BAHIR DAR UNIVERSITY
COLLEGE OF AGRICULTURE AND ENVIRONMENTAL
SCIENCE
DEPARTMENT OF RURAL DEVELOPMENT AND
AGRICULTURAL EXTENSION
GRADUATE PROGRAM
VALUE CHAIN ANALYSIS OF SOYBEAN: THE CASE OF PAWE
DISTRICT, NORTH WESTERN ETHIOPIA
MSc. Thesis
By
Takele Atnafu Delele
December, 2020
Bahir Dar
i
BAHIR DAR UNIVERSITY
COLLEGE OF AGRICULTURE AND ENVIRONMENTAL
SCIENCE
DEPARTMENT OF RURAL DEVELOPMENT AND
AGRICULTURAL EXTENSION
GRADUATE PROGRAM
VALUE CHAIN ANALYSIS OF SOYBEAN: THE CASE OF PAWE
DISTRICT, NORTH WESTERN ETHIOPIA
MSc. Thesis
By
Takele Atnafu Delele
THESIS SUBMITTED IN PARTIAL FULFILMENT OF THE
REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE
(MSc.) IN RURAL DEVELOPMENT MANAGEMENT
MAJOR ADVISOR: ALMAZ GIZIEW (PhD)
CO-ADVISOR: BIRHANU MELESSE (ASSISTANT PROFESSOR)
December, 2020
BAHIR DAR
ii
BAHIR DAR UNIVERSITY
COLLEGE OF AGRICULTURE AND ENVIRONMENTAL SCIENCE
DEPARTMENT OF RURAL DEVELOPMENT AND
AGRICULTURAL EXTENSION
Approval of Thesis for Defense Result
As a member of the Board of Examiners of the Master of Science (MSc.) thesis open defense
examination, we certify that we have read and evaluated this thesis prepared by Mr. Takele
Atnafu entitled Value Chain Analysis of Soybean: the case of Pawe district, North
western Ethiopia. We hereby certify that the thesis is accepted for fulfilling the
requirements for the award of the degree of Master of Sciences (MSc.) in Rural
Development Management.
Board of Examiners
Derjew Fentie (PhD)
Name of External Examiner Signature Date
Beneberu Assefa (PhD) 14/12/2020
Name of Internal Examiner Signature Date
Yenesew Sewnet (Assistant professor)
Name of Chairman Signature Date
iii
DECLARATION
This is to certify that this thesis entitled “Value Chain Analysis of Soybean: The Case of
Pawe District, North Western Ethiopia” submitted in partial fulfillment of the
requirements for the award of the degree of Master of Science in “Rural Development
Management” to the Graduate Program of College of Agriculture and Environmental
Sciences, Bahir Dar University by Mr. Takele Atnafu (ID. No. BDU 1100541) is an
authentic work carried out by him under our guidance. The matter embodied in this project
work has not been submitted earlier for an award of any degree or diploma to the best of our
knowledge and belief.
Name of the Student
Takele Atnafu
Signature & date 13/12/2020
Name of the Major Advisor
1) Almaz Giziew (PhD)
Signature & date 14/12/2020
Name of the Co-advisor
2) Birhanu Melesse (Assistant professor)
Signature & date 14/12/2020
iv
DEDICATION
I dedicate this thesis paper to my beloved mother Siraye Yitayih and my father Atnafu
Delele as well as my wife Yalganesh Hunegnaw for their unlimited moral encouragement
and financial support starting from elementary school for the success of my life.
v
ACKNOWLEDGMENT
First and for most, my gratitude goes to the Almighty of God for being successful in my
journey. My heartfelt appreciation and gratitude go to my major advisor Dr. Almaz Giziew
and co-advisor Mr. Birhanu Melesse for their unlimited and unreserved support to make this
thesis work successful. They helped me starting from topic selection up to the completion
of this work. Their willingness to share knowledge and materials and their way of advice to
produce competent citizens will never be forgotten. Their inspiration and brotherly
encouragement were critical for the success of this output.
My special thanks also go to Dr. Beneberu Assefa for his critical advice and brotherly
support for the overall journey of my thesis research work. I am also grateful to Dr. Zemen
Ayalew for sharing of some Stata commands and his technical support for some
specification tests.
My deepest gratitude goes to Mr. Derese Mekonnen director of agricultural extension and
communication research directorate from Ethiopia Institute of Agricultural Research for his
critical advice and resource arrangements for the completion of this thesis work.
I am extremely thankful to my wife, Yalganesh Hunegnaw, for her moral encouragement
and financial support from the beginning up to the end of my research thesis to be successful.
Special thanks go to my friend Koyachew Arega for his great cooperation in the process of
data collection at Addis Ababa. I would like to extend my gratitude to Pawe Agricultural
Research Center for vehicle and other resource arrangements for the overall process of data
collection. I also express my special thanks to enumerators from Pawe Agricultural
Research Center staff and respondents for their unreserved cooperation and patience from
the beginning up to the end of data collection.
vi
TABLE OF CONTENTS
Contents Page
DECLARATION............................................................................................ iii
DEDICATION................................................................................................ iv
ACKNOWLEDGMENT ................................................................................ v
LIST OF TABLES ......................................................................................... ix
LIST OF FIGURES ........................................................................................ x
LIST OF TABLES AND FIGURES IN THE APPENDICES ................... xi
ABBREVIATIONS AND ACRONYMS..................................................... xii
ABSTRACT ..................................................................................................xiii
Chapter 1. INTRODUCTION ....................................................................... 1
1.1 Background and Justification .................................................................................. 1
1.2 Statement of the Problem ......................................................................................... 3
1.3 Objectives of the Study ............................................................................................. 5
1.4 Significance of the Study ........................................................................................... 5
1.5 Scope and Limitation of the Study ........................................................................... 6
Chapter 2. REVIEW OF RELATED LITERATURE ................................ 7
2.1 Definition and Basic Concepts .................................................................................. 7
2.1.1 Concepts related to value chain ......................................................................... 7
2.1.2 Value chain analysis ........................................................................................... 8
2.1.3 Value chain mapping .......................................................................................... 8
2.2 Theories of Value Chain Approaches ...................................................................... 8
2.2.1 The Filiere concept.............................................................................................. 9
2.2.2 The Porter approach .......................................................................................... 9
2.2.3 Global value chain analysis ................................................................................ 9
2.2.4 Global commodity chain approach ................................................................. 10
2.2.5 Global production networks approach ........................................................... 10
2.2.6 Social network theory ....................................................................................... 10
2.3 The Basic Model of Porters’ Value Chain Distinction ......................................... 11
2.4 Status of Soybean Production in Ethiopia ............................................................ 12
2.5 Ethiopian Import-Export Trends of Soybean and Byproducts .......................... 13
vii
TABLE OF CONTENTS (continued)
2.6 Soybean Value Chain in Ethiopia .......................................................................... 15
2.7 The Relevance of Value Chain to the Poor ........................................................... 15
2.8 The Food System Framework ................................................................................ 16
2.9 Review of Empirical Studies ................................................................................... 16
2.9.1 Main value chain actors and their roles.......................................................... 16
2.9.2 Marketing margin and profit shares in the value chain ............................... 18
2.9.3 Determinants of market supply ....................................................................... 18
2.9.4 Factors affecting value addition decision........................................................ 20
2.10 Conceptual- Framework of the Study ................................................................. 21
Chapter 3. RESEARCH METHODOLOGY ............................................. 23
3.1 Description of the Study Area ................................................................................ 23
3.2 Sampling Techniques and Procedures .................................................................. 24
3.3 Sample Size Determination ..................................................................................... 25
3.4 Type, Source, and Methods of Data Collection .................................................... 27
3.5 Methods of Data Analysis ....................................................................................... 28
3.5.1 Descriptive statistical analysis ......................................................................... 29
3.5.2 Econometric analysis ........................................................................................ 29
3.5.3 Marketing margin analysis .............................................................................. 32
3.6 Definition of Variables and Working Hypothesis ................................................ 33
3.6.1 Dependent variables ......................................................................................... 33
3.5.2 Independent variables ...................................................................................... 34
Chapter 4. RESULTS AND DISCUSSION ................................................ 43
4.1 Descriptive and Inferential Statistics ..................................................................... 43
4.1.1 Household heads characteristics ..................................................................... 43
4.1.2 Institutional characteristics of soybean producers ........................................ 47
4.1.3 In put utilization................................................................................................ 52
4.1.4 Soybean production and marketing ................................................................ 53
4.2 Description of Sample Traders and Consumers ................................................... 56
4.2.1 Household characteristics of sampled traders ............................................... 56
4.2.2 Price setting strategies of traders for soybean purchasing ........................... 56
4.2.3 Initial and working capital of traders ............................................................. 57
4.2.4 Soybean oil production ..................................................................................... 58
viii
TABLE OF CONTENTS (continued)
4.2.5 Household characteristics of consumers ......................................................... 61
4.3 Main Value Chain Actors and Functions .............................................................. 62
4.3.1 Primary value chain actors and their functions ............................................. 62
4.3.2 Support service providers and their functions ............................................... 65
4.3.3 Map of soybean value chain ............................................................................. 68
4.3.4 Marketing channels along soybean value chain ............................................. 69
4.4 Marketing Margin Analysis ................................................................................... 72
4.4.1 Production cost of soybean in the study area ................................................. 72
4.4.2 Marketing margin and profit shares of actors in the value chain ................ 75
4.5 Econometrics Analysis ............................................................................................ 80
4.5.1 The determinant factors affecting soybean market supply .......................... 80
4.5.2 Factors affecting farmers’ participation on value addition .......................... 85
Chapter 5. CONCLUSION AND RECOMMENDATIONS .................... 90
5.1 Conclusion ................................................................................................................ 90
5.2 Recommendations ................................................................................................... 90
6. REFERENCES .......................................................................................... 93
7. APPENDICES ......................................................................................... 102
AUTHOR BIOGRAPHICAL SKETCH .................................................. 120
ix
LIST OF TABLES
Tables Page
Table 2.1 Soybean value chain actors and their functions .................................................. 17
Table 3. 1 Sample size and households of the study kebeles .............................................. 25
Table 3. 2 Sampled traders of the study .............................................................................. 27
Table 3.3 Summary of Research Methodology .................................................................. 33
Table 3.4 Description of explanatory variables & hypothesis in MLR model ................... 38
Table 3.5 Description of explanatory variables in the Probit model .................................. 42
Table 4.1 Socio-demographic characteristics of soybean producers .................................. 44
Table 4.2 Socio-demographic characteristics of sampled household (t-test) ...................... 45
Table 4.3 Relationship of quantity of soybean market supply with categorical variables .. 47
Table 4.4 Socio-economic characteristics of soybean producers........................................ 49
Table 4.5 Socio-economic characteristics of sampled households (t-test) ......................... 50
Table 4.6 Extension contact and education level of soybean producers ............................. 52
Table 4.7 Utilization of agricultural inputs for soybean production ................................... 53
Table 4.8 Soybean production and marketing in 2018/19 cropping season ....................... 55
Table 4.9. Socio-demographic characteristics of sample traders ........................................ 56
Table 4.10 Time of soybean purchasing and price setting strategies .................................. 57
Table 4.11 Initial, working capital & credit source ............................................................ 58
Table 4.12 Purchase price of soybean oil by traders and consumers .................................. 59
Table 4.13 Socio-demographic characteristics of soybean oil consumers .......................... 61
Table 4.14 Primary actors and supporters along soybean value chain in the study area .... 62
Table 4.15 Soybean value chain supporters and their functions ......................................... 66
Table 4.16 Production cost of soybean producers .............................................................. 73
Table 4.17 Labor cost of soybean production for producers .............................................. 74
Table 4.18 Value addition and margin of producers ........................................................... 75
Table 4.19 Margin & profit shares of actors along soybean value chain ............................ 76
Table 4.20 Gross margin following marketing channel 3 ................................................... 76
Table 4.21 Distribution of value addition among major actors .......................................... 77
Table 4.22 Production cost of soybean oil processor .......................................................... 78
Table 4.23 Value addition & margin by soybean grain traders & processor ...................... 79
Table 4.24 Value addition and margin by soybean oil traders ............................................ 80
Table 4.25 Regression results of factors affecting quantity of soybean market supply ...... 81
Table 4.26 Probit estimation of factors influencing value addition .................................... 86
x
LIST OF FIGURES
Figures Page
Figure 2.1 Models of porter’ value chain (Porter, 1985) .................................................... 12
Figure 2.2 Trends of soybean production, area coverage & yield in (Ethiopia 2007-2017 13
Figure 2.3 Import value trends of soybean & Soy Sause (2007-2016) ............................... 14
Figure 2.4 Export value trends of soybean & soy Sause (2007-2016)................................ 14
Figure 2. 5 Ethiopian soybean export (2017/18) ................................................................. 15
Figure 2.6 Conceptual frame work of the study .................................................................. 22
Figure 3.1 Geographical location of the study area (Pawe district) .................................... 24
Figure 3.2 Sampling procedures of sample respondents ..................................................... 26
Figure 4.1 Quantity of soybean oil imported and domestically produced, 2019 ................ 59
Figure 4.2 Quantity of soybean grain exported & domestic consumption for processing .. 60
Figure 4.3 Value Chain Actors, Functions and Support service providers ......................... 67
Figure 4.4 Map of soybean value chain .............................................................................. 68
Figure 4.5 Soybean value chain marketing channels .......................................................... 71
xi
LIST OF TABLES AND FIGURES IN THE APPENDICES
Appendices Page
Appendix Table 1. Livestock conversion factors .............................................................. 102
Appendix Table 2. Test of multicollinearity for continuous explanatory variables ......... 102
Appendex Table 3. Contigency coefficient for dummy/categorical variables .................. 103
Appendix Table 4. ANOVA table for F-statistics ............................................................. 103
Appendix 5. Questionnaires and interview guides for different stakeholders ................. 103
xii
ABBREVIATIONS AND ACRONYMS
AGRA Alliance for the Green revolution in Africa
ANOVA Analysis of Variance
ATA Agricultural Transformation Agency
BGRS Benishangul Gumuz Regional State
CBSM Community Based Seed Multiplication
DA Development Agent
ECX Ethiopian Commodity Exchange
EIAR Ethiopian Institute of Agricultural Research
FAO Food and Agriculture Organization
FGD Focus Group Discussion
FTC Farmer’s Training Center
IFAD International Fund for Agricultural Development
IITA International Institute of Tropical Agriculture
MBI Menagesha Biotechnology Institute
MLR Multiple Linear Regression
OLS Ordinary Least Square
PARC Pawe Agricultural Research Center
PLC Private Limited company
SPSS Statistical Package for Social Science
SSA Sub-Saharan Africa
TLU Tropical Livestock Unit
TVET Technical Vocational and Education Training
UNCTAD United Nations Conference on Trade and Development
UNIDO United Nations Industrial Development Organization
USA United States of America
USAID United States Agency for International Development
VCA Value Chain Analysis
VIF Variance Inflation Factor
WBCSD World Business Council for sustainable development
xiii
Value Chain Analysis of Soybean: The Case of Pawe District,
Northwestern Ethiopia
ABSTRACT
This study focused on analysis of soybean value chain in Pawe district of Metekel zone with
specific objectives of mapping of actors in the value chain, marketing margin analysis,
determinants of soybean market supply and value addition. Although there is untapped
potential on soybean, the current production status is unable to meet its rapidly increasing
demands for export and domestic processing. This implies that there is a high production
gap to satisfy market demands. The crop has been provided to the market without adding
significant values besides production constraints and that is why the expected return could
not be realized. Primary data were collected from 228 farmers, 23 traders and 15 consumers
through the interview schedule. Descriptive and inferential statistics, marketing margin,
multiple linear regression and Probit models were used to analyze the primary data. Results
show that input suppliers, farmers, local traders, whole-sellers, cooperatives, ECX,
exporters, processors, retailers, and consumers were the main value chain actors in the
study area. Local-traders received the highest profit margin (377.75 birr per quintal) and
this implies that intervention is needed to increase the net profit share of producers by
experts and concerned bodies. Soybean meal and hulls contributed 60-62% of total revenues
and that is why soybean meal is regarded as the main driving force for soybean oil
production industries. Results of multiple linear regression model indicate that productivity,
lagged price, market information, soybean farm experience, cultivated land, credit
utilization and extension contact influenced the quantity of soybean market supply posively
and significantly. Results of Probit model also indicate that age, quantity produced, market
price and Packaging material influenced the likelihood of farmers to add values on soybean
positively and significantly and emphasis has to be given for each significant variable.
Therefore, provision of improved soybean technologies with full recommended packages to
producers and strengthening of linkages among actors will realize a sustainable production
with significant value addition and viable value chain on soybean.
Key words: Margin, Multiple linear regression, Pawe, Soybean, Probit model, Value Chain
1
Chapter 1. INTRODUCTION
1.1 Background and Justification
Soybean (Glycine max.) is the major legume crop in the world and belongs to the
Leguminosae family. It was originated in east Asia and recognized as a food crop for the
first time in North-eastern China around 1700-1100 B.C (UNCTAD, 2016). Currently,
soybean oil is the 2nd most important vegetable oil after palm oil and accounts for 25% in
world’s oil production (Urgessa Tilahun, 2015). The global average production volume of
soybean reached 557.5 million metric tons per annum over the last eight years. USA, Brazil,
Argentina, China, and India are the five leading soybean producers in the world. USA
(34%), Brazil (30%), and Argentina (18%) these three countries account for 82% of the
overall soybean production worldwide (ECX, 2017). USA is the leading in soybean
production and successful value chain with annual production of more than 89.8 million
metric tons (United Soybean Board, 2012). The ultimate objective of the value chain is to
produce a value-added product for a market (Trienekens, 2011). The value chain is so
important in this era of rapid globalization to penetrate global markets successfully and to
enhance the efficiency of actors (Gereffi &Fernandez, 2011).
Soybean was first introduced to SSA by Chinese traders in the 19th century and cultivated
as an economic crop in the early 1903 in South Africa (Khojely, et al., 2018). Soybean
industry is expanding in Eastern & Southern Africa. Although world population growth at
a slower rate, in Eastern and Southern Africa, is alarming and this will be a major driver of
the economy in these countries (Meyer, et al., 2018). South Africa is the leading soybean
producer in SSA (Byrne, 2018). The global share of soybean production in all African
countries is less than 1% (Varia, 2011). South Africa and Zambia are successful in soybean
value chain since they have a commercial production system with high processing capacity
industries. There is a strong linkage and fair distributions of benefits among actors in the
value chain. Actors are well informed about the price of domestic as well as international
markets (Meyer, et al., 2018).
Soybean was introduced to Ethiopia for the first time in the early 1950s (Shurtleff & Aoyagi,
2009). Although soybean is a recent introduction to Ethiopia, land size and production
2
increased from 6352 - 39021 hectares and 58,490-840,330 quintals respectively from 2007
to 2017 consecutive years (FAOSTAT, 2019). The average production volume of the crop
increased by 37% per annum in the last ten years (ECX, 2017). The main producing areas
are in the western parts of the country in Oromia, Benishangul Gumuz, and Amhara region
(Byrne, 2018). Soybean has been produced by smallholders and commercial farmers each
have a 50% account in the national production (Lehr & Sertse, 2018). However, the overall
performance of the value chain of soybean is still not successful due to some reasons
(Kumilachew Achamyelh et al., 2020).
In Benishangul Gumuz, Soybean was introduced during the massive resettlement program
of the Dreg regime in 1985 (Addisu Getahun & Erimias Assefa, 2016). The crop has been
produced in all three administrative zones of the region namely Assosa, Metekel, and
Kamashi. As BGRS 2018/19 report, 836, 754 quintals of soybean were produced by
cultivating 31,670 hectares of land. But the region has 156,000 hectares of land which is
suitable for soybean production (Musba Kedir, 2019). Metekel is the highest potential zone
in soybean production and produced 550,112 quintals from 14,399 hectares in the 2018/19
production season. Producers, unions, local traders, regional traders, brokers, central whole-
sellers, exporters, and consumers were the main soybean value chain actors in Metekel as
well as the region (Addisu Getahun & Erimias Assefa, 2016). But the value chain was not
successful as a result of some reasons and that is why insignificant value is added on
soybean.
Pawe district is the major soybean producer among Metekel zone districts. Almost all
farmers are producing soybean in most parts of the district. More than 35% of cultivated
land was allocated for soybean production during 2015/16 cropping season (Birhanu
Ayalew, et al., 2018). According to Pawe agriculture office report (2019), the district
covered 7109.6 hectares of land by soybean and produced 122,973 quintals in 2018/19
production season. Almost all producers in the district are chain actors in the value chain
and cannot influence the selling price that is why they are price takers. Hence, the study was
initiated to fill the knowledge gaps on margins, factors affecting soybean market supply,
and farmers’ participation in value addition as well as the roles of actors in the value chain.
3
1.2 Statement of the Problem
In the current situation of the soybean production system in Ethiopia, development and
research gaps were identified in different parts of the country. Metekel Zone is the leading
zone of Ethiopia in soybean production and contributes 65.7% of the region & 24.69% of
the national production (BGRS & FAOSTAT, 2018). Pawe is the major soybean producer
among Metekel zone districts. The quantity of soybean market supply is increasing over
time due to the rising price of soybean (Lehr & Sertse, 2018). As a result, land size and
production of soybean increased from 6352-39,021ha and 58,490-840,330 quintals
respectively in the 2007-2017 fiscal years (FAOSTAT, 2019).Besides soybean dishes like
bread, Kukis, milk, and oil are becoming the common food items in the study area and that
is why the price and demands of soybean are increasing with time.
Apart from its food and market values, its byproducts are important for, fattening, dairy, and
poultry production which are the main job opportunity areas for unemployed youths in the
study area. For these activities, feed is the major input and soybean is the best and preferred
ingredient for feed formulation since the crop is highly reached in protein (Urgessa Tilahun,
2015). This implies that soybean is highly compatible with the crop-livestock farming
system. Two big edible oil factories that can be used soybean as a major input are also under
establishing at Bure town and Addis Ababa. These factories have been designed with high
processing capacity to cover the domestic oil consumption through import substitution.
However, the current production status of soybean across the country is unable to meet all
the above demands.
Besides to production constraints, soybean products are provided without adding significant
values since all producers in the study area are chain actors. They simply produce and sell
their product without influencing the selling price (Addisu Getahun & Erimias Assefa,
2016). Products without significant value addition couldn’t able to compete in the domestic
as well as international markets and it is difficult to realize a viable economic growth. On
average, less than 10% value is added to agricultural products in Ethiopia (Byrne, 2018).
However, developed and developing countries added US$185 & US$40 value respectively
by processing a tone of agricultural products (UNIDO, 2009). In Kenya, 30-290 Kenyan
shilling (8.19-79.21 ETB) value has been added by processing 1-kilogram soybean seed
4
(Nyongesa, et al., 2018). Although soybean can be processed into different feed and food
items, the existing domestic soybean processors are unable to satisfy the consumption needs.
The increased demand for soybean for local processing has led to importing the crop starting
in 1995 (FAOSTAT, 2019). According to this report, the quantity of soybean imports
increased from 172-12,630 quintals (0.151-2.513 million USD value) from 2007-2016
consecutive years. Additionally, Ethiopia has paid more than 320 million birr for importing
byproducts of soybean which shows that nearly 400 million birr is paid annually for
importing grain & byproducts (Mekonnen Hailu & Kaleb Kelemu, 2014; FAOSTAT,
2019). This has been a cause for the emergence of different value chain actors being
involved in soybean production, marketing, and processing (Addisu Getahun & Erimias
Assefa, 2016). Development initiatives have been undertaken by the government extension
programs and development partners to increase soybean production to satisfy the demands
of domestic processing and to promote export. Research centers designed research programs
on soybean to improve its production status. CBSM is one of the research projects to reduce
seed shortage problems for soybean producers across the country. AGRA and N2-Africa
projects also invest in soybean variety development and technology promotion to improve
the status of soybean production.
The problem of unable to meet the domestic demand both in soybean grain & byproducts
can be associated with soybean production and marketing along the value chain includes
lack of knowledge, lack of improved technologies, low and improper extension service,
input scarcity, market access and price of the commodity (Urgessa Tilahun, 2015).
Researches conducted related to soybean value chain is scanty. Most researches and
literature in the past focused on breeding, production, and some on marketing. A study was
conducted on the assessment of soybean value chain in Metekel Zone (Addisu Getahun &
Erimias Tefera, 2016). However, the study emphasized on constraints of soybean
production by using 15 respondents from each district which couldn’t reflect the whole
population. Similarly, Soybean value chain analysis was conducted at Buno Bedele Zone
(Esayas Negasa & Mustefa Bati, 2019). Analysis of cost and return of soybean also
conducted at Assosa Zone and Pawe district of Metekel Zone (Birhanu Ayalew, et al., 2018
& Afework Hagos and Adam Bekele, 2018). All these studies indicated that the determinant
factors that affect soybean supply to the market and value addition as well as profit margins
5
of each actor along the value chain needs future investigation. Overall, such issues have not
been well studied and documented in the study area and that is why this study was initiated
to fill the knowledge gaps on the determinant factors affecting soybean supply and value
addition as well as profit margins of all actors and their roles along soybean value chain in
Pawe district of Metekel zone.
1.3 Objectives of the Study
The general objective of the study is to analyze soybean value chain in Pawe district of
Metekel zone.
Specific objectives
1. Mapping of actors in soybean value chain in the study area
2. To analyse marketing margin of actors in soybean value chain
3. To analyze the determinants of soybean supply to the market in the study area
4. To determine factors affecting value addition on soybean
Research questions
1. What are the main soybean value chain actors and their roles in the study area?
2. Who is benefited more in the value chain? What is the profit share of each actor in
the value chain?
3. What are the determinant factors that affect soybean supply to the market in the
required quantity in the study area?
4. What influences producers from adding value to soybean before providing to the
buyers?
1.4 Significance of the Study
This study provides information on the roles of direct and indirect actors, marketing margin,
and benefit shares of actors along soybean value chain. The study also provides information
on determinants of quantity of soybean market supply and farmers’ participation in value
addition. The result of the study is helpful for soybean producers and traders to make the
6
appropriate decision regarding soybean production and marketing in the study area. The
information generated in this study can also helpful for development organizations, research
institutions, extension service providers, government, and non-governmental organizations
to formulate soybean value chain development programs and guidelines for interventions
that would improve the efficiency of soybean value chain analysis in the study area. Besides,
the findings of this study can be used as a source for further investigations.
1.5 Scope and Limitation of the Study
This study focused on soybean value chain from input suppliers to end-users in the study
area. The study was conducted in Pawe district and important information was collected
from sampled households and other value chain actors involved in the study area and from
Addis Ababa. However, there were spatial and temporal limitations to make the study more
representatives in terms of wider area coverage and time horizon. For this study, data were
collected from Addis Ababa soybean processor and other traders besides to Pawe district.
To collect the required data on time from each actor particularly at Addis Ababa was so
challenging due to the COVID-19 pandemic. This study incorporated soybean oil processor
and other feed and food processors not incorporated due to time limitation as a result of
COVID-19 and this makes difficult to conclude the whole soybean processors at the country
level. Although COVID-19 challenges, the researcher tried to collect the data by
communicating with the sponsoring institution by writing official letters particularly to big
institutions (Health Care Food Manufacturer plc, central ECX, and Ministry of Trade and
Industry).
7
Chapter 2. REVIEW OF RELATED LITERATURE
2.1 Definition and Basic Concepts
2.1.1 Concepts related to value chain
Value chain: The value chain is the full range of activities that are required to bring a
product or service from conception, through different phases of production and processing,
delivery to final consumers, and final disposal/recycling (Ponte, 2014, p. 2). The value chain
describes the full range of activities that firms and workers perform to bring a product from
its conception to end-use and beyond (Gereffi &Fernandez, 2011, p.7). The term value chain
refers to the full life cycle of a product or process including material sourcing, production,
consumption and disposal/recycling processes (WBCSD, 2011). A value chain is the set of
input activities that a company carries out in order to create value for its valued customers
(Porter, 1985).
Value-addition: Value added is a measure of the value created in the economy (IITA-IFAD,
2010). Value-added ideally represents the value created during the manufacturing process
conducted by each industrial establishment. It is measured as the difference between the
value of all goods and services produced and the value of those purchased non-labor inputs
which have been used in the production process (UNIDO, 2009). Value addition is the
difference between output value and the cost of raw material and other inputs in processing
(Surni, et al., 2019).
Value chain actors: Actors are all the individuals or organizations, enterprises, and public
agencies related to a value chain and therefore important for understanding the functioning
and performance of the value chain. Value chain actors are those who are actually directly
involved in value chain activities. Supporting actors can play an important role, but they are
not directly involved in value chain activities (Christian & Barron, 2017). Value chain
supporters provide support services and represent the common interest of the value chain
operators (Engida Gebre et al., 2019). Input suppliers, producers, harvesters, consolidators,
processors, and exporters are the most important value chain actors (FAO, 2018).
8
2.1.2 Value chain analysis
Value chain analysis is the process of breaking a chain into its constituent parts in order to
better understand its structure and functioning. The analysis consists of identifying chain
actors at each stage and discerning their functions and relationships (UNIDO, 2009, p. 4).
Value chain analysis is an effective way to examine the interaction among different players
in a given industry (Zamora, 2016). Value chain analysis is a valuable tool to investigate the
role that value chains can play in achieving specific policy objectives, such as poverty
alleviation, sustained growth, and inequality reduction. VCA is the assessment of a portion
of an economic system where upstream agents in production and distribution processes are
linked to downstream partners by technical, economic, territorial, institutional, and social
relationships (Bellu, 2013). Value chain analysis is about understanding how activities and
actors that are involved in bringing a product from production to consumption are linked
(Stein & Barron, 2017). VCA is used to identify where the firm can increase the value or
reduce costs to the customer at each stage of the value chain (Simatupang et al., 2017).
2.1.3 Value chain mapping
Value chain mapping can be an important means to better understand what opportunities
and/or constraints producers face if they are to benefit from participating in value chains.
The combination of value chain mapping with visual network research approaches and
participatory statistics has the potential to complement existing value chain analysis
approaches and to generate new insights that would be difficult to obtain using traditional
questionnaire surveys alone (Christian & Barron, 2017). Value chain maps provide an easily
digestible way to understand the process and pathways to production and sale by illustrating,
in a simple form, complexities of an industry sector and its value chain (Kerr, et al., 2015).
2.2 Theories of Value Chain Approaches
Different theories of value chain approaches have been developed by different scientists in
different countries which are talking about value chain from different perspectives.
However, all these approaches were not mentioned here for this study. Hence, theories of
value chain approaches that have some contributions to this study in one or another way
were discussed.
9
2.2.1 The Filiere concept
The main objective of this approach is to map out actual commodity flows and to identify
agents and activities which is viewed as a physical flow-chart of commodities and
transformations. It deals more directly with issues of trade and marketing. This approach
focuses on empirical analysis and has mainly attempted to measure inputs and outputs,
prices, and value-added along a commodity chain. It mostly focuses on local or national
level chains which are less functional to analyze the global world economy (Raikes, et al.,
2000). The filiere approach is seen by many adherents as a neutral and purely empirical
category.
2.2.2 The Porter approach
The term value chain was introduced by Michael Porter for the first time in 1985 in the book
of competitive advantage. Porter developed modern value chain analysis as an instrument
for identifying the value of each steep of the production (Porter, 1985). This approach is
used as an ultimate tool for analyzing the value formed at each stage of the production
process. It focused on actual and potential areas of competitive advantage for the
organization. According to Porter, the value chain is used to analyze the flow of value-
adding activities from the raw material supplier to the end customer. However, this approach
fails to focus on the interconnections and relationships between vertically grouped actors.
2.2.3 Global value chain analysis
Global value chain analysis originates from the commodity chain approaches and focuses
on the position of the lead firm in value chains and power relationships between developing
country producers and western markets or multi-national companies. In this theoretical
stream of power relationships and information, asymmetry is key concepts in the analysis
of global value chains (Roko & Opusunju, 2016). Global value chain analysis provides a
holistic view of global industries both from the top-down and from the bottom-up (Gereffi
& Fernandez, 2011).
10
2.2.4 Global commodity chain approach
This approach has been primarily developed for industrial commodity chains. It is a concept
that is mainly focusing on the power relations in the coordination of dispersed but linked
production systems (Gereffi, 1999). The main intension of the global commodity chain
approach is governance relationships between actors in the value chain. One of the major
hypotheses of this approach is that development requires linking up with the most significant
lead firms in an industry. A strong point of the global commodity chain is its inclusion of
power in economic relations and transactions and the willingness to include the aspects of
power excluded from other analyses of international production and trading relations
(Raikes, et al., 2000). This approach has generated little quantitative analysis and its
conceptual structure and definition would need further elaboration.
2.2.5 Global production networks approach
The global production network/GPN/ is a concept in developmental literature that refers to
the nexus of interconnected functions, operations, and transactions through which specific
products and services produced, distributed, and consumed. It is a conceptual framework
that is capable of grasping the global, regional, and local economic and social dimensions
of the process. These frameworks combine the insights of the global value chain analysis at
network theory and literature are providing of capitalism. The global production network
provides the relationship of a framework that aims to encompass all the relevant actors in
the production systems. The concept combines a sequence of interconnected activities in the
process of value creation. This approach is a direct refinement of the global commodity
chains/GCC/ approach. It allows for far greater complexity and geographical variation in
producer-consumer relations than the global commodity chain approach (Henderson, et al.,
2002).
2.2.6 Social network theory
This theory focuses on the inter-relationships between economic and social interactions in
production networks composed of multiple horizontal and vertical relationships between
value chain actors. The vertical dimension of network theory reflects the flow of products
and services from the primary producer up to the end-consumer and its horizontal dimension
11
reflects relationships between actors in the same chain link (Trienekens, 2011). This theory
can be used as a tool to explain the performance of value chains (Mapanga, et al., 2017). It
views companies as embedded in a complex of horizontal, vertical, and business value chain
relationships with other companies and organizations supporting inputs and services (Roko
& Opusunju, 2016).
Overall, a combination of filiere, Porter, and global production network approaches and
social network theory guided this study to meet the studded objectives. As explained above,
the filiere approach shows the physical flow of inputs and outputs and focused on empirical
analysis of the value chain with national boundaries. The flow of value-adding activities
starting from a raw material supplier to end customer can be analyzed by using a value chain
according to the Porter. The concepts of global production networks also combine a
sequence of interconnected activities in the process of value creation from the local to the
global levels. On the other hand, social network theory focuses on inter-relationships
between economic and social transactions along the value chain from the vertical and
horizontal perspectives of actors. Hence, the stated objectives were addressed under the
guides of these combined approaches. But, the global commodity chain and global value
chain analysis approach less contributed to guide this study.
2.3 The Basic Model of Porters’ Value Chain Distinction
Porter makes a clear distinction between primary and support activities. Primary activities
are directly concerned with the creation or delivery of a product or service (Porter, 1985).
According to porter, primary activities can be grouped into five main areas which are
inbound logistics, operations, outbound logistics, marketing and sales, and service.
Procurement, technology development, human resource management, and infrastructure are
the four main areas of support activities. Each of these primary activities is connected to
support activities that help to improve their effectiveness or efficiency.
12
Figure 2.1 Models of porter’ value chain (Porter, 1985)
The term ‚Margin’ here implies that organizations realize a profit margin that depends on
their ability to manage the linkages between all activities in the value chain. In other words,
the organization is able to deliver a product/service for which the customer is willing to pay
more than the sum of the costs of all activities in the value chain (Porter, 1985).
2.4 Status of Soybean Production in Ethiopia
Ethiopia is the potential country in soybean production in Eastern Africa (FAOSTAT,
2019). According to this report, production trends and area coverage of soybean commodity
have been increased over time even though not significant as compared to its potential. As
indicated in Fig. 2.2, production has been increased from 58,490 quintals to 840,330 quintals
in the last ten years. In the same consecutive years, productivity has been increased from
9.2qt/ha to 21.5 qt/ha. This data indicates that the quantity of soybean produced in Ethiopia
was underestimated. Because of more than 700,000 quintals of soybean produced in that
particular production season in Benishangul Gumuz region excluding the Amhara and
Oromiya regions. According to the data collected from the Ministry of Trade and Industry
and health care food manufacturer plant, 942,038.10 quintals of soybean were exported and
75,000 quintals were used for domestic soybean oil production in the 2019 fiscal year. This
indicates that 1,017,038.10 quintals of soybean were produced in the 2018/19 production
season in the country excluding the local consumptions for seed and different soybean
dishes.
Infrastructure
Human resource management
Technology development
Procurement
Inbo
und
logis
tics
Opera
tions
Outbou
nd
logistics
Marketing
& sales Service
Margin
Margin
Support
ive
Act
ivit
ies
Primary Activities
13
Figure 2.2 Trends of soybean production, area coverage & yield in (Ethiopia 2007-2017
2.5 Ethiopian Import-Export Trends of Soybean and Byproducts
Ethiopia started soybean export grain in 2004 (Mekonnen Hailu & Kaleb Kelemu, 2014).
India, China, Vietnam, Canada, and Pakistan are the five most destination countries for
soybean export (Byrne, 2018). According to this report, 59,042 metric tons of soybean were
exported in the 2017/18 production period. However, according to Mekonnen Hailu &
Kaleb Kelemu (2014), most of the demand for soybean for local processing as well as
consumption as a byproduct has been covered through import. As the results of this study,
there were about 138,000 quintals trade deficit in the country which is the difference
between import and export of the commodity. As Fig. 2.3 and Fig. 2.4 show below, Ethiopia
exporting soybean grain only but the byproduct export almost null up to now. This indicates
that the country is exporting the grain without adding significant values and import back it
again as byproducts for domestic consumption by spending on average 400 million birr per
annum (Mekonnen Hailu & Kaleb Kelemu, 2014; FAOSTAT, 2019). This is huge money
in Ethiopian capacity and it can be invested in other development activities if the domestic
processors capacitated enough to meet domestic consumption.
0
200
400
600
800
1000
1200
2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
Area covered ('000 ha) Production ('000 Qt) Productivity (Qt/ha)
14
Figure 2.3 Import value trends of soybean & Soy Sause (2007-2016)
Figure 2.4 Export value trends of soybean & soy Sause (2007-2016)
0
1000
2000
3000
4000
5000
6000
2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
Soybean grain import value ('000 USD)
Soy sause imoprt value ('000 USD)
0
5000
10000
15000
20000
25000
30000
2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
Soybean grain export value ('000 USD)
Soy sause export value ('000 USD)
15
Source: (Byrne, 2018)
Figure 2. 5 Ethiopian soybean export (2017/18)
2.6 Soybean Value Chain in Ethiopia
The soybean value chain in Ethiopia has not been successful due to weak linkages among
actors and inefficiency of each actor in the value chain. Insignificant value is added at each
stage of the value chain (Kumilachew Achamyelh et al., 2020). The main obstacle to more
value-added production lies in access to finance, particularly in the skills necessary to access
investment and finance and an acceleration program for entrepreneurs in value-added
production could be a good way to remove this obstacle (Lehr & Sertse, 2018). Currently,
ATA developing a value chain map for linking the commodities to agro-industrial parks,
and soybean is included as one of the value chains at Bure-Agro-industrial park (Lehr &
Sertse, 2018). According to this report, in the national pulse strategy developed recently,
soybean is one of the priority pulses under the ministry of agriculture due to its high demand
in the domestic as well as international markets. However, limited production of the
commodity has put as a threat in the overall process of the soybean value chain.
2.7 The Relevance of Value Chain to the Poor
Agriculture continues to play a central role in economic development and poverty reduction
in many parts of the world. However, agriculture alone will not be sufficient to tackle
26,576
20,284
5,148 2,680 1,386 1,364
57,438
1,604
59,042
0
10,000
20,000
30,000
40,000
50,000
60,000
70,000
Vo l u me o f S o y b ea n ( Mt s )
16
poverty and inequality problems that are so pervasive in today’s world. It is crucial for
policy makers to focus on immediate attention on agro-industries. Agro-industries
established along efficient value chains can increase significantly the rate and scope of
industrial growth. In addition, the marked trend to break down production processes into
specific tasks opens up new opportunities for developing countries to specialize and take a
more profitable part in global trade provided they meet increasingly stringent market
requirements (UNIDO, 2009). VCA can be a practical way to help the rural poor to
participate advantageously in local, regional and global trade. It begins by explaining why
value chains have emerged as a helpful entry point for discussions on rural poverty. By
pulling together lessons learned on using value chain analysis and development effectively
as a tool to enhance the incomes of poor people in rural areas ( Mitchell, et al., 2009).
2.8 The Food System Framework
A key characteristic of the food system is the extensive linkages, interdependencies, and
feedback loops between value chain stages and the wider environment, society, and
economy. For example, the food system is dependent on natural resources and has a
significant impact on the global environment. The food system also has a major influence
on human health and is an important global source of employment and economic value. It
has also cultural significance in many societies. Growing environmental pressures,
including climate change, soil degradation, disruption of water cycles, expanding pathogen
ranges, and increasing regularity of extreme weather events, coupled with population
growth and migration impact on and will continue to affect the food system (Gregory, 2016).
2.9 Review of Empirical Studies
2.9.1 Main value chain actors and their roles
Value chains are a way of understanding the interaction of people and firms in the domestic
as well as global markets. Primary actors perform a selection of primary functions along the
value chains. These typically include input supply, production, processing, storage,
wholesale, retail, and consumption. Actors who perform similar functions are regarded as
occupying the same functional node, for example, the input supply node, production node,
retail node, and so on. Secondary actors or support service providers perform supporting
17
activities for primary actors. Support service providers facilitate the overall process of
primary functions to realize the objectives of primary actors (Mitchell, et al., 2009).
Value chain actors can be categorized as input suppliers, direct market actors, and enablers
as the study conducted on chickpea value chain in Southern Ethiopia (Tewodros Tefera,
2014). The study conducted at Metekel zone indicated that producers, traders,
cooperatives/unions, brokers, central whole-sellers, processors, exporters, and consumers
are the main soybean value chain actors (Addisu Getahun & Erimias Assefa, 2016).
Table 2.1 Soybean value chain actors and their functions
Actors Functions
Producers perform & Manage overall farm level production process
Keeping the quality of their product before delivery
Delivery their product to local as well as district traders
Local traders Collect, measure, & pack the product by paying cash on the delivery
Store grain and delivery to local whole sellers
Sell seeds to local consumers
Regional
traders Pay cash on the delivery to collectors and farmers
Delivery the collected product to central whole sellers & processors
Brokers Receive the product transferred from local & regional whole sellers
Facilitating the process of selling the product
Negotiate with the buyers about the selling price
Central whole
sellers Negotiate with the commission agents
Pay cash to the commission agents on the delivery
Export or sell the received product to processing factories
Exporters Maintain the quality of the product and pack it
Deal with export clearance
Pay necessary fees for export
Export the product and remit income
Processors Buying the product from producers and whole sellers
Process the product in to different feeds and foods
Sell the processed products to retailors, supermarkets or consumers
Consumers Consumption of the processed products
Source: Addisu Getahun & Erimias Assefa (2016)
18
2.9.2 Marketing margin and profit shares in the value chain
Marketing margin is commonly used to measure the performance of the marketing system.
It is associated with the selling price of the producers and the price paid by the end
customers. This indicates that the total gross marketing margin is the difference between the
price received by producers and paid by the final consumers. Gross marketing can also be
calculated by subtracting the purchase price from the selling price at each consecutive stage
of the marketing channel. Most of the time the shortest marketing channel is preferred by
the consumers since it reduces the purchase prices by reducing the numbers of
intermediaries in the market. The marketing and profit margins of each actor in the market
are different along different marketing channels (Wondim Awoke and Dessalegn Molla,
2018). According to this study, processors incurred the highest cost since they are
performing more value-adding activities among traders in the market. Producers can receive
relatively good gross profit as compared to traders (Nugusa Abajobir, 2018). As the results
of this study, producers received an average gross profit of 378.7 birr per quintal along
different maize marketing channels. However, this may not be always true in the marketing
system. As the results of some studies, the profit margin of producers is calculated without
considering the cost of family labor and that is why the profit margin of producers inflated.
A higher market share determines the profitability of marketing actors (Saripalle, 2018). It
depends according to the performance of actors and market efficiency. Market
characteristics and technological capabilities of actors are the determinant factors to get a
better profit share in the market (Porter, 1985). Input logistics, scientific and technological
development, and purchase price are significant factors for value chain profitability and
profitability (Strakova, et al., 2020).
2.9.3 Determinants of market supply
There are several factors that influence market supply as well as participation in the market.
Age of household head, distance to the nearest market, distance to the urban market, literacy
level, contract farming, access to training, and extension services have significant effects on
market supply and participation (Taye Melese et al., 2018). According to this study
conducted at South Gondar zone, the results of the Heckman selection -stage two model,
age of the household head negatively and significantly influences market participation and
19
it implies that market supply is reduced as age increased. The marginal effect also indicates
that the probability of participating in the market decreases by 0.2% as the age of the
household increased by one year. Similarly, the above-mentioned factors have a significant
influence on the overall process of market supply as well as participation.
The amount of income received from off-farm, yield, sex of the household, farming
experience, and family size have a significant impact on supplying maize products to the
market (Nugusa Abajobir, 2018). According to the results of this study conducted on value
chain analysis of maize in the Horro Guduru Wollega zone, all the above factors
significantly affect maize supply to the market. The quantity of maize supply increases by
1.404 quintals as the income received from off-farm increases by one birr. Maize supply
increases by 0.149 quintal for a year increase in farm experience. Similarly, the quantity of
maize supply increases by 0.521 quintal as productivity increased by one quintal. The
finding by Almaz Giziew (2018) also confirmed that the quantity of onion market supply is
increased by 0.0185 quintal as the farm experience of onion producers increased by one
year.
Quantity of the commodity produced, landholding size, numbers of livestock owned and
family size of the household are the determinant factors that influence the amount of the
product supplied to the market (Sultan Usman, 2016). According to the results of this study,
all the above factors have a significant impact on wheat supply to the market. As the results
of the robust regression of the OLS model, the amount of wheat supplied to the market
increases by 0.623 quintal as the quantity produced is increased by one quintal. Similarly,
the number of wheat supply increases by 4.25 quintals and 0.37 quintal as farmland size and
numbers of livestock owned increased by 1 hectare and 1TLU. The wheat supply decreases
by 0.05 quintal if the household size increases by one. As the study conducted at South
Gondar zone and Horo Guduru wpllega zones, quantity of teff market supply and family
size negatively corelated (Tadie Marie & Lema Zemedu, 2018; Edosa Tadesa, 2018). the
study conducted at Southern Ethiopia also indicates that quantity of sesame market supply
decreased by 0.24 quintal as distance to the nearest market incresead by a kilometer
(Dagnayegbaw Goshme et al., 2018).
20
2.9.4 Factors affecting value addition decision
The quantity of value-added can be calculated by subtracting the value of raw material and
other inputs used in the processing from the output value (Surni, et al., 2019). Value-added
agriculture is a movement that has created a life of its own. It greatly focuses on production
or manufacturing processes, marketing, or services that increase the value of primary
agricultural commodities, perhaps by increasing appeal to the consumer and consumers’
willingness to pay a premium over similar but undifferentiated products (USDA, 2014).
Agriculture is one of the main sectors to realize economic growth, particularly for
developing countries. Because it has huge resources like arable land and surface and
underground water for production. These opportunities foster to improve productivity.
Recently, studies are trying to give more emphasis on human development in order to add
value to the agriculture sector in selected developing countries through the panel data
method and the results indicate a meaningful effect on vale addition to this sector (Badri, et
al., 2017).
The results of the OLS econometric model show that education, health, domestic credit to
the private sector, and gross fixed capital formation have a positive effect on value addition
to the agriculture sector (Badri, et al., 2017). As a result of this model, a unit increase of
expenditure to education leads to 0.31 value is added to agriculture. Similarly, a unit
increase in expenditure for health care services leads to a 0.24 value addition to this sector.
Agriculture is one of the economic growth sectors for cooperatives. Cooperatives can add
value to agriculture products if the conditions are suitable and there are different factors that
influence the cooperatives in the overall process of value addition. Capital grants to help
finance, the ability of the board of directors, specialization in marketing, and the absence of
investment-friendly institutional arrangements have a significant positive effect on value
addition to the agriculture sector (Esnard, 2016).
The study conducted at Assosa and Kamashi zone indictates that the likelihood of faremers
to add values to soybean is influenced by disease incidence due to yield loss quality
deteorartion (Minyahil Kebede& Assefa Gidesa, 2016). Market destination, existing
government policies, strategic decisions, and Personnel skills are the main influencing
factors for value addition on tea (S. Grace & Fridah, 2016). The size of the value-added is
decided by the willingness to pay by the end customers (Porter, 1985). Sufficient finance,
21
credit facilities, and equitable government tax are important and significant factors for value
addition to soybean (Otu & Okibeya, 2018). Availability of packaging materisls positively
and significantly influenced value-addition (Obute et al., (2019). Astudy conducted at
Assosa zone showed that availability of good storage condition positively and significantly
influenced soybean value-addition (Afework Hagos & Adam Bekele, 2018).
Overall, researches conducted in related to soyabean value chain is scantity and most
researhces in the past focused on breeding, production and to some extent on marketing.
They emphasized on production constraints of soybean. Some studies also conducted on
marketing and marketing and profit margins of marketing actors were estimated. However,
as the results reviewed from the literature, marketing and proft margins were calculated
without considering the cost of family labor in the production stream which causes to inflate
the profit and marketing margins of producers.
Issues related to determinants of soybean supply and farmers participation on value addition
as well as profit margins of value chain actors were not investigated in the study area and
Metekel zone. One study was conducted on cost and return analysis of soybean under
smallholder farmers in Pawe during 2015/16 production season. On this study, only proft
margins of farmers was estimated and it is difficult to know which actor is benefited more
in the chain and difficult to take an intervention based on scientific evidences that is why
this study was initiated to fill the knowledge gaps on such issues.
2.10 Conceptual- Framework of the Study
The ultimate goal of promoting the agribusiness value chain is to improve the
competitiveness of agriculture at the national and international levels for the purpose of the
market as well as consumption. In the overall processes of production, marketing, and
consumption as well as waste management, many value chain actors are involved. The
process starts from the input supply and passes through production up to the final stages. In
addition to these direct actors involved in the chain, there are also support service providers
who have an indirect role along the value chain. The numbers and types of producers affect
the quantity of soybean market supply. If the system is well functional, sustainable
production can exist. Socio-economic and institutional factors influence the quantity of
market supply and value addition as well as the fair distributions of benefits/profit shares
22
for each actor in the value chain. In order to improve soybean production and value chain
in the study area, there is a need to identify the determinant factors for market supply, to
analyze the factors affecting value addition of soybean, and analyzing the marketing margin
of actors in Pawe Woreda of Metekel zone. Accordingly, the following figure has been
drawn to show the conceptual framework of the soybean value chain in the study area.
Source: Own sketch by reviewing different literatures, 2018
Figure 2.6 Conceptual frame work of the study
Soybean Value chain
Socio-economic factors
• Travel time (hrs.)
• Lagged price
• Off/non-farm
• Lagged price
• Coops.membership
• Land size
• Livestock owned
• Market price
• Quantity produced
• Disease
Value Addition
Market
supply
Institutional factors
• Access to market
information
• Access to transport
• Access to market
• Cooperatives/unions
• Credit utilization
• Extension service
Market performance
• Marketing margin
• Marketing cost
Demographic factors
• Age
• Family size
• Sex
• Education level
• Farm experience
• Training
23
Chapter 3. RESEARCH METHODOLOGY
3.1 Description of the Study Area
Metekel Zone: It is one of the three administrative zones of Benishangul Gumuz Regional
State located in Western Ethiopia. It has borderlines with Awe zone in the East, Western
Gondar in the North, North Sudan in the West, and Kamashi zone in the South. It has 7
districts and three agro-ecologies i.e. Dega, Kola, and Woynadega. One district is the high
land agroecology part of Merkel as well as the region. The rest 2 and 4 districts are the
Woynadega and Kola agroecology parts respectively. It is the leading zone of the region as
well as the country in soybean production. Soybean has been produced in all 7 districts of
the Metekel zone in which 5 of them are highly potential in soybean production. The area
receives an average annual rainfall and temperature ranges from 600 -1540 mm/y and 12 -
41 0C respectively. The altitude ranges from 580 -2730 m.a.s.l. It has a total population of
470,684 and out of which 233,040 are males and 237,644 are females. More than 98% of
the rural population depends on agriculture and 95% of them are living in rural areas. Maize,
sorghum, soybean, sesame, and groundnut are the major crops producing in Metekel zone.
Pawe District: The district is located in the northwestern parts of the Metekel zone which
is 575 km away from Addis Ababa. According to the district agriculture office report, 2019,
the district has a total area of 64,300 ha. From the total area, 50.4% of the land is arable
which is used for crop production. From the total cultivated land in the district, more than
35% was covered by soybean (Birhanu Ayalew et al, 2018). The district is one of the major
soybean producers in Metekel zone. It constitutes 20 kebeles and 16 kebeles are producing
soybean as a major crop mainly for marketing purposes. The district is located at Latitude;
110 09’ N Longitude; 360 03’ E. It has an Altitude of 1120 m.a.s.l. According to PARC
metrology data, Pawe has an average temperature and rainfall of 32.7 0C and 1582 mm/y
respectively over the last 30 years. The district has great opportunities to cultivate soybean
intensively. From those opportunities, Pawe Agricultural Research Center which
coordinates soybean research at the national level is being existed at Pawe. Lands,
temperature, and rainfall distribution are suitable for soybean production. The farming
system of the district is mixed farming both livestock rearing and crop production.
24
According to the district agriculture office report 2019, there are about a total population of
67,862 off which 35,407 are males and 32,455 are females. From the total households of the
district (10,899), 2383 are female-headed households and the rest 8516 are male-headed
households. 2105 male and 1326 female a total of 3431 youths is existing in the district.
Maize, Soybean, rice, sorghum, sesame, and groundnut are the major crops producing in the
district. There are about 29 different soybean varieties in which 6 of these are reached to the
end-users. All the varieties have been released by PARC and other collaborative research
centers in which they can be categorized into three groups based on their maturity. Those
varieties are classified as early maturity, medium maturity, and late maturity and they have
their own maturity dates which have been verified by PARC. In the 2018/19 production
season, 122,973 quintals of soybean yield were produced from 7109.6 hectares of land
(Pawe agriculture office report, 2019).
Source: Own draw
Figure 3.1 Geographical location of the study area (Pawe district)
3.2 Sampling Techniques and Procedures
A multistage sampling technique was employed to select sample households in the study
area. In the first stage, out of 20 kebeles in Pawe district, 16 kebeles were selected
purposively due to their potentials in soybean production. In second stage, out of 16 kebeles,
25
4 kebeles were selected through simple random sampling technique from those potential
kebeles. Because, these kebeles have similar rainfall distribution, soil type and temperature.
Third, sample respondents were selected from the prepared sampling frame based on
proportional size to the population in the selected kebeles.
3.3 Sample Size Determination
The required sample size was selected by using Yamane (1967) formula with the precision
level of 7% from each sampled kebele. Since almost all farmers in Pawe are soybean
producers and have similar agroecology, there is no high degree of variability regarding
soybean production among farmers and that is why the precision level of 7% was used by
the researcher. Therefore, the required sample size was calculated as follows.
Table 3. 1 Sample size and households of the study kebeles
Kebeles Total households Sample households
Village 23/45 1003 75
Village 49 990 74
Village 30 602 45
Village 28/29 455 34
Total 3050 228
𝒏 =𝑵
𝟏+𝑵(𝒆𝟐)
Where, n is the required sample size
N is the total population size
e is the level of precision
n = 3050/1+3050(0.072) =3050/15.945 = 191
Therefore, based on the above formula a total of 191 farm households were selected through
a simple random sampling technique by using a lottery method to pick each sample in the
sampling frame. Additionally, 19 respondents for non-response rate and 18 for invalid data
were included as compensation. Finally, the quantitative primary data was collected from
26
228 respondents out of 3050 total households in the four sampled kebeles. Overall primary
data was collected from 20 February to 20 March 2020 from these respondents in the
selected four kebeles.
Source: Own design
Figure 3.2 Sampling procedures of sample respondents
Sampling of Traders & Consumers: Whole-sellers, retailers, unions/cooperatives,
processors, and consumers as well as Ethiopian commodity exchange were interviewed
besides individual farmers. The researcher selected purposively 5 whole-sellers and 9 local
traders in Pawe based on the quantity of soybean bought and sold. Mama union and 4
BGRS
Assosa Metekel
wombera Bullen Dibate Pawe Mandura DangureGuba
Kamashi
75 45 34 74
V-23/45
1003
V-30
602
V-28/29
455
V-49
990
Stage 2
Simple random sampling Stage 3
Simple random sampling 228
16 kebeles
Stage 1
Purposively
27
primary basic cooperatives one from each sampled kebele also interviewed in the district to
collect data related to soybean marketing. Because there is only one primary cooperative
per kebele and one union in Pawe. Similarly, Health care food manufacturer PLC from
Addis Ababa was interviewed and data related to soybean processing and its marketing
process from oil production up to consumption as well as the linkages of the processors with
suppliers and retailers was collected. Two soybean oil whole-sellers and 4 retailers were
selected purposively due to their closeness to enumerator to collect data easily.15 consumers
(5 from Addis Ababa and 10 from Pawe) were also selected purposively by taking in to
account whether the consumer consumes soybean oil or not. Central ECX and Almu ECX
were interviewed to get data especially related to soybean grading and the quantity
marketed. Data from these stakeholders were collected from the end of April to 25 May
2020.
Table 3. 2 Sampled traders of the study
Types of traders Towns
Pawe Almu V-23 V-49 V-
30
V-
28
Addis
Ababa
Total
Local traders 0 0 3 3 1 2 0 9
Whole-sellers 4 0 0 0 1 0 2 7
Processor 0 0 0 0 0 0 1 1
Cooperatives 0 0 1 1 1 1 0 4
Union 1 0 0 0 0 0 0 1
ECX 0 1 0 0 0 0 1 2
Retailor 0 0 0 0 0 0 2 2
Consumers 10 0 0 0 0 0 5 15
Total 15 1 4 4 3 3 11 41
3.4 Type, Source, and Methods of Data Collection
Types of data: Both quantitative and qualitative data types were used by the researcher in
the overall process of this study. To complement the quantitative data, qualitative data was
collected from 4 FGDs with members of 6 per group and 8 key informants through
28
interviews from 4 sampled kebeles with DAs to triangulate the quantitative data. FGD
members were selected farmers base on their experience and knowledge about soybean
production and marketing. During FGDs, the deep discussion was conducted on issues of
soybean production, marketing, and their linkages with buyers & other actors with the
selected group members and DAs. From this discussion, in-depth information was collected
that used to complement the quantitative primary data.
Sources of data: Both primary and secondary data were the sources for the researcher in
this study. Secondary data were obtained by reviewing published journals, proceedings,
books, reports, and to some extent from the unpublished reports as well as the internet. The
primary data was collected through various instruments that have designed in line with the
study objectives and research questions. Questionnaires and checklists have been developed
independently for each actor. Unions/cooperatives, Addis Ababa soybean processing
factory, district and zone offices, and local traders were the target groups for the study to
collect data besides to individual farmers. Data related to the import and export of soybean
grain and byproducts were collected from the Ministry of Trade and Industry through a
semi-structured questionnaire. The quantitative data was gathered through household
surveys from each sampled respondent with face to face interview. There was a close and
friendly relationship with each sampled kebele DAs and district experts from the beginning
up to the end of this study to get the required data from each sampled respondent. Every
process of data collection was completed early by the researcher with DAs before the
enumerators went to the field. There were clear appointments with the respondents about
where and when the data have to be collected. As much as possible, the time was efficiently
used to reduce time wastage for farmers. Enumerators were highly respected respondents’
culture and dignity. Although the appointed time was not comfortable for some respondents;
other appointments were arranged since it was difficult to get the required data for the
situation that was not suitable for them.
3.5 Methods of Data Analysis
Three types of data analysis methods which are descriptive statistical analysis, Econometric
analysis, and marketing margin analysis were undertaken to analyze the primary data
through STATA (version 14.2) and SPSS (version 20) software packages.
29
3.5.1 Descriptive statistical analysis
Descriptive statistics like mean, percentage, maximum, minimum, frequency, and standard
deviation were used. And also, inferential statistics such as t-test (to test the significance of
the parameters to be estimated), Breusch-Pagan/Cook-Weisberg test (to test the presence of
heteroscedasticity) and VIF (to test the presence of multicollinearity problem) were used.
Independent t-test used to see significant association and mean difference between value
addition participants and non-participants. Chi-square test (χ2) was also used to test the
relationship of the dichotomous dependent variable (value-addition) with categorical
variables. One-way ANOVA was used to test the presence of significant differences
between the groups (education level and frequency of extension contact) in terms of the
quantity of soybean market supply. Tables and graphs also used to present the collected
data.
3.5.2 Econometric analysis
Econometric models that are useful to analyze the factors affecting soybean supply to the
market and factors influencing value addition were specified below.
Factors affecting soybean market supply: In order to estimate the factors that affect the
quantity supplied to the market, using the OLS model is applicable if and only if all of the
households or respondents are participating in the marketing of the given commodity.
However, if all the participants are not participating in the marketing of the selected
commodity, using the OLS model by excluding the non-participants from the analysis leads
to selectivity biases to the model. In such a condition, it is better to use Tobit, Double-
Hurdle, and Heckman's two-stage procedures to overcome the problem. If our interest is
analyzing the probability of selling, in this case, Probit and logit model can address such
problems.
In Pawe district of Metekel zone, almost all of the farmers in 16 kebeles are producing
soybean for marketing purposes since direct consumption of soybean as food is not habited.
To study factors affecting soybean supply to the market in the study area, a multiple linear
regression model was used because here the quantity of soybean supplied to the market is
continuous and all the sample respondents in the sampling frame were producing and
30
providing their soybean products to the market in 2018/19 cropping season. This model is
simple and practical to apply for identifying such factors. The econometric model of
specification for this supply function in matrix notation is given below.
Y = X’β + U
Where, Y = The quantity of soybean market supply
X = Explanatory variables that affect soybean market supply
β = Are the parameters to be estimated
U = The disturbance term i.e. unobserved factors to the researcher but affects market supply
Factors affecting value addition on soybean: The factors that affect the decision to engage
in value-addition was determined by using a Probit model. The decision to add value is a
discrete and dichotomous i.e. either adds value or not adds value. Discrete decisions are
analyzed by using qualitative response models and among them, Probit is the one that used
to analyze such factors. Logit and LPM are other qualitative response models. LPM has
problems of non-normality and heteroscedasticity of the disturbance term, the possibility of
the estimated dependent variable lying outside 0-1 ranges, and the questionable values of
R2. Due to these problems, the LPM/linear probability model not recommended to use for
analyzing such factors. Most of the time to analyze such types of factors logit and Probit
models can be used. However, there is no compelling reason to choose the one over the
other (Gujarati, 2004). Logit models are used to analyze the data that has a logistic
cumulative distribution function. But this study assumes a normal cumulative distribution
function and that is why the researcher decided to use the Probit model for analyzing factors
affecting farmers’ participation in value addition. Empirically, the Probit model is defined
as follows.
y* = β0 + β1x1 + β2x2 +…βnxn + εi
yi = 1 if y* > 0
yi = 0 if y* < 0
Where, y* = is a latent (unobservable) variable which represents farmers’ discrete decision
whether to adds value on soybean or not
β’s = are the parameters to be estimated
X’s = are the predictor variables
31
εi = is the error term or factors that affect farmers’ participation on value addition but not
observable to the researcher and assumed not correlated with predictor variables
y = is the dependent variable that takes the value 1 if farmers add values and 0 other wise.
Soybean value-addition is the dependent variable and a household was considered as a
participant in value-addition if at least one type of value added by a household. In this Probit
regression analysis, farm households only analyzed, but other actors were not included in
the model. Because the numbers of sample sizes are below the minimum requirement to run
the model i.e. less than 30 and it was captured through descriptive statistical analysis.
Specification Tests: It is important to check multicollinearity and heteroscedasticity
problems before running the model. Multicollinearity is the existence of a correlation
between the explanatory variables and it is difficult to identify the separate effects of each
explanatory variable on the dependent variable due to strong relationships among them
(Gujarati, 2004). The variance inflation factor is a technique used to detect the existence of
multicollinearity for continuous explanatory variables and contingency coefficient (CC) was
used to test multicollinearity of dummy variables. According to Gujarati,2004, the VIF (Xj)
can be defined as follows.
VIF=1/(1-(Rj)2)
Where R2j = is the multiple correlation coefficient between the explanatory variables. The
higher the value of R2j is the higher value of VIF and that indicates the existence of high
collinearity among explanatory variables. If the value of VIF>10, there is a series problem
of multicollinearity. Similarly, contigency coefficient for dummy variables were calculated
as follows.
Where N = total sample size and if the value of CC is greater than 0.75, the variable are said
to be colinear
Heteroscedasticity: The problem was detected through the Breusch-Pagan/Cook-
Weisberg test by using STATA (version 14.2) software. It is commonly occurring in cross-
32
sectional data. There is no constant variance across the variables if there is a problem of
heteroscedasticity.
3.5.3 Marketing margin analysis
Marketing margin analysis indicates that the comparison of prices at different levels of the
marketing chain over the same period of time. As Nugusa Abajobir (2018) cited
Mendoza,1995, the share of the final selling price is received by a particular actor in the
marketing chain and that is always related to the final selling price expressed in percentage
can be measured through marketing margin analysis. Calculating total gross marketing
margin (TGMM) is always related to the final price paid by the end customer or buyer and
that is expressed in percentage form.
TGMM =Consumer price − Producer price X100%
Consumer price
Where TGMM = Total Gross Marketing Margin
The same concept was applied to calculate the benefit share of each actor in in the marketing
chain with some adjustments. In order to analyze the margins, TGMM was calculated first.
It is the difference between farmer’s/producer’s price and consumers price i.e.
TGMM = Consumer’s price – Farmer’s price.
Therefore, the marketing margin at a given stage, i (GMMi) will be computed as follows.
GMMi =Spi − Ppi X100%
TGMM
Where Spi = is the selling price of the commodity at ith stage or link
Ppi = is the purchase price of the commodity at ith link
The trade margin of this study was calculated with the average prices of the commodity at
each level of market chain and the various charges incurred by each actor.
Total Gross Profit Margin was computed as follows.
TGPM = TGMM-TOE
Where TGPM = is total gross profit margin
33
TGMM = is total gross marketing margin
TOE = is total operating expenses
Table 3.3 Summary of Research Methodology
Research
objectives Data types
Data
collection
methods
Data analysis Models used
Mapping of value
chain actors Demographic KI & FGDs Descriptive
Socio-economic
Marketing margin
analysis Socio-economic
Interview
schedule
Marketing
margin
analysis
Determinants of
market supply Demographic
Interview
schedule Descriptive
Multiple linear
regression
(OLS)
Socio-economic KI & FGDs Econometric
Inferential
Factors affecting
value addition Demographic
Interview
schedule Descriptive Probit model
Socio-economic KI & FGDs Econometric
3.6 Definition of Variables and Working Hypothesis
To identify the factors that affect soybean supply to the market, and value addition to
soybean crop, the following variables were assumed to affect the dependent variables in the
overall processes of this study.
3.6.1 Dependent variables
Quantity of soybean supply to the market: It is a dependent variable that represents the
amount of soybean supplied by the households to the market in 2019 measured in quintals.
Factors affecting value addition: It is the dependent variable that represents whether the
farmer participates in value addition or not. It is a dummy variable that takes the value 2 =
for these farmers participating in value addition and 1= for these farmers not participating
in value addition and measurement is in terms of participation in value addition.
34
3.5.2 Independent variables
The independent variables that were hypothesized to affect the dependent variables are
presented below.
a) Independent variables for determinant factors affecting soybean market supply
Productivity: It is a continuous variable that refers to the quantity of soybean produced in
quintals per hectare in 2018/19 production season. A study conducted by Nugusa Abajobir
(2018) indicated that productivity positively and significantly influences the quantity of
maize supply to the market. This implies that those households that get more outputs per
hectare can supply more to the market. So, it was hypothesized that productivity of soybean
influences the quantity of market supply positively and significantly.
Lagged price (LAGGED PRICE): It is a continuous variable measured in terms of birr per
quintal. It represents the average price of soybean that farmers receive in the previous year.
The good price of the commodity in the previous year stimulates farmers to produce more
output for supplying to the market. The market price of the given commodity is the
determinant factor for market supply. There is a direct relationship between the quantity
supplied and its price (Chad, 2019). Soybean producers are involving intensively in soybean
production to supply the market in sufficient quantity if the price of the crop increases. If
the price of the crop increases, the quantity of the crop supplied to the market is also
increasing. Therefore, it was hypothesized that previous years’ market price of soybean has
a positive and significant influence on the quantity of soybean supply.
The education level of household head (EDLEVEL): It is a categorical variable that
ranges from illiterate to TVET and above education levels. Farmers who attend more years
in formal schooling were more productive and can supply more outputs to the market than
their counterparts (Wondimu Tefaye & Hassen Beshir, 2014). Education improves the level
of knowledge for farmers to engage in production for marketable products. Education
increases the skills of producers in the overall process of production and marketing for each
commodity. The researcher hypothesized that education has a significant and positive effect
on the quantity of soybean supplied to the market.
35
Age of household head (AGE): It is a continuous variable measured in terms of the number
of years of the household head. Age can influence the quantity of soybean market supply
either positively or negatively. The age of the household head has a negative and significant
influence quantity of market supply (Taye Melese, et al., 2018). According to the results of
the Heckman selection-two stage model, the level of market participation decreased by 0.2%
as the age of the household head increased by a year. But to some extent, aged households
are wise in resource use and can allocate more lands for marketable crops than their
counterparts and supply more outputs to the market. Here, the researcher was hypothesized
that the age of the household head has either a negative or positive significant influence on
the quantity of soybean market supply.
Extension contact (EXCT): It is a categorical variable measured in terms of the frequency
of contacts with extension agent that ranges from daily to rarely extension contacts during
2018/19 production season. The more frequent contacts with the extension agent the higher
the knowledge and market information have obtained. Extension service improves farmers’
awareness of production and market surplus with better market prices (Sultan Usman, 2016).
Therefore, the researcher hypothesized that more frequent extension contact influenced the
quantity of soybean market supply positively and significantly.
Market distance (DISTANCE): It is a continuous variable that is measured in an hour
from the residence to the nearest market. The longer the distance between the residence of
the household is the lower the quantity supplied to the market (Dagnaygebaw Goshme, et
al., 2018). Therefore, it was hypothesized that the closeness of the households to the nearest
market has a positive effect on the quantity of soybean market supply.
Credit utilization (CREDIT): It is a continuous variable that refers to the quantity of credit
used measured in birr in the 2018/19 cropping season. The finding of Shewaye Abera et al.
(2016) confirmed that credit utilization positively correlated with the quantity of haricot
bean marketed. It improves farmers’ capacity to purchase different agricultural inputs for
production and this leads to an increase in the quantity marketed. So, the researcher
hypothesized utilization of credit influences the quantity of soybean supplied to the market
positively and significantly.
36
Sex of household head (SEX): It is a dummy variable and assigned 1 for female head
household and 2 for the male head household. Being a male-headed household positively
and significantly affected teff market participation with a marketed surplus (Haregitu
Nitsuh, 2019). Males are aware better than females of improved agricultural technologies
due to the participation of males in different training programs and field day events. This
leads to enhancing the level of adoption of different agricultural technologies and increases
the production of surplus outputs to the market. So, being maleness hypothesized to have a
positive and significant effect on the quantity of soybean market supply.
Training (TRAINING): It is a dummy variable and took the value 2 for those households
who took training on soybean production and management in 2018/19 production and 1
otherwise. Training positively and significantly influences the quantity of soybean market
supply (Banda, et al., 2017). Provision of appropriate training on production management,
pest control, and pre- and post-harvest handling techniques increases the production and
productivity of farmers by reducing yield loss. Therefore, this variable hypothesized that to
have a positive and significant effect on the quantity of soybean marketed surplus.
Off/non-farm income (OFF-NONFAM): Dummy variable and assigned 2 for households
who participate on-off/non-farm activities as income alternatives and 1 for households not
participate in any off/non-farm activities in the 2018/19 cropping season. The availability
of alternative sources of income for farmers other than own agricultural activities increases
the purchasing power of different agricultural inputs and this leads to increase production
surplus further to supply more outputs to the market. There was a positive and significant
relationship between maize market supply and off/non-farm income (Nugusa Abajobir,
2018) So, it was hypothesized that off/non-farm income positively and significantly
influences the quantity of soybean market supply.
Cooperative membership (COOPMB): It is a dummy variable and assigned 2 for those
farmers who are member a cooperative and 1 for non-members in the 2018/19 production
season. Farmers can produce more outputs in bulk if they are organized into a cooperative.
Because coming together into a cooperative increasing the purchasing power of agricultural
inputs and can produce surplus outputs for the market. Maize market supply and cooperative
membership positively and significantly correlated (Nugusa Abajobir, 2018). Therefore, it
37
was hypothesized to have a positive and significant influence on the quantity of soybean
market supply.
Family size (FAMSIZ): This is a continuous variable measured in terms of the number of
family members per household head. The finding by Sultan Usman (2016) founds that
quantity of wheat market supply is decreased by 0.05 quintal as family size increased by
one. But the finding by Afouda et al. (2019) confirmed that significant and positive
correlation between soy production and household size due to labor force contribution in
North-East Benin. The researcher hypothesized that family size can have either a negative
or positive and significant effect on the quantity of soybean market supply.
Access to market information (MKTINFN): It is a dummy variable that took the value 2
if the farmer has access to market information and 1 for not accessed. Smallholder producers
cannot produce surplus outputs for the market as a result of poor access to market
information. Access to market information increases the quantity of potato supply to the
market (Wondim Awoke & Dessalegn Molla, 2018). Farmers also become price takers for
their products due to the absence of reliable market information. Therefore, the researcher
hypothesized that access to market information positively and significantly affects the
quantity of soybean market supply.
Soybean farming experience (SOYFAMEXP): It is a continuous variable measured in
terms of the number of years of the household head involving in soybean production. If the
number of years of experience increases in farming, farmers can have knowledge of
production as well as marketing activity by adding some value to the commodity. Farming
experience significantly and positively increases the quantity of sesame supplied to the
market (Tamirat Girma, 2017). In this study, the researcher hypothesized that experience
has a significant and positive effect on the quantity of soybean to the market.
Cultivated land (CLAND): A continuous variable measured in a hectare. It refers to the
total farmland that farmers cultivated during the 2018/19 production period. The quantity
of haricot bean supply increased by 2.03 quintals as the farmland size increased by a hectare
(Wogayehu Abele & Tewodros Tefera, 2015). This indicates that farmland size has a
positive and significant effect on the quantity of the commodity supplied to the market. So,
38
it was hypothesized that cultivated land affects positively and significantly the quantity of
soybean market supply.
Table 3.4 Description of explanatory variables & hypothesis in MLR model
Variables Symbol Type Measurement Expected
sign
Productivity PRODUCTIVITY Continuous Quintal ha-1 +
Lagged price LAGGED PRICE Continuous Birr per kg-1 +
Education level EDLEVEL Categorical
1= unable to read
&write 2=read &
write 3=primary
school (1-8)
4=secondary
school (9-10)
5=preparatory
school (11-12)
6=TVET &
above
+
Age HH AGE Continuous Years +/-
Extension contact FEXTCONT Categorical
1=rarely
2=monthly
3=twice a month
4=weekly
5=daily
+
Market distance DISTANCE Continuous Walking hour +/-
Coops. membership COOPMB Dummy 2= Yes 1= No +
Credit utilization CREDIT Continuous Birr +
Sex HH SEX Dummy 1= Female 2=
Male +
Training TRAINING Dummy 2= Yes 1= No +/-
off/non-farm income OFF-NONFAM Dummy 2= Yes 1= No +
Family size FAMSIZ Continuous Years +
Market information MKTINFN Dummy 2= Yes 1= No +
Farm experience SOYFAMEXP Continuous Years +
Cultivated land CLAND Continuous Hectare +
b) Independent variables affecting value addition
Age of the household head (AGE): A continuous variable which is the number of years of
the household heads in the study area. Aged people are wise and have good experience in
the overall process of agricultural production and value addition to get a better price in the
39
market. They have relatively good knowledge about how to increase the values of their
product in the market. Unlike the youngsters, aged households give more value for each
agricultural product to receive a good return. Aged people are also familiar with the market
requirements of the product and performing some tasks to increase the values of their
product by targeting the end return. So, it was hypothesized that aged households were more
likely to add values on soybean.
The education level of household head (EDLEVEL): It is a categorical variable that
ranges from illiterate to TVET and above education levels. Education improves the level of
knowledge for farmers to engage in production for marketable products to provide the
market with better quality and quantity by adding some values to their products in order to
receive a better price (Taye Melese, et al., 2018). Education is the key to improve farmers’
skills and knowledge on value addition (Badri, et al., 2017). As the results of this study, the
likelihood of value addition to the agriculture sector is increased by 1% as the expenditure
costs to improve education increased by 0.31%. This implies that educated households are
focusing on value addition in order to receive a good return by providing their quality
product to their valued customers. So, education was hypothesized to increase the likelihood
of farmers to add values on soybean.
Market distance (DISTANCE): It is a continuous variable that refers to the amount of time
taken to reach the nearest market measured in walking hours. Distance to the nearest urban
market affects negatively and significantly the likelihood of farmers to add values to their
soybean product. The finding by Sultan Usman (2016) found that the probabilities of
farmers to add values on wheat is decreased by 0.03% as the distance of the residence to the
nearest market increased by a kilometer. Therefore, it was hypothesized that the longer the
travel time to reach the nearest market the less likely to add values to soybean.
Family size (FAMSIZ): It is a continuous variable measured in terms of the number of
family members per household head in 2018/19 production season. The existence of more
family members per household reduces the likelihood of producers adding values to
soybean. The finding by Nyongesa et al. (2018) confirmed that there is an inverse
relationship between maize value addition and household family size. Because the product
is sold immediately after harvesting to cover student fees and different home expenditures
40
instead of storing the product to add some values. So, it was hypothesized to have a negative
and significant influence on the likelihood of farmers to add values on soybean.
Livestock owned (TLU): It is a continuous variable that refers to the numbers of livestock
owned in 2018/19 production season measured in terms of tropical livestock unit.
Ownership of more livestock increases farmers’ capacity to purchase packaging materials
and to prepare suitable storage conditions for value addition. On the other hand, farmers
who own more livestock focus on their livestock raring and give less attention to increase
their soybean products. So, it was hypothesized that to have either a positive or negative
effect on farmers’ likelihood to add values on soybean.
The market price of the commodity (MKT PRICE): It is a continuous variable that refers
to the price of soybean in birr per kilogram in 2019 marketing year. A good market price of
the commodity has a positive influence on the improvement of product quality by
performing some value-adding activities. If there is a good market price, producers give
more attention to the quality of their product in order to get a good return in the market.
Because quality products are highly demanded by all the buyers and can establish good
customer relationships by building trust among each other. So, the researcher hypothesized
selling price influences the likelihood of farmers to add values on soybean.
Disease incidence: It a categorical variable range from very serious constraint to not
constraint for the farm households in the study area during the 2018/19 cropping season.
Disease incidence has a negative influence on the quantity as well as the quality of each
commodity in the overall agricultural production. If farmers are facing a disease incidence,
it makes difficult the process of value addition since it deteriorates the biological and
physical appearance of the crop. The study by Bandara et al. (2020) also found that diseases
negatively and significantly impacted value addition on soybean due to quality deterioration
and loss of production. Therefore, it was hypothesized that diseases reduce farmers’
likelihood to add values on soybean.
Training (TRAINING): It is a dummy variable and assigned 2 (yes) for households who
took training in the 2018/19 cropping year on soybean pre- and post-harvest handling
techniques and 1 (no) for those not took the training. Technical training improves the skill
of farmers about the way how to increase their level of production with significant value
41
addition. So, it was hypothesized to have a positive and significant effect on farmers’
likelihood to add values.
Quantity of soybean produced (QUPROD): It is a continuous variable that refers to the
amount of soybean produced in 2018/19 production season and measured in quintals. The
quantity of soybean produced in quintals affects the decision of farmers to participate in
value addition positively and significantly. Farmers who produce more yields of soybean
emphasized on the quality of their product by performing some value-adding activities like
cleaning, Packaging, storing, and transporting to sell with a better price for their customers
as well as to provide directly their product to ECX. Since soybean is an export commodity
and passes through ECX by considering some standards, farmers give more attention to
perform some value-adding tasks to meet the requirements of their buyers further to increase
the value of their soybean product. So, it was hypothesized that the quantity of soybean
produced influences farmers’ likelihood of value addition to soybean positively and
significantly.
Improved seed (IMPSEED): It is a dummy variable that represents 2 for those households
use improved soybean seed and 1 otherwise in 2018/19 production season. The seed is one
of the major inputs that the farm households use in the production process. In order to get
the required yield with better quality, farmers highly recommended using improved variety
seed. Producers can produce surplus outputs with better quality that maximizes the value of
their product if they are applying improved seed with other extension packages. Therefore,
it was hypothesized that the use of improved seed influences farmers’ likelihood to add
values on soybean.
Packaging material (PACKMT): Dummy variable and assigned 2 (yes) for households
who accessed appropriate packaging materials and 1(no) for those not accessed. After the
soybean has been threshed, it has to be packed appropriately to keep its quality for a long
time. Appropriately packed soybeans can be stayed for a long time without losing the quality
until sold (Obute, et al., 2019). The researcher hypothesized that this variable can increase
the value of soybean with time positively and significantly.
Storage problem (STORAGE): Dummy variable and assigned 2 (yes) for households who
have problems of storage and 1 (no) for those not facing storage problems. Good storage is
42
one of the post-harvest technologies that increase the value of crops by keeping their quality
a long time. Safe storage keeps soybean for a long period of time without deteriorating
nutrient composition (Prabakaran, et al., 2018). Storage is a great problem even at the
country level which causes yield loss due to quality deterioration as a result of poor storage.
So, it was hypothesized that the storage problem negatively and significantly influences
soybean value addition.
Table 3.5 Description of explanatory variables in the Probit model
Variables Symbol Type Measurement Expecte
d sign
Age AGE Continuous Years +
Education level EDLEVEL Categorical
1 = illiterate 2 = read &
Write 3 = Primary
school (1-8) 4 =
secondary school (9-10)
5 = Preparatory school
(11-12) 6 = TVET &
above
+
Market distance DISTANCE Continuous Walking Hr. -
Family size FAMSIZ Continuous Number -
Livestock TLU Continuous TLU +/-
Market price MKTPRICE Continuous Birr kg-1 +
Disease incidence DISEACON Categorical
1 = very serious 2 =
serious 3 = moderately
serious 4 = not serious 5
= not constraint at all
-
Training TRAINING Dummy 2= Yes 1 = No +
Quantity produced QUPROD Continuous Quintal +
Improved seed IMPSEED Dummy 2= Yes 1 = No +
Packaging material PACKMT Dummy 2= Yes 1 = No +
Storage problem STORAGE Dummy 2= Yes 1 = No -
43
Chapter 4. RESULTS AND DISCUSSION
This chapter presents the major findings of the study. Descriptive and inferential statistics
were employed to analyze the demographic and socioeconomic characteristics of
households, traders, and consumers. The determinant factors that affect the quantity of
soybean supply to the market and value addition in the study area were analyzed through
econometric analysis. Soybean value chain was analyzed through marketing margin analysis
which includes a map of the value chain, actors and their respective roles, marketing
margins, marketing channels, and benefit shares of each actor along the value chain were
discussed.
4.1 Descriptive and Inferential Statistics
4.1.1 Household heads characteristics
Household characteristics include age, sex, training, education level, family size, and farm
experience of the household head. The results of the study revealed that all of the households
are producing soybean mainly for marketing purposes since direct consumption of soybean
as a food is not habited. All households have been provided their soybean product to the
market after reserving seed for next year production. The study result indicated that 204
sample households participated in some value-adding activities whereas 24 households
didn’t participate in any value-adding activities. Although most of them participated in
value-adding activities, the estimated value-added was not significant as compared to other
developing countries even as compared to the neighboring country with Kenya. Because
Kenya was added 30-290 Kenyan shillings (8.19-79.21 ETB) value by processing 1-
kilogram soybean seed (Nyongesa, et al., 2018).
Sex of household head: Of the total interviewed households, 3.95% were female-headed
households and 96.05% were males. The survey result indicated that none of the female-
headed households participated in any value-adding activities. This may be due to either
inclusion of few female respondents in the sampling due to random sampling or low
awareness of female-headed households about value-addition as compared to their
counterparts.
44
Training of household head: The survey result indicated that 58.33% of participant
households took training on pre-and post-harvest handling techniques and 31.14 not took
the training. All non-participants did not take the training of pre-and post-harvest handling
techniques on soybean. The chi-square result (χ2 = 37.55***) is evidence for the presence
of a significant association between the two groups at less than a 1% level of significance.
The finding concurs with Roy et al. (2013) who found a significant association between
training and tomato value addition. The result is also similar to Opolot et al. (2018) who
confirmed the existence of a significant association between training of farmers and soybean
value-addition.
Education level: As indicated in Table 4.1 below, 40.35% of participant households were
completed primary school education while only 5.26% of non-participants completed their
primary school education. Although the chi-square test (χ2 = 3.046) is not evidence for the
presence of a significant association between the two groups, relatively more educated
households are involved in value-addition.
Table 4.1 Socio-demographic characteristics of soybean producers
Participation on value addition
Variables Category Participant
(N=204)
Non-participant
(N=24)
All cases
(N=228) χ2
Sex of HH Male 195 (85.53) 24 (10.53) 219 (96.05) 1.1
Female 9 (3.95) 0 (0.00) 9 (3.95) Training of HH Yes 133 (58.33) 0(0.00) 133 (58.33) 37.55***
No 71(31.14) 24(10.53) 95 (41.67)
Education level Illiterate 57 (25.00) 6 (2.63) 63 (27.63) 3.05
Read & Write 36 (15.79) 6 (2.63) 42 (18.42)
Primary (1-8) 92 (40.35) 12 (5.26) 104 (45.61)
Secod. (9-10) 13 (5.70) 0 (0.00) 13 (5.70) Prep. (11-12) 3 (1.32) 0 (0.00) 3 (1.32) TVET & above 3 (1.32) 0 (0.00) 3 (1.32)
Source: Own survey result, 2020
Family size: The average family size of participant and non-participant households on value
addition was 5.71 and 6.13 respectively.
45
The mean age of participant and non-participant households was 45.28 and 42.46 years
respectively. Age reflects the productivity of the population since it has a bearing on the
overall situation of health within the community. Aged members of the society are more
prone to diseases particularly in developing countries and thus leads to less productive. It
implies the employment pattern, spatial mobility, and quality of work done. Age plays an
important role in any type of business, especially in agriculture. The t-test (-1.36) indicates
that there was no significant mean difference between the two groups in terms of age.
Farm experience: The survey result indicated that average overall farming and soybean
farming experience of the total sampled households was 23.20 and 9.28 years respectively.
Soybean farming experience of participant households was 9.49 years and 7.5 years for non-
participants. The t-test (-1.75*) is evidence for the presence of a significant mean difference
between the two groups in terms of farming experience on soybean. The negative sign for
t-value indicates that households who did not participate in value-addition were less
experienced on soybean production. The finding is similar to Regasa Dibaba et al. (2019)
who confirmed that farm experience of soybean significantly influences the knowledge and
skills of farmers to increase their technical efficiency of value addition on soybean.
Experienced households have the knowledge and skill on the overall processes of the
agricultural production system and can produce more outputs from a small amount of inputs
used and can supply more output to the market with significant value addition.
Table 4.2 Socio-demographic characteristics of sampled household (t-test)
Variables Category Participant
(N=204)
Non-participant
(N=24)
All cases
(N=228) t-value
Family size Mean 5.71 6.13 5.75 1.02
SD 1.92 1.78 1.91 Age Mean 45.28 42.46 44.99 -1.36
SD 9.87 7.24 9.59 Farm experience Mean 23.45 21.08 23.20 -1.26
SD 8.79 7.63 8.67
Soybean experience Mean 9.49 7.5 9.28 -1.75*
SD 5.29 5.08 5.27
Source: Own survey result, 2020
46
The results depicted in Table 4.3 show that the average quantity of soybean market supply
by a household who had access and not access market information was 20.55 and 5.55
quintals respectively. The t-test (7.31***) indicates that there was a significant mean
difference between the two groups in terms of the quantity of soybean market supply at less
than 1% significant level. The positive sign of t-value implies that farmers who have
accessed market information supplied more out puts to the market. The result is in line with
Wondim Awoke and Dessalegn Molla (2018) who confirmed the existence of a significant
correlation between potato market supply and market information. As a result of FGDs,
friends/neighbors were the major sources of market information for most of the households
in the study area although traders, development agents, radio & television were additional
sources. The existence of reliable market information helps farmers to sell their products a
better price since farmers can choose a profitable mode of transaction.
As the results indicated in Table 4.3 below, 57.46% of households were cooperative
members and 42.54% of them were not a member of a cooperative. On average, 19.57 and
10.28 quintals of soybean were supplied to the market by cooperative members and non-
members respectively. The t-test (4.47***) is evidence for the presence of statistical mean
difference among cooperative members and non-members regarding the quantity of soybean
products delivered to the market. Positive sign of t-value implies cooperative delivered more
soybean to the market as compared to non-members. The finding is similar to Kumilachew
Achamyelh et al. (2020) who found that cooperative membership significantly affects
quantity of sesame market supply. Cooperatives are crucial in the study area particularly for
input supply and to buy the products in bulk. However, the local communities were not
satisfied by the cooperatives as well as unions in regarding to marketing as the data collected
from FGDs & KI interviews. Even cooperative members sold their soybean and other
products to local traders instead of supplying to the cooperative. Because cooperatives were
not able to pay money on time for their products and not able to provide different services
as expected. On the other hand, local traders can give sacks free for their soybean product
and give money and other inputs like seed in credit in time of scarcity and this attracts most
producers to supply their products to those traders.
The average quantity of soybean supplied to the market by households who took and not
took training last year on soybean production and management was 21.07 and 13.88 quintals
respectively. The t-test (2.92***) implies that there was a significant mean difference
47
between the two groups in terms of quantity of soybean market supply at less than 1%
significant level. The finding agrees with Opolot et al. (2018) who confirmed that training
significantly influenced the quantity of soybean marketed. The result is also in line with
Banda et al. (2017) who found that training of framers significantly influences surplus
production of soybean to supply more to the market.
Table 4.3 Relationship of quantity of soybean market supply with categorical variables
Quantity supplied (qt)
Variables Category Response Mean SD t-value
Market information Yes 153 20.55 17.69 7.31***
No 75 5.55 2.11 All cases 228 15.62 12.57 Coops. membership Yes 131 19.57 18.89 4.47***
No 97 10.28 9.16 All cases 228 15.62 14.75 Training of HH Yes 55 21.07 23.68 2.92***
No 173 13.88 12.48 All cases 228 15.62 15.19
Source: Own survey result, 2020
4.1.2 Institutional characteristics of soybean producers
The socio-economic development in general and the well-being of individuals in particular,
can be enhanced through the provision of adequate services to the communities by different
institutions. It has a positive contribution regarding improving production and productivity
and this leads to an increase the supply level of marketable crops further to increase the
income level of smallholder farmers. The most important services that are expected to
deliver for users to promote the production and marketing of soybean in the study area are
explained below.
The survey results indicated in Table 4.4 showed that 63.16% of participant households on
value-addition had access to market information whereas 26.32% of them couldn’t access
the information about their product. From the non-participants, 3.95% accessed market
information and 6.58% of households not accessed the information. The chi-square test (χ2
= 10.65***) indicates that there was a significant association between access to market
48
information and value addition at less than a 1% level of significance. The finding is
consistent with Magesa et al. (2020) who found a significant association between the use of
market information and value addition.
As depicted in Table 4.4 below, 72.37% of the households responded that there was a price
difference due to value addition whereas 27.63% were responded no price difference at all
as a result of value addition. The chi-square test (χ2 =70.25***) is evidence for the presence
of a significant association between the two groups at less than 1% level of significance.
The finding by Kyomugisha et al. (2018) also confirmed that existence of significant
association between selling price and value addition of potato.
The survey result indicated that 54.39% and 35.09% of households were cooperative
members from participants and non-participants respectively. From the non-participant
respondents, 3.07% of them were cooperative members and 7.46% were not members. The
chi-square test (χ2 = 8.78***) shows that there was a significant association between
cooperative membership and value addition at less than 1% significant level. The finding is
similar to Kolade and Harpham (2014) who found a significant association between
cooperative membership and technological adoption for value addition.
As the results indicated below, 49.12% of participant households were used improved
soybean seed for production, and 40.35% of them were not used improved seed. From the
non-participants, 6.14% of them used improved seed, and 4.39% not used improved seed.
The chi-square result (χ2 = 0.10) shows there was no significant relationship between use
of improved seed and value addition.
49
Table 4.4 Socio-economic characteristics of soybean producers
Variables Category Participant
(N=204)
Non-participant
(N=24)
All cases
(N=228) χ2
Market information Yes 144 (63.16) 9 (3.95) 153 (67.11) 10.65***
No 60 (26.32) 15 (6.58) 75 (32.89)
Price difference Yes 165 (72.37) 0 (0.00) 165 (72.37) 70.25***
No 39 (17.11) 24 (10.53) 63 (27.63)
Coops. membership Yes 124 (54.39) 7 (3.07) 131 (57.46) 8.78***
No 80 (35.09) 17 (7.46) 97 (42.54)
Use of improved seed Yes 112 (49.12) 14 (6.14) 126 (55.26) 0.1
No 92 (40.35) 10 (4.39) 102 (44.74)
Source: Own survey result, 2020
The study result revealed that average travel time for participant and non-participant
households was 0.41 and 0.69 hours respectively to reach the nearest market. The t-value (-
2.78***) is evidence for the presence of a significant mean difference between the two
groups in terms of travel time at less than 1% significant level. The negative sign of t-value
implies that participants traveled less time as compared to non-participants due to their
closeness of the market. The finding agrees with Orinda et al. (2017) who affirmed a
significant association between distance and sweet potato value addition. On average,
participant and non-participant households paid 18.67- and 17.00-birr qt-1 respectively to
transport their soybean product. The t-test (-0.69) indicates that there was no significant
mean difference between the two groups regarding to transportation cost of their product.
The study result showed that 47.4% of respondents had access to credit service but only
3.9% of them took the credit. Microfinance, local money lenders, and saving and credit
associations were the main credit sources for farmers in the study area. The mean credit
utilization of participant and non-participant households was 139.89 and 83.33 birr with a
standard deviation of 892.57 and 408.25 respectively. The result of the t-test (-0.31)
indicates that there was no significant mean difference between the two groups in terms of
credit utilization. As the data collected from FGDs, religious taboo, high-interest rate,
complex process, and no need for credit due to self-sufficiency were the main factors that
hinder farmers from utilizing credit.
50
The survey result showed that the overall average selling price of participants of value
addition was 1141.19 birr per quintal whereas 987.50 birr per quintal for non-participants.
The t-test ( 3.63***) revealed that there was a significant mean difference between the two
groups in terms of selling price at less than a 1% level of significance. The positive sign of
t-value implies that participant households in value-addition sold their product with better
price as compared to their counter parts. The finding agrees with Kyomugisha et al. (2018)
who confirmed that selling price significantly influences producers to add values to potato.
The study result shows that 1.75 birr per kilogram value was added by participant
households with a standard deviation of 1.68. The t-test (5.09***) indicates that there was
a significant mean difference at less than 1% significant level among the two groups
regarding estimated value addition.
The amount of livestock owned by the household was measured in terms of tropical
livestock unit. The mean livestock holding of participants and non-participants in terms of TLU
was 6.04 and 4.60 respectively. The result of t-test (-1.59) showed that there is no evidence for
the presence of a significant mean difference between the two groups in terms of TLU.
Table 4.5 Socio-economic characteristics of sampled households (t-test)
Variables Category Participant
(N=204)
Non-participant
(N=24)
All cases
(N=228) t-value
Travel time (hrs.) Mean 0.41 0.69 0.44 -2.78***
SD 0.47 0.44 0.47
Product transport cost Mean 18.67 17.00 18.49 -0.69
SD 11.46 8.32 11.13
Credit utilization Mean 139.89 83.33 133.93 0.31
SD 892.57 408.25 841.59
Selling price Birr Qt Mean 1141.19 987.5 1125.01 3.63***
SD 202.58 126.19 194.54
Value addition Mean 1.75 0.00 1.57 5.09***
SD 1.68 0.00 1.51
livestock in TLU Mean 6.04 4.60 5.89 -1.59
SD 4.28 3.23 4.17
Source: Own survey result, 2020
51
As the results of this study, 39.5% of soybean producers confirmed that they rarely contacted
with extension agents and 60.5% had contacts that ranges from daily to monthly. The result
of the analysis of variance ANOVA (F = 16.006, p = 0.000) is evidence for the presence of
a statistical mean difference between the groups regarding to quantity of soybean supplied
to the market. The finding by Dagnaygebaw Goshme et al. (2018) also confirmed that the
frequency of extension contact significantly affects the quantity of sesame market supply at
less than 1% significant level. The provision of appropriate agricultural extension service
takes a lion share in the overall journey of improving the living standards of farmers. It
provides assistance for farmers to improve their production and productivity by applying
scientific knowledge. Scientific findings can be put into practice by farmers with close
assistance of DAs and experts. Agricultural extension service is crucial to convince farmers
on the adoption of new agricultural technologies by taking risks. It enables farmers to be
aware of and get a better understanding of the research findings that increase their level of
production and productivity. It also plays a significant role to promote and disseminate
improved technologies to the majority of farmers.
The government of Ethiopia has been assigned five development agents (DAs) per each
kebele administration and building one farmers’ training center (FTC) to fill the knowledge
gaps of farmers and poverty reduction. Development agents are the major source of
extension service for farmers and they are expected to support farmers in their day to day
farming activities. Different organizations like the development group and one to five
groups have been established to enhance the provision of extension service and knowledge
transfer among groups. However, as the data collected from FGDs & KIs, farmers’
organization is not functioning according to the objectives. Although DAs have been
assigned to assisting the farmers for only agricultural activities, they are forced to perform
different political and other missions out of their professions and this affects the
performance of farmers for their production and productivity. Extension agents are expected
to give support for farmers by contacting frequently in their everyday life. However, more
frequently extension contact of farmers with DAs doesn’t necessarily give the expected
result, rather it is better to deliver appropriate extension services through service providers.
The education level of sample household heads in the study area ranges from illiteracy to
TVET and above levels. The proportion of household heads were illiterate (27.6%), read
and write (18.4%), primary school (45.6%), secondary school (5.7%), preparatory school
52
and TVET, and above each 1.3%. The ANOVA result (F = 0.536) indicates that there was
no statistical evidence for the presence of significant difference between the groups in terms
of the quantity of soybean supplied to the market.
Table 4.6 Extension contact and education level of soybean producers
Source of variation Sum of squares df Mean squares F Sig.
Frequency of extension contact
Between groups 13217 4 3304.26 16.006 0.000
Within groups 46036.4 223 206.441
Total 59253.4 227
Education level of household head
Between groups 706.651 5 141.33 0.536 0.749
Within groups 58546.8 222 263.724
Total 59253.4 227
Source: Own computation from survey result, 2020
4.1.3 In put utilization
DAP fertilizer, seed, and herbicides are the major agricultural inputs used by farmers in the
study area for soybean production. Very few farmers have used biofertilizer by mixing with
DAP and sugar that has been supplied by PARC and mama union. These inputs were
supplied to farmers through cooperatives/unions, bureau of agriculture, and private traders.
Unions and cooperatives are the major suppliers of fertilizer for producers in the study area.
The average quantity of improved soybean seed applied by participant and non-participant
households was 0.76 and 0.74 quintal per hectare respectively. The t-test (0.10) indicates
that there was no significant mean difference among the two groups in regarding to seed
rate. On average, participants applied 0.35 quintal DAP fertilizer per hectare and 0.32
quintal for non-participants. Statistically, there was no significant mean difference between
the two groups in terms of fertilizer application. The mean herbicides applied by participants
and non-participants were 2.16 and 1.60 liters per hectare respectively. Similarly, the t-test
indicates that there was not significant mean difference between the two groups in terms of
herbicide application. Round up and 2-4D are the two types of herbicides that farmers are
applying before sowing. These herbicides were completely supplying to farmers through
53
private traders at Pawe and Gilgelbeles town and any pesticide is not recommended to use
for soybean commodity.
To get the required production and marketable supply, farmers recommended to use these
agricultural inputs with the recommended rate given by PARC. PARC recommends
applying 100kg DAP ha-1 but applying UREA fertilizer for soybean is not recommended
since the crop itself replaces UREA. However, farmers in the study area were not applying
each input as the recommendation and that is why they received the yield per hectare that
was far from nation productivity. This finding is similar to Afework Hagos and Adam
Bekele (2018) who obtained that soybean yield per hectare was far away from the national
productivity due to limited use of improved seed and other recommended packages. Some
farmers also perceived that their plot is fertile and no need of applying any artificial fertilizer
for soybean production which is a great problem in the study area.
Table 4.7 Utilization of agricultural inputs for soybean production
Variables Category Participant
(N=204)
Non-participant
(N=24)
All cases
(N=228) t-value
Improved seed Qt ha-1 Mean 0.76 0.74 0.76 0.10
SD 0.17 0.19 0.18
DAP fertilizer Qt ha-1 Mean 0.35 0.32 0.35 0.526
SD 0.40 0.43 0.41
Herbicide applied lit ha-1 Mean 2.16 1.60 2.10 0.104
SD 2.52 3.05 2.57
Source: Own computation from survey result, 2020
4.1.4 Soybean production and marketing
Ethiopia is one of the potential countries in soybean production in East Africa as described
in Chapter 1. Similarly, Metekel is the leading zone of Ethiopia in soybean production and
contributes 65.7% of the region and 24.69% of the national production. Soybean is the
dominant legume crop-producing in Metekel zone as well as the region. It is not the legume
crop only, but also the known oil crop in the study area next to sesame and groundnut. In
Pawe district, all farmers (100%) are soybean producers and allocated a large proportion of
their lands for soybean production. According to Birhanu Ayalew et al. (2018), soybean
54
took a 35% share of the land among other crops produced in Pawe in the 2015/16 cropping
season.
Land is an important measure of wealth and the most significant factor of production in the
study area. It is the major source of income and improves the status of people in the
community. The survey result showed that the mean cultivated land of participant and non-
participant households in the 2018/19 cropping season was 3.47 and 2.73 hectares
respectively. The t-test (t = 2.30**) is evidence for the presence of a significant mean
difference between the two groups in terms of cultivated land at a 5% level of significance.
Positive sign of t-value indicates that participants in value-addition owned more cultivated
land as compared to non-participants. The finding is similar to Orinda et al. (2017) who
found a significant association between land size and value addition to sweet potato.
Similarly, the average land size allocated for soybean production by participants and non-
participants was 1.40 and 0.85 hectares respectively for the same production season. The t-
test (3.01***) indicates that there was a significant mean difference among the two groups
regarding allocating lands for soybean at less than 1% significant level. Similarly, the
positive sign of t-value implies that participants allocated more proportion of land for
soybean production. Overall, out of the total cultivated lands, 39.53% (1.34 ha) of their
lands were used for soybean in the 2018/19 cropping season since the area is known for its
soybean potential. This shows a 4.53% increment as compared to the 2015/16 cropping
season in terms of lands allocated for soybean.
As the results of this study, 3823.21 quintal soybean was produced by sample households
in the 2018/19 cropping season. The average quantity of soybean produced by participant
and non-participant households was 17.74 and 8.52 quintal respectively. The t-test (2.55**)
indicates that there was a significant mean difference between the two groups regarding
soybean production at a 5% level of significance. The positive t-value implies that
participants produced more outputs as compared to their counter parts. This finding agrees
with Surni et al. (2019) who confirmed that quantity of farmers production significantly
influences value addition at less than 1% level of significance. Households who participated
in value addition was produced 12.26 quintals per hectare and 10.26 quintals for non-
participants. The t-test (1.54) indicates that there was no significant mean difference
between the two groups in terms of productivity. Overall productivity of soybean in the
study area was 12.05 quintals per hectare which is far from the national productivity of 21.5
55
quintals. As the data collected from FGDs & KIs, low use of improved variety seed, limited
use of fertilizer, improper application of agrochemicals, and other agronomic practices
without the recommended rate were the major inhibiting factors for productivity. This result
is in line with Afework Hagos and Adam Bekele (2018) who affirmed that soybean
productivity was far from the national productivity due to limited use of improved soybean
varieties and other recommended packages.
The survey result revealed that 93.11% (3559.95 qt) of soybean was provided to the market
out of the total volume of 3823.21quintal produced and 6.89% (263.26qt) was reserved for
seed in the next season production and local consumption. As indicated in Table 4.8, the
mean quantity of soybean sold by participant and non-participant households was 16.50 and
8.13 quintals respectively. The t-value (2.43**) is evidence for the presence of a significant
mean difference between the two groups regarding to quantity of soybean sold. The positive
sign of t-value indicates that paricipants were provided and sold more soybean product as
compared to non-participants households.
Table 4.8 Soybean production and marketing in 2018/19 cropping season
Variables Category Participant
(N=204)
Non-participant
(N=24)
All cases
(N=228) t-value
Cultivated land (ha.) Mean 3.47 2.73 3.39 2.30**
SD 1.50 1.38 1.49
Soybean land size (ha.) Mean 1.40 0.85 1.34 3.01***
SD 0.86 0.55 0.83
Soybean production (qt.) Mean 17.74 8.52 16.77 2.55**
SD 17.52 7.11 16.43
Productivity qt. ha-1 Mean 12.26 10.26 12.05 1.54
SD 6.09 5.30 6.01
Sold soybean (qt.) Mean 16.50 8.13 15.62 2.43**
SD 16.71 6.83 15.62
Source: Own survey result, 2020
56
4.2 Description of Sample Traders and Consumers
4.2.1 Household characteristics of sampled traders
All sampled traders in the study area were males. Out of the total 14 traders interviewed, 13
were married and one trader was single. The majority (42.86%) of traders can read and write
and 21.43% of them had an education level of primary school and TVET and above. The
result showed that 57.14% and 42.86% of traders are Orthodox and Muslim religious
followers respectively. The mean age and family size of traders were 41.86 and 4.14 with a
standard deviation of 6.57 and 1.03 respectively. On average, traders have 5.93 years of
experience in soybean trading.
Table 4.9. Socio-demographic characteristics of sample traders
Variable (N = 14) Frequency Percent
Sex Male 14 100
Marital status Married 13 92.86
Single 1 7.14
Education Read and write 6 42.86
Primary 3 21.43
Secondary 1 7.14
Preparatory 1 7.14
TVET and above 3 21.43
Religion Orthodox 8 57.14
Muslim 6 42.86
Mean Std. Deviation
Age 41.86 6.57
Family size 4.14 1.03
Experience of trade on soybean 5.93 2.02
Source: Own survey result, 2020
4.2.2 Price setting strategies of traders for soybean purchasing
Most traders (85.71%) have been purchased soybean during the pick periods of the year.
Few (14.29%) traders have purchased soybean throughout the year although there are few
months in which soybean is scarce. Terms of payment for purchasing are in cash and credit
57
in the study area. The study result showed that 64.29% of traders purchased soybean both
in cash and credit whereas 35.71% of traders purchased only in cash. According to the
information obtained from FGDs, most of the local traders have been purchased soybean
through credit from their long-standing customers. Most (85.71%) of the traders responded
that the purchase price of soybean was set by the market and 2 traders responded that it was
set through negotiation and by the traders. Out of the total 14 traders, 64.29% of them said
that the purchase price is set early in the market during the market day. 14.29% and 21.43%
of traders responded that the purchase price is set one day before the market day and at the
time of purchase respectively. However, the responses of traders contradict with farmers in
regarding to price setting strategies. Because most farmers responded that the selling price
of soybean was set by traders and they couldn’t influence the selling price.
Table 4.10 Time of soybean purchasing and price setting strategies
Variables (N = 14) Frequency Percent
Time of purchase year-round 2 14.29
During pick period 12 85.71
Terms of payment Cash 5 35.71
Both cash & credit 9 64.29
Price setting strategy Negotiation 1 7.14
By the market 12 85.71
By trader 1 7.14
Time of price setting one before market day 2 14.29
Early in the morning during market day 9 64.29
At the time of purchase 3 21.43
Source: Own computation from survey result, 2020
4.2.3 Initial and working capital of traders
The average initial and working capital of traders in the study area were 288,751.43 and
441,071.43 birr respectively. The majority (78.57%) of traders have their own saved sources
of capital for starting the business. Three traders took credit from banks, private money
lenders, and other traders as a starting capital for the trading business. The study result
indicated that most traders in the study area started their trading business on soybean and
other trading items by using their own saved capital.
58
Table 4.11 Initial, working capital & credit source
Variable (N= 14) Frequency Percent
Source of capital Own saved 11 78.57
Credit 3 21.43
Mean Std. Deviation
Initial capital 288751.43 276895.07
Working capital 441071.43 332566.82
Source: Own survey result, 2020
4.2.4 Soybean oil production
Soybean can be processed as feed and food for animal feeding and human consumption with
an excellent nutrient composition. The estimated annual consumption of oil in Ethiopia is
about 394 million kg which indicates that around 11 billion ETB is spending per annum
(Sopov & Sertse, 2014). Three-fourths of the edible oil demand has been covered through
import. Most of the soybean and sunflower edible oils have been covered through import
since there is only one soybean edible oil processing industry (health care food manufacturer
PLC) in the country (Sopov & Sertse, 2014). The imported soybean edible oil costs US$ 4
per litter whereas it cost US$ 3 per litter for domestic processed oil. This implies that US$1
can be saved if the domestic soybean oil production substitutes the imported oil.
Health care food manufacture PLC: It is the only soybean oil-producing plant in Ethiopia
and produces and distributes the produced soybean oil and soybean meal and hulls to the
end-users through its distributors. The byproducts of soybean meal and hulls were selling
directly to consumers after the oil has been extracted. As the data obtained from the factory,
extracted oil was distributed through distributors/whole-sellers but not directly sold to
retailers and consumers, unlike byproducts. The processing plant bought soybean grain from
Addis Ababa whole sellers and producers before the crop being an ECX commodity and
that was so challenging to get soybean in the required quantity as well as quality. Currently,
soybean becomes an ECX commodity starting from 2019 and the factory has been bought
from ECX. Now, the factory can get relatively a quality soybean from ECX even though
still the quantity is limited. In 2019, the processing plant bought about 75,000 quintals
soybean and produced 900,000-937,500 litters of soybean oil. As the data collected from
59
the factory, to produce 1 litter oil, 8kg soybean grain is needed. This indicates that from
100kg soybean grain, 12.5 litters of oil can be produced. After oil production, the oil is
delivered to whole sellers with a price of 67.77 birr per litter. Whole sellers also sold to
retailers at the price of 76.07 birr per litter and finally consumers purchased by 85 birr per
litter.
Table 4.12 Purchase price of soybean oil by traders and consumers
Actors Purchase price of soybean oil per litter
Whole sellers 67.77
Retailors 76.07
Consumers 85.00
Source: Own survey result, 2020
Palm, soybean, and sunflower oils are the major imported oils in Ethiopia (Sopov & Sertse,
2014). According to the data collected from the Ministry of Trade and Industry and health
care food manufacturer plant, the quantity of domestically produced soybean oil shows
some increment although the existing soybean oil-producing plant is still one. As indicated
in Figure 4.1, the country imported 8115.46kg soybean oil besides to other oils in 2019
marketing year. At the same time, the domestic soybean oil processor produced 781,250kg
soybean oil. This implies that the establishment of additional processing plants and capacity
improvement of the existing processing plant needs great attention to substitute the imported
oils as well as to save foreign currency.
Source: Ministry of Trade & Industry & Health care food manufacturer plant, 2019
Figure 4.1 Quantity of soybean oil imported and domestically produced, 2019
Imported
soybean oi l
Domest ic
product ion
soybean oi l
8115.46 781250.00
Volume of soybean oi l (kg)
60
Exporters: Exporters are those traders who buy soybean from central ECX and export the
commodity abroad. According to the data collected from the ministry of trade and industry
report (2019), 137 exporters were involved in soybean export in the 2019 fiscal year. In
2019 marketing season, 942,038.10 quintal of soybean was exported by those 137 exporters.
Quantity of soybean grain exported increased by 37.37% as compared to 590,042 quintals
exported in the 2017/18 production season (Ministry of Trade and Industry & Byrne, 2018,
2019). As the data indicated below in Figure 4.2, from the total volume provided to the
market, the majority (92.63%) of soybean grain was exported and 7.37% was used for
domestic processing. This implies that still now, the country exports soybean grain without
adding significant values and import back it again the processed products by spending huge
money. Even though the government of Ethiopia promotes export of the commodity, more
attention has to be given to processing to meet the domestic consumption of the byproducts
as well as to promote export by adding significant values to the product instead of exporting
soybean grain. Because viable economic growth can be realized through the addition of
significant values to each agricultural product and this enhances the competitiveness in the
domestic and international markets.
Source: Ministry of Trade & Industry & Health care food manufacturer plant, 2019
Figure 4.2 Quantity of soybean grain exported & domestic consumption for processing
80%
90%
100%
Volume (Qt) Percent
942,038.10 92.63
75,000 7.37
Soybean exported & domestically used for processing
Quantity exported quantity used for domestic processing
61
4.2.5 Household characteristics of consumers
Out of the total interviewed consumers, 66.67% were females and 33.33% of consumers
were males. These consumers are households that are consuming soybean oil which were
found in Addis Ababa city and Pawe town. Five consumers were from Addis Ababa and 10
of them were from Pawe. The education level of consumers ranges from illiterate to
certificate holder and above levels. The result showed that 86.67% of consumers were
married and 13.33 % were single households. Government employment, trading, and daily
labor were the main livelihoods for 46.67%, 40%, and 13.33% of consumers respectively.
The mean age and family size of consumers were 32.13 years and 3.67 family members
respectively. Soybean oil is a recently produced consumption oil in which most people are
not habited to consume it. The average soybean oil consumption experience of the
respondent was 1.60 years. The oil has been provided from Addis Ababa whole sellers to
retailors in each corner of the country. Finally, consumers purchased the oil from there
nearby retailors.
Table 4.13 Socio-demographic characteristics of soybean oil consumers
Variables (N = 15) Frequency Percent
Sex Male 5 33.33
Female 10 66.67
Education Illiterate 4 26.67
Read and Write 3 20.00
Certificate & above 8 53.33
Marital Status Married 13 86.67
Single 2 13.33
Means of income for consumers Trade 6 40.00
Employment 7 46.67
Daily labor 2 13.33
Mean Std. Deviation
Age 32.13 6.61
Family size 3.67 1.50
soy oil consumption experience 1.60 0.63
Purchase price of soy oil (birr/lit) 85.00 1.36
Source: Own survey result, 2020
62
4.3 Main Value Chain Actors and Functions
4.3.1 Primary value chain actors and their functions
Table 4.14 Primary actors and supporters along soybean value chain in the study area
Primary Actors Supportive Actors
Cooperative Development agents
Unions Experts
Agriculture office Pawe Agricultural Research Center
Pawe Agricultural Research Center Ethiopian Commodity Exchange
Private Traders/agro chemical suppliers District agriculture office
MBI/Menagesha Bio-technology Institute Zone agriculture bureau
Farmers District trade and industry office
Local traders/village level traders N2-Africa project
District whole sellers AGRA project
Ethiopian Commodity Exchange BG saving credit association
Processor
Exporters
Oil whole sellers
Retailors
Consumers
Input suppliers, producers, local traders, cooperatives/unions, district whole-sellers,
Ethiopian commodity exchange, Addis Ababa wholesalers, processors, exporters, retailers,
and consumers were the primary actors in soybean value chain in the study area.
Input suppliers: This segment of the value chain consists of the actors that provide the
starting materials for the proper functioning of the subsequent soybean value chain. The
actors include in this segment were seed supplier, fertilizer supplier, herbicide supplier, and
extension service providers as well as other technical and financial supporters.
Cooperatives, Mama union, private traders, MBI, and Pawe Agricultural Research Center
were the main input suppliers in the study area. Cooperatives were the major suppliers of
fertilizer, seeds, and agrochemicals. Cooperatives also collected soybean and other products
from cooperative members and delivered to Mama union. On the other hand, Mama union
distributes fertilizer, improved seeds, agrochemicals, oil, sugar, and other sanitary goods to
basic primary cooperatives. The union received the collected soybean and other products
63
from basic cooperatives and delivered the bulk products to Ethiopian commodity exchange
at Almu branch. Private traders from Pawe and Gilgelbeles town were the main
agrochemical suppliers. Inoculants (mar-1495) were supplied through Menagesha
Biotechnology Institute (MBI) and PARC. PARC provides basic and pre-basic initial seeds
and technical trainings with full packages to seed producers, DAs, experts, and other
stakeholders in the overall process of production and marketing.
Producers: Soybean producers are the major value chain actors following input suppliers
in soybean value chain. All soybean producers in the study area were smallholder farmers
having different land size. The smallholder producers in Pawe district were providing their
soybean product to village level traders, basic cooperatives, and to district whole sellers.
Farmers in the study area were producing soybean only for marketing purposes. The
production process of soybean undergoes through the following basic farming operations
from land preparation to final post-harvest handling. As the data obtained from FGDs and
household surveys, Seed preparation, chemical application, and storing were the basic
farming operations which are performed through family labor only. However, land
preparation, w plowing, sowing, weeding, harvesting, damping, and threshing farming
operations were performed by both family and daily labor. Cleaning, packaging,
transportation, storing, and by very few farmers sorting were the post-harvest handling tasks
performed by farmers along soybean value chain. Finally, producers provided their
produced soybean to local traders, district whole-sellers, cooperatives, ECX, and other
producer farmers for seed and investors outside the district. The study result indicated that
the majority (77.97%) of the total produced was sold to local traders.
Local/village traders: Were the major soybean buyers among all traders in the study area.
Local traders had a strong linkage with farmers. According to the FGDs and key informants’
interviews in the four sampled kebeles, local traders give different inputs and food grains as
well as money for farmers in time of scarcity as a means of attraction. Some farmers also
received sacks free from the local traders for soybean packaging. Due to such attraction
mechanisms, 83.77% of sampled farmers were sold their soybean production to local
traders. After collection of the product from producers, all local traders finally delivered to
ECX at Almu branch since all of them have a trading license.
64
Pawe whole sellers: These whole sellers can be received soybean products from farmers,
local traders, and even from Mama union and delivered it to Addis Ababa whole sellers and
processors before the crop is becoming an ECX commodity. Currently, soybean becomes an
export ECX commodity starting 2019 and Pawe whole sellers were forced to buy soybean
only from farmers and delivered it to ECX at Almu branch. As the data obtained from those
whole sellers during the interview, they complain about the existence of ECX and not
willing to expand soybean trading business since the market alternative was limited and
monopolized by ECX. Such an issue was not a problem of whole sellers only, but also to
local traders and producers due to misperceptions and low awareness about the roles of
ECX.
Ethiopian Commodity Exchange: ECX is a national multi-commodity exchange to
provide market integrity by guaranteeing the product grade and quality. It is designed to be
a marketplace where buyers and sellers meet to trade, assured of quality, delivery, and
payment (Meijerink G & Dawit Alemu, 2014). ECX is a modern trading system based on
standard crop contracts by establishing standard parameters for commodity grades,
transaction, size, payment, and delivery as well as trading order matching. The main role of
ECX is to bring buyers and sellers together to trade at the trading floor more efficiently and
transparently (Bizualem Assefa & Saron Mebratu, 2018). ECX has a positive impact on the
existing marketing system and for the development of the agricultural value chain in
Ethiopia through a more reliable way to connect buyers and sellers efficiently. It delivers
timely market price information to all marketing actors. Central ECX received the collected
soybean production from each branch across the country and stored in the warehouse until
the bulk collection to sell to buyers. After bulk collection, the product was sold to exporters
and domestic processors with clear bid competition on the trading floor. As data collected
from the Ministry of Trade and Industry and health care food manufacturer, 92.63% soybean
production was sold to exporters, and the rest 7.37% soybean sold to processors in 2019
marketing year.
Exporters: According to the data collected from the Ministry of Trade and Industry, there
were about 137 soybean exporters in the 2019 marketing season. These exporters bought
soybean from central ECX and exported the crop abroad.
65
Processors: Buy soybean from central ECX and process soybean into food and feed
products. After processing, soybean oil is delivered to distributers/whole-sellers to reach to
final consumers through retailers across the country. Although soybean oil is the main
product in soybean oil production, byproducts of soybean meal and hulls are the main
driving force for soybean oil production since it contributes 60-62% of the total revenues
received by the processor. Soybean meal and hulls can be sold directly to poultry production
centers, beef, and dairy cattle producers at a better price.
Processed product whole sellers: There were three whole-sellers/distributers from Addis
Ababa to receive the soybean oil from health care food manufacturer plant and to sell the
oil to retailers across the country.
Retailors: Received soybean oil from the three whole sellers and delivered to consumers.
Consumers: Rural and urban dwellers, poultry production centers, beef, and dairy cattle
producers were the main consumers after soybean has been processed. Soybean oil can be
received from retailers in different corners of the country. However, soybean meal and hulls
were directly received from the processor by poultry production centers, beef and dairy
cattle producers for direct consumption as well as ration formulation.
4.3.2 Support service providers and their functions
Along soybean value chain in the overall process of this study, the following are the lead
organizations to provide support to primary actors at different stages of the value chain.
66
Table 4.15 Soybean value chain supporters and their functions
Indirect/supportive actors Functions
Development agents Delivery of advisory service and follow up
Training of farmers
Field day preparation on model farmers’ farm & FTC
Agriculture offices Provision of advisory service
Training of farmers and development agents
Field supervision and follow up
Technical support and facilitation to cooperatives and
union
Trade and industry office Provision of trading license
Kebele administration Community mobilization and facilitation
District administration Facilitation and controlling
Mama union Provides market information to basic cooperatives
Orientation to basic cooperative and members on the
quality of soybean product and packaging systems
Pawe Agricultural
Research Center
Development and adaptation of improved soybean varieties
On farm demonstration of improved soybean varieties
Establishment of CBSM to alleviate seed shortage
Provision of technical training and advisory service to DAs,
experts, cooperatives/unions and different stakeholders
Field day event preparation and experience sharing
programs
Government
market
regulators
(Export
marketing)
Ministry of
trade
Providing trading license to exporters
Ministry of
agriculture
Provides quality, safety and healthy certification for export
Ministry of
revenue
Clearing and documentation
National
Bank of
Ethiopia
NBE provides export permits through commercial banks
and control hard currency repatriation
facilitate & provide guarantee to exporter payment systems
Transport
service
providers
Cross
boarder
transporting
companies
Transporting the products to markets in the neighboring
country or to ports
Local
transporters
Transport the products from farmers plot to local markets
Transport the products from local markets to Almu ECX
and
to Central ECX Addis Ababa
Financial
service
providers
Saving and
credit
association
and Banks
Credit services
Solve the financial problems of farmers, traders and
exporters
Source: Own survey result, 2020
67
As indicated in Figure 4.3, primary actors connect with their primary functions. The Figure
also shows the roles of support services providers and the types of support for primary actors
at each stage of the value chain.
Source: Adapted from Almaz Giziew, 2018
Figure 4.3 Value Chain Actors, Functions and Support service providers
Input Supplier Producer Transporter Consumer Trader
Coops. /union Private traders, Research centres & Farmers
MBI
Farmers
Workers
Vehicle owners
Drivers
Cart drivers
Exporters, Whole sellers, Local traders, Cooperatives/unions, Processors, retailors & shops
Rural dwellers & Urban dwellers
Poultry centres
Beef & dairy cattle producers
Inputs transport
ation Production consum
ption
Trading/whole selling
exporting, processing & retailing,
processing,
Fertilizer, inoculants, chemicals & improved seeds, Technical trainings
Loading,
unloading, take
the produce to
Almu ECX,
central ECX,
plants, ports on
cart, vehicle &
aircraft
Land preparation, chemical application, Ploughing, Sowing, Weeding, harvesting, Threshing
Sorting,
Packaging,
Grading,
Transporting,
buying, selling
to the local
market or
abroad
Buying the product for consumption and ration formulation
GOs, Pawe Agricultural Research
Centre, BoARD, District trade &
industry office, Mama union, &
MBI, N2-Africa and AGRA projects,
BG saving and credit association
Transport
service
providers
GOs, Mama union& Almu and
central Ethiopian Commodity
Exchanges, Commercial Bank
of Ethiopia, Ministry of trade &
Industry, Ministry of Revenue
Value chain functions
Value chain supporters
Value chain operators
68
4.3.3 Map of soybean value chain
Value chain mapping is the first step to conduct value chain analysis. It is a system of
sketching to show the product flow from producers up to final consumers bypassing
different stages. The map indicates the activities of actors, their relationship, and value-
added at each stage of the value chain.
Source: Own sketch from survey result, 2020
Figure 4.4 Map of soybean value chain
Producers
Cooperatives Local traders
Consumers
Investors/
other
farmers
Mama
union
Pawe whole sellers
Ethiopian Commodity Exchange
Exporters Processor
Oil whole sellers Oil retailors
Production
Value chain actors Value Chain supporters Value Chain functions
Agriculture
office
Cooperatives Traders
Consumption
Input
supply
GOs, PARC, BoARD,
Trade & industry
office, Mama union, &
MBI, BG saving and
credit association
GOs, NGOs, BoARD,
Cooperatives/Mama
union, PARC, DAs
and experts, BG
saving and credit
association
GOs, Mama
union& Almu and
central Ethiopian
Commodity
Exchanges,
Commercial Bank
of Ethiopia,
Ministry of trade
& Industry,
Ministry of
Revenue,
Ministry of
agriculture &
Transport service
providers
Marketing
Trading/wh
ole selling
Exporting
Processing
Retailing
69
4.3.4 Marketing channels along soybean value chain
A marketing channel is a pathway in which the product is moving from the point of
production origin to the final consumption destination. In the study area, producers were
providing their soybean product to five major buyers with varying degrees of volumes. All
soybean products were collected by Pawe whole-sellers, local traders, and cooperatives and
then delivered to ECX at Almu branch except the first channel in which producers directly
sold to other farmers or investors for production purposes. After the product has been
collected by ECX, it was sold to exporters and processors at Addis Ababa by central ECX
based on its marketing rules. According to the data collected from traders and farmers, the
local traders and district whole sellers sold their collected product to Exporters and
processors through ECX. The rest value chain actors sold the product to exporters only
through ECX.
In the overall process of this study, nine marketing channels were identified. A total volume
of 3823.21 quintal soybean was produced by sample households in the 2018/19 cropping
season. From this production volume, 93.11% (3559.95qt) was provided to the market and
sold along the following nine marketing channels. The rest 6.89% (263.26qt) was reserved
for next season production and local consumption by the households.
Channel I: Farmer _consumer/investor: This channel was the shortest among nine
marketing channels when producers directly sold to other farmers and investors for
production purpose. In this channel, producers sold the produced quality product which can
be used as a seed for further production and a good price was received by producers. This
channel represents a 4.94% share among all other channels.
Channel II: Farmer_ ECX_ Exporter: It is also the second shortest channel in which the
farmer directly provides to ECX and then to the exporter. Only 1.40% of the product volume
was sold in this channel. This channel was considered as a good channel for producers since
it has good return due to no intermediaries in the chain. Because farmers can directly provide
to ECX by considering some standard quality parameters.
Channel III: Farmer _ Local trader _ ECX _ Exporter: On this channel, the local traders
were collected soybean from farmers on each kebele and delivered to ECX and then sold to
70
exporters. This channel was the main marketing channel in which the largest volume of
soybean was sold on it which represents 71.68% of share from all marketing channels.
Channel IV: Farmer _ WSPawe _ ECX_ Exporter: Along this channel, farmers were
provided their soybean to Pawe district whole sellers. On this marketing channel, 13.26%
of the total volume was sold which represents the second-largest volume next to channel III.
Then Pawe whole sellers sold the collected product to exporters through ECX.
Channel V: Farmer _ Coops. _ Union _ ECX _ Exporter: Only 2.42% of soybean
production was sold along this marketing channel.
Channel VI: Farmer _Local trader _ ECX_ Processor _ Consumer: On this marketing
channel, the local traders purchased soybean from producers and resale it to a processor
through ECX. After the oil has been extracted by the processor, soybean meals and hulls
were directly sold to poultry production centers, beef and dairy cattle producers for direct
consumption as well as ration formulation. This channel represents 4.41% of the volume
sold.
Channel VII: farmer _ WSPawe _ ECX _ Processor _ Consumer: Pawe whole sellers
purchased soybean from farmers and resale it to a processor through ECX as channel VI.
Byproducts of soybean meal and hulls were directly sold to poultry production centers, beef,
and dairy producers and represent 0.75% of share among other channels.
Channel VIII: Farmer _ Local trader _ ECX _ Processor _ WSoil _ RToil _ Consumer:
This channel is the largest and represents 1.89% share along the nine channels. In this
channel, the processor has been extracted soybean oil and delivered this oil to whole-
sellers/distributers from Addis Ababa further to provide to retailers in different corners of
the country. Finally, consumers purchased soybean oil from their nearby retailers.
Channel IX: Farmer _ WSPawe _ ECX _ Processor _ WSoil _ RToil _ Consumer: On this
channel also, a processor purchased soybean from central ECX delivered by Pawe whole
sellers and extracted soybean oil further to distribute the oil to whole sellers. Retailers
received the oil from whole sellers and then sold to consumers. This marketing channel
accounts only 0.32% share along all marketing channels.
71
Channel I: Farmer consumer/investor (4.94%)
Channel II: Farmer ECX exporter (1.4%)
Channel III: Farmer L. trader ECX Exporter (71.68%)
Channel IV: Farmer WSPawe ECX Exporter (13.26%)
Channel V: Farmer Coops. Union ECX Exporter (2.42%)
Channel VI: Farmer L. trader ECX Processor Consumer (4.41%)
Channel VII: Farmer WSPawe ECX Processor Consumer (0.75%)
Channel VIII: Farmer L. trader ECX Proc WSoil RToil Consumer (1.89%)
Channel IX: Farmer WSPawe ECX Proc. WSoil RToil consumer (0.32%)
Figure 4.5 Soybean value chain marketing channels
Producers
Consumers
Investors/oth
er farmers
(4.94%)
Mama union
Local traders (77.68%) Cooperatives
(2.42%)
Pawe whole
sellers (13.26%)
Ethiopian Commodity Exchange
(95.06%)
Exporters (92.63%)
3134.55 qt
Processor (7.37%) 249.4 qt
Oil whole sellers (30%) or
3117.5 liters
Oil retailors (30%)
70% (SBM & hulls)
1.4%
72
4.4 Marketing Margin Analysis
4.4.1 Production cost of soybean in the study area
Cost identification and profit estimation is one of the tasks of value chain analysis. Before
calculating the profit shares of each actor, each cost type has to be identified. As the survey
result indicated in Table 4.16 below, the total production cost of the sample households was
9264.28-birr ha-1. On average, sampled households obtained 12.05 qt yield ha-1. As the result
depicted in Table 4.16, the production cost of the household per quintal was 768.82 birr.
Households were received a total revenue of 13,556.37 birr per hectare with a gross profit
of 4292.09 birr by considering family labor. By excluding family labor in the production
stream, farmers can receive a gross profit of 6985.30 birr per hectare. Weeding, harvesting,
and land preparation-sowing are ranked 1st, 2nd, and 3rd in terms of costs incurred for
soybean producers in the study area. The highest cost was incurred at the time of weeding
followed by harvesting. As the results of this study, 1607.47- and 1580.60-birr were incurred
per hectare for labor and chemical costs respectively to control weeds. Households incurred
1069.91 birr per hectare for labor cost at the time of harvesting and 701.99-birr was incurred
during land preparation to sowing.
73
Table 4.16 Production cost of soybean producers
List of cost types Cost in Birr per ha Percent
Seed purchase 1352.38 14.60
Fertilizer purchase 1369.42 14.78
Chemical purchase 1580.60 17.06
Family Labor 2693.21 29.07
Land preparation – sowing 97.20 1.05
Weeding 1071.66 11.57
Harvesting 599.49 6.47
Threshing 59.00 0.64
Damping 73.27 0.79
Cleaning 1.25 0.01
Product transportation 222.80 2.40
Packaging Material 144.00 1.55
Total cost in birr per hectare 9264.28 100.00
Average selling price in birr per quintal 1125.01 Yield in quintal per hectare 12.05 Revenue per hectare of production 13556.37 Gross Profit in birr per hectare 4292.09
Source: Own survey result, 2020
In the study area, labor was one of the main factors of production for soybean, and family
labor took the lion share. As indicated in Table 4.17, the total cost of family and daily labor
was 2693.21- and 1901.87-birr ha-1 respectively. Households were incurred 4595.08-birr ha-
1 for labor cost only which implies that more than half of the production cost for soybean
was labor in the study area.
74
Table 4.17 Labor cost of soybean production for producers
Cost type Unit Family labor cost Daily labor cost Total cost
Land preparation-sowing ETB/ha 604.79 97.20 701.99
Weeding ETB/ha 535.80 1071.66 1607.46
Chemical application ETB/ha 307.08 0.00 307.08
Harvesting ETB/ha 470.42 599.49 1069.91
Damping ETB/ha 306.28 73.27 379.55
Threshing ETB/ha 358.41 59.00 417.41
Cleaning ETB/ha 110.43 1.25 111.68
Total labor cost ETB/ha 2693.21 1901.87 4595.08
Source: Own survey result, 2020
The overall average selling price of soybean was 1125.01 birr per quintal by incurring an
average production cost of 768.82 birr. The majority (83.77%) of respondents were provided
their soybean to local traders and received the least price when they were supplied to them.
The finding by Toure and Wang (2013) also confirmed that producers received the least
price from their Potato product when they sold at the farm gate to village level traders.
Farmers received a profit margin of 356.19 birr per quintal by considering family labor.
However, they can receive a profit margin of 579.69 birr per quintal by excluding the cost
of family labor from soybean production. Farmers received a gross profit of 4292.09- and
6985.30 Birr ha-1 with including and excluding of family labor cost respectively. This
indicates that soybean production is profitable for smallholder farmers in Pawe as well as
the region although the expected return cannot be realized due to insignificant value addition
and less productivity. The finding is similar to Afework Hagos and Adam Bekele (2018)
who found that farmers received a gross profit of 3931.952 Birr ha-1 from soybean in the
2016 marketing season. The result is also in line with Hichaambwa et al. (2014) who
confirmed that soybean production is quite profitable for smallholder producers even with
below-recommended levels of inoculant and fertilizer utilization.
75
Table 4.18 Value addition and margin of producers
Attribute Unit Amount
Yield Qt/ha 12.05
Total production cost ETB/ha 9264.28
Cost Per quintal (Value Addition) including family labor ETB/Qt 768.82
Cost Per quintal (Value Addition) excluding family labor ETB/Qt 545.32
Average Selling Price ETB/Qt 1125.01
Farmer's Margin including family labor ETB/Qt 356.19
Gross Profit including family labor ETB/ha 4292.09
Farmer's Margin excluding family labor ETB/Qt 579.69
Gross Profit excluding family labor ETB/ha 6985.30
Source: Own computation from survey result, 2020
4.4.2 Marketing margin and profit shares of actors in the value chain
The survey result indicated in Table 4.19 shows that the processor received the highest
marketing margin which is 669.26 Birr qt-1 followed by local traders and district grain whole
sellers with the respective marketing margin of 431.56- and 423.25-Birr qt-1. Producers and
cooperatives also received the respective marketing margin of 386.63 and 392.38 Birr qt-1.
Even though the processor received the highest marketing margin, its profit share was the
least (6.64%) among grain traders due to incurring the highest processing and marketing
cost. The finding is consistent with Wondim Awoke and Dessalegn Molla (2018) who found
that potato processors were incurred the highest cost among traders since processors perform
different value adding activities. The highest profit share was received by local traders
which is 377.75 Birr qt-1. This finding agrees with Esayas Negasa and Mustefa Bati (2019)
and Kumilachew Achamyelh et al. (2020) who confirmed that profit margin of traders is
more than that of soybean producer farmers. Producers, Pawe whole sellers and cooperatives
received 23.15%, 23.13% and 22.53% profit shares respectively.
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Table 4.19 Margin & profit shares of actors along soybean value chain
Cost Items Producers Local
traders WSPawe Cooperatives Processor
Sale price (Birr/qt) 1125.01 1523.00 1571.25 1496.67 2229.26
GMM (Birr/qt) 386.63 431.52 423.25 392.38 669.26
% share Market margin 16.79 18.74 18.38 17.04 29.06
Gross cost (Birr/qt) 768.82 1145.25 1215.48 1149.99 2127.17
Gross profit (birr/qt) 356.19 377.75 355.78 346.68 102.09
% share Gross profit 23.15 24.55 23.13 22.53 6.64
Source: Own computation from survey result, 2020
Along the nine marketing channels in the overall process of soybean value chain, channel 3
was selected to estimate value addition and total costs incurred among actors. Channel 3
was selected due to 71.68% of soybean production volume sold along this channel. The
maximum profit margin was received by local or village level traders which is 377.75 birr
per quintal following channel 3. Producers received a profit margin of 356.19 birr per quintal
which indicates that producers were price takers since they were chain actors in the value
chain. Because chain actors cannot influence the selling price within the value chain.
Table 4.20 Gross margin following marketing channel 3
Actors Cost incurred per quintal Sales price in birr per quintal Gross margin
Producers 768.82 1125.01 356.19
Local traders 1145.25 1523.00 377.75
Source: Own computation from survey result, 2020
The survey result indicated in Table 4.21 shows that local traders and producers were added
a gross value of 3.7775 and 3.5619 birr per kilogram respectively. The result revealed that
producers were not benefited from their effort and that is why they were exploited by traders
particularly village-level traders. Local traders received relatively good returns from
soybean without exerting more effort as compared to other actors in the value chain. A total
value of 7.3394 birr per kilogram was added by local traders and smallholder producers
along this marketing channel.
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Table 4.21 Distribution of value addition among major actors
Value chain
Sales price in birr per kg 11.2501 15.23
Cost of raw material birr per kg 7.6882 11.4525
Gross value added in birr per kg 3.5619 3.7775
% of total value-added 48.5312 51.4648
Source: Own computation from survey result, 2020
Total value added = 7.3394 Birr per kilogram
Health Care Food Manufacturer PLC: It is the only soybean oil processing plant in
Ethiopia located at Addis Ababa around Kaliti. The plant is producing oil from soybean
besides to other oils and sales the residues of soybean after the oil has been extracted. The
hexane extraction process is the most commonly used approach in soybean oil processing
due to its high oil recovery and lower production cost. A well-adjusted interest rate and high
annual soybean oil production capacity can realize the profitability of soybean oil
production industries (Cheng & Rosetrater, 2017). According to the data obtained from the
factory, soybean oil is the main product, and soybean meal (SBM) and hulls are the
byproducts in the overall process of soybean oil production. However, out of the total
revenues received from soybean processing, soybean oil contributes only 38-39%, and the
majority (60-62%) of the revenues received from the byproducts/residues of soybean meal
and hulls. As the result indicated from Table 4.22, from 100kg of soybean grain, 12.5 litters
of soybean oil can be extracted since 8kg soybean grain is needed to produce a litter of
soybean oil. At the same time after the oil has been extracted, SBM is produced and about
1382.14 birr can be received by selling the byproducts to poultry production centers, beef,
and dairy cattle producers with a better price. This implies that the processor can receive a
total revenue of 2229.26 birr per 100kg soybean grain after processing by incurring a gross
cost of 2127.16 birr. The processor received a marketing margin and gross profit of 669.26
and 102.10 birr per 100kg soybean grain respectively.
Soybean purchase price, selling price of soybean oil, and soybean meal are the determinant
factors in the overall process of soybean oil extraction. The finding is similar to Cheng &
Rosentrater (2017) who found that soybean meal is regarded as an important driving force
for soybean oil production. Even though soybean oil is the main product for soybean oil
Producer Local trader
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processors, soybean meal is considered as the main driving force for soybean oil production
industry due to its higher productivity and higher revenues than soybean oil.
Table 4.22 Production cost of soybean oil processor
Cost items to process 100kg soybean grain Total cost incurred in birr
Soybean purchase price 1560.00
Labor cost 9.13
Chemical cost 96.63
Gas oil cost 36.25
Utilities cost 31.13
Bottles and labels 126.50
Packaging 188.13
Transport cost 20.00
Loading/unloading 12.00
Tax and fee 40.00
License fee 6.67
Telephone cost 0.74
Total cost 2127.16
Sale of 12.5 litters soybean oil 847.12
Sale of Soybean meal & hulls 1382.14
Total sale in birr 2229.26
Market margin 669.26
Gross profit 102.10
Source: Own survey result, 2020
Perfuming different value-adding activities increases the value of the product in the market
and can be received a good return. Products with significant value addition can well
competitive in domestic as well as international markets. The survey result indicated in
Table 4.23 showed that the highest value was added by the processor which is 567.17 birr
by processing 100kg soybean. Pawe whole-sellers, local traders, and cooperatives added
67.48, 53.77, and 45.70 values respectively on soybean from marketing of 100kg soybean.
Accordingly, most of the value-adding activities performed by the processor was form
values and to some extent place and time value after processing. However, other actors
perform time and place value by storing and transporting the product respectively. The only
form value activity performed by these actors is packing which was not significant.
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Table 4.23 Value addition & margin by soybean grain traders & processor
Cost Items Unit Local traders WSPawe Cooperatives Processor
Purchase price ETB/qt 1091.48 1148.00 1104.29 1560.00
Production cost ETB/qt 0.00 0.00 0.00 299.63
Transport ETB/qt 13.89 22.00 20.00 20.00
Loading/unloading ETB/qt 8.11 7.80 10.00 12.00
Packaging cost ETB/qt 12.49 12.38 12.00 188.13
Tax and fee ETB/qt 10.00 15.00 0.00 40.00
License fee ETB/qt 5.08 6.52 0.00 6.67
Storage cost ETB/qt 0.00 0.00 0.00 0.00
Telephone cost ETB/qt 0.75 0.33 0.25 0.74
Warehouse rent ETB/qt 3.45 3.45 3.45 0.00
Total cost ETB/qt 1145.25 1215.48 1149.99 2127.17
Sales price ETB/qt 1523.00 1571.25 1496.67 2229.26
Market margin ETB/qt 431.52 423.25 392.38 669.26
Value added ETB/qt 53.77 67.48 45.70 567.17
Source: Own computation from survey result, 2020
After processing, the processor distributes the soybean oil to consumers through its
distributers/whole sellers. The factory has three distributors/whole-sellers at Addis Ababa.
The whole sellers received the soybean oil from the factory at the price of 67.77 birr per
litter and sold to retailers by 76.07 birr per litter. Finally, consumers purchased the oil with
an average price of 85 birr per litter. Whole sellers and retailors added a value of 3.30 and
3.71 birr per litter and 8.30 and 8.93 birr per litter marketing margin respectively.
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Table 4.24 Value addition and margin by soybean oil traders
Cost Items Unit Oil whole seller Retailor
Purchase price ETB/lit 67.77 76.07
Processing cost ETB/lit 0.00 0.00
Transport ETB/lit 0.28 0.50
Loading/unloading ETB/lit 0.32 0.32
Packaging material ETB/lit 0.00 0.00
Tax and fee ETB/lit 2.20 2.00
License fee ETB/lit 0.25 0.20
Storage cost ETB/lit 0.00 0.56
Telephone cost ETB/lit 0.25 0.13
Warehouse rent ETB/lit 0.00 0.00
Total cost ETB/lit 71.07 79.78
Sales price ETB/lit 76.07 85.00
Market margin ETB/lit 8.30 8.93
Value added ETB/lit 3.30 3.71
Source: Own computation from survey result, 2020
4.5 Econometrics Analysis
4.5.1 The determinant factors affecting soybean market supply
Multiple linear regression model (OLS) was employed to analyze the determinant factors
affecting the quantity of soybean supply to the market. As the result indicated in Table 4.25,
all the coefficients of the independent variables were indicated which shows that the amount
of change in the quantity of soybean supply for a unit change of the listed independent
variables. The coefficient of determination (R2) that shows the explanatory power of the
model also indicated. According to the result of the OLS model, the coefficient of
determination (R2) was 0.8937. This implies that 89.37% of the variation on the dependent
variable i.e. quantity of soybean supply to the market is due to the independent variables
that are included in the model. The F-statistics calculated value (F = 118.84, p > 0.0000)
indicated that the overall model is significant at less than 1% level of significance.
Multicollinearity problem was checked through variance inflation factor (VIF) for
continuous explanatory variables and results obtanined ranges from 1.01 to 1.97 which
shows that there is no problem of multicollinearity among explanatory variables.
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Multicollinearity for dummy/categorical variables was tested through contingency
coefficient (CC) and results show that no problem of multicollinearity between the
explanatory variables that are included in the model. Similarly, heteroscedasticity problem
was tested by using Breusch-Pagan/Cook-Weisberg test. Since the result showed the
existence of heteroscedasticity, it was corrected through robust regression.
Table 4.25 Regression results of factors affecting quantity of soybean market supply
Variables Coefficients Robust Std. Err. t-value P>t
PRODUCTIVITY 0.415*** 0.054 7.64 0.000
LAGGED PRICE 2.539*** 0.256 9.91 0.000
EDLEVEL 0.029 0.036 0.82 0.413
AGE -0.131 0.111 -1.19 0.237
FEXTCONT 0.065* 0.037 1.79 0.076
DISTANCE -0.036* 0.018 -1.93 0.055
COOPMB 0.096 0.060 1.59 0.113
CREDIT 0.232*** 0.087 2.66 0.008
SEX 0.128 0.227 0.56 0.573
TRAINING 0.085 0.056 1.50 0.136
OFF-NONFAM 0.036 0.065 0.56 0.577
FAMSIZ -0.117*** 0.043 -2.74 0.007
MKTINFN 0.415*** 0.071 5.84 0.000
SOYFAMEXP 0.089** 0.036 2.48 0.014
CLAND 0.361*** 0.056 6.42 0.000
Constant -16.863*** 1.818 -9.28 0.000
N = 228, R2 = 0.8937, ***, **, * are significant levels at 1%, 5% and 10% respectively
Productivity (PRODUCTIVITY): It is a continuous variable which refers to the quantity
of soybean produced in quintals per hectare in 2018/19 production season. Productivity
influences the quantity of soybean supply to the market positively and significantly at less
than 1% level of significance as hypothesized. The amount of soybean supplied to the
market can be increased if the households produce more yield per hectare. The result of
multiple linear regression (OLS) model indicated that quantity of soybean market supply is
increased by 0.415 quintal as the productivity increased by 1 quintal. This implies that
increment of productivity positively enforces producers to produce soybean intensively and
this leads to increase quantity market supply. The finding is similar to Mengistu Berhe et al.
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(2019) who found that quantity of sesame market supply increased by 3.499 quintals for a
unit increase of sesame productivity.
Lagged price (LAGGED PRICE): It is a continuous variable measured in terms of birr per
quintal. The lagged price of the commodity affects positively and significantly the quantity
of soybean supply to the market at less than 1% level of significance as expected. Good
price of soybean in the previous year attracts farmers to produce more further to supply
more outputs to the market in the next year by allocating more of their lands for soybean.
As the results of the OLS model, the quantity of soybean supply is increased by 2.539
quintals as the market price of the crop in the previous year increased by 1 birr per kilogram.
The finding is similar with Tadie Mirie and Lemma Zemedu (2018) and Nugusa Abajobir
(2018) who confirmed that teff and maize marketed surplus positively and significantly
correlated with the lagged price.
Frequency of extension contact (FCONTACT): It is a categorical variable which refers
to the frequency of contacts of sampled farmers with development agents during 2018/19
cropping season. Frequency of extension contact positively and significantly affected the
quantity of soybean market supply at 10% significant level as hypothesized. This might be
due to knowledge and skill improvement of farmers on farming operations and this leads to
increase production and productivity. Farmers who frequently contact with development
agents can able to get different information on different soybean varieties with full
recommended packages and they can be convinced to apply these technologies. Use of
improved soybean varieties with recommended packages increases farmers’ production and
productivity and this leads to increase the quantity soybean market supply. The results of
multiple linear regression model indicates that quantity of soybean market supply is
increased by 0.065 quintal as frequency extension contact increased by one. The finding
agrees with Sultan Usman (2016) who found that the existence of positive relationship
between wheat market supply and extension service. The result is also in line with
Kumilachew Achamyelh et al. (2020) who confirmed positive correlation of extension
contact and market participation of soybean producer farmers with surplus outputs. The
findings of Nugusa Abajobir (2018) also confirmed that the quantity of maize market supply
is increased by 1.404 quintals as the number of extension contact increased by one.
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Market distance (DISTANCE): It is a continuous variable that refers to the length of time
taken to reach the nearest market for the household measured in hours. Distance to the
nearest market affects the quantity of soybean supply negatively and significantly at 10%
significant level as expected. Results of the OLS model indicated that the quantity of
soybean market supply is decreased by 0.036 quintal as the travel time to reach the nearest
market increased by an hour. Households who are far from markets forced to pay more costs
for input and product transportation as well as forced to travel for long hours on foot and
this reduces their level of participation on production. This might be the reason for limited
supply of soybean for those distant farmers. The finding is consistent with Rehima Mussema
et al. (2013) who confirmed that a minute decrease in walking distance to reach the nearest
market increases households’ market participation by 0.97%. The finding by Amare Tesfaw
(2013) and Tadele Melaku and Ashalatha (2016) confirmed that distance to reach the nearest
market affects quantity of pepper and teff market supply negatively and significantly. The
finding is also in line with Dagnaygebaw Goshme et al. (2018) who found that quantity of
sesame market supply is decreased by 0.24 quintal as distance to the nearest market
increased by a kilometer.
Credit utilization (CREDIT): It is a continuous variable that refers to the quantity of credit
utilized by a household in 2018/19 cropping season measured in birr. The amount of credit
utilization affects quantity of soybean market supply positively and significantly at less than
1% level of significant as expected. Credit can improve farmers’ purchasing power of
agricultural inputs and this leads to increase surplus production of the crop for the market
due to improvement of productivity. Results of OLS model indicated that quantity of
soybean market supply is increased by 0.232 quintal as credit utilization increased by one
birr. The result is consistent with Ali and Awade (2019) who confirmed that having a full
amount of credit positively and significantly influences surplus production of soybean and
market return. The result is also similar to Seven and Tumen (2020) who found that credit
utilization increases farmers’ productivity and this leads to an increase quantity of marketed
surplus.
Family size (FAMSIZ): It was hypothesized that to have either a positive or negative
influence on the quantity of soybean market supply. The model result indicated that family
size affects the quantity of soybean market supply negatively and significantly at less than
1% significant level. As a result of OLS model, the quantity of soybean supply to the market
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is decreased by 0.117 quintal as the number of family size increased by one. This implies
that even though consumption of soybean in the study area is not habited, currently, PARC
promotes soybean dishes by giving training on the nutritional values of soybean and way of
preparing soy foods like porridge, soy milk, bread, and kookis for local consumption. This
opportunity increases the level of soy foods consumption in the area and those households
who have more family members use more soybean for consumption and this may be the
reason for the reduction of quantity of soybean market supply. The finding is similar to
Nugusa Abajobir (2018) and Sultan Usman (2016) who confirmed the quantity of maize
and wheat supply is decreased by 0.379 and 0.05 quintal respectively as the family size
increased by one. The result is also in line with Edosa Tadesa (2018) who found negative
and significant relationships between the quantity of teff marketed and family size of the
household.
Access to market information (MKTINFN): Access to market information is an important
factor for farmers to produce surplus outputs for marketing purposes. Access to market
information influences the quantity of soybean market supply positively and significantly at
less than 1% level of significance as expected. This implies that those farmers who cannot
able to get market price information on a certain commodity, they are not producing more
outputs and cannot allocate more lands for production and this reduces market supply. As a
result of the OLS model, the quantity of soybean supply to the market is increased by 0.415
quintal when farmers have got market information. The finding is consistent with Wondim
Awoke and Dessalegn Molla (2018) who confirmed that the quantity of potato market
supply increased by 7.316 quintals for a household who has accessed market information.
Zamasiya et al. (2014) also found a positive correlation between access to market
information and soybean marketed surplus.
Soybean farm experience (SOYFAMEXP): It is a continuous variable that refers to the
number of years of the households involving in soybean production. Soybean farm
experience of the household influences positively and significantly the quantity of soybean
market supply at less than 5% level of significance as hypothesized. The model result
indicated that the quantity of soybean market supply is increased by 0.089 quintal as the
farm experience of soybean producers increased by a year. This implies that experienced
households have the knowledge and skills of applying agronomic practices and other
farming operations very well. This leads to increase their production and productivity and
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this might be the reason why experienced households supplied more outputs to the market.
The finding is consistent with Modeste et al. (2018) and Tadele Melaku and Ashalatha
(2016) who found that significant and positive relationships between the quantity of market
supply and soybean and teff farm experience. The finding is also similar to Almaz Giziew
(2018) who obtained that onion market supply is increased by 0.0185 quintal as the farm
experience of onion producers increased by a year. This result also agrees with the findings
of Ali et al. (2015) and Tamirat Girma et al. (2017) who found a strong and positive
relationship between farm experience and quantity of sesame and haricot bean marketed.
Landholding size (CLAND): It is a continuous variable which refers to the total lands that
the household cultivated during 2018/19 cropping season measured in hectares. The
availability of more cultivated land affects the quantity of soybean supply to the market
positively and significantly at less than 1% level of significance as hypothesized. Farmers
can allocate more lands for soybean production if they have more cultivated land. Because
farmers are producing different food grains for home consumption besides to cash crop and
cannot allocate lands for soybean if the owned small cultivated land. This might be the
reason why producers supplied less outputs to the market due to small size cultivated land.
The quantity of soybean supply to the market is increased by 0.361 quintal as the amount of
cultivated land increased by a hectare. The finding is similar to Shewaye Abera et al. (2016)
and Wogayehu Abele and Tewodros Tefera (2015) who confirmed that the quantity of
haricot bean market supply increased by 2.1 and 2.03 quintals respectively for a hectare
increase of landholding size for the farm households. The finding by Dagnaygebaw Goshme
et al. (2018) also confirmed that sesame market supply increased by 6.8 quintals for a
hectare increase of land size. The finding of Besufekad Belayneh et al. (2018) indicated that
quantity of common bean market supply increased by 2.97 quintals as cultivated land
increased by a hectare. The finding is also in line with (Edosa Tadesa, 2018, Regasa Dibaba
& Degye Goshu, 2018, Falmata Gezachew, 2018 and Yegon et al., 2015) who confirmed
that positive and significant correlation of land size and quantity of (teff, wheat, and
soybean) market supply.
4.5.2 Factors affecting farmers’ participation on value addition
Probit model was employed for the estimation of factors affecting the probabilities of
sampled households to add values to soybean as indicated in Table 4.26. The Table below
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also contains the marginal effects that are evaluated at the means of all other independent
variables. Marginal effects indicate a unit change in an exogenous variable on the
probability that an individual farmer adds value to his/her soybean product. Pseudo R2
indicated that the explanatory variables included in the Probit model explain a significant
proportion of the variation in soybean producer farmers’ likelihood to add values to soybean.
As the result indicated in Table 4.26, the Probit model explains 40.54% of the variations in
the likelihood of soybean producer farmers to add values on their product.
Table 4.26 Probit estimation of factors influencing value addition
Variables Coefficients Std. Error Marginal effects
(dy/dx) P>|z|
AGE 0.061*** 0.0216 0.0011 0.005
EDLEVEL 0.249 0.1607 0.0047 0.122
DISTNCE -0.486* 0.2902 -0.0091 0.094
FAMSIZ -0.091 0.0866 -0.0017 0.294
TLU -0.053 0.0506 -0.001 0.291
MKTPRICE 0.005*** 0.0014 0.0001 0.001
DISEACON -0.354** 0.1501 -0.0067 0.018
TRAINING 0.348 0.4298 0.0065 0.419
QUPROD 0.087*** 0.0296 0.0016 0.003
IMPSEED 0.307 0.3437 0.0058 0.372
PACKMT 0.379*** 0.133 0.0071 0.004
STORAGE -0.309* 0.1693 -0.0058 0.068
Constant -7.135*** 2.4522 0.004
N = 228, Pseudo R2 = 0.4054, LR Chi2 = 62.21, P > Chi2 = 0.0000, ***, **, * are significant
levels at 1%, 5% and 10% respectively
Age of the household head (AGE): A continuous variable which is the number of years of
the sampled households in the study area. The Probit model indicated in the above table
shows the age of the household head influenced positively and significantly the likelihood
of farmers to add values on soybean at 1% significant level. Aged people are wise and have
good experience with age in the overall process of agricultural production and marketing.
They know relatively better than the youngsters how to increase the value of their products
by performing some value-adding activities to increase the qualities of their product to get
a better price in the market. The result indicates that the likelihood of farmers to add values
to soybean is increased by 0.11% as the age of the household head increased by a year. The
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finding agrees with Kyomugisha et al. (2018) who found that the age of the household
positively and significantly influenced the probabilities of farmers to add values to the
potato at less than 1% significant level.
Market distance (DISTANCE): Distance to the nearest urban market affects negatively
and significantly the likelihood of farmers to add values to soybean product as expected.
This implies that those producers who are far from their nearby markets are less likely to
add values on soybean due to unable to get value adding materials and high cost product
transportation. The result indicates that the likelihood of farmers to add values to soybean
is decreased by 0.91% as the travel time to reach the nearest urban center increased by an
hour. The reason behind here is that those farmers who are near to the urban centers can be
able to get the packaging materials, can prepare relatively good silos/storage conditions for
their product, and have good know-how about the value-adding activities. The finding
concurs with Sultan Usman (2016) who confirmed that the likelihood of farmers to add
values on wheat products is decreased by 0.3% as the distance to the nearest market
increased by a kilometer.
Market price of the commodity (MKTPRICE): It is a continuous variable that refers to
the price of soybean in birr per kilogram in 2019. Selling price affects positively and
significantly the likelihood of farmers to add values to soybean at 1% level of significance
as hypothesized. A good selling price of the commodity has positive implication for the
improvement of product quality by performing some value adding activities. According to
the results of the Probit model indicated above, the probabilities of farmers’ likelihood to
add values on soybean is increased by 0.01% as the selling price of soybean increased by
one birr per kilogram. The finding agrees with Kyomugisha et al. (2018) who confirmed
that selling price positively and significantly influences potato value addition at less than
1% significant level.
Disease constraint (DISEACON): The incidence of diseases on different agricultural
products in general and soybean in particular negatively influences the returns received due
to yield loss and quality deterioration. Disease negatively and significantly affects the
likelihood of soybean producers to add values to soybean at less than 5% level of
significance as expected. The model result indicated that the probabilities of farmers’
likelihood to add values to soybean is decreased by 0.67% as the level of disease incidence
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increased by one level. The finding by Minyahil Kebede and Assefa Gidesa (2016) also
confirmed that leaf blotch and brown spot soybean diseases highly reduce the quality and
quantity of soybean produced. This finding is also similar with Bandara et al. (2020) and
Murithi et al. (2015) who found that diseases negatively and significantly impacted value
addition on soybean due to high deterioration of yield quality and loss of production.
Quantity of soybean produced (QUPROD): The amount of soybean produced in quintals
affects the decision of farmers to participate in value addition positively and significantly at
1% level of significance as expected. As the results of the Probit model, the probability of
farmers to add values on soybean is increased by 0.16% as the quantity of soybean produced
increased by one quintal. This indicates that farmers who produce more outputs of soybean
give more attention to the quality of their product by performing some value-adding
activities like cleaning, Packaging, storing and transporting to sell with a better price for
their customers and to provide directly to ECX. Because soybean is an export commodity
and passes through ECX by considering some standards. To provide the product directly to
ECX, it needs a minimum of 50 quintals with some quality parameters. The finding is
consistent with Orinda et al. (2017) who found that positive and significant relationships
between the quantity of potato produced and farmers’ participation in value addition. The
result also agrees with the findings of Surni et al. (2019) who confirmed that the production
quantity of farmers influences value addition positively and significantly at less than 1%
level of significance.
Packaging material (PACKMT): Packaging material influences the likelihood of soybean
producers to add values to soybean at less than a 1% level of significance as hypothesized.
The availability of appropriate packaging materials is crucial to increase the value of a
product by storing and transporting to different places without quality deterioration. As the
results of the Probit model, the probabilities of farmers to add values to soybean is increased
by 0.71% when farmers have accessed packing materials. The result is in line with
Kyomugisha et al. (2018) who confirmed the existence of a positive and significant
association between packaging materials and potato value addition. This finding is also
similar to Obute et al. (2019) who found that packaging materials have a significant effect
on soybean to keep its quality for a long period of time.
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Storage (STORAGE): The absence of appropriate storage/silos negatively and
significantly influenced farmers’ likelihood to add values on soybean as expected. Storage
plays a significant role to store the products for a long period by keeping the qualities and
this maximizes the time value of the product. The model result depicted that farmers’
likelihood to add values to soybean is decreased by 0.58% when farmers have faced storage
problems. This is in line with Afework Hagos and Adam Bekele (2018) and Prabakaran et
al. (2018) who confirmed that suitable storage condition increases the time value of soybean
products by storing for a long time without losing the nutrient composition. This finding
also agrees with Esayas Negasa and Mustefa Bati (2019) who found that soybean producers
were not maximized the time value of their soybean product due to poor storage conditions.
As the data collected from FGDs, most producers had not good storage conditions and they
were forced to deliver their product immediately after harvest to local traders and this leads
farmers to be a price taker.
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Chapter 5. CONCLUSION AND RECOMMENDATIONS
5.1 Conclusion
The overall findings of this study concluding that producers, local-traders, whole-sellers,
cooperatives/unions, exporters, processors, retailers, and consumers were the primary value
chain actors with assistance of different supporters. Soybean production is a profitable crop
for smallholder farmers in the study area although productivity is far from the national
average that is why 39.53% of cultivated land was allocated for soybean. The contribution
of this soybean sub-sector in the overall economic growth is not as expected since most of
the product was exported as a grain without adding significant values. Local-traders
received the highest profit margin since all producers were chain actors and cannot influence
the selling price that is why they are price takers. More than half of the production costs for
soybean producers was labor and family labor took the lion share. Soybean meal is an
important driving force for soybean oil production due to its higher productivity and return
than soybean oil. According to the results of multiple linear regression (OLS) model,
productivity, lagged price, distance, family size, market information, soybean farm
experience, size of cultivated land, credit utilization and extension contact were the main
determinant factors affecting the quantity of soybean market supply. Results of the Probit
model also indicating age, distance, quantity produced, selling price, disease incidence,
packaging material, and storage were the determinant factors that influence the probabilities
of farmers’ likelihood to add values on soybean in the study area.
5.2 Recommendations
Based on the findings of this study, the following recommendations are relevant to improve
and develop a sustainable and viable soybean value chain that is locally adaptable and
expected to increase competitiveness.
Ethiopia imported large volume of byproducts of soybean oil and others annually by
spending huge money to cover domestic consumption. Because there is only one soybean
oil processing plant in the country at Addis Ababa. Therefore, the government has to give
more emphasis on the establishment of additional soybean oil processing plants on the
91
potential producing parts of the country to satisfy domestic consumption and to save foreign
currency through import substitution.
All producers in the study area are small-scale farmers and most of them provided their
product to local traders with the least price since they are chain actors. Actors have poor
linkages and the product passes different stages to reach processors which are not viable for
both actors. Therefore, producers have to be directly linked to processors through unions to
get the expected return from soybean and to keep sustainable production.
Currently, edible oil factories are under establishment and they designed to cover the
domestic oil consumption by substituting the imported oil and soybean is considered as the
main crop for oil production. However, the current production status is so limited and this
is the main threat to the factories. Therefore, unrestricted and unreserved effort is needed
from producers, experts, researchers and other concerned bodies to increase the status of
soybean production to ensure sustainable domestic oil production.
The result of multiple linear regression analysis indicates that productivity, lagged price,
market information, credit utilization, extension contact, soybean farming experience, and
size of cultivated land influence the quantity of soybean market supply positively and
significantly. Therefore, to increase the volume of soybean market supply, these variables
should get more attention and has to be promoted. Increasing surplus production can be
realized by improving the production and productivity of soybean through the use of
improved varieties with full packages and other recommended agronomic practices with
close assistance of development agents, experts and other concerned bodies.
The decision of farmers to participate in value addition to soybean was influenced by the
quantity of soybean produced, age, market price, diseases, distance, packaging material, and
storage condition of the farm households. Therefore, more emphasis has to be given to each
significant variable by concerned bodies to enhance the contribution of what is expected
from this subsector to ensure sustainable economic growth through surplus soybean
production with significant value-addition that can be competitive in the domestic and
international markets.
92
This study focused on soybean value chain analysis from producers to exporters and
soybean oil processor. The profit and marketing margins of each actor along this value chain
was investigated except exporters. However, there are different soy foods and feed
processing plants besides soybean oil in the country. Therefore, further investigation is
needed to know the processes of feed and food processing and way of delivering the
processed products to consumers as well as to estimate value addition of the processing
industries. Also, future investigation is required to estimate the profit margins of soybean
exporters in the international markets.
93
6. REFERENCES
Addisu Getahun & Erimias Tefera. 2016. Soybean Value Chain Assessment Study in
Northwestern Ethiopia, Metekel Zone. Asian Journal of Agricultural Extension,
Economics & Sociology, 14(4), pp. 1-14.
Afework Hagos & Adam Bekele. 2018. Cost and returns of soybean production in Assosa
Zone of Benishangul Gumuz Region of Ethiopia. Journal of Development and
Agricultural Economics, 10(11), pp. 377-383.
Afouda, I. M., Afouda, I.M., Tama, C., Akpo, I.F. and Yabi, J.A. 2019. Determinants of the
Economic Profitability of Soy Production in North-East Benin. European Journal
of Scientific Research, 154(2), pp. 270-280.
Ali, A., Salawu, A.J. and Sani, R.M. 2015. Factors Influencing Sesame (Sesamun Indicum
L) Marketing in Jigawa State, Nigeria. Journal of Agricultural Extension, 19(2),
pp.126-133.
Ali, E. and Awade, N.E., 2019. Credit constraints and soybean farmers' welfare in
subsistence agriculture in Togo. Heliyon, 5(4), p.e01550.
Almaz Giziew. 2018. Analysis of gender and determinants of market supply of onion in
Dugda District, East Shoa, Ethiopia. Journal of Agriculture and Environmental
Sciences, 3(1), pp.39-55.
Amare Tesfaw. 2013. Determinants of agricultural commodity market supply: A case study
in the upper watershed of the Blue Nile, northwestern Ethiopia. Journal of
Agribusiness and Rural Development, 30(4), pp.243-256.
Badri, . K. A., Tabrizi, Y. E. & Badri, P. K. 2017. Factors Affecting the Value Added of
Agriculture Sector in Selected Developing Countries Emphasising on Human
Capital. Noble International Journal of Social Sciences Research, 2(9), pp. 88-94.
Banda, G.N., Nyengere, J. and Chinkhata, D. 2017. Factors contributing to continued
dependence on family food and income among graduate farmers of School of
Agriculture for Family Independence (SAFI). Journal of Agricultural Extension and
Rural Development, 9(9), pp.202-206.
Bandara, A.Y., Weerasooriya, D.K., Bradley, C.A., Allen, T.W. and Esker, P.D. 2020.
Dissecting the economic impact of soybean diseases in the United States over two
decades. https://doi.org/10.1371/journal.pone.0231141, 15(4), pp. 1-28.
Bellu, L.G. 2013. Value chain analysis for policy making: Methodological guidelines and
country cases for a quantitative approach. Easy Pol Series, (129), p.178.
94
Besufekad Belayneh, Tewodros Tefera & Thomas Lemma. 2018. Determinants of Common
Bean (Phaseolus Vulagris L.) Marketed Surplus Among Smallholder Farmers in
Humbo and Damot Gale Woredas, Southern Ethiopia. Journal of Food Industry,
2(1), pp. 20-29
BGRS. 2019. Annual report of the regional government, unpublished document.
Birhanu Ayalew, Yalew Mazengia and Adam Bekele. 2018. Analysis of Cost and Return of
Soybean Production Under Smallholder farmers in Pawe District, North Western
Ethiopia. Journal of Natural Sciences Research, 8(1), pp.1-8.
Bizualem Assefa & Saron Mebratu. 2018. The role of Ethiopian commodity exchange
(ECX) in crop value chain development in Ethiopia. International Journal of
Business and Economics Research, 7(6), pp.183-190.
Byrne, J. 2018. Feed Navigator, https://www.feednavigator.com Soybean-production-on-
the-rise-in-Ethiopia, retrieved on 21 Augest 2019.
Cheng, M.H.& Rosentrater, K. A. 2017. Economic feasibility analysis of soybean oil
production by hexane extraction. Journal industrial crops and products, volume
108, pp. 777-785.
Christian, S. & Barron, J. 2017. Mapping Actors along Value Chains: Integrating Visual
Network Research and ParticipatoryStatistics into Value Chain Analysis, s.l.:
CGIAR.
Dagnaygebaw Goshme, Bosena Tegegne and Lemma Zemedu. 2018. Determinants of
sesame market supply in Melokoza District, Southern Ethiopia. International
Journal of Research Studies in Agricultural Sciences (IJRSAS), 4(10), pp.1-6.
ECX. 2017.Crop and livestock production, http://www.ecx.com.et/pages/soya beans.aspx,
retrieved on 30Augest 2019
Edosa Tadesa. 2018. Determinants of commercialization of teff crop in Abay Chomen
District, Horo Guduru Wollega zone, Oromia Regional State, Ethiopia. Journal of
Agricultural Extension and Rural Development, 10(12), pp.251-259.
Engida Gebre, Kusse Haile and Agegnehu Workye. 2019. Value Chain Analysis of Sesame
the Case of Bench Maji Zone, Southwest Ethiopia. Journal of Agriculture and
Crops, 5(11), pp.226-236.
Esayas Negasa and Mustefa Bati. 2019. Analysis of Soybean Value Chain in Buno Bedele
Zone, South Western Ethiopia. Ethiopian Journal of Environmental Studies &
Management, 12(5), pp.509-519.
95
Esnard, R. 2016. Institutional factors affecting value added by agricultural cooperatives in
St. Lucia (Doctoral dissertation, Lincoln University), 102pp.
Falmata Gezachew. 2018. Factors Affecting Marketing Intensity of Wheat Growers in
Southeastern Ethiopia. Journal of Agricultural Science andFood Research , 9(1), pp.
1-6.
FAO. 2018. Market and value chain analysis of selected sectores for diversification of rural
economy and womens' economic empowerment, Central Asia: UN.
FAOSTAT. 2019. Agricultural production and trade, http://www.fao.org/faostat/en/
retrieved on 29 Augest 2019.
Frederick, S. 2016. Global value chains, https://globalvaluechains.org/concept-tools
retrieved on1 September 2019.
Gereffi, G. 1999. International trade and industrial upgrading in the apparel commodity
chain. Journal of international economics, 48(1), pp.37-70.
Gereffi, G. and Fernandez-Stark, K. 2011. Global value chain analysis: a primer. Center on
Globalization, Governance & Competitiveness (CGGC), Duke University, North
Carolina, USA.
Gregory, D. 2016. Food System Framework: a Focus on food sustainability, London:
Institute of Food Science Technology.
Gujarati. 2004. Basic Econometrics. 4th ed. s.l.:The McGraw−Hill Companies.
Haregitu Nitsuh. 2019. Market Chain Analysis of Teff (Eragrostistef): The Case of Dejen
District, East Gojam Zone, MSc Thesis, Harromaya University, Ethiopia, 78pp.
Henderson, J., Dicken, P., Hess, M., Coe, N. and Yeung,.H., W. 2002. Global Production
Networks and the Analysis of Economic Development. Review Journal of
International Political Economy, 9(3), pp. 436-464.
Hichaambwa, M., Chileshe, C., Chimai-Mulenga, B., Chomba, C. and Mwiinga-Ngcobo,
M. 2014. Soybean Value Chain and Market Analysis. Indaba Agricultural Policy
Research Institute (IAPRI), Final Draft Report, p.67.
IITA-IFAD. 2010. Value Chain Analysis: Analytical toolkit and approaches to guide the
development of sustainable African Agrifood Chains, s.l.: Research to Nourish
Africa.
96
Kerr, F., Tulloch, C. & Roos, G. 2015. Using value chain mapping to build comparative
advantage, SouthAustralia: South Australia Economic Development Board.
Khojely, D. M., Ibrahim, S. E., Sapey, E. & Han, T. 2018. History, current status, and
prospects of soybean production and research in sub-Saharan Africa. The crop
journal , 3(6), pp. 226-235.
Kolade, O. and Harpham, T. 2014. Impact of cooperative membership on farmers' uptake
of technological innovations in Southwest Nigeria. Development Studies Research.
An Open Access Journal, 1(1), pp.340-353.
Kumilachew Achamyelh, Zekariyas Shumeta, Abush Tesfaye & Mesfin Hailemariam.
2020. Soybean (Glycine max (L.) Merril) Value Chain Analysis in case of Jimma
Zone, Southwestern Ethiopia. International Journal of Economic and
BusinessManagement , 8(1), pp. 1-10.
Kyomugisha, H., Sebatta, C. and Mugisha, J. 2018. Potato market access, marketing
efficiency and on-farm value addition in Uganda. Scientific African, 1, p.e00013.
Lehr, H. & Sertse, Y. 2018. Value chain analysis of pulses and oilseeds from Ethiopia, s.l.:
Knowledge to value.
Magesa, M.M., Michael, K. and Ko, J. 2020. Access and use of agricultural market
information by smallholder farmers: Measuring informational capabilities. The
Electronic Journal of Information Systems in Developing Countries, pp.1-21.
Mapanga, A., Miruka, C. & Mavetera, N. 2017. Using Social Network Theory to Explain
Performance in Value Chains. Taipei, Taiwan.
Meijerink G, & Dawit Alemu. 2014. Formal institutions and social capital in value chains:
The case of the Ethiopian Commodity Exchange, Food Policy.
Mekonnen Hailu & Kaleb Kelemu. 2014. Trends in Soy Bean Trade in Ethiopia. Research
Journal of Agriculture and Environmental Management, 9(3), pp. 477-484.
Mengistu Berhe, Worku Tessema, Girma Gezimu, Kahsay Tadesse and Mewael Kiros.
2019. Value chain analysis of sesame (Sesamum indicum L.) in Humera district,
Tigray, Ethiopia. Cogent Food & Agriculture, 5(1), p.1705741.
Meyer, F., Traub, L.N., Davids, T., Chisanga, B., Kachule, R., Tostão, E., Vilanculos, O.,
Popat, M., Binfield, J. and Boulanger, P. 2018. Modelling soybean markets in
Eastern and Southern Africa. JRC Technical Reports.
97
Ministry of Trade and Industry. 2019. Annual import and export reports of unpublished
document.
Minyahil Kebede & Assefa Gidesa. 2016. Survey and Identification of Diseases on Major
Crops of Assosa and Kamashi Zones, Ethiopia. Galore International Journal of
Applied Sciences and Humanities, 1(1), pp. 27-31.
Mitchell, J., Coles, C. & Keane, J. 2009. Trade and Poverty in Latine America :Upgrading
Along Value Chains: Strategiesfor Poverty ReducUCtion in Latin America, s.l.:
COPLA.
Modeste, M., Mulyungi, P., Wanzala, F.N., Eric N., & Aimable, N. 2018. Effect of social-
economic factors on profitability of soya bean in Rwanda. International Journal of
Scientific & Engineering Research, 9(9), pp. 828-833.
Murithi, H. M., Beed, F., Tukumuhabwa, P., Thomma, B.P.H.J. & Joosten, M.H.A. 2015.
Soybean production in eastern and southern Africa and threat of yield loss due to
soybean rust caused by Phakopsora pachyrhizi. Review Journal on Plant Pathology,
pp. 1-13.
Musba Kedir. 2019. Impact of Soybean (Belesa-95) Variety on Income among Smallholder
Farmers in Bambasi Woreda, Benishangul Gumuz Regional State. Greener Journal
of Agricultural Science, 9(2), pp. 119-137.
Nugusa Abajobir. 2018. Analysis of Maize Value Chain: The Case of Guduru Woreda ,
Horro Guduru Wollega Zone of Oromia Regional State, MSc Thesis, Harromaya
University, Ethiopia, 107pp.
Nyongesa, D., Mabele, R.B., Mutoni, C.K. and Esilaba, A.O. 2018. An economic analysis
of gender roles in soya bean value addition and marketing in Kenya: a case of
smallholder farms in Western Kenya. International Journal of Agricultural
Resources, Governance and Ecology, 14(3), pp.237-259.
Obute, J. O., Irtwange, S. V. & Vange, T. 2019. Effect of Packaging Materials and Storage
Periods on the Protein Content of Three Soybean Varieties from Makurdi, Benue
State, Nigeria. International Journal of Plant & Soil Science, 30(3), pp. 1-7.
Opolot, H.N., Isubikalu, P., Obaa, B.B. and Ebanyat, P. 2018. Influence of university
entrepreneurship training on farmers’ competences for improved productivity and
market access in Uganda. Cogent Food & Agriculture, 4(1), p.1469211.
Orinda, M., Lagat, J. & Mshenga, P. 2017. Analysis of the Determinants of Sweet Potato
Value Addition by Smallholder Farmers in Kenya. Journal of Economics and
Sustainable Development, 8(8), pp. 1-11.
98
Otu. I, E. & Okibeya F., O. 2018. Economic Analysis of Value Addition by Soybean
Processing Firms in Cross River State Central Agricultural Zone, Nigeria.
International Journal of Agriculture Innovations and Research, 6(6), pp. 277-279.
Pawe District Agriculture Office. 2019. Annual report, unpublished document.
Ponte, S. 2014. Governance and Upgrading in Value Chains: Opportunities and Challenges.
Copenhagen, Copenhagen Businee School.
Porter, M. 1985. The Competitive Advantage: Creating and Sustaining Superior
Performance. NY: Free Press.
Prabakaran, M., Lee, K.J., An, Y., Kwon, C., Kim, S., Yang, Y., Ahmad, A., Kim, S.H. and
Chung, I.M. 2018. Changes in soybean (Glycine max L.) flour fatty-acid content
based on storage temperature and duration. Molecules,23(10), p.2713.
Raikes, P., Friis Jensen, M. and Ponte, S. 2000. Global commodity chain analysis and the
French filière approach: comparison and critique. Economy and society, 29(3),
pp.390-417.
Regasa Dibaba & Degye Goshu. 2018. Factors Affecting Market Supply of Wheat by
Smallholder Farmers in Ethiopia. Journal of Natural Sciences Research, 8(19), pp.
56-64.
Regasa Dibaba, Mesay Yami and Adam Bekele. 2019. Determinants of Productivity and
Technical Efficiency in Soybean Production among Small-Holder
Farmers. International Journal of Agriculture & Agribusiness, 3(2), pp.227-42.
Rehima Mussema, Belay Kasa, Dawit Alemu & Rashid S. 2013. Analysis of the
Determinants of Small-Scale Farmers’ Grain Market Participations in Ethiopia: The
Contribution of Transaction Costs. Ethiopian Journal of Agricultural Sciience,
Volume 23, pp. 75-94.
Roko, L.P. and Opusunju, M.I. 2016. Value chain and performance in agro allied small and
medium scale enterprise in Sokoto state, Nigeria. International Journal of Business
and Social Research, 6(9), pp.8-19.
Roy, Ranjan, M. Shivamurthy, and Rama B. Radhakrishna. 2013. Impact of value addition
training on participants of farmers training institutes. World Applied Sciences
Journal, 22(10), pp. 1401-1411.
Saripalle, M. 2018. Determinants of profitability in the Indian logistics industry. Int. J.
Logistics Economics and Globalisation, 7(1), pp. 13-27.
99
Seven, U. and Tumen, S. 2020. Agricultural credits and agricultural productivity: Cross-
country evidence.in Togo. Heliyon, 5(4), p.e01550.
S. Grace, T. & Fridah, T. 2016. Factors Affecting Value Addition to Tea by Buyers within
the Kenyan Tea Trade Value Chain. International Journal of Humanities Social
Sciences and Education, 3(2), pp. 133-142.
Shewaye Abera, Dawit Alemu & Lemma Zemedu. 2016. Determinants of Haricot Bean
Market Participation in Misrak Badawacho District, Hadiya zone, SouthernNations
Nationalities and Peoples Regional State, Ethiopia. Ethiopian Journal of
Agricultural Science., 26(2), pp. 69-81.
Shurtleff, W. & Aoyagi, A. 2009. History of soybeans and soy foods in Africa. 1st ed. USA:
Soyinfo Center.
Simatupang, T.M., Piboonrungroj, P. and Williams, S.J., 2017. The emergence of value
chain thinking. International Journal of value chain management, 8(1), pp.40-57.
Sopov, M. and Sertse, Y. 2015. Investment opportunities in the Ethiopian. Soy sub-sector,
Business Opportunities Report Soy, 9, pp.1-32.
Stein, C. and Barron, J. 2017. Mapping actors along value chains: Integrating visual network
research and participatory statistics into value chain analysis (Vol. 5). International
Water Management Institute (IWMI). CGIAR Research Program on Water, Land
and Ecosystems.
Straková, J., Rajiani, I., Pártlová, P., Váchal, J. and Dobrovič, J. 2020. Use of the Value
Chain in the Process of Generating a Sustainable Business Strategy on the Example
of Manufacturing and Industrial Enterprises in the Czech
Republic. Sustainability, 12(4), p.1520.
Sultan Usman. 2016. Analysis of Wheat Value Chain: The Case of Sinana District, Bale
Zone, Oromia Region, MSc Thesis, Harromaya University, Ethiopia, 127pp.
Surni, Padangaran, A. M., La Ola, T. & Saediman, H. 2019. Determinants of Value Addition
in Sago Processing in Southeast Sulawesi, Indonesia. IOSR Journal of Agriculture
and Veterinary Science (IOSR-JAVS), 12(1), pp. 72-76.
Tadele Melaku & Ashalatha, D. 2016. Determinants of Teff and Wheat Market Suppiy In
Dendi District, West Shoa Zone, Ethiopia. International Journal of Current
Research, 8(10), pp. 40716-40721.
Tadie Mirie and Lemma Zemedu. 2018. Determinants of market participation and intensity
of marketed surplus among teff producers in Dera district of South Gondar Zone,
Ethiopia. Journal of Development and Agricultural Economics, 10(10), pp.359-366.
100
Tamirat Girma, Tewodros Tefera & Deribe Kaske. 2017. Determinants and Resource Use
Efficiency of Haricot BeanProduction in Halaba Special District, Southern Ethiopia.
Journal of Economics and Sustainable Development, 8(17), pp. 12-19.
Taye Melese, Abebe Birara and Tadie Mirie. 2018. Determinants of commercialization by
smallholder onion farmers in Fogera district, South Gondar Zone, Amhara national
regional State, Ethiopia. Journal of Development and Agricultural
Economics, 10(10), pp.339-351.
Tewodros Tefera. 2014. Analysis of chickpea value chain and determinants of market
options choice in selected districts of southern Ethiopia. Journal of Agricultural
Science, 6(10), pp.26-40.
Toure, M. & Wang, J. 2013. Marketing margin analysis of tomato in the district of Bamako,
Republic of Mali. Journal of Agricultural Economics and Development, 2(3), pp.
084-089.
Trienekens, J. H. 2011. Agricultural value chains in developing countries a frame work for
analysis. International food and agribusiness management review, 14(2), pp. 51-77.
UNCTAD. 2016. An INFOCOMM Commodity Profile on soybeans:UNCTAD Trust Fund
on Market Information on Agricultural Commodities, Geneva: Nations unies.
UNIDO. 2009. Agro-Value Chain Analysis and Development: The UNIDO Approach a
staff working paper, Vienna: United Nations Industrial Development Organization.
Urgessa Tilahun. 2015. Empirical Review of Production, Productivity and Marketability of
Soya Bean in Ethiopia. International Journal of u-ande- Service, Science and
Technology, 8(1), pp. 1-6.
United Soybean Board. 2012. Farm to market: a soybean’s journey from field to consumer,
USA: Informa economics.
Varia, N. 2011. Technical Report:Soybean Value Chain, Southern Africa Hub: USAID.
WBCSD. 2011. World Business Council for Sustainable Development,
http://www.org/pages/Edocuments, retrieved on 5 Augest 2019.
Wogayehu Abele and Tewodros Tefera. 2015. Factors Affecting Production and Market
Supply of Haricot Bean in Southern Ethiopia. Journal of Economics and Sustainable
Development, 6(15), pp.103-109.
101
Wondim Awoke & Dessalegn Molla. 2018. Market chain analysis of potato and factors
affecting market supply in West Gojam Zone, Ethiopia. Journal of Development and
Agricultural Economics, 2(11), pp. 43-51.
Wondimu Tefaye & Hassen Beshir. 2014. Determinants of Technical Efficiency in Maize
Production: The Case of Smallholder Farmers in Dhidhessa District of Illuababora
Zone, Ethiopia. Journal of Economics and Sustainable Development, 5(15), pp. 274-
284.
Yegon, P. K., Kibet, L. K. & Lagat, J. K. 2015. Determinants of technical efficiency in
smallhoder soybean production in Bomet District, Kenya. Journal Development and
Agricultural Economics, 7(5), pp. 190-194.
Zamasiya, B., Mango, N., Nyikahadzoi, K. & Siziba, S. 2014. Determinants of soybean
market participation by smallholder farmers in Zimbabwe. Journal of Development
and Agricultural Economics, 6(2), pp. 49-58.
Zamora, E.A. 2016. Value chain analysis: A brief review. Asian Journal of Innovation and
Policy, 5(2), pp.116-128.
102
7. APPENDICES
Appendix Table 1. Livestock conversion factors
Livestock category Conversion factors
Ox 1.1
Cow 0.8
Bull 1.1
Heifer 0.5
Calf 0.2
Sheep 0.09
Goat 0.09
Mule 0.8
Donkey 0.36
Hen 0.01
Source: ILRI (International Livestock research Institute)
Appendix Table 2. Test of multicollinearity for continuous explanatory variables
Variable VIF 1/VIF
LAGPRICE 1.97 0.51
PRODUCTIVITY 1.82 0.55
CLAND 1.45 0.69
SOYFAMEXP 1.23 0.82
FAMSIZ 1.17 0.85
AGE 1.11 0.90
TTIME 1.07 0.93
CREDIT 1.01 0.99
Mean VIF 1.35
Source: Own computation from survey result, 2020
103
Appendex Table 3. Contigency coefficient for dummy/categorical variables
Mkt_inf
n
Ed_
level
Ex_conta
ct
Coops.m/shi
p
sex trainin
g
off/non-
farm
Mkt_infn 1
education
level
0.045 1
Ex_contact 0.178 0.026 1
Coops.m/ship 0.266 -0.065 0.211 1
sex -0.050 0.221 -0.080 -0.099 1
training 0.220 -0.138 0.110 0.084 -
0.018
1
off/non-farm 0.050 0.009 0.230 -0.025 0.004 -0.080 1
Appendix Table 4. ANOVA table for F-statistics
Source SS df MS F Sig. value
Model 127.945 15 8.5296 118.84 0.0000
Residual 15.216 212 0.0718
Total 143.161 227
Appendix 5. Questionnaires and interview guides for different stakeholders
Questionnaire developed for Farmer’s Survey to conduct a study on value chain
analysis of soybean in Pawe district of Metekel Zone /2018/19
Name of district_________________________________
Name of Kebele ________________________________
Name of Household head_________________________
Name of respondent_____________________________
Name of Enumerator ____________________________
Date of Interview _________Date _________Month ____________Year
104
I. Household/respondents’ general information
1. Sex of household head 1= Male 0 = Female
2. Age of household head in years _______________
3. Religion of household head 1 = Orthodox 2 = Muslim 3 =Protestant 4=
Catholic 5 = others (specify ___________)
4. Education level of household head 1 = Illiterate 2 = Read and Write 3 =
primary (1-8) 4 = Secondary (9-10) 5 =
preparatory (11-12) 6 = TVET and above
5. Marital status 1 = married 2 = single 3 = Divorced 4 =
widowed
6. Distance of your residence to reach the nearest market ___Km, time taken on foot in
min__
7. Total family size of the household _______________
Sex category <15 years 15 to 30 years 31 to 65 years >65 years
Male
Female
Total
II. Resource ownership: Land holding size and farming characteristics
1.Total land size operated in ha ________________
Land ownership description Amount in hectare
Owen land
Cultivated land
Grazing land
Fallow land
Land rented in
Land rented out
Land share in
Land shared out
2. Farming experience in years___________________________
3. Soybean farming experience in years__________________
4. Did you use improved soybean varieties in your soybean farming experience? 1 = Yes 2=
No
105
5. If no for Q4. Why? 1 = not available 2 = Lack of awareness 3 = the seed is expensive 4
= not productive 5 = others
6. If yes in question No. 4, what type of varieties that you have used?
1 =TGX 2 = Gishama 3 = Belesa-95
4 = Pawe -1 5 = Pawe-2 6 =Pawe-3 7 = others
7. From question No. 6 which variety is best for you? _____ Why? _________________
8. If yes for Q4 from above, what is the source of seed? 1 = government 2 = PARC 3 = other
farmers 4 = own saved 5 = Others
9. Have you used Inoculant for soybean production? 1= Yes 2 =No, if yes time of use
____E.C
10. If yes for question No.9, what is the source of Inoculant? 1 = government 2 = PARC 3
= agro dealers 4 = unions 5 = Others
11. Did you produce soybean in 2018/19 cropping season? 1 = Yes 2 = No
12. If yes in question No. 11, the total land allocated for soybean production is _______ha.
13. Crops produced in 2018/19 cropping season
Type of
crop
Variety Area
covered
(ha)
Amount
produced
(Qnt.)
Amount
consumed
(Qnt.)
Amount
sold (Qnt.)
Income
received
from sold
(Birr)
1 = Local
2 =
Improved
3 = Both
Maize
Soybean
Sorghum
Millet
Ground
nut
Sesame
Rice
106
14. Livestock ownership
Livestock type Number owned
Ox
Cow
Bull
Heifer
Calf
Sheep
Goat
Mule
Hen
Donkey
III. Sources of Income
1. What are your major sources of income? 1 = sales of crops 2 = sales of livestock/ products
3 = off/non-farm income 4 = others
2. Estimation of annual cash income received from:
a) sales of crops ______________Birr/year
b) sales of livestock __________Birr/year
c) sales of livestock products (Butter, milk and egg…) ______________Birr/year
d) off/non-farm income ________________Birr/year) others
3. Are you involved the following off/non-farm activities? 1 Yes 2 = No
Source of income 1 = Yes 2 = No Estimated income received per year (Birr)
Daily labour
Handicraft
Petty trade
Fire wood
Employment
Remittance
Other (specify)
Total income received
4. What are the crops that you sold frequently? _____________________ (put in the order
of importance from the given crops) 1 = soybean 2 = maize 3 = sorghum 4 = rice 5 = ground
nut 6 = sesame 7= Others
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IV. Soybean production
Input supply
1. Have you used agricultural inputs (fertilizer, improved seed, agro chemicals etc.) for
soybean production in 2018/19 cropping season? 1 = Yes 2 = No
Input type Did you use for
soybean?
1 = Yes
2 = No
Pri
ce/Q
t.
/lit
ter/
Pac
ks
Am
ount
use
d /
ha
Tota
l am
ount
use
d
(Qt/
lit/
pac
ks)
Tota
l co
st i
ncu
rred
(Bir
r)
Source
1 = own saved
2 = government
3 =cooperative
4 = PARC
5 = private
traders
6= other farmers
Improved seed
Fertilizer DAP
Inoculant
Herbicide
Pesticides
Others (specify__)
Credit services
1. Did you have access to credit for different purposes? 1 = Yes 2 = No
2. If your answer for Q1 yes, did you take credit in cash last year? 1 = Yes 2 = No
3. If yes for Q 2, how much you took in Birr _____________________?
4. If yes for Q2, for what purpose you took the credit? 1 = house hold consumption 2 = to
purchase farm inputs = 3 = livestock purchase 4 = student fee 5 = land rent fee 6 = others
5. where is the source of your credit? 1 = micro finance 2 = NGOs 3 = local money lenders
4 = saving and credit association 6 = Banks 7 = others
6. If the answer for Q2 is No, why? 1 = high interest rate 2 = no need credit 3 = fear of
repayment due to in ability 4 = no service 5 = lack of awareness about the service 6 = others
7. What was the precondition to get credit service? 1 = personal guarantee 2 = membership
3 = land holding 4 = partial saving 5 = others
Extension and information services
1. Did you have an extension contact? 1 = Yes 2 = No
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2. If yes for Q1, frequency of contact with the extension agents per year? 1 = daily 2 =
weekly 3 = twice a month 4 = monthly 5 = rarely
Did you participate in the following trainings?
No. Type training 1 = yes
2 = no
By whom
1 = Research center
2 = Bureau of agriculture
3 = NGO
4 = University
5 = Others
(specify_____)
How many
times per
year
1 Crop
management/protection
2 Input use
3 Use of credit service
4 Marketing of agri. products
5 Pre and postharvest
handling
6 About seed production
7 Field day and
demonstration
Farming activities and associated costs
1. What is your means of cultivation? 1= hand tool 2= Own oxen/donkeys 3= rented oxen 4
= rented donkeys 4= rented tractor
2. If your answer is rented oxen, how much the cost of rented oxen per day in ETB
_______total oxen days used _________total cost paid for oxen rent in ETB____________
If rented donkeys, cost per day ________ETB, Total cost Paid _________ETB.
If rented tractor, how much the cost of the rented tractor per hectare in ETB? ___________
Total cost paid for tractor rent in ETB____________
3. Are you weeding your soybean manually? 1= Yes 2= No
4. Frequency of weeding? 1 = once only 2 = two times 3 = three times
5. If yes for Q3. what is/are your source of labor? 1= family labor 2= hired labor
3= daily labor 4 = family labor & hired labor 5 = family labor & daily labor
6. If Q5 is hired labor, how much you pay for him/her per month? _______ETB.
7. If Q5 is daily labor, numbers of labors used _______how much you pay for him/her per
man per day ________ETB, total cost paid for daily labors in ETB ___________
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8. Did you use daily labor for spraying chemical? 1= Yes 2= No
9. If yes for Q8, how much you pay per hectare? ___ETB, total cost paid for spraying in
ETB___
10. How did you harvest your soybean? 1= through family labor 2= daily labor 3= both
11. If through daily labor for Q10, how much you pay per man per day in ETB _____total
cost paid for harvesting in ETB ______________
12. What are the major soybean production constraints?
No. Constraints Rank of constraints
1= very serious
2= serious
3= moderate
4= not serous
5= not a constraint
Remark
1 diseases
2 Lack of improved varieties
3 Lack of inoculants and late arrival of
fertilizers
4 Weeds
5 High Cost of inputs
6 High cost of labor
7 Low productivity
8 Shortage of improved seeds
9 Lack of market facilities
10 Low awareness about agronomic practices
11 Low price of the commodity
12 Erratic rainfall and hill storm
V. Marketing
1. Did you sell soybean last year/ 2019? 1= Yes 2 = No
2. If yes for Q1, for whom you prefer to sell your product MRAP? 1= local traders 2= broker
3= district whole sellers 4= unions/cooperatives 5 = ECX 6= processors
3. Why you prefer for selling the selected actor? 1= price difference from others 2=
closeness of the buyer in distance 3= transport availability 4= customer relationship 5=
others
4. If you sold your product to more than one actor for Q2, please estimate the volume of sell
for each actor in quintals. _____________________________________
5. For how many months you store your soybean product for sale? on average ____months.
110
6. The selling price of your soybean product immediately after harvesting _______
ETB/100kg
7. Where did you sell your soybean product? 1 = Farm gate 2= village market 3 = district
market 4 = outside district market 5 = ECX
8. Is there price difference for soybean in different places and to different buyers? 1 Yes 2
= No
9. If yes for Q8, indicate the price of sale in different places and to different buyers.
Place of sale Price when the product is sold to (Birr/100kg) in 2019
Village traders District whole sellers cooperatives
Farm gate
Village market
District market
Outside district market
11. What is your means of transportation for transporting soybean to the market? 1=
Donkey 2= cart 3= vehicle 4= others (specify________________)
12. Do you owned means of transportation? 1= Yes 2= No
13. If no for Q12. How much you cost for transporting ETB per 100kg? ___________
Marketing Association
1. What type of relation you have with buyer/s? 1= customer relation 2 = no relation 3=
friend 4= relative 5 = others (specify_____________________)
2. Do you have long standing customer (buyer)? 1= Yes 2= No
3. Have you sold your soybean product in credit basis? 1 =Yes 2= No
4. If yes for Q3, how long you wait for the payment? ______________
5. To decide for whom to sell, what factors you consider?
____________________________
Price information
1. Selling price of soybean in birr/100kg in 2018 _______
2. What is the trends of soybean price for the last five years? 1 = increasing 2= decreasing
3= no change
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3. Who is the decision maker for the price of soybean during selling? 1= Traders 2 = brokers
3= negotiation between farmers and traders 4 = others
4. If broker/middle men negotiate on price, who will pay for him? 1= trader 2 = farmer
5. If farmer pay, how much you pay per quintal ____ETB. Total cost for broker ______ETB.
Supply information
1. When did you sell your soybean product last year? 1 = immediately after harvest 2= one
month later 3 = more than two months later
2. If you sell immediately after harvest, why you did sell immediately? 1= storage problem
2= better price 3= fear of price fall 4= financial problems for home expenditure 5= others
3. What do you consider while supplying your soybean product to the market? 1= When we
need money, supply to the market 2= assessing market price information and supply if it has
better price 3 = others
Value addition
4. Are you keep the quality of your product? 1 = Yes 2 = No
5. If yes for Q4., what value adding activities you perform?
1= cleaning, cost per quintal _______________ETB.
2. storing, cost per quintal for storage ____________ETB.
3. Packaging, cost per quintal __________________ETB
4. transporting, cost per quintal for sale of transportation ____________ETB.
5. Sorting, cost per quintal/sacks __________________ETB
6. others
6. Is there price difference due to value addition? 1 = Yes 2 = No
7. If yes for Q6., what is your estimate of price difference due to value addition?
___ETB/Kg.
VI. Sources of market information
1. Do you get market information before providing your soybean product to the market? 1
= Yes 2= No
112
Information source category Sources of information 1= Yes 2 = No
Professional/ personal networks Traders
Friends/neighbor
Development Agent
Others
(specify_________)
Public information system From market bulletins
Radio
Television
Message blackboards at
market
places/ECX board
Others (specify ______)
VII. Average return of soybean
Selling
price in
ETB/qt
Total cost ETB/qt
Packaging
material
Loading
/unloading
Transportation Storage
rent
Weight
loss
Revenue
VIII. Membership in cooperatives
1. Are you a member of farmers’ cooperatives? 1 = Yes 2= No
3. What is the advantage of being a member of a cooperative?
1 =The cooperative provides better price
2 = The cooperative tries to hold the cost down
3 = Provide guaranteed outlet (market)
4 = Give field service or technical assistance __
5 = It makes timely payment _____
6 = gives an input through credit
7 = gives oil and sugar
4. Who is the member of cooperative from your family? 1. Husband 2. Wife 3 = Children
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Marketing constraints
No. Constraints 1 =Yes
2 = No
Rank according to importance
1 = very serious
2 = series
3 = Moderately
4 = not series
5 = not constraint at all
1 Low price
2 Less/no market information
3 Price fluctuation
4 No buyer or lack of market
5 Lack of transport facility
6 Problem of packing
7 Poor linkage of actors
8 Quality problem
9 Storage problem
Questionnaires for Traders
General information
Name of organization ____________________________________
Name of enumerator: __________________ Signature: _____________
Date: ___________/____________/_____________
Address: ___________Region: __________Zone: ________Woreda: _______Town:
______
Demographic characteristics of Traders
1. Name of the Trader
2. Sex of the Trader 1 = male 2 = female
3. Marital status of the trader 1 = married 2 = single 3 = divorced 4 =
widowed
4. Education level of the trader 1 = illiterate 2 = primary school (1-8) 2 =
secondary school (9-10) 3 = preparatory (11-
12) 4 = TVET and above
5. Religion of the trader 1 = Orthodox 2= Muslim 3 = Protestant 4 =
Catholic 5 = Other
6. Business type of trader 1 = Retailor 2 = Whole seller 3 = Collector 4 =
Broker 5 = Cooperative 6 = union 7 = Exporter
8 = processor
7.Position of respondent on the
business
1 = Owner 2 = Spouse 3 = Employed manager
4 = relative of business owner 5 = others
8. Trade type 1 = Alone 2 = Partnership 3 = other
114
9. If your answer is partnership for
Q8.
Number of members involved in joint
venture___
Number of women_________
Number of men__________
10. Year of involvement on trade
11. Time of trading 1 = Year-round 2 = When purchase price is low
3 = During high supply 4 = Other
12. Number of market days you
purchase soybean per week
1 = once a week 2 = two times 3 = throughout
the week 4 = others
13. Are you involved in trading other
than soybean?
1 = Yes 2 = No
14. If yes for Q13, what is that?
15. What was the amount of your
initial working capital when you start
soybean trading business in ETB?
___________Birr.
16. Sources of your initial starting
capital for trading
1 = Own saved 2 = Credit
17. If your source is credit, what is
the sources of your credit?
1= Relative 2 = Bank 3 = Micro finance 4 =
private money lenders 5 = Friends 6 = Other
traders 7 = Others
18. Reasons of credit 1= To extend soybean trading 2 = To purchase
soybean transporting vehicles 3 = To extend the
items of trading crop 4 = Others.
19. Is/Are there entry barriers for
trading?
1 = Yes 2 = No
20. If yes for Q20., What type of
entry barriers you face?
1 = Social barriers 2 = Legal barriers 3 =
Political barriers 4 = Financial barriers 5 =
administrative problems 6 = Coopetition of
unlicensed traders 7 = Discrimination 8 =
Others
21. With whom do you have a
linkage?
1 = Farmers 2 = Retailors 3 = Whole sellers 4 =
Consumers 5 = Collectors 6 = Brokers 7 =
Others
Purchasing part
22. What is/are your means of attracting your suppliers? 1= By giving credit to purchase
inputs and other expenditures 2 = By giving better price relative others 3 = By fair Weighing
4 = By giving other food items and seed through credit 5 = Others
23. How do you attract your buyer/s? 1 = By providing a quality product 2 = By giving fair
price relative to others 3 = By giving bonus 4 = Others
24. From which market you bought soybean in 2019? 1= Village market 2 = Pawe woreda
market 3 = Jawe woreda market 4 = Zonal market 5 = Addis Ababa market
115
25. From whom you bought soybean in 2019? 1 = Purchased from sellers 2 = Farmers 3 =
Retailors 4 = Whole sellers 5 = from rural collectors 6 = cooperatives/unions
26. How do you purchase soybean? 1 = Year-round 2 = During pick periods 3 = Others
27. How many quintals of soybean you purchased per month? _________________
28. How many quintals of soybean you purchased totally in 2019? ______price
birr/kg______
29. Terms of payment for the purchase: 1 = Cash 2= Credit 3 = Both
30. Who sets the purchase price? 1 = Negotiation 2 = By the market 3 = By the trader 4 =
By seller 5 = others
31. If you decide the purchase price by yourself, how do you set the price? 1 = Individually
2 = By agreeing with other traders 3 = By considering the current situation of the market 4
= Others
32. When did you set the purchase price? 1 = one day before the market day 2 = a week
before the market day 3 = Early in the morning during the market day 4= At the time of
purchase
33. Did you use brokers for soybean purchase? 1 = Yes 2 = No
34. If brokers used, what problems you face? 1 = Brokers cheat the quality 2 = Wrong price
information 3 = Cheating scaling (weighing) 4 = Charged high brokerage 5 = Others
35. Did you use brokers for selling your collected soybean? 1= Yes 2 = No
36. What problems you face during selling through your brokers? 1 = Wrong price
information 2 = Cheating scaling (weighing) 3 = Charged high brokerage 4 = Others
37. What is the preferable season of the year for purchasing soybean in terms price? Lowest
price of soybean ________________months.
38. How do you measure your purchase? 1= By weighing (quintals) 2 = By weighing
through traditional weighing materials 3 = others
39. Have you ever stopped purchasing soybean due to lack of supply? 1 = Yes 2 = No
40. If yes for Q38., for how long? ________________________.
41. Indicate the average costs incurred per quintal in trading activities
116
Activities Cost/quintal, Selling price and revenue
Purchase price
Transportation
Labor for packing
Loading/unloading
fee Sorting
Storage cost
Telephone cost
Material cost
License and taxes
Total cost
Selling price
Revenue
42. What is your estimation of price difference due to value addition? _________ETB/100kg
43. What are the prices of soybean during scarce and abundant seasons? Fill below table
Price Soybean during scarce ETB/kg Soybean during abundant ETB/kg
Maximum Minimum Maximum Minimum
Selling price
Purchase
44. Is soybean needs trade license in your locality? 1 = Yes 2 = No
45. If yes for Q42, how do you see the license procedures? 1 = Complicated 2 = Easy
46. Did you have soybean trade license? 1 = Yes 2 = No
47. How much did you pay for soybean trade license for the beginning? _________ETB.
48. Are there any trade restrictions for unlicensed soybean traders? 1 = Yes 2 = No
49. Are there charges (taxes) imposed by the government or community officials at the
market? 1 = Yes 2 = No
50. If yes for Q47, _________amount (ETB) based on sales volumes of products? And what
is the basis of payment? ____________________________________________________
51. Do you expand soybean trading? 1 = Yes 2 = No
52. If yes for Q49, why? ___________________________________________________
53. If No for Q49, why? ___________________________________________________
54. Are there problems in soybean marketing? 1 = Yes 2 = No
55. If yes for Q52, what are the problems? 1 = Credit and capital shortage 2 = Supply
shortage 3 = Storage problem 4 = Lack of demand 5 = Inadequate information 6 = Quality
117
problem 7 = Government, Telephone cost, Absence of government support, Problem of road
access, High competition with unlicensed traders
56. What do you think the causes of the problems and what interventions is needed to solve
this problem in your opinion? ____________________________________________
Interview schedule for healthcare food manufacture plant
1. Quantity of soybean bought for processing in 2019? ___________quintals
2. Average purchase price of soybean in 2019 in birr per quintal ________
3.How many litters of soybean oil produced from one quintal soybean grain? ______
4. Average selling price of one litter soybean oil in Birr for whole seller ________for
retailor____ for consumer_________
5. How many whole sellers you have for soybean oil? ________
6.How many retailors you have for soybean oil? ________
7. Estimated cost of labor to produce one litter oil including hired labor _______Birr
8. Do you have commission agents? 1 = Yes 2 = No
9. If your answer is yes, how much you pay per unit for commission agents? _______Birr
Table 1: List of major ingredients and associated costs for producing one litter oil in 2019
No. Type inputs used to produce
one litter oil from soybean
Unit Amount used Cots per unit
1 Soybean
2
3
4
Table 2: List of major buyers of soybean oil and byproducts in 2019
No. Buyers Unit Amount Price of oil per unit
1 Whole sellers
2 Retailors
3 Consumers
10. Average return received from selling of soybean meal and hulls/byproducts from
100kg soybean grain after the oil has been extracted in birr_________________
118
Questionnaires for Ministry of Trade and Industry
1. Total numbers of exporters involved for soybean export in 2011 E.C? ___________
2. Total volume of soybean grain exported in 2011E.C_____________ quintal
3. Average selling price of soybean exported in birr per quintal _____
4. Quantity of soybean grain imported to Ethiopia in 2011 E.C ________quintals.
5. Average import price of soybean grain in birr per quintal __________
Table 1: List of major imported soybean processed byproducts
No. Type of soybean byproduct Unit Amount Import price in birr per unit
1 Soybean oil
2 Soy milk
3
Table 2: List of major exported soybean processed byproducts
No. Type of soybean byproduct Unit Amount Export price in birr per unit
1 Soybean oil
2 Soy milk
3
Consumers Interview Schedule
1. Name of respondent _________________________
2. Zone_________Woreda___________Kebele__________Village_____________
3. Sex of consumer: 1 = Male 2 = Female
4. Age of consumer in years ______________________
5. Marital status of consumer: 1= married 2 = single 3 = divorced 4 = widowed
6. Education level of consumer: 1 = illiterate 2 = read and write 3 = primary school (1-
8) 4 = secondary school (9- 12) 5 = TVET and above
6. Religion of consumer: 1 = Orthodox 2 = Muslim 3 = Protestant 4 = Catholic 5 =
others
7. Means of income for consumers: 1 = farming 2 = trade 3 = employment 4= daily
laborer
8. What type of soybean byproduct you consume? 1 = oil 2 = soy milk 3 = bread 4 =
testy soya 5 = processed feed 6 = others
119
9. If oil for Q.No.7, have you used other types of oil other than soybean oil? 1 = Yes 2 = No
10. Types other oils used: 1 = sunflower oil 2 = nug oil 3 = sesame oil 4 = vegetable oil
11. If yes for Q. No. 8, how do you evaluate the cost and taste of that oil as compared to
soy-oil. 1 = cheap and has good taste 2 = expensive and has good taste 3 = cheap but has
not good taste and not suitable for health 4 = others
12. What is your preference from soy-oil and others type of oils? 1 = soy-oil 2 = others
13. If for Q. No. 10 is soy-oil, why? __________if others type of oil, why? _____________
14. Source of soybean byproducts you consume: 1 = village retailors 2 = district retailors 3
= zonal whole sellers/retailors 4 = cooperatives/unions 5 = processors 6 = supper markets
14. How much the cost soybean byproducts? 1 = Soy oil ___________ETB/litter
2 = Processed feed _________ETB/kg
3 = Soy milk __________ETB/litter.
15. Consumers linkage with commercial soybean value chain actors: MRAP. 1 = byproduct
retailors 2 = brokers 3 = cooperatives/unions 4 = processors 5 = whole sellers 6 = others
16. Do you think soybean value chain is complex & has many intermediaries? 1 =Yes 2 =
No
17. Do you think that retailors and whole sellers of soybean byproducts marketing are
efficient and fair? 1 = Yes 2 = No
18. If No for Q. No. 16, what are the problems in regarding to soybean byproduct marketing?
1 = high price of the byproducts 2 = unfair distribution of the byproducts for consumers 3 =
unfair price set by sellers 4 = lack of clear information about the exact price of soybean
byproducts 5 = existence of many intermediaries in the market 6 = cheating by sellers and
brokers due to weak follow up of the government at different levels 7 = others
19. If yes for Q. No. 16, how do you evaluate the overall process of soybean byproduct
marketing? 1= very good 2 = good 3 = fair according to the existing situation.
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AUTHOR BIOGRAPHICAL SKETCH
The author was born in North Mecha Woreda, West Gojam zone of Amhara Regional State
in May 1989. He attended his primary education at Wotet Ber primary school and his junior
at Densabata junior school. He attended his secondary school education at Adet secondary
high school and his preparatory at Merawi preparatory school in West Gojam zone. After
completion of his preparatory school education, he joined Bahir Dar University College of
Agriculture in October 2008 and graduated with BSc. Degree in Rural Development in
2010. Soon after his graduation, he was employed by Menge agriculture and rural
development office and served as an agricultural extension and communication expert for
about five years. Starting from 2016, the author joined the Ethiopian Institute of Agricultural
Research at Pawe Agricultural Research Center and served as an assistant researcher in the
Agricultural extension and communication research directorate. Finally, he joined Bahir Dar
University in 2019 to Pursue his MSc. Degree in Rural Development Management in the
regular program.