SCENARIOS FOR COMMUNITY-BASED MANAGEMENT OF …

SCENARIOS FOR COMMUNITY-BASED MANAGEMENT OF

CUTOVER NATIVE FOREST IN PAPUA NEW GUINEA

Cossey Keosai Yosi

Submitted in total fulfilment of the requirements

for the degree of Doctor of Philosophy

July 2011

Melbourne School of Land and Environment

Department of Forest and Ecosystem Science

The University of Melbourne

ldquoProduced on archival quality paperrdquo

ii

ABSTRACT

There is an increasing demand for multiple objectives from forest management

worldwide and this is particularly challenging for tropical forests due to their diverse

composition structure and a wide range of stakeholder expectations and requirements

In Papua New Guinea (PNG) forest management is generally considered to be

unsustainable and commercial harvesting leaves behind large forest areas to degrade

overtime with little attention paid to their future management There were four

objectives of this study The first was to assess the current condition and future

production potential of cutover forests in PNG The second objective focussed on

developing scenario analyses and evaluation tools for assisting decision making in

community-based management of cutover native forests In the third objective the

study tested the tools developed under the second objective in two case study sites

where extensive harvesting of primary forest had taken place in the past The fourth

objective of this study was to develop a conceptual framework for community-based

management of cutover native forests in PNG

The methodology used in this study was a combination of qualitative analyses of

community interests and expectations in small-scale harvesting and quantitative

analyses of permanent sample plots (PSPs) forest resources and cash-flow associated

with different management scenarios in two case study sites Analyses of PSPs in

cutover forests showed that there was a gradual increase in residual stand basal area

(BA) and timber volume over time and these forests generally showed a high degree

of resilience following harvesting In the two case study sites timber volume for the

residual stand and aboveground forest carbon (C) in the Yalu community forest were

estimated at 127 m3

ha-1

plusmn 45 (SD) and 1499 MgC ha-1

plusmn 375 (SD) respectively In

the Gabensis community forest timber volume and forest C were estimated at 152 m3

ha-1


plusmn 506 (SD) respectively Analyses of field

interviews in communities in the two case study sites showed that community

sawmill local processing log export and carbon trade were the main options

preferred by the communities for the future management of their cutover forests

Scenario analyses using a planning tool showed that a management regime with a

short cutting cycle (10-20 years) a reduced cut proportion (50) at the initial harvest

iii

and removing a proportion of only commercial timber species was sustainable

Longer cutting cycles have lower short-term yields but potentially higher yields in the

long term because the forest has a greater time to recover to higher volumes for later

cutting cycles

This study developed decision analyses models for community-based management of

cutover forest in PNG With the data available the models were tested in the Yalu

case study site and depending on the input variables in the model the expected

monetary value (EMV) returned was determined by the related cash flow associated

with each scenario For example sensitivity analysis of the EMV showed that in a

local processing scenario the annual sawn timber production and sawn timber price in

the overseas certified market had the largest impact on the EMV

An integrated conceptual framework for community-based forest management

(CBFM) was developed in this study The framework is appropriate for application in

CBFM throughout PNG

This study concludes that the scenario evaluation and analyses tools developed are a

new approach in tropical forest management and its application is justified in the

context of CBFM because of the complexity and uncertainty affecting tropical forests

and their management A new policy direction in community forestry is therefore

necessary for the application of these systems in CBFM and utilisation in PNG

iv

DECLARATION

This is to certify that

i) the thesis comprises only my original work

ii) due acknowledgement has been made in the text to all other material used

iii) the thesis is less than 100000 words in length exclusive of tables maps

references and appendices

___________________

Cossey Keosai Yosi

July 2011

v

DEDICATION

This thesis is dedicated to the pioneering teachers of the Zare Aingse primary school

in Morobe Patrol Post of the Huon District in Papua New Guinea who set the

foundation for my education and career In 1964 when the Zare Aingse primary

school was being established I was born at Kaingze hamlet near Aingse village The

pioneering teachers at that time were Mr Eike Guguwa Mr Arataung Kuru and the

late Mr Naira During that time because there were no classrooms school children

were taught in a small hut at Zare village From 1966 to 1969 the school was

relocated and a small patch of coconut trees near Aingse village was cleared by the

village people and a few classrooms were built from the bush material During those

days the English language was non-existent and the school children were taught in

the Zia dialect In 1970 the school was relocated to Seboro near what is now the Wizi

hamlet At this stage the official English language was used to teach the school

children and I was among the first village school children to enrol at the school when

English was introduced at primary school level in this part of the country From 1970

to 1976 the following teachers taught in the school using English as the official

language for education Mr Zama Mr Bera Koi Mr Amo Ms Anake Guguwa Ms

Zane Tunina late Mr Mainuwe Kelly Seregi Mr Tingkeo Puro Mr Waria Woreti

and Mr Don Amos In 1976 I completed my Year 6 and in 1977 I said goodbye to my

village my school and my village friends when I was among the seven local students

selected by the Education Department to start a new life of modern education in the

urban centre of Lae (now PNGlsquos second city) My modern education started then at

the Bugandi High School (now Bugandi Secondary School) and in 1980 I completed

my Year 10 education After completing Year 12 in 1982 at the Passam National

High School in Wewak East Sepik Province (one of PNGlsquos four national high

schools at that time) I went on to study a three year Diploma in Forestry course at the

PNG Forestry College in Bulolo and graduated in 1985 Three years later I received a

PNG Government scholarship and completed a Forest Science Degree course at the

PNG University of Technology in Lae and graduated in 1992 Since then it has taken

me 19 long years to have reached this far a PhD I humbly salute the pioneering

teachers of the Zare Aingse primary school those who have passed away and those

who are still alive for starting this challenging journey for me

vi

PREFACE

PSP data used in Chapter 3 are the property of the Papua New Guinea Forest

Authority (PNGFA) and its Research Institute and the International Tropical Timber

Organisation (ITTO) research Project number PD16292

Data for the forest assessment in case study sites in Chapter 4 are from the

implementation of a collaborative research project between The University of

Melbourne and PNG project partners PNG Forest Research Institute (PNGFRI) and

Village Development Trust (VDT) under the ACIAR Project number FST2004061

The Decision Tree Models developed in Chapter 6 are based on a Spreadsheet

Modelling and Decision Analysis technique Two Excel Spreadsheet add-ins called

TreePlan and SensIT were used to develop the models and carry out sensitivity

analyses TreePlan and SensIT were developed by Professor Michael R Middleton at

the University of San Francisco and modified for use at Fuqua (Duke) by Professor

James E Smith

The following sections of this thesis are contained in publications

Parts of Chapter 1 and 2 are contained in

Yosi CK Keenan JR and Fox JC 2011 Forest management in Papua New

Guinea historical development and future directions In J C Fox R J Keenan C

L Brack and S Saulei (Eds) Native forest management in Papua New Guinea

advances in assessment modelling and decision-making ACIAR Proceeding No

135 18-31 Australian Center for International Agricultural Research Canberra

Chapter 3 has been published in

Yosi CK Keenan RJ and Fox JC 2011 Forest dynamics after selective timber

harvesting in Papua New Guinea Forest Ecology and Management 262 895-905


Yosi CK Keenan RJ Coote DC and Fox JC 2011 Evaluating scenarios for

community-based management of cutover forests in Papua New Guinea In J C Fox

R J Keenan C L Brack and S Saulei (Eds) Native forest management in Papua

New Guinea advances in assessment modelling and decision-making ACIAR

Proceeding No 135 185-201 Australian Center for International Agricultural

Research Canberra

vii

ACKNOWLEDGEMENTS

This thesis would not have been completed without the support of various people and

organisations Firstly I would like to extend my special appreciation to my

supervisors Professor Rodney J Keenan and Dr Julian C Fox for their professional

advice encouragement and support provided throughout this study The regular

consultations meetings and networking that I have had with the two of you had

motivated me to stay focused on the completion of this thesis and I sincerely thank

you both very much I also thank both of you for your willingness to provide

constructive discussions feedback and comments on draft chapters and related

support during the duration of my study Dr Yue Wang formerly of Melbourne

School of Land and Environment (MSLE) and Dr Andrew Haywood of Department

of Sustainability and Environment (DSE) Victorian Government are also

acknowledged for providing some advice during the initial stages of this study

The Department of Forest and Ecosystem Science (DFES) of the University of

Melbourne are acknowledged for the use of University facilities in the completion of

this study

Many thanks are extended to PNGFA and PNGFRI for releasing me for the duration

of my study The ITTO Project PD 16292 and PNGFRI are acknowledged for the use

of their permanent sample plot (PSP) data set to undertake the study in Chapter 3

Those staff of PNGFRI who assisted in the PSP data collection included Forova

Oavika Joseph Pokana and Kunsey Lavong The field assistants who undertook field

work for the PSP data collection were Stanley Maine Matrus Peter Timothy Urahau

Amos Basenke Gabriel Mambo Silver Masbong Dingko Sinawi and late Steven

Mathew Janet Sabub provided data entry services for the PSPs Their efforts and

related support are gratefully acknowledged

This study is a component of ACIAR Project FST2004-061 which I have been

involved with for the last four years The data for forest assessment in the case study

sites in Chapter 5 are a part of the work carried out under this ACIAR Project The

staff of the Project involved in the forest assessment work are acknowledged for their

assistance

viii

In PNG where this research was conducted various stakeholders participated in this

study I would like to thank the following for their assistance in one way or another

Desmond Celecor of TFTC Kenneth Mamu of PNGFA Madang office Robert

Songan of VDT Israel Bewang and Emmanual Mu of FPCD Cosmos Makamet and

Oscar Pileng of FORCERT Ltd Francis of Ditib Eco-Timber Abraham of Narapela

Wei Ltd Mr Kabusoda of Santi Timbers Ltd Watam Afing and Bernard Bobias of

LBC Ltd and Emmaus Tobu of Madang Timbers Ltd

My special appreciation is extended to Francis Inude of VDT for assisting with field

interviews of community groups The following community groups are acknowledged

for their participation in this study Konzolong Clan of Yalu village TN Eco-Timber

of Gabensis village and Sogi Eco-Timber of Madang province

My special thanks are offered to ACIAR for awarding me the John Allwright

Fellowship to pursue PhD study at the Department of Forest and Ecosystem Science

of The University of Melbourne The AusAID team including Lucia Wong and Jacqui

are acknowledged for administering my award and other related support at The

University of Melbourne during the duration of this study

Above all I give Glory and Honour to the Almighty God for his guidance throughout

the difficult and challenging times of my study and up to the successful completion of

this thesis ―Praise be to God from Whom all things come

I also would like to thank my wife Relly and our three lovely children Cerbera

Cassandra and Caleb for their time patience encouragement and support given to me

throughout the duration of my study

Finally but not the least my deep gratitude goes to my mother Mrs Aratamase

Bawang Ainase and my late father Mr Yosi Guwa Ami for nurturing me to become

the man that I am today

TABLE OF CONTENTS

ABSTRACT II DECLARATION IV DEDICATION V PREFACE VI ACKNOWLEDGEMENTS VII TABLE OF CONTENTS IX LIST OF TABLES XIII LIST OF FIGURES XIV LIST OF ACRONYMS XV

INTRODUCTION 1

CHAPTER 1 THESIS INTRODUCTION AND OVERVIEW 2

11 THESIS INTRODUCTION 2 12 FOREST MANAGEMENT ISSUES AND PROBLEMS IN PNG 4 13 BACKGROUND 7

131 History of Timber Harvesting in PNG 8 132 Papua New Guinearsquos National Forest Policy 12 133 Papua New Guinearsquos Forest Resources and Timber Production 14 134 Certification Efforts in PNG 18 135 Case Study Sites 20 136 The PNGFRI Permanent Sample Plot Network 22

14 RESEARCH QUESTIONS AND OBJECTIVES 27 15 THESIS OUTLINE 28

REVIEW OF THE LITERATURE 27

CHAPTER 2 AN OVERVIEW OF CURRENT ISSUES IN TROPICAL FOREST

MANAGEMENT 28

21 FOREST DYNAMICS 28 211 Introduction 28 212 Overview of Tropical Forests 30 213 Tropical Forest Dynamics 31 214 Forest Types 32 215 Species Diversity 33 216 Species Distribution 35 217 Regeneration Mechanisms 36 218 Shade Tolerance 39 219 Stand Structure 40 2110 Responses of Forest to Disturbances 40 2111 Discussion 44 2112 Conclusions 46

22 CURRENT ISSUES IN TROPICAL FOREST MANAGEMENT 47 221 Introduction 47 222 Illegal Logging 49 223 Deforestation 50 224 Climate Change 52 225 Community Forest Management in the Tropics 56 226 Certification 58 227 Governance 60 228 Discussion 62

x

229 Conclusions 63 23 FOREST MANAGEMENT APPROACHES 65

231 The Management Strategy Evaluation (MSE) 65 232 The Scenario Method 67 233 The Bayesian Belief Network (BBN) 69 234 Discussion 70 235 Conclusions 71

CONDITION OF CUTOVER FOREST 72

CHAPTER 3 FOREST DYNAMICS AFTER SELECTIVE TIMBER HARVESTING

IN PNG 65

3 1 INTRODUCTION 65 32 MATERIALS AND METHODS 67

321 PNGFRI Permanent Sample Plots ndash Background 67 322 Study Sites and PSP Locations 68 323 PSPs used in this Study and Data Analyses 69 324 Analyses of Stand Structure 70 325 Assessing the Dynamics of Cutover Forests 71 326 Basal Area and Volume Growth 72 327 Estimating Mortality due to the 1997-98 El Nino Drought 74 328 Shannon-Wiener Index (H

1) 74

33 RESULTS 75 331 Change in Stand Structure after Harvesting 75 332 Trends in Stand Basal Area 78 333 Basal Area Growth since Harvesting 79 334 Critical Threshold Basal Area for Recovery of Harvested Forest 81 335 Trends in Timber Volume 81 336 Timber Yield since Harvesting 83 337 Mortality due to the Fire Caused During the 1997-98 El Nino Drought 83 338 Species Diversity in Cutover Forest 84

34 DISCUSSION 85 35 CONCLUSIONS 90

CHAPTER 4 FOREST ASSESSMENT IN CASE STUDY SITES 91

41 INTRODUCTION 91 42 BACKGROUND 92

421 Yalu Community Forest 92 422 Gabensis Community Forest 93

43 FOREST ASSESSMENT METHODS 94 44 DATA ANALYSIS 95

441 Estimating Stems per Hectare 95 442 Timber Volume 96 443 Aboveground Live Biomass 96 444 Determining Sample Size 97

45 RESULTS 98 451 Size Class Distribution 98 452 Residual Timber Volume 100 a The table excludes other non-commercial and secondary timber species 100

453 Mean Residual Timber Volume 101 454 Aboveground Forest Carbon 101 455 Sample Size 101 456 Summary of Resource 102


xi

SCENARIO ANALYSES AND EVALUATION TOOLS 106

CHAPTER 5 EVALUATION OF SCENARIOS FOR COMMUNITY-BASED

FOREST MANAGEMENT 107


521 The Scenario Approach 108 522 Modelling Tropical Forest Growth and Yield 109

53 METHODOLOGY 110 531 Criteria for Developing Scenarios 110 532 Field Interviews using the PAR Protocol as a Guide 111 533 Scenario development 112 534 Scenario Analysis using a Spreadsheet Tool 114

54 RESULTS 118 541 Current Forest Uses and Future Forest Management Options 118 542 Scenario Indicators 122 543 Estimating Timber Yield under Different Management Scenarios 123 544 Analyses of Residual Timber Volume over a 60 Year Cycle 129 545 Projection of Annual Yield over a 60 Year Cycle 130

55 DISCUSSION 131 551 Outcomes from Field Interviews 131 552 Analyses Output from the Planning Tool 131

56 CONCLUSIONS 134

CHAPTER 6 DECISION TREE MODELS FOR COMMUNITY-BASED FOREST

MANAGEMENT IN PNG 136

61 INTRODUCTION 136 62 BACKGROUND ndash DECISION TREE MODELS 138 63 METHODOLOGY 138

631 Building the Decision Tree 139 632 Nodes and Branches 139 633 Terminal Values 140 634 Expected Monetary Values (EMV) 140 635 Application of the Decision Tree Models 141 636 Decision Tree Model Parameters 145

64 RESULTS 146 641 Decision Tree Model 1 Community Sawmill 146 642 Decision Tree Model 2 Local Processing 149 643 Decision Tree Model 3 Log Export 155 644 Decision Tree Model 4 Carbon Trade 160

65 DISCUSSION 164 651 Silvicultural Management of Rainforests 164 652 Testing the Decision Tree Models 165

66 CONCLUSIONS 169

CHAPTER 7 SCENARIO EVALUATION FRAMEWORK FOR COMMUNITY-

BASED FOREST MANAGEMENT 170


721 The Management Strategy Evaluation (MSE) approach 171 722 Overview of Forest Planning in PNG 173 723 Small-Scale Timber Harvesting in PNG 176 724 Requirements for Certification 176

73 METHODOLOGY 181 731 Stakeholder Consultation 181 732 Forest Inventory 181

xii

733 Planning System 182 734 Decision Analysis Tools 182 735 Sensitivity Analyses 182

74 RESULTS 183 741 A Scenario Analyses and Evaluation Framework 183


CONCLUSIONS 187

CHAPTER 8 CONCLUSIONS AND RECOMMENDATIONS 188

81 INTRODUCTION 188 82 RESEARCH OBJECTIVES AND QUESTIONS 188

821 Research Objectives 188 822 Research Questions 189

83 KEY OUTPUTS OF THE STUDY 191 84 APPLICATION OF THE TOOLS DEVELOPED IN THIS STUDY 192 85 CONTRIBUTIONS OF THE PRESENT STUDY 192 86 LIMITATIONS OF THE STUDY 193

861 Forest Management Implications 193 87 FUTURE DIRECTIONS 194

871 Future Research Needs 194 872 Future Policy Directions 195


REFERENCES 198

APPENDICES 219

APPENDIX 3-1 SUMMARY OF PSPS USED IN THE STUDY 219 APPENDIX 3-2 SUMMARY OF THE PSPS IN UNLOGGED FOREST 219 APPENDIX 3-3 UN-BURNED PSPS IN HARVESTED FOREST WITH INCREASING BA 220 APPENDIX 3-4 UNBURNED PSPS IN HARVESTED FOREST WITH FALLING BA 222 APPENDIX 3-5 PSPS BURNED BY FIRE DURING THE DROUGHT 223 APPENDIX 3-6 10 PSPS SEVERELY BURNED DURING THE DROUGHT 223 APPENDIX 4-1 SAMPLING POINT DATA-YALU COMMUNITY FOREST AREA 224 APPENDIX 4-2 INVENTORY DATA-GABENSIS COMMUNITY FOREST 237 APPENDIX 5-1 PNGFA MINIMUM EXPORT PRICE SPECIES GROUP 240 APPENDIX 5-2 CURRENT FOREST USES IN CASE STUDY SITES 241 APPENDIX 5-3 FUTURE FOREST USES IN CASE STUDY SITES 242 APPENDIX 6-1 REQUIREMENTS ndash COMMUNITY SAWMILL 243 APPENDIX 6-2 REQUIREMENTS ndash LOCAL PROCESSING 244 APPENDIX 6-3 REQUIREMENTS ndash MEDIUM-SCALE LOG EXPORT 245 APPENDIX 6-4 REQUIREMENTS - CARBON TRADE 246

LIST OF TABLES

Table 1-1 Location of the 72 PSPs and their forest types (Yosi 1999) 23 Table 1-2 Description of Vegetation Types according to CSIRO 24

Table 3-1 Mean BAI for plots with increasing and falling BA 79 Table 3-2 Comparison of results of this study with similar studies 87

Table 4-1 Unmeasured Components of AGLBge10cm (AGLBge10cm) 97 Table 4-2 Size Class Distribution 98 Table 4-3 Residual Merchantable Volume for Major Timber Species

a 100

Table 4-4 Mean Residual Timber Volume ge 20cm DBH (m3 ha

-1) 101

Table 4-5 Aboveground Forest Carbon (MgC ha-1

) with SD in parenthesis 101 Table 4-6 Estimate of number of samples 102 Table 4-7 Summary Results 102

Table 5-1 Yalu community forest area 115 Table 5-2 Yalu community forest inventory data 116 Table 5-3 Data for a management regime with 50 constant cut proportion 116 Table 5-4 Data for a management regime with 75 constant cut proportion 117 Table 5-5 Data for a management regime with 20 years constant cutting cycle 117 Table 5-6 Management regime with a constant cut proportion of 50 123 Table 5-7 Management regime with a constant cut proportion of 75 124 Table 5-8 Management regime with a constant cutting cycle of 20 years 124 Table 5-9 Residual and annual volume over a 60 year cutting cycle 129 Table 5-10 Comparison of shorter and longer cutting cycles 133

Table 6-1 Sensitivity data - Community sawmill 146 Table 6-2 Sensitivity data ndash Local processing 149 Table 6-3 Sensitivity data ndash Medium-scale log export 155 Table 6-4 Sensitivity data ndash Carbon trade 161 Table 6-5 Comparison of the four management scenarios 168

Table 7-1 Forest Planning and inventory requirements in Papua New Guinea 175 Table 7-2 Strengths and weaknesses of certification 177

xiv

LIST OF FIGURES

Figure 1-1 Timber Volume and Area harvested from 1988 to 2007 (PNGFA 2007) 17 Figure 1-2 Export of Primary Products by PNG (ITTO 2006) 17 Figure 1-3 Map of case study sites selected for the study 22 Figure 1-4 Plot layout in the field (adapted from Romijn (1994a) 25 Figure 1-5 Permanent Sample Plots Location Map (adapted from (Fox et al 2010) 26

Figure 2-1 Key features of the general MSE Framework (Sainsbury et al 2000) 67

Figure 3-1 Map of PNG showing study sites and permanent sample plot locations 69 Figure 3-2 Trends in stem and BA distribution since harvesting 76 Figure 3-3 Representation of trends in commercial and non-commercial tree species 77 Figure 3-4 Trends in BA since harvesting for the 84 un-burned plots 78 Figure 3-5 Average trends in MBAI since harvesting 80 Figure 3-6 BA growth of harvested forest in PNG 81 Figure 3-7 Trends in timber volume for trees ge 20cm DBH 82 Figure 3-8 Timber yield of trees ge 20cm DBH in the residual stand 83 Figure 3-9 Ingrowth recruitment and mortality for the 10 burned plots 84 Figure 3-10 Species diversity represented by the change in Shannon-Wiener Index 85

Figure 4-1 An aster image of the Yalu community forest 93 Figure 4-2 An aster image of the Gabensis community forest 94 Figure 4-3 Size Class Distribution for tress ge10cm DBH in the Yalu study site 99 Figure 4-4 Size Class Distribution for trees ge20cm DBH in the Gabensis study site 99

Figure 5-1 Example output of the Planning tool (Keenan et al 2005) 114 Figure 5-2 Current main forest uses in Yalu and Gabensis villages 118 Figure 5-3 Future forest management options in case study sites 119 Figure 5-4 Factors influencing community attitudes towards small-scale harvesting 121 Figure 5-5 Graphical presentation of the frequencies from field interviews 122 Figure 5-6 Timber yield under different scenarios with a 50 cut proportion 126 Figure 5-7 Timber yield under different scenarios with a 75 cut proportion 127 Figure 5-8 Timber yield for a constant cutting cycle of 20 years 128 Figure 5-9 Residual timber volume for a 100 year cycle 130 Figure 5-10 Annual Yield for a 60 year cycle 130

Figure 6-1 Basic framework for decision analyses 142 Figure 6-2 Main Features of decision tree model 1 - Community sawmill 148 Figure 6-3 Main features of decision tree model 2 ndash Local processing 151 Figure 6-4 EMV sensitivity at +-10 of the base case ndash Local processing 153 Figure 6-5 Impact of input variables on the EMV at +-10 ndash Local processing 154 Figure 6-6 Main features of decision tree model 3 ndash Medium-scale log export 157 Figure 6-7 EMV sensitivity at +-10 of the base case ndash Log export 159 Figure 6-8 Impact of input variables on the EMV at +-10 - Log export 160 Figure 6-9 Main features of decision tree model 4 ndash Carbon trade 162 Figure 6-10 EMV sensitivity at +-10 of base case ndash Carbon trade 163 Figure 6-11 Impact of input variables on the EMV at +-10 - Carbon trade 164

Figure 7-1 The MSE framework for natural resource management 173 Figure 7-2 Certification model promoted by FORCERT in PNG 180 Figure 7-3 A conceptual framework for community-based forest management 184

xv

LIST OF ACRONYMS

ACIAR Australian Centre for International Agricultural Research

APFC Asia Pacific Forestry Commission

AR Afforestation Reforestation

asl Above Sea Level

BA Basal Area

BBN Bayesian Belief Network

C Carbon

CBOs Community Based Organisations

CBFM Community-based Forest Management

CBFT Community-based Fair Trade

CCAMLR Commission for Conservation of Antarctica Marine Living

Resources

CDM Clean Development Mechanism

CERFLOR Certificacao Florestal

CIFOR Centre for International Forestry Research

CMU Central Marketing Unit

CO2 Carbon Dioxide

CSIRO Commonwealth Scientific and Industrial Research Organisation

D Simpsonrsquos Index

DBH Diameter at Breast Height

DBHOB Diameter at Breast Height Over Bark

DEC Department of Environment and Conservation

DFES Department of Forest and Ecosystem Science of The University of

Melbourne

DFID Department for International Development

DSE Department of Sustainability and Environment of Victorian

Government

EMV Expected Monetary Value

ENSO El Nino Southern Oscillation

ESD Ecologically Sustainable Development

FAO Food and Agricultural Organisation of The United Nations

FIP Forest Industry Participant

xvi

FLEG Forest Law Enforcement and Governance

FORCERT Forest Management and Production Certification Service

FPCD Foundation for People and Community Development

FSC Forest Stewardship Council

FRA Forest Resource Assessment

GHG Green House Gases

GTP Gogol Timber Project

HCV High Conservation Value

HCVF High Conservation Value Forest

HCVFT High Conservation Value Forest Toolkit

H1 Shannon-Wienner Index

ILG Incorporated Land Group

IRR Internal Rate of Return

ITTA International Tropical Timber Agreement

ITTO International Tropical Timber Organisation

IWC International Whaling Commission

JANT Japan And New Guinea Timbers

LBC Lae Builders and Contractors

LULUCF Land use land-use change and forestry

MBAI Mean Basal Area Increment

MEP Minimum Export Price

MFROA Madang Forest Resource Owners Association

m2 ha

-1 Basal Area in square meters per hectare

m3 ha

-1 Timber Volume in Cubic meters per hectare

mm annum-1

Rainfall in millimetres per annum

MOMASE Morobe Madang Sepik

MSE Management Strategy Evaluation

MSLE Melbourne School of Land and Environment

MVOLI Mean Volume Increment

NFDP National Forest Development Programme

NGOs Non-Government Organisations

N ha-1

Number of stems per hectare

NPV Net Present Value

NTFP Non Timber Forest Product

xvii

OECD Organisation for Economic Co-operation and Development

PAR Participatory Action Research

PEFC Programme for the Endorsement of Forest Certification

PERSYST Permanent Sample Plot data management System

PES Payment for Environmental Services

PFE Permanent Forest Estate

PINFORM PNG and ITTO Natural Forest Model

PNG Papua New Guinea

PNGFA Papua New Guinea Forest Authority

PNGFRI Papua New Guinea Forest Research Institute

PNGK Papua New Guinea Kina

PPP Public Procurement Policies

PRA Participatory Rapid Appraisal

PSP Permanent Sample Plot

PSR Pressure State Response

RAI Ramu Agri Industry

REDD Reduced Emission from Deforestation and forest Degradation

RIL Low Impact Logging

SABLs Special Agricultural and Business Leases

SEQHWP South East Queensland Healthy Waterways Partnership

SFM Sustainable Forest Management

SPCGTZ South Pacific Commission German

TFAP Tropical Forest Action Plan

TFTC Timber and Forestry Training College

TRP Timber Rights Purchase

TSH Time Since Harvesting in years

UK United Kingdom

UNFCCC United Nations Framework Convention on Climate Change

UNEP United Nations Environment Program

UNESCO United Nations Education Scientific and Cultural Organisation

USA United States of America

UTM Universal Traverse Mercator

VDT Village Development Trust

WWF World Wide Fund for Nature

INTRODUCTION

2

CHAPTER 1

THESIS INTRODUCTION AND OVERVIEW

11 THESIS INTRODUCTION

Forest management worldwide is increasingly focused on values such as biodiversity

conservation carbon water and recreation as well as timber production Ownership

and governance arrangements are also changing with an increase in private ownership

of forest resources focused on timber production and devolution of management and

control from the state to the community-level Due to overexploitation of tropical

forests there has been a widespread concern about how tropical forests are being

managed however according to Poore (1989) tropical forests can be managed for

sustainable production of timber at a number of different intensities Whitmore (1990)

points out that tropical forest can be managed not only for timber production but also

for multiple purposes to meet the needs of conservation as well as to produce other

useful products In terms of sustainable forest management (SFM) if long-term

sustainability of timber production is sought from tropical mixed forests their

economic performance must be improved by transforming or replacing the original

growing stock (Lamprecht 1989)

These concerns have given rise to institutions such as the Tropical Forest Action Plan

(TFAP) and International Tropical Timber Agreement (ITTA) to address issues

relating to SFM in the tropics While that is so Non Government Organisations

(NGOs) have been vocal critics of tropical forest management While SFM may be a

concept which is quite new to many tropical countries for those countries which are

members of the International Tropical Timber Organisation (ITTO) achieving

ITTOlsquos year 2000 Objective still remains a major challenge The ITTO year 2000

Objective calls for all forest products for export to come from forests managed in a

sustainable way In PNG some efforts have been put to meet the ITTO year 2000

Objective by enforcing strict controls on timber harvesting practices through the

introduction and adoption of the PNG Logging Code of practice Despite varying

difficulties in the region there has been significant progress towards SFM in the

tropics since ITTO conducted an initial survey in 1988 (ITTO 2006) According to

3

ITTO (2006) there is positive progress towards SFM in that countries are now

beginning to establish and implement forest policies that address SFM and more

forest areas are being allocated as permanent forest estates (PFE) for production or

protection Some PFEs in the region are being certified however the proportion of

natural production forest under SFM in the region is still low and SFM is distributed

unevenly across the tropics (ITTO 2006)

ITTOlsquos focus in SFM is to improve the social and economic livelihoods of poor

communities who depend on their forests for survival whilst also maintaining

ecosystem services like provision of clean water and conservation of biodiversity To

support SFM and assist monitoring ITTO has developed a set of seven key criteria

and indicators for sustainable management of tropical forest (ITTO 1998) which

have evolved into the requirements for forest certification In terms of progress

towards SFM findings from Forest Resource Assessment (FRA) 2005 indicated that

forest management is generally improving in the global context however the

scenario changed dramatically when information is interpreted at the regional level

with alarming trends in several tropical sub-regions (FAO 2006)

PNG has a significant area of tropical forest composed of a wide range of forest types

and environments However these forests are increasingly under threat from high

human population growth and industrial activities such as mining and logging These

activities are also contributing to the increase in deforestation rates of over 1 per

year (see Ericho 1998 Shearman et al 2009b) Most of the forest in PNG is under

the customary ownership of indigenous people with a similarly high ethnic and

cultural diversity Local people have used forest land and resources for thousands of

years for subsistence and cultural needs For the past 20 years much of the focus of

formal forest management and policy in PNG has been concentrated on large-scale

conventional harvesting to meet national requirements for economic development and

little attention has been given to community-level forest management The current

management system is considered by many to be unsustainable and as commercial

timber resources in primary forests have been extracted there have been few

examples of future management plans for cutover forests This has resulted in

extensive cutover forest areas being left to degrade over time

A new policy approach is therefore required for forest management in PNG that

reflects changing local and international expectations from forests and the current

4

state and future requirements for forest resources This should include consideration

for the future production capacity of cutover and degraded forests and development of

the capacity of local forest owner communities This will assist communities to

participate in small-scale forest management and utilization for example through

management systems that are compliant with requirements of certification bodies

This thesis is focused on assisting decision-making in community-based management

of cutover forests in PNG and at the same time support the capacity of PNGFA and

set a new direction for an integrated regional forest planning and management system

for cutover forests in PNG

12 FOREST MANAGEMENT ISSUES AND PROBLEMS IN PNG

There is an increasing demand for multiple objectives to forest management world-

wide and particularly tropical forests are complex hence their management is

challenging Due to their diverse composition structure wide range of stakeholder

expectations and requirements tropical forest management is associated with many

difficulties Uncertainty is also a characteristic of many situations in tropical forest

management (Wollenberg et al 2000) hence traditional methods such as straight

forward projections of growth and yield may not be able to meet these challenges

Uncertainties in tropical forest management also make SFM in the region a major

challenge for governments NGOs local communities and the timber industry

Therefore new management approaches creative processes and policy directions are

required to meet these challenges

PNG has abundant natural resources with very diverse ecosystems and the country is

home to an estimated 15000 or more native plant species (Beehler 1993 Sekhran

and Miller 1994) However the country is faced with many challenges in terms of

resource development as the government looks for alternative ways to improve and

sustain the livelihoods of a large rural population PNG has 394 million hectares of

forests (PNGFA 1998) As it has always been in many communities throughout the

country forests are a part of the peoples way of life and over 80 of the population of

the country depend on them for food shelter medicine and cultural benefits and 97

of the forest are under customary ownership by individuals or community groups

(PNGFA 1998) According to ITTO on average each citizen of PNG has rights over

about 64 hectares of forest however the majority of people still live in extreme

5

poverty (ITTO 2006) The forestry sector is the countrys third major contributor to

government revenue For example in 2003 PNG earned US$126 million from the

export of tropical timber (ITTO 2006) This revenue has been generated from

primary forests Given customary ownership arrangements the future management of

cutover forests is likely to be decided by local community groups This is because in

the past there was lack of landowner participation in forest management decision-

making However today community groups are beginning to accept that their forests

provide many values and services apart from timber products Therefore they would

like to participate in decision-making and also manage their own forests to get

maximum benefits and improve their livelihoods

Due to the fact that most global wood production comes from natural or semi-natural

forests rather than plantations (Johns 1997) natural forests research and management

elsewhere as well as in PNG remains an important basis to assist SFM As natural

forests are being exhausted in PNG through commercial timber harvesting and other

land uses such as large-scale forest conversion to agriculture and shifting cultivation1

forest management will begin to focus on cutover secondary forests and a new

paradigm in forest use and management is likely to emerge when cutover forest areas

are taken over by community landowner groups

A major challenge is the development of sustainable management systems for cutover

forests that meet the needs of community forest owners Another concerning

development and challenge for land owning communities is the PNG governmentlsquos

rapid expansion of Special Agricultural and Business Leases (SABLs) SABLs may

limit landowner rights and their access to traditional lands and forests In SABLs

forest lands which may be originally intended for agricultural development usually

for a lease period of 99 years could be diverted to other land uses by foreign or

multinational corporations especially for large-scale harvesting interests without

proper landowner consent (Wwwpostcouriercompg)

In PNG there are many problems associated with forest management For example

apart from stakeholder demands land and forest ownership arrangements are

complicated issues Generally forest management in PNG is considered unsustainable

and this is compounded by high deforestation rates Evidence suggests that forest

cover in PNG declined at an estimated annual rate of 113000 hectares (04) 1 Shifting cultivation is a traditional method of subsistence farming that contributes to loss of forest cover

6

between 1990 and 2000 (FAO 2005) Reports from PNGFA suggest that PNGlsquos

natural forests are being exploited at an overwhelming rate with estimates that forest

areas are decreasing at a rate of 120000 ha per annum (PNGFA 2003) through

logging agricultural activities mining and other land uses Current statistics from

PNGFA (2007) also show that from 1988 to 2007 well over 2 million hectares of

primary forest have been harvested through commercial logging Evidence from a

recent study (Shearman et al 2009a Shearman et al 2009b) showed that the

deforestation rate in PNG increased from 046 to 141 from 1972 to 2002

although there is some debate about the assumptions underlying this figure (Filer et

al 2009) Generally the main drivers of forest cover change including deforestation

in PNG are subsistence agriculture timber harvesting fire plantation conversion and

mining (Filer et al 2009 Keenan 2009 Shearman et al 2009b) There have also

been ongoing problems of illegal logging in PNG From 2000 to 2005 the PNG

government reviewed the operations of the logging industry and found that none of

the projects were operating legally with the exception of only two projects (Forest

Trends 2006) However Curtin (2005) claims that the World Bank sponsored audit

of the PNG timber industry from 2000 to 2004 found full compliance by the industry

with the countrylsquos Forestry Act 1991 Despite these various reviews of the timber

industry it is a general understanding by the public that illegal logging in PNG seems

to continue

At present the timber production capacity of cutover forest areas and secondary

forests in PNG are poorly understood and the future of marketing wood products from

native forests is also uncertain This study will attempt to address these uncertainties

and to develop a framework whereby information will be generated and made

available to all stakeholders to assist community management of cutover native

forests in PNG This research study will develop methods for analysis of management

scenarios for cutover forests in PNG

7

13 BACKGROUND

The background of this study presents the historical development of forest

management in PNG in terms of history of harvesting Forest Policy development

forest resources and timber production PNGlsquos efforts in certification particularly at

community-level are discussed Some background about the case study sites and

PNGlsquos comprehensive PSP network are also given in this section

Subsection 131 is the history of timber harvesting in PNG which is based on an

earlier study by Lamb (1990) This subsection provides details of timber exploitation

before and after the Second World War As far as the history of timber harvesting in

PNG is concerned in the early 1970s and 1980s harvesting of primary forests started

and this has increased extensively in the 1990s Since the 2000s harvesting has

increased rapidly and the PNGFA records show that about 10 of accessible primary

forests have been harvested by 2007 under commercial logging (PNGFA 2007)

In Subsection 132 Forest Policy development in PNG is discussed PNGlsquos Forest

Policy was adopted in 1990 and has been focused mainly on large-scale commercial

harvesting of primary forests with little or no attention given to management of the

residual stand after harvesting Therefore the 1990 National Forest Policy does not

provide directions on technical aspects of management of logged-over forest areas in

PNG and there are no guidelines for land use plans after logging Although the 1991

Forestry Act has been amended numerous times since 1991 (PNGFA 2007) there

have been no provisions made in the Act for the management of forest areas left

behind after harvesting This study sets the basis for policy changes in order to

facilitate sustainable management of cutover forest areas in PNG

The overview of PNGlsquos forest resources and timber production are given in

Subsection 133 This includes the major forest types found in the country with

lowland tropical forests found most commonly throughout PNG PNG is considered

as a country blessed with abundant natural resources with 70 of the country under

forest cover (ITTO 2006) Details of PNGlsquos production and trade of primary products

from 2002 to 2007 are also discussed in this subsection and this includes products

such as logs and sawn timber A record of PNGlsquos timber production and trade shows

that in 2003 the country was the worldlsquos second largest exporter of tropical logs after

8

Malaysia (ITTO 2004 ITTO 2005) The forest industry in PNG still remains the

third largest revenue earner for the country

In Subsection 134 certification efforts in PNG are discussed Efforts are increasing

particularly at community-level forest management and this initiative is likely to bring

significant benefits to communities However evidence shows that only a small

number of forest management certificates have been granted for village-based timber

operations in the Asia-Pacific region including PNG (Scheyvens 2009) With the

assistance of the Forest Stewardship Council (FSC) a high conservation value forest

(HCVF) toolkit for PNG has been developed to be used in forest management

certification (PNG FSC 2006) This toolkit is now being promoted by NGOs and

used to support certification in PNG

Details of case study sites in this research are given in Subsection 135 The study

sites are located in two village communities near Lae in Morobe province where

large-scale timber harvesting has taken place in the past Field interviews and data

collection for the study have been undertaken in the two villages

Subsection 136 of the background section gives details of the PNGFRI PSP network

Extensive work on establishment and measurement of PSPs have taken place since

1993 and the field procedures of plot measurements and recording (Romijn 1994a)

are included in this subsection

131 History of Timber Harvesting in PNG2

The then Forestry Department in PNG was established in 1938 and began operations

but these initial operations were interrupted by the advent of World War II (Lamb

1990) During the Second World War in 1942 some timber harvesting occurred and a

few forest resource surveys were also carried out These were mainly for military

purposes Several years after the second World War forestry activities resumed and

efforts were then concentrated on producing timber for post-war reconstruction and

building In the 1950s timber harvesting started in the Bulolo area where a ply mill

was established to process Araucaria logs from natural forest stands

2 The history of timber harvesting in PNG is based on earlier study by Lamb (1990)

9

In 1951 the first official statement on forest policy in PNG was issued by the then

Minister for Territories in the Australian Parliament (Lamb 1990) The Ministerlsquos

policy statement called for location assessment and regulation of availability of

forest resources for the development of PNG Although several years of surveys and

research followed by 1957 progress was still slow

Following on from 1957 the PNG Administration issued a five year Forestry Plan for

1962-1967 In 1963 the Administration had 548000 hectares of forest areas available

for exploitation most of these were allocated for temporary Timber Rights Purchase

(TRP) In the 1980s and early 1990s TRP areas were allocated by the government for

timber extraction The procedures involved purchase of timber and harvesting rights

by the government from the landowners from designated forest areas The

government then transferred the harvesting rights to in many cases an international

harvesting company for timber exploitation The extraction timber volumes in the

TRP areas depended on the density of commercial species The 1991 Forest Policy

and Act replaced the TRP system with what is now the forest management areas

(FMAs) Typically the procedures for the government to acquire an FMA from the

landowners are similar to those of TRPs but permits for granting a licence for an

FMA area are for forest areas that exceed 80000 ha Since 2000 up to now allocation

of forest areas for timber extraction under the FMA arrangement has increased In

such areas the extraction volumes differ from one concession area to another but

average timber volume removed during harvesting is about 15m3 ha

-1 (Keenan et al

2005)

During 1963 there were about 82 sawmills with a combined capacity of 930m3

per

day The timber industry in PNG at that time was fairly small as reflected by the low

amount of export Prior to 1962 annual log exports were less than 5000m3 and sawn

timber exports less than 800m3 (Office of Forest 1979) At that time the only major

timber development in the country was in Bulolo where the large ply mill was based

on Araucaria forests (Lamb 1990)

In 1964 a World Bank report indicated extensive forest resources in PNG and this

warranted large scale commercial exploitation By this time it was also indicated that

PNG would take advantage of a major timber deficit as anticipated in South Asia

East Asia and Oceania by 1975 however an expansion in the timber industry was

difficult at that time because of a high diversity of timber species and difficult terrain

10

in most forested areas throughout the country (Lamb 1990) The World Bank further

called for the need to attract large companies with marketing skills managerial

abilities and financial resources to make the timber industry successful

In 1963 and 1964 large timber areas in Bougainville and Madang were offered for

sale by public tender and by now there was an increase in timber areas allocated

throughout PNG under TRP arrangements Between 1964 and 1969 over 36 million

hectares of forest areas were assessed and by now the Forestry Department had some

11 million hectares under TRP (Lamb 1990) During the same period harvested log

volumes increased from 183000m3 to 421000m

3 ha

-1 In 1968 the Administration

prepared a Five Year Development Plan for the country and the Forestry component

of the plan called for further increases in production and downstream processing of

timber

In 1959 the first reconnaissance survey of the timber resources of the Gogol Valley

was carried out to assess the potential for timber development in the area The survey

covered an estimated area of 15000 hectares and in 1962 and 1963 detailed surveys

were carried out which used temporary plots of 01 hectares in size Data analysis

from these surveys recommended timber development in the Gogol Valley thus a

TRP was designated In 1964 the Gogol Valley timber resource was offered for

tender by the PNG Administration however as no successful tender was received by

the Administration the timber resources still remained undeveloped for some time In

1968 timber rights were again offered for tender and this time a Japanese consortium

submitted an application and began a feasibility study to determine the potential of

developing the timber resources for making pulp from the mixed timber species The

Japanese consortiumlsquos application was rejected by the PNG Administration because it

failed to meet the requirements for Australian or PNG equity in the project (DeAth

1980)

In 1970 when the potential for pulpwood development was considered a further

survey was carried out to assess the volume of smaller size class timber This survey

identified high volumes of sawlog size timber on the flatter areas of the flood plain

while pulpwood size timbers were located in most secondary forests Similar surveys

were carried out in adjacent forest areas including the Gum Naru and North Coast

Blocks and arrangement for TRPs were also carried out The estimated area included

11

in the Gogol Timber Project (GTP) was about 88000 hectares which contained an

estimated 7 million m3 of timber

The GTP was signed in 1971 between Japan and New Guinea Timbers (JANT) a

local company called Wewak Timbers and the PNG Administration for the

development of the Gogol Valley timber resources JANT started harvesting timber

for pulpwood in most parts of the GTP area while Wewak Timberslsquo harvesting

operations covered parts of Madang North Coast area In 1974 JANT shipped the

first woodchips from the GTP to Honshu Paper Co (Lamb 1990) By 1980 JANTlsquos

operations had covered most parts of the GTP area and harvesting for pulpwood

continued throughout the Naru and Gum Blocks By 1981 JANT had taken control of

timber resources of the Gogol Valley and its clear-felling operations spread into most

areas of the GTP and extended to cover the Western boundary of the existing Gogol

TRP

Before the 1980s Australian companies also carried out small-scale timber harvesting

in some parts of PNG The period 1980s to 1990s saw an influx of Japanese and

Malaysian companies carrying out harvesting operations in the country Currently the

timber industry in PNG is dominated by Asian companies and more than 80 of all

timber concessions are controlled by the Malaysian logging giant Rimbunan Hijau

From 2000 up to now allocation of new timber concession areas increased and in

2007 ten new areas have been released for harvesting

The history of harvesting in PNG from this literature review shows that there has been

an extensive logging of primary forests over the years This suggests that primary

forests in PNG are under extreme pressure from industry and the amount of cutover

forest is rapidly increasing

12

132 Papua New Guinearsquos National Forest Policy

The National Goals and Directive Principles as set out in PNGlsquos Constitution in

particular the Fourth Goal of the Constitution provides the basis for the countrylsquos

forest policies which is to ensure that the forest resources of the country are used and

replenished for the collective benefit of all Papua New Guineans now and for future

generations The countrylsquos new National Forest Policy has been designed and

formulated to remedy the shortcoming of the previous policy of 1987 to address the

recommendations of the Barnett Forest Industry Inquiry3 of 1989 and the World Bank

Review of 1990 and to adjust to new situations in the forestry and forest industry

sectors (Ministry of Forests 1991a) The National Forest policy was approved in

1990 followed by passing of the Forestry Act in the National Parliament in July 1991

(Ministry of Forests 1991b) The new Forestry Act replaced the previous national

legislation on forestry matters and reflects the objectives and strategies of the new

Forest Policy

The two main objectives of the countrylsquos forest policies are management and

protection of the nationlsquos forest resources as a renewable natural asset and utilisation

of the nationlsquos forest resources to achieve economic growth employment creation

greater PNG participation in industry and increased viable domestic processing The

Policy also calls for skills and technology transfer and the promoted export of value-

added products However up to now little progress has been made in terms of phasing

out log exports and increasing domestic processing although a lot of attempts have

been made in the past In 2008 the National Minister for Forests announced the phase

out of log exports from PNG by 2010 and increasing downstream processing of wood

products (ITTO 2008)

After the approval of the Policy and passing of the Act in 1990 and 1991 several new

pieces of forestry legislation have been put in place (PNGFA 2007) These include

the following

Forest Regulation No 15 1992 was introduced to enable registration of forest

industry participants and consultants under the Act Forestry (Amendment) Act 1993

was certified in April 1993 and provided for a clear administrative function of the

3 Inquiry carried out into the Forest Industry by former National Court judge Justice Tos Barnett which uncovered

mal-practices and corrupt dealings in the timber industry

13

Board the National Forest Service through the Managing Director and the Provincial

Forest Management Committees (PNGFA 1993) The National Forest Development

Guidelines were issued by the Minister for Forests and endorsed by the National

Executive Council during September 1993 The Guidelines were an implementation

guide for aspects covered in the new Forest Act especially in terms of sustainable

production domestic processing forest revenue training and localisation review of

existing projects forest resource acquisition and allocation and sustainable

development The National Forest Plan is prepared by the Forest Authority under the

Forestry Act 1991 (as amended) as required under the Act to provide a detailed

statement of how the national and provincial governments intend to manage and

utilise the countrylsquos forest resources (Ministry of Forests 1991b PNGFA 1996b)

The National Forest Development Programme (NFDP) under the Plan is now under

implementation

The PNG Logging Code of Practice (PNGLCP) was finalised in February 1996 and

tabled in Parliament in July 1996 (PNGFA and DEC 1996) The PNG Code is

inconsistent with the Regional Code proposed at the 1995 Suva Heads of Forestry

Meeting but is more specific to PNG operating conditions and was made mandatory

in July 1997 The 1996 Forestry Regulations which cover all aspects of the industry

procedures and control were approved by the National Executive Council in 1996 in

principle subject to some changes to be finalized later These Regulations provide the

legal status for the implementation of many of the requirements specified under the

Forestry Act 1991 (as amended)

The Forestry (Amendment no 2) Act 1996 was passed by Parliament and certified on

11 October 1996 (PNGFA 1996a) The major amendment requires the membership to

the Board to have eight representatives including the representatives of a National

Resource Owners Association and the Association of Foresters of PNG

Since the Forestry Act was first enacted in 1991 it has been amended four times

(PNGFA 2007) The first was in 1993 and this was followed by additional

amendments in 1996 2000 and 2005 (PNGFA 2001)

The Forest policy is administered by the PNG Forest Authority (PNGFA) under the

provisions of the Forestry Act 1991 Section 5 (Ministry of Forests 1991b) Section 7

of the Act specifies among the functions of the PNGFA (a) to provide advice to the

Forest Minister on forest policies and legislation pertaining to forestry matters (b) to

14

prepare and review the National Forest Plan and recommend to the National

Executive Council for approval and (c) to direct and supervise the National Forest

Service through the Managing Director Implementation of the Forest Policy Act and

Regulations have been have been problematic over the years This is because the

PNGFA is under-staffed and has limited capacity to fully enforce legal instruments

such as the PNGLCP Enforcement of rules and regulations in timber concession

areas has been difficult due to funding constraints and the isolation of many timber

harvesting project sites

In the case of landuse planning after harvesting there is no clear policy direction on

the management of cutover forest areas in PNG This study addresses some aspects of

National Forest Policy Part II Section 3 Sustained Yield Management The 1991

National Forest Policy does not provide directions on technical aspects of

management of cutover forest areas in PNG and there are no guidelines for land use

plans after harvesting This research will set the basis for development of new policy

guidelines for the management of cutover forest areas in PNG

133 Papua New Guinearsquos Forest Resources and Timber

Production

PNG is located on the eastern half of the Island of New Guinea and lies 160 km north

of Australia (Keenan 2007 ) The country comprises both the mainland and some 600

offshore islands It has a total land area of 470000 Km2 The country covers a total

landmass of about 46 million hectares of which 86 (394 million hectares) are

forested land while 14 (66 million hectares) is non-forested The estimated 394

million hectares of forested land are productive and have potential for some form of

forest development while the 66 million hectares of non-forested land remain un-

productive (PNGFA 1998) While two thirds of PNG is under forest cover the

official timber harvest is well below the estimated national sustainable timber yield of

47 million m3 (ITTO 2006)

15

1331 Forest Types

Different authors have described PNGlsquos vegetation and forest types using their own

terminology (for example Johns 1978) however the countrylsquos vegetation and forest

types have been described in detail and classified based on structural formations

(Hammermaster and Saunders 1995 Paijmans 1975 Paijmans 1976 Saunders

1993) Generally PNG has a wide range of floristic composition which is a

characteristic of the lowland tropical forests At sea level mangrove forests are

common while savannah grasslands can be found in the valleys and on foothills In

higher altitude areas montane forests are common although many of the forest types

in the country are representative of the floristic composition of a typical lowland

tropical forest

The vegetation types in Melanesia including PNG have been broadly described by

Mckinty (1999) to fall into three main types These include lowland moist rain forest

lower montane rainforest and upper montane rainforest However other vegetation

types common in the region are mangrove forests savannah and subalpine In PNG

all these vegetation types occur including the subalpine The lowland moist rain forest

is the most widespread and floristically rich vegetation type It occurs on flat gentle

and undulating terrain of the alluvial plains and foothills It is also found on steeper

hills extending up to 1500m above sea level (asl) Some of the major emergent tree

species that occur in this forest type include Pometia pinnata Intsia bijuga

Anisoptera thurifera Toona sureni Terminalia spp and Planchonela spp

As altitude increases and temperature decreases lowland rainforest is replaced by

lower montane rainforest from about 1000-1200m and extends up to below 3000m

asl (Mckinty 1999) One common feature of the montane rainforest is the dense moss

and tree trunks on the forest floor Some dominant canopy tree species in this forest

type are Castanopsis spp and Nothofagus spp

The upper montane forest occurs above about 3000m asl and tree species are more

stunted This forest type is very dense with mosses and epiphytes Major conifers in

the genera such as Dacrycarpus Papuacedrus and Podocarpus are common trees

found and may extend up to the tree-line at about 3900m asl The subalpine

vegetation comprises mainly grassland and Danthonia and Deschampsia species are

common The grasslands are dominated by small trees and shrubs and colourful

orchids such as Rhododendron are common in many parts of PNG Above 4000m

16

altitude plant growth is limited because of decreasing temperature and occurrence of

frost This is common on PNGlsquos highest mountain Mt Wilhelm which is about

4800m asl

Mangrove forests are salt-tolerant and occur at sea level on tidal flats and the saline

estuarine plains of larger rivers such as the Fly and Kikori in the southern part of PNG

and the Sepik river in the north The main mangrove genera that occur throughout

PNG include Sonneratia Avicennia Bruguiera and Rhizophora

Savannas are anthropogenic in nature and on the mainland of PNG grasslands of

Themeda and Imperata are common Tree genera of Eucalypts melaleuca and Acacia

are associated with savannas and grow well on savanna grassland The savanna

vegetation in PNG is similar to the flora in the northern part of Australia

1332 Timber Production and Trade

In 2003 PNG produced an estimated 72 million m3 of round wood of which about

76 (55 million m3) was fuel wood for domestic use (FAO 2005) Total industrial

tropical log production was an estimated 230 million m3 in 2003 which is an increase

from 210 million m3 in 1999 (ITTO 2004 ITTO 2005) though well below the

estimated sustainable yield of 47 million m3

The forest industry in PNG is predominantly based on log exports As such an

estimated 202 million m3 of tropical logs were exported in 2003 an increase from

198 million m3 in 1999 (ITTO 2004 ITTO 2005) which made PNG the worldlsquos

second largest exporter of tropical logs after Malaysia PNG earned US$126 million

in 2003 from exports of tropical timber $US109 million of which were from logs

(ITTO 2005) The principal log export markets for PNG logs in 2003 were China

(62 of all log exports) Japan (20) and Korea (9) (ITTO 2005) Unfortunately

the current level of harvesting by the timber industry is considered unsustainable and

accessible primary forests are likely to be exhausted in the next 15 years (Keenan

2007 )

PNGFA statistics estimated that the area harvested under commercial logging from

1988 to 2007 was over 2 million hectares and timber volume harvested in the form of

logs during the same period was over 39 million m3 (Figure 1-1) (PNGFA 2007) All

17

in all the forestry sector in the country has contributed 1773 million PNG Kina4 year

-

1 on average in the form of foreign exchange between 1998 and 2007 PNGlsquos export

of logs increased from 2002 to 2003 and then became stable from 2003 to 2007

(Figure 1-2) In 2002 log export totalled 1854000m3 and that increased to

2008000m3 in 2007

Figure 1-1 Timber Volume and Area harvested from 1988 to 2007 (PNGFA 2007)

Figure 1-2 Export of Primary Products by PNG (ITTO 2006)

4 As at 2007 the PNG local currency of 1 PNG Kina was equivalent to 040 Australian Dollars

0

50

100

150

200

250

300

00

05

10

15

20

25

30

35

40

Are

a H

arv

este

d (

00

0 h

a)

Harv

este

d T

imb

er V

olu

me

(Mil

lion

m3)

Year

Harvested

volume

Harvested area

0

500

1000

1500

2000

2500

2002 2003 2004 2005 2006 2007

Volu

me

(0

00

m3

)

Year

Logs

Sawn

Ply

Veneer

18

134 Certification Efforts in PNG

PNG has a national Forest Stewardship Council (FSC) working group in place and

has developed national certification standards (ITTO 2006 PNG FSC 2006) The

extent of FSC-certified forest areas in PNG is one area of 19215 hectares consisting

of semi-natural and mixed plantation forests and natural forests This figure may have

increased since then as in recent years non-governmental organisations and

environmental groups have been very active under the banner of FSC to certify

projects in various parts of the country For example efforts of some recognised non-

governmental organisations in PNG include Forest Management and Product

Certification Service (FORCERT) in West New Britain World Wide Fund for Nature

(WWF) in Western Province Village Development Trust (VDT) in Lae and

Foundation for People and Community Development (FPCD) in Madang FSC

activities in PNG include training and capacity building for local NGO partners

FORCERT is a PNG Not-For-Profit company that uses FSC certification as a

management and marketing tool to help small-scale sawmilling businesses practice

good forest management and strengthen their businesses (Scheyvens 2009) Together

with partner organisations FORCERT has established a FSC Group Certification

Service Network where community based timber producers come together under one

umbrella certificate and are linked with central timber yards FORCERT and its

partner organisations have also helped community groups in PNG to manage their

forest and business and assists in finding good markets for a wide range of species

Those community groups who become a member of this network receive training and

support in many aspects of running a portable sawmilling business and they are

expected to meet all forest certification requirements

The FORCERT Group Certification Service Network was developed in 2003 and

2004 by a wide range of stakeholders village sawmill managers timber yard staff and

managers eco-forestry environmental and social NGOlsquos and training educational

and research institutions (Scheyvens 2009)

Community groups in PNG have very little capacity to achieve FSC certification

standards and find that meeting certification requirements is quite difficult and the

costs of becoming certified are high It is a requirement that community groups have

to comply with international standards and organise and pay for an independent

19

auditor to assess their forest and business operation For the community groups to go

through the certification requirements and processes are difficult This is why

FORCERT is managing a so called FSC Group Certificate The group certification

system works in that individual small-scale producers that meet the set group

certificate standards can become group members The costs of managing the group

certificate are shared between the members who pay an annual fee plus a small levy

per cubic meter on all certified timber sold

Certified timber needs to be followed down the ―marketing chain from the forest

from which it was extracted all the way to the final buyer of the timber product This

―chain of custody guarantees buyers of certified products that the timber used did

come from well managed forests Therefore any trader in certified timber is required

to maintain their own Chain of Custody certificate FORCERT also manages a group

Chain-of-Custody certificate and offers membership to a number of selected small

central timber yards (Central Marketing Units or CMUlsquos) to which certified

producers can sell their timber

In terms of SFM in PNG according to ITTO (2006) forest areas designated for

management totalled five million hectares of which one and half million hectares

have been considered to be managed sustainably and are expected to undergo

certification in the near future

20

135 Case Study Sites

Two sites were selected for this study in a region where extensive harvesting of

primary forests had occurred in the past in PNG (Figure 1-3a) These sites were

located in Yalu and Gabensis villages outside Lae PNGlsquos second city The first site

was the Yalu community forest which is located on Grid Zone 55 492977 UTM East

and 9269368 UTM North (Figure 1-3b) The community harvesting project in this

village comes under the name Yalu Eco-forestry Project and is run by the Konzolong

clan The community forest area is approximately 2000 ha and the area allocated for

small-scale harvesting is about 1800 ha The total population of Yalu village is about

2000 people and about 30 are members of the Konzolong clan (600 clan members)

In terms of accessibility into the Yalu village and the community forest area there is a

government road connecting the community to Lae city The road is generally in good

condition however the community forest area is approximately five kilometres away

from the village and can be accessed by a 4x4 wheel drive vehicle on an all-weather

road which is often in a bad condition during wet seasons The Yalu community

owns a portable sawmill that was used in the past for small-scale harvesting however

it has broken down and is no longer being used On a few occasions their project has

sold sawn timber to the domestic market for about 450 PNG Kina per cubic meter

(PNGK per m3) The average price for exporting sawn timber to the overseas market

is approximately PNGK900 per m3 The Woodage in Sydney (Peter Musset) offers

PNGK2250 (AUD$900) per m3 for Intsia biguga (Kwila) and PNGK1500

(AUD$600) per m3 for mixed hardwood species

The majority of the people in Yalu community are engaged in subsistence farming as

their daily activity while a handful of them are employed by private companies in

Lae as tradesmen in various fields The main sources of income for the Yalu

community are selling local garden produce fermented cocoa beans and selling

poultry farm products at nearby local markets and the main market in Lae Other

small-scale economic activities that the community is engaged in to earn some income

include cocoa copra piggery operating trade stores and public transport The

community also has future plans for development of a large-scale oil palm plantation

in their area in partnership with a private agriculture development company called

Ramu Agri Industry (RAI) Recently the community has developed interest in eco-

timber production and marketing and there is a proposal in place for establishment of

21

a central marketing unit (CMU) for downstream processing and marketing of sawn

timber

The second case study site is the Gabensis village community forest area which is

located on Grid Zone 55 469240 UTM East and 9256166 UTM North (Figure 3-1a

and b) In this village only one family is involved in small-scale timber harvesting

Their family group name is the TN Eco-timber The total forest area available in the

Gabensis community forest is approximately 150 ha and about 60 ha are considered

as the operable area that can be easily accessible for harvesting

Like in the Yalu community the majority of the local people in Gabensis village are

involved in subsistence farming as their daily activity Other economic activities in

Gabensis village included cocoa farming poultry piggery and operation of local

trade stores and public transport to and from Lae city Operation of the portable

sawmill by the TN Eco-Timber currently serves as a direct income generating activity

for the one family involved in small-scale harvesting and at the same time supports

the Gabensis community with other community services These include the supply of

sawn timber as building materials for a local school clinic church building and a

community hall

The investigations and data collection in the case study sites form the basis for studies

in Chapter 4 5 6 and 7

22

Figure 1-3 Map of case study sites selected for the study

(a) region in PNG where extensive harvesting has taken place in the past and (b)

approximate location of the two communities (Yalu and Gabensis) in Morobe province

where the study sites are located

136 The PNGFRI Permanent Sample Plot Network

Currently 135 PSPs are being maintained by PNGFRI since 1992 to monitor forest

growth and dynamics with a measurement history extending over 15 years The PSP

network is comprised of 122 plots on selectively-harvested forest with 411

measurements and 13 plots on unlogged forests with 23 measurements (Fox et al

2010) These plots have been initially established and measured through an ITTO

funded research Project (Alder 1997) and maintained over the years by PNGFRI with

funding support from ACIAR (Keenan et al 2002) A large database has been

developed (Romijn 1994b) to store and manage all data from the PSP network

Earlier work by Alder (1998) evaluated data from some of these plots and concluded

that all the plots could be regarded as having rather similar floristic composition

characteristic of the lowland tropical forests of PNG Research work done at PNGFRI

to classify forest types on PSPs showed that these plots fall on one of lowland plain

lowland foothill lowland hill and lower mountain forest types (Yosi 1999 Yosi

2004) however these have been re-classified and integrated using the CSIRO

Vegetation Type maps for the 72 PSPs initially established under the ITTO funding

(a)

(b)

23

(Table 1-1) Since ITTOlsquos funding of the re-measurements of these plots came to an

end the rest of the PSPs have been established and measured by PNGFRI with

funding assistance from ACIAR Details of vegetation classification of the whole of

PNG are contained in Hammermaster and Saunders (1995) and Bellamy and

McAlpine (1995)

Table 1-1 Location of the 72 PSPs and their forest types (Yosi 1999)

Province Locations No Of

Plots

Date of

Establishment

Forest Type

Gulf

Western

Oro

Milne Bay

Central

Turama

Vailala

Oriomo

Wawoi Guavi

Embi Hanau

Gara Modewa

Ormand Lako

Iva Inika

2

2

2

2

4

2

2

2

091194

271194

121094

261094

200594

120694

070894

160396

Lowland Foot Hills

Lowland Plain

W (Lowland Plain)

HmFswWsw (Lowland

FHills)

Pl (Lowland Plain)

Hm (Lowland Foothills)

Hs (Lowland Hill)

Ps (Lowland Foot Hills)

Morobe

Madang

East Sepik

Sandaun

Oomsis

Trans Watut

Umboi

Kui

Yema Gaiapa

North Coast

Rai Coast

Hawain

Pual

Krisa

2

2

2

2

1

2

2

2

2

2

260593

261093

151294

121194

150596

200395

060495

090894

240894

100994

Hm (Lowland Foot Hills)

LN (Lower Mountain)

Hl (Lowland Plain)

Hm (Lowland Hill)

Hm (Lowland Hill)

Hm9 (Lowland Hill)

Hm (Lowland Hill)

(Lowland Hill)

(Lowland Foot Hills)

(Lowland Hill)

Southern

Highlands

MtGiluwe 2

211293 LsN (Mountain)

West New

Britain

East New Britain

New Ireland

Manus

Kapiura

Mosa Leim

Kapuluk

Central Arawe

Anu Alimbit

Pasisi Manua

Open Bay

Gar

Waterfall Bay

Lassul Bay

Cape Orford

Inland Pomio

Kaut

Umbukul

Central NI

Lark

West Coast

2

2

2

2

2

2

2

2

2

2

2

1

2

2

2

2

2

230793

110893

300893

060595

200695

070795

180893

270793

290893

090695

270695

280795

230993

011093

021195

181095

290395

Hm (Lowland Hill)

Hm8 (Lowland Hill)

Hm (Lowland Hill)

Hl (Lowland Foothills)

Hm8 (Lowland Foothills)

Hm8Hs8 (Lowland Hills)



(Lowland Foothills)

(Lowland Foothills)

(Lowland Foothills)

(Lowland Hill)



(Lowland Foothills)

(Lowland Foothills)

HmeHm6 (Lowland

Foothills)

14 Provinces 36 Locations 72 Plots

24

The different forest types on which 72 of the PSPs were established have been

classified according to the CSIRO Vegetation Type Maps (Hammermaster and

Saunders 1995 Bellamy and McAlpine 1995) The CSIRO description and

classification of vegetation in the PSPs are represented by fifteen codes (Table 1-2)

For example a code of Hm representing a medium crown forest according to the

CSIRO classification will represent a lowland foothill or lowland hill forest in the

PNG tropical forest context

Table 1-2 Description of Vegetation Types according to CSIRO

Code Vegetation Type

W Woodland

Hm Medium crowned forest

Fsw Mixed swamp forest

Wsw Swamp woodland

Pl Large to medium crowned forest

Hs Small crowned forest (low altitude on Uplands)

Ps Small crowned forest (low altitude on Plains and Ferns)

Hm9 Medium crown forest (degree of disturbance class 9 is slightly

disturbed)

LN Small crowned forest with Nothofagus

Hl Large crowned forest

LsN Very small crowned forest with Nothofagus

Hm8 Medium crown forest (degree of disturbance class 8 is slightly disturbed)

Hs8 Small crowned forest (low altitude on Plains and Ferns degree of

disturbance 8 is slightly disturbed

Hme Medium crowned forest with an even canopy

Hm6 Medium crowned forest (degree of disturbance class 6 is moderate

disturbance

25

1361 Plot Design and Layout

During the establishment of PSPs all the plots were randomly located and established

in pairs All the plots are one hectare in size and divided into 25 sub-plots of 20 m x

20 m (Romijn 1994a) The field procedures for establishment and measurements of

the plots were adapted from Alder and Synnot (1992) During plot measurement all

tree species of 10 cm in diameter and above were assessed Measurements taken on

trees included diameter at breast height (DBH) or above buttress height crown

diameter crown classes (Dawkins 1958) and an initial basal area count for each tree

was undertaken Plots on selectively-harvested forest were established and measured

either immediately or sometime between then and 10 years after harvesting For plots

accessible by road re-measurements have been taken on an annual basis while the

initial re-measurement of the other plots were carried out on a two-year interval but

have been re-scheduled for re-measurements on a five-year interval due to funding

constraints In the assessment of trees in the plot a standard quadrat numbering

system was used This system uses quadrat numbers on the basis of coordinates or

offsets from the plot origin for example south-west corner (Figure 1-4)

NW NE

08 28 48 68 88

06 26 46 66 86

04 24 42 64 84

02 22 42 62 82

00 20 40 60 80

SW SE

Figure 1-4 Plot layout in the field (adapted from Romijn (1994a)

Plot origin

where

measurement

starts

N

100 m

100 m

26

1362 PSP Locations

Most of the plots have been recorded on lowland tropical forests distributed

throughout PNG as these are where most harvesting activities have taken place

(Figure 1-5) Only two plots have been established in higher altitude montane forest

dominated by the genera Castanopsis and Nothofagus in Southern Highlands

province Twenty three of PSPs are located on the island of New Britain where

there are large areas of selectively-harvested forest

Figure 1-5 Permanent Sample Plots Location Map (adapted from (Fox et al 2010)

The data from the PSP network discussed in chapter 1 section 13 forms the basis for

the study in chapter 3 (Dynamics of natural tropical forest after selective timber

harvesting in PNG)

27

14 RESEARCH QUESTIONS AND OBJECTIVES

This research study involved use of scenarios (Wollenberg et al 2000) which is a

new approach that requires a participatory approach to forest management in PNG

This approach has been considered appropriate for the PNG situation because

landowner expectations and requirements have not been taken into account in forest

planning and management in the past This study anticipates to bridge this gap

The overall aim of this study was to investigate and identify frameworks that support

community decision-making regarding the future use of cutover forests in PNG

In order to achieve this a management strategy evaluation (MSE) framework

(Butterworth and Punt 1999 Sainsbury et al 2000) was adopted to develop and

demonstrate practical science-based methods that will support community-based

planning and management of cutover forests in PNG

There were four main objectives of this research study The first was to assess the

current condition and future production potential of cutover forests in PNG This was

achieved from the analyses of existing PSPs and the assessment of the forest

resources in two case study sites Secondly this study aims to develop scenario

analysis and evaluation tools for assisting decision-making in community-based

management of cutover native forests In consultation with stakeholders a

participatory action research protocol (Creswell et al 2007) was used as a guide to

analyse stakeholder interests and expectations through field interviews Based on this

consultation and interviews future forest management options were investigated

These options were further analysed and forest management scenarios were developed

using existing planning tools These were tested and analysed using the scenario

analysis and evaluation tools developed under objective two Effects of scenario

analyses were compared and evaluated Thirdly the scenario analyses and evaluation

tools developed under the second objective were tested in case study sites in cutover

native forests in PNG The two case study areas were selected in a pilot region where

extensive timber harvesting had taken place in the past The fourth objective of this

study was to develop a scenario analyses and evaluation framework for community-

based management of cutover native forests in PNG Scenario outcomes from the

exercises in the second and third objectives of the study were integrated into this

framework The systems developed were based on sound information compliance

28

with expectations of forest certification bodies and meeting the needs of local

communities

The four main questions this study addressed were

1 What is the current condition and future production potential of cutover forests

in PNG

2 What are the potential options for community-based management of cutover

forests in PNG

3 How can information on the structure and dynamics of forests and the

potential uses of forest resources be used to support effective decision-making

in community-based management of cutover native forests in PNG

4 What type of scenario method is appropriate for adaptive management of

cutover native forests in PNG

15 THESIS OUTLINE

The structure of this thesis consists of eight chapters organised around five main parts

These parts are introduction (Chapter 1) literature review (Chapter 2) condition of

cutover forest (Chapters 3 and 4) scenario analyses and evaluation tools (Chapters 5

6 and 7) and the conclusion (Chapter 8) Chapter 1 introduces the thesis and discusses

some major forest management issues and problems in PNG Some background

information is provided including the history of timber harvesting in PNG national

forest policy PNGlsquos forest resources and timber production and certification efforts

in PNG The background section in Chapter 1 also describes the case study sites and

the PSP network The research questions and objectives of this study and the outline

of this thesis are also included in the introductory chapter

Chapter 2 is the literature review and discusses the current issues in tropical forest

management in the regional context and gives some examples of the PNG situation

The literature review also includes three different management approaches that may

be considered for the management of cutover forests in PNG These approaches are

the management strategy evaluation (MSE) the scenario method and the Bayesian

Belief Network (BBN)

As part of this research study dynamics of natural tropical forest after selective

timber harvesting in PNG have been analysed using historical data from an extensive

29

PSP network that have been managed by the PNGFRI for over 15 years These

involved quantitative analyses of forest structure data from PSPs Details of these

analyses include growth and dynamics and recovery and degradation of cutover native

forests in PNG and are presented in Chapter 3 In this research two case study sites

have been selected in PNG The details of forest resource assessment in the two sites

are given in Chapter 4 These details also include some background information about

the two study sites and results of analyses of forest assessment which includes

residual timber volume and aboveground forest carbon Evaluation of scenarios for

CBFM is discussed in Chapter 5 These involved qualitative analyses of field

interviews in case study sites and quantitative analyses of timber yields under

different management scenarios in community-based harvesting Analyses of timber

yields in this case have been facilitated with the application of a planning tool and the

outputs are discussed

In Chapter 6 decision analysis models developed in this study for cutover forests in

PNG are described The models have been tested using data available in case study

sites and the results and outputs are discussed The two sites that have been used as

case studies in this research are Yalu and Gabensis villages outside Lae in Morobe

province PNG

Based on the MSE approach and the outputs from the studies in Chapter 5 and 6 an

integrated conceptual framework has been developed for community-based

management of cutover forests in PNG and the details are discussed in Chapter 7

The thesis is concluded in Chapter 8 by discussing the implications of applying the

tools developed in this study for community-based management of cutover native

forests in PNG

27

REVIEW OF THE LITERATURE

28

CHAPTER 2

AN OVERVIEW OF CURRENT ISSUES IN TROPICAL FOREST MANAGEMENT

21 FOREST DYNAMICS

211 Introduction

Subsection 211 gives a general introduction of tropical forests and topics such as

species diversity composition distribution structure and disturbance regimes are

highlighted

Forests are dynamic ecosystems that are continuously changing (Shao and Reynolds

2006) These changes relate to the growth succession mortality reproduction and

associated changes that are taking place in forest ecosystems Usually these changes

are projected to obtain relevant information for decision-making and are the basis of

forest simulation models that describe forest dynamics Projection and simulation

have been widely used in forest management to update inventory and to predict

future yields species composition and ecosystem structure and function under

changing environmental conditions

Tropical forests are biologically diverse and there are complexity and a great diversity

of interactions within rainforest ecosystems For example studies done by Nicholson

(1985) showed that the estimated number of tree species in north Queensland

rainforest are about 900 In terms of species distribution in tropical forests it is

common for a lot of tree species to be represented by few individuals In some forest

areas in the tropics abundance of seed resources and heavy fruit production

encourages those areas to have dense and clumped seedling and young sapling

distribution on the forest floor Examples of these type of forests are the Dipterocarp

forests in Peninsula Malaysia (UNESCOUNEPFAO 1978) Tropical rainforests are

always heterogeneous and often it is difficult to describe its structure In terms of

disturbances to tropical rainforests particularly logging activities the impacts may

occur in various forms However apart from changes in environment including

29

changes in microclimate and soil timber harvesting affects the forest structure

(Kobayashi 1992)

In Subsection 212 the review gives an overview of the extent of tropical forests

Most of this information have been compiled from work done under the FAO Forest

Resource Assessment (FRA) 2000 (FAO 2000) as well as the description of tropical

rainforests in the region according to Westoby (1989)

Some background on forest dynamics relating to forest succession and the associated

changes that take place in a forest stand are discussed in Subsection 213 Forest

dynamics relates to the growth mortality reproduction and the associated changes

that take place in a forest These and the factors that influence the dynamics in a forest

area are discussed in this subsection

In Subsection 214 the details of the different forest types in the tropics are described

and the difficulties in the classification of these forests are pointed out To give some

examples PNGlsquos vegetation and forest types are described

Subsection 215 is species diversity of tropical forests Tropical forests are considered

as biologically and genetically diverse and the species richness of some countries in

the region are discussed as examples in this subsection Impact of harvesting on

growth and species diversity in tropical forests are discussed in detail in Subsection

2151

Species distribution in tropical forests and the environmental factors that influence

their distribution pattern are discussed in Subsection 216 The review gives some

examples from the PNG situation where some tree species that are common in higher

altitude areas are able to grow well in lower altitude environments

Regeneration is an important aspect regarding the sustainability of timber extraction

in tropical forests In Subsection 217 regeneration mechanism and the

environmental factors that determine the extent of regeneration in tropical forests are

discussed The silvicultural systems applied in tropical forests are described in

Subsection 2171 and this review is mainly based on earlier studies by Dawkins and

Philip (1998) and Mckinty (1999) Examples of application of these systems in

selected tropical countries are given

In tropical forests those tree species that are slow growing and are able to grow under

shade are referred to as shade tolerant while tree species that are light demanding and

30

are able to grow under the forest canopy with limited light levels are called shade

intolerant In Subsection 218 different aspects of shade tolerance in relation to light

demanding tree species and those that are able to grow under limited light are

discussed in detail

Subsection 219 is the review on the subject of stand structure of tropical forests To

describe the structure of tropical forests accurately is difficult because these forests

are complex and heterogeneous structurally These aspects are discussed in detail

under this subsection

All forests are subjected to both naturally-occurring disturbances as well as human-

induced ones In Subsection 2110 responses of tropical forests to both of these

disturbances are described Natural disturbances include such as phenomena as

flooding or landslips and human-induced disturbances are particularly activities such

as timber harvesting Tropical forest responses to natural disturbances are detailed in

Subsection 21101 and in Subsection 21102 how these forests respond to human

activities for example timber harvesting is discussed Some examples in the tropics

relating to the changes in stand structure after logging activities are highlighted with

examples in PNG from research studies on natural forests (Yosi 2004)

The literature review in Subsection 2111 discusses key issues of forest dynamics in

the tropics and some general conclusions are drawn from these discussions in

Subsection 2112 The objective of Section 21 from the literature review is to

understand the complex structure of tropical forests and how these forests response to

disturbances

212 Overview of Tropical Forests

Tropical forests are considered to be the most biologically diverse of the worldlsquos

ecosystems Though they cover only 5 of the globe (ITTO 2007) tropical forests

harbour more than half of the worldlsquos terrestrial plant and animal species Tropical

forest landscapes are home to hundreds of millions of people For many of these

people who live in or near the forests tropical forests provide a large proportion of the

goods and services they use in their daily lives including fruits vegetables game

water and building materials They also play an important and complex cultural role

particularly in indigenous communities In PNG a majority of the population who live

in rural areas depend on forests for their livelihoods

31

FAO FRA 2000 classified the tropical forests into six ecological zones which

include tropical rain forest tropical moist deciduous forests tropical dry forest

tropical shrub land tropical desert and tropical mountain systems (FAO 2000) Of

these six ecological zones the rain forest moist forests and dry forests are

distinguished to be the most important as far as timber production is concerned

According to Westoby (1989) the tropical evergreen rainforests are concentrated in

the Amazon Congo basin and equatorial Africa and Indo-Malaysian region covering

South East Asia and PNG There are important climatic differences between these

three regions but all are characterized by a great diversity of tree species From a

forest management perspective serious damage can occur to the generally poor soils

by unmanaged removal of trees and loss of nutrients caused by burning The diversity

of vegetation ranging from species-rich rainforest to barren desert provides

enormous variety in the tropics the variation which is a result of variation in rainfall

(Evan 1982)

Tropical moist deciduous forests are widespread in the Northern part of South

America particularly Brazil Venezuela and the Guyana Shield In Asia they are found

in parts of India Sri Lanka Thailand Laos Cambodia Vietnam Burma and southern

China (Cooper 2003) In Africa these forests are less extensive than in Asia and

South America and occur in the southern and eastern fringes of the Congo basin

Dry forests occur over much of Sub-Sahara Africa not covered by the equatorial rain

forests Many of these areas are savannah woodlands with sparse tree cover In Asia

these forests are found in parts of India southern China and continental South East

Asia South American tropical dry forests are found in north eastern Brazil the

Caribbean coast and in the Argentinean Chaco

213 Tropical Forest Dynamics

Forest dynamics relates to the growth mortality reproduction and associated changes

in a forest stand (Avery and Burkhart 1994) These changes can be predicted through

field observations in existing forest stands while past growth and mortality trends are

used to infer future trends in the forest stands observed Forest dynamics describes the

physical and biological forces that shape and change a forest and this process is in a

continuous state of change that alters the composition and structure of a forest

32

According to Shugart (1984) forest dynamics reflect more generally on the

phenomenon of succession Succession in this case is considered to involve the

changes in natural systems and the understanding of the causes and direction of those

changes Forest succession and forest disturbance are considered to be the two main

factors that influence the ongoing process of forest dynamics in a forest area In forest

disturbances the events that may cause changes in the structure and composition of a

forest include fires flooding windstorm earthquake mortality caused by insects and

disease outbreak Human activities also contribute to these changes for example

timber harvesting anthropogenic disturbances such as forest clearing and introduction

of exotic species

Forest succession refers to the orderly changes in the composition or structure of an

ecological community The two levels of forest succession are primary succession and

secondary succession Primary succession is usually caused by formation of a new

unoccupied habitat community from such events as a lava flow or a severe landslide

On the other hand secondary succession is often initiated by some form of

disturbance caused by for example fire severe wind-throw or logging activities

Ecological changes in a forest can be influenced by site conditions species

interactions stochastic factors such as colonizers and seeds or weather conditions at

the time of disturbance

214 Forest Types

According to Dawkins and Philip (1998) classification of tropical forest types fall

into three major categories as

i) Tropical wet evergreen which has rainfall over 2500mm per annum

ii) Tropical semi-evergreen with rainfall between 2000 and 2500mm per annum

iii) Moist deciduous forest having rainfall between 1500 and 2500 mm per annum

Some common characteristics of regions with tropical forest types are an enormous

range in precipitation seasonality temperatures relative humidity frequency of

extreme climatic features such as violent storms hail hurricanes and severe

droughts Forests in the region with an equatorial climate can usually have severe

drought making them prone to fires for example in the case of Nigeria in 1973 in

parts of Indonesia in 1982 1983 1988 1991 and 1994 and in the Amazon basin in

1995 (Dawkins and Philip 1998)

33

In some parts of the tropical region there may be forest stands that are dominated by

one particular species as is the case in Malaysia and Indonesia where Dipterocarp

forests are commonly found (Whitmore 1984) the varzea forests of Amazon basin

and the teak forests of India and Burma (Champion 1936)

The classification of tropical forest types is notoriously difficult and contentious

(ITTO 2006) however different authors have described forest types in the tropics

using their own terminology For example Tracey (1982) and Webb and Kikkawa

(1990) described rainforests of North Queensland using habitat features as well as

physiognomic features such as canopy layering Generally rainforests in Australia

cover various structural and floristic types which are described by reference to

climatic features The major forest types in North Queensland rainforests fall into the

categories of tropical sub-tropical monsoonal and temperate (Truswell 1990)

PNGlsquos vegetation and forest types have been described in detail based on structural

formations (Hammermaster and Saunders 1995 Paijmans 1975 Paijmans 1976

Saunders 1993) however generally PNG has a wide range of floristic composition

which is a characteristic of the lowland tropical forests At sea level mangrove forests

are common while savannah grasslands can be found in the valleys and on foothills

and in higher altitude areas Montane forests are common although much of the forest

types in the country represent the floristic composition of a typical lowland tropical

forest

215 Species Diversity

Tropical rainforests are considered to harbour the greatest wealth of biological and

genetic diversity of any terrestrial community (Hubbell and Foster 1983) These

forests are also known for their high numbers of different plant species Earlier studies

in several tropical rainforest sites around the world in a 08 ha plot by Whitmore

(1998) revealed highest levels of tree species diversity at around 120 different species

per hectare in PNG 150 in Malaysia and 250 in Peru However recent studies and

botanical collections may have otherwise increased the number of species found in

these countries Usually most species are patchily distributed many are random and a

few are uniformly spaced For example according to studies carried out in Panama

(Hubbell and Foster 1983) complete mapping of all trees over 20cm DBH in a 50

hectare plot of tropical rainforest has shown patterns of tropical tree distribution and

34

abundance over a large area in unprecedented detail In their study it was found that

among the patchily distributed species several tree species were found to closely

follow the topographic features of the plot It is considered that the patchiness has a

major effect on the species composition of local stands

The island of New Guinea (PNG and Indonesian western province of Irian Jaya) has a

great diversity in vegetation and a flora which is one of the richest in the world

(Loffler 1979) One of the unique features of tropical mixed forest is that the great

diversity of the plants are trees ranging in size from 1-2 meters to some of the worldlsquos

tallest for example Araucaria hunsteinii can grow to almost 90m (Mckinty 1999)

2151 Impact of harvesting on growth and species diversity

In tropical forests growth of most primary species under shade can be very slow for a

long time often ceasing for many years (Mckinty 1999) Growth rate then increases

for a primary tree species when it is released by the formation of a gap or if it grows

tall enough for its crown to be no longer overshadowed by its neighbours

Studies to examine the effects of logging and treatments on growth rates and yield of

tropical forests showed that diameter increments basal area and volume production

were strongly affected by reduction in stocking resulting from logging and treatment

Reduction in stocking and basal area by felling or treatments such as poisoning results

in faster mean increments of remaining trees This is evident in studies carried out in

Suriname (Synnot 1978) and north Queensland rainforest (Nicholson et al 1988)

Studies of effects of treatments on desirable trees (eliminating unwanted trees by

poisoning or felling them for firewood or charcoal) resulted in faster average diameter

increments of larger trees than those of smaller trees

Studies carried out to assess stand changes in North Queensland rainforests after

logging by Nicholson et al (1988) on ninety permanent plots some of which have

been treated silviculturally showed that species diversity was lowered and this change

was found to be correlated with the severity of logging as evidenced from

measurement of basal area loss Data obtained from their study indicated that a certain

level of disturbance in the rainforest is required to encourage higher level of species

diversity In this case logging generally provided this disturbance and there were

evidence of regeneration and species diversity after logging activities which

enhanced potential for future production It is considered that most rainforests are

35

very rich in species for example PNG and South-East Asian region rainforests are

considered richer in species than North Queensland rainforests whereas the African

rainforests are considered poorer in terms of species richness

Lindemalm and Rogers (2001) carried out studies on impacts of conventional logging

and portable sawmill logging operations on tree diversity in tropical forests of PNG

Their studies compared impacts of conventional high intensity logging and low

intensity portable sawmill logging on tree diversity six years after harvesting Results

from their study indicated that tree diversity was significantly lower after high

intensity logging in comparison to low intensity logging and unlogged forest

Usually species richness is best indicated by the number of species while species

diversity is indicated by the Shannon-Wiener Index (Stocker et al 1985) Studies in

tropical forests of PNG showed that in low intensity logging there was a reduction in

tree diversity of 5 and 25 for the Shannon Wiener Index (H1) and Simpsonlsquos

Index (D) of diversity respectively in comparison to unlogged forest (Lindemalm and

Rogers 2001) Diameter growth rates of many PNG tree species are found to be in

excess of 20 mm yr-1

(Alder 1998 Lindemalm and Rogers 2001) and the study of

diameter increment of tree species in PSPs (Alder 1998) showed that the increment

for all tree species averaged 047 cm yr-1

(47 mm)

216 Species Distribution

In tropical rainforests a lot of species are uncommon while fewer are common and it

is also known that a lot of species are represented by few individuals This is

supported by studies carried out by Poore (1968) on a 23 hectares area of lowland

tropical forest in Jengka Penninsula Malaysia in which 377 tree species were

assessed The results of his study indicated that 81 (307) of the total number of

species were represented by only one to ten individuals each while less than 143

species (38) were found to be represented by only a single individual

Tropical forest tree species distribution may be influenced by environmental factors

such as soil rainfall temperature and altitude however certain tree species may be

able to adapt to any environmental condition while some may be suited to specific

site and environmental conditions For example in PNG the commercially important

Araucaria species A hunsteinii (Klinkii pine) and A cunninghamii (Hoop pine)

though common in higher altitude forest types are also able to adapt well on coastal

36

vegetation environments close to sea level These two tree species are common in the

Bulolo and Watut area on lower montane forest types (over 600 meters asl) but have

been also found along the Huon coast near Kui-Buso village (below 100 meters asl)

Related research carried out by Pokana (2002) to study the relationship between soil

groups and tree species on logged-over forests also showed that none of the natural

forest tree species studied had a strong relationship with the three environmental

variables (vegetation type soil type and rainfall) observed This may suggest that a

large number of native forest tree species occurring in PNG may be suited to any

environmental and site conditions in the country

217 Regeneration Mechanisms

Extent of regeneration is often determined by factors controlling the fate of seeds and

seedlings and the main influencing factors are soil seed bank light humidity

predation and defoliation by animals as well as seed sterility

Regeneration of commercial tree species is an important aspect regarding

sustainability of logging in tropical forests A study carried out in Bolivia

(Fredericksen and Mostacedo 2000) compared density species composition and

growth of timber species seedlings and sapling regeneration 14 months after selection

logging This study indicated that there were highest density and greatest initial height

growth rates of tree regeneration in areas with the greatest amount of soil disturbance

including log landings and logging roads Regeneration in this case was high due to

high densities of light-seeded shade intolerant species such as Anaderanthera

colubrina and Astronium urundeuva This situation is similar to what happens after

selective logging in PNG where gaps skid tracks and logging roads are quickly

conquered by pioneer light demanding species such as Macaranga Alphitonia and

Trema orientalis In many cases the invasive species Piper is very common Studies

done by Park et al (2005) on natural regeneration in a four year chronosequence in a

Bolivian tropical forest also showed that pioneer regeneration was more abundant

than that of commercial species in all harvest years

In tropical forest conditions it has been proposed that forests regenerating after

timber harvesting are not expected to grow and achieve the heights of the original

forests because the lowered vegetational matrix will lower the biological clear bole-

height of developing young trees Usually height reduction of 25-50 may be

37

expected and this will reduce the living space (volume) of the forest by an equivalent

amount (Ng 1983)

After logging operations silvicultural treatment in residual stands may be required in

tropical forests to encourage regeneration and growth of commercially viable timber

species If logged over forests are not encouraged to regenerate commercial timber

species they are more susceptible to conversion to other land uses when accessible to

different users (Fredericksen and Putz 2003) Natural regeneration forms an essential

component of selection harvesting systems used in rainforest management and long-

term yield forecasts must take account of the presence and amount of this

regeneration (Vanclay 1992)

Due to abundance of seed resources and periodic heavy fruit production in tropical

rainforests a lot of forest areas are found to have dense and clumped seedling and

young sapling distribution on the forest floor Examples of these type of forests

according to UNESCOUNEPFAO (1978) are Malaysian mixed Dipterocarp forests

mixed lowland forest in Irian Venezuela Sumatrana mixed swamp forests and

Araucaria forests in PNG

2171 Silvicultural Systems

The two main silviculture systems applicable for forest management are selection and

uniform (clear-cutting) systems (Dawkins and Philip 1998 Mckinty 1999)

Silvicultural systems for commercially valuable native forests are largely concerned

with their regeneration (Mckinty 1999) From the two silvicultural systems the four

common methods of forest regeneration applied in both tropical and temperate forests

are selection shelter-wood seed-tree and clear-cutting In all the methods

regeneration is assumed to arise from natural or induced seed-fall sowing or planting

or a combination of these However in tropical forests the principal source of

regeneration of primary species following selection harvesting is usually advanced

growth (Mckinty 1999)

The two silvicultural systems may be further classified as monocyclic or polycyclic

Monocyclic systems are even-aged regeneration methods where all saleable trees are

harvested from a site over a short time-frame The length of the cycle in this system is

equal to the time it takes the trees to mature to achieve rotation age

38

Polycyclic systems are uneven-aged regeneration methods that involve returning to

the one area to harvest selected trees at short intervals in a continuing series of felling

cycles In this system the length of the cycle is less than the rotation age of the trees

During the post-1900 to the late 1950s silviculture of natural tropical forests was

evident in India Burma Indonesia and Malaysia (Dawkins and Philip 1998) The

main tree species being developed into plantation crops at that time were teak

(Tectona grandis) and Shorea robusta However progress was hampered by the

World economic depression of 1930 the wars and shortages of experienced staff

From the 1950s up to the early 1990s as population increased World trade in wood

production expanded giving rise in demand for sawn timber in the tropics During this

period the intensity of felling rose in the tropics and in countries such as Sabah and

Indonesia logging operations destroyed the canopy removed significant part of the

seed bearers and encouraged the growth of pioneer species (Dawkins and Philip

1998)

Ongoing cases of success in tropical rainforest management and silviculture are now

seen in not all but few countries in the tropics For example in Peninsular Malaysia

the uniform system has been used to manage Dipterocarp forest while selective

logging system has been used in the Philippines The uniform system used in

Peninsular Malaysia has been associated with a diameter increment of about 08-

10cm per year (Poore 1989)

Generally in selective harvesting systems used in the region timber harvesting is

carried out on the basis of minimum felling diameter limits For example in PNG the

diameter cutting limit for selective felling system is 50cm dbh This means that in a

timber harvesting operation all commercial trees with a diameter of 50cm and above

across the board are harvested The selective system used in PNG is associated with

an average diameter increment on all commercial timber species to be about 047-

10cm per year (Alder 1998)

39

218 Shade Tolerance

Forest tree species that are able to tolerate low light levels and are able to grow under

shade are usually referred to as shade tolerant and these species are mostly slow

growing Often these tree species can regenerate in areas where lower levels of light

reach the forest floor For example Vitex lucens and Dysoxylum spectabile are shade

tolerant tree species that are able to regenerate in areas where lower levels of light

reach ground level while Agathis australis is a much more light demanding tree and

requires larger gaps to regenerate In PNG one of the most important commercial

timber species Pometia pinnata (Taun) is a shade tolerant species which is able to

regenerate under canopy and limited light levels For light demanding tree species

(shade intolerant) they may be able to persist without significant growth in deep

shade until a gap appears

It is also quite common in tropical forest logging that mortality rates are usually high

on shade tolerant species This is supported by studies carried out on vegetation

structure and regeneration in tree-fall gaps of reduced-impact logged of subtropical

forests in Bolivia (Felton et al 2006) This study showed that ground disturbance

during timber harvesting caused higher rates of mortality to shade tolerant species in

advance stages of regeneration This resulted in the removal of the competitive height

advantage needed by shade tolerant species to compete for gaps and therefore further

encourages opportunities for pioneer species to dominate gap regeneration

In temperate forests if there is less accumulation of organic matter in a forest stand

understory trees remain more vigorous during transitional growth stages (Oliver et al

1985) and in this situation trees which eventually form the overstory during true old

growth stage can be either tolerant or intolerant of shade Sometimes shade tolerant

species become established in the understory re-initiation stage and slowly grow

upward as the overstory releases growing space Some examples of shade tolerant tree

species found in temperate forest types are for example in the Pacific north-western

United States where western hemlocks Pacific silver firs and grand firs which grow

beneath old Douglas fir canopies (Oliver et al 1985)

40

219 Stand Structure

Stand structure of a forest may be investigated to observe how a forest behaves over

time which is quite important for forest management purposes If a forest stand has

past management history or some forms of disturbance such as commercial harvesting

or other human and animal influence often it will be necessary to assess its quality

before future management decisions are made

To describe the structure of tropical forests accurately either in words or in

quantitative terms presents considerable problems (Richards 1983) It is often

difficult to describe the structure of tropical forests as rainforests are always very

heterogeneous structurally however single dominant tropical rainforests show clearly

defined strata while mixed forests usually do not

In a tropical forest ecosystem the structure of forest also controls the distribution of

smaller plants like the epiphytes Primary rainforests have numerous gaps due to

death of large old trees and often also gaps caused by lightning strikes windfalls

landslips and other natural causes

Often the distribution of the number of tree stems between diameter size classes and

distribution of individual stems amongst basal area size classes are the measures that

are used to examine the structure of a stand which are more informative As well as

that size class distribution of individual tree species in a stand is also useful to

examine the structure of the stand

2110 Responses of Forest to Disturbances

All forests are subjected to a number of naturally-occurring disturbances and many to

human-induced ones which produce a range of different-sized gaps in the canopy

(Mckinty 1999) The death and falling of a large dominant tree and the associated

damage of its neighbours could produce a gap of some 100-800 m2 (Lamprecht 1989

Richards 1996) Gaps caused by the death of trees are of different quality to those

caused by fire landslip or human disturbances such as logging or traditional farming

41

21101 Tropical forest response to Natural Disturbances

Various natural disturbances in tropical forests create a mosaic of vegetation types

with strong species diversity between them (Mckinty 1999 Whitmore 1990) This

diversity occurs from place to place within the same community For example violent

annual flooding in the Peruvian Amazon forest resulted in the occurrence of high

species diversity from the formation of a mosaic of forest types (Whitmore 1990)

PNG is a land wracked by continual catastrophe such as earthquakes landslides

volcanic activities and strong winds In dry periods forests that are slightly seasonal

become dry hence frequent fires can be experienced (Whitmore 1990) In PNG

shifting cultivation and associated regrowth are also extensive Timber tree species for

a tract of lowland rainforest usually include a considerable proportion of pioneers

such as the species of Albizzia Paraserianthes and Serianthes besides strong light-

demanding climax species for example Campnosperma spp Pometia pinnata and

Terminalia spp

In the Melanesia region (PNG-Solomon Island-Vanuatu) cyclones earthquakes

volcanic eruptions and periodic fires are frequent and can destroy large areas of forest

(Mckinty 1999) Prolonged heavy rainfall or tectonic activity causes landslips and

other mass movement of the soil surface in Melanesia They may be also caused by

fires or inappropriate roading The most common form of natural disturbance is the

formation of gaps caused by the death of trees

Gaps caused by landslips can be extensive for example Whitmore (1998) estimated

that 8-16 per century of the land surface of PNG is disturbed by landslides Lava

and heat from volcanic eruptions can also destroy an entire rainforest

Tropical mixed forests are not fire-prone nor do they require fire for their

regeneration however tropical forests are vulnerable to extensive fires during

prolonged drought for example in an El Nino Southern Oscillation (ENSO) event

(Mckinty 1999) Rainforests have been destroyed by fire during drier weather periods

for over several thousand years (Whitmore 1991) Fire can be caused by volcanic

eruptions or lightning in drier forests Human induced fire in the tropics is much more

frequent and widespread This can be from fires lit during cooking or more frequently

from activities of shifting cultivation for example in PNG extensive areas of forests

were burnt during the ENSO event of 199798

42

21102 Tropical forest response to harvesting

Generally in a commercial logging operation in a tropical environment large size

class trees with economic value are removed for timber During the process of timber

extraction excessive damage may be done to the small size class trees which are not

always caused by felling itself but by the movement of machinery in and out of the

forest as well as the construction of logging tracks and skidding trails There are also

damage to existing regeneration and the residual stand as a direct result of logging It

is often obvious especially in the tropical region in uncontrolled logging operation

that mortality rates are quite high immediately after logging

Harvesting and removal of logs using logging machinery creates gaps on the forest

floor to which the forest responds The amount of damage to a forest and the nature of

the response depends on how many trees are felled than on the volume harvested

(Mckinty 1999) Usually felling damage is in the form of breakage of the crowns and

snapping of the stems of some of the remaining trees In many situations in tropical

forest logging skidding operations damage tree roots and boles For example in

PNG the most common forms of damage to the residual stand during selection

harvesting are to the bole and crowns and the presence of lianas is the major factor

affecting crowns (Sam 1999)

Effects of timber harvesting on tropical rainforest may occur in various forms

however apart from changes in the environment including changes in microclimate

and soil harvesting affects the forest structure According to studies carried out in

Brunei by Kobayashi (1992) the density of standing trees decrease after timber

harvesting but analysis of size class distribution revealed a similar pattern Similar

studies were carried out by Yosi (2004) in which a comparison was made between

seven plots on unlogged and seven plots on cutover tropical forests from initial

measurements of PSPs in PNG to assess the impact of timber harvesting on stocking

and basal area Results from his study showed that there was a 32 reduction in stem

numbers while basal area was reduced by 40 after timber harvesting In relation to

the study by Kobayashi (1992) the PNG data (Yosi 2004 Yosi et al 2009 Yosi et

al 2011) also showed that the size class distribution pattern displayed the reverse-J

shape pattern which is a typical characteristic of uneven-aged mixed natural forest

Several studies carried out in the past in PNGlsquos tropical forest are worth mentioning

here Yosi (2004) showed that the average basal area of seven plots on unlogged

43

forest was about 269m2 ha

-1 and when the forest was disturbed through logging it

was reduced to about 178m2 ha

-1 a study by Oavika (1992) showed that after

conventional logging operations initial basal area may be reduced to as low as 10m2

ha-1

while related research studies done on diagnostic sampling conducted in PNGlsquos

Oomsis forest by Kingston and Nir (1988a) suggested that the maximum basal area

for free growth of natural forest in PNG is around 30m2 ha

-1 and data analysis under

an ITTO funded project by Alder (1998) also indicated that an un-logged forest in

PNG achieves a dynamic equilibrium of about 32m2 ha

-1

It is generally understood that forest disturbances from logging may change the

structure and species composition and may also upset the ecological balance of a

forest On the other hand logging may encourage a new balance of regeneration

especially where the canopy is opened and gaps are created in the forest Studies on

effects of reduced impact logging (RIL) on stand structure and regeneration in a

lowland hill forest of PNG (Rogers 2010) showed that timber harvesting using a

portable-sawmill cutting 1-2 trees ha-1

caused 1-6 of ground area to be heavily

disturbed Logging gaps created from operations of portable-sawmill promoted

abundant regeneration of primary and secondary species His study also showed that

early regeneration was recorded at 61 for secondary species but after 61 months

primary species became dominant and secondary species accounted for only 9

Johns (1986) reported that initial losses of trees through logging may be compensated

in the short term by leaf flush in the remaining trees in response to conditions of

physiological drought and rapid growth of pioneer species This is quite common in

tropical rainforests as immediately after timber harvesting through logging short-

lived pioneers (for example in PNG Macaranga Trema and Altofia) quickly conquer

the openings and gaps created on the forest floor

According to Ng (1983) in selective timber harvesting removal of large size trees

also destroys the upper canopy of the forest as well as much of the lower canopy For

example studies carried out in Kalimantan in Indonesia (Abdulhadi et al 1981)

showed that removal of a single large tree in a logging operation resulted in the

destruction of 17 other trees and crown and branch damage to 41 of the surviving

trees

44

2111 Discussion

The literature review on the subject of forest dynamics in Section 21 highlighted not

all but some issues in tropical forests The review related to an overview of tropical

forests (Subsection 212) showed that apart from the diverse ecosystems and complex

structure of tropical forests they support the livelihoods of millions of people who

depend on them for their survival

Tropical forest dynamics (Subsection 213) relate to the various changes in natural

systems that take place continuously in a forest stand and these changes are explained

by the phenomenon of succession As explained earlier forest succession and forest

disturbance are the two main factors that influence the ongoing process of forest

dynamics in a forest area (Shugart 1984) In the review it was pointed out that

classification of tropical forests are difficult (Subsection 214) (ITTO 2006)

however the characteristics of these types of forests include high precipitation

seasonality temperatures humidity violent storms hail hurricane and severe

droughts In terms of species diversity (Subsection 215) tropical forests still remain

the worldlsquos most complex and diverse ecosystems of any terrestrial environment

Tropical forests are known for their mixed species composition and their species

distribution (Subsection 216) are influenced by environmental factors such as soil

rainfall temperature and altitude

Regeneration in tropical forests (Subsection 217) is controlled by factors such as soil

seed bank light humidity predation and defoliation by animals and seed sterility

Sustainability of timber harvesting in tropical forests is also affected by the

regeneration capacity of commercial tree species Review under this subsection points

out that the two main silvicultural systems for the management of tropical forests are

selection and uniform (clear-cutting) systems (Subsection 2171) As is commonly

known this literature review pointed out that shade tolerant tree species (Subsection

218) are able to grow under shade while shade intolerant species are light

demanding and require larger gaps to regenerate Usually timber harvesting in tropical

forests affects shade tolerant tree species due to high mortality rates caused from

harvesting activities (Felton et al 2006) Describing the structure of tropical forests

(Subsection 219) is often difficult because of their heterogeneous structure

45

However the distribution of tree numbers between diameter classes and individual

stems amongst basal area classes can easily describe the structure of a stand

Tropical forest environments respond to disturbances in many ways As pointed out in

this review (Subsection 2110) forests respond to natural disturbances (Subsection

21101) as well as human-induced disturbances such as timber harvesting

(Subsection 21102) which affect the environment structure and species

composition On the other hand harvesting also opens up the canopy and gaps are

created in the forest floor hence encouraging regeneration

As indicated in the literature many research studies have been carried out in tropical

forests relating to stand dynamics and changes that follow after disturbances such as

logging activities Many of these studies are not reported in this review however

research studies on this subject carried out in North Queensland (for example

Nicholson 1985 Nicholson et al 1988) and research in tropical rainforests of Bolivia

(Fredericksen and Mostacedo 2000 Fredericksen and Putz 2003) point out the need

for silvicultural interventions to be applied to the residual stands to promote

regeneration and growth of commercial tree species

46

2112 Conclusions

From the review in Section 21 the following general conclusions are made

Silvicultural treatments after logging to enhance forest growth have been

successful in North Queensland tropical rainforests for example increasing

basal area indicating good response to treatments (Nicholson et al 1988)

Using the North Queensland experience there is a need to adopt similar

practices to other tropical forests in the region especially in the Pacific-Asia

region

Silvicultural treatments in residual stands may be required after logging to

encourage regeneration and growth of commercially viable timber species

(Fredericksen and Putz 2003)

Post-harvest competition control treatments may be necessary to encourage

regeneration of commercial tree species (Fredericksen and Mostacedo 2000)

Out-planting programs may be needed to ensure successful regeneration of

commercial timber tree species (Park et al 2005)

In the case of PNG currently there are few or no silvicultural treatments

applied to residual stands to promote regeneration of desirable timber species

or to enhance forest recovery after logging activities There is now a need for

research into post-harvest silvicultural treatments and other silvicultural

interventions on cut-over native forests in the country This may be necessary

to promote regeneration and growth of commercial timber species as well as to

improve stocking and density on cut-over forests which may otherwise be left

to degrade over time Silvicultural treatments may involve liberation and

refinement treatments while the way forward in terms of other silvicultural

interventions on cut-over native forests may be enrichment and gap planting

The objective of Section 21 was to understand the complex structure of tropical

forests and how these forests response to disturbances Tropical forests are diverse in

terms of their structure and composition and they respond differently to both natural

and human-induced disturbances such as timber harvesting Due to their mixed and

diverse species composition SFM is a challenge however appropriate management

systems are required to address these challenges

47

22 CURRENT ISSUES IN TROPICAL FOREST MANAGEMENT

221 Introduction

Subsection 221 gives a general introduction of the current issues in tropical forest

management The issues that are high on the agenda of international discussion

regarding tropical forest management are highlighted based on (FAO 2007) These

issues are discussed briefly under this subsection to set the scene for the details that

follow

Due to global demand for timber products tropical forests are under enormous

pressure from harvesting while governments in the region rely on revenues generated

from export of timber products to supplement internal budgets It is also considered

that as most global wood production comes from either natural or semi-natural forests

rather than plantations natural forest management and research elsewhere and in the

tropics still remain as an important aspect for SFM

Based on the most recent information available from the Global Forest Resource

Assessment 2005 (FRA 2005) by FAO (2007) the current issues high on the agenda

globally include climate change forest landscape restoration invasive species

wildlife management and wood energy The tropical region is part of the global

community hence while most of the global issues are also important in the region the

important topics for discussion and debate include illegal logging deforestation

climate change certification and governance

In Subsection 222 the review discusses illegal logging in the tropics and gives some

specific examples in the region World-wide campaigns against illegal logging have

emerged and have much support from the international community especially OECD

countries (Curtin 2005) and particularly Australia However there have been also a

lot of efforts and cooperation in combating illegal logging and the associated timber

trade In this subsection detailed aspects of illegal logging in the tropical region are

pointed out

Deforestation is a major factor contributing to global warming which leads to climate

change This is a widespread concern and the review discusses the associated

problems with deforestation in Subsection 223

48

Subsection 224 discusses detailed aspect of climate change There is now a growing

concern that global warming is the major cause of climate change and the review

points out the importance of the role of tropical forests in causing and solving the

problems of climate change Under this Subsection an overview of the Kyoto

Protocol and the role it plays in addressing issues relating to climate change are also

given in Subsection 2241 Some aspects of carbon sequestration the process that

removes carbon from the atmosphere that may assist in solving the problems of global

warming are highlighted in Subsection 2242

In Subsection 225 community forest management in the tropics is discussed It is

now widely recognised that community groups are increasingly involved in forest

management at the community-level in the tropics The review give details of the

efforts of Non-government organisations (NGOs) Community-based Organisations

(CBOs) and international agencies in promoting CBFM in the tropics

Certification efforts by various schemes in the tropics are highlighted as these

processes are a necessary requirement for SFM In Subsection 226 the review firstly

gives some details of the establishment of certification bodies worldwide and also

gives some examples of the countries in the tropics which are developing their own

certification systems ITTOlsquos role in promoting certification programs in its member

countries are also discussed in this subsection

The review in Subsection 227 emphasises that governance at local national and

regional levels is important to address problems such as corruption and deforestation

Details of efforts by international organisations to improve governance in developing

countries are discussed in this subsection In the review some specific examples from

PNG have been highlighted

The literature review in Subsection 228 summarises the discussions relating to the

current issues in tropical forest management and some general conclusions are drawn

from these discussions in Subsection 229 The objective of Section 22 is to point out

and discuss the current issues which are themselves problems and challenges facing

tropical forest management These key issues are high on the agenda in policy debate

and discussions by governments and stakeholders in international meetings

49

222 Illegal Logging

The world-wide campaign against illegal logging in developing countries especially

Africa Asia and the Pacific is attracting support from governments of OECD

countries including USA UK and Australia (Curtin 2005) However there is also an

argument that these governments are more concerned in protecting their own timber

industries from competition from producers especially in the tropical region

including countries such as Indonesia and Papua New Guinea (Curtin 2005)

According to Australian Ministry for Fisheries Forestry and Conservation citing a

report by Jaakko Poyry (2005) illegal logging is defined as harvesting without

authority in national parks or conservation reserves and avoiding full payment of

royalty taxes or charges It is generally understood that illegal logging involves the

harvest transportation purchase or sale of timber in violation of national laws

There has also been much of international effort and cooperation in combating illegal

timber trade These efforts have been supported following the adoption of an anti-

timber trafficking resolution at the meeting of the United Nations Economic and

Social Council (UNESCO) in Vienna April 2007 These initiatives are receiving

support from developing countries For example Indonesia has been the first country

in the world to change its laws relating to money laundering to include crimes against

the environment and illegal logging In PNG the government commissioned five

separate reviews of the administration and operations of the logging industry from

2000 to 2005 (Forest Trends 2006) These reviews were conducted in response to

concerns raised by the public that the operations of the timber industry were not

providing long-term benefits to the country and its peoples and to assess the

implementation of amendments to the 1991 PNG Forestry Act (Ministry of Forests

1991b) Of the 14 active logging operations investigated under one of the five

reviews it was stated that none of these projects were operating legally with the

exception of only two projects which were found to be better than average

compliance to existing laws and regulations The report by Forest Trends (2006) is

contradictory to claims by Curtin (2005) in which he points out that audits of the PNG

timber industry sponsored by the World Bank from 2000 to 2004 found full

compliance by the industry with the countrylsquos Forestry Act 1991

50

Quite recently Australia has been one of the countries engaging with issues relating to

illegal timber trafficking Australialsquos efforts have been boosted when trade officials

from Australian Embassy visited the Centre for International Forestry Research

(CIFOR) in 2006 to discuss the question of illegal timber exports Also in April 2007

the Australian Minister for Environment and Water Resources visited CIFOR as part

of the launch of the Global Initiative on Forests and Climate

According to ITTO (2006) in many ITTO producer member countries illegal logging

is a critical obstacle to SFM in both production and protection forest areas however

efforts to combat illegal logging and illegal trade through bilateral agreements are

emerging For example in Indonesia and Malaysia governments have developed a

system of government-to-government timber trade in 2004 whereby only logs

received through government designated ports would be considered legal Multilateral

initiatives have also been put in place to address illegal logging For example the

2001 introduction of Forest Law Enforcement and Governance (FLEG) (ITTO 2006)

in East Asia which resulted in the Bali Ministerial Declaration in which both

producer and consumer countries agreed to take actions to suppress illegal logging

223 Deforestation

Deforestation in tropical countries has been a major point of discussion in recent

years As Grainger (1983) points out deforestation is temporary or permanent

removal of forest cover whether for agricultural or other purposes FAO has estimated

the rate of deforestation in the humid tropics to be about 16 million hectares per year

from studies done in thirteen countries in the tropics including Malaysia and PNG

(FAO 2006) However these estimates were doubtful as Lanleylsquos systematic

approach (Lanley 1981) in 55 tropical countries estimated the deforestation rate in

the tropics to be 6 million hectares per year

According to FAO FRA 2005 each year about 13 million hectares of the worldlsquos

forests are lost due to deforestation (FAO 2006) From 1990 to 2000 net forest loss

was 89 million hectares per year from which primary forest was lost at a rate of 6

million hectares per year through deforestation or selective logging Among the ten

leading countries that have the largest net forest loss per year between 2000 and 2005

Brazil Indonesia Myanmar and Zambia were top of the list During the same period

net forest loss was 73 million hectares per year which is equivalent to 200 km2 per

51

day (wwwfaoorgforestrysite28679en 2008) According to Greenpeace Indonesia

had the fastest rate of deforestation in the world with an area of forest equivalent to

300 soccer pitches destroyed every hour (wwwsciamcom 2007)

Recently at a high level meeting on Forests and Climate held in Sydney it was

pointed out that land use change especially deforestation in developing countries

contributes 20 of annual global greenhouse gas emissions

(httpwwwciforcgiarorg) This high level meeting followed the Australian

Governmentlsquos launch earlier of a $200 million initiative to reduce global greenhouse

gas emissions caused by forest loss especially in developing countries FAO (2007)

also pointed out that most developing countries especially those in tropical areas

continue to experience high rates of deforestation and forest degradation and countries

with highest rates of poverty and civil conflict are those that face the most serious

challenges in achieving SFM (wwwfaoorgforestrysite28679en) Freeman (2006)

also argues that the ongoing problems of illegal logging and forest conversion to other

land uses in developing countries are arguably the most significant threats to

achieving SFM With widespread concern about the fast depletion of tropical forests

logging activities in the region have been taken as a sensitive issue Apart from the

day to day human influence on the forests as well as the many complex factors and

issues causing the fast depletion of the tropical forests logging activities in the region

have been understood to be a major contributing factor to forest degradation With

higher rate of exploitation tropical forests are now under threat from conversion to

different land uses In earlier estimates by Dawkins and Philip (1998) 02 km2

of

rainforests are lost every year of which 25 is a direct result of logging activities

carried out in the region while an estimated 51 million ha of forest degrade every

year as a direct result of logging

Like many other developing countries in the tropics PNGlsquos natural forests are being

exploited at an overwhelming rate Estimates show that the countrylsquos forests are

decreasing at a rate of 120000 ha per annum (PNGFA 2003) through logging

agricultural activities mining and other land uses Earlier on the 2000 World Bank

statistics estimated that from 1980 to 1990 the deforestation rate in PNG was 03

annually (Forestry Compendium 2003) In 1992 forest areas committed for timber

concessions throughout the country were about 57 million hectares while the total

52

logged-over forest was estimated to be about 850000 hectares (Bun 1992) and this

has increased to an estimated figure of one million hectares (Nir 1995)

224 Climate Change

There is now a growing concern throughout the world about global warming which

causes global climate change Tropical forests are considered to play an important

role in causing and solving the problems of global climate change global biodiversity

and sustainability Tropical deforestation is considered a major factor contributing to

carbon dioxide (CO2) emission into the atmosphere It is estimated that the total

global C stored in plant biomass is 106 Kg C (Healey 2003) Tropical forests

especially moist forests are important for their capacity to store C Therefore their

conversion and degradation can potentially have a massive effect

There is also concern about human-induced climate change which is affecting ever-

wider areas of energy and land use policy as evidenced by the United Nations 1997

Climate Change Conference at Kyoto and further ratification in Bonn (Healey 2003)

The major cause of global warming according to the Green house effect theory is the

increasing concentration of atmospheric CO2 which lets short wavelengths radiation

from the sun penetrate whilst blocking the long wavelengths radiation emitted by the

much cooler surface of the earth Because of the importance of forests in the global C

cycle it is widely recognised that their management could play a large role in

mitigating this mechanism The potential for increasing terrestrial C storage by

increasing forest biomass has also been recognised in many parts of the world It is

also considered that the high productivity of moist tropical forests means that they

have the potential to fix a lot of CO2 to counteract recent global climate change

In 1990 it was estimated that the contribution of tropical forest conversion and

degradation to the C cycle was 22 At present global forestry is acting as a net

absorber of atmospheric CO2 Experts are more and more certain that the so called

―Missing Sink for CO2 is greater than previously expected absorption by terrestrial

vegetation One of the reasons for forests being the net C fixation includes the

increase in productivity of existing forests Also important is the large amount of

plantation forestry established in the past 30 years These forests are still in their

building phase when their biomass is rapidly increasing and they are major sinks for

CO2 Despite the evidence of forests currently acting as a net C sink the extent of this

53

and in particular itlsquos time duration are very uncertain It is predicted that there could

be a catastrophic switch of the whole Amazon ecosystem from net sink to net source

of C Studies carried out in Indonesia show that deforestation and slash and burn

agriculture had a dramatic impact on global climate change (Healey 2003)

There is a potential technical improvement in tropical forestry to current conventional

commercial logging practices The improvement in the technique of Reduced Impact

Logging (RIL) include the prohibition of logging in the more vulnerable areas and

the adoption of better planned and implemented felling and skidding operations are

considered to be one means of reducing the C emissions held responsible for global

warming While deforestation in developing countries contributes significantly to

greenhouse gas emission PNG and countries in the Pacific may potentially benefit

from a system of Payment of Environment Services (PES) or Avoided Deforestation

(httpwwwciforcgiarorg) to compensate and provide incentives for them to reduce

deforestation

2241 Kyoto Protocol

The Kyoto Protocol is the international treaty on global warming The treaty was

negotiated as an amendment to United Nations Framework Convention on Climate

Change (UNFCCC) in Rio de Janeiro in 1992 In 1997 the Protocol was negotiated in

Kyoto and opened for signatures in 1998 Among those countries who signed the

Agreement PNG also signed the Agreement in 1999 and ratified the Protocol in 2002

The two main objectives of the Kyoto Protocol are to assist developed countries to

meet emission reduction targets and to assist developing countries to meet the

objectives of sustainable development The mechanism that allows developed and

developing countries to collaborate is the Clean Development Mechanism (CDM)

Eligibility of lands for implementing CDM project activities are required to comply

with international rules and national regulations and priorities Land use land-use

change and forestry (LULUCF) requirements under the CDM are limited to

afforestation and reforestation later known as AR CDM in the first commitment

period Under the Protocollsquos standards (Murdiyarso et al 2005) afforestation is the

direct human-induced conversion of land that has not been forested for a period of at

least 50 years to forested land through planting seedling and human-induced

promotion of natural seed sources Reforestation is the direct human-induced

54

conversion of non-forested land to forested land through planting seedling and

human-induced promotion of natural seed sources on land that was forested but that

has been converted to non-forested land Implementation of AR CDM is required to

comply with strict rules concerning methodologies to determine baseline to monitor

greenhouse gas removals and leakages and the monitoring plan The scheme for

LULUCF activities called small-scale AR CDM gives smallholder rural communities

an opportunity to participate Small-scale projects are able to sequester a maximum

of 8 Kt CO2 year-1

(Murdiyarso et al 2005) The magnitude of such projects could

involve an area of 500-800 ha depending on the species chosen and management of

the project

2242 Carbon Sequestration

C sequestration is the process that removes C from the atmosphere This can be done

in a long-term storage of C in terrestrial vegetation underground in organic matter

and soils and in oceans This process removes or slows down CO2 accumulation in the

atmosphere While artificial capturing and storing C is possible natural processes of

storing C in terrestrial biomass are also important

The most obvious way to reduce atmospheric CO2 is for forest plantations to be

established in currently non-forest low-biomass land This can be difficult due to high

investment costs and shortages of available land If the socio-economic conditions are

favourable for continued establishment of new forest plantations this will establish a

larger flexible C store As an alternative to the continuous establishment of new

plantations attention should be turned to massively reducing the rate of conversion

and degradation of existing forests

As far as the Kyoto Protocol is concerned developing countries especially in the

tropical region could possibly benefit from developed country investment in increased

C storage This may be possible through the CDM which allows developed and

developing countries to collaborate

Considering the global context Cooper (2003) estimated that afforestation in

temperate forests is 33 tropical is 61 and boreal forests is 6 The key to

contribution of afforestation to reducing atmospheric CO2 is the fate and utilisation of

the resulting wood products C fixed during forest re-growth in the short term will

eventually be converted back to CO2 by respiration or burning Therefore it would be

better for the C balance if one could make more positive use of this fixed C

55

Stuart and Sekhran (1996) proposed that there was a potential for C-offset projects to

fund forest management or forest conservation in PNG Participation in this case will

depend on organisational management capacity and appropriate legal instruments that

secure C rights for buyers and give security on issues such as leakage and permanence

(Keenan 2001) This may ultimately depend on transformation of indigenous

property relations Activities that might allow PNG communities to benefit from

developed country investment in increased C storage or reduced emissions in forests

according to Keenan (2001) are

Development of forest plantations on cleared land particularly degraded

Imperata grasslands

Rehabilitation of forest areas degraded by previous logging operations

through enrichment planting weeding and tending or other intervention

Development of woodlots tree farming and domestication of PNG indigenous

species in the rural communities

Reducing green house gas (GHG) emissions associated with harvesting

operations

Conserving forest areas that are currently designated for harvesting or

conversion to agriculture

56

225 Community Forest Management in the Tropics

Increased devolution of forest ownership and management rights to local control has

the potential to promote both conservation and livelihood development in remote

tropical regions (Duchelle et al 2011) However such shifts in property rights can

generate conflicts particularly when combined with rapidly increasing values of

forest resources Multiple uses of forests are now being recognised at community-

level and apart from timber local people also value their forests for other goods and

services such as NTFP carbon and biodiversity conservation According to Kainer et

al (2009) it is highly unlikely that large tracts of tropical forests will be conserved

without engaging local people who depend on them daily for their livelihoods This is

because stakeholders who reside in bio-diverse ecosystems such as tropical forests

are the largest direct users and ultimate decision-makers of forest fate therefore can

be important investors in conservation Their local ecological knowledge can also

complement western science and frequently have long-term legitimate claims on lands

where they reside

Throughout tropical countries communities have raised concern that very few

benefits have been reaching the owners of land and forests whenever there are major

forest development projects initiated by the government As well as that local people

value forests for not only timber products but also other benefits and services hence

there have been an increasing number of local community groups involved in small-

scale forestry projects Many of these projects are community based and have

involved small-scale sawmilling with the primary aim of producing sawn timber to

build a decent home and to sell surplus sawn timbers to generate some income for the

community groups to improve livelihoods

In PNG some NGOs CBOs and conservation groups have participated in community

forestry related activities over the last 15 years Some of these groups include the

Village Development Trust (VDT) World Wide Fund for Nature (WWF) Foundation

For People and Community Development (FPCD) and Madang Forest Resource

Owners Association (MFROA) VDT is an indigenous non-governmental

organisation that has been working in the communities in PNG and throughout the

south pacific since 1990 (wwwglobalnetpgvdt) Some of its activities include eco-

forestry forest conservation education and training in forestry village eco-timber

57

projects integrated conservation and development projects In Fiji a collaborative

effort between the Fiji Forestry Department and Drawa Forest Landowners Co-

operative Ltd has been established This collaborative arrangement has been

supported by the SPCGTZ Pacific-German Regional Forestry and the Drawa

Community-based SFM regime for native forest in 1994

(wwwspcintlrdHighlights_Archivehighlights_Drawa_Modelhtm) The Drawa

Project has been established as a model area for community and resource owner

participation in forest management Under this project forest management and land

use plans have been drawn to provide a regulatory framework for community-based

natural resource management

In countries such as India Nepal and Philippines community forestry and joint

forest management initiatives have been found to be quite successful (Mery et al

2005 Wardle et al 2003) These initiatives have been successful because community

forestry related activities promoted the customary management systems which existed

before the state assumed control of forest lands Experiences show that local

institutions make better use of forests manage them more sustainably and contribute

more equitably to livelihoods than central government agencies

Small-scale forestry elsewhere outside the tropics has been also proven to be

successful For example in Lithuania where 35 of total forest area is under small-

scale private forestry (Mizaras et al 2007) small-scale forestry activities include use

of logging residues and other non-used wood for fuel use of non-wood forest

products and sales of environmental services including CO2 sequestration These

activities have increased income from forests for small-scale forestry Experiences in

Australia show that small-scale farm forestry has continued to grow since the 1980lsquos

and has the potential to influence the Australian national forest estate Research

carried out by Cox (2004) indicates that exposure of small-scale forestry to

international trade can create an impetus for change that would be beneficial for

small-scale forestry sector

The review of community forest management in the tropics has not covered all the

literature available however from those materials consulted it can be seen that more

NGOs CBOs and community groups are increasingly involved in forest management

at the community-level in the tropics Most of these groupslsquo involvement in forest

management at community-level is usually at a small scale however there is

58

evidence that direct benefits may flow to the communities For tropical countries

where central governments have direct control over forest lands communities could

adopt the systems used in India Nepal and the Philippines by promoting the

customary management systems in CBFM This will not be the case in PNG because

majority of the forests in the country are owned by community groups

226 Certification

Forest certification has been developed as a way of providing timber consumers with

information about the management of forests from which certain timber products have

originated The first forest certification started in 1990 with a teak plantation in

Indonesia certified as well managed by SmartWood a program of the New York-

based Rainforest Alliance (Dickinson 1999 Dickinson et al 1996) In 1992 the

Woodworkers Alliance for Rainforest Protection in the United States proposed the

creation of the Forest Stewardship Council (FSC) and in the following year in 1993

the FSC founding assembly was held and in 1995 the council began to accredit

certifiers (Viana et al 1996) When forest certification started it was intended as a

tool for saving tropical forests however from the tropical forest management point of

view it was generally understood that logging practices in temperate and boreal

forests are if anything more destructive than is logging in tropical forests Therefore

certification of good forest management is now being quickly adopted in almost all

forest types throughout the world (Viana et al 1996)

Tropical forests are biodiversity hotspots of the world and are vital for the survival of

millions of indigenous people (httpwwwfscorgtropicalforestshtml) They also

provide social and environmental benefits to sustain the livelihoods of local

communities Tropical forests are managed for a wide variety of reasons For

example timber production source of firewood water catchment and biodiversity

conservation Due to overwhelming demands from society tropical forests are under

enormous pressure for exploitation and this continues to escalate with emerging

challenges FSC certification can offer communities in the tropics financially

competitive alternatives to poor practices illegal logging and land conversion for

cattle ranching or bio-fuel production (httpwwwfscorgtropicalforestshtml) FSC

standards are recognised as the highest social and environmental standards for forest

management worldwide Certification of tropical forests can result in substantial

59

social and environmental improvements and ultimately support the conservation and

long-term maintenance of these forests

In recent years several certification bodies have been established by interest groups to

provide a framework in which certification initiatives can be pursued and managed

The two largest schemes are the FSC which was established in 1993 and is driven

largely by environmental non-governmental organisations and the Programme for the

Endorsement of Forest Certification (PEFC) which was established in 1999 with the

support of international forest industry and trade organisations and associations

representing woodland owners in Europe Several countries in Europe New Zealand

and Japan have also developed Public Procurement Policies (PPP) to promote SFM

and good forest governance and promote sustainable use of forest products by

consumers (Freeman 2006) Some tropical countries are also now developing their

own certification systems These include the Malaysian Timber Certification Council

in Malaysia the Ecolabelling Institute in Indonesia and the Certificacao Florestal

(CERFLOR) in Brazil Countries in Africa are also developing a regional initiative

According to ITTO (2007) there has been a lot of progress in certification

requirements in ITTO producer countries however more than 90 of currently

certified forests worldwide are outside the tropics This scenario indicates the

difficulties associated with implementing SFM in the tropics In the report on Forests

for the New Millennium Mery et al (2005) noted that almost 200 million hectares of

forests had been certified at global level At regional level according to FSC 2009

figures 15 million hectares of tropical forest are FSC certified representing 14

percent of the total global area certified to the FSC Principles and Criteria

(httpwwwfscorgtropicalforestshtml) However in the regional context one in

five certificates lies in the tropics and the top three countries with the highest total

certified forest area are Brazil Bolivia and the Republic of Congo

At global level certification is now being quickly adopted in almost all forest types

however at regional level in many developing countries adoption of certification

requirements are very slow This is because of the difficulties associated with

implementing SFM as well as other related problems such as poor governance weak

laws and regulations lack of skilled personnel lack of enforcement of regulations for

implementing SFM and the direct and indirect costs associated with meeting the

requirements of certification

60

It is a general understanding that the process of forest certification is a market driven

approach that focuses on improving forest management by linking consumer concerns

about social issues and the environment to good practices Certification schemes

provide consumers governments retailers and individuals with an assurance that

they are buying products that come from forests which are sustainably managed in a

socially responsible way ITTO plays a significant role in certification in that it

undertakes policy related work by commissioning studies convenes conferences and

workshops and promotes debate among member countries ITTOlsquos assistance in

member countries are in the following capacity building and promoting forest

auditing systems strengthening certification programs helping companies to get their

forests certified and funding private sector and civil society partnerships to promote

SFM and certification

227 Governance

The World Bank defines governance as consisting of the traditions and institutions by

which authority in a country is exercised and includes the processes by which

governments are selected monitored and replaced the capacity of the government to

effectively formulate and implement sound policies and the respect of citizens and

the state for the institutions that govern economic and social interactions among them

(wwwworldbankreportsgovernanceampanti-corruptionWGI1996-

2007interactivehomemht) This definition is considered as political however

according to a report on the State of the Worldlsquos Forests by FAO (2007) the Asia

Pacific Forestry Commission (APFC) recognises the issue of governance to involve

the process of making and implementing decisions about forests and forest

management at local national and regional levels APFC emphasises that

frameworks such as forest legislation regulations criteria and indicators and codes of

conduct are important in the decision-making process

In most developing countries communities living in and around forest areas do not

have recognised property rights to the forest products that are important to their

livelihoods and their concerns are not taken care of in forest policy decision-making

processes National and local level governments also lack the necessary authority

capacity and accountability to fulfil their obligations to forest management and

therefore failures in governance also cause pressing problems such as deforestation in

61

many parts of the tropical region Over time the scenario has taken a shift as rapid

changes relating to expectations and demands on forests by society confronts the

forestry sector and those institutions and agencies involved in forest management are

now putting in place reforms in order to cope with these changes In PNG the Forest

Authority is now implementing the countrylsquos logging code of practice (PNGFA and

DEC 1996) Among other controls the code has a 24 step procedure that has to be

met before granting a license or permit for any major timber project to start The PNG

logging code of practice has received a lot of support from agencies and stakeholders

within the country as well as the international community The APFC is now

implementing a study in the Asia-Pacific region to provide member countries with

recommendations about how existing forestry agencies can be re-structured or

modernised to ensure their continued effectiveness and relevance

(wwwfaoorgforestrysite28679en)

The Special Project on World Forests Society and Environment of the International

Union of Forest Research Organisations (IUFRO) in 2005 (Mery et al 2005)

recommended that decentralization in developing countries should be pursued when

the conditions are right However the process of decentralization must be seen to

overcome corruption and establish new structures of governance at the local level

through participative democracy and self-management It is considered that these

processes may not be easy especially in developing countries in the tropical region as

multi-national corporations with their wealth and monetary power influence

government policies to their own advantage in terms of resource development in

sectors such as forestry and mining To support this argument it is not surprising that

the Word Bank Corruption Index (wwwworldbankreportsgovernanceampanti-

corruptionWGI1996-2007interactivehomemht) has recently ranked many developing

countries in the tropical region among the 20 most corrupt nations in the world

including PNG being ranked number 15

62

228 Discussion

Based on the review in Section 22 illegal logging is understood to be a major

problem in the tropics However there are also a considerable effort and cooperation

from international organisations in combating this issue Deforestation is mostly

experienced in developing countries in the tropics and contributes 20 of annual

GHG emissions with Indonesia having the fastest rate of deforestation in the world A

major contributing factor to global warming which causes climate change is tropical

deforestation but the importance of forests in the global carbon cycle has been widely

recognised hence their management could play a large role in mitigating this

mechanism Apart from illegal logging deforestation in the tropical region is also a

threat to achieving SFM (Freeman 2006) High rates of deforestation in the tropics

are associated with high rates of poverty and civil conflict and these are major barriers

to achieving SFM

Climate change is a global issue and tropical forests play an important role in causing

and solving problems of global climate change This is because tropical forests are not

only a major contributing factor to CO2 emission into the atmosphere which causes

global warming they are also important for their capacity to store carbon Provisions

in the Kyoto Protocol such as the Land Use and Land Use Change and Forestry

(LULUCF) under the CDM will potentially sequester CO2 from the atmosphere

thereby reducing global warming In terms of community forest management in the

tropics this review pointed out that more stakeholders are involved While some

communities have very little capacity to participate in community forestry

community forest management has been successful in India Nepal and the

Philippines (Mery et al 2005 Wardle et al 2003) Certification is seen as a tool for

assisting SFM There is now a growing support from international organisations in

developing certification bodies that focus on improving forest management by linking

consumer concerns about sound issues and environment to good practices

In many tropical countries there is a break-down and failure in governance and these

have given rise to pressing problems such as deforestation and corruption However

positive changes are now taking place as efforts from organisations such as the World

Bank and Asia Pacific Forestry Commission (APFC) are assisting to improve

governance in the tropics

63

Most of the issues discussed in Section 22 are problems and challenges that create

difficulties in achieving SFM in the region Until management of tropical forests

adopts the principles of sustainable forestry and until regulators enforce forest laws

effectively in the region forest management in the region will be subject to

unsustainable practices and biodiversity conservation and sustainable use of forest

products and other values will remain a major challenge

229 Conclusions

The literature review in Section 22 identified the following key issues

SFM in the tropics still remains a major challenge however there have been

some progress made to date with support from international organisations such

as ITTO and FAO (FAO 2007 ITTO 2007)

Illegal logging is a major problem in the tropics and is usually fuelled by

corruption and poor governance however recently there have been a lot of

efforts from international organisations to combat this problem

Deforestation and global warming which cause climate change are a

worldwide concern and international treaties such as the Kyoto Protocol have

the responsibility to assist developed countries meet their emission reduction

targets and assist developing countries by providing incentives for them to

meet the objectives of sustainable development

There is now a growing concern about global warming which is the major

cause of climate change but the importance of the role of tropical forests in

causing and solving the problems of climate change have been widely

recognised

Communities in the tropics are increasingly involved in forest management

and utilisation at small-scale

Forest certification is seen as a tool for assisting SFM and focuses on

improving forest management by linking consumer concerns about social

issues and environment to good practice However adoption of certification

requirements is very slow in tropical forests in developing countries because

of the difficulties associated with implementing SFM

Poor governance in the developing world is seen as a set-back to SFM as it

gives rise to problems such as corruption and deforestation however efforts

64

and assistance from international bodies such as the World Bank and APFC

are now putting in place systems that would improve governance

Considering the current issues discussed in Section 22 and relating them to the

overall objectives of the thesis the discussion points out problems and challenges

facing tropical forest management However there are efforts and approaches at local

level that can assist SFM in the region and this thesis addresses some of those aspects

For example scenario analyses tools developed in this study (Chapter 6 and 7) will be

applied by communities who own the majority of forests as is the case in PNG

Therefore the application of these tools will involve low impact harvesting and this

will contribute to sustainable forest use and overall SFM

65

23 FOREST MANAGEMENT APPROACHES

231 The Management Strategy Evaluation (MSE)

MSE is a frame work commonly used for fishery resource management This

approach has been considered for possible application for management of logged-over

forests in PNG The MSE framework was developed by Walters and Hilborn (1976)

for adaptive management of fishery resources Further work on MSE was carried out

by scientists working for the International Whaling Commission (Kirkwood 1993)

Since then work on the framework has been extended by Australian scientists and

others on multiple use models and spatial models (Butterworth and Punt 1999 Little

et al 2007 McDonald et al 2005 Sainsbury et al 2000) In resource management

multiple-use MSE has so far been mainly focused on sectors such as oil and gas

conservation fisheries and coastal development (McDonald et al 2005) In the

fishery sector the objective of adopting the MSE framework has been to develop and

demonstrate practical science-based methods that support integrated regional planning

and management of coastal marine ecosystems An integrated MSE developed by

CSIRO (McDonald et al 2005) has been applied successfully to fisheries and has

been further enhanced for providing scientific decision support for multiple use

management of coastal regions and estuaries

A framework such as MSE requires active participation of stakeholders and facilitates

the generation of ideas identification of problems and approaches for solving them as

well as anticipation of real world impacts This type of approach is usually motivated

and supported by the needs of management agencies Associated with an MSE

approach are the three main elements strategy specification and scenario A strategy

is a planned course of action by one or more people while a specification is a

computer representation or a model of the real system A scenario is a future

projection of various factors that impact on the system but which are not included

explicitly or dynamically in any of the computer representation or model of the

system (McDonald et al 2005) Usually these factors are represented as data inputs to

the model The factors projected into the future include things such as human

population growth patterns industrial development climate change and variability

and anticipated changes in recreational or industrial usage of natural resources

66

According to Sainsbury et al (2000) methods to design and evaluate operational

management strategies have advanced considerably in the past decade These MSE

methods have relied on simulation testing of the whole management process using

performance measures derived from operational objectives This approach involves

selecting operational management objectives specifying performance measures

specifying alternative management strategies and evaluating these using simulations

The MSE framework emphasises the identification and modelling of uncertainties and

propagates these through to their effects on the performance measures An example

application of the MSE approach has been in the fishery sector when the scientific

methods for evaluating fishery management strategies were applied through two

parallel initiatives These are adaptive management (Walters and Hilborn 1976) and

comprehensive assessment and management procedure evaluation developed by the

International Whaling Commission (De la Mare 1996 Donovan 1989 Kirkwood

1993 Magnusson and Stefansson 1989)

Both adaptive management and management procedure evaluation approaches are

similar in terms of their concept and have been termed as MSE Use of MSE is now

widely recognised as providing a successful and appropriate framework for scientific

input to fishery management (Cooke 1999 Sainsbury 1998) In resource

management the goals of MSE have been to support informed selection of a

management strategy by means of quantitative analysis to make clear the trade-offs

among the management objectives for any given strategy and to identify the

requirements for successful management MSE uses simulation modelling to examine

the performance of alternative strategies and therefore requires that all five of the

below elements be specified in a way that allows quantitative analysis A management

strategy consists of specifications for

o Monitoring program

o Measurements that will be made

o How these measurements will be analysed and used in the scientific

assessment

o How results of the assessment will be used in management

o How any decision will be implemented

The MSE framework can be used to compare alternative aspects of any part of a

strategy from monitoring options through the scientific assessment and its use in

decision-making and implementation (Figure 2-1)

67

Figure 2-1 Key features of the general MSE Framework (Sainsbury et al 2000)

The MSE framework has been used successfully for providing scientific decision

support in resource management The MSE approach may be considered for adoption

in the management of cutover forests in PNG because forest owners and community

demands expectations and problems vary under different circumstances therefore

this option is expected to address these issues

The objective of Section 23 is to investigate appropriate management approaches for

cutover native forest in PNG from the literature review and Subsections 231

(Management Strategy Evaluation) Subsection 232 (Scenario Method) and

Subsection 233 (Bayesian Belief Network) aim to discuss these approaches as the

alternative management systems

232 The Scenario Method

Use of scenarios can provide a tool for planning creatively for the future and

scenario-based approaches tap peoplelsquos imagination in anticipating the future

Because of the complexity of tropical forests and in PNG in particular compounded

by a complicated land and forest resource ownership systems the scenario method is

considered an applicable approach for adaptive management of cutover forest by

communities in PNG CIFORlsquos scenario method (httpwwwciforcgiarorg) for

68

adaptive management is considered an appropriate approach for management of

cutover forest in PNG

Scenarios are used with the objective of helping people change their habits of thinking

or mental maps of how things work so they can deal better with the uncertainties of

the future and perceive the consequences of their actions in the short and long term In

the context of community forestry scenarios are applicable when there is a need to

explore possibilities Scenario-based techniques are tools for improving anticipatory

rather than retrospective learning (Wollenberg et al 2000) They may assist forest

managers make decisions based on an anticipated range of changes Elements of the

scenario approach suitable for community forests are based on participatory rapid

appraisal (PRA) that may be appropriate to village and community settings

The major steps for using scenario methods include the following

o Defining the scenariolsquos purpose

o Choosing the type of scenario that best suits the purpose

o Selecting participants facilitators and setting for learning and follow-up action

According to Wollenberg et al (2000) the four sorts of scenario approaches are the

following

o Vision ndash a vision of the desired ideal future

o Projection ndash best guesses about the expected future

o Pathway ndash determination of how to get from the present to the future by

comparing present and desired future (vision) scenarios

o Alternatives ndash a comparison of options through multiple scenarios of either the

vision projection or pathway type

In the case of this PhD research study in the PNG situation scenario methods were

integrated into the MSE framework for evaluation The best possible approach in the

management of cutover forests in PNG is the use of alternative scenarios as this will

represent the expectations of different stakeholders such as the community groups and

timber industry

69

233 The Bayesian Belief Network (BBN)

The Bayesian Belief Network (BBN) has been considered as a possible approach for

management of cutover native forest in PNG BBNs are models that graphically and

probabilistically represent correlative and causal relationships among variables and

have been used in a broader decision support framework in resource management

(Cain 2001) McCann et al (2006) suggested that BBNs are useful tools for

representing expert knowledge of an ecosystem evaluating potential effects of

alternative management decisions and communicating with non experts about making

natural resource management decisions

Development of BBNs started in the 1990s (Pearl 1995) drawing on a deep body of

the theory developed for graphical models Later BBN techniques have been used by

ecologists and resource managers (Ellison 1996) Crome et al (1996) showed that

Bayesian methods may be useful and applicable in the context of tropical forest

management for modelling uncertainties involved when forest systems are disturbed

While developing models to predict the impact of non-timber forest products (NTFP)

commercialisation on livelihoods studies in Mexico and Bolivia adopted the

Department For International Development (DFID) livelihood framework as a basis

for constructing the BBN (Asley and Carney 1999) This framework is based on the

concept that people require a range of assets in order to achieve positive livelihood

outcomes According to DFID (1999) the five different types of assets including

both material and social resources are natural capital physical capital human capital

financial capital and social capital Following the DIFID approach Newton et al

(2006) considered that communities and individuals involved in NTFP

commercialization would require access to each of the five types of asset in order for

commercialisation to be successful

Considering the DIFIDlsquos livelihoods framework for resource management adoption

of BBN for community management of cutover native forests in PNG may not be

appropriate The main reason for this would be that many individuals and

communities in PNG may not have direct access to the five different types of material

and social assets

70

234 Discussion

The literature review in Section 23 covered three approaches to the development and

assessment of alternative forest management scenarios These are the MSE scenario

methods and BBN The MSE approach has been widely used in resource management

particularly in the fishery sector (McDonald et al 2005) The key steps of MSE

involves turning broad objectives into specific and quantifiable performance

indicators identifying and incorporating key uncertainties in the evaluation and

communicating the results effectively to client groups and decision-makers (Smith et

al 1999) The review pointed out that a successful application of an MSE approach

to natural resource management requires a collaborative effort between the decision-

makers technical experts and an MSE analyst

There is now an increasing emphasis on community participation in natural resource

management through group formation in all forms of development intervention

(Agawal 2001) In the context of natural resource management such as forests

devolving greater power to village community groups is now widely accepted by

governments international agencies and NGOs Community-based organisations

involved in forestry activities represent a rapidly expanding attempt at participatory

approaches to development and effective participation requires peoplelsquos involvement

such as a village group In community forestry scenarios are applicable in order to

explore different forest management options (Wollenberg et al 2000) In the context

of CBFM use of scenarios and the MSE approach are recommended for application

in PNG because both of these approaches require a participatory approach to forest

management by different stakeholders

BBNs are used in complex ecological systems that require a multidisciplinary

approach and this approach is considered useful in tropical forest management for

modelling uncertainties (McCann et al 2006 Newton et al 2006 Pearl 1995)

Adoption of BBN may require access to the different types of material and social

assets hence application of this approach may not be appropriate for CBFM in PNG

because communities generally have no or very little capacity to have access to these

assets

71

235 Conclusions

Not all topics related to the forest management approaches in tropical forests have

been covered in Section 23 of the literature review This is a broad area and the

review considered only the three approaches (MSE scenario methods and BBN) that

may be applicable to cutover forest management in PNG In PNG forest management

in general is associated with many key issues and problems Concern for the

sustainability of the current management practice illegal logging traditional land

tenure systems and lack of participation by forest owning communities in decision-

making are not all but some key challenges in forest management in PNG The

literature review in Section 23 pointed out that the three approaches are useful in

tropical forest management The MSE and scenario approaches require stakeholder

participation in forest management while BBNs are applicable where there are

uncertainties

Based on the objectives of PNG forest landowning communities lack of participation

in decision-making by communities in forest management and the available data it

was decided to use an approach that integrated development of management scenarios

and the MSE framework for community-based management of cutover forests in

PNG

72

CONDITION OF CUTOVER FOREST

65

CHAPTER 3

FOREST DYNAMICS AFTER SELECTIVE TIMBER HARVESTING IN PNG

3 1 INTRODUCTION

Tropical forests are subject to extensive human disturbance such as clearance for

agriculture infrastructure development fires and mining There has been considerable

debate about timber harvesting in tropical forests and its impacts on environmental

cultural and social values The implementation of SFM in tropical forests is a

widespread goal of the international community but while there is some evidence of

improvement few forest areas are currently considered to be managed sustainably

(ITTO 2006) More recently international attention on implementation of SFM has

increased as a result of the focus on greenhouse gas emissions associated with

deforestation and forest degradation in the tropics and the potential to reduce

emissions from these sources as a low cost climate change mitigation option

(UNFCCC 2006 UNFCCC 2009)


exploited at a rapid rate Current estimates of forest loss vary It is estimated that

primary forests are decreasing at a rate of 113000-120000 ha year-1

(FAO 2005

PNGFA 2003) through logging agricultural activities mining and other land uses

Other statistics indicate that the annual deforestation rate is decreasing From 1980 to

1990 the rate was estimated at 03 and between 1990 and 2000 at 044 with a

further increase to 046 from 2000 to 2005 (FAO 2005 FAO 2007 ITTO 2006)

Other studies have suggested that the rate of forest loss through deforestation or forest

harvesting and subsequent decline is currently 14 year-1

(Shearman et al 2009b)

although there is debate about this figure (Filer et al 2009)

In PNG timber harvesting is occurring under policies and regulations that are

intended to provide for a sustainable supply of timber from designated forest

management areas (FMA) as stipulated in the National Forestry Act 1991 (PNGFA

1991) These operations are largely undertaken by international companies for the log

66

export market There is considerable uncertainty about the sustainability of current

management practices the recovery of forests after harvesting and the potential of

forests to provide timber or other community needs (Filer et al 2009 Shearman et

al 2009a)

Current rates of timber harvesting in PNG are considered unsustainable (Shearman et

al 2009a) The current status of selectively harvested forest in PNG is such that total

areas harvested through logging increased from 850000 ha in 1992 to over one

million ha in 1995 (Bun 1992 Nir 1995) Recent PNGFA statistics also indicate that

from 1988 to 2007 the estimated total area affected by commercial harvesting has

increased to over 2 million ha and total timber volume harvested in the form of logs

during the same period was over 39 million m3 (PNGFA 2007) Selectively-harvested

forests in PNG amount to 10 of forested areas but the condition and future

production potential of these forests is uncertain Some authors have suggested that

selectively-harvested forest in PNG generally degrade over time after harvesting


Much of the international debate about tropical forest harvesting and its impacts on

forests are primarily around impacts on biodiversity (Chazdon et al 2009 Gardner et

al 2009 Kobayashi 1992 Lamb 1998) and a global concern about the loss of

species through tropical deforestation particularly in some of the worldlsquos biodiversity

hotspots (Myers et al 2000 Pimm and Raven 2000 Stork 2010)

However there is now a wider range of values to be considered including capacity of

harvested forests to provide timber sequester carbon or other community benefits

There is considerable uncertainty about how harvesting impacts on these values due to

the lack of knowledge about the extent of impacts and rate of recovery of forests after

harvesting

More broadly there have been a relatively limited number of studies of forest

dynamics and changes in stand structure of tropical forests after harvesting (Breugel

et al 2006 Kobayashi 1992 Nicholson 1958 Nicholson et al 1988) Most of the

research in the area has focused on the rehabilitation and restoration of degraded areas

after large-scale clearance for agriculture and subsequent abandonment or

disturbances such as fire (Lamb 1998 Lanley 2003 Shono et al 2007) Other

studies have focused on the impact of drought on tropical forest dynamics (Nakagawa

et al 2000)

67

The aims of the study in Chapter 3 are to (1) examine the impacts of selective

harvesting on stand structure in PNG forests by analysing the diameter and BA

distribution after harvesting (2) assess the dynamics of selectively-harvested forest in

terms of trends in stand BA and residual timber volume (3) determine whether there

is a critical threshold BA for forest recovery by testing a model developed in

Queensland tropical forests to analyse BA growth for harvested forests (4) assess the

impact of the El Nino induced forest fire of 1997-98 on BA growth and mortality rates

of the burned plots and (5) investigate the impacts of harvesting on species diversity

of selectively-harvested tropical forests in PNG

32 MATERIALS AND METHODS

321 PNGFRI Permanent Sample Plots ndash Background

Forests in PNG are characterised by high species and structural diversity There are

over 15000 or more native plant species (Beehler 1993 Sekhran and Miller 1994) of

which over 400 are currently considered commercial (Lowman and Nicholls 1994)

Forests cover a wide altitudinal range and occur across a range of rainfall conditions

and soil types Disturbance has been an integral part of dynamics of PNG forests For

example fire has been shaping PNGlsquos vegetation patterns through thousands of years

of human settlement (Haberle et al 2001 Johns 1989) At high altitudes fire may

result in permanent conversion of forests to grasslands (Corlett 1987)

135 PSPs were established in mostly lowland tropical forests by the PNGFRI These

plots have a measurement history extending over 15 years These comprise 122 plots

in selectively-harvested forest with a total of 411 measurements and 13 plots in un-

harvested forests with a total of 23 measurements (Fox et al 2010) Alder (1998)

indicated these plots had floristic composition characteristic of the lowland tropical

forests of PNG During the measurement period some plots have been abandoned due

to difficulty in access or measurement has been discontinued due to fire or conversion

of the forest to subsistence gardens

The selective harvesting system used in PNG involves felling commercial timber

species with a diameter limit of 50 cm and above generally in larger-scale operations

for log export The size of openings and gaps created in this type of harvesting are

between 20-40 m in diameter Usually the area allocated for harvesting is over 80000

68

ha and the average timber volume removed during harvesting depends on the density

of commercial species and averages about 15 m3ha

-1 (Keenan et al 2005) The

planned return period for a future harvest is 35-40 years although this depends on the

stand structure residual merchantable volume and stand growth rates (Keenan et al

2005)

During the establishment of PSPs plots were randomly located and established in

pairs All the plots are one hectare in size and divided into 25 sub-plots of 20 m x 20

m (Romijn 1994a Romijn 1994b) The field procedures for establishment and

measurement of the plots were adopted from Alder and Synnot (1992) In the

assessment of trees in the plot a standard quadrat numbering system was used This

system uses quadrat numbers on the basis of coordinates or offsets from the plot

origin for example south-west corner All tree species ge 10cm diameter at breast

height (DBH) were measured Measurements taken on trees included DBH height

crown diameter and crown classes according to Dawkins (1958) For plots in

selectively-harvested forests initial establishment ranged from immediately after to

more than 10 years after harvesting For plots accessible by road re-measurements

have been taken on an annual basis Re-measurement of the other plots varied from

two to five years depending on funding

322 Study Sites and PSP Locations

The majority of the PSPs were located in lowland tropical forest types distributed

throughout PNG where most harvesting activities have taken place (Figure 3-1) Only

two plots have been established in higher altitude montane forest dominated by the

genera Castanopsis and Nothofagus in the Southern Highlands part of the country

Twenty three percent of PSPs are located on the island of New Britain Annual

rainfall in these plots averages over 3000 mm Plots were located on a range of soil

groups with the most common being Alfisols Entisols Inceptsols and Mollisols

(Pokana 2002)

69

Figure 3-1 Map of PNG showing study sites and permanent sample plot locations

(adapted from Fox et al 2011b)

323 PSPs used in this Study and Data Analyses

For the purpose of this study data from a total of 118 PSPs were used (105 in

selectively-harvested and 13 in un-harvested forests) Of the 105 plots in harvested

forest 84 were selected for analyses of dynamics of stand BA timber volume and

species diversity These 84 plots excluded those burned by fire during the 1997-98 El

Nino drought those with short measurement period and plots affected by erroneous

measurements An analysis of mortality was undertaken on burned plots Apart from

the disturbance by the El Nino event field observations also showed evidence of other

disturbance such as traditional land uses for example shifting cultivation in some of

the harvested plots

High variability are an inherent problem in sampling tropical natural forests subject to

harvesting (Gerwing 2002) To assess the dynamics of selectively-harvested forest in

this study a preliminary investigation was undertaken to test the normality of

response variables (BA and VOL) and the independent variable (TSH) Analyses

showed that data were homogeneous and normally distributed Examination of

70

residual plots also showed similar results Hence it was not considered necessary to

transform the dependent variables to stabilize variances

In the data analyses MS Excel was used for processing PSP data and the softwares

SPSS ver18 SigmaPlot ver11 and Minitab ver15 were used for statistical analysis

Linear and logarithmic regression analyses were carried out to establish the

relationship between the response (dependent) and independent variables

Significance of these relationships have been tested at 95 CI and significant results

have been considered as plt005 Graphical outputs for the results have been

generated from SigmaPlot ver 11

324 Analyses of Stand Structure

The number of trees per hectare (stems ha-1

) and BA are measures of stand density

and their distribution among diameter classes are often used to examine the structure

of a stand Both of these measures were analysed in order to describe the impacts of

harvesting on stand structure of natural forest in PNG This study focused on

dynamics of selectively harvested forest however analyses were also undertaken on

the stem and BA distribution of 13 plots in the un-harvested primary intact forest in

order to make comparisons with the structure of selectively-harvested forest These 13

plots have shorter re-measurement histories than those in selectively-harvested forest

Tree species in the study were divided into two groups at stand level consisting of

commercial and non-commercial species Trends in stocking BA and timber volume

were analysed for these two groups The commercial group consists of the PNGFAlsquos

group I and II commercial species (dominant species in Group I include those from

the genera Burckella Calophyllum Canarium Planchonella Pometia Intsia and

those in Group II are Hopea Vitex Aglaia and Endospermum) while the non-

commercial group consists other species including the secondary and pioneer species

from the genera such as Trema Althopia Alphitonia and Ficus (PNGFA 2005)

71

325 Assessing the Dynamics of Cutover Forests

The dynamics of selectively-harvested forest was assessed by analysing changes over

time in stand BA and timber volume To examine the condition of the forest after

harvesting a relationship was established between time since harvesting (TSH) and

BA for each plot In the analyses the starting BA is referred to as the plot BA at the

first census and final BA as the plot BA at the last census after harvesting These

denotations also apply to the analyses of residual timber volume A linear regression

analysis was carried out to examine the relationship between TSH and BA A similar

analysis was carried out to examine the relationship between TSH and residual timber

volume for trees ge 20cm DBH remaining after selective timber harvesting in order to

make comparisons with the change in timber volume in the 13 un-harvested plots

Basal area is a commonly used measure of forest stocking and stand structure and this

measure has been used as an indicator to determine patterns of change in stand

structure over time Patterns of change in timber volume were determined for

commercial and non-commercial timber species for trees ge 20 cm in DBH This

provides an indication of current and future production potential for cutover forests

(generally trees gt 50 cm DBH)

Currently there are no volume equations for individual natural forest tree species in

PNG however there are two systems of equations used for calculating volumes of

indigenous trees by PNGFA (Alder 1998) The single entry equation comprises only

the tree diameter with form and coefficients (equation 3-1)

(3-1)

Where V is bole volume overbark and D is girth at breast height

The second equation is a double entry system and comprises both diameter and height

with form and coefficient These set of equations are for calculating volume for trees

over 50 cm DBH (equation 3-2) and for those trees between 20 and 50 cm DBH

(equation 3-3)

72

(3-2)

(3-3)

In the second sets of equation V is bole volume overbark D is diameter at breast

height or above buttress and H is bole length

In the PSP analyses residual timber volume for commercial and non-commercial tree

species was estimated using the second set of volume equations

326 Basal Area and Volume Growth

Mean BA increment (MBAI) and mean volume increment (MVOLI) were calculated

for each plot To investigate the existence of a critical threshold BA below which a

harvested forest generally does not recover a model developed for native tropical

forest in Queensland (Vanclay 1994) was tested A logarithmic regression analysis

was carried out to establish the relationship between the starting BA after harvesting

and MBAI Although the model developed for tropical forest in Queensland was in

native forest dominated by uneven-aged stands of Callitris spp growing on drier sites

this model was applied to the dataset in this study because those forests have similar

environmental conditions to parts of PNG

This model takes the form as shown below

(3-4)

Where ΔG = stand basal area increment G = stand basal area (m2 ha

-1) Shd = site

form (m) an estimate of site productivity based on height-diameter relationship

Vanclay and Henry (1988) defined site form as an index of site productivity given by

the expected tree height (m) at some index diameter

Fox et al (2010) developed species-specific height-diameter models for PSPs in

natural tropical forests in PNG from the same dataset as the one used in this study In

the context of the present study site form was estimated from the height-diameter

models developed by Fox et al (2010) This estimate was used to test the above

model to determine the stand BA increment in this study

hd

73

In these analyses the relationship between starting BA and MBAI was used to

determine whether the forest was recovering (positive trend in BA) degrading

(negative trend in BA) or neither recovering nor degrading (constant BA) The mean

BAI was also determined for plots with an increasing BA (63 plots) and those with

decreasing BA (21 plots) in order to examine the trend in mean BAI after harvesting

To examine the change in mean BAI over time after harvesting the relationship

between mean TSH and mean BAI was investigated The differences in MBAI for

plots measured lt 10 years and gt 10 years since harvesting were also tested using a

two-way ANOVA Result for this test was insignificant (p = 094) hence details are

not reported in the results section

Environmental factors such as rainfall and altitude can affect BA growth A

correlation analysis was carried out to establish whether or not an association existed

between these two variables and BA growth These tests showed insignificant results

(Pearsonlsquos correlation r = 0124 for rainfall and mean BAI and r = -0039 for altitude

and mean BAI) therefore are not reported in the results section Twenty one plots

were not burned by fire but had negative BA increment due to losses from mortality

resulting from natural causes and the effects of the drought on BA growth These plots

were located on lowland forest types where large-scale harvesting has taken place and

50 of these plots are in very remote areas on the islands of New Britain New

Ireland and Manus (Figure 3-1) During plot measurement it was observed that there

were harvesting damages to the residual stand

To assess the trend in timber yield over time since harvesting the fit of a model

developed in the Philippines which is based on an empirical function of initial BA

site quality and time since harvesting was investigated (Mendoza and Gumpal 1987

Vanclay 1994) The equation takes the form

(3-5)

Where Vt = timber yield (m3 ha

-1) t = years after harvesting Go = residual basal area

(m2 ha

-1) after harvesting Sh = site quality (m) estimated as the average total height of

residual trees

t = 134 + 0394 ln Go + 0346 ln t + 000275 Sh t -1

74

To apply the model in this study the average total tree height estimated from the PSP

analyses (Fox et al 2010) was used Logarithmic regression was used to test the

relationship between TSH and timber yield of harvested forests using this model

327 Estimating Mortality due to the 1997-98 El Nino Drought

Twenty one PSPs in harvested forests were burned by widespread forest fires

occurring during the 1997-98 El Nino induced drought In this analysis ten of these

plots were selected to estimate annual mortality rates caused during the drought and

fire period Only the ten burned plots were considered for further analyses because

they were re-measured after the fire and had sufficient data while the other burned

plots had either a short measurement period or no re-measurement data after the El

Nino fire event These particular analyses aimed to provide an example of the impact

of fire during the El Nino event on BA losses due to mortality caused by this event In

this case we used the following equation to determine annual tree mortality rates

(Sheil and May 1996)

(3-6)

Where X is the initial BA at the first census and D is the BA lost due to mortality

during n years For the purpose of this study BA for the two measurements before the

fire was used to determine BA gained and the two measurements after the fire were

used to determine BA lost (annual tree mortality rates) caused by fire during the El

Nino drought

328 Shannon-Wiener Index (H1)

To examine the pattern of change in tree species diversity over time after harvesting

the Shannon-Wiener Index (H1) was estimated for all tree species using the equation

below (Nicholson et al 1988 Williams et al 2007)

(3-7)

Where pi = niN ni is the number of individuals present of species i N is the total

number of individuals and s is the total number of species

75

33 RESULTS

331 Change in Stand Structure after Harvesting

The total stocking for all size classes (ge 10 cm DBH) averaged 351 stems ha-1

plusmn 100

(SD) in selectively-harvested plots (Figure 3-2 a) and 531 stems ha-1

plusmn 138 (SD) in the

un-harvested plots (Figure 3-2 b) Average BA was 1735 m2 ha

-1 plusmn 417 (SD) and

2901 m2

ha-1

plusmn 577 (SD) in selectively-harvested and un-harvested plots respectively

(Figure 3-2 c and d) There was a significant increase in stem numbers in the lower

diameter classes (10-29 cm DBH) while there is an absence of trees in the larger size

classes (gt 70cm DBH) in the harvested forest This is as expected because the

selective harvesting system in PNG is such that a majority of the trees ge 50 cm DBH

are removed during harvesting There was a significant increase in BA over time since

harvesting in almost all size classes in the harvested forest This indicated the

evidence of recruitment of smaller size class stems into the ge 10 cm DBH class and

in-growth and related diameter increment occurring in the larger diameter classes In

the un-harvested plots there was no marked increase in stem numbers over time

however there was evidence of an increase in the size classes 30-49 cm DBH at 5-10

years BA in the harvested forest increased in the size classes 30-49 cm and 70-89 cm

DBH at 5-10 years As expected the stem distribution in selectively-harvested plots

(Figure 3-2a) and un-harvested plots shown on common-log scale on the y-axis to

represent fewer stems in the larger size classes (Figure 3-3b) and BA distribution in

selectively-harvested plots (Figure 3-3c) and un-harvested plots (Figure 3-3d) showed

a reverse-J pattern The plots in the un-harvested forest had short measurement

history and fewer re-measurement data were available but there did not appear to be

any marked changes in the number of stems and BA in the range of diameter classes

over time in these plots

76

(a)L

og

Sto

ckin

g (

ste

ms h

a-1

)

1

10

100

1000

0 - 5 years

5 - 10 years

10 - 15 years

15 - 20 years

Diameter Class (cm)

10-29 30-49 50-69 70-89 90+

Lo

g S

tockin

g (

ste

ms h

a-1

)

1

10

100

1000

(b)

(c)

Basal

Are

a (

m2 h

a-1

)

0

2

4

6

8

10

12

Diameter Class (cm)

10-29 30-49 50-69 70-89 90+

Basal

Are

a (

m2 h

a-1

)

0

2

4

6

8

10

12

(d)

Figure 3-2 Trends in stem and BA distribution since harvesting

(a) stem distribution in selectively-harvested plots (b) stem distribution in un-harvested

plots shown on a common log scale on the y-axis to represent fewer stems in the larger

size classes (c) BA distribution in selectively-harvested plots and (d) BA distribution in

un-harvested plots

At stand level the change in stocking basal area and residual timber volume for trees

ge 20 cm DBH showed similar trends over time (Figure 3a-c) These three density

indices increased for the commercial group 15-20 years after timber harvesting There

was also a marked increase in stocking for the non-commercial species group 0-10

years after harvesting as a result of recruitment of secondary and pioneer species

colonising the gaps and openings created by harvesting

77

Bas

al

Are

a (

m2 h

a-1

)

0

5

10

15

20

25

Sto

ck

ing

(ste

ms

ha

-1)

0

100

200

300

400 Commercial

NonCommercial

Time Since Harvesting (Years)

0-5 5-10 10-15 15-20

Res

idu

al

Tim

ber

Vo

lum

e (

m3 h

a-1

)

0

20

40

60

80

100

120

140

160

180

(a)

(b)

(c)

Figure 3-3 Representation of trends in commercial and non-commercial tree species

(ge 20 cm DBH) groups at stand-level since harvesting showing (a) stocking (b) basal

area and (c) residual timber volume

78

332 Trends in Stand Basal Area

Mean stand BA generally increased with time since harvesting although the

increment trajectory varied considerably between plots (Figure 3-4) Variability over

time also increased A scatter plot with linear regression showed that the relationship

between BA and TSH was relatively weak (r2= 007 p = 0016) when analysed with

the whole dataset including consecutive re-measurements for the un-burned plots

because of the variability in the data However the trend in BA across the 84 un-

burned plots showed a consistent recovery of natural forest after timber harvesting

Overall there is an increasing BA over time since harvesting suggesting that in

general these forests are recovering after harvesting but there is considerable

variability and this is discussed further below

r2 = 007

p = 0016

Time Since Harvesting (years)

0 5 10 15 20 25

Bas

al

Are

a (

m2 h

a-1

)

0

5

10

15

20

25

30

35

Figure 3-4 Trends in BA since harvesting for the 84 un-burned plots

represented by a scatter plot with linear regression for the whole dataset including

consecutive re-measurements

79

333 Basal Area Growth since Harvesting

Seventy five percent of the 84 un-burned plots indicated increasing BA after

harvesting with a mean BAI of 042 m2 ha

-1 year

-1 (SD 042) (Table 3-1) For the 21

plots showing a decline in BA after harvesting average BAI was -058 m2 ha

-1 year

-1

(SD 053) The mean BAI across the un-burned plots was 017 m2 ha

-1 year

-1 (SD

062) Apart from the other anthropogenic disturbances and the effect of the El Nino

drought on the declining plots harvesting damage causing injuries to the residual

stand resulted in high mortality rates in these un-burned plots The other factors

affecting BA growth of the declining plots are the site effects such as rainfall and soil

types In an earlier study in the same forest Alder (1998) observed that factors such as

variations in water regime and soil fertility in those sites affected tree increment Plot

background and measurement history showed that fifty percent of the un-harvested

plots had no or fewer re-measurement data and the mean BAI increment was negative

(-172 plusmn 316) (Table 3-1)

Table 3-1 Mean BAI for plots with increasing and falling BA

Forest Condition No of Plots

Mean BAI (m2 ha

-1 year

-1)

a

Un-harvested 13

-172 plusmn 316

Selectively-harvested

Increasing BA (un-burned) 63 042 plusmn 042

Falling BA (un-burned) 21

-058 plusmn 053

(All un-burned) 84b

017 plusmn 062)

Burned during 1997-98 El Nino

drought 21

-067 plusmn 085

Total 118

a Mean basal area increment plusmn standard deviation given in italics

b Total un-burned plots with increasing and falling BA combined

80

Regression analyses showed mean BAI increased throughout the plot measurement

period although the relationship between Ln MBAI and mean TSH is weak (r2 = 037)

(Figure 3-5) The results here are significant at 005 level (p = 0028) The scatter plot

with line and linear regression with error bars show average trends in mean BAI for

selectively-harvested forests The data points are the mean BAI at each time period

since harvesting while the error bars in this case represent standard deviation from

the mean

r2 = 037

p = 0028

Mean TSH (years)

5 10 15 20

Ln

Mean

BA

I (m

2 h

a-1

year-1

00

02

04

06

08

10

12

14

16

18

Figure 3-5 Average trends in MBAI since harvesting

The data points are the mean BAI at each time period since harvesting while the error

bars in this case represent standard deviation from the mean

81

334 Critical Threshold Basal Area for Recovery of Harvested

Forest

The data from this study showed a good fit with the model (equation 3-4) developed

in Queensland (Vanclay 1994) There was a strong relationship between the mean

BAI and starting BA after harvesting when the model was fitted to the data from this

study (r2 = 075 p lt 005) (Figure 3-6) Almost all plots had a relatively high residual

BA after harvesting (greater than 10 m2 ha

-1) and at this level residual BA was not a

determinant of whether BA increment after harvesting was positive or negative

r2 = 074

p = 0000

Starting BA after harvesting (m2 ha

-1)

0 5 10 15 20 25 30

Ln

Mean

BA

I (m

2 h

a-1

year-1

)

-6

-4

-2

0

2

4

Figure 3-6 BA growth of harvested forest in PNG

The scatter plot with logarithmic regression was generated from a model developed in

north Queensland rainforest (Vanclay 1994)

335 Trends in Timber Volume

Timber volume for the harvested plots showed a positive trend over time since

harvesting (r2 = 006 p = 0031) (Figure 3-7 a) In the un-harvested plots analyses

also showed an increase in timber volume since the plot establishment period but with

an insignificant result (r = 024 p = 0087) (Figure 3-7 b) due to the variability in the

data Regression analyses indicated a consistent increase in residual timber volume for

trees ge 20 cm DBH for harvested plots

82

r2 = 024

p = 0087

Time Since Plot Establishment (years)

0 1 2 3 4 5 6

Tim

be

r V

olu

me

gt2

0c

m D

BH

(m

3 h

a-1

)

0

50

100

150

200

250

300

r2 = 006

p = 0031


0 5 10 15 20

Tim

be

r V

olu

me

gt20

cm

DB

H (

m3

ha

-1)

0

50

100

150

200

250

300

Figure 3-7 Trends in timber volume for trees ge 20cm DBH

represented by scatter plot with linear regression for (a) 84 un-burned plots in

harvested forest and (b) 13 plots in un-harvested forest The unharvested plots have a

short measurement history with fewer data and show high variability in the data with

insignificant relationship between time since plot establishment and timber volume

(a)

(b)

83

336 Timber Yield since Harvesting

Test of the model (equation 3-5 Figure 3-8) developed in the Philippines tropical

forests (Mendoza and Gumpal 1987 Vanclay 1994) showed that timber yield of un-

burned plots (63 with increasing BA and 21 with falling BA) in harvested forest for

trees ge 20 cm DBH averages to 296 m3 ha

-1 plusmn 024 (SD) and gradually increases over

the measurement period while mean VOLI is estimated at 233 m3 ha

-1 year

-1 plusmn 809

(SD) Test of this model showed a good fit between the model and the dataset from

this study (r2

= 083 p = 0000) (Figure 3-8)

r2 = 083

p = 0000


0 5 10 15 20

Ln

Tim

be

r Y

ield

gt2

0c

m D

BH

(m

3 h

a-1

)

00

02

04

06

08

10

12

14

16

Figure 3-8 Timber yield of trees ge 20cm DBH in the residual stand


the Philippines natural forests (Mendoza and Gumpal 1987 Vanclay 1994)

337 Mortality due to the Fire Caused During the 1997-98 El

Nino Drought

Ten plots were severely affected due to the fire and had sufficient measurements for

analyses of mortality There was evidence of in-growth and recruitment in the form of

BA gained in the ten plots before the fire with a marked increase in BA for the

Kapul01 and Lark01 plots (Figure 3-9) The BA gained before the fire in Lark01 plot

had exceeded BA lost due to the fire and the trend is almost similar with the Lark02

plot The trend in the two plots indicated that these plots are recovering after they

84

have been burned by the fire The average annual mortality rate estimated (using

equation 3-6) for the ten severely burned plots was 1282 year-1

plusmn 836 (SD) Annual

mortality rates increased dramatically for the Kapul01 and Kapul02 plots due to the

fire

PlotID

CNIR

D01

CNIR

D02

IVAIN

01

IVAIN

02

KAPU

L01

KAPU

L02

LARK01

LARK02

WIM

AR01

WIM

AR02

Pe

rcen

tag

e B

A g

ain

ed

or

lost

()

0

10

20

30

40

BA gained before fire

BA lost due to fire

Figure 3-9 Ingrowth recruitment and mortality for the 10 burned plots

Ingrowth and recruitment are expressed as percentage BA gained before the fire and

mortality is expressed as percentage BA losses after the fire for the 10 severely burned

plots during the 1997-98 El Nino drought After the fire mortality rates are high as a

result of trees dying and the resulting BA losses with the exception of the Lark01 plot

The error bars represent standard deviation from the mean

338 Species Diversity in Cutover Forest

Species diversity measured using the Shannon-Wiener Index (equation 3-7) for the 13

un-harvested plots was higher (49 plusmn 021 SD) than in selectively-harvested forests

(35 plusmn 033 SD) The un-harvested forest had fewer plots hence detailed analyses and

comparison could not be made between intact plots and those in harvested forests

however species diversity remained almost constant without increasing over time for

plots on harvested forest since harvesting

85

r2 = 016

p = 0069


0 5 10 15 20 25 30

Sh

an

no

n-W

ien

er

Ind

ex

(H

-1)

0

1

2

3

4

5

Figure 3-10 Species diversity represented by the change in Shannon-Wiener Index

since harvesting At 005 level there is no significant relationship between time since

timber harvesting and the Shannon Wiener Index (p = 0069)

34 DISCUSSION

As would be expected analyses of the impact of selective timber harvesting on stand

structure showed that in the harvested plots the number of stems increased in the

smaller size classes (Figure 3-2 a) while stand BA increased in almost all size classes

over the plot measurement period (Figure 3-2 d) The un-harvested plots had a short

measurement history and there was no marked increase in stem numbers over the

range of diameter classes (Figure 3-2 b) while BA for size classes 30-49cm and 70-

89cm DBH increased at 5-10 years (Figure 3-2 b and d)

There was a slight increase in commercial stocking while the non-commercial

(including secondary and pioneer species) species continue to increase at 0-10 years

and 15-20 years for harvested plots (Figure 3-3 a) Marked increases in BA and

volume (trees ge 20cm DBH) were evident in the commercial species group but the

increase in both measures in the non-commercial group exceeded that of the

commercial group by over 50 (Figure 3-3 b and c) These trends provide evidence

that a higher proportion of non-commercial species occupy gaps and openings

immediately up to about 20 years after harvesting This result also supports

projections made by Alder (1998) for the same studied forest in which he observed a

86

significant tendency for higher proportions of pioneers to occur at higher recruitment

levels There was some evidence of recovery of stocking BA and volume in

commercial species (Figure 3-3 a b and c) Commercial volume recovery includes

recruitment into the gt 20 cm DBH size class and growth in the larger size classes

Results from analyses of impact of harvesting on stand dynamics of selectively-

harvested forests showed there was an increase in stand BA (Figure 3-4) In PNGlsquos

natural forests earlier research studies indicated that BA in undisturbed forests was

about 30-32 m2

ha-1

(Alder 1998 Kingston and Nir 1988b Oavika 1992) The

present study found that average BA in plots on forests disturbed from selective

harvesting is about 17 m2 ha

-1 a reduction of about 43 from the original un-

harvested intact primary forest

Residual timber volume in the harvested plots increased significantly over time while

there was a general increase in timber volume for the un-harvested plots but this

increase appeared insignificant because of the insufficient data resulting in higher

variability in these plots (Figure 3-5a and b) The increase in residual timber volume

in harvested plots is due to the recruitment and ingrowth associated with diameter and

BA growth occurring after harvesting

When a comparison was made between the change and growth in BA since selective

harvesting from this study with similar studies in tropical forests in other regions

(Table 3-2) results from this study are within the ranges of those studies For

example similar studies carried out by Nicholson et al (1988) in north Queensland

rainforest showed that BA was reduced due to selective harvesting by between 8

and 43 Studies of Smith and Nichols (2005) and Pelissier et al (1998) also showed

similar figures for BA in primary and harvested forests Although the mean BAI after

selective harvesting for the 84 plots in this study is lower (017-042 m2 ha

-1 year

-1)

than that of the study by Smith and Nichols (2005) (032-075 m2 ha

-1 year

-1) overall

stand BA continued to increase over the plot measurement period (Figure 3-4) The

mean increment for the 75 of un-burned plots with increasing BA (042 m2 ha

-1

year-1

) is more consistent with the international data It is also considered that BA

increment after harvesting is generally the contribution of recruitment whereby

smaller size class trees are growing into the ge 10cm DBH class and the ingrowth

occurring where trees in smaller size classes are putting on diameter increment and

passing on to the next larger size classes These two processes suggest that when there

87

is a positive BA increment harvested forests are in a recovering condition As

indicated in this study the increase in BA after harvesting (Figure 3-4) suggests that

selectively-harvested forests in PNG have the potential to recover following

harvesting This has also been observed in other regions (eg north Queensland

rainforest see Nicholson et al 1988) The estimates of BA and mean BAI in this

study are comparable to similar international studies carried out in other tropical

regions focusing on the impact of harvesting on change and growth of basal area for

tree stems ge10cm DBH (Table 3-2)

Table 3-2 Comparison of results of this study with similar studies

Region

Primary Forest

Mean BA

(m2 ha

-1)

a

Harvested Forest

Mean BA (m2 ha

-1 )

Mean BAI

after harvesting

(m2 ha

-1 year

-1)

Source

PNG

2901

1735

017

Current study

PNGb

30 - 33

10 - 20

Kingston amp Nir

1988 Oavika 1992

Alder 1998

Sub tropical

Australia

515

12 - 58

032 ndash 075

Smith et al 2005

North

Queensland

Australia

3794 ndash 7342

2586 ndash 4160

Nicholson et al 1988

South Indiac

393

348

Pelissier et al 1998

a Primary forest mean basal area are for un-harvested forests

b Earlier studies carried out in similar forest types in PNG

c Study carried out in dense moist evergreen forest in Western Ghats

South India

If the sample plots in this study are generally representative of selectively-harvested

forests in PNG the change in BA over time in this study suggests that a significant

proportion of native forests in PNG are recovering after disturbance from

conventional harvesting This contrasts with the suggestion of Shearman et al (2009a)

that harvested forests in PNG generally degrade over time To address this disparity

detailed research studies are required in the future to quantify the extent of

degradation after harvesting native forests in PNG A degraded forest or forest

degradation does not involve a reduction in the forest area but rather a decrease in

forest quality or condition (Lanley 2003) In the context of this study forest

88

degradation is examined as the decrease in forest condition after selective-harvesting

in the plots studied The present study shows through direct evidence from ground-

based monitoring of PSPs that a relatively high proportion of harvested native forests

in PNG are recovering over time

Test of the model developed for sub-tropical forests in the nearby region of north

Queensland (equation 3-4) (Vanclay 1994) to determine BA growth in this study

showed that there was a good fit to this model despite the fact that it was developed

for forests with quite different forest type and stand structure and that it may be a

useful basis for modeling future growth of PNG forests Application of the

Queensland model using the dataset from this study showed no evidence of a single

critical threshold BA below which the BA growth of harvested forest decreases

(Figure 3-6) This suggests that forest recovery capacity is dependent on other factors

such as the extent of damage to residual trees degree of soil disturbance or the

presence of seedlings and saplings that can rapidly grow into gaps created by

harvesting Earlier studies in PNG suggested that stands with BA below 25m2 ha

-1

should be able to recover to at least their original stocking before harvesting (Alder

1998)

Application of the model developed in the Philippines (equation 3-5) (Mendoza and

Gumpal 1987 Vanclay 1994) using the dataset from this study produced reasonable

estimates (Figure 3-8) The objective to test this model was to assess the trend in

timber yield over time since harvesting however because of the diverse forest types

and species composition in the PNG situation the Philippines model may not be

applicable to PNG forests Therefore this study recommends the need for

development of similar models for application in the future management of natural

forests in PNG

In parts of PNG that are subject to periodic fire forest can readily convert to

savannah particularly in proximity to settlements (Alder 1998) The effects of the

fire following the severe El Nino of 1997-98 on stand mortality (Figure 3-9) were

similar to those in a tropical forest in Sarawak impacted by severe drought associated

with the same event (Nakagawa et al 2000) In their study of a core plot (138 ha

plot at the centre of a larger plot of 8 ha) mortality during non-drought period was

089 year-1

and during the drought period this increased to 637 year-1

in the same

plot Their study also indicated that the BA lost in the drought interval (1997-98) was

89

34 times that of the annual BA increment of the measurement period 1993-97

Annual mortality rates assessed as BA losses in this study are considered higher than

the Nakagawa et al (2000) study due to the combined effects of drought and fire

Currently there is an increasing concern about the impacts of timber harvesting on

biodiversity and other forest values in tropical forests (Kobayashi 1992 Stork 2010

Stork and Turton 2008) Tropical forests are characterized by a high diversity of

woody species (Clark and Clark 1999) as is the case in PNG Species diversity is best

indicated by the Shannon-Wiener Index (H1) (Stocker et al 1985) Studies carried out

in north Queensland showed that timber harvesting had only a minimal affect on

species diversity (Nicholson et al 1988) This was probably due to the type of

harvesting and goal of maintaining species composition in that forest In this study

harvested plots had considerable lower mean species diversity than un-harvested plots

and species diversity did not increase over time This suggests that some species were

continuing to be lost while pioneer and secondary species became established in

gaps Further research is required to establish the effect of timber harvesting and

species diversity in different forest types

Lindemalm and Rogers (2001) showed that conventional harvesting caused reduction

in tree diversity of 25 (H1) in comparison to unlogged forest as a result of initial

losses from high harvesting intensities high post harvest mortality and low diversity

of new recruitment Diversity index (H1) for un-harvested and harvested plots in the

current study is consistent with studies of Wright et al (1997) They found H1 values

of 4 and 5 in PNG forests in comparison to values around 1 in the Lindemalm and

Rogers (2001) study

Options for future utilisation of forests in the current study sites will depend on their

status Forests that have been heavily impacted by harvesting with declining BA will

require intervention to rehabilitate and restore species composition and production

potential For forests in similar condition to the 75 of plots that are in a recovering

state maintaining their production potential will depend on protection from fire or

other human disturbances Data from this study suggests that in these types of forests

it is likely to take a minimum of 50 years after harvest before they have sufficient

standing volume to provide for a similar level of harvest to the first cut

These forests can potentially sustain harvesting of lower volumes per hectare in small-

scale operations to supply portable sawmills or local mills but this type of operation

90

will be limited to areas accessible from existing roads with intact bridges and other

infrastructure The production potential of these types of operations is being

investigated in further research associated with this study

35 CONCLUSIONS

Evidence from this study of 105 PSPs suggests that a major proportion of native

forests show increasing BA and stand volume following selective timber harvesting in

PNG Mean BA after harvesting was about 17 m2 ha

-1 and BA increment after

harvesting was positive on 63 (75) of 84 plots with an average BA increment on

these plots of 042 m2 ha

-1 year

-1 Average BA increment across the 84 un-burned

plots over up to 25 years after harvesting was 017 m2 ha

-1 year

-1 Based on the 75 of

the plots with positive BA increment recovering plots may reach the BA of

undisturbed stands within 40-50 years after harvest but the capacity for a future large-

scale harvest will depend on the recovery of commercial timber volume Factors such

as residual stand damage impacts on soil understorey and tree regeneration are likely

to determine the direction of BA increment and the rate of recovery after harvesting

Impacts of drought-related fires and other human or natural disturbances are factors

that will affect the recovery of harvested forests in the future In this study it was

found that BA is affected by the high mortality rates caused by the 1997-98 El Nino

related fire across PNG The future fate of these forests will depend on the period of

time before future timber harvests and the effects of activities undertaken by

communities living near the forest such as subsistence gardening that result in a

change in land cover or species composition To avoid the type of on-going decline

observed on 25 of sites it is recommended that harvesting activities are more

effectively managed and implemented to limit the damage to retained trees soil and

regeneration and trees in smaller size classes of commercially-important species This

study suggests that intervention such as assisted regeneration should be considered as

an option to assist recovery in currently declining sites Given the time frame for

commercial volume recovery of the residual stand harvested forests are unlikely to

attract large-scale commercial harvesting in the near future There is a need for

development of appropriate strategies and options for sustainable future management

of selectively-harvested forests in PNG focusing on smaller-scale CBFM and

utilisation

91

CHAPTER 4

FOREST ASSESSMENT IN CASE STUDY SITES

41 INTRODUCTION

In the late 1950s the first recorded forest inventories in PNG were carried out with

the use of helicopter surveys to assess the countrylsquos forest resources for the first time

for exploitation and the aim was to assess as large an area as possible in the shortest

time (Vatasan 1989) Survey teams were dropped by a helicopter in the middle of the

forest and the survey proceeded to use circular sample plots of 20 meters radius set at

100 meters between centre distances on lines radiating from camp sites In those

surveys the sampling intensity was often very low (less than 1) This was

compensated to an extent by the randomness of line selection and dispersion of the

plots

In the late 1970s and early 1980s the then Department of Forest (now PNGFA)

adopted the systematic sampling method for forest resource inventories (Ambia and

Yosi 2001) This inventory system is currently being used by the PNGFA and is

based on a systematic sampling through parallel equidistant strip lines The procedure

consists of establishing strip lines at equal distances from each other starting from a

base line All trees over 50 centimetres in diameter at breast height (DBH) are

measured as saw logs while trees of over 20 centimetres DBH are measured as pulp

logs Measurement of trees is taken on a strip of 20 meters wide or 10 meters on either

side of the centre line Each 100 meter length of the strip line is considered as a plot of

2000 m2 which is 02 hectares in size Often a measurement staff is used to estimate

the diameter of stems above the buttress however when possible the diameter is

measured with a tape The merchantable height (log length) of stems is often

estimated however just as a check measurements of some trees are taken using a

clinometer and a measuring tape Tree species identifications are made on the spot in

the field while samples of unknown species are collected by the inventory teams and

identified later

While collecting data on trees information about the topography soil and forest type

is also collected An earlier study under the ACIAR Project FST1998-118 (Keenan et

92

al 2005) indicated that the systematic sampling method currently used by PNGFA

generally overestimates forest resource timber volume in a given concession area and

field procedures are costly

In Chapter 4 the forest resource assessment carried out in the two case study sites are

described and results are presented to include residual timber volume and

aboveground forest carbon The objectives of this chapter are to estimate the residual

timber volume and aboveground forest carbon in the two case study sites in order to

use this data to test the scenario analysis and evaluation tools (decision tree models)

developed in Chapter 6

The two study sites have been selected for this research in areas where there has been

significant harvesting of primary forest in the past These sites are the Yalu and

Gabensis villages located outside Lae in Morobe province PNG The two study sites

are approximately 17km apart and located close to easily accessible infrastructure

such as roads and within similar forest types which is the lowland foothill forest as

indicated from field observations

42 BACKGROUND

421 Yalu Community Forest

The detailed background about the Yalu case study site have been given in Chapter 1

(Section 13) The Yalu community forest consists of cutover secondary forest

primary intact forests and areas allocated for gardens (Figure 4-1) In earlier studies

carried out by PNGFRI (Yosi 2004) the CSIRO vegetation type map classified the

forest type in Yalu as Hm (medium crown forest) (Hammermaster and Saunders

1995 Bellamy and McAlpine 1995) Forest assessment and inventory data from field

work carried out by VDT in the Yalu community forest in the past also indicated that

the major timber tree species included Toona sureni Mastixiodendron spp

Pterocarpus spp Intsia spp Terminalia spp Pometia spp Celtis spp and

Bischofia spp (VDT 2006a VDT 2008) VDTlsquos analysis of forest inventory data of

the Yalu forest area indicated that the average timber volume is 2767 m3 ha

-1 (VDT

2006a) The Yalu community forest area is approximately 2200 ha in size

93

Figure 4-1 An aster image of the Yalu community forest

422 Gabensis Community Forest

Details of the Gabensis case study site have been given earlier (Chapter 1 Section

13) This community forest area is near Gabensis village which has been extensively

harvested in the past and the forest left behind are patches of primary intact forest

cutover secondary forest as well as areas allocated for traditional uses including

gardening (Figure 4-2) In the Gabensis community forest area earlier forest

assessment carried out by VDT (VDT 2006b) indicated that the major timber tree

species are Pometia pinnata Anthocephalus chinensis Pterocarpus indicus Vitex

cofassus Terminalia spp and Octomeles sumatrana The total forest area allocated

94

for community forest management in the Gabensis case study site is approximately

150 ha and can be easily accessible for harvesting

Figure 4-2 An aster image of the Gabensis community forest

43 FOREST ASSESSMENT METHODS

In the two case study sites the sampling method that was used as a guide to assess the

residual timber volume and aboveground forest carbon in their community forest

areas involved a stratified random point sampling technique This technique was not

fully implemented because the community forests were relatively small areas and did

not warrant full stratification The basic field procedures in the sampling without full

stratification are summarised below

The respective community forest areas were accessed by walking through

bush tracks and strata in each study site were identified in the field

Each stratum in the respective forest areas were randomly sampled

95

Because the two community forest areas were relatively small bush tracks

previously used by the village people were used to locate and establish points

for sampling

A basal area factor 2 (BAF2) prism wedge was used to take a sweep at each

point in a clockwise direction at a particular point During the sweep each tree

whose DBHOB subtended an angle larger than that identified by the gauge

was counted as IN In the count how close a tree is to the sampling point

determines whether or not this tree is included and is counted as IN Usually

small trees are not included in the count if they are some distance from the

sampling point while larger trees will be included at even greater distances In

this technique only the ―IN trees are counted as sample trees and are

recorded and measured

When recording and assessing each sample at each point features such as

gardens scared sites villages and traditional sites were recorded

GPS was used to record location of each sampling point

At each sampling point the records and measurements taken included timber

species diameter merchantable height and total height of each tree sampled

From the parameters measured on each sampled tree the timber volume and

biomass of each tree were estimated

44 DATA ANALYSIS

441 Estimating Stems per Hectare

In the point sampling technique used in the assessment of forest resources in the two

case study sites a prism gauge with a basal area factor (BAF) of 2 contributes 2m2

ha-

1 of BA for each ―IN tree For example an ―IN tree of 50cm dbhob has g = 020m

2

ha-1

Therefore the stems per hectare are estimated using the equation below

(4-1)

Where BAF is basal area factor and g is tree basal area For example 2020 gives 10

stems ha-1

96

The formula for calculating g takes the form as shown below

(4-2)

Where g is tree basal area and D is tree diameter

442 Timber Volume

The following equation was used to calculate the residual merchantable timber

volume for each tree sampled (Fox et al 2011b)

(4-3)

Where MV is merchantable timber volume D is tree diameter MH is merchantable

tree height and form factor is 05

443 Aboveground Live Biomass

To calculate the aboveground live biomass (AGLB ge 10cm) of each sampled tree a

model developed for wet tropical forests by Chave et al (2005) was used This

equation was developed from data collected from tropical countries including PNG

Malaysia and Indonesia When applying this model Chave et al (2005) found that

locally the error on the estimation of a treelsquos biomass was on the order of plusmn 5 This

approach is internationally accepted when calculating forest C and the model

developed by Chave et al (2005) takes the form as indicated below

(4-4)

Where AGLB is aboveground live biomass p is wood specific gravity D is tree

diameter and TH is total tree height

In this case the wood specific gravity for most PNG timber species have been derived

from Eddowes (1977) The methodology for estimating AGLB and forest C in

Chapter 4 has been adapted from Fox et al (2010) In that study they developed a

methodology for estimating the aboveground forest C and reported the first estimates

of forest C in lowland tropical forest in PNG While currently there is an absence of

97

allometrics and biomass equations for calculating AGLB in PNG Fox et al (2010)

estimated AGLB ge 10cm from PSPs and from these measured component and previous

established relationships (Brown and Lugo 1990 Chave et al 2003 Edwards and

Grubb 1977) they determined the total aboveground forest C in tropical forests in

PNG The ratios applied by Fox et al (2010) to estimate the unmeasured aboveground

pools in harvested secondary forest are for three major forest types (Table 4-1) In this

case the unmeasured pools include AGLB lt 10cm fine litter (FL) and course wood

debris (CWD)

Table 4-1 Unmeasured Components of AGLBge10cm (AGLBge10cm)

Harvested Secondary Forest

Lowland Forest Lower Montane Mid Montane

AGLBlt10cm 10 10 10

FL 1 25 25

CWD 25 25 25

In the present study of the forest assessment in the two community forest areas the

AGLB ge 10cm was determined from the point sampling and using the above ratios the

unmeasured component of AGLB lt 10cm FL and CWD were estimated in order to

determine the total AGLB and consequently the estimate of total aboveground forest

C in the two study sites After estimating the unmeasured components the total

AGLB was determined from the equation below

(4-5)

444 Determining Sample Size

The objective of the forest resource and aboveground forest C estimates were for the

purpose of obtaining the necessary data from the two case study sites in order to test

the decision analysis model developed in Chapter 6 However the estimates of the

mean values of the different parameters and the sample size can be improved by

applying the formula according to Philip (1994)

(4-6)

ge 10cm lt 10cm

98

Where n = number of samples CV = coefficient of variation t = studentlsquos t value for a

90 confidence interval at a specified degree of freedom and E = acceptable level of

error for example 10 of the true mean

45 RESULTS

451 Size Class Distribution

Analyses of point samples shows the number of stems recorded for each diameter

class in the point samples and the estimated number of stems per hectare (Table 4-2)

With the use of the wedge prism of BAF 2 the stems per hectare in each diameter

class have been estimated and recorded In this case each sampled tree contributes

2m2 ha

-1 of basal area and by dividing the BAF with the basal area g of each tree the

stems per hectare is then estimated

Table 4-2 Size Class Distribution

Diameter Class No of Stems Predicted

(cm) in sample Stemsha

10-20 69 119

20-30 93 42

Yalu Community 30-40 55 23

Forests 40-50 23 13

50-60 22 8

60-70 13 6

70-80 10 5

80-90 2 4

90-100 1 3

100+ 7 1

20-30 9 33

30-40 6 22

Gabensis Community 40-50 5 14

Forests 50-60 11 8

60-70 3 6

70-80 2 5

80-90 1 4

90-100 1 3

99

The graphical presentation represents the diameter distribution of the stems of all

timber species combined for the Yalu community and Gabensis community forest

areas respectively (Figure 4-3 Figure 4-4) The distribution represents the actual and

predicted number of stems per hectare in the sample

Figure 4-3 Size Class Distribution for tress ge10cm DBH in the Yalu study site

Figure 4-4 Size Class Distribution for trees ge20cm DBH in the Gabensis study site

0

20

40

60

80

100

120

140

10-20 20-30 30-40 40-50 50-60 60-70 70-80 80-90 90-100 100+

No

of

Ste

ms

(N h

a-1

)

Diameter Class (cm)

Actual

Predicted

0

5

10

15

20

25

30

35

20-30 30-40 40-50 50-60 60-70 70-80 80-90 90-100

No

of

Ste

ms

(Nh

a)

Diameter Class (cm)

Actual

Predicted

100

452 Residual Timber Volume

In the present study the major timber species found in the two community forests

include those in the PNGFA Minimum Export Price (MEP) groups (Table 4-3) with

the estimated residual merchantable timber volume per hectare and the total volume in

each study site

Table 4-3 Residual Merchantable Volume for Major Timber Speciesa

Yalu Community Forest

Timber Species Representation()

Merch Vol (m3

ha-1

)

Total Merch

Vol (m3)

Pterocarpus indicus 116 90 20000

Celtis sp 68 179 39000

Pometia pinnata 51 142 31000

Terminalia sp 34 170 37000

Intsia sp 14 168 37000

Vitex sp 14 119 26000

Endiandra sp 14 165 36000

Canarium sp 14 161 35000

Toona sureni 07 134 29000

Dracontomelon sp 03b

178 39000

Gabensis Community Forest


Chionanthus sp 189 169 2500



Intsia sp 54 144 2100

Hernandia sp 54 152 2300

Planchonella sp 27 149 2200

Mastixiodendron sp 27 186 2800

a The table excludes other non-commercial and secondary timber species

b Dracontomelon sp is represented by only few trees in the sample but they are in the

large diameter class therefore the average volume estimated is high

101

453 Mean Residual Timber Volume

From the forest assessment in the two community forests the mean residual

merchantable timber volume in the two study sites have been estimated (Table 4-4)

The estimates are for all timber species combined


-1)

Yalu Community Forest Gabensis Community

Forest

Mean 1269 1519

SD 450 277

454 Aboveground Forest Carbon

The measured component of AGB (AGLB ge 10cm) the estimated unmeasured

component (AGLB lt 10cm FL CWD) and hence the total AGB in the Yalu and

Gabensis community forest areas are reported (Table 4-5)


) with SD in parenthesis

Component Yalu Community Forest Gabensis Community

Forest

AGLBge10cm 11019 ( 2758) 11921 (3719)

AGLBlt10cm 1102 1192

FL 110 119

CWD 2755 2980

Total AGB 14985 ( 3751) 16212 (5058)

455 Sample Size

Data analyses to improve the estimates of the mean values and the sample size show

the required number of samples n for timber volume and AGB in the two case study

sites (Table 4-6) In this case the number of samples required to improve the

estimates of timber volume and AGB in the Yalu community forest area at 10

acceptable level of error are 22 and 11 In the Gabensis community forest the

numbers of samples required are 31 and 92 for timber volume and AGB respectively

102

Table 4-6 Estimate of number of samples


Mean SD CV

No of

Observation DF

E

() t-value n

Volume

(m2 ha

-1) 1269 450 035 17 16 10 1337 22

AGB

(MgC ha-1

) 14985 3751 025 17 16 10 1337 11


Volume

(m2 ha

-1) 1519 277 018 2 1 10 3078 31

AGB

(MgC ha-1

) 16212 5058 031 2 1 10 3078 92

SD is Standard deviation CV is Coefficient of variation DF is Degrees of freedom E is

Error and n is number of samples required

456 Summary of Resource

The summary of the forest resource in the two study sites from the point sampling

carried out in the present study include the residual timber volume and forest C (Table

4-7) CO2 emissions resulting from selective timber harvesting in PNG have been

estimated to be about 55 from PSP analyses (Fox and Keenan 2011 Fox et al

2011a Fox et al 2011b) based on conventional harvesting practice using heavy

equipment therefore in a community-based timber harvesting future CO2 emissions

in cutover forests are likely to be less Considering a CO2 equivalent of 4412 CO2

emission from large-scale industrial timber harvesting that took place in the past in the

study sites are estimated at 665500 Mg CO2 (181319 Mg C) in Yalu forest area and

49042 Mg CO2 (13375 Mg C) in the Gabensis community forest area

Table 4-7 Summary Results

Yalu Community Forest Gabensis Community Forest

Total Forest Area

2200 ha

150 ha

Total Residual Volume

28000 m3

2300 m3

Mean Residual Volume

1269 m3 ha

-1

1519 m3 ha

-1

Total Forest Carbon

329670 Mg C

24318 Mg C

Mean Forest Carbon

14985 Mg C ha-1

16212 Mg C ha-1

Estimated Emission

from Past Harvesting

181319 Mg C

13375 Mg C

103

46 DISCUSSION

Following on from the objectives of this chapter this study generally shows that the

two case study sites have been extensively harvested in the past and the forests in

these areas have been left in a degraded condition This is reflected from the residual

timber volume and aboveground forest carbon estimated from this study The residual

timber volume in Yalu and Gabensis community forests were estimated at 127 plusmn 45

m3 ha

-1 and 152 plusmn 28 m

3 ha

-1 respectively These estimates are considered lower than

the average timber volumes in fully-stocked primary forests in PNG which is about

30-40 m3 ha

-1 (PNGFA 2007) Looking at the Fox et al (2010) estimates of

aboveground forest C in selectively-harvested forests (902 MgC ha-1

) and primary

forests (1208 MgC ha-1

) in PNG the estimates in the two case study sites are much

higher given the situation that these two community forests had some larger size class

(gt 70cm DBH) and relatively tall trees left behind after harvesting (Figure 4-2) These

community forests are small areas that have been repeatedly harvested in the past and

there have been also evidence of extensive traditional land uses prior to this study

The study estimated aboveground forest C in Yalu community forest at 1499 plusmn 375

Mg C ha-1

while in Gabensis it was estimated to be about 1621 plusmn 506 MgC ha-1

The

issue about additionality and its relationship to C stocks in CBFM is considered in this

study The concept of additionality is firmly grounded in international climate law and

discussed in international climate change negotiations The UNFCC (1992 Article

43) the Kyoto Protocol (1997 Article 112) the Bali Action Plan (2007 Paragraph

1e) and the Copenhagen Accord (2009 Paragraph 8) all call for developed countries

to provide ―new and additional climate change financing to developing countries

(Ballesteros and Moncel 2011) However within climate change policy and

environmental markets the concept of additionality is not clearly understood and

creates disagreement and confusion (Gillenwater 2011) At the heart of these

reactions is not simply a policy debate but there is a more fundamental obstacle

preventing constructive discussion and debate One of the difficulties of the CDM is

in judging whether or not projects truly make additional savings in GHG emissions

(Carbon Trust 2009) The baseline which is used in making this comparison is not

observable According to the Carbon Trust (2009) some projects have been clearly

additional For example the fitting of equipment to remove HFCs and N2O and some

104

low-carbon electricity supply projects were also thought to have displaced coal-

powered generation

Additionality is the process of assessing whether a proposed activity is different than

its baseline scenario For example in the context of climate change policy the

question of additionality is whether GHG emissions from a proposed activity will be

different than baseline scenario emissions

REDD+ is an emerging initiative that has the potential to provide alternative income

for communities who would like to conserve their forest and participate in SFM that

enhances the forest C stock

In the context of this study there is a potential to avoid future emissions from timber

harvesting or other activities that may enable communities to participate in REDD+

projects For example if communities adopt small-scale more sustainable reduced

impact harvesting techniques rather than agreeing to larger-scale industrial operations

they may be able to calculate and benefit from the difference in emissions In

addition some of their forest areas will be protected under smaller-scale operations

conserving biodiversity and other forest values for traditional uses These activities

will therefore avoid emissions that would otherwise have taken place in more

extensive operations

It is clear from this study that the residual timber volume in the two community

forests may not be able to attract large-scale harvesting This is because of insufficient

volumes that may not be able to sustain a bigger operation However volumes

available in the case study sites can support a small-scale harvesting under CBFM

because some large size commercial trees have been left behind after conventional

harvesting in the past The residual timber volume in the study sites is lower than the

average timber volume (30-40m3 ha

-1) in fully-stocked primary forest in PNG The

merchantable timber volume in these forests may be lower than the estimates from the

study (equation 4-3) because trees lt 50cm DBH were also considered during the

inventory If the FSC promoted guidelines of harvesting 2-3 trees ha-1

(Rogers 2010)

is adopted in CBFM in these forests SFM can be anticipated because lower volumes

will be harvested per year and the forest will be left to recover for future harvest

The community forest areas have a high aboveground forest C compared to estimates

for lowland tropical forests in PNG from an earlier study by Fox et al (2010) The

high aboveground forest C in the two study areas can be seen as a result of some large

105

and tall non-merchantable trees with high density left behind after the past harvesting

operations Therefore the options available now in the Yalu and Gabensis community

forest areas are small-scale forest management and utilisation as well as other benefits

from community C trade and participation in the REDD+ initiative

47 CONCLUSIONS

The objectives of Chapter 4 have been to estimate the residual timber volume and

aboveground forest carbon in the two case study sites in order to use this data to test

the scenario analysis and evaluation tools (decision tree models) developed in Chapter

6 These objectives have been achieved and the residual timber volumes and AGLB in

the case study sites have been determined

The residual commercial timber volume estimated in the case study sites 127 m3 ha

-1

in Yalu and 152 m3 ha

-1 in Gabensis forest areas can support a smaller-scale

harvesting operation in CBFM The high aboveground forest C estimates in the two

study sites (1499 MgC ha-1

in Yalu and 1621 MgC ha-1

in Gabensis) provide an

option for communities to manage their cutover forests for C benefits

Results from the assessment of the current condition and future production potential

of cutover forests in the case study sites suggest that communities in these areas may

participate in small-scale timber harvesting and certification schemes manage their

forests for C benefits and participate in REDD and REDD+ activities

106

SCENARIO ANALYSES AND EVALUATION

TOOLS

107

CHAPTER 5

EVALUATION OF SCENARIOS FOR COMMUNITY-BASED FOREST MANAGEMENT

51 INTRODUCTION

In research involving qualitative data collection there are specific methodologies that need

to be followed however review of these methodologies indicated that there are also

difficulties in such methodological choices (Creswell et al 2007) The qualitative research

designs include such methodologies as the participatory action research (PAR) approach

particularly used by psychologists In PAR a major focus is to produce social change

(Maguire 1987) and improve the quality of life (Stringer 1999) in oppressed and exploited

communities While PAR commonly targets silenced groups it is also necessary to involve

groups such as decision-makers as participants of the research (Bodorkos and Pataki 2009)

The PAR method is unique in that the researcher and the members of the community are

engaged at all level of the research process (Whyte et al 1991) The origins of PAR are

traced back to the late 1960s and early 1970s in the United States (Brydon-Miller 2001

Freire 1970) Brydon-Miller (2001) also indicated that PAR has been conducted all over

the world especially in third-world countries Also in past decades the PAR approach was

common in the field of social sciences involving research in education community

development work life and health (Nielsen and Svensson 2006) however recently there

have been increasing interests in adopting this method to address current pressing issues

such as climate change biodiversity loss and other sustainability issues (Fals-Borda and

Mora-Osejo 2003 Reason 2007)

There are two parts to the study in Chapter 5 In the first part a PAR protocol has been

used as a guide to investigate options for the future management of cutover forests in PNG

This involved qualitative interviews of two community groups in a region in PNG where

extensive harvesting of primary forests had occurred in the past The PAR involved group

meetings to explain the purpose of the research followed by one to one interviews in the

108

two case study sites Structured interviews were conducted to investigate local peopleslsquo

preference in how they would like to manage their forests in the future The outcome from

these interviews provided the basis to develop forest management scenarios for cutover

forests

In the second part of the study local peopleslsquo preferences in the future management of their

forests identified in the first part of the study have been analysed The outcomes from these

analyses have been used to develop forest management scenarios by using a spreadsheet

planning tool developed under a previous forest research project in PNG funded by ACIAR

(Keenan et al 2005) Scenarios developed in this chapter have been further tested using

decision tree models developed in Chapter 6

The first objective of Chapter 5 is to investigate options for future management of cutover

forests by using the PAR approach as a guide with two community groups namely Yalu

and Gabensis villages in PNG The second objective of the study is to develop management

scenarios for CBFM

52 BACKGROUND

521 The Scenario Approach

The literature review in Chapter 2 discussed the scenario and MSE methods as the

alternative forest management approaches for cutover forests in PNG Chapter 5 describes

the application of the MSE approach (Sainsbury et al 2000 Smith et al 1999) to evaluate

scenarios for CBFM The details of the MSE approach are given in a framework developed

by Sainsbury et al (2000) (Chapter 2 Figure 2-1)

Scenarios are stories or models for planning and decision-making in situations where

complexity and uncertainty are high for example management of tropical forest

ecosystems (Nemarundwe et al 2003) The use of future scenarios assists in defining

alternative options and identifying strategies to achieve desired results Use of scenarios is

applicable when there are many stakeholders from local groups to decision makers

Scenario methods are applicable to village communities (Wollenberg et al 2000) and in

109

Chapter 5 these approaches have been used as a guide to develop scenarios for CBFM in

PNG

522 Modelling Tropical Forest Growth and Yield

Forest simulation models have a long history in forestry and have proven to be useful tools

for forest management (Shao and Reynolds 2006) Early work on forest yields in the

tropics were started in Burma for Teak and over the years different approaches have

emerged in the development of suitable models for tropical forests (Mariaux 1981

Vanclay 1994) In the tropics there has been a lot of progress made in the development of

growth and yield models for tropical mixed forests Some of these efforts include

development of a growth model for north Queensland by Vanclay (1994) stand table

projection model for Sarawak by Korsgaard (1989) and development of the PINFORM

growth model for lowland tropical forests in PNG by Alder (1998) More recently there

have been examples of work on growth and yield modelling of tropical forests in north

Queensland Brazil Ghana Costa Rica Malaysia and PNG However regardless of these

efforts the very diverse forest types mixed species and lack of continuity in data

collection are some barriers that make it difficult to make predictions on the growth of

tropical forests Work on prediction simulation models and forest growth models in the

tropics generally use inventory data based on PSPs

Analyses of timber yields under different forest management scenarios in this Chapter 5 are

based on the spreadsheet planning tool (Keenan et al 2005)

110

53 METHODOLOGY

531 Criteria for Developing Scenarios

The basic procedures for creating the scenarios in the study included the following steps

using the PAR approach as a guide

o In consultation with stakeholders including government agencies timber

companies NGOs and community groups criteria for selecting scenarios were

developed

o Inform and discuss different approaches to forest management with community and

industry based on information available from existing management tools (for

example PINFORM ACIAR Planning Tool) and analysis of current forest growth

data

o Allow stakeholders to collectively create broad categories of scenarios based on an

informed decision

o In consultation with stakeholders develop a scenario preference scoring sheet

o Distribute scenario scoring sheet during field interviews to research participants for

them to mark the scenarios of their preferences

o In consultation with the research participants select scenarios with highest scores

o Develop scenario analysis and evaluation tools

o Test and analyse selected scenarios using the scenario analysis and evaluation tools

developed

o Compare and evaluate effects of scenarios

o Develop an integrated conceptual framework for CBFM and integrate scenario

outcomes into the framework

111

532 Field Interviews using the PAR Protocol as a Guide

The initial fieldwork in this study involved an extensive consultation in the form of field

visits and meetings to explain the purpose of the research to a wide range of stakeholders in

PNG This was done in order to gauge views from stakeholders about general forest

management issues in the country and to assess their interests and expectations on how they

would like to manage their forests in the future Stakeholders included the following

government agencies (PNGFA FRI University TFTC) timber companies (Lae builders

Ltd Madang timbers Ltd Santi timbers Ltd) NGOs (VDT FPCD FORCERT CMUs) and

the communities (Yalu Gabensis Sogi villages) The research focussed on two community

groups (Yalu and Gabensis villages) that were selected in consultation with the project

partner NGO the Village Development Trust The approach taken in this study involved

the general procedures of PAR but the methodologies of a PAR protocol were not fully

implemented in the study Based on the objectives of the study the PAR approach involved

only the conventional forms of data gathering in the form of village meetings discussions

and interviews The interviews were conducted in order to understand the current uses of

forest by communities and how they would like to manage their forests in the future In this

process research participants in the two communities were asked to indicate their

preferences in questionnaires on what options they preferred in the future management of

their cutover forests

In the PNG context few individuals or families usually involve in small-scale timber

harvesting but they represent the interests of a village or community In such cases sawn

timbers harvested are sometimes used for building local schools community halls church

buildings and other infrastructure The selection of the participants for the interviews was

based on their involvement in small-scale timber harvesting in the past and those that were

interested in the future management of their cutover forests Furthermore the interviews

were not intended as a detailed social survey in the study sites rather it targeted individuals

and families that were interested in the future management of their cutover forests

Eleven individual structured interviews (8 in Yalu village and 3 in Gabensis village) were

conducted within the two community groups The groups were from two villages that are

located in a region where there have been an extensive timber harvesting of primary forests

112

in the past and the forests that are left behind are mostly secondary cutover forests with

residual stand

Despite the sample in this study not being representative of the region due to the sample

size of 11 (8 interviewees in Yalu village and 3 interviewees in Gabensis village) the main

aim of the interview was to understand community attitudes towards small-scale timber

harvesting The outcome of the interviews provided the background on how communities

would like to manage their forests in the future The individuals interviewed were local

people who were not only interested to participate in small-scale timber harvesting rather

they were members of the two community groups who had been actually involved in small-

scale timber harvesting for the last 10 years but with very little capacity to expand their

operations Therefore the interviews served its purpose of understanding community

attitudes towards small-scale timber harvesting a process which is considered as a

prerequisite or background to developing forest management scenarios

The data from field interviews were analysed using both the quantitative data analysis

software SPSS (analysis of scenario indicators) and qualitative data analysis software

NVIVO (current and future uses of forest community attitudes towards small-scale timber

harvesting)

533 Scenario development

Scenarios for CBFM were developed from local communitieslsquo participation in meetings

discussions and interviews in the study The analysis of local peoplelsquos current and future

uses of forests and their preferences on how they would like to manage their forests in the

future form the basis of scenario development The key component of the field interviews

was the scoring of local peoplelsquos preferences Their preferences were analysed as scenario

indicators which were then used to develop the scenarios The initial PAR approach in the

case study sites with the participation of the two communities and the results from analyses

of the field interviews have identified four main forest management options These are

community sawmill local processing medium-scale log export and carbon trade These

options have been analysed using the ACIAR planning tool (Keenan et al 2005) in order

to develop forest management scenarios

113

The scenarios developed in Chapter 5 are community sawmill local processing medium-

scale log export and carbon trade however under the community-based harvesting the

three latter scenarios have been analysed using the planning tool The four scenarios for

CBFM including the carbon trade scenario have been tested using the decision analyses

model developed in Chapter 6 The details and description of the activities that take place

under each scenario are summarised below

Community sawmill that a sawmill is managed by the community itself with little

capacity and light equipment Timber is felled and milled in situ according to buyer

specifications All sawn timber produced are sold in the domestic market and for other

community uses There is no value adding and no export of sawn timber to the overseas

market All production and marketing are the responsibility of the community

Local processing that a local processing is managed by an entity referred to as the central

marketing unit (CMU) with the use of mechanised equipment to increased capacity and

production for the overseas export market The CMU add value to the sawn timber from a

timber storage shed equipped with planner-moulder breakdown saw crosscut saw and

other backup All the processed timber are exported to an overseas certified market and the

production and marketing of sawn timber are the responsibility of the CMU

Medium-scale log export that a medium-scale log export enterprise is managed by a

CMU for the export market with the use of mechanised equipment and increased log

production Logs are exported to the overseas market The CMU is responsible for the

production and marketing of logs from the operation

Carbon trade that a community forest C project is managed for selling C credits to either

a compliance or voluntary market CBFM activities involve reduced impact harvesting and

some of their forest areas are protected thereby avoiding emissions that would otherwise

have taken place This enables the community to participate in the REDD+ initiative

114

534 Scenario Analysis using a Spreadsheet Tool

The forest management options investigated during the field interviews with the

participation of the two community groups (Yalu and Gabensis villages) were further

analysed using a spreadsheet planning tool (Figure 5-1) This tool was developed in a

previous forest research project to improve timber inventory and strategic forest planning in

PNG under the funding support of ACIAR (Keenan et al 2005) The tool basically

facilitates the integration of forest area inventory and growth information from the Yalu

case study site (Yalu community forest) to estimate the timber yields under different

management scenarios in community-based harvesting

Figure 5-1 Example output of the Planning tool (Keenan et al 2005)

Data input in the system include cutting cycle pre-harvest volume in each diameter class for

each species groups and cut fraction

Project NameManagement optionAnalyst Cossey Yosi University of Melbourne Date 3062011

A Cycle length (yrs) 50

Total

Diameter class (cm) 20-50 50-65 65+ 20-50 50-65 65+ 20-50 50-65 65+ Merch

Pre-harvest (m3ha) 210 270 430 90 100 120 50 50 70 1040

Cut fraction () 0 0 100 0 0 100 0 0 0

Post-harvest (m3ha) 210 270 00 90 100 00 50 50 70 490 60

YIELD (m3ha) 00 00 430 00 00 120 00 00 00 550

Ingrowth (m3yr) 028 028 028 008 008 008 000 000 000 07

Growth (m3yr) 000 000 000 000 000 000 000 000 000 00

DeathDamage (m3yr) 000 000 000 000 000 000 000 000 000

Upgrowth (m3yr) 028 028 008 008 000 000

Pre-harvest (m3ha) 210 270 139 90 100 39 50 50 70 667

Cut fraction () 0 0 100 0 0 100 0 0 0

Post-harvest (m3ha) 210 270 00 90 100 00 50 50 70 490 83

YIELD (m3ha) 00 00 139 00 00 39 00 00 00 177

Ingrowth (m3yr) 022 022 022 006 006 006 000 000 000 06

Growth (m3yr) 000 000 000 000 000 000 000 000 000 00

DeathDamage (m3yr) 000 000 000 000 000 000 000 000 000

Upgrowth (m3yr) 022 022 006 006 000 000

Pre-harvest (m3ha) 210 270 112 90 100 31 50 50 70 633

Cut fraction () 0 0 100 0 0 100 0 0 0

Post-harvest (m3ha) 210 270 00 90 100 00 50 50 70 490 85

YIELD (m3ha) 00 00 112 00 00 31 00 00 00 143

Ingrowth (m3yr) 021 021 021 006 006 006 000 000 000 05

Growth (m3yr) 000 000 000 000 000 000 000 000 000 00

DeathDamage (m3yr) 000 000 000 000 000 000 000 000 000

Upgrowth (m3yr) 021 021 006 006 000 000

Left after Harvest1

Cycle

Number

Left after Harvest

Left after Harvest


3

2

B Inventory growth and yield data (ha)

MEP-code 36 OtherMEP-code 12

Local processing Small-scale higher values trees only

115

The gross area of the Yalu community forest was 2200 ha The area available for

harvesting was assessed by considering the need to set aside areas for conservation

reserves slopes fragile areas stream buffers and other areas for community use (Table 5-

1) The pre-harvest volume classified under the PNGFA merchantable species classes and

net volume growth in the case study site are categorised under each size class (Table 5-2)

Table 5-1 Yalu community forest area

Yalu Area Data (ha)

Forest area allocated for CBFM 2000

Exclusions from 1st cycle

Conservation Reserve 50

Slope outside conservation 20

Fragile 15

Streamline Buffers not in

above

10

Community reserves not in

above

10

Other inaccessible 20

1st cycle net area (ha) 1875

Additional Exclusions after 1st cycle (ha)

Conversion to gardens

20

Regrowth area 15

Roading 10

Other

25

2nd

amp3rd

cycle net area (ha) 1805

116

Table 5-2 Yalu community forest inventory data

Diameter Class

(cm)

Volume MEP1

(m3 ha

-1)

Volume MEP2

(m3 ha

-1)

Others

(m3 ha

-1)

lt 20 0301 0307 7029

20-50 4950 6961 34991

50-65 6634 11885 18539

65+

Volume Growth

(m3 ha

-1 year

-1)

0-20 0117 0301 0203

20-50 0129 0124 0244

50-65 0041 0080 0073

65+ 0127

The data available from the case study site was input in the planning tool to analyse timber

yields under different management scenarios Three levels of analysis were carried out

using the planning tool The first was a management regime involving a constant cut

proportion of 50 with different cutting cycles in each scenario removing timber species

in MEP codes 1 and 2 only with a DBH of gt 50cm (Table 5-3)

Table 5-3 Data for a management regime with 50 constant cut proportion

Scenario Cutting Cycle

(Years)

Cut Proportion

()

Diameter

LimitMEP

Codes

Community

sawmill

10 50 gt 50cm

MEP1 MEP2

Local processing

20 50 gt 50cm

MEP1 MEP2

Local processing 30 50 gt 50cm

MEP1 MEP2

Medium-scale log

export

40 50 gt 50cm

MEP1 MEP2

117

The second analysis was a management regime with a constant cut proportion of 75 but

with the same settings (cutting cycles and species groups) in each scenario as the first

regime (Table 5-4) In community-based harvesting only valuable timber species are

felled hence only timber species group in the PNGFA MEP codes 1 and 2 have been

considered in this study The main timber species in MEP code 1 include the genera

Burckella Calophyllum Canarium Planchonella Pometia Intsia and those in Group II

are Hopea Vitex Aglaia and Endospermum



(Years)

Cut Proportion

()

Diameter

LimitMEP Codes

Community sawmill 10 75 gt 50cm

MEP1 MEP2

Local processing

20 75 gt 50cm

MEP1 MEP2


MEP1 MEP2

Medium-scale log export 40 75 gt 50cm

MEP1 MEP2

In the third analyses (Table 5-5) a management regime with a constant cutting cycle of 20

years under a local processing scenario was tested but with 50 and 75 cut intensities

and DBH limit of gt 50cm and gt 65cm in the same species groups (MEP 1 and 2) as in the

first and second management regimes

Table 5-5 Data for a management regime with 20 years constant cutting cycle


(Years)

Cut Proportion

()

Diameter

LimitMEP Codes


MEP1 MEP2

Local processing

20 50 gt 65cm

MEP1 MEP2


MEP1 MEP2


MEP1 MEP2

118

54 RESULTS

541 Current Forest Uses and Future Forest Management Options

The current forest uses in the two communities are hunting gardening and small-scale

harvesting (Figure 5-2) A higher number of people indicated that they were currently using

their forests for small-scale harvesting in Yalu village than in Gabensis village Analyses of

field interviews showed that the local people were currently using some of their forests for

small-scale harvesting while still maintaining other forest lands for traditional uses such as

hunting and gardening (Figure 5-2)

Figure 5-2 Current main forest uses in Yalu and Gabensis villages

X-axis represents the number of interviewees in each village

119

According to the interviews the preferred forest management options for the future

included reforestation local processing carbon trade conservation and sawn timber export

(Figure 5-3) A higher number of local people interviewed in Yalu village also indicated

reforestation as another option for future management of their cutover forests than in

Gabensis village

Figure 5-3 Future forest management options in case study sites


Current forest use by gender indicated that a higher numbers of males were engaged in

hunting and small-scale harvesting than females Forest uses for gardening were higher for

females (Appendix 5-2)

Analyses of future forest uses by villages from the interviews indicated that higher numbers

of people were interested in managing their forests for small-scale harvesting both in Yalu

and Gabensis communities (Appendix 5-3) The other future forest uses recorded in the two

case study sites included non-timber forest products (NTFP) reforestation gardening

120

local timber processing conservation and community development Analyses of future

forest use by gender showed that both males and females were interested in managing their

forests for small-scale harvesting (Appendix 5-3)

Village meetings discussions and interviews carried out in the two case study sites (Yalu

and Gabensis villages) provided evidence that lack of social services including education

health community infrastructure and church facilities influenced community interest in

engaging in small-scale timber harvesting (Figure 5-4) The factors influencing a familylsquos

engagement in small-scale timber harvesting included lack of income difficulties in raising

school fees for sending children to school and better homes Sawn timber demand timber

price certification benefits and markets influenced local peopleslsquo commercial interest in

engaging in small-scale timber harvesting in the two communities (Figure 5-4)

121

Figure 5-4 Factors influencing community attitudes towards small-scale harvesting

This model was generated from the qualitative software Nvivo

122

542 Scenario Indicators

Analyses of field interviews showed high frequencies for local processing (6 55) small-

scale harvesting (4 36) and management for carbon values (5 46) (Figure 5-5)

Frequencies recorded in this case represent the total number of persons under each level of

preference for a particular forest management option in the two case study sites A total of

11 participants were interviewed in the two case study sites Frequency recorded for no

preference was high (6 counts) for the log export scenario

Figure 5-5 Graphical presentation of the frequencies from field interviews

Frequency (left Y-axis) represents number of counts and the equivalent counts are

represented as percentage (right Y-axis)

0

20

40

60

80

100

0

2

4

6

8

10

12

high

preference

low

preference

no

preference

not sure

Percen

tag

e (

)

Freq

uen

cy

(N

)

Do you prefer small-scale harvesting

0

20

40

60

80

100

0

2

4

6

8

10

12

high

preference

low

preference

no

preference

not sure

Percen

tag

e (

)

Freq

uen

cy

(N

)

Do you prefer local processing

0

20

40

60

80

100

0

2

4

6

8

10

12

high

preference

low

preference

no

preference

not sure

Percen

tag

e (

)

Freq

uen

cy

(N

)

Do you prefer log export

0

20

40

60

80

100

0

2

4

6

8

10

12

high

preference

low

preference

no

preference

not sure

Percen

tag

e (

)

Freq

uen

cy

(N

)

Do you prefer management for carbon values

0

20

40

60

80

100

0

2

4

6

8

10

12

high

preference

low

preference

no

preference

not sure

Percen

tag

e (

)

Freq

uen

cy

(N

)

Do you prefer no harvesting

123

543 Estimating Timber Yield under Different Management

Scenarios

Analysis outputs from the planning tool showed that with a cut proportion of 50 of total

volume per hectare in commercial tree species with a DBH gt 50cm in MEP1 and MEP2

merchantable categories in a 10 year cutting cycle for a community sawmilling project

resulted in a relatively even distribution of annual yield of about 3000 m3 in the first

second and third cutting cycles (Table 5-7) Total yield over the three cycles (30 years) in a

10 year cutting cycle is estimated at about 87000m3 In this management regime as the

cutting cycle is increased yield decreases in the first cycle but increases in the second and

third cycles

Table 5-6 Management regime with a constant cut proportion of 50

Scenario

Cutting Cycle

(years)

Annual

Yield Cycle 1

(m3 year

-1)

Annual

Yield Cycle 2

(m3 year

-1)

Annual

Yield Cycle 3

(m3 year

-1)

Total

Yield

(m3)

Community

sawmill

10

3166

2865

2718

87490

Local

processing

20

1583

2100

2890

131500

Local

processing

30

1055

1846

3307

186060

Medium-scale

log export

40

792

1718

3780

251600

In a management regime with a higher cut proportion of 75 but with the same input

variables (gt 50cm DBH MEP1 and 2 groups) under a 10 year cutting cycle annual yield

increased to about 5000 m3 in the first cutting cycle but reduces to about 2000 and 1000

m3 respectively in the second and third cycles (Table 5-8) Further analysis showed that a

yield of about 2000 m3

was evenly distributed over the first second and third cycles under

a 30 year cutting cycle in a local processing scenario The general trend in this management

regime is that with an increased cutting cycle and cut intensity yield decreases

124


Scenario

Cutting

Cycle (years)

Annual Yield

Cycle 1 (m3)

Annual Yield

Cycle 2 (m3)

Annual Yield

Cycle 3 (m3)

Total

Yield

(m3)

Community

sawmill

10

4749

2316

1229

82940

Local

processing

20

2375

1743

1294

108240

Local

processing

30

1583

1551

1574

141240

Medium-scale

log export

40

1187

1456

1802

177800

A management regime under a constant cutting cycle of 20 years showed that with a

reduced cut fraction (50) removing a lesser volume of commercial tree species with a

DBH limit of gt 50cm resulted in an annual yield of about 1600m3 year

-1 in the first cycle

but provided for increases to about 2000m3 year

-1 and 3000m

3 year

-1 in the second and

third cycles respectively In this management regime an increased cutting cycle and

removing more commercial trees (gt 50cm DBH) resulted in an increased annual yield in

the initial harvest however when the cut intensity is increased (75) with an increased

cutting cycle annual yield generally decreases over the consecutive cycles

Table 5-8 Management regime with a constant cutting cycle of 20 years

Scenario

DBH Limit

Species Grp

Annual

Yield Cycle 1

(m3 year

-1)

Annual

Yield Cycle 2

(m3 year

-1)

Annual

Yield Cycle 3

(m3 year

-1)

Total Yield

(m3)

Local

processing

50 gt 50cm

MEP 1 2

1583

2100

2890

131460

Local

processing

50 gt 65cm

MEP 1 2

623

703

805

42620

Local

processing

75 gt 50cm

MEP 1 2

2375

1743

1361

276463

Local

processing

75 gt 65cm

MEP 1 2

934

603

415

39040

125

Analyses of timber yield with an initial cut proportion of 50 under four different cutting

cycles (10 20 30 and 40 years) showed that in a shorter cutting cycle (10 years) under a

community sawmill scenario (Figure 5-6a) annual volume was higher and evenly

distributed over the first second and third cycles A 20 years cutting cycle in a local

processing scenario (Figure 5-6b) showed similar results In longer cutting cycles (30-40

years) under a local processing scenario (Figure 5-6c) and medium-scale log export

scenario (Figure 5-6d) annual volume is lower initially but increases in the second and

third cycles because there is more time between harvests for the forest to recover and

increase in volume

In a similar analysis but with a cut proportion of 75 shorter cutting cycles for example

10 years in a community sawmill (Figure 5-7a) and 20 years in a local processing scenario

(Figure 5-7b) showed a higher annual volume initially which reduced over the consecutive

cycles Longer cutting cycles (30-40 years) showed a lower annual volume for the initial

cut and then evenly distributed over the second and third cycles under a local processing

and medium-scale scenarios (Figure 5-7c and d)

Analyses with a constant cutting cycle of 20 years removing timber species in the same

commercial group (MEP 1 and 2) with a DBH gt 50cm showed that a reduced cut intensity

(50) resulted in a lower annual volume in the first cycle (Figure 5-8a) Maintaining the

same cut proportion (50) and removing commercial trees only with a DBH gt 65cm

(Figure 5-8b) resulted in a low annual volume in the first second and third cycles When

the cut proportion was increased (75) annual volume in the first cycle was increased

(Figure 5-8c) but decreased in the latter cycles With a cut fraction of 75 removing tree

species in the same merchantable categories and only in the DBH class gt 65cm resulted in

a lower annual volume initially and there were no marked increases in the consecutive

cycles (Figure 5-8d)

126

Figure 5-6 Timber yield under different scenarios with a 50 cut proportion

The management regimes are for four cutting cycles (a) 10 years (b) 20 years (c) 30 years and (d) 40 years

0

1

2

3

4

5

1 - 10 11 - 20 21 - 30

An

nu

al

Vo

lum

e (1

00

0 m

3 y

r-1

)

Cutting Cycle (Years)

MEP-code2 65+

MEP-code2 50-65

MEP-code1 65+

MEP-code1 50-65

0

1

2

3

4

5

1 - 20 21 - 40 41 - 60

An

nu

al

Vo

lum

e (1

00

0 m

3 y

r-1

)


MEP-code2 65+

MEP-code2 50-65

MEP-code1 65+

MEP-code1 50-65

0

1

2

3

4

5

1 - 30 31 - 60 61 - 90

An

nu

al

Vo

lum

e (1

00

0 m

3 y

r-1

)


MEP-code2 65+

MEP-code2 50-65

MEP-code1 65+

MEP-code1 50-65

0

1

2

3

4

5

1 - 40 41 - 80 81 - 120

An

nu

al

Vo

lum

e (1

00

0 m

3 y

r-1

)


MEP-code2 65+

MEP-code2 50-65

MEP-code1 65+

MEP-code1 50-65

(b) (a)

(d) (c)

127


The management regimes are for the four cutting cycles (a) 10 years (b) 20 years (c) 30 years and (d) 40 years

0

1

2

3

4

5

1 - 20 21 - 40 41 - 60

An

nu

al

Vo

lum

e (1

00

0 m

3 y

r-1

)


MEP-code2 65+

MEP-code2 50-65

MEP-code1 65+

MEP-code1 50-65

0

1

2

3

4

5

1 - 10 11 - 20 21 - 30

An

nu

al

Vo

lum

e (1

00

0 m

3 y

r-1

)


MEP-code2 65+

MEP-code2 50-65

MEP-code1 65+

MEP-code1 50-65

0

1

2

3

4

5

1 - 30 31 - 60 61 - 90

An

nu

al

Vo

lum

e (1

00

0 m

3 y

r-1

)


MEP-code2 65+

MEP-code2 50-65

MEP-code1 65+

MEP-code1 50-65

0

1

2

3

4

5

1 - 40 41 - 80 81 - 120

An

nu

al

Vo

lum

e (1

00

0 m

3 y

r-1

)


MEP-code2 65+

MEP-code2 50-65

MEP-code1 65+

MEP-code1 50-65

(a) (b)

(c) (d)

128

Figure 5-8 Timber yield for a constant cutting cycle of 20 years

The management regimes are for different cut proportions and diameter limits (a) 50 and DBH gt 50cm (b) 50 and DBH gt

65cm (c) 75 and DBH gt 50cm and (d) 75 and DBH gt 65cm

0

1

2

3

4

5

1 - 20 21 - 40 41 - 60

An

nu

al

Vo

lum

e (1

00

0 m

3 y

r-1

)


MEP-code2 65+

MEP-code2 50-65

MEP-code1 65+

MEP-code1 50-65

0

1

2

3

4

5

1 - 20 21 - 40 41 - 60

An

nu

al

Vo

lum

e (1

00

0 m

3 y

r-1

)


MEP-code2 65+

MEP-code1 65+

0

1

2

3

4

5

1 - 20 21 - 40 41 - 60

An

nu

al

Vo

lum

e (1

00

0 m

3 y

r-1

)


MEP-code2 65+

MEP-code2 50-65

MEP-code1 65+

MEP-code1 50-65

0

1

2

3

4

5

1 - 20 21 - 40 41 - 60

An

nu

al

Vo

lum

e (1

00

0 m

3 y

r-1

)


MEP-code2 65+

MEP-code1 65+

(a) (b)

(c) (d)

129

544 Analyses of Residual Timber Volume over a 60 Year

Cycle

The starting timber volume (pre-harvest volume) in the Yalu case study site was 305

m3 ha

-1 At a cut proportion of 50 in a community-based harvesting in the study site

harvesting size class gt 50cm DBH in the MEP1 and 2 merchantable groups showed

that the residual timber volume continues to increase over a 60 year period (Table 5-

9) At year 50 the residual timber volume is estimated at about 213 m3 ha

-1 and

increases to about 286 m3 ha

-1 at year 60

Table 5-9 Residual and annual volume over a 60 year cutting cycle

Cutting

Cycle

(Years)

Cut

Proportion

()

Diameter Limit

MEP Codes

Starting

Pre-Harvest

Volume

(m3 ha

-1)

Residual

Volume After

3rd

Cycle

(m3 ha

-1)

Annual

Yield

(m3 year

-1)

10 50 gt 50cm MEP1 amp 2 305 271 8750

20 50 gt 50cm MEP1 amp 2 305 577 6574

30 50 gt 50cm MEP1 amp 2 305 989 6208

40 50 gt 50cm MEP1 amp 2 305 1508 6290

50 50 gt 50cm MEP1 amp 2 305 2132 6550

60 50 gt 50cm MEP1 amp 2 305 2861 6899

Projection output from the planning tool showed that at year 0 the starting volume

(pre-harvest volume available) in the Yalu community forest was 305 m3

ha-1

and

under the 10 year cutting cycle this is reduced to 271 m3 ha

-1 after the third cycle

(Figure 5-9) During the consecutive cutting cycles residual timber volume increases

in a positive trend over the 60 year period

130

Figure 5-9 Residual timber volume for a 100 year cycle

545 Projection of Annual Yield over a 60 Year Cycle

At the initial cut the annual yield is high (8750 m3 year

-1) at year 10 but is reduced to

6208 m3 year

-1 at year 30 (Figure 5-10) Yield then is almost constant up to year 40

and starts to increase over the projection period

Figure 5-10 Annual Yield for a 60 year cycle

0

50

100

150

200

250

300

350

10 20 30 40 50 60

Re

sid

ual

Vo

lum

e (

m3

ha-1

)


0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

10 20 30 40 50 60

An

nu

al V

olu

me

(m

3Y

ear

-1)


131

55 DISCUSSION

551 Outcomes from Field Interviews

The field interviews enabled understanding of community attitudes towards small-

scale harvesting Although the sample size (11 individual interviewees) was not

representative of the whole region where the study was undertaken the interviews

served their purpose Community participation in the study has enabled the

identification of the forest management options preferred by the communities for the

future management of their forests This was achieved through preference scoring of

how communities would like to manage their cutover forests in the future While the

study was only able to interview relatively few landowners the whole process of

initial consultations and village meetings to the actual interviews in the two case study

sites provided a basis for further analyses using the planning tool in order to develop

scenarios for community-based management of cutover forests

552 Analyses Output from the Planning Tool

In this study timber yields under different management scenarios have been estimated

using the planning tool (Keenan et al 2005) and scenarios for community-based

management of cutover forests have been developed In community-based harvesting

in a shorter cutting cycle (for example 10 years) sustainability can be achieved in

terms of sawn timber production as is the case in this study (Figure 5-6a)

The study indicated that there was a trade-off between cutting cycle and yield in these

cutover forests Maintaining the same cut proportion (50) and removing commercial

tree species in the same merchantable categories (50cm DBH MEP1 and 2) but in a

20 year cutting cycle under the local processing scenario results in a yield of about

2000m3 year

-1 in the first and second cutting cycles and then an increase in the third

cycle to about 3000m3 year

-1 This management regime under the Local Processing

scenario can achieve sustainability and an even flow of sawn timber in a community

project (Figure 5-6b)

With an increased cutting cycle to 30 years there was a reduced yield of about

1000m3 year

-1 in the first cycle but an increase to 2000 and 3000 m

3 year

-1 in the

132

second and third cycles respectively in a community local processing project (Figure

5-6c)

When the cutting cycle is increased to 40 years in a medium-scale community log

export project there was a reduced yield of about 1000 m3 year

-1 in the first cutting

cycle but an increase to 2000 and 4000 m3 year

-1 respectively in the second and third

cycles (Table 5-6d)

Thus longer cutting cycles have lower short-term yields but potentially higher yields

in the long term because the forest has a greater time to recover to higher volumes for

later cutting cycles Communities will need to assess their time preference for income

associated with harvesting in order to consider the choice between these options

With the same data input as the management regime with a 50 cut proportion but

with an increased cut fraction to 75 yield is higher in a shorter cutting cycle (10

years) initially but reduces in the second and third cycles (Figure 5-7a)

In a 20 year cutting cycle under a local processing scenario with the same data input

in the planning tool yield was same in the first and second cycles (2000 m3 year

-1)

but reduces to 1000 m3 year

-1 in the third cycle (Figure 5-7b)

Analysis showed an even distribution of yield (2000 m3 year

-1) in the first second

and third cycles in a 30 year cutting cycle under a local processing scenario This

management regime can therefore be sustainable in a local community processing

project (Figure 5-7c)

In a community medium-scale log export scenario under a 40 year cutting cycle

analysis showed a reduced yield of about 1000 m3 year

-1 in the first and second

cycles but an increased to 2000 m3 year

-1 in the third cycle (Figure 5-7d)

Analyses of timber yield under a constant cutting cycle (20 years) showed that

removal of commercial timber species in DBH class gt 50cm results in a high annual

volume when the cut fraction is increased (Figure 5-8c) but when only fewer trees in

the gt 65cm DBH class in MEP 1 and 2 groups are cut annual volume is low in the

initial cycle and no marked increases over the consecutive cycles (Figure 5-8 b and c)

A Management regime with a higher diameter limit and shorter cutting cycle may not

produce sufficient volume to support a sustainable community-based harvesting

A comparison was made between shorter and longer cutting cycles with their

resulting annual yield under a constant cut proportion removing half (50) of the

pre-harvest volume available and harvesting only those commercial species in MEP1

133

and 2 groups with a DBH of gt 50cm (Table 5-10) It can be seen that in a shorter

cycle (10-20 years) annual yield can be higher in community-based harvesting

However total yield over the consecutive cycles can be high in longer cutting cycles

(30-40 years) because of longer time periods between the cuts can potentially result in

volume growth for the next harvest For example in a management regime with 50

cut proportion under a 40 year cutting cycle total yield was estimated to be over

250000 m3 (Table 5-6)

Table 5-10 Comparison of shorter and longer cutting cycles

Cutting Cycle Cut Proportion Diameter Limit

Annual

Yield

(Years) () Species Group (m3 year

-1)

10 50

gt 50 cm MEP

1amp2 8750

20 50

gt 50 cm MEP

1amp3 6574

30 50

gt 50 cm MEP

1amp4 6208

40 50

gt 50 cm MEP

1amp5 6290

A similar analyses of timber yields under different management scenarios in a 84000

ha fully-stocked primary forest in the middle Ramu area in PNG (Keenan et al 2005)

showed that a management regime with a lighter cut in a longer cutting cycle taking

only a proportion of higher quality timber species resulted in a longer term even flow

of wood for a community Their study was conducted in a fully-stocked primary

forest while the present study was carried out in a site which had been previously

harvested hence there was lower stocking in the residual timber volume

Projections from the planning tool in the present study showed that residual timber

volume in the case study site increased in a positive trend from year 0 to 60 (Figure 5-

9) while initial yield was high at year 0 to 10 and then decreases at about 30 in year

30 Annual yield increases again in a positive trend after year 40 (Figure 5-10)

Alder (1998) developed a whole stand growth and yield model called PINFORM for

lowland tropical forests in PNG Test of this model in an earlier study suggested that a

harvesting regime with longer cutting cycle example 35 years with gt 50cm DBH

cutting limit was considered unsustainable Projections from PINFORM showed that

134

an increase in the diameter cutting limit from gt 50cm DBH to 65cm+ DBH is

considered more sustainable PINFORM also suggested that shorter cutting cycles for

example 20 years with a regulated volume to be felled at 10m3 ha

-1 are considered

sustainable The results from analyses of timber yields under different management

scenarios in this study supports earlier projections by Alder (1998)

56 CONCLUSIONS

The main aim of the field interview was to understand community attitudes towards

small-scale harvesting to inform the development of scenarios for CBFM These have

been achieved by using the PAR protocol as a guide and involving the participation of

the Yalu and Gabensis village communities Analyses of the field interviews have

identified five main options for the management of cutover forests These are

community sawmill local processing medium-scale log export Carbon trade and no

harvest

In developing scenarios analyses output from the planning tool showed that in

CBFM a reduced cut proportion to about half (50) with a shorter cycle for

example 10 to 20 years removing only commercial trees with a DBH gt 50cm in

MEP1 and MEP2 merchantable categories can result in an even flow of sawn timber

in a community sawmilling or local processing scenario This management regime is

considered sustainable in small-scale harvesting by communities in PNG Similarly in

a longer cutting cycle (30 years) with an increased cut proportion (75) under a local

processing scenario there is an even distribution of yield across the first second and

third cycles however the initial cut is excessive and the yield is low in the first cycle

hence this management regime is considered unsustainable A management regime

under a constant cutting cycle for example 20 years is considered unsustainable

because an increased cut intensity and removal of only fewer commercial timber

species results in low annual yield Outputs from the planning tool provides evidence

that with a light intensity harvest and removal of only a proportion of commercial

timber species can result in a continued increase in the residual timber volume over a

longer period of time in community-based harvesting Annual yield can be high or

low depending on the initial cut fraction in community-based harvesting however it

can increase over a longer period of time as suggested here Projections from the

135

planning tool over 100 years suggest that community-based harvesting can be

sustainable over a longer period of time

A forest management regime with a short cycle (10-20 years) with a reduced cut

proportion (50) removing only a proportion of commercial timber species is

recommended for application in community-based harvesting in PNG

In the PNG situation implementation of control and monitoring systems as far as

forest management (conventional harvesting operations of the industry as well as

small-scale harvesting) is concerned is a major challenge for government authorities

Forest management in general is associated with many problems such as under-

staffing of the PNGFA lack of continuous funding for monitoring logging operations

and corruption at higher level in the timber industry There are also many problems

associated with the implementation of sustainable community-managed timber

production systems in PNG The certification process can address many of the issues

with corruption and short-term financial gain that can drive unsustainable practices

However communities themselves will need to develop agreed internal rules and

controls and political processes to ensure that these are adhered to The mechanisms

for achieving this were beyond the scope of the current study

136

CHAPTER 6

DECISION TREE MODELS FOR COMMUNITY-BASED FOREST MANAGEMENT IN PNG

61 INTRODUCTION

Decision-making is a management and decision science (Ragsdale 2007) SFM

necessitates decision-making which recognises and incorporates diverse ecological

economic and social processes a multitude of variables and conflicting objectives

and constraints (Varma et al 2000)

A decision-support system is a tool that offers a decision maker direct support during

the decision process and integrates a decision makerlsquos own insights with a computerlsquos

information processing capabilities for improving the quality of decision making

(Keen and Scott-Morton 1978 Shao and Reynolds 2006 Turban 1993) On the

other hand a decision analysis tool offers powerful structured analytical technique

about how the actions taken in a decision would lead to a result (Lieshout 2006)

Decision-support systems also assist the decision maker with the evaluation of

alternatives or substantiating decisions Unlike evaluation and analysis systems

decision-support systems involve valuation and rating techniques and inference

methods such as knowledge-based systems originating from the domain of artificial

intelligence (Shao and Reynolds 2006) Generally the application of decision-

support systems to assist SFM has been successful worldwide (Varma et al 2000)

However the use of decision analysis techniques has not been applied in forest

management before Most work on decision analysis has been applied in economic

analysis and decision making in investment scenarios by corporate bodies and

businesses (Ragsdale 2007)

There are different types of modelling techniques that are used to help managers gain

an in-depth understanding about the decision problems they face However models

do not make decisions but people do While the insight and understanding gained by

modelling problems can be helpful decision making often remains a difficult task

The two primary causes for this difficulty are uncertainty regarding the future and

conflicting values or objectives (Ragsdale 2007) The goal of decision analysis is to

137

help individuals make good decisions however it is important to understand that

good decisions do not always result in good outcomes Using a structured approach to

make decisions should give us enhanced insight and sharper intuition about the

decision problems we face As a result it is reasonable to expect good outcomes to

occur more frequently when using a structured approach to decision making than if

we make decision in a more haphazard manner

Although all decision problems are somewhat different they share certain

characteristics such as when a decision must involve at least two alternatives for

addressing or solving a problem An alternative is a course of action intended to solve

a problem Alternatives are evaluated on the basis of the value they add to one or

more decision criteria The criteria in a decision problem represent various factors that

are important to the decision maker and influenced by the alternatives The impact of

the alternatives on the criteria is of primary importance to the decision maker Not all

criteria can be expressed in terms of monetary value making comparisons of the

alternatives more difficult The values assumed by the various decision criteria under

each alternative depend on the different states of nature that occur The states of

nature in a decision problem correspond to future events that are not under the

decision makerlsquos control

There are various useful decision analysis techniques such as influence diagrams

decision trees sensitivity analysis and tornado diagrams as well as more traditional

accounting techniques such as net present value (NPV) (Lieshout 2006) In the

current study the application of a decision analysis technique in CBFM in PNG is a

new approach to tropical forest management This type of technique is justified for

application in tropical forests because of the complexity and uncertainty (Wollenberg

et al 2000) these type of forests present in their management In the context of forest

management in PNG community forest owners have very little capacity to make

decisions on how they would like to manage their forests The decision analyses tools

such as the four decision tree models developed in this study will assist the

community forest owners to make the best decisions in order to get the maximum

return from the different forest management scenarios before them The decision

analyses tools developed in this study are the four decision tree models for

community-based management of cutover forest in PNG The objectives of Chapter 6

138

are to develop scenario analysis and evaluation tools for assisting decision-making in

CBFM and test these tools in two case study sites in PNG

62 BACKGROUND ndash DECISION TREE MODELS

Decision trees are models for sequential decision problems under uncertainty

(Middleton 2001) Decision tree models describe graphically the decisions to be

made the events that may occur and the outcomes associated with combinations of

decisions and events Probabilities are assigned to the events and values are

determined for each outcome A major goal of decision analysis is to determine the

best decisions

Two Excel spreadsheet add-ins called TreePlan and SensIT are the packages used to

build tree diagrams and carryout sensitivity analyses TreePlan and SensIT were

developed by Professor Michael R Middleton at the University of San Francisco and

modified for use at Fuqua (Duke) by Professor James E Smith (Middleton 2001)

This work is based on spreadsheet modelling and decision analysis (Ragsdale 2007)

63 METHODOLOGY

In the previous Chapters (Chapter 1 and 4) some background information about the

two case study sites have been given The forest resource assessment and

aboveground forest carbon data obtained from the study in Chapter 4 as well as other

related costs and income data for timber harvesting and marketing described in

Chapter 5 are used in the Decision Tree Models in Chapter 6 The methodologies for

developing scenarios for CBFM which are guided by a PAR protocol have been

described in Chapter 5 In Chapter 6 these scenarios are tested using the decision tree

models developed in the study Given the data requirements to test the decision

analysis models developed in this study the models are tested using data from the

Yalu case study site only The Yalu case study site had sufficient forest area to

support a CBFM project while the community forest area in Gabensis village was

considered insufficient to support such a project

139

631 Building the Decision Tree

Decision tree models include such concepts as nodes branches terminal values

strategy payoff distribution certainty equivalents and the rollback method When

using decision tree models for decision analysis there are usually two main

approaches Analysis of a single-stage decision problem in which a single decision

has to be made while in multi-stage decision problems most decisions lead to other

decisions thus multi-stage decision problems can be modelled and analysed using a

decision tree (Ragsdale 2008) In this study the multi-stage decision analysis

approach has been used to develop four decision tree models for community forest

management in PNG

To construct the tree diagrams and carry out sensitivity analysis two Excel

spreadsheet add-ins called TreePlan and SensIT have been used

To build the decision trees TreePlanlsquos dialog boxes are used to develop the structure

The branch name branch cash flow and branch probability (for an event) are entered

in the cells above and below the left side of each branch As you build the tree

diagram TreePlan enters formulas in the other cells

632 Nodes and Branches

A decision tree has three kinds of nodes and two kinds of branches A decision node

is shown as a square and this is a point where a choice must be made The branches

extending from a decision node are decision branches and they represent one of the

possible alternatives or course of action available at that point An event node (chance

node) is a point where uncertainty is resolved and is shown as a circle The event set

consists of the event branches extending from an event node and represents one of the

possible events that may occur at the point Each event in a decision tree is assigned a

probability and the sum of probabilities for the events in a set must equal one

In general decision nodes and branches represent the factors that can be controlled in

a decision problem while event nodes and branches represent factors that cannot be

controlled Decision nodes and event nodes are arranged in order of subjective

chronology For example the position of an event node corresponds to the time when

the decision maker learns the outcome of the event The third kind of node is a

terminal node which represents the final result of a combination of decisions and

140

events Terminal nodes are the endpoints of a decision and shown at the end of a

branch

633 Terminal Values

In a decision tree each terminal node has an associated terminal value referred to as a

payoff value Each payoff value measures the result of a scenario or the sequence of

decisions and events along the decision branches leading from the initial decision

node to a specific terminal node The payoff value is determined by assigning a cash

flow value to each decision branch and event branch and then summing the cash flow

values on the branches leading to a terminal node Given the number of probability

and financial estimates used as inputs to a decision tree tornado and spider charts are

generated to identify the inputs that have the greatest impact on the expected

monetary value (EMV) Graphical outputs such as the tornado and spider charts can

be generated from the SensIT for sensitivity analysis to summarise the impact on the

decision treelsquos EMV of each input cell

In the decision tree models that have been developed in this study for community-

based management of cutover forests in PNG the key inputs into the models are

actual costs and income (cash flows) associated with each scenario The five scenarios

for forest management that have been tested using these models include community

sawmill local processing medium-scale log export carbon trade and no harvest

634 Expected Monetary Values (EMV)

In decision analysis using decision trees a decision maker uses a rollback method to

determine the EMV for the decision he makes in each scenario A rollback is a

process that is used to determine the decision with the highest EMV by starting with

each payoff and working from the right to left through the decision tree and

computing the expected values for each node This system is used to select the largest

EMV The EMV for a decision alternative is the average payoff for making a

particular decision In a decision tree an EMV with the highest value is the decision

alternative that is expected to return the highest monetary value for a particular

scenario being considered and in this case an EMV represents profit values The

EMV approach differs from more traditional accounting techniques such as NPV in

that EMV estimation is for annual basis only while income and expenditure are

141

required over a period of time for the estimation of NPV In the case of the current

study EMV calculation was derived from the analyses of income and costs along

each decision and event branch in the decision tree

To select the decision alternative with the largest EMV the following equation was

used (Ragsdale 2007)

(6-1)

Where rij is the payoff for alternative i under the jth state of nature pj is the

probability of the jth state of nature

635 Application of the Decision Tree Models

Decision tree models allow sensitivity data to be linked to a cash flow model and the

cash flow model to be linked to the decision tree model (Figure 6-1) Decision

alternatives and uncertain events are then analysed along the decision and event

branches which result in a payoff value for a particular decision alternative The

payoff value is further analysed using a rollback method by working from the right to

the left of the decision tree to identify the highest EMV for a particular decision

alternative

The main features of the decision tree models developed in this study to test the

community sawmill (Figure 6-2) local processing (Figure 6-3) medium-scale log

export (Figure 6-6) and carbon trade (Figure 6-9) scenarios have the management

arrangement and type of market as the decision alternatives while the anticipated

demand for various forest products and values and their estimated market prices are

uncertain events In the decision tree models the cash flows associated with each

scenario are either negative (costs) or positive (income) and all cash flows are in

PNGK To apply the models the four forest management scenarios have been tested

using data available from the case study site

Local communities in PNG require immediate income to improve their livelihoods

therefore the aim of the analyses using the decision tree approach is to estimate

annual profits (EMV) from the different scenarios being tested in the decision tree

models In terms of the equipment used under different scenarios (for example Lucas

142

Mill) depreciation costs are not considered in the analyses therefore a Lucas Mill in

this case may be written-off or undergo major service after a 12 month operation

Figure 6-1 Basic framework for decision analyses

6351 Scenario 1 ndash Community Sawmill

The two decision alternatives for consideration are community sawmill or no

harvesting (Figure 6-1) If a community or a decision-maker chooses community

sawmill the two uncertain events anticipated are whether the demand for sawn timber

is high or low in the domestic market These events are followed by consideration for

three decision alternatives to sell sawn timber to industry central marketing unit

(CMU) or nearby local market After a decision has been made the last uncertain

events to consider are whether the sawn timbers produced from the sawmill are sold at

high or low price The analysis of the decision alternatives and the events along the

decision tree are expected to return either a zero negative or a positive EMV in profit

terms during the operation of the community sawmill

Field interviews and discussions with the groups involved in small-scale sawmilling

indicated that on average 20m3 of sawn timber are produced from portable mills per

annum and this is for 8 productive months of operation Because communities do not

work continuously in the operation of the mill for 12 months as they may be engaged

EMV

Spider

Charts

Tornado

Charts

Decision Tree

Model

Decision

Alternatives

Uncertain

Events

Cash Flow

Model

Sensitivity

Data

Decision

Analyses

Sensitivity

Analyses

Payoff

Strategy

143

in other village activities such as gardening and due to other factors for example bad

weather and machinery breakdown low annual production volumes are anticipated

The production and marketing requirements for the community sawmill scenario

include costs for the start-up kit operational costs marketing costs and sawn timber

prices (Appendix 6-1)

The examples of calculation of EMVs (profits) estimated for the community sawmill

scenario are as follow (Figure 6-2)

EMV at 2nd

node = (06 x -59850) + (04 x -63850) = PNGK-61450

EMV at 3rd

node = (06 x -61450) + (04 x -76350) = PNGK-67410

6352 Scenario 2 - Local Processing

The two first decision alternatives analysed under the local processing scenario using

the decision tree are the central marketing unit (CMU) managed processing and

community managed processing (Figure 6-3) For a start the decision maker

encounters the first two uncertainties high or low sawn timber demand (ST-Demand

High ST-Demand Low) and the second alternative decisions to be considered are

sawn timber production for Export Market or Domestic Market After a decision has

been made the last uncertainties (events) encountered are selling sawn timber at high

or low prices in both export and domestic markets In the export market prices for

sawn timber are high in a certified market while in a non-certified market sawn

timber prices are low In the domestic market sawn timber prices are either high or

low

Under the local processing scenario with increased capacity and use of mechanized

equipment in a community managed processing the annual production volume is

increased to 50m3 and under the local processing scenario managed by a CMU

annual production volume is further increased to 200m3

The production and marketing requirements for a community-based processing

scenario covers costs for the starting capital operation transport marketing and

sawn timber prices for domestic and certified overseas market (Appendix 6-2)

The examples of the calculation of EMVs (profits) estimated under the local

processing scenario are as follow (Figure 6-3)

EMV at 1st event node = (06 x 199800) + (04 x 19800) = PNGK127800

EMV at 2nd

event node = (06 x 127800) + (04 x -112200) = PNGK31800

144

6353 Scenario 3 ndash Medium-Scale Log Export

CMU managed log export or community managed log export are the two first

decision alternatives to consider under the medium-scale log export scenario (Figure

6-6) When a decision is made the uncertain events that follow are whether the

demand for log export in the overseas export market is high or low After those

uncertain events the next two decision alternatives to consider are whether to export

the logs to an Asian market (60 round logs from the forest industry sector in PNG

are exported to the Asian market) or to other markets (for example Australia and

New Zealand) The last uncertain events to consider are whether the logs are exported

for high or low log prices The related costs and log prices for the international market

(Asia and others) under the medium-scale log export scenario for a community have

been estimated in the PNG context (Appendix 6-3)

The example of calculation of EMVs (profit) estimated under the medium-scale log

export scenario are as follow (Figure 6-6)


EMV at 2nd

event node = (06 x 4159318) + (04 x 3659318) = PNGK3959318

6354 Scenario 4 ndash Carbon Trade

C trade and the emergence of REDD and REDD+ are now increasingly of interest to

many communities in PNG While the exact costs and the benefit sharing

arrangements for C trade are still uncertain in PNG these analyses have been carried

out based on the assumption that a community involved in a forest C project

anticipates to sell its C credits to either a voluntary or compliance market primarily at

an estimated US$20 per tonne The alternative decisions considered by a community

are whether to manage their forests for C trade or do nothing (Figure 6-9) The two

uncertain events that are encountered for the start are whether there is high or low

demand for C credits as a commodity in the C market Two decision alternatives are

then considered whether to sell the C credits to a compliance market or a voluntary

market The last uncertain events that follow are whether the community sells its C

credits for a high or low price The costs for a community forest C project including

the field forest C assessment and accounting administrative expenses and

requirements for the trading of credits have been estimated based on the PNG

community context The analyses for a community forest C assessment and marketing

145

have been based on some crude estimates to demonstrate an example of the likely

costs and benefits for communities in a C trade scenario (Appendix 6-4)

The estimated benefits (EMV or profit) from C trade have been based on estimates of

above ground forest C in the Yalu case study site The average forest C in the study

site was estimated at 150 t C ha-1

giving a total aboveground forest C of 329670 t C

Based on the C emission rate from large-scale selective harvesting in PNG which is

estimated at 55 (Fox et al 2010 Fox and Keenan 2011 Fox et al 2011a Fox et

al 2011b) the total C emission in the study site was estimated at 181319 t C

However considering a CO2 equivalent of 4412 emission from the Yalu case study

site was estimated at 665500 t CO2 Therefore the avoided emission to be sold by the

community is 665500 t CO2 and the average price for C assumed is US$20 per tonne

(compliance market) and US$15 per tonne (voluntary market) In this analysis the

CO2 emission was estimated from the past large-scale selective harvesting that took

place in the study site and the estimated income from selling the avoided emission is

for one year

Below are the examples of calculation of EMVs (profits) under the C trade scenario

(Figure 6-9)


EMV at 2nd

event node = (06 x76321135) + (04 x 67669635) = PNGK72860535

636 Decision Tree Model Parameters

The basic model parameters that are input in the decision tree models are the cost and

income (cash flow) associated with each scenario For the community sawmill local

processing and medium-scale log export scenarios the main costs that are input in the

models are for equipment fuel maintenance wages and transport while the income

associated with all the scenarios are dependent on timber price and annual production

(Table 6-1 6-2 and 6-3 and Appendix 6-1 6-2 and 6-3) The cost estimates used in

this study are based on actual figures obtained from communities and NGOs who are

involved in CBFM using portable sawmills in the region where this study was

undertaken (Morobe Madang and West New Britain provinces) For example the

costs of Lucas mill and chainsaw are actual costs obtained from supplies in PNG

during the time of field data collection and interviews The costs associated with

146

wages are based on the PNG Minimum Wages Standards and direct wages paid to

workers by NGOs and communities involved in CBFM

In the case of the C trade scenario the costs and income that are input in the model

are based on crude estimates in order to demonstrate the likely costs and benefits for a

community C trade project For example C price in USD are estimates only while

forest C C emission and avoided CO2 emission (Table 6-4 and Appendix 6-4) to be

sold by the community have been calculated from the forest assessment carried out in

the Yalu case study site (Chapter 4)

64 RESULTS

641 Decision Tree Model 1 Community Sawmill

Under the community sawmill scenario the sensitivity data input to the decision tree

includes variables such as costs for equipments for example Lucas mill and

chainsaw variable costs operational costs and prices for sawn timber (Table 6-1)

Table 6-1 Sensitivity data - Community sawmill

Input Description

Variation

(10) Variable range

Abs var -var base case +var

Lucas mill (PNGK)5 85000 8500 76500 85000 93500

Chainsaw (PNGK) 6000 600 5400 6000 6600

Manager wages (PNGKm3) 80 8 72 80 88

Fuel and oil (PNGKm3) 120 12 108 120 132

Maintenance amp repairs (PNGKm3) 70 7 63 70 77

Transport local market (PNGKm3) 60 6 54 60 66

Transport town market (PNGKm3) 255 255 2295 255 2805

Timber price - community market

(PNGKm3) 500 50 450 500 550

Timber price - local market (PNGKm3) 600 60 540 600 660

Timber price ndash industry (PNGKm3) 750 75 675 750 825

Timber price ndash CMU (PNGKm3) 1000 100 900 1000 1100

Average sawn timber production

(m3annum) 20 2 18 20 22

No of fortnights (per 8 productive

months) 16 16 144 16 176

5 At the time of this study PNGK1 was equivalent to AUD045

147

Cash flow analysis shows that the main costs under the community sawmill scenario

are the starting capital (K91000) (costs of equipment including portable mill and

chainsaw) and the costs for selling sawn timber to industry CMU or the local market

(Figure 6-2)

Input of cash flows in the decision tree model for the two decision alternatives

(Community sawmill and No harvesting) resulted in the community sawmill returning

an EMV of zero (Figure 6-2) Although the community has the option of selling their

sawn timber to either industry CMU or local market such an enterprise with very

limited capacity and capital is unlikely to generate enough income for the community

and in many cases may make a loss in one year of operation

Income expected are when sawn timber is sold for either a high or low price to

industry CMU or the local market (Figure 6-2) In a community project the local

people also use some of the sawn timber produced for building homes or fuel wood at

no costs to the project

Sensitivity analysis to identify those input variables that impacted the EMV showed

that none of the variables had any impact on the EMV This is because such an

operation had made a loss hence returning a zero EMV under the community

sawmill scenario This particular analysis is not supported by tornado and spider

charts

148

Figure 6-2 Main Features of decision tree model 1 - Community sawmill

Decision Tree Model 1 Community Sawmill 06 Payoff

High Price (PNGK)

-64850

Sell ST-Industry 15000 -64850

-8850 -66050 04

Low Price

-67850

12000 -67850

06

High Price

06 -59850

ST Demand High Sell ST-CMU 20000 -59850

2

20000 -61450 -8850 -61450 04

Low Price

-63850

16000 -63850

06

High Price

-63950

Sell ST-Local Market 12000 -63950

CommSawmill -4950 -64750 04

Low Price

-91000 -67410 -65950

10000 -65950

06

High Price

-75950

Sell ST-Local Comm 10000 -75950

-4950 -76350 04

2 04 Low Price

0 ST Demand Low -76950

1 9000 -76950

10000 -76350

Comm Use

-81000

0 -81000

No Harvest

0

0 0

149

642 Decision Tree Model 2 Local Processing

The sensitivity data input to the decision tree under the local processing scenario

includes equipment costs operational costs and prices for sawn timber (Table 6-2)

An absolute variable in this type of analysis is the input variable (for example cost of

a Lucas mill) multiplied by the range in percentage as set (for example +-10)

Table 6-2 Sensitivity data ndash Local processing

Input Description

Variation

(10) Variable range


Lucas mill (PNGK) 85000 8500 76500 85000 93500

Chainsaw (PNGK) 6000 600 5400 6000 6600

Wages manager (PNGKm3) 80 8 72 80 88

Wages mill operator (PNGKm3) 80 8 72 80 88

Fuels amp oil -CM (PNGKm3) 126 126 1134 126 1386

Maintenance amp repairs - CM (PNGKm3) 735 735 6615 735 8085

4WD truck ndash CMU (PNGK) 260000 26000 234000 260000 286000

4WD tractor ndash CMU (PNGK) 162000 16200 145800 162000 178200

Planner Moulder ndash CMU (PNGK) 100000 10000 90000 100000 110000

Breakdown saw ndash CMU (PNGK) 50000 5000 45000 50000 55000

Cross-cut saw ndash CMU (PNGK) 50000 5000 45000 50000 55000

Fuel amp oil - CMU (PNGKm3) 132 132 1188 132 1452

Maintenance amp repairs - CMU (PNGKm3) 77 77 693 77 847


Transport wharfexport (PNGKm3) 255 255 2295 255 2805

Certification requirements (PNGKm3) 50 5 45 50 55

Fumigation (PNGK) 720 72 648 720 792

Wharf handling (PNGK) 950 95 855 950 1045

Customs clearance (PNGK) 330 33 297 330 363

Sawn timber price -domestic market

(PNGKm3) 700 70 630 700 770

Max timber price -certified market

(PNGKm3) 2400 240 2160 2400 2640

Max timber price - noncert Market

(PNGKm3) 1500 150 1350 1500 1650

Sawn timber production - CM (m3year) 50 5 45 50 55

Sawn timber production - CMU (m3year) 200 20 180 200 220

No of fortnights (per 8 productive months) 16 16 144 16 176

150

In the local processing scenario input of cash flow of the two decision alternatives

(CMU managed processing and Community managed processing) resulted in the

CMU managed processing returning an EMV of PNGK 31800 in profit terms in one

year of operation (Figure 6-3) Analyses showed that when local processing is

managed by the community itself the estimated EMV is PNGK-89494 therefore

resulting in a loss in the first year

151

Figure 6-3 Main features of decision tree model 2 ndash Local processing

Decision Tree Model 2 Local Processing 06 Payoff

CertMarket HP

199800

Export Market 480000 199800

-69200 127800 04

Non-CertMarket LP

06 19800

ST-Demand High 300000 19800

1

480000 127800 06

ST High Price

-124450

Domestic Market 140000 -124450

-53450 -132450 04

ST Low Price

-144450

CMU Mng Process 120000 -144450

-691000 31800 06

CertMarket HP

-40200

Export Market 480000 -40200

-6920000 -112200 04

Non-CertMarket LP

04 -220200

ST-Demand Low 300000 -220200

1

240000 -112200 06

ST High Price

-364450


-5345000 -372450 04

ST Low Price

-384450

120000 -384450

1

31800 06

CertMarket HP

-474938


-24494 -654938 04

Non-CertMarket LP

06 -924938

ST-Demand High 75000 -924938

1

120000 -654938 06

ST High Price

-120494


-12494 -122494 04

ST Low Price

-125494

CommMng Process 30000 -125494

-263000 -894938 06

CertMarket HP

-107494


-2449375 -125494 04

Non-CertMarket LP

04 -152494

ST-Demand Low 75000 -152494

1

60000 -125494 06

ST High Price

-180494


-1249375 -182494 04

ST Low Price

-185494

30000 -185494

152

Sensitivity analysis shows that the annual sawn timber production under a CMU

managed processing has the largest impact on the EMVlsquos range followed by the

maximum sawn timber price in the overseas certified market at +-10 of the EMV

(Figure 6-4) The input variable in the decision tree with the smallest impact on the

EMV is the customs clearance of sawn timber before export The input variable with

either the smallest or no impact on the EMV is shown at the bottom of the Tornado

chart (Figure 6-4)

153

Figure 6-4 EMV sensitivity at +-10 of the base case ndash Local processing

180

2160

286000

178200

1350

110000

1080

93500

55000

88

22000

2805

55

6600

1452

55

220

2640

234000

145800

1650

90000

1320

76500

45000

72

18000

2295

45

5400

1188

45

-60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60 70 80 90 100110120

Sawn timber production - CMU (m3year)

Max timber price -certified market (Km3)

4WD truck - CMU (PNGK)

4WD tractor - CMU (PNGK)

Max timber price - noncert Market (Km3)

Planer Moulder - CMU (PNGK)

Min timber price -certified market (Km3)

Lucas mill (PNGK)

Breakdown saw - CMU (PNGK)

Wages casual worker (Km3)

Cross-cut saw - CMU (PNGK)

Transport wharfexport (Km3)

Sawn timber production - CM (m3year)

Chainsaw (PNGK)

Fuels amp oil - CMU (Km3)

Certification requirements (Km3)

Scenario income value (PNGK)

Tornado chart showing effect on scenario income of +-10 input variation

154

Cash flow (input variables) in the decision tree that impact the EMV represented by

the spider chart (Figure 6-5) shows that the annual sawn timber production by the

CMU and the maximum sawn timber price in the overseas certified market have the

largest impact on the EMV at +-10 of the base case At the inflection point (100

of base case and about PNGK30000 expected EMV) the annual sawn timber

production in a CMU managed local processing is expected to increase by 10

Figure 6-5 Impact of input variables on the EMV at +-10 ndash Local processing

-60000

-50000

-40000

-30000

-20000

-10000

0

10000

20000

30000

40000

50000

60000

70000

80000

90000

100000

110000

120000

86 90 94 98 102 106 110

EMV

(PN

GK

+-

10

B

ase

Cas

e)

Input Value as of Base Case

Spider chart for Local timber processing scenario income with +-10 variation






Planner Moulder - CMU (PNGK)


Lucas mill (PNGK)


155

643 Decision Tree Model 3 Log Export

The sensitivity data under the medium-scale log export that are linked to the cash flow

model are all the costs for equipments operations roading transport marketing and

log prices for overseas market (Table 6-3)

Table 6-3 Sensitivity data ndash Medium-scale log export

Input Description

Variation

(10) Variable range

Abs var -var

base

case +var

Chainsaw (PNGK) 6000 600 5400 6000 6600

Logging truck - CM (PNGK) 120000 12000 108000 120000 132000

4WD tractor - CM (PNGK) 162000 16200 145800 162000 178200

Front-end loader -CM (PNGK) 162000 16200 145800 162000 178200

Wages Manager (PNGKfortnight) 250 25 225 250 275

Wages - Casual (PNGK) 175 175 1575 175 1925

Fuel amp oil - CM (PNGKm3) 144 144 1296 144 1584

Maintenance repairs spare parts - CM

(PNGm3) 84 84 756 84 924

Logging truck - CMU (PNGK) 150000 15000 135000 150000 165000

Dozer D6 - CMU (PNGK) 200000 20000 180000 200000 220000

Skidder D7 - CMU (PNGK) 240000 24000 216000 240000 264000

Front-end loader -CMU (PNGK) 240000 24000 216000 240000 264000


Maintenance repairs spare parts - CMU

(PNGm3) 105 105 945 105 1155

Transport export (PNGKm3) 255 255 2295 255 2805

Roading cost - CM (PNGKKm) 6000 600 5400 6000 6600

Roading cost - CMU (PNGKKm) 40000 4000 36000 40000 44000

Distance to wharf - CM (Km) 15 15 135 15 165

Distance to wharf - CMU (Km) 10 1 9 10 11

Wharf handling fees (PNGK) 950 95 855 950 1045


Log export tax (PNGKm3) 10 1 9 10 11

Government registration (PNGK) 250 25 225 250 275

Sawn timber price - Asia market (PNGKm3) 600 60 540 600 660

Sawn timber price - other market (PNGKm3) 450 45 405 450 495

Annual log production - CM (m3) 2500 250 2250 2500 2750

Annual log production - CMU (m3) 5000 500 4500 5000 5500

No of fortnights 16 16 144 16 176

156

In a medium-scale log export managed by a CMU the data input into the decision tree

model returns an EMV of PNGK 3959317 in profit terms during 8 productive

months of operation (Figure 6-6) If the community manages the log export itself it is

likely to make an estimated profit of PNGK 1987692

The main cost variables input in the decision tree under the log export scenario are

associated with the starting capital and exporting of logs to the overseas market The

export of logs in an operation managed by a CMU or a community group is to either

an Asian market or other markets

157

Figure 6-6 Main features of decision tree model 3 ndash Medium-scale log export

Decision Tree Model 3 Medium-scale Log Export 06 Payoff

Log Price High (PNGK)

4359317

Asia Market 3000000 4359317

-798683 4159317 04

Log Price Low

06 3859317

Log Demand High 2500000 3859317

1

3000000 4159317 06

Log Price High

3609317

Other Market 2250000 3609317

-798683 3509317 04

Log Price Low

3359317

CMU Mng Log Export 2000000 3359317

-842000 3959317 06

Log Price High

3859317

Asia Market 3000000 3859317

-798683 3659317 04

Log Price Low

04 3359317

Log Demand Low 2500000 3359317

1

2500000 3659317 06

Log Price High

3109317

Other Market 2250000 3109317

-798683 3009317 04

Log Price Low

2859317

2000000 2859317

1

3959317 06

Log Price High

2187692

Asia Market 1500000 2187692

-338308 2087692 04

Log Price Low

06 1937692


1

1500000 2087692 06

Log Price High

1812692

Other Market 1125000 1812692

-338308 1762692 04

Log Price Low

1687692

CommMng Log Export 1000000 1687692

-474000 1987692 06

Log Price High

1937692

Asia Market 1500000 1937692

-338308 1837692 04

Log Price Low

04 1687692


1

1250000 1837692 06

Log Price High

1562692

Other Market 1125000 1562692

-338308 1512692 04

Log Price Low

1437692

1000000 1437692

158

Sensitivity analysis represented by the Tornado chart shows that the annual log

production by a central marketing unit has the biggest impact on the EMV in the

medium-scale scale log export scenario The second input variable in the decision tree

that had the biggest impact on the EMV is the log price in the Asian market followed

by the costs of transport associated with the logging operations (Figure 6-7) The

input variable that has the smallest impact on the EMV is the distance from the

logging operation site to the wharf for transportation of logs for overseas export

159

Figure 6-7 EMV sensitivity at +-10 of the base case ndash Log export

4500

540

2805

198

1155

44000

11

11

176

1045

363

275

5400

108000

145800

145800

225

1575

1296

756

135000

180000

216000

216000

5400

135

405

2250

5500

660

2295

162

945

36000

9

9

144

855

297

225

6600

132000

178200

178200

275

1925

1584

924

165000

220000

264000

264000

6600

165

495

2750

33000003400000350000036000003700000380000039000004000000410000042000004300000440000045000004600000

Annual log production - CMU (m3)

Log price - Asia market (PNGKm3)

Transport export (PNGKm3)

Fuel amp oil - CMU (PNGKm3)

Maintenance repairs spare parts - CMU (PNGm3)

Roading cost - CMU (PNGKKm)

Distance to wharf - CMU (Km)

Log export tax (PNGKm3)

No of fortnights

Wharf handling fees (PNGK)

Customs clearance (PNGK)

Government registration (PNGK)

Chainsaw (PNGK)

Logging truck - CM (PNGK)

4WD tractor - CM (PNGK)

Front-end loader -CM (PNGK)

Wages Manager (PNGKfortnight)

Wages - Casual (PNGK)

Fuel amp oil - CM (PNGKm3)

Maintenance repairs spare parts - CM (PNGm3)

Logging truck - CMU (PNGK)

Dozer D6 - CMU (PNGK)

Skidder D7 - CMU (PNGK)

Front-end loader -CMU (PNGK)

Roading cost - CM (PNGKKm)

Distance to wharf - CM (Km)

Log price - other market (PNGKm3)

Annual log production - CM (m3)

PNGK (+- 10 Base case)

160

The spider chart represents the same information as the tornado chart but with

additional details (Figure 6-8) The inflection point where the associated lines

(representing each input variable) meet in the chart is when annual log production in

the medium-scale operation by the CMU is increased by 10

Figure 6-8 Impact of input variables on the EMV at +-10 - Log export

644 Decision Tree Model 4 Carbon Trade

Sensitivity data (Table 6-4) for the C trade scenario are based on a crude assumption

that communities in PNG will engage in selling C credits from their forests to either a

compliance or voluntary market The cost assumption covers areas such as landowner

issues and social mapping equipments for forest C assessment logistics and

transport verification and validation and selling of credits in the international C

market

3300000

3400000

3500000

3600000

3700000

3800000

3900000

4000000

4100000

4200000

4300000

4400000

4500000

4600000

860 880 900 920 940 960 980 1000 1020 1040 1060 1080 1100 1120

EMV

(PN

GK

+-

10

B

ase

cas

e)










No of fortnights




Chainsaw (PNGK)
















161

Table 6-4 Sensitivity data ndash Carbon trade

Input Description

Variation

(10) Variable range


Landowner issuessocial mapping

(PNGK) 30000 3000 27000 30000 33000

Measuring tapes (Ktape) 35 35 315 35 385

Diameter tapes (Ktape) 70 7 63 70 77

Suunto clinnometer (Kclinnometer) 85 85 765 85 935

Compass (Kcompass) 65 65 585 65 715

GISMapping (PNGK) 20000 2000 18000 20000 22000

Logisticstransport (PNGK) 10000 1000 9000 10000 11000

Wages team leader (Kfortnight) 250 25 225 250 275

Inventory field staff (Kfortnight) 175 175 1575 175 1925

Consultancy (PNGK) 10000 1000 9000 10000 11000

Other paper work (PNGK) 2000 200 1800 2000 2200

VerificationValidation (PNGK) 20000 2000 18000 20000 22000

MarketingTrading (PNGK) 10000 1000 9000 10000 11000

Administration (PNGK) 10000 1000 9000 10000 11000

Carbon price - Compliance ($UStC) 20 2 18 20 22

Carbon price - Voluntary ($UStC) 15 15 135 15 165

Average aboveground forest carbon (t

Cha) 150 15 135 150 165

Rate of CO2 Emission () 55 0055 0495 055 0605

Average community forest area (ha) 2200 220 1980 2200 2420

No of fortnights (8 productive

months) 16 16 144 16 176

Application of the decision tree model shows that if a community decides to manage

its forests for C trade the EMV anticipated from analysis of the decisions and events

along the decision tree is estimated at PNGK72860535 over a one year period

(Figure 6-9) The cost input into the decision tree model includes the estimated

starting capital (PNGK60765) and the costs of trading C credits in the overseas

market (PNGK17500)

162

Figure 6-9 Main features of decision tree model 4 ndash Carbon trade

The tornado chart shows that the average aboveground forest C average community

forest area C price in the compliance market and the rate of CO2 equivalent emission

had equal impacts on the EMV under the C trade scenario (Figure 6-10) The other

input variables in the decision tree had either small or no impact on the EMV Results

from the sensitivity analysis are as expected because most of the costs and income

(cash flow) associated with the community C trade scenario are based on crude data

from communities in PNG

Decision Tree Model 4 Carbon Trade 06 Payoff

High Price (PNGK)

79781735

Compliance Market 39930000 79781735

-17500 76321135 04

Low Price

06 71130235

High Demand 31278500 71130235

1

39930000 76321135 06

High Price

69799235

Voluntary Market 29947500 69799235

-17500 67203785 04

Low Price

63310610

Carbon Trade 23458875 63310610

-60765 72860535 06

High Price

71130235


-17500 67669635 04

Low Price

04 62478735

Low Demand 31278500 62478735

1

1 31278500 67669635 06

72860535 High Price

61147735


-17500 58552285 04

Low Price

54659110

23458875 54659110

Do Nothing

0

0 0

163

Figure 6-10 EMV sensitivity at +-10 of base case ndash Carbon trade

The spider chart shows that C price in the compliance market available forest C and

average community forest area the variables that have the direct impact on the EMV

(Figure 6-11) At the inflection point these three input variables are expected to

increase by 10

135

1980

18

50

33000

22000

11000

22000

176

1925

11000

11000

11000

275

2200

935

77

715

385

135

165

2420

22

61

27000

18000

9000

18000

144

1575

9000

9000

9000

225

1800

765

63

585

315

165

47000004800000490000050000005100000520000053000005400000550000056000005700000580000059000006000000

Average aboveground forest carbon (t Cha)

Average community forest area (ha)

Carbon price - Compliance ($UStC)

Rate of CO2 Emission ()

Landowner issuessocial mapping (PNGK)

GISMapping (PNGK)

Logisticstransport (PNGK)

VerificationValidation (PNGK)

No of fortnights (8 productive months)

Inventory field staff (Kfortnight)

Consultancy (PNGK)

MarketingTrading (PNGK)

Administration (PNGK)

Wages team leader (Kfortnight)

Other paper work (PNGK)

Suunto clinnometer (Kclinnometer)

Diameter tapes (Ktape)

Compass (Kcompass)

Measuring tapes (Ktape)

Carbon price - Voluntary ($UStC)

EMV (PNGK +- 10 Base Case)

164

Figure 6-11 Impact of input variables on the EMV at +-10 - Carbon trade

65 DISCUSSION

Forest management requires decision-making hence management tools are required

Application of decision analyses systems in forest management worldwide has not

been common while decision support systems have been widely applied in natural

resource management including the forestry sector

The decision analyses tools developed in this chapter are new techniques in tropical

forest management The major goal of this type of technique is to assist the decision-

maker determine the best decision when presented with different alternatives and

future uncertainties (Middleton 2001) This approach is an analytical technique that

facilitates a structured approach to decision-making

651 Silvicultural Management of Rainforests

The decision tree models developed in Chapter 6 are appropriate tools that can assist

the silvicultural management of rainforests However there have been a few examples

of long-term silvicultural management of native tropical rainforests For example the

Malayan Uniform System (MUS) applied in parts of Malaysia for the management of

4700000

4800000

4900000

5000000

5100000

5200000

5300000

5400000

5500000

5600000

5700000

5800000

5900000

6000000

880 900 920 940 960 980 1000 1020 1040 1060 1080 1100 1120

EMV

(PN

GK

+-

10

B

ase

Cas

e)







GISMapping (PNGK)





Consultancy (PNGK)







Compass (Kcompass)



165

Dipterocarp forest dominated by a single species (about 50) such as Virola Carapa

and Irianthera (Dawkins and Philip 1998 Mckinty 1999) The MUS involves a

single felling and post-felling treatment For example for a shade-tolerant species

such as Dryobalanops aromatic its advance regeneration could stand the sudden

change in light conditions following heavy felling The key to the success of MUS is

the presence of seedling regeneration of the economic species on the ground at the

time of felling

In 1989 the Indonesian government regulations required natural forests to be

managed under one of three systems (Dawkins and Philip 1998) the Indonesian

selective felling which involves multiple use and benefits of the forest soil and water

conservation sustainable timber production conservation of nature and economics of

harvesting The second system involved a clear-cutting practice with natural

regeneration a natural forest stand is managed in a longer cutting cycle and natural

regeneration is encouraged The third system is clear-cutting with planting and this

involves natural advance growth or artificial enrichment In this system 25 candidate

trees ha-1

with DBH gt 20cm are selected to be felled in each cutting cycle of 35 years

In PNG FORCERT has promoted FSC guidelines for sustainable management of

native forests in the communities Basically the silvicultural system involves the

application of RIL by selective harvesting of 1-2 trees ha-1

(Rogers 2010) Logging

gaps created from operations of portable-sawmill promoted abundant regeneration of

primary and secondary species Communities involved in small-scale silvicultural

management of their forests in West New Britain and Madang provinces in PNG were

able to share the financial benefits of exporting their sawn timber to the overseas FSC

certified markets

652 Testing the Decision Tree Models

When the decision tree approach was tested in the case study site (Yalu community

forest) results showed that in a community sawmill scenario because of limited

capacity high starting capital lack of mechanised equipment and low annual sawn

timber production such an operation is likely to make a loss in one year of operation

However whether a high low or no EMV is returned in such an operation is

dependent on costs and income (cash flow) associated with this scenario

The application of this model using data from the case study site showed that when

the two decision alternatives (CMU and community managed processing) were

166

considered in a local processing scenario the EMV returned for the CMU managed

processing was higher (PNGK 31800) in profit terms while the community managed

processing returned an EMV in the form of a loss of PNGK-89494 during the first

year (Figure 6-3) Sensitivity analysis of the EMV showed that the annual sawn

timber production is the model input that has the largest impact on the EMV followed

by the sawn timber price in the certified market at +-10 (Figure) In this case the

profit is dependent on sawn timber prices for exports to certified and non-certified

overseas market The price differential here is justified as sensitivity analyses provide

evidence that prices in the certified market also had a high impact on the profit

(EMV)

The application of the model is flexible in that depending on the cash flow associated

with each decision alternative the EMV is determined by the related costs and income

input into the model For example in a CMU managed local processing facility with

an increased capacity addition of mechanised equipment increased sawn timber

production and high sawn timber price in the certified market is expected to make a

reasonable profit in one year The aim of the EMV analysis is to estimate profits for

only one year and this is dependent on the cash flow (costs and income) associated

with each scenario Although under the community sawmill scenario and if the option

of the local processing being managed by the community is considered (Figure 6-2 6-

3) a loss is made but this loss is only for one year of operation One limitation of the

EMV analysis is that it assigns all the costs of purchasing equipment to one year

rather than spreading the costs over a longer production period of several years or

more The loss is made in the first year of operation because the costs of equipment

are high relative to production sales This does not mean that over a longer period

community sawmilling cannot be viable There is evidence in community sawmilling

in PNG that such operations can be viable if the equipment costs are spread out over

several years (FORCERT 2010 Scheyvens 2009)

This study considered the EMV approach to estimate annual profits and income and

overlooked other analyses techniques such as NPV and internal rate of return (IRR)

because in PNG communities there is a lack of income and local people are in

desperate need for immediate financial benefits to pay for their basic needs to

improve their livelihoods Therefore the EMV analysis was considered appropriate in

the case of the study in Chapter 6 because communities can anticipate monetary

benefits sooner than later

167

Analyses of input variables in the decision tree model under the medium-scale log

export scenario that is managed by a CMU returned a positive EMV

(PNGK3959317) in profit terms Sensitivity analyses showed that the input variables

that had the largest impact on the EMV were annual log production and log price in

the overseas Asian market Results were similar when the log export was managed by

the community itself but with a lower EMV of PNGK1987692

Decision analyses along the decision tree under the C trade scenario resulted in an

estimated EMV of PNGK72860535 With crude data applied in this scenario and

assumption of most of the cash flow input in the model sensitivity analyses showed

that the C price in the compliance market and the rate of CO2 equivalent emission are

two of the four main input variables that had the largest impact on the EMV

Estimates of the EMV under the C trade scenario are based on 150 t C ha-1

in the Yalu

case study site and 55 rate of emission from selective timber harvesting in PNG

(Fox et al 2010 Fox and Keenan 2011 Fox et al 2011a Fox et al 2011b) and

considering a CO2 equivalent of 4412 This particular analysis has been undertaken

to demonstrate to communities the decision tree approach in considering options such

as C trade in the management of cutover forests in PNG Because of insufficient data

available to test the C trade scenario and most of the input variables (costs and

income) in the decision tree model have been based on assumptions the outputs from

the analyses are considered weak and do not provide a strong basis for the anticipated

income from selling C credits by communities in PNG The profit and income

estimated under the C trade scenario are based on crude data and assumptions The

issue of timing of costs and benefits are not considered in this particular analysis

however given the situation that if the community chose to participate in a REDD+

project the income anticipated is assumed to be paid upfront in one lump sum in the

first year While this is unlikely in practice it is consistent with the approach used for

financial analysis of other management options and the best basis for comparison As

C credits are produced over the accounting period of the project usually about 30

years hence payment may be conditional on periodic verification of performance

Considering these uncertainties the analyses undertaken under the C trade scenario

demonstrates the likely costs and benefits for a C project if a community participates

in a REDD+ project

168

A comparison of the starting capital and estimated annual EMV (profit) is made

between the scenarios tested using the decision tree (Table 6-5) Test results showed

that the community sawmill was unable to make any profit in a community-based

operation during the first year of operation This is because the community lacked

capacity management skills and could not bear the operational costs therefore no

profit was made in such an operation In a community managed local processing an

annual loss (PNGK-89494) is anticipated while a CMU managed local processing

makes a profit in one year (PNGK31 800) of operations Analyses outputs from the

decision tree indicated that both the CMU and community managed medium-scale log

export projects make annual profits estimated at PNGK4 million and PNGK2 million

respectively C trade scenario is the option that is expected to generate huge profits if

the community decides to manage its forests for C benefits As mentioned earlier the

analyses outputs for the C trade scenario are uncertain because of the assumptions

made in the costs and income that were input in the decision tree model

Table 6-5 Comparison of the four management scenarios

Scenarios

Starting

Capital

(PNGK)

Annual

EMVProfitLoss

(PNGK)

Community Sawmill 91000 0

Local Processing

CMU Managed 691000 31800a

Community Managed 263000 -89494b

Log Export

CMU Managed 842000 3959317

Community Managed 474000 1987692

Carbon Trade 60765c

72860535

a positive figure represent estimated annual profit

b denotes estimated annual loss

c starting capital for carbon trade scenario based on crude estimates

169

66 CONCLUSIONS

The objectives of Chapter 6 had been to develop scenario analysis and evaluation

tools for assisting decision-making in CBFM and test these tools in two case study

sites in PNG Generally the objectives of this chapter have been achieved There are

four decision analysis models developed in this chapter These are presented in

diagrammatic form which is commonly known as decision trees or decision tree

models The models represent the four management scenarios for CBFM These are

community sawmill local processing log export and carbon trade

Test of the decision tree models with data available from the case study site provided

evidence that depending on the costs and income associated with each scenario the

EMV (whether it is a profit or loss) is generally dependent on the variables such as

cash flow that are input in the model In this case the price differential (for example

sawn timber price in a domestic market versus prices in the overseas certified market)

is a key factor that should be taken into account in the sensitivity analyses

The study in Chapter 6 did not consider the combination of scenarios to test the

decision analyses models for example combining community sawmilling and

REDD+ as one scenario but recommends that future analyses should investigate this

In this case multiple use forest for example community sawmilling and REDD+

project should be considered with the objective of increasing income in CBFM

Currently many community forests in PNG are potentially subject to further

industrial logging or the impact of SPBALs This study does not address these issues

in detail but recommends that community forests that are potentially subject to future

industrial-scale harvesting should be considered for REDD demonstration projects

The tools developed in this study are appropriate for community-based forest

managed in PNG and can be applied in tropical forest management elsewhere in the

region

170

CHAPTER 7

SCENARIO EVALUATION FRAMEWORK FOR COMMUNITY-BASED FOREST MANAGEMENT

71 INTRODUCTION

More than 80 of PNGlsquos population depends on forests in some ways for their survival As

PNGlsquos population increases at a rate of over 3 per annum (wwwpostcouriercompg)

increasing pressure are put on the environment including the forest resources of the

country Currently accessible primary forests are being exhausted for commercial

exploitation but the future management of areas left after harvesting is not the agenda of

governments timber industry and communities Areas left after harvesting is currently

estimated to be 10 of the total forest area in PNG (PNGFA 2007) However because of

the cultural ties between rural communities in PNG and their environments areas left after

harvesting which are considered as secondary or cutover forests are likely to be taken over

by the communities in the future However communities also face a big challenge because

the traditional rights to their land including cutover forests are being limited by a land lease

concept called special purpose business and agricultural leases (SPBALs)

(Wwwpostcouriercompg) implemented by the PNG government This land lease concept

has received a lot of criticism from local groups and international bodies such as the

Association of Tropical Biology and Conservation When local communities and

stakeholders are faced with challenges on how they would like to manage their forest

resources there is a need to deliver to them appropriate tools for assisting decision-making

in CBFM

In developed countries forestry frameworks have long been adopted For example Boyle et

al (1997) developed a forestry framework for the Oregon State Department of Forestry for

evaluation of cumulative effects of forestry practices on the environment In a detailed

framework for forest management the systems that should be taken into account include

measurement monitoring and decision-making (Boyle et al 1997)

171

The objective of Chapter 7 is to develop a framework for community-based management of

cutover forests in PNG

72 BACKGROUND

The background in Chapter 7 covers the MSE approach an overview of forest planning in

PNG small-scale harvesting and requirements for certification in PNG A review of forest

planning in the country shows that the PNGFA has got adequate systems in place but these

systems have been ineffective in terms of implementation In the 1980s small-scale

harvesting by communities in PNG started as an alternative to large-scale conventional

harvesting While this industry has grown particularly at community level there have been

various problems associated with their operations for example the low capacity of

communities and the high starting capital requirements In Subsection 721 some

background of the MSE framework (Sainsbury et al 2000) is provided The MSE approach

has been originally developed and widely applied in fisheries and marine management

(SEQHWP 2007) and this approach forms the basis of the development of an integrated

conceptual framework for assisting decision-making in CBFM in this chapter A framework

such as the MSE seeks to provide the decision maker with the information on which to

base a rational decision given their own objectives and attitudes to risk (Sainsbury et al

2000 Smith et al 1999)

721 The Management Strategy Evaluation (MSE) approach

MSE is a simulation technique developed more than 20 years ago to consider the

implication of alternative management strategies for the robust management of natural

resources (Punt and Smith 1999 Sainsbury et al 2000) MSE is often used to assess the

effects of a range of management strategies and present the results in a way which lays

bare the tradeoffs in performance across a range of management objectives This approach

anticipates to provide the decision maker with the information on which to base a rational

decision given their own objectives preferences and attitudes to risks (Sainsbury et al


The MSE method has been used by organizations such as the International Whaling

Commission (IWC) and Commission for the Conservation of Antarctic Marine Living

172

Resources (CCAMLR) (de la Mare and Williams 1997 Kirkwood 1993) It has been

adopted successfully as a standard management tool for the fishery sector in a number of

countries including South Africa Europe New Zealand and Australia (Punt and Smith

1999) The MSE approach has not been applied in forest management before although most

of its application has been common in other natural resource management sectors such as

the fisheries and watersheds As the need for multi-disciplinary approaches to forest

management are increasing there is a need to investigate the utility of systems such as the

MSE method

The indicator concept is common in environmental and fishery management for an

integrated approach (Rochet et al 2007) The concept works in that all environmental

variables cannot be monitored in a complex natural ecosystem therefore indicators

summarise the information required Indicators are usually incorporated in broader

approaches or frameworks (FAO 1999) however working operational frameworks for

their use in decision-making are still lacking (Rochet et al 2007) To date the most

developed frameworks are the hierarchical structure of the Australian Ecologically

Sustainable Development (ESD) reporting framework which divides well-being into

ecological human and economic components and then further sub-divides these

components (Chesson and Clayton 1998) Another complex framework is the pressure-

state-response (PSR) promoted by FAO (FAO 1999)

The more detailed MSE framework describes the simulation technique for natural resource

management (Punt and Smith 1999 Sainsbury et al 2000) (Figure 7-1)

173

Figure 7-1 The MSE framework for natural resource management

722 Overview of Forest Planning in PNG

The requirements for the National Forest Plan and National Forest Inventory in PNG are set

out in the Forestry Act 1991(Amended 2000) (Table 7-1) The Forestry Act sec 47 (1)

provides provision for a National Forest Plan Section 47 (2) (b) National Forest Inventory

and sec 49 (1) Provincial Forest Plan (Ministry of Forests 1991a) Data and other related

information collected from forest inventories by the PNGFA provides the basis for drawing

up forest plans in PNG Basically forest plans are developed at two levels National Forest

Plan to provide a detailed statement of how the national and provincial governments intend

to manage the countrylsquos forest resources and the Provincial Forest Plans to be drawn up by

174

the provincial government The National Forest Plan is to be consistent with the 1991

national forest policy and relevant government policies and be based on a certified National

Forest Inventory and also consist of the National Forestry Development Guidelines and the

National Forest Development Programme The Provincial Forest Plans contain Provincial

Forestry Development Guidelines and a five year rolling forest development program The

1991 National Forest Policy also has provision for all agreements and permits to be

conditional upon broad land use plans However there is currently no comprehensive land

use planning process in place in PNG (Keenan et al 2005) The PNGFA has adequate

systems in place for planning requirements however they are not currently integrated

effectively for strategic forest planning As it is now there is a lack of understanding of the

overall forest planning framework within PNG (Keenan et al 2005)

175

Table 7-1 Forest Planning and inventory requirements in Papua New Guinea

Planning Level

Inventory Planning

Requirement

Standard Specification Responsibility Comment

National Forest Plan

Forestry Act s 47(1) 1 sample process with

FIPS FIMS and PNGRIS

PNGFA

National Forest Inventory

Forestry Act s 47(2) 1 sample

same as above

PNGFA Significant inventory work

done but not a

comprehensive National

Forest Inventory

Provincial Plans

Forestry Act s 47(2) 1 sample same as above

Compiled for each province

Provincial Forest Officers

Forest Management

Agreement Project

Statement (Feasibility study

tender)

Forestry Act s 100 1 sample from company

plots different to above

PNGFA Significant inventory done

1 inventory not necessary

for sound statistics

5 Year Working Plan

Forestry Act s 101 with

detailed prescription in the

Planning Monitoring and

Control Procedures (PMCP)

1 sample PMCP states

estimate of net harvestable

volume must be based at a

minimum of a 1 sample of

the gross loggable area

Details of net harvestable

volumes presented must be

based of actual inventory of

the areas to be logged and

not on historical data from

previously logged areaslsquo

Company As above

Annual Logging Plan

Forestry Act s 102 and

PMCP

1 Company As above

Operational set-up plan

(harvesting plan)

PMCP At minimum consist of 10

sample of the loggable area

Company Companies prefer to a 20

sample of trees selected to

be harvested Some

companies asses 100 of

trees planned for harvest

(Source Keenan et al 2002)

176

723 Small-Scale Timber Harvesting in PNG

Large-scale commercial timber harvesting of primary forest began in PNG in the

1970s and 80s In the mid 1980s small-scale harvesting particularly by private

operators and community groups started as an alternative income generating activity

as well as to supply sawn timber to build decent homes and community infrastructures

such as buildings for community halls schools hospitals and churches By then

there were over 5000 small-scale portable sawmills sold throughout PNG however in

the 1990s 1500 of these sawmills were still operational with the estimated capacity to

produce 75000m3 of sawn timber per year with the value of AUS$10 million in the

local market (wwwforcertorgpg)

Small-scale timber harvesting in PNG started in the mid 1980lsquos as an alternative to

large-scale logging this was the result of local communities and forest owners

receiving very little services and other benefits from large-scale logging operations

Since then up to now small-scale harvesting has rapidly increased in many

communities throughout PNG Usually this involves individuals family groups clan

groups or community groups harvesting on small blocks of forest land using small-

scale portable sawmills Small-scale harvesting is community-based and most of their

activities have been supported primarily through funding assistance from overseas aid

donors

724 Requirements for Certification

Certification of good forest management represents a new approach in the global

effort to sustain the diverse forest ecosystems and this is being seen as a necessary

requirement particularly in the forestry sector in the tropics (Alder et al 2002

Dickinson 1999) The market for certified products is relatively new and small

compared with the overall wood trade there are few brokers and as yet there are no

trade magazines and few product shows

FSC is a global certification body and its goals are to promote environmentally

responsible socially beneficial and economically viable management of forests

through the establishment of worldwide standards for good forest management

(Dickinson 1999 FSC 1996 FSC 1999) One of the roles of FSC is to accredit

177

organizations that in turn offer independent third-party certification of forest

operations

Certification has been developed as an instrument for promoting SFM (Durst et al

2006) Although initially certification was focused on tropical forests it rapidly

shifted to cover other forest types Ten years after the first certification schemes were

developed about 92 of the 271 million hectares of forests that have been certified

are located in Europe and North America In developing countries only 13 percent of

certified forests are located while only 5 percent of the certified forests are located in

the tropics (Durst et al 2006) There are challenges facing certification and eco-

labelling of forest products in developing countries but the strengths of certification

are promising (Table 7-2)

Table 7-2 Strengths and weaknesses of certification

STRENGTHS

WEAKNESSES

Standards for forest management and

chain of custody are developed

through multistakeholder processes

Forest and chain of custody

management are audited by accredited

third party assessors

Legality and sustainability are

verified under public and private

procurement policies

Broad guidance to forest managers

and assurance to markets

Market is guaranteed for certified

products

Chain of custody guarantees buyers of

certified products

Market driven approach to improve

forest management and address

consumer concerns about social issues

and the environment to good practice

Assurance to consumers that products

they buy are from sustainably

managed forest

Weak market demand for certified

products in the global market

Wide gaps between existing

management standards and

certification requirements

Requirements of certification not

consistent with FSC standards and

guidelines

Weak implementation of national

forest legislation policies and

programs in developing countries

Insufficient capacity to implement

SFM at forest management unit level

and to develop standards and delivery

mechanisms

High direct and indirect costs of

obtaining certification in developing

countries

178

Despite these challenges and constraints many developing countries are increasingly

interested in pursuing certification Recently some promising developments have

emerged that may give further encouragement to developing countries efforts such as

supportive codes of forestry practice stepwise approaches to certification and

increasing interest in forest certification and certified products in the Asia-Pacific

region (Durst et al 2006)

In PNG while there is a national FSC working group in place (FSC 2005) interests

in adopting certification standards are increasing in community-level forest

management While various agencies such as FORCERT FPCD and VDT are

promoting FSC certification standards in CBFM the requirements for certification are

very costly and time consuming and community groups have very little capacity to

comply with the standards and guidelines Certification of village-based timber

operations require heavy subsidisation of not only the certification process but also

the subsequent production transport and marketing of timber (Scheyvens 2009) and

this is a major challenge in PNG

Although PNG communities have very little capacity are financially disadvantaged

and have difficulties in complying with FSC standards certification has a potential to

offer alternative income and benefits through the promotion of SFM When CBFM in

PNG can demonstrate that FSC standards have been met communities will be

rewarded with economic benefits such as continued market access financially

competitive alternatives to poor practice illegal logging and conversion to other land-

uses For those who are able to meet the requirements for certification the financial

benefits of having access to overseas certified markets may be significant For

example FORCERT and FPCD have in the past exported A Grade sawn timber to the

Woodage in Sydney for a price that is almost three times higher than the price in the

local market However with the recent establishment of the PNG Liquefied Natural

Gas (PNG LNG) project in PNG there is currently high demand for sawn timber in

the domestic market Therefore local groups who are unable to comply with the

certification requirements and are unable to sell their products to the overseas certified

market can benefit from higher prices in the domestic market

The FSC has also developed a High Conservation Value Forest Toolkit for PNG to be

used in forest management certification The toolkit is intended to be used by forest

managers to comply with Principle 9 of the FSC standards to assist managers to

179

identify any high conservation values (HCVs) that occur within their individual forest

management units and manage them in order to maintain or enhance the values

identified Examples of HCVF in PNG include the following

Forest areas containing globally regionally or nationally significant

concentrations of biodiversity values (for example endemism endangered

species refugia)

Forest areas that are in or contain rare threatened or endangered ecosystems

(for example breeding sites migratory sites)

The toolkit is intended for use by forest managers undergoing FSC accredited forest

management certification and by FSC accredited certification auditors assessing or

monitoring conservation values in PNG as a part of a complete FSC assessment or

evaluation process The toolkit will assist in making FSC certification acceptable

within the forest industry in PNG

There are three certification models promoted by FORCERT in CBFM in PNG and

the requirements come under three main phases (Figure 7-2) These include

Community Based Fair Trade (CBFT) status Pre-certification status and FSC Group

Certification membership or full certification status There are several criteria for a

community group to comply with and this is a step-wise process for them to move

towards FSC certification

180

Figure 7-2 Certification model promoted by FORCERT in PNG

Phase 2 Pre-certified

Awareness on FORCERT group

certification service network in the group

Carry out 1 forest inventory in its forest

area

Group must be starting the ILG application

process

Application to be lodged for a company or

business name registration

Group to integrate business plan with

community needs

Socio-economic and environmental baseline

survey must be completed

Landuse plan must be in place

Group must undergo chain of custody

training

Must undergo training on operational health

and safety procedures

Enter into a service and production

agreement with a CMU

Must enter into procedure membership

agreement with FORCERT

After achieving pre-certification status

group must progress to FSC certified

producer status with 2 years

Phase 1 CBFT Community must own a good forest resource of

sufficient size

Must have the management right over the forest

area

Group working well with members of its clan

and there are no disputes over the forest area

Awareness on FORCERT group certification

service network in the group

Harvesting to not occur in the buffer zones

Group to undergo training on chain of custody

Must understand the coding system with 3-letter

producer code on both ends of all individual

timber species

Group must enter into a service and production


Must enter into producer membership


After achieving a CBFT status group must

progress to the pre-certified producer status

within 2 years

Phase 3 FSC certified Awareness on FORCERT group certification


Carry out 1 forest inventory in its forest area

Complete the ILG process and submit to

relevant government agency

Have a company or business name registered


survey completed

Landuse plan must be completed

Group must be registered as a member of FIP

Have forest management plan in place

Carry out 10 inventory of the first 5 years

working forest area

Complete set-up establishment

Group must have the chain of custody processes

in place

After achieving the FSC certified producer

status group must meet the FORCERT member

training requirements within 1 year

181

73 METHODOLOGY

In this chapter an integrated conceptual framework for scenario analyses and

evaluation is presented for CBFM The framework is based on the MSE approach

(Sainsbury et al 2000 Smith et al 1999) which has been discussed earlier (Section

721) and the outcomes of the study on scenario analyses (Chapter 5) and decision

tree models developed and tested in case study sites (Chapter 6) The details of the

MSE approach have been given in the literature review (Chapter 2 Figure 2-1) These

are represented by the MSE framework developed by (Sainsbury et al 2000)

The framework for management of cutover forest in PNG was developed after

consultation with local communities (Yalu Gabensis and Sogi villages) government

agencies (PNGFA FRI TFTC) timber industries (LBC Madang Timbers Santi

Timbers) and NGOs (VDT FORCERT FPCD CMUs) in the pilot region where this

research was carried out The procedures were guided by the PAR protocol and

included field visits meetings discussions and interviews with those stakeholders in

the pilot region

731 Stakeholder Consultation

The stakeholder consultation in case study sites leading up to the development of the

framework involved the PAR approach in communities These involved village

meetings and research participants were interviewed and different forest management

options for the future were investigated for cutover forests Outputs from this

investigation and forest management options were fed into a planning systems for

further analyses

732 Forest Inventory

Forest inventory data forms an important part of input data in the planning system for

scenario analyses Data from case study sites including volume growth timber

volume in different size classes and available forest area information were fed into

the planning system The integration of forest inventory data forest growth and area

from the case study site facilitated the estimates of timber yields under different

scenarios

182

733 Planning System

The framework has a spreadsheet-based planning system (Keenan et al 2005) that

analyses forest growth different management options and annual timber yield

estimates to develop scenarios for CBFM The details of the planning tool have been

discussed earlier (Chapter 5 Figure 5-1) In this chapter the planning tool integrates

forest inventory growth and area from the case study site to analyse timber yields

734 Decision Analysis Tools

In the framework the decision analyses tools are models that have been developed

based on spreadsheet modelling and decision analyses technique The models have

been developed in four parts to represent the different forest management scenario for

community-based management of cutover forests (see details in Chapter 6)

For the purpose of this framework a decision analyses tool called decision tree model

analyses decision alternatives and uncertain events along the branches and a payoff

value is determined at the end of the analyses The payoff value is further analysed to

determine the largest EMV for a particular decision alternative

735 Sensitivity Analyses

Sensitivity analyses is facilitated by an Excel Add-in called SensIT to consider how

sensitive the recommended decision is to changes in values in the decision tree

(Ragsdale 2008) This approach is carried out to determine which of the input

variables in the decision tree model have the largest impact on the EMVs range for

example at +-10 Tornado and spider charts are generated using SensIT to identify

the input variables in the decision tree that if changed have the greatest impact on the

EMV Tornado and spider charts summarise the impact on the decision treelsquos EMV of

each input variable being set at for example +-10 of the original EMV (base case)

183

74 RESULTS

The main result in Chapter 7 is the framework presented in this study for assisting

decision-making in CBFM in PNG The framework integrates outputs from

stakeholder consultations (communities industry) a PAR protocol to analyse

stakeholder interests and expectations and management options from field interviews

into an integrated spreadsheet-based scenario analyses and evaluation system The

framework involves decision analyses modelling and evaluation systems and delivers

scenario outputs which can be further evaluated for action

741 A Scenario Analyses and Evaluation Framework

A conceptual framework for scenario analysis has been presented in this study for

community-based management of cutover forests in PNG (Figure 7-1) This approach

has been adopted from earlier studies carried out by Sainsbury et al (2000) for marine

and fishery resource management Their earlier study has been used as a basis to

develop an integrated scenario analyses and evaluation framework in Chapter 7 for

CBFM because of the following reasons

(i) Active participation of different stakeholders and generation of ideas by those

involved in forest management in PNG such as the timber industry community

groups NGOs and PNGFA

(ii) Different stakeholders will have different expectations and requirements on how

they would like to manage their forests hence this framework will accommodate their

interests

(iii) Support the capacity of PNGFA to develop an integrated regional planning and

management system for cutover native forests in PNG

The framework in Chapter 7 has been presented based on the MSE approach

(Sainsbury et al 2000) and the outputs from the studies in Chapter 5 and 6 The

framework integrates different processes from the PAR protocol in the case study

sites testing of scenarios using a planning tool (Chapter 5) and decision analyses tools

(Chapter 6) The framework is an integration of qualitative data from interviewing

communities and quantitative data from forest inventory that have been input in to the

planning and decision analyses systems (Figure 7-2) Sensitivity analyses are carried

out on the outputs of these systems before a decision is implemented

184

Figure 7-3 A conceptual framework for community-based forest management

75 DISCUSSION

Participatory approaches to tropical forest management are increasing and have been

successful because opportunities arise for more inclusive and better informed

decision-making by communities (Evans and Guariguata 2008) Similar studies such

as the one in this chapter have developed tools to assist decision-making in CBFM

For example Anil (2004) developed a GIS-based participatory 3-dimensional model

(3PDM) for transforming landscape information into a format that communities in

Sasatgre in India can use to monitor their forests to make management decisions

Participatory approaches developed in the Brazilian Amazon (Shanley and Gaia

2002) for communities to manage NTPF in their forests and biodiversity management

in Nepal (Lawrence et al 2006) have also been successful Studies in the Philippines

involving community participation in forest management with the application of the

criteria and indicators framework (Hartanto et al 2002) a vegetation monitoring

system developed in India (Roy 2004) for community participation in assessing their

An integrated conceptual framework for scenario evaluation and decision analyses for community-based forest management

Stakeholder

Consultation

Field Interviews

PAR

Investigate

Options

Forest Inventory

Data

Planning System

Growth Data

Decision

Analyses Tools

Spreadsheet

Planning Tool

Decision Tree

Model

Annual Yield

Estimates

Management

Options

Payoff

Strategy

Decision

Alternatives

Uncertain

Events

EMV

Tornado

Chart

Spider Chart

Sensitivity

Analyses

Scenario

Evaluation amp

Analyses

Decision

Implementation

Scenario

Output

Feedback to

Stakeholders

185

vegetation status and other related systems developed for community management of

plantations to assist in decision-making have been also successful

The framework presented in Chapter 7 involved a participatory approach in

communities development of scenarios and analyses of timber yields under different

management scenarios and testing these scenarios using decision analyses models

The framework can be described as having a data input system three simple

spreadsheet-based analyses and modelling systems (planning system decision

analyses tools and sensitivity analyses system) for scenario analyses and evaluation

and a scenario output system for decision implementation

Currently there is a shortfall in the overall forest planning in PNG in that land use

planning process is inadequate and PNGFAlsquos planning systems are ineffective Forest

certification and good practice forestry are not the goal of the government but they are

widely promoted by NGOs and international organisations Small-scale forest

management is usually funded by international donor agencies with very limited or no

support from the government The framework presented in this chapter addresses

these shortfalls from the participation by communities in decision-making and small-

scale timber harvesting to the marketing of products in an overseas certified market

The framework requires forest management options to be investigated from

stakeholder consultations and interviews and forest inventory data to be fed into a

planning system The planning tool integrates inventory data growth and area from a

forest for example a community forest area and estimates annual yields under

different management scenarios The outputs from the planning tool are tested using

decision analyses tools In the decision analyses system a spreadsheet-based model

analyses decision alternatives and uncertain events and at the end of the decision tree

a payoff value is determined The decision tree model has a roll-back system that

analyses the payoff value to determine the largest EMV in profit terms When the

largest EMV is selected and before the decision is implemented the EMV is further

analysed by applying sensitivity analyses to determine which input variables (costs

and income associated with a scenario) have the largest impact on the EMVlsquos range

(at for example +-10) Finally the decision alternative with the largest EMV is

implemented and feedback is given to the stakeholders

186

76 CONCLUSIONS

The objective of Chapter 7 was to present a framework for community-based

management of cutover forests in PNG Unlike decision support systems the system

developed in this chapter is an analytical approach and decision analyses follow a

structured methodology The system developed in this study will build the capacity of

NGOs and communities and assist in decision-making in forest management This

will require stakeholder participation in forest management especially at the

community level A framework such as the one developed in this study has not been

used in PNG hence application of the system will assist decision-making in

community-based management of cutover forests

Since there is no planning system in place for the management of cutover forests in

PNG the framework presented in this chapter will assist the PNGFA develop a

regional forest planning system Application of the framework will involve

community participation in small-scale harvesting in cutover forests and export of

their sawn timber to the overseas certified markets in Australia and New Zealand

The conceptual framework developed in this study is an integrated system for

scenario analyses and evaluation and is applicable to a participatory approach to

tropical forest management in PNG and elsewhere in the tropical region

187

CONCLUSIONS

188

CHAPTER 8

CONCLUSIONS AND RECOMMENDATIONS

81 INTRODUCTION

The overall aim of the thesis was to investigate and identify frameworks that support


Generally this aim has been achieved The objectives of Chapter 8 are to summarise

the outputs of the overall study draw some conclusions and point out the future

directions for forest management in PNG The research questions and objectives of

the thesis are restated and how they have been achieved are discussed (Section 82)

The key outputs of the study are summarised (Section 83) and the application of the

tools developed in the study by stakeholders in CBFM are discussed (Section 84) In

Section 85 the contributions of the current study to knowledge are presented The

study had some short-falls and limitations and these are highlighted (Section 86) and

in section 87 future directions in research and policy are discussed Finally the

outputs of the thesis are discussed and some comparisons are made with the literature

(Section 88) and some conclusions and recommendations are given (Section 89)

82 RESEARCH OBJECTIVES AND QUESTIONS

821 Research Objectives

In this section the objectives of the thesis are restated and how they have been

addressed are discussed The details of how the objectives of the study have been

addressed are as follow

i) to assess the current condition and future production potential of cutover

forests in PNG

The first objective of the study has been achieved from the outcomes of analyses of

PSPs (Chapter 3) and forest resources in the two case study sites (Chapter 4)

Evidence from analyses of PSPs suggest that cutover forests in PNG showed a high

degree of resilience following harvesting Residual timber volume and aboveground

189

forest carbon determined in case study sites are adequate for communities to

participate in small-scale harvesting and REDD+ projects

ii) to develop scenario analyses and evaluation tools for assisting decision-

making in community-based management of cutover native forests in PNG

This objective has been addressed in Chapter 5 and 6 Scenarios have been analysed

and evaluated in community-based harvesting and decision analyses models have

been developed The scenario analyses and evaluation tools developed under the

second objective have been tested in case study sites

iii) to test the scenario analyses and evaluation tools developed under the second

objective in case study sites

The decision tree models developed in this study have been tested using actual data in

the Yalu case study site Data relating to cash flow (costs and income) associated with

community sawmill local processing medium scale log export and carbon trade were

input into the decision tree model and tested

iv) to develop a scenario analysis and evaluation framework for community-based


This objective has been achieved and an integrated conceptual framework has been

developed in the study based on the MSE approach (Sainsbury et al 2000) This

MSE type of management approach has been successfully applied in fishery and

marine resource management (Butterworth and Punt 1999 Kirkwood 1993)

822 Research Questions

There were four questions that have been addressed in this thesis These questions are

restated and how they have been addressed are discussed The questions are addressed

as follow

i) what is the current condition and future production potential of cutover forests

in PNG

This question has been adequately addressed from the outputs of the study on the

structure and dynamics of cutover forests (Chapter 3) and forest resource estimates in

case study sites (Chapter 4) Analyses of PSPs suggest that a majority of plots showed

increasing BA and stand volume following selective timber harvesting but there were

190

also on-going decline in 25 of sites studied In the two case study sites residual

timber volumes estimated can be able to support small-scale timber harvesting while

high estimates of forest carbon in these sites provide an option for communities to

manage their forests for carbon benefits

ii) what are the potential options for future management of cutover forests by

communities

The study in Chapter 5 has addressed this question and from the outputs of the

qualitative interviews in the case study sites the following were the future

management options for cutover forests community sawmill local processing

medium-scale log export and carbon trade

iii) How can information on the structure and dynamics of forests and the


in community management of cutover native forests in PNG

Outputs from the studies in Chapter 3 (Forest dynamics after selective timber

harvesting) Chapter 4 (Forest resources in case study sites) Chapter 5 (Evaluation of

scenarios) and Chapter 6 (Testing of scenarios using decision analysis models) have

addressed this question Data related to forest structure dynamics and timber yields

under different management scenarios have been analysed using the planning tool and

further tested using the decision analyses models These outputs have been integrated

in the conceptual framework that has been presented in this study (Chapter 7)

Therefore this framework will support effective decision making in community-based


iv) what type of scenario methods are appropriate for adaptive management of


The literature review (Chapter 2) has addressed this last question and the scenario

method and MSE approach have been applied in this study In the review different

forest management approaches were investigated for possible application in the

management of cutover forests in PNG This study recommends that the type of

scenario methods appropriate for adaptive management of cutover forests in PNG is

the MSE approach (Butterworth and Punt 1999 Sainsbury et al 2000) The MSE

approach has been used as the basis to present a new conceptual framework (Chapter

191

7) for community-based management of cutover forests in PNG The tools developed

in this study are appropriate for application in PNG and other tropical regions

83 KEY OUTPUTS OF THE STUDY

There are three key outputs of the overall study reported in this chapter The first is

the scenario analysis and evaluation tools developed for assisting decision making in

community-based management of cutover native forests in PNG These tools have

been developed from the outputs of the analyses of timber yields under different

management scenarios and the study of decision tree models for community-based

management of cutover forests in PNG The different management regimes developed

from an existing planning tool are applicable to CBFM The decision tree models

developed in the study are based on a spreadsheet modelling and decision analyses

technique (Ragsdale 2007 Ragsdale 2008) This type of modelling technique has

been mainly applied in making investment decisions under uncertain circumstances

for example application of decision analyses in the selection of a product

development strategy or investing in a real estate business by a company (Lieshout

2006 Middleton 2001 Ragsdale 2007)

The second output of the study was the testing of the scenario analyses and evaluation

tools in the case study sites When the decision analysis model (Decision Tree Model

2 Local Processing) was tested in the Yalu case study site analyses indicated that

depending on the input variables in the model the expected monetary value (EMV)

returned is determined by the related cash flow associated with each scenario

An integrated conceptual framework for CBFM has been developed in the study and

this relates to the third key output of the overall study The framework integrates

outputs from scenario analyses and evaluation and testing of the scenarios using the

decision analyses models Development of this framework has been guided by the

PAR approach with the two communities that have participated in this study for the

past four years

192

84 APPLICATION OF THE TOOLS DEVELOPED IN THIS

STUDY

Currently there is no overall policy framework in place for community-based

management of cutover forests in PNG Scenarios and approaches developed in this

study can support the development of national and provincial policies and local-level

decision-making for cutover natural forests in PNG NGOs who are currently

supporting small-scale forest management in PNG may be the most likely initial

users Some NGOs have good capacity and are supported by international

organisations Hence these models can be applied by them in promoting small-scale

harvesting in communities throughout PNG Workshop-based exercises can provide a

basis for equipping NGOs and communities with the skills required for the practical

application of the decision analyses tools developed in this study

The conceptual framework developed in this study is a new tool for forest

management in PNG The framework can be applied by NGOs and conservation

groups involved in small-scale harvesting and those engaged in promoting

certification in PNG However wider application of these tools and the analytical

framework will depend on development of supporting policy at national and

provincial levels in PNG that aims to increase the capacity and control of local forest

owners and facilitate their involvement in implementing sustainable forest

management objectives

85 CONTRIBUTIONS OF THE PRESENT STUDY

While decision support systems have been commonly applied in natural resource

management decision analyses and evaluation techniques have not been applied in

tropical forest management before The systems developed in this study necessitate a

structured approach to decision-making in tropical forest management Therefore the

present study contributes knowledge in the area of decision analyses and modelling in

tropical forest management This study has also contributed to knowledge in the form

of one publication in an international journal and two papers in a book chapter (see

the preface on page vi)

The study of forest dynamics after selective timber harvesting in Chapter 3 is the first

detailed analyses in the tropical forest of PNG based on a comprehensive set of

193

permanent sample plot data Scenario analyses and evaluation are new approaches to

tropical forest management and the types of analyses undertaken in this study are new

as far as forest management in PNG is concerned In the context of forest

management in PNG the outputs from the present study will assist decision-making

in CBFM

A framework such as the one presented in this study has never been applied in forest

management in PNG before Therefore this framework will assist the stakeholders

including communities in the management of cutover forests in PNG

86 LIMITATIONS OF THE STUDY

The decision analyses models developed in Chapter 6 relied on data available from

case study sites However insufficient data was obtained from the study areas to test

the C trade scenario using the decision tree model The costs and income estimated in

the analyses are based on crude data only at the community-level and do not provide a

strong basis for such analyses Therefore the results obtained in the estimation of the

EMV (profit) under the C trade scenario are only for the purpose of demonstrating the

application of decision analyses models to assist decision-making in communities to

consider different forest management options Based on the current in-country

situation C trade has not officially started yet and issues such as REDD and REDD+

are still being discussed at policy level

861 Forest Management Implications

As more community groups become involved in small-scale harvesting the need for

application of management tools such as the systems developed in this study will be

necessary This will put additional pressure on the PNGFA to control the increase in

participation of communities in small-scale harvesting Land and forest owning

communities who would like to participate in small-scale harvesting may want to

expand their operations to cover bigger forest areas which will in turn call for

compliance with PNGFA and government policy requirements Therefore the

government will need to consider putting in place regulatory systems not only to

control small-scale operations but also to assist and promote small-scale harvesting

by communities in order for them to get maximum benefits from the management of

their cutover forest resources

194

87 FUTURE DIRECTIONS

After over two decades of large-scale commercial harvesting of primary forests in

PNG there are still no land use plans for the management of forest areas after

harvesting A major challenge for the PNGFA and the government is the development

of appropriate management systems for cutover forest Management planning should

include consideration of the future production capacity of cutover and degraded

forests and the development of the capacity of local forest owner communities to

participate in small-scale forest management and utilisation for example through


871 Future Research Needs

In Chapter 3 the study used forest structure data to assess the current condition and

future production potential of cutover forests in PNG However the study fall-short of

the required data to adequately address the issue of forest degradation after selective

timber harvesting Therefore future research is required to quantify the extent of

degradation after harvesting The study also tested models developed in other tropical

regions to assess the growth of harvested forests in PNG Research is also required to

develop country-specific growth models for sustainable management of tropical

forests in PNG

The study in Chapter 5 assessed timber yields under different management scenarios

in community-based harvesting to recommend a regime that is sustainable and can

continuously supply sawn timber for communities The study has not considered the

question of optimisation in the analyses Future research is therefore necessary to

investigate optimisation in community-based harvesting to address a research

question such as how can an intensity of cut be optimised in community-based

harvesting In Chapter 6 the decision analyses relating to C trade are based on

unreliable data to estimate annual EMV from managing forests for C benefits by

communities Future research is necessary to study detailed economic analyses (costs

and benefits) for participation by communities in C trade in PNG Further

investigation is also necessary to consider the combination of scenarios to test the


REDD+ as one scenario with the objective of increasing income in CBFM

195

872 Future Policy Directions

The present study has addressed some aspects of PNG Forest Policy 1991 Currently

there are no policy instruments in place to address issues relating to cutover forest

management and community forestry A new direction in Forest Policy is now

necessary to meet the increasing demands and expectations of stakeholders in PNG as

well as the international community There is a need for policy change to reflect the

changing circumstances in forest management As the need for a multi-disciplinary

approach to natural resource management is increasing worldwide policy must be

changed to address the need for an integrated and participatory approach to the

management of forests that have been over-exploited Capacity building is required at

the community-level to address the needs of forest owners and other stakeholderlsquos

expectations and the demands for small-scale forest management and utilisation in

PNG

88 DISCUSSION

This study has focused on analyses and evaluation of scenarios for the management of

cutover tropical forests in PNG To the knowledge of the author scenario analyses

and evaluation are new approaches to tropical forest management therefore there is

limited literature available on the subject However approaches such as the MSE have

been widely applied in other natural resource management sectors such as fishery and

marine resources (Butterworth and Punt 1999 Sainsbury et al 2000)

Studies at CIFOR have embarked on work relating to scenarios but this has been

mainly focused on participatory approaches to decision-making in community-based

management of natural resources including tropical forests (Nemarundwe et al 2002

Nemarundwe et al 2003 Wollenberg et al 2000 Wollenberg et al 1998) Work at

CIFOR has concentrated on providing training through workshop-based exercises for

trainers to equip them with skills to develop scenarios for natural resource

management in community settings

In developed countries detailed studies have been carried out in modelling forest

management scenarios across landscapes for example studies by Tappe et al (2004)

involved use of satellite imagery in conjunction with field data to quantify differences

196

in landscape that can aid in making management decisions in ecologically and

socially complex forests

The present study does not involve complex modelling of scenarios for forest

management in PNG The study rather provides an analytical system approach that is

appropriate for application in community decision-making in tropical forest

management The tools developed in the study are spreadsheet-based analyses and

modelling applications hence can be made available to stakeholders in PNG

The outputs from this study have provided some basis for the review of PNGlsquos 1991

National Forest Policy Part II Section 3 Sustained Yield Management At the

moment there are no policy framework and guidelines in place for the management of

cutover forests The tools developed in this study provide the framework to be used

for the development of new policies for the management of cutover forests in PNG

Policy change should be directed at addressing stakeholder requirements and

expectations especially at community-level in the management of the 10 of forest

areas that are now regarded as cutover and degraded These policy changes should

also address international issues relating to SFM biodiversity conservation climate

change and meet the needs of the global community

89 CONCLUSIONS

The current condition of cutover forests in PNG requires management interventions

and the future production potential of these forests will depend on frequency of future

harvests and other land uses such as conversion to agricultural lands and traditional

farming activities for example land cultivation for gardening In community-based

harvesting shorter cycles for example 10-20 years and removing about 50 of

available pre-harvest volume only in commercial timber species groups at each cycle

are recommended

There are four decision analysis models developed in this study (Chapter 6) to

represent the decision tree models for community sawmill local processing medium-

scale log export and C trade

The integrated conceptual framework for scenario analyses and evaluation presented

in this study will assist the capacity of NGOs and communities in the management of


197

The application of the systems developed in this study will assist communities in the

management of the extensive cutover forests in PNG by participating in small-scale

harvesting and marketing of sawn timber to generate income This will have forest

management implications in the activities of stakeholders such as the PNGFA timber

industry NGOs and community groups A new policy direction in forest management

is therefore necessary in PNG in order to apply these systems particularly at

community level forest management and utilisation

198

REFERENCES

ABDULHADI R KARTAWINATA K amp SUKARDJO S 1981 Effects of

mechanised logging in the lowland Dipterocarp forest at Lempake East

Kalimantan Malaysian Forester 44 407-418

AGAWAL B 2001 Participatory Exclusions Community Forestry and Gender An

analyses of South Asia and a conceptual framework World Development 29

1623-1648

ALDER D 1997 Data Analysis and Growth Modeling with the ITTO Plots in Papua

New Guinea Lae PNG Forest Research Institute ITTO Project PD162-

91PNGFRI

ALDER D 1998 PINFORM A Growth Model for Lowland Tropical Forest in

Papua New Guinea PNG Forest Institute ITTO Project PD162-91PNGFRI

Lae

ALDER D OAVIKA F SANCHEZ M SILVA J N M VAN DER HOUT P

amp WRIGHT H L 2002 A comparison of species growth rates from four

moist tropical forest regions using increment-size ordination International

Forestry Review 4

ALDER D amp SYNNOT T J 1992 Permanent sample plot techniques for mixed

tropical forests Tropical Forestry Papers No 25 Oxford Forestry Institute

University of Oxford

AMBIA V amp YOSI C K 2001 A review of sampling methods for forest resource

inventories in Papua New Guinea PNGFA and PNGFRI PNG Forest

Research Institute Lae 28 June 2001

ANIL C N 2004 Participatory 3 Dimensional Modeling for ecological monitoring

in mountainous areas ICIMODIFAD Partnership Programme

ASLEY C amp CARNEY D 1999 Sustainable Livelihoods In (DFID) D F I D

(ed) London UK DFID

AVERY T E amp BURKHART H E 1994 Forest Measurements Fourth Edition

McGraw Hill Inc USA

BALLESTEROS A amp MONCEL R 2011 Additionality of Climate Finance

Available online at httpwwwwriorg [Online] Washington DC World

Resource Institute [Accessed November 2011]

199

BEEHLER B M (ed) 1993 Papua New Guinea conservation needs assessment

Vol2 Washington DC Biodiversity Support Program

BELLAMY J A amp MCALPINE J R 1995 Papua New Guinea Inventory of

Natural Resources Population Distribution and land use handbook Australia

June 1995 PNGRIS Publication No 6 CSIRO

BODORKOS B amp PATAKI G 2009 Local communities empowered to plan

Applying PAR to establish democratic communicative spaces for sustainable

rural development Action Research 7 313-334

BOYLE J R WARILA J E BESCHTA R L REITER M CHAMBERS C C

GIBSON W P GREGORY S V GRIZZEL J HACAR J C LIT J L

MCCOM W C PARZYBOK T W amp TAYLOR G 1997 Cumulative

effects of forestry practices An example framework for evaluation from

Oregon USA Biomass and Bioenergy 13 223-245

BREUGEL M V RAMOS M M amp BONGERS F 2006 Community dynamics

during early secondary succession in Mexican tropical rainforest Journal of

Tropical Ecology 22 663-674

BROWN S amp LUGO A E 1990 Tropical secondary forests Journal of Tropical

Ecology 6 1-32

BRYDON-MILLER M 2001 Education research and action theory methods of

participatory action research In TOLMAN D L amp BRYDON-MILLER M

(eds) From subjects to subjectivities A handbook of interpretative and

participatory methods New York New York University Press

BUN Y A Year Present status of logged-over forest in Papua New Guinea In NIR

E amp SRIVASTARVA P eds Management of logged-over forests 1992 Lae

PNG JICA PNGFRI

BUTTERWORTH D S amp PUNT A E 1999 Experiences in the evaluation and

implementation of management procedures ICES Journal of Marine Science

56 985-998

CAIN J 2001 Planning improvements in natural resources management Guidelines

for using Bayesian networks to support the planning and management of

development programmes in the water sector and beyond Wallingford UK

Center for Ecology and Hydrology Crowmarsh Gifford

CARBON TRUST 2009 Carbon Mechanism Available online at

wwwcarbontrustcomuk [Online] [Accessed November 2011]

200

CHAMPION H G 1936 A preliminary survey of the forest types of India and

Burma Dehra Dun India Indian Forest Record Silviculture

CHAVE J ANDALO C BROWN S CAIRNS M A CHAMBERS J Q

EAMUS D FOumlLSTER H FROMARD F HIGUCHI N KIRA T

LESCURE J P PUIG H RIEacuteRA B amp YAMAKURA T 2005 Tree

allometry and improved estimation of carbon stocks and balance in tropical

forests Oecologia 145 87-99

CHAVE J CONDIT R LAO S CASPERSEN J P FOSTER R B amp

HUBBELL S P 2003 Spatial and temporal variation of biomass in a tropical

forest results from a 20 large census plot in Panama Journal of Ecology 91

240-252

CHAZDON R L PERES C A DENT D SHEIL D LUGO A E LAMB D

STORK N E amp MILLER S 2009 Where are the wild things Assessing the

potential for species conservation in tropical secondary forests Conservation

Biology 23 1406-1417

CHESSON J amp CLAYTON H 1998 A framework for assessing fisheries with

respect to ecologically sustainable development Canberra Bureau of Rural

Sciences Report 60 pp

CLARK D A amp CLARK D B 1999 Assessing the growth of rainforest trees

issues for forest modeling and management Ecological Applications 9 981-

997

COOKE J G 1999 Improvement of fishery management advice through simulation

testing of harvest algorithms ICES Journal of Marine Science 56 797-810

COOPER R 2003 Lecture notes on World Forest Resources and Commercial

Timbers MSc in Environmental Forestry Programme Bangor UK School of

Agricultural and Forest Sciences University of Wales

CORLETT R T 1987 Post-fire succession on Mt Wilhelm Papua New Guinea

Biotropica 19 157-160

COX M 2004 Exporting for small-scale forestry enterprises International

symposium on contributions of family farm enterprises to sustainable rural

development Gengenbach Germany Queensland University of Technology

Australia

201

CRESWELL J W HANSON W E CLARK PLANO V L amp MORALES A

2007 Qualitative Research Designs Selection and Implementation The

Counseling Psychologist 35 236-264

CROME F H J THOMAS M R amp MOORE L A 1996 A novel Bayesian

approach to assessing impacts of rainforest logging Ecological Applications

6 1104-1123

CURTIN T 2005 The great illegal logging swindle Commonwealth Forestry

Association Newsletter No 37 June 2007 Shropshire UK Edited by

Pottinger A

DAWKINS H C 1958 The management of the natural tropical high forest with

special reference to Uganda Imperial Forestry Institute University of

Oxford UK

DAWKINS H C amp PHILIP M S 1998 Tropical moist forest silviculture and

management A history of success and failure UK CAB International

DEATH C 1980 The throw away people Social impact of the Gogol Timber

Project Madang Province Monograph 13 Institute of Applied Social and

Economic Research Papua New Guinea

DE LA MARE W K 1996 Some recent developments in the management of marine

living resources In FLOYD R B SHEPHERD A W amp DE BARRO P J

(eds) Frontiers of population Ecology Melbourne Australia CSIRO

Publishing

DE LA MARE W K amp WILLIAMS R 1997 Abundance of Patagonian toothfish at

Macquarie Island estimated from tagging studies SAFAG976

DFID 1999 Sustainable livelihoods guidance sheets DIFID London UK[online]

URLhttpwwwlivelihoodsorg Department for International Development

DICKINSON J C 1999 Forest Management Certification as a tool for

Conservation Geographical Review 89 431-439

DICKINSON M B DICKINSON J C amp PUTZ F E 1996 Natural Forest

Management as a Conservation Tool in the Tropics Divergent Views on

Possibilities and Alternatives Commonwealth Forestry Review 4 309-315

DONOVAN G P (ed) 1989 The comprehensive assessment whale stocks The

Early Years Reports of the International Whaling Commission (Special Issue

II) 210pp

202

DUCHELLE A E CRONKLETON P KAINER K A amp GUANCOMA G 2011

Resource theft in tropical forest communities implications for non-timber

management livelihoods and conservation Ecology and Society 16(1) 4

[online] URL httpwwwecologyandsocietyorgvol16iss1art4 [Online]

[Accessed November 2011]

DURST P B MCKENZIE P J BROWN C L amp APPANAH S 2006

Challenges facing certification and eco-labelling of forest products in

developing countries International Forestry Review 8 193-200

EDDOWES P J 1977 Commercial Timbers of Papua New Guinea Their

Properties and Uses Papua New Guinea Office of Forests

EDWARDS P J amp GRUBB P J 1977 Studies of Mineral Cycling in a Montane

Rain Forest in New Guinea The distribution of organic matter in the

vegetation and soil Journal of Ecology 65 943-969

ELLISON A M 1996 An introduction to Bayesian inference for ecological research

and environmental decision-making Ecological Application 6 1036-1046

ERICHO J 1998 Lessons from Crater Mountain ICAD Project and Some

Suggestions In SAULEI M A amp ELIS J (eds) The Reports of Matupore

Conference ICAD Practitionersrsquo views from the field DEC and UNDP

Rome Pp168-173

EVAN J 1982 Plantation Forestry in the Tropics Oxford United Kingdom ELBS

and Clarendon Press

EVANS K amp GUARIGUATA M R 2008 Participatory monitoring in tropical

forest management a review of tools concepts and lessons learned Bogor

Indonesia Center for International Forestry Research (CIFOR)

FALS-BORDA O amp MORA-OSEJO L E 2003 Context and diffusion of

knowledge A critique of Eurocentrism Action Research 1 29

FAO 1999 Indicators for sustainable development of marine capture fisheries FAO

Technical Guidelines

for Responsible Fisheries 8 68 pp

FAO 2000 Global Forests Resource Assessment FAO Rome Italy

FAO 2005 State of the Worlds Forests 2005 FAO Rome Italy

FAO 2006 Global Forest Resources Assessment 2005 A progress towards

sustainable forest management FAO Forestry Paper 147 Food and

Agriculture Organization of the United Nations Rome Italy 2006

203


FELTON A FELTON A M WOOD J amp LINDENMAYER D B 2006

Vegetation structure phenology and regeneration in the natural and

anthropogenic tree-fall gaps of a reduced-impact logged subtropical Bolivian

forest Forest Ecology and Management 235 186-193

FILER C ALLEN B J KEENAN R J amp MCALPHINE J R 2009

Deforestation and degradation in Papua New Guinea Annals of Forest

Science 66 813-825

FORCERT 2010 Forest Management and Product Certification Service Group

certification membership requirements FORCERT Walindi WNBP

Working Document

FOREST TRENDS 2006 Logging Legality and Livelihoods in Papua New Guinea

Synthesis of official assessments of the large-scale logging industry Volume

I Jakarta Indonesia

FORESTRY COMPENDIUM 2003 Selected Statistics World Bank 2000

FOX J YOSI C NIMIAGO P OAVIKA F POKANA J LAVONG K amp

KEENAN R 2010 Assessment of Aboveground Carbon in Primary and

Selectively Harvested Tropical Forest in Papua New Guinea Biotropica 42

410-419

FOX J C amp KEENAN R J 2011 Modelling CO2 emissions from selective-

harvesting in Papua New Guinea In FOX J C KEENAN R J BRACK

C L amp SAULEI S (eds) Native forest management in Papua New Guinea

advances in assessment modelling and decision-making ACIAR Proceeding

No 135 136-151 Canberra Australian Center for International Agricultural

Research

FOX J C VIEILLEDENT G YOSI C K POKANA J N amp KEENAN R J

2011a Aboveground forest carbon dynamics in Papua New Guinea isolating

the influence of selective-harvesting and El Nintildeo Ecosystems 14 1276-1288

FOX J C YOSI C K amp KEENAN R J 2011b Assessment of timber and carbon

stocks for community forest managment In FOX J C KEENAN R J

BRACK C L amp SAULEI S (eds) Native forest management in Papua New

Guinea advances in assessment modelling and decision-making ACIAR

Proceeding No 135 42-52 Canberra Australian Center for International

Agricultural Research

204

FREDERICKSEN T S amp MOSTACEDO B 2000 Regeneration of timber species

following selection logging in a Bolivian tropical dry forest Synthesis of

official assessments of the large-scale logging industry Forest Ecology and

Management 131 47-55

FREDERICKSEN T S amp PUTZ F E 2003 Silvicultural intensification for tropical

forest conservation Biodiversity and Conservation 12 1445-1453

FREEMAN M 2006 Globalisation and international trade in forest products How

does it impact on forest management A literature review School of Forest

and Ecosystem Science The University of Melbourne

FREIRE P 1970 Predatory of the oppressed New York Continium

FSC 1996 Forest Stewardship Council Principles and Criteria for Forest

Management Oaxaca Mexico FSC Mimeographed

FSC 1999 Forest Stewardship Council [httpwwwfscoaxorg] Accessed 2009

[Online] [Accessed 2008]

FSC 2005 Forest Stewardship Council-Certificates Worldwide FSC International

Center and Working Group Germany October 2005

GARDNER T A BARLOW R L CHAZDON R L HARVEY C A PEREZ

C A amp SODHI N 2009 Prospects for tropical forest biodiversity in human

modified world Ecology Letters 12 561-582

GERWING J J 2002 Degradation of forests through logging and fire in the eastern

Brazilian Amazon Forest Ecology and Management 157 131-141

GILLENWATER M 2011 What is additionality Part 2 A framework for more

precise definition and standardized approaches Greenhouse Gas Management

Institute Silver Spring MD

GRAINGER A 1983 Quantifying changes in forest cover in the humid tropics

Overcoming current limitations Journal of the World Forest Resource

Management (Vol Pages)

HABERLE S G HOPE G S amp VAN DER KAARS S 2001 Biomass burning in

Indonesia and Papua New Guinea Natural and human induced fire events in

the fossil record Palaeogeogr Palaeoecol 171 259-268

HAMMERMASTER E T amp SAUNDERS J C 1995 Forest Resources and

Vegetation Mapping of Papua New Guinea PNGRIS Publication No 4

CSIRO Australia May 1995

205

HARTANTO H LORENZO M C B amp FRIO A L 2002 Collective action and

learning in developing a local monitoring system A review of tools concepts

and lessons learned International Forestry Review 4 184-195

HEALEY J 2003 Lecture notes on Forest Ecology MSc in Environmental Forestry

Programme School of Agricultural and Forest Sciences University of Wales

Bangor UK

HTTPWWWCIFORCGIARORG [Accessed June 2008]

HTTPWWWFSCORGTROPICALFORESTSHTML Tropical forest

certification [Online] [Accessed 12 December 2011]

HUBBELL S T amp FOSTER R B 1983 Diversity of canopy trees in a neotropical

forest and implications for conservation In SUTTON S L WHITMORE T

C amp CHADWICK A C (eds) Tropical Rainforest Ecology and

Management Oxford UK Blackwell Scientific Publication

ITTO 1998 Criteria and Indicators for sustainable management of natural tropical

forests ITTO Policy Development Series No10 ITTO Yokohama Japan

ITTO 2004 Annual Review and Assessment of the World Timber Situation 2003

International Tropical Timber Organisation Yokohama Japan



ITTO 2006 Status of Tropical Forest Management 2005 ITTO Technical Series No

24 Yokohama Japan

ITTO 2007 Making SFM Work ITTOrsquos First Twenty Years International Tropical

Timber Organization Yokohama Japan

ITTO 2008 Tropical Timber Market Report International Tropical Timber

Organization (ITTO) Market Information Services (MIS) Vol 13 No 4 16-29

February 2008 Yokohama Japan

JAAKKO POYRY 2005 Report of overview of illegal logging Department for

Agriculture Fisheries and Forestry (DAFF) Canberra 2005

JOHNS A D 1986 Effects of selective logging on the behavioural Ecology of West

Malaysia Primates Ecology Letters 67 684-694

JOHNS A D 1997 Timber production and biodiversity conservation in tropical

rainforests Cambridge University Press

JOHNS R J 1978 Vegetation of Papua New Guinea A manual for the PNG

Forestry College Bulolo Morobe Province Papua New Guinea

206

JOHNS R J 1989 The influence of drought on tropical rainforest vegetation in

Papua New Guinea Mountain Research and Development 9 248-251

KAINER K A DIGIANO M L DUCHELLE A E WADT L H O BRUNA

E amp JONATHAN L D 2009 Partnering for Greater Success Local

Stakeholders and Research in Tropical Biology and Conservation Biotropica

41 555ndash562

KEEN P G W amp SCOTT-MORTON M S 1978 Decision Support Systems An

Organizational Perspective Reading MA Addition-Wesley 264pp

KEENAN R 2007 Assessment management and marketing of goods and services

from cutover native forests in Papua New Guinea ACIAR Project FST2004-

061 Project Document The University of Melbourne Australia

KEENAN R J 2001 Planning Methods for Sustainable Management of Timber

Stocks in Papua New Guinealsquos Forests ACIAR Project Document Number

FST1998118 Bureau of Rural Sciences Canberra Australia

KEENAN R J 2009 Disturbance degradation and recovery forest dynamics and

climate change mitigation Paper presented at the XIII World Forestry

Congress Buenos Aires Argentina October 2009

KEENAN R J AMBIA V BRACK C BRAGG C FRAKES I GERRAND

A GOLMAN M LAVONG K TICKLE P VANCLAY J amp YOSI C

2002 Forest planning growth modeling and yield regulation in natural forests

Paper presented to the PNG Forest Industry Association Forest Investment

Seminar 19-20 March 2002 Port Moresby

KEENAN R J AMBIA V BRACK C FRAKES I GERRAND A GOLMAN

M LAVONG K TICKLE P SAM N VANCLAY J amp YOSI C K

2005 Improved timber inventory and strategic forest planning in Papua New

Guinea Bureau of Rural Sciences Canberra and PNG Forest Research

Institute Lae

KINGSTON B amp NIR E 1988a A Report on Diagnostic Sampling conducted in

Oomsis Forest FAO DPPNG84003 Working Document No 9 Lae

Morobe Province

KINGSTON B amp NIR E 1988b A Report on Diagnostic Sampling conducted in

Oomsis Forest Morobe Province FAOUNDPPNG84003 Working

Document No 9

207

KIRKWOOD G P 1993 Incorporating allowance for risk in management The

Revised Management Procedure of the International Whaling Commission

KOBAYASHI S 1992 Effects of Harvesting Impact on Tropical Rainforests In

NIR E amp SRIVASTARVA P (eds) Management of logged-over forests

proceedings of the JICA-PNGFRI Research Project Seminar Lae PNG

JICA-PNGFRI

KORSGAARD S 1989 The standtable projection simulation model In MOHD W

R CHAN H T amp APPANAH S (eds) Proceedings of seminar in Growth

and Yield in Tropical MixedMoist Forests Kuala Lumpur Kepong Forest

Research Institute Malaysia

LAMB D 1990 Exploiting the tropical rainforest An account of pulpwood logging

in Papua New Guinea Man and the Biosphere Series Vol 3

LAMB D 1998 Large-scale ecological restoration of degraded tropical forest lands

The potential role of timber plantations Restoration Ecology 6 271-279

LAMPRECHT H 1989 Silviculture in the tropics Tropical forest ecosystems and

their tree species possibilities and methods for their long-term utilisation

Eschborn Germany Dt Ges Fur TechnZusammenarbeit (GTZ) GmbH

LANLEY J P (ed) 1981 Tropical Resource Assessment Project (GEMS) Tropical

Africa Tropical Asia Tropical America (4 vols) FAOUNDP Rome

LANLEY J P 2003 Deforestation and Forest Degradation Factors Paper presented

to the XII World Forestry Congress Quebec City Canada

LAWRENCE A PAUDEL K BARNES R amp MALLA Y 2006 Adaptive value

of participatory biodiversity monitoring in community forestry Environmental

Conservation 33 325-334

LIESHOUT R V 2006 Using Decision Analysis tools for Innovation Whitepaper

Decision analysis tools 12 Simbon Innovation Management Solutions

LINDEMALM F amp ROGERS H M 2001 Impacts of conventional logging and

portable sawmill logging operations on tree diversity in East New Britain

Papua New Guinea Australian Forestry 64 26-31

LITTLE L R PUNT A E MAPSTONE B D PANTUS F SMITH A D M

DAVIES A R amp MCDONALD A D 2007 ELFSimmdashA model for

evaluating management options for spatially structured reef fish populations

An illustration of the ―larval subsidylsquo effect Ecological Modeling 205 381-

396

208

LOFFLER E 1979 Papua New Guinea Hutchinson Group Victoria Australia

LOWMAN B amp NICHOLLS S 1994 Forestry in Papua New Guinea In

SEKHRAN N amp MILLER S (eds) Papua New Guinea Country Study on

Biodiversity PNG Department of Environment and Conservation

Conservation Resource Centre Boroko Pp155-167

MAGNUSSON K G amp STEFANSSON G 1989 A feedback strategy to regulate

catches from a whale stock In DONOVAN G P (ed) Reports of the

International Whaling Commission (Special issue II) pp 171-189

MAGUIRE P 1987 Doing participatory research A feminist approach University

of Massachusetts Press

MARIAUX A 1981 Past efforts in measuring age and annual growth in tropical

trees In BORMANN F H amp BERLYN G (eds) Age and Growth Rate of

Tropical Trees Proceedings of Workshop on Age and Growth Rate

Determination for Tropical Trees Yale University School of Forestry and

Environmental Studies Harvard Forest MA

MCCANN R K MARCOT B G amp ELLIS R 2006 Bayesian belief networks

application in ecology and natural resource management Can J For Res

36 3053-3062

MCDONALD A D LITTLE L R GRAY R FULTON E SAINSBURY K J

amp LYNE V D 2005 Multiple-use Management Strategy Evaluation for

Coastal Marine Ecosystems using In Vitro In ZERGER A amp ARGENT R

M (eds) MODSIM 2005 International Congress on Modeling and Simulation

Modeling and Simulation Society of Australia and New Zealand December

2005

MCKINTY M H 1999 Silviculture of tropical mixed forests of Melanesia (Papua

New Guinea ndash Solomon Islands ndash Vanuatu) Lae PNG The Papua New

Guinea University of Technology

MENDOZA G A amp GUMPAL E C 1987 Growth projection of a selectively cut-

over forest based on residual inventory Forest Ecology and Management 20

253-263

MERY G ALFARO R KANNINEN M LOBOVIKOV M VANHANEN H amp

PYE-SMITH C (eds) 2005 Forests for the New Millennium Ministry for

Foreign Affairs of Finland International Union of Forest Research

Organisations

209

MIDDLETON M R 2001 Decision Trees Using TreePlan Add-in for Microsoft

Excel School of Business and Management University of San Francisco San

Francisco CA

MINISTRY OF FORESTS 1991a National Forest Policy Independent State of Papua

New Guinea Hohola September 1991

MINISTRY OF FORESTS 1991b Forestry Act 1991 No 30 of 1991 Independent

State of Papua New Guinea Hohola 16 October 1991

MIZARAS S MIZARAITE D SADAUSKIENE L amp OZOLINCIUS R 2007

Improving Incomes From Small-scale Forestry of Lithuania In HARRISON

S MURDIYARSO A D HERAWATI H amp ISKANDAR H (eds)

Carbon Sequestration and Sustainable Livelihoods A workshop synthesis

Borgor Indonesia April 2005 Centre For International Forestry Research

(CIFOR)

MURDIYARSO D HERAWATI H amp ISKANDAR H 2005 Carbon

Sequestration and Sustainable Livelihoods A workshop synthesis Centre For

International Forestry Research (CIFOR) Borgor Indonesia April 2005

MYERS N MITTERMEIER R A MITTERMEIER C G DA FONSECA G A

B amp KENT J 2000 Biodiversity Hotspots for Conservation Priorities

Nature 403 853-858

NAKAGAWA M TANAK A K NAKASHIZUKA T OHKUBO T KATO T

MAEDA T SATO K MIGUCHI H NAGAMASU H OGINO K TEO

S HAMID A A amp SENG L H 2000 Impact of severe drought associated

with the 1997-1998 El Nino in a tropical forest in Sarawak Journal of


NEMARUNDWE N DEJONG W amp CRONKLETON P 2002 Future Scenarios

as an Instrument for Forest Management Bolivia Workshop Report Amboro

Eco-Resort Buena Vista Bolivia-CIFOR Bogor May 7-11 2002

NEMARUNDWE N JONG W amp CRONKLETON P 2003 Future scenarios as

an instrument for forest management Manual for training facilators of future

scenarios Bogor Indonesia Center for International Forestry Research

(CIFOR)

210

NEWTON A C MARSHALL E SCHRECKENBERG K GOLICHER D TE

VELDE D W EDOUARD F amp ARANCIBIA E 2006 Use of Bayesian

Belief Network to predict the impacts of commercializing Non-timber Forest

Products on Livelihoods Ecology and Society 11 (2) 24 [online]

URLhttpwwwecologyandsocietyorgvol11iss2art24

NG F S P 1983 Ecological Principles of tropical lowland rain forest conservation

In SUTTON S L WHITMORE T C amp CHADWICK A C (eds)

Tropical Rain Forest Ecology and Management Oxford UK Blackwell

Scientific Publication

NICHOLSON D I 1958 An analysis of logging damage in a tropical rainforest

North Borneo Mal For 21 235-245

NICHOLSON D I 1985 The Development of Silviculture in North Queensland

Rainforests In SHEPHERD K R amp RICHTER H V (eds) Managing the

Tropical Forest Canberra Development Studies Centre The Australian

National University pp203-223

NICHOLSON D I HENRY N B amp RUDDER J 1988 Stand Changes in North

Queensland rainforests Proc Ecology Society Aust 15 61-80

NIELSEN K A amp SVENSSON L G (eds) 2006 Action research and interactive

research Maastricht Shaker Publishing

NIR E 1995 Current status of Oomsis forest Papua New Guinea In OAVIKA F

(ed) Report of Workshop on Permanent Sample Plots in Logged Natural

Forest Lae Papua New Guinea ITTO Project PD 16291 Internal Report

OAVIKA F 1992 Report on the Establishment of PSPTSI in Turama Forest Papua

New Guinea Forest Research Institute Internal Report Lae PNG

OFFICE OF FOREST 1979 Compendium of Statistics Office of Forests Department

of Primary Industry Port Moresby

OLIVER C D ADAM A B amp ZASOSKI R J 1985 Disturbance patterns and

forest development in a recently deglaciated valley in the northwestern

Cascade Range of Washington USA Canadian Journal of Forest Research

15 221-232

PAIJMANS K 1975 Vegetation of Papua New Guinea [map with explanatory

notes] Scale 11000 000 CSIRO Melbourne

PAIJMANS K (ed) 1976 New Guinea Vegetation Canberra Australian National

University Press

211

PARK A JUSTINIANO M J amp FREDERICKSEN T S 2005 Natural

regeneration and environmental relationships of tree species in logging gaps in

a Bolivian tropical forest Forest Ecology and Management 217 147-157

PEARL J 1995 Causal diagrams for empirical research Biometrika 82 669-688

PELISSIER R PASCAL J P HOULLIER F amp LABORDE H 1998 Impact of

selective logging on the dynamics of a low elevation dense moist evergreen

forest in the Western Ghats (South India) Forest Ecology and Management

105 107-119

PHILIP M S 1994 Measuring Trees and Forests Second Edition Wallinford

United Kingdom CAB International

PIMM S L amp RAVEN P H 2000 Extinction by numbers Nature 403 843-845

PNG FSC 2006 High Conservation Value Forest Toolkit for Papua New Guinea Port

Moresby First Edition November 2005 FPCD

PNGFA 1991 Forestry Act 1991 No 30 of 1991 Independent State of Papua New

Guinea Hohola 21 April 1993

PNGFA 1993 Forestry (Amendment) Act 1993 No 3 of 1993 Independent State of

Papua New Guinea Hohola 21 April 1993

PNGFA 1996a Forestry (Amendment) Act 1996 No 27 of 1996 Independent State

of Papua New Guinea Hohola 11 October 1996

PNGFA 1996b National Forest Plan for Papua New Guinea PNG Forest Authority

Hohola May 1996

PNGFA 1998 Forest Resource Acquisition General Information PNG Forest

Authority Port Moresby June 1998

PNGFA 2001 Forestry (Amendment) Act 2000 Independent State of Papua New

Guinea PNG Forest Authority Hohola 29 January 1996

Author 2003 Forest Resources of Papua New Guinea The National Newspaper

Article Port Moresby 20 September 2003

PNGFA 2005 PNG Forest Authority Minimum Export Price Species List Appendix -

List of species codes and names in alphabetical order Port Moresby October

2005

PNGFA 2007 Forest Resource Area Status and Harvested Figures PNG Forest

Authority Port Moresby

PNGFA AND DEC 1996 Papua New Guinea Logging Code of Practice First

Edition Port Moresby April 1996

212

POKANA J N 2002 Assessing the relationship between the soil groups and species

composition in logged-over rainforests of Papua New Guinea MSc Thesis

University of Wales

POORE D 1989 No Timber without Trees In BURGESS P PALMER J

RIETBERGEN S amp SYNNOTT T (eds) Sustainability in the Tropical

Forest London Earthscan

POORE M E D 1968 Studies in Malaysian Rainforests The Forest on the Triassic

Sediments in Jengka Forest Reserve Journal of Ecology 56 143-196

PUNT A E amp SMITH A D M 1999 Harvest strategy evaluation for the eastern

stock of gemfish (Rexea solandri) ICES Journal of Marine Science 56 860-

875

RAGSDALE C T 2007 Spreadsheet Modeling amp Decision Analysis A Practical

Introduction to Management Science Fifth Edition

RAGSDALE C T 2008 Spreadsheet Modeling and Decision Analysis A Practical

Introduction to Management Science Fifth Edition Revised South-Western

Cengage Learning

REASON P 2007 Education for Ecology Science aesthetics spirit and ceremony

Management Learning 38 27-44

RICHARDS P W 1983 The three dimensional structure of tropical rain forest In

SUTTON S L WHITMORE T C amp CHADWICK A C (eds) Tropical

Rainforest Ecology and management Oxford UK Blackwell Scientific

Publications

RICHARDS P W 1996 The Tropical Rainforest An ecological study UK Second

Edition Cambridge University Press

ROCHET M-J TRENKEL V M FOREST A LORANCE P amp MESNIL B

2007 How could indicators be used in an ecosystem approach to fisheries

ICES 2006 R 05

ROGERS H M 2010 Impacts of portable-sawmill logging on stand structure and

regeneration in the lowland forests of West New Britain Papua New Guinea

Australian Forestry 73 12-23

ROMIJN K 1994a PSP Standards and Procedures (A 5 Part Manual) ITTO Project

PD 16291 Internal Report PNG Forest Research Institute Lae PNG

213

ROMIJN K 1994b PERSYST A Data Management System for permanent sample

plots in natural forest ITTO Project PD 16291 Internal Report PNG Forest

Research Institute Lae PNG

ROY S B 2004 Participatory vegetation monitoring Examples from West Bengal

In BAHUGUNA V K MITRA K CAPISTRANO D amp SAIGAL S

(eds) Root to Canopy Regenerating Forests through Community-State

Partnerships New Delhi India Winrock International IndiaCommonwealth

Forestry Association-India Chapter

SAINSBURY K J 1998 Living marine resource assessment for the 21st Century

What will be needed and how will it be provided In FUNK F QUINN

T J HEIFETZ J IANELLI J N POWERS J E SCHWEIGERT J F

SULLIVAN P J amp ZHANG C I (eds) Fishery Stock Assessment Models

Alaska Sea Grant College Program Report N AK-SG-98-01 University of

Alaska Farbanks Alaska USA pp1-40

SAINSBURY K J PUNT A E amp SMITH A D M 2000 Design of operational

management strategies for achieving fishery ecosystem objectives ICES

Journal of Marine Science 57 731-741

SAM N 1999 Damage assessment in logged forests Seminar Beyond the First

Harvest Lae PNG PNG Forest Research Institute

SAUNDERS J C 1993 Forest Resources of Papua New Guinea PNGRIS

Publication No2 CSIRO Australia

SCHEYVENS H 2009 Socio-Economic Impact Survey Forest Conservation

Livelihoods and Rights Project Institute for Global Environmental Strategies

(IGES) Japan Forest Management and Product Certification (FORCERT)

PNG

SEKHRAN N amp MILLER S (eds) 1994 Papua New Guinea country study on

biological diversity Waigani Papua New Guinea Department of

Environment and Conservation

SEQHWP 2007 South East Queensland Healthy Waterways Strategy 2007-2012

Management Strategy Evaluation Action Plan Waterways December 2007

SHANLEY P amp GAIA G R 2002 Equitable ecology collaborative learning for

local benefit in Amazonia Agricultural Systems 73 83-97

214

SHAO G amp REYNOLDS K M (eds) 2006 Computer Applications in Sustainable

Forest Management Including Perspectives on Collaboration and

Integration The Netherlands Springer

SHEARMAN P L BRYAN J E ASH J HUNNAM P MACKEY B amp

LOKES B 2009a The State of the Forests of Papua New Guinea Mapping

the extent and Condition of Forest Cover and Measuring the Drivers of Forest

Change in the Period 1972-2002 Port Moresby University of Papua New

Guinea


LOKES B 2009b Forest conversion and degradation in Papua New Guinea


SHEIL D amp MAY R M 1996 Mortality and recruitment rate evaluations in

heterogeneous tropical forests Journal of Ecology 84 91-100

SHONO K CADAENG E A amp DURST P B 2007 Application of assisted

natural regeneration to restore degraded tropical forestlands Restoration

Ecology 15 620-626

SHUGART H H 1984 The Theory of Forest Dynamics The Ecological

Implications of Forest Succession Models New York Springer-Verlag

SMITH A D M SAINSBURY K J amp STEVENS R A 1999 Implementing

effective fisheries management systems - management strategy evaluation and

the Australian partnership approach ICES Journal of Marine Science 56

967-979

SMITH R G B amp NICHOLS J D 2005 Patterns of basal area increment mortality

and recruitment were related to logging intensity in subtropical rainforest in

Australia over 35 years Forest Ecology and Management 218 319-328

STOCKER G C UNWIN G L amp WEST P W 1985 Measures of richness

evenness and diversity in tropical rainforest Australian Journal of Botany 33

131-137

STORK N E 2010 Reassessing Extinction Rates Biodiversity and Conservation

19 357-371

STORK N E amp TURTON S M 2008 Living in a Dynamic Tropical Forest

Landscape Lessons from Australia Oxford 650pp Blackwells

STRINGER E T 1999 Action research (2nd ed) CA Thousand Oaks Sage

215

STUART M amp SEKHRAN N 1996 Developing externally financed greenhouse

gas mitigation projects in Papua New Guinealsquos forestry sector a review of

concepts opportunities and links to biodiversity conservation Department of

Environment and ConservationUNDP Port Moresby 80 p

SYNNOT T J 1978 Tropical Rainforest Silviculture A Research Project Report

Occasional Paper No 10 Oxford Commonwealth Forestry Institute

TAPPE P A WEIH R C THILL R E MELCHIORS M A amp WIGLEY T B

2004 Ecosystem management research In GULDIN J M (ed) Ouachita

and Ozark Mountains symposium Southern Research Station Asheville NC

US Department of Agriculture Forest Service 321p

TRACEY J G 1982 The vegetation of the humid tropical region of north

Queensland Melbourne CSIRO

TRUSWELL E M 1990 Australian Rainforests The 100 Million Year Record

Melbourne CSIRO

TURBAN E 1993 Decision Support and Expert Systems Management Support

Systems New York Macmillan 833pp

UNESCOUNEPFAO 1978 Tropical Forest Ecosystems A state of the art report

prepared by UNESCOUNEPFAO UNESCO Paris

UNFCCC 2006 Background paper for the workshop on reducing emissions from

deforestation in developing countries Part I Scientific socio-economic

technical and methodological issues related to deforestation in developing

countries UN Working paper No 1 (a) August 2006 Rome Italy

UNFCCC 2009 Report on the expert meeting on methodological issues relating to

reference emission levels and reference levels Subsidiary body for scientific

and technological advice Thirtieth session FCCCSBSTA20092 Bonn 1-

10 June 2009

VANCLAY J K 1992 Modelling regeneration and recruitment in a tropical

rainforest Canadia Journal of Forest Resources 22 1235-1248

VANCLAY J K 1994 Modeling Forest Growth and Yield Application to Mixed

Tropical Forests Wallingford UK CAB International

VANCLAY J K amp HENRY N B 1988 Assessing site productivity of indigenuous

cypress pine forest in Southern Queensland Commonwealth Forestry

Review 67 53-64

216

VARMA V K FERGUSON I amp WILD I 2000 Decision support system for the

sustainable forest management Forest Ecology and Management 128 49-55

VATASAN G S 1989 Forest Resource Inventories ndash Main tool for the

Management of the rainforest Lae Forestry Department PNG University of

Technology

VDT 2006a Konzolong Small-scale Logging Yalu Village sustainable forest

management plan Village Development Trust Internal Report Lae Morobe

Province Papua New Guinea

VDT 2006b Gabensis Village Small-scale Logging Sustainable forest management

plan Village Development Trust Internal Report Lae Morobe Province

Papua New Guinea

VDT 2008 Baseline data information survey for Yalu village VDT-ACIAR Project

VDT Internal Report Lae Morobe Province Papua New Guinea

VIANA V M ERVIN J DONORAN R Z ELLIOT C amp GHOLZ (eds) 1996

Certification of forest products Issues and perspectives Washington DC

Island Press

WALTERS C J amp HILBORN R 1976 Adaptive control of fishing systems

Journal of the Fisheries Board of Canada 33 145-159

WARDLE P JANSKY L MERY G PALO M UUSIVUORI J amp

VANHANEN H 2003 World Forests Society and Environment - Executive

Summary World Forests Volume I The United Nations University Tokyo

WEBB L T amp KIKKAWA J (eds) 1990 Australian Tropical Rainforests

Melbourne CSIRO

WESTOBY J 1989 Introduction to World Forestry People and their trees Oxford

Basil Blackwell Ltd

WHITMORE T C 1984 Tropical Rainforests of the far East Second Edition

Oxford Clarendon PressOxford University Press

WHITMORE T C 1990 An introduction to Tropical Rainforest Oxford Clarendon

Press

WHITMORE T C 1991 Tropical rainforest dynamics and its implications for

management In GOMEZ-POMPA A WHITMORE T C amp HADLEY M

(eds) Rainforest regeneration and management Carnforth UNESCO Paris

and Parthenon Publishing

217

WHITMORE T C 1998 An Introduction to Tropical Rainforest Second Edition

UK Oxford University Press

WHYTE W F GREENWOOD D J amp LAZES P 1991 Participatory action

research through practice to science in social research In WHYTE W F

(ed) Participatory action research CA Thousand Oaks Sage

WILLIAMS M RYAN C M REES R M SAMBANE E FERNANDO J amp

GRACE J 2007 Carbon sequestration and biodiversity of re-growing

miombo woodlands in Mozambique Forest Ecology and Management 254

145-155

WOLLENBERG E EDMUNDS D amp BUCK L 2000 Anticipating Change

Scenarios as a Tool for Adaptive Forest Management A Guide Bogor


WOLLENBERG E D EDMUNDS D amp BUCK L 1998 Using Scenarios to

Make Decision about the Future Anticipating Learning for the Adaptive Co-

Management of Community Forests Paper presented at a Symposium on

Adaptive Co-Management in Proteced Areas Cornell University Ithaca New

York 17-19 September 1998

WRIGHT D D JESSEN J H BURKE P amp DE SILVA GARZA H G 1997

Tree and Liana enumeration and diversity on a one-hectare plot in Papua New

Guinea Biotropica 29 250-260

WWWFAOORGFORESTRYSITE28679EN [Accessed August 2008]

WWWFAOORGFORESTRYSITE28679EN 2008 [Accessed August 2010]

WWWFORCERTORGPG [Accessed September 2009]

WWWGLOBALNETPGVDT [Accessed October 2009]

WWWPOSTCOURIERCOMPG [Accessed June 2008]

WWWPOSTCOURIERCOMPG The Association for Tropical Biology and

Conservation opposes land lease in PNG [Online] Port Moresby Postcourier

PNG [Accessed April 2011]

WWWSCIAMCOM 2007 Indonesia deforestation fastest in the World [Online]

Shropshire UK Commonwealth Forestry Association Newsletter No 37

June 2007 [Accessed 2007]

WWWSPCINTLRDHIGHLIGHTS_ARCHIVEHIGHLIGHTS_DRAWA_MODE

LHTM [Accessed March 2009]

218

WWWWORLDBANKREPORTSGOVERNANCEampANTI-

CORRUPTIONWGI1996-2007INTERACTIVEHOMEMHT [Accessed

April 2008]

YOSI C K 1999 Final Status Report - PSP Database Management ITTO Project

PD 16291 Internal Report PNG Forest Research Institute Lae November

1999

YOSI C K 2004 Impact of logging on short-term trends in forest structure

composition and population of lowland tropical forest in Papua New Guinea

MSc Thesis University of Wales

YOSI C K FOX J C KEENAN R J amp POKANA J N 2009 Condition and

changes in stand structure of lowland tropical forests after selective timber

harvesting in Papua New Guinea In PUANGCHIT L amp DILOKSUMPUM

S (eds) Proceedings of the FORTROP II international conference on

Tropical Forestry Change in a Changing World Bangkok THAILAND 17-

20 November 2008 Kasetsart University

YOSI C K KEENAN R amp FOX J C 2011 Forest dynamics after selective timber

harvesting in Papua New Guinea Forest Ecology and Management 262 895-

905

219

APPENDICES

APPENDIX 3-1 SUMMARY OF PSPS USED IN THE STUDY

Forest Condition

No of Plots

Un-harvested 13


Increasing BA (un-burnt) 63

Falling BA (un-burnt) 21

Burnt during 1997-98 El nino drought 21

Total 118

APPENDIX 3-2 SUMMARY OF THE PSPS IN UNLOGGED FOREST

PLOTNO PLOTID

FIRST

CENS

LAST

CENS

BA_FIRST

CENS

(m2ha

-1)

BA_LAST

CENS

(m2ha

-1)

1 DANAR03 2006

208470 No data

2 DANAR04 2006

77838 No data

3 HUVIV02 1999

253617 No data

4 KAUP_03 1998 2000 242586 216303

5 MARE_03 2001

237487 No data

6 SAGAR03 1998 2005 321673 332807

7 SASER03 2005

248061 No data

8 SASER04 2005

293279 No data

9 SOGER03 1998 2003 217693 239859

10 WATUT05 1997 1999 338812 253121

11 WATUT06 1997 1999 441607 286389

12 WCOST05 1998 2001 336952 344092

13 WCOST06 1998 2001 314374 328569

220

APPENDIX 3-3 UN-BURNED PSPS IN HARVESTED FOREST WITH

INCREASING BA

PLOTNO PLOTID LOGDATE

FIRST

CENS

LAST

CENS

BA_FIRST

CENS

(m2ha

-1)

BA_LAST

CENS

(m2ha

-1)

MBAI

(m2ha

-1yr

-

1)

1 ANUAL01 1993 1995 1999 168828 179791 02741

2 ANUAL02 1993 1995 1999 209696 214081 01096

3 ARI__01 1995 1996 2003 118680 164226 06506

4 ARI__02 1995 1996 2003 112410 134710 03186

5 CARAW01 1991 1995 2004 194671 221647 02997

6 CARAW02 1991 1995 2004 188221 212092 02652

7 CFORD01 1994 1995 2004 302147 340191 04227

8 EMBIH01 1992 1994 1999 130070 135086 01003

9 EMBIH02 1992 1994 1999 95760 103879 01624

10 EMBIH03 1993 1994 1999 138590 159763 04235

11 EMBIH04 1993 1994 1999 125500 164194 07739

12 GAR__01 1991 1993 1999 150426 172383 03660

13 GAR__02 1991 1993 1999 142926 165673 03791

14 GARAM01 1991 1994 2000 201981 221105 03187

15 GILUW01 1987 1993 2003 125896 137937 01204

16 GILUW02 1991 1994 2003 198455 199718 00140

17 HAWAN01 1993 1994 2002 130935 171417 05060

18 HAWAN02 1994 1994 2002 133950 168687 04342

19 KAPIU01 1991 1993 1997 130361 226460 24025

20 KAPIU02 1991 1993 2003 116672 282623 16595

21 KAUP_01 1996 1996 2000 195241 198719 00869

22 KAUP_02 1996 1996 2000 223736 229669 01483

23 KRISA01 1991 1994 1996 164044 174124 05040

24 KRISA02 1991 1994 1996 231445 239709 04132

25 KUI__01 1994 1994 2002 180250 204151 02988

26 LARK_03 1994 1996 1999 186482 186841 00120

27 MALAM01 1995 1995 2000 165864 219264 10680

28 MOKOL01 1980 1993 2004 243010 291990 04453

29 MOKOL02 1981 1993 2004 218361 242578 02202

30 MORER01 1997 1997 1999 161786 170147 04180

31 MOSAL01 1992 1993 2003 124213 199976 07576

32 MOSAL02 1992 1993 1997 119561 196195 19159

33 MUSAU01 1996 1996 1999 170058 174021 01321

34 MUSAU02 1995 1996 1999 170392 178642 02750

35 PASMA01 1993 1997 2004 172060 214776 04746

36 PASMA02 1993 1997 1999 195182 206363 05591

37 PUAL_01 1993 1994 2000 191461 191960 00083

38 PUAL_02 1994 1994 2000 151644 175568 03987

221


FIRST

CENS

LAST

CENS

BA_FIRST

CENS

(m2ha

-1)

BA_LAST

CENS

(m2ha

-1)

MBAI

(m2ha

-1yr

-1)

39 PUAL_03 1996 1996 1998 165854 175962 05054

40 PUAL_04 1996 1996 2004 172923 186604 01710

41 PULIE02 1997 1997 2004 109713 118248 01219

42 PULIE03 1997 1997 1999 198100 204913 03406

43 SAGAR01 1997 1998 2005 141514 153152 01663

44 SEMBE01 1996 1997 1999 134691 137005 01157

45 SERA_02 1996 1996 1998 174719 178179 01730

46 TURAM01 1994 1994 1998 245674 256188 02629

47 UMBOI01 1993 1994 2004 219117 245082 02597

48 UMBOI02 1993 1994 2001 174360 198924 03509

49 UMBUK01 1993 1993 2007 132607 163482 02205

50 UMBUK02 1993 1993 1999 107566 121284 02286

51 VAILA01 1993 1994 2002 146811 190990 05522

52 VAILA02 1993 1994 2002 175963 188018 01507

53 WASAP01 1986 1990 2003 184658 285293 07741

54 WASAP02 1987 1995 2003 131157 165941 04348

55 WATUT01 1992 1993 2003 139136 202128 06299

56 WATUT02 1992 1993 1998 138267 149267 02200

57 WAWOI01 1991 1994 1998 234345 256670 05581

58 WCOST03 1996 1996 2003 154697 189326 04947

59 WCOST04 1996 1996 2003 103386 104722 00191

60 WFBAY02 1981 1993 1999 182790 183297 00085

61 YALU_01 1995 1995 2007 126460 233236 08898

62 YALU_02 1995 1995 2007 162517 197775 02938

63 YEMA_01 1995 1996 2002 183911 201508 02933

222

APPENDIX 3-4 UNBURNED PSPS IN HARVESTED FOREST WITH

FALLING BA


FIRST

CENS

LAST

CENS

BA_FIRST

CENS

(m2ha

-1)

BA_LAST

CENS

(m2ha

-1)

MBAI

(m2ha

-1yr

-1)

1 CFORD02 1995 1995 2004 1651825 1580870 -007883

2 GARAM02 1991 1994 1998 1806829 1620510 -031054

3 INPOM01 1993 1995 1997 1942872 1707170 -117852

4 KUI_02 1994 1994 2002 1561649 1478340 -010413

5 LARK_04 1994 1996 1999 1609460 1592510 -005649

6 MALAM02 1995 1995 2003 1959570 1434840 -065591

7 MORER02 1997 1997 1999 1443625 1390560 -026533

8 ORLAK01 1994 1994 2000 1891138 993640 -149582

9 ORLAK02 1994 1994 1994 1674760 1085680 -098180

10 PULIE01 1997 1997 2004 1807768 1076690 -104440

11 SAGAR02 1997 1998 2005 1735408 1716280 -002732

12 SEMBE02 1996 1997 1999 945672 888900 -028387

13 SERA_01 1996 1996 2000 2129906 2107070 -005708

14 TURAM02 1994 1994 1997 2540949 2561880 -010010

15 TURAM03 1996 1997 1999 1582846 1481270 -050786

16 VUDAL01 1997 1997 1999 762256 705470 -028393

17 VUDAL02 1996 1997 1999 1215035 1070640 -072196

18 WAWOI02 1994 1994 2000 2325639 1142410 -197204

19 WCOST01 1989 1995 1999 1202939 907100 -073959

20 WCOST02 1989 1995 1999 2470524 2172310 -074554

21 WFBAY01 1980 1993 1999 1720145 1404070 -052680

223

APPENDIX 3-5 PSPS BURNED BY FIRE DURING THE DROUGHT


FIRST

CENS

LAST

CENS

BA_FIRST

CENS

(m2ha

-1)

BA_LAST

CENS

(m2ha

-1)

MBAI

(m2ha

-1yr

-

1)

1 CNIRD01 1994 1995 2004 236627 71393 -18359

2 CNIRD02 1994 1995 2007 230366 35539 -16236

3 HUVIV01 1997 1997

152131 Short measurement

4 IVAIN01 1995 1996 2003 163578 58564 -15002

5 IVAIN02 1995 1996 2003 99191 49083 -07158

6 IVAIN03 1995 1996 1998 130492 119804 -05344

7 IVAIN04 1995 1996 1998 168716 129575 -19570

8 KAPUL01 1993 1993 1999 146181 96334 -08308

9 KAPUL02 1993 1993 2003 117906 26473 -09143

10 KAUT_01 1993 1993 1997 129425 146797 04343

11 KAUT_02 1993 1993 1997 122872 124960 00522

12 LARK_01 1994 1995 1999 236381 191211 -11292

13 LARK_02 1994 1995 1999 214359 236409 05513

14 MAUBU01 1995 1996


15 MAUBU02 1995 1996


16 OOMSI01 1979 1993 1997 209554 221536 02996

17 OOMSI02 1980 1993 1997 189978 211015 05259

18 SOGER01 1996 1996


19 SOGER02 1996 1996


20 WIMAR01 1993 1994 2000 185575 170570 -02501

21 WIMAR02 1993 1994 2000 230218 160777 -11574

APPENDIX 3-6 10 PSPS SEVERELY BURNED DURING THE DROUGHT

BA BA

BA

gained BA BA

BA lost

After

Pre-

1997 1997

Meas

Period

Before

Fire 1997

Post-

1997

Meas

Period Fire

PLOTID

(m2ha

-

1)

(m2ha

-

1) (years) ()

(m2ha

-

1)

(m2ha

-

1) (years) ()

CNIRD01 2366 2443 2 163 2443 714 7 1612

CNIRD02 2304 2355 2 023 2355 355 10 1723

IVAIN01 1636 1680 1 269 1680 586 6 1611

IVAIN02 992 993 1 009 993 491 6 1108

KAPUL01 1462 1736 4 506 1736 963 2 2550

KAPUL02 1180 1299 4 264 1299 265 6 2328

LARK01 1961 2364 2 891 2364 1912 2 104

LARK02 2144 2231 2 205 2231 2364 2 317

WIMAR01 1856 1924 3 124 1924 1706 3 394

WIMAR02 2264 2302 3 056 2302 1608 3 1078

224

APPENDIX 4-1 SAMPLING POINT DATA-YALU COMMUNITY FOREST

AREA

Plot East North Date

Tree

No Species POM Diameter Description

1 484643 9268927 4072009 1 PTE IND 13 18

Secondary

Forest - Yalu

1 484643 9268927 4072009 2 TRE 13 27

Secondary

Forest - Yalu

1 484643 9268927 4072009 3 HIB 13 29

Secondary

Forest - Yalu

1 484643 9268927 4072009 4 MAC 13 17

Secondary

Forest - Yalu

1 484643 9268927 4072009 5 HIB 13 335

Secondary

Forest - Yalu

1 484643 9268927 4072009 6 13 30

Secondary

Forest - Yalu

1 484643 9268927 4072009 7 13 30

Secondary

Forest - Yalu

1 484643 9268927 4072009 8 PTE IND 13 51

Secondary

Forest - Yalu

1 484643 9268927 4072009 9 TRE 13 33

Secondary

Forest - Yalu

1 484643 9268927 4072009 10 13 20

Secondary

Forest - Yalu

1 484643 9268927 4072009 11 POM PIN 13 245

Secondary

Forest - Yalu

1 484643 9268927 4072009 12 13 40

Secondary

Forest - Yalu

1 484643 9268927 4072009 13 13 30

Secondary

Forest - Yalu

1 484643 9268927 4072009 14 HIB 13 39

Secondary

Forest - Yalu

1 484643 9268927 4072009 15 TRE 13 225

Secondary

Forest - Yalu

1 484643 9268927 4072009 16 TER 13 26

Secondary

Forest - Yalu

2 484713 9268265 4072009 1 AIL 2 88

Primary Forest

- Yalu

2 484713 9268265 5072009 2 MYR 13 22

Primary Forest

- Yalu

2 484713 9268265 6072009 3 CEL PHI 13 175

Primary Forest

- Yalu

2 484713 9268265 7072009 4 STE 13 60

Primary Forest

- Yalu

225


Tree


2 484713 9268265 8072009 5 CEL LAT 13 335

Primary Forest

- Yalu

2 484713 9268265 9072009 6 VIT 2 95

Primary Forest

- Yalu

2 484713 9268265 10072009 7 POM TOM 13 123

Primary Forest

- Yalu

2 484713 9268265 11072009 8 CHN 13 18

Primary Forest

- Yalu

2 484713 9268265 12072009 9 MYR 13 129

Primary Forest

- Yalu

2 484713 9268265 13072009 10 NEU 13 225

Primary Forest

- Yalu

2 484713 9268265 14072009 11 PTE IND 13 47

Primary Forest

- Yalu

2 484713 9268265 15072009 12 POM PIN 13 48

Primary Forest

- Yalu

2 484713 9268265 16072009 13 LIT 2 29

Primary Forest

- Yalu

2 484713 9268265 17072009 14 PIM AMB 13 27

Primary Forest

- Yalu

2 484713 9268265 18072009 15 LIT 2 435

Primary Forest

- Yalu

2 484713 9268265 19072009 16 MYR 13 42

Primary Forest

- Yalu

2 484713 9268265 20072009 17 CEL PHI 3 73

Primary Forest

- Yalu

2 484713 9268265 21072009 18 CEL PHI 2 40

Primary Forest

- Yalu

3 484634 9268819 17062009 1 TRH 13 365

Secondary

Forest - Yalu

3 484634 9268819 17062009 2 TRH 13 359

Secondary

Forest - Yalu

3 484634 9268819 17062009 3 SEM 13 110

Secondary

Forest - Yalu

3 484634 9268819 17062009 4 TER 13 600

Secondary

Forest - Yalu

3 484634 9268819 17062009 5 STE 13 253

Secondary

Forest - Yalu

3 484634 9268819 17062009 6 POM PIN 13 570

Secondary

Forest - Yalu

3 484634 9268819 17062009 7 TER 13 630

Secondary

Forest - Yalu

3 484634 9268819 17062009 8 HIB 13 435

Secondary

Forest - Yalu

226


Tree


3 484634 9268819 17062009 9 INO FAG 13 600

Secondary

Forest - Yalu

3 484634 9268819 17062009 10 BUC 13 230

Secondary

Forest - Yalu

3 484634 9268819 17062009 11 TRH 13 313

Secondary

Forest - Yalu

3 484634 9268819 17062009 12 PIS UMB 13 220

Secondary

Forest - Yalu

3 484634 9268819 17062009 13 PTE IND 13 120

Secondary

Forest - Yalu

4 484630 9268763 17062009 1 POM PIN 13 280

Secondary

Forest - Yalu

4 484630 9268763 17062009 2 POM PIN 13 359

Secondary

Forest - Yalu

4 484630 9268763 17062009 3 END 13 370

Secondary

Forest - Yalu

4 484630 9268763 17062009 4 13 300

Secondary

Forest - Yalu

4 484630 9268763 17062009 5 MAC 13 225

Secondary

Forest - Yalu

4 484630 9268763 17062009 6 TOO SUR 13 325

Secondary

Forest - Yalu

4 484630 9268763 17062009 7 TOO SUR 13 305

Secondary

Forest - Yalu

4 484630 9268763 17062009 8 MAC 13 230

Secondary

Forest - Yalu

4 484630 9268763 17062009 9 PTE IND 13 220

Secondary

Forest - Yalu

4 484630 9268763 17062009 10 PTE IND 13 239

Secondary

Forest - Yalu

4 484630 9268763 17062009 11 TRH 13 235

Secondary

Forest - Yalu

4 484630 9268763 17062009 12 VIT 13 163

Secondary

Forest - Yalu

4 484630 9268763 17062009 13 SEM 13 128

Secondary

Forest - Yalu

4 484630 9268763 17062009 14 TRI 13 306

Secondary

Forest - Yalu

4 484630 9268763 17062009 15 TRI 13 284

Secondary

Forest - Yalu

4 484630 9268763 17062009 16 POM PIN 13 250

Secondary

Forest - Yalu

5 484646 9268686 17062009 1 TIM 13 143

Secondary

Forest - Yalu

227


Tree


5 484646 9268686 17062009 2 GUI 13 129

Secondary

Forest - Yalu

5 484646 9268686 17062009 3 PTE IND 13 130

Secondary

Forest - Yalu

5 484646 9268686 17062009 4 PTE IND 13 253

Secondary

Forest - Yalu

5 484646 9268686 17062009 5 FIC 13 335

Secondary

Forest - Yalu

5 484646 9268686 17062009 6 TRI 13 286

Secondary

Forest - Yalu

5 484646 9268686 17062009 7 FIC 13 278

Secondary

Forest - Yalu

5 484646 9268686 17062009 8 PTE IND 13 253

Secondary

Forest - Yalu

5 484646 9268686 17062009 9 TRH 13 411

Secondary

Forest - Yalu

5 484646 9268686 17062009 10 ELA 13 583

Secondary

Forest - Yalu

5 484646 9268686 17062009 11 STE 13 272

Secondary

Forest - Yalu

5 484646 9268686 17062009 12 ART 13 301

Secondary

Forest - Yalu

5 484646 9268686 17062009 13 PTE IND 13 204

Secondary

Forest - Yalu

5 484646 9268686 17062009 14 PTE IND 13 153

Secondary

Forest - Yalu

5 484646 9268686 17062009 15 SEM 13 95

Secondary

Forest - Yalu

5 484646 9268686 17062009 16 SEM 13 118

Secondary

Forest - Yalu

5 484646 9268686 17062009 17 TRI 13 275

Secondary

Forest - Yalu

5 484646 9268686 17062009 18 TRH 13 258

Secondary

Forest - Yalu

5 484646 9268686 17062009 19 TRH 13 250

Secondary

Forest - Yalu

5 484646 9268686 17062009 20 TRH 13 328

Secondary

Forest - Yalu

5 484646 9268686 17062009 21 TIM 13 288

Secondary

Forest - Yalu

6 _ _ 17062009 1 TRH 13 167

Secondary

Forest - Yalu

6 _ _ 17062009 2 PTE IND 13 152

Secondary

Forest - Yalu

228


Tree


6 _ _ 17062009 3 PTE IND 13 192

Secondary

Forest - Yalu

6 _ _ 17062009 4 PTE IND 13 158

Secondary

Forest - Yalu

6 _ _ 17062009 5 FIC 13 506

Secondary

Forest - Yalu

6 _ _ 17062009 6 TIM 13 218

Secondary

Forest - Yalu

6 _ _ 17062009 7 STR 13 101

Secondary

Forest - Yalu

6 _ _ 17062009 8 LIT 13 249

Secondary

Forest - Yalu

6 _ _ 17062009 9 MAC 13 264

Secondary

Forest - Yalu

6 _ _ 17062009 10 FIC 13 275

Secondary

Forest - Yalu

6 _ _ 17062009 11 PTE IND 13 350

Secondary

Forest - Yalu

6 _ _ 17062009 12 DYS 13 183

Secondary

Forest - Yalu

6 _ _ 17062009 13 TRH 13 235

Secondary

Forest - Yalu

6 _ _ 17062009 14 TRH 13 266

Secondary

Forest - Yalu

6 _ _ 17062009 15 ART 13 212

Secondary

Forest - Yalu

6 _ _ 17062009 16 TRI 13 260

Secondary

Forest - Yalu

6 _ _ 17062009 17 TRI 13 117

Secondary

Forest - Yalu

7 484761 9268629 17062009 1 TIM 13 159

Secondary

Forest - Yalu

7 484761 9268629 17062009 2 TIM 13 156

Secondary

Forest - Yalu

7 484761 9268629 17062009 3 EUO 13 351

Secondary

Forest - Yalu

7 484761 9268629 17062009 4 TRH 13 215

Secondary

Forest - Yalu

7 484761 9268629 17062009 5 TRH 13 336

Secondary

Forest - Yalu

7 484761 9268629 17062009 6 PTE IND 13 305

Secondary

Forest - Yalu

7 484761 9268629 17062009 7 POM PIN 13 284

Secondary

Forest - Yalu

229


Tree


7 484761 9268629 17062009 8 INT 13 256

Secondary

Forest - Yalu

7 484761 9268629 17062009 9 ANT CHI 13 172

Secondary

Forest - Yalu

7 484761 9268629 17062009 10 MYR 13 142

Secondary

Forest - Yalu

7 484761 9268629 17062009 11 TIM 13 226

Secondary

Forest - Yalu

7 484761 9268629 17062009 12 13 470

Secondary

Forest - Yalu

7 484761 9268629 17062009 13 ART 13 313

Secondary

Forest - Yalu

7 484761 9268629 17062009 14 VIT COF 13 241

Secondary

Forest - Yalu

7 484761 9268629 17062009 15 PTE IND 13 198

Secondary

Forest - Yalu

7 484761 9268629 17062009 16 MAC 13 398

Secondary

Forest - Yalu

7 484761 9268629 17062009 17 MAC 13 214

Secondary

Forest - Yalu

7 484761 9268629 17062009 18 MAC 13 190

Secondary

Forest - Yalu

7 484761 9268629 17062009 19 GUI 13 244

Secondary

Forest - Yalu

7 484761 9268629 17062009 20 TIM 13 247

Secondary

Forest - Yalu

7 484761 9268629 17062009 21 SEM 13 142

Secondary

Forest - Yalu

7 484761 9268629 17062009 22 SEM 13 156

Secondary

Forest - Yalu

7 484761 9268629 17062009 23 SEM 13 163

Secondary

Forest - Yalu

7 484761 9268629 17062009 24 PTE IND 13 316

Secondary

Forest - Yalu

7 484761 9268629 17062009 25 ANT CHI 13 251

Secondary

Forest - Yalu

7 484761 9268629 17062009 26 ANT CHI 13 210

Secondary

Forest - Yalu

7 484761 9268629 17062009 27 TIM 13 266

Secondary

Forest - Yalu

7 484761 9268629 17062009 28 TIM 13 151

Secondary

Forest - Yalu

8 484610 9268470 17062009 1 TRH 13 260

Secondary

Forest - Yalu

230


Tree


8 484610 9268470 17062009 2 EUO 13 142

Secondary

Forest - Yalu

8 484610 9268470 17062009 3 EUO 13 118

Secondary

Forest - Yalu

8 484610 9268470 17062009 4 TIM 13 211

Secondary

Forest - Yalu

8 484610 9268470 17062009 5 PTE IND 13 294

Secondary

Forest - Yalu

8 484610 9268470 17062009 6 HIB 13 792

Secondary

Forest - Yalu

8 484610 9268470 17062009 7 TRH 13 411

Secondary

Forest - Yalu

8 484610 9268470 17062009 8 ART 13 1135

Secondary

Forest - Yalu

8 484610 9268470 17062009 9 PTE IND 13 198

Secondary

Forest - Yalu

8 484610 9268470 17062009 10 TRH 13 520

Secondary

Forest - Yalu

8 484610 9268470 17062009 11 MAC 13 233

Secondary

Forest - Yalu

8 484610 9268470 17062009 12 POL 13 261

Secondary

Forest - Yalu

8 484610 9268470 17062009 13 CAN 13 316

Secondary

Forest - Yalu

8 484610 9268470 17062009 14 POM PIN 13 472

Secondary

Forest - Yalu

8 484610 9268470 17062009 15 EUO 13 116

Secondary

Forest - Yalu

8 484610 9268470 17062009 16 PTE IND 13 114

Secondary

Forest - Yalu

8 484610 9268470 17062009 17 CAN 13 281

Secondary

Forest - Yalu

8 484610 9268470 17062009 18 POM PIN 13 561

Secondary

Forest - Yalu

8 484610 9268470 17062009 19 ANT CHI 13 283

Secondary

Forest - Yalu

8 484610 9268470 17062009 20 POM PIN 13 196

Secondary

Forest - Yalu

8 484610 9268470 17062009 21 EUO 13 500

Secondary

Forest - Yalu

8 484610 9268470 17062009 22 FIC 13 246

Secondary

Forest - Yalu

8 484610 9268470 17062009 23 FIC 13 246

Secondary

Forest - Yalu

231


Tree


8 484610 9268470 17062009 24 TRI 13 153

Secondary

Forest - Yalu

9 484522 92685314 17062009 1 SEM 13 540

Secondary

Forest - Yalu

9 484522 92685314 17062009 2 INO FAG 13 550

Secondary

Forest - Yalu

9 484522 92685314 17062009 3 BUC 13 369

Secondary

Forest - Yalu

9 484522 92685314 17062009 4 ANT CHI 13 505

Secondary

Forest - Yalu

9 484522 92685314 17062009 5 GUI 13 195

Secondary

Forest - Yalu

9 484522 92685314 17062009 6 LIT 13 355

Secondary

Forest - Yalu

9 484522 92685314 17062009 7 PIS UMB 13 300

Secondary

Forest - Yalu

9 484522 92685314 17062009 8 SEM 13 371

Secondary

Forest - Yalu

9 484522 92685314 17062009 9 PIS UMB 13 172

Secondary

Forest - Yalu

9 484522 92685314 17062009 10 PIS UMB 13 153

Secondary

Forest - Yalu

9 484522 92685314 17062009 11 BRI 13 1800

Secondary

Forest - Yalu

9 484522 92685314 17062009 12 VIT COF 13 1800

Secondary

Forest - Yalu

9 484522 92685314 17062009 13 TER 13 201

Secondary

Forest - Yalu

9 484522 92685314 17062009 14 PIS UMB 13 196

Secondary

Forest - Yalu

9 484522 92685314 17062009 15 PTE IND 13 1850

Secondary

Forest - Yalu

10 484446 9268164 17062009 1 END 13 381

Secondary

Forest - Yalu

10 484446 9268164 17062009 2 CAN 13 548

Secondary

Forest - Yalu

10 484446 9268164 17062009 3 MAC 13 346

Secondary

Forest - Yalu

10 484446 9268164 17062009 4 MAC 13 289

Secondary

Forest - Yalu

10 484446 9268164 17062009 5 MAC 13 336

Secondary

Forest - Yalu

10 484446 9268164 17062009 6 PTE IND 13 324

Secondary

Forest - Yalu

232


Tree


10 484446 9268164 17062009 7 CAN 13 375

Secondary

Forest - Yalu

10 484446 9268164 17062009 8 MAC 13 274

Secondary

Forest - Yalu

10 484446 9268164 17062009 9 MAC 13 393

Secondary

Forest - Yalu

10 484446 9268164 17062009 10 PTE IND 13 180

Secondary

Forest - Yalu

10 484446 9268164 17062009 11 ANT CHI 13 507

Secondary

Forest - Yalu

10 484446 9268164 17062009 12 STE 13 165

Secondary

Forest - Yalu

10 484446 9268164 17062009 13 CEL 13 570

Secondary

Forest - Yalu

10 484446 9268164 17062009 14 LIT 13 394

Secondary

Forest - Yalu

10 484446 9268164 17062009 15 STE AMP 13 107

Secondary

Forest - Yalu

10 484446 9268164 17062009 16 PTE IND 13 195

Secondary

Forest - Yalu

10 484446 9268164 17062009 17 LIT 13 130

Secondary

Forest - Yalu

10 484446 9268164 17062009 18 PIM AMB 13 234

Secondary

Forest - Yalu

10 484446 9268164 17062009 19 ANT CHI 13 517

Secondary

Forest - Yalu

10 484446 9268164 17062009 20 AGL 13 180

Secondary

Forest - Yalu

10 484446 9268164 17062009 21 ALS 13 192

Secondary

Forest - Yalu

10 484446 9268164 17062009 22 STE 13 265

Secondary

Forest - Yalu

10 484446 9268164 17062009 23 MIC 13 201

Secondary

Forest - Yalu

10 484446 9268164 17062009 24 PTE IND 13 1860

Secondary

Forest - Yalu

11 484612 9268157 17062009 1 FIC 13 375

Secondary

Forest - Yalu

11 484612 9268157 17062009 2 PLA 13 130

Secondary

Forest - Yalu

11 484612 9268157 17062009 3 INO FAG 13 242

Secondary

Forest - Yalu

11 484612 9268157 17062009 4 STE 13 690

Secondary

Forest - Yalu

233


Tree


11 484612 9268157 17062009 5 PIM AMB 13 466

Secondary

Forest - Yalu

11 484612 9268157 17062009 6 GNE GNE 13 158

Secondary

Forest - Yalu

11 484612 9268157 17062009 7 PIM AMB 13 255

Secondary

Forest - Yalu

11 484612 9268157 17062009 8 PIM AMB 13 385

Secondary

Forest - Yalu

11 484612 9268157 17062009 9 GNE GNE 13 130

Secondary

Forest - Yalu

11 484612 9268157 17062009 10 PIM AMB 13 255

Secondary

Forest - Yalu

11 484612 9268157 17062009 11 PIM AMB 13 260

Secondary

Forest - Yalu

11 484612 9268157 17062009 12 CEL 13 180

Secondary

Forest - Yalu

11 484612 9268157 17062009 13 CEL 13 255

Secondary

Forest - Yalu

11 484612 9268157 17062009 14 GUI 13 290

Secondary

Forest - Yalu

11 484612 9268157 17062009 15 CEL 13 715

Secondary

Forest - Yalu

11 484612 9268157 17062009 16 STE 13 700

Secondary

Forest - Yalu

11 484612 9268157 17062009 17 MIC 13 210

Secondary

Forest - Yalu

11 484612 9268157 17062009 18 PIM AMB 13 346

Secondary

Forest - Yalu

11 484612 9268157 17062009 19 MIS 13 246

Secondary

Forest - Yalu

11 484612 9268157 17062009 20 CEL 13 700

Secondary

Forest - Yalu

11 484612 9268157 17062009 21 CEL 13 496

Secondary

Forest - Yalu

12 484699 9268074 17062009 1 INT 20 926

Secondary

Forest - Yalu

12 484699 9268074 17062009 2 TER 13 634

Secondary

Forest - Yalu

12 484699 9268074 17062009 3 SEM 13 430

Secondary

Forest - Yalu

12 484699 9268074 17062009 4 TER 13 293

Secondary

Forest - Yalu

12 484699 9268074 17062009 5 PIM AMB 13 260

Secondary

Forest - Yalu

234


Tree


12 484699 9268074 17062009 6 PIM AMB 13 250

Secondary

Forest - Yalu

12 484699 9268074 17062009 7 PIM AMB 13 310

Secondary

Forest - Yalu

12 484699 9268074 17062009 8 SYZ 20 560

Secondary

Forest - Yalu

12 484699 9268074 17062009 9 TRI 20 1300

Secondary

Forest - Yalu

12 484699 9268074 17062009 10 13 180

Secondary

Forest - Yalu

12 484699 9268074 17062009 11 PIS UMB 13 320

Secondary

Forest - Yalu

12 484699 9268074 17062009 12 LIT 13 150

Secondary

Forest - Yalu

12 484699 9268074 17062009 13 TRI 13 471

Secondary

Forest - Yalu

12 484699 9268074 17062009 14 STE 13 284

Secondary

Forest - Yalu

12 484699 9268074 17062009 15 CER 13 252

Secondary

Forest - Yalu

12 484699 9268074 17062009 16 INT 13 825

Secondary

Forest - Yalu

12 484699 9268074 17062009 17 TER 30 450

Secondary

Forest - Yalu

13 484743 9268126 17062009 1 PIM AMB 13 420

Secondary

Forest - Yalu

13 484743 9268126 17062009 2 CEL 13 490

Secondary

Forest - Yalu

13 484743 9268126 17062009 3 MIC 13 130

Secondary

Forest - Yalu

13 484743 9268126 17062009 4 PTE IND 13 530

Secondary

Forest - Yalu

13 484743 9268126 17062009 5 CEL 13 761

Secondary

Forest - Yalu

13 484743 9268126 17062009 6 CEL 13 420

Secondary

Forest - Yalu

13 484743 9268126 17062009 7 CEL 13 340

Secondary

Forest - Yalu

13 484743 9268126 17062009 8 PTE IND 40 705

Secondary

Forest - Yalu

13 484743 9268126 17062009 9 MAC 13 320

Secondary

Forest - Yalu

13 484743 9268126 17062009 10 MAC 13 460

Secondary

Forest - Yalu

235


Tree


13 484743 9268126 17062009 11 END 13 300

Secondary

Forest - Yalu

13 484743 9268126 17062009 12 MAC 13 190

Secondary

Forest - Yalu

13 484743 9268126 17062009 13 MAC 13 203

Secondary

Forest - Yalu

13 484743 9268126 17062009 14 ART 13 220

Secondary

Forest - Yalu

13 484743 9268126 17062009 15 PTE IND 13 525

Secondary

Forest - Yalu

13 484743 9268126 17062009 16 MAC 13 124

Secondary

Forest - Yalu

13 484743 9268126 17062009 17 AGL 13 415

Secondary

Forest - Yalu

14 484837 9268212 17062009 1 GAR 20 291

Secondary

Forest - Yalu

14 484837 9268212 17062009 2 AGL 13 280

Secondary

Forest - Yalu

14 484837 9268212 17062009 3 TER 13 364

Secondary

Forest - Yalu

14 484837 9268212 17062009 4 TER 13 330

Secondary

Forest - Yalu

14 484837 9268212 17062009 5 PIS UMB 13 156

Secondary

Forest - Yalu

14 484837 9268212 17062009 6 POM PIN 13 584

Secondary

Forest - Yalu

14 484837 9268212 17062009 7 TER 13 365

Secondary

Forest - Yalu

14 484837 9268212 17062009 8 END 13 396

Secondary

Forest - Yalu

14 484837 9268212 17062009 9 TER 13 233

Secondary

Forest - Yalu

14 484837 9268212 17062009 10 STE 13 630

Secondary

Forest - Yalu

15 484784 9268298 17062009 1 CEL 13 367

Secondary

Forest - Yalu

15 484784 9268298 17062009 2 PIM AMB 13 360

Secondary

Forest - Yalu

15 484784 9268298 17062009 3 CEL 15 619

Secondary

Forest - Yalu

15 484784 9268298 17062009 4 DYS 13 240

Secondary

Forest - Yalu

15 484784 9268298 17062009 5 LIT 13 465

Secondary

Forest - Yalu

236


Tree


15 484784 9268298 17062009 6 FIC 40 1500

Secondary

Forest - Yalu

15 484784 9268298 17062009 7 POM PIN 13 579

Secondary

Forest - Yalu

15 484784 9268298 17062009 8 MIS 13 278

Secondary

Forest - Yalu

15 484784 9268298 17062009 9 CEL 40 570

Secondary

Forest - Yalu

15 484784 9268298 17062009 10 LIT 13 294

Secondary

Forest - Yalu

15 484784 9268298 17062009 11 ANT CHI 13 434

Secondary

Forest - Yalu

15 484784 9268298 17062009 12 PIS UMB 13 236

Secondary

Forest - Yalu

15 484784 9268298 17062009 13 GNE GNE 13 150

Secondary

Forest - Yalu

15 484784 9268298 17062009 14 CEL 15 603

Secondary

Forest - Yalu

16 484840 9268332 17062009 1 INT 13 570

Secondary

Forest - Yalu

16 484840 9268332 17062009 2 MIC 13 246

Secondary

Forest - Yalu

16 484840 9268332 17062009 3 CEL 40 750

Secondary

Forest - Yalu

16 484840 9268332 17062009 4 POM PIN 20 286

Secondary

Forest - Yalu

16 484840 9268332 17062009 5 MIC 13 240

Secondary

Forest - Yalu

16 484840 9268332 17062009 6 TRI 13 176

Secondary

Forest - Yalu

16 484840 9268332 17062009 7 FIC 13 120

Secondary

Forest - Yalu

16 484840 9268332 17062009 8 PIM AMB 13 287

Secondary

Forest - Yalu

16 484840 9268332 17062009 9 GNE GNE 13 146

Secondary

Forest - Yalu

16 484840 9268332 17062009 10 PIM AMB 13 250

Secondary

Forest - Yalu

16 484840 9268332 17062009 11 BIS JAV 13 605

Secondary

Forest - Yalu

16 484840 9268332 17062009 12 STE 13 553

Secondary

Forest - Yalu

16 484840 9268332 17062009 13 PIM AMB 13 378

Secondary

Forest - Yalu

237


Tree


17 484890 9268434 17062009 1 PTE IND 13 323

Secondary

Forest - Yalu

17 484890 9268434 17062009 2 ART 15 733

Secondary

Forest - Yalu

17 484890 9268434 17062009 3 POM PIN 30 705

Secondary

Forest - Yalu

17 484890 9268434 17062009 4 DRA 30 680

Secondary

Forest - Yalu

17 484890 9268434 17062009 5 HOR 13 250

Secondary

Forest - Yalu

17 484890 9268434 17062009 6 MAC 13 143

Secondary

Forest - Yalu

17 484890 9268434 17062009 7 PTE IND 15 623

Secondary

Forest - Yalu

17 484890 9268434 17062009 8 CEL 30 664

Secondary

Forest - Yalu

17 484890 9268434 17062009 9 PTE IND 13 220

Secondary

Forest - Yalu

17 484890 9268434 17062009 10 PTE IND 13 170

Secondary

Forest - Yalu

17 484890 9268434 17062009 11 PTE IND 13 140

Secondary

Forest - Yalu

APPENDIX 4-2 INVENTORY DATA-GABENSIS COMMUNITY FOREST


Tree


1 469324 9256048 4062009 1 POM PIN 3 695

Logged Forest -

Gabensis

1 469324 9256048 4062009 2 INT 13 59

Logged Forest -

Gabensis

1 469324 9256048 4062009 3 CHN 13 61

Logged Forest -

Gabensis

1 469324 9256048 4062009 4 TER 2 43

Logged Forest -

Gabensis

1 469324 9256048 4062009 5 POM PIN 2 59

Logged Forest -

Gabensis

1 469324 9256048 4062009 6 POM PIN 2 59

Logged Forest -

Gabensis

1 469324 9256048 4062009 7 POM PIN 13 70

Logged Forest -

Gabensis

238


Tree


1 469324 9256048 4062009 8 CHN 13 555

Logged Forest -

Gabensis

1 469324 9256048 4062009 9 INT 15 28

Logged Forest -

Gabensis

1 469324 9256048 4062009 10 TER 2 535

Logged Forest -

Gabensis

1 469324 9256048 4062009 11 TER 13 40

Logged Forest -

Gabensis

1 469324 9256048 4062009 12 HRN 13 365

Logged Forest -

Gabensis

1 469324 9256048 4062009 13 CHN 18 52

Logged Forest -

Gabensis

1 469324 9256048 4062009 14 CNN 18 575

Logged Forest -

Gabensis

1 469324 9256048 4062009 15 CHN 18 385

Logged Forest -

Gabensis

1

469324

9256048

4062009

16

CHN

18

33

Logged Forest-

Gabensis

1 469324 9256048 4062009 17 POM PIN 13 305

Logged Forest -

Gabensis

1 469324 9256048 4062009 18 PLA 13 30

Logged Forest -

Gabensis

1 469324 9256048 4062009 19 13 20

Logged Forest -

Gabensis

2 470782 9257001 4062009 1 HRN 13 43

Secondary Forest -

Gabensis

2 470782 9257001 4062009 2 POM PIN 2 55

Secondary Forest -

Gabensis

2 470782 9257001 4062009 3 CHN 2 94

Secondary Forest -

Gabensis

2 470782 9257001 4062009 4 13 30

Secondary Forest -

Gabensis

2 470782 9257001 4062009 5 13 30

Secondary Forest -

Gabensis

2 470782 9257001 4062009 6 PTE IND 2 85

Secondary Forest -

Gabensis

2 470782 9257001 4062009 7 13 30

Secondary Forest -

Gabensis

2 470782 9257001 4062009 8 13 30

Secondary Forest -

Gabensis

2 470782 9257001 4062009 9 13 30

Secondary Forest -

Gabensis

2 470782 9257001 4062009 10 13 30

Secondary Forest -

Gabensis

239


Tree


2 470782 9257001 4062009 11 PTE IND 2 57

Secondary Forest -

Gabensis

2 470782 9257001 4062009 12 PTE IND 13 31

Secondary Forest -

Gabensis

2 470782 9257001 4062009 13 MAS 2 55

Secondary Forest -

Gabensis

2 470782 9257001 4062009 14 POM PIN 2 41

Secondary Forest -

Gabensis

2 470782 9257001 4062009 15 POM PIN 2 47

Secondary Forest -

Gabensis

2 470782 9257001 4062009 16 13 30

Secondary Forest -

Gabensis

2 470782 9257001 4062009 17 POM PIN 15 43

Secondary Forest -

Gabensis

2 470782 9257001 4062009 18 PTE IND 15 80

Secondary Forest -

Gabensis

240

APPENDIX 5-1 PNGFA MINIMUM EXPORT PRICE SPECIES GROUP

GroupSpecies ID Species Group Species ID Species Group Species ID Species

EAG Eaglewood

1 2 3

BUR Burckella AGL Aglaia AMB Amberoi

CAL Calophyllum AMO Amoora [Pacific Maple] CAH Camphorwood PNG [Cinnamomum]

CAG Canarium Grey ANT Antiaris CAM Campnosperma

CAR Canarium Red BAS Basswood PNG CEH Celtis Hard

CEP Cedar Pencil CEM Cedar Mangrove CEL Celtis Light

DIL Dillenia CER Cedar Red CRY Cryptocarya [Medang]

ERI Erima BEW Elmerrillia [Beech Wau] DYS Dysox

HEK Hekakoro (Gluta) HOH Hopea Heavy END Endiandra [Medang]

KWI Kwila HOL Hopea Light GAG Garo Garo

LOP Lophopetallum [Perupok] KAM Kamarere GUW Gum Water[Syzygium]

MAL Malas KEM Kempas [PNG] HER Heritiera

MER Mersawa [PNG] LAB Labula LIT Litsea [Medang]

PLR Planchonella Red VIT Vitex PNG SAP Satin[wood]heart Pink [Buchanania]

PLW Planchonella White SIW Siris White [Ailantus]

TAU Taun

TEA Teak

TER Terminalia

WAL Walnut PNG

4 4 Conthellip 4 Conthellip

ALB Albizia Brown GON Gonostyllus OWT Oak White Tulip

ALW Albizia White GOR Gordonia OPS Oreocallis [Oak Pink Silky]

ALH Alstonia Hard HAY Hardwood Yellow RWD Oriomo Redwood

ASH Ash Hickory HEN Hernandia PAN Pangium

ASP Ash Papuan HIB Hibiscus [Bulolo Ash] PAS Parastemon

ASG Ash Scaly [Ganophyllum] IRS Ironbark Scrub [Bridelia] PAR Paratocarpus

BAR Barringtonia IVW Ivorywood PNG PER Pericopsis

BEP Beech PNG KAN Kandis PIM Pimeleodendron

BIP Birch Pink KAP Kapiak [Artocarpus] PLA Planchonia

BOM Bombax KAK Kasi Kasi PLB Plum Busu

BOS Box Swamp PNG KIN Kingiodendron PLT Plum Tulip

BOW Boxwood PNG (Zanthophyllum) KIS Kiso OAP PNG Oak

MGB Brown Mangrove LAP Lapome [PNG] TUL PNG Tulipwood

BTO Brown Tulip Oak MAC Macaranga POL Polyalthia

CAN Cananga MAH Malaha QUA Quandong PNG

CAD Candlenut MAN Mango [Mangifera] VAT Resak [Vatica]

CLL Carallia MAB Mangrove Black RHU Rhus

CEJ Cedar Java [Bischofia] MAM Mangrove Milky SAH Saffron Heart

CWW Cheesewood White [Milky Pine] MAR Mangrove Red SAS Sassafras PNG

CWY Cheesewood Yellow MAW Mangrove White SAG Satinheart Green

CHR Chrysophyllum MAK Manilkara SEM Semicarpus

COW Coachwood [PNG] MAT Maniltoa SIL Silkwood (Silver Maple)

DRY Drypetes MAS Maple Scented [Flindersia] ASS Silkwood Ash

DUA Duabunga MIG Milkwood Grey [Cerbera] SLO Sloanea

EUH Euodia [Heavy] NEO Neoscortechinia SPO Spondias

EUL Euodia [Light] NEU Neuburgia STE Sterculia

FIG Fig PNG HOR Nutmeg [Horsfieldia] TET Tea Tree

FLA Flacourtia NUT Nutmeg [Myristica] TEM Tetrameles

GAL Galbulimima [White Magnolia] OAR Oak Red TRC Trichadenia

GAR Garuga OSC Oak She (Casuarina) TRI Tristiropsis

GLO Glochidion OAS Oak Silky WAB Wattle Brown PNG

GME Gmelina [White beech] OAW Oak White WAR Wattle Red PNG

AMW White Almond Alphitonia

5 6

BLB Blackbean POB [Brown] Podocarp

CTE Ctenolophon POH [Highland] Podocarp

ELE Eleocarpus ARA Araucaria (Hoop pine Klinki pine)

EUG Eugenia [Syzygium] BAL Balsa

EXA Exanto CLP Celery-Top PNG Pine

FIR Firmiana COR Cordia

GAS Gastonia DAC Dacrydium

ILE Ilex DIO Diospyros

MIR Mix Red EBO Ebony PNG

MIW Mix White AGA Kauri PNG [Agathis]

MIX Mixed Species KEW Kerosene Wood

PRO Protium LIB Libocedrus

PRU Prunus POD Podocarpus

SCH Schima ROS Rosewood PNG

STR Steropsis

241

APPENDIX 5-2 CURRENT FOREST USES IN CASE STUDY SITES

242

APPENDIX 5-3 FUTURE FOREST USES IN CASE STUDY SITES

243

APPENDIX 6-1 REQUIREMENTS ndash COMMUNITY SAWMILL

A sawmill project is managed by a community to supply the local market with little

capacity and light equipment All sawn timber produced are sold in the domestic market

and for other community use All costs are in PNG Kina The production and marketing

requirements for such a project are as follow

1 x Lucas mill 1 x Stihl 90 chainsaw + accessories

40m3 of logs harvested8 productive months

At a 50 recovery production of 20m3 sawn timber8 productive months

7 men team on wages K80m3

Maintenance repairs spare parts K70m3

Fuel and oil consumption K120

Transport of sawn timber to local market K60m3

Sawn timber sold at the local market K600m3

244

APPENDIX 6-2 REQUIREMENTS ndash LOCAL PROCESSING

Decision Alternative 1 CMU managed processing

Local processing is managed by a community entity referred to as the central marketing

unit (CMU) with mechanised equipment and increased capacity and production for the

export market Production and marketing requirements that have been used to determine

the cash flow as input variables in the decision tree model are as following


1 x 4WD truck Hino FTGT 500 series

1 x 4 WD tractor Massey Ferguson-72HD




10 increase in maintenance repairs spare parts K77m3

10 increase in fuel and oil consumption K132m3

Transport of sawn timber to wharf for export market K255m3

Sawn timber sold to overseas certified market K2400m3 and CBFT market

K1500m3

Other costs for certification

o Certification requirements K50m3

o Fumigation K720 one-off payment

o Wharf handling fees K950 one-off payment

o Custom clearance K330 one-off payment

Decision Alternative 2 Community managed processing

Local processing is managed by the community itself with light equipment and limited

capacity for the export market The following production and marketing requirements

apply









K1500m3






245

APPENDIX 6-3 REQUIREMENTS ndash MEDIUM-SCALE LOG EXPORT

Decision Alternative 1 CMU managed log export

A medium-scale log export enterprise is managed by a CMU for the export market with

mechanised equipment and increased log production The following production and

marketing requirements apply

2 x Stihl 90 chainsaw + accessories

1 x Dozer (D6) for roading

1 x Skidder (D7) to move logs from felling site to road side

1 x Front-end loader for loading logs into logging truck

1 x logging truck for transport of logs to wharf

5000m3 of logs harvested8 productive months through TA arrangement

15 men logging team on wages K250fortnight for manager and other members

K175fortnight for 8 productive months (16 fortnights)



Roading costs K40000Km3

Transport of logs to wharf for overseas export K255m3

CMU logging site is approximately 10km from wharf facilities

Logs sold to overseas market K600m3in Asia and other overseas markets at

K450m3

Other costs for log export



o Log export tax K10m3

o TA registration with PNGFA K250 one-off payment

Decision Alternative 2 Community managed log export

A medium-scale log export enterprise is managed by a Community for the export market

with increased capacity and limited mechanised equipment The following production and





1 x 4WD tractor Massey Fergusson-72HD for moving logs to road side






Roading costs K6000Km


Community logging site is approximately 15km from wharf facilities


K450m3






246

APPENDIX 6-4 REQUIREMENTS - CARBON TRADE

A community forest carbon project is managed for selling carbon credits to either a

compliance or voluntary market The estimated costs of logistics carbon accounting

administration and marketing at the community level used to determine the cash flows as

input variables in the decision analysis model are as follow

Landowner mobilizationsocial mapping K30000

Equipment for ground-based forest carbon assessment K765

GIS Mapping K20000

Logistics transport K10000

8 men team for forest carbon assessment Team leader K250fortnight 5 men

inventory team K175personfortnight international consultancy K10000

other requirement K2000

Verification Validation K20000

Marketing K10000

Other administration requirement K10000

Carbon credits sold to compliance market USD20 per tonne C and to voluntary

market USD15 per tonne C

Average aboveground forest carbon 150 Mg C ha-1

in the case study site

Carbon emission from selective timber harvesting is 55

CO2 equivalent of aboveground forest carbon in the case study site is 4412

Total CO2 emission from case study site is 665500 t CO2

Community forest area in the case study site is 2200 ha

16 fortnights 8 productive months

Minerva Access is the Institutional Repository of The University of Melbourne

Authors

Yosi Cossey Keosai

Title

Scenarios for community-based management of cutover forest in Papua New Guinea

Date

2011

Citation

Yosi C K (2011) Scenarios for community-based management of cutover forest in Papua

New Guinea PhD thesis Melbourne School of Land and Environment - Forest and

Ecosystem Science The University of Melbourne

Persistent Link

httphdlhandlenet1134337028

File Description


Terms and Conditions

Terms and Conditions Copyright in works deposited in Minerva Access is retained by the

copyright owner The work may not be altered without permission from the copyright owner

Readers may only download print and save electronic copies of whole works for their own

personal non-commercial use Any use that exceeds these limits requires permission from

the copyright owner Attribution is essential when quoting or paraphrasing from these works

ii

ABSTRACT

There is an increasing demand for multiple objectives from forest management

worldwide and this is particularly challenging for tropical forests due to their diverse

composition structure and a wide range of stakeholder expectations and requirements

In Papua New Guinea (PNG) forest management is generally considered to be

unsustainable and commercial harvesting leaves behind large forest areas to degrade

overtime with little attention paid to their future management There were four

objectives of this study The first was to assess the current condition and future

production potential of cutover forests in PNG The second objective focussed on

developing scenario analyses and evaluation tools for assisting decision making in

community-based management of cutover native forests In the third objective the

study tested the tools developed under the second objective in two case study sites

where extensive harvesting of primary forest had taken place in the past The fourth

objective of this study was to develop a conceptual framework for community-based


The methodology used in this study was a combination of qualitative analyses of

community interests and expectations in small-scale harvesting and quantitative

analyses of permanent sample plots (PSPs) forest resources and cash-flow associated

with different management scenarios in two case study sites Analyses of PSPs in

cutover forests showed that there was a gradual increase in residual stand basal area

(BA) and timber volume over time and these forests generally showed a high degree

of resilience following harvesting In the two case study sites timber volume for the

residual stand and aboveground forest carbon (C) in the Yalu community forest were

estimated at 127 m3

ha-1


plusmn 375 (SD) respectively In

the Gabensis community forest timber volume and forest C were estimated at 152 m3

ha-1


plusmn 506 (SD) respectively Analyses of field

interviews in communities in the two case study sites showed that community

sawmill local processing log export and carbon trade were the main options

preferred by the communities for the future management of their cutover forests

Scenario analyses using a planning tool showed that a management regime with a

short cutting cycle (10-20 years) a reduced cut proportion (50) at the initial harvest

iii




cutting cycles










CBFM throughout PNG






iv

DECLARATION






___________________

Cossey Keosai Yosi

July 2011

v

DEDICATION
































vi

PREFACE













James E Smith

















Research Canberra

vii

ACKNOWLEDGEMENTS















this study














assistance

viii


























TABLE OF CONTENTS


INTRODUCTION 1







MANAGEMENT 28



x





IN PNG 65



1) 74











xi









56 CONCLUSIONS 134







66 CONCLUSIONS 169






xii




CONCLUSIONS 187








REFERENCES 198

APPENDICES 219


LIST OF TABLES




a 100


-1) 101






xiv

LIST OF FIGURES








xv

LIST OF ACRONYMS




asl Above Sea Level

BA Basal Area


C Carbon





Resources





CO2 Carbon Dioxide







Melbourne



Government






xvi























m2 ha


m3 ha


mm annum-1








N ha-1




xvii



























UK United Kingdom








INTRODUCTION

2

CHAPTER 1






























3


































4

































5

































6




















to continue








7

13 BACKGROUND































8






























9






















-1 (Keenan et al

2005)


per











10










3 ha




timber














1980)







11













TRP












12














Forest Policy













the following






13













implementation





















14

















Production











15

1331 Forest Types










tropical forest























16



4800m asl
























2007 )




17


-




2008000m3 in 2007




0

50

100

150

200

250

300

00

05

10

15

20

25

30

35

40

Are

a H

arv

este

d (

00

0 h

a)

Harv

este

d T

imb

er V

olu

me

(Mil

lion

m3)

Year

Harvested

volume

Harvested area

0

500

1000

1500

2000

2500

2002 2003 2004 2005 2006 2007

Volu

me

(0

00

m3

)

Year

Logs

Sawn

Ply

Veneer

18

































19




















20


































21


timber















community hall



22





















(a)

(b)

23





McAlpine (1995)



Plots

Date of

Establishment

Forest Type

Gulf

Western

Oro

Milne Bay

Central

Turama

Vailala

Oriomo

Wawoi Guavi

Embi Hanau

Gara Modewa

Ormand Lako

Iva Inika

2

2

2

2

4

2

2

2

091194

271194

121094

261094

200594

120694

070894

160396

Lowland Foot Hills

Lowland Plain

W (Lowland Plain)

HmFswWsw (Lowland

FHills)

Pl (Lowland Plain)


Hs (Lowland Hill)


Morobe

Madang

East Sepik

Sandaun

Oomsis

Trans Watut

Umboi

Kui

Yema Gaiapa

North Coast

Rai Coast

Hawain

Pual

Krisa

2

2

2

2

1

2

2

2

2

2

260593

261093

151294

121194

150596

200395

060495

090894

240894

100994


LN (Lower Mountain)

Hl (Lowland Plain)

Hm (Lowland Hill)

Hm (Lowland Hill)

Hm9 (Lowland Hill)

Hm (Lowland Hill)

(Lowland Hill)


(Lowland Hill)

Southern

Highlands

MtGiluwe 2


West New

Britain

East New Britain

New Ireland

Manus

Kapiura

Mosa Leim

Kapuluk

Central Arawe

Anu Alimbit

Pasisi Manua

Open Bay

Gar

Waterfall Bay

Lassul Bay

Cape Orford

Inland Pomio

Kaut

Umbukul

Central NI

Lark

West Coast

2

2

2

2

2

2

2

2

2

2

2

1

2

2

2

2

2

230793

110893

300893

060595

200695

070795

180893

270793

290893

090695

270695

280795

230993

011093

021195

181095

290395

Hm (Lowland Hill)

Hm8 (Lowland Hill)

Hm (Lowland Hill)






(Lowland Foothills)

(Lowland Foothills)

(Lowland Foothills)

(Lowland Hill)



(Lowland Foothills)

(Lowland Foothills)

HmeHm6 (Lowland

Foothills)


24










W Woodland



Wsw Swamp woodland





disturbed)









disturbance

25

















NW NE

08 28 48 68 88

06 26 46 66 86

04 24 42 64 84

02 22 42 62 82

00 20 40 60 80

SW SE


Plot origin

where

measurement

starts

N

100 m

100 m

26

1362 PSP Locations










harvesting in PNG)

27

































28


communities



in PNG


forests in PNG






15 THESIS OUTLINE



















29


















province PNG






forests in PNG

27


28

CHAPTER 2


21 FOREST DYNAMICS

211 Introduction



highlighted





















29


(Kobayashi 1992)

















2151














30




discussed in detail


















disturbances











31














(Evan 1982)


















32










of exotic species










214 Forest Types













33




















forest













34

































35




















(47 mm)















36

































37


amount (Ng 1983)






























38














1998)














39

218 Shade Tolerance





























40

219 Stand Structure



























41














Terminalia spp



















42


































43






ha-1







-1
























trees

44

2111 Discussion































45

















46

2112 Conclusions







region
























47


221 Introduction





follow




















pointed out




48































49

222 Illegal Logging































50

















223 Deforestation
















51























of











52



224 Climate Change






























53














deforestation

2241 Kyoto Protocol


















54








of 8 Kt CO2 year-1



the project
























55










Imperata grasslands






operations



56















where they reside


















57


































58






226 Certification



























59


































60













227 Governance




















61




























62

228 Discussion












to achieving SFM




















63







229 Conclusions
















recognised










64











65
































66





























assessment






67



















68

















following











timber industry

69






























and social assets

70

234 Discussion





























assets

71

235 Conclusions












uncertainties





PNG

72


65

CHAPTER 3


3 1 INTRODUCTION















(FAO 2005













66




al 2009a)





















harvesting








et al 2000)

67
































68






2005)






















(Pokana 2002)

69












the harvested plots






70




























71






















(3-1)





(equation 3-3)

72

(3-2)

(3-3)
















(3-4)


-1) Shd = site









hd

73


























(3-5)



(m2 ha


residual trees

t = 134 + 0394 ln Go + 0346 ln t + 000275 Sh t -1

74















(3-6)





Nino drought





(3-7)



75

33 RESULTS



plusmn 100





2901 m2

ha-1





















76

(a)L

og

Sto

ckin

g (

ste

ms h

a-1

)

1

10

100

1000

0 - 5 years

5 - 10 years

10 - 15 years

15 - 20 years

Diameter Class (cm)

10-29 30-49 50-69 70-89 90+

Lo

g S

tockin

g (

ste

ms h

a-1

)

1

10

100

1000

(b)

(c)

Basal

Are

a (

m2 h

a-1

)

0

2

4

6

8

10

12

Diameter Class (cm)

10-29 30-49 50-69 70-89 90+

Basal

Are

a (

m2 h

a-1

)

0

2

4

6

8

10

12

(d)





un-harvested plots







77

Bas

al

Are

a (

m2 h

a-1

)

0

5

10

15

20

25

Sto

ck

ing

(ste

ms

ha

-1)

0

100

200

300

400 Commercial

NonCommercial


0-5 5-10 10-15 15-20

Res

idu

al

Tim

ber

Vo

lum

e (

m3 h

a-1

)

0

20

40

60

80

100

120

140

160

180

(a)

(b)

(c)




78












r2 = 007

p = 0016


0 5 10 15 20 25

Bas

al

Are

a (

m2 h

a-1

)

0

5

10

15

20

25

30

35




79




-1 year

-1 (SD 042) (Table 3-1) For the 21


-1 year

-1


-1 year

-1 (SD









(-172 plusmn 316) (Table 3-1)



Mean BAI (m2 ha

-1 year

-1)

a

Un-harvested 13

-172 plusmn 316




-058 plusmn 053

(All un-burned) 84b

017 plusmn 062)


drought 21

-067 plusmn 085

Total 118



80







the mean

r2 = 037

p = 0028

Mean TSH (years)

5 10 15 20

Ln

Mean

BA

I (m

2 h

a-1

year-1

00

02

04

06

08

10

12

14

16

18




81


Forest








r2 = 074

p = 0000


-1)

0 5 10 15 20 25 30

Ln

Mean

BA

I (m

2 h

a-1

year-1

)

-6

-4

-2

0

2

4











82

r2 = 024

p = 0087


0 1 2 3 4 5 6

Tim

be

r V

olu

me

gt2

0c

m D

BH

(m

3 h

a-1

)

0

50

100

150

200

250

300

r2 = 006

p = 0031


0 5 10 15 20

Tim

be

r V

olu

me

gt20

cm

DB

H (

m3

ha

-1)

0

50

100

150

200

250

300






(a)

(b)

83








-1 year

-1 plusmn 809


this study (r2

= 083 p = 0000) (Figure 3-8)

r2 = 083

p = 0000


0 5 10 15 20

Ln

Tim

be

r Y

ield

gt2

0c

m D

BH

(m

3 h

a-1

)

00

02

04

06

08

10

12

14

16





Nino Drought







84





fire

PlotID

CNIR

D01

CNIR

D02

IVAIN

01

IVAIN

02

KAPU

L01

KAPU

L02

LARK01

LARK02

WIM

AR01

WIM

AR02

Pe

rcen

tag

e B

A g

ain

ed

or

lost

()

0

10

20

30

40


BA lost due to fire














85

r2 = 016

p = 0069


0 5 10 15 20 25 30

Sh

an

no

n-W

ien

er

Ind

ex

(H

-1)

0

1

2

3

4

5




34 DISCUSSION

















86








about 30-32 m2

ha-1




















-1 year

-1)


-1 year

-1) overall



-1

year-1






87










Region

Primary Forest

Mean BA

(m2 ha

-1)

a

Harvested Forest

Mean BA (m2 ha

-1 )

Mean BAI

after harvesting

(m2 ha

-1 year

-1)

Source

PNG

2901

1735

017

Current study

PNGb

30 - 33

10 - 20

Kingston amp Nir

1988 Oavika 1992

Alder 1998

Sub tropical

Australia

515

12 - 58

032 ndash 075

Smith et al 2005

North

Queensland

Australia

3794 ndash 7342

2586 ndash 4160


South Indiac

393

348





South India










88
















-1


1998)








forests in PNG







089 year-1


in the same


89























Rogers (2001) study











90




35 CONCLUSIONS







-1 year



-1 year























utilisation

91

CHAPTER 4


41 INTRODUCTION









plots
















identified later



92
















42 BACKGROUND













-1 (VDT


93











94














95



for sampling

















44 DATA ANALYSIS




ha-


2

ha-1


(4-1)


stems ha-1

96


(4-2)


442 Timber Volume



(4-3)











(4-4)








97








debris (CWD)




AGLBlt10cm 10 10 10

FL 1 25 25

CWD 25 25 25







(4-5)







(4-6)

ge 10cm lt 10cm

98




45 RESULTS






2m2 ha






10-20 69 119

20-30 93 42


Forests 40-50 23 13

50-60 22 8

60-70 13 6

70-80 10 5

80-90 2 4

90-100 1 3

100+ 7 1

20-30 9 33

30-40 6 22


Forests 50-60 11 8

60-70 3 6

70-80 2 5

80-90 1 4

90-100 1 3

99







0

20

40

60

80

100

120

140

10-20 20-30 30-40 40-50 50-60 60-70 70-80 80-90 90-100 100+

No

of

Ste

ms

(N h

a-1

)

Diameter Class (cm)

Actual

Predicted

0

5

10

15

20

25

30

35

20-30 30-40 40-50 50-60 60-70 70-80 80-90 90-100

No

of

Ste

ms

(Nh

a)

Diameter Class (cm)

Actual

Predicted

100





each study site




Merch Vol (m3

ha-1

)

Total Merch

Vol (m3)


Celtis sp 68 179 39000



Intsia sp 14 168 37000

Vitex sp 14 119 26000





178 39000






Intsia sp 54 144 2100







101






-1)


Forest

Mean 1269 1519

SD 450 277








Forest

AGLBge10cm 11019 ( 2758) 11921 (3719)


FL 110 119

CWD 2755 2980

Total AGB 14985 ( 3751) 16212 (5058)

455 Sample Size







102



Mean SD CV

No of

Observation DF

E

() t-value n

Volume

(m2 ha

-1) 1269 450 035 17 16 10 1337 22

AGB

(MgC ha-1

) 14985 3751 025 17 16 10 1337 11


Volume

(m2 ha

-1) 1519 277 018 2 1 10 3078 31

AGB

(MgC ha-1

) 16212 5058 031 2 1 10 3078 92
















Total Forest Area

2200 ha

150 ha


28000 m3

2300 m3


1269 m3 ha

-1

1519 m3 ha

-1

Total Forest Carbon

329670 Mg C

24318 Mg C

Mean Forest Carbon

14985 Mg C ha-1

16212 Mg C ha-1

Estimated Emission


181319 Mg C

13375 Mg C

103

46 DISCUSSION






m3 ha


3 ha



30-40 m3 ha



) and primary








Mg C ha-1


The
















104


powered generation




























(Rogers 2010)






105





47 CONCLUSIONS







-1












106


TOOLS

107

CHAPTER 5


51 INTRODUCTION
























108




forests










scenarios for CBFM

52 BACKGROUND













109


PNG



















110

53 METHODOLOGY






developed




data


informed decision







developed




111































112


residual stand















harvesting)













113


























114















Total


Pre-harvest (m3ha) 210 270 430 90 100 120 50 50 70 1040

Cut fraction () 0 0 100 0 0 100 0 0 0

Post-harvest (m3ha) 210 270 00 90 100 00 50 50 70 490 60

YIELD (m3ha) 00 00 430 00 00 120 00 00 00 550

Ingrowth (m3yr) 028 028 028 008 008 008 000 000 000 07

Growth (m3yr) 000 000 000 000 000 000 000 000 000 00

DeathDamage (m3yr) 000 000 000 000 000 000 000 000 000

Upgrowth (m3yr) 028 028 008 008 000 000

Pre-harvest (m3ha) 210 270 139 90 100 39 50 50 70 667

Cut fraction () 0 0 100 0 0 100 0 0 0

Post-harvest (m3ha) 210 270 00 90 100 00 50 50 70 490 83

YIELD (m3ha) 00 00 139 00 00 39 00 00 00 177

Ingrowth (m3yr) 022 022 022 006 006 006 000 000 000 06

Growth (m3yr) 000 000 000 000 000 000 000 000 000 00

DeathDamage (m3yr) 000 000 000 000 000 000 000 000 000

Upgrowth (m3yr) 022 022 006 006 000 000

Pre-harvest (m3ha) 210 270 112 90 100 31 50 50 70 633

Cut fraction () 0 0 100 0 0 100 0 0 0

Post-harvest (m3ha) 210 270 00 90 100 00 50 50 70 490 85

YIELD (m3ha) 00 00 112 00 00 31 00 00 00 143

Ingrowth (m3yr) 021 021 021 006 006 006 000 000 000 05

Growth (m3yr) 000 000 000 000 000 000 000 000 000 00

DeathDamage (m3yr) 000 000 000 000 000 000 000 000 000

Upgrowth (m3yr) 021 021 006 006 000 000

Left after Harvest1

Cycle

Number

Left after Harvest

Left after Harvest


3

2




115







Yalu Area Data (ha)





Fragile 15


above

10


above

10





20

Regrowth area 15

Roading 10

Other

25

2nd

amp3rd


116


Diameter Class

(cm)

Volume MEP1

(m3 ha

-1)

Volume MEP2

(m3 ha

-1)

Others

(m3 ha

-1)

lt 20 0301 0307 7029

20-50 4950 6961 34991

50-65 6634 11885 18539

65+

Volume Growth

(m3 ha

-1 year

-1)

0-20 0117 0301 0203

20-50 0129 0124 0244

50-65 0041 0080 0073

65+ 0127








(Years)

Cut Proportion

()

Diameter

LimitMEP

Codes

Community

sawmill

10 50 gt 50cm

MEP1 MEP2

Local processing

20 50 gt 50cm

MEP1 MEP2


MEP1 MEP2

Medium-scale log

export

40 50 gt 50cm

MEP1 MEP2

117










(Years)

Cut Proportion

()

Diameter

LimitMEP Codes


MEP1 MEP2

Local processing

20 75 gt 50cm

MEP1 MEP2


MEP1 MEP2


MEP1 MEP2







(Years)

Cut Proportion

()

Diameter

LimitMEP Codes


MEP1 MEP2

Local processing

20 50 gt 65cm

MEP1 MEP2


MEP1 MEP2


MEP1 MEP2

118

54 RESULTS










119





Gabensis village










120












121



122











0

20

40

60

80

100

0

2

4

6

8

10

12

high

preference

low

preference

no

preference

not sure

Percen

tag

e (

)

Freq

uen

cy

(N

)


0

20

40

60

80

100

0

2

4

6

8

10

12

high

preference

low

preference

no

preference

not sure

Percen

tag

e (

)

Freq

uen

cy

(N

)


0

20

40

60

80

100

0

2

4

6

8

10

12

high

preference

low

preference

no

preference

not sure

Percen

tag

e (

)

Freq

uen

cy

(N

)


0

20

40

60

80

100

0

2

4

6

8

10

12

high

preference

low

preference

no

preference

not sure

Percen

tag

e (

)

Freq

uen

cy

(N

)


0

20

40

60

80

100

0

2

4

6

8

10

12

high

preference

low

preference

no

preference

not sure

Percen

tag

e (

)

Freq

uen

cy

(N

)


123


Scenarios








third cycles


Scenario

Cutting Cycle

(years)

Annual

Yield Cycle 1

(m3 year

-1)

Annual

Yield Cycle 2

(m3 year

-1)

Annual

Yield Cycle 3

(m3 year

-1)

Total

Yield

(m3)

Community

sawmill

10

3166

2865

2718

87490

Local

processing

20

1583

2100

2890

131500

Local

processing

30

1055

1846

3307

186060

Medium-scale

log export

40

792

1718

3780

251600









124


Scenario

Cutting

Cycle (years)

Annual Yield

Cycle 1 (m3)

Annual Yield

Cycle 2 (m3)

Annual Yield

Cycle 3 (m3)

Total

Yield

(m3)

Community

sawmill

10

4749

2316

1229

82940

Local

processing

20

2375

1743

1294

108240

Local

processing

30

1583

1551

1574

141240

Medium-scale

log export

40

1187

1456

1802

177800






-1 and 3000m

3 year







Scenario

DBH Limit

Species Grp

Annual

Yield Cycle 1

(m3 year

-1)

Annual

Yield Cycle 2

(m3 year

-1)

Annual

Yield Cycle 3

(m3 year

-1)

Total Yield

(m3)

Local

processing

50 gt 50cm

MEP 1 2

1583

2100

2890

131460

Local

processing

50 gt 65cm

MEP 1 2

623

703

805

42620

Local

processing

75 gt 50cm

MEP 1 2

2375

1743

1361

276463

Local

processing

75 gt 65cm

MEP 1 2

934

603

415

39040

125









increase in volume

















126



0

1

2

3

4

5

1 - 10 11 - 20 21 - 30

An

nu

al

Vo

lum

e (1

00

0 m

3 y

r-1

)


MEP-code2 65+

MEP-code2 50-65

MEP-code1 65+

MEP-code1 50-65

0

1

2

3

4

5

1 - 20 21 - 40 41 - 60

An

nu

al

Vo

lum

e (1

00

0 m

3 y

r-1

)


MEP-code2 65+

MEP-code2 50-65

MEP-code1 65+

MEP-code1 50-65

0

1

2

3

4

5

1 - 30 31 - 60 61 - 90

An

nu

al

Vo

lum

e (1

00

0 m

3 y

r-1

)


MEP-code2 65+

MEP-code2 50-65

MEP-code1 65+

MEP-code1 50-65

0

1

2

3

4

5

1 - 40 41 - 80 81 - 120

An

nu

al

Vo

lum

e (1

00

0 m

3 y

r-1

)


MEP-code2 65+

MEP-code2 50-65

MEP-code1 65+

MEP-code1 50-65

(b) (a)

(d) (c)

127



0

1

2

3

4

5

1 - 20 21 - 40 41 - 60

An

nu

al

Vo

lum

e (1

00

0 m

3 y

r-1

)


MEP-code2 65+

MEP-code2 50-65

MEP-code1 65+

MEP-code1 50-65

0

1

2

3

4

5

1 - 10 11 - 20 21 - 30

An

nu

al

Vo

lum

e (1

00

0 m

3 y

r-1

)


MEP-code2 65+

MEP-code2 50-65

MEP-code1 65+

MEP-code1 50-65

0

1

2

3

4

5

1 - 30 31 - 60 61 - 90

An

nu

al

Vo

lum

e (1

00

0 m

3 y

r-1

)


MEP-code2 65+

MEP-code2 50-65

MEP-code1 65+

MEP-code1 50-65

0

1

2

3

4

5

1 - 40 41 - 80 81 - 120

An

nu

al

Vo

lum

e (1

00

0 m

3 y

r-1

)


MEP-code2 65+

MEP-code2 50-65

MEP-code1 65+

MEP-code1 50-65

(a) (b)

(c) (d)

128




0

1

2

3

4

5

1 - 20 21 - 40 41 - 60

An

nu

al

Vo

lum

e (1

00

0 m

3 y

r-1

)


MEP-code2 65+

MEP-code2 50-65

MEP-code1 65+

MEP-code1 50-65

0

1

2

3

4

5

1 - 20 21 - 40 41 - 60

An

nu

al

Vo

lum

e (1

00

0 m

3 y

r-1

)


MEP-code2 65+

MEP-code1 65+

0

1

2

3

4

5

1 - 20 21 - 40 41 - 60

An

nu

al

Vo

lum

e (1

00

0 m

3 y

r-1

)


MEP-code2 65+

MEP-code2 50-65

MEP-code1 65+

MEP-code1 50-65

0

1

2

3

4

5

1 - 20 21 - 40 41 - 60

An

nu

al

Vo

lum

e (1

00

0 m

3 y

r-1

)


MEP-code2 65+

MEP-code1 65+

(a) (b)

(c) (d)

129


Cycle


m3 ha





-1 and


-1 at year 60


Cutting

Cycle

(Years)

Cut

Proportion

()

Diameter Limit

MEP Codes

Starting

Pre-Harvest

Volume

(m3 ha

-1)

Residual

Volume After

3rd

Cycle

(m3 ha

-1)

Annual

Yield

(m3 year

-1)

10 50 gt 50cm MEP1 amp 2 305 271 8750

20 50 gt 50cm MEP1 amp 2 305 577 6574

30 50 gt 50cm MEP1 amp 2 305 989 6208

40 50 gt 50cm MEP1 amp 2 305 1508 6290

50 50 gt 50cm MEP1 amp 2 305 2132 6550

60 50 gt 50cm MEP1 amp 2 305 2861 6899



ha-1

and





130





6208 m3 year




0

50

100

150

200

250

300

350

10 20 30 40 50 60

Re

sid

ual

Vo

lum

e (

m3

ha-1

)


0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

10 20 30 40 50 60

An

nu

al V

olu

me

(m

3Y

ear

-1)


131

55 DISCUSSION























2000m3 year







1000m3 year


3 year

-1 in the

132


5-6c)






cycles (Table 5-6d)










-1)























133







250000 m3 (Table 5-6)



Annual

Yield


-1)

10 50

gt 50 cm MEP

1amp2 8750

20 50

gt 50 cm MEP

1amp3 6574

30 50

gt 50 cm MEP

1amp4 6208

40 50

gt 50 cm MEP

1amp5 6290
















134




-1 are considered



56 CONCLUSIONS







harvest



















135


















136

CHAPTER 6


61 INTRODUCTION



























137

































138









best decisions






63 METHODOLOGY













139










management in PNG






















140


branch

633 Terminal Values





























141






(6-1)










alternative














142



















EMV

Spider

Charts

Tornado

Charts

Decision Tree

Model

Decision

Alternatives

Uncertain

Events

Cash Flow

Model

Sensitivity

Data

Decision

Analyses

Sensitivity

Analyses

Payoff

Strategy

143








EMV at 2nd

node = (06 x -59850) + (04 x -63850) = PNGK-61450

EMV at 3rd

node = (06 x -61450) + (04 x -76350) = PNGK-67410












low











EMV at 2nd


144
















EMV at 2nd


















145
















for one year


(Figure 6-9)


EMV at 2nd














146









64 RESULTS






Input Description

Variation

(10) Variable range


Lucas mill (PNGK)5 85000 8500 76500 85000 93500

Chainsaw (PNGK) 6000 600 5400 6000 6600







(PNGKm3) 500 50 450 500 550





(m3annum) 20 2 18 20 22


months) 16 16 144 16 176


147




(Figure 6-2)















charts

148



High Price (PNGK)

-64850


-8850 -66050 04

Low Price

-67850

12000 -67850

06

High Price

06 -59850


2

20000 -61450 -8850 -61450 04

Low Price

-63850

16000 -63850

06

High Price

-63950



Low Price

-91000 -67410 -65950

10000 -65950

06

High Price

-75950


-4950 -76350 04

2 04 Low Price


1 9000 -76950

10000 -76350

Comm Use

-81000

0 -81000

No Harvest

0

0 0

149







Input Description

Variation

(10) Variable range


Lucas mill (PNGK) 85000 8500 76500 85000 93500

Chainsaw (PNGK) 6000 600 5400 6000 6600















Fumigation (PNGK) 720 72 648 720 792




(PNGKm3) 700 70 630 700 770


(PNGKm3) 2400 240 2160 2400 2640


(PNGKm3) 1500 150 1350 1500 1650




150







151



CertMarket HP

199800


-69200 127800 04

Non-CertMarket LP

06 19800


1

480000 127800 06

ST High Price

-124450


-53450 -132450 04

ST Low Price

-144450


-691000 31800 06

CertMarket HP

-40200


-6920000 -112200 04

Non-CertMarket LP

04 -220200

ST-Demand Low 300000 -220200

1

240000 -112200 06

ST High Price

-364450


-5345000 -372450 04

ST Low Price

-384450

120000 -384450

1

31800 06

CertMarket HP

-474938


-24494 -654938 04

Non-CertMarket LP

06 -924938


1

120000 -654938 06

ST High Price

-120494


-12494 -122494 04

ST Low Price

-125494


-263000 -894938 06

CertMarket HP

-107494


-2449375 -125494 04

Non-CertMarket LP

04 -152494


1

60000 -125494 06

ST High Price

-180494


-1249375 -182494 04

ST Low Price

-185494

30000 -185494

152







chart (Figure 6-4)

153


180

2160

286000

178200

1350

110000

1080

93500

55000

88

22000

2805

55

6600

1452

55

220

2640

234000

145800

1650

90000

1320

76500

45000

72

18000

2295

45

5400

1188

45

-60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60 70 80 90 100110120








Lucas mill (PNGK)






Chainsaw (PNGK)





154








-60000

-50000

-40000

-30000

-20000

-10000

0

10000

20000

30000

40000

50000

60000

70000

80000

90000

100000

110000

120000

86 90 94 98 102 106 110

EMV

(PN

GK

+-

10

B

ase

Cas

e)










Lucas mill (PNGK)


155






Input Description

Variation

(10) Variable range

Abs var -var

base

case +var

Chainsaw (PNGK) 6000 600 5400 6000 6600








(PNGm3) 84 84 756 84 924


Dozer D6 - CMU (PNGK) 200000 20000 180000 200000 220000





(PNGm3) 105 105 945 105 1155















156









157




4359317

Asia Market 3000000 4359317

-798683 4159317 04

Log Price Low

06 3859317


1

3000000 4159317 06

Log Price High

3609317

Other Market 2250000 3609317

-798683 3509317 04

Log Price Low

3359317


-842000 3959317 06

Log Price High

3859317

Asia Market 3000000 3859317

-798683 3659317 04

Log Price Low

04 3359317


1

2500000 3659317 06

Log Price High

3109317

Other Market 2250000 3109317

-798683 3009317 04

Log Price Low

2859317

2000000 2859317

1

3959317 06

Log Price High

2187692

Asia Market 1500000 2187692

-338308 2087692 04

Log Price Low

06 1937692


1

1500000 2087692 06

Log Price High

1812692

Other Market 1125000 1812692

-338308 1762692 04

Log Price Low

1687692


-474000 1987692 06

Log Price High

1937692

Asia Market 1500000 1937692

-338308 1837692 04

Log Price Low

04 1687692


1

1250000 1837692 06

Log Price High

1562692

Other Market 1125000 1562692

-338308 1512692 04

Log Price Low

1437692

1000000 1437692

158








159


4500

540

2805

198

1155

44000

11

11

176

1045

363

275

5400

108000

145800

145800

225

1575

1296

756

135000

180000

216000

216000

5400

135

405

2250

5500

660

2295

162

945

36000

9

9

144

855

297

225

6600

132000

178200

178200

275

1925

1584

924

165000

220000

264000

264000

6600

165

495

2750

33000003400000350000036000003700000380000039000004000000410000042000004300000440000045000004600000









No of fortnights




Chainsaw (PNGK)

















160












market

3300000

3400000

3500000

3600000

3700000

3800000

3900000

4000000

4100000

4200000

4300000

4400000

4500000

4600000

860 880 900 920 940 960 980 1000 1020 1040 1060 1080 1100 1120

EMV

(PN

GK

+-

10

B

ase

cas

e)










No of fortnights




Chainsaw (PNGK)
















161


Input Description

Variation

(10) Variable range



(PNGK) 30000 3000 27000 30000 33000





GISMapping (PNGK) 20000 2000 18000 20000 22000












Cha) 150 15 135 150 165




months) 16 16 144 16 176






market (PNGK17500)

162










High Price (PNGK)

79781735


-17500 76321135 04

Low Price

06 71130235

High Demand 31278500 71130235

1

39930000 76321135 06

High Price

69799235


-17500 67203785 04

Low Price

63310610

Carbon Trade 23458875 63310610

-60765 72860535 06

High Price

71130235


-17500 67669635 04

Low Price

04 62478735

Low Demand 31278500 62478735

1

1 31278500 67669635 06

72860535 High Price

61147735


-17500 58552285 04

Low Price

54659110

23458875 54659110

Do Nothing

0

0 0

163





increase by 10

135

1980

18

50

33000

22000

11000

22000

176

1925

11000

11000

11000

275

2200

935

77

715

385

135

165

2420

22

61

27000

18000

9000

18000

144

1575

9000

9000

9000

225

1800

765

63

585

315

165

47000004800000490000050000005100000520000053000005400000550000056000005700000580000059000006000000






GISMapping (PNGK)





Consultancy (PNGK)







Compass (Kcompass)




164


65 DISCUSSION















4700000

4800000

4900000

5000000

5100000

5200000

5300000

5400000

5500000

5600000

5700000

5800000

5900000

6000000

880 900 920 940 960 980 1000 1020 1040 1060 1080 1100 1120

EMV

(PN

GK

+-

10

B

ase

Cas

e)







GISMapping (PNGK)





Consultancy (PNGK)







Compass (Kcompass)



165







time of felling









trees ha-1










certified markets










166










(EMV)

























167













in the Yalu




















in a REDD+ project

168
















Scenarios

Starting

Capital

(PNGK)

Annual

EMVProfitLoss

(PNGK)


Local Processing



Log Export

CMU Managed 842000 3959317


Carbon Trade 60765c

72860535




169

66 CONCLUSIONS

























region

170

CHAPTER 7


71 INTRODUCTION


















in CBFM






171



72 BACKGROUND



























172








MSE method














173











174












175


Planning Level

Inventory Planning

Requirement





PNGFA



same as above


done but not a


Forest Inventory

Provincial Plans




Forest Management

Agreement Project


tender)






5 Year Working Plan
















Company As above

Annual Logging Plan


PMCP

1 Company As above


(harvesting plan)





be harvested Some




176



















donors












177


operations











STRENGTHS

WEAKNESSES













products


certified products







managed forest








guidelines







mechanisms



countries

178


































179






species refugia)














180






area


process




community needs





training











sufficient size


area









timber species







within 2 years








survey completed





working forest area



in place




181

73 METHODOLOGY














the pilot region







further analyses







scenarios

182

733 Planning System
























183

74 RESULTS




















interests











184


75 DISCUSSION















Stakeholder

Consultation

Field Interviews

PAR

Investigate

Options

Forest Inventory

Data

Planning System

Growth Data

Decision

Analyses Tools

Spreadsheet

Planning Tool

Decision Tree

Model

Annual Yield

Estimates

Management

Options

Payoff

Strategy

Decision

Alternatives

Uncertain

Events

EMV

Tornado

Chart

Spider Chart

Sensitivity

Analyses

Scenario

Evaluation amp

Analyses

Decision

Implementation

Scenario

Output

Feedback to

Stakeholders

185
































186

76 CONCLUSIONS


















187

CONCLUSIONS

188

CHAPTER 8


81 INTRODUCTION




















forests in PNG





189
























as follow


in PNG





190






communities


























191



























past four years

192


STUDY






























193





in CBFM



























194


















forests in PNG














195













PNG

88 DISCUSSION

















196


















89 CONCLUSIONS







are recommended







197








198

REFERENCES






1623-1648



91PNGFRI



Lae




Forestry Review 4










(ed) London UK DFID


McGraw Hill Inc USA




199


















Ecology 6 1-32







PNG JICA PNGFRI



56 985-998







200











240-252




Biology 23 1406-1417






997











Australia

201






6 1104-1123



Pottinger A



Oxford UK









Publishing












II) 210pp

202



















Rome Pp168-173


and Clarendon Press














203








Science 66 813-825



Working Document



I Jakarta Indonesia





410-419






Research










204


































205






Bangor UK















24 Yokohama Japan














206






41 555ndash562





















Institute Lae



Morobe Province



Document No 9

207






JICA-PNGFRI




























396

208


















36 3053-3062






2005






253-263




Organisations

209



Francisco CA










(CIFOR)






Nature 403 853-858












(CIFOR)

210






























15 221-232




University Press

211








105 107-119













Hohola May 1996









2005





212



University of Wales








875





Cengage Learning






Publications





ICES 2006 R 05






213

























PNG








214








Guinea








Ecology 15 620-626






967-979






131-137


19 357-371




215














Melbourne CSIRO












10 June 2009







Review 67 53-64

216





Technology






Papua New Guinea





Island Press







Melbourne CSIRO


Basil Blackwell Ltd




Press





217









145-155

























218



April 2008]



1999












905

219

APPENDICES


Forest Condition

No of Plots

Un-harvested 13





Total 118


PLOTNO PLOTID

FIRST

CENS

LAST

CENS

BA_FIRST

CENS

(m2ha

-1)

BA_LAST

CENS

(m2ha

-1)

1 DANAR03 2006

208470 No data

2 DANAR04 2006

77838 No data

3 HUVIV02 1999

253617 No data

4 KAUP_03 1998 2000 242586 216303

5 MARE_03 2001

237487 No data

6 SAGAR03 1998 2005 321673 332807

7 SASER03 2005

248061 No data

8 SASER04 2005

293279 No data

9 SOGER03 1998 2003 217693 239859

10 WATUT05 1997 1999 338812 253121

11 WATUT06 1997 1999 441607 286389

12 WCOST05 1998 2001 336952 344092

13 WCOST06 1998 2001 314374 328569

220


INCREASING BA


FIRST

CENS

LAST

CENS

BA_FIRST

CENS

(m2ha

-1)

BA_LAST

CENS

(m2ha

-1)

MBAI

(m2ha

-1yr

-

1)

1 ANUAL01 1993 1995 1999 168828 179791 02741

2 ANUAL02 1993 1995 1999 209696 214081 01096

3 ARI__01 1995 1996 2003 118680 164226 06506

4 ARI__02 1995 1996 2003 112410 134710 03186

5 CARAW01 1991 1995 2004 194671 221647 02997

6 CARAW02 1991 1995 2004 188221 212092 02652

7 CFORD01 1994 1995 2004 302147 340191 04227

8 EMBIH01 1992 1994 1999 130070 135086 01003

9 EMBIH02 1992 1994 1999 95760 103879 01624

10 EMBIH03 1993 1994 1999 138590 159763 04235

11 EMBIH04 1993 1994 1999 125500 164194 07739

12 GAR__01 1991 1993 1999 150426 172383 03660

13 GAR__02 1991 1993 1999 142926 165673 03791

14 GARAM01 1991 1994 2000 201981 221105 03187

15 GILUW01 1987 1993 2003 125896 137937 01204

16 GILUW02 1991 1994 2003 198455 199718 00140

17 HAWAN01 1993 1994 2002 130935 171417 05060

18 HAWAN02 1994 1994 2002 133950 168687 04342

19 KAPIU01 1991 1993 1997 130361 226460 24025

20 KAPIU02 1991 1993 2003 116672 282623 16595

21 KAUP_01 1996 1996 2000 195241 198719 00869

22 KAUP_02 1996 1996 2000 223736 229669 01483

23 KRISA01 1991 1994 1996 164044 174124 05040

24 KRISA02 1991 1994 1996 231445 239709 04132

25 KUI__01 1994 1994 2002 180250 204151 02988

26 LARK_03 1994 1996 1999 186482 186841 00120

27 MALAM01 1995 1995 2000 165864 219264 10680

28 MOKOL01 1980 1993 2004 243010 291990 04453

29 MOKOL02 1981 1993 2004 218361 242578 02202

30 MORER01 1997 1997 1999 161786 170147 04180

31 MOSAL01 1992 1993 2003 124213 199976 07576

32 MOSAL02 1992 1993 1997 119561 196195 19159

33 MUSAU01 1996 1996 1999 170058 174021 01321

34 MUSAU02 1995 1996 1999 170392 178642 02750

35 PASMA01 1993 1997 2004 172060 214776 04746

36 PASMA02 1993 1997 1999 195182 206363 05591

37 PUAL_01 1993 1994 2000 191461 191960 00083

38 PUAL_02 1994 1994 2000 151644 175568 03987

221


FIRST

CENS

LAST

CENS

BA_FIRST

CENS

(m2ha

-1)

BA_LAST

CENS

(m2ha

-1)

MBAI

(m2ha

-1yr

-1)

39 PUAL_03 1996 1996 1998 165854 175962 05054

40 PUAL_04 1996 1996 2004 172923 186604 01710

41 PULIE02 1997 1997 2004 109713 118248 01219

42 PULIE03 1997 1997 1999 198100 204913 03406

43 SAGAR01 1997 1998 2005 141514 153152 01663

44 SEMBE01 1996 1997 1999 134691 137005 01157

45 SERA_02 1996 1996 1998 174719 178179 01730

46 TURAM01 1994 1994 1998 245674 256188 02629

47 UMBOI01 1993 1994 2004 219117 245082 02597

48 UMBOI02 1993 1994 2001 174360 198924 03509

49 UMBUK01 1993 1993 2007 132607 163482 02205

50 UMBUK02 1993 1993 1999 107566 121284 02286

51 VAILA01 1993 1994 2002 146811 190990 05522

52 VAILA02 1993 1994 2002 175963 188018 01507

53 WASAP01 1986 1990 2003 184658 285293 07741

54 WASAP02 1987 1995 2003 131157 165941 04348

55 WATUT01 1992 1993 2003 139136 202128 06299

56 WATUT02 1992 1993 1998 138267 149267 02200

57 WAWOI01 1991 1994 1998 234345 256670 05581

58 WCOST03 1996 1996 2003 154697 189326 04947

59 WCOST04 1996 1996 2003 103386 104722 00191

60 WFBAY02 1981 1993 1999 182790 183297 00085

61 YALU_01 1995 1995 2007 126460 233236 08898

62 YALU_02 1995 1995 2007 162517 197775 02938

63 YEMA_01 1995 1996 2002 183911 201508 02933

222


FALLING BA


FIRST

CENS

LAST

CENS

BA_FIRST

CENS

(m2ha

-1)

BA_LAST

CENS

(m2ha

-1)

MBAI

(m2ha

-1yr

-1)

1 CFORD02 1995 1995 2004 1651825 1580870 -007883

2 GARAM02 1991 1994 1998 1806829 1620510 -031054

3 INPOM01 1993 1995 1997 1942872 1707170 -117852

4 KUI_02 1994 1994 2002 1561649 1478340 -010413

5 LARK_04 1994 1996 1999 1609460 1592510 -005649

6 MALAM02 1995 1995 2003 1959570 1434840 -065591

7 MORER02 1997 1997 1999 1443625 1390560 -026533

8 ORLAK01 1994 1994 2000 1891138 993640 -149582

9 ORLAK02 1994 1994 1994 1674760 1085680 -098180

10 PULIE01 1997 1997 2004 1807768 1076690 -104440

11 SAGAR02 1997 1998 2005 1735408 1716280 -002732

12 SEMBE02 1996 1997 1999 945672 888900 -028387

13 SERA_01 1996 1996 2000 2129906 2107070 -005708

14 TURAM02 1994 1994 1997 2540949 2561880 -010010

15 TURAM03 1996 1997 1999 1582846 1481270 -050786

16 VUDAL01 1997 1997 1999 762256 705470 -028393

17 VUDAL02 1996 1997 1999 1215035 1070640 -072196

18 WAWOI02 1994 1994 2000 2325639 1142410 -197204

19 WCOST01 1989 1995 1999 1202939 907100 -073959

20 WCOST02 1989 1995 1999 2470524 2172310 -074554

21 WFBAY01 1980 1993 1999 1720145 1404070 -052680

223



FIRST

CENS

LAST

CENS

BA_FIRST

CENS

(m2ha

-1)

BA_LAST

CENS

(m2ha

-1)

MBAI

(m2ha

-1yr

-

1)

1 CNIRD01 1994 1995 2004 236627 71393 -18359

2 CNIRD02 1994 1995 2007 230366 35539 -16236

3 HUVIV01 1997 1997


4 IVAIN01 1995 1996 2003 163578 58564 -15002

5 IVAIN02 1995 1996 2003 99191 49083 -07158

6 IVAIN03 1995 1996 1998 130492 119804 -05344

7 IVAIN04 1995 1996 1998 168716 129575 -19570

8 KAPUL01 1993 1993 1999 146181 96334 -08308

9 KAPUL02 1993 1993 2003 117906 26473 -09143

10 KAUT_01 1993 1993 1997 129425 146797 04343

11 KAUT_02 1993 1993 1997 122872 124960 00522

12 LARK_01 1994 1995 1999 236381 191211 -11292

13 LARK_02 1994 1995 1999 214359 236409 05513

14 MAUBU01 1995 1996


15 MAUBU02 1995 1996


16 OOMSI01 1979 1993 1997 209554 221536 02996

17 OOMSI02 1980 1993 1997 189978 211015 05259

18 SOGER01 1996 1996


19 SOGER02 1996 1996


20 WIMAR01 1993 1994 2000 185575 170570 -02501

21 WIMAR02 1993 1994 2000 230218 160777 -11574


BA BA

BA

gained BA BA

BA lost

After

Pre-

1997 1997

Meas

Period

Before

Fire 1997

Post-

1997

Meas

Period Fire

PLOTID

(m2ha

-

1)

(m2ha

-

1) (years) ()

(m2ha

-

1)

(m2ha

-

1) (years) ()

CNIRD01 2366 2443 2 163 2443 714 7 1612

CNIRD02 2304 2355 2 023 2355 355 10 1723

IVAIN01 1636 1680 1 269 1680 586 6 1611

IVAIN02 992 993 1 009 993 491 6 1108

KAPUL01 1462 1736 4 506 1736 963 2 2550

KAPUL02 1180 1299 4 264 1299 265 6 2328

LARK01 1961 2364 2 891 2364 1912 2 104

LARK02 2144 2231 2 205 2231 2364 2 317

WIMAR01 1856 1924 3 124 1924 1706 3 394

WIMAR02 2264 2302 3 056 2302 1608 3 1078

224


AREA


Tree


1 484643 9268927 4072009 1 PTE IND 13 18

Secondary

Forest - Yalu

1 484643 9268927 4072009 2 TRE 13 27

Secondary

Forest - Yalu

1 484643 9268927 4072009 3 HIB 13 29

Secondary

Forest - Yalu

1 484643 9268927 4072009 4 MAC 13 17

Secondary

Forest - Yalu

1 484643 9268927 4072009 5 HIB 13 335

Secondary

Forest - Yalu

1 484643 9268927 4072009 6 13 30

Secondary

Forest - Yalu

1 484643 9268927 4072009 7 13 30

Secondary

Forest - Yalu

1 484643 9268927 4072009 8 PTE IND 13 51

Secondary

Forest - Yalu

1 484643 9268927 4072009 9 TRE 13 33

Secondary

Forest - Yalu

1 484643 9268927 4072009 10 13 20

Secondary

Forest - Yalu

1 484643 9268927 4072009 11 POM PIN 13 245

Secondary

Forest - Yalu

1 484643 9268927 4072009 12 13 40

Secondary

Forest - Yalu

1 484643 9268927 4072009 13 13 30

Secondary

Forest - Yalu

1 484643 9268927 4072009 14 HIB 13 39

Secondary

Forest - Yalu

1 484643 9268927 4072009 15 TRE 13 225

Secondary

Forest - Yalu

1 484643 9268927 4072009 16 TER 13 26

Secondary

Forest - Yalu

2 484713 9268265 4072009 1 AIL 2 88

Primary Forest

- Yalu

2 484713 9268265 5072009 2 MYR 13 22

Primary Forest

- Yalu

2 484713 9268265 6072009 3 CEL PHI 13 175

Primary Forest

- Yalu

2 484713 9268265 7072009 4 STE 13 60

Primary Forest

- Yalu

225


Tree


2 484713 9268265 8072009 5 CEL LAT 13 335

Primary Forest

- Yalu

2 484713 9268265 9072009 6 VIT 2 95

Primary Forest

- Yalu

2 484713 9268265 10072009 7 POM TOM 13 123

Primary Forest

- Yalu

2 484713 9268265 11072009 8 CHN 13 18

Primary Forest

- Yalu

2 484713 9268265 12072009 9 MYR 13 129

Primary Forest

- Yalu

2 484713 9268265 13072009 10 NEU 13 225

Primary Forest

- Yalu

2 484713 9268265 14072009 11 PTE IND 13 47

Primary Forest

- Yalu

2 484713 9268265 15072009 12 POM PIN 13 48

Primary Forest

- Yalu

2 484713 9268265 16072009 13 LIT 2 29

Primary Forest

- Yalu

2 484713 9268265 17072009 14 PIM AMB 13 27

Primary Forest

- Yalu

2 484713 9268265 18072009 15 LIT 2 435

Primary Forest

- Yalu

2 484713 9268265 19072009 16 MYR 13 42

Primary Forest

- Yalu

2 484713 9268265 20072009 17 CEL PHI 3 73

Primary Forest

- Yalu

2 484713 9268265 21072009 18 CEL PHI 2 40

Primary Forest

- Yalu

3 484634 9268819 17062009 1 TRH 13 365

Secondary

Forest - Yalu

3 484634 9268819 17062009 2 TRH 13 359

Secondary

Forest - Yalu

3 484634 9268819 17062009 3 SEM 13 110

Secondary

Forest - Yalu

3 484634 9268819 17062009 4 TER 13 600

Secondary

Forest - Yalu

3 484634 9268819 17062009 5 STE 13 253

Secondary

Forest - Yalu

3 484634 9268819 17062009 6 POM PIN 13 570

Secondary

Forest - Yalu

3 484634 9268819 17062009 7 TER 13 630

Secondary

Forest - Yalu

3 484634 9268819 17062009 8 HIB 13 435

Secondary

Forest - Yalu

226


Tree


3 484634 9268819 17062009 9 INO FAG 13 600

Secondary

Forest - Yalu

3 484634 9268819 17062009 10 BUC 13 230

Secondary

Forest - Yalu

3 484634 9268819 17062009 11 TRH 13 313

Secondary

Forest - Yalu

3 484634 9268819 17062009 12 PIS UMB 13 220

Secondary

Forest - Yalu

3 484634 9268819 17062009 13 PTE IND 13 120

Secondary

Forest - Yalu

4 484630 9268763 17062009 1 POM PIN 13 280

Secondary

Forest - Yalu

4 484630 9268763 17062009 2 POM PIN 13 359

Secondary

Forest - Yalu

4 484630 9268763 17062009 3 END 13 370

Secondary

Forest - Yalu

4 484630 9268763 17062009 4 13 300

Secondary

Forest - Yalu

4 484630 9268763 17062009 5 MAC 13 225

Secondary

Forest - Yalu

4 484630 9268763 17062009 6 TOO SUR 13 325

Secondary

Forest - Yalu

4 484630 9268763 17062009 7 TOO SUR 13 305

Secondary

Forest - Yalu

4 484630 9268763 17062009 8 MAC 13 230

Secondary

Forest - Yalu

4 484630 9268763 17062009 9 PTE IND 13 220

Secondary

Forest - Yalu

4 484630 9268763 17062009 10 PTE IND 13 239

Secondary

Forest - Yalu

4 484630 9268763 17062009 11 TRH 13 235

Secondary

Forest - Yalu

4 484630 9268763 17062009 12 VIT 13 163

Secondary

Forest - Yalu

4 484630 9268763 17062009 13 SEM 13 128

Secondary

Forest - Yalu

4 484630 9268763 17062009 14 TRI 13 306

Secondary

Forest - Yalu

4 484630 9268763 17062009 15 TRI 13 284

Secondary

Forest - Yalu

4 484630 9268763 17062009 16 POM PIN 13 250

Secondary

Forest - Yalu

5 484646 9268686 17062009 1 TIM 13 143

Secondary

Forest - Yalu

227


Tree


5 484646 9268686 17062009 2 GUI 13 129

Secondary

Forest - Yalu

5 484646 9268686 17062009 3 PTE IND 13 130

Secondary

Forest - Yalu

5 484646 9268686 17062009 4 PTE IND 13 253

Secondary

Forest - Yalu

5 484646 9268686 17062009 5 FIC 13 335

Secondary

Forest - Yalu

5 484646 9268686 17062009 6 TRI 13 286

Secondary

Forest - Yalu

5 484646 9268686 17062009 7 FIC 13 278

Secondary

Forest - Yalu

5 484646 9268686 17062009 8 PTE IND 13 253

Secondary

Forest - Yalu

5 484646 9268686 17062009 9 TRH 13 411

Secondary

Forest - Yalu

5 484646 9268686 17062009 10 ELA 13 583

Secondary

Forest - Yalu

5 484646 9268686 17062009 11 STE 13 272

Secondary

Forest - Yalu

5 484646 9268686 17062009 12 ART 13 301

Secondary

Forest - Yalu

5 484646 9268686 17062009 13 PTE IND 13 204

Secondary

Forest - Yalu

5 484646 9268686 17062009 14 PTE IND 13 153

Secondary

Forest - Yalu

5 484646 9268686 17062009 15 SEM 13 95

Secondary

Forest - Yalu

5 484646 9268686 17062009 16 SEM 13 118

Secondary

Forest - Yalu

5 484646 9268686 17062009 17 TRI 13 275

Secondary

Forest - Yalu

5 484646 9268686 17062009 18 TRH 13 258

Secondary

Forest - Yalu

5 484646 9268686 17062009 19 TRH 13 250

Secondary

Forest - Yalu

5 484646 9268686 17062009 20 TRH 13 328

Secondary

Forest - Yalu

5 484646 9268686 17062009 21 TIM 13 288

Secondary

Forest - Yalu

6 _ _ 17062009 1 TRH 13 167

Secondary

Forest - Yalu

6 _ _ 17062009 2 PTE IND 13 152

Secondary

Forest - Yalu

228


Tree


6 _ _ 17062009 3 PTE IND 13 192

Secondary

Forest - Yalu

6 _ _ 17062009 4 PTE IND 13 158

Secondary

Forest - Yalu

6 _ _ 17062009 5 FIC 13 506

Secondary

Forest - Yalu

6 _ _ 17062009 6 TIM 13 218

Secondary

Forest - Yalu

6 _ _ 17062009 7 STR 13 101

Secondary

Forest - Yalu

6 _ _ 17062009 8 LIT 13 249

Secondary

Forest - Yalu

6 _ _ 17062009 9 MAC 13 264

Secondary

Forest - Yalu

6 _ _ 17062009 10 FIC 13 275

Secondary

Forest - Yalu

6 _ _ 17062009 11 PTE IND 13 350

Secondary

Forest - Yalu

6 _ _ 17062009 12 DYS 13 183

Secondary

Forest - Yalu

6 _ _ 17062009 13 TRH 13 235

Secondary

Forest - Yalu

6 _ _ 17062009 14 TRH 13 266

Secondary

Forest - Yalu

6 _ _ 17062009 15 ART 13 212

Secondary

Forest - Yalu

6 _ _ 17062009 16 TRI 13 260

Secondary

Forest - Yalu

6 _ _ 17062009 17 TRI 13 117

Secondary

Forest - Yalu

7 484761 9268629 17062009 1 TIM 13 159

Secondary

Forest - Yalu

7 484761 9268629 17062009 2 TIM 13 156

Secondary

Forest - Yalu

7 484761 9268629 17062009 3 EUO 13 351

Secondary

Forest - Yalu

7 484761 9268629 17062009 4 TRH 13 215

Secondary

Forest - Yalu

7 484761 9268629 17062009 5 TRH 13 336

Secondary

Forest - Yalu

7 484761 9268629 17062009 6 PTE IND 13 305

Secondary

Forest - Yalu

7 484761 9268629 17062009 7 POM PIN 13 284

Secondary

Forest - Yalu

229


Tree


7 484761 9268629 17062009 8 INT 13 256

Secondary

Forest - Yalu

7 484761 9268629 17062009 9 ANT CHI 13 172

Secondary

Forest - Yalu

7 484761 9268629 17062009 10 MYR 13 142

Secondary

Forest - Yalu

7 484761 9268629 17062009 11 TIM 13 226

Secondary

Forest - Yalu

7 484761 9268629 17062009 12 13 470

Secondary

Forest - Yalu

7 484761 9268629 17062009 13 ART 13 313

Secondary

Forest - Yalu

7 484761 9268629 17062009 14 VIT COF 13 241

Secondary

Forest - Yalu

7 484761 9268629 17062009 15 PTE IND 13 198

Secondary

Forest - Yalu

7 484761 9268629 17062009 16 MAC 13 398

Secondary

Forest - Yalu

7 484761 9268629 17062009 17 MAC 13 214

Secondary

Forest - Yalu

7 484761 9268629 17062009 18 MAC 13 190

Secondary

Forest - Yalu

7 484761 9268629 17062009 19 GUI 13 244

Secondary

Forest - Yalu

7 484761 9268629 17062009 20 TIM 13 247

Secondary

Forest - Yalu

7 484761 9268629 17062009 21 SEM 13 142

Secondary

Forest - Yalu

7 484761 9268629 17062009 22 SEM 13 156

Secondary

Forest - Yalu

7 484761 9268629 17062009 23 SEM 13 163

Secondary

Forest - Yalu

7 484761 9268629 17062009 24 PTE IND 13 316

Secondary

Forest - Yalu

7 484761 9268629 17062009 25 ANT CHI 13 251

Secondary

Forest - Yalu

7 484761 9268629 17062009 26 ANT CHI 13 210

Secondary

Forest - Yalu

7 484761 9268629 17062009 27 TIM 13 266

Secondary

Forest - Yalu

7 484761 9268629 17062009 28 TIM 13 151

Secondary

Forest - Yalu

8 484610 9268470 17062009 1 TRH 13 260

Secondary

Forest - Yalu

230


Tree


8 484610 9268470 17062009 2 EUO 13 142

Secondary

Forest - Yalu

8 484610 9268470 17062009 3 EUO 13 118

Secondary

Forest - Yalu

8 484610 9268470 17062009 4 TIM 13 211

Secondary

Forest - Yalu

8 484610 9268470 17062009 5 PTE IND 13 294

Secondary

Forest - Yalu

8 484610 9268470 17062009 6 HIB 13 792

Secondary

Forest - Yalu

8 484610 9268470 17062009 7 TRH 13 411

Secondary

Forest - Yalu

8 484610 9268470 17062009 8 ART 13 1135

Secondary

Forest - Yalu

8 484610 9268470 17062009 9 PTE IND 13 198

Secondary

Forest - Yalu

8 484610 9268470 17062009 10 TRH 13 520

Secondary

Forest - Yalu

8 484610 9268470 17062009 11 MAC 13 233

Secondary

Forest - Yalu

8 484610 9268470 17062009 12 POL 13 261

Secondary

Forest - Yalu

8 484610 9268470 17062009 13 CAN 13 316

Secondary

Forest - Yalu

8 484610 9268470 17062009 14 POM PIN 13 472

Secondary

Forest - Yalu

8 484610 9268470 17062009 15 EUO 13 116

Secondary

Forest - Yalu

8 484610 9268470 17062009 16 PTE IND 13 114

Secondary

Forest - Yalu

8 484610 9268470 17062009 17 CAN 13 281

Secondary

Forest - Yalu

8 484610 9268470 17062009 18 POM PIN 13 561

Secondary

Forest - Yalu

8 484610 9268470 17062009 19 ANT CHI 13 283

Secondary

Forest - Yalu

8 484610 9268470 17062009 20 POM PIN 13 196

Secondary

Forest - Yalu

8 484610 9268470 17062009 21 EUO 13 500

Secondary

Forest - Yalu

8 484610 9268470 17062009 22 FIC 13 246

Secondary

Forest - Yalu

8 484610 9268470 17062009 23 FIC 13 246

Secondary

Forest - Yalu

231


Tree


8 484610 9268470 17062009 24 TRI 13 153

Secondary

Forest - Yalu

9 484522 92685314 17062009 1 SEM 13 540

Secondary

Forest - Yalu

9 484522 92685314 17062009 2 INO FAG 13 550

Secondary

Forest - Yalu

9 484522 92685314 17062009 3 BUC 13 369

Secondary

Forest - Yalu

9 484522 92685314 17062009 4 ANT CHI 13 505

Secondary

Forest - Yalu

9 484522 92685314 17062009 5 GUI 13 195

Secondary

Forest - Yalu

9 484522 92685314 17062009 6 LIT 13 355

Secondary

Forest - Yalu

9 484522 92685314 17062009 7 PIS UMB 13 300

Secondary

Forest - Yalu

9 484522 92685314 17062009 8 SEM 13 371

Secondary

Forest - Yalu

9 484522 92685314 17062009 9 PIS UMB 13 172

Secondary

Forest - Yalu

9 484522 92685314 17062009 10 PIS UMB 13 153

Secondary

Forest - Yalu

9 484522 92685314 17062009 11 BRI 13 1800

Secondary

Forest - Yalu

9 484522 92685314 17062009 12 VIT COF 13 1800

Secondary

Forest - Yalu

9 484522 92685314 17062009 13 TER 13 201

Secondary

Forest - Yalu

9 484522 92685314 17062009 14 PIS UMB 13 196

Secondary

Forest - Yalu

9 484522 92685314 17062009 15 PTE IND 13 1850

Secondary

Forest - Yalu

10 484446 9268164 17062009 1 END 13 381

Secondary

Forest - Yalu

10 484446 9268164 17062009 2 CAN 13 548

Secondary

Forest - Yalu

10 484446 9268164 17062009 3 MAC 13 346

Secondary

Forest - Yalu

10 484446 9268164 17062009 4 MAC 13 289

Secondary

Forest - Yalu

10 484446 9268164 17062009 5 MAC 13 336

Secondary

Forest - Yalu

10 484446 9268164 17062009 6 PTE IND 13 324

Secondary

Forest - Yalu

232


Tree


10 484446 9268164 17062009 7 CAN 13 375

Secondary

Forest - Yalu

10 484446 9268164 17062009 8 MAC 13 274

Secondary

Forest - Yalu

10 484446 9268164 17062009 9 MAC 13 393

Secondary

Forest - Yalu

10 484446 9268164 17062009 10 PTE IND 13 180

Secondary

Forest - Yalu

10 484446 9268164 17062009 11 ANT CHI 13 507

Secondary

Forest - Yalu

10 484446 9268164 17062009 12 STE 13 165

Secondary

Forest - Yalu

10 484446 9268164 17062009 13 CEL 13 570

Secondary

Forest - Yalu

10 484446 9268164 17062009 14 LIT 13 394

Secondary

Forest - Yalu

10 484446 9268164 17062009 15 STE AMP 13 107

Secondary

Forest - Yalu

10 484446 9268164 17062009 16 PTE IND 13 195

Secondary

Forest - Yalu

10 484446 9268164 17062009 17 LIT 13 130

Secondary

Forest - Yalu

10 484446 9268164 17062009 18 PIM AMB 13 234

Secondary

Forest - Yalu

10 484446 9268164 17062009 19 ANT CHI 13 517

Secondary

Forest - Yalu

10 484446 9268164 17062009 20 AGL 13 180

Secondary

Forest - Yalu

10 484446 9268164 17062009 21 ALS 13 192

Secondary

Forest - Yalu

10 484446 9268164 17062009 22 STE 13 265

Secondary

Forest - Yalu

10 484446 9268164 17062009 23 MIC 13 201

Secondary

Forest - Yalu

10 484446 9268164 17062009 24 PTE IND 13 1860

Secondary

Forest - Yalu

11 484612 9268157 17062009 1 FIC 13 375

Secondary

Forest - Yalu

11 484612 9268157 17062009 2 PLA 13 130

Secondary

Forest - Yalu

11 484612 9268157 17062009 3 INO FAG 13 242

Secondary

Forest - Yalu

11 484612 9268157 17062009 4 STE 13 690

Secondary

Forest - Yalu

233


Tree


11 484612 9268157 17062009 5 PIM AMB 13 466

Secondary

Forest - Yalu

11 484612 9268157 17062009 6 GNE GNE 13 158

Secondary

Forest - Yalu

11 484612 9268157 17062009 7 PIM AMB 13 255

Secondary

Forest - Yalu

11 484612 9268157 17062009 8 PIM AMB 13 385

Secondary

Forest - Yalu

11 484612 9268157 17062009 9 GNE GNE 13 130

Secondary

Forest - Yalu

11 484612 9268157 17062009 10 PIM AMB 13 255

Secondary

Forest - Yalu

11 484612 9268157 17062009 11 PIM AMB 13 260

Secondary

Forest - Yalu

11 484612 9268157 17062009 12 CEL 13 180

Secondary

Forest - Yalu

11 484612 9268157 17062009 13 CEL 13 255

Secondary

Forest - Yalu

11 484612 9268157 17062009 14 GUI 13 290

Secondary

Forest - Yalu

11 484612 9268157 17062009 15 CEL 13 715

Secondary

Forest - Yalu

11 484612 9268157 17062009 16 STE 13 700

Secondary

Forest - Yalu

11 484612 9268157 17062009 17 MIC 13 210

Secondary

Forest - Yalu

11 484612 9268157 17062009 18 PIM AMB 13 346

Secondary

Forest - Yalu

11 484612 9268157 17062009 19 MIS 13 246

Secondary

Forest - Yalu

11 484612 9268157 17062009 20 CEL 13 700

Secondary

Forest - Yalu

11 484612 9268157 17062009 21 CEL 13 496

Secondary

Forest - Yalu

12 484699 9268074 17062009 1 INT 20 926

Secondary

Forest - Yalu

12 484699 9268074 17062009 2 TER 13 634

Secondary

Forest - Yalu

12 484699 9268074 17062009 3 SEM 13 430

Secondary

Forest - Yalu

12 484699 9268074 17062009 4 TER 13 293

Secondary

Forest - Yalu

12 484699 9268074 17062009 5 PIM AMB 13 260

Secondary

Forest - Yalu

234


Tree


12 484699 9268074 17062009 6 PIM AMB 13 250

Secondary

Forest - Yalu

12 484699 9268074 17062009 7 PIM AMB 13 310

Secondary

Forest - Yalu

12 484699 9268074 17062009 8 SYZ 20 560

Secondary

Forest - Yalu

12 484699 9268074 17062009 9 TRI 20 1300

Secondary

Forest - Yalu

12 484699 9268074 17062009 10 13 180

Secondary

Forest - Yalu

12 484699 9268074 17062009 11 PIS UMB 13 320

Secondary

Forest - Yalu

12 484699 9268074 17062009 12 LIT 13 150

Secondary

Forest - Yalu

12 484699 9268074 17062009 13 TRI 13 471

Secondary

Forest - Yalu

12 484699 9268074 17062009 14 STE 13 284

Secondary

Forest - Yalu

12 484699 9268074 17062009 15 CER 13 252

Secondary

Forest - Yalu

12 484699 9268074 17062009 16 INT 13 825

Secondary

Forest - Yalu

12 484699 9268074 17062009 17 TER 30 450

Secondary

Forest - Yalu

13 484743 9268126 17062009 1 PIM AMB 13 420

Secondary

Forest - Yalu

13 484743 9268126 17062009 2 CEL 13 490

Secondary

Forest - Yalu

13 484743 9268126 17062009 3 MIC 13 130

Secondary

Forest - Yalu

13 484743 9268126 17062009 4 PTE IND 13 530

Secondary

Forest - Yalu

13 484743 9268126 17062009 5 CEL 13 761

Secondary

Forest - Yalu

13 484743 9268126 17062009 6 CEL 13 420

Secondary

Forest - Yalu

13 484743 9268126 17062009 7 CEL 13 340

Secondary

Forest - Yalu

13 484743 9268126 17062009 8 PTE IND 40 705

Secondary

Forest - Yalu

13 484743 9268126 17062009 9 MAC 13 320

Secondary

Forest - Yalu

13 484743 9268126 17062009 10 MAC 13 460

Secondary

Forest - Yalu

235


Tree


13 484743 9268126 17062009 11 END 13 300

Secondary

Forest - Yalu

13 484743 9268126 17062009 12 MAC 13 190

Secondary

Forest - Yalu

13 484743 9268126 17062009 13 MAC 13 203

Secondary

Forest - Yalu

13 484743 9268126 17062009 14 ART 13 220

Secondary

Forest - Yalu

13 484743 9268126 17062009 15 PTE IND 13 525

Secondary

Forest - Yalu

13 484743 9268126 17062009 16 MAC 13 124

Secondary

Forest - Yalu

13 484743 9268126 17062009 17 AGL 13 415

Secondary

Forest - Yalu

14 484837 9268212 17062009 1 GAR 20 291

Secondary

Forest - Yalu

14 484837 9268212 17062009 2 AGL 13 280

Secondary

Forest - Yalu

14 484837 9268212 17062009 3 TER 13 364

Secondary

Forest - Yalu

14 484837 9268212 17062009 4 TER 13 330

Secondary

Forest - Yalu

14 484837 9268212 17062009 5 PIS UMB 13 156

Secondary

Forest - Yalu

14 484837 9268212 17062009 6 POM PIN 13 584

Secondary

Forest - Yalu

14 484837 9268212 17062009 7 TER 13 365

Secondary

Forest - Yalu

14 484837 9268212 17062009 8 END 13 396

Secondary

Forest - Yalu

14 484837 9268212 17062009 9 TER 13 233

Secondary

Forest - Yalu

14 484837 9268212 17062009 10 STE 13 630

Secondary

Forest - Yalu

15 484784 9268298 17062009 1 CEL 13 367

Secondary

Forest - Yalu

15 484784 9268298 17062009 2 PIM AMB 13 360

Secondary

Forest - Yalu

15 484784 9268298 17062009 3 CEL 15 619

Secondary

Forest - Yalu

15 484784 9268298 17062009 4 DYS 13 240

Secondary

Forest - Yalu

15 484784 9268298 17062009 5 LIT 13 465

Secondary

Forest - Yalu

236


Tree


15 484784 9268298 17062009 6 FIC 40 1500

Secondary

Forest - Yalu

15 484784 9268298 17062009 7 POM PIN 13 579

Secondary

Forest - Yalu

15 484784 9268298 17062009 8 MIS 13 278

Secondary

Forest - Yalu

15 484784 9268298 17062009 9 CEL 40 570

Secondary

Forest - Yalu

15 484784 9268298 17062009 10 LIT 13 294

Secondary

Forest - Yalu

15 484784 9268298 17062009 11 ANT CHI 13 434

Secondary

Forest - Yalu

15 484784 9268298 17062009 12 PIS UMB 13 236

Secondary

Forest - Yalu

15 484784 9268298 17062009 13 GNE GNE 13 150

Secondary

Forest - Yalu

15 484784 9268298 17062009 14 CEL 15 603

Secondary

Forest - Yalu

16 484840 9268332 17062009 1 INT 13 570

Secondary

Forest - Yalu

16 484840 9268332 17062009 2 MIC 13 246

Secondary

Forest - Yalu

16 484840 9268332 17062009 3 CEL 40 750

Secondary

Forest - Yalu

16 484840 9268332 17062009 4 POM PIN 20 286

Secondary

Forest - Yalu

16 484840 9268332 17062009 5 MIC 13 240

Secondary

Forest - Yalu

16 484840 9268332 17062009 6 TRI 13 176

Secondary

Forest - Yalu

16 484840 9268332 17062009 7 FIC 13 120

Secondary

Forest - Yalu

16 484840 9268332 17062009 8 PIM AMB 13 287

Secondary

Forest - Yalu

16 484840 9268332 17062009 9 GNE GNE 13 146

Secondary

Forest - Yalu

16 484840 9268332 17062009 10 PIM AMB 13 250

Secondary

Forest - Yalu

16 484840 9268332 17062009 11 BIS JAV 13 605

Secondary

Forest - Yalu

16 484840 9268332 17062009 12 STE 13 553

Secondary

Forest - Yalu

16 484840 9268332 17062009 13 PIM AMB 13 378

Secondary

Forest - Yalu

237


Tree


17 484890 9268434 17062009 1 PTE IND 13 323

Secondary

Forest - Yalu

17 484890 9268434 17062009 2 ART 15 733

Secondary

Forest - Yalu

17 484890 9268434 17062009 3 POM PIN 30 705

Secondary

Forest - Yalu

17 484890 9268434 17062009 4 DRA 30 680

Secondary

Forest - Yalu

17 484890 9268434 17062009 5 HOR 13 250

Secondary

Forest - Yalu

17 484890 9268434 17062009 6 MAC 13 143

Secondary

Forest - Yalu

17 484890 9268434 17062009 7 PTE IND 15 623

Secondary

Forest - Yalu

17 484890 9268434 17062009 8 CEL 30 664

Secondary

Forest - Yalu

17 484890 9268434 17062009 9 PTE IND 13 220

Secondary

Forest - Yalu

17 484890 9268434 17062009 10 PTE IND 13 170

Secondary

Forest - Yalu

17 484890 9268434 17062009 11 PTE IND 13 140

Secondary

Forest - Yalu



Tree


1 469324 9256048 4062009 1 POM PIN 3 695

Logged Forest -

Gabensis

1 469324 9256048 4062009 2 INT 13 59

Logged Forest -

Gabensis

1 469324 9256048 4062009 3 CHN 13 61

Logged Forest -

Gabensis

1 469324 9256048 4062009 4 TER 2 43

Logged Forest -

Gabensis

1 469324 9256048 4062009 5 POM PIN 2 59

Logged Forest -

Gabensis

1 469324 9256048 4062009 6 POM PIN 2 59

Logged Forest -

Gabensis

1 469324 9256048 4062009 7 POM PIN 13 70

Logged Forest -

Gabensis

238


Tree


1 469324 9256048 4062009 8 CHN 13 555

Logged Forest -

Gabensis

1 469324 9256048 4062009 9 INT 15 28

Logged Forest -

Gabensis

1 469324 9256048 4062009 10 TER 2 535

Logged Forest -

Gabensis

1 469324 9256048 4062009 11 TER 13 40

Logged Forest -

Gabensis

1 469324 9256048 4062009 12 HRN 13 365

Logged Forest -

Gabensis

1 469324 9256048 4062009 13 CHN 18 52

Logged Forest -

Gabensis

1 469324 9256048 4062009 14 CNN 18 575

Logged Forest -

Gabensis

1 469324 9256048 4062009 15 CHN 18 385

Logged Forest -

Gabensis

1

469324

9256048

4062009

16

CHN

18

33

Logged Forest-

Gabensis

1 469324 9256048 4062009 17 POM PIN 13 305

Logged Forest -

Gabensis

1 469324 9256048 4062009 18 PLA 13 30

Logged Forest -

Gabensis

1 469324 9256048 4062009 19 13 20

Logged Forest -

Gabensis

2 470782 9257001 4062009 1 HRN 13 43

Secondary Forest -

Gabensis

2 470782 9257001 4062009 2 POM PIN 2 55

Secondary Forest -

Gabensis

2 470782 9257001 4062009 3 CHN 2 94

Secondary Forest -

Gabensis

2 470782 9257001 4062009 4 13 30

Secondary Forest -

Gabensis

2 470782 9257001 4062009 5 13 30

Secondary Forest -

Gabensis

2 470782 9257001 4062009 6 PTE IND 2 85

Secondary Forest -

Gabensis

2 470782 9257001 4062009 7 13 30

Secondary Forest -

Gabensis

2 470782 9257001 4062009 8 13 30

Secondary Forest -

Gabensis

2 470782 9257001 4062009 9 13 30

Secondary Forest -

Gabensis

2 470782 9257001 4062009 10 13 30

Secondary Forest -

Gabensis

239


Tree


2 470782 9257001 4062009 11 PTE IND 2 57

Secondary Forest -

Gabensis

2 470782 9257001 4062009 12 PTE IND 13 31

Secondary Forest -

Gabensis

2 470782 9257001 4062009 13 MAS 2 55

Secondary Forest -

Gabensis

2 470782 9257001 4062009 14 POM PIN 2 41

Secondary Forest -

Gabensis

2 470782 9257001 4062009 15 POM PIN 2 47

Secondary Forest -

Gabensis

2 470782 9257001 4062009 16 13 30

Secondary Forest -

Gabensis

2 470782 9257001 4062009 17 POM PIN 15 43

Secondary Forest -

Gabensis

2 470782 9257001 4062009 18 PTE IND 15 80

Secondary Forest -

Gabensis

240



EAG Eaglewood

1 2 3















TAU Taun

TEA Teak

TER Terminalia

WAL Walnut PNG



































5 6















STR Steropsis

241


242


243














244

















K1500m3









apply









K1500m3






245




















K450m3























K450m3






246








GIS Mapping K20000






Marketing K10000












Authors

Yosi Cossey Keosai

Title


Date

2011

Citation




Persistent Link


File Description








iii




cutting cycles










CBFM throughout PNG






iv

DECLARATION






___________________

Cossey Keosai Yosi

July 2011

v

DEDICATION
































vi

PREFACE













James E Smith

















Research Canberra

vii

ACKNOWLEDGEMENTS















this study














assistance

viii


























TABLE OF CONTENTS


INTRODUCTION 1







MANAGEMENT 28



x





IN PNG 65



1) 74











xi









56 CONCLUSIONS 134







66 CONCLUSIONS 169






xii




CONCLUSIONS 187








REFERENCES 198

APPENDICES 219


LIST OF TABLES




a 100


-1) 101






xiv

LIST OF FIGURES








xv

LIST OF ACRONYMS




asl Above Sea Level

BA Basal Area


C Carbon





Resources





CO2 Carbon Dioxide







Melbourne



Government






xvi























m2 ha


m3 ha


mm annum-1








N ha-1




xvii



























UK United Kingdom








INTRODUCTION

2

CHAPTER 1






























3


































4

































5

































6




















to continue








7

13 BACKGROUND































8






























9






















-1 (Keenan et al

2005)


per











10










3 ha




timber














1980)







11













TRP












12














Forest Policy













the following






13













implementation





















14

















Production











15

1331 Forest Types










tropical forest























16



4800m asl
























2007 )




17


-




2008000m3 in 2007




0

50

100

150

200

250

300

00

05

10

15

20

25

30

35

40

Are

a H

arv

este

d (

00

0 h

a)

Harv

este

d T

imb

er V

olu

me

(Mil

lion

m3)

Year

Harvested

volume

Harvested area

0

500

1000

1500

2000

2500

2002 2003 2004 2005 2006 2007

Volu

me

(0

00

m3

)

Year

Logs

Sawn

Ply

Veneer

18

































19




















20


































21


timber















community hall



22





















(a)

(b)

23





McAlpine (1995)



Plots

Date of

Establishment

Forest Type

Gulf

Western

Oro

Milne Bay

Central

Turama

Vailala

Oriomo

Wawoi Guavi

Embi Hanau

Gara Modewa

Ormand Lako

Iva Inika

2

2

2

2

4

2

2

2

091194

271194

121094

261094

200594

120694

070894

160396

Lowland Foot Hills

Lowland Plain

W (Lowland Plain)

HmFswWsw (Lowland

FHills)

Pl (Lowland Plain)


Hs (Lowland Hill)


Morobe

Madang

East Sepik

Sandaun

Oomsis

Trans Watut

Umboi

Kui

Yema Gaiapa

North Coast

Rai Coast

Hawain

Pual

Krisa

2

2

2

2

1

2

2

2

2

2

260593

261093

151294

121194

150596

200395

060495

090894

240894

100994


LN (Lower Mountain)

Hl (Lowland Plain)

Hm (Lowland Hill)

Hm (Lowland Hill)

Hm9 (Lowland Hill)

Hm (Lowland Hill)

(Lowland Hill)


(Lowland Hill)

Southern

Highlands

MtGiluwe 2


West New

Britain

East New Britain

New Ireland

Manus

Kapiura

Mosa Leim

Kapuluk

Central Arawe

Anu Alimbit

Pasisi Manua

Open Bay

Gar

Waterfall Bay

Lassul Bay

Cape Orford

Inland Pomio

Kaut

Umbukul

Central NI

Lark

West Coast

2

2

2

2

2

2

2

2

2

2

2

1

2

2

2

2

2

230793

110893

300893

060595

200695

070795

180893

270793

290893

090695

270695

280795

230993

011093

021195

181095

290395

Hm (Lowland Hill)

Hm8 (Lowland Hill)

Hm (Lowland Hill)






(Lowland Foothills)

(Lowland Foothills)

(Lowland Foothills)

(Lowland Hill)



(Lowland Foothills)

(Lowland Foothills)

HmeHm6 (Lowland

Foothills)


24










W Woodland



Wsw Swamp woodland





disturbed)









disturbance

25

















NW NE

08 28 48 68 88

06 26 46 66 86

04 24 42 64 84

02 22 42 62 82

00 20 40 60 80

SW SE


Plot origin

where

measurement

starts

N

100 m

100 m

26

1362 PSP Locations










harvesting in PNG)

27

































28


communities



in PNG


forests in PNG






15 THESIS OUTLINE



















29


















province PNG






forests in PNG

27


28

CHAPTER 2


21 FOREST DYNAMICS

211 Introduction



highlighted





















29


(Kobayashi 1992)

















2151














30




discussed in detail


















disturbances











31














(Evan 1982)


















32










of exotic species










214 Forest Types













33




















forest













34

































35




















(47 mm)















36

































37


amount (Ng 1983)






























38














1998)














39

218 Shade Tolerance





























40

219 Stand Structure



























41














Terminalia spp



















42


































43






ha-1







-1
























trees

44

2111 Discussion































45

















46

2112 Conclusions







region
























47


221 Introduction





follow




















pointed out




48































49

222 Illegal Logging































50

















223 Deforestation
















51























of











52



224 Climate Change






























53














deforestation

2241 Kyoto Protocol


















54








of 8 Kt CO2 year-1



the project
























55










Imperata grasslands






operations



56















where they reside


















57


































58






226 Certification



























59


































60













227 Governance




















61




























62

228 Discussion












to achieving SFM




















63







229 Conclusions
















recognised










64











65
































66





























assessment






67



















68

















following











timber industry

69






























and social assets

70

234 Discussion





























assets

71

235 Conclusions












uncertainties





PNG

72


65

CHAPTER 3


3 1 INTRODUCTION















(FAO 2005













66




al 2009a)





















harvesting








et al 2000)

67
































68






2005)






















(Pokana 2002)

69












the harvested plots






70




























71






















(3-1)





(equation 3-3)

72

(3-2)

(3-3)
















(3-4)


-1) Shd = site









hd

73


























(3-5)



(m2 ha


residual trees

t = 134 + 0394 ln Go + 0346 ln t + 000275 Sh t -1

74















(3-6)





Nino drought





(3-7)



75

33 RESULTS



plusmn 100





2901 m2

ha-1





















76

(a)L

og

Sto

ckin

g (

ste

ms h

a-1

)

1

10

100

1000

0 - 5 years

5 - 10 years

10 - 15 years

15 - 20 years

Diameter Class (cm)

10-29 30-49 50-69 70-89 90+

Lo

g S

tockin

g (

ste

ms h

a-1

)

1

10

100

1000

(b)

(c)

Basal

Are

a (

m2 h

a-1

)

0

2

4

6

8

10

12

Diameter Class (cm)

10-29 30-49 50-69 70-89 90+

Basal

Are

a (

m2 h

a-1

)

0

2

4

6

8

10

12

(d)





un-harvested plots







77

Bas

al

Are

a (

m2 h

a-1

)

0

5

10

15

20

25

Sto

ck

ing

(ste

ms

ha

-1)

0

100

200

300

400 Commercial

NonCommercial


0-5 5-10 10-15 15-20

Res

idu

al

Tim

ber

Vo

lum

e (

m3 h

a-1

)

0

20

40

60

80

100

120

140

160

180

(a)

(b)

(c)




78












r2 = 007

p = 0016


0 5 10 15 20 25

Bas

al

Are

a (

m2 h

a-1

)

0

5

10

15

20

25

30

35




79




-1 year

-1 (SD 042) (Table 3-1) For the 21


-1 year

-1


-1 year

-1 (SD









(-172 plusmn 316) (Table 3-1)



Mean BAI (m2 ha

-1 year

-1)

a

Un-harvested 13

-172 plusmn 316




-058 plusmn 053

(All un-burned) 84b

017 plusmn 062)


drought 21

-067 plusmn 085

Total 118



80







the mean

r2 = 037

p = 0028

Mean TSH (years)

5 10 15 20

Ln

Mean

BA

I (m

2 h

a-1

year-1

00

02

04

06

08

10

12

14

16

18




81


Forest








r2 = 074

p = 0000


-1)

0 5 10 15 20 25 30

Ln

Mean

BA

I (m

2 h

a-1

year-1

)

-6

-4

-2

0

2

4











82

r2 = 024

p = 0087


0 1 2 3 4 5 6

Tim

be

r V

olu

me

gt2

0c

m D

BH

(m

3 h

a-1

)

0

50

100

150

200

250

300

r2 = 006

p = 0031


0 5 10 15 20

Tim

be

r V

olu

me

gt20

cm

DB

H (

m3

ha

-1)

0

50

100

150

200

250

300






(a)

(b)

83








-1 year

-1 plusmn 809


this study (r2

= 083 p = 0000) (Figure 3-8)

r2 = 083

p = 0000


0 5 10 15 20

Ln

Tim

be

r Y

ield

gt2

0c

m D

BH

(m

3 h

a-1

)

00

02

04

06

08

10

12

14

16





Nino Drought







84





fire

PlotID

CNIR

D01

CNIR

D02

IVAIN

01

IVAIN

02

KAPU

L01

KAPU

L02

LARK01

LARK02

WIM

AR01

WIM

AR02

Pe

rcen

tag

e B

A g

ain

ed

or

lost

()

0

10

20

30

40


BA lost due to fire














85

r2 = 016

p = 0069


0 5 10 15 20 25 30

Sh

an

no

n-W

ien

er

Ind

ex

(H

-1)

0

1

2

3

4

5




34 DISCUSSION

















86








about 30-32 m2

ha-1




















-1 year

-1)


-1 year

-1) overall



-1

year-1






87










Region

Primary Forest

Mean BA

(m2 ha

-1)

a

Harvested Forest

Mean BA (m2 ha

-1 )

Mean BAI

after harvesting

(m2 ha

-1 year

-1)

Source

PNG

2901

1735

017

Current study

PNGb

30 - 33

10 - 20

Kingston amp Nir

1988 Oavika 1992

Alder 1998

Sub tropical

Australia

515

12 - 58

032 ndash 075

Smith et al 2005

North

Queensland

Australia

3794 ndash 7342

2586 ndash 4160


South Indiac

393

348





South India










88
















-1


1998)








forests in PNG







089 year-1


in the same


89























Rogers (2001) study











90




35 CONCLUSIONS







-1 year



-1 year























utilisation

91

CHAPTER 4


41 INTRODUCTION









plots
















identified later



92
















42 BACKGROUND













-1 (VDT


93











94














95



for sampling

















44 DATA ANALYSIS




ha-


2

ha-1


(4-1)


stems ha-1

96


(4-2)


442 Timber Volume



(4-3)











(4-4)








97








debris (CWD)




AGLBlt10cm 10 10 10

FL 1 25 25

CWD 25 25 25







(4-5)







(4-6)

ge 10cm lt 10cm

98




45 RESULTS






2m2 ha






10-20 69 119

20-30 93 42


Forests 40-50 23 13

50-60 22 8

60-70 13 6

70-80 10 5

80-90 2 4

90-100 1 3

100+ 7 1

20-30 9 33

30-40 6 22


Forests 50-60 11 8

60-70 3 6

70-80 2 5

80-90 1 4

90-100 1 3

99







0

20

40

60

80

100

120

140

10-20 20-30 30-40 40-50 50-60 60-70 70-80 80-90 90-100 100+

No

of

Ste

ms

(N h

a-1

)

Diameter Class (cm)

Actual

Predicted

0

5

10

15

20

25

30

35

20-30 30-40 40-50 50-60 60-70 70-80 80-90 90-100

No

of

Ste

ms

(Nh

a)

Diameter Class (cm)

Actual

Predicted

100





each study site




Merch Vol (m3

ha-1

)

Total Merch

Vol (m3)


Celtis sp 68 179 39000



Intsia sp 14 168 37000

Vitex sp 14 119 26000





178 39000






Intsia sp 54 144 2100







101






-1)


Forest

Mean 1269 1519

SD 450 277








Forest

AGLBge10cm 11019 ( 2758) 11921 (3719)


FL 110 119

CWD 2755 2980

Total AGB 14985 ( 3751) 16212 (5058)

455 Sample Size







102



Mean SD CV

No of

Observation DF

E

() t-value n

Volume

(m2 ha

-1) 1269 450 035 17 16 10 1337 22

AGB

(MgC ha-1

) 14985 3751 025 17 16 10 1337 11


Volume

(m2 ha

-1) 1519 277 018 2 1 10 3078 31

AGB

(MgC ha-1

) 16212 5058 031 2 1 10 3078 92
















Total Forest Area

2200 ha

150 ha


28000 m3

2300 m3


1269 m3 ha

-1

1519 m3 ha

-1

Total Forest Carbon

329670 Mg C

24318 Mg C

Mean Forest Carbon

14985 Mg C ha-1

16212 Mg C ha-1

Estimated Emission


181319 Mg C

13375 Mg C

103

46 DISCUSSION






m3 ha


3 ha



30-40 m3 ha



) and primary








Mg C ha-1


The
















104


powered generation




























(Rogers 2010)






105





47 CONCLUSIONS







-1












106


TOOLS

107

CHAPTER 5


51 INTRODUCTION
























108




forests










scenarios for CBFM

52 BACKGROUND













109


PNG



















110

53 METHODOLOGY






developed




data


informed decision







developed




111































112


residual stand















harvesting)













113


























114















Total


Pre-harvest (m3ha) 210 270 430 90 100 120 50 50 70 1040

Cut fraction () 0 0 100 0 0 100 0 0 0

Post-harvest (m3ha) 210 270 00 90 100 00 50 50 70 490 60

YIELD (m3ha) 00 00 430 00 00 120 00 00 00 550

Ingrowth (m3yr) 028 028 028 008 008 008 000 000 000 07

Growth (m3yr) 000 000 000 000 000 000 000 000 000 00

DeathDamage (m3yr) 000 000 000 000 000 000 000 000 000

Upgrowth (m3yr) 028 028 008 008 000 000

Pre-harvest (m3ha) 210 270 139 90 100 39 50 50 70 667

Cut fraction () 0 0 100 0 0 100 0 0 0

Post-harvest (m3ha) 210 270 00 90 100 00 50 50 70 490 83

YIELD (m3ha) 00 00 139 00 00 39 00 00 00 177

Ingrowth (m3yr) 022 022 022 006 006 006 000 000 000 06

Growth (m3yr) 000 000 000 000 000 000 000 000 000 00

DeathDamage (m3yr) 000 000 000 000 000 000 000 000 000

Upgrowth (m3yr) 022 022 006 006 000 000

Pre-harvest (m3ha) 210 270 112 90 100 31 50 50 70 633

Cut fraction () 0 0 100 0 0 100 0 0 0

Post-harvest (m3ha) 210 270 00 90 100 00 50 50 70 490 85

YIELD (m3ha) 00 00 112 00 00 31 00 00 00 143

Ingrowth (m3yr) 021 021 021 006 006 006 000 000 000 05

Growth (m3yr) 000 000 000 000 000 000 000 000 000 00

DeathDamage (m3yr) 000 000 000 000 000 000 000 000 000

Upgrowth (m3yr) 021 021 006 006 000 000

Left after Harvest1

Cycle

Number

Left after Harvest

Left after Harvest


3

2




115







Yalu Area Data (ha)





Fragile 15


above

10


above

10





20

Regrowth area 15

Roading 10

Other

25

2nd

amp3rd


116


Diameter Class

(cm)

Volume MEP1

(m3 ha

-1)

Volume MEP2

(m3 ha

-1)

Others

(m3 ha

-1)

lt 20 0301 0307 7029

20-50 4950 6961 34991

50-65 6634 11885 18539

65+

Volume Growth

(m3 ha

-1 year

-1)

0-20 0117 0301 0203

20-50 0129 0124 0244

50-65 0041 0080 0073

65+ 0127








(Years)

Cut Proportion

()

Diameter

LimitMEP

Codes

Community

sawmill

10 50 gt 50cm

MEP1 MEP2

Local processing

20 50 gt 50cm

MEP1 MEP2


MEP1 MEP2

Medium-scale log

export

40 50 gt 50cm

MEP1 MEP2

117










(Years)

Cut Proportion

()

Diameter

LimitMEP Codes


MEP1 MEP2

Local processing

20 75 gt 50cm

MEP1 MEP2


MEP1 MEP2


MEP1 MEP2







(Years)

Cut Proportion

()

Diameter

LimitMEP Codes


MEP1 MEP2

Local processing

20 50 gt 65cm

MEP1 MEP2


MEP1 MEP2


MEP1 MEP2

118

54 RESULTS










119





Gabensis village










120












121



122











0

20

40

60

80

100

0

2

4

6

8

10

12

high

preference

low

preference

no

preference

not sure

Percen

tag

e (

)

Freq

uen

cy

(N

)


0

20

40

60

80

100

0

2

4

6

8

10

12

high

preference

low

preference

no

preference

not sure

Percen

tag

e (

)

Freq

uen

cy

(N

)


0

20

40

60

80

100

0

2

4

6

8

10

12

high

preference

low

preference

no

preference

not sure

Percen

tag

e (

)

Freq

uen

cy

(N

)


0

20

40

60

80

100

0

2

4

6

8

10

12

high

preference

low

preference

no

preference

not sure

Percen

tag

e (

)

Freq

uen

cy

(N

)


0

20

40

60

80

100

0

2

4

6

8

10

12

high

preference

low

preference

no

preference

not sure

Percen

tag

e (

)

Freq

uen

cy

(N

)


123


Scenarios








third cycles


Scenario

Cutting Cycle

(years)

Annual

Yield Cycle 1

(m3 year

-1)

Annual

Yield Cycle 2

(m3 year

-1)

Annual

Yield Cycle 3

(m3 year

-1)

Total

Yield

(m3)

Community

sawmill

10

3166

2865

2718

87490

Local

processing

20

1583

2100

2890

131500

Local

processing

30

1055

1846

3307

186060

Medium-scale

log export

40

792

1718

3780

251600









124


Scenario

Cutting

Cycle (years)

Annual Yield

Cycle 1 (m3)

Annual Yield

Cycle 2 (m3)

Annual Yield

Cycle 3 (m3)

Total

Yield

(m3)

Community

sawmill

10

4749

2316

1229

82940

Local

processing

20

2375

1743

1294

108240

Local

processing

30

1583

1551

1574

141240

Medium-scale

log export

40

1187

1456

1802

177800






-1 and 3000m

3 year







Scenario

DBH Limit

Species Grp

Annual

Yield Cycle 1

(m3 year

-1)

Annual

Yield Cycle 2

(m3 year

-1)

Annual

Yield Cycle 3

(m3 year

-1)

Total Yield

(m3)

Local

processing

50 gt 50cm

MEP 1 2

1583

2100

2890

131460

Local

processing

50 gt 65cm

MEP 1 2

623

703

805

42620

Local

processing

75 gt 50cm

MEP 1 2

2375

1743

1361

276463

Local

processing

75 gt 65cm

MEP 1 2

934

603

415

39040

125









increase in volume

















126



0

1

2

3

4

5

1 - 10 11 - 20 21 - 30

An

nu

al

Vo

lum

e (1

00

0 m

3 y

r-1

)


MEP-code2 65+

MEP-code2 50-65

MEP-code1 65+

MEP-code1 50-65

0

1

2

3

4

5

1 - 20 21 - 40 41 - 60

An

nu

al

Vo

lum

e (1

00

0 m

3 y

r-1

)


MEP-code2 65+

MEP-code2 50-65

MEP-code1 65+

MEP-code1 50-65

0

1

2

3

4

5

1 - 30 31 - 60 61 - 90

An

nu

al

Vo

lum

e (1

00

0 m

3 y

r-1

)


MEP-code2 65+

MEP-code2 50-65

MEP-code1 65+

MEP-code1 50-65

0

1

2

3

4

5

1 - 40 41 - 80 81 - 120

An

nu

al

Vo

lum

e (1

00

0 m

3 y

r-1

)


MEP-code2 65+

MEP-code2 50-65

MEP-code1 65+

MEP-code1 50-65

(b) (a)

(d) (c)

127



0

1

2

3

4

5

1 - 20 21 - 40 41 - 60

An

nu

al

Vo

lum

e (1

00

0 m

3 y

r-1

)


MEP-code2 65+

MEP-code2 50-65

MEP-code1 65+

MEP-code1 50-65

0

1

2

3

4

5

1 - 10 11 - 20 21 - 30

An

nu

al

Vo

lum

e (1

00

0 m

3 y

r-1

)


MEP-code2 65+

MEP-code2 50-65

MEP-code1 65+

MEP-code1 50-65

0

1

2

3

4

5

1 - 30 31 - 60 61 - 90

An

nu

al

Vo

lum

e (1

00

0 m

3 y

r-1

)


MEP-code2 65+

MEP-code2 50-65

MEP-code1 65+

MEP-code1 50-65

0

1

2

3

4

5

1 - 40 41 - 80 81 - 120

An

nu

al

Vo

lum

e (1

00

0 m

3 y

r-1

)


MEP-code2 65+

MEP-code2 50-65

MEP-code1 65+

MEP-code1 50-65

(a) (b)

(c) (d)

128




0

1

2

3

4

5

1 - 20 21 - 40 41 - 60

An

nu

al

Vo

lum

e (1

00

0 m

3 y

r-1

)


MEP-code2 65+

MEP-code2 50-65

MEP-code1 65+

MEP-code1 50-65

0

1

2

3

4

5

1 - 20 21 - 40 41 - 60

An

nu

al

Vo

lum

e (1

00

0 m

3 y

r-1

)


MEP-code2 65+

MEP-code1 65+

0

1

2

3

4

5

1 - 20 21 - 40 41 - 60

An

nu

al

Vo

lum

e (1

00

0 m

3 y

r-1

)


MEP-code2 65+

MEP-code2 50-65

MEP-code1 65+

MEP-code1 50-65

0

1

2

3

4

5

1 - 20 21 - 40 41 - 60

An

nu

al

Vo

lum

e (1

00

0 m

3 y

r-1

)


MEP-code2 65+

MEP-code1 65+

(a) (b)

(c) (d)

129


Cycle


m3 ha





-1 and


-1 at year 60


Cutting

Cycle

(Years)

Cut

Proportion

()

Diameter Limit

MEP Codes

Starting

Pre-Harvest

Volume

(m3 ha

-1)

Residual

Volume After

3rd

Cycle

(m3 ha

-1)

Annual

Yield

(m3 year

-1)

10 50 gt 50cm MEP1 amp 2 305 271 8750

20 50 gt 50cm MEP1 amp 2 305 577 6574

30 50 gt 50cm MEP1 amp 2 305 989 6208

40 50 gt 50cm MEP1 amp 2 305 1508 6290

50 50 gt 50cm MEP1 amp 2 305 2132 6550

60 50 gt 50cm MEP1 amp 2 305 2861 6899



ha-1

and





130





6208 m3 year




0

50

100

150

200

250

300

350

10 20 30 40 50 60

Re

sid

ual

Vo

lum

e (

m3

ha-1

)


0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

10 20 30 40 50 60

An

nu

al V

olu

me

(m

3Y

ear

-1)


131

55 DISCUSSION























2000m3 year







1000m3 year


3 year

-1 in the

132


5-6c)






cycles (Table 5-6d)










-1)























133







250000 m3 (Table 5-6)



Annual

Yield


-1)

10 50

gt 50 cm MEP

1amp2 8750

20 50

gt 50 cm MEP

1amp3 6574

30 50

gt 50 cm MEP

1amp4 6208

40 50

gt 50 cm MEP

1amp5 6290
















134




-1 are considered



56 CONCLUSIONS







harvest



















135


















136

CHAPTER 6


61 INTRODUCTION



























137

































138









best decisions






63 METHODOLOGY













139










management in PNG






















140


branch

633 Terminal Values





























141






(6-1)










alternative














142



















EMV

Spider

Charts

Tornado

Charts

Decision Tree

Model

Decision

Alternatives

Uncertain

Events

Cash Flow

Model

Sensitivity

Data

Decision

Analyses

Sensitivity

Analyses

Payoff

Strategy

143








EMV at 2nd

node = (06 x -59850) + (04 x -63850) = PNGK-61450

EMV at 3rd

node = (06 x -61450) + (04 x -76350) = PNGK-67410












low











EMV at 2nd


144
















EMV at 2nd


















145
















for one year


(Figure 6-9)


EMV at 2nd














146









64 RESULTS






Input Description

Variation

(10) Variable range


Lucas mill (PNGK)5 85000 8500 76500 85000 93500

Chainsaw (PNGK) 6000 600 5400 6000 6600







(PNGKm3) 500 50 450 500 550





(m3annum) 20 2 18 20 22


months) 16 16 144 16 176


147




(Figure 6-2)















charts

148



High Price (PNGK)

-64850


-8850 -66050 04

Low Price

-67850

12000 -67850

06

High Price

06 -59850


2

20000 -61450 -8850 -61450 04

Low Price

-63850

16000 -63850

06

High Price

-63950



Low Price

-91000 -67410 -65950

10000 -65950

06

High Price

-75950


-4950 -76350 04

2 04 Low Price


1 9000 -76950

10000 -76350

Comm Use

-81000

0 -81000

No Harvest

0

0 0

149







Input Description

Variation

(10) Variable range


Lucas mill (PNGK) 85000 8500 76500 85000 93500

Chainsaw (PNGK) 6000 600 5400 6000 6600















Fumigation (PNGK) 720 72 648 720 792




(PNGKm3) 700 70 630 700 770


(PNGKm3) 2400 240 2160 2400 2640


(PNGKm3) 1500 150 1350 1500 1650




150







151



CertMarket HP

199800


-69200 127800 04

Non-CertMarket LP

06 19800


1

480000 127800 06

ST High Price

-124450


-53450 -132450 04

ST Low Price

-144450


-691000 31800 06

CertMarket HP

-40200


-6920000 -112200 04

Non-CertMarket LP

04 -220200

ST-Demand Low 300000 -220200

1

240000 -112200 06

ST High Price

-364450


-5345000 -372450 04

ST Low Price

-384450

120000 -384450

1

31800 06

CertMarket HP

-474938


-24494 -654938 04

Non-CertMarket LP

06 -924938


1

120000 -654938 06

ST High Price

-120494


-12494 -122494 04

ST Low Price

-125494


-263000 -894938 06

CertMarket HP

-107494


-2449375 -125494 04

Non-CertMarket LP

04 -152494


1

60000 -125494 06

ST High Price

-180494


-1249375 -182494 04

ST Low Price

-185494

30000 -185494

152







chart (Figure 6-4)

153


180

2160

286000

178200

1350

110000

1080

93500

55000

88

22000

2805

55

6600

1452

55

220

2640

234000

145800

1650

90000

1320

76500

45000

72

18000

2295

45

5400

1188

45

-60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60 70 80 90 100110120








Lucas mill (PNGK)






Chainsaw (PNGK)





154








-60000

-50000

-40000

-30000

-20000

-10000

0

10000

20000

30000

40000

50000

60000

70000

80000

90000

100000

110000

120000

86 90 94 98 102 106 110

EMV

(PN

GK

+-

10

B

ase

Cas

e)










Lucas mill (PNGK)


155






Input Description

Variation

(10) Variable range

Abs var -var

base

case +var

Chainsaw (PNGK) 6000 600 5400 6000 6600








(PNGm3) 84 84 756 84 924


Dozer D6 - CMU (PNGK) 200000 20000 180000 200000 220000





(PNGm3) 105 105 945 105 1155















156









157




4359317

Asia Market 3000000 4359317

-798683 4159317 04

Log Price Low

06 3859317


1

3000000 4159317 06

Log Price High

3609317

Other Market 2250000 3609317

-798683 3509317 04

Log Price Low

3359317


-842000 3959317 06

Log Price High

3859317

Asia Market 3000000 3859317

-798683 3659317 04

Log Price Low

04 3359317


1

2500000 3659317 06

Log Price High

3109317

Other Market 2250000 3109317

-798683 3009317 04

Log Price Low

2859317

2000000 2859317

1

3959317 06

Log Price High

2187692

Asia Market 1500000 2187692

-338308 2087692 04

Log Price Low

06 1937692


1

1500000 2087692 06

Log Price High

1812692

Other Market 1125000 1812692

-338308 1762692 04

Log Price Low

1687692


-474000 1987692 06

Log Price High

1937692

Asia Market 1500000 1937692

-338308 1837692 04

Log Price Low

04 1687692


1

1250000 1837692 06

Log Price High

1562692

Other Market 1125000 1562692

-338308 1512692 04

Log Price Low

1437692

1000000 1437692

158








159


4500

540

2805

198

1155

44000

11

11

176

1045

363

275

5400

108000

145800

145800

225

1575

1296

756

135000

180000

216000

216000

5400

135

405

2250

5500

660

2295

162

945

36000

9

9

144

855

297

225

6600

132000

178200

178200

275

1925

1584

924

165000

220000

264000

264000

6600

165

495

2750

33000003400000350000036000003700000380000039000004000000410000042000004300000440000045000004600000









No of fortnights




Chainsaw (PNGK)

















160












market

3300000

3400000

3500000

3600000

3700000

3800000

3900000

4000000

4100000

4200000

4300000

4400000

4500000

4600000

860 880 900 920 940 960 980 1000 1020 1040 1060 1080 1100 1120

EMV

(PN

GK

+-

10

B

ase

cas

e)










No of fortnights




Chainsaw (PNGK)
















161


Input Description

Variation

(10) Variable range



(PNGK) 30000 3000 27000 30000 33000





GISMapping (PNGK) 20000 2000 18000 20000 22000












Cha) 150 15 135 150 165




months) 16 16 144 16 176






market (PNGK17500)

162










High Price (PNGK)

79781735


-17500 76321135 04

Low Price

06 71130235

High Demand 31278500 71130235

1

39930000 76321135 06

High Price

69799235


-17500 67203785 04

Low Price

63310610

Carbon Trade 23458875 63310610

-60765 72860535 06

High Price

71130235


-17500 67669635 04

Low Price

04 62478735

Low Demand 31278500 62478735

1

1 31278500 67669635 06

72860535 High Price

61147735


-17500 58552285 04

Low Price

54659110

23458875 54659110

Do Nothing

0

0 0

163





increase by 10

135

1980

18

50

33000

22000

11000

22000

176

1925

11000

11000

11000

275

2200

935

77

715

385

135

165

2420

22

61

27000

18000

9000

18000

144

1575

9000

9000

9000

225

1800

765

63

585

315

165

47000004800000490000050000005100000520000053000005400000550000056000005700000580000059000006000000






GISMapping (PNGK)





Consultancy (PNGK)







Compass (Kcompass)




164


65 DISCUSSION















4700000

4800000

4900000

5000000

5100000

5200000

5300000

5400000

5500000

5600000

5700000

5800000

5900000

6000000

880 900 920 940 960 980 1000 1020 1040 1060 1080 1100 1120

EMV

(PN

GK

+-

10

B

ase

Cas

e)







GISMapping (PNGK)





Consultancy (PNGK)







Compass (Kcompass)



165







time of felling









trees ha-1










certified markets










166










(EMV)

























167













in the Yalu




















in a REDD+ project

168
















Scenarios

Starting

Capital

(PNGK)

Annual

EMVProfitLoss

(PNGK)


Local Processing



Log Export

CMU Managed 842000 3959317


Carbon Trade 60765c

72860535




169

66 CONCLUSIONS

























region

170

CHAPTER 7


71 INTRODUCTION


















in CBFM






171



72 BACKGROUND



























172








MSE method














173











174












175


Planning Level

Inventory Planning

Requirement





PNGFA



same as above


done but not a


Forest Inventory

Provincial Plans




Forest Management

Agreement Project


tender)






5 Year Working Plan
















Company As above

Annual Logging Plan


PMCP

1 Company As above


(harvesting plan)





be harvested Some




176



















donors












177


operations











STRENGTHS

WEAKNESSES













products


certified products







managed forest








guidelines







mechanisms



countries

178


































179






species refugia)














180






area


process




community needs





training











sufficient size


area









timber species







within 2 years








survey completed





working forest area



in place




181

73 METHODOLOGY














the pilot region







further analyses







scenarios

182

733 Planning System
























183

74 RESULTS




















interests











184


75 DISCUSSION















Stakeholder

Consultation

Field Interviews

PAR

Investigate

Options

Forest Inventory

Data

Planning System

Growth Data

Decision

Analyses Tools

Spreadsheet

Planning Tool

Decision Tree

Model

Annual Yield

Estimates

Management

Options

Payoff

Strategy

Decision

Alternatives

Uncertain

Events

EMV

Tornado

Chart

Spider Chart

Sensitivity

Analyses

Scenario

Evaluation amp

Analyses

Decision

Implementation

Scenario

Output

Feedback to

Stakeholders

185
































186

76 CONCLUSIONS


















187

CONCLUSIONS

188

CHAPTER 8


81 INTRODUCTION




















forests in PNG





189
























as follow


in PNG





190






communities


























191



























past four years

192


STUDY






























193





in CBFM



























194


















forests in PNG














195













PNG

88 DISCUSSION

















196


















89 CONCLUSIONS







are recommended







197








198

REFERENCES






1623-1648



91PNGFRI



Lae




Forestry Review 4










(ed) London UK DFID


McGraw Hill Inc USA




199


















Ecology 6 1-32







PNG JICA PNGFRI



56 985-998







200











240-252




Biology 23 1406-1417






997











Australia

201






6 1104-1123



Pottinger A



Oxford UK









Publishing












II) 210pp

202



















Rome Pp168-173


and Clarendon Press














203








Science 66 813-825



Working Document



I Jakarta Indonesia





410-419






Research










204


































205






Bangor UK















24 Yokohama Japan














206






41 555ndash562





















Institute Lae



Morobe Province



Document No 9

207






JICA-PNGFRI




























396

208


















36 3053-3062






2005






253-263




Organisations

209



Francisco CA










(CIFOR)






Nature 403 853-858












(CIFOR)

210






























15 221-232




University Press

211








105 107-119













Hohola May 1996









2005





212



University of Wales








875





Cengage Learning






Publications





ICES 2006 R 05






213

























PNG








214








Guinea








Ecology 15 620-626






967-979






131-137


19 357-371




215














Melbourne CSIRO












10 June 2009







Review 67 53-64

216





Technology






Papua New Guinea





Island Press







Melbourne CSIRO


Basil Blackwell Ltd




Press





217









145-155

























218



April 2008]



1999












905

219

APPENDICES


Forest Condition

No of Plots

Un-harvested 13





Total 118


PLOTNO PLOTID

FIRST

CENS

LAST

CENS

BA_FIRST

CENS

(m2ha

-1)

BA_LAST

CENS

(m2ha

-1)

1 DANAR03 2006

208470 No data

2 DANAR04 2006

77838 No data

3 HUVIV02 1999

253617 No data

4 KAUP_03 1998 2000 242586 216303

5 MARE_03 2001

237487 No data

6 SAGAR03 1998 2005 321673 332807

7 SASER03 2005

248061 No data

8 SASER04 2005

293279 No data

9 SOGER03 1998 2003 217693 239859

10 WATUT05 1997 1999 338812 253121

11 WATUT06 1997 1999 441607 286389

12 WCOST05 1998 2001 336952 344092

13 WCOST06 1998 2001 314374 328569

220


INCREASING BA


FIRST

CENS

LAST

CENS

BA_FIRST

CENS

(m2ha

-1)

BA_LAST

CENS

(m2ha

-1)

MBAI

(m2ha

-1yr

-

1)

1 ANUAL01 1993 1995 1999 168828 179791 02741

2 ANUAL02 1993 1995 1999 209696 214081 01096

3 ARI__01 1995 1996 2003 118680 164226 06506

4 ARI__02 1995 1996 2003 112410 134710 03186

5 CARAW01 1991 1995 2004 194671 221647 02997

6 CARAW02 1991 1995 2004 188221 212092 02652

7 CFORD01 1994 1995 2004 302147 340191 04227

8 EMBIH01 1992 1994 1999 130070 135086 01003

9 EMBIH02 1992 1994 1999 95760 103879 01624

10 EMBIH03 1993 1994 1999 138590 159763 04235

11 EMBIH04 1993 1994 1999 125500 164194 07739

12 GAR__01 1991 1993 1999 150426 172383 03660

13 GAR__02 1991 1993 1999 142926 165673 03791

14 GARAM01 1991 1994 2000 201981 221105 03187

15 GILUW01 1987 1993 2003 125896 137937 01204

16 GILUW02 1991 1994 2003 198455 199718 00140

17 HAWAN01 1993 1994 2002 130935 171417 05060

18 HAWAN02 1994 1994 2002 133950 168687 04342

19 KAPIU01 1991 1993 1997 130361 226460 24025

20 KAPIU02 1991 1993 2003 116672 282623 16595

21 KAUP_01 1996 1996 2000 195241 198719 00869

22 KAUP_02 1996 1996 2000 223736 229669 01483

23 KRISA01 1991 1994 1996 164044 174124 05040

24 KRISA02 1991 1994 1996 231445 239709 04132

25 KUI__01 1994 1994 2002 180250 204151 02988

26 LARK_03 1994 1996 1999 186482 186841 00120

27 MALAM01 1995 1995 2000 165864 219264 10680

28 MOKOL01 1980 1993 2004 243010 291990 04453

29 MOKOL02 1981 1993 2004 218361 242578 02202

30 MORER01 1997 1997 1999 161786 170147 04180

31 MOSAL01 1992 1993 2003 124213 199976 07576

32 MOSAL02 1992 1993 1997 119561 196195 19159

33 MUSAU01 1996 1996 1999 170058 174021 01321

34 MUSAU02 1995 1996 1999 170392 178642 02750

35 PASMA01 1993 1997 2004 172060 214776 04746

36 PASMA02 1993 1997 1999 195182 206363 05591

37 PUAL_01 1993 1994 2000 191461 191960 00083

38 PUAL_02 1994 1994 2000 151644 175568 03987

221


FIRST

CENS

LAST

CENS

BA_FIRST

CENS

(m2ha

-1)

BA_LAST

CENS

(m2ha

-1)

MBAI

(m2ha

-1yr

-1)

39 PUAL_03 1996 1996 1998 165854 175962 05054

40 PUAL_04 1996 1996 2004 172923 186604 01710

41 PULIE02 1997 1997 2004 109713 118248 01219

42 PULIE03 1997 1997 1999 198100 204913 03406

43 SAGAR01 1997 1998 2005 141514 153152 01663

44 SEMBE01 1996 1997 1999 134691 137005 01157

45 SERA_02 1996 1996 1998 174719 178179 01730

46 TURAM01 1994 1994 1998 245674 256188 02629

47 UMBOI01 1993 1994 2004 219117 245082 02597

48 UMBOI02 1993 1994 2001 174360 198924 03509

49 UMBUK01 1993 1993 2007 132607 163482 02205

50 UMBUK02 1993 1993 1999 107566 121284 02286

51 VAILA01 1993 1994 2002 146811 190990 05522

52 VAILA02 1993 1994 2002 175963 188018 01507

53 WASAP01 1986 1990 2003 184658 285293 07741

54 WASAP02 1987 1995 2003 131157 165941 04348

55 WATUT01 1992 1993 2003 139136 202128 06299

56 WATUT02 1992 1993 1998 138267 149267 02200

57 WAWOI01 1991 1994 1998 234345 256670 05581

58 WCOST03 1996 1996 2003 154697 189326 04947

59 WCOST04 1996 1996 2003 103386 104722 00191

60 WFBAY02 1981 1993 1999 182790 183297 00085

61 YALU_01 1995 1995 2007 126460 233236 08898

62 YALU_02 1995 1995 2007 162517 197775 02938

63 YEMA_01 1995 1996 2002 183911 201508 02933

222


FALLING BA


FIRST

CENS

LAST

CENS

BA_FIRST

CENS

(m2ha

-1)

BA_LAST

CENS

(m2ha

-1)

MBAI

(m2ha

-1yr

-1)

1 CFORD02 1995 1995 2004 1651825 1580870 -007883

2 GARAM02 1991 1994 1998 1806829 1620510 -031054

3 INPOM01 1993 1995 1997 1942872 1707170 -117852

4 KUI_02 1994 1994 2002 1561649 1478340 -010413

5 LARK_04 1994 1996 1999 1609460 1592510 -005649

6 MALAM02 1995 1995 2003 1959570 1434840 -065591

7 MORER02 1997 1997 1999 1443625 1390560 -026533

8 ORLAK01 1994 1994 2000 1891138 993640 -149582

9 ORLAK02 1994 1994 1994 1674760 1085680 -098180

10 PULIE01 1997 1997 2004 1807768 1076690 -104440

11 SAGAR02 1997 1998 2005 1735408 1716280 -002732

12 SEMBE02 1996 1997 1999 945672 888900 -028387

13 SERA_01 1996 1996 2000 2129906 2107070 -005708

14 TURAM02 1994 1994 1997 2540949 2561880 -010010

15 TURAM03 1996 1997 1999 1582846 1481270 -050786

16 VUDAL01 1997 1997 1999 762256 705470 -028393

17 VUDAL02 1996 1997 1999 1215035 1070640 -072196

18 WAWOI02 1994 1994 2000 2325639 1142410 -197204

19 WCOST01 1989 1995 1999 1202939 907100 -073959

20 WCOST02 1989 1995 1999 2470524 2172310 -074554

21 WFBAY01 1980 1993 1999 1720145 1404070 -052680

223



FIRST

CENS

LAST

CENS

BA_FIRST

CENS

(m2ha

-1)

BA_LAST

CENS

(m2ha

-1)

MBAI

(m2ha

-1yr

-

1)

1 CNIRD01 1994 1995 2004 236627 71393 -18359

2 CNIRD02 1994 1995 2007 230366 35539 -16236

3 HUVIV01 1997 1997


4 IVAIN01 1995 1996 2003 163578 58564 -15002

5 IVAIN02 1995 1996 2003 99191 49083 -07158

6 IVAIN03 1995 1996 1998 130492 119804 -05344

7 IVAIN04 1995 1996 1998 168716 129575 -19570

8 KAPUL01 1993 1993 1999 146181 96334 -08308

9 KAPUL02 1993 1993 2003 117906 26473 -09143

10 KAUT_01 1993 1993 1997 129425 146797 04343

11 KAUT_02 1993 1993 1997 122872 124960 00522

12 LARK_01 1994 1995 1999 236381 191211 -11292

13 LARK_02 1994 1995 1999 214359 236409 05513

14 MAUBU01 1995 1996


15 MAUBU02 1995 1996


16 OOMSI01 1979 1993 1997 209554 221536 02996

17 OOMSI02 1980 1993 1997 189978 211015 05259

18 SOGER01 1996 1996


19 SOGER02 1996 1996


20 WIMAR01 1993 1994 2000 185575 170570 -02501

21 WIMAR02 1993 1994 2000 230218 160777 -11574


BA BA

BA

gained BA BA

BA lost

After

Pre-

1997 1997

Meas

Period

Before

Fire 1997

Post-

1997

Meas

Period Fire

PLOTID

(m2ha

-

1)

(m2ha

-

1) (years) ()

(m2ha

-

1)

(m2ha

-

1) (years) ()

CNIRD01 2366 2443 2 163 2443 714 7 1612

CNIRD02 2304 2355 2 023 2355 355 10 1723

IVAIN01 1636 1680 1 269 1680 586 6 1611

IVAIN02 992 993 1 009 993 491 6 1108

KAPUL01 1462 1736 4 506 1736 963 2 2550

KAPUL02 1180 1299 4 264 1299 265 6 2328

LARK01 1961 2364 2 891 2364 1912 2 104

LARK02 2144 2231 2 205 2231 2364 2 317

WIMAR01 1856 1924 3 124 1924 1706 3 394

WIMAR02 2264 2302 3 056 2302 1608 3 1078

224


AREA


Tree


1 484643 9268927 4072009 1 PTE IND 13 18

Secondary

Forest - Yalu

1 484643 9268927 4072009 2 TRE 13 27

Secondary

Forest - Yalu

1 484643 9268927 4072009 3 HIB 13 29

Secondary

Forest - Yalu

1 484643 9268927 4072009 4 MAC 13 17

Secondary

Forest - Yalu

1 484643 9268927 4072009 5 HIB 13 335

Secondary

Forest - Yalu

1 484643 9268927 4072009 6 13 30

Secondary

Forest - Yalu

1 484643 9268927 4072009 7 13 30

Secondary

Forest - Yalu

1 484643 9268927 4072009 8 PTE IND 13 51

Secondary

Forest - Yalu

1 484643 9268927 4072009 9 TRE 13 33

Secondary

Forest - Yalu

1 484643 9268927 4072009 10 13 20

Secondary

Forest - Yalu

1 484643 9268927 4072009 11 POM PIN 13 245

Secondary

Forest - Yalu

1 484643 9268927 4072009 12 13 40

Secondary

Forest - Yalu

1 484643 9268927 4072009 13 13 30

Secondary

Forest - Yalu

1 484643 9268927 4072009 14 HIB 13 39

Secondary

Forest - Yalu

1 484643 9268927 4072009 15 TRE 13 225

Secondary

Forest - Yalu

1 484643 9268927 4072009 16 TER 13 26

Secondary

Forest - Yalu

2 484713 9268265 4072009 1 AIL 2 88

Primary Forest

- Yalu

2 484713 9268265 5072009 2 MYR 13 22

Primary Forest

- Yalu

2 484713 9268265 6072009 3 CEL PHI 13 175

Primary Forest

- Yalu

2 484713 9268265 7072009 4 STE 13 60

Primary Forest

- Yalu

225


Tree


2 484713 9268265 8072009 5 CEL LAT 13 335

Primary Forest

- Yalu

2 484713 9268265 9072009 6 VIT 2 95

Primary Forest

- Yalu

2 484713 9268265 10072009 7 POM TOM 13 123

Primary Forest

- Yalu

2 484713 9268265 11072009 8 CHN 13 18

Primary Forest

- Yalu

2 484713 9268265 12072009 9 MYR 13 129

Primary Forest

- Yalu

2 484713 9268265 13072009 10 NEU 13 225

Primary Forest

- Yalu

2 484713 9268265 14072009 11 PTE IND 13 47

Primary Forest

- Yalu

2 484713 9268265 15072009 12 POM PIN 13 48

Primary Forest

- Yalu

2 484713 9268265 16072009 13 LIT 2 29

Primary Forest

- Yalu

2 484713 9268265 17072009 14 PIM AMB 13 27

Primary Forest

- Yalu

2 484713 9268265 18072009 15 LIT 2 435

Primary Forest

- Yalu

2 484713 9268265 19072009 16 MYR 13 42

Primary Forest

- Yalu

2 484713 9268265 20072009 17 CEL PHI 3 73

Primary Forest

- Yalu

2 484713 9268265 21072009 18 CEL PHI 2 40

Primary Forest

- Yalu

3 484634 9268819 17062009 1 TRH 13 365

Secondary

Forest - Yalu

3 484634 9268819 17062009 2 TRH 13 359

Secondary

Forest - Yalu

3 484634 9268819 17062009 3 SEM 13 110

Secondary

Forest - Yalu

3 484634 9268819 17062009 4 TER 13 600

Secondary

Forest - Yalu

3 484634 9268819 17062009 5 STE 13 253

Secondary

Forest - Yalu

3 484634 9268819 17062009 6 POM PIN 13 570

Secondary

Forest - Yalu

3 484634 9268819 17062009 7 TER 13 630

Secondary

Forest - Yalu

3 484634 9268819 17062009 8 HIB 13 435

Secondary

Forest - Yalu

226


Tree


3 484634 9268819 17062009 9 INO FAG 13 600

Secondary

Forest - Yalu

3 484634 9268819 17062009 10 BUC 13 230

Secondary

Forest - Yalu

3 484634 9268819 17062009 11 TRH 13 313

Secondary

Forest - Yalu

3 484634 9268819 17062009 12 PIS UMB 13 220

Secondary

Forest - Yalu

3 484634 9268819 17062009 13 PTE IND 13 120

Secondary

Forest - Yalu

4 484630 9268763 17062009 1 POM PIN 13 280

Secondary

Forest - Yalu

4 484630 9268763 17062009 2 POM PIN 13 359

Secondary

Forest - Yalu

4 484630 9268763 17062009 3 END 13 370

Secondary

Forest - Yalu

4 484630 9268763 17062009 4 13 300

Secondary

Forest - Yalu

4 484630 9268763 17062009 5 MAC 13 225

Secondary

Forest - Yalu

4 484630 9268763 17062009 6 TOO SUR 13 325

Secondary

Forest - Yalu

4 484630 9268763 17062009 7 TOO SUR 13 305

Secondary

Forest - Yalu

4 484630 9268763 17062009 8 MAC 13 230

Secondary

Forest - Yalu

4 484630 9268763 17062009 9 PTE IND 13 220

Secondary

Forest - Yalu

4 484630 9268763 17062009 10 PTE IND 13 239

Secondary

Forest - Yalu

4 484630 9268763 17062009 11 TRH 13 235

Secondary

Forest - Yalu

4 484630 9268763 17062009 12 VIT 13 163

Secondary

Forest - Yalu

4 484630 9268763 17062009 13 SEM 13 128

Secondary

Forest - Yalu

4 484630 9268763 17062009 14 TRI 13 306

Secondary

Forest - Yalu

4 484630 9268763 17062009 15 TRI 13 284

Secondary

Forest - Yalu

4 484630 9268763 17062009 16 POM PIN 13 250

Secondary

Forest - Yalu

5 484646 9268686 17062009 1 TIM 13 143

Secondary

Forest - Yalu

227


Tree


5 484646 9268686 17062009 2 GUI 13 129

Secondary

Forest - Yalu

5 484646 9268686 17062009 3 PTE IND 13 130

Secondary

Forest - Yalu

5 484646 9268686 17062009 4 PTE IND 13 253

Secondary

Forest - Yalu

5 484646 9268686 17062009 5 FIC 13 335

Secondary

Forest - Yalu

5 484646 9268686 17062009 6 TRI 13 286

Secondary

Forest - Yalu

5 484646 9268686 17062009 7 FIC 13 278

Secondary

Forest - Yalu

5 484646 9268686 17062009 8 PTE IND 13 253

Secondary

Forest - Yalu

5 484646 9268686 17062009 9 TRH 13 411

Secondary

Forest - Yalu

5 484646 9268686 17062009 10 ELA 13 583

Secondary

Forest - Yalu

5 484646 9268686 17062009 11 STE 13 272

Secondary

Forest - Yalu

5 484646 9268686 17062009 12 ART 13 301

Secondary

Forest - Yalu

5 484646 9268686 17062009 13 PTE IND 13 204

Secondary

Forest - Yalu

5 484646 9268686 17062009 14 PTE IND 13 153

Secondary

Forest - Yalu

5 484646 9268686 17062009 15 SEM 13 95

Secondary

Forest - Yalu

5 484646 9268686 17062009 16 SEM 13 118

Secondary

Forest - Yalu

5 484646 9268686 17062009 17 TRI 13 275

Secondary

Forest - Yalu

5 484646 9268686 17062009 18 TRH 13 258

Secondary

Forest - Yalu

5 484646 9268686 17062009 19 TRH 13 250

Secondary

Forest - Yalu

5 484646 9268686 17062009 20 TRH 13 328

Secondary

Forest - Yalu

5 484646 9268686 17062009 21 TIM 13 288

Secondary

Forest - Yalu

6 _ _ 17062009 1 TRH 13 167

Secondary

Forest - Yalu

6 _ _ 17062009 2 PTE IND 13 152

Secondary

Forest - Yalu

228


Tree


6 _ _ 17062009 3 PTE IND 13 192

Secondary

Forest - Yalu

6 _ _ 17062009 4 PTE IND 13 158

Secondary

Forest - Yalu

6 _ _ 17062009 5 FIC 13 506

Secondary

Forest - Yalu

6 _ _ 17062009 6 TIM 13 218

Secondary

Forest - Yalu

6 _ _ 17062009 7 STR 13 101

Secondary

Forest - Yalu

6 _ _ 17062009 8 LIT 13 249

Secondary

Forest - Yalu

6 _ _ 17062009 9 MAC 13 264

Secondary

Forest - Yalu

6 _ _ 17062009 10 FIC 13 275

Secondary

Forest - Yalu

6 _ _ 17062009 11 PTE IND 13 350

Secondary

Forest - Yalu

6 _ _ 17062009 12 DYS 13 183

Secondary

Forest - Yalu

6 _ _ 17062009 13 TRH 13 235

Secondary

Forest - Yalu

6 _ _ 17062009 14 TRH 13 266

Secondary

Forest - Yalu

6 _ _ 17062009 15 ART 13 212

Secondary

Forest - Yalu

6 _ _ 17062009 16 TRI 13 260

Secondary

Forest - Yalu

6 _ _ 17062009 17 TRI 13 117

Secondary

Forest - Yalu

7 484761 9268629 17062009 1 TIM 13 159

Secondary

Forest - Yalu

7 484761 9268629 17062009 2 TIM 13 156

Secondary

Forest - Yalu

7 484761 9268629 17062009 3 EUO 13 351

Secondary

Forest - Yalu

7 484761 9268629 17062009 4 TRH 13 215

Secondary

Forest - Yalu

7 484761 9268629 17062009 5 TRH 13 336

Secondary

Forest - Yalu

7 484761 9268629 17062009 6 PTE IND 13 305

Secondary

Forest - Yalu

7 484761 9268629 17062009 7 POM PIN 13 284

Secondary

Forest - Yalu

229


Tree


7 484761 9268629 17062009 8 INT 13 256

Secondary

Forest - Yalu

7 484761 9268629 17062009 9 ANT CHI 13 172

Secondary

Forest - Yalu

7 484761 9268629 17062009 10 MYR 13 142

Secondary

Forest - Yalu

7 484761 9268629 17062009 11 TIM 13 226

Secondary

Forest - Yalu

7 484761 9268629 17062009 12 13 470

Secondary

Forest - Yalu

7 484761 9268629 17062009 13 ART 13 313

Secondary

Forest - Yalu

7 484761 9268629 17062009 14 VIT COF 13 241

Secondary

Forest - Yalu

7 484761 9268629 17062009 15 PTE IND 13 198

Secondary

Forest - Yalu

7 484761 9268629 17062009 16 MAC 13 398

Secondary

Forest - Yalu

7 484761 9268629 17062009 17 MAC 13 214

Secondary

Forest - Yalu

7 484761 9268629 17062009 18 MAC 13 190

Secondary

Forest - Yalu

7 484761 9268629 17062009 19 GUI 13 244

Secondary

Forest - Yalu

7 484761 9268629 17062009 20 TIM 13 247

Secondary

Forest - Yalu

7 484761 9268629 17062009 21 SEM 13 142

Secondary

Forest - Yalu

7 484761 9268629 17062009 22 SEM 13 156

Secondary

Forest - Yalu

7 484761 9268629 17062009 23 SEM 13 163

Secondary

Forest - Yalu

7 484761 9268629 17062009 24 PTE IND 13 316

Secondary

Forest - Yalu

7 484761 9268629 17062009 25 ANT CHI 13 251

Secondary

Forest - Yalu

7 484761 9268629 17062009 26 ANT CHI 13 210

Secondary

Forest - Yalu

7 484761 9268629 17062009 27 TIM 13 266

Secondary

Forest - Yalu

7 484761 9268629 17062009 28 TIM 13 151

Secondary

Forest - Yalu

8 484610 9268470 17062009 1 TRH 13 260

Secondary

Forest - Yalu

230


Tree


8 484610 9268470 17062009 2 EUO 13 142

Secondary

Forest - Yalu

8 484610 9268470 17062009 3 EUO 13 118

Secondary

Forest - Yalu

8 484610 9268470 17062009 4 TIM 13 211

Secondary

Forest - Yalu

8 484610 9268470 17062009 5 PTE IND 13 294

Secondary

Forest - Yalu

8 484610 9268470 17062009 6 HIB 13 792

Secondary

Forest - Yalu

8 484610 9268470 17062009 7 TRH 13 411

Secondary

Forest - Yalu

8 484610 9268470 17062009 8 ART 13 1135

Secondary

Forest - Yalu

8 484610 9268470 17062009 9 PTE IND 13 198

Secondary

Forest - Yalu

8 484610 9268470 17062009 10 TRH 13 520

Secondary

Forest - Yalu

8 484610 9268470 17062009 11 MAC 13 233

Secondary

Forest - Yalu

8 484610 9268470 17062009 12 POL 13 261

Secondary

Forest - Yalu

8 484610 9268470 17062009 13 CAN 13 316

Secondary

Forest - Yalu

8 484610 9268470 17062009 14 POM PIN 13 472

Secondary

Forest - Yalu

8 484610 9268470 17062009 15 EUO 13 116

Secondary

Forest - Yalu

8 484610 9268470 17062009 16 PTE IND 13 114

Secondary

Forest - Yalu

8 484610 9268470 17062009 17 CAN 13 281

Secondary

Forest - Yalu

8 484610 9268470 17062009 18 POM PIN 13 561

Secondary

Forest - Yalu

8 484610 9268470 17062009 19 ANT CHI 13 283

Secondary

Forest - Yalu

8 484610 9268470 17062009 20 POM PIN 13 196

Secondary

Forest - Yalu

8 484610 9268470 17062009 21 EUO 13 500

Secondary

Forest - Yalu

8 484610 9268470 17062009 22 FIC 13 246

Secondary

Forest - Yalu

8 484610 9268470 17062009 23 FIC 13 246

Secondary

Forest - Yalu

231


Tree


8 484610 9268470 17062009 24 TRI 13 153

Secondary

Forest - Yalu

9 484522 92685314 17062009 1 SEM 13 540

Secondary

Forest - Yalu

9 484522 92685314 17062009 2 INO FAG 13 550

Secondary

Forest - Yalu

9 484522 92685314 17062009 3 BUC 13 369

Secondary

Forest - Yalu

9 484522 92685314 17062009 4 ANT CHI 13 505

Secondary

Forest - Yalu

9 484522 92685314 17062009 5 GUI 13 195

Secondary

Forest - Yalu

9 484522 92685314 17062009 6 LIT 13 355

Secondary

Forest - Yalu

9 484522 92685314 17062009 7 PIS UMB 13 300

Secondary

Forest - Yalu

9 484522 92685314 17062009 8 SEM 13 371

Secondary

Forest - Yalu

9 484522 92685314 17062009 9 PIS UMB 13 172

Secondary

Forest - Yalu

9 484522 92685314 17062009 10 PIS UMB 13 153

Secondary

Forest - Yalu

9 484522 92685314 17062009 11 BRI 13 1800

Secondary

Forest - Yalu

9 484522 92685314 17062009 12 VIT COF 13 1800

Secondary

Forest - Yalu

9 484522 92685314 17062009 13 TER 13 201

Secondary

Forest - Yalu

9 484522 92685314 17062009 14 PIS UMB 13 196

Secondary

Forest - Yalu

9 484522 92685314 17062009 15 PTE IND 13 1850

Secondary

Forest - Yalu

10 484446 9268164 17062009 1 END 13 381

Secondary

Forest - Yalu

10 484446 9268164 17062009 2 CAN 13 548

Secondary

Forest - Yalu

10 484446 9268164 17062009 3 MAC 13 346

Secondary

Forest - Yalu

10 484446 9268164 17062009 4 MAC 13 289

Secondary

Forest - Yalu

10 484446 9268164 17062009 5 MAC 13 336

Secondary

Forest - Yalu

10 484446 9268164 17062009 6 PTE IND 13 324

Secondary

Forest - Yalu

232


Tree


10 484446 9268164 17062009 7 CAN 13 375

Secondary

Forest - Yalu

10 484446 9268164 17062009 8 MAC 13 274

Secondary

Forest - Yalu

10 484446 9268164 17062009 9 MAC 13 393

Secondary

Forest - Yalu

10 484446 9268164 17062009 10 PTE IND 13 180

Secondary

Forest - Yalu

10 484446 9268164 17062009 11 ANT CHI 13 507

Secondary

Forest - Yalu

10 484446 9268164 17062009 12 STE 13 165

Secondary

Forest - Yalu

10 484446 9268164 17062009 13 CEL 13 570

Secondary

Forest - Yalu

10 484446 9268164 17062009 14 LIT 13 394

Secondary

Forest - Yalu

10 484446 9268164 17062009 15 STE AMP 13 107

Secondary

Forest - Yalu

10 484446 9268164 17062009 16 PTE IND 13 195

Secondary

Forest - Yalu

10 484446 9268164 17062009 17 LIT 13 130

Secondary

Forest - Yalu

10 484446 9268164 17062009 18 PIM AMB 13 234

Secondary

Forest - Yalu

10 484446 9268164 17062009 19 ANT CHI 13 517

Secondary

Forest - Yalu

10 484446 9268164 17062009 20 AGL 13 180

Secondary

Forest - Yalu

10 484446 9268164 17062009 21 ALS 13 192

Secondary

Forest - Yalu

10 484446 9268164 17062009 22 STE 13 265

Secondary

Forest - Yalu

10 484446 9268164 17062009 23 MIC 13 201

Secondary

Forest - Yalu

10 484446 9268164 17062009 24 PTE IND 13 1860

Secondary

Forest - Yalu

11 484612 9268157 17062009 1 FIC 13 375

Secondary

Forest - Yalu

11 484612 9268157 17062009 2 PLA 13 130

Secondary

Forest - Yalu

11 484612 9268157 17062009 3 INO FAG 13 242

Secondary

Forest - Yalu

11 484612 9268157 17062009 4 STE 13 690

Secondary

Forest - Yalu

233


Tree


11 484612 9268157 17062009 5 PIM AMB 13 466

Secondary

Forest - Yalu

11 484612 9268157 17062009 6 GNE GNE 13 158

Secondary

Forest - Yalu

11 484612 9268157 17062009 7 PIM AMB 13 255

Secondary

Forest - Yalu

11 484612 9268157 17062009 8 PIM AMB 13 385

Secondary

Forest - Yalu

11 484612 9268157 17062009 9 GNE GNE 13 130

Secondary

Forest - Yalu

11 484612 9268157 17062009 10 PIM AMB 13 255

Secondary

Forest - Yalu

11 484612 9268157 17062009 11 PIM AMB 13 260

Secondary

Forest - Yalu

11 484612 9268157 17062009 12 CEL 13 180

Secondary

Forest - Yalu

11 484612 9268157 17062009 13 CEL 13 255

Secondary

Forest - Yalu

11 484612 9268157 17062009 14 GUI 13 290

Secondary

Forest - Yalu

11 484612 9268157 17062009 15 CEL 13 715

Secondary

Forest - Yalu

11 484612 9268157 17062009 16 STE 13 700

Secondary

Forest - Yalu

11 484612 9268157 17062009 17 MIC 13 210

Secondary

Forest - Yalu

11 484612 9268157 17062009 18 PIM AMB 13 346

Secondary

Forest - Yalu

11 484612 9268157 17062009 19 MIS 13 246

Secondary

Forest - Yalu

11 484612 9268157 17062009 20 CEL 13 700

Secondary

Forest - Yalu

11 484612 9268157 17062009 21 CEL 13 496

Secondary

Forest - Yalu

12 484699 9268074 17062009 1 INT 20 926

Secondary

Forest - Yalu

12 484699 9268074 17062009 2 TER 13 634

Secondary

Forest - Yalu

12 484699 9268074 17062009 3 SEM 13 430

Secondary

Forest - Yalu

12 484699 9268074 17062009 4 TER 13 293

Secondary

Forest - Yalu

12 484699 9268074 17062009 5 PIM AMB 13 260

Secondary

Forest - Yalu

234


Tree


12 484699 9268074 17062009 6 PIM AMB 13 250

Secondary

Forest - Yalu

12 484699 9268074 17062009 7 PIM AMB 13 310

Secondary

Forest - Yalu

12 484699 9268074 17062009 8 SYZ 20 560

Secondary

Forest - Yalu

12 484699 9268074 17062009 9 TRI 20 1300

Secondary

Forest - Yalu

12 484699 9268074 17062009 10 13 180

Secondary

Forest - Yalu

12 484699 9268074 17062009 11 PIS UMB 13 320

Secondary

Forest - Yalu

12 484699 9268074 17062009 12 LIT 13 150

Secondary

Forest - Yalu

12 484699 9268074 17062009 13 TRI 13 471

Secondary

Forest - Yalu

12 484699 9268074 17062009 14 STE 13 284

Secondary

Forest - Yalu

12 484699 9268074 17062009 15 CER 13 252

Secondary

Forest - Yalu

12 484699 9268074 17062009 16 INT 13 825

Secondary

Forest - Yalu

12 484699 9268074 17062009 17 TER 30 450

Secondary

Forest - Yalu

13 484743 9268126 17062009 1 PIM AMB 13 420

Secondary

Forest - Yalu

13 484743 9268126 17062009 2 CEL 13 490

Secondary

Forest - Yalu

13 484743 9268126 17062009 3 MIC 13 130

Secondary

Forest - Yalu

13 484743 9268126 17062009 4 PTE IND 13 530

Secondary

Forest - Yalu

13 484743 9268126 17062009 5 CEL 13 761

Secondary

Forest - Yalu

13 484743 9268126 17062009 6 CEL 13 420

Secondary

Forest - Yalu

13 484743 9268126 17062009 7 CEL 13 340

Secondary

Forest - Yalu

13 484743 9268126 17062009 8 PTE IND 40 705

Secondary

Forest - Yalu

13 484743 9268126 17062009 9 MAC 13 320

Secondary

Forest - Yalu

13 484743 9268126 17062009 10 MAC 13 460

Secondary

Forest - Yalu

235


Tree


13 484743 9268126 17062009 11 END 13 300

Secondary

Forest - Yalu

13 484743 9268126 17062009 12 MAC 13 190

Secondary

Forest - Yalu

13 484743 9268126 17062009 13 MAC 13 203

Secondary

Forest - Yalu

13 484743 9268126 17062009 14 ART 13 220

Secondary

Forest - Yalu

13 484743 9268126 17062009 15 PTE IND 13 525

Secondary

Forest - Yalu

13 484743 9268126 17062009 16 MAC 13 124

Secondary

Forest - Yalu

13 484743 9268126 17062009 17 AGL 13 415

Secondary

Forest - Yalu

14 484837 9268212 17062009 1 GAR 20 291

Secondary

Forest - Yalu

14 484837 9268212 17062009 2 AGL 13 280

Secondary

Forest - Yalu

14 484837 9268212 17062009 3 TER 13 364

Secondary

Forest - Yalu

14 484837 9268212 17062009 4 TER 13 330

Secondary

Forest - Yalu

14 484837 9268212 17062009 5 PIS UMB 13 156

Secondary

Forest - Yalu

14 484837 9268212 17062009 6 POM PIN 13 584

Secondary

Forest - Yalu

14 484837 9268212 17062009 7 TER 13 365

Secondary

Forest - Yalu

14 484837 9268212 17062009 8 END 13 396

Secondary

Forest - Yalu

14 484837 9268212 17062009 9 TER 13 233

Secondary

Forest - Yalu

14 484837 9268212 17062009 10 STE 13 630

Secondary

Forest - Yalu

15 484784 9268298 17062009 1 CEL 13 367

Secondary

Forest - Yalu

15 484784 9268298 17062009 2 PIM AMB 13 360

Secondary

Forest - Yalu

15 484784 9268298 17062009 3 CEL 15 619

Secondary

Forest - Yalu

15 484784 9268298 17062009 4 DYS 13 240

Secondary

Forest - Yalu

15 484784 9268298 17062009 5 LIT 13 465

Secondary

Forest - Yalu

236


Tree


15 484784 9268298 17062009 6 FIC 40 1500

Secondary

Forest - Yalu

15 484784 9268298 17062009 7 POM PIN 13 579

Secondary

Forest - Yalu

15 484784 9268298 17062009 8 MIS 13 278

Secondary

Forest - Yalu

15 484784 9268298 17062009 9 CEL 40 570

Secondary

Forest - Yalu

15 484784 9268298 17062009 10 LIT 13 294

Secondary

Forest - Yalu

15 484784 9268298 17062009 11 ANT CHI 13 434

Secondary

Forest - Yalu

15 484784 9268298 17062009 12 PIS UMB 13 236

Secondary

Forest - Yalu

15 484784 9268298 17062009 13 GNE GNE 13 150

Secondary

Forest - Yalu

15 484784 9268298 17062009 14 CEL 15 603

Secondary

Forest - Yalu

16 484840 9268332 17062009 1 INT 13 570

Secondary

Forest - Yalu

16 484840 9268332 17062009 2 MIC 13 246

Secondary

Forest - Yalu

16 484840 9268332 17062009 3 CEL 40 750

Secondary

Forest - Yalu

16 484840 9268332 17062009 4 POM PIN 20 286

Secondary

Forest - Yalu

16 484840 9268332 17062009 5 MIC 13 240

Secondary

Forest - Yalu

16 484840 9268332 17062009 6 TRI 13 176

Secondary

Forest - Yalu

16 484840 9268332 17062009 7 FIC 13 120

Secondary

Forest - Yalu

16 484840 9268332 17062009 8 PIM AMB 13 287

Secondary

Forest - Yalu

16 484840 9268332 17062009 9 GNE GNE 13 146

Secondary

Forest - Yalu

16 484840 9268332 17062009 10 PIM AMB 13 250

Secondary

Forest - Yalu

16 484840 9268332 17062009 11 BIS JAV 13 605

Secondary

Forest - Yalu

16 484840 9268332 17062009 12 STE 13 553

Secondary

Forest - Yalu

16 484840 9268332 17062009 13 PIM AMB 13 378

Secondary

Forest - Yalu

237


Tree


17 484890 9268434 17062009 1 PTE IND 13 323

Secondary

Forest - Yalu

17 484890 9268434 17062009 2 ART 15 733

Secondary

Forest - Yalu

17 484890 9268434 17062009 3 POM PIN 30 705

Secondary

Forest - Yalu

17 484890 9268434 17062009 4 DRA 30 680

Secondary

Forest - Yalu

17 484890 9268434 17062009 5 HOR 13 250

Secondary

Forest - Yalu

17 484890 9268434 17062009 6 MAC 13 143

Secondary

Forest - Yalu

17 484890 9268434 17062009 7 PTE IND 15 623

Secondary

Forest - Yalu

17 484890 9268434 17062009 8 CEL 30 664

Secondary

Forest - Yalu

17 484890 9268434 17062009 9 PTE IND 13 220

Secondary

Forest - Yalu

17 484890 9268434 17062009 10 PTE IND 13 170

Secondary

Forest - Yalu

17 484890 9268434 17062009 11 PTE IND 13 140

Secondary

Forest - Yalu



Tree


1 469324 9256048 4062009 1 POM PIN 3 695

Logged Forest -

Gabensis

1 469324 9256048 4062009 2 INT 13 59

Logged Forest -

Gabensis

1 469324 9256048 4062009 3 CHN 13 61

Logged Forest -

Gabensis

1 469324 9256048 4062009 4 TER 2 43

Logged Forest -

Gabensis

1 469324 9256048 4062009 5 POM PIN 2 59

Logged Forest -

Gabensis

1 469324 9256048 4062009 6 POM PIN 2 59

Logged Forest -

Gabensis

1 469324 9256048 4062009 7 POM PIN 13 70

Logged Forest -

Gabensis

238


Tree


1 469324 9256048 4062009 8 CHN 13 555

Logged Forest -

Gabensis

1 469324 9256048 4062009 9 INT 15 28

Logged Forest -

Gabensis

1 469324 9256048 4062009 10 TER 2 535

Logged Forest -

Gabensis

1 469324 9256048 4062009 11 TER 13 40

Logged Forest -

Gabensis

1 469324 9256048 4062009 12 HRN 13 365

Logged Forest -

Gabensis

1 469324 9256048 4062009 13 CHN 18 52

Logged Forest -

Gabensis

1 469324 9256048 4062009 14 CNN 18 575

Logged Forest -

Gabensis

1 469324 9256048 4062009 15 CHN 18 385

Logged Forest -

Gabensis

1

469324

9256048

4062009

16

CHN

18

33

Logged Forest-

Gabensis

1 469324 9256048 4062009 17 POM PIN 13 305

Logged Forest -

Gabensis

1 469324 9256048 4062009 18 PLA 13 30

Logged Forest -

Gabensis

1 469324 9256048 4062009 19 13 20

Logged Forest -

Gabensis

2 470782 9257001 4062009 1 HRN 13 43

Secondary Forest -

Gabensis

2 470782 9257001 4062009 2 POM PIN 2 55

Secondary Forest -

Gabensis

2 470782 9257001 4062009 3 CHN 2 94

Secondary Forest -

Gabensis

2 470782 9257001 4062009 4 13 30

Secondary Forest -

Gabensis

2 470782 9257001 4062009 5 13 30

Secondary Forest -

Gabensis

2 470782 9257001 4062009 6 PTE IND 2 85

Secondary Forest -

Gabensis

2 470782 9257001 4062009 7 13 30

Secondary Forest -

Gabensis

2 470782 9257001 4062009 8 13 30

Secondary Forest -

Gabensis

2 470782 9257001 4062009 9 13 30

Secondary Forest -

Gabensis

2 470782 9257001 4062009 10 13 30

Secondary Forest -

Gabensis

239


Tree


2 470782 9257001 4062009 11 PTE IND 2 57

Secondary Forest -

Gabensis

2 470782 9257001 4062009 12 PTE IND 13 31

Secondary Forest -

Gabensis

2 470782 9257001 4062009 13 MAS 2 55

Secondary Forest -

Gabensis

2 470782 9257001 4062009 14 POM PIN 2 41

Secondary Forest -

Gabensis

2 470782 9257001 4062009 15 POM PIN 2 47

Secondary Forest -

Gabensis

2 470782 9257001 4062009 16 13 30

Secondary Forest -

Gabensis

2 470782 9257001 4062009 17 POM PIN 15 43

Secondary Forest -

Gabensis

2 470782 9257001 4062009 18 PTE IND 15 80

Secondary Forest -

Gabensis

240



EAG Eaglewood

1 2 3















TAU Taun

TEA Teak

TER Terminalia

WAL Walnut PNG



































5 6















STR Steropsis

241


242


243














244

















K1500m3









apply









K1500m3






245




















K450m3























K450m3






246








GIS Mapping K20000






Marketing K10000












Authors

Yosi Cossey Keosai

Title


Date

2011

Citation




Persistent Link


File Description








iv

DECLARATION






___________________

Cossey Keosai Yosi

July 2011

v

DEDICATION
































vi

PREFACE













James E Smith

















Research Canberra

vii

ACKNOWLEDGEMENTS















this study














assistance

viii


























TABLE OF CONTENTS


INTRODUCTION 1







MANAGEMENT 28



x





IN PNG 65



1) 74











xi









56 CONCLUSIONS 134







66 CONCLUSIONS 169






xii




CONCLUSIONS 187








REFERENCES 198

APPENDICES 219


LIST OF TABLES




a 100


-1) 101






xiv

LIST OF FIGURES








xv

LIST OF ACRONYMS




asl Above Sea Level

BA Basal Area


C Carbon





Resources





CO2 Carbon Dioxide







Melbourne



Government






xvi























m2 ha


m3 ha


mm annum-1








N ha-1




xvii



























UK United Kingdom








INTRODUCTION

2

CHAPTER 1






























3


































4

































5

































6




















to continue








7

13 BACKGROUND































8






























9






















-1 (Keenan et al

2005)


per











10










3 ha




timber














1980)







11













TRP












12














Forest Policy













the following






13













implementation





















14

















Production











15

1331 Forest Types










tropical forest























16



4800m asl
























2007 )




17


-




2008000m3 in 2007




0

50

100

150

200

250

300

00

05

10

15

20

25

30

35

40

Are

a H

arv

este

d (

00

0 h

a)

Harv

este

d T

imb

er V

olu

me

(Mil

lion

m3)

Year

Harvested

volume

Harvested area

0

500

1000

1500

2000

2500

2002 2003 2004 2005 2006 2007

Volu

me

(0

00

m3

)

Year

Logs

Sawn

Ply

Veneer

18

































19




















20


































21


timber















community hall



22





















(a)

(b)

23





McAlpine (1995)



Plots

Date of

Establishment

Forest Type

Gulf

Western

Oro

Milne Bay

Central

Turama

Vailala

Oriomo

Wawoi Guavi

Embi Hanau

Gara Modewa

Ormand Lako

Iva Inika

2

2

2

2

4

2

2

2

091194

271194

121094

261094

200594

120694

070894

160396

Lowland Foot Hills

Lowland Plain

W (Lowland Plain)

HmFswWsw (Lowland

FHills)

Pl (Lowland Plain)


Hs (Lowland Hill)


Morobe

Madang

East Sepik

Sandaun

Oomsis

Trans Watut

Umboi

Kui

Yema Gaiapa

North Coast

Rai Coast

Hawain

Pual

Krisa

2

2

2

2

1

2

2

2

2

2

260593

261093

151294

121194

150596

200395

060495

090894

240894

100994


LN (Lower Mountain)

Hl (Lowland Plain)

Hm (Lowland Hill)

Hm (Lowland Hill)

Hm9 (Lowland Hill)

Hm (Lowland Hill)

(Lowland Hill)


(Lowland Hill)

Southern

Highlands

MtGiluwe 2


West New

Britain

East New Britain

New Ireland

Manus

Kapiura

Mosa Leim

Kapuluk

Central Arawe

Anu Alimbit

Pasisi Manua

Open Bay

Gar

Waterfall Bay

Lassul Bay

Cape Orford

Inland Pomio

Kaut

Umbukul

Central NI

Lark

West Coast

2

2

2

2

2

2

2

2

2

2

2

1

2

2

2

2

2

230793

110893

300893

060595

200695

070795

180893

270793

290893

090695

270695

280795

230993

011093

021195

181095

290395

Hm (Lowland Hill)

Hm8 (Lowland Hill)

Hm (Lowland Hill)






(Lowland Foothills)

(Lowland Foothills)

(Lowland Foothills)

(Lowland Hill)



(Lowland Foothills)

(Lowland Foothills)

HmeHm6 (Lowland

Foothills)


24










W Woodland



Wsw Swamp woodland





disturbed)









disturbance

25

















NW NE

08 28 48 68 88

06 26 46 66 86

04 24 42 64 84

02 22 42 62 82

00 20 40 60 80

SW SE


Plot origin

where

measurement

starts

N

100 m

100 m

26

1362 PSP Locations










harvesting in PNG)

27

































28


communities



in PNG


forests in PNG






15 THESIS OUTLINE



















29


















province PNG






forests in PNG

27


28

CHAPTER 2


21 FOREST DYNAMICS

211 Introduction



highlighted





















29


(Kobayashi 1992)

















2151














30




discussed in detail


















disturbances











31














(Evan 1982)


















32










of exotic species










214 Forest Types













33




















forest













34

































35




















(47 mm)















36

































37


amount (Ng 1983)






























38














1998)














39

218 Shade Tolerance





























40

219 Stand Structure



























41














Terminalia spp



















42


































43






ha-1







-1
























trees

44

2111 Discussion































45

















46

2112 Conclusions







region
























47


221 Introduction





follow




















pointed out




48































49

222 Illegal Logging































50

















223 Deforestation
















51























of











52



224 Climate Change






























53














deforestation

2241 Kyoto Protocol


















54








of 8 Kt CO2 year-1



the project
























55










Imperata grasslands






operations



56















where they reside


















57


































58






226 Certification



























59


































60













227 Governance




















61




























62

228 Discussion












to achieving SFM




















63







229 Conclusions
















recognised










64











65
































66





























assessment






67



















68

















following











timber industry

69






























and social assets

70

234 Discussion





























assets

71

235 Conclusions












uncertainties





PNG

72


65

CHAPTER 3


3 1 INTRODUCTION















(FAO 2005













66




al 2009a)





















harvesting








et al 2000)

67
































68






2005)






















(Pokana 2002)

69












the harvested plots






70




























71






















(3-1)





(equation 3-3)

72

(3-2)

(3-3)
















(3-4)


-1) Shd = site









hd

73


























(3-5)



(m2 ha


residual trees

t = 134 + 0394 ln Go + 0346 ln t + 000275 Sh t -1

74















(3-6)





Nino drought





(3-7)



75

33 RESULTS



plusmn 100





2901 m2

ha-1





















76

(a)L

og

Sto

ckin

g (

ste

ms h

a-1

)

1

10

100

1000

0 - 5 years

5 - 10 years

10 - 15 years

15 - 20 years

Diameter Class (cm)

10-29 30-49 50-69 70-89 90+

Lo

g S

tockin

g (

ste

ms h

a-1

)

1

10

100

1000

(b)

(c)

Basal

Are

a (

m2 h

a-1

)

0

2

4

6

8

10

12

Diameter Class (cm)

10-29 30-49 50-69 70-89 90+

Basal

Are

a (

m2 h

a-1

)

0

2

4

6

8

10

12

(d)





un-harvested plots







77

Bas

al

Are

a (

m2 h

a-1

)

0

5

10

15

20

25

Sto

ck

ing

(ste

ms

ha

-1)

0

100

200

300

400 Commercial

NonCommercial


0-5 5-10 10-15 15-20

Res

idu

al

Tim

ber

Vo

lum

e (

m3 h

a-1

)

0

20

40

60

80

100

120

140

160

180

(a)

(b)

(c)




78












r2 = 007

p = 0016


0 5 10 15 20 25

Bas

al

Are

a (

m2 h

a-1

)

0

5

10

15

20

25

30

35




79




-1 year

-1 (SD 042) (Table 3-1) For the 21


-1 year

-1


-1 year

-1 (SD









(-172 plusmn 316) (Table 3-1)



Mean BAI (m2 ha

-1 year

-1)

a

Un-harvested 13

-172 plusmn 316




-058 plusmn 053

(All un-burned) 84b

017 plusmn 062)


drought 21

-067 plusmn 085

Total 118



80







the mean

r2 = 037

p = 0028

Mean TSH (years)

5 10 15 20

Ln

Mean

BA

I (m

2 h

a-1

year-1

00

02

04

06

08

10

12

14

16

18




81


Forest








r2 = 074

p = 0000


-1)

0 5 10 15 20 25 30

Ln

Mean

BA

I (m

2 h

a-1

year-1

)

-6

-4

-2

0

2

4











82

r2 = 024

p = 0087


0 1 2 3 4 5 6

Tim

be

r V

olu

me

gt2

0c

m D

BH

(m

3 h

a-1

)

0

50

100

150

200

250

300

r2 = 006

p = 0031


0 5 10 15 20

Tim

be

r V

olu

me

gt20

cm

DB

H (

m3

ha

-1)

0

50

100

150

200

250

300






(a)

(b)

83








-1 year

-1 plusmn 809


this study (r2

= 083 p = 0000) (Figure 3-8)

r2 = 083

p = 0000


0 5 10 15 20

Ln

Tim

be

r Y

ield

gt2

0c

m D

BH

(m

3 h

a-1

)

00

02

04

06

08

10

12

14

16





Nino Drought







84





fire

PlotID

CNIR

D01

CNIR

D02

IVAIN

01

IVAIN

02

KAPU

L01

KAPU

L02

LARK01

LARK02

WIM

AR01

WIM

AR02

Pe

rcen

tag

e B

A g

ain

ed

or

lost

()

0

10

20

30

40


BA lost due to fire














85

r2 = 016

p = 0069


0 5 10 15 20 25 30

Sh

an

no

n-W

ien

er

Ind

ex

(H

-1)

0

1

2

3

4

5




34 DISCUSSION

















86








about 30-32 m2

ha-1




















-1 year

-1)


-1 year

-1) overall



-1

year-1






87










Region

Primary Forest

Mean BA

(m2 ha

-1)

a

Harvested Forest

Mean BA (m2 ha

-1 )

Mean BAI

after harvesting

(m2 ha

-1 year

-1)

Source

PNG

2901

1735

017

Current study

PNGb

30 - 33

10 - 20

Kingston amp Nir

1988 Oavika 1992

Alder 1998

Sub tropical

Australia

515

12 - 58

032 ndash 075

Smith et al 2005

North

Queensland

Australia

3794 ndash 7342

2586 ndash 4160


South Indiac

393

348





South India










88
















-1


1998)








forests in PNG







089 year-1


in the same


89























Rogers (2001) study











90




35 CONCLUSIONS







-1 year



-1 year























utilisation

91

CHAPTER 4


41 INTRODUCTION









plots
















identified later



92
















42 BACKGROUND













-1 (VDT


93











94














95



for sampling

















44 DATA ANALYSIS




ha-


2

ha-1


(4-1)


stems ha-1

96


(4-2)


442 Timber Volume



(4-3)











(4-4)








97








debris (CWD)




AGLBlt10cm 10 10 10

FL 1 25 25

CWD 25 25 25







(4-5)







(4-6)

ge 10cm lt 10cm

98




45 RESULTS






2m2 ha






10-20 69 119

20-30 93 42


Forests 40-50 23 13

50-60 22 8

60-70 13 6

70-80 10 5

80-90 2 4

90-100 1 3

100+ 7 1

20-30 9 33

30-40 6 22


Forests 50-60 11 8

60-70 3 6

70-80 2 5

80-90 1 4

90-100 1 3

99







0

20

40

60

80

100

120

140

10-20 20-30 30-40 40-50 50-60 60-70 70-80 80-90 90-100 100+

No

of

Ste

ms

(N h

a-1

)

Diameter Class (cm)

Actual

Predicted

0

5

10

15

20

25

30

35

20-30 30-40 40-50 50-60 60-70 70-80 80-90 90-100

No

of

Ste

ms

(Nh

a)

Diameter Class (cm)

Actual

Predicted

100





each study site




Merch Vol (m3

ha-1

)

Total Merch

Vol (m3)


Celtis sp 68 179 39000



Intsia sp 14 168 37000

Vitex sp 14 119 26000





178 39000






Intsia sp 54 144 2100







101






-1)


Forest

Mean 1269 1519

SD 450 277








Forest

AGLBge10cm 11019 ( 2758) 11921 (3719)


FL 110 119

CWD 2755 2980

Total AGB 14985 ( 3751) 16212 (5058)

455 Sample Size







102



Mean SD CV

No of

Observation DF

E

() t-value n

Volume

(m2 ha

-1) 1269 450 035 17 16 10 1337 22

AGB

(MgC ha-1

) 14985 3751 025 17 16 10 1337 11


Volume

(m2 ha

-1) 1519 277 018 2 1 10 3078 31

AGB

(MgC ha-1

) 16212 5058 031 2 1 10 3078 92
















Total Forest Area

2200 ha

150 ha


28000 m3

2300 m3


1269 m3 ha

-1

1519 m3 ha

-1

Total Forest Carbon

329670 Mg C

24318 Mg C

Mean Forest Carbon

14985 Mg C ha-1

16212 Mg C ha-1

Estimated Emission


181319 Mg C

13375 Mg C

103

46 DISCUSSION






m3 ha


3 ha



30-40 m3 ha



) and primary








Mg C ha-1


The
















104


powered generation




























(Rogers 2010)






105





47 CONCLUSIONS







-1












106


TOOLS

107

CHAPTER 5


51 INTRODUCTION
























108




forests










scenarios for CBFM

52 BACKGROUND













109


PNG



















110

53 METHODOLOGY






developed




data


informed decision







developed




111































112


residual stand















harvesting)













113


























114















Total


Pre-harvest (m3ha) 210 270 430 90 100 120 50 50 70 1040

Cut fraction () 0 0 100 0 0 100 0 0 0

Post-harvest (m3ha) 210 270 00 90 100 00 50 50 70 490 60

YIELD (m3ha) 00 00 430 00 00 120 00 00 00 550

Ingrowth (m3yr) 028 028 028 008 008 008 000 000 000 07

Growth (m3yr) 000 000 000 000 000 000 000 000 000 00

DeathDamage (m3yr) 000 000 000 000 000 000 000 000 000

Upgrowth (m3yr) 028 028 008 008 000 000

Pre-harvest (m3ha) 210 270 139 90 100 39 50 50 70 667

Cut fraction () 0 0 100 0 0 100 0 0 0

Post-harvest (m3ha) 210 270 00 90 100 00 50 50 70 490 83

YIELD (m3ha) 00 00 139 00 00 39 00 00 00 177

Ingrowth (m3yr) 022 022 022 006 006 006 000 000 000 06

Growth (m3yr) 000 000 000 000 000 000 000 000 000 00

DeathDamage (m3yr) 000 000 000 000 000 000 000 000 000

Upgrowth (m3yr) 022 022 006 006 000 000

Pre-harvest (m3ha) 210 270 112 90 100 31 50 50 70 633

Cut fraction () 0 0 100 0 0 100 0 0 0

Post-harvest (m3ha) 210 270 00 90 100 00 50 50 70 490 85

YIELD (m3ha) 00 00 112 00 00 31 00 00 00 143

Ingrowth (m3yr) 021 021 021 006 006 006 000 000 000 05

Growth (m3yr) 000 000 000 000 000 000 000 000 000 00

DeathDamage (m3yr) 000 000 000 000 000 000 000 000 000

Upgrowth (m3yr) 021 021 006 006 000 000

Left after Harvest1

Cycle

Number

Left after Harvest

Left after Harvest


3

2




115







Yalu Area Data (ha)





Fragile 15


above

10


above

10





20

Regrowth area 15

Roading 10

Other

25

2nd

amp3rd


116


Diameter Class

(cm)

Volume MEP1

(m3 ha

-1)

Volume MEP2

(m3 ha

-1)

Others

(m3 ha

-1)

lt 20 0301 0307 7029

20-50 4950 6961 34991

50-65 6634 11885 18539

65+

Volume Growth

(m3 ha

-1 year

-1)

0-20 0117 0301 0203

20-50 0129 0124 0244

50-65 0041 0080 0073

65+ 0127








(Years)

Cut Proportion

()

Diameter

LimitMEP

Codes

Community

sawmill

10 50 gt 50cm

MEP1 MEP2

Local processing

20 50 gt 50cm

MEP1 MEP2


MEP1 MEP2

Medium-scale log

export

40 50 gt 50cm

MEP1 MEP2

117










(Years)

Cut Proportion

()

Diameter

LimitMEP Codes


MEP1 MEP2

Local processing

20 75 gt 50cm

MEP1 MEP2


MEP1 MEP2


MEP1 MEP2







(Years)

Cut Proportion

()

Diameter

LimitMEP Codes


MEP1 MEP2

Local processing

20 50 gt 65cm

MEP1 MEP2


MEP1 MEP2


MEP1 MEP2

118

54 RESULTS










119





Gabensis village










120












121



122











0

20

40

60

80

100

0

2

4

6

8

10

12

high

preference

low

preference

no

preference

not sure

Percen

tag

e (

)

Freq

uen

cy

(N

)


0

20

40

60

80

100

0

2

4

6

8

10

12

high

preference

low

preference

no

preference

not sure

Percen

tag

e (

)

Freq

uen

cy

(N

)


0

20

40

60

80

100

0

2

4

6

8

10

12

high

preference

low

preference

no

preference

not sure

Percen

tag

e (

)

Freq

uen

cy

(N

)


0

20

40

60

80

100

0

2

4

6

8

10

12

high

preference

low

preference

no

preference

not sure

Percen

tag

e (

)

Freq

uen

cy

(N

)


0

20

40

60

80

100

0

2

4

6

8

10

12

high

preference

low

preference

no

preference

not sure

Percen

tag

e (

)

Freq

uen

cy

(N

)


123


Scenarios








third cycles


Scenario

Cutting Cycle

(years)

Annual

Yield Cycle 1

(m3 year

-1)

Annual

Yield Cycle 2

(m3 year

-1)

Annual

Yield Cycle 3

(m3 year

-1)

Total

Yield

(m3)

Community

sawmill

10

3166

2865

2718

87490

Local

processing

20

1583

2100

2890

131500

Local

processing

30

1055

1846

3307

186060

Medium-scale

log export

40

792

1718

3780

251600









124


Scenario

Cutting

Cycle (years)

Annual Yield

Cycle 1 (m3)

Annual Yield

Cycle 2 (m3)

Annual Yield

Cycle 3 (m3)

Total

Yield

(m3)

Community

sawmill

10

4749

2316

1229

82940

Local

processing

20

2375

1743

1294

108240

Local

processing

30

1583

1551

1574

141240

Medium-scale

log export

40

1187

1456

1802

177800






-1 and 3000m

3 year







Scenario

DBH Limit

Species Grp

Annual

Yield Cycle 1

(m3 year

-1)

Annual

Yield Cycle 2

(m3 year

-1)

Annual

Yield Cycle 3

(m3 year

-1)

Total Yield

(m3)

Local

processing

50 gt 50cm

MEP 1 2

1583

2100

2890

131460

Local

processing

50 gt 65cm

MEP 1 2

623

703

805

42620

Local

processing

75 gt 50cm

MEP 1 2

2375

1743

1361

276463

Local

processing

75 gt 65cm

MEP 1 2

934

603

415

39040

125









increase in volume

















126



0

1

2

3

4

5

1 - 10 11 - 20 21 - 30

An

nu

al

Vo

lum

e (1

00

0 m

3 y

r-1

)


MEP-code2 65+

MEP-code2 50-65

MEP-code1 65+

MEP-code1 50-65

0

1

2

3

4

5

1 - 20 21 - 40 41 - 60

An

nu

al

Vo

lum

e (1

00

0 m

3 y

r-1

)


MEP-code2 65+

MEP-code2 50-65

MEP-code1 65+

MEP-code1 50-65

0

1

2

3

4

5

1 - 30 31 - 60 61 - 90

An

nu

al

Vo

lum

e (1

00

0 m

3 y

r-1

)


MEP-code2 65+

MEP-code2 50-65

MEP-code1 65+

MEP-code1 50-65

0

1

2

3

4

5

1 - 40 41 - 80 81 - 120

An

nu

al

Vo

lum

e (1

00

0 m

3 y

r-1

)


MEP-code2 65+

MEP-code2 50-65

MEP-code1 65+

MEP-code1 50-65

(b) (a)

(d) (c)

127



0

1

2

3

4

5

1 - 20 21 - 40 41 - 60

An

nu

al

Vo

lum

e (1

00

0 m

3 y

r-1

)


MEP-code2 65+

MEP-code2 50-65

MEP-code1 65+

MEP-code1 50-65

0

1

2

3

4

5

1 - 10 11 - 20 21 - 30

An

nu

al

Vo

lum

e (1

00

0 m

3 y

r-1

)


MEP-code2 65+

MEP-code2 50-65

MEP-code1 65+

MEP-code1 50-65

0

1

2

3

4

5

1 - 30 31 - 60 61 - 90

An

nu

al

Vo

lum

e (1

00

0 m

3 y

r-1

)


MEP-code2 65+

MEP-code2 50-65

MEP-code1 65+

MEP-code1 50-65

0

1

2

3

4

5

1 - 40 41 - 80 81 - 120

An

nu

al

Vo

lum

e (1

00

0 m

3 y

r-1

)


MEP-code2 65+

MEP-code2 50-65

MEP-code1 65+

MEP-code1 50-65

(a) (b)

(c) (d)

128




0

1

2

3

4

5

1 - 20 21 - 40 41 - 60

An

nu

al

Vo

lum

e (1

00

0 m

3 y

r-1

)


MEP-code2 65+

MEP-code2 50-65

MEP-code1 65+

MEP-code1 50-65

0

1

2

3

4

5

1 - 20 21 - 40 41 - 60

An

nu

al

Vo

lum

e (1

00

0 m

3 y

r-1

)


MEP-code2 65+

MEP-code1 65+

0

1

2

3

4

5

1 - 20 21 - 40 41 - 60

An

nu

al

Vo

lum

e (1

00

0 m

3 y

r-1

)


MEP-code2 65+

MEP-code2 50-65

MEP-code1 65+

MEP-code1 50-65

0

1

2

3

4

5

1 - 20 21 - 40 41 - 60

An

nu

al

Vo

lum

e (1

00

0 m

3 y

r-1

)


MEP-code2 65+

MEP-code1 65+

(a) (b)

(c) (d)

129


Cycle


m3 ha





-1 and


-1 at year 60


Cutting

Cycle

(Years)

Cut

Proportion

()

Diameter Limit

MEP Codes

Starting

Pre-Harvest

Volume

(m3 ha

-1)

Residual

Volume After

3rd

Cycle

(m3 ha

-1)

Annual

Yield

(m3 year

-1)

10 50 gt 50cm MEP1 amp 2 305 271 8750

20 50 gt 50cm MEP1 amp 2 305 577 6574

30 50 gt 50cm MEP1 amp 2 305 989 6208

40 50 gt 50cm MEP1 amp 2 305 1508 6290

50 50 gt 50cm MEP1 amp 2 305 2132 6550

60 50 gt 50cm MEP1 amp 2 305 2861 6899



ha-1

and





130





6208 m3 year




0

50

100

150

200

250

300

350

10 20 30 40 50 60

Re

sid

ual

Vo

lum

e (

m3

ha-1

)


0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

10 20 30 40 50 60

An

nu

al V

olu

me

(m

3Y

ear

-1)


131

55 DISCUSSION























2000m3 year







1000m3 year


3 year

-1 in the

132


5-6c)






cycles (Table 5-6d)










-1)























133







250000 m3 (Table 5-6)



Annual

Yield


-1)

10 50

gt 50 cm MEP

1amp2 8750

20 50

gt 50 cm MEP

1amp3 6574

30 50

gt 50 cm MEP

1amp4 6208

40 50

gt 50 cm MEP

1amp5 6290
















134




-1 are considered



56 CONCLUSIONS







harvest



















135


















136

CHAPTER 6


61 INTRODUCTION



























137

































138









best decisions






63 METHODOLOGY













139










management in PNG






















140


branch

633 Terminal Values





























141






(6-1)










alternative














142



















EMV

Spider

Charts

Tornado

Charts

Decision Tree

Model

Decision

Alternatives

Uncertain

Events

Cash Flow

Model

Sensitivity

Data

Decision

Analyses

Sensitivity

Analyses

Payoff

Strategy

143








EMV at 2nd

node = (06 x -59850) + (04 x -63850) = PNGK-61450

EMV at 3rd

node = (06 x -61450) + (04 x -76350) = PNGK-67410












low











EMV at 2nd


144
















EMV at 2nd


















145
















for one year


(Figure 6-9)


EMV at 2nd














146









64 RESULTS






Input Description

Variation

(10) Variable range


Lucas mill (PNGK)5 85000 8500 76500 85000 93500

Chainsaw (PNGK) 6000 600 5400 6000 6600







(PNGKm3) 500 50 450 500 550





(m3annum) 20 2 18 20 22


months) 16 16 144 16 176


147




(Figure 6-2)















charts

148



High Price (PNGK)

-64850


-8850 -66050 04

Low Price

-67850

12000 -67850

06

High Price

06 -59850


2

20000 -61450 -8850 -61450 04

Low Price

-63850

16000 -63850

06

High Price

-63950



Low Price

-91000 -67410 -65950

10000 -65950

06

High Price

-75950


-4950 -76350 04

2 04 Low Price


1 9000 -76950

10000 -76350

Comm Use

-81000

0 -81000

No Harvest

0

0 0

149







Input Description

Variation

(10) Variable range


Lucas mill (PNGK) 85000 8500 76500 85000 93500

Chainsaw (PNGK) 6000 600 5400 6000 6600















Fumigation (PNGK) 720 72 648 720 792




(PNGKm3) 700 70 630 700 770


(PNGKm3) 2400 240 2160 2400 2640


(PNGKm3) 1500 150 1350 1500 1650




150







151



CertMarket HP

199800


-69200 127800 04

Non-CertMarket LP

06 19800


1

480000 127800 06

ST High Price

-124450


-53450 -132450 04

ST Low Price

-144450


-691000 31800 06

CertMarket HP

-40200


-6920000 -112200 04

Non-CertMarket LP

04 -220200

ST-Demand Low 300000 -220200

1

240000 -112200 06

ST High Price

-364450


-5345000 -372450 04

ST Low Price

-384450

120000 -384450

1

31800 06

CertMarket HP

-474938


-24494 -654938 04

Non-CertMarket LP

06 -924938


1

120000 -654938 06

ST High Price

-120494


-12494 -122494 04

ST Low Price

-125494


-263000 -894938 06

CertMarket HP

-107494


-2449375 -125494 04

Non-CertMarket LP

04 -152494


1

60000 -125494 06

ST High Price

-180494


-1249375 -182494 04

ST Low Price

-185494

30000 -185494

152







chart (Figure 6-4)

153


180

2160

286000

178200

1350

110000

1080

93500

55000

88

22000

2805

55

6600

1452

55

220

2640

234000

145800

1650

90000

1320

76500

45000

72

18000

2295

45

5400

1188

45

-60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60 70 80 90 100110120








Lucas mill (PNGK)






Chainsaw (PNGK)





154








-60000

-50000

-40000

-30000

-20000

-10000

0

10000

20000

30000

40000

50000

60000

70000

80000

90000

100000

110000

120000

86 90 94 98 102 106 110

EMV

(PN

GK

+-

10

B

ase

Cas

e)










Lucas mill (PNGK)


155






Input Description

Variation

(10) Variable range

Abs var -var

base

case +var

Chainsaw (PNGK) 6000 600 5400 6000 6600








(PNGm3) 84 84 756 84 924


Dozer D6 - CMU (PNGK) 200000 20000 180000 200000 220000





(PNGm3) 105 105 945 105 1155















156









157




4359317

Asia Market 3000000 4359317

-798683 4159317 04

Log Price Low

06 3859317


1

3000000 4159317 06

Log Price High

3609317

Other Market 2250000 3609317

-798683 3509317 04

Log Price Low

3359317


-842000 3959317 06

Log Price High

3859317

Asia Market 3000000 3859317

-798683 3659317 04

Log Price Low

04 3359317


1

2500000 3659317 06

Log Price High

3109317

Other Market 2250000 3109317

-798683 3009317 04

Log Price Low

2859317

2000000 2859317

1

3959317 06

Log Price High

2187692

Asia Market 1500000 2187692

-338308 2087692 04

Log Price Low

06 1937692


1

1500000 2087692 06

Log Price High

1812692

Other Market 1125000 1812692

-338308 1762692 04

Log Price Low

1687692


-474000 1987692 06

Log Price High

1937692

Asia Market 1500000 1937692

-338308 1837692 04

Log Price Low

04 1687692


1

1250000 1837692 06

Log Price High

1562692

Other Market 1125000 1562692

-338308 1512692 04

Log Price Low

1437692

1000000 1437692

158








159


4500

540

2805

198

1155

44000

11

11

176

1045

363

275

5400

108000

145800

145800

225

1575

1296

756

135000

180000

216000

216000

5400

135

405

2250

5500

660

2295

162

945

36000

9

9

144

855

297

225

6600

132000

178200

178200

275

1925

1584

924

165000

220000

264000

264000

6600

165

495

2750

33000003400000350000036000003700000380000039000004000000410000042000004300000440000045000004600000









No of fortnights




Chainsaw (PNGK)

















160












market

3300000

3400000

3500000

3600000

3700000

3800000

3900000

4000000

4100000

4200000

4300000

4400000

4500000

4600000

860 880 900 920 940 960 980 1000 1020 1040 1060 1080 1100 1120

EMV

(PN

GK

+-

10

B

ase

cas

e)










No of fortnights




Chainsaw (PNGK)
















161


Input Description

Variation

(10) Variable range



(PNGK) 30000 3000 27000 30000 33000





GISMapping (PNGK) 20000 2000 18000 20000 22000












Cha) 150 15 135 150 165




months) 16 16 144 16 176






market (PNGK17500)

162










High Price (PNGK)

79781735


-17500 76321135 04

Low Price

06 71130235

High Demand 31278500 71130235

1

39930000 76321135 06

High Price

69799235


-17500 67203785 04

Low Price

63310610

Carbon Trade 23458875 63310610

-60765 72860535 06

High Price

71130235


-17500 67669635 04

Low Price

04 62478735

Low Demand 31278500 62478735

1

1 31278500 67669635 06

72860535 High Price

61147735


-17500 58552285 04

Low Price

54659110

23458875 54659110

Do Nothing

0

0 0

163





increase by 10

135

1980

18

50

33000

22000

11000

22000

176

1925

11000

11000

11000

275

2200

935

77

715

385

135

165

2420

22

61

27000

18000

9000

18000

144

1575

9000

9000

9000

225

1800

765

63

585

315

165

47000004800000490000050000005100000520000053000005400000550000056000005700000580000059000006000000






GISMapping (PNGK)





Consultancy (PNGK)







Compass (Kcompass)




164


65 DISCUSSION















4700000

4800000

4900000

5000000

5100000

5200000

5300000

5400000

5500000

5600000

5700000

5800000

5900000

6000000

880 900 920 940 960 980 1000 1020 1040 1060 1080 1100 1120

EMV

(PN

GK

+-

10

B

ase

Cas

e)







GISMapping (PNGK)





Consultancy (PNGK)







Compass (Kcompass)



165







time of felling









trees ha-1










certified markets










166










(EMV)

























167













in the Yalu




















in a REDD+ project

168
















Scenarios

Starting

Capital

(PNGK)

Annual

EMVProfitLoss

(PNGK)


Local Processing



Log Export

CMU Managed 842000 3959317


Carbon Trade 60765c

72860535




169

66 CONCLUSIONS

























region

170

CHAPTER 7


71 INTRODUCTION


















in CBFM






171



72 BACKGROUND



























172








MSE method














173











174












175


Planning Level

Inventory Planning

Requirement





PNGFA



same as above


done but not a


Forest Inventory

Provincial Plans




Forest Management

Agreement Project


tender)






5 Year Working Plan
















Company As above

Annual Logging Plan


PMCP

1 Company As above


(harvesting plan)





be harvested Some




176



















donors












177


operations











STRENGTHS

WEAKNESSES













products


certified products







managed forest








guidelines







mechanisms



countries

178


































179






species refugia)














180






area


process




community needs





training











sufficient size


area









timber species







within 2 years








survey completed





working forest area



in place




181

73 METHODOLOGY














the pilot region







further analyses







scenarios

182

733 Planning System
























183

74 RESULTS




















interests











184


75 DISCUSSION















Stakeholder

Consultation

Field Interviews

PAR

Investigate

Options

Forest Inventory

Data

Planning System

Growth Data

Decision

Analyses Tools

Spreadsheet

Planning Tool

Decision Tree

Model

Annual Yield

Estimates

Management

Options

Payoff

Strategy

Decision

Alternatives

Uncertain

Events

EMV

Tornado

Chart

Spider Chart

Sensitivity

Analyses

Scenario

Evaluation amp

Analyses

Decision

Implementation

Scenario

Output

Feedback to

Stakeholders

185
































186

76 CONCLUSIONS


















187

CONCLUSIONS

188

CHAPTER 8


81 INTRODUCTION




















forests in PNG





189
























as follow


in PNG





190






communities


























191



























past four years

192


STUDY






























193





in CBFM



























194


















forests in PNG














195













PNG

88 DISCUSSION

















196


















89 CONCLUSIONS







are recommended







197








198

REFERENCES






1623-1648



91PNGFRI



Lae




Forestry Review 4










(ed) London UK DFID


McGraw Hill Inc USA




199


















Ecology 6 1-32







PNG JICA PNGFRI



56 985-998







200











240-252




Biology 23 1406-1417






997











Australia

201






6 1104-1123



Pottinger A



Oxford UK









Publishing












II) 210pp

202



















Rome Pp168-173


and Clarendon Press














203








Science 66 813-825



Working Document



I Jakarta Indonesia





410-419






Research










204


































205






Bangor UK















24 Yokohama Japan














206






41 555ndash562





















Institute Lae



Morobe Province



Document No 9

207






JICA-PNGFRI




























396

208


















36 3053-3062






2005






253-263




Organisations

209



Francisco CA










(CIFOR)






Nature 403 853-858












(CIFOR)

210






























15 221-232




University Press

211








105 107-119













Hohola May 1996









2005





212



University of Wales








875





Cengage Learning






Publications





ICES 2006 R 05






213

























PNG








214








Guinea








Ecology 15 620-626






967-979






131-137


19 357-371




215














Melbourne CSIRO












10 June 2009







Review 67 53-64

216





Technology






Papua New Guinea





Island Press







Melbourne CSIRO


Basil Blackwell Ltd




Press





217









145-155

























218



April 2008]



1999












905

219

APPENDICES


Forest Condition

No of Plots

Un-harvested 13





Total 118


PLOTNO PLOTID

FIRST

CENS

LAST

CENS

BA_FIRST

CENS

(m2ha

-1)

BA_LAST

CENS

(m2ha

-1)

1 DANAR03 2006

208470 No data

2 DANAR04 2006

77838 No data

3 HUVIV02 1999

253617 No data

4 KAUP_03 1998 2000 242586 216303

5 MARE_03 2001

237487 No data

6 SAGAR03 1998 2005 321673 332807

7 SASER03 2005

248061 No data

8 SASER04 2005

293279 No data

9 SOGER03 1998 2003 217693 239859

10 WATUT05 1997 1999 338812 253121

11 WATUT06 1997 1999 441607 286389

12 WCOST05 1998 2001 336952 344092

13 WCOST06 1998 2001 314374 328569

220


INCREASING BA


FIRST

CENS

LAST

CENS

BA_FIRST

CENS

(m2ha

-1)

BA_LAST

CENS

(m2ha

-1)

MBAI

(m2ha

-1yr

-

1)

1 ANUAL01 1993 1995 1999 168828 179791 02741

2 ANUAL02 1993 1995 1999 209696 214081 01096

3 ARI__01 1995 1996 2003 118680 164226 06506

4 ARI__02 1995 1996 2003 112410 134710 03186

5 CARAW01 1991 1995 2004 194671 221647 02997

6 CARAW02 1991 1995 2004 188221 212092 02652

7 CFORD01 1994 1995 2004 302147 340191 04227

8 EMBIH01 1992 1994 1999 130070 135086 01003

9 EMBIH02 1992 1994 1999 95760 103879 01624

10 EMBIH03 1993 1994 1999 138590 159763 04235

11 EMBIH04 1993 1994 1999 125500 164194 07739

12 GAR__01 1991 1993 1999 150426 172383 03660

13 GAR__02 1991 1993 1999 142926 165673 03791

14 GARAM01 1991 1994 2000 201981 221105 03187

15 GILUW01 1987 1993 2003 125896 137937 01204

16 GILUW02 1991 1994 2003 198455 199718 00140

17 HAWAN01 1993 1994 2002 130935 171417 05060

18 HAWAN02 1994 1994 2002 133950 168687 04342

19 KAPIU01 1991 1993 1997 130361 226460 24025

20 KAPIU02 1991 1993 2003 116672 282623 16595

21 KAUP_01 1996 1996 2000 195241 198719 00869

22 KAUP_02 1996 1996 2000 223736 229669 01483

23 KRISA01 1991 1994 1996 164044 174124 05040

24 KRISA02 1991 1994 1996 231445 239709 04132

25 KUI__01 1994 1994 2002 180250 204151 02988

26 LARK_03 1994 1996 1999 186482 186841 00120

27 MALAM01 1995 1995 2000 165864 219264 10680

28 MOKOL01 1980 1993 2004 243010 291990 04453

29 MOKOL02 1981 1993 2004 218361 242578 02202

30 MORER01 1997 1997 1999 161786 170147 04180

31 MOSAL01 1992 1993 2003 124213 199976 07576

32 MOSAL02 1992 1993 1997 119561 196195 19159

33 MUSAU01 1996 1996 1999 170058 174021 01321

34 MUSAU02 1995 1996 1999 170392 178642 02750

35 PASMA01 1993 1997 2004 172060 214776 04746

36 PASMA02 1993 1997 1999 195182 206363 05591

37 PUAL_01 1993 1994 2000 191461 191960 00083

38 PUAL_02 1994 1994 2000 151644 175568 03987

221


FIRST

CENS

LAST

CENS

BA_FIRST

CENS

(m2ha

-1)

BA_LAST

CENS

(m2ha

-1)

MBAI

(m2ha

-1yr

-1)

39 PUAL_03 1996 1996 1998 165854 175962 05054

40 PUAL_04 1996 1996 2004 172923 186604 01710

41 PULIE02 1997 1997 2004 109713 118248 01219

42 PULIE03 1997 1997 1999 198100 204913 03406

43 SAGAR01 1997 1998 2005 141514 153152 01663

44 SEMBE01 1996 1997 1999 134691 137005 01157

45 SERA_02 1996 1996 1998 174719 178179 01730

46 TURAM01 1994 1994 1998 245674 256188 02629

47 UMBOI01 1993 1994 2004 219117 245082 02597

48 UMBOI02 1993 1994 2001 174360 198924 03509

49 UMBUK01 1993 1993 2007 132607 163482 02205

50 UMBUK02 1993 1993 1999 107566 121284 02286

51 VAILA01 1993 1994 2002 146811 190990 05522

52 VAILA02 1993 1994 2002 175963 188018 01507

53 WASAP01 1986 1990 2003 184658 285293 07741

54 WASAP02 1987 1995 2003 131157 165941 04348

55 WATUT01 1992 1993 2003 139136 202128 06299

56 WATUT02 1992 1993 1998 138267 149267 02200

57 WAWOI01 1991 1994 1998 234345 256670 05581

58 WCOST03 1996 1996 2003 154697 189326 04947

59 WCOST04 1996 1996 2003 103386 104722 00191

60 WFBAY02 1981 1993 1999 182790 183297 00085

61 YALU_01 1995 1995 2007 126460 233236 08898

62 YALU_02 1995 1995 2007 162517 197775 02938

63 YEMA_01 1995 1996 2002 183911 201508 02933

222


FALLING BA


FIRST

CENS

LAST

CENS

BA_FIRST

CENS

(m2ha

-1)

BA_LAST

CENS

(m2ha

-1)

MBAI

(m2ha

-1yr

-1)

1 CFORD02 1995 1995 2004 1651825 1580870 -007883

2 GARAM02 1991 1994 1998 1806829 1620510 -031054

3 INPOM01 1993 1995 1997 1942872 1707170 -117852

4 KUI_02 1994 1994 2002 1561649 1478340 -010413

5 LARK_04 1994 1996 1999 1609460 1592510 -005649

6 MALAM02 1995 1995 2003 1959570 1434840 -065591

7 MORER02 1997 1997 1999 1443625 1390560 -026533

8 ORLAK01 1994 1994 2000 1891138 993640 -149582

9 ORLAK02 1994 1994 1994 1674760 1085680 -098180

10 PULIE01 1997 1997 2004 1807768 1076690 -104440

11 SAGAR02 1997 1998 2005 1735408 1716280 -002732

12 SEMBE02 1996 1997 1999 945672 888900 -028387

13 SERA_01 1996 1996 2000 2129906 2107070 -005708

14 TURAM02 1994 1994 1997 2540949 2561880 -010010

15 TURAM03 1996 1997 1999 1582846 1481270 -050786

16 VUDAL01 1997 1997 1999 762256 705470 -028393

17 VUDAL02 1996 1997 1999 1215035 1070640 -072196

18 WAWOI02 1994 1994 2000 2325639 1142410 -197204

19 WCOST01 1989 1995 1999 1202939 907100 -073959

20 WCOST02 1989 1995 1999 2470524 2172310 -074554

21 WFBAY01 1980 1993 1999 1720145 1404070 -052680

223



FIRST

CENS

LAST

CENS

BA_FIRST

CENS

(m2ha

-1)

BA_LAST

CENS

(m2ha

-1)

MBAI

(m2ha

-1yr

-

1)

1 CNIRD01 1994 1995 2004 236627 71393 -18359

2 CNIRD02 1994 1995 2007 230366 35539 -16236

3 HUVIV01 1997 1997


4 IVAIN01 1995 1996 2003 163578 58564 -15002

5 IVAIN02 1995 1996 2003 99191 49083 -07158

6 IVAIN03 1995 1996 1998 130492 119804 -05344

7 IVAIN04 1995 1996 1998 168716 129575 -19570

8 KAPUL01 1993 1993 1999 146181 96334 -08308

9 KAPUL02 1993 1993 2003 117906 26473 -09143

10 KAUT_01 1993 1993 1997 129425 146797 04343

11 KAUT_02 1993 1993 1997 122872 124960 00522

12 LARK_01 1994 1995 1999 236381 191211 -11292

13 LARK_02 1994 1995 1999 214359 236409 05513

14 MAUBU01 1995 1996


15 MAUBU02 1995 1996


16 OOMSI01 1979 1993 1997 209554 221536 02996

17 OOMSI02 1980 1993 1997 189978 211015 05259

18 SOGER01 1996 1996


19 SOGER02 1996 1996


20 WIMAR01 1993 1994 2000 185575 170570 -02501

21 WIMAR02 1993 1994 2000 230218 160777 -11574


BA BA

BA

gained BA BA

BA lost

After

Pre-

1997 1997

Meas

Period

Before

Fire 1997

Post-

1997

Meas

Period Fire

PLOTID

(m2ha

-

1)

(m2ha

-

1) (years) ()

(m2ha

-

1)

(m2ha

-

1) (years) ()

CNIRD01 2366 2443 2 163 2443 714 7 1612

CNIRD02 2304 2355 2 023 2355 355 10 1723

IVAIN01 1636 1680 1 269 1680 586 6 1611

IVAIN02 992 993 1 009 993 491 6 1108

KAPUL01 1462 1736 4 506 1736 963 2 2550

KAPUL02 1180 1299 4 264 1299 265 6 2328

LARK01 1961 2364 2 891 2364 1912 2 104

LARK02 2144 2231 2 205 2231 2364 2 317

WIMAR01 1856 1924 3 124 1924 1706 3 394

WIMAR02 2264 2302 3 056 2302 1608 3 1078

224


AREA


Tree


1 484643 9268927 4072009 1 PTE IND 13 18

Secondary

Forest - Yalu

1 484643 9268927 4072009 2 TRE 13 27

Secondary

Forest - Yalu

1 484643 9268927 4072009 3 HIB 13 29

Secondary

Forest - Yalu

1 484643 9268927 4072009 4 MAC 13 17

Secondary

Forest - Yalu

1 484643 9268927 4072009 5 HIB 13 335

Secondary

Forest - Yalu

1 484643 9268927 4072009 6 13 30

Secondary

Forest - Yalu

1 484643 9268927 4072009 7 13 30

Secondary

Forest - Yalu

1 484643 9268927 4072009 8 PTE IND 13 51

Secondary

Forest - Yalu

1 484643 9268927 4072009 9 TRE 13 33

Secondary

Forest - Yalu

1 484643 9268927 4072009 10 13 20

Secondary

Forest - Yalu

1 484643 9268927 4072009 11 POM PIN 13 245

Secondary

Forest - Yalu

1 484643 9268927 4072009 12 13 40

Secondary

Forest - Yalu

1 484643 9268927 4072009 13 13 30

Secondary

Forest - Yalu

1 484643 9268927 4072009 14 HIB 13 39

Secondary

Forest - Yalu

1 484643 9268927 4072009 15 TRE 13 225

Secondary

Forest - Yalu

1 484643 9268927 4072009 16 TER 13 26

Secondary

Forest - Yalu

2 484713 9268265 4072009 1 AIL 2 88

Primary Forest

- Yalu

2 484713 9268265 5072009 2 MYR 13 22

Primary Forest

- Yalu

2 484713 9268265 6072009 3 CEL PHI 13 175

Primary Forest

- Yalu

2 484713 9268265 7072009 4 STE 13 60

Primary Forest

- Yalu

225


Tree


2 484713 9268265 8072009 5 CEL LAT 13 335

Primary Forest

- Yalu

2 484713 9268265 9072009 6 VIT 2 95

Primary Forest

- Yalu

2 484713 9268265 10072009 7 POM TOM 13 123

Primary Forest

- Yalu

2 484713 9268265 11072009 8 CHN 13 18

Primary Forest

- Yalu

2 484713 9268265 12072009 9 MYR 13 129

Primary Forest

- Yalu

2 484713 9268265 13072009 10 NEU 13 225

Primary Forest

- Yalu

2 484713 9268265 14072009 11 PTE IND 13 47

Primary Forest

- Yalu

2 484713 9268265 15072009 12 POM PIN 13 48

Primary Forest

- Yalu

2 484713 9268265 16072009 13 LIT 2 29

Primary Forest

- Yalu

2 484713 9268265 17072009 14 PIM AMB 13 27

Primary Forest

- Yalu

2 484713 9268265 18072009 15 LIT 2 435

Primary Forest

- Yalu

2 484713 9268265 19072009 16 MYR 13 42

Primary Forest

- Yalu

2 484713 9268265 20072009 17 CEL PHI 3 73

Primary Forest

- Yalu

2 484713 9268265 21072009 18 CEL PHI 2 40

Primary Forest

- Yalu

3 484634 9268819 17062009 1 TRH 13 365

Secondary

Forest - Yalu

3 484634 9268819 17062009 2 TRH 13 359

Secondary

Forest - Yalu

3 484634 9268819 17062009 3 SEM 13 110

Secondary

Forest - Yalu

3 484634 9268819 17062009 4 TER 13 600

Secondary

Forest - Yalu

3 484634 9268819 17062009 5 STE 13 253

Secondary

Forest - Yalu

3 484634 9268819 17062009 6 POM PIN 13 570

Secondary

Forest - Yalu

3 484634 9268819 17062009 7 TER 13 630

Secondary

Forest - Yalu

3 484634 9268819 17062009 8 HIB 13 435

Secondary

Forest - Yalu

226


Tree


3 484634 9268819 17062009 9 INO FAG 13 600

Secondary

Forest - Yalu

3 484634 9268819 17062009 10 BUC 13 230

Secondary

Forest - Yalu

3 484634 9268819 17062009 11 TRH 13 313

Secondary

Forest - Yalu

3 484634 9268819 17062009 12 PIS UMB 13 220

Secondary

Forest - Yalu

3 484634 9268819 17062009 13 PTE IND 13 120

Secondary

Forest - Yalu

4 484630 9268763 17062009 1 POM PIN 13 280

Secondary

Forest - Yalu

4 484630 9268763 17062009 2 POM PIN 13 359

Secondary

Forest - Yalu

4 484630 9268763 17062009 3 END 13 370

Secondary

Forest - Yalu

4 484630 9268763 17062009 4 13 300

Secondary

Forest - Yalu

4 484630 9268763 17062009 5 MAC 13 225

Secondary

Forest - Yalu

4 484630 9268763 17062009 6 TOO SUR 13 325

Secondary

Forest - Yalu

4 484630 9268763 17062009 7 TOO SUR 13 305

Secondary

Forest - Yalu

4 484630 9268763 17062009 8 MAC 13 230

Secondary

Forest - Yalu

4 484630 9268763 17062009 9 PTE IND 13 220

Secondary

Forest - Yalu

4 484630 9268763 17062009 10 PTE IND 13 239

Secondary

Forest - Yalu

4 484630 9268763 17062009 11 TRH 13 235

Secondary

Forest - Yalu

4 484630 9268763 17062009 12 VIT 13 163

Secondary

Forest - Yalu

4 484630 9268763 17062009 13 SEM 13 128

Secondary

Forest - Yalu

4 484630 9268763 17062009 14 TRI 13 306

Secondary

Forest - Yalu

4 484630 9268763 17062009 15 TRI 13 284

Secondary

Forest - Yalu

4 484630 9268763 17062009 16 POM PIN 13 250

Secondary

Forest - Yalu

5 484646 9268686 17062009 1 TIM 13 143

Secondary

Forest - Yalu

227


Tree


5 484646 9268686 17062009 2 GUI 13 129

Secondary

Forest - Yalu

5 484646 9268686 17062009 3 PTE IND 13 130

Secondary

Forest - Yalu

5 484646 9268686 17062009 4 PTE IND 13 253

Secondary

Forest - Yalu

5 484646 9268686 17062009 5 FIC 13 335

Secondary

Forest - Yalu

5 484646 9268686 17062009 6 TRI 13 286

Secondary

Forest - Yalu

5 484646 9268686 17062009 7 FIC 13 278

Secondary

Forest - Yalu

5 484646 9268686 17062009 8 PTE IND 13 253

Secondary

Forest - Yalu

5 484646 9268686 17062009 9 TRH 13 411

Secondary

Forest - Yalu

5 484646 9268686 17062009 10 ELA 13 583

Secondary

Forest - Yalu

5 484646 9268686 17062009 11 STE 13 272

Secondary

Forest - Yalu

5 484646 9268686 17062009 12 ART 13 301

Secondary

Forest - Yalu

5 484646 9268686 17062009 13 PTE IND 13 204

Secondary

Forest - Yalu

5 484646 9268686 17062009 14 PTE IND 13 153

Secondary

Forest - Yalu

5 484646 9268686 17062009 15 SEM 13 95

Secondary

Forest - Yalu

5 484646 9268686 17062009 16 SEM 13 118

Secondary

Forest - Yalu

5 484646 9268686 17062009 17 TRI 13 275

Secondary

Forest - Yalu

5 484646 9268686 17062009 18 TRH 13 258

Secondary

Forest - Yalu

5 484646 9268686 17062009 19 TRH 13 250

Secondary

Forest - Yalu

5 484646 9268686 17062009 20 TRH 13 328

Secondary

Forest - Yalu

5 484646 9268686 17062009 21 TIM 13 288

Secondary

Forest - Yalu

6 _ _ 17062009 1 TRH 13 167

Secondary

Forest - Yalu

6 _ _ 17062009 2 PTE IND 13 152

Secondary

Forest - Yalu

228


Tree


6 _ _ 17062009 3 PTE IND 13 192

Secondary

Forest - Yalu

6 _ _ 17062009 4 PTE IND 13 158

Secondary

Forest - Yalu

6 _ _ 17062009 5 FIC 13 506

Secondary

Forest - Yalu

6 _ _ 17062009 6 TIM 13 218

Secondary

Forest - Yalu

6 _ _ 17062009 7 STR 13 101

Secondary

Forest - Yalu

6 _ _ 17062009 8 LIT 13 249

Secondary

Forest - Yalu

6 _ _ 17062009 9 MAC 13 264

Secondary

Forest - Yalu

6 _ _ 17062009 10 FIC 13 275

Secondary

Forest - Yalu

6 _ _ 17062009 11 PTE IND 13 350

Secondary

Forest - Yalu

6 _ _ 17062009 12 DYS 13 183

Secondary

Forest - Yalu

6 _ _ 17062009 13 TRH 13 235

Secondary

Forest - Yalu

6 _ _ 17062009 14 TRH 13 266

Secondary

Forest - Yalu

6 _ _ 17062009 15 ART 13 212

Secondary

Forest - Yalu

6 _ _ 17062009 16 TRI 13 260

Secondary

Forest - Yalu

6 _ _ 17062009 17 TRI 13 117

Secondary

Forest - Yalu

7 484761 9268629 17062009 1 TIM 13 159

Secondary

Forest - Yalu

7 484761 9268629 17062009 2 TIM 13 156

Secondary

Forest - Yalu

7 484761 9268629 17062009 3 EUO 13 351

Secondary

Forest - Yalu

7 484761 9268629 17062009 4 TRH 13 215

Secondary

Forest - Yalu

7 484761 9268629 17062009 5 TRH 13 336

Secondary

Forest - Yalu

7 484761 9268629 17062009 6 PTE IND 13 305

Secondary

Forest - Yalu

7 484761 9268629 17062009 7 POM PIN 13 284

Secondary

Forest - Yalu

229


Tree


7 484761 9268629 17062009 8 INT 13 256

Secondary

Forest - Yalu

7 484761 9268629 17062009 9 ANT CHI 13 172

Secondary

Forest - Yalu

7 484761 9268629 17062009 10 MYR 13 142

Secondary

Forest - Yalu

7 484761 9268629 17062009 11 TIM 13 226

Secondary

Forest - Yalu

7 484761 9268629 17062009 12 13 470

Secondary

Forest - Yalu

7 484761 9268629 17062009 13 ART 13 313

Secondary

Forest - Yalu

7 484761 9268629 17062009 14 VIT COF 13 241

Secondary

Forest - Yalu

7 484761 9268629 17062009 15 PTE IND 13 198

Secondary

Forest - Yalu

7 484761 9268629 17062009 16 MAC 13 398

Secondary

Forest - Yalu

7 484761 9268629 17062009 17 MAC 13 214

Secondary

Forest - Yalu

7 484761 9268629 17062009 18 MAC 13 190

Secondary

Forest - Yalu

7 484761 9268629 17062009 19 GUI 13 244

Secondary

Forest - Yalu

7 484761 9268629 17062009 20 TIM 13 247

Secondary

Forest - Yalu

7 484761 9268629 17062009 21 SEM 13 142

Secondary

Forest - Yalu

7 484761 9268629 17062009 22 SEM 13 156

Secondary

Forest - Yalu

7 484761 9268629 17062009 23 SEM 13 163

Secondary

Forest - Yalu

7 484761 9268629 17062009 24 PTE IND 13 316

Secondary

Forest - Yalu

7 484761 9268629 17062009 25 ANT CHI 13 251

Secondary

Forest - Yalu

7 484761 9268629 17062009 26 ANT CHI 13 210

Secondary

Forest - Yalu

7 484761 9268629 17062009 27 TIM 13 266

Secondary

Forest - Yalu

7 484761 9268629 17062009 28 TIM 13 151

Secondary

Forest - Yalu

8 484610 9268470 17062009 1 TRH 13 260

Secondary

Forest - Yalu

230


Tree


8 484610 9268470 17062009 2 EUO 13 142

Secondary

Forest - Yalu

8 484610 9268470 17062009 3 EUO 13 118

Secondary

Forest - Yalu

8 484610 9268470 17062009 4 TIM 13 211

Secondary

Forest - Yalu

8 484610 9268470 17062009 5 PTE IND 13 294

Secondary

Forest - Yalu

8 484610 9268470 17062009 6 HIB 13 792

Secondary

Forest - Yalu

8 484610 9268470 17062009 7 TRH 13 411

Secondary

Forest - Yalu

8 484610 9268470 17062009 8 ART 13 1135

Secondary

Forest - Yalu

8 484610 9268470 17062009 9 PTE IND 13 198

Secondary

Forest - Yalu

8 484610 9268470 17062009 10 TRH 13 520

Secondary

Forest - Yalu

8 484610 9268470 17062009 11 MAC 13 233

Secondary

Forest - Yalu

8 484610 9268470 17062009 12 POL 13 261

Secondary

Forest - Yalu

8 484610 9268470 17062009 13 CAN 13 316

Secondary

Forest - Yalu

8 484610 9268470 17062009 14 POM PIN 13 472

Secondary

Forest - Yalu

8 484610 9268470 17062009 15 EUO 13 116

Secondary

Forest - Yalu

8 484610 9268470 17062009 16 PTE IND 13 114

Secondary

Forest - Yalu

8 484610 9268470 17062009 17 CAN 13 281

Secondary

Forest - Yalu

8 484610 9268470 17062009 18 POM PIN 13 561

Secondary

Forest - Yalu

8 484610 9268470 17062009 19 ANT CHI 13 283

Secondary

Forest - Yalu

8 484610 9268470 17062009 20 POM PIN 13 196

Secondary

Forest - Yalu

8 484610 9268470 17062009 21 EUO 13 500

Secondary

Forest - Yalu

8 484610 9268470 17062009 22 FIC 13 246

Secondary

Forest - Yalu

8 484610 9268470 17062009 23 FIC 13 246

Secondary

Forest - Yalu

231


Tree


8 484610 9268470 17062009 24 TRI 13 153

Secondary

Forest - Yalu

9 484522 92685314 17062009 1 SEM 13 540

Secondary

Forest - Yalu

9 484522 92685314 17062009 2 INO FAG 13 550

Secondary

Forest - Yalu

9 484522 92685314 17062009 3 BUC 13 369

Secondary

Forest - Yalu

9 484522 92685314 17062009 4 ANT CHI 13 505

Secondary

Forest - Yalu

9 484522 92685314 17062009 5 GUI 13 195

Secondary

Forest - Yalu

9 484522 92685314 17062009 6 LIT 13 355

Secondary

Forest - Yalu

9 484522 92685314 17062009 7 PIS UMB 13 300

Secondary

Forest - Yalu

9 484522 92685314 17062009 8 SEM 13 371

Secondary

Forest - Yalu

9 484522 92685314 17062009 9 PIS UMB 13 172

Secondary

Forest - Yalu

9 484522 92685314 17062009 10 PIS UMB 13 153

Secondary

Forest - Yalu

9 484522 92685314 17062009 11 BRI 13 1800

Secondary

Forest - Yalu

9 484522 92685314 17062009 12 VIT COF 13 1800

Secondary

Forest - Yalu

9 484522 92685314 17062009 13 TER 13 201

Secondary

Forest - Yalu

9 484522 92685314 17062009 14 PIS UMB 13 196

Secondary

Forest - Yalu

9 484522 92685314 17062009 15 PTE IND 13 1850

Secondary

Forest - Yalu

10 484446 9268164 17062009 1 END 13 381

Secondary

Forest - Yalu

10 484446 9268164 17062009 2 CAN 13 548

Secondary

Forest - Yalu

10 484446 9268164 17062009 3 MAC 13 346

Secondary

Forest - Yalu

10 484446 9268164 17062009 4 MAC 13 289

Secondary

Forest - Yalu

10 484446 9268164 17062009 5 MAC 13 336

Secondary

Forest - Yalu

10 484446 9268164 17062009 6 PTE IND 13 324

Secondary

Forest - Yalu

232


Tree


10 484446 9268164 17062009 7 CAN 13 375

Secondary

Forest - Yalu

10 484446 9268164 17062009 8 MAC 13 274

Secondary

Forest - Yalu

10 484446 9268164 17062009 9 MAC 13 393

Secondary

Forest - Yalu

10 484446 9268164 17062009 10 PTE IND 13 180

Secondary

Forest - Yalu

10 484446 9268164 17062009 11 ANT CHI 13 507

Secondary

Forest - Yalu

10 484446 9268164 17062009 12 STE 13 165

Secondary

Forest - Yalu

10 484446 9268164 17062009 13 CEL 13 570

Secondary

Forest - Yalu

10 484446 9268164 17062009 14 LIT 13 394

Secondary

Forest - Yalu

10 484446 9268164 17062009 15 STE AMP 13 107

Secondary

Forest - Yalu

10 484446 9268164 17062009 16 PTE IND 13 195

Secondary

Forest - Yalu

10 484446 9268164 17062009 17 LIT 13 130

Secondary

Forest - Yalu

10 484446 9268164 17062009 18 PIM AMB 13 234

Secondary

Forest - Yalu

10 484446 9268164 17062009 19 ANT CHI 13 517

Secondary

Forest - Yalu

10 484446 9268164 17062009 20 AGL 13 180

Secondary

Forest - Yalu

10 484446 9268164 17062009 21 ALS 13 192

Secondary

Forest - Yalu

10 484446 9268164 17062009 22 STE 13 265

Secondary

Forest - Yalu

10 484446 9268164 17062009 23 MIC 13 201

Secondary

Forest - Yalu

10 484446 9268164 17062009 24 PTE IND 13 1860

Secondary

Forest - Yalu

11 484612 9268157 17062009 1 FIC 13 375

Secondary

Forest - Yalu

11 484612 9268157 17062009 2 PLA 13 130

Secondary

Forest - Yalu

11 484612 9268157 17062009 3 INO FAG 13 242

Secondary

Forest - Yalu

11 484612 9268157 17062009 4 STE 13 690

Secondary

Forest - Yalu

233


Tree


11 484612 9268157 17062009 5 PIM AMB 13 466

Secondary

Forest - Yalu

11 484612 9268157 17062009 6 GNE GNE 13 158

Secondary

Forest - Yalu

11 484612 9268157 17062009 7 PIM AMB 13 255

Secondary

Forest - Yalu

11 484612 9268157 17062009 8 PIM AMB 13 385

Secondary

Forest - Yalu

11 484612 9268157 17062009 9 GNE GNE 13 130

Secondary

Forest - Yalu

11 484612 9268157 17062009 10 PIM AMB 13 255

Secondary

Forest - Yalu

11 484612 9268157 17062009 11 PIM AMB 13 260

Secondary

Forest - Yalu

11 484612 9268157 17062009 12 CEL 13 180

Secondary

Forest - Yalu

11 484612 9268157 17062009 13 CEL 13 255

Secondary

Forest - Yalu

11 484612 9268157 17062009 14 GUI 13 290

Secondary

Forest - Yalu

11 484612 9268157 17062009 15 CEL 13 715

Secondary

Forest - Yalu

11 484612 9268157 17062009 16 STE 13 700

Secondary

Forest - Yalu

11 484612 9268157 17062009 17 MIC 13 210

Secondary

Forest - Yalu

11 484612 9268157 17062009 18 PIM AMB 13 346

Secondary

Forest - Yalu

11 484612 9268157 17062009 19 MIS 13 246

Secondary

Forest - Yalu

11 484612 9268157 17062009 20 CEL 13 700

Secondary

Forest - Yalu

11 484612 9268157 17062009 21 CEL 13 496

Secondary

Forest - Yalu

12 484699 9268074 17062009 1 INT 20 926

Secondary

Forest - Yalu

12 484699 9268074 17062009 2 TER 13 634

Secondary

Forest - Yalu

12 484699 9268074 17062009 3 SEM 13 430

Secondary

Forest - Yalu

12 484699 9268074 17062009 4 TER 13 293

Secondary

Forest - Yalu

12 484699 9268074 17062009 5 PIM AMB 13 260

Secondary

Forest - Yalu

234


Tree


12 484699 9268074 17062009 6 PIM AMB 13 250

Secondary

Forest - Yalu

12 484699 9268074 17062009 7 PIM AMB 13 310

Secondary

Forest - Yalu

12 484699 9268074 17062009 8 SYZ 20 560

Secondary

Forest - Yalu

12 484699 9268074 17062009 9 TRI 20 1300

Secondary

Forest - Yalu

12 484699 9268074 17062009 10 13 180

Secondary

Forest - Yalu

12 484699 9268074 17062009 11 PIS UMB 13 320

Secondary

Forest - Yalu

12 484699 9268074 17062009 12 LIT 13 150

Secondary

Forest - Yalu

12 484699 9268074 17062009 13 TRI 13 471

Secondary

Forest - Yalu

12 484699 9268074 17062009 14 STE 13 284

Secondary

Forest - Yalu

12 484699 9268074 17062009 15 CER 13 252

Secondary

Forest - Yalu

12 484699 9268074 17062009 16 INT 13 825

Secondary

Forest - Yalu

12 484699 9268074 17062009 17 TER 30 450

Secondary

Forest - Yalu

13 484743 9268126 17062009 1 PIM AMB 13 420

Secondary

Forest - Yalu

13 484743 9268126 17062009 2 CEL 13 490

Secondary

Forest - Yalu

13 484743 9268126 17062009 3 MIC 13 130

Secondary

Forest - Yalu

13 484743 9268126 17062009 4 PTE IND 13 530

Secondary

Forest - Yalu

13 484743 9268126 17062009 5 CEL 13 761

Secondary

Forest - Yalu

13 484743 9268126 17062009 6 CEL 13 420

Secondary

Forest - Yalu

13 484743 9268126 17062009 7 CEL 13 340

Secondary

Forest - Yalu

13 484743 9268126 17062009 8 PTE IND 40 705

Secondary

Forest - Yalu

13 484743 9268126 17062009 9 MAC 13 320

Secondary

Forest - Yalu

13 484743 9268126 17062009 10 MAC 13 460

Secondary

Forest - Yalu

235


Tree


13 484743 9268126 17062009 11 END 13 300

Secondary

Forest - Yalu

13 484743 9268126 17062009 12 MAC 13 190

Secondary

Forest - Yalu

13 484743 9268126 17062009 13 MAC 13 203

Secondary

Forest - Yalu

13 484743 9268126 17062009 14 ART 13 220

Secondary

Forest - Yalu

13 484743 9268126 17062009 15 PTE IND 13 525

Secondary

Forest - Yalu

13 484743 9268126 17062009 16 MAC 13 124

Secondary

Forest - Yalu

13 484743 9268126 17062009 17 AGL 13 415

Secondary

Forest - Yalu

14 484837 9268212 17062009 1 GAR 20 291

Secondary

Forest - Yalu

14 484837 9268212 17062009 2 AGL 13 280

Secondary

Forest - Yalu

14 484837 9268212 17062009 3 TER 13 364

Secondary

Forest - Yalu

14 484837 9268212 17062009 4 TER 13 330

Secondary

Forest - Yalu

14 484837 9268212 17062009 5 PIS UMB 13 156

Secondary

Forest - Yalu

14 484837 9268212 17062009 6 POM PIN 13 584

Secondary

Forest - Yalu

14 484837 9268212 17062009 7 TER 13 365

Secondary

Forest - Yalu

14 484837 9268212 17062009 8 END 13 396

Secondary

Forest - Yalu

14 484837 9268212 17062009 9 TER 13 233

Secondary

Forest - Yalu

14 484837 9268212 17062009 10 STE 13 630

Secondary

Forest - Yalu

15 484784 9268298 17062009 1 CEL 13 367

Secondary

Forest - Yalu

15 484784 9268298 17062009 2 PIM AMB 13 360

Secondary

Forest - Yalu

15 484784 9268298 17062009 3 CEL 15 619

Secondary

Forest - Yalu

15 484784 9268298 17062009 4 DYS 13 240

Secondary

Forest - Yalu

15 484784 9268298 17062009 5 LIT 13 465

Secondary

Forest - Yalu

236


Tree


15 484784 9268298 17062009 6 FIC 40 1500

Secondary

Forest - Yalu

15 484784 9268298 17062009 7 POM PIN 13 579

Secondary

Forest - Yalu

15 484784 9268298 17062009 8 MIS 13 278

Secondary

Forest - Yalu

15 484784 9268298 17062009 9 CEL 40 570

Secondary

Forest - Yalu

15 484784 9268298 17062009 10 LIT 13 294

Secondary

Forest - Yalu

15 484784 9268298 17062009 11 ANT CHI 13 434

Secondary

Forest - Yalu

15 484784 9268298 17062009 12 PIS UMB 13 236

Secondary

Forest - Yalu

15 484784 9268298 17062009 13 GNE GNE 13 150

Secondary

Forest - Yalu

15 484784 9268298 17062009 14 CEL 15 603

Secondary

Forest - Yalu

16 484840 9268332 17062009 1 INT 13 570

Secondary

Forest - Yalu

16 484840 9268332 17062009 2 MIC 13 246

Secondary

Forest - Yalu

16 484840 9268332 17062009 3 CEL 40 750

Secondary

Forest - Yalu

16 484840 9268332 17062009 4 POM PIN 20 286

Secondary

Forest - Yalu

16 484840 9268332 17062009 5 MIC 13 240

Secondary

Forest - Yalu

16 484840 9268332 17062009 6 TRI 13 176

Secondary

Forest - Yalu

16 484840 9268332 17062009 7 FIC 13 120

Secondary

Forest - Yalu

16 484840 9268332 17062009 8 PIM AMB 13 287

Secondary

Forest - Yalu

16 484840 9268332 17062009 9 GNE GNE 13 146

Secondary

Forest - Yalu

16 484840 9268332 17062009 10 PIM AMB 13 250

Secondary

Forest - Yalu

16 484840 9268332 17062009 11 BIS JAV 13 605

Secondary

Forest - Yalu

16 484840 9268332 17062009 12 STE 13 553

Secondary

Forest - Yalu

16 484840 9268332 17062009 13 PIM AMB 13 378

Secondary

Forest - Yalu

237


Tree


17 484890 9268434 17062009 1 PTE IND 13 323

Secondary

Forest - Yalu

17 484890 9268434 17062009 2 ART 15 733

Secondary

Forest - Yalu

17 484890 9268434 17062009 3 POM PIN 30 705

Secondary

Forest - Yalu

17 484890 9268434 17062009 4 DRA 30 680

Secondary

Forest - Yalu

17 484890 9268434 17062009 5 HOR 13 250

Secondary

Forest - Yalu

17 484890 9268434 17062009 6 MAC 13 143

Secondary

Forest - Yalu

17 484890 9268434 17062009 7 PTE IND 15 623

Secondary

Forest - Yalu

17 484890 9268434 17062009 8 CEL 30 664

Secondary

Forest - Yalu

17 484890 9268434 17062009 9 PTE IND 13 220

Secondary

Forest - Yalu

17 484890 9268434 17062009 10 PTE IND 13 170

Secondary

Forest - Yalu

17 484890 9268434 17062009 11 PTE IND 13 140

Secondary

Forest - Yalu



Tree


1 469324 9256048 4062009 1 POM PIN 3 695

Logged Forest -

Gabensis

1 469324 9256048 4062009 2 INT 13 59

Logged Forest -

Gabensis

1 469324 9256048 4062009 3 CHN 13 61

Logged Forest -

Gabensis

1 469324 9256048 4062009 4 TER 2 43

Logged Forest -

Gabensis

1 469324 9256048 4062009 5 POM PIN 2 59

Logged Forest -

Gabensis

1 469324 9256048 4062009 6 POM PIN 2 59

Logged Forest -

Gabensis

1 469324 9256048 4062009 7 POM PIN 13 70

Logged Forest -

Gabensis

238


Tree


1 469324 9256048 4062009 8 CHN 13 555

Logged Forest -

Gabensis

1 469324 9256048 4062009 9 INT 15 28

Logged Forest -

Gabensis

1 469324 9256048 4062009 10 TER 2 535

Logged Forest -

Gabensis

1 469324 9256048 4062009 11 TER 13 40

Logged Forest -

Gabensis

1 469324 9256048 4062009 12 HRN 13 365

Logged Forest -

Gabensis

1 469324 9256048 4062009 13 CHN 18 52

Logged Forest -

Gabensis

1 469324 9256048 4062009 14 CNN 18 575

Logged Forest -

Gabensis

1 469324 9256048 4062009 15 CHN 18 385

Logged Forest -

Gabensis

1

469324

9256048

4062009

16

CHN

18

33

Logged Forest-

Gabensis

1 469324 9256048 4062009 17 POM PIN 13 305

Logged Forest -

Gabensis

1 469324 9256048 4062009 18 PLA 13 30

Logged Forest -

Gabensis

1 469324 9256048 4062009 19 13 20

Logged Forest -

Gabensis

2 470782 9257001 4062009 1 HRN 13 43

Secondary Forest -

Gabensis

2 470782 9257001 4062009 2 POM PIN 2 55

Secondary Forest -

Gabensis

2 470782 9257001 4062009 3 CHN 2 94

Secondary Forest -

Gabensis

2 470782 9257001 4062009 4 13 30

Secondary Forest -

Gabensis

2 470782 9257001 4062009 5 13 30

Secondary Forest -

Gabensis

2 470782 9257001 4062009 6 PTE IND 2 85

Secondary Forest -

Gabensis

2 470782 9257001 4062009 7 13 30

Secondary Forest -

Gabensis

2 470782 9257001 4062009 8 13 30

Secondary Forest -

Gabensis

2 470782 9257001 4062009 9 13 30

Secondary Forest -

Gabensis

2 470782 9257001 4062009 10 13 30

Secondary Forest -

Gabensis

239


Tree


2 470782 9257001 4062009 11 PTE IND 2 57

Secondary Forest -

Gabensis

2 470782 9257001 4062009 12 PTE IND 13 31

Secondary Forest -

Gabensis

2 470782 9257001 4062009 13 MAS 2 55

Secondary Forest -

Gabensis

2 470782 9257001 4062009 14 POM PIN 2 41

Secondary Forest -

Gabensis

2 470782 9257001 4062009 15 POM PIN 2 47

Secondary Forest -

Gabensis

2 470782 9257001 4062009 16 13 30

Secondary Forest -

Gabensis

2 470782 9257001 4062009 17 POM PIN 15 43

Secondary Forest -

Gabensis

2 470782 9257001 4062009 18 PTE IND 15 80

Secondary Forest -

Gabensis

240



EAG Eaglewood

1 2 3















TAU Taun

TEA Teak

TER Terminalia

WAL Walnut PNG



































5 6















STR Steropsis

241


242


243














244

















K1500m3









apply









K1500m3






245




















K450m3























K450m3






246








GIS Mapping K20000






Marketing K10000












Authors

Yosi Cossey Keosai

Title


Date

2011

Citation




Persistent Link


File Description








Date post:	11-Feb-2022
Category:	Documents
Upload:	others
View:	2 times
Download:	0 times

SCENARIOS FOR COMMUNITY-BASED MANAGEMENT OF …

Documents