A Thesis Submitted to the Department of Agricultural Economics, School ...... cultivated land have a significant negative effect on allocative and economic efficiencies, ..... certified seed production is carried out by the ESE, which relies on its own ...... seed is produced by farmers with close monitoring of ASE; hence there is no.
ECONOMIC EFFICIENCY OF WHEAT SEED PRODUCTION: AMHARA REGION, ETHIOPIA
MSc Thesis
Solomon Bizuayehu
October, 2012
Haramaya University
Economic Efficiency of Wheat Seed Production: The Case of Smallholders in Womberma Woreda of West Gojjam Zone
A Thesis Submitted to the Department of Agricultural Economics, School of Graduate Studies of Haramaya University
In Partial Fulfilment of the Requirements for the Degree of MASTERS OF SCIENCE IN AGRICULTURE (AGRICULTURAL ECONOMICS)
By
Solomon Bizuayehu
October, 2012
Haramaya University
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SCHOOL OF GRADUATE STUDIES HARAMAYA UNIVERSITY As thesis research advisor, I hereby certify that I have read and evaluated this thesis prepared under my direction, by Solomon Bizuayehu, entitled “Economic Efficiency of Wheat Seed
Production: The Case of Smallholders in Womberma Woreda of West Gojjam Zone” and recommend that it be accepted as fulfilling the thesis requirement.
__________________________ Name of thesis advisor
________________ Signature
_________________ Date
As members of the Examining Board of the Final MSc Open Defence, we certify that we read and evaluated the thesis prepared by Solomon Bizuayehu and recommend that it be accepted as fulfilling the thesis requirement for the Degree of Master of Science in Agricultural Economics.
1. Name of Chairman
2. ______________________ Name of Internal Examiner
3. ______________________ Name of External Examiner
________________ Signature
________________ Date
________________
________________
Signature
Date
________________
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Signature
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Date
DEDICATION
I dedicate this piece of work for my mom ETIHUN DEMILE whom I know what real love is while living in this world and the memories of my brother ALEMANTE ESKEZIYAW whom I lost in September 2008.
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STATEMENT OF AUTHOR First I declare that this thesis is my bona-fide work and that all sources of materials used for this thesis have been duly acknowledged. This thesis has been submitted in partial fulfillment of the requirement for an advanced MSc degree at the Haramaya University and is deposited at the university library to be made available to borrowers under the rule of the library. I solemnly declare that this thesis is not submitted to any other institution anywhere for the award of any academic degree diploma or certificate.
Brief quotations from this thesis are allowable without special permission provided that accurate acknowledgement of source is made. Requested for permissions for extended quotation from or reproduction of manuscript in whole or in part may be granted by the head of major department or the dean of the school of graduate studies when in his or her judgment the proposed use of the material is in the interests of scholarship. In all other instances, however, permission must be obtained from author.
Name: Solomon Bizuayehu
Signature _____________
Place: Haramaya University Date of submission: August______2012
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LIST OF ABBREVAITIONS AND ACRONYMS ADB
Africa Development Bank
AE
Allocative Efficiency
ASE
Amhara Seed Enterprise
COLS
Corrected Ordinary Least Squares
CSA
Central Statistics Agency
DEA
Data Envelopment Analysis
EE
Economic Efficiency
EEA
Ethiopia Economic Association
ESC
Ethiopian Seed Corporation
ESE
Ethiopia Seed Enterprise
FAO
Food and Agriculture Organization
GDP
Gross Domestic Product
MD
Man Day
ML
Maximum Likelihood
NSC
National Seed Council
OSE
Oromia Seed Enterprise
OLS
Ordinary Least Squares
SFA
Stochastic Frontier Analysis
SPF
Stochastic Production Frontier
TE
Technical Efficiency
TLU
Tropical Livestock Unit
VIF
Variance Inflation Factor
WFP
World Food Programme
WOAaRD
Woreda Office of Agriculture and Rural Development
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BIOGRAPHICAL SKETCH The author, Solomon Bizuayehu, was born in Debre Tabor on August 08/ 1984. He attended his elementary and junior school education at Shembit Elementary school, and Fasilo Junior Secondary School, Bahir Dar, respectively.
He has completed his high school education at
Tewodros II Secondary School, Debre Tabor. After completion of his high school education, he joined Mekelle University in November 2002 and graduated with Bachelor of Science Degree in Natural Resources Economics and Management in 2006.
Soon after graduation, he joined Debre Birhan Agricultural Research Center of Amhara Regional Agricultural Research Institute as junior researcher in Socio Economics Research Division and worked there until October 2009. In October 2009 he joined the School of Graduate Studies of Haramaya University, Department of Agricultural Economics for his MSc degree in agricultural economics.
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ACKNOWLEDGEMENTS First and for most, I would like to be grateful for the loving, kindness, and faithfulness of the Almighty God and His Mother St. Merry in bestowing health, strength, patience and protection throughout the study period.
I express my sincere gratitude and heartfelt appreciation to my advisor Dr. Jema Haji as without his consistent advice, understanding, guidance and supervision the completion of this work would not have been possible. He was so helpful and I had full freedom to communicate him any time and in any place, which was so nice of him. He deserves big respect and thanks.
I am greatly indebted to my mother Ms. Etihun Demile (Nanye) for her special love and prayer throughout my life. My gratitude also goes for all my sisters and my brother for their moral and material support during my study time. My sincere appreciation and thanks also go to my colleagues, Daniel, Yalfal, Dagnenet, Hana and Abebayehu for the remarkable memories and constant moral support during the study period. I must thank Dr Fitsum Hagos, my hero in my professional life, for his critical comments in improving this work. I also thank Beneberu Tefera and Leuleseged Kassa for their valuable comments.
I am very grateful to LSB project of Bahirdar University and Ethiopian Development Research Institute for recognizing the importance of this research and providing financial and material assistance, which led to the implementation and finalization of this study. Also my keen appreciation should go to ARARI and Debre Birhan Agricultural Research Center for providing the chance to attend my study. I need to extend my heartily appreciation for Tsegaye Getachew and Genet Taye for administering my salary while I was on study.
I also feel great to express my thanks to the farmers who participated in the study for sparing their precious time and for responding positively to the lengthy interview schedule. I would also like to thank the enumerators (especially Leakemareyam and Esubalew) who have tolerated the hardship and for filling the questions patiently.
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TABLE OF CONTENTS STATEMENT OF AUTHOR .................................................................................................. v LIST OF ACRONYMS ...........................................................................................................vi BIOGRAPHICAL SKETCH ................................................................................................ vii ACKNOWLEDGEMENTS .................................................................................................. viii LIST OF TABLES ...................................................................................................................xi LIST OF FIGURES ............................................................................................................... xii LIST OF TABLES IN THE APPENDIX ............................................................................ xiii ABSTRACT ............................................................................................................................xiv 1. INTRODUCTION ................................................................................................................. 1 1.1. Background .................................................................................................................... 1 1.2. Statement of the Problem.............................................................................................. 2 1.3. Objectives of the Study.................................................................................................. 3 1.4. Scope and Limitations of the Study ............................................................................. 3 1.5. Significance of the Study ............................................................................................... 4 1.6. Organization of the Thesis ............................................................................................ 5 2. LITERATURE REVIEW..................................................................................................... 6 2.1. Performance of Agriculture in Ethiopia ...................................................................... 6 2.2. Concept of Seed and Seed System ................................................................................ 7 2.2.1. Formal seed sector in Ethiopia .......................................................................... 7 2.2.2. Informal seed sector in Ethiopia ........................................................................ 8 2.3. Concept and Measures of Efficiency ............................................................................ 9 2.3.1. Input oriented measures ................................................................................... 10 2.3.2. Output oriented measure.................................................................................. 12 2.4. Models of Efficiency Measurement ............................................................................ 13 2.4.1. Non-parametric approach: Data Envelopment Analysis .............................. 13 2.4.2. Parametric approaches ..................................................................................... 14 2.5. Empirical Studies on Economic Efficiency ............................................................... 16 3. RESEARCH METHODOLOGY ...................................................................................... 21 ix
TABLE OF CONTENTS (Continued) 3.1. Description of the Study Area .................................................................................... 21 3.2. Types and Sources of Data .......................................................................................... 22 3.3. Sampling Technique and Sample Size ....................................................................... 23 3.4. Methods of Data Analysis ........................................................................................... 23 3.4.1. Specification of econometric model ................................................................. 24 3.4.2. Definition of variables and hypotheses ............................................................ 29 4. RESULTS AND DISCUSSION ......................................................................................... 35 4.1. Descriptive Statistics.................................................................................................... 35 4.1.1. Social and demographic characteristics of sample households .................... 35 4.1.2 General farming characteristics ....................................................................... 36 4.1.3. Economic status and income sources............................................................... 43 4.1.4. Summary of variables used in the model ........................................................ 44 4.2. Econometric Result...................................................................................................... 47 4.2.1. Estimation of production and cost functions .................................................. 48 4.2.2. Test of hypothesis .............................................................................................. 50 4.2.3. Efficiency scores ................................................................................................ 52 4.2.4. Determinants of efficiency differentials .......................................................... 55 5. SUMMARY, CONCLUSSION AND RECOMMENDATION ....................................... 58 5.1. Summary ...................................................................................................................... 58 5.2. Conclusion .................................................................................................................... 59 5.3. Recommendation and Policy Implications ................................................................ 60 6. REFERENCES .................................................................................................................... 62 7. APPENDICES ..................................................................................................................... 66
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LIST OF TABLES Table
Page
Table 1 Review of economic efficiency researches done in different countries...................... 20 Table 2 Distribution of sample households by Kebele, Womberma woreda. .......................... 23 Table 3 Family structure and labour force of sample households during 2010/11. ................. 35 Table 4 Education status of sampled household heads during 2010/11................................... 36 Table 5 Farming characteristics and land distribution of sampled households in 2010/11 ..... 37 Table 6 Distribution of oxen among sampled households during 2010/11 ............................. 37 Table 7 Livestock holding of sampled households in the year 2010/11. ................................. 38 Table 8 Major crops produced by sampled farmers by production and area coverage............ 39 Table 9 Production and distribution of improved seeds by ESE in 2010 (quintals) ................ 41 Table 10 Participants of wheat seed production at Womberma woreda .................................. 41 Table 11 Distribution of income among sampled households in 2010/11. .............................. 43 Table 12 Asset owner ship of sampled households in the year 2010/11 ................................. 44 Table 13 Summary statistics of variables in the production function. ..................................... 44 Table 14 Summary statistics of variables in the cost function. ................................................ 45 Table 15 Summary of efficiency model variables ................................................................... 46 Table 16 Estimates of the average and Cobb Douglas production function. ........................... 48 Table 17 Elasticities and return to scale of the parameters in the production Function. ......... 50 Table 18 Generalized likelihood ratio tests of hypothesis for the parameters of the SPF. ...... 51 Table 19 Descriptive statistics of efficiency measures. ........................................................... 52 Table 20 Tobit model estimates for different efficiency measures. ......................................... 55
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LIST OF FIGURES Figures
page
Figure 1 Input-oriented Measures of technical and allocative efficiencies.............................. 11 Figure 2 Output oriented measures for technical and allocative efficiencies .......................... 12 Figure 3 Piecewise linear convex isoquant .............................................................................. 14 Figure 4 Location of study area in Amhara National Regional State ...................................... 22 Figure 5 Wheat seed production trend in Womberma woreda ................................................ 42 Figure 6 Distribution of technical efficiency scores. ............................................................... 53 Figure 7 Distribution of allocative efficiency scores. .............................................................. 54 Figure 8 Distribution of economic efficiency scores ............................................................... 54
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LIST OF TABLES IN THE APPENDIX Appendix Table Page Appendix 1 Conversion factor of man equivalent and adult equivalent. ................................. 67 Appendix 2 Conversion factors used to Estimate Tropical Livestock Unit Equivalents. ........ 67 Appendix 3 Technical efficiency score of the sample farmers (SPF) ...................................... 68 Appendix 4 Allocative efficiency score of the sample farmers (SPF) ..................................... 69 Appendix 5 Economic efficiency score of the sample farmers (SPF) ..................................... 70 Appendix 6 Variance Inflation Factor (VIF) for input and efficiency variables. .................... 71 Appendix 7 Variance Inflation Factor (VIF) for variables in the production function model . 71 Appendix 8 Contingency coefficient of dummy efficiency variables. .................................... 72 Appendix 9 Questionnaire........................................................................................................ 73
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ECONOMIC EFFICIENCY OF WHEAT SEED PRODUCTION: THE CASE OF SMALLHOLDERS IN WEST GOJJAM ZONE OF AMHARA REGION.
BY: SOLOMON BIZUAYEHU MAJOR ADVISOR: JEMA HAJI (PhD) ABSTRACT There is huge seed supply shortage of in Ethiopia, though it is basic input in crop production. One way to increase seed supply and crop productivity is through enhancing the efficiency of seed production, because it is the basic input with out which production is impossible. The aim of the study was to measure the level of technical, allocative and economic efficiencies of wheat seed production and to identify factors affecting them in the study area. The study was conducted using cross-sectional data collected in 2010/11 production season from total 150 households randomly selected at Womberma Woreda of West Gojjam zone, Amhara National Regional State. Stochastic production frontier model has been used to estimate technical, allocative and economic efficiency levels, where as tobit model has been used to identify factors affecting efficiency. The results indicate that there was significant amount of inefficiency in wheat seed production. Accordingly, the mean technical, allocative and economic efficiencies of sampled households were 79.9%, 47.7% and 37.3%, respectively. Results of tobit model prevails that interest in wheat seed bussines and total income positively and significantly affect technical efficiency while total expenditure has a negative and significant effect. Education level and livestock ownership have a significant positive impact on allocative and economic efficiencies while land ownership and total cultivated land have a significant negative effect on allocative and economic efficiencies, respectively. The mean technical efficiency levels further suggest that wheat seed producer farmers in the study area could increase their production by 20% without using extra inputs. Alternatively, farmers can reduce, on average, their cost of production by 52.3% without reducing the existing level of output. As a concluding remark, the result of the study indicates that there is a room to increase the efficiency of wheat seed production, which is very limiting input. Therefore, policies and strategies of the government should be geared towards the above mentioned factors.
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1. INTRODUCTION 1.1. Background
Ethiopian agriculture is explained by low productivity, caused by an adverse combination of agro climatic, demographic, economic and institutional constraints and shocks. About 5.23 million people are in need of relief food assistance and the national relief pipeline has a shortfall of 290,000 tonnes of food grain (FAO and WFP, 2010). The smallholder farmers, who are providing the major share of the agricultural output in the country, commonly employ backward production technology and limited modern inputs (World Bank, 2007). Hence, being an agriculturally dependent country with a food deficit gap, increasing crop production and productivity is not a matter of choice rather a must to attain food self-sufficiency.
Currently, the Ethiopian government has designed a five year growth and transformation plan which aims at boosting the national Gross Domestic Product (GDP). According to the plan smallholder farmers are among the major target groups where increased agricultural productivity is believed to be achieved (David et al., 2011). One of the basic strategies of the Ethiopian government in improving agricultural productivity is to adopt new technologies, especially seed. Among others, seed is considered to be the basic input that enhances agricultural production and productivity.
In Ethiopia only less than 10 % farmers use improved seeds. This is partly a supply problem due to the inability of the various suppliers (the Ethiopian Seed Enterprise and other suppliers including international firms such as Pioneer seed company and cooperative seed producers) to meet the demand (FAO and WFP, 2010). Production and distribution of improved seed has been stagnant since about 2000. At about this same time, the supply of improved seed channelled through the regional extension and input supply system began to fall short of official estimates of demand, with a 72 percent shortfall in 2008 for the five major cereals and 24 percent for wheat (David et al., 2011).
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The total annual seed requirement by the agricultural sector in Ethiopia by the year 2011 was estimated to be about 700,000 tonnes. During same year, about 15 percent of this was met by the formal sector, distributing over 105,000 tonnes of improved seeds. Commercial imports of cereals, mainly wheat, have risen strongly since 2008, showing the Government‟s effort to stabilize prices following the significant increase in domestic food prices. The supply demand gap of wheat at national level for the year 2011 was 335,000 tonnes, which was not abled to be covered by either domestic production or import (David et al., 2011).
Agricultural production and productivity can be enhanced through increased use of input or improvement in technology given some level of input. The other way of improving productivity is to enhance the efficiency of producers. In countries like Ethiopia (having capital constraint) it is worthwhile to benefit from increasing productivity through improving efficiency in use of resources available. Hence this study tries to analyse Economic Efficiency (EE) of wheat seed production, and determinants that explain the variation in efficiency.
1.2 Statement of the Problem
The significant share of Ethiopia‟s agricultural output comes from subsistence farmers operating under traditional practices; mainly using local varieties with inadequate amount of fertilizer. Ethiopia is known as a centre of diversity for some of the crops widely grown. However, besides their importance in some traits like disease resistance, the productivity of the land races is low due to their low genetic potential (Yealembirhan, 2006). Hence, crop productivity on smallholdings is low, averaging 17.8 qt/ha for wheat and 15.4 qt/ha for cereal. The cereal import requirement of the country in 2010 was about 1.16 million tonnes of which 520,000 tonnes were imported commercially (FAO and WFP, 2010).
According to EEA (2005), wheat yield per hectare can be increased on average by more than two fold if farmers use the technologies (agronomic management, seed and fertilizer) at the recommended rate. Even though the prevailing problem in productivity is attributed to many factors, less availability of improved seed on the required time is among major factors. Although, it is not to the satisfactory level, there is an experience of improved seed 2
multiplication by different stakeholders, mainly by the Ethiopian Seed Enterprise (ESE) and Amhara and Oromia regional seed enterprises. The other and relatively simple way for increasing access to seed for the farmers is local seed production system, which is managed by the farmers at farm level. The merit of this system over the formal one is that it can reach the farmer quickly.
Despite the huge demand of seed of different crops by the farmers, the formal sector (mainly ESE) cannot reach everywhere. As a result, the majority (95%) of the total area allocated for seed production is covered by informal sector (Dawit, 2010). Therefore, due attention should be given to strengthen the performance of this sector. However, there is limited number of studies done in this regard in general and there is no study done on efficiency of smallholder wheat seed producers in Ethiopia, particularly in the study area. Hence, there is a need to fill this gap and provide possible intervention areas to improve the existing seed production and productivity.
1.3. Objectives of the Study
The overall objective of this study is to asses the efficiency of wheat seed production in Womberma woreda of West Gojjam zone.
The specific objectives of the study are: i. To estimate the technical, allocative and economic efficiencies of the wheat seed producers in the study area; ii. To identify factors determining technical, allocative and economic efficiencies of the wheat seed producers.
1.4. Scope and Limitations of the Study
This study was done at Womberma woreda, West Gojjam Zone. The major limitations of this study are: it covers only one district and used a cross-sectional data. The other limitation of 3
the study is that given different crops in the study area, it only focuses on wheat seed production.
1.5. Significance of the Study
The measurement of efficiency (technical, allocative and economic) has remained an area of important research, especially in developing countries, where resources are scanty and opportunities for developing by adopting better technologies are dwindling (Bedasa and Krishnamoorthy, 1997). Given the current technological conditions and the structure of production in Ethiopia, pushing the production area further is difficult due to the already existing pressures on the land (ADB, 2010). This makes the study to be problem oriented and important for the country in general, and to stakeholders working on the area in particular.
Different people, on different sectors, have done many performance evaluation studies in Ethiopia. However, much of the works are constrained to technical efficiency (Mohammed et al., 2000; Temesgen, 2003; Kinde, 2005). But focusing only on TE understates the benefits that could be derived by producers from improvements in overall performance. Unlike TE, researches done on EE are limited (Jema, 2008 and Wondimu, 2010). However, none of these works deal with EE of wheat seed production. Therefore, this study is the first of its kind in the study area.
In this study priority has been given to wheat because of the following reasons. First, wheat seed takes the lion‟s share of the whole seed distributed by ESE at national level. The amount of wheat seed distributed by ESE shows an increasing trend since 2007. Wheat seed takes 54% of the total seed distributed by the ESE, which is the major supplier of seed for the country, in the year 2009. The other rational for focusing on wheat seed is that farmers in the study area have more than 12 years of experience in wheat seed production. The last but not least rational is that Ethiopia imported 639,000 tonnes of crops in the year 2010, out of which major share (72%) is wheat (FAO and WFP, 2010). Hence if the efficiency of the farmers producing wheat seed (the basic input for production) is improved, this significant amount of currency which is incurred to import wheat could be at least reduced and shift to other 4
products which can‟t be produced domestically, which is in line with import substitution policy of the country.
In the study, possible sources of intervention that would help stakeholders working to improve wheat seed production in the area are recommended. Generally besides improving the EE of the area, it will contributed literature for further study since there is no such a research done before in the study area.
1.6. Organization of the Thesis
The remaining chapters are organized in such a way that chapter two deals with concepts of efficiency in general and various methodological aspects concerning efficiency measurements in particular are briefly reviewed. The major part of this chapter is devoted to explain models that are commonly used in efficiency measurements. However, special emphasis was given to stochastic frontier model and empirical review of literature. Chapter three is all about the methodology used in the study. It includes the study area, sources of data and sampling design. Moreover, the econometric model and the variable definition are also briefly illustrated. Chapter four starts by presenting descriptive statistics results about sampled households. The integral part of this study, econometric result, is also clearly discussed in this chapter. Chapter six is summary, conclusion and policy implications. It briefly summarizes main results of the study and also indicates what policy implications have these results in improving the efficiency of farmers in the study area.
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2. LITERATURE REVIEW In this chapter, a review of performance of the Ethiopian agriculture, concepts of efficiency and efficiency measurements and empirical studies on efficiency are discussed briefly as follows:
2.1. Performance of Agriculture in Ethiopia
Developing countries face the dual tasks of increasing agricultural productivity and ensuring sustainability of the resource base on which agriculture fundamentally depends. Agriculture is the foundation of Ethiopian economy and also the major contributing sector to food security. However, despite the existence of huge potential for development, the performance of the sector remains weak as it is heavily influenced by weather conditions. Ethiopia‟s economy is highly vulnerable to exogenous shocks by virtue of its dependence on primary commodities and rain fed agriculture (ADB, 2010). The growing gap between food demand and supply in Ethiopia is mainly attributed to the very low productivity of the agricultural sector (EEA, 2005). Low productivity is partly due to limited use of improved varieties and associated technologies, so the availability and use of improved varieties and seeds play an important role in this endeavor (Thijssen et al., 2008). The increased use of modern agricultural inputs particularly fertilizer, through government‟s participatory demonstration and training extension systems, have all possibly contributed to the increase in the overall output although there was no much change in productivity figures at national level. Moreover, this recent improvement in total food crop production has failed to change the negative trend in per capital agricultural production observed in the last three decades. Per capita production in 2003/04 is lower by 40kg compared to the level 20 years back (EEA, 2005). The current data from FAO and WFP (2010) indicates that the per capita consumption comprises 47 kg of maize, 38.5 kg of wheat, 33.5 kg of teff, 31 kg of sorghum, 14 kg of barley, 6 kg of millet, 14 kg of pulses and 1.2 kg of other cereal crops.
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In the current Ethiopian agricultural system there is only a little room for further significance increase in the area under cultivation. Even if there is expansion this will be to lands that are often fragile and susceptible to erosion (ADB, 2010). Hence improving productivity is the best way to boost production and mitigate the terrible consequence of increased human and livestock pressure on limited natural resource. It is also through improved productivity that sustainability can be introduced in to the farming system and the attempt to transfer the agricultural sector in to an economic activity can be realized.
2.2. Concept of Seed and Seed System
Seed can be defined as any part of the plant that is used for reproduction, both generative (true seed) and vegetative parts (Yealembirhan, 2006). Seed is considered to be basic to agricultural production and a vital input to enhance productivity. One could consider seed security as part and parcel of food security where by agricultural development is expected to play a major role in order to alleviate the problem of food insecurity.
Though agriculture is the leading sector in most developing countries, farmers have been using their own seed saved from the previous crop, or obtained, usually in exchange for grains or other commodities from neighbouring farmers. Usually these seeds are of varieties described as „land races‟, which have been going through a natural evolution process over time to produce new genetic versions of varieties to fit each agro-ecological condition. However the land races are well adapted to the local agro-ecological situations resistant to factors like disease, insect attack or even moisture stress, they are generally low yielding when compared to improved high yielding varieties of seeds in agricultural research stations (Thijssen et al., 2008).
2.2.1. Formal seed sector in Ethiopia
The formal seed sector in Ethiopia owes its origin to the establishment of Alemaya College of Agriculture (currently Haramaya University) in the mid of 1950s and the Ethiopian Institute of Agricultural Research in the mid 1960s, when these institutions engaged in developing 7
improved varieties of seeds for cereal, pulse and oilseed crops. Seeds developed by the above mentioned institutions were initially introduced to the farmers in 1967 (Dawit, 2010). The then Ethiopian Seed Corporation (ESC), (currently, the Ethiopian Seed Enterprise, ESE) was established in 1979 following the recommendations of a National Seed Council formed in 1976 (Yealembirhan, 2006).
In history of Ethiopian seed system, producers are both public and private. The type and number of actors in the formal seed system in general, and cereal seed system in particular, have been changing along with the institutional and policy changes in the system. Basic and certified seed production is carried out by the ESE, which relies on its own farms alongside private companies, private subcontractors, state farms, and cooperatives, to bulk up seed that is supplied to the regional extension and input supply systems. More recently, state-owned regional seed enterprises have also emerged in Oromia and Southern Peoples Nations and Nationalities and in Amhara (David et al., 2010). Unlike the former times, active participation of stakeholders helps to raise the total available improved seed, but the use of such seed still remains less than 10 % of all crops grown (FAO and WFO, 2010).
2.2.2. Informal seed sector in Ethiopia The informal seed system under Ethiopian context is defined as seed production and distribution along with the different actors where there is no legal certification in the process (Dawit, 2010). This includes retained seed by farmers, farmer to farm seed exchange, cooperative based seed multiplication and distribution, nongovernmental organization based seed multiplication and distribution. For centuries, the Ethiopian farmers have used land races for agricultural production. Around 85 % of Ethiopian farmers are believed to be dependent on these land races (Yealembirhan, 2006). In order to overcome the serious food security concerns of the country in general a mix of formal and informal seed sector development strategies were adopted. The formal sector plays a limited role in addressing the demand for many crops in different areas; the seed supply is often irregular and limited to a few varieties. For this reason ESE has designed a new approach, Farmer Based Seed Production and Marketing Scheme, in 1998/99 and 8
currently the scheme accounts for more than 30 % of ESE total seed production (Thijssen et al., 2008). 2.3. Concept and Measures of Efficiency
Basically productivity can be enhanced in two ways. One can either improve the state of technology by inventing a new technology (ploughs, pesticides, etc). This is commonly referred to as technological change and can be represented by an upward shift in the production frontier. Alternatively one can implement procedures, such as improved farmer education, to ensure farmers use the existing technology more efficiently. This will be represented by the firms operating more closely to the existing frontier. Hence, productivity growth may attribute to either technological progress or efficiency improvement.
Farrell (1957) in his first work on efficiency proposed that the efficiency of a firm consists of two components: Technical Efficiency (TE), which reflects the ability of the firm to obtain maximum output from a given set of inputs, and Allocative Efficiency (AE), which reflect the ability of the firm to use the inputs in optimal proportions, given their respective prices. These two measures are then combined to provide a measure of total Economic Efficiency (EE).
Empirical works in the field of economics, including agricultural economics, have adopted Ordinary Least Squares (OLS) regression, which estimate a line of best fit through the sample data. According to Coelli and Battesse (1995) in analysing efficiency, fitting a frontier model performs better than OLS regression. The two main benefits of estimating the frontier function, rather than average (e.g. OLS) functions, are that:
i. Estimation of an average function will provide a picture on the shape of technology of an average firm, while the estimation of the frontier function will be most heavily influenced by the best performing firm and hence reflect the technology they are using.
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ii. The frontier function represents a best practice technology against which the efficiency of firms within the industry can be measured. It is this second use of frontiers, which leads to widely application of estimating frontier functions.
There are two approaches of measuring efficiency: output oriented approach (often referred to as primal approach) and input oriented approach (referred as dual approach). In the primal approach the interest is by how much output could be expanded from a given level of inputs, hence known as output shortfall. Where as in the input oriented approach the concern is the amount by which all inputs could be proportionately reduced to achieve efficient level of production, hence, known as input over use. Both measures will coincide when the technology exhibits constant returns to scale, but are likely to vary otherwise (Coelli, 2005).
2.3.1. Input oriented measure
Farrell (1957) illustrated his idea about measuring efficiency using a simple example involving firms, which use two inputs (X1 and X2) to produce a single output (Y). In figure 1 bellow SS‟ is an isoquant, representing technically efficient combinations of inputs, X1 and X2, used in producing output Q. SS‟ is also known as the best practice production frontier. AA' is an isocost line, which shows all combinations of inputs X1 and X2 to be used in such a way that the total cost of inputs is equal at all points. However, any firm intending to maximize profits has to produce at Q', which is a point of tangency and representing the least cost combination of X1 and X2 in production of Q. At point Q' the producer is economically efficient.
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Figure 1 Input-oriented Measures of technical and allocative efficiencies.
Source: Coelli, 1995. The same figure (Figure 1) is employed to measure the technical, allocative and economic efficiency. Suppose a farmer is producing his output depicted by isoquant SS‟ with input combination level of (X1 and X2). Production at input combination of point (P) is not technically efficient because the level of inputs needed to produce the same quantity is Q on isoquant SS‟. In other words, the farmer can produce at any point on SS‟ with fewer inputs (X1 and X2), in this case at Q in an input-input space. The degree of TE of such a farm is measured as OQ OP , which is proportional in all inputs that could theoretically be achieved without reducing the output.
In Figure 1, AA' represents input price ratio or isocost line which gives the minimum expenditure for which a firm intending to maximize profit should adopt. The same firm using (X1 and X2) to produce output with input combination at point P would be allocatively inefficient in relation to R. Its level of AE is represented by OR OQ , since the distance RQ represents the reduction in production costs if the farmer using the combination of input (X1 and X2) was to produce at any point on AA', particularly at point R instead of P.
The overall (economic) efficiency is measured as the product of OQ OP and OR OQ , which is OR OP . All three measures of efficiency are bounded between zero and one. This follows
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from interpretation of distance RP as the reduction in costs if a technically and allocatively inefficient producer at P were to become efficient (both technically and allocatively) at Q' (Coelli, 1995).
2.3.2. Output oriented measure
In the output oriented perspective, efficiency is evaluated keeping inputs constant. Knowledge of the fully efficient production possibility curve as well as the isorevenue line makes it possible to measure and interpret the level of EE.
Output oriented measures can be illustrated by considering the case where production involves two outputs (Y1 and Y2) and a single input (L). If the input quantity is held fixed at a particular level, the technology can be represented by a production possibility curve in two dimensions as follows:
Figure 2 Output oriented measures for technical and allocative efficiencies
Source: Coelli et al., 1998.
The production possibility curve is represented by the curve AB in Figure 2, which represents technically efficient combinations of production of outputs Y1/L and Y2/L. The economically efficient point is H where the marginal rate of product transformation equals the slope of the isorevenue line CD. Consider a firm situated at point Q. Its economic output efficiency is OQ/OF. TE is represented by OQ/OG and the AE is OG/OF. 12
The point of tangency between the isorevenue line CD and the production possibility curve AB (at point H) represents the economically efficient method of production, which is 100% technically and allocatively efficient. Again, all these three measures are between by zero and one.
2.4. Models of Efficiency Measurement
Efficiency measurements basically are carried out using frontier methodologies, which shift the average response functions to the maximum output or to the efficient firm. These methodologies are broadly categorized under two frontier models; namely parametric and non-parametric. The parametric models are basically estimated based on econometric methods and the non-parametric model, often referred to as Data Envelopment Analysis (DEA), involves the use of linear programming method to construct a non-parametric 'piece-wise' surface (or frontier) over the data (Coelli et al., 1998). Efficiency measures assume as production function of the fully efficient firm is known. But this is different in practice, and the efficient isoquant must be estimated from the sample data. Farrell (1957) suggests the use of either (a) a non-parametric piecewise linear convex isoquant constructed in such a way that no observed points should lie to the left or below it, or (b) a parametric function such as Cobb Douglas production function.
2.4.1. Non-parametric approach: Data envelopment analysis (DEA)
The most widely used method of analysis in dealing with non parametric approach is DEA. This method employs mathematical linear programming to construct a frontier from a sample of data points. It is commonly used to evaluate the efficiency of a group of decision making units such as producers, firms, farmers, etc.
Data envelopment analysis is flexible and can accommodate multiple inputs and outputs with different units. It doesn‟t require an assumption of a functional form relating inputs to outputs. However DEA has also limitations (Coelli et al., 1998). The major once are:
13
The piece wise – linear convex isoquant (see figure 3) assumes that no observed point lies to the left or bellow it. It assumes all deviation from frontier is inefficiency (do not capture noise) hence it is very sensitive for outliers. Since a standard formulation of DEA creates a separate linear program for each decision making units, large problems can be computationally intensive.
Figure 3 Piecewise linear convex isoquant
Firms operating on the frontier, point C and D are efficient firms while point B and A are inefficient firms. The magnitude of efficiency for point B is OD/OB i.e. firm B can reduce both inputs by (BD/OB) * 100%.
2.4.2. Parametric approaches
Parametric frontier model can further be classified into deterministic and stochastic frontier methods. The deterministic model assumes that any deviation from the frontier is due to inefficiency, while the stochastic approach allows for statistical noise.
14
2.4.2.1. Non- stochastic/deterministic
According to Aigner and Chu (1968) a Cobb Douglas production function for a sample of N firms can be specified as:
ln(Yi ) F ( Xi; i ) Ui , i 1,2,...N
(2.1)
Where Yi is the output of the ith firm; Xi is the vector of input quantities used by the ith firm; is a vector of unknown parameters to be estimated; F ( . ) denotes an appropriate function (Cobb Douglas); and Ui is a non negative variable representing the inefficiency in production. Generally non-stochastic/deterministic production frontier can be estimated using linear programming or econometric techniques such as Corrected Ordinary Least Square (COLS). The limitation of this model is that, it treats random components (like measurement error, bad weather, etc) as part of inefficiency. Hence when there is high random error on the data or where there are few outliers, the inefficiency estimates will be exaggerated as compared to other models, which take into account random errors. Coelli (1995) argues that one of the criticisms of the deterministic approach is that no account is taken of the possible influences of measurement errors and other noises up on the shape and positioning of the estimated frontier.
2.4.2.2. Stochastic frontier production function
To solve the limitation of deterministic approach of Aigner and Chu (1968), Timmer (1971) designed a method that involves dropping a percentage of firms closest to the estimated frontier, and re-estimating the frontier using the reduced sample. The arbitrary nature of the selection of some percentage of observation to omit has meant, however, that Timmer‟s probabilistic approach has not been widely followed (Coelli, 1995).
Finally, Aigner et al. (1977) and Meeusen and Van den Broeck (1977) independently come up with the estimation of Stochastic Frontier Production (SFP) function, where the disturbance 15
term has two components; the error component (v) and the stochastic noise (u). The other merit of the SFP function over the former (deterministic) is that the estimation of standard errors and tests of hypothesis is possible, which the deterministic model fails to fulfil because of the violation of the maximum likelihood regularity conditions (Coelli, 1995).
Stochastic frontier production function can be estimated using Maximum likelihood (ML) or COLS method. The COLS is advised to use, for its simplicity in analysis. However, ML method is asymptotically efficient than COLS. These days there are computer software to manipulate the complication in analysing numerical solutions of likelihood. Given this rational ML method is preferred than COLS whenever possible.
2.5. Empirical Studies on Economic Efficiency
A recent study by Wondimu (2010) applied both stochastic and DEA methods of efficiency analysis to analyse economic efficiency of haricot bean production in East Shewa zone. The results indicate that using SFP methodology, the relative deviation from the frontier due to inefficiency was 69%, 65% and 85% for technical, allocative and economic efficiencies, respectively. The study also prevailed that the efficiency scores obtained by DEA were lower than that of SFP. According to Wondimu (2010) family size, education and livestock were the significant determinants of technical, allocative and economic efficiencies of haricot beans production in the study area. Fragmentation had a significant impact only on technical efficiency, whereas credit service had a significant effect on allocative efficiency. Extension visit was an important factor in determining both technical and allocative efficiencies.
Kehinde and Awoyemi (2009) used a stochastic frontier approach to estimate a self dual Cobb Douglas production function which gave estimates of technical, allocative and economic efficiencies of sawn production in Ondo and Osun states, Nigeria. The study used a sample of 170 sawn wood producers to estimate the technical, allocative and economic efficiencies. Accordingly, the result indicated as there was high potential (by 32%) to increase production efficiency. The AE was relatively better (81%) having inefficiency of 19%. In the study the
16
total EE was calculated to be 54%. The study prevailed as saw millers‟ level of efficiency could be improved if sawlog, electricity and capital are effectively used.
Andreu (2008) applied the concept of EE on Kansas farms. In his study, he has considered capital, labor, land and purchased inputs. The data for this study were of a 10 year (19982007) on the farms belonging to Kansas farm management association. DEA techniques were used to construct a non-parametric efficiency frontier and calculate TE, AE, and EE for each farm and each year. None of the farms in the data sample were technically, allocatively or economically efficient in all 10 years of the study. On his study, Andreu (2008) confirmed that larger farms were more efficient than smaller ones. Farms specializing in livestock products, such as dairy and beef, were reported to be slightly efficient than crop or mixed farms.
A study by Jema (2008) on EE of vegetable production, Ethiopia, used both parametric and non parametric approaches. The result revealed that there was similarity in the estimates of efficiency in both methodologies. Jema used the two stage approach in determining factors affecting efficiency. He also compared the efficiency of the whole farm with vegetable production, out of which the level of EE was 0.53 and 0.43, respectively. As to the researcher, this difference might attribute to limited access to capital markets, high consumer spending and large family size. The mean technical, allocative and economic efficiency, estimated by the non parametric DEA, were 91%, 60% and 56 % respectively.
Kareem et al. (2008) applied the concept of efficiency for comparing different pond structures in Ogun state of Nigeria. They have used SFP to estimate efficiency among the fish farmers using concrete and earthen pond systems. The result of EE has shown an average of 76% in concrete pond system while earthen pond system made as high as 84% EE level. The mean TE for the concrete pond system was estimated to be 88% while earthen pond system was 89%. Similarly, the AE results revealed that concrete pond system was 79 % efficient while earthen pond had 85%. The SFP function models resulted that pond area, quantity of lime used, and number of labor used were significant factors that contributed to the TE of concrete
17
pond system while pond, quantity of feed and labor were the significant factors in earthen pond system.
Hansson (2007) applied DEA method on her Doctoral thesis entitled with driving and restraining forces for economic and technical efficiency of dairy farms in Sweden. Her study was based on farm accounting data from a database of gross margin budgets for different agricultural production lines and regions in Sweden, a dairy cow recording scheme and questionnaire. Hansson, on her study concluded that especially the allocative input efficiency could be improved and the technical and allocative efficiency scores were typically affected by farm size positively.
Ike and Inoni (2006) on their study of the determinants of yam production in South Eastern Nigeria; use a SFP function to estimate efficiency of yam producers. A Cobb Douglas production function was fitted in their study and they use a farm level data, which was collected from a sample of 120 farmers in Enugu state. The predicted efficiencies differ substantially among the farmers, ranging between 0.07 and 0.85, with mean efficiency of 0.41. The low mean EE is an indication of inefficiency in resource use by yam farmers in South Eastern Nigeria. The results further indicated that labor and material inputs were the major factors that influence changes in yam output. The study also examined the effects of selected farmer specific socio-economic characteristics on observed inefficiencies among the farmers. Accordingly, variables such as education, farming experience and access to credit, had significant impact in explaining the observed variation in efficiency among yam producers.
According to Bravo and Pinheiro (1997) work on peasant farming efficiency in Congo the mean value of technical, allocative and economic efficiencies were 70, 44 and 31 %, respectively. These results suggested that substantial gains in output and/or decreases in cost could be attained given existing technology. Data for this study was collected from 60 peasant farmers in Dajabon region, which is situated in the North West corner of the Dominican Republic. In their study, Bravo and Pinheiro (1997) use ML techniques to estimate a Cobb Douglas production frontier, which was then used to derive its corresponding dual cost. 18
Finally the study suggests that policymakers should foster the development of medium size farms, while promoting contract arrangements between peasant farmers and agribusinesses.
Bravo and Pinheiro (1993) have done an intensive work in reviewing farm level efficiency in developing countries. They have critically reviewed 30 studies of 14 countries. The country that has received most attention was India, while rice has been the most studied agricultural product. According to them an average of few studies reported allocative and economic efficiency to be 68% and 43%, respectively. These results recommend that there is considerable room to increase agricultural output without additional inputs and given existing technology. They also proved as there is considerable farm level variation in TE which attribute to variables like farmer education and experience, contacts with extension, access to credit, and farm size.
The literature review above prevails that there is limited work in area of efficiency, with special emphasis on wheat seed production in Ethiopia. Moreover, the stochastic frontier approach is adopted to study efficiency of different sectors, indicating that it has wider application and is also appropriate to use for agricultural sector. Hence, this study adopted the most widely applicable method of estimating efficiency, SFA, in estimating TE, AE and EE of wheat seed production in Womberma woreda of West Gojjam zone, Amhara region.
19
Table 1Review of economic efficiency researches done in different countries
No
Author
Year
Approach
Functional
Country
form 1
Wondimu
2
2010
Average Efficiency (%) TE
AE
EE
DEA and SFA
Cobb-Douglas
Ethiopia
73
62
45
(Kehinde and Awoyemi, 2009) 2009
SFA
Cobb-Douglas
Nigeria
68 & 79 1
81&83
54&67
3
Andreu
2008
DEA
___
America
4
Jema Haji
2008
DEA and SFA
Cobb-Douglas
Ethiopia
91
60
56
5
Kareem et al.,
2008
SFA
Cobb-Douglas
Nigeria
88& 89
79& 85
76&84
6
Hansson. H
2007
DEA
___
Sweden
___
___
___
7
Ike and inoni
2006
SFA
Cobb-Douglas
Nigeria
__
__
0.41
8
Bravo and Pinheiro
1997
SFA
Cobb-Douglas
Congo
70
44
31
9
Bravo and Pinheiro
1993
DEA and SFA
Cobb-Douglas
India 2
__
68
43
Source: Own review
1
Done in two districts having different efficiency (See Kehinde and Awoyemi, 2009; Kareem et al., 2008)
2
It is done in 14 countries with main focus in India (See Bravo and Pinheiro, 1993) 20
3. RESEARCH METHODOLOGY In this chapter, the study area and the research methodology followed are briefly explained. Besides, the definition and hypothesis for input, output and inefficiency variables are clearly described.
3.1. Description of the Study Area
West Gojjam zone is one of eleven administrative zones of Amhara National Regional State. It covers a total area of about 13760 Sq. Kms, which accounts for 8.5% of the area of the region. The administrative zone is bounded in the North by the North and South Gondar zones, in the East by East Gojjam zone, in the South by Oromia region and in the West by Benishangul Gumuz region. The administrative zone has 11 woredas and 358 Kebeles3, out of which only 28 are urban (Figure 4).
The study was conducted in Womberma woreda, which is one of the eleven woredas of West Gojjam zone. The woreda is bounded by Bure woreda in the East, Awi zone in the West and North, and Oromia region in the South. The total area coverage of the woreda is 1353.77 Sq Km. The annual rain fall in the woreda ranges from 1100 to 1430 mm. Based on the traditional agro-climatic classification, Womberma woreda has two climatic divisions, namely, Kola (52%) and Woyna Dega (48%). The major crops grown in the area are maize, wheat, pepper and teff (WOAaRD, 2011).
The woreda has 19 rural kebelles, out of which 13 of them participate in wheat seed production. Its capital town “Shendy” is found 17 km far away from Bure town, 172 km from Bahir Dar and 427 km from Addis Ababa through Bure town. Based on figures published by Central Statistics Agency (CSA), Womberma woreda has an estimated total population of 106,954 of whom 54,205 were male and 52,749 were female. The population density of the
3
Kebele is the smallest administrative unit in the study area 21
woreda is 79 persons/ Sq Km and from the total population 90,732 are rural dwellers (CSA, 2010).
Figure 4 Location of study area in Amhara National Regional State
Source: Amhara Region Bureau of Planning and Economic Development, 2001.
3.2. Types and Sources of Data
The study principally employed primary data. A structured questionnaire was prepared, pretested in the field and adjusted accordingly. Five enumerators having qualification of diploma and above were trained for two days on how to administer the data collection work. The data was collected, with due supervision of the researcher, from sample of wheat seed producers. Finally the focus group discussion was held.
The content of the questionnaire mainly emphasized on household characteristics (family size, sex and age category, etc), land holding (own, rented and leased, etc), availability and use of inputs (labor, fertilizer, seed, etc), institutional factors (credit, market, etc), income of the household (on farm and non/off farm income), allocation and price of each type of farm resource to produce wheat seed, quantity of wheat seed produced, access to agricultural extension services, problems and opportunities in wheat seed production. 22
Secondary data were also gathered from governmental and non-governmental sources located around the study area so as to backup the primary data. Specifically the price information for basic inputs and output were taken from the Womberma woreda agriculture office (fertilizer, pesticide and seed) and ASE. Moreover, regular and statistical reports of the Ministry of Agriculture and Rural Development, CSA and ESE were consulted. 3.3. Sampling Technique and Sample Size
In this study, a combination of both purposive and multiple stage random sampling techniques were employed to draw an appropriate sample. Womberma woreda was selected purposively for its long year experience in seed production and being an innovation site for Bahirdar University local seed business project. Since the research focus basically on seed production, wheat seed producer of the kebeles were the major target areas for sample selection. In the woreda there are 13 wheat seed producer kebeles out of which two (Marwoled and Wogedade) were selected randomly in the first stage. In the second stage a total of 150 sample wheat seed producer farmers were selected randomly, using probability proportional to size sampling technique (Table 2). After data entry and cleaning four observations were found to have insufficient information, hence a total of 146 households were used for further analysis.
Table 2. Distribution of sample households by Kebele, Womberma woreda
Total number of
Wheat seed producer
households
households
Kebele
Sampled households
Total
Male
Female
Total
Male
Female
Total
Male
Female
Marwoled
855
589
266
161
145
16
71
66
5
Wogedade
919
779
140
170
158
12
75
72
3
Total
1774
1368
331
303
28
146
138
8
406
Source: Own computation, 2011
3.4. Methods of Data Analysis 23
The study employed both descriptive and econometric methods. The descriptive analysis was used to summarize some important characteristics of the sample households. This method includes the application of means, percentages and standard deviations. The econometric model was used to measure the level and determinants of efficiency.
Basically there are two approaches in measuring the efficiency of a decision making unit; input oriented and output oriented. Given that we are considering developing country settings input quantities appear to be decision variables (Jema, 2008). Hence for this study preference is made to the input oriented/primal approach4.
In the context of developing world where random errors (measurement error, weather, and natural disasters, etc.) are prevalent, SFP is a relatively better measure of efficiency. SFP approach can estimate the standard errors and help to test hypothesis (Coelli, 2005). With this all background the SFP function approach was used for this study.
3.4.1. Specification of econometric model
The SFP function was autonomously developed by Aiger et al. (1977) and Meeusen and van den Broeck (1977). The approach offers some sensible advantages over the other methods that are usually used in efficiency analysis. In the first place, it is easy to implement and interpret. Most importantly, the model allows segregating the effect of statistical noises from systematic sources of inefficiency. Besides, the technique is consistent with most of the agricultural production efficiency studies (Ike and Inoni, 2006; Jema, 2008; Kareem et al., 2008; Kehinde and Awoyemi, 2009).
Following Aigner et al. (1977) and Meeusen and van den Broeck (1977), the SFP model is defined as:
4
Output and the input oriented measures of technical efficiency are equal under constant
returns to scale, while the former is greater (less) than the latter under decreasing (increasing) returns to scale (Giannis and Vangelis, 2001). 24
6
ln Yi o ln jXij i j 1
i vi ui
(3.1)
Here ln denotes the natural logarithm; j represents the number of inputs used; i represents the ith farm in the sample; Yi represents the observed wheat seed production of the i th farmer;
Xij denotes j th farm input variables used in wheat seed production of the i th farmer; ß stands for the vector of unknown parameters to be estimated; i is a composed disturbance term made up of two elements ( vi and u i ). The random error ( vi ) accounts for the stochastic effects beyond the farmer‟s control, measurement errors as well as other statistical noises and u i captures the technical inefficiency.
Aigner et al. (1977) proposed the log likelihood function for the model in equation (3.1) assuming half normal distribution for the technical inefficiency effects ( u i ). They expressed the likelihood function using parameterization, where is the ratio of the standard errors of the non-symmetric to symmetric error term (i.e. u v ). However, Battese and Corra (1977) proposed that the parameterization, where 2 u ( v u ) , to be used instead 2
2
of . The reason is that could be any non-negative value while ranges from zero to one and better measures the distance between the frontier output and the observed level of output resulting from technical inefficiency. However, there is association between and λ. According to Bravo and Pinheiro (1997) gamma (γ) can be formulated as:
[2 (1 2 )]
(3.2)
Hence, by following Battese and Corra (1977) the log likelihood function of the model is specified as:
25
ln( L)
N N ln ln 2 ln 1 i 2 2 2 i 1
1 1 2 2
N
i 1
2
(3.3)
i
Where ei = lnYi - ln Xi b is the residual of (3.1); N is the number of observations; (.) is the standard normal distribution; 2 v u , and u 2 are variance parameters. 2
2
2
The minimization of (3.3) with respect to , 2 and and solving the resulting partial derivatives simultaneously, produces the ML estimates of , 2 , and . The parameter is used to test whether the technical inefficiency affects output or not. 2 Likewise the significance of indicate whether the conventional average production
function adequately represent the data or not.
According to Aigner et al. (1977), the symmetric component ( vi ) is assumed to be independently and identically distributed as N (0, v ) . On the other hand, u i is non-negative 2
truncated half normal random variable with zero mean and constant variance, u . 2
The production function could also be estimated through an alternative form, called dual, such as cost or profit function. According to Arega and Rashid (2005), inadequate farm level price data together with little or no input price variation across farms in Ethiopia precludes any econometric estimation of a cost function. This problem will be simplified if one uses functional forms which are not flexible such as Cobb Douglas production function (Kumbhakar, 1991). The self dual functional form allows the cost frontier to be derived and used to estimate EE when farmers face same price.
With regard to functional forms, Coelli and Battese (1995) discussed three common functional forms namely Cobb Douglas, Translog and Zellner-Revankar generalized production functions. Each functional form has its own strength and shortcomings. The Cobb 26
Douglas production function is mostly used for its simplicity; ease of estimation and interpretation. On the other hand, the Translog functional form imposes no restrictions upon returns to scale or substitution possibilities and the Zellner-Revankar form removes the return to scale restriction. However, these functional forms are susceptible to multicollinearity and degree of freedom problem.
Even though Cobb Douglas model assumes unitary elasticity of substitution, constant production elasticity and constant factor demand; if the interest is to analyse the efficiency measurement and not analysing the general structure of production function, it will have adequate representation of the technology and insignificant impact on measurement of efficiency (Coelli et al., 1998). When farmers operate in small farms, the technology is unlikely to be substantially affected by variable returns to scale (Coelli, 1995). Moreover, Cobb Douglas production function has been employed in many researches dealing with efficiency (Bravo and Pinheiro, 1997; Jema, 2008; Kareem et al., 2008; Kehinde and Awoyemi, 2009). Therefore, it was also adopted for this study.
The dual cost function of the Cobb-Douglas production function can be specified as: 6
lnCi = a o + åa i lnWij + a 7 lnY *i
(3.4)
j=1
Where i refers to the ith sample farm; j is number of input; Ci is the minimum cost of production; Wi denotes input prices5; Y
*
refers to farm output which is adjusted for noise vi
and αs are parametrs to be estimated.
Sharma et al. (1999) suggests that the corresponding dual cost frontier of the Cobb Douglas production functional form in equation (3.1) can be rewritten as:
5
Scaling all factor prices equally or each factor price individually will have no effect on the
input-oriented measure of inefficiency. Hence, this is important in studies where there is no variation in price data for individual producers (Giannis K. and T. Vangelis, 2001). 27
Ci C (Wi, Y * ; )
(3.5)
Where Ci , Y , Wi and are as described above. The economically efficient input vector of *
the ith firm Xie is drived by applying Shepards‟ lemma (Jema, 2008; Kehinde and Awoyemi, 2009) and substituting the firms input prices and adjusted output level, a system of minimum cost input demand equation can be expressed as: Ci / Wn Xie (Wi, Y * ; )
(3.6)
Where n is the number of inputs used. The observed, technically and economically efficient costs of production of the ith firm are then equal to W‟Xi, W‟Xit and W‟ie ; respectiveslly. According to Sharma et al. (1999) the above cost measures are used to estimate the technical, allocative and economic efficiencies respectivelly.
TE W ' Xit / W ' iXi
(3.7)
EE W ' Xie / W ' iXi
(3.8)
Following Farrell (1957), the AE index can be derived from Equations (3.7) and (3.8) as follows:
AE W ' Xie / W ' iXit .
(3.9)
Thus the total cost or economic inefficiency component of the ith firm ( W ' Xie W ' iXi ) can
be
( W ' Xit
decomposed
in
to
its
technical
( W ' Xi W ' Xie )
and
allocative
W ' iXie ) components.
After estimating the level of efficiency ( E * ) parameters, tobit model was estimated to identify factors affecting TE, AE and EE.
28
Following Gujarati (2004) the tobit model was estimated as follows:
E* o KZ i v, v / z Normal(0, 2 ) (3.10) E max( 0, E*)
Where i represent the i th farm in the sample; k is the number of factors affecting efficiency; Zi represents farm specific factors affecting efficiency; δ is parameter to be estimated; E is efficiency (TE, AE and EE) measure.
Equation (3.10) implies that the above observed variable, E , equals E * when E * > 0, but E 0 when E * < 0.
For this study the tobit model is estimated using STATA version 11 software.
3.4.2. Definition of variables and hypotheses
I) Output (YILD)
Physical output as a measure of production does not account the differences among the qualities of products. Therefore, it is advised to take total economic value of the output per household to represent the dependent variable in the production function model. But, specific to this study, seed is produced by farmers with close monitoring of ASE; hence there is no considerable difference in quality. Moreover, producers fetch the same price since they supply their products for the same organization (ASE). Hence the Wheat seed output measured in quintals was taken as dependent variable in the production function.
II) Inputs
29
Land (LAND): The data was collected using the local measurement unit “Timad”6. The land may belong to the farmer, obtained by means of hiring, leasing or through share cropping arrangements. Hence, area of the plot allocated for wheat seed production, in hectare, during 2010/11 production season was considered for analysis.
Human labor (LABOR): This input captures family and hired labor used for different agronomic practices of wheat seed production in the 2010/11 production season. But the differences in sex and age among labor would be expected. Hence to make a homogeneous group of labor to be added, the individual labor was changed in to Man Days (MDs) using the standard of Storck et al. (1991 as cited in Arega and Rashid, 2005). Therefore, the human labor input is expressed in terms of total MDs employed to perform land preparation, planting, input application, cultivation, harvesting and threshing.
Oxen labor (OXEN): Given small-scale farmers and less mechanized farming exercise in the study area, oxen labor is among major inputs of production. In the study area, task of ploughing and threshing is done using oxen. Hence, oxen labor was measured using the total amount of oxen days allocated for different activities of wheat seed production in 2010/11 production season. Urea and DAP7 (UREA, DAP): Fertilizer is a key input and its application along with other technologies will have a great potential to increase crop productivity. Urea is applied on the farm land once or using split application, but DAP is usually applied during planting time only. As input variables, the total amount of Urea and DAP used (Kg) for the 2010/11 year wheat seed production were considered in this study.
Seed (SEED): Seed is one of the principal inputs out of which production is unthinkable. For this study, it refers to the quantity of wheat seed (kg) used for wheat seed production during 2010/11 production season.
6
One timad is equivalent to 0.25 hectare.
7
DAP refers to Di Ammonium Phosphate 30
III) Input prices
Input costs that were used to estimate the cost function were collected using primary and secondary information. The unit price/cost for land was estimated using the local average rental land value in the area, in Birr/ha. The wage rate in the area vary significantly during slack and peak periods of the year; hence the average wage rate during the time that different agronomic practices are undertaken was taken to calculate the expenditure for labor, 35 Birr/MD. For oxen labor the average rental value of pair of oxen per day (Birr 55) was taken. As far as seed and fertilizers (DAP and urea) are concerned, the market values of inputs (Birr/Kg) were taken.
IV) Factors affecting efficiency
After a thorough review of previous studies and the prevailing situation in the study area, socio economic and institutional factors that would affect efficiency were hypothesized as follow:
Age (AGEHH): This refers to the age of the household head measured in years. It is believed that age can serve as a proxy for experience. In this case farmers with more years of experience are expected to be more efficient. On the other hand, older farmers are relatively unlikely to change their long life farming exercise, which is usually traditional and less efficient. Moreover, labor productivity decreases with age; younger farmers tend to be relatively more productive, because of the tough nature of farm operations (Ike and Inoni, 2006). Therefore, in this study we hypothesized indeterminate relationship between age and efficiency.
Education (EDUCLVL): This variable is measured in years of schooling and can be used as a proxy variable for managerial ability. Farmers with more years of formal schooling tend to be more efficient, presumably due to their enhanced ability to acquire technical knowledge,
31
which makes them closer to the frontier. Therefore, education of the household head, measured in years of formal schooling, is hypothesized to affect efficiency positively.
Family size (FAMSIZE): Family is an important source of labor supply in rural areas. It is expected that households with many family members have better advantage of being able to use labor resources at the right time, particularly during peak cultivation periods. But family size could have positive effect in raising the farmers‟ production efficiency, if actually the members are in the working force. In this study, the number of persons the household head administers/supposed to manage was considered as family members, regardless of blood relationship. This was measured in man equivalent; to capture the difference in age and sex. Given labor is main input in the production of wheat seed, it is hypothesized to influence efficiency positively.
Total cultivated land (TOTCULTLND): This refers to the area of cultivated (own, shared or rented in) land the household managed during 2010/11 production season. Farmers with larger area of cultivated land have the capacity to use compatible technologies that could increase the efficiency of the farmer, enjoy economies of scale. According to Andreu (2007) larger farms are relatively better efficient than small size farms. Hence, we hypothesized that farm size would affect efficiency positively.
Land ownership (LNDOWNER): This is a dummy variable measured as 1 if the farm for production of wheat seed is on sharecropping basis and 0 otherwise. Farmers are expected to give priority, especially during peak periods of farming like weeding, to their own land or the rented in land as they would acquire the whole output that would be obtained from the land. In share cropping the risk of crop failure is shared by the tenant and share cropper. Therefore, ownership measured in relation with sharecropping was hypothesized to determine efficiency negatively.
Land fragmentation (LNDFRAG): This is defined as the total number of plots that the farmer has managed during the 2010/11 production season. Increased land fragmentation leads to inefficiency by creating shortage of family labor, costing time and other resources 32
that should have been available at the same time (Wondimu, 2010). Hence, this study hypothesized a negative association between fragmentation and efficiency.
Number of livestock (LIVSTOCK): This is the total number of livestock in terms of Tropical Livestock Unit (TLU). Livestock could support crop production in many ways; they can be source of cash, draft power and manure that will be used to maintain soil fertility. It also serves as shock absorber to an unexpected hazard in crop failure and the main sources of animal labor in crop production. Therefore, in this study the effect of livestock on efficiency was hypothesized to be positive.
Interest in seed production (INTSEED): This variable is a dummy with the value of 1 for farmers who have a plan to increase area of seed production in the coming year and 0 otherwise. This can serve as a proxy variable to measure the interest of farmers in wheat seed production business. Thus, the presence of interest in seed production was hypothesized to have a positive effect on efficiency of wheat seed producers.
Extension service about seed (EXTSERV): Access to extension services is a medium for the diffusion of new technologies among farmers, and hence improves the efficiency of farmers (Ike and Inoni, 2006). This is a dummy variable measured as 1 if the farmer has access to extension service about wheat seed production and 0 otherwise. Therefore for our study, extension service was expected to have a positive effect on efficiency.
Radio ownership (RADIO): The dummy variable for radio ownership assigns the value of 1 if the respondent has radio and 0 if not. Radio ownership can be considered as a proxy variable for information. It is expected that farmers having radio will have more access to information about technologies, like use of fertilizer, improved seed and best practices. Hence, in this study it was hypothesized as radio ownership have a positive effect on the efficiency of wheat seed producers. Total income (TOTINCOM): This includes all income from on farm, off farm and nonfarm activities of the household. It is a continuous variable measured in the amount of income (birr) the household head and/or other members get per year. It can be argued as extra income 33
could serve as cash source to buy agricultural inputs, which may positively contribute in reduction of inefficiency. Hence, in this study it was hypothesized to have positive influence on efficiency.
Total Expenditure (TOTEXP): This is a continuous variable measured in monetary terms (birr). This includes all expenditures of the household consumption, education, medication and social obligation. But this does not include expenses related to purchase of agricultural input and investments made on agriculture to avoid double counting. Since this competes for the limited cash available, it was hypothesized to have a negative effect on efficiency.
34
4. RESULTS AND DISCUSSION Chapter four is further divided in to two sub-chapters; descriptive statistics and econometric results. In this chapter the results of the study along with previous research findings are briefly discussed. 4.1. Descriptive Statistics Before embarking on discussing results obtained from the models, it is important to briefly describe socioeconomic variables using descriptive statistics. This would help to draw a general picture about the study area and sampled households. 4.1.1. Social and demographic characteristics of sample households
Family size and age structure
The average family size of the sampled households was 6.24, with the maximum household size of 13. The average age of the sampled households, during the survey period, was about 44 years with the minimum of 22 and maximum of 78.
Table 3 Age, family structure and labor force of sample households during 2010/11.
Variable description
Minimum
Maximum
Mean
Std. Deviation
Age
22
78
44.05
9.25
Family size
2
13
6.24
2.15
Adult equivalent
1.60
12.20
5.07
1.94
Man equivalent
1.20
11.40
3.92
1.86
Dependency ratio
0.48
1.00
0.76
0.13
Source: Own computation. Table 3 shows that, on average, 3.92 out of 5.07 adult equivalents can provide labor force in man equivalent and actively engage in an economic activity, indicating that 24 % of family 35
members depend on this labor force for subsistence. Alternatively, only 76 % of the family labor is within economically active age group.
Education status
Education is an instrument to enhance the quality of labor through improving the managerial skill and the tendency to adopt new technologies. Education together with increased experience could guide farmers to better manage their farm activities.
Table 4 indicates that out of the total sampled households only 16 % cannot read and write and 28.5 % do have formal education. Alternatively, 84 % of sampled households, during the survey period, can read and write.
Table 4 Education status of sampled household heads during 2010/11
Education category
Frequency
Percent
Cumulative Percent
Illiterate
23
16.0
16.0
Read and write only
80
55.5
71.5
Formal education
41
28.5
100.0
Source: Own survey 4.1.2. General farming characteristics
There is wide range of farming experience in the study area, varying from 2 to 60 years. The average farming experience of sampled households was 23.23 years. Specific to the experience of sampled households in wheat seed production, the variation was less relatively. The average wheat seed production experience was 5.33 years.
The average area of cultivated, homestead and grazing land was 2.19, 0.15 and 0.08 ha, respectively (Table 5). The average land size of the household ranges from 0.38 to 7.75 ha, with the mean of 2.37 ha. The average number of plots of the sampled households during the 36
survey period was greater than three in number. This indicates that there is land fragmentation in the area, with the number of plots varying from one to six. On average, the farm plots of the household take around 21 minutes on foot journey.
Table 5 Farming characteristics and land distribution of sampled households in 2010/11 Variables
N
Minimum Maximum Mean
Std. Deviation
Farming experience (years)
140
2
60
23.23
10.81
Wheat seed production experience (Years)
145
1
20
5.33
3.62
Cultivated land (ha)
145
0.25
7.50
2.19
0.97
Homestead land (ha)
132
0.04
1.50
0.15
0.16
Grazing land (ha)
70
0.02
0.13
0.08
0.04
Total own farm land (ha)
146
0.38
7.75
2.37
1.00
Time to reach to farm plots (minute)
143
0.83
90.00
21.39
11.92
The number farm plots (Number)
145
1
6
3.32
1.19
Source: Own survey
4.1.2.1. Livestock ownership
Given a mixed farming system in the study area, livestock has considerable contribution for household income and food security. Among others, ox is a major input in crop production process serving as a source of draft power. Farmers in the study area use oxen to undertake different agronomic practices, out of which plough and threshing were the major once. Conventionally, land preparation is done using a pair of oxen; as a result 2.1 % of the sampled households cannot independently plough their farm using own oxen. Hence as an alternative, they will go for exchange with or rent in from others.
There was variability in oxen ownership among farmers in the study area, ranging from one to more than five. Generally 21.4% and 16.2 % of sampled households at Marwoled and Wogedade kebeles have had only a pair of oxen, respectively. In both kebeles, Wogedade and 37
Marwoled, more than 35 % of sampled households have two pairs of oxen. Moreover, out of the total sample households during the survey period, 19.4 % have had at least five oxen. Alternatively, most (55.5 %) sampled households have had at least two pairs of oxen.
Table 6 Distribution of oxen among sampled households during 2010/11
Oxen ownership category
Name of the kebele Marwoled
Total sample
Wogedade
households
Number
Percent
Number
Percent
Number
Percent
1 ox
3
4.3
0
.0
3
2.1
2 oxen
15
21.4
12
16.2
27
18.8
3 oxen
19
27.1
15
20.3
34
23.6
4 oxen
25
35.7
27
36.5
52
36.1
> 5 oxen
8
11.4
20
27.0
28
19.4
Source: Own survey
In the study area, farmers rear animals for different economical and social reasons; such as source of income, source of food, drought coping mechanism. Basically cattle, small ruminants, donkey and poultry are the major group of livestock at Womberma woreda. To make comparison of livestock among farmers, Storck et al. (1991) as cited in Arega and Rashid (2005) conversion factor was used to convert the herd size in to TLU.
Table 7 Livestock holding of sampled households in the year 2010/11.
38
Name of kebeles TLU range
Marwoled
Wogedade
Total sample households
Frequency
Percent
Frequency Percent Frequency
Percent
0.00 – 5.00
10
14.1
6
8.0
16
11.0
5.01 – 10.00
34
47.9
28
37.3
62
42.5
10.01 – 15.00
22
31.0
31
41.3
53
36.3
15.01 – 20.00
5
7.0
7
9.3
12
8.2
> 20.01
0
.0
3
4.0
3
2.1
Source: Own computation.
Table 7 shows that 38% and 54.6 % of sampled households at Marwoled and Wogedade kebeles, respectively, have had more than 10 TLU. Alternatively, 46.6 % of the total sampled households have had more than 10 TLU. Only 14.1 and 11 % of the sampled households at Marwoled and Wogedade, respectively, have had less than or equal to five TLU. This means, on average, only 11 % of the households have had less or equal to five TLU.
4.1.2.2. Crops production and area coverage
The study area is well known for its crop production. The major crops grown in the area includes wheat, maize, pepper, teff and finger millet. Table 8 demonstrates the production and area coverage of these major crops.
Table 8 Major crops produced by sampled farmers by production and area coverage.
39
Production area (ha) Crop type
Production (Qt)
N
Mean
Percent8
Mean
Percent
Wheat grain
118
0.78
18.61
19.27
19.92
Wheat seed
145
0.70
16.70
18.36
18.97
Maize.
144
0.98
23.39
37.38
38.63
Pepper
142
0.77
18.38
10.88
11.24
Teff
48
0.61
14.55
5.38
5.56
Finger millet
21
0.35
8.35
5.48
5.66
Source: Own survey
On average, sampled households allocate nearly one and half hectare (more than 35 %) of the land for wheat production, out of which 0.70 ha or 16.7 % was for wheat seed production (Table 8). Next to wheat, maize and pepper were crops that take the lion‟s share of the farmer`s total (both own and rented in) cultivated land covering 0.98 and 0.77 ha of land, respectively.
Table 8 also demonstrates the average production of major crops in quintals. Given the difference in productivity (yield/ha) among crops, sampled farmers on average got equivalent amount of wheat and maize yield in quintals. The total average production of wheat was 37.63 qt (38.89 % of the total major crop production), out of which 18.36 qt or nearly 19 % was wheat seed. Sampled households on average, during the survey period, got 37.38 qt of maize. Even though farmers allocate (on average) more than half hectare of land for teff production, its average production was limited to 5.38 qt, because of its low productivity. Hence, unlike in the case of area coverage, average production of finger millet was greater than teff taking 5.66 % of the total major crop production measured in quintals.
4.1.2.3. Wheat seed production
8
The percent is calculated taking total of major crops as a reference, not from all crops. 40
According to FAO and WFP (2010) the major commodity that takes the lion‟s share of seed distributed by ESE was wheat, constituting 77.8% and 73.5 % of the total seed distribution in Ethiopia and Amhara region, respectively. Generally ESE has distributed more than half million (510025) quintals of different crops‟ seed at national level during the year 2010 (Table 9). Following wheat, maize and teff takes the major share of the ESE total seed distribution both at national and Amhara region level.
Table 9 Production and distribution of improved seeds by ESE in 2010 (quintals)
Region Crops
Amhara
Oromia
SNNP9
Tigray
Others
Ethiopia
Wheat
98293
137610
98293
39317
19658
393172
Maize (hybrid only)
19029
12115
21963
56
6395
59558
Sorghum
554
680
127
206
16
1583
Barley
3927
11227
2717
1482
-
19365
Teff
7214
8160
2966
4302
-
22642
Faba bean
1501
1592
637
773
46
4549
Haricot bean
147
720
736
25
8
1636
Chick peas
3008
2557
376
1429
150
7520
Source: FAO and WFP, 2010
Womberma woreda has long years of experience in wheat seed production. The dominant system of seed production in the study area is farmer based seed production scheme, in which farmers are the main actors. Among improved wheat seed varieties in the study area HAR1685 was widely adopted, found in every farmer‟s hand.
Table 10 Participants of wheat seed production at Womberma woreda
9
SNNP refers to Southern Nations and Nationalities People 41
Wheat seed production Participants Year
Variety
Area (ha)
Male
Female
Total
2007/08
HAR1685; HAR2501
280; 30
333
7
340
2008/09
HAR1685
350
423
17
440
2009/10
HAR1685
800
1035
58
1093
2010/11
HAR1685
988
1497
80
1577
Source: WOAaRD, 2011
According to WOAaRD (2011) the number of participants of wheat seed production in the woreda has shown an increasing trend during the last four years. The total number of participants, which was limited to 340 households in 2007/8 has increased to 1577 during the survey period. In line with the total number of participants, active involvement of female headed households has also increased in number during the last four years. Moreover, the area covered under wheat seed production has shown an increasing trend. The area allocated for wheat seed production was only 280 ha in 2007/8 but it has increased to more than 900 ha during 2010/11.
Figure 5 Wheat seed production trend in Womberma woreda
Source: WOAaRD, 2011. Figure 5 designates that there was a yearly increasing trend of wheat seed in terms of production measured in quintals. The total amount of wheat seed produced in the woreda 42
during 2007/8 production season was 5845 qt. but the yield has increased by more than two fold (13853 qt) during the 2009/10 production season.
4.1.3. Economic status and income sources
4.1.3.1. Income sources
For our study income sources were broadly categorized into two groups; off/non farm income and farm income. The average off and/or non-farm income of sampled households, during the survey period, was 7377.23 birr/year. The nonfarm income sources in the study area include house rent, petty trade and income from grain mill. There was wide range of off farm and/or non farm income among farmers, with minimum of 240 and maximum of 60000 birr/year. The average farm income of sampled households was 49700 birr/year, during the survey period. This may be because, unlike many areas in the region, the study area is well known for its productivity and long years of experience in seed production. The major share of farm income was obtained from pepper followed by maize and wheat seed production. Out of the total income of the households, on average, farm income contributes more than 95 %. Generally the total income of sampled households during the survey period was 52100 birr/year, on average.
Table 11. Distribution of income among sampled households in 2010/11.
Income source
N
Minimum Maximum
Mean
Std. Deviation
Total non/off farm income (Birr)
47
240
60000
7377.23
12334.46
Total farm income (Birr)
146
1400
407510
49700.00
46375.95
Total income of households (Birr)
146
2360
407510
52100.00
47124.17
Source: Own survey 4.1.3.2. Asset ownership
43
Asset ownership can be used as a proxy for wealth status of the households. People living in different areas own different assets, based on the socioeconomic and cultural values of these assets in their areas. The major assets, of sampled households, are explained as follow:
Table 12. Asset ownership of sampled households in the year 2010/11 Yes Variables
No
Frequency
Percent
Frequency
Percent
Iron sheet house ownership
144
99.3
1
0.7
Mobile ownership
66
45.5
79
54.5
Radio ownership
134
92.4
11
7.6
Own house at Shendy
60
41.7
84
58.3
Source: Own survey
More than 99 % of sampled households live in iron roofed house. Given the value of mobile for communication, farmers in the study area were found to be aware of using mobile. Out of the total sampled households 45.5 % of them had access to mobile. Similarly more than 92 % of the sampled households had access to radio. In the study area, farmers with better economic ground have houses in Shendy, capital town of the woreda. As a result, out of the total sampled households during the survey period, 41.7 % of them have house at Shendy. 4.1.4. Summary of variables used in the model
The production function for this study was estimated using six input variables. To draw some picture about the distribution and level of inputs, the mean and range of input variables is discussed as follows:
Table 13 Summary statistics of variables used to estimate the production function
44
Variable description
Minimum
Maximum
Mean
Std. deviation
Yield (Qt)
4
40
17.58
7.79
Land (ha)
0.25
1.5
0.65
0.25
Seed (Kg)
37.5
225
100.81
43.48
Labor (MDs)
4
135
41.05
17.90
Oxen (Oxen days)
8
110
41.77
18.49
Urea (Kg)
25
200
75.94
41.70
DAP (Kg)
50
300
114.15
50.36
Source: Own survey
On average farmers got 17.58 qt of wheat seed, which is dependent variable in the production function. The land allocated for wheat seed production, by sampled farmers during the survey period, ranges from 0.25 to 1.5 ha with average of 0.65 ha. The other very important variable, out of which production is impossible, is seed. The amount of seed that sampled households‟ used was 100.81 Kg, on average. Like other inputs human and animal labor inputs were also decisive, given a traditional farming system in the study area. Sampled households, on average, use 41.05 man equivalent labor and 41.77 oxen days for the production of wheat seed during 2010/11 production season. In the study area farmers use both urea and DAP for wheat seed production. Hence, on average farmers used 75.94 Kg and 114.15 Kg of urea and DAP respectively.
Similar to the production function, the mean and standard deviation of each of the variables used in the cost function along with their contribution to the total cost of production are depicted as follows:
Table 14 Summary statistics of variables used to estimate the cost function
45
Variables
Mean
Standard deviation
Percentage of total cost
17.58
7.79
-
Total cost of production
8273.08
2938.91
-
Cost of land (Birr)
3597.60
1391.59
43.49
Cost of seed (Birr)
725.89
313.11
8.77
Cost of human labor (Birr)
821.10
358.12
9.92
Cost of oxen labor (Birr)
1670.96
739.76
20.20
Cost of urea (Birr)
508.81
279.42
6.15
Cost of DAP (Birr)
913.21
402.88
11.04
Yield (Quintal)
Source: Own survey
The total cost of 8273.08 was required to produce 17.58 qt of wheat seed. Among the various factors of production, the cost of land accounted for the highest share (43.49 %). Given the high productivity and greater opportunity cost of land in the area, it is logical to get this result. Following the cost of land, cost of oxen labor takes major share out of total cost of production. Among other inputs, cost of urea takes the smallest (only 6.15 %) share out of the total cost of wheat seed production. Alternatively, farmers on average invest 3597.60 and 1670.96 birr for land and oxen labor respectively.
A total of 12 variables were hypothesized to affect efficiency of wheat seed producers, out of which four of them were dummy variables. Table 15 illustrates summary of these variables.
Table 15 Summary of efficiency model variables
46
Percentage of the
Percentage of
Std.
mean with
the mean with
deviation
Dummy = 1
Dummy = 0
44.05
8.832
-
-
Education (years of schooling)
1.7
2.928
-
-
Family size (MDs)
3.92
1.86
-
-
Total cultivated land (ha)
2.19
0.865
-
-
Land fragmentation (Number)
3.32
1.194
-
-
Livestock (TLU)
9.68
4.213
-
-
Total expenditure (Birr)
7779.78
4547.041
-
-
Total income (Birr)
52100.0
47124.180
-
-
Land ownership
-
-
21.2
78.8
Interest in seed production
-
-
71.0
29.0
Extension service
-
-
84.9
15.1
Radio
-
-
92.40
7.60
Variables
Age (years)
Mean
Source: Own survey
Most of the variables are discussed in the above sections. Hence here we discuss only some of the variables in the efficiency model. Education level of the households measured in years of schooling indicates that the average level of education is less than grade two. The range in level of education varies from zero (referring to illiterate) to grade 10. Sampled households, on average, invest 7779.78 birr/year for household consumption and other related costs. Out of the total respondents, 78.8 % of them used their own plot for wheat seed production during the survey period. The majority (84.9 %) of farmers reported as they have taken extension service about wheat seed production. Similarly, 71 % of the farmers have had an interest in wheat seed production business. This could be explained as wheat seed production has come to be a good choice for farmers in the study area. 4.2. Econometric Result
This section presents the econometric results of the study. In this sub chapter the production and cost functions, efficiency scores and determinants of efficiency are discussed clearly. 47
Before running to the econometric analysis, the data was tested against different econometric problems. Accordingly, the data was checked for hetroskedasticity using Breusch - Pagan test, and the result showed that there was no serious problem of hetroskedasticity. Multicollinearity test for all variables was also done using Variance Inflation Factor (VIF). Specific to dummy variables contingency coefficient was calculated to test for the existence of multicollinearity problem. Test for multicollinearity using both methodologies also confirm that there is no serious linear relation among explanatory variables (Appendices 6-8). Moreover, specification tests were done to improve the fitness of the model. 4.2.1 Estimation of production and cost functions
The ML estimates of the parameters, of the SPF specified in equation (3.1), were obtained using the STATA 11 computer program. These results together with the standard OLS estimates of the average production function are presented in Table 16.
Among the total of six variables considered in the production function, three (land, labor and DAP) have a significant effect in explaining the variation in wheat seed production among farmers. The coefficients of the production function are interpreted as elasticity. Hence, high elasticity of output to land (0.721) suggests that wheat seed production was relatively sensitive to land. As a result, 100 % increase in area of land will result in 72.06 % increase in the wheat seed production, keeping other factors constant. Alternatively, this indicates wheat seed production was responsive to land, followed by labor and DAP respectively.
Table 16. Estimates of the average and Cobb Douglas production function
OLS
ML estimate
48
Variables
Coefficients
Std. Err
Coefficients
Std. Err
Constant
2.4078***
0.6916
2.0782***
0.5551
Land
0.8076***
0.1378
0.7206***
0.1121
Seed
-0.1169
0.1312
-0.0350
0.1106
Labor
0.1830**
0.1795
0.2021***
0.0695
Oxen
-0.0477
0.0565
-0.0100
0.0515
Urea
0.0586
0.0715
0.0114
0.0662
DAP
0.1193
0.0944
0.1513*
0.0873
Adjusted R2
0.7174
F statistics
61.94***
2 v2 u2
-
0.1096***
u v
-
2.5278***
Log likelihood
-
16.8697
Note: *, ** and *** refers to 10%, 5% and 1% significance level, respectively. Source: Own computation. The diagnostic statistics of inefficiency component reveals that sigma squared (δ2) was statistically significant at 1 percent (Table 16). This indicates goodness of fit, and the correctness of the distributional form assumed for the composite error term.
The returns to scale analysis can serve as a measure of total factor productivity (Ogundari and Ojo, 2007). The scale coefficient was calculated to be 1.0404, indicating increasing returns to scale (Table 17). This implies that there is potential for wheat seed producers to continue to expand their production because they are in the stage I of the production surface, where resource use and production is believed to be inefficient. In other words, a percent increase in all inputs proportionally will increase the total production by 1.04 %. This result is consistent with Fikadu (2006) who estimated the returns to scale to be 1.08 in his study of TE of wheat production in Machakel woreda. But a study by Ogundari and Ojo (2006) in Nigeria found returns to scale to be 0.84, which falls in stage II of production surface.
49
Table 17 Elasticities and return to scale of the parameters in the production function.
Variables
Elasticities
Land
0.7206
Seed
-0.0350
Labor
0.2021
Oxen
-0.0100
Urea
0.0114
DAP
0.1513
Returns to scale
1.0404
Source: Own computation.
The dual cost function which is specified in equation (3.4) and derived analytically from the stochastic production function is given as follows:
ln Cwsi 1.589 0.45Wi1 0.106Wi 2 0.089Wi3 0.203Wi 4 0.069Wi5 0.074Wi6 0.0012 ln Y * i
(4.1)
Where Cwsi is cost of producing wheat seed; Wi1 refers to the average rent value of land per hectare, Wi2 is price of a Kg of wheat seed; Wi3 is average wage rate; Wi4 is the average oxen rent value in the study area; Wi5 is price of Kg of urea; Wi6 is price of Kg of DAP and Y* is total amount of wheat seed produced in quintals adjusted for statistical noise.
4.2.2. Test of hypothesis
50
Before discussing the model output, we begin with likelihood ratio (LR) tests to assess various assumptions related to the model specification. Generally the log likelihood form can be defined as:
2[log L( Ho) log L( Ha)]
(4.2)
Where, L(Ho) and L(Ha) are the values of the log-likelihood function under the null and alternative hypotheses, Ho and Ha, respectively. Tests of hypotheses for the parameters of the frontier model were conducted using the generalized likelihood ratio statistics, λ, defined by Equation (4.2). Accordingly we test two hypotheses, one for the existence of inefficiency and the other for variables that explain the difference in efficiency.
Table 18 Generalized likelihood ratio tests of hypothesis for the parameters of the SPF.
Null hypothesis
Log likelihood
Critical value
value
λ
(χ2, 0.95)
Decision
Ho: γ = 0
16.86
12.03
3.84
Reject Ho
Ho: δ1= δ2= . . .= δ13=0
84.56
30.78
21.02
Reject Ho
Source: Own computation. The first null hypothesis was Ho: γ = 0, which specifies that the inefficiency effects in the SPF were not stochastic. The null hypothesis was rejected (Table 18). This implies the traditional average production function does not adequately represent the data. Similarly according to the model result (Table 16) of the production function the value of λ is 2.528. Applying equation (3.2), gamma (γ) which measures the effect of technical inefficiency in the variation of observed output, was estimated to be 0.865. This implies that 86.5% of the total variation among wheat seed producer farmers, in wheat seed production, is explained by technical inefficiency parameter.
51
The second test was the null hypothesis of all coefficients that explain inefficiency is equal to zero. The result confirms as the null hypothesis was rejected, implying that there is at least one variable that explain the difference in efficiency.
4.2.3. Efficiency scores
The results of the efficiency scores indicate that there were wide ranges of differences in TE, AE and EE among wheat seed producer farmers. The mean TE of sampled households during the survey period was 79.94 %. The TE among farmers ranges from 41.24 to 96.65 %, with standard deviation of 0.114. Similarly, the mean AE and EE of farmers were 47.71 and 37.31%, respectively. Unlike TE there was low average EE score, which was attributed to both allocative and technical inefficiency of wheat seed producers. Generally there is a considerable amount of efficiency variation among wheat seed producer farmers in all measures of efficiency. This result, wide variation in farmer specific efficiency levels, is consistent with study of Ike and Inoni (2006).
Table 19. Descriptive statistics of efficiency measures.
Efficiency parameter
Minimum
Maximum
Mean
Std.dev
TE
0.4124
0.9665
0.7994
0.1140
AE
0.2395
0.9832
0.4771
0.1341
EE
0.2012
0.7774
0.3731
0.0892
Source: Own computation.
The results in Table 19 indicate that farmers on average could decrease inputs (land, labor and DAP) by 20.04 % if they were technically efficient. Similarly, wheat seed producer farmers can save 52.29 % of their current cost of inputs by behaving in a cost minimizing way. Conversely, EE value of 37.31 % prevails that an economically efficient farmer can produce 62.69 % additional wheat seed.
52
The distribution of the TE scores shows skewed distribution to the right; with the majority (more than 60 %) of the sampled households have TE score greater than 80 % (Figure 6). But there are also farmers whose TE levels were limited to the range 40 to 50 % only. Farmers in this group have a room to enhance their wheat seed production at least by 50 %, on average. Out of the total sample, only 17 % of the farmers have TE of greater than 90 %. This implies that around 83 % of the farmers can increase their production at least by 10 %.
Figure 6 Distribution of technical efficiency scores
Source: Own computation.
According to Figure 7 the AE distribution scores indicate that the largest efficiency group of wheat seed producers (32 %) operates between 40 and 49.99 %. Only less than 5 % of the total wheat seed producers have an AE score of greater than 90 %. This shows that there is great opportunity to increase the efficiency of wheat seed producers by reallocation of resources in cost minimizing way.
53
Figure 7 Distribution of allocative efficiency scores
Source: Own computation.
The distribution of EE scores implies that the majority of the farmers were performing under the average efficiency level. The low average level of EE was the total effect of both technical and allocative inefficiencies. This also indicates the existence of substantial economic inefficiency in the production of wheat seed during the study period.
Figure 8 Distribution of economic efficiency scores
Source: Own computation. 54
4.2.4. Determinants of efficiency differentials among farmers
The results obtained from the first stage estimations indicate that the average efficiency scores were low and there exists efficiency variations among farmers. The technical, allocative and economic efficiency estimates derived from the model were regressed on socioeconomic and institutional variables that explain variations in efficiency across farm households using tobit regression model. Hence table 20 illustrates the socioeconomic and institutional factors that affect efficiency in wheat seed production.
Table 20. Tobit model estimates for different efficiency measures
TE Variables
AE
Marginal
EE
Marginal
Marginal
Effect
Std. Err.
Effect
Std. Err.
Effect
Std. Err.
CONSTANT
0.6535***
0.0794
0.5519***
0.0911
0.3660***
0.0550
AGEHH
0.0021
0.0017
0.0007
0.0019
0.0013
0.0012
EDUCLEVEL
-0.0015
0.0040
0.0091*
0.0047
0.0067**
0.0028
FAMSIZE
-0.0096
0.0092
-0.0162
0.0105
-0.0185***
0.0063
LNDFRAG
-0.0080
0.0104
0.0060
0.0120
0.0010
0.0072
CULTLAND
-0.0060
0.0163
-0.0373**
0.0188
-0.0244**
0.0113
LNDOWNER
0.0232
0.0166
-0.0453**
0.0190
-0.0168
0.0115
LIVSTOCK
0.0002
0.0028
0.0055*
0.0032
0.0036*
0.0019
INTSEED
0.0516**
0.0260
0.0014
0.0298
0.0237
0.0180
EXTSERVICE
0.0017
0.0357
0.0163
0.0409
0.0038
0.0247
RADIO
0.0319
0.0240
-0.0086
0.0275
0.0129
0.0166
TOTEXPEN
-0.0001***
0.0000
0.0000
0.0000
0.0000
0.0000
TOTINCOM
0.0001***
0.0000
0.0000
0.0000
0.0000
0.0000
LOG L
84.1160
70.4020
122.2923
Note: *, ** and *** refers to 10%, 5% and 1% significance level, respectively. Source: Own computation. 55
Total income has a significant and positive impact on TE, as expected. Income was expected to directly affect efficiency, because farmers with limited income will be constrained form purchasing agricultural inputs (like seed and fertilizer). Therefore, in line with result of Arega and Rashid (2005), in our study income of the household had significant and positive effect on improving farmers‟ TE. Unlike total income of the household, household expenditure has turn out to have negative and significant effect on TE. This shows, as in the case of Jema (2008), households with much expenditure are usually constrained to buy inputs that would support their agricultural activities.
The interest of farmers in wheat seed production were measured using a dummy variable; represented by value of 1 for farmers who have a plan to increase area of land to be allocated for wheat seed and 0 other wise. The model result confirms that those farmers who have had better interest in the business have higher level of TE, on average.
Education had significant impact on AE and EE with expected sign. Positive and significant impact of education on AE and EE confirms the importance of education in increasing the efficiency of wheat seed production. The result indicates that, AE and EE require better knowledge and managerial skill than TE. In other words, educated farmers have relatively better capacity for optimal allocation of inputs. In line with this study, research done by Arega and Rashid (2005) in Eastern Ethiopia, Kehinde and Awoyemi (2009) and Ogundari and Ojo (2007) both in Nigeria found education to influence AE and EE positively and significantly.
Total cultivated or farm land was found to have significant and negative impact on AE and EE, which is contradictory to the hypothesis made. However, this is in conformity with the results of (Jema, 2008) and Wondimu (2010). Out of the total farm land only 16.7 % of the land is allocated for wheat seed production (Table 8). Give the requirement of close monitoring and labor intensive production, since it is produced for seed purpose, the negative effect total farm size on AE and EE could be because of the managerial and input competition.
56
Family size was hypothesized to have positive effect on all measures of efficiency (TE, AE and EE), since family is the major source of labor for agricultural activities. However it has turned out to influence EE negatively and significantly. There is significant correlation of 0.516 (significant at 1%) between area of land allocated for pepper production and family size. Hence, given the higher opportunity cost of labor in the study area, application of more labor for other cash crops (like pepper) will affect EE of wheat seed production negatively. The result is consistent with Jema (2008) in his study of EE and marketing of vegetable production in Eastern and central parts of Ethiopia and Mulwa et al. (2009). However, there are also other findings (Okoruwa et al., 2006) that are contradictory with this result.
Positive and significant impact of livestock ownership on AE and EE, as in the case of Wondimu (2010), confirms the considerable contribution of livestock in reducing the current cost of inputs in wheat seed production. Given the importance of livestock in the crop production system (as source of draft power, income and fertilizer) the model result seems logical to affect AE and EE positively. However, the land arrangement or land ownership has negative and significant effect on AE. Farmers who use their own land for wheat seed production have relatively better level of AE, as expected. This could be attributed to the reason that, farmers who produce on their own or rented in land use inputs properly and give priority during peak farming periods since the whole product belongs for them (unlike in case of share cropping).
As to the interpretation, the marginal effect value of education (0.0091) to AE implies that, an increase in level of education by one unit will increase the AE score by 0.0091. Similarly, the value of marginal effect (0.0516) for the variable interest in wheat seed production to TE indicates that, for the sample period, if a farmer has interest in wheat production business the TE score will be increased by 0.0516.
57
5. SUMMARY, CONCLUSSION AND RECOMMENDATION In this chapter the main findings of the study are summarized and policy recommendations drawn from the study are briefly discussed.
5.1. Summary
Productivity can be enhanced in two ways; by improving the state of technology or enhancing the efficiency of producers. Alternatively, productivity growth may attribute to either technological progress or efficiency improvement. Improving efficiency of the farmer plays a great role in increasing productivity, given the current state of technology. This paper analysed the technical, allocative and economic efficiencies and factors that explain the variation in efficiency among wheat seed producer farmers in Womberma woreda of West Gojjam zone, Amhara region.
Both purposive and multistage random sampling procedures were implemented to select a sample of 146 wheat seed producer farmers that represents the population. Besides the primary data collected using structured schedule, secondary data from different sources were consulted and group discussion was also organized.
Stochastic production frontier model was used to estimate the production and cost functions. The study result indicates as land, labor and DAP were significant variables that positively affect the production of wheat seed. The positive coefficient of these parameters indicates that, increased use of these inputs will increase the production level to greater extent. The test statistics undertaken also confirmed as there is significant amount of variation in efficiency among farmers. Alternatively, the traditional average response function is not an adequate representation of production frontier. The study result shows as 79.9%, 47.7% and 37.3% are the mean levels of TE, AE and EE, respectively. This in turn implies that farmers can increase their wheat seed production on average by 20.01 % when they were technically efficient.
58
Similarly, wheat seed producers can reduce current cost of inputs, on average, by 52.3 % if they were allocatively efficient.
To identify the factors that explain the variation in efficiency, tobit model was used. The model results indicate as interest in wheat seed production and total income of household positively and significantly affect TE. However, total expenditure has negative and significant effect on TE. Education level and livestock ownership have positive and significant impact on AE. But total cultivated land and land ownership (dummy) affect AE negatively and significantly. Education level and livestock ownership of the household head have positive and significant impact on EE. However, total cultivated land and family size affects EE negatively and significantly.
In general, the SPF model shows that production can be improved by increasing the use of inputs. There is considerable room to enhance the efficiency of farmers in wheat seed production. The implication is that, there will be considerable gain in production level or reduction in cost of production if introduction and dissemination of agricultural technologies is coupled with improving the existing level of efficiency.
5.2. Conclusion An important conclusion stemming from the analysis of the efficiency of wheat seed production is that, there exists a considerable room to enhance the level of technical, allocative and economic efficiency of wheat seed producer farmers. Result of the production function indicates that land, labor and DAP were limiting constraints, all with positive sign as expected. The average TE, AE and EE values of the sampled households was calculated to be 79.9, 47.7 and 37.3 %, respectively.
The factors that affect the level of efficiency are identified, to help different stakeholders to enhance the current level of efficiency in wheat seed production. Accordingly, interest in wheat seed production and total income of households has positive and significant impact on TE. However, total expenditure has negative and significant effect on TE. This implies that 59
farmers who have interest in wheat seed production business are better off in their level of TE than others. But total expenditure of the household tends to affect TE negatively, as expected.
Allocative efficiency, ability to use least cost combination of inputs to produce a given output, of farmers was affected by education level and livestock ownership positively and significantly. Farmers who have better schooling are more efficient in allocating their resources. Similarly, given the importance of livestock in wheat seed production, livestock ownership has significant and positive impact on AE. But total cultivated land and land ownership affect AE negatively and significantly.
Last but not least, level of education and livestock ownership has positive and significant impact on EE. This also implies as education is basic tool to enhance the level of economic or overall level of efficiency. Unlike education, family size and total cultivated land has negative and significant influence on overall efficiency of wheat seed production.
5.3. Recommendation and Policy Implications
Given the substantial shortage of wheat seed, in the country in general and in the study area in particular, improving the level of efficiency of wheat seed producer farmers is quite important. Arising from the results of the study, the following recommendations are drawn:
Education of the household heads, measured in years of schooling affects both allocative and economic efficiencies of wheat seed producer farmers. Hence policy makers should devote a great effort to create access to formal education for farmers in the study area.
Farmers who have interest in wheat seed production business were technically efficient than others. Hence, the seed production business should be done entirely with the interest of farmers. Moreover, there should to have an awareness creating training, monitoring and guidance with regards to the marketing and production constraints to create interest on farmers in the seed production business. 60
Farmers who produce wheat seed on their own farm or rent in farm were more efficient in allocation of resources than those who produce on share cropping arrangements. Hence the seed production system, mainly managed by ESE and ASE, in the study area should give priority for farmers who produce on their own land.
Given the mixed farming system in the study area, farmers with better number of livestock were relatively better in the allocation of resources. Hence technologies that would support the productivity of livestock should be adopted, which in turn will enhance the efficiency of wheat seed production.
Total expenditure of the household for non agricultural activities has a negative impact in TE of wheat seed production. Hence there should to have strong work on awareness creating on how to allocate the resources, mainly cash. There is a need to introduce activities that would enhance the income of households so that the farmers would be in a position to invest the required amount of investment in wheat seed production.
61
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Kareem. R. O, A. O. Dipeolu, A. B. Aromolaran, and A. Samson, 2008. Analysis of Technical, Allocative and Economic Efficiency of Different Pond Systems in Ogun State, Nigeria. African Journal of Agricultural Research, 3 (4): 246-25. Kehinde, A.L. and T.T. Awoyemi, 2009. Analysis of Economic Efficiency in Sawnwood Production in South West Nigeria. Journal of Human Ecology, 26(3): 175-183. Kinde Teshome, 2005. Analysis of Technical Efficiency of Wheat Production: A Case of Smallholder Farmers in Assosa Woreda, Ethiopia. An MSc Thesis Presented to the School of Graduate Studies of Alemaya University. Kumbhakar. C. S., 1991. The Measurment and Decomposition of Cost-Inefficiency: The Translog Cost System. Economic Papers, 43: 667-683. Meeusen, W. and J. Van den Broeck, 1977. Efficiency Estimation from Cobb- Douglas Production Functions with Composed Error. International Economic Review, 18: 435-444. Mohammed Hasson., F. Hassan, W. Mwangi and Belay Kassa, 2000. Factors Influencing Technical Efficiencies of Barley Production in Assasa District of South-Eastern Ethiopia. Ethiopian Journal of Agricultural Economics, 4(1 and 2): 1-21. Mulwa, R., A. Emrouznejad and L. Muhammad, 2009. Economic Efficiency of smallholder maize producers in Western Kenya: a DEA meta-frontier analysis, International Journal of Operational Research,4 (3): pp.250–267. Ogundari, K and S. O. Ojo, 2007. Economic Efficiency of Small Scale Food Crop Production in Nigeria: A Stochastic Frontier Approach. Journal of Social Science, 14(2): 123-130. Okoruwa, V.O., O. O. Ogundele and B. O. Oyewusi, 2006. Efficiency and Productivity of Farmers in Nigeria: A Study of Rice Farmers in North Central Nigeria. Poster Paper Prepared for Presentation at the International Association of Agricultural Economists Conference, Gold Coast, Australia. Schultz, T.W., 1964. Transforming Traditional Agriculture. New Haven: Yale University Press. Sharma, K. R, P. Leung, and H. M. Zaleski, 1999. Technical, Allocative and Economic Efficiencies in Swine Production In Hawaii: A Comparison Of Parametric and Nonparametric Approaches. Agricultural Economics, 20: 23-35. Temesgen Bogale, 2003. Efficiency of Use in the Production of Irrigated Potato in Case of Awi Zone. An MSc Thesis Presented to the School of Graduate Studies of Alemaya University.
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7. APPENDICES
66
Appendix 1 Conversion factor of man equivalent and adult equivalent. Man Equivalent
Adult Equivalent
Age group (years)
Male
Female
Male
Female
50
0.7
0.5
1.0
0.75
Source: Storck, et al. (1991 as cited in Arega and Rashid, 2005).
Appendix 2 Conversion factors used to estimate Tropical Livestock Unit equivalents. Animal Category
TLU
Calf
0.25
Donkey (Young)
0.35
Weaned Calf
0.34
Camel
1.25
Heifer
0.75
Sheep and Goat (adult)
0.13
Caw and Ox
1.00
Sheep and Goat young
0.06
Horse
1.10
Chicken
0.013
Donkey (adult)
0.70
Source: Storck, et al. (1991 as cited in Arega and Rashid, 2005).
67
Appendix 3 Technical efficiency score of the sample farmers (SPF)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
0.931147 0.735792 0.686811 0.829924 0.951559 0.882038 0.871481 0.803604 0.859055 0.918608 0.760417 0.830124 0.904435 0.840002 0.886907 0.86164 0.875035 0.503807 0.816319 0.880709 0.943327 0.948185 0.881966 0.875172 0.614723 0.869428 0.811995 0.874713 0.870271 0.632301
31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
0.80976 0.817359 0.679626 0.835291 0.902201 0.667552 0.904256 0.739562 0.920527 0.66062 0.914874 0.763148 0.587057 0.834425 0.655288 0.863787 0.638539 0.870747 0.634791 0.632398 0.912759 0.966481 0.777478 0.788634 0.880787 0.453771 0.8101 0.777258 0.473751 0.522767
61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90
0.820838 0.720609 0.768273 0.69672 0.853236 0.837455 0.664693 0.742011 0.412355 0.692884 0.900412 0.704579 0.869921 0.862236 0.652561 0.688955 0.662224 0.94123 0.706392 0.852867 0.804223 0.802023 0.746427 0.925984 0.843647 0.762165 0.711627 0.73618 0.913715 0.860555
Source: Own computation.
68
91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120
0.777059 0.747118 0.625938 0.814275 0.522421 0.841494 0.897357 0.796364 0.828587 0.934721 0.527905 0.627894 0.792758 0.636637 0.856243 0.917433 0.796032 0.89045 0.776085 0.897821 0.874809 0.912827 0.77522 0.901108 0.84165 0.874047 0.852825 0.837826 0.861217
121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146
0.847315 0.755935 0.821749 0.876793 0.825108 0.765283 0.800682 0.859973 0.571931 0.877971 0.901295 0.888593 0.812142 0.828986 0.897127 0.852838 0.901128 0.890258 0.895182 0.897211 0.908094 0.900647 0.759955 0.884937 0.739018 0.910523
Appendix 4 Allocative efficiency score of the sample farmers (SPF)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
0.535509 0.412153 0.603472 0.534115 0.306923 0.518669 0.341462 0.522513 0.507577 0.360239 0.355176 0.451124 0.482154 0.239478 0.294433 0.706527 0.480451 0.803823 0.52528 0.466049 0.407138 0.408389 0.460729 0.385167 0.615758 0.541981 0.482167 0.467777 0.303452 0.373227
31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
0.462505 0.474851 0.59798 0.383521 0.861685 0.407661 0.426264 0.533643 0.353445 0.605409 0.417695 0.295729 0.674773 0.292466 0.483048 0.600876 0.612096 0.365252 0.652538 0.690043 0.492906 0.365237 0.499904 0.462593 0.461918 0.566453 0.358646 0.26305 0.618105 0.676603
61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90
0.46208 0.612756 0.314183 0.968831 0.466617 0.456801 0.601992 0.587626 0.9832 0.43674 0.67941 0.548739 0.419203 0.462855 0.515294 0.403247 0.813516 0.376026 0.437981 0.471539 0.480409 0.316848 0.458772 0.299404 0.562442 0.389906 0.588569 0.41568 0.294325 0.473489
Source: Own computation.
69
91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120
0.634098 0.615427 0.684074 0.34906 0.584149 0.422162 0.50044 0.58605 0.569344 0.306376 0.489678 0.514618 0.494093 0.573484 0.469958 0.357897 0.587468 0.303299 0.558431 0.247755 0.493062 0.310385 0.530128 0.32239 0.526076 0.328805 0.500361 0.342326 0.406971
121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146
0.331298 0.526604 0.572803 0.464014 0.484063 0.564342 0.503112 0.341595 0.472124 0.320945 0.525811 0.318886 0.501569 0.423952 0.466711 0.295328 0.325478 0.322248 0.482373 0.509766 0.486119 0.318713 0.600567 0.445219 0.550876 0.274439
Appendix 5 Economic efficiency score of the sample farmers (SPF)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
0.498638 0.303259 0.414471 0.443275 0.292055 0.457486 0.297578 0.419894 0.436036 0.330919 0.270082 0.374489 0.436077 0.201162 0.261135 0.608772 0.420411 0.404972 0.428796 0.410453 0.384064 0.387228 0.406347 0.337088 0.378521 0.471213 0.391517 0.409171 0.264085 0.235992
31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
0.374518 0.388123 0.406403 0.320352 0.777413 0.272135 0.385452 0.394662 0.325356 0.399945 0.382139 0.225685 0.39613 0.244041 0.316535 0.519029 0.390847 0.318042 0.414225 0.436382 0.449904 0.352995 0.388664 0.364816 0.406851 0.25704 0.290539 0.204458 0.292828 0.353705
61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90
0.379293 0.441558 0.241378 0.675004 0.398134 0.38255 0.40014 0.436025 0.405427 0.30261 0.61175 0.38663 0.364674 0.39909 0.336261 0.277819 0.53873 0.353927 0.309386 0.40216 0.386356 0.254119 0.34244 0.277243 0.474503 0.297173 0.418841 0.306015 0.268929 0.407463
Source: Own computation.
70
91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120
0.492732 0.459797 0.428188 0.284231 0.305172 0.355246 0.449073 0.466709 0.471751 0.286376 0.258503 0.323126 0.391696 0.365101 0.402398 0.328346 0.467644 0.270073 0.43339 0.22244 0.431335 0.283328 0.410966 0.290508 0.442771 0.287391 0.42672 0.286809 0.35049
121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146
0.280713 0.398078 0.4707 0.406844 0.399404 0.431881 0.402833 0.293762 0.270022 0.28178 0.47391 0.28336 0.407345 0.35145 0.418699 0.251867 0.293297 0.286884 0.431812 0.457368 0.441442 0.287048 0.456404 0.393991 0.407107 0.249883
Appendix 6 Variance Inflation Factor (VIF) for input and efficiency variables. No.
Variable
VIF
(1/VIF)
1
Age of household head
2.02
0.4949
2
Family size
1.49
0.6722
3
Total cultivated land
2.08
0.4807
4
Number of plots
1.5
0.6658
5
Livestock
1.16
0.8656
6
Education level of household head
1.34
0.7449
7
Total income
1.33
0.7494
8
Total Expenditure
1.59
0.6285
9
Land ownership
1.22
0.8191
10
Interest in wheat seed production
1.25
0.7984
11
Extension service
1.36
0.7330
12
Radio ownership
1.46
0.6844
Mean VIF
1.48
Source: Own computation. Appendix 7 Variance Inflation Factor (VIF) for variables in the production function model
No. 1
Seed
Variable
VIF 8.24
(1/VIF) 0.465
2
Land
7.79
0.495
3
Urea
4.30
0.526
4
DAP
3.83
0.715
5
Labor
2.15
0.725
6
Oxen
1.60
Mean VIF
4.65
Source: Own computation. 71
Appendix 8 Contingency coefficient of dummy efficiency variables.
Variables
Land ownership
Interest in wheat
Extension service
Radio
- 0.178
0.105
0.096
1.00
0.218
- 0.147
1.00
- 0.087
seed production Land ownership
1.00
Interest in wheat seed production Extension service Radio ownership
1.00
Source: Own computation.
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Appendix 9 Questionnaire
Economic Efficiency of Wheat Seed Production: Case of Smallholders in Womberma Woreda of West Gojjame Zone. Instruction for Enumerators:
Tell the purpose of the study and introduce yourself before starting interview. For all closed questions put (X) mark where appropriate and use the space provided for open ended questions. Name of Enumerator …………………………………….. Date ………………………… Questioner Number ……………………………. Kebele
(__________________________)
A. General Household and Farm Characteristics 1. Name of the Respondent (Household head)…………………………………………………. 2. Sex of the Household head (M/F) ………………
Age …………………… years
3. What is the educational status of the household head? A. Illiterate
B. Read and write
4. Marital status: 1. Single 2. Married
C. formal education (______ the grade). 3. Divorced
4. Widow
5. Number of family members and structure No.
Name
Age
73
Sex
Kinship
6. How long have you been in farming? …………….years. 7. Land use pattern? Total Area of land ………… (ha)
Homestead land………….. (ha)
Cultivated land…………….. (ha)
Fallow land………………. (ha)
Grazing land……………….. (ha)
Other land………………… (ha)
8. Distribution (size), fertility status and distance of cultivated land from home. Fertility status Plot number
Area of plot (ha)
(G=good, M=medium,
Time to reach on foot (minute)
P=poor)
Cultivation land 1 Cultivation land 2 Cultivation land 3 Cultivation land 4 9. Have you involved in share cropping during 2010/11 production season? A. Yes 10. If yes, what is the size and rate of shared land? Description In kind In cash Size (ha) Rate Size (ha) Rate Rent in Rent out 11. Household livestock possession. Class of livestock Number Purpose for maintaining Cows Oxen Heifers Bulls Calves Sheep Goat Donkey Horse Mule Poultry Bee Hives 74
B. No
Other Size (ha) Rate
Remark
B. Crop Production 1. Crops produced and type of inputs used in 2010/11 production season? Land allocated
Production
(ha)
(Qt)
Type of Crop
Type of seed used
Do you use fertilizer?
Local
Modern
Yes
No
Wheat Grain Wheat seed Maize Pepper Teff Finger millet
2. How long have you been since you begin producing wheat seed? ……………. Years. 3. Give the major reasons that initiate you to produce wheat seed? ………………………………………………………………………………………… ……………………………………………………………………………………….... ……………………………………………………………………………………… 4. Why do you prefer the improved variety over the local wheat variety? A. Grain yields
B. Straw yield
C. Disease resistance capacity
D. Better
market price E. Better cooking quality
F. Early maturing
G. other …………………….
5. Have you observed any shortcoming of the improved wheat varieties compared to the local variety?
A. Yes
B. No
6. If yes, state the limitations of improved wheat variety compared to the local variety? ………………………………………………………………………………………… ………………………………………………………………………………………… ………………………………………………………………………………………… 7. How far is your most distant wheat seed farm from your home? ……. Minute (on foot). 75
8. What was your source for wheat seed during last cropping season? A. Own
B. From market
C. Agriculture office cooperatives
D. Other
9. What is your plan with regards to the area to be allocated for wheat seed production in the coming production seasons? A. Increase
B. Decrease
C. No change
10. What is your reason for the response in the above question? …………………………………………………………………………………………… …………………………………………………………………………………………… …………………………………………………………………………………………… 11. Do you have problem with regards to wheat seed marketing?
A. Yes
B. No
12. If yes, what are the major problems? …………………………………………………………………………………………… …………………………………………………………………………………………… ……………………………………………………………………………..…………… 13. In your opinion, what would be the solution for this problem? ………………………………………………………………………………………… ………………………………………………………………………………………… ……………………………………………………..…………………………………… 14. Have you applied fertilizer for production of wheat seed in the 2010/11 year? A. Yes
B. No
15. Do you have problem in supply and marketing of artificial fertilizer? A. Yes
B. No
16. If yes, what are the major problems of supply and marketing of artificial fertilizer? A. Not supplied timely high
B. There is shortage of fertilizer supply D. The market is far from home
C. The price is
E. Other
17. Have you used herbicide in the production of wheat seed by the 2010/11 production season?
A. Yes
B. No
18. If yes, from where did you get from? A. Shendy market
B. Wogedad market C. Cooperatives
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D. Other
C. Input used and Output produced 1. How many plots did you used to produce wheat seed in the year 2010/11? …… plots. 2. What technique you use to apply fertilizer? A. Split application
B. One time only
3. What was your source of oxen for the 2010/11 production season? A. Own
B. Rented
C. Shared
D. Other (specify)__________________
4. Have you hired labor for your wheat seed farm in the last (2010/11) production season?
A. Yes
B. No
5. Area allocated, fertilizer applied and seed used for wheat seed production in 2010/11. fertilizer
Plot
Application 1st round
Plot 1
application
Plot 2
Area of plot
Seed used
DAP applied
Urea Applied
(ha)
(Kg)
(Kg)
(Kg)
Plot 3 Split (2nd
Plot 1
X
X
round)
Plot 2
X
X
application
Plot 3
X
X
6. How many times do you plow your wheat seed farm? _______ Times.
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7. Amount of human and oxen labor allocated in the process of wheat seed production? Activities
Animal labor (Number)
Family labor (Number) Men Women Children
Hired labor (Number)
Wonfel (Number)
st
1 Plowing 2nd plowing 3rd Plowing 4th Plowing 5th Plowing 6th plowing Sowing & top dressing Apply insecticide 1st weeding 2nd Weeding Split application Harvesting Threshing Transportation 8. Was there disease and pest occurrence on your wheat seed farm in the year 2010/11 production season?
A. Yes
B. No
9. If yes, did you apply any chemical for control?
A. Yes
B. No
10. How much was the total cost of herbicide used for production of wheat seed in 2010/11 year? No.
Name of Herbicide
Unit
Amount
Unit price (Br)
Total price (Br)
1 2 Total cost of Herbicide
11. Did you bought wheat seed for the last production season? A. Yes
B. No
12. Is there price difference for wheat seed during different months of a year? A. Yes
B. No
13. If yes, when is the price of wheat seed very high? …………….. (Month) 14. Did you sold your wheat seed for the Amhara seed enterprise before? A. Yes 78
B. No
15. If yes, do you believe that the standard to measure the quality of wheat seed by expertise of Amhara seed enterprise is correct?
A. Yes
B. No
16. If no (for question 15) why? ………………………………………………………………………………………… ………………………………………………… 17. On what land did you produce the wheat seed? A. Own
B. Rented in
C. Other
18. Amount of wheat seed produced and consumed in 2010/11 production season? Type of land
Produced (Qt)
Sold (Qt)
Consumed/used for home (Qt)
From own land Plot1 Plot2 Plot3 From rent in land (shared) Plot1 Plot2 Plot3
C. Socio economic and institutional factors 1. Do you have access for credit?
A. Yes
2. If yes, from whom do you get the credit? C. Cooperatives
B. No A. ACSI
D. Relatives
B. Banks
E. Others
3. If yes, for what purpose you used the credit? A. To buy fertilizer
B. to buy wheat seed
C. To buy oxen
D. Other (specify ………………………………..) 4. Do you have a problem in credit market?
A. Yes
B. No
5. If yes, please state the major problems that exist in the credit market? A. It has high interest rate
B. the amount of money for loan is limited (small)
C. the repayment period is short
D. It has administration problem
79
E. Other
6. Do you have enough market demand for your wheat seed production? A. Yes
B. No
7. If no, what are the major reasons? ………………………………………………………………………………………… 8. Do you believe that the current market price for seed is fair (good)? A. Yes
B. No
9. If no, what are the major reasons? ………………………………………………………………………………………… 10. How is the price for your wheat seed product decided in the market? A. By the farmer himself C. By the market
B. By the enterprise together with the farmers D. Other (Specify …………………………………)
11. How far is the market that you sold your wheat seed from your home? ….. Minutes on foot. 12. How do you transport your wheat seed product to the market? A. Using human labor
B. By car
C. Other (Animals)
13. Do you have communication with extension agents?
A. Yes
B. No
14. If yes (Qn. 13), about what did they usually used to give you an advice? ………………………………………………………………………………………… 15. If yes (Qn. 13), have you ever take an advice about wheat seed?
A. Yes
B. No
16. If yes (Qn. 15) what lessons did you get from them? ………………………………………………………………………………………… 17. Have you ever participate on demonstration site?
A. Yes
B. No
18. Asset ownership of the household No.
Description
Do you have the following list of assets Yes
1
Corrugated Iron roof house
2
Mobile
3
Radio
4
Farm Machineries/equipments
5
House in town
6
80
No
19. For whom did you sell your last season wheat seed product? A. Amhara seed enterprise
B. Market
C. from cooperatives
D. Other
20. Do you know the market price for wheat seed at primary and secondary markets (B/dar and Bure) while you are selling your output?
A. Yes
21. If yes did you sell your product on the market with better price?
B. No A. Yes
B. No
22. If not, why? ………………………………………………………………………………………… 23. Do you participate in social activities in your kebele?
A. Yes
B. No
24. If yes, what is your role? A. Leadership
B. Committee
C. Member
D. Other
25. Does your responsibility have significant negative impact in farming activities? A. Yes
B. No
26. How many days on average do you spend per month to in your responsibility? ……….. Days. 27. Who will take care of the farming activities while executing your responsibility? A. Family members
B. Hired workers
C. Relatives
D. The society E. Other
28. What is the criterion for assigning wealth status of people in your area? ………………………………………………….……………………………………… ……………………………………………… 29. With the above criteria which group are you?
A. Rich
30. Are you a member of agriculture office cooperative?
B. Medium
C. Poor
A. Yes
B. No
31. Are you a member of seed producer and marketing cooperative?
A. Yes
B. No
32. If yes, how long have you been since you join the cooperative? …………… years. 33. If yes (Qn. 30), what benefits you get from the cooperative? A. It supply fertilizer and seed B. Purchase our products
C. dividend
D. Other
34. If not (for Qn.30), what is your reason for being a non member? ………………………………………………………………………………………
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35. On-farm and off-farm income generating activities of the households. Activity Off-farm and Non farm House rent
Income earned per year (Birr)
Predominant participant Male Female Both
On-farm Sale of annual crops Wheat Maize Pepper Sale of animals
Sale of perennial crops
36. Do you buy any crop in the last season for household consumption?
A. Yes
B. No
37. If yes, how many months food deficit does your family had in the 2010/11?______ Months. 38. General household expenditure of the household in the year 2010/11. Description
Annual expense (Birr)
Remark only cash expense for consumption
Consumption Closing Education Social obligation (wedding, . . . Medication Others (mill, …)
Thank U!!
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