Technical Efficiency of Sesame Production

Table of Contents

ACKNOWLEDGEMENT

ACRRONYMS

LIST OF TABLES

LIST OF FIGURES

LIST OF TABLES IN THE APPENDEX

ABSTRACT

CHAPTER ONE

1. INTRODUCTION
1.1. Background of the study
1.2. Statement of the Problem
1.3. Objectives of the Study
1.4. Hypothesis of the Study
1.5. Significance of the Study
1.6. Scope and Limitation of the Study
1.7. Organization of the thesis

CHAPTER TWO

2. LITERATURE REVIEW
2.1. Theoretical Concept
2.1.1. Concepts of Technical Efficiency
2.1.2. Measurement Issues of Technical Efficiency
2.2. Empirical Evidence
2.2.1. World Sesame Production
2.2.2. Sesame Production in Ethiopia
2.2.3. Major Sesame Seed Producing Zones
2.2.4. Empirical Findings Outside Ethiopia
2.2.5. Empirical Finding on Ethiopian Farms
2.3. Conceptual Framework

CHAPTER THREE

3. RESEARCH METHODOLOGY
3.1. Description of the Study Area
3.2. Sampling Technique
3.3. Sources and Method of Data Collection
3.4. Methods of Data Analysis
3.4.1 Efficiency Estimation
3.5. Variables as measured in the Model

CHAPTER FOUR

4. RESULTS AND DISCUSSION
4.1. Descriptive Results
4.1.1. Demographic Characteristics
4.1.2. Input Basis of the Farmers
4.1.3. Production Practices
4.1.4. Institutional Support
4.1.5. Major Production Constraints
4.2. Econometric Analysis Results
4.2.1. Maximum Likelihood Estimates of Parameters of the Models
4.2.2. Technical Efficiency Scores
4.2.3. Actual - potential level of sesame output
4.2.4. Technical Inefficiency

CHAPTER FIVE

5. CONCLUSIONS AND RECOMMONDATIONS
5.1. Conclusions
5.2. Recommendation
5.3. Suggestions for Future Research

6. REFERENCES

7. APPENDICES

Appendix 1

Appendix 2

Appendix 3

Appendix 4

ACKNOWLEDGEMENT

First and for most I would like to thank the eternal God. I would also deeply grateful and indebted to MilkessaWakjira (PhD), my advisor, for his encouragement, suggestions, guidance and overall assistance. Successful accomplishment of this research would have been very difficult without his generous time devotion from the early work of the thesis to the final write-up of the thesis by adding valuable, constructive and ever teaching comments and thus I am indebted to him for his kind and tireless efforts that enabled me to finalize the study.

I humbly acknowledge to the cooperation of sampled respondent’s in replying to all questions patiently and active participation in the discussion made and five enumerators that participated in the data collection period.

I would like to thank also Mr. Anemaw Gedamu, Mr.Natan Mesfin and M/s Dagem Anmaw (who provided STATA software manual, translates the questionnaire in to Tigrigna Language and final thesis printing and editorial support respectively) and give their valuable suggestions throughout my study.

I am also glad to express my gratitude to Development Bank of Ethiopia for allowing me to spare time to undertake the study. And my appreciation goes to entire DBE staff and my friends particularly Yibeltal Yayeh and Dagne Mulatu for their continuous encouragement and moral support.

Last but not the least; I would like to thank my family: my Mother Walellegn Melese, for giving birth to me at the first place and supporting me spiritually throughout my life.

ACRRONYMS

Abbildung in dieser Leseprobe nicht enthalten

LIST OF TABLES

Table 2.1: Regional Sesame Production, Trade, and Consumption

Table 2.2: Major sesame producers

Table 2.3: Sesame production by regions

Table 2.4: Share of total production of major producing regions /zones

Table 3. 1: Total number of Household and sample size selected

Table 3.2: Variables used in the model

Table 4.1: Age Distribution

Table 4.2: Family size

Table 4.3: Education Level of household heads

Table 4.4: Martial Status

Table 4.5: Area of sesame

Table 4.6: Average livestock holding

Table 4.7: Labor use of sample farmers for conducting different activities

Table 4.7: Frequency of ploughing

Table 4.8: Land Fragmentation

Table 4.9: Off farm activities

Table 4.10: Credit accessibility

Table 4.11: Major problems of sesame production in the study area

Table 4.12: Hypothesis testing on the stochastic Frontier Functional form

Table 4.13: Maximum Likelihood (ML) Estimates of the parameters for stochastic Frontier production function

Table 4.14: Technical efficiency (TE) frequencies for farmers in the study area

Table 4.15: The potential and the actual yield of the sample farmers

Table 4.16: Factors Affecting Inefficiency of Farmers' in the area

Table 4.18: Age groups and TE of sample household heads

Table 4.19: Relationship between technical efficiency level and education

Table 4.20: Relationship between average TE and access to and use of credit

Table 4.21: Relationship between the mean TE and involvement in off farm activity

Table 4.22: Extension contact and mean level of technical efficiency

LIST OF FIGURES

Figure 1: Farrell's measure of technical and allocative efficiencies

Figure 2: The Stochastic Frontier Production Function

Figure 3: The Conceptual Framework

Figure 4: Proportion of farmers in the efficiency group

LIST OF TABLES IN THE APPENDEX

Table 1: Maximum Likelihood (ML) Estimates of the parameters for stochastic frontier production function

Table 2: Factors Affecting Inefficiency of Farmers' in the area

Table 3: Technical efficiency (TE) frequencies for farmers in the study area

ABSTRACT

This study aimed to analyze the technical efficiency of sesame production in Humera area and to identify major factors that cause efficiency differentials of smallholder farmers. The objective of the study is to measure the technical efficiency of small holder farmers in sesame production. The study was conducted using a cross sectional data collected in 2015/2016-production year from a total sample of 110 households. Cobb-Douglas function was employed to estimate technical efficiency of smallholder farmers in sesame production. The finding of the study indicated that there is inefficiency in the production of sesame in the study area. The estimation of the frontier model with inefficiency variables shows that the mean technical efficiency of farmers is 0.69 (69%). This implies that production of sesame can be increased by 31 percent given the existing technological level. This indicates that the farmers did not using production inputs efficiently in such a way that they give their maximum potential. The estimated stochastic production frontier model together with the inefficiency parameters suggests that any attempt to strengthen technical efficiency of smallholder farmers in the study area must give due attention to the improvement of the principal causes for efficiency differentials such as education, age, extension contact, credit availability, off farm activities and proximity, which were found to be significant determinants of efficiency level. The negative coefficient of educational status, age, credit availability, extension contact and off farm activities means these factors are important in determining the existing efficiency of farmers positively and significantly. While the positive coefficients of proximity indicate that the increments in these factors increase inefficiency. Given the limited resources in the study area will enable the concerned parties engaged in efforts for improvement of the product and productivity of this part of the community to bring about the desired changes in a cost effective way than trying to inject an investment on the production of sesame.

Key words; Sesame, Technical Efficiency, Smallholder farmers and stochastic production function

CHAPTER ONE

1. INTRODUCTION

1.1. Background of the study

Agriculture is the pillar of Ethiopia economy, providing employment opportunity for more than 85% of the country’s population, the main income-generating sector for the majority of the rural population and accounts for more than 45.9% of the total GDP of the country. It also serves as the main source of food, generates 90% of the foreign exchange earnings and also provides raw materials for more than 70% of the country’s industries with in agriculture, 60% of the output of the agricultural GDP comes from crop production. Whereas, 30% and 7% is from livestock and forestry, respectively (World Bank, 2012).

Despite its importance in the livelihood of the people and its potential, the sector has been still dominated by subsistence production and traditional technologies are predominant. Hence, level of productivity in agriculture is very low due to low rate of quality production among others. Consequently, the agricultural sector has failed to meet adequately its primary objectives such as providing adequate food, raw materials, exports earnings, and resources inevitable in itself and other sectors of the economy.

In order to reverse these horrifying situations, the government has put agriculture at the heart of its policies since the last two decades to accelerate economic growth and development. All agricultural development programs of the country are on agricultural growth and provide support to small farmers, pastoralists and large-scale commercial farms. In particular, attempts have been made to increase agricultural production in the country by intensifying the use of fertilizers, improved seeds, trained manpower, improved cultural practices and reclamation of waste lands. Moreover, to mitigate the food insecurity problem and increase export earnings from agricultural commodities, the country has focused on diversification of agricultural production, especially growing high value commodities for export and adoption of appropriate newly introduced crops and livestock technologies. Amongst others, for instance, adopting of the newly introduced and released sesame varieties and other additional oils crops are an alternatives for the production of commercially oriented high value crops (Haile, 2009). At present, the Ethiopian government devotes considerable resources to research and extension activities to encourage farmers to increase their production and productivity of high value crops such as sesame to increase export earnings of the country (Kinde Teshome, 2005).

Sesame is predominantly grown in the Humera area in the Tigray region of Ethiopia. This crop as grown both by large- scale farms and smallholder farmers. There is also strong support from the government and Non-government organizations (NGOs) to small farmers to invite in the production of this high value crops.

Despite this, the productivity of the crop is still low and the government plan in the respect to diversity expert is not yet achieved. i.e., there is low efficiency in the production of the crop. In line with this, the solution for the productivity problem time question now is what are factors behind that low productivity? This being the case, the present study has attempted to identify the factors behind the low productivity of the crop using technical efficiency measures.

1.2. Statement of the Problem

A large number of studies made to date in different regions and sectors in Ethiopia found that inefficiency exists (kinde, 2005; Alemu et al., 2009 and Ahmed , 2013). These studies are crop specific. Attempt has been made to investigate the productivity of sesame in the study area through creation of sectoral atmosphere likely to improve the technical efficiency of sesame producers and thereby improve sesame productivity.

Assessing the efficiency of farmers may be one of the ways that helps improving the performance of sesame production in the region. In this regard there is lack of pertinent research findings on the technical efficiency of the farmers in the study area and to identify the determinants of the variability of the efficiency levels among farmers. The present study is an attempt towards analyzing the technical efficiency of the farmers in the study area and aims to bridge the prevailing information gap on the contextual factors contributing to efficiency differentials in the production of sesame.

1.3. Objectives of the Study

1.3.1. General Objective

The general objective of the study is to investigate the technical efficiency of smallholders involved in sesame production.

1.3.2. Specific Objective

The specific objectives of the study are:-

1. To measure the level of technical efficiency of smallholders involved in sesame production.
2. To identify the principal factors that causes efficiency differentials among smallholder sesame producers in the study area.

1.4. Hypothesis of the Study

The following hypotheses requires testing with the generalized likelihood ratio test, The following null hypotheses were formulated for the above objectives; lLR = 2[L(H1)-L(H0)], where L(H 1 ) and L(H0) are the values of the log likelihood functions under the alternative and null hypothesis, respectively. The null hypothesis is rejected when lLR > xC2

H0=ßij=0, this hypothesis is concerned with selection of appropriate functional form that describes the data set adequately. So, in this study it is hypothesized that Cob-Douglas production function describes the data adequately or Trans log production function is not an adequate representation of the data.

H0; u=0, Sesame producers in Humera area are technically efficient and no production loss is attached to technical inefficiency of sesame producers.

H 0; l = s 0 = s 1 = s 2 =… s p= 0, variables included under the inefficiency effects model are not jointly affecting technical inefficiency of the farmers in the study area. Meaning that; age, educational level and experience of farmers, gender of house hold head, proximity to market, distance, number of livestock owned, agricultural extension workers’ contact with sesame producers and accessibility to credit do not significantly influence the farmer’s technical inefficiency.

1.5. Significance of the Study

A thorough analysis of efficiency of a farm is of a paramount importance in developing agricultural economies since it can be considered as one of the worthwhile factors of productivity and growth. Especially in areas where resources are scarce, making such measurement is highly crucial to improve production and productivity without injecting investment.

Knowing the existence of efficiency differentials among farmers in advance is a key and crucial factor that helps much to improve efficiency. In addition to this identifying the different factors contributing to the inefficiency differentials among farmers is vital and a thorough understanding of them is essential in order to formulate a better policy for intervention with a view to bring a desired change in the sector.

Finally, it is wise to indicate that providing empirical evidences on the degree of producers' technical efficiency and the principal causes of inefficiency of farmers might have a substantial contribution in assisting policy makers as well as development workers to focus on those determinants identified and to make improvement. If this is so, it is possible to boost production and productivity of the sesame farmers and helps them to give more land coverage for the production of this crop. Moreover, the study will hopefully serve as a springboard for further and detailed study in the region.

1.6. Scope and Limitation of the Study

The study has certain limitations, which emanate primarily from shortage of time, budget, and facilities. In the first place, the study was conducted using a cross-sectional data of 110 respondent farmers to analyze the technical efficiency level and the principal determinants for the variation among farmers, which only reflects farmers’ circumstances in a given year. This may be affected by the specific climate of the year as agriculture in the country in general, and in the study area in particular, is dependent on weather. Moreover, the results of cross-sectional data do not show the change over time that is important for a follow up development strategy.

Secondly, the present study focused only on one aspect of production efficiency, technical efficiency, to establish the existence of crop specific inefficiency among farmers and it may not give the clear picture of the efficiency differentials among farmers in the study area. In addition to these, respondent farmers were very dubious to offer some important data for the analysis, for instance yield. Despite these shortcomings this study can provide a spring board for a lot of useful policy formulation and intervention.

1.7. Organization of the thesis

The rest of the thesis is organized in to four chapters. The analytical foundation of theoretical and empirical literature of efficiency measurements are well discussed in the chapter two. Following this, chapter three discusses about research data and methodology. Whereas, chapters four is being devoted to results and discussions based on the theoretical and empirical foundations discussed in the chapter two and three. Finally, the last chapter concludes and stressed the policy implications based on the findings.

CHAPTER TWO

2. LITERATURE REVIEW

2.1. Theoretical Concept

2.1.1. Concepts of Technical Efficiency

In production theory the main choices center on what to produce (i.e., which product or combination of products), how much to produce (the levels of output) and how to produce (the combination of inputs to use). The decision making unit is the firm which is defined as a “distinct agent specialized in the conversion of inputs into desired goods as outputs”. (Farrell, 1957). Technical efficiency may be defined as the ability of a firm to produce as much output as possible with a specified level of inputs, given the existing technology. Technical efficiency concerns the way in which physical quantities of input are converted into physical quantities of output. Farmers are said to be technically efficient if they achieve maximum feasible output from inputs.

According to the neoclassical definition of technical efficiency, a production process is technically efficient if and only if it yields the maximum possible output for a specified technology and input set. The concept of efficiency can be explained more easily using input or output oriented approaches. Farrell (1957) used an input-oriented approach to illustrate the measurement of efficiency. He used a simple example involving firms, which use two inputs, x1 and x2, to produce a single output y, under the assumption of constant returns to scale.

The constant returns to scale assumption allows to represent the technology using a unit iso-quant. Farrell discussed the extension of his method so as to accommodate more than two inputs. Knowledge of the unit iso-quant of the fully efficient firm, represented by TT' in figure 1, permits the measurement of technical efficiency. If a given firm uses quantities of inputs, defined by the point k, to produce a unit of output, the technical inefficiency of that firm could be represented by the distance yk, which is the amount by which all inputs could be proportionally reduced without a reduction in output. This is usually expressed in percentage by which all inputs could be reduced.

Abbildung in dieser Leseprobe nicht enthalten

Figure 1: Farrell's measure of technical and allocative efficiencies

The Technical efficiency (TE) of a firm operating at k is measured by the ratio, TEk= oy/ok, which is equal to one minus yk/ok. TEk will take a value between zero and one, and hence provides an indicator of the degree of technical inefficiency of the firm. A value of one indicates the firm is fully technically efficient. For example, the point y is technically efficient because it lies on the efficient iso-quant.

Farrell has also demonstrated that the unit iso-quant provides a set of standards for measuring allocative efficiency. The iso-cost line SS’ gives the minimum cost of producing one unit of output given relative input prices. The allocative efficiency (AE) of the firm operating at k is defined to be the ratio, AEk= oz/oy, since the distance zk represents the reduction in production costs that would occur if production were to occur at the allocatively (and technically) efficient point y*, instead of at the technically efficient, but allocatively inefficient, point y. The total economic efficiency (EE) is defined to be the ratio, EEk= oz/ok, where the distance zk can also be interpreted in terms of a cost reduction. Thus, the product of technical and allocative efficiency provides the overall economic efficiency measure.

Producers that achieve different output-input ratios may actually face different technologies, or the differences may arise from random disturbances, or some produce may be more successfully than others in exploiting the same technology. The failure of producers facing the same set of prices and production function to achieve the same level of efficiency arises from two sources: (1) the failure of some to avoid waste by producing as much output as input usage allows or by using as little input as output production allows i.e., the failure to operate on the technically efficient production frontier, and (2) the failure of some to combine inputs and outputs in optimal proportions in light of prevailing prices i.e., the failure to apply the level of inputs that maximize profits. The above two points, enable one to identify technical and allocative or price inefficiencies (Coelli et al., 1998).

The input oriented measure of efficiency addresses the question “by how much can input quantities be proportionally reduced without changing the output quantities produced?” (Coelli. et al., 1998). A farm can be on or above the unit iso-quant on the input per unit of output space and cannot be below or to the left to it. A departure from the unit iso-quant indicates technical inefficiency and the more a farm is far from the unit iso-quant the more it is inefficient.

Efficiency measure can also be expressed by using the output-oriented approach. “ By how much can output be increased without increasing the amount of input use?” (Coelli et al., 1998). Such a question can be answered by using the output-oriented measure of efficiency. The failure of producers facing the same set of prices and production function to achieve the same level of efficiency arises from two sources: (1) the failure of some to avoid waste by producing as much output as input usage allows or by using as little input as output production allows i.e., the failure to operate on the technically efficient production frontier, and (2) the failure of some to combine inputs and outputs in optimal proportions in light of prevailing prices i.e., the failure to apply the level of inputs that maximize profits. The above two points, enable one to identify technical and allocative or price inefficiencies (Coelli et al., 1998).

2.1.2. Measurement Issues of Technical Efficiency

As indicated above, economic efficiency is composed of two components, namely, technical efficiency and allocative efficiency. Allocative efficiency refers to the extent to which a farmer combines inputs to achieve the greatest financial gain. As with allocative efficiency, if not more so, policy maker's conception of technical efficiency in peasant agriculture may influence the shape of development strategies.

Technical efficiency, which can have an output augmenting orientation or an input conserving orientation, is the measure of a firm's success in producing maximum output from a given set of inputs. A producer is said to be technically efficient if an increase in any output requires a reduction in at least one other output or an increase in at least one input and if a reduction in any input requires an increase in at least one other input or a reduction in at least one output.

Technical efficiency is concerned with the efficiency of the transformation of inputs to physical output. That is, for efficient production, farm output should lie on the envelope curve, or production function, which traces out the maximum quantities of output from varying quantities of inputs under a given technology. When technical efficiency is defined in terms of maximum output from a given bundle of measured inputs, it means that only those farmers who are technically efficient will operate on the production frontier. Farmers whose input- output performance falls below that of farms on the production function are classified being technically inefficient.

According to Farrell (1957) technical inefficiency arises when more than the least bundle of inputs is used to produce one unit of output. He puts several techniques for the measurement of efficiency of production. These techniques can be broadly categorized into two approaches: Parametric and non-parametric. The parametric production frontier production function approach and the non- parametric mathematical programming approach, commonly referred to as Data Envelopment Analysis (DEA) are the two most popular techniques used in efficiency analysis.

Abbildung in dieser Leseprobe nicht enthalten

Figure 2: The Stochastic Frontier Production Function

Source: Adopted from Coelli et al. (1998)

Recent advances in the theory and practice of estimating stochastic ‘frontier functions” have brought empirical estimates of production functions much closer to their theoretical definition of envelop curves or frontiers. Among many authors, Coelli et al (1995) present the most recent review of various techniques used in efficiency measurement, including their limitations, strengths and applications in agricultural production.

The main strengths of the stochastic frontier approach are that it deals with stochastic noise and permits statistical tests of hypothesis pertaining to production structure and the degree of inefficiency. It is the only technique that distinguishes between random factors and inefficiency. However, the estimation of efficiency using stochastic method requires a prior specification of functional form.

Moreover, the estimation of ui (random term) needs distributional assumptions (half normal, gamma, truncated, etc.) (Coelli et al, 1998). In stochastic frontier, method technical efficiency is measured by estimating a production function. Different production functions such as Cobb-Douglas, Translog, Transcendental, and Quadratic etc. can be used to estimate the frontier. The Translog and Cobb-Douglas specifications are commonly used functional forms to estimate the frontier; but both have their merits and demerits.

Stochastic frontier production functions have been applied in a large number of empirical studies to account for the existence of technical inefficiencies of production. In most cross-sectional studies, the technical inefficiency effects have been assumed to be independently and identically distributed, generally as half- normal or exponential random variables. However, in some empirical papers, the parameters of stochastic frontier production functions have been estimated and then the predicted technical inefficiency effects have been regressed on various farmer specific variables believed to be significant in explaining the level of technical inefficiency of the farmers involved. This two-stage approach involves contradictory assumptions in that the technical inefficiency effects in the stochastic frontier are assumed to be independently and identically distributed to obtain prediction for their unknown values.

However, expressing the predicted technical inefficiency effects in terms of a regression model involving other explanatory variables is not consistent with the assumptions of identically distributed technical inefficiency effects in the stochastic frontier. Coelli and Battesse (1995) propose a model in which the technical inefficiency effects in a stochastic production frontier are a function of other explanatory variables.

2.2. Empirical Evidence

2.2.1. World Sesame Production

World production of sesame seeds is estimated at 2.25 million tones, of which, 70% is consumed in the producing countries themselves. Annual trading volumes are estimated at 600,000 mt, valued at US$500 million. India, China, Myanmar in Asia, Nigeria, and Ethiopia and Tanzania in Africa and Guatemala in Central America are the major producing and exporting countries with Japan the world's largest importer (FAO, 2012/13).

Production of sesame is shown by geographic region in Table 2.1 below. According to FAO statistics, one can also observe that: Asia produces 64% of the world's supply of seed and Africa 31%, the two regions contribute a total of 95.3% of total global production, 28% of sesame produced in the world enters international trade.

Table 2.1. Regional Sesame Production, Trade, and Consumption

Abbildung in dieser Leseprobe nicht enthalten

Source: UN / FAO, 2012

Of the fourteen countries who are the major producers in the world, as shown in table 2.2 below, six are in Asia, seven in Africa and one in Latin America. These countries together accounts for 84% of the world sesame production.

Table 2.2. Major sesame producers 2012

Abbildung in dieser Leseprobe nicht enthalten

Source: UN / FAO, 2012

FAO statistics reveals that in 2012; 16.49 million acres were cultivated, producing 3.1 million tons of sesame seed for an average yield of 390 lbs/acre. Generally speaking, there is a high correlation between yield and the amount of rainfall. Much of the sesame in the world is grown in semi-arid areas where there is very little irrigation.

2.2.2. Sesame Production in Ethiopia

Ethiopia ranks among the top 6 world producers of sesame. Owing to the availability of suitable soils and climate, oilseeds are important agricultural commodities widely grown in Ethiopia. The major oilseeds are sesame, Noug or Niger, groundnuts, soy sesames (partly used for oil extraction), rape seed (Gomenzer), linseed, sunflower, cottonseed and others. Most of the sesame production is used for export to Middle East, European and Far East countries (FAO, 2012).

In the North West and South Western lowland areas of the country, sesame is currently cultivated on fertile lands and there seems to be less need for fertilizers. During the year 2012/13, there were about 527,819 sesame growers with an average acreage of 0.3 ha are involved in sesame seed production who produced 18,677.3 tons most of which are small holder farmers. Besides small holders, there are a limited number of investors or large commercial farmers (having more than 100 ha). Due to the low input levels, sesame production in Ethiopia meets organic standards. This is the case for most small holders and large commercial farms. The main Ethiopian sesame production regions are situated in the North West and South West. This fragmented faming system has resulted in many collectors gathering quantities suitable for trading.

Table 2.3. Sesame production by regions

Abbildung in dieser Leseprobe nicht enthalten

Source: CSA, 2012

The major producing areas of sesame in Ethiopia are Tigray, Amahra, Oromia and Benshangul Gumuz. However, a small amount of sesame is currently produced in SNNPR. Tigray, Amahra and Oromia have a total share of 92.7% of 2012/13 production. In most of the cases sesame is produced in low land areas of the country where relatively large tracts of arable land exist. In Humera area alone, there were 358 small, medium and large scale commercial farms who cultivated 110,000 ha of land in 2012. However, the extension service provided to the small holders of sesame farmers is minimal (MoARD 2012).

Data obtained from the crop and livestock utilization report of the (CSA 2012) reveals that the percentage of sesame sales varies across the producing regions of the country. In 2012, it was as high as 79.8% in Tigray and 76.9 and 63.2 percent in Benshangul, Gumuz and Amhara regions respectively. While that of Oromia region was nearly equivalent to the national average. This high proportion of sales volume implies that sesame can be regarded as a cash crop with high price elasticity of supply and the market price of sesame could have a huge impact on sesame production and supply.

2.2.3. Major Sesame Seed Producing Zones

According to the CSA 2012 survey report, the major sesame seed producing areas in the country are Tigray region, Western and North Western zones, (especially Humera, Tsegede and Welkaite woredas); Amhara region, North Gondar Awi and west Gojam zones (specifically Metema, kuara, West Armachiho, Tach Armachiho and Tegede woredas); Oromiya region, (Western, Eastern and Kelum wollega, Jimma, Illubabor as well as West Hararge zones); and Benshangul Gumuz region (Metekel Assossa, and Kemashi zones). The total estimated yield of sesame in the major producing zones of the regions is presented in table 2.4 below.

Table 2.4. Share of total production of major producing regions /zones (2012)

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Source: CSA Agricultural sample survey Report 2012.

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