1
ASSESSMENT OF SUSTAINABILITY OF FOOD CROP PRODUCTION IN THE FADAMA
OF SOUTHERN GUINEA SAVANNA OF NIGER STATE, NIGERIA.
LAWAL, ALIMI FOLORUNSO.
(84/8344)
DEPARTMENT OF AGRICULTURAL ECONOMICS AND FARM MANAGEMENT
UNIVERSITY OF ILORIN, NIGERIA.
APRIL, 2008.
2
ASSESSMENT OF SUSTAINABILITY OF FOOD CROP PRODUCTION IN THE FADAMA
OF SOUTHERN GUINEA SAVANNA OF NIGER STATE, NIGERIA.
LAWAL, ALIMI FOLORUNSO (84/8344).
Dip. Agric (Ife), B. Agric. (Hons) Ilorin, M.Sc Agric Economics, (Ibadan).
A thesis in the Department of Agricultural Economics and Farm Management submitted In Partial Fulfilment of the Requirement for the award of Doctor of Philosophy (Ph.D,) of the University of Ilorin, Nigeria.
April, 2008.
3
DEDICATION
This thesis is dedicated to my father Alhaji AbdulRaheem A. Lawal for his love for quality
education for his children.
ii
4
ACKNOWLEDGEMENT
All praises is due to Almighty Allah, the creator of the heavens and earth, the
fountain of knowledge for His mercies, guidance and protection in my life.
I gratefully acknowledge the support of my supervisor, Professor Omotesho, O.A. for
his constructive comments, suggestions, fatherly advice and encouragement towards the
successful completion of this programme.
Special thanks go to Dr M.O. Adewumi for his assistance during the course of the
programme. It is difficult to envision the completion of this work without his contribution.
Between November 2002 and November 2007, several individuals participated and
spent time attending seminars, validating indices, helped in executing field operation,
analysis of data, and read through draft of seminars and the thesis. I would like to mention
Professor E.T.O. Oyatoye of the department, Professor B.A.Oyejola and Dr A.A Adewara of
Statistics Department, Dr Ayodele Jimoh and Dr M. Ijaya of Economics Department. Dr
O.A. Adekunle, Dr I Ogunlade, and Dr G. B. Adesiji of Agric. Extension Department, Dr Y.
A. Abayomi and Dr (Mrs) E.E.A. Oyedunmade of Crop Production Department and late Dr
O. Alabi of NCRI Badeggi. The assistance of Dr M.A. Y. Rahji of Department of
Agricultural Economics, University of Ibadan in data analysis and reading the draft of the
thesis is recognized with thanks. The efforts of Mallam Tanko Sonfada and Mallam Jibril
Mohamed Mokwa in data collection and Mallam M.K. Salihu and Mallam Sanusi Shehu in
editing the work are appreciated.
iii
5
The assistance received from the following lecturers in the department; R.O
Babatunde, Mrs E.O. Oloruntoba, Mr S. B. Fakayode, Mrs E.O. Ayinde and most especially
Mr A. Muhammad-Lawal, is recognized with thanks. The cooperation of other Ph.D
candidates, Col B. A Tsoho, M. Hussien, M Hassan and Dr I. O. Fatoba is acknowledged.
I recognized with sincere appreciation the assistance of my friend Mallam H. N. Z.
Koroka for giving me unlimited use of his laptop computer during the programme.
My appreciation goes to my friends and colleagues; Bello B.A, Abubakar M, Liman
A, Ibrahim D, Dr Aliyu Garba , M. M. Izom, M. K. Ndaman M. I .Kodan, T. Y. Loko, Mrs
B. Babalola and Samuel N. Yisa for their encouragement and support.
I sincerely acknowledge the contribution of my parents Alhaji AbdulRaheem A.
Lawal and Alhaja A. A. Lawal for giving me the opportunity to have quality education. I
appreciate the support of my sibling; Lawal I. G, Lawal O A, Raimi D and Mrs Adelabu A
M during the programme. The understanding, love and unalloyed support of my wife,
Wasilat Abdulsalam-Lawal and my children AbdulRaseed, Mariam and Mohammed Bashir
during the course of the programme is recognized with thanks.
I wish to express my gratitude to Dr E.K. Tsado the former Provost and Dr M.T.
Nagya the Provost of Niger State College of Agriculture, Mokwa and his management team
for giving me the opportunity to complete this programme.
To all that I mentioned and others, too numerous to mention especially my sampled
fadama farming household heads, I pray that Almighty Allah reward you abundantly.
iv
6
CERTIFICATION
I certify that Lawal, A Folorunso in the Department of Agricultural Economics and
Farm Management of the University of Ilorin, Nigeria carried out this study under my
supervision.
____________________ _____________
Supervisor. Date
O.A. Omotesho. B.Sc; M.Sc.;Ph.D (Ibadan). Professor of Agricultural Economics.
7
v
TABLE OF CONTENT
Title page i
Dedication ii
Acknowledgement iii
Certification v
Table of content vi
List of Tables xi
List of Figures xiii
Abstract xiv
CHAPTER ONE 1
1.0 INTRODUCTION 1
1.1: Background to the Study. 1
1.2: Inland Valleys (FADAMA) and sustainable food crop production. 2
1.3: Research Problem. 3
1.4: Objectives of the Study. 5
1.5: Justification for the Study. 5
1.6: Plan of study. 7
CHAPTER TWO
2.0: THEORETICAL FRAMEWORK AND LITERATURE REVIEW. 8
2.1: THEORETICAL FRAMEWORK. 8
8
vi
2.1.1: Economic concept of sustainability. 8
2.1.2: Sustainability Measurement. 10
2.1.3: Farm budgeting model. 11
2.1.4: Resource Productivity and Efficiency. 12
2.1.5: Deterministic parametric Frontier. 16
2.1.6: Stochastic Production Frontier 17
2.2: LITERATURE REVIEW. 21
2.2.1: Sustainable Agricultural Production. 21
2.2.2: Measurement of Sustainability of Agricultural Production. 25
2.2.3: Fadama Land Use, Soil Fertility Management and Sustainability. 32
2.2.4: Economic Environmental Modeling. 37
2.2.5: Efficiency/Productivity Measurement in Agriculture. 42
2.2.6: Empirical Application of Stochastic Frontiers Model-The Model for the Study. 45
CHAPTER THREE. 51 3.0: METHODOLOGY. 51
3.1: Area of Study. 51
3.2: Method of Data Collection. 52
3.3: Sampling Procedure. 53
3.4: Analytical Techniques. 54
3.4.1. Land Use Pattern Indices. 55
3.4.1.1: Crop Diversification Index. 55
9
vii
3.4.1.2: Nutrient Intake Index (NII). 56
3.4.1.3:Ruthenberg -Value. 57
3.4.2: Farm Budget Analysis. 57
3.4.3: Production Function Analysis. 59
3.4.3.1: Description of the Variable of the Frontier Model. 62
3.4.3.2: Elasticity of Production and Return to Scale Measurement. 66
3.4.4: Measurement of Short-Run Sustainability Index (S.R.S.I). 67
3.4.4.1: Basic Assumption of the SRSI Estimation. 67
3.4.5: Construction of Indicator of Sustainable Agricultural Practices (ISAP). 68
3.4.5.1: Scoring and Weighting Sustainability of Farming Practices. 69
3.4.5.2: Assumption for Constructing Indicator for Sustainable Agricultural
Practices (I.S.A.P). 72
3.4.5.3: Index Validation. 72
3.5: Limitations of the study. 73
CHAPTER FOUR 74
4.0: RESULTS AND DISCUSSION. 74
4.1: SOCIO-ECONOMIC CHARACTERISTICS OF FADAMA FOOD CROPS FARM
HOUSEHOLD HEADS. 74
4.2: THE STRUCTURE OF AGRICULTURAL PRODUCTION IN FADAMA. 77
4.2.1: Land Ownership Pattern. 77
4.2.2: Farm plots of the fadama farming Households. 79
4.2.3: Farm size of fadama farming households. 79
10
viii
4.2.4: Farm Labour Supply and Utilization 80
4.2.5: Farm Capital Utilization 82
4.2.6: Farm Input Characteristics. 85
4.3: ANALYSIS OF CROPPING PATTERN AND INDEX OF DIVERSITY. 86
4.3.1: Land Use Pattern 86
4.3.2: Major Crop Output. 92
4.3.3: Reason for crop preference. 93
4.3.4: Index of crop diversification. 94
4.3.5: Index of Soil Nutrient Intake. 95
4.3.6: Ruthberg Index. 97
4.4: COST AND RETURNS IN FOOD CROP PRODUCTION IN FADAMA. 98 4.4.1: Farm Production Cost. 98
4.4.2: Farm Revenue. 98
4.4.3: Gross Margin Analysis. 99 4.5: INDICATOR OF SUSTAINABLE AGRICULTURAL PRACTICES (I.S.A.P) 102
4.6: ANALYSIS OF STOCHASTIC FRONTIER ESTIMATION. 107
4.6.1: MLE Estimates of the parameter of the stochastic Production
Function. 109
4.6.2: Inefficiency Factor. 113
4.6.3: Technical Efficiency. 115
4.6.4: Distribution of Production Elasticity. 117 4.7: SHORT-RUN SUSTAINABILITY INDEX (SRSI) 118
ix
11
4.7.1: Relationship between Short Run Sustainability Index and Output of
the Farms. 120
CHAPTER FIVE 121 5.0: SUMMARY OF MAJOR FINDINGS, CONCLUSION, POLICY IMPLICATION,
AND SUGGESTIONS FOR FURTHER RESEARCH 121
5.1: Summary of major findings 121
5.1.1: Socio-Economic characteristics of respondent production pattern. 121
5.1.2: Land use pattern analysis. 123
5.1.3 Farm Budgetary Analysis. 123
5.1.4: Indicators of Sustainable Agricultural Practices for Fadama
of Southern Guinea Savanna, Niger state, Nigeria. 124
5.1.6: Technical Efficiency Estimate and Inefficiencies Factors. 125
5.1.7: Short-Run Sustainability Index
126.
5.2: Conclusion. 126 5.3: Policy Implications and Recommendations. 127
5.4: Suggestions for further study. 130
REFERENCES 131
APPENDIX: Interview Schedule 147
APPENDIX II: Distribution of Technical efficiency among Sampled Fadama Farmers Southern Guinea Savanna Niger State, Nigeria. 159
12
APPENDIX III: Gram equivalent Conversion Factors for Cultivated
Food Commodities. 163
APPENDIX IV: Estimation of Lambda 164
x
LIST OF TABLES
page
Table 1 : A Classification of Indicators of Sustainable Development. 27
Table 2 : Sampled Fadama Food Crop Farming Households Distribution Pattern. 54
Table 3: Farm Practices Used in the Construction of I.S.A.P. 68
Table 4: Scoring Farm Practices with Respect to Sustainability. 70
Table 5 : Demographic Characteristic of the fadama food crops households heads. 75
Table6: Land Ownership Pattern in the study Area. 78
Table7: Distribution of Number of Farm plots Per Household. 79
Table 8 : Distribution of Fadama Food Crop Farming Households by Farm Size. 80
Table 9: Farm Labour Supply and Utilization by an Average Fadama Food Crop
Farming Household. 81
Table 10: Distribution of Farm Fixed Input and Their Costs. 82
Table 11: Distribution of Sources of Credit, Number of Extension Visits, and
Major Constraint of Food Crop Farming Households. 83
Table 12: Description of Farm Inputs Characteristics of an Average Fadama
Farming Households. 85
13
Table 13: Distribution of Area Cultivated (Ha) to Sole Crop Enterprises. 87
Table 14: Distribution of Area Cultivated (Ha) to Mixed Crop Enterprises. 89
Table 15: Distribution of Fadama Households by Sources of Seeds PTalanted. 91
Table 16: Major crops grown in the area and their yields. 92
xi
Table 17: Distribution of Main Reason for Crop Preference. 93
Table 18: Herfidahl Index of Crop Diversification. 94
Table 19: Distribution of Nutrient Intake Index among Sampled Households. 96
Table 20: Gross Margin per Hectare by Enterprises for Food Crops Cultivated by
Sampled Fadama Food crop Households. 100
Table 21: Mean value of I.S.A.P components. 104
Table 22: Distribution of Indicator of Sustainable Agricultural Practices (ISAP) scores
106.
Table 23: Stochastic Frontier Estimation (MLE) Result. 107
Table 24: Distribution of farm specific Technical Efficiency indices among
Sampled Fadama Farming Households 115
Table 25: Distribution of Production Elasticity among the variables 117
Table 26: Distribution of Farm Specific Short Run Sustainability Index (SRSI). 119
14
xii
LIST OF FIGURES
.
Figure 1: Farell’s Efficiency Measures. 13
Figure 2: Stochastic Frontier Production Function. 19
Figure3: Mean score of I.S.A P. Components. 104
15
xiii
ABSTRACT
An assessment of the sustainability of food crop production in the fadamas of southern
guinea savanna of Niger State, Nigeria was carried out within the framework of small-scale
farming households utilizing fadama for the cultivation of food crops. The study identified
the pattern of land use and management, estimated technical efficiency, identified
determinants of technical inefficiency and determined productivity and profitability of food
crop production in the fadama. It also determined Indicators of Sustainable Agricultural
Practices (ISAP). A two- stage simple random sampling technique was used to obtain 149
food crop farming households interviewed for the study. The data were collected between
August 2004 and September 2005. The data were analyzed by using descriptive statistics
such as frequency distribution, mean, standard deviation, bar chart, and sustainability web.
Estimates of Crop Diversification Index (CDI), Nutrient Intake Index (NII), Ruthberg Index,
Farm Budgeting Model, Cobb-Douglas based Stochastic Frontier Model using Maximum
Likelihood Estimate (MLE) and Short Run sustainability Index (SRSI) were determined.
A typical household had seventeen persons and planted an average of 3.44 ha scattered in
three plots. The major enterprises were sole crop rice and maize – based mixtures in about
0.70 ha and 2.59ha respectively. Estimated mean CDI was 0.651 implying stability of
income and sustainability of mixed crop enterprises while mean NII was 1.89 showing that
combined crops have low tendency to deplete soil nutrient. The Ruthberg index value of
0.393 implied that a six years fallow period may be adequate to restore natural fertility
depending on the farming practices employed by households. The Indicator of Sustainable
Agricultural Practices (ISAP) mean score of 0.517 implied that crop production practices of
sampled households were sustainable. The study showed that methods of pest control, weed
16
control, and soil fertility maintenance were the critical indices to monitor for the
improvement of sustainability. The farm budget analysis showed that the sampled fadama
food crop farming households had positive net return for all enterprises. Maize/cowpea
enterprise had the highest gross margin of N25,663/ha while leafy vegetables (e.g. spinach)
had the highest return on investment (2.39). The average return on investment for all the
farms studied was 1.89. Mixed cropping the dominant cropping system generally adopted by
the fadama farming households gave higher gross margin per hectare. The MLE of the
Stochastic Frontier Model revealed the presence of short run increasing
xiv
return to scale with a mean technical efficiency of 58%. This result indicated the possibility
of improving efficiency of sampled fadama households by 42% with the existing resources
and technology. Farm size, farm experience, access to credit, educational level and extension
contact had negative and significant relationship with inefficiency. This implied that
increase in these variables would lead to less inefficiency. Household size had positive and
significant relationship on inefficiency which implies that increase would lead to higher
inefficiency. The mean SRSI of -0.14 showed an average productivity decline of 14%,
which could be reversed by preventive and remedial action.
In conclusion, production of food crop in the fadama of the Guinea Savanna of Niger State,
Nigeria is fairly sustainable. Mixed cropping, consolidation of household resources, increased
use of animal traction and organic fertilizer as well as integrated pest management is
recommended In order for the foregoing to be effective, they must be
accompanied by improved extension service delivery, aggressive adult education programme,
regulated use of agro-chemical, improved access to credit and availability of subsidized inputs.
17
xv
CHAPTER ONE
INTRODUCTION
1.1: Background to the Study.
Agricultural growth is a catalyst for broad based economic growth and development
in most low-income countries: Agricultural linkages to the non-farm economy generate
employment, income and growth in the remaining part of the economy. Very few countries
have experienced rapid economic growth without strong agricultural growth. Agricultural
growth and development also help meet growing food needs driven by rapid population
growth and urbanization. Therefore, maintenance of sustainable productivity in the
agricultural sector is the pivot of development. However, most developing economies have
witnessed substantial decrease in productivity of agricultural sector and food import has
continued to increase (World Bank, 1996).
Thus, in writing the mandate for International Institute for Tropical Agriculture
(IITA), the institute’s founding father shows a clear awareness that improving agriculture in
the humid and sub-humid tropics of Africa would require a somewhat different strategy from
the one that had yielded dramatic result elsewhere especially in Asia. They realized that the
18
long term success of any effort to raise the productivity of food crops in this region would
depend on the ability of agricultural research bodies to find new ways to maintain the
productivity of the land under continuous cultivation. Therefore, sustainability was
recognised as a critical pre-condition for putting food production in Sub-Saharan Africa on
the path towards steady improvement long before sustainability becomes the by-world of
agricultural development and was incorporated into the goals of Consultative Group on
International Agricultural Research System (CGIAR) (IITA,1992).
1.2: Inland Valleys (Fadama) and sustainable food crop production.
Fadama is a Hausa word meaning the seasonally flooded plain along major rivers and
or depression on the adjacent low terraces. These contracts sharply with the surrounding dry
top land in terms of resources. It is the relative wetness and dryness and the seasonal and
inter annual variation of the moisture level of the top land and bottomland component of the
system that are particularly important from the point of view of economic resource
management (Kolawole and Scoones, 1995). The fadama size of Nigeria is estimated at
about 4.6 million hectares. Out of this, Niger State has an estimated 495,000 hectares. This
is second to Adamawa State with 625,000 hectares, the largest in the country (Ingawa, 1998).
The potential of this agro-system is based on two distinguishing features. One is the
presence of sufficient soil moisture that reduces crop risk in the wet season and permits the
cultivation of a wide variety of crops during the dry season. The second is the presence of
soil that is generally more fertile than that of upland. If the potential of the inland valleys for
intensive crop production could be realized, they might serve as a kind of safety valve for
relieving pressure in other agro ecosystem particularly the humid forest and moist savanna.
(World Bank, 1992).
19
However, some problems that can prevent the realization of sustainable crop
production in the agro ecosystem include, prevalence of diseases, difficulty in clearing
natural vegetation, erosion induced by run off, inadequate water control, intensive weed
infestation, frequent outbreak of pest and diseases, and poor land use and management
practices. Largely for these problems, the potentials of the inland valleys are not fully
utilized.
1.3: Research Problem.
Fadama are relatively more fertile than the surrounding upland areas. They reduce
the risk of crop failure and have potential for longer period of agricultural activities in a year.
They present a unique opportunity towards reversing the declining per capita food production
in Nigeria.
The quest for harnessing the benefit of fadama land has ushered in technological
innovations such as development of small irrigation pumps, small earthen dams and shallow
tube wells. This has led to intensification in the use and management of fadama for
agricultural activities. The Ministry of Water Resource and National Fadama Development
Project (Fadama II, World Bank and African Development Bank assisted project) is
especially active in fadama development in eighteen (18) states of the country. By and large
the inland valleys are cultivated by small holders whose land utilization and management
with limited resources are aimed at achieving farm level objectives in term of food security,
and economic viability. Their land use practices have a short term planning horizon with
little attention to the status and management of agricultural land (Krusemen et al. (1996);
Pannell and Glenn, 2000; Adewumi and Omotesho, 2002). Production objectives of short
20
term food security and income that guarantees economic production by the farmer might be
achieved. However, the achievement may have come at the expense of long-term
sustainability of land resources and development. Sustainable development is consistent with
increasing environmental assets or development without destroying the future of natural
capital stock.
The most critical issue that this thesis addresses is whether fadama land utilization is
consistent with sustainable development in view of some factors threatening sustainability
around the fadama of Northern Nigeria. Kolawole (1991) reported that development
intervention, changing land tenureship and population pressure were among the factors
threatening sustainability of fadama in Nigeria. Lawal (2001) also reported that the
construction of hydroelectricity Dams at Jebba and Shiroro have had adverse effect on socio-
economic lives of the riparian communities. This has led to the loss of crop and farm land to
flooding as a result of flash-flood occasioned by opening of the dams.
Within the context of sustainable agriculture, land use and management must aim at
addressing the simultaneous aspect of production and conservation. Sustainable agriculture
must of necessity involve farming practices that enhance productivity of land, labour and
other physical resources and/or improve plant and animal productivity in order to meet
present economic needs without compromising the benefits of future generation.
According to Saka et al. (2005), given the natural endowment of human, material
resources and the available technology, Nigeria should be self sufficient in production of
major food items. This will however, depend on a sustained efficient use of production
resources at the farm level. Improving farm productivity and combating land degradation
problems are the key issues in sustainable agricultural development. Therefore, within the
21
foregoing context, some questions become fundamental in the assessment of fadama land
use;
(1) Are the farming practices adequate to improve soil fertility, nutrient recycling and
enhance sustainable production?
(2) Can the present cropping practices enhance economic benefits?
(3) What are the farming practices to monitor as indicators of sustainability?
(4) Are fadama food crop farming households efficient in the use of inputs?
(5) What are the factors that determine the level of inefficiency of the farming
household?
(6) Is food crop production in the fadama sustainable?
1.4: Objectives of the Study.
The main objective of the study is to evaluate the sustainability of fadama in food
crop production in the Southern Guinea Savanna of Niger State of Nigeria. The specific
objectives are to;
(1) identify the patterns of land use and management.
(2) determine costs and return to food crop production in the fadama.
(3) determine indicators of sustainability of agricultural practices (ISAP) in fadama.
(4) determine the technical efficiency and productivity of resources used by fadama
farming households.
(5) identify the determinants of technical inefficiency of fadama farming households
(6) evaluate the short run sustainability of food crop production in the fadama.
1.5: Justification for the Study.
22
FAO (1989) adopted the definition of sustainable agricultural development as the
management and conservation of the natural resource base and the orientation of
technological and instructional changes in such a manner as to ensure the attainment and
continue satisfaction of human needs for present and future generation. Such sustainable
development in agriculture, forestry and fisheries sectors conserve land, water, plants and
animal genetic resources, is environmentally non-degrading, technically appropriate,
economically viable and socially acceptable. Therefore, a work that assesses sustainability of
food crop production in fadama at the small holder farm level is relevant in determining the
extent to which the low external input agriculture (LEIA) farming households in the fadama
allocate, use and manage this delicate land resource.
Fadama areas, although scattered, fragmented and usually small relative to the
overall mass of dry land, are relatively more fertile and have potentials for longer period of
agricultural activities in a year. This potential will remain elusive until the sustainability of
this agro-ecosystem can be assessed which is the focus of this study.
A farm level evaluation of sustainability of food crop production of this type in a
fadama setting is necessary to provide information to policy makers in assessing the
economic rationale of different land management options in order to maintain the bio-
physical condition of the land.
Furthermore, this study determined sustainability indicators that identified land use
indices to monitor in a typical smallholder fadama agro-ecosystem.
With rapid growth in population, increasing pressure on natural endowed resources,
diminishing traditional fallows, production needs to be efficient to ensure sustainability of
23
the land resources. Estimate of the extent of inefficiency would also help in deciding whether
to improve efficiency or to develop new technologies to raise agricultural productivity.
There is also the research need to move away from the stereo type studies on peasant
agriculture which is based on neo-classical economic optimization objective without any
particular attention paid to non-economic variables like land management practices and their
effects on crop production which is one of the focuses of this study. Hence, these attempts at
the empirical quantification of the different issues/assertions raised constitute the justification
for the current study.
1.6: Plan of Study.
This study is divided into five chapters. In the first chapter, the background to the
study is presented; statement of the problem, the means by which the research questions will
be answered and justification are presented. The second chapter highlights the review of
theory as it affects the research as well as relevant literature from past studies. In chapter
three, methodology of the research is presented. Chapter four presents the results of the
analysis of the data collected and the discussion of the results. The final chapter, chapter five,
presents the summary of major findings, policy implication, conclusion of the study,
recommendations and suggestions for further studies.
24
CHAPTER TWO
THEORETICAL FRAMEWORK AND LITERATURE REVIEW.
2.1: THEORETICAL FRAMEWORK.
2.1.1: Economic Concept of Sustainability.
The unpriced outputs of agricultural system both positive and negative have become
of increased importance as compared with conventional marketed outputs. There is concern
that, while technical changes have brought major benefits to consumers in terms of reduced
food prices, some developments have taken us into unchartered waters. Society sensitized by
problem of pollution, health and other issues arising from earlier technical developments,
appears less ready to accept change, which may involve consequences that might be
damaging and irreversible, hence, the need for caution and reappraisal. Such concern
underlies the move to a spectrum of production systems ranging from “organic,” alternative,”
to “integrated,” farming system. The question is how we can induce progress towards
farming systems, which are sustainable and how we might measure that progress.
25
Nix (1990) pointed out that the idea of sustainability is central to attempts to define
farm income. He quoted Hicks (1946) who defined income as that which could be consumed
in a given period leaving the consumer as well off at the end of the period as at start. Recent
theories of economic growth have built upon neoclassical foundation (Solow, 1992) and
recognize that aggregate capital (K) consists of man made capital (Km), natural capital (Kn),
human capital (Kh), and social capital (Ks), such that;
K = Km + Kn + Kh + Ks
The distinction between weak sustainability and strong sustainability hinges upon
whether substitution is permitted between the components of K in equation 1 (Turner, 1993).
Weak sustainability implies that substitution may take place in order to maintain K. In other
words, rates of substitution or elasticity’s are assumed to exist. In contrast, strong
sustainability does not permit substitution between components of K and the emphasis is
placed on conserving K
(1)
The quantity and quality of this capital determines the level of provision of utility for
mankind on a year by year basis (Pearce, 1999). The notion of sustainability arises when it is
required that the capacity of K to produce utility from one period to the next does not decline.
Important modifiers to K include technological change, which may be regarded as
endogenous or exogenous and population growth, which may have positive or negative
impacts on the component of capital (Webster, 1999).
n (and each of its separate sub-components) at all costs. Arguments
for such an approach include irreversibility of some changes in natural capital e.g. species
extinction (Tversky and Kahneman, 1981). It is further argued that natural capital is
impossible to value by whatever means and that no trade-offs could be made within Kn or
any level of subdivision of Kn. However, Vander Hamsfort and Latacz-Lohnman (1998)
26
have argued for strong sustainability on the basis of the second law of thermodynamics (the
entropy law), which implies a limit to the stock of energy available to mankind for
transforming low entropy natural capital through manufactured capital to high entropy waste
products. It is argued that Km and Kn should be complements rather than substitutes.
2.1.2: Sustainability Measurement.
Inputs are applied to the land and outputs are produced. The most frequently
employed measure of agricultural productivity is yield, generally expressed as grain yield per
hectare per crop. Inputs are relevant to sustainability. It would be difficult to agree that a
system that requires increasing inputs to maintain constant outputs over time is sustainable.
Total factor productivity (TFP) which measures an index of total output relative to an index
of total inputs is a better measure of productivity than yield because it recognizes the use of
all inputs, including seeds, water, labour, traction, pesticides, fertilizer, micro-organisms etc.
TFP is a ratio of an index of aggregate output to aggregate inputs. By constructing an index
of TFP, it is possible to assess objectively the productivity performance of the system. Total
factor productivity as suggested by Lynam and Herdt (1989) and Keys and Mcbride (2003)
are given as follows:
TFP = Y TVC +TFC (2)
Y = ∑P
iQi or PQ if one output (3)
27
TVC = ∑PiXi (4)
TFP = PQ Values of output ∑PiXi + TFC = Values of input (5)
Where ;
Y = value of output (N)
P = price of output (N)
Q = quantity of output.
Pi = price of ith variable input (N).
Xi = quantity of ith variable input
TVC = Total variable cost.
TFC = Total fixed cost.
2.1.3: Farm Budgeting Model.
The Farm Budgeting tool is widely used in Farm Management and Production
Economic Studies. The farm budget tool is an operation leading to the determination of costs
and revenue for a given production period (Olayide and Heady, 1992). The farm budget tool
is used to determine the overall average Net Farm Income (NFI) derived from various
enterprises (profitability). Comparisons are made between cost incurred and return obtained
on a particular venture. Profit is made when returns are greater than costs, while losses
occurred when the reverse is the case. With the budgeting technique NFI can be estimated.
According to Olukosi and Erhabor (1988), NFI is expressed as:
NFI = GI - TVC - TFC (6)
Where ;
NFI = Net Farm Income
GI = Gross Income (Total Revenue)
TVC = Total Variable Cost
28
TFC = Total Fixed Cost
Izac and Swift (1994); Udoh (2000) and Alamu and Coker (2005) have used
profitability of production system obtained from farm budgeting model as a measure of
sustainability.
2.1.4: Resource Productivity and Efficiency.
Agricultural productivity may be defined as the index of the ratio of the value of total
farm output to the value of the total input used in farm production. Any increase in the
productivity of resource employed with a given amount of effort in farm production amounts
to progress. Increases in agricultural productivity will contribute to the well-being of the
economy as a whole. Maximum resource productivity will imply obtaining the maximum
possible output from the minimum possible sets of input. In this content, optimal
productivity of resources implies an efficient utilization of resources in the production
process. Several attempts have been made to define economic efficiency and to measure it in
an empirical sense.
Production efficiency is concerned with the relative performance of the process used
in transforming inputs into output. Technically, a firm is said to be efficient if it produces as
much output as possible from a given set of input or it uses the smaller possible amount of
input for a given level of output and input mix. To be economically efficient, a firm has to
29
be technically efficient (Atkinson and Cornwell, 1994). The analysis of efficiency is
generally associated with the possibility of farms producing a certain optimal level of output
from a given bundle of resources or certain level of output at least cost.
Farrel (1957) who drew extensively upon the work of Debrew (1951) and Koopamans
(1951) distinguish between three types of efficiency. Firstly, he defines ‘technical’
efficiency (TE) as the measure of a firm’s success in producing maximum output from a
given set of inputs. Secondly, he defines, “price” efficiency (PE) as the measure of firm’s
success in choosing an optimal set of outputs. This is indication of the gain that can be
obtained by varying the input ratio on certain assumptions about future price structure.
Thirdly, he defines “overall” efficiency or total economic efficiency as the simple product of
the technical and price efficiencies.
Graphical presentation of Farrel’s definition assumes as efficient isoquant which is
SS1 in Fig 1. Given the efficient isoquant SS1 and the isocost line CC1, the three efficiency
measures of Farrel are given by
TE = OQ = Technical Efficiency OP (7) PE = OR = Price Efficiency OQ (8) OE = OQ OR = OR
X
Overall efficiency OP OQ OP (9)
2 S1
Labour *T P
C1
*E
* D
R
Isocline
S1
30
Q*
0 C1 (Land) X1
Figure 1: Farell’s Efficiency Measures.
Farrel’s (1957) three efficiency measures are represented in Figure 1. Isoquant SS1 is
an outer bound production function, whereby given the current state of technology, it is not
technically feasible to produce the level of output represented by SS1 on an alternative
isoquant which is closer to the origin O or below SS1. The unit isoquant SS1 defines the
locus of input per unit of output ratios associated with the most efficient use of the inputs to
produce the unit output involved. Any observed deviations of actual input per unit of output
ratios from this unit isoquant are considered to be associated with technical inefficiency in
the firms involved. Therefore, firm that operates on isoquant SS1
Allocative efficiency demands that inputs be combined in the same proportion as their
relative prices. In Figure 1, the slope of the isocost line CC
such as that at Q or T is
technically efficient but any firm which is on the space above the isoquant such as at D or E
is technically inefficient.
1 gives relative price of input X1
(land) and X2 (Labour). Hence Q is allocatively efficient whereas T is allocatively
inefficient. Firms D and E may or may not be allocatively efficient depending on whether
the slopes of their technically inefficient isoquant (which are not shown) at these points are
equal to the slope of CC1 (Ghatak and Ingersent, 1984). Farrel’s measure of efficiency
assumes the existence of an efficient production function with which the observed
performance of a firm can be compared. A production function based on the best practical
31
result would have to be used as a reference for measuring individual firm’s performance.
Farrel’s original measure of technical efficiency assumes constant return to scale.
Fare et al. (1985) introduced another method of measuring efficiency across firms,
which extended Farrel’s approach by relaxing the restrictive assumption of constant return to
scale which is the major criticism of Farrel’s method. According to Fare et al. (1985),
efficiency by a firm in input necessarily implies efficiency on outputs. Technical, allocative
and other efficiency measures on output cannot however, be determined from corresponding
measures in inputs or vice versa. This is because output and input efficiency focus on
different aspects of production.
Efficiency measurement is very important because, it is a factor for productivity
growth. Such a measurement is undertaken to;
(1) Determine the total factor productivity indices in agriculture with regards to individual
inputs like land, labour, capital etc.
(2) Monitor and evaluate the level of trend of economic development or trend of resource
productivity as in sustainability measurement.
(3) Generate data used in national planning.
(4) To obtain a pattern or direction of resource adjustment and bring into focus a realistic
determinant of the well-being of the populace.
Measurement of this function and the potentials deviation from such frontier involve
numerous methods. The four alternative production frontier models are;
(a) A deterministic production frontier estimated via linear programming.
(b) A statistical production frontier estimated by corrected ordinary least square
(COLS).
32
(c) A statistical production frontier that assumes a gamma distributed efficiency term
and is estimated using maximum likelihood technique (MLE).
(d) A stochastic production frontier with a composed error structure, which is also,
estimated using maximum likelihood techniques (Bravo-Ureta and Rieger, 1991).
The first three are deterministic, which means that the entire deviation from the
frontier is attributed to inefficiency. The fourth model, by contrast attributes only a part of
the deviation from the frontier to inefficiency.
2.1.5: Deterministic Parametric Frontier.
The term deterministic is generally used to describe that group of methods which
assumes a parametric form for production frontiers along with a strict one sided error term.
Pioneers in this area are the work of Aigner and Chu (1968), Seitz (1970) and Afriat (1972).
The deterministic frontier model is defined by
Y = f (Xi: β ) + e i = (1 2 ---,N) (10)
Where;
Y = output .
Xi
Afriat (1972) specified model is similar to equation 10 except that the error term were
assumed to have a gamma distribution and the parameter of the model were estimated using
= vector of inputs.
β = vector of unknown parameters.
f = appropriate functional form..
e = error term.
33
the maximum likelihood (ML) method. However, the maximum likelihood procedures are
not independent of the distribution assumption of the error term (gamma, exponential, half
normal etc) and as such different distribution assumptions lead to different maximum
likelihood estimate. To eliminate this limitation, Richmond (1974) observed that the
parameter of Afriat model could be estimated using a method that is known as corrected
ordinary least square (COLS). Where the corrected ordinary least square (COLS) method
provides unbiased estimates of the slope parameter by shifting the OLS constant parameters
estimate upward until no residual is positive and consistent parameter is estimated. Schmidt
(1976) added to the discussion on ML frontier by noting that the linear and quadratic
programming estimates proposed by Aigner and Chu (1968) are ML estimators if the error
term were assumed to be distributed as exponential or half normal random variable
respectively. The Afrait- Richmond production frontier or Data Envelopment Analysis
(DEA) approach suffers from the criticism that it takes no account of the possible influence
of measurement errors and other noise data which are common in agriculture since all
iobserved deviations from the estimated frontier are assumed to be the result of technical
inefficiency Coelli (1995); Coelli and Battese (1996). There has been a number of research
works that modified and applied the DEA using linear programming methodology {Ali and
Chaudhry (1990), Aromolaran (1992)}, linear programming and the Minimization of Total
Absolute Deviation (MOTAD) Belete et al. (1993) and dynamic programming (Olayide and
Olowude, 1972).
Furthermore, because of the way technical inefficiency is defined, deterministic
parametric frontier are susceptible to outlier. The inherent problems of deterministic
approach are better handled by stochastic approach.
34
2.1.6: Stochastic Production Frontier.
A stochastic production frontier comprises a production function of the usual
regression type and a composite disturbance term equal to the sum of two error components.
The stochastic frontier production is defined by
Y = f (Xi : β) exp (Vi - Ui) i = (1, 2 ---, N) (11)
Where Vi is a random error having zero mean, which is associated with random
factor (e.g. weather, measurement error in production etc) not under the control of the firm.
Ui, is a non-negative random variable, called technical inefficiency effects, which are
associated with technical inefficiency factor in production.
Aigner et al. (1977) and Meeusen and Van den Broeck (1977) independently proposed this
stochastic model. The model is such that the possible production of (Yi) is bounded above
by the stochastic quantity f (Xi : β) exp (Vi – Ui): hence the term stochastic frontier. The
error Vi = 1 2 ---, N are assumed to be independently and identically distributed as N ( 0 δ2 )
random variables, independent of the Ui, which is assumed to be non negative truncation of
the N (0 δ2
The basic structure of the stochastic frontier model is depicted in Fig 2.0 in which the
productive activities of two firms, represented by
) distribution (half normal distribution) or have exponential distribution i.e
gamma distribution.
i and j are considered. Firm i used inputs
with values given by vector Xi and obtains the output Yi but the frontier output, Yi*, exceeds
the value on the deterministic production function f(Xi ; β) because its productive activity is
associated with favourable conditions for which the random error, Vi, is positive. However,
firms j uses inputs with values given by the vector Xj and the frontier output Yj* is below the
value on the deterministic production functions, f(Xj:β) because its productive activity is
35
associated with unfavourabale conditions for which the random error Vj is negative. In both
cases the observed production values are less than the corresponding frontier values, but the
unobservable frontier production values will be around the deterministic production function
associated with the firms involved.
Given the assumption of the stochastic frontier model, inference about the parameters
of the model can be based on the maximum-likelihood estimates because the standard regular
conditions hold. Technical efficiency of an individual firm is defined in terms of the ratio of
the observed output to the corresponding frontier output, condition on the levels of inputs
used by that firm. Thus the technical efficiency of a firm (Tei) in the context of the
stochastic frontier production function is the same as for the deterministic frontier model,
namely
Tei = exp (-u) (Battese, 1992).
Tei = Yi/Yi* (12)
= f(Xi ; β) exp (Vi-Ui)/ f(Xi ; β) exp (Vi)
= exp (-Ui
) (13)
Y
Y
Observed output Yj
Frontier output Yj*<Y if Vj < 0
Frontier output Yi*>Y if Vi > 0
Output Y
Deterministic production function Y= f(xi; β)
36
Fig 2: Stochastic Frontier Production Function. The primary advantage of stochastic frontier is that they allow for technical
inefficiency to be measured separately from statistical noise, whereas both the deterministic,
statistical and the non-parametric frontier lump random effects and measurement errors with
technical inefficiency.
Battese and Coelli (1988) suggest that the parameter of the frontier and the density
function of Vi and Ui can be predicted by using the conditional expectation, given the
composed error Vi – Ui evaluated at the maximum – likelihood estimates of the parameters
of the model.
Although the technical efficiency of a firm associated with the deterministic and
stochastic frontier models is the same, it is significant to note that they have different values
for the two models. The stochastic frontier can be estimated by different methods other than
maximum likelihood such as corrected OLS method suggested by Richmond (1974) because
of ease of compilation. However, Battese (1992) suggested that distribution assumptions of
the MLE are basic to obtaining accurate predictions for technical efficiencies. The ML
estimator is asymptotically more efficient than the COLS estimator and also the availability
of automated ML routines using computer program FRONTIER VERSION 4.1 (Coelli,
1996) has made its application easier.
Observed output Yi
0 Xi Xj Inputs
37
Stochastic frontier, however are not without short-comings. Assumption about the
distribution of inefficiency Ui and statistical noise Vi
Rigby et al. (2001) in a study on constructing a farm level indicator of sustainable
agricultural practice noted that developments in modern agriculture have led to doubts
regarding the long-term viability of current production systems. These developments include
heavy reliance on chemical fertilizers, pesticides and herbicides, the destruction of wildlife
are necessary for their estimation and
the derived results are not independent of such assumption. Also, the estimates of technical
efficiency are inconsistent (Jondrow et al. 1982).
2.2: LITERATURE REVIEW.
The relevant literatures for the study are based on the following:
(i) Sustainable agricultural production;
(a) Meaning and definitions.
(b) Measurement of sustainable agricultural production.
(ii) Fadama land use and soil fertility measurement.
(iii) Economic environment modeling.
(iv) Agricultural productivity/efficiency Measurement.
2.2.1: Sustainable Agricultural Production.
38
habitats, environmental pollution and risk to human health. These have led to development
of several agricultural approaches such as “organic farming”, ‘Alternative agriculture’ and
the discussion on sustainable agricultural production.
Pearce (1998) in his report prepared for the World Commission on Environment and
Development (WCED) workshop on Agric-Environmental indicators stated that the concept
of sustainability was probably first advanced in 1980 by the international union for the
conservation of Natural and National Resources. And that sustainability means different
things to different people; there are at least 356 definitions of sustainable development in
literature.
FAO (1989) adopted sustainable agricultural development as the management and
conservation of the natural resource base and the orientation of technological and
instructional changes in such a manner as to ensure the attainment and continue satisfaction
of human needs for present and future generation. Such sustainable development in
agriculture, forestry and fisheries sectors conserves land, water, plants and animal genetic
resources, is environmentally non-degrading, technically appropriate, economically viable
and socially acceptable.
Mac Rae et al (1989) regard sustainability as management procedure that work with
natural processes to conserve all resources, minimize waste and environmental impact,
prevent problems and promote agro ecosystem resilience, self-regulation, evolutional
production for the nourishment and fulfillment of all.
According to O’Rioden (1985), a sustainable agriculture combines the social,
economic and environmental component of sound husbandry into a united package. Izac and
39
Swift (1994) argued that a cropping system is sustainable if it has acceptable level of
production of harvested yield, which shows non-declining trend from cropping cycle over the
long-term. Lynam and Herdt (1989), Spencer and Swift (1992) and IITA (1992) in their
separate work supported this concept of sustainability as a non-declining trend in output.
This concept introduces a number of the issues which should be included in discussion of
sustainability: spatial scale and boundaries, productivity – biological and economical in term
of potential and expectation, trend over-time and the time scale for assessing sustainability.
Fashad and Zinck (1993) further noted that the concept of sustainability is a
multifaceted one involving economic, agronomic, environmental, social and ethical
consideration. Hence an average definition of sustainable agriculture would include such
statement as soil fertility and productivity (rotations, integrated pest management, tillage
method, crop sequence), management strategies (choice of hybrids, low cost input etc)
human needs (demand for food and fibre) economic viability, social acceptability, ecological
soundness and time span.
NRC (1993) consequently accepted that land use is sustainable when its productivity
is economically adequate, socially just and culturally viable.
Ikerd (1990) noted that economic perspective of sustainable agriculture focuses on
the economic performance and viability of farming system which are sufficient to adequately
reward procedures and thereby maintain operation. In commercial agriculture, farms which
are unable to generate sufficient profit because of low farm prices, reduced yield, higher cost
of production or whatever reason are not self sustainable. Izac and Swift (1994) argued that
for small scale farming in Sub-Saharan Africa, an agro-ecosystem is sustainable when over a
period of decade or more, the annual yield of agricultural product shows a non declining
40
trend at a mean level sufficient to satisfy the nutritional and economic base needs of the
individual farmer and the community he inhabits. The agro ecosystem must have capacity to
resist and correct fluctuation that have a 33% or greater probability of occurrence in any
given year.
In the opinion of Okigbo (1989), a sustainable agriculture is one, which maintains an
acceptable and increasing level of productivity that satisfies prevailing needs and is
continuously adapted to meet the future needs for increasing the capacity of the resource base
and other worthwhile human needs. He emphasized an integral approach to overall
sustainable likelihood and development strategy that gives priority to better management of
resources so that their uses in satisfying human needs minimize damage to the environment.
The most quoted principle of sustainable development is that offered by the WCED
(1987) as “development that meets the needs of the present without compromising the ability
of future generation to meet their own needs”. Three necessary conditions for sustainable
development can be identified within the definition of sustainable development which is
implicit in the WCED definition and explicit in the definition offered by economists.
1. Intergenerational equity as implied in WCED definition
2. Ecosystem resilience, this stresses the interdependence of natural systems and the
processes which bring about environmental change.
3. Equity in opportunity and human development
The implementation of these principles fundamentally leads to environment and
equity constraint on economic optimization (Pearce, 1999).
Nijkamp and Vreeker (2000) reported that studies on sustainable agricultural
production are multidimensional, the range of subject is vast, although there is clear bias
41
towards agricultural and land use studies. The theoretical frameworks and methodological
foundation also vary widely. But specifically, these studies could be grouped into at least
three dimensions; the biophysical, the social and economic. Therefore, any study that seeks
to evaluate agricultural production on any of these three dimensions is actually a
sustainability study. They presented a framework within which the sustainability of
agriculture can be assessed. Their model recognized biophysical, socio political and techno-
economic dimensions. They concluded that the productive capacity of land resources is the
basis of ecological (environmental) view of sustainability.
Scholes (1994) outline the determinant of sustainable land use to include physical,
biological and socio-economic factors. The socio-economic determinants include technical
policies, population, infrastructure, inputs, institutions and cost-benefit relationships. Kidd
and Pimental (1992) argued that soil resources are logical indicators of economic limits
because soil is widely viewed as the greatest limits to long term productivity in low input
agriculture. However, Pannell and Glenn (2000) reported that crop yield represent an easily
quantifiable measurement of agro-ecological productivity that is central to the need of
subsistence farmers.
2.2.2: Measurement of Sustainability of Agricultural Production
Sustainability is a constantly use word by agricultural development expert and
environmental economist but few have venture to define the concept clearly, even fewer
suggest how it may be measured (Rothmans and Bouyn, 1998). The most frequently
employed measure of agricultural productivity is yield generally expressed as grain yield per
hectare per crop. Total factor productivity was suggested by Lynam and Herdt (1989), for
measuring sustainability of a production system. Their operational approach is based on
42
trends in output as a single criterion of sustainability. They define sustainability “as the
capacity of a system to maintain output at a level approximately equal to or greater than its
historical average, with the approximation determined by the historical level of variability”.
They measured sustainability by the ratio of value of output and value of input.
Elui and Spencer (1993) measured sustainability by estimating the interspatial and
inter-temporal total factor productivity of alternative farming systems, paying special
attention to valuation of natural stock and flow.
Harrington (1990) reviewed alternative approach of measuring sustainability of an
agricultural system and concluded that it should include yields, total factor productivity and
the production function.
Pannel and Schilizzi (1999) argued that sustainable indicators are a practical and
reasonable vehicle for attempting to deal with the multifaceted nature of the ambiguous term
sustainability. There is a large and growing literature on different types of indicators for
sustainable development (SD). These indicators attempt to capture important aspects of the
broad concept of sustainable development. In other words, alternative definitions of SD lead
naturally to alternative ideal measure although these measures vary in the extent to which
they are empirically observable. They argued that indicator ought to be useful at a number of
different levels; for research purposes, for policy makers and as a source of information for
the general public.
Gallopin (1997) surveys a wide range of literature and reports that in different sources
an environmental indicators has been identified as a “variable --- a parameter, --- a measure –
a statistical measure --- proxy, -- a value, -- meter or measuring instrument – a fraction --- an
index -- a piece of information, --- a single quantity – an empirical model – sign”. Moxey
43
(1998) argued that there is no wide spread agreement on design and use of what he calls
Agric-Environmental Indicator. Glenn and Pannell (1998) submitted that “an indicator is a
quantitative measure against which some aspects of policy performance or management
strategy can be assessed”.
From an examination of the literature, Hamley et al. (1999) identified the following
classification of indicators of sustainable development.
Table 1: A Classification of Indicators of Sustainable Development.
Group Example/Unit Studies Ecological/ environmental Single Air quality NO4ppm Department of the Environment SO4ppm 1996 or UNEP, various Water quality DO mg/l Soil erosion Tonnes/ha/year Aggregate Net primary productivity Energy/m2 or ton/ha Vitouusek et al (1986) Ecological footprint ha/person Ree & Wackernagel (1994) Economic Single Consumption per capita $/ person Scottish Office (1996) Real wages $/person Unemployment No unemployed/region Aggregate Green Net National Product $ Hartwick (1990) Genuine saving $ Pearce & Atkinson (1993)
44
Socio political Single Literary Literacy rate/1000 World Bank (1995) Mortality Death/1000 WRI, Various. Index of social $/person Economic welfare Daly &Cobb (1989) Aggregate Genuine progress indicators $ /person Cobb et al.(1995) Human development index Index UNDP 1996 Source: Hamley et al. (1999).
Example of indicators relevant to agriculture identified by Pannell and Glenn (2000) include.
- Microbial biomass within the soil
- Organic matter in soils
- Protein levels of crops
- Diversity of production
- Earthworm density in soil
- Pesticide usage
- Effective crop root depth
- Depth to ground water table
Izac and Swift (1994) have earlier identified indicators for sustainability assessment
of agro ecosystem of sub-Saharan Africa to include the following:
(a) Cropping system scale indicators
45
(1) ratio of annual yield for all product to potential and or farmer’s target
yield.
(2) soil acidity and exchangeable aluminum content
(3) soil loss and compaction
(4) ratio of soil microbial biomass to total soil organic matter.
(b) Farming system scale indicators
(5) profit of farm production
(6) ratio of profit to farmer’s target income
(7) ratio of aggrading to degrading land area
(8) nutritional status of household
(9) drinking water quality
(10) source and availability of fuel.
(c) Village-catchments scale indicators.
(11) bounded rationally version of economic efficiency.
(12) bounded rationally version of social welfare.
(13) ratio of aggrading to degrading land area.
(14) stream turbidity, nutrient concentration and acidity.
(15) nutritional status of community.
(16) human diseases and disease vectors.
(17) biodiversity and complexity.
Most of the proposed indicators are strongly technical in focus with no close link to
management decision. It has been recognized that the type of indicators most useful to
different group of users (e.g. on farm and off farm) are likely to differ. From the perspective
46
of agricultural policy, there are two broad decisions to make: which indicator to recommend
to farmers and which indicators to collect to assist in policy making. As earlier mentioned,
these two sets are likely to differ. However, whatever is recommended to farmers, it has to be
recognized that farmers will make their own independent choice based solely on their own
perception about whether indicators are worth monitoring. Indicators should not make
unrealistic large demand on farmer’s time and energy (Glenn and Pannell, 1998).
Webster (1999) noted that while farmers may attach value to sustainability goods,
they are unlikely to adopt socially optimal levels without regulation or incentives. Since
sustainability issues at the farm level are usually long run, dynamic and have social
dimension, a central task for farm management researchers lies in investigation which allows
trade off between different sustainability criteria to be determined and then optimized
according to society’s norm. Bablier (1989) observed that poverty is likely to force farmers
to use unsustainable management practices.
Izac and Swift (1994) proposed that the principal way in which shorter-term
assessment of sustainability can be made is the use of cross-sectional studies. They argued
that the relative values of the key indicators applied to a cross section of different agricultural
land use system at a given point in time may in itself give an indicator of relative stability.
Ali (1996) quantifies the socio - economic determinant of sustainable crop production
among wheat farmers in the Tarui of Nepal. He collected 150 samples in a cross sectional
study and utilized production function in his analysis. The result shows that soil fertility has
deteriorated owing to long term practices that are incompatible with soil and drainage
condition; the farmers are inefficient in resource use and that increasing population pressure
47
on land, decreasing livestock number per crop area, and diminishing fuel wood sources, have
significantly reduced farm base nutrient cycling and sustainability.
Byiringiro and Reardon (1996) also employed a production function in assessing the
effect of farm size, soil erosion and soil conservation investments on land and labour
productivity and allocative efficiency in Rwanda. The results showed that farms with greater
investment in soil conservation have much better land productivity than average and that
smaller farms are not more eroded than large farms, but have twice the soil conservation
investment.
Udoh (2000) studied land use and efficiency of food crop production among farmer in
Eastern Nigeria with a cross sectional data from 300 farmers. His estimates of short run
sustainability index (SRSI) showed that 73 percent of the farmers impaired farm productivity
while 27 percent improved farm productivity and the index was significantly related to crop
yield. The result further showed that different land use and management practices in term of
length of fallow, crop diversification and crop configuration had impact on sustainability
index of the farms.
Rigby et al. (2001) developed an indicator of sustainable agricultural practices ISAP
at the farm level for a sample of 237 UK horticultural producers. The pattern of ISAP scores
generated indicates that the discrete categorization of organic farms as sustainable and non-
organic farm as unsustainable may be gross over simplification in many cases and that even
suggesting that all organic farms as a group have progressed further toward sustainability
may also be inappropriate. They argued that indicators can be extremely useful in that they
force those involved in discussion of sustainability to identify the key aspects of
sustainability of agriculture and to assign weight to them.
48
Gustauso et al. (1999) reported that it would be more fruitful and cost effective to
focus attention on a small number of indicators within selected indicator classes. And that
proliferation of indicators would not necessarily improve the reliability of models to provide
informed policy guidance.
This study utilized a cross sectional data obtained from 149 fadama farming
households to develop an Indicator of Sustainable Agricultural Practices (ISAP) in the
fadama. In addition, profitability, return on Naira invested, technical efficiency, inefficiency
factors and productivity of fadama cropping system were calculated as part of the approaches
used to measure the sustainability of the food crop production in fadama of Southern Guinea
Savanna; Niger State, Nigeria.
2.2.3: Fadama Land Use, Soil Fertility Management and Sustainability.
Kolawole (1991) reported that the relative wetness and dryness of fadama land and
the seasonal and interannual variations of the moisture level of the top and bottom land
components of the system are particularly important from the point of view of environmental
resource management. This allows extension of the production period in dry season and
reduces the risk of crop failure during the wet season. Although spatially scattered and
fragmentary, wetland constitutes a significant proportion of total area in the dry lands of
Northern Nigeria. The production decisions involved in fadama land use are made within
particular socio-agronomic and economic contents.
Kolawole and Scoones (1995) reported that portfolio and investment model is an
effective and efficient response mechanism used by fadama farmers to counter environment
49
risk and uncertainties. The strongest component of this portfolio management strategy is
agricultural diversification. Farmers also get involved in occupational diversification; this is
when agricultural activities are complemented with other occupations.
Odo and Gwary (1996) reported that there are important criteria which determine the
cropping pattern in Jere bowl in Nigeria Sudan Savanna. The criteria are;
1. The timing of the period of tilling of the soil, between the harvest of one crop and
planting of the next on the same or different farm lands
2. The possible crop combinations that allow for complementary yield responses and
reduced intercrop competition.
3. The sequential planting of crops which improve the soil with those that deplete the
soil nutrient.
4. The rational utilization of labour, irrigation pumps technology and other technical
services.
They identified three (3) major cropping systems, involving cereals and legumes.
The crop mixtures are millet-cowpea, millet-groundnut and sorghum-cowpea with or without
leafy vegetables.
According to Kolawole and Scoones (1995), sustainable development is consistent
with increasing environmental assets or development without destroying the future of the
natural capital stock. The natural capital stock involved in the fadama are the agricultural
land, fisheries, forestry’s, wildlife, water resources, grazing land, and other flora and fauna.
They reported that evidence suggests that fadama is probably one of the most endangered
environmental resources in Northern Nigeria. They identified factors threatening its
50
sustainability are development intervention, changing land tenure system, and population
pressure.
Adams (1983) reported that irrigation development has resulted in the construction of
large multipurpose dams and flood control measure that have impacted on the nature and
scope of flooding of fadama land. In the Sokoto Rima Basin area, the construction of
Bakalori Dam led to the loss of about 14,700 ha of fadama land while another 20,000 ha of
fadama down stream had their productivity affected in order to irrigate just, 22,500 ha of
land.
Yakubu (1991) reported that Bakalori Dam and Gonroyo Dam have combined to
reduce the total area flooded from 92% before their construction to 33%. In addition, the
various flood control measures seems to be failing, as flash flood have become more
common in down stream area like Argungu in Kebbi State. This has heightened the risk and
uncertainties resulting in drastic change in cropping pattern from rice cultivation to less
productive crop millet.
According to Lawal (2001), the construction of Jebba Dam has led to changes in
socio-economic life and cropping pattern of the riparian communities down stream to the
dam. The frequent flash flood experienced has led to changes from rice cultivation to less
productive crop guinea corn. School, health care centre, markets and houses were frequently
flooded; this leads to disruption of socio-economic life of some communities and eventual
resettlement in other areas upland.
Kolawole et al. (1990) reported that the quest for improvement of fadama has ushered
in technological innovation such as development of small irrigation pump, small earthen
dam, wash bore and shallow tube wells. With such innovation, fadama land is witnessing
51
intensive exploitation accompanied by diversification and intensification of land use. As a
very valuable resource, fadama land is most affected by land use decree with impact
depending largely on proximity to urban areas. Fadama in remote areas like Hantsu in
Hadejia valley of Kano State are mostly owned by indigenous farmers and population
pressure is low unlike the Danbatta, Tomas and Galma fadama where plots are controlled by
urban dwellers. With high population pressure, per capita holding size is decreasing and
intensification of cropping is increasing with resultant effects on productivity and
sustainability.
In his work on “fadama resource sustainability: lessons from the Jere rice bowl
experience, Borno State Nigeria”. Odihi (1996) makes the following observation about
effects of development intervention on fadama areas.
1. Dam construction in Nigeria dry belt may have adverse economic and social
effects on down stream communities if water supply to the area is inadequate.
2. Downstream communities suffer from reduced farm size and yield due to the
decreased nature of flood variable such as frequency, depth, extent and duration to
which economic activities have been tailored.
3. Development may be biased both in conception and reality. It is therefore
necessary to specify the target population for whom development is meant. This
will allow for incorporation of necessary wider interests.
4. Dam construction is capable of destabilizing the fadama-based economy by
presenting obstacles to effective resource utilization.
52
5. Fadama resources and their use must not be taken for granted. Adequate care
must be taken to ensure their sustainability.
According to Mahalanobis and Adak (1994), sustainable land use requires planning
for conservation, management and development of land. Land use planning should ensure
that every piece of land is put under proper use to which it is best suited. That is, there
should be continuous assessment of the status of land use and its productivity vis-à-vis socio-
economic conditions of the people.
In a bid to evolve the most profitable crop combination and geometry that would be
effective in checking soil erosion and loss of water via run-off, Mandal and Nitra (1989)
confirmed intercropping as the best option.
In the word of Lai (1995), effective land management systems and technologies are
crucial to overcoming the misuse of soil in agricultural production. According to him, the
solution lies in controlling land use and restoring its productivity, which can be achieved via
innovative technology options. These could be in terms of land clearing and development,
tillage methods and other agronomic practices. Research in tropical regions has indicated
advantages of conventional (manual) over machine clearing (Lai et al. 1986). Kaoneka and
Solberg (1997) ascribed major causes of land degradation to increased deforestation coupled
with inappropriate farming practices including over growing.
Nitant and Tiwari (1990) noted that proper agro-techniques in land use are necessary
in order to achieve higher yield and productivity in addition to sustainable soil and water
conservation measures.
Campbell et al. (1998) examined soil fertility management in small-scale farming
systems investigating, whether mixed farming system are sustainable. It was discovered that
53
farmers maintain crop production by using inorganic fertilizers and variety of locally derived
fertilizers, manure, crop residues, woodland litters, composts and house waste. Mixed
farming was found to be profitably and sustainable. Rai (1995), noted that decisions
concerning the land use, management and conservation are made by farmers in context of
family concerns and priorities.
Spio (1996) noted that traditional agricultural production safeguards against
outbreaks of pests and diseases and the vagaries of the weather by planting a variety of crops
in the same field. In this manner, farmers could reduce the risk of total failure in the food
supply. It was concluded that this practice gave higher output and stability of income to the
farmer and sustainability of land use pattern.
Adewumi (1999) investigated irrigation management practices of the fadama farmers
along Asa river in Ilorin, Kwara State, Nigeria with a cross sectional data from 70 farmers.
The study revealed that the farmers are not making efficient use of their resources. There is
therefore a need for adjustment to improve efficiency and farm net income to ensure
sustainability of the small-scale fadama irrigation of the farmers. Continuous provision of
adequate and accessible inputs at subsidized price was recommended as an integral part of
strategy aimed at increasing productivity.
Alamu and Coker (2005) assessed the sustainability of fadama farming in northern
Nigeria. 120 fadama farmers involved in cultivation of tomatoes and pepper were selected in
a cross sectional survey. Simple descriptive statistics and farm budgeting techniques were
used for the analysis. The results show that farmers who practiced mixed cropping of tomato
and pepper made higher profit of N70, 215/ha than those who cultivated tomato and pepper
sole who made N54, 192.40 and N11, 711.70 per hectare respectively. The fadama farming
54
was considered sustainable because of the profit made by the farmer. The study
recommended mixed cropping for sustainable fadama farming.
2.2.4: Economic Environmental Modeling.
In an attempt to measure sustainability, interdisciplinary modeling has been
developed and literature contains some useful attempts to grapple with the interaction
between environmental functions and economic actions. At conceptual level Tschirhart and
Crocker (1987); Costanza and Daly (1992); Costanza et al, (1993) and Russell (1993) have
much to explain. To draw from the vernacular of Barbier (1990), “the key question raised in
environmental economic modeling is what useful economic functions does the environment
provide and how are these functions affected by the process of economic environmental
interaction?” He used two different formal approaches to answer these questions; the more
conventional approach that is concerned with the optimal allocation of economically valuable
exhaustible resources and an alternative analysis that considers the trade off between
environmental quality and economic optimality.
Griffith and Zepeda (1994) examined trade-off between costs and production
practices of intensification of milk production. The result of their study showed that
significant economic and environmental trade off is found in response to low labour
productivity or availability and low protein content of forages. Moreso, costs were identified
as the focus on the economic side while on the environment side, intensification incentive to
deforest, use of cut feed versus more erosive pasture, and manure versus chemical
fertilization were identified.
Lutze and Manasinghe (1994) tried to integrate environmental concerns into
economic analyses of projects. They concluded that efficient use of natural resources is a
55
vital prerequisite for economic and social development. Therefore, one key role of
ecological economics is to help value environmental and natural resources more precisely
and internalize the cost and benefits of using such resources into the decision making
process.
In a way to analyze deforestation and economically sustainable farming system,
Kaoneka and Solberg (1997) noted that for existing farming systems to become economically
sustained, it is important to improve farming technology which could increase crop
production via improvement of land productivity and to increase income from other sources.
Such strategy will meet the increased food demand as a result of population growth as well as
limiting the expansion of farmland via forest clearing.
Phillis and Andriantiatsaholiniaina (2001) developed a model called Sustainability
Assessment by Fuzzy Evaluation (SAFE) which provides mechanism for measuring
development sustainability. Ecological, (land, water, air and biodiversity) and human
(economical, social, educational and political) inputs are treated individually and then
combined with the aid of fuzzy logic to provide an overall measure. The output of the model
is a degree (percentage) of sustainability of the system under examination.
Grossman and Krueger (1995) used Environment Kuznet Curve (EKC) to describe
the relationship between some pollutant and income level as an inverted U. That is,
increasing level of pollutant for people living in lower income countries and declining levels
of pollution for higher per capita incomes. The relationship derives its name from the work
of Kuznet (1995) who postulated a similar association between income inequality and
income levels. The results of these relationships lent evidence for the suggestion that
56
economic growth can be compatible with environmental improvement, if appropriate policy
responses are taken.
Torras and Boyce (1998) while probing the environmental Kuznet hypothesis agree
with Grossman and Krueger (1996) that putting brake on economic growth in the developing
world is not an acceptable or even a wise response to the pressing environmental concern of
our time. They however added that effort to achieve a more equal distribution of income,
wider literacy and greater political liberties and civil right can positively affect environmental
quality especially in low income countries. There is evidence that rising average income in
the upper income range countries is associated with renewed deterioration in some dimension
of environmental quality.
Modeling cropping systems after the sustainable agriculture demonstration farm,
Kelly et al. (1996) utilized EPIC (Erosion Productivity Impact Calculator) to simulate long-
term impacts of different cropping system in terms of the trade off among net returns and
different components of environmental quality. By a way of simulation via EPIC it was quite
computationally convenient to obtain crop yield, soil erosion, and the environmental impact
of Nitrogen, Phosphorus and herbicides in response to weather and management practices
over a simulated 30 years period.
Midmore et al. (1996) executed an economic environmental impact study to evaluate
current production pattern and profitability of vegetable production, their result showed
environmental impacts and externally cost of soil erosion and management practices to have
adverse effect on vegetable production.
Furthermore, Jones (1991) explained the versatile nature of EPIC in environmental
economic modeling. They noted that EPIC was designed to help decision-makers analyze
57
alternate cropping systems and predict their socio-economic and environmental
sustainability.
Deybe and Flichman (1991) developed a regional agricultural model using EPIC to
determine the possible effects of economic changes on regional supply and revenue as well
as the level of farm structure and farm relationship. They concluded that the use of
agronomic plant growth models and mathematical programming models is a possible
methodology for obtaining results that combine political and economic effects with
environmental aspect at a regional level.
Schans (1991) employed an interactive multiple goal linear programming technique
(IMGLP) for the optimization of arable farming systems that integrate economic and
ecological goals. Production systems ranging from low-input to high-input are generated
with a computer by a systematic variation of major crop management components. The
farming systems selected with the models serve as a guideline for the introduction of
sustainable arable farming systems.
Manyong and Degand (1995) utilized multiple objective mathematical (goal)
programming in socio-economic and environmental modeling of smallholder farming
system. The result of their study showed that the system could be made to be more efficient
if new technologies are introduced.
Bockstrael et al. (1995) attempted an ecological and economic modeling in order to
improve basic understanding of regional systems, assess potential future impacts of various
land-use, development, and agricultural policy options, and to better assess the value of
ecological systems.
58
According to Hewitt and Lohr (1995), researchers may ignore economic feasibility as
a criterion for selecting among technologically feasible alternative when designing and
implementing alternative cropping sequences for on-farm testing. They developed a
consistent approach for simulating economic returns and environmental characteristics of
field level production system using ex-date evaluation.
Apparently, attempts at environmental-economic modeling are extremely complex
and cumbersome, as only a few functional relationships have been quantified. This study
takes a parallel modeling approach utilized by Ali (1996), Byiringiro and Reardon (1996) and
Udoh (2000). It shows how index of short run sustainability at farm level can be estimated
within a stochastic frontier context.
2.2.5: Efficiency/Productivity Measurement in Agriculture.
Economists have used various models to carry out empirical analysis of efficiency in
agricultural production. The specific models for measuring efficiency reviewed in this
section are statistical production function, mathematical programming and econometric
estimation.
Production function describes the relationship between inputs and output in a given
state of technology. In production analysis, various functional forms can be used (Heady and
Dillon, 1962). Studies on farm productivity using cross sectional data have mostly used
single equation approach applying the OLS estimation technique. Hence the estimated
function could be more appropriately described as response or average function (Battese,
1992).
59
Efficiency and productivity growth are usually related as productivity is generally
defined in terms of the efficiency with which input are transformed into useful output within
the production process. Fried et al. (1993) emphasized that productivity varies due to
differences in production technology, differences in efficiency of the production process and
differences in the environment in which production occurs. Hussain and Perera (2004)
classified the determinants of productivities as land and water related factors, such as farm
size, water course, location and sources of water; climatic factors, agronomic factors, socio-
economic factors, as well as farm management factors like adoption of modern production
technologies, farm planning and management practices.
Jayaram et al. (1992) carried out a production function analysis of farms on Mandya
district of India. The objective of the study was to estimate the efficiency of production in
the study area. The analytical framework was based on the frontier production function built
around the concept of efficiency developed by Farrell (1957). The result indicates that
farmers were operating at constant returns to scale. The levels of output efficiency were
generally high. The average output efficiency for the large farmers was 96 percent while the
average for small farmer was almost the same at 97.5 percent this suggesting that the farmers
achieved relatively high levels of efficiency on rice production.
Oredipe (1998) examined resource use efficiency in maize farms in Ogun State of
Nigeria. The production function was used to analyze resource productivity in the small plot
Adoption Trial (SPAT) and farmers’ adjacent plot (FAP). The study demonstrates that there
are both intra and inter efficiency differentials on the study area. Estimated technical
efficiency indices indicate that the least efficient farmers could obtain output that is 33 to 38
percent above current level. In each of the two scenario studied, 8 – 37 percent of the farmer
60
had efficiency scores that were 30 percent below the efficiency frontier which average zonal
indicates were 27.5 percent below the frontier. Others who have used production functions
in various studies in Nigeria include Lawal (1998), Adewumi and Omotesho (2002), and
Babatunde et al. (2005).
Estimation of efficiency through mathematical programming is also available in many
literatures. Many previous studies in Africa employed static linear programming models (see
Clayton, 1961; Ogunfowora, 1970; Abalu, 1976 and Crawford, 1982).
Belete et al. (1993) in their study of efficiency of small-scale farmers in Ethiopia
employed linear programming and MOTAD (minimization of total absolute log deviation).
The study indicated a substantial potential for increasing net cash income by efficient
allocation of available resources under current level of technology.
Kangasniemi and Reardon (1997) studied the demographic pressure and the
sustainability of land use in Rwanda by utilizing regression analysis for crop cover index.
Their interest was whether increasing land scarcity, reflected in fragmentation of farms is
associated with Rwanda farmers having unsustainable land uses. The result showed that the
estimated relationship between farm size and protective cover depends crucially on how the
measure of vegetative cover is adjusted to account for high cropping densities. Without any
adjustment the association between land scarcity and erosive land use is strong; with the
adjustment, it disappears, except for high altitude areas, where bananas, the only major food
crop that protect land well against erosion, do not grow well.
Liewelyn and Williams (1996) concluded a non-parametric analysis of technical
efficiency for irrigated farms using linear programming techniques. Their procedure allowed
the relative technical efficiency for each firm to be determined and for inefficiency to be
61
decomposed into pure technical inefficiency and scale inefficiency. The result showed that
farmers operating inefficiently do so more often because of scale inefficiencies rather than
pure technical inefficiencies as majority of the farms operate in the region of decreasing
return to scale rather than increasing return to scale. Farmers’ age, level of diversification of
cropping activities and education were found to be related to technical efficiency.
An extension of utility efficient programming to the non linear discrete stochastic
programming method was developed and used in the analysis of economic efficiency of a
sample of farmers in Iran by Torkaman and Hardaker (1996). The result indicated that it
would be feasible to increase substantial farmer’s total net revenue by increasing their
economic efficiency in term of technical and allocative efficiencies. The study further
suggested that risk aversion plays an important role in farmer’s behaviour.
In Nigeria, other researchers like Ogunfowora (1970), Abalu (1976), Aromolaran
(1992), Tsoho (2004), and Ndanitsa (2005) have also carried out various studies on Nigeria
agriculture using linear programming technique.
2.2.6: Empirical Application of Stochastic Frontiers Model-The Model for the Study.
Econometric modeling of stochastic frontier methodology of Aigner et al. (1977)
associated with the estimation of efficiency has been an important area of research in recent
years. Basically these studies are mostly based on Cobb-Douglas function or trancedental
logarithmic (translog) function that could be specified as either production function or cost
function. Panel data, time series data and cross sectional data are mostly used. The first
application of stochastic frontier model to farm level agricultural data was by Battese and
Corra (1977). But technical efficiency of farms was not directly addressed in the work.
62
Taylor and Shonkwiler (1986) estimated both deterministic and stochastic production
frontiers of Cobb-Douglass type for participants and non-participants of the World Bank
sponsored Credit programmes for farmers in Brazil. The report only highlighted the
parameters of the frontiers on the basis that the farm effects had gamma distribution in the
deterministic frontier and half-normal for the stochastic frontier. However, Taylor and
Shonkwiler (1986) did not report any comparison of the deterministic and stochastic frontier
used and later concluded that their results were somewhat confusing as to the impact of the
World Bank programme.
Bravo-Ureta and Rieger (1991) employed both the deterministic and stochastic
frontier production to estimate the efficiency of sampled diary farms in the north-eastern
states of the U.S.A over a two year period. The maximum-likelihood method estimates of
the stochastic Cobb-Douglass functional form showed results that were not significantly
different from that of the deterministic frontier. The study showed that frontier function
models are neutrally upwardly scaled version of the OLS or the average model. Second, they
obtained and compared estimates of technical efficiency from four different models. They
find that even though levels of technical efficiency vary from one estimation method to the
other, they are highly correlated. This suggests that the ranking of the farm according to their
efficiency is not affected by the choice of model.
Battese and Tassema (1993) applied their panel data model incorporating time-
varying firm effects in the analysis of data for paddy farmers in an Indian village who were
observed for up to ten years. Given the specifications of a stochastic frontier production
function with time-invariant parameters, the time trend included was not significantly
63
different from the average response function i.e. technical inefficiencies could be considered
absent from the model.
Habibullah and Ismail (1994) determined the status of technical efficiency for a
sample of bee keepers in Malaysia using Cobb-Douglass based stochastic frontier production
function. The technical efficiency index computed shows a mean efficiency ratio of 0.625
implying that substantial inefficiency exists among the Malaysian bee keepers in the sample
under study.
Kabede (2001) estimated a stochastic frontier production for rice farmers in the mid-
hills of Nepal. The study also assesses the various distributional assumptions made on the
estimation of the stochastic frontier models and compare estimation results for technical
efficiency. Moreover attempt was made to demonstrate the alternative approach to
estimating normal-gamma stochastic frontier model based on the method of simulated
maximum likelihood estimation as in Green (2000). The results indicated a useful extension
of the stochastic frontier model empirically, supporting the operational aspect of the model.
Singh and Singh (1995) studied land degradation and economic stability in an attempt
to measure the impact of soil salinity and water level in terms of resource use, productivity
and profitability of crop production and its consequent effect on employment of overall
labour in the affected areas. They utilized Cobb Douglas production function. The study
observed a huge cut in non-land resource use on problem (salt affected) soil as compare to
normal soils, which consequently resulted in low farm production and income and as a
consequent, employment opportunities were also not great on problem soils.
In Nigeria, Ajibefun and Abdulkadri (1999) carried out a study that investigated
technical inefficiency of production among food crop farmers in Ondo State, Nigeria. The
64
study considered translog stochastic frontier production function in which the technical
inefficiency effects were defined by three different sub-models. Given the specifications of
the frontier production function, the null hypothesis, that the frontier is adequately
represented by the Cobb-Douglas function, is accepted. But the null hypothesis that the
farmers are fully technically efficient, which implies that inefficiency effects are absent from
the model, is rejected. Further, the null hypothesis of half-normal distribution for the
inefficiency effects is rejected. Predicted technical efficiencies vary widely across farms,
ranging between 21.7% and 87.8% and a mean technical efficiency of 67%.
Ogunjobi (1999) applied stochastic production frontier for efficiency analysis in
smallholder cocoa farmers in Ondo State of Nigeria. The results of the study reveal that
technical efficiencies vary between 0.21 and 0.94, with a mean technical efficiency of 0.63.
The determinants of technical efficiency among the sample farmers include farmer’s age,
which was found to be negatively related to production efficiency, while education and age
of cocoa trees have positive influence on production efficiency.
Ajibefun et al. (2002) used a translog stochastic frontier production function to study
the factors influencing the technical efficiency of food crop farmers in Oyo State in Nigeria.
The estimated technical efficiencies of the sample farmers varied widely, ranging from 19%
and 95% with a mean of 83%, indicating that the farmers are 83% efficient in the use of their
production inputs. Age of farmers, farming experience, level of education, size of farm
holdings as well as, ratio of hired labour to total labour use were factors that significantly
influenced the level of technical efficiency.
Rahji (2003) applied stochastic production and cost function to broiler production in
Oyo State, Nigeria. The study reported that the broiler firms attained about 80.5 percent
65
technical efficiency. The allocative efficiency is about 74.9 percent while economic
efficiency stood at 60.3 percent. The result obtained implies that the average firm is
potentially capable of increasing production without increasing its use of inputs.
Awoyemi and Adekanye (2004) undertook a gender analysis of economic efficiency
of cassava based farm holding using stochastic parametric decomposition functional form.
The results indicated that average overall productive efficiency in the sample was 78.69
percent. The average economic and allocative efficiency of male respondent was better than
the female respondent. However, female respondent was reported to be more technically
efficient than male respondent.
Awoyinka and Ikpi (2004) utilized stochastic frontier production function to assess
resource use efficiency in sugarcane production among farmers in Jigawa State, Nigeria. The
study reveals an average compounded gross margin and average compounded net farm
income of N101, 696.48 and N90, 154.93 per farmer respectively. All variables in the
stochastic frontier function except irrigation water were significant at1% level of probability.
The mean technical efficiency was 0.94 and the farmers were operating at increasing return
to scale.
Ohajianya (2005) estimated the economic efficiency among small scale poultry
farmers in Imo state, Nigeria with a stochastic frontier production model. The predicted
efficiencies differ substantially among the farmers ranging between 0.16 and 0.89 with a
mean efficiency of 0.43. The mean economic efficiency of the poultry farmer is low, which
is an indication of inefficiency in resource use by the poultry farmers.
Onyenweaku and Effiong (2005) measured the level of technical efficiency and its
determinants in pig production in Akwa Ibom State, Nigeria using a stochastic frontier
66
production function. The results show that technical efficiency in pig production in Akwa
Ibom State is relatively high with a mean of 83.6%. Farming experience, farm size,
membership of cooperative societies/farmers association and extension contact were the
important factors directly related to technical efficiency.
Okoruwa and Ogundele (2005) examined technical efficiency differentials between
farmers farming two varieties of rice: the traditional and improved rice varieties in Nigeria.
The study used stochastic frontier production functions in which the technical inefficiency
effects are assumed to be functions of education status of farmers, number of contact with
extension personnel, rice farming experience and household size. The results indicated that
significant increase recorded in output of rice in the country could be traceable mainly to area
expansion. The estimated average technical efficiencies for the two groups were greater than
90 percent which implies that there is little opportunity for increase efficiency given the
present state of technology.
In another study, Rahji (2005) used the stochastic frontier production function to
investigate the production efficiency differential between two rice farmers’ groups in Niger
State, Nigeria. These farmers’ groups are categorised as adopters and non-adopters of the
recommended improved rice technologies based on an adoption score of 40 percent.
Multiple regression analysis involving the estimation of stochastic frontier production
function was used in analyzing the data set for the groups. The results indicated that, land,
labour, and seed were significant factors that influence rice production. The result also
showed over-utilization of labour in rice production system in the study area. The two
farmers’ groups attained 79 and 65 percent technical efficiency levels respectively. Farm
67
size, education, household size, and distance to input source were found to affect the
technical efficiency of the farmers.
In a recent work, Fatoba (2007) used stochastic frontier production function to
determine the technical efficiencies of production technologies for rice soyabean and
sugarcane in the Middle Belt Zone of Nigeria. The study revealed mean output oriented
technical efficiency of 0.65, 0.51 and 0.99 for soyabean, rice and sugarcane farming
households respectively. The study further reported the presence of short-run increasing
return to scale for soyabean and rice, and decreasing return to scale for sugarcane.
CHAPTER THREE
METHODOLOGY
This chapter presents the methodology that was employed in this study.
3.1: Area of Study.
The study was carried out in the fadama areas of Niger State, in the Southern Guinea
Savanna of Nigeria. Fadama are seasonally flooded plains along major rivers and or
depressions on the adjacent low terraces. The fadama along river Niger and river Kaduna
and other minor rivers and floodable plains in Niger State were used for the study.
Niger State lies between longitude 8o 11’ and 11o 20’ north of the equator and
between 4o 30’ east of the equator. It covers an estimated land area of 4240 km sq. The
vegetation of the state is mainly Southern Guinea Savanna. The mean annual rainfall ranges
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between 1110 mm in the north and 1600 mm in the south. The average annual number of
raining days ranges between 187 and 220 days. The rain starts in late April and ends in
October with the peak being in July. The average minimum temperature is about 26o C while
the average maximum temperature is about 36o
Data used for this study were from both primary and secondary sources. The relevant
primary data were obtained through a farm management survey of fadama food crops
C. The mean humidity ranges between 60%
(January to February) and 80% (June to September). The vegetation supports the cultivation
of root crops and grains. The predominant crops are; rice, sorghum, millet, yam, groundnut
and cotton. The soil is of two types, which could be used for both agriculture and as raw
materials for industries (e.g. pottery industry). The two types of soils are (i) Ku soil –
Ferruginous soil of alluvial and colloidal origin. (ii) Ya soil – containing high level of
organic matter. The predominant occupation is farming while the main ethnic groups are
Nupe, Hausa, and Gwari (NCRI, 1997).
Niger State is divided into three zones by the Niger State Agricultural Development Project
(NSADP). The zonal distribution is in line with the agro-ecological characteristics and
cultural similarities of the areas. The zones are I, II, and III or Bida, Kuta and Kontagora
zones respectively (NSADP, 2001).
Zone I : - Bida, Katcha, Gbako, Agaie, Lapai, Lavun, Edati, and Mokwa LGAs
Zone II :- Bosso, Chanchaga, Gurara, Minna, Munya, Paiko, Shiroro and Suleja, Tafa
LGAs
Zone III:- Agwara, Borgu , Kotangora, Magama, Mashegu, Mariga, Rafi and Wushishi
LGAs
3.2: Method of Data Collection.
69
farming households conducted during the 2004/2005 cropping seasons. The main instrument
for data collection was a structured Questionnaire. This was administered on sampled head of
fadama food crop farming households by trained enumerators under the supervision of the
researcher. Data collected covered information on fadama food crop farming households
head characteristics (age, level of education, family size etc), land use and management
practices, input and output data, as well as their prices, crop combination and diversification
etc. The secondary data were mainly information from official records and edited journals.
3.3: Sampling Procedure.
The target population for this study is the fadama food crops farming households in
Niger State, Southern Guinea Savanna, Nigeria. A two stage simple random sampling
technique was used to select sample for the study.
The first stage involved the random selection of fadama farming villages in the three
ADP zones of the State. The 1991 fadama village listing of Niger State Agricultural
Development Project (NSADP) served as the sampling frame for the selection. Information
from NSADP revealed that about fifty percent of fadama farming villages/ households are in
zone I while the remaining are in zones II and III in fairly equal proportion. About five
percent of the total fadama farming villages in each of the zones of NSADP that make up the
study area were randomly selected for the study (Table 2).
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The second stage of sampling involved the random selection of fadama farming
households. The NSADP fadama households listing of 1991 served as the sampling frame.
About ten percent of the fadama farming households in each of the selected villages were
sampled for the study. A minimum of five households and a maximum of fifteen were
sampled from each of the sixteen selected villages based on the proportion of fadama food
crop farming households in each village.
A cross sectional data from 160 fadama food crop farming households were
collected for study. In the end, only data from 149 (93%) households were analysed as
others were discarded for inconsistencies or discrepancies.
Table 2 : Sampled Fadama Food Crop Farming Households Distribution Pattern.
NSADP Zone
No of Fadama Farming Villages
No of Fadama Farming Villages Sampled
Names of Villages Sampled
No of Fadama Farming Households
No of Households Sampled
Zone I (Bida)
173 8 Jaagi Wuya suma Edozhigi Ebbo Rakapa Landzun Kwakwagi(mambe) Muye
102 95 156 102 53 112 47 146
10 10 15 10 5 10 5 15
Zone II 87 4 Kafinkoro Gusouro Zunkushi Tufa
105 76 83 151
10 8 8 14
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Zone III 78 4 Kuruwasa Gadaoli Shagunu Debbe (Wushish)
147 153 52 47
15 15 5 5
Total 338 16 1627 160 Source: Adapted from NSADP (1991) Field Survey (2004/2005) 3.4: Analytical Techniques.
Combinations of analytical techniques were employed in this study.
(1) Descriptive statistics such as mean, standard deviation, frequency distribution
were used to capture the socio economics characteristics and pattern of land use
management. (Objective 1). In addition to descriptive statistics the following
indices were calculated to further investigate the influence of pattern of land use
and management on sustainability of fadama land.
(i) Crop Diversification Index
(i) Nutrient Intake Index
(iii) Ruthberg -Value (R-Value)
(2) Farm budgeting analysis was used to capture costs and return to food crop
production in the fadama. (Objective 2).
(3) Sustainability of agricultural practices in the fadama (Objective 3) was measured
with indicators of sustainable agricultural practices (ISAP) through scoring and
weighting of fadama production practices. The indexes were further presented
with sustainability web or radar diagram.
(4) Multiple regression model based on stochastic frontier was used to measure
technical efficiency, inefficiency factor, and productivity of resources, indices of
sustainable land use and management and short run sustainability index of the
fadama food crop farming households. (Objectives 4, 5 and 6).
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3.4.1: Land Use Pattern Indices.
3.4.1.1: Crop Diversification Index (CDI).
Stability of yield and revenue from crop planted are indicators of sustainable farm practice,
this was captured with crop diversification index. The crop diversification variable was
measured in this study by the Herfidahl index given as;
CDIj ∑=
n
iPi
1 = j22
2 (14)
Where:
CDIj is the crop diversification index for the jth household.
PiJ
The index was estimated to reflect how crop diversity pattern can affect nutrient
depletion and sustainability of farmland. This was measured as a ratio of crop configuration
to number of crops in combination. Crop configuration was derived by assigning different
weights to different classes of crop in combination and summing the weighted value for each
farm, and then dividing the value by the number of crops in such combination. The assigned
weights to the respective classes were based on nutrient depletion ability of crops in an
environment where nutrient augmenting input like fertilizer is inadequate (Fageria and
Baligar, 1993). It is expected that the yield of crops in combination would be affected if the
combined crops were mostly of the same class of crops. For instance, a combination of
= Proportion of total income from each crop in a particular enterprise.
A value approaching 1.0 indicates specialization whereas smaller values reflect
increasing diversification, stability of income and sustainability of land use pattern (Spio,
1996 and Udoh, 2000).
3.4.1.2: Nutrient Intake Index (NII).
i = 1
∑pij n ∑pij n
2
73
melon/maize/yam would not deplete soil nutrient as the case of cassava/yam/cocoyam
mixture. Therefore, combining crops that would deplete soil nutrients heavily do not show
sustainable land use practice.
The Nutrient Intake Index is given as
NII = ΣWi i = (2 ----, n) (15) n Where:
NII = Nutrient Intake Index..
n = Number of crop in combination
Wi
c + f (16) Where
c = Number of cropping years. This was obtained as the average number of years a land was
used before fallow.
= Particular weight attached to type of class of crop planted (legume = 1,
vegetable/cereal = 2, Root tuber = 3, stem tuber = 4)
Nutrient intake index is meant to capture the vulnerability of farm total output to
different crop combination. The index value ranges between 1 and 4. The higher the NII the
more the likelihood that crop combinations can affect nutrient depletion, land degradation
and sustainability of farmland (Fageria and Baligar, 1993; Ali, 1996 and Udoh, 2000).
(iii) Ruthenberg -Value.
The Ruthberg index value shows the land use intensity for an area. It shows if the
length of fallow may be adequate for soils to restore natural fertility. It is given as;
R – Value = c
f = Number of fallow years. This was obtained as average number of years a land was
allowed to fallow before further cultivation.
74
R – Value = 1 for permanent cultivation
The further the value is from one the more the likelihood that fallow would be
adequate to restore natural fertility and improve sustainability (Udoh, 2000).
Degrees of soil erosion, flooding and drainage were also part of variables used to
measure extent of likely land degradation, the level of nutrient depletion and fadama
sustainability.
3.4.2: Farm Budget Analysis.
The budgetary analysis was used for the estimation of costs, returns and profitability
of the various food crop enterprises by the fadama farming households. In developing the
farm budget, estimates of production cost and gross revenue from crop outputs during the
survey year were collected from the cross sectional survey of the fadama food crop farming
households. Comparisons were made between costs incurred and return obtained from each
enterprise. Profit is made when return are greater than costs, while losses occurred when
reverse is the case.
Following Olukosi and Erhabor (1988), farm budget was estimated on per hectare
basis as
NFI = GI - TVC - TF (17)
GM = GI - VC (18)
Where NFI = Net farm income
GM = Gross margin per hectare
Gross Income (GI) per hectare is the naira (N) value of the total output of the
enterprise or enterprise combination in a hectare. It was obtained by multiplying the yield/ha
75
in kg by price of output per kg obtained from each sampled farming households engaged in a
particular enterprise.
Total Variable Cost (TVC) comprises of cost incurred on the farm as production is
undertaken. It changes with the level of production. It includes the cost of fertilizers, seeds,
casual labour, herbicide, insecticide etc. It was obtained by finding the price of input(s) and
multiplying by the quantity of input(s) used per hectare for each farming household sampled.
One man-day of labour cost N275 during the survey.
Total Fixed Cost (TFC) is the cost incurred by firm which did not change with the
level of production. They are overhead cost that must be paid irrespective of the level of
production. The fixed inputs used during the survey consist of inputs such as baskets, hoes,
cutlasses and rain boot. The depreciated values of these assets were negligible. Therefore,
GM/ha was used for profitability analysis.
GM/ha was calculated for all the enterprises that the fadama farming households were
engaged in during the survey period. GM allowed for comparison of the profit of the
different enterprises cropped by the sampled households during the survey.
Return on a Naira Investment (RNI) which provides a measure of economic performance of
each enterprise in terms of revenue accruing to the households and cost of inputs employed
was determined as;
Return on a Naira Investment (RNI) GI
3.4.3: Production Function Analysis.
(19) TVC RNI > 1 for enterprise to be sustainable (Alamu and Coker, 2005)
Return on a Naira Investment was used to rank the enterprises of the sampled fadama food
crop farming household.
76
Multiple regression model based on stochastic frontier was used to measure technical
efficiency, inefficiency factor, productivity of resources and indices of sustainable land use
and management of the food crop fadama farming household .(Objectives 3 and 5).
The production frontier model derived from the composed error model of Aigner et
al. (1977); Meeusen and Van den Broeck (1977) and Forsund et al. (1980) as used by Coelli
and Battese (1996) and Rahji (2003) was adopted for this study. The frontier production
model begins by considering a stochastic production function with a multiplicative
disturbance term of the form.
Y = f (Xi ; β) e ε (20)
Where;
Y = quantity of agricultural output in grain equivalent.
Xi = vector of input quantities.
β = vector of parameters.
e = error term
Where ε is a stochastic disturbance term consisting of two independent element V and U
where
ε = V- U (21)
The symmetric component V, accounts for random variation in output due to factors outside
the farmer’s control such as weather and diseases. It is assumed to be independently and
identically distributed as N ~ (0, δu2
A one – sided component U ≤ 0 reflect technical inefficiency relative to the stochastic
frontier, f (X
)
i; β) e ε. Thus, U = 0 for a farm output which lies on the frontier and U < 0
77
for output which is below the frontier as N ~ (0.δu2 ) hence, the distribution of U is half
normal.
The frontier of the farm is given by combining equation (20) and (21) as
Y = f (Xi ; β) e (v - u) (22)
The variance of e is therefore,
δ2 = δu2 + δv
2 (23)
The ratio of two standard deviations is defined by
λ = δu/δv
Jondrow et al. (1982) have shown that measuring efficiency at the individual farm
level can be obtained from the error term ε = V - U for each farm, the measure is the
expected value of u conditional on ε i.e.
E (u/ε) =
(24)
δu.δv f (εi λδ - (εi λ) δ 1-F(εi
A number of empirical work Kalijaran (1981); Parikh and Shah (1994); Liewelyn and
Williams (1996); Ajibefun and Abdulkadir (2002); Awoyemi and Adekanye (2004);
Awoyinka and Ikpi (2004) and Ohajianya (2005) have investigated the determinants of
λ/δ) δ (25) where f and F are the standard normal density function and the standard normal distribution
function respectively, evaluated at ε λ/δ. Estimation values for ε, λ and δ are used to evaluate
the density and distribution functions.
Measures of efficiency for each farm can be calculated as;
TE = [ε (u/ ε)] (26)
Technical inefficiency = 1 - [ε (u/ ε)] (27)
78
technical efficiency among firm in different industry by regressing the predicted efficiencies,
obtained from an estimated stochastic frontier on a vector of farmer specific factors such as
age of farmer, level of education, access to extension etc in a two stage regression. The
identification of factors that influence the level of technical efficiency is a valuable exercise
because the factors are important for policy formulation.
However, Coelli (1995) has identified a fundamental contradiction in the two-stage
approach. In the first stage the efficiency factors are assumed to be independently and
identically distributed while, in the second stage, they are assumed to be a function of a
number of firm-specific factors which implies that they are not independently distributed.
Battese and Coelli (1995) resolved the inconsistency in the two-stage approach by
specifying stochastic frontier models in which the inefficiency factors are made an explicit
function of the firm-specific factors and all parameters are estimated in a single-stage
maximum likelihood procedure. This single stage approach is less objectionable from a
statistical point of view and is expected to lead to more efficient estimator. This work used
this single stage model to estimate the parameters of the stochastic frontier function model
using the computer program FRONTIER version 4.1 (Coelli, 1996).
The production technology of fadama food crop farming household is assumed to be
specified by frontier production function defined as follows:
Q = f (X, R, T, M ; β) exp. (Vi –Ui). (28)
Where ;
Q = Output of crops measured in grain equivalent per household
X = Vector of physical inputs measured in unit per household.
R = Land resource quality variables measured as dummy variables.
79
T = Vector of land use variables measured as indices.
M= Vector of land management practices assumed to have impact on land quality
measured as numbers and dummy.
β = Vector of parameters.
Vi = Random error due to mis-specification of the model.
Ui = Inefficiency component of error terms.
3.4.3.1: Description of the Variable of the Frontier Model.
Q = This is the output of the farm and the dependent variable. During the survey,
information on the quantity of the crop harvested at the end of the season (including produce
sold, consumed at home or given away as gifts) was obtained from the fadama food crop
farming household heads. Direct weighing of unit of measurement such as basket in the case
of tomato, sugarcane in bundles, yam in heaps, grains in bags was employed in getting the
average unit weight of measurements used by the household. Total weight of crops was
obtained by multiplying the average unit of measurement (kg) by the total units harvested in
all the plots. However, the non-homogenous nature of crop planted by various households
and differences in prices of produce at points of sale makes it necessary to device a standard
value of output. In this study the standard output used was the grain equivalent. This is the
energy content (Kcal/kg) of the various output in reference to energy content of maize (see
appendix iii). The grain equivalents of the entire crops planted were now added to obtain the
output for each of the households sampled.
Independent Variables.
A: Physical inputs (Xi)
80
X1 = Land was measured in hectares of cultivated area. The output of crop is partly
dependant on the cultivated land area. In the literature, Coelli and Battese (1996), and
Amaza (2000) reported positive production elasticity with respect to land in India and
Nigeria respectively. By a prior expectation, the coefficient for land area is expected to be
positive. ΒX1 > 0.
X2 = Family labour was expressed as adult male-equivalent man-days, contributed by
members of the farming household. In a situation where labour is limited, it is expected that
labour coefficient would be positive (βX2 > 0). However, if there is excess labour, the
relationship between labour and output may be negative (βX2 < 0) (Meir 1989).
X3 = Hired labour is the labour contributed by people who worked and get paid for by the
farming household heads. This was expressed as adult male equivalent man-days. Hired
labour coefficient is expected to be positive (βX3 > 0) (Ohajianya 2005).
X4 = Capital was expressed as the depreciation value of all the crude tools used in the season.
The tools that are freely used by the households in all enterprises were used. A straight line
method of depreciation was used. The priori expectation is a positive sign (βX4 > 0)
(Ohajianya 2005).
X5 = Cost of purchased input is the value of variable input used per farm. It consists of
planting materials (cuttings, seeds and tubers), rental value for planting and agrochemical (if
any). The a priori expectation is a positive sign (ΒX5
D
> 0) (Babatunde et al. 2005).
B: Land Resource Quality Variable (R)
1: Drainage was expressed as dummy: D1 = 1 good drainage and D1 = 0 for poor drainage.
It was used as a proxy to capture the condition of the farm as one expects a poorly drained
soil to be poor in nutrient status and requires more land augmenting practices for crop
81
production to be sustainable. Bhalla and Roy (1988) and Udoh (2000) incorporated farmland
quality in their analysis of the relationship between farm size and productivity, and land use
and sustainability respectively. βD1 > 0 for well drained soil.
D2: Terraces was expressed as dummy: D2 = 1 for flat topography and D2 = 0 for slope
topography. It was used as a proxy to capture the condition of farmland as sloppy and
undulating land were marginal farmlands and farming on such lands require special
management practices to maintain or improve the soil fertility. βD 2
X
> 0 for land with better
terrace (Ali, 1996 and Udoh, 2000).
C: Land Use Variable (T).
6 = Crop Diversification Index was used to capture stability of yield and income from the
cropping pattern adopted by the households and calculated with Herfidahl index as given in
equation 14. Ali (1996); Spio (1996); Udoh (2000); Alamu and Coker (2005) reported that
mixed cropping has a higher total productivity per unit of land and greater stability of yield
and revenue than mono-cropping, thus a priori expectation is βX6
X
> 0.
7
Nutrient intake index was used to capture the vulnerability of farm total output (or grain
equivalent) to different crop combination on a farm as a result of nutrient depletion. It is
expected to be negatively associated with output level βX
= Nutrient Intake Index was measured as a ratio of crop configuration to number of crops
in combination. The nutrient intake index was estimated as given in equation 15.
7
D: Land management variable (M)
< 0 (Fageria and Baligar, 1993;
Ali, 1996 and Udoh, 2000).
D3 = Level of tillage was measured as dummy D3 = 1 for conventional tillage and D3 = 0 for
zero tillage. Maduakor et al. (1984) reported that level of tillage can affect the level of crop
82
productivity. Spencer (1989) also reported that soils that have been tilled is likely to be
degraded faster and loses nutrient easily. It is expected to be negatively associated with
output level βD3 < 0 (Spencer and Swift, 1992).
X8 = Length of fallow was expressed as the number of years a piece of land is allowed to
stay before cultivation is carried on it. By a priori expectation, it is expected to be positively
associated with output level βX8 > 0 (Ali, 1996 and Udoh, 2000).
X9 = Fertilizer used (measured in kilogram) during the period of the study. By a priori
expectation, it is expected to be positively associated with output level. βX9 > 0; (Ali, 1996)
and Udoh, 2000).
Vi = Random error due to mis-specification of the model and variation in output due to
factors outside the farmer’s control such as weather and diseases.
Ui = Inefficiency component of error term. It is assumed that the inefficiency effects are
independently distributed and Ui truncation (at zero) of the normal distribution with mean 0
and variance δu2 where Ui is specified as:
Ui = a0+a1lnZ1+a2lnZ2+a3lnZ3+a4lnZ4+a5lnZ5 (29)
Where;
Ui = Technical inefficiency of fadama food crop farming household.
Z1 =Access to credit expressed as a dummy, 1 for access and 0 for no access.
Z2= Fadama farming experience expressed in years.
Z3 =Highest educational level expressed in years.
Z4= Number of extension contact in years.
Z5 = Household size expressed as the number of people in a household.
83
a0, ai, i = 1 5 are parameters estimated.
3.4.3.2: Elasticity of Production and Return to Scale Measurement.
Other estimates derived from our stochastic equation (29) for food crops farming
household in the fadama are elasticity of production (EOP) and return to scale (RTS).
EOP is the same as the estimated coefficients of the independent variables (Kumbhakar,1994)
RTS = ΣEOPi i = 1…,n (30)
Inferentially, RTS < 1, decreasing return to scale
RTS > 1, increasing return to scale
3.4.4: Measurement of Short-Run Sustainability Index (S.R.S.I).
This involves 2 – step measurement used by Ali, 1996 and Udoh, 2000. First, is the
estimation of farm index of sustainable land use and management (ISM). Secondly product
of the ISM index with technical inefficiency index gives the measure of short run
sustainability index (SRSI).
ISM (1- Te) = SRSI (31)
Farm specific index of sustainable land use and management (ISM) was estimated as
the partial productivity of equation (29) with respect to all the agronomic practices i.e. land
resource quality (R), land use (T), and land management practices (M).
Inferentially, if the value of ISM is zero, the land use and management practices give
no change in land quality. If it is positive, there has been improvement in the use and
management of the land, and if it is negative, land use and management practices have
adverse effects on the land resources. When SRSI is positive, this indicates that the
productivity improves owing to the net balance of resources use and environmental
management and vice-versa if it is negative Ali (1996) and Udoh (2000).
84
3.4.4.1: Basic Assumption of the SRSI Estimation
. The estimation of SRSI is built on the following assumptions.
1. Farming households are faced with the same climatic factor and same soil type.
2. The households farming practices can either improve the productivity of the soil
as well as internal nutrient cycling or deteriorate nutrient status of the soil.
3. Land use and management practices of households are identical for each
cropping season.
4. The agronomic practices have net carry over effect on the soil and their effect is
captured by the estimated frontier.
5. The farm specific output level is jointly determined by input use and agronomic
practices undertaken at farm level.
3.4.5: Construction of Indicator of Sustainable Agricultural Practices (ISAP)
This was used to satisfy the objective of developing appropriate indicators of
sustainable agricultural practices in fadama land use. The information that was used to
generate the indicator of sustainable agricultural practices relates to seven aspects of fadama
crop production. These are:
1. Seed source
2. Weed control
3. Crop management
4. Pest/disease control
5. Maintenance of soil fertility
6. Tillage
7. Method of water control
85
The different farming practices within each of these categories are identified in Table 3.
Table 3: Farm Practices Used in the Construction of ISAP. Seed source Weed control Crop
management
Pest/
diseases
control
Maintenance of soil
fertility
Tillage Method of
water control
- Own farm
- Conventional
supplier
Chemical herbicides
Manual/mechanical
- Biological e.g.
Crop cover
- Sole Crop
crop rotation
Intercropping
Natural
pest control
Synthetic
pesticide
Synthetic fertilizer e.g.
NPK
- Natural fertilizer e.g.
bones, wood wash
- Organic manure
Poultry manure
Composted fertilizer
Green manure
Zero tillage
Conventional
tillage
Mould
Bond
Source: Adapted with modification from Rigby et al. (2001).
3.4.5.1: Scoring and Weighting Sustainability of Farming Practices.
The impact of the farming practices in Table 3 on farm sustainability was assessed by
identifying from literature criteria commonly adopted for agricultural sustainability, and then
allocating simple scores to each farming practice according to whether a particular practice is
considered to improve or diminish a farm’s performance.
Rigby et al. (2001) identified four (4) criteria of sustainability based on literature on
impact of farming practices on sustainability. The criteria are with regards to the effects of
the practices on increased yields and reduced losses while
* Minimizing off-farm inputs (Hodge, 1993; Petty, 1995).
* Minimizing input from non-renewable sources (Hodge, 1995).
* Maximizing use of natural biological processes (Ikerd, 1993; US Farm Bill, 1993).
* Promoting local biodiversity/environmental quality (Petty, 1995)
86
The scoring system is shown in Table 4 which combines information from Table 3 on
farming practices with sustainability criteria identified by Rigby et al. (2001).
Table 4: Scoring Farm Practices with Respect to Sustainability.
Dimension of sustainability Promotes local Biodiversify
Total Farm practice Minimizes off-
farm inputs Minimizes Non-Renewable inputs
Maximize Natural Biological Processes
1. Seed sourcing Own farm +1 0 0 0 + 1 Conventional supplier 0 0 0 0 0 2. Weed control - Chemicals -1 -1 -1 -0.5 -3.5 - Manual +1 +0.5 +1 +0.5 +3 - Biological (e.g. cover crop) +1 +1 +1 +1 +4 3. Crop management - Bush fallow 0 0 +2 +1 +3 - Crop Rotation +0.5 +0.5 +1 0 +2 - Inter-cropping +1 +1 +1 1 +4 4. Pest & Diseases - Natural pest control 0 +0.5 +1 +1 +2.5 - Synthetic pesticide -1 -1 -3 -3 -8
87
5. Maintenance of soil fertility - Synthetic fertilizer e.g. NPK -1 -1 -1 0 -3 - Natural fertilizer e.g. wood -1 -1 0 0 -2 - Organic manual e.g. poultry manure +1 0 0 0 +1 - Compost +1 +1 0 0 +2 - Green manure +1 +1 +1 0 +3 6. Tillage - Zero tillage +1 0 +1 0 +2 - Conventional tillage -1 0 0 0 -1 7. Method of water control - Mould 0 0 +1 0 + 1 - Bond 0 0 +1 +1 + 2 Source: Adapted with modification from Rigby et al. (2001).
Following Rigby et al. (20001) each farming practice scored in absolute term ranges
between 0, and 3 points for each criterion. The scoring system could be interpreted as 0 for
no significant impact, 0.5 indicates marginal impact, 1.0 indicates significant impact, 2.0
indicates strong significant impact, 3.0 indicates very strong significant impact. As could be
observed from Table 4, the seven categories of farm practice represent different proportion of
the total number of points obtainable. The proportional contributions of each farm practice
to sustainability of fadama land are as follows;
- Seed source 3%
-Tillage methods 7%
-Pest/disease control methods 8%
- Method of water control 10%
-Maintenance of soil fertility 20%
- Weed control methods 22%
-Crop management methods 30%
88
. The score for each household was calculated by adding the total score attributed to
each farming practice in table 4 as used by each household. Index value calculated this way
can range between -17.5 and +30.5 depending on each households pattern of inputs use in
fadama food crop production. Linear transformation was used to convert the index score to
between 0 and 1 for each of the fadama food crop farming households. The closer the linear
transformation score to one (1) the more sustainable the farm practices being used by the
household on the fadama land.
To overcome problem of single score aggregation as identified by Bockstreal (1997)
that compensation can occur between the values of components that are aggregated, this
study used mean score of aggregated components for sustainability webs (Gomez et al. 1996;
Bockstreal et al. 1997; Swete-Kelly, 1999 and Webster, 1999). These simultaneously display
scores for different components in addition to the linear transformation scores of each
households. This is to show the contribution of the components to the overall mean ISAP
score of the sampled households. It also allows the identification of critical farm practices to
monitor in order to improve the sustainability of the fadama land.
3.4.5.2: Assumption for Constructing Indicator for Sustainable Agricultural Practices (I.S.A.P)
1. The indicator is constructed according to pattern of input used rather than their
impacts.
2. Farming practices are scored on prediction about their impacts on ecological
processes and local biodiversity.
3. It is assumed that insecticides would in general be more damaging (via impact on
animal food chain) than herbicides.
89
4. It is assumed that synthetic insecticides and herbicides not found in nature are
more likely to be damaging than synthetic fertilizer, which supplies the same
nutrients as organic manure but in soluble form.
5. Organic form of fertilizer is assumed to be better than synthetic fertilizer because
they are likely to confer physical improvement to the soil in addition to nutrition
supply.
3.4.5.3: Index Validation.
In developing I.S.A.P for Malaysia, Taylor et al. (1993) circulated the proposed
scoring schedule to natural scientist and Rigby et al. (2001) followed these in their work on
U.K. horticultural producers. In this work, the scoring of agricultural practices in table 4 was
circulated among natural scientist and agronomist at University of Ilorin and National Cereal
Research Institute Baddegi, Nigeria. Adjustment was made on the scores as suggested before
indicators for sustainable agricultural practices among food crop producers in the fadama
southern guinea savanna, Niger State, Nigeria were finally constructed.
3.5: Limitations of the Study
The study utilized cross sectional data to assess sustainability of fadama. This is
acceptable but trend over a period of decade or more may give a better understanding of the
sustainability of food crop production in the area.
There is also the problem of poor and or complete absence of farm records kept by
the farming household. The information provided by the heads of households was mainly
based on memory to answer questions relating to input supply, output realized, prices,
allocation and utilization of farm inputs. Difficulty with the “memory recall” system has
been documented (Norman, 1973). There is therefore the possibility of errors, such as the
90
computational error, measurement error, and selective bias. This may affect the validity and
accuracy of the information pertaining to the levels of input employed and output obtained by
the farming household.
However, to minimize the above mentioned limitations, a preliminary survey of the
study areas using the interview schedule used for this study was carried out before the actual
survey. Again, the model used in this study i.e. stochastic frontier model, adequately makes
provision for statistical noise.
CHAPTER FOUR
RESULTS AND DISCUSSION
This chapter presents the results of the analysis of data collected and discussion on it.
4.1: SOCIO-ECONOMIC CHARACTERISTICS OF FADAMA FOOD CROPS
FARM HOUSEHOLD HEADS.
This section highlights the features of the fadama food crops farming household
heads who are the major decision makers in each of the households sampled during the study.
Table 5, shows the demographic characteristics of fadama food crops households heads.
There are more male (97.32%) than female (2.68%) as heads of family. This result conforms
to the cultural and religious setting of the study area. Islam, where man is expected to be
head of the family is the predominant religion in the area. This finding is similar to that of
91
Nagya (2003), Ndanitsa (2005) and Fatoba (2007) who all conducted different studies in
Niger State.
Age is important in hoe and cutlass technology employed by sampled fadama
households. As people advance in age, they wear in energy and may no longer be capable of
providing the type of effort required in the traditional farming practiced within the fadama of
Niger State. About 83.87% of the sampled household heads are between 20 and 50 years.
The mean age of the household heads is 44.5 years and the modal class is 41-50 years with
standard deviation of 11.52. This age is still young and active for farming and their
productivity and output is expected to be high. This may affect the land use and management
of the fadama land and its sustainability. Awolola (1995); Onu et al. (2000) and Adewumi
and Omotesho (2002) reported that age affects labour productivity and adoption.
Table 5 : Demographic Characteristic of the Fadama Food Crops Households Heads.
Characteristics Frequency Percentage (i) Gender Male Female Total (ii) Age Group 20 - 30 31 - 40 41 - 50 51 - 60 > 61 Total (iii)Highest Educational level No formal education Quranic Adult Primary Secondary Tertiary Total (iv) Household size 1 - 5
145 4 149
9 33 83 14 10
149
17 54 24 43 9 3
149
15
97..32 2.68
100.00
6.04 22.15 55.70 9.40 6.71
100.00
11.41 34.89 16.11 28.86 6.04 2.01
100.00
10.07
92
6 - 10 11 - 15 16 - 20 21 - 25 > 26 Total (v) Major occupation Farming Blacksmith Clergy Tailoring Public service Total (vi) Experience in years 0 - 5 6 - 10 11 - 15 16 - 20 21 - 25 26 - 30 > 31 Total
27 42 51 9 5
149
137 2 1 4 5
149
11 23 51 33 13 9 9
149
18.12 28.18 34.23 6.04 3.36
100.00
91..95 1.34 0.67 2.68 3.36
100.00
7.38 15.44 14.22 22.15 18.13 6.04 6.04
100.00 Source: Field Survey, (2004/2005).
The highest educational level distribution as shown in Table 5 reveals an appreciable
high level of literacy among household heads (54%). They are likely to have good potentials
to acquire and interpretes messages relating to their farming operation. These may have
significant impact on productivity and their ability to effectively adopt better management
practices that could improve crop yield and sustainability of fadama land.
The size of farm household is important in peasant agriculture. This is because the
availability of labour for farm production, the total land area cultivated to different crop
enterprises, the quantity of farm product retained for domestic consumption are all to a large
extent determined by the size of farm household. The mean household size during the study
was 17 persons. The composition of an average household reveals that there were
approximately seven (7) numbers of adult male, four (4) numbers of adult female and six (6)
numbers of children below 15 years. The Islamic religion the dominant faith of the sample
93
households favours polygamy hence the large household size. These may result in more
pressure on fadama land. Generally fadama lands are small and patchy relative to the dry
land, to a larger extent there may be fragmentation of holdings and smaller fallow time which
may affect the sustainability of the fadama for crop production.
Table 5, shows that 91.94 percent of the farming household heads considered
farming as their main occupation. It was however, revealed that some other members of the
households engage in other non-farm occupation to complement the household earnings from
farming operations. This finding shows that the sample used for this study are likely going to
give adequate information about food crop production in the fadama because farming is their
main job. It is expected that they should be receptive of new technologies that can improve
their farm output, income and efficiency because they have no other means of livelihood.
Consistent high output and high efficiency are indicators of sustainability.
Table 5 also reveals that about 77.18 percent of household heads involved in food
crop production in the fadama of Niger State have more than ten (10) years of experience.
The mean farming experience was 17.5 years. This is expected to positively influence
technology acceptance and compliance that could transform to high farm output. The result
of this study shows that the sampled households’ cultivated crops that are profitable and the
management practices adopted might be due to the farming experience in the fadama land.
The finding is similar to that of Nagya (2003) who reported a mean farming experience of 26
years among rice farmers in Niger state and that those farmers with longer experience in
farming accepted newly introduced technologies more readily than those with less.
4.2: THE STRUCTURE OF AGRICULTURAL PRODUCTION.
This section deals with description of pattern of ownership and utilization of factors of
production by fadama food crops farming household during the study.
94
4.2.1: Land Ownership Pattern.
Land availability is very important to the livelihood of peasants. The ownership
structure under the land tenure system in rural settings determines to a large extent how the
land can be used. In the study area, different ownership pattern characterized the land as
shown in Table 6.
Table 6: Land Ownership Pattern in the Study Area.
Mode of ownership Frequency Percentage Average land size
Per household (Ha)
Communal
Inheritance and Communal
Family and Inheritance
Rented/Leased
Purchased
16
37
73
17
6
10.74
24.83
48.99
11.41
4.03
2.21
4.65
2.28
6.31
1.75
Total 149 100.00 3.44
Source: Field Survey, (2004/2005).
Five types of land ownership patterns were identified as shown in Table 6. Major
parts of the cultivated farm land (53.02 percent) were acquired through tenure systems that
provide property right to the farming households. These are farm lands acquired through
family, inheritance, and purchase. An important feature of the ownership pattern is that
fadama land was seldom sold for agricultural purposes in the study area. Only 4.03 percent
95
of the cultivated land was purchased. The distribution of land ownership pattern permits the
fadama land to be used for both perennial and annual crop cultivation. The ownership pattern
is an important factor in investments decision that could lead to higher productivity of farm
resources. The result of the study shows that fadama farming households in their attempt at
meeting their goals of farming coincidentally planted crops and adopted production practices
that seems mindful of the long run effects of farm practices on the environment.
4.2.2: Farm Plots of the Fadama Farming Households.
Farm plot as estimated in this study refers to a separate contiguous parcel of land
cultivated to a crop or crops combination in a farm location. Due to different land ownership
pattern, sampled households have farm plots at different locations as shown in Table 7.
Table 7: Distribution of Number of Farm Plots Per Household.
Number of Farm Plots Frequency Percentage
1 - 2
3 - 4
5 - 6
> 6
52
78
16
3
34.90
52.35
10.74
2.01
Total 149 100.00
Source: Field Survey, (2004/2005).
Table 7 reveals that more than half (52.35 percent) of the sampled households
cultivated between 3 and 4 plots and only 2.01 percent cultivated more than 6 plots. The
average number of plots cultivated by the sampled fadama household is 3 plots. The land
available to household is mostly small and scattered at different locations under different
land holding arrangement. However, because of large household size (average 17 persons)
96
farming takes place in many locations. It should however be noted that the household depend
mostly on human effort and crude tools for their production and this thus limits the number
of plots and hectares that can be cultivated by an household in a cropping season.
4.2.3: Farm Size of Fadama Farming Households.
The size of farm cultivated by a household determines to a large extent how other
farm resources are combined for efficient production. The distribution of respondent based
on the sizes of their farm holdings is shown in Table 8.
Table 8 : Distribution of Sampled Fadama Food Crop Farming Households by Farm Size.
Farm size (Ha) Frequency Percentage
0.01 - 1.00 1.01 - 1.50 1.51 - 2.00 2.01 - 2.50 2.51 - 3.00 3.01 - 3.50 3.51 - 4.00 4.01 - 4.50 4.51 - 5.00 > 5
3 11 19 25 23 47 17 6 7 1
1.52 7.83
12.75 16.79 15.44 31.54 11.41 4.43 4.70 0.67
Total 149 100.00 Source: Field Survey, (2004/2005).
The mean farm size per household was 3.44 hectares held in about 3 plots. The modal
class is 3.01-3.50 with standard deviation of 7.86. The survey reveals that the food crop
farming households in the fadama of Southern Guinea Savanna of Niger State, Nigeria are
small-scale farmers who cultivate their land in fragmented plots. This scenario impedes
mechanization, and may affect adoption of new technology that can increase output and
make farming more efficient.
4.2.4: Farm Labour Supply and Utilization.
97
Agricultural production under peasant settings as witnessed during the survey makes
minimal use of capital; rely heavily on available labour supply. Farm labour supply play a
crucial role in determining the amount of land area farming household cultivates successfully
in a season. Two sources of labour were identified in the study area during the survey. These
are family labour and hired labour. Farm labour input supply and utilization by average
fadama farming household is presented in Table 9.
Table 9: Farm Labour Supply and Utilization by an Average Fadama Food Crop Farming Household.
Major occupation Sources of Labour (Man-days)
Family Hired Total Land preparation Planting Cultural operation Harvesting Post harvest operation
18.35 (62.63) 23.12 (91.14) 49.64 (70.04) 16.02 (49.86) 11.14 (65.21)
10.95 (37.37) 2.21 (8.81) 21.23 (29.96) 16.10 (50.14) 5.22 (31.79)
29.30 (16.84) 25.33 (14.56) 70.87 (40.74) 32.12 (18.46) 16.36 (9.40)
Total 118.27 (67.98) 55.71 (32.02) 173.98(100) * Figures in parenthesis are percentages Source: Field Survey, (2004/2005S).
Table 9 reveals that an average fadama farming household utilized about 173.98 man-
days of labour in cultivating about 3.44 hectares of land. Out of this figure 118.27 (67.98
percent) were family labour while 55.71 (32.02 percent) were hired labour. The table further
reveals high demand for labour for cultural practices (i.e. thinning, fertilizer application,
weeding, bird scaring, goat scaring) and harvesting. About 41 percent and 18 percent of total
labour used were required for cultural practices and harvesting respectively. During these
periods, most farm crop simultaneously requires the same cultural operations which family
labour alone cannot easily cope with. The table shows that about 30 percent of labour utilized
for cultural operations and 50 percent of labour utilized for harvesting were hired. This
implies that, for food crop production to be sustainable, the farm household should be able to
98
have sufficient fund to hire the critical labour required to supplement family labour at the
right time.
4.2.5: Farm Capital Utilization
Capital as defined in this study refers to durable assets mainly crude tools that are
commonly used in all enterprises. The number and value of durable assets owned by
sampled fadama farming household is shown in Table 10.
Table 10: Distribution of Farm Fixed Input and Their Costs.
Farm tools Total number of fadama farming household
Percentage of sampled households possessing tools
Total value of farm tools (N
Annual depreciation costs () N)
Hoes Cutlasses
Axes Knap sack
sprayer
149 149 95 8
100 100 65 5
260,550 338,500 74,250 68,000
125 300 150 1700
Total 741,300 4,075 Source: Field Survey, (2004/2005).
Table 10 reveals that the entire households sampled owned and used hoes and
cutlasses as the basic tools for their farming operations. None of the households owned ox-
plow, tractor, or any other equipment for mechanization of farm operations. This result
implies that food crop farming is not capital-intensive in the study area. The small sizes of
farm holding impede mechanization of farm operation.
99
Table 11: Distribution of Sources of Credit, Number of Extension Visits, and Major
Constraints of Sampled Fadama Food Crop Farming Households.
No of farm plots Frequency Percentage
(i) Sources of Credits. No credit Family and friend Money lenders Cooperative society Community Bank Total Reason for lack of access to formal credits. Inadequate awareness Remote nature of fadama areas Bureaucratic processes Total (ii) No of extension visits. 0 1 - 2 3 - 5 6 - 8 9 - 11 > 12 Total (iii) Major constraints Inadequate credit High input cost Poor access to farm Implement Pest and diseases Flooding Nomad encroachment Poor prices of produce Inadequate rainfall Total
99 30 9 7 4
149
68 46 35
149
88 22 18 16 - 5
149
40 24 23 15 15 12 10 10
149
66.44 20.13 6.05 4.70 2.68 100.00
30.88 45.63 23.49
100.00
59.06 14.76 12.08 10.74
- 3.36
100.00
26.85 16.11 15.44 10.07 10.07
8.05 6.71 6.71
100.00
100
Source: Field Survey, (2004/2005)
Access to farm credit plays a significant role in agricultural production. It enables
farming household to finance their production processes easily; input such as fertilizer,
agrochemical etc are purchased, and hired labour and other inputs are paid for. It could be
observed from Table 11 that, about 66% of fadama farming households did not have access
to credit. This shows that access to farm credit is inadequate in the fadama of Niger State.
The response of fadama household heads shows that inadequate awareness of formal
financial institution (45.63%), remote nature of fadama area (30.88%), 5%) and bureaucratic
processes (23.49%) were the major constraints to seeking for loan from banks to finance
production processes. The implication of this is that the households may not be able to
finance investments in technology that can improve productivity and fadama sustainability.
The sources of credit are also important to the households as they can affect the ease and
amount of credit they can acquire. Only 2.68 percent of fadama households have access to
bank loans for credit.
The study area is covered by the activities of the Niger State Agricultural
Development Project (NSADP) and Ministry of Agriculture. The extension agents are
expected to visit the farmers periodically to give advice and introduce new technologies to
farm households. Table 11 also shows that 59.06 percent of the sampled fadama farming
households did not receive any extension visit at all during the survey year. Only 21 percent
had one extension worker’s visit in every other month while 5 households (about 3.36
percent) had at least one visits a month during the year. This level of extension visit to the
fadama area seems too low, and may impact negatively on access to, and adoption of new
technologies by these households. It may also affect their efficiency level.
101
The fadama food crops farming households faced a number of constraints in their
farming operations. They had in most cases multiple constraints but only the one ranked as
the most important are considered. Inadequate credit, high cost of inputs especially fertilizer,
poor access to farm implement for land preparation were ranked by sample fadama farming
household as 1st, 2nd and 3rd
Description
major constraints respectively.
4.2.6: Farm Input Characteristics. Table 12: Description of Farm Inputs Characteristics of an Average Fadama
Farming Household.
Sample mean Standard
Deviation Minimum
value Maximum
value Farm size (Ha) Number of plots Family labour (man-days) Hired labour (man-day) Family labour N Hired labour N Total labour Pesticide (litres) Pesticide N Capital N Fertilizer (kg) Fertilizer N Other operating cost
3.44 3.00
118.74 55.71
32,524 15,320 47,845 1.84 2870 2275
78 3978 6624 N
2.68 9.56 38.99 11.63 43.68 37.8
218.54 1.57 53.43 953 2.76 2.76
2014.3
0.1 1 7 2 37
1750 50 0
9250 0 0
850 0
6.3 6
220 70
290 55,000 17,500
7 72,500 12,000
8 9000
38,500 * Hired labour cost average N
Table 12 reveals the farm characteristics of an average household in term of physical
input used during the cropping season. On the average the farm size cultivated was 3.44
hectare in about 3 plots. Hired labour was about 32 percent of total labour used for
production. One of the probable reasons for hiring little labour could be the fact that an
average fadama farming household head is still young (about 44 years), active and married
with about 17 family members who provided the needed manpower for farm operations. On
the average, the cost of labour used constitutes about 73.17 percent of total expenditure on
275 per man-day during the survey. Source: Field Survey, (2004/2005)
102
farm inputs. This phenomenon clearly demonstrates the dominance of labour in food crop
production in the fadama area. Labour is the most important factor of production because
farming activities in the area are mostly labour intensive.
4.3: ANALYSIS OF CROPPING PATTERN AND INDEX OF DIVERSITY.
4.3.1: Land Use Pattern.
Cropping pattern defined in this study as the yearly sequence and spatial arrangement
of crops determined by households based on their priorities and main reason of cultivation.
As shown in table 16 (reason for crop preference) food security and income generation are
the main reasons for selecting crops in the area. As such crops are planted and combined at
different degrees so as to meet the basic needs of the household.
In the study area, sole cropping and intercropping are the common cropping pattern
during the survey. The sampled farm household cultivated altogether, twenty-four (24)
different crop enterprise combinations. Thirteen (13) as sole crop and eleven (11) as crop
mixture during the survey. A typical fadama food crop farming household planted rice as
sole crop and maize based crop mixture with any of the other crops during the survey.
The study reveals that the fadama farming household adopted different agricultural
diversification strategies to fully utilize the fadama land and cope with risks and
uncertainties. The strategies include:-
(i) An act of cultivating flood tolerant crop such as rice and sugarcane during the
early wet season
(ii) Cultivation of drought resistant crops: millet, cowpea, sorghum, cassava later
in the season at the drier part of fadama area.
103
(iii) Cultivation of crops with short gestation period: Okra, leafy vegetable, late
sorghum and cowpea to utilize moist land during the early part of dry season.
These strategies assure adequate utilization of the fadama land, which satisfy the food
security and income generation objectives of the households. The distribution of area of land
in hectares cultivated to sole crops and mixed crops during the survey by sampled fadama
food crops farming households in Niger State, Nigeria is presented in Tables 13 and 14
respectively.
Table 13 shows the sole crop planted during the survey. The survey reveals that rice
is the dominant and most preferred crop planted as sole crop, it was the only crop planted by
all the sampled households. It was planted in 18.30 percent (104.3 Ha) of the land area
utilized for crops production during the survey. Onion is the least preferred crop accounting
for only 0.26 percent (1.50Ha) of land area cultivated.
104
Table 13: Distribution of Area Cultivated (Ha) to Sole Crop Enterprises.
Enterprise No of Households
Area cultivated. (Ha)
% of Area cultivated.
Mean area cultivated (Ha)
Minimum area cultivated. (Ha).
Maximum area cultivated (Ha).
Standard Deviation.
Cowpea Groundnut Maize Okra Onion Pepper Rice Sugarcane Sorghum Soybean Tomato Vegetables Yam
8 8 10 12 2 6 149 7 5 3 14 14 6
6.4 16.00 22.30 7.20 1.50 4.80 104.3 5.6 8.25 3.00 11.90 12.46 5.40
1.12 2.81 3.91 1.26 0.26 0.84 18.32 0.98 1.45 0.53 2.09 2.19 0.95
0.80 2.00 2.23 0.60 0.75 0.80 0.70 0.80 1.65 1.00 0.85 0.89 0.90
0.20 0.50 0.80 0.20 0.50 0.20 0.30 0.30
0.60 0.50 0.30 0.25 0.50
3.00 2.50 3.5 2.00 1.00 3.50 4.50 3.00 2.80 1.50 1.75 1.00 1.50
2.37 1.89 2.45 0.89 2.74 1.25 0.96 0.58 1.33 1.28 0.45 0.33 0.53
209.11 36. 70 0.86
Source: Field Survey, (2004/2005)
105
Table 14 shows that the sampled fadama food crop farming household planted plots
with 2 crops combination and 3 crops combination. The survey reveals that 60.08 percent of
the mixed crop enterprises grow two crops per plot while 439.92 percent grow three crops
per plot. Maize/cowpea was the most preferred mixed crop enterprises. It was planted in
about 12.52 percent (71.34 Ha) of the land area (Ha) cultivated during the survey. The least
preferred among the crops combination is Sorgum/Bambara, only about 0.23 percent (1.30
Ha) of land area cultivated during the survey was planted with it. Maize based cropping
system was the dominant enterprises among the household. It accounted for about 48.16
percent (274.42 Ha) of land area used during the survey.
The cropping pattern clearly reveals preference for maize, rice, sorghum, yam and
cowpea as the most important food crops grown by household. The dominance of these
crops may be explained by the fact that they are the predominant stable food crop in the
study area. The households therefore, need to produce them in adequate quantity as food.
The average household in the study area had 17 persons.
Under crops combination, cowpea is the predominant second crop grown in
association with cereal crop such as maize and sorghum. The legumes in the combination
may have largely served as substitute to fertilizer which tends to increase the yields of other
crops usually cereals in the combination. The adoption of this cropping system is largely
attributable to the low level of fertilizer used by most households. During the survey an
average fadama food crop farming household used only 78kg of fertilizer in about 3.44
hectares of land. Hence growing legumes in combination may be one of the feasible options
available for the maintenance of the fertility of cultivated land in the absence of adequate
fertilizer. This practice might have ensured stability of yields of crops planted and
106
Table 14: Distribution of Area Cultivated (Ha) to Mixed Crop Enterprises.
Enterprise No of Households
Area of land cultivated (Ha).
%of Area of land cultivated
Mean area cropped (Ha)
Minimum area cropped (Ha)
Maximum area cropped (Ha).
Standard Deviation.
Maize/sorghum
Mellon/maize/sorghum
Maize/sorghum/cowpea
G/nut/sorghum/cowpea
Sorghum/cowpea
Sorghum/Bambara
Maize/cowpea
Maize/cassava
Maize/yam
Millet/groundnut
Millet/cowpea
29
28
36
12
29
1
58
10
32
20
13
30.72
46.94
66.96
16.80
43.84
1.30
71.34
8.64
27.52
28.20
18.46
5. 39
8.24
11.75
2. 95
7. 69
0. 23
12. 52
1. 52
4.83
4. 95
3. 24
1.28
1.68
1.86
1.40
1.56
1.30
1.23
0.86
0.86
1.41
1.42
0.20
1.0
1.0
0.5
0.2
1.30
0.5
0.4
0.3
1.0
0.6
4.00
6.50
4.50
7.00
3.50
1.30
4.00
2.50
3.00
2.00
3.50
2.46
2.58
2.74
1.85
2.25
0.00
2.35
1.67
3.98
2.11
2.64
360.72 63.30 1.37
Source: Field Survey, (2004/2005).
107
sustainability of the fadama land for food crop production. This result is similar to the work of
Amaza. (1991) who reported that in savanna ecological region of Northern Nigeria, farmers who
practice mixed cropping commonly do so under cereal/legume combination.
In general, the sampled food crops farming household in fadama of Southern Guinea
Savanna, Niger State Nigeria cultivated crops in 569.83 hectares during the survey. 209. 11
hectares (36.70 percent) and 360.72 hectares (63.30 percent) were cultivated to sole crop
enterprises and mixed crops enterprises respectively.
Table 15: Distribution of Fadama Households by Sources of Seeds Planted.
Types of crops planted.
Major source of seeds planted ADP and other Agencies Local Markets Personal Stock Frequency Percentage Frequency Percentage Frequency Percentage
Bambaranut Cowpea Cassava Groundnut Sorghum Maize Mellon Millet Onion Okra Pepper Rice Soybean Sugarcane Tomatoes Vegetable Yam
- 16 8 8 15 26 - - 2 6 - 56 - 5 8 4 -
- 12.80 80.00 15.38 14.70 24.76 - 100.00 50.00 - 37.59 - 29.41 57.14 28.57 -
2 44 - 12 18 21 10 5 - 2 - 24 3 - - 4 6
33.33 35.20 - 23.08 17.65 20.00 35.71 15.15 - 16.67 - 16.11 100.00 - - 28.57 20.00
4 65 2 32 69 58 18 28 - 4 16 64 - 12 6 16 24
66.67 52.00 20.00 61.54 67.65 55.24 64.29 84.85 - 33.33 100.00 46.31 - 70.59 42.86 42.86 80.00
Source: Field Survey, (2004/2005).
Table 15 shows that the sampled households used more of personal stock and seeds
obtained from local market as planting material in most crops cultivated during the survey
period. It was only in the cultivation of Onion, Cassava, Tomatoes and Okra that about 100%,
80%, 57% and 50% respectively of the seeds planted were obtained from ADP and other
108
agencies. The local seeds generally have low vigour and might be one the main reasons for low
yield recorded by sampled households.
4.3.2: Major Crop Output.
Table 16: Average Yield of Major Crops Grown During the Survey.
S/No Crops Average yield (kg/Ha) Expected Yield (Kg/Ha)*
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
Bambara nut
Cowpea
Cassava
Groundnut
Sorghum
Maize
Mellon
Millet
Onion
Okra
Pepper
Rice
Soybean
Sugarcane
Tomatoes
Vegetable
Yam
824.5
786.48
7142.86
1121.36
1057.22
877.31
925.11
805.28
2145.2
1205.2
866.34
1697.27
1056.11
5412.54
2479.75
2571.29
12043.51
1700
1400
30000
1400
1800
2500
1450
1700
Not available
“
1200
2500
3200
40000
Not Available
"
35000.
* Expected yield based on best farm management practices as reported by NSADP (2001).
Source: Field Survey, (2004/2005).
Table 16 shows the average yield of major crops grown in the fadama during the survey.
This result shows that the average yield obtained is lower than the expected yield in all crops
cultivated. The most important food crops cultivated (maize, rice, sorghum yam and cowpea)
gave yield of between one-third and half of the expected yield. The low yield experienced
generally may be due to the fact that majority of the sampled household (86.57%) used last
season harvest as seed in their plots. These seed are mostly with low vigour. The use of fertilizer
to argument soil nutrient is also poor as reported earlier. This result implies that farm households
109
needs to be encouraged to use improved seed and land augmenting material (fertilizer, organic
manure) to obtain consistently highs yields on farms. This would ensure higher yield from food
crop production and sustainability of the fadama land.
4.3.3: Reason for Crop Preference.
Sampled fadama farm household offered different reasons for their choice of crop grown
on the farms. The main reason for crop preference among the fadama farming households is
given in Table 17.
Table 17: Main Reason for Crop Preference.
S/No Major Reason for cultivation Frequency Percentage 1 2 3 4 5 6
Food security Income generation Risk management Ease of cultivation/management Land management Hobby
55 51 28 7 4 4
36.91 34.24 18.79 4.70 2.68 2.68
149 100.00 Source: Field Survey, (2004/2005).
Table 17 shows that food security objective predominates the household choice of crops
grown. This is followed by income generation objectives with land management and hobby
being the least reason for crop preference. The table reveals that only 2.68 percent grow crops for
land management. This shows that very few households were mindful of the type of crops
cultivated and their relation with soil properties and land management. This decision may have
implication for sustainability of the land and its long run ability to support good yield from the
crop planted. This result is similar to the finding of Ogunkunle and Eghaghara, (1992) and Udoh,
(2000) who individually reported that under small-scale peasant farming, land use and crop
choice are rarely closely associated with soil type.
4.3.4: Index of Crop Diversification.
110
The pattern of land use as regards stability of yield and revenue from crops planted are
indicators of sustainable farm practice (Webster, 1999). This is measured with the indices of crop
diversification. The index used in this study is Herfidahl index. This is modelled in terms of
proportion of net income from the various crops in each combination (see equation 14).
Table 18: Herfidahl Index of Crop Diversification.
Description of cropping
pattern
Mean Herfidahl
Index
SD Minimum
value
Maximum
Value
CV %
Sole
Two-crops combination
Three-crops combination
1
0.625
0.462
1
0.189
0.106
1
0.384
0.264
1
0.946
0.753
100
30
23
Whole farm 0.651 0.302 0.264 1 47
Source: Field Survey, (2004/2005).
Table 18 shows that the mean diversification index for all the sampled fadama food crops
farming household was 0.651. This implies increasing diversification among majority of fadama
food crops farming households which could ensure stability of yield, income and sustainability
of fadama land. About 39 percent of the plots covered during the survey were planted with two
crops combination with mean of diversification index of 0.625. Three crops combination was
practiced in about 23 percent of plots under cultivation with mean of diversification index of
0.462. These crop combinations could also be regarded as environmentally and economically
sound practice.
It is environmentally friendly because when two or more crops are planted under low use
of land augmenting material like fertilizers as observed during the survey, the negative effect of
such material on the environment is reduced. The cultivated crop depended mostly on the
111
available soil nutrient for their growth and development. Each crop uses the fertility of the soil in
its own particular way especially when the rooting systems of the crops differ. Mixed
cropping planted this way during the survey generally gives more revenue and higher gross
margin per hectare than mono cropping. This finding is similar to the work of Spio (1996);
Alamu and Coker (2005) who reported that mixed cropping in Ghana and Nigeria give higher
yields and revenue per hectare respectively.
4.3.5: Index of Soil Nutrient Intake
To achieve the maximum advantage of inter cropping or mixed cropping, combined crops
must be grown in such a way that each crop in mixture uses the nutrient of the soil in different
ways as to eliminate the risk of competition for the available soil nutrients. Table 19 shows the
distribution of the fadama farming households based on how their crop diversity pattern can
affect nutrient depletion and sustainability of farmland. This was calculated using equation 15.
Table 19: Distribution of Nutrient Intake Index among Sampled Households.
Nutrient Intake index Frequency Percentage
1 - 1.5
1.6 - 2.0
2.1 - 2.5
23
99
27
15.44
66.44
18.12
Total 149 100
112
Source: Field Survey, (2004/2005).
From Table 19, it could be observed that nutrient intake index (NII) ranges between 1.0 and 2.5.
The mean NII was 1.89. This index measured the intensity of likely nutrient depletion by the
combined crop. This result implies that the combined crops have very low tendency to deplete
soil nutrient. These may not be unconnected to the fact that eight (8) of these crop combinations
had a leguminous crop in addition to the cereal in the combination. So the cropping pattern is
such that could not adversely affect soil nutrient and crop nutrition. These could translate to
consistently good yield of fadama farm land which is an indication of sustainability of the
cropping pattern of the fadama food crops farming households. Udoh, (2000) reported NII of
3.25 among farmers in eastern Nigeria, most of these farmers planted root/tuber crops in
combination with other crops.
4.3.6: Ruthberg -Value.
The Ruthberg-Value shows the land use intensity for the sampled households. It was
estimated as 0.393 during the survey. This implies that the length of fallow which was about 6
years may be adequate for soils to restore natural fertility. The sustainability of the land use
would however depend on the agronomic practices adopted by the households.
113
Analysis of data shows that about 65 percent of the farms sampled had different levels of
soil erosion symptoms indicating incidence of soil degradation and subsequent nutrient loss. The
farmlands were not adequately covered with crop canopies. In the long run, without adequate
remedial and preventive measures, soil fertility may be affected which could affect the
sustainability of fadama land of Niger State in Nigerian Southern Guinea Savanna.
4.4: COSTS AND RETURN IN FOOD CROP PRODUCTION IN FADAMA OF SOUTHERN
GUINEA SAVANNA OF NIGER STATE, NIGERIA.
4.4.1: Farm Production Cost: These are the sum of all costs of variable inputs used by the
fadama farming households during the survey. The distribution of the variable cost by
enterprises is presented in Table 20.
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Based on the cost incurred per hectare in each of the enterprises, maize/yam intercrop
have the highest cost of N36, 684, followed by rice and maize/cowpea intercrop with average
cost of N35, 650 and N31,837 per hectare respectively. Bambara nut planted solely was the
enterprise with the lowest average cost of N8461 per hectare. The average variable cost incurred
by a typical farming household cultivating 3.44 hectares of food crop was N63, 592 which was
about N
Table 20 reveals that maize/cowpea intercrop gave the highest revenue of
18,486 per hectare. The variability in cost across the enterprises did not follow a given
sequence. The production cost varies across the households and it did not necessarily depend on
the type of crop planted in the enterprises.
4.4.2: Farm Revenue: The average revenue obtained from enterprises cultivated by sampled
fadama food crop farming households during the survey is presented in Table 20.
N57,500 per
hectare, while the lowest revenue of N14, 412 per hectare came from sole bambara. The average
gross revenue per hectare during the survey was N33, 662. As observed in cost of production,
the variability in gross revenue does not follow a definite pattern across the farming household
and/or enterprises. However, it is interesting to note that enterprises that account for substantial
proportion of the gross farm revenue are those that mostly incorporate cowpea and maize.
Cowpea and maize based enterprises accounted for 37.31% and 39.35% of gross farm revenue
respectively. The relative better performance of these cowpea and maize based enterprises may
be due to three major factors. Firstly, cowpea command relatively higher price and it requires no
fertilizer. As a result, the removal of price subsidies on fertilizer by government in recent years
and their high cost seems to encourage farmers to grow cowpea based crops. Secondly, cowpea,
sorghum and millet are crops that are relatively drought resistant (World Bank, 1986). Hence,
environmental problems associated with inadequate rainfall seem to favour the production of
115
these crops. Thirdly, maize as earlier noted is one of the major staples in the study area;
households tend to grow them for subsistence as well as for the market.
4.4.3: Gross Margin Analysis.
Gross margin/ha as stated in equation 18 was used to capture profitability of food crop
production among the farming household in fadama of Niger State, Nigeria. The gross margin
calculated on enterprise basis per hectare as observed during the survey is presented in Table 20.
Table 20 reveals that maize/cowpea enterprise with N25, 663/ha has the highest gross
margin per hectare. The bambara nut enterprise with N
Enterprises
5951/ha was the least in gross margin per
hectare. With the exception of sole rice, enterprises planted as crop combinations generally
performed better than sole crop enterprises in terms of gross margin per hectare.
Table 20: Gross Margin per Hectare by Enterprises for Food Crops Planted by Sampled Fadama Farming Households.
Mean farm Revenue/ha
Mean variable cost per hectare
Gross margin per hectare
Ranked based on GM//ha
Return on Naira Investment.
Ranked by Return on Naira Investment
Maize/Cowpea Sorghum/Cowpea Maize//sorghum/Cowpea Maize / Yam Sugarcane Sorghum / Bambara Millet /Cowpea Soybean
57,500 40,100. 42,700 56,250 41,250 35,600 36,400 41,593
31,837 17,886 22,122 36,684 27,550 17,850 19,750 24,946
25,663 22,214 20,578 19,566 18,700 17,550 16,650 16,627
1st 2nd 3rd 4th 5th
6th 7th 8
1.81 2.25 1.85 1.53 1.68 1.89 1.84 1.57 th
10th 3rd 7th 21st 17th 6th 8th 19th
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G/Nut / Sorghum Cowpea Okra Mellon / Maize /Cowpea Maize / Cassava Maize / Sorghum Rice Yam Pepper Sorghum Tomatoes Cowpea Millet /G/nut Vegetables Maize Onion Bambara
39,700 28,850 36,585 39,600 38,631 50,600 34,200 32,487 31,767 28,850 31,500 28,300 23,500 29,592 21,600 14412
23,283 12,502 20,670 23,755
22,2716 25,650 19,461 18,195 17,413 14,650 17,685 14,595 9,850 16.250 14,000 8,461
16,417 15,950 15,915 15,845 15,684 14,950 14,739 14,292 14,267 13,850 13,815 13,705 13,650 13,342 7,660 5,951
9th 10th 11th 12th 13th 14th
15th 16th 17th 18th 19th 20th 21st 22nd 23rd 24
1.71 2..30 1.73 1.68 1.61 1.42 1.76 1.79 1..82 1..95 1.78 1.94 2.39 1.70 1.54 1.70 th
15th 2nd 14th 17th 18th 22nd 13th 11th 9th 4th 12th 4th 1st
16th 20stt
16th Whole farm 33, 623 18, 486 15, 137 1.89
Source: Field Survey, (2004/2005). Using the GM/ha to rank the enterprises, maize/cowpea, sorghum/cowpea and
mellon/maize/sorghum are the 1st, 2nd and 3rd enterprises respectively. Seven out of the ten
enterprises ranked 1st to 10th were planted as enterprise combinations. This results support the
findings of Alamu and Coker (2005) that reported higher GM/ha among farmers who cultivated
crops in mixture than those who cultivated sole crops in fadama of Kaduna State, Nigeria.
Using return on Naira investment to rank the enterprise, leafy vegetable (spinach), okra
and sorghum/cowpea were the 1st, 2nd and 3rd enterprises respectively. These enterprises returns
N2.39k, N2.30k and N2.25k on every N1 invested in the enterprises respectively. The estimated
average net farm-income for a typical food crops farming household was N52, 071 and the
average return on Naira investment was N1.89. This shows that on financial consideration the
farm operations of food crop farming households in the Fadama of Guinea Savanna, Niger State,
Nigeria was profitable and therefore, financially sustainable.
For a typical food crop fadama farming household, the summary of cost and revenue
structure as observed during the survey is as follows:
Average farm size 3.44 hectares
Average total revenue = N115, 663.
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Average total cost = N63, 592.
Average Net farm income = N52, 071.
Average gross margin/ha = N
Seven categories of farming practices were used to generate a single score used to
describe each of the household on sustainable farming practices. The seven categories of farming
practices used in generating the ISAP score are; seed source, weed control, pest control, water
management, fertility management, crop management and tillage methods. As could be
observed from Table 21, the seven categories of farm practices represent different proportional
contributions to sustainability of fadama land based on the scores obtainable from different
15, 137
1
4.5: INDICATOR OF SUSTAINABLE AGRICULTURAL PRACTICES (ISAP) FOR SAMPLED
FADAMA FOOD CROPS FARMING HOUSEHOLDS.
Indicator of sustainable agricultural practices (ISAP) scores were calculated for each of
the 149 food crops farming household in the fadama of Southern Guinea Savanna, Niger State,
Nigeria using the method outlined in section 3.4.5 of this work. The ISAP score was used to
access the effect of the agricultural farm practices adopted by individual farming household on
the sustainability of the fadama land in food crop production.
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categories. Table 21 presents the mean score of the ISAP components for the food crop farming
household in fadama of Southern Guinea Savanna, Niger State, Nigeria.
Table 21: Mean Value of I.S.A.P Components.
Score Seed
source 3%
Method of weed
control 22%
Crop mgt 30%
Pest control
8%
Fertility mtance
method 20%
Tillage method
7%
Water mgt method
10%
Expected
Mean
1
0.5
(50)
7
2.06
(19.62)
9
6.17
(68.55)
2.5
0.36
(14.56)
6
1.53
(25.45)
2
0.91
(45.67)
3
1.56
(52)
* Figure in parenthesis is mean score as a percentage of expected score.
Source: Field Survey, (2004/2005).
The mean scores in Table 21 were used to draw “sustainability web”. Each spine of the
web is calibrated from zero at the origin to highest percentage of the index weight farthest from
its origin. So the farther the web is to the origin the “better” the categories within the ISAP
index.
119
120
Table 21 and Figure 3 shows that fadama food crop farming households ISAP
components mean score is low in the adopted farming practices with respect to methods of weed
Fig 3: Mean score of I.S.A P. Components.
0
5
10 seed 3%
weed ctr 22%
Crop mgt 30%
pest ctr 8% fert mtnc 20%
tillage mtd7%
water mgt 10%
Expected
Mean
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control, pest control and soil fertility maintenance. The adopted farming practices in these
categories fell short on sustainability scale. These three categories scored 19.2 percent, 14.86
percent and 25.45 percent of the expected sustainable practices scores respectively. The crop
management method was above average (68.55 percent) while seed source (50 percent), water
management method (52 percent), and tillage method (45.67 percent) are within the average
range on the sustainable practices score. This result implies that the critical indexes to monitor
with respect to sustainability of food crops production in the fadama area of Southern Guinea
Savanna; Niger State, Nigeria are the methods of fertility maintenance, pest and weed control.
The aggregation of the scores of individual farming household across the seven
categories of farm practices identified earlier was used to obtain the ISAP score for each of the
households. The distribution of the ISAP scores among food crops fadama farming households is
presented in Table 22.
Table 22: Distribution of Indicator of Sustainable Agricultural Practices (ISAP)
Scores.
ISAP scores Frequency Percentage
0.31 - 0.40
0.41 - 0.50
0.51 - 0.60
0.61 - 0.70
0.71 - 0.80
14
50
84
-
1
9.40
33.56
56.37
-
0.67
149 100
Source: Field Survey, (2004/2005).
Table 22 show that the ISAP scores generated is between 0.31 and 0.80. About 43
percent of the sampled household had ISAP score of 0.50 and below while majority of the
sampled household (57 percent) had ISAP score more than 0.50. The mean ISAP score for all the
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households was 0.517. This implies that the combined effects of all the farming practices used in
generating the ISAP score would result in a fairly sustainable production of food crops in the
fadama of Niger State, Nigeria. And that the current production practices of majority of the
farming household would sustain food crop production using current farming practices.
However, it is important to monitor and improve upon the levels and methods of fertility
maintenance, pest and weed management. The results of this study is similar to the work of
Taylor et al. (1993); Izac and Swift, (1994); and Rigby et al, ( 2001) that reports a mean ISAP
score of 0.657, 0.535 and 0.652 among rice producers in Malaysia, small scale farmers in
Rwanda, and horticultural producers in England respectively. Izac and Swift (1994) identified
pest control, fertility maintenance and tillage methods while Rigby et al. (2001) reported pest
management method as the farm practice to monitor in order to improve sustainability.
4.6: ANALYSIS OF STOCHASTIC FRONTIER ESTIMATION.
The production frontier was estimated using maximum likelihood estimation approach
(MLE) through the FRONTIER 4.1 program developed and licensed by Coelli (1996).
The results of the MLE are given in table 23.
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Table 23: Stochastic Frontier Estimation (MLE) Result.
Variable parameter Coefficient Standard Error (SE)
t-ratio
Physical Input (xi) Constant (x0) Farm size(x1) Family labour (x2) Hired labour (x3) Capital (x4 ) Cost of purchase input (x5) Land use variables Crop diversification index (x6) Nutrient intake index (x7) Land Management Variable Length of fallow (x8) Fertilizer used (x9) Tillage used (D1 ) Land resource Quality variable Drainage (D2) Terrace (D3) Inefficiency model Constant Term Credit (Z1) Farming Experience (Z2) Education (Z3) Extension Contact (Z4) Household size (Z5) Diagnostic statistics Sigma square Gamma Lambda 6.3785 Likelihood ratio (Ho ) -97.3668 Likelihood ratio (H1 ) -111.8277 LR Test 28.92*** δu2 = 0.6762 δv2
β
= 0.0167
0 β1 β2 β3 β4 β5
β6 β7
β8 β9 β12
β10 β11
a0 a1 a2 a3 a4 a5
δ
3.953** 0 .487**
0.1320** 0.1989** 0.3896**
1.5505***
-0.0048** - 0.7180***
0.0594**
0.2292*** -0.1213
0.2597**
0.0017
0.2847 -0.1341** -0.3624** -0.2074** -0.1695** 0.4373**
0.6959*** 0.9759***
2
γ λ
1.6910 0.1954 0.0569 0.0788 0.1949
0.3374
0.0019 0.2266
0.0283 0.0537 0.2560
0.1307 0.1153
0.2315 0.0606 0.1828 0.0841 0.0735 0.2213
0.1346 0.0355
2.337 2.492 2.3216 2.5240 1.9989 4.5960
2.5260 -3.1686
2.0989 4.2682 -0.4738
1.9870 0.0147
1.2298 2.2129 1.9821 2.4663 2.3061 1.9761
5.1706
27.5327
*** Significant at 1%, ** Significant at 5 % Source: Summarised from computed output of Frontier 4.1
Table 23 shows the MLE of the stochastic production function (Eq 28) for all the
sampled farm households during the study. The estimate of sigma-square (δ2) is 0.6959. This is
large and statistically significant at 0.01. Lambda (λ) estimated at 6.3785 (Appendix IV) is
greater than one. This indicates a good fit and the correctness of the specified distributional
assumption of the composite error term (Tradesse and Krishmamoorthy, 1997). The variance
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ratio represented by gamma (γ) is estimated to be as high as 97.59 percent. This suggests that
systematic influences that are unexplained by the production function are the dominant sources
of random error. That is to say that the presence of technical inefficiency among the sampled
farm explains about 98 percent variation in error observed in the estimated stochastic production
frontier. The generalised likelihood ratio is significant at 0.01 level suggesting the presence of
the one sided error component. This implies that technical inefficiency is significant and a
classical regression model of production function based on OLS estimation techniques would be
inadequate representation of the data. Thus, the results of the diagnostic statistics confirm the
relevance of stochastic parametric production frontier and maximum likelihood estimator for this
work.
4.6.1: MLE Estimates of the Parameter of the Stochastic Production Function.
The estimated parameters and the related statistical test result from the analysis are
presented in Table 23. All the parameters in the model have the expected sign and many of the
coefficients are statistically significant at 5 percent level of probability or less. The coefficients
can be interpreted as the elasticity of the output with respect to input at the data point
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(Kumbhakar, 1994). The estimated coefficients for methods of tillage and drainage were not
discussed because they were not significant in explaining the estimated stochastic production
frontier.
Physical Inputs (X).
Farm Size (X1
Family Labour (X
):- The estimated coefficient for farm size is positive, which conform to a priori
expectation. It is significant at 5% level. The magnitude of the coefficient of farm size is 0.487.
This shows an inelastic nature of output with respect to farm size. This implies that increasing
farm size by 10 percent, output level would increase by a margin of 4.87 in a ceteris paribus
case. It was reported earlier in the work that the yield observed among the sampled household
vary widely and it does not reflect the land holding of the household. This result shows that
despite the inelastic nature of the coefficient, there is still some scope for increasing output per
plot by expanding farm size. This could be in term of economics of scale or in the benefit of
attaining optimum farm size.
2): The elasticity of output with respect to family labour is 0.1320 and
significant at 5 percent level. The positive coefficient of family labour conforms to a priori
expectation. It implies that the output from food crops is expected to increase with increase in
family labour input and vice-versa. With the level of technology and high dependence of farming
household on human effort available family labour form the fulcrum of most farming activities.
Table 9 shows that family labour use was more than double the hired labour use. It constitutes
about 68 percent of the total labour use. In most households, in the study area every member of
the household including the school children is involved in the farming business. Therefore, such
farming business is not strictly taken as business but a way of life. Hence management of the
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farm might not be as effective as what obtains in pure commercial farms whose main objective is
profit making.
Hired Labour (X3): The coefficient of hired labour; which is 0.1989 is positive as expected and
is statistically significant at 5 percent level. The production elasticity with respect to hired labour
is positive and less than one (0.1989), indicating that output in crop production is inelastic to
changes in the amount of hired labour used. It suggests that a 10 percent increase in labour would
cause an increase of 1.989 percent in output and vice versa. However, this shows that hired
labour is more productive than family labour.
Hired labour constitutes 32 percent of total labour used for all farm operations by an
average farm family. The share of hired labour in crop production seems to vary directly with the
size of farm. As the farm size increases, farmers tend to employ more hired labour, since the
sizes of family labour tend to be relatively fixed. Hired labour might be an important factor
explaining changes in output as farm size increases.
Capital (X4
Cost of Purchased Input (X
): The coefficient of variable associated with capital inputs is positive and
statistically significant at 5 percent level. The production elasticity of capital input which is
0.389 suggests that a 10 percent increase in capital input utilized would induce an increase of 3.9
percent in the output of food crop and vice versa.
5): The production elasticity with respect to cost of purchased
inputs is 1.5505 showing an elastic situation. This is statistically significant at 1 percent level and
appears to be the most important input in the area. By increasing the cost of purchased input by
10 percent, output level would improve by a margin of 15.5 percent in a ceteris paribus case. It
implies that there exists high scope for increasing output per plot by increasing the use of
127
purchase input, especially when improved seed and land augmenting materials such as
fertilizer/manure are adequately applied.
Land Use Variables (T).
Crop Diversification Index (X6): Crop diversification Index (CDI) is shown to have significant
relationship at 5 percent level with output. The value of the estimate is positive (0.0048)
indicating that higher level of crop diversification is associated with increasing output (GE) of
combined crops. The diversification practices adopted by the sampled households combined
different classes of crops in a mixture that do not compete for soil nutrient with one another.
These ensure higher output of crop mixture than sole crop enterprise. This result supports the
finding of Spio (1996); and Alamu and Coker (2005) who reported higher stability of yield and
revenue in mixed crop enterprises.
Nutrient Intake Index (X7
Length of Fallow (X
): The value of the estimated coefficient is negative at –0.7180 and is
statistically significant at one percent level. This implies that output from food crop production is
expected to decrease with increase in nutrient intake index. This result is consistent with the a
priori expectation that crops which have heavy soil nutrient depleting abilities would have lower
aggregate yield where soil is poor in nutrient status and land augmenting resources is sparsely
added to the soil. The estimated value further shows that majority of the sampled farm
households cultivated more than one crop that compete for soil nutrient on a plot of land. The
distribution of nutrient intake index shown in table 18 confirm that majority of the farmers grow
more than one crop on a plot of land.
Land Management Variables (M).
8): The estimated coefficient is positively related to output and statistically
significant at 5 percent. This shows that fallow for longer period of time can improve crop
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productivity especially under a farming system where nutrient restoration depends on the
regrowth of vegetation. The coefficient 0.0594 shows that output would increase by only 0.59
percent if land is allowed to fallow for ten years. This implies that land productivity can be
improved only marginally with fallow only. Adequate soil augmenting material in addition to
proper farm management practice is important to restore nutrient to fadama farmland.
Fertilizer Used (X9
Land Resource Quality Variable (R).
): The estimated coefficient (0.2292) is statistically significant at one percent
level and is positively related to output level. The coefficient shows that a 10 percent increase in
the present level of fertilizer usage would lead to a 2 percent increase in output. This is not
surprising as the current rate of fertilizer usage is poor. An average farmer uses only about 78kg
of NPK 15-15-15 fertilizers as against average of about 300 – 400 kg/ha recommended (NSADP,
2001) for grains crops in Niger State of Nigeria. Therefore, there is need to improve on fertilizer
use by farming household to improves productivity.
Drainage (X10
4.6.2: Inefficiency Factor.
): The value of the estimated coefficient is 0.2597 and is statistically significant at
5 percent level. The result is consistent with a priori expectation that coefficient of drainage is
positive for well-drained soil. This implies that yield increase as the drainage condition
improves. This is very true of the fadama area. Fadama land can be waterlogged very easily and
this can reduce crop yield.
The determinants of technical inefficiency in food crop production in the fadama of Southern
Guinea Savanna, Niger State, Nigeria as presented in Table 23 are discussed below.
Credit Assess (Z1).
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Assess to credit shows negative relationship with technical inefficiency. The estimated
coefficient is statistically significant at 5 percent level. This implies that assess to credit can
improve level of technical efficiency of sampled household head. This result agrees with those of
Bravo-Ureta and Evenson (1994); Onyenweaku and Nwaru (2004). However, the result
disagrees with those of Okike (2000) who found a negative relationship between credit and
technical efficiency and Rahji (2005) who found no significant relationship between access to
credit and technical efficiency.
Fadama Farming Experience (Z2).
The result shows that there is a negative and significant relationship at 5 percent level, between
technical inefficiency and fadama farming experience among sampled households. This result
implies that households with more experience tend to be less inefficient. These might be because
most of the household head have more than fifteen years experience in fadama farming and
might be receptive to innovations. This result is in line with that of Ajibefun et al. (2002) who
reported a negative and significant relationship between farming experience and technical
inefficiency. However, it differs from that of Onu et aI. (2000) whose result showed a positive
relationship between farming experience and technical inefficiency.
Highest Educational Level of Household Heads (Z3
The coefficient of educational level of fadama food crops farming household heads is negatively
related to technical inefficiency and significant at 5 percent level. This implies that access to
quality education can reduce the technical inefficiency of the fadama farming household, which
will invariably increase sustainability of fadama as a result of higher productivity. This result
):
130
agrees with those of Kalijaran and Shand (1986), Bravo-Ureta and Pinheiro (1993), and Rahji
(2005). However, the result disagrees with Kalijaran and Shand (1985), Bravo-Ureta and
Evanson (1994), Onyenweaku and Effiong (2005) and Fatoba (2007) whose result showed no
significant relationship between education and technical efficiency.
Extension Contact (Z4).
The coefficient of extension contact is negatively and significantly related to the technical
inefficiency at 5 percent. This is in accordance with the a priori expectation that extension
contact leads to more efficient transmission of information to farmers as well as enhancing the
adoption of innovation. This implies that more extension contact would lead to lower technical
inefficiency and higher productivity of the farming household. This result is similar to that of
Rahji (2005) who reported a positive and significant relationship between extension contact and
technical efficiency.
Fadama Farming Household Size (Z5
The technical efficiencies differ substantially among the sampled fadama-farming households
ranging between 0.06 and 0.95 with a mean technical efficiency index of 0.58. This leaves an
inefficiency gap of 0.42. This suggests that reasonable marketable output is sacrificed and there
is resource wastage. The result implies that about 42 percent higher production could be
).
The coefficient of household size is positively related to technical inefficiency and significant at
5% level. The implication is that increase in the number of farming household will increase
technical inefficiency. Household size, which can be a proxy for labour supply, is presently at an
average of 17 people. So any increase may lead to excess labour supply.
4.6.3: Technical Efficiency.
131
achieved without additional resources or inputs could be reduced by 42 percent to achieve the
same level of output. The distribution of the technical efficiencies is presented in Table 24.
Table 24: Distribution of farm specific Technical Efficiency indices among Sampled
Fadama Farming Households.
Class interval of efficiency indices Frequency Percentage
0.01 - 0.10
0.11 - 0.20
0.21 - 0.30
0.30 - 0.40
0.41 - 0.50
0.51 - 0.60
0.61 - 0.70
0.71 - 0.80
0.81 - 0.90
0.91 - 1.00
3
4
8
10
29
30
20
19
19
7
2.01
2.69
5.37
6.71
19.46
20.13
13.42
12.75
12.75
4.70
Total 149 100 Mean = 0.58 Standard deviation 0.20
Min value = 0.06 Maximum value 0.95
Source: Summarized from MLE result frontier 4.1
From Table 24, the frequencies of occurrence of the technical efficiency in deciles
ranges indicate that the highest number of farming household have technical efficiencies between
0.5 and 0.6. The sample frequency distribution indicates a gradual rising from left to highest; it
then falls to the right of the distribution. The modal class did not fall into any of the extreme
classes. Therefore, the assumption of a general truncated normal distribution for the inefficiency
term (u1) is therefore justified.
132
Although, there is a wide range between the maximum and minimum values of technical
efficiencies, the estimated technical efficiencies clustered around 0.5 and 0.6 ranges, with
reasonable spread among the range. About 64 percent of the farming households have technical
efficiency value of 0.50 and above while only 17 percent have technical efficiency value of less
than 0.40. This result is an indication of a fairly efficient group of farming households. Given
the wide variation in the level of technical efficiency, there appears to be considerable room for
improvements in the technical efficiencies of sampled fadama food crops farming households.
The distribution of efficiency estimates over a wide range agree with previous works carried out
in other peasant farming settings (see Ali (1996); Parikh and Shah (1995); Coelli and Battese
(1996); Ajibefun et al. (1998); Udoh (2000); Amaza (2000); Amaza and Olayemi (2002);
Oyenweaku and Effiong (2004); Okoruwa and Ogundele (2005; and Fatoba (2007). It should be
noted that the estimated efficiencies are purely output oriented technical efficiency derived as the
ratio of observed to maximum feasible output, condition on technology and observed input
usage.
Basically efficient use of resources that implies fewer inputs to produce the same level of
output or higher output at the same level of input is an important part of sustainability. This is
because efficiency improves productivity of fixed resources and thus sustainability of the system.
However, to actually measure the contribution of technical efficiency to the production process,
an index of technical inefficiency (1 – e-u ) is used together with the marginal effects of land-use
and management practices on production to obtain short run sustainability index (SRSI) as
suggested by Ali (1996) and Udoh) (2000) (see equation 32 ). This is discussed later in the work
in section 4.60.
4.6.4: Distribution of Production Elasticity.
133
Table 25: Distribution of Production Elasticity among the Variables.
Set of variables Estimated value Scale of Production
Physical inputs
Land use and land management variables
2.9872
-0.2848
SR-Increasing return to scale
SR-Decreasing return to scale
Total (V) 2.7058 Increasing return to scale
Source: Computed from MLE result of Frontier 4.1
From the estimates on Table 25 the sum of elasticity of output with respect to physical
input generates an estimated scale elasticity that is greater than one (2.9872 > 1). This is the case
of short run increasing return to scale. The estimate of the sum of elasticity of output with respect
to land use and management variables is less than one (-0.2848). This is the case of short run
decreasing or diminishing returns to scale.
The estimate of the overall return to scale measured as the sum of production elasticity of
all variables (∑ βi
4.7: Short-Run Sustainability Index (SRSI).
), is greater than one. The return to scale parameter (2.7058) indicates the
presence of short run increasing return to scale. This implies that every addition to production
input would lead to more than proportionate addition to the output. Thus, fadama food crops
farming households could still get more output by intensifying on the use of there resources until
they are able to achieve economic optimum. This result is in line with the findings of Ajibefun et
al. (2002); Awotunde and Ikpi (2004) who reported a short-run increasing return to scale among
smallholder food crop farmers in Oyo State and Sugarcane farmers in Jigawa State, Nigeria
respectively.
The indices of sustainable land use and management (ISM) show the accumulated
marginal effect of the land use and management practices on the land resource quality. The ISM
value of -0.2848 shows that on the aggregate the land use and management practices adopted by
134
the farming household has adverse effect on land resource because the value is negative. The
ISM value estimated have to a large extent, measured the effect of land use and management
practices normally adopted by food crop farming households on fadama land in the Southern
Guinea Savanna area.
The Short-run Sustainability Index (SRSI) is a product of farm specific technical
inefficiency and indices of sustainable land use and management (ISM). It was estimated using
equation 32. The distributions of the estimated indices are presented in Table 26. The SRSI
shows at farm level, the economic and environmental impact of crop production under low-
external input agriculture as practiced in the fadama of Southern Guinea Savanna, Niger State of
Nigeria.
Table 26: Distribution of Farm Specific Short -Run Sustainability Index (SRSI).
SRSI Frequency Percentage
(0.26 - 0.30)
(0.21 - 0.25)
(0.16 - 0.20)
(0.11 - 0.15)
4
10
23
52
2.69
6,71
15.44
34.90
135
(0.06 - 0.10)
(0.01 - 0.05)
30
30
20.13
20.13
149 100
• Values in parenthesis are negative values.
Source: - Computed from the MLE result and Field Survey, (2004/2005).
Table 26 clearly shows a normal distribution of the S.R.S.I indices with the highest
number of the farming households falling between -0.11 to -0.15 class intervals. The modal class
did not fall into any of the extreme class. The entire sampled households had negative S.R.S.I.
This implies that the farming household productivity declined owing to the net balance effect of
the technical inefficiency and effect of land use and management practices. SRSI distribution
shows that productivity decline of 1% to 30%, was observed among the sampled fadama food
crops farming household. None of the households had more than 30% productivity decline. The
mean productivity decline was 14%. However, the high concentration of the households (97
percent) within the SRSI ranges of -0.01 and -0.25 shows that in the short-run, remedial and
preventive measures can easily bring about improvement in productivity decline.
4.7.1: Relationship between Short Run Sustainability Index and Output of the Farms.
High crop yield is a measure of sustainability of crop production, and the major concern
of farming households is how much crop yield they get from the production process. So, it is
expected that lower productivity expressed in poor yield should reflect on the sustainability
index estimated in this study. A simple correlation analysis was used to capture the relationship
between short-run sustainability index and average crop yield (GE) at farm level.
136
Under the assumption of joint distribution of SRSI and crop yield, a correlation
coefficient r was estimated to be 0.648. A test of significance at 0.01 probability level shows that
r =0.648 is statistically significant and different from zero. It can safely be concluded that there
exist a positive joint movement of SRSI and average yield, so higher SRSI are accompanied with
higher average crop yield.
This show that a farm with higher technical efficiency index is likely going to have high
output and that short-run sustainability index is a good proxy to determine farms that are within
the path to sustainable farming practices.
CHAPTER FIVE
SUMMARY OF MAJOR FINDINGS, CONCLUSION, RECOMMENDATIONS
AND SUGGESTIONS FOR FURTHER RESEARCH.
This summary of the study is presented in this chapter, with emphasis on the major
empirical findings, the conclusion arising from the analysis, policy implication of the findings,
recommendations and suggestions for further research.
5.1: Summary of Major Findings.
137
5.1.1: Socio-Economic Characteristics of Respondent Production Pattern.
An analysis of socio-economic characteristics of farming household heads and their food
crop production pattern reveals that an average household head was about 44 years, males (97.32
percent), married (98 percent) with a household size of 17 persons. The composition of an
average household reveals that there are seven (7) numbers of adult male, four (4) numbers of
adult female and six (6) numbers of children below 15 years. Farming is the main
occupation of majority (91.94%) of the respondent and their mean fadama farming experience
was about 17.5 years. About 59.06 percent of the sampled farming households had no extension
contact. The mean extension contact was 1.5 visits per household per annum.
Five types of land ownership patterns were identified. These `were communal land
(10.74%), Inheritance and communal land (24.83%), Family and inheritance (48.99),
Rented/Lease (11.41%), and purchased land (4. 03%). The mean farm size per household was
3.44 hectares. This is mostly held in about 3 plots comprising of sole crop Rice and two plots of
maize based mixtures in about 0.7 ha and 1.37 ha respectively. Labour is the most important
factor, as the average cost of labour constitutes about 73.47 percent of the variable cost of
production. Family labour and hired labour contributed 68 percent and 32 percent of total labour
utilized for production respectively. Hired labour was mostly utilized for cultural practices and
harvesting. About 30 percent of labour utilized for cultural operations and 50 percent of labour
utilized for harvesting were hired.
The study revealed that mixed cropping was the most common farming system. The
sampled households cultivated thirteen (13) types of crops as sole enterprises and eleven (11)
types of mixed crops enterprises during the survey. 209. 11 hectares (36.70 percent) and 360.72
hectares (63.30 percent) of the cultivated land area during the survey were used for sole crop
138
enterprises and mixed crops enterprises respectively. Results show that farming households use
more of their own stock of local varieties, which inherently possess low yield potentials as
planting material.
Rice is the dominant and most preferred crop planted as sole crop, it was the only crop
planted by all the sampled households. It was planted in 17.14 percent (104.3 Ha) of the land
area utilized for crops production during the survey. Maize/cowpea mixture was the most
preferred enterprises among the mixed crop farms accounting for 10.36 percent (63.05 Hectares)
of the total area cultivated by sampled households. The cropping pattern clearly reveals
preference for rice, maize, sorghum, yam and cowpea as the most important food crops grown by
the households. Under crops combination, cowpea is the predominant second crop grown in
association with cereal crop such as maize and sorghum.
Food security (36.9 percent) and income generation (34.24 percent) were the major
reasons for choice and preference of crops grown with only 2.68 percent of the households
choosing crops for cultivation based on land management.
5.1.2: Land Use Pattern Analysis.
The Ruthberg value shows the land use intensity for the sampled households. It was
estimated as 0.393 during the survey. This implies that the average length of fallow of about 6
years may be adequate for soils to restore natural fertility. Depending on the agronomic practices
adopted by the households, the land use may be sustainable. About 65 percent of the farms
sampled had different levels of soil erosion symptoms indicating incidence of soil degradation
and subsequent nutrient loss. The farmlands were not adequately covered with crop canopies.
139
The mean Herfidahl index of crop diversification was 0.651. This implies increasing
diversification among majority of the sampled fadama food crops farming households which
could ensure stability of yield, income and sustainability of fadama land. Two-crop combination
(62.22 percent) was the major crop diversification pattern.
The mean Nutrient intake Index (NII) was 1.89. This index measured the intensity of
likely nutrient depletion by the combined crop. This result implies that the combined crops have
very low tendency to deplete soil nutrient. The cropping pattern is such that could not adversely
affect soil nutrient and crop nutrition.
5.1.3: Farm Budgetary Analysis.
Analysis of cost shows that maize/yam with N36, 684/hectare has the highest cost per
hectare. Among the sole crop enterprise, rice has the higher cost per hectare (N35, 650). The
least cost enterprises was sole Bambara nut production with N8,461. The farm revenue analysis
shows that maize/cowpea enterprise with N57, 500/hectare gave the highest revenue per hectare,
while the least revenue of N14, 412/ha also comes from Bambara enterprise.
Gross margin analysis shows that maize/cowpea enterprise with N25, 663/ha has the
highest GM/ha during the survey. Based on the profitability ratio leafy vegetable (spinach), okra,
and sorghum/cowpea ranked first, second and third respectively. These enterprises returns
N2.39k, N2.30k and N2.25k on every N1 invested in the enterprises respectively.
The average gross margin per hectare for a representative farm was N 15,137 while
average net farm-income was N52, 071. The average return on a Naira investment ratio for all
farms was 1.89 showing that on financial consideration the farm operation of food crop farming
households in the Fadama of Southern Guinea Savanna, Niger State, Nigeria was profitable and
therefore sustainable.
140
5.1.4: Indicators of Sustainable Agricultural Practices (ISAP) for Fadama of Southern
Guinea Savanna of Niger state, Nigeria.
The critical farming practices to monitor in order to improve the sustainability of the
fadama are methods weeds control, pest/ diseases control and fertility maintenance. These three
categories scored 19.2 percent, 14.86 percent and 25.45 percent of the expected sustainable
practices scores respectively. The ISAP score generated ranges between 0.31 and 0.80. About 56
percent of the sampled households have ISAP score of between 0.51 – 0.60. The mean ISAP
score estimate was 0.517. These shows that the production practices of the sampled fadama
farming households are fairly sustainable because the ISAP score is more than 0.50.
5.1.5: Technical Efficiency Estimate and Inefficiencies Factors.
The farm specific technical efficiency was estimated using Stochastic Frontier Production
Function The estimated production frontier fulfilled the attributes of well-behaved production
frontier and the diagnostic statistics suggested the presence of component error term, thus the use
of stochastic parametric estimation. The estimates of all physical input have required sign and
are statistically significant at 5 percent or less. The MLE estimate of technical efficiency
revealed a general truncated normal distribution with a minimum efficiency index of 0.06 and
the maximum efficiency value of 0.95. The average technical efficiency in the sample was 0.58
leaving an inefficiency gap of 0 .42. This implies that about 42 percent higher production could
141
be achieved without additional input or input which could be reduced by 42 percent to achieve
the same level of output.
The return to scale parameter (2.7058) indicates the presence of short run increasing
return to scale. This implies that every addition to production input would lead to more than
proportionate addition to the output. Thus, fadama food crops farming households could still get
more output by intensifying on the use of there resources until they are able to achieve economic
optimum.
Farm size, family labour, hired labour, capital, cost of purchased inputs, length of fallow,
quantity of fertilizer used, crop diversification index, drainage and nutrient intake index were
factors that significantly (P< 0.05) influenced the estimated technical efficiency.
Access to credit, fadama farming experience, educational level of head of households,
and extension contact had negative and significant relationship (P< 0.05) with inefficiency level.
This implies that increase in these variables would lead to less inefficiency.
Household size had positive and significant relationship (P< 0.05) on inefficiency which
implies that increase would lead to higher inefficiency.
5.1.6: Short-Run Sustainability Index.
The indices of sustainable land use and management (ISM) value of -0.2848 show that
land use and management practices adopted by the fadama farming households had adverse
effect on land resources. The entire sampled fadama food crop farming households have negative
farm specific short-run sustainability index (SRSI) which shows that farmer productivity decline
owing to the net balance effect of the resource use inefficiency and effect of land use and
management practices.
142
Average productivity decline of about 14 percent was observed. The distribution of
sampled households based on short-run sustainability index showed that remedial and preventive
measure could easily bring about improvement. There is a significant positive relationship (P <
0.01) between the farm specific short-run sustainability index (SRSI) and farm output (GE). This
shows that short-run sustainability index is a good proxy to determine households that are within
the path to sustainable farming practices.
5.2: Conclusion.
The general conclusion drawn from the study is that the production of food crops in the
fadama of Niger State, Nigeria is sustainable. All the food crop enterprises produced have
positive net farm income, profitability and return on Naira investment ratio that is greater than
one. The levels of diversification of the enterprises do not impact negatively on the nutrient
intake index (NII) of the crops. The estimated short-run sustainability index (SRSI), indicator of
sustainable agricultural practices (ISAP) score, the Ruthberg index value, erosion and drainage
situation shows that remedial and preventive measure is required to ensure sustainability of
fadama land in the Southern Guinea Savanna of Niger State, Nigeria.
5.3: Recommendations.
Based on the results of the analysis carried out in this study, the following policy
implications and recommendations are suggested.
1. The result of the study showed that the sampled households are operating at a state of
increasing return to scale. This result supports intensifying the use of production
resources especially purchased inputs like improve seeds, fertilizers, and agrochemicals
to achieve optimum output.
143
2. To further pursue the policy of intensification of production resources on farms,
farming household should be encouraged to form fadama users’ cooperative society
where resources may be consolidated for purchase of inputs and marketing of outputs.
The current effort of the NFDP (FADAMA II) to organize the fadama users into fadama
users association (FUA) is appropriate and should be intensified.
3. Most of the sampled households used manual labour for land preparation. Government
should facilitate purchase of tractors and implements at subsidized rate for fadama users
cooperatives. Vigorous effort should also be made to encourage the farm households to
employ animal traction involving oxen or camel with appropriate implements.
4. The result shows that farming households use more of their own stock of local varieties
in most of the crops cultivated except Onion, Cassava, Tomatoes and Okra which
inherently possess low yield potentials as planting material. These may be one of the
major reasons for low yield from production especially in cereals and legumes.
Therefore, there is need to introduce and encourage the use of improved seeds varieties
with high yield potentials together with package of required crop husbandry.
5. The farming households scored low with regard to soil fertility maintenance and fertilizer
use was found to have positive relationship with efficiency and output in food crop
production in the fadama. There is therefore the need, to maintain fadama soil fertility
status at a reasonable level for sustainability of food crop production. This could be
achieved through (a) addition of organic material (green manure and compost) (b)
adoption of appropriate legume based cropping system and (c) use of chemical fertilizers.
The chemical fertilizers are not only expensive but also frequently unavailable at the right
time and place. It is recommended that households should use organic fertilizer and
144
legume based cropping system as substitute to chemical fertilizers. The organic material
should be adjusted appropriately with respect to the environment and cropping system so
as to derive the maximum benefit. Government should reinstate subsidy on fertilizers and
design appropriate policy that would eliminate unintended beneficiaries of subsidy
program in fertilizer procurement and distribution.
6. Government should provide adequate and assessable inputs such as work oxen, improve
seeds, herbicides, and fertilizers to fadama food crop farming households. The input
should be provided at subsidized rate to encourage their use.
7. Mixed cropping is recommended for food crop production in the fadama of southern
guinea savanna, Niger State, Nigeria. This would ensure stability of output and income;
and profitability of production.
8. The use of agrochemicals-herbicides and insecticides by farming households should be
properly monitor to protect the environment. Appropriate government agency such as
National Agency for Food Administration and Control (NAFDAC) and produce section
of Ministry of Agriculture at all levels of government should monitor and regulate the
importation and use of agrochemical.
9. The food crop-farming households in the study area are not making efficient use of their
resources especially family labour, capital and fertilizers. There is therefore the need for
adjustment in order to improve the efficiency of these resources. Aggressive adult
education programme and improved assess to extension services are recommended.
Extension workers should be properly motivated through provision of appropriate vehicle
for their movement and prompt payment of their salary and allowances. The activities of
145
the extension agents should also be monitored to ensure that they carried out their duty
diligently.
10. Extension agencies and other farm support services should sensitize fadama food crop
farming households on the need to take advantage of the various private and government
farm credit schemes to enable them acquire production resources necessary to expand
their farmland and produce more efficiently.
11. Finally, to avoid conflict, fadama development must be undertaken in an integrated
fashion taking into consideration the interest of different categories of users (farmers,
pastoralists, and fisherman,) and specifying and enforcing their property rights to the
fadama resources.
5.4: Suggestions for Further Study.
1. This thesis focused on assessment of sustainability of food crop production in fadama
area at the farm level, and estimated technical efficiency from cross sectional data. There
is the need to measure sustainability of agriculture at the farming system and
village/catchments system level to take care of long term environmental/ecological,
economical and socio-political dimension of sustainability. This would require time-
series or panel data to measure impact trade-off and capture year-to-year fluctuation of
agricultural output and prices in efficiency measurement.
2. This study is limited to only food crops production and excludes other fadama resources-
livestock, fisheries, and forestry. There is the need to assess the general utilization of
fadama resources and its effect on fadama sustainability.
146
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162
APPENDIX I
UNIVERSITY OF ILORIN DEPARTMENT OF AGRIC ECONS & FARM MANAGEMENT
ASSESSMENT OF SUSTAINABILITY OF CROP PRODUCTION IN FADAMA OF NIGER STATE, NIGERIA.
This questionnaire is designed for the above named study. You are hereby requested to kindly
supply the information in the questionnaire as required. The information supplied will be used
purely for academic purpose and kept strictly confidential.
SECTION 1: - Socio-Economic Characteristics and Crop Establishment Activities (SECEA)
(A) Socio-Economic Characteristics
1. Name of village:……………………………………………………………………
2. Name of farmer (or serial No.):…………………………………………..
3. Age of farmer:…………………………………………………………….
4. Sex (tick one): Male [ ] Female [ ]
163
5. Marital Status (tick one): Single [ ] Married [ ]
6. Family size (number)
Adult Male:………………………………………………………..
Female:……………………………………………………...
Children (13 years)
Male:-……………………………………………………………...
Female:…………………………………………………………….
7. Highest educational level (tick one):
(i) No Formal education [ ] (ii) Primary education [ ]
(iii) Secondary education [ ] (iv) Tertiary education [ ]
(v) Adult education [ ] (v) Quranic education [ ]
8. What is your main occupation?
…………………………………………………………………………….
9. For how many years have you been farming in the fadama?
…………………………………………………………………………….
10. Are you an indigene of this area? (a) Yes [ ] (b) No [ ]
11. If No in 10, when did you arrive in the area (or at what approximate age)?
…………………………………………………………………………….
(B) Crop Establishment Activities (CPA)
1. Are all your farming activities this season on one site?
Yes [ ] No [ ]
2. If No in 1, how many plots have you cultivated this planting season?
3. Please provide information on your farm as follows
Plot No. Type of crop or
crop mixture
Unit of farm size
measurement
Plots size (Ha)
164
Planting Activities
(i) Please give information on your planting activity.
Plot No. Crop planted Area planted
Family Manday
Labour used: Hired Manday
Cost
1.
2.
3.
4.
5.
(ii) Please give information on quantity of seed used in planting.
Crop planted Quantity
used in
measure
(N
Price per
measure
)
Total paid
N
Quantity
Kg N
Unit
price/kg
Total paid
(iii) What is the source of your seeds (tick as appropriate)
Type of crop ADP Local market Personal stock Other specify
165
iv
Plot No. How long
have you been
cultivating on
the plot?
How do you
acquire your
land?
Nature of land
flat (F) Sloppy
(S)
How long do you
cultivate on the land
before you allow it
fallow?
4. Please give information on the method of tillage practice on your farm (tick as
appropriate)
(i) Plough/Ridge with machinery
(ii) Hand ridge
(iii) Non-tillage (zero tillage)
166
5. Land Clearing Activity
Labour Used Labour
Cost (N) Plot No. Area cleared in
ha
Family
Labour
Manday
Hired Man-
day
1.
2.
3.
4.
5.
6. Ploughing Activity
Plot No. Area plough in
(ha)
Tools used in ploughing
Ox-Plough
Cost (N
Tractor Cost
() N
Hand (Hoe) Cost (
)
N)
Family Hired Cost (N)
7. From your view point, describe the soil you cultivate
…………………………………………………………………………………………
…………………………………………………………………………
167
8. What is the drainage condition of your farm?
…………………………………………………………………………………………
…………………………………………………………………………
9. How do you manage water in your farm? (tick as appropriate)
(a) Used of bond [ ] (b) The use of mould [ ]
(c) Others:…………………………………………………………………
10. Do you experience flooding in your farm? (a) Yes [ ] (b) No [ ]
11. If Yes, what do you do to protect the soil?
…………………………………………………………………………………
12. Is your plot on a slope? (a) Yes [ ] (b) No [ ]
13. Since you started cultivating on the farm, have you notice any change in the colour of
the soil? If yes, explain what could be the reason for the change.
…………………………………………………………………………………………
…………………………………………………………………………
14. Were crops grown on the same land before the present crop?
(a) Yes [ ] (b) No [ ]
15. If yes, try to establish the cropping sequence
…………………………………………………………………………………
16. In your opinion what is the best time to rest a field after cropping?
…………………………………………………………………………………
17. By which of the following method did you acquire your farmland?
(a) Communual [ ] (b) Inheritance [ ] (c) Lease [ ]
(d) Family [ ] (e) Gift [ ] (f) Others………………...
18. Are there any restriction on the purpose for which you may use your land?
………………………………………………………………………………..
19. If you were to increase your farmland next year, could you easily acquire additional
land?
…………………………………………………………………………………..
20. If yes, by what method?
…………………………………………………………………………………..
168
21. Do you think the present land tenure arrangement encourage you to invest in land?
…………………………………………………………………………………..
22. If no, how could it be improved?
…………………………………………………………………………………..
23. If no, how could it be improved?
…………………………………………………………………………………..
24 What are your main reasons for farming?--------------------------------------------------
---------------------------------------------------------------------------------------------------
SECTION II: CROP PRODUCTION ACTIVITIES (CPA)
(A) Weeding situation
1. By which method do you weed your farm? (Tick as appropriate)
(a) Manual [ ] (b) Chemical (herbicides) [ ]
(iii) Biological e.g. cover crops [ ]
2. If weeding is by use of chemicals (Herbicides) provide information on
herbicide used.
Plot
No
Herbicide used Labour used
Trade
Name
Quantity
used
Cost
(N
Family
labour
manday
)
Cost
(N
Hired
labour
Manday
)
Cost
(N)
169
3. How many times do you weed your farm per cropping season? Give
information on your first weeding activities.
Plot No Crop planted Plot size Cost of weeding (N)
4. Do you use fertilizer on your farm? (tick one)
(a) Yes [ ] (b) No [ ]
5. If yes, provide the information on your use of fertilizer.
Plot
No
Type
of
crops
Area
planted
ha
Type of fertilizer used
Chemical fertilizer FYM, organic fertilizer
Type Quantity Cost
(N
Type
)
Quantity
Cost
(N)
6. Please give information on your second weeding activity if any
Plot No Crops Labour used in second weeding
Family labour Hired labour
170
Manday Cost(N Mandays Cost () N)
7. Do you use pesticide on your farm? (tick one)
(a) Yes [ ] (b) No [ ]
8. If yes, for how long have you been applying pesticides?
…………………………………………………………………………..
9. Please give information on your pesticide application
Farm
plot
Crops Area
Ha
Type
used
Quantity Total
Cost
(N
Labour used in application
)
Family laour hired labour
Mandays Cost
Manday
Cost (N)
.
171
Other Activity
Please give information on other production activities not covered by the questions
asked before now e.g. thinning Transplanting 3rd
Plot
No.
weeding, scare crow etc.
Activity Input
used
Labour used
Quantity Cost Family
Manday
Hired
Manday
Cost (N)
SECTION III: YIELD AND MARKETING ACTIVITIES (YMA)
1. Please complete the following table
2. Did you carry out some processing of your crops at the farm level?
(a) Yes [ ] (b) No [ ]
3. If yes, provide information on farm level processing.
Plot
No
crops Types of
processing
Labour used
Family
Mandays
Hired
Mandays
Cost (N)
4. Please provided information on marketing of your crops.
172
Cr
op
Output
produced
kg or bag
and others
Quantity for
consumption
kg or bag
and others
Gift kg of
bag and
others
Quantity
marketed
kg or bag
and others
Market
price per
unit kg
bag and
others
Selling price N
Per bag ,kg and
other units
5. How is your product bought ? (tick as appropriate)
(a) At farm gate [ ] (b) In the market [ ] (c) In the house [ ]
6. If in the market, how do you transport your farm produce (tick as appropriate)
(i) Vehicle [ ] (ii) Head porterage [ ] (c) Bicycle [ ]
(iv) Motorcycle [ ] (v) Donkey (other specify) [ ]
7 estimate the cost of transportation per bag to the market.
EXTENSION
1. How many times did an extension staff visit you this cropping season (Official visit to
give you advice)
……………………………………………………………………………………
2. Would you like him to visit you more? (a) Yes [ ] (b) No [ ]
3. If yes, how many visit would you like?
……………………………………………………………………………………
4. Did extension staff visit you more regularly last cropping?
……………………………………………………………………………………
5. What problems do you encounter in food production last season?
……………………………………………………………………………………
……………………………………………………………………………………
6. Suggest possible ways that can increase your food production
173
……………………………………………………………………………………
……………………………………………………………………………………
7. What are the sources of credit available to you during the current cropping season?
(a)
Personal
(b) From Relative (c) Agric Bank (d) Co-operative
Society
(e) Others
8. If you did not use bank loan, identify the main reason why you did not take bank loan.
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9. What is your main source of agricultural technological information?
(a) Extension agent [ ] (b) Friends [ ] (C) Radio [ ]
(d) Other farmers [ ]
THANK YOU
174
APPENDIX II Distribution of Technical efficiency among Sampled Fadama farmers in Southern Guinea Savanna, Niger State, Nigeria
Farmers
S/No Efficiency 1 0.24 2 0.36 3 0.43 4 0.21 5 0.47 6 0.45 7 0.44 8 0.46 9 0.53 10 0.18 11 0.49 12 0.58 13 0.85 14 0.75 15 0.72 16 0.56 17 0.5 18 0.69 19 0.54 20 0.48
175
21 0.49 22 0.86 23 0.89 24 0.91 25 0.79 26 0.64 27 0.7 28 0.89 29 0.69 30 0.83 31 0.61 32 0.78 33 0.53 34 0.51 35 0.35 36 0.53 37 0.45 38 0.59 39 0.68 40 0.48 41 0.74 42 0.51 43 0.45 44 0.52 45 0.28 46 0.26 47 0.48 48 0.46 49 0.61 50 0.25 51 0.41 52 0.07 53 0.54 54 0.52 55 0.61 56 0.58 57 0.77 58 0.5 59 0.59 60 0.43 61 0.56 62 0.33 63 0.15 64 0.54 65 0.35 66 0.18
176
67 0.06 68 0.58 69 0.82 70 0.41 71 0.5 72 0.37 73 0.76 74 0.06 75 0.22 76 0.88 77 0.54 78 0.73 79 0.74 80 0.67 81 0.56 82 0.34 83 0.33 84 0.86 85 0.31 86 0.63 87 0.45 88 0.37 89 0.35 90 0.24 91 0.6 92 0.78 93 0.59 94 0.74 95 0.67 96 0.77 97 0.53 98 0.92 99 0.59 100 0.88 101 0.69 102 0.54 103 0.62 104 0.9 105 0.53 106 0.42 107 0.54 108 0.94 109 0.5 110 0.53 111 0.67 112 0.14
177
113 0.79 114 0.86 115 0.9 116 0.89 117 0.62 118 0.95 119 0.92 120 0.84 121 0.62 122 0.49 123 0.64 124 0.45 125 0.71 126 0.88 127 0.93 128 0.6 129 0.75 130 0.88 131 0.43 132 0.5 133 0.32 134 0.64 135 0.83 136 0.81 137 0.64 138 0.57 139 0.48 140 0.54 141 0.88 142 0.83 143 0.89 144 0.45 145 0.68 146 0.49 147 0.29 148 0.88 149 0.76
Mean Efficiency : 0.58
178
APPENDIX III
Gram equivalent Conversion Factors for Cultivated food Commodities Commodity No of calories per kg of
food Conversion factors
Maize
Millet
Sorghum
Rice
Other cereals
Cassava
Sweet potatoes
Yam
Groundnut
Cowpea
Soybean
Mellon
Vegetable/leafy
Vegetable (fruits)
Sugarcane
3600
3320
3430
3600
3240
1090
9701
700
5436
3420
3960
5580
216
360
830
1.00
0.92
0.95
1.00
0.90
0.30
0.27
0.25
1.51
0.95
1.10
1.55
0.06
0.10
0.23
Source: Adapted from Oyenuga (1978).
179
APPENDIX IV
Estimation of Lambda (λ) δ2 = δv2 + δu2 γ = δu2 δ2 λ = δu δ Rahji (2005) MLE equation (29) gave the following estimate γ = 0.9760 δ2 = 6959 γ = δu2
δ2
δu2 = δ2 (γ) = (0.6959) (0.9760) = 0.6792 δv2 = δ2 - δu2 = 0.6959 - 6792 = 0.0167
180
λ = δu δv
δu2 = 0.6792,, δu2 = 0.8241 δv = 0.0167 δv = 0.1292
λ = 0.824 0.1292
= 6.3785
