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BAHIR DAR UNIVERSITY COLLEGE OF AGRICULTURE AND ENVIRONMENTAL SCIENCE DEPARTMENT OF RURAL DEVELOPMENT AND AGRICULTURAL EXTENSION GRADUATE PROGRAM VALUE CHAIN ANALYSIS OF SOYBEAN: THE CASE OF PAWE DISTRICT, NORTH WESTERN ETHIOPIA MSc. Thesis By Takele Atnafu Delele December, 2020 Bahir Dar

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Page 1: BAHIR DAR UNIVERSITY COLLEGE OF AGRICULTURE AND

BAHIR DAR UNIVERSITY

COLLEGE OF AGRICULTURE AND ENVIRONMENTAL

SCIENCE

DEPARTMENT OF RURAL DEVELOPMENT AND

AGRICULTURAL EXTENSION

GRADUATE PROGRAM

VALUE CHAIN ANALYSIS OF SOYBEAN: THE CASE OF PAWE

DISTRICT, NORTH WESTERN ETHIOPIA

MSc. Thesis

By

Takele Atnafu Delele

December, 2020

Bahir Dar

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BAHIR DAR UNIVERSITY

COLLEGE OF AGRICULTURE AND ENVIRONMENTAL

SCIENCE

DEPARTMENT OF RURAL DEVELOPMENT AND

AGRICULTURAL EXTENSION

GRADUATE PROGRAM

VALUE CHAIN ANALYSIS OF SOYBEAN: THE CASE OF PAWE

DISTRICT, NORTH WESTERN ETHIOPIA

MSc. Thesis

By

Takele Atnafu Delele

THESIS SUBMITTED IN PARTIAL FULFILMENT OF THE

REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE

(MSc.) IN RURAL DEVELOPMENT MANAGEMENT

MAJOR ADVISOR: ALMAZ GIZIEW (PhD)

CO-ADVISOR: BIRHANU MELESSE (ASSISTANT PROFESSOR)

December, 2020

BAHIR DAR

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BAHIR DAR UNIVERSITY

COLLEGE OF AGRICULTURE AND ENVIRONMENTAL SCIENCE

DEPARTMENT OF RURAL DEVELOPMENT AND

AGRICULTURAL EXTENSION

Approval of Thesis for Defense Result

As a member of the Board of Examiners of the Master of Science (MSc.) thesis open defense

examination, we certify that we have read and evaluated this thesis prepared by Mr. Takele

Atnafu entitled Value Chain Analysis of Soybean: the case of Pawe district, North

western Ethiopia. We hereby certify that the thesis is accepted for fulfilling the

requirements for the award of the degree of Master of Sciences (MSc.) in Rural

Development Management.

Board of Examiners

Derjew Fentie (PhD)

Name of External Examiner Signature Date

Beneberu Assefa (PhD) 14/12/2020

Name of Internal Examiner Signature Date

Yenesew Sewnet (Assistant professor)

Name of Chairman Signature Date

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DECLARATION

This is to certify that this thesis entitled “Value Chain Analysis of Soybean: The Case of

Pawe District, North Western Ethiopia” submitted in partial fulfillment of the

requirements for the award of the degree of Master of Science in “Rural Development

Management” to the Graduate Program of College of Agriculture and Environmental

Sciences, Bahir Dar University by Mr. Takele Atnafu (ID. No. BDU 1100541) is an

authentic work carried out by him under our guidance. The matter embodied in this project

work has not been submitted earlier for an award of any degree or diploma to the best of our

knowledge and belief.

Name of the Student

Takele Atnafu

Signature & date 13/12/2020

Name of the Major Advisor

1) Almaz Giziew (PhD)

Signature & date 14/12/2020

Name of the Co-advisor

2) Birhanu Melesse (Assistant professor)

Signature & date 14/12/2020

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DEDICATION

I dedicate this thesis paper to my beloved mother Siraye Yitayih and my father Atnafu

Delele as well as my wife Yalganesh Hunegnaw for their unlimited moral encouragement

and financial support starting from elementary school for the success of my life.

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ACKNOWLEDGMENT

First and for most, my gratitude goes to the Almighty of God for being successful in my

journey. My heartfelt appreciation and gratitude go to my major advisor Dr. Almaz Giziew

and co-advisor Mr. Birhanu Melesse for their unlimited and unreserved support to make this

thesis work successful. They helped me starting from topic selection up to the completion

of this work. Their willingness to share knowledge and materials and their way of advice to

produce competent citizens will never be forgotten. Their inspiration and brotherly

encouragement were critical for the success of this output.

My special thanks also go to Dr. Beneberu Assefa for his critical advice and brotherly

support for the overall journey of my thesis research work. I am also grateful to Dr. Zemen

Ayalew for sharing of some Stata commands and his technical support for some

specification tests.

My deepest gratitude goes to Mr. Derese Mekonnen director of agricultural extension and

communication research directorate from Ethiopia Institute of Agricultural Research for his

critical advice and resource arrangements for the completion of this thesis work.

I am extremely thankful to my wife, Yalganesh Hunegnaw, for her moral encouragement

and financial support from the beginning up to the end of my research thesis to be successful.

Special thanks go to my friend Koyachew Arega for his great cooperation in the process of

data collection at Addis Ababa. I would like to extend my gratitude to Pawe Agricultural

Research Center for vehicle and other resource arrangements for the overall process of data

collection. I also express my special thanks to enumerators from Pawe Agricultural

Research Center staff and respondents for their unreserved cooperation and patience from

the beginning up to the end of data collection.

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TABLE OF CONTENTS

Contents Page

DECLARATION............................................................................................ iii

DEDICATION................................................................................................ iv

ACKNOWLEDGMENT ................................................................................ v

LIST OF TABLES ......................................................................................... ix

LIST OF FIGURES ........................................................................................ x

LIST OF TABLES AND FIGURES IN THE APPENDICES ................... xi

ABBREVIATIONS AND ACRONYMS..................................................... xii

ABSTRACT ..................................................................................................xiii

Chapter 1. INTRODUCTION ....................................................................... 1

1.1 Background and Justification .................................................................................. 1

1.2 Statement of the Problem ......................................................................................... 3

1.3 Objectives of the Study ............................................................................................. 5

1.4 Significance of the Study ........................................................................................... 5

1.5 Scope and Limitation of the Study ........................................................................... 6

Chapter 2. REVIEW OF RELATED LITERATURE ................................ 7

2.1 Definition and Basic Concepts .................................................................................. 7

2.1.1 Concepts related to value chain ......................................................................... 7

2.1.2 Value chain analysis ........................................................................................... 8

2.1.3 Value chain mapping .......................................................................................... 8

2.2 Theories of Value Chain Approaches ...................................................................... 8

2.2.1 The Filiere concept.............................................................................................. 9

2.2.2 The Porter approach .......................................................................................... 9

2.2.3 Global value chain analysis ................................................................................ 9

2.2.4 Global commodity chain approach ................................................................. 10

2.2.5 Global production networks approach ........................................................... 10

2.2.6 Social network theory ....................................................................................... 10

2.3 The Basic Model of Porters’ Value Chain Distinction ......................................... 11

2.4 Status of Soybean Production in Ethiopia ............................................................ 12

2.5 Ethiopian Import-Export Trends of Soybean and Byproducts .......................... 13

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TABLE OF CONTENTS (continued)

2.6 Soybean Value Chain in Ethiopia .......................................................................... 15

2.7 The Relevance of Value Chain to the Poor ........................................................... 15

2.8 The Food System Framework ................................................................................ 16

2.9 Review of Empirical Studies ................................................................................... 16

2.9.1 Main value chain actors and their roles.......................................................... 16

2.9.2 Marketing margin and profit shares in the value chain ............................... 18

2.9.3 Determinants of market supply ....................................................................... 18

2.9.4 Factors affecting value addition decision........................................................ 20

2.10 Conceptual- Framework of the Study ................................................................. 21

Chapter 3. RESEARCH METHODOLOGY ............................................. 23

3.1 Description of the Study Area ................................................................................ 23

3.2 Sampling Techniques and Procedures .................................................................. 24

3.3 Sample Size Determination ..................................................................................... 25

3.4 Type, Source, and Methods of Data Collection .................................................... 27

3.5 Methods of Data Analysis ....................................................................................... 28

3.5.1 Descriptive statistical analysis ......................................................................... 29

3.5.2 Econometric analysis ........................................................................................ 29

3.5.3 Marketing margin analysis .............................................................................. 32

3.6 Definition of Variables and Working Hypothesis ................................................ 33

3.6.1 Dependent variables ......................................................................................... 33

3.5.2 Independent variables ...................................................................................... 34

Chapter 4. RESULTS AND DISCUSSION ................................................ 43

4.1 Descriptive and Inferential Statistics ..................................................................... 43

4.1.1 Household heads characteristics ..................................................................... 43

4.1.2 Institutional characteristics of soybean producers ........................................ 47

4.1.3 In put utilization................................................................................................ 52

4.1.4 Soybean production and marketing ................................................................ 53

4.2 Description of Sample Traders and Consumers ................................................... 56

4.2.1 Household characteristics of sampled traders ............................................... 56

4.2.2 Price setting strategies of traders for soybean purchasing ........................... 56

4.2.3 Initial and working capital of traders ............................................................. 57

4.2.4 Soybean oil production ..................................................................................... 58

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TABLE OF CONTENTS (continued)

4.2.5 Household characteristics of consumers ......................................................... 61

4.3 Main Value Chain Actors and Functions .............................................................. 62

4.3.1 Primary value chain actors and their functions ............................................. 62

4.3.2 Support service providers and their functions ............................................... 65

4.3.3 Map of soybean value chain ............................................................................. 68

4.3.4 Marketing channels along soybean value chain ............................................. 69

4.4 Marketing Margin Analysis ................................................................................... 72

4.4.1 Production cost of soybean in the study area ................................................. 72

4.4.2 Marketing margin and profit shares of actors in the value chain ................ 75

4.5 Econometrics Analysis ............................................................................................ 80

4.5.1 The determinant factors affecting soybean market supply .......................... 80

4.5.2 Factors affecting farmers’ participation on value addition .......................... 85

Chapter 5. CONCLUSION AND RECOMMENDATIONS .................... 90

5.1 Conclusion ................................................................................................................ 90

5.2 Recommendations ................................................................................................... 90

6. REFERENCES .......................................................................................... 93

7. APPENDICES ......................................................................................... 102

AUTHOR BIOGRAPHICAL SKETCH .................................................. 120

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LIST OF TABLES

Tables Page

Table 2.1 Soybean value chain actors and their functions .................................................. 17

Table 3. 1 Sample size and households of the study kebeles .............................................. 25

Table 3. 2 Sampled traders of the study .............................................................................. 27

Table 3.3 Summary of Research Methodology .................................................................. 33

Table 3.4 Description of explanatory variables & hypothesis in MLR model ................... 38

Table 3.5 Description of explanatory variables in the Probit model .................................. 42

Table 4.1 Socio-demographic characteristics of soybean producers .................................. 44

Table 4.2 Socio-demographic characteristics of sampled household (t-test) ...................... 45

Table 4.3 Relationship of quantity of soybean market supply with categorical variables .. 47

Table 4.4 Socio-economic characteristics of soybean producers........................................ 49

Table 4.5 Socio-economic characteristics of sampled households (t-test) ......................... 50

Table 4.6 Extension contact and education level of soybean producers ............................. 52

Table 4.7 Utilization of agricultural inputs for soybean production ................................... 53

Table 4.8 Soybean production and marketing in 2018/19 cropping season ....................... 55

Table 4.9. Socio-demographic characteristics of sample traders ........................................ 56

Table 4.10 Time of soybean purchasing and price setting strategies .................................. 57

Table 4.11 Initial, working capital & credit source ............................................................ 58

Table 4.12 Purchase price of soybean oil by traders and consumers .................................. 59

Table 4.13 Socio-demographic characteristics of soybean oil consumers .......................... 61

Table 4.14 Primary actors and supporters along soybean value chain in the study area .... 62

Table 4.15 Soybean value chain supporters and their functions ......................................... 66

Table 4.16 Production cost of soybean producers .............................................................. 73

Table 4.17 Labor cost of soybean production for producers .............................................. 74

Table 4.18 Value addition and margin of producers ........................................................... 75

Table 4.19 Margin & profit shares of actors along soybean value chain ............................ 76

Table 4.20 Gross margin following marketing channel 3 ................................................... 76

Table 4.21 Distribution of value addition among major actors .......................................... 77

Table 4.22 Production cost of soybean oil processor .......................................................... 78

Table 4.23 Value addition & margin by soybean grain traders & processor ...................... 79

Table 4.24 Value addition and margin by soybean oil traders ............................................ 80

Table 4.25 Regression results of factors affecting quantity of soybean market supply ...... 81

Table 4.26 Probit estimation of factors influencing value addition .................................... 86

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LIST OF FIGURES

Figures Page

Figure 2.1 Models of porter’ value chain (Porter, 1985) .................................................... 12

Figure 2.2 Trends of soybean production, area coverage & yield in (Ethiopia 2007-2017 13

Figure 2.3 Import value trends of soybean & Soy Sause (2007-2016) ............................... 14

Figure 2.4 Export value trends of soybean & soy Sause (2007-2016)................................ 14

Figure 2. 5 Ethiopian soybean export (2017/18) ................................................................. 15

Figure 2.6 Conceptual frame work of the study .................................................................. 22

Figure 3.1 Geographical location of the study area (Pawe district) .................................... 24

Figure 3.2 Sampling procedures of sample respondents ..................................................... 26

Figure 4.1 Quantity of soybean oil imported and domestically produced, 2019 ................ 59

Figure 4.2 Quantity of soybean grain exported & domestic consumption for processing .. 60

Figure 4.3 Value Chain Actors, Functions and Support service providers ......................... 67

Figure 4.4 Map of soybean value chain .............................................................................. 68

Figure 4.5 Soybean value chain marketing channels .......................................................... 71

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LIST OF TABLES AND FIGURES IN THE APPENDICES

Appendices Page

Appendix Table 1. Livestock conversion factors .............................................................. 102

Appendix Table 2. Test of multicollinearity for continuous explanatory variables ......... 102

Appendex Table 3. Contigency coefficient for dummy/categorical variables .................. 103

Appendix Table 4. ANOVA table for F-statistics ............................................................. 103

Appendix 5. Questionnaires and interview guides for different stakeholders ................. 103

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ABBREVIATIONS AND ACRONYMS

AGRA Alliance for the Green revolution in Africa

ANOVA Analysis of Variance

ATA Agricultural Transformation Agency

BGRS Benishangul Gumuz Regional State

CBSM Community Based Seed Multiplication

DA Development Agent

ECX Ethiopian Commodity Exchange

EIAR Ethiopian Institute of Agricultural Research

FAO Food and Agriculture Organization

FGD Focus Group Discussion

FTC Farmer’s Training Center

IFAD International Fund for Agricultural Development

IITA International Institute of Tropical Agriculture

MBI Menagesha Biotechnology Institute

MLR Multiple Linear Regression

OLS Ordinary Least Square

PARC Pawe Agricultural Research Center

PLC Private Limited company

SPSS Statistical Package for Social Science

SSA Sub-Saharan Africa

TLU Tropical Livestock Unit

TVET Technical Vocational and Education Training

UNCTAD United Nations Conference on Trade and Development

UNIDO United Nations Industrial Development Organization

USA United States of America

USAID United States Agency for International Development

VCA Value Chain Analysis

VIF Variance Inflation Factor

WBCSD World Business Council for sustainable development

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Value Chain Analysis of Soybean: The Case of Pawe District,

Northwestern Ethiopia

ABSTRACT

This study focused on analysis of soybean value chain in Pawe district of Metekel zone with

specific objectives of mapping of actors in the value chain, marketing margin analysis,

determinants of soybean market supply and value addition. Although there is untapped

potential on soybean, the current production status is unable to meet its rapidly increasing

demands for export and domestic processing. This implies that there is a high production

gap to satisfy market demands. The crop has been provided to the market without adding

significant values besides production constraints and that is why the expected return could

not be realized. Primary data were collected from 228 farmers, 23 traders and 15 consumers

through the interview schedule. Descriptive and inferential statistics, marketing margin,

multiple linear regression and Probit models were used to analyze the primary data. Results

show that input suppliers, farmers, local traders, whole-sellers, cooperatives, ECX,

exporters, processors, retailers, and consumers were the main value chain actors in the

study area. Local-traders received the highest profit margin (377.75 birr per quintal) and

this implies that intervention is needed to increase the net profit share of producers by

experts and concerned bodies. Soybean meal and hulls contributed 60-62% of total revenues

and that is why soybean meal is regarded as the main driving force for soybean oil

production industries. Results of multiple linear regression model indicate that productivity,

lagged price, market information, soybean farm experience, cultivated land, credit

utilization and extension contact influenced the quantity of soybean market supply posively

and significantly. Results of Probit model also indicate that age, quantity produced, market

price and Packaging material influenced the likelihood of farmers to add values on soybean

positively and significantly and emphasis has to be given for each significant variable.

Therefore, provision of improved soybean technologies with full recommended packages to

producers and strengthening of linkages among actors will realize a sustainable production

with significant value addition and viable value chain on soybean.

Key words: Margin, Multiple linear regression, Pawe, Soybean, Probit model, Value Chain

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Chapter 1. INTRODUCTION

1.1 Background and Justification

Soybean (Glycine max.) is the major legume crop in the world and belongs to the

Leguminosae family. It was originated in east Asia and recognized as a food crop for the

first time in North-eastern China around 1700-1100 B.C (UNCTAD, 2016). Currently,

soybean oil is the 2nd most important vegetable oil after palm oil and accounts for 25% in

world’s oil production (Urgessa Tilahun, 2015). The global average production volume of

soybean reached 557.5 million metric tons per annum over the last eight years. USA, Brazil,

Argentina, China, and India are the five leading soybean producers in the world. USA

(34%), Brazil (30%), and Argentina (18%) these three countries account for 82% of the

overall soybean production worldwide (ECX, 2017). USA is the leading in soybean

production and successful value chain with annual production of more than 89.8 million

metric tons (United Soybean Board, 2012). The ultimate objective of the value chain is to

produce a value-added product for a market (Trienekens, 2011). The value chain is so

important in this era of rapid globalization to penetrate global markets successfully and to

enhance the efficiency of actors (Gereffi &Fernandez, 2011).

Soybean was first introduced to SSA by Chinese traders in the 19th century and cultivated

as an economic crop in the early 1903 in South Africa (Khojely, et al., 2018). Soybean

industry is expanding in Eastern & Southern Africa. Although world population growth at

a slower rate, in Eastern and Southern Africa, is alarming and this will be a major driver of

the economy in these countries (Meyer, et al., 2018). South Africa is the leading soybean

producer in SSA (Byrne, 2018). The global share of soybean production in all African

countries is less than 1% (Varia, 2011). South Africa and Zambia are successful in soybean

value chain since they have a commercial production system with high processing capacity

industries. There is a strong linkage and fair distributions of benefits among actors in the

value chain. Actors are well informed about the price of domestic as well as international

markets (Meyer, et al., 2018).

Soybean was introduced to Ethiopia for the first time in the early 1950s (Shurtleff & Aoyagi,

2009). Although soybean is a recent introduction to Ethiopia, land size and production

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increased from 6352 - 39021 hectares and 58,490-840,330 quintals respectively from 2007

to 2017 consecutive years (FAOSTAT, 2019). The average production volume of the crop

increased by 37% per annum in the last ten years (ECX, 2017). The main producing areas

are in the western parts of the country in Oromia, Benishangul Gumuz, and Amhara region

(Byrne, 2018). Soybean has been produced by smallholders and commercial farmers each

have a 50% account in the national production (Lehr & Sertse, 2018). However, the overall

performance of the value chain of soybean is still not successful due to some reasons

(Kumilachew Achamyelh et al., 2020).

In Benishangul Gumuz, Soybean was introduced during the massive resettlement program

of the Dreg regime in 1985 (Addisu Getahun & Erimias Assefa, 2016). The crop has been

produced in all three administrative zones of the region namely Assosa, Metekel, and

Kamashi. As BGRS 2018/19 report, 836, 754 quintals of soybean were produced by

cultivating 31,670 hectares of land. But the region has 156,000 hectares of land which is

suitable for soybean production (Musba Kedir, 2019). Metekel is the highest potential zone

in soybean production and produced 550,112 quintals from 14,399 hectares in the 2018/19

production season. Producers, unions, local traders, regional traders, brokers, central whole-

sellers, exporters, and consumers were the main soybean value chain actors in Metekel as

well as the region (Addisu Getahun & Erimias Assefa, 2016). But the value chain was not

successful as a result of some reasons and that is why insignificant value is added on

soybean.

Pawe district is the major soybean producer among Metekel zone districts. Almost all

farmers are producing soybean in most parts of the district. More than 35% of cultivated

land was allocated for soybean production during 2015/16 cropping season (Birhanu

Ayalew, et al., 2018). According to Pawe agriculture office report (2019), the district

covered 7109.6 hectares of land by soybean and produced 122,973 quintals in 2018/19

production season. Almost all producers in the district are chain actors in the value chain

and cannot influence the selling price that is why they are price takers. Hence, the study was

initiated to fill the knowledge gaps on margins, factors affecting soybean market supply,

and farmers’ participation in value addition as well as the roles of actors in the value chain.

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1.2 Statement of the Problem

In the current situation of the soybean production system in Ethiopia, development and

research gaps were identified in different parts of the country. Metekel Zone is the leading

zone of Ethiopia in soybean production and contributes 65.7% of the region & 24.69% of

the national production (BGRS & FAOSTAT, 2018). Pawe is the major soybean producer

among Metekel zone districts. The quantity of soybean market supply is increasing over

time due to the rising price of soybean (Lehr & Sertse, 2018). As a result, land size and

production of soybean increased from 6352-39,021ha and 58,490-840,330 quintals

respectively in the 2007-2017 fiscal years (FAOSTAT, 2019).Besides soybean dishes like

bread, Kukis, milk, and oil are becoming the common food items in the study area and that

is why the price and demands of soybean are increasing with time.

Apart from its food and market values, its byproducts are important for, fattening, dairy, and

poultry production which are the main job opportunity areas for unemployed youths in the

study area. For these activities, feed is the major input and soybean is the best and preferred

ingredient for feed formulation since the crop is highly reached in protein (Urgessa Tilahun,

2015). This implies that soybean is highly compatible with the crop-livestock farming

system. Two big edible oil factories that can be used soybean as a major input are also under

establishing at Bure town and Addis Ababa. These factories have been designed with high

processing capacity to cover the domestic oil consumption through import substitution.

However, the current production status of soybean across the country is unable to meet all

the above demands.

Besides to production constraints, soybean products are provided without adding significant

values since all producers in the study area are chain actors. They simply produce and sell

their product without influencing the selling price (Addisu Getahun & Erimias Assefa,

2016). Products without significant value addition couldn’t able to compete in the domestic

as well as international markets and it is difficult to realize a viable economic growth. On

average, less than 10% value is added to agricultural products in Ethiopia (Byrne, 2018).

However, developed and developing countries added US$185 & US$40 value respectively

by processing a tone of agricultural products (UNIDO, 2009). In Kenya, 30-290 Kenyan

shilling (8.19-79.21 ETB) value has been added by processing 1-kilogram soybean seed

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(Nyongesa, et al., 2018). Although soybean can be processed into different feed and food

items, the existing domestic soybean processors are unable to satisfy the consumption needs.

The increased demand for soybean for local processing has led to importing the crop starting

in 1995 (FAOSTAT, 2019). According to this report, the quantity of soybean imports

increased from 172-12,630 quintals (0.151-2.513 million USD value) from 2007-2016

consecutive years. Additionally, Ethiopia has paid more than 320 million birr for importing

byproducts of soybean which shows that nearly 400 million birr is paid annually for

importing grain & byproducts (Mekonnen Hailu & Kaleb Kelemu, 2014; FAOSTAT,

2019). This has been a cause for the emergence of different value chain actors being

involved in soybean production, marketing, and processing (Addisu Getahun & Erimias

Assefa, 2016). Development initiatives have been undertaken by the government extension

programs and development partners to increase soybean production to satisfy the demands

of domestic processing and to promote export. Research centers designed research programs

on soybean to improve its production status. CBSM is one of the research projects to reduce

seed shortage problems for soybean producers across the country. AGRA and N2-Africa

projects also invest in soybean variety development and technology promotion to improve

the status of soybean production.

The problem of unable to meet the domestic demand both in soybean grain & byproducts

can be associated with soybean production and marketing along the value chain includes

lack of knowledge, lack of improved technologies, low and improper extension service,

input scarcity, market access and price of the commodity (Urgessa Tilahun, 2015).

Researches conducted related to soybean value chain is scanty. Most researches and

literature in the past focused on breeding, production, and some on marketing. A study was

conducted on the assessment of soybean value chain in Metekel Zone (Addisu Getahun &

Erimias Tefera, 2016). However, the study emphasized on constraints of soybean

production by using 15 respondents from each district which couldn’t reflect the whole

population. Similarly, Soybean value chain analysis was conducted at Buno Bedele Zone

(Esayas Negasa & Mustefa Bati, 2019). Analysis of cost and return of soybean also

conducted at Assosa Zone and Pawe district of Metekel Zone (Birhanu Ayalew, et al., 2018

& Afework Hagos and Adam Bekele, 2018). All these studies indicated that the determinant

factors that affect soybean supply to the market and value addition as well as profit margins

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of each actor along the value chain needs future investigation. Overall, such issues have not

been well studied and documented in the study area and that is why this study was initiated

to fill the knowledge gaps on the determinant factors affecting soybean supply and value

addition as well as profit margins of all actors and their roles along soybean value chain in

Pawe district of Metekel zone.

1.3 Objectives of the Study

The general objective of the study is to analyze soybean value chain in Pawe district of

Metekel zone.

Specific objectives

1. Mapping of actors in soybean value chain in the study area

2. To analyse marketing margin of actors in soybean value chain

3. To analyze the determinants of soybean supply to the market in the study area

4. To determine factors affecting value addition on soybean

Research questions

1. What are the main soybean value chain actors and their roles in the study area?

2. Who is benefited more in the value chain? What is the profit share of each actor in

the value chain?

3. What are the determinant factors that affect soybean supply to the market in the

required quantity in the study area?

4. What influences producers from adding value to soybean before providing to the

buyers?

1.4 Significance of the Study

This study provides information on the roles of direct and indirect actors, marketing margin,

and benefit shares of actors along soybean value chain. The study also provides information

on determinants of quantity of soybean market supply and farmers’ participation in value

addition. The result of the study is helpful for soybean producers and traders to make the

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appropriate decision regarding soybean production and marketing in the study area. The

information generated in this study can also helpful for development organizations, research

institutions, extension service providers, government, and non-governmental organizations

to formulate soybean value chain development programs and guidelines for interventions

that would improve the efficiency of soybean value chain analysis in the study area. Besides,

the findings of this study can be used as a source for further investigations.

1.5 Scope and Limitation of the Study

This study focused on soybean value chain from input suppliers to end-users in the study

area. The study was conducted in Pawe district and important information was collected

from sampled households and other value chain actors involved in the study area and from

Addis Ababa. However, there were spatial and temporal limitations to make the study more

representatives in terms of wider area coverage and time horizon. For this study, data were

collected from Addis Ababa soybean processor and other traders besides to Pawe district.

To collect the required data on time from each actor particularly at Addis Ababa was so

challenging due to the COVID-19 pandemic. This study incorporated soybean oil processor

and other feed and food processors not incorporated due to time limitation as a result of

COVID-19 and this makes difficult to conclude the whole soybean processors at the country

level. Although COVID-19 challenges, the researcher tried to collect the data by

communicating with the sponsoring institution by writing official letters particularly to big

institutions (Health Care Food Manufacturer plc, central ECX, and Ministry of Trade and

Industry).

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Chapter 2. REVIEW OF RELATED LITERATURE

2.1 Definition and Basic Concepts

2.1.1 Concepts related to value chain

Value chain: The value chain is the full range of activities that are required to bring a

product or service from conception, through different phases of production and processing,

delivery to final consumers, and final disposal/recycling (Ponte, 2014, p. 2). The value chain

describes the full range of activities that firms and workers perform to bring a product from

its conception to end-use and beyond (Gereffi &Fernandez, 2011, p.7). The term value chain

refers to the full life cycle of a product or process including material sourcing, production,

consumption and disposal/recycling processes (WBCSD, 2011). A value chain is the set of

input activities that a company carries out in order to create value for its valued customers

(Porter, 1985).

Value-addition: Value added is a measure of the value created in the economy (IITA-IFAD,

2010). Value-added ideally represents the value created during the manufacturing process

conducted by each industrial establishment. It is measured as the difference between the

value of all goods and services produced and the value of those purchased non-labor inputs

which have been used in the production process (UNIDO, 2009). Value addition is the

difference between output value and the cost of raw material and other inputs in processing

(Surni, et al., 2019).

Value chain actors: Actors are all the individuals or organizations, enterprises, and public

agencies related to a value chain and therefore important for understanding the functioning

and performance of the value chain. Value chain actors are those who are actually directly

involved in value chain activities. Supporting actors can play an important role, but they are

not directly involved in value chain activities (Christian & Barron, 2017). Value chain

supporters provide support services and represent the common interest of the value chain

operators (Engida Gebre et al., 2019). Input suppliers, producers, harvesters, consolidators,

processors, and exporters are the most important value chain actors (FAO, 2018).

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2.1.2 Value chain analysis

Value chain analysis is the process of breaking a chain into its constituent parts in order to

better understand its structure and functioning. The analysis consists of identifying chain

actors at each stage and discerning their functions and relationships (UNIDO, 2009, p. 4).

Value chain analysis is an effective way to examine the interaction among different players

in a given industry (Zamora, 2016). Value chain analysis is a valuable tool to investigate the

role that value chains can play in achieving specific policy objectives, such as poverty

alleviation, sustained growth, and inequality reduction. VCA is the assessment of a portion

of an economic system where upstream agents in production and distribution processes are

linked to downstream partners by technical, economic, territorial, institutional, and social

relationships (Bellu, 2013). Value chain analysis is about understanding how activities and

actors that are involved in bringing a product from production to consumption are linked

(Stein & Barron, 2017). VCA is used to identify where the firm can increase the value or

reduce costs to the customer at each stage of the value chain (Simatupang et al., 2017).

2.1.3 Value chain mapping

Value chain mapping can be an important means to better understand what opportunities

and/or constraints producers face if they are to benefit from participating in value chains.

The combination of value chain mapping with visual network research approaches and

participatory statistics has the potential to complement existing value chain analysis

approaches and to generate new insights that would be difficult to obtain using traditional

questionnaire surveys alone (Christian & Barron, 2017). Value chain maps provide an easily

digestible way to understand the process and pathways to production and sale by illustrating,

in a simple form, complexities of an industry sector and its value chain (Kerr, et al., 2015).

2.2 Theories of Value Chain Approaches

Different theories of value chain approaches have been developed by different scientists in

different countries which are talking about value chain from different perspectives.

However, all these approaches were not mentioned here for this study. Hence, theories of

value chain approaches that have some contributions to this study in one or another way

were discussed.

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2.2.1 The Filiere concept

The main objective of this approach is to map out actual commodity flows and to identify

agents and activities which is viewed as a physical flow-chart of commodities and

transformations. It deals more directly with issues of trade and marketing. This approach

focuses on empirical analysis and has mainly attempted to measure inputs and outputs,

prices, and value-added along a commodity chain. It mostly focuses on local or national

level chains which are less functional to analyze the global world economy (Raikes, et al.,

2000). The filiere approach is seen by many adherents as a neutral and purely empirical

category.

2.2.2 The Porter approach

The term value chain was introduced by Michael Porter for the first time in 1985 in the book

of competitive advantage. Porter developed modern value chain analysis as an instrument

for identifying the value of each steep of the production (Porter, 1985). This approach is

used as an ultimate tool for analyzing the value formed at each stage of the production

process. It focused on actual and potential areas of competitive advantage for the

organization. According to Porter, the value chain is used to analyze the flow of value-

adding activities from the raw material supplier to the end customer. However, this approach

fails to focus on the interconnections and relationships between vertically grouped actors.

2.2.3 Global value chain analysis

Global value chain analysis originates from the commodity chain approaches and focuses

on the position of the lead firm in value chains and power relationships between developing

country producers and western markets or multi-national companies. In this theoretical

stream of power relationships and information, asymmetry is key concepts in the analysis

of global value chains (Roko & Opusunju, 2016). Global value chain analysis provides a

holistic view of global industries both from the top-down and from the bottom-up (Gereffi

& Fernandez, 2011).

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2.2.4 Global commodity chain approach

This approach has been primarily developed for industrial commodity chains. It is a concept

that is mainly focusing on the power relations in the coordination of dispersed but linked

production systems (Gereffi, 1999). The main intension of the global commodity chain

approach is governance relationships between actors in the value chain. One of the major

hypotheses of this approach is that development requires linking up with the most significant

lead firms in an industry. A strong point of the global commodity chain is its inclusion of

power in economic relations and transactions and the willingness to include the aspects of

power excluded from other analyses of international production and trading relations

(Raikes, et al., 2000). This approach has generated little quantitative analysis and its

conceptual structure and definition would need further elaboration.

2.2.5 Global production networks approach

The global production network/GPN/ is a concept in developmental literature that refers to

the nexus of interconnected functions, operations, and transactions through which specific

products and services produced, distributed, and consumed. It is a conceptual framework

that is capable of grasping the global, regional, and local economic and social dimensions

of the process. These frameworks combine the insights of the global value chain analysis at

network theory and literature are providing of capitalism. The global production network

provides the relationship of a framework that aims to encompass all the relevant actors in

the production systems. The concept combines a sequence of interconnected activities in the

process of value creation. This approach is a direct refinement of the global commodity

chains/GCC/ approach. It allows for far greater complexity and geographical variation in

producer-consumer relations than the global commodity chain approach (Henderson, et al.,

2002).

2.2.6 Social network theory

This theory focuses on the inter-relationships between economic and social interactions in

production networks composed of multiple horizontal and vertical relationships between

value chain actors. The vertical dimension of network theory reflects the flow of products

and services from the primary producer up to the end-consumer and its horizontal dimension

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reflects relationships between actors in the same chain link (Trienekens, 2011). This theory

can be used as a tool to explain the performance of value chains (Mapanga, et al., 2017). It

views companies as embedded in a complex of horizontal, vertical, and business value chain

relationships with other companies and organizations supporting inputs and services (Roko

& Opusunju, 2016).

Overall, a combination of filiere, Porter, and global production network approaches and

social network theory guided this study to meet the studded objectives. As explained above,

the filiere approach shows the physical flow of inputs and outputs and focused on empirical

analysis of the value chain with national boundaries. The flow of value-adding activities

starting from a raw material supplier to end customer can be analyzed by using a value chain

according to the Porter. The concepts of global production networks also combine a

sequence of interconnected activities in the process of value creation from the local to the

global levels. On the other hand, social network theory focuses on inter-relationships

between economic and social transactions along the value chain from the vertical and

horizontal perspectives of actors. Hence, the stated objectives were addressed under the

guides of these combined approaches. But, the global commodity chain and global value

chain analysis approach less contributed to guide this study.

2.3 The Basic Model of Porters’ Value Chain Distinction

Porter makes a clear distinction between primary and support activities. Primary activities

are directly concerned with the creation or delivery of a product or service (Porter, 1985).

According to porter, primary activities can be grouped into five main areas which are

inbound logistics, operations, outbound logistics, marketing and sales, and service.

Procurement, technology development, human resource management, and infrastructure are

the four main areas of support activities. Each of these primary activities is connected to

support activities that help to improve their effectiveness or efficiency.

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Figure 2.1 Models of porter’ value chain (Porter, 1985)

The term ‚Margin’ here implies that organizations realize a profit margin that depends on

their ability to manage the linkages between all activities in the value chain. In other words,

the organization is able to deliver a product/service for which the customer is willing to pay

more than the sum of the costs of all activities in the value chain (Porter, 1985).

2.4 Status of Soybean Production in Ethiopia

Ethiopia is the potential country in soybean production in Eastern Africa (FAOSTAT,

2019). According to this report, production trends and area coverage of soybean commodity

have been increased over time even though not significant as compared to its potential. As

indicated in Fig. 2.2, production has been increased from 58,490 quintals to 840,330 quintals

in the last ten years. In the same consecutive years, productivity has been increased from

9.2qt/ha to 21.5 qt/ha. This data indicates that the quantity of soybean produced in Ethiopia

was underestimated. Because of more than 700,000 quintals of soybean produced in that

particular production season in Benishangul Gumuz region excluding the Amhara and

Oromiya regions. According to the data collected from the Ministry of Trade and Industry

and health care food manufacturer plant, 942,038.10 quintals of soybean were exported and

75,000 quintals were used for domestic soybean oil production in the 2019 fiscal year. This

indicates that 1,017,038.10 quintals of soybean were produced in the 2018/19 production

season in the country excluding the local consumptions for seed and different soybean

dishes.

Infrastructure

Human resource management

Technology development

Procurement

Inbo

und

logis

tics

Opera

tions

Outbou

nd

logistics

Marketing

& sales Service

Margin

Margin

Support

ive

Act

ivit

ies

Primary Activities

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Figure 2.2 Trends of soybean production, area coverage & yield in (Ethiopia 2007-2017

2.5 Ethiopian Import-Export Trends of Soybean and Byproducts

Ethiopia started soybean export grain in 2004 (Mekonnen Hailu & Kaleb Kelemu, 2014).

India, China, Vietnam, Canada, and Pakistan are the five most destination countries for

soybean export (Byrne, 2018). According to this report, 59,042 metric tons of soybean were

exported in the 2017/18 production period. However, according to Mekonnen Hailu &

Kaleb Kelemu (2014), most of the demand for soybean for local processing as well as

consumption as a byproduct has been covered through import. As the results of this study,

there were about 138,000 quintals trade deficit in the country which is the difference

between import and export of the commodity. As Fig. 2.3 and Fig. 2.4 show below, Ethiopia

exporting soybean grain only but the byproduct export almost null up to now. This indicates

that the country is exporting the grain without adding significant values and import back it

again as byproducts for domestic consumption by spending on average 400 million birr per

annum (Mekonnen Hailu & Kaleb Kelemu, 2014; FAOSTAT, 2019). This is huge money

in Ethiopian capacity and it can be invested in other development activities if the domestic

processors capacitated enough to meet domestic consumption.

0

200

400

600

800

1000

1200

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Area covered ('000 ha) Production ('000 Qt) Productivity (Qt/ha)

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Figure 2.3 Import value trends of soybean & Soy Sause (2007-2016)

Figure 2.4 Export value trends of soybean & soy Sause (2007-2016)

0

1000

2000

3000

4000

5000

6000

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

Soybean grain import value ('000 USD)

Soy sause imoprt value ('000 USD)

0

5000

10000

15000

20000

25000

30000

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

Soybean grain export value ('000 USD)

Soy sause export value ('000 USD)

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Source: (Byrne, 2018)

Figure 2. 5 Ethiopian soybean export (2017/18)

2.6 Soybean Value Chain in Ethiopia

The soybean value chain in Ethiopia has not been successful due to weak linkages among

actors and inefficiency of each actor in the value chain. Insignificant value is added at each

stage of the value chain (Kumilachew Achamyelh et al., 2020). The main obstacle to more

value-added production lies in access to finance, particularly in the skills necessary to access

investment and finance and an acceleration program for entrepreneurs in value-added

production could be a good way to remove this obstacle (Lehr & Sertse, 2018). Currently,

ATA developing a value chain map for linking the commodities to agro-industrial parks,

and soybean is included as one of the value chains at Bure-Agro-industrial park (Lehr &

Sertse, 2018). According to this report, in the national pulse strategy developed recently,

soybean is one of the priority pulses under the ministry of agriculture due to its high demand

in the domestic as well as international markets. However, limited production of the

commodity has put as a threat in the overall process of the soybean value chain.

2.7 The Relevance of Value Chain to the Poor

Agriculture continues to play a central role in economic development and poverty reduction

in many parts of the world. However, agriculture alone will not be sufficient to tackle

26,576

20,284

5,148 2,680 1,386 1,364

57,438

1,604

59,042

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

Vo l u me o f S o y b ea n ( Mt s )

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poverty and inequality problems that are so pervasive in today’s world. It is crucial for

policy makers to focus on immediate attention on agro-industries. Agro-industries

established along efficient value chains can increase significantly the rate and scope of

industrial growth. In addition, the marked trend to break down production processes into

specific tasks opens up new opportunities for developing countries to specialize and take a

more profitable part in global trade provided they meet increasingly stringent market

requirements (UNIDO, 2009). VCA can be a practical way to help the rural poor to

participate advantageously in local, regional and global trade. It begins by explaining why

value chains have emerged as a helpful entry point for discussions on rural poverty. By

pulling together lessons learned on using value chain analysis and development effectively

as a tool to enhance the incomes of poor people in rural areas ( Mitchell, et al., 2009).

2.8 The Food System Framework

A key characteristic of the food system is the extensive linkages, interdependencies, and

feedback loops between value chain stages and the wider environment, society, and

economy. For example, the food system is dependent on natural resources and has a

significant impact on the global environment. The food system also has a major influence

on human health and is an important global source of employment and economic value. It

has also cultural significance in many societies. Growing environmental pressures,

including climate change, soil degradation, disruption of water cycles, expanding pathogen

ranges, and increasing regularity of extreme weather events, coupled with population

growth and migration impact on and will continue to affect the food system (Gregory, 2016).

2.9 Review of Empirical Studies

2.9.1 Main value chain actors and their roles

Value chains are a way of understanding the interaction of people and firms in the domestic

as well as global markets. Primary actors perform a selection of primary functions along the

value chains. These typically include input supply, production, processing, storage,

wholesale, retail, and consumption. Actors who perform similar functions are regarded as

occupying the same functional node, for example, the input supply node, production node,

retail node, and so on. Secondary actors or support service providers perform supporting

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activities for primary actors. Support service providers facilitate the overall process of

primary functions to realize the objectives of primary actors (Mitchell, et al., 2009).

Value chain actors can be categorized as input suppliers, direct market actors, and enablers

as the study conducted on chickpea value chain in Southern Ethiopia (Tewodros Tefera,

2014). The study conducted at Metekel zone indicated that producers, traders,

cooperatives/unions, brokers, central whole-sellers, processors, exporters, and consumers

are the main soybean value chain actors (Addisu Getahun & Erimias Assefa, 2016).

Table 2.1 Soybean value chain actors and their functions

Actors Functions

Producers perform & Manage overall farm level production process

Keeping the quality of their product before delivery

Delivery their product to local as well as district traders

Local traders Collect, measure, & pack the product by paying cash on the delivery

Store grain and delivery to local whole sellers

Sell seeds to local consumers

Regional

traders Pay cash on the delivery to collectors and farmers

Delivery the collected product to central whole sellers & processors

Brokers Receive the product transferred from local & regional whole sellers

Facilitating the process of selling the product

Negotiate with the buyers about the selling price

Central whole

sellers Negotiate with the commission agents

Pay cash to the commission agents on the delivery

Export or sell the received product to processing factories

Exporters Maintain the quality of the product and pack it

Deal with export clearance

Pay necessary fees for export

Export the product and remit income

Processors Buying the product from producers and whole sellers

Process the product in to different feeds and foods

Sell the processed products to retailors, supermarkets or consumers

Consumers Consumption of the processed products

Source: Addisu Getahun & Erimias Assefa (2016)

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2.9.2 Marketing margin and profit shares in the value chain

Marketing margin is commonly used to measure the performance of the marketing system.

It is associated with the selling price of the producers and the price paid by the end

customers. This indicates that the total gross marketing margin is the difference between the

price received by producers and paid by the final consumers. Gross marketing can also be

calculated by subtracting the purchase price from the selling price at each consecutive stage

of the marketing channel. Most of the time the shortest marketing channel is preferred by

the consumers since it reduces the purchase prices by reducing the numbers of

intermediaries in the market. The marketing and profit margins of each actor in the market

are different along different marketing channels (Wondim Awoke and Dessalegn Molla,

2018). According to this study, processors incurred the highest cost since they are

performing more value-adding activities among traders in the market. Producers can receive

relatively good gross profit as compared to traders (Nugusa Abajobir, 2018). As the results

of this study, producers received an average gross profit of 378.7 birr per quintal along

different maize marketing channels. However, this may not be always true in the marketing

system. As the results of some studies, the profit margin of producers is calculated without

considering the cost of family labor and that is why the profit margin of producers inflated.

A higher market share determines the profitability of marketing actors (Saripalle, 2018). It

depends according to the performance of actors and market efficiency. Market

characteristics and technological capabilities of actors are the determinant factors to get a

better profit share in the market (Porter, 1985). Input logistics, scientific and technological

development, and purchase price are significant factors for value chain profitability and

profitability (Strakova, et al., 2020).

2.9.3 Determinants of market supply

There are several factors that influence market supply as well as participation in the market.

Age of household head, distance to the nearest market, distance to the urban market, literacy

level, contract farming, access to training, and extension services have significant effects on

market supply and participation (Taye Melese et al., 2018). According to this study

conducted at South Gondar zone, the results of the Heckman selection -stage two model,

age of the household head negatively and significantly influences market participation and

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it implies that market supply is reduced as age increased. The marginal effect also indicates

that the probability of participating in the market decreases by 0.2% as the age of the

household increased by one year. Similarly, the above-mentioned factors have a significant

influence on the overall process of market supply as well as participation.

The amount of income received from off-farm, yield, sex of the household, farming

experience, and family size have a significant impact on supplying maize products to the

market (Nugusa Abajobir, 2018). According to the results of this study conducted on value

chain analysis of maize in the Horro Guduru Wollega zone, all the above factors

significantly affect maize supply to the market. The quantity of maize supply increases by

1.404 quintals as the income received from off-farm increases by one birr. Maize supply

increases by 0.149 quintal for a year increase in farm experience. Similarly, the quantity of

maize supply increases by 0.521 quintal as productivity increased by one quintal. The

finding by Almaz Giziew (2018) also confirmed that the quantity of onion market supply is

increased by 0.0185 quintal as the farm experience of onion producers increased by one

year.

Quantity of the commodity produced, landholding size, numbers of livestock owned and

family size of the household are the determinant factors that influence the amount of the

product supplied to the market (Sultan Usman, 2016). According to the results of this study,

all the above factors have a significant impact on wheat supply to the market. As the results

of the robust regression of the OLS model, the amount of wheat supplied to the market

increases by 0.623 quintal as the quantity produced is increased by one quintal. Similarly,

the number of wheat supply increases by 4.25 quintals and 0.37 quintal as farmland size and

numbers of livestock owned increased by 1 hectare and 1TLU. The wheat supply decreases

by 0.05 quintal if the household size increases by one. As the study conducted at South

Gondar zone and Horo Guduru wpllega zones, quantity of teff market supply and family

size negatively corelated (Tadie Marie & Lema Zemedu, 2018; Edosa Tadesa, 2018). the

study conducted at Southern Ethiopia also indicates that quantity of sesame market supply

decreased by 0.24 quintal as distance to the nearest market incresead by a kilometer

(Dagnayegbaw Goshme et al., 2018).

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2.9.4 Factors affecting value addition decision

The quantity of value-added can be calculated by subtracting the value of raw material and

other inputs used in the processing from the output value (Surni, et al., 2019). Value-added

agriculture is a movement that has created a life of its own. It greatly focuses on production

or manufacturing processes, marketing, or services that increase the value of primary

agricultural commodities, perhaps by increasing appeal to the consumer and consumers’

willingness to pay a premium over similar but undifferentiated products (USDA, 2014).

Agriculture is one of the main sectors to realize economic growth, particularly for

developing countries. Because it has huge resources like arable land and surface and

underground water for production. These opportunities foster to improve productivity.

Recently, studies are trying to give more emphasis on human development in order to add

value to the agriculture sector in selected developing countries through the panel data

method and the results indicate a meaningful effect on vale addition to this sector (Badri, et

al., 2017).

The results of the OLS econometric model show that education, health, domestic credit to

the private sector, and gross fixed capital formation have a positive effect on value addition

to the agriculture sector (Badri, et al., 2017). As a result of this model, a unit increase of

expenditure to education leads to 0.31 value is added to agriculture. Similarly, a unit

increase in expenditure for health care services leads to a 0.24 value addition to this sector.

Agriculture is one of the economic growth sectors for cooperatives. Cooperatives can add

value to agriculture products if the conditions are suitable and there are different factors that

influence the cooperatives in the overall process of value addition. Capital grants to help

finance, the ability of the board of directors, specialization in marketing, and the absence of

investment-friendly institutional arrangements have a significant positive effect on value

addition to the agriculture sector (Esnard, 2016).

The study conducted at Assosa and Kamashi zone indictates that the likelihood of faremers

to add values to soybean is influenced by disease incidence due to yield loss quality

deteorartion (Minyahil Kebede& Assefa Gidesa, 2016). Market destination, existing

government policies, strategic decisions, and Personnel skills are the main influencing

factors for value addition on tea (S. Grace & Fridah, 2016). The size of the value-added is

decided by the willingness to pay by the end customers (Porter, 1985). Sufficient finance,

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credit facilities, and equitable government tax are important and significant factors for value

addition to soybean (Otu & Okibeya, 2018). Availability of packaging materisls positively

and significantly influenced value-addition (Obute et al., (2019). Astudy conducted at

Assosa zone showed that availability of good storage condition positively and significantly

influenced soybean value-addition (Afework Hagos & Adam Bekele, 2018).

Overall, researches conducted in related to soyabean value chain is scantity and most

researhces in the past focused on breeding, production and to some extent on marketing.

They emphasized on production constraints of soybean. Some studies also conducted on

marketing and marketing and profit margins of marketing actors were estimated. However,

as the results reviewed from the literature, marketing and proft margins were calculated

without considering the cost of family labor in the production stream which causes to inflate

the profit and marketing margins of producers.

Issues related to determinants of soybean supply and farmers participation on value addition

as well as profit margins of value chain actors were not investigated in the study area and

Metekel zone. One study was conducted on cost and return analysis of soybean under

smallholder farmers in Pawe during 2015/16 production season. On this study, only proft

margins of farmers was estimated and it is difficult to know which actor is benefited more

in the chain and difficult to take an intervention based on scientific evidences that is why

this study was initiated to fill the knowledge gaps on such issues.

2.10 Conceptual- Framework of the Study

The ultimate goal of promoting the agribusiness value chain is to improve the

competitiveness of agriculture at the national and international levels for the purpose of the

market as well as consumption. In the overall processes of production, marketing, and

consumption as well as waste management, many value chain actors are involved. The

process starts from the input supply and passes through production up to the final stages. In

addition to these direct actors involved in the chain, there are also support service providers

who have an indirect role along the value chain. The numbers and types of producers affect

the quantity of soybean market supply. If the system is well functional, sustainable

production can exist. Socio-economic and institutional factors influence the quantity of

market supply and value addition as well as the fair distributions of benefits/profit shares

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for each actor in the value chain. In order to improve soybean production and value chain

in the study area, there is a need to identify the determinant factors for market supply, to

analyze the factors affecting value addition of soybean, and analyzing the marketing margin

of actors in Pawe Woreda of Metekel zone. Accordingly, the following figure has been

drawn to show the conceptual framework of the soybean value chain in the study area.

Source: Own sketch by reviewing different literatures, 2018

Figure 2.6 Conceptual frame work of the study

Soybean Value chain

Socio-economic factors

• Travel time (hrs.)

• Lagged price

• Off/non-farm

• Lagged price

• Coops.membership

• Land size

• Livestock owned

• Market price

• Quantity produced

• Disease

Value Addition

Market

supply

Institutional factors

• Access to market

information

• Access to transport

• Access to market

• Cooperatives/unions

• Credit utilization

• Extension service

Market performance

• Marketing margin

• Marketing cost

Demographic factors

• Age

• Family size

• Sex

• Education level

• Farm experience

• Training

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Chapter 3. RESEARCH METHODOLOGY

3.1 Description of the Study Area

Metekel Zone: It is one of the three administrative zones of Benishangul Gumuz Regional

State located in Western Ethiopia. It has borderlines with Awe zone in the East, Western

Gondar in the North, North Sudan in the West, and Kamashi zone in the South. It has 7

districts and three agro-ecologies i.e. Dega, Kola, and Woynadega. One district is the high

land agroecology part of Merkel as well as the region. The rest 2 and 4 districts are the

Woynadega and Kola agroecology parts respectively. It is the leading zone of the region as

well as the country in soybean production. Soybean has been produced in all 7 districts of

the Metekel zone in which 5 of them are highly potential in soybean production. The area

receives an average annual rainfall and temperature ranges from 600 -1540 mm/y and 12 -

41 0C respectively. The altitude ranges from 580 -2730 m.a.s.l. It has a total population of

470,684 and out of which 233,040 are males and 237,644 are females. More than 98% of

the rural population depends on agriculture and 95% of them are living in rural areas. Maize,

sorghum, soybean, sesame, and groundnut are the major crops producing in Metekel zone.

Pawe District: The district is located in the northwestern parts of the Metekel zone which

is 575 km away from Addis Ababa. According to the district agriculture office report, 2019,

the district has a total area of 64,300 ha. From the total area, 50.4% of the land is arable

which is used for crop production. From the total cultivated land in the district, more than

35% was covered by soybean (Birhanu Ayalew et al, 2018). The district is one of the major

soybean producers in Metekel zone. It constitutes 20 kebeles and 16 kebeles are producing

soybean as a major crop mainly for marketing purposes. The district is located at Latitude;

110 09’ N Longitude; 360 03’ E. It has an Altitude of 1120 m.a.s.l. According to PARC

metrology data, Pawe has an average temperature and rainfall of 32.7 0C and 1582 mm/y

respectively over the last 30 years. The district has great opportunities to cultivate soybean

intensively. From those opportunities, Pawe Agricultural Research Center which

coordinates soybean research at the national level is being existed at Pawe. Lands,

temperature, and rainfall distribution are suitable for soybean production. The farming

system of the district is mixed farming both livestock rearing and crop production.

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According to the district agriculture office report 2019, there are about a total population of

67,862 off which 35,407 are males and 32,455 are females. From the total households of the

district (10,899), 2383 are female-headed households and the rest 8516 are male-headed

households. 2105 male and 1326 female a total of 3431 youths is existing in the district.

Maize, Soybean, rice, sorghum, sesame, and groundnut are the major crops producing in the

district. There are about 29 different soybean varieties in which 6 of these are reached to the

end-users. All the varieties have been released by PARC and other collaborative research

centers in which they can be categorized into three groups based on their maturity. Those

varieties are classified as early maturity, medium maturity, and late maturity and they have

their own maturity dates which have been verified by PARC. In the 2018/19 production

season, 122,973 quintals of soybean yield were produced from 7109.6 hectares of land

(Pawe agriculture office report, 2019).

Source: Own draw

Figure 3.1 Geographical location of the study area (Pawe district)

3.2 Sampling Techniques and Procedures

A multistage sampling technique was employed to select sample households in the study

area. In the first stage, out of 20 kebeles in Pawe district, 16 kebeles were selected

purposively due to their potentials in soybean production. In second stage, out of 16 kebeles,

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4 kebeles were selected through simple random sampling technique from those potential

kebeles. Because, these kebeles have similar rainfall distribution, soil type and temperature.

Third, sample respondents were selected from the prepared sampling frame based on

proportional size to the population in the selected kebeles.

3.3 Sample Size Determination

The required sample size was selected by using Yamane (1967) formula with the precision

level of 7% from each sampled kebele. Since almost all farmers in Pawe are soybean

producers and have similar agroecology, there is no high degree of variability regarding

soybean production among farmers and that is why the precision level of 7% was used by

the researcher. Therefore, the required sample size was calculated as follows.

Table 3. 1 Sample size and households of the study kebeles

Kebeles Total households Sample households

Village 23/45 1003 75

Village 49 990 74

Village 30 602 45

Village 28/29 455 34

Total 3050 228

𝒏 =𝑵

𝟏+𝑵(𝒆𝟐)

Where, n is the required sample size

N is the total population size

e is the level of precision

n = 3050/1+3050(0.072) =3050/15.945 = 191

Therefore, based on the above formula a total of 191 farm households were selected through

a simple random sampling technique by using a lottery method to pick each sample in the

sampling frame. Additionally, 19 respondents for non-response rate and 18 for invalid data

were included as compensation. Finally, the quantitative primary data was collected from

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228 respondents out of 3050 total households in the four sampled kebeles. Overall primary

data was collected from 20 February to 20 March 2020 from these respondents in the

selected four kebeles.

Source: Own design

Figure 3.2 Sampling procedures of sample respondents

Sampling of Traders & Consumers: Whole-sellers, retailers, unions/cooperatives,

processors, and consumers as well as Ethiopian commodity exchange were interviewed

besides individual farmers. The researcher selected purposively 5 whole-sellers and 9 local

traders in Pawe based on the quantity of soybean bought and sold. Mama union and 4

BGRS

Assosa Metekel

wombera Bullen Dibate Pawe Mandura DangureGuba

Kamashi

75 45 34 74

V-23/45

1003

V-30

602

V-28/29

455

V-49

990

Stage 2

Simple random sampling Stage 3

Simple random sampling 228

16 kebeles

Stage 1

Purposively

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primary basic cooperatives one from each sampled kebele also interviewed in the district to

collect data related to soybean marketing. Because there is only one primary cooperative

per kebele and one union in Pawe. Similarly, Health care food manufacturer PLC from

Addis Ababa was interviewed and data related to soybean processing and its marketing

process from oil production up to consumption as well as the linkages of the processors with

suppliers and retailers was collected. Two soybean oil whole-sellers and 4 retailers were

selected purposively due to their closeness to enumerator to collect data easily.15 consumers

(5 from Addis Ababa and 10 from Pawe) were also selected purposively by taking in to

account whether the consumer consumes soybean oil or not. Central ECX and Almu ECX

were interviewed to get data especially related to soybean grading and the quantity

marketed. Data from these stakeholders were collected from the end of April to 25 May

2020.

Table 3. 2 Sampled traders of the study

Types of traders Towns

Pawe Almu V-23 V-49 V-

30

V-

28

Addis

Ababa

Total

Local traders 0 0 3 3 1 2 0 9

Whole-sellers 4 0 0 0 1 0 2 7

Processor 0 0 0 0 0 0 1 1

Cooperatives 0 0 1 1 1 1 0 4

Union 1 0 0 0 0 0 0 1

ECX 0 1 0 0 0 0 1 2

Retailor 0 0 0 0 0 0 2 2

Consumers 10 0 0 0 0 0 5 15

Total 15 1 4 4 3 3 11 41

3.4 Type, Source, and Methods of Data Collection

Types of data: Both quantitative and qualitative data types were used by the researcher in

the overall process of this study. To complement the quantitative data, qualitative data was

collected from 4 FGDs with members of 6 per group and 8 key informants through

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interviews from 4 sampled kebeles with DAs to triangulate the quantitative data. FGD

members were selected farmers base on their experience and knowledge about soybean

production and marketing. During FGDs, the deep discussion was conducted on issues of

soybean production, marketing, and their linkages with buyers & other actors with the

selected group members and DAs. From this discussion, in-depth information was collected

that used to complement the quantitative primary data.

Sources of data: Both primary and secondary data were the sources for the researcher in

this study. Secondary data were obtained by reviewing published journals, proceedings,

books, reports, and to some extent from the unpublished reports as well as the internet. The

primary data was collected through various instruments that have designed in line with the

study objectives and research questions. Questionnaires and checklists have been developed

independently for each actor. Unions/cooperatives, Addis Ababa soybean processing

factory, district and zone offices, and local traders were the target groups for the study to

collect data besides to individual farmers. Data related to the import and export of soybean

grain and byproducts were collected from the Ministry of Trade and Industry through a

semi-structured questionnaire. The quantitative data was gathered through household

surveys from each sampled respondent with face to face interview. There was a close and

friendly relationship with each sampled kebele DAs and district experts from the beginning

up to the end of this study to get the required data from each sampled respondent. Every

process of data collection was completed early by the researcher with DAs before the

enumerators went to the field. There were clear appointments with the respondents about

where and when the data have to be collected. As much as possible, the time was efficiently

used to reduce time wastage for farmers. Enumerators were highly respected respondents’

culture and dignity. Although the appointed time was not comfortable for some respondents;

other appointments were arranged since it was difficult to get the required data for the

situation that was not suitable for them.

3.5 Methods of Data Analysis

Three types of data analysis methods which are descriptive statistical analysis, Econometric

analysis, and marketing margin analysis were undertaken to analyze the primary data

through STATA (version 14.2) and SPSS (version 20) software packages.

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3.5.1 Descriptive statistical analysis

Descriptive statistics like mean, percentage, maximum, minimum, frequency, and standard

deviation were used. And also, inferential statistics such as t-test (to test the significance of

the parameters to be estimated), Breusch-Pagan/Cook-Weisberg test (to test the presence of

heteroscedasticity) and VIF (to test the presence of multicollinearity problem) were used.

Independent t-test used to see significant association and mean difference between value

addition participants and non-participants. Chi-square test (χ2) was also used to test the

relationship of the dichotomous dependent variable (value-addition) with categorical

variables. One-way ANOVA was used to test the presence of significant differences

between the groups (education level and frequency of extension contact) in terms of the

quantity of soybean market supply. Tables and graphs also used to present the collected

data.

3.5.2 Econometric analysis

Econometric models that are useful to analyze the factors affecting soybean supply to the

market and factors influencing value addition were specified below.

Factors affecting soybean market supply: In order to estimate the factors that affect the

quantity supplied to the market, using the OLS model is applicable if and only if all of the

households or respondents are participating in the marketing of the given commodity.

However, if all the participants are not participating in the marketing of the selected

commodity, using the OLS model by excluding the non-participants from the analysis leads

to selectivity biases to the model. In such a condition, it is better to use Tobit, Double-

Hurdle, and Heckman's two-stage procedures to overcome the problem. If our interest is

analyzing the probability of selling, in this case, Probit and logit model can address such

problems.

In Pawe district of Metekel zone, almost all of the farmers in 16 kebeles are producing

soybean for marketing purposes since direct consumption of soybean as food is not habited.

To study factors affecting soybean supply to the market in the study area, a multiple linear

regression model was used because here the quantity of soybean supplied to the market is

continuous and all the sample respondents in the sampling frame were producing and

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providing their soybean products to the market in 2018/19 cropping season. This model is

simple and practical to apply for identifying such factors. The econometric model of

specification for this supply function in matrix notation is given below.

Y = X’β + U

Where, Y = The quantity of soybean market supply

X = Explanatory variables that affect soybean market supply

β = Are the parameters to be estimated

U = The disturbance term i.e. unobserved factors to the researcher but affects market supply

Factors affecting value addition on soybean: The factors that affect the decision to engage

in value-addition was determined by using a Probit model. The decision to add value is a

discrete and dichotomous i.e. either adds value or not adds value. Discrete decisions are

analyzed by using qualitative response models and among them, Probit is the one that used

to analyze such factors. Logit and LPM are other qualitative response models. LPM has

problems of non-normality and heteroscedasticity of the disturbance term, the possibility of

the estimated dependent variable lying outside 0-1 ranges, and the questionable values of

R2. Due to these problems, the LPM/linear probability model not recommended to use for

analyzing such factors. Most of the time to analyze such types of factors logit and Probit

models can be used. However, there is no compelling reason to choose the one over the

other (Gujarati, 2004). Logit models are used to analyze the data that has a logistic

cumulative distribution function. But this study assumes a normal cumulative distribution

function and that is why the researcher decided to use the Probit model for analyzing factors

affecting farmers’ participation in value addition. Empirically, the Probit model is defined

as follows.

y* = β0 + β1x1 + β2x2 +…βnxn + εi

yi = 1 if y* > 0

yi = 0 if y* < 0

Where, y* = is a latent (unobservable) variable which represents farmers’ discrete decision

whether to adds value on soybean or not

β’s = are the parameters to be estimated

X’s = are the predictor variables

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εi = is the error term or factors that affect farmers’ participation on value addition but not

observable to the researcher and assumed not correlated with predictor variables

y = is the dependent variable that takes the value 1 if farmers add values and 0 other wise.

Soybean value-addition is the dependent variable and a household was considered as a

participant in value-addition if at least one type of value added by a household. In this Probit

regression analysis, farm households only analyzed, but other actors were not included in

the model. Because the numbers of sample sizes are below the minimum requirement to run

the model i.e. less than 30 and it was captured through descriptive statistical analysis.

Specification Tests: It is important to check multicollinearity and heteroscedasticity

problems before running the model. Multicollinearity is the existence of a correlation

between the explanatory variables and it is difficult to identify the separate effects of each

explanatory variable on the dependent variable due to strong relationships among them

(Gujarati, 2004). The variance inflation factor is a technique used to detect the existence of

multicollinearity for continuous explanatory variables and contingency coefficient (CC) was

used to test multicollinearity of dummy variables. According to Gujarati,2004, the VIF (Xj)

can be defined as follows.

VIF=1/(1-(Rj)2)

Where R2j = is the multiple correlation coefficient between the explanatory variables. The

higher the value of R2j is the higher value of VIF and that indicates the existence of high

collinearity among explanatory variables. If the value of VIF>10, there is a series problem

of multicollinearity. Similarly, contigency coefficient for dummy variables were calculated

as follows.

Where N = total sample size and if the value of CC is greater than 0.75, the variable are said

to be colinear

Heteroscedasticity: The problem was detected through the Breusch-Pagan/Cook-

Weisberg test by using STATA (version 14.2) software. It is commonly occurring in cross-

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sectional data. There is no constant variance across the variables if there is a problem of

heteroscedasticity.

3.5.3 Marketing margin analysis

Marketing margin analysis indicates that the comparison of prices at different levels of the

marketing chain over the same period of time. As Nugusa Abajobir (2018) cited

Mendoza,1995, the share of the final selling price is received by a particular actor in the

marketing chain and that is always related to the final selling price expressed in percentage

can be measured through marketing margin analysis. Calculating total gross marketing

margin (TGMM) is always related to the final price paid by the end customer or buyer and

that is expressed in percentage form.

TGMM =Consumer price − Producer price X100%

Consumer price

Where TGMM = Total Gross Marketing Margin

The same concept was applied to calculate the benefit share of each actor in in the marketing

chain with some adjustments. In order to analyze the margins, TGMM was calculated first.

It is the difference between farmer’s/producer’s price and consumers price i.e.

TGMM = Consumer’s price – Farmer’s price.

Therefore, the marketing margin at a given stage, i (GMMi) will be computed as follows.

GMMi =Spi − Ppi X100%

TGMM

Where Spi = is the selling price of the commodity at ith stage or link

Ppi = is the purchase price of the commodity at ith link

The trade margin of this study was calculated with the average prices of the commodity at

each level of market chain and the various charges incurred by each actor.

Total Gross Profit Margin was computed as follows.

TGPM = TGMM-TOE

Where TGPM = is total gross profit margin

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TGMM = is total gross marketing margin

TOE = is total operating expenses

Table 3.3 Summary of Research Methodology

Research

objectives Data types

Data

collection

methods

Data analysis Models used

Mapping of value

chain actors Demographic KI & FGDs Descriptive

Socio-economic

Marketing margin

analysis Socio-economic

Interview

schedule

Marketing

margin

analysis

Determinants of

market supply Demographic

Interview

schedule Descriptive

Multiple linear

regression

(OLS)

Socio-economic KI & FGDs Econometric

Inferential

Factors affecting

value addition Demographic

Interview

schedule Descriptive Probit model

Socio-economic KI & FGDs Econometric

3.6 Definition of Variables and Working Hypothesis

To identify the factors that affect soybean supply to the market, and value addition to

soybean crop, the following variables were assumed to affect the dependent variables in the

overall processes of this study.

3.6.1 Dependent variables

Quantity of soybean supply to the market: It is a dependent variable that represents the

amount of soybean supplied by the households to the market in 2019 measured in quintals.

Factors affecting value addition: It is the dependent variable that represents whether the

farmer participates in value addition or not. It is a dummy variable that takes the value 2 =

for these farmers participating in value addition and 1= for these farmers not participating

in value addition and measurement is in terms of participation in value addition.

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3.5.2 Independent variables

The independent variables that were hypothesized to affect the dependent variables are

presented below.

a) Independent variables for determinant factors affecting soybean market supply

Productivity: It is a continuous variable that refers to the quantity of soybean produced in

quintals per hectare in 2018/19 production season. A study conducted by Nugusa Abajobir

(2018) indicated that productivity positively and significantly influences the quantity of

maize supply to the market. This implies that those households that get more outputs per

hectare can supply more to the market. So, it was hypothesized that productivity of soybean

influences the quantity of market supply positively and significantly.

Lagged price (LAGGED PRICE): It is a continuous variable measured in terms of birr per

quintal. It represents the average price of soybean that farmers receive in the previous year.

The good price of the commodity in the previous year stimulates farmers to produce more

output for supplying to the market. The market price of the given commodity is the

determinant factor for market supply. There is a direct relationship between the quantity

supplied and its price (Chad, 2019). Soybean producers are involving intensively in soybean

production to supply the market in sufficient quantity if the price of the crop increases. If

the price of the crop increases, the quantity of the crop supplied to the market is also

increasing. Therefore, it was hypothesized that previous years’ market price of soybean has

a positive and significant influence on the quantity of soybean supply.

The education level of household head (EDLEVEL): It is a categorical variable that

ranges from illiterate to TVET and above education levels. Farmers who attend more years

in formal schooling were more productive and can supply more outputs to the market than

their counterparts (Wondimu Tefaye & Hassen Beshir, 2014). Education improves the level

of knowledge for farmers to engage in production for marketable products. Education

increases the skills of producers in the overall process of production and marketing for each

commodity. The researcher hypothesized that education has a significant and positive effect

on the quantity of soybean supplied to the market.

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Age of household head (AGE): It is a continuous variable measured in terms of the number

of years of the household head. Age can influence the quantity of soybean market supply

either positively or negatively. The age of the household head has a negative and significant

influence quantity of market supply (Taye Melese, et al., 2018). According to the results of

the Heckman selection-two stage model, the level of market participation decreased by 0.2%

as the age of the household head increased by a year. But to some extent, aged households

are wise in resource use and can allocate more lands for marketable crops than their

counterparts and supply more outputs to the market. Here, the researcher was hypothesized

that the age of the household head has either a negative or positive significant influence on

the quantity of soybean market supply.

Extension contact (EXCT): It is a categorical variable measured in terms of the frequency

of contacts with extension agent that ranges from daily to rarely extension contacts during

2018/19 production season. The more frequent contacts with the extension agent the higher

the knowledge and market information have obtained. Extension service improves farmers’

awareness of production and market surplus with better market prices (Sultan Usman, 2016).

Therefore, the researcher hypothesized that more frequent extension contact influenced the

quantity of soybean market supply positively and significantly.

Market distance (DISTANCE): It is a continuous variable that is measured in an hour

from the residence to the nearest market. The longer the distance between the residence of

the household is the lower the quantity supplied to the market (Dagnaygebaw Goshme, et

al., 2018). Therefore, it was hypothesized that the closeness of the households to the nearest

market has a positive effect on the quantity of soybean market supply.

Credit utilization (CREDIT): It is a continuous variable that refers to the quantity of credit

used measured in birr in the 2018/19 cropping season. The finding of Shewaye Abera et al.

(2016) confirmed that credit utilization positively correlated with the quantity of haricot

bean marketed. It improves farmers’ capacity to purchase different agricultural inputs for

production and this leads to an increase in the quantity marketed. So, the researcher

hypothesized utilization of credit influences the quantity of soybean supplied to the market

positively and significantly.

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Sex of household head (SEX): It is a dummy variable and assigned 1 for female head

household and 2 for the male head household. Being a male-headed household positively

and significantly affected teff market participation with a marketed surplus (Haregitu

Nitsuh, 2019). Males are aware better than females of improved agricultural technologies

due to the participation of males in different training programs and field day events. This

leads to enhancing the level of adoption of different agricultural technologies and increases

the production of surplus outputs to the market. So, being maleness hypothesized to have a

positive and significant effect on the quantity of soybean market supply.

Training (TRAINING): It is a dummy variable and took the value 2 for those households

who took training on soybean production and management in 2018/19 production and 1

otherwise. Training positively and significantly influences the quantity of soybean market

supply (Banda, et al., 2017). Provision of appropriate training on production management,

pest control, and pre- and post-harvest handling techniques increases the production and

productivity of farmers by reducing yield loss. Therefore, this variable hypothesized that to

have a positive and significant effect on the quantity of soybean marketed surplus.

Off/non-farm income (OFF-NONFAM): Dummy variable and assigned 2 for households

who participate on-off/non-farm activities as income alternatives and 1 for households not

participate in any off/non-farm activities in the 2018/19 cropping season. The availability

of alternative sources of income for farmers other than own agricultural activities increases

the purchasing power of different agricultural inputs and this leads to increase production

surplus further to supply more outputs to the market. There was a positive and significant

relationship between maize market supply and off/non-farm income (Nugusa Abajobir,

2018) So, it was hypothesized that off/non-farm income positively and significantly

influences the quantity of soybean market supply.

Cooperative membership (COOPMB): It is a dummy variable and assigned 2 for those

farmers who are member a cooperative and 1 for non-members in the 2018/19 production

season. Farmers can produce more outputs in bulk if they are organized into a cooperative.

Because coming together into a cooperative increasing the purchasing power of agricultural

inputs and can produce surplus outputs for the market. Maize market supply and cooperative

membership positively and significantly correlated (Nugusa Abajobir, 2018). Therefore, it

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was hypothesized to have a positive and significant influence on the quantity of soybean

market supply.

Family size (FAMSIZ): This is a continuous variable measured in terms of the number of

family members per household head. The finding by Sultan Usman (2016) founds that

quantity of wheat market supply is decreased by 0.05 quintal as family size increased by

one. But the finding by Afouda et al. (2019) confirmed that significant and positive

correlation between soy production and household size due to labor force contribution in

North-East Benin. The researcher hypothesized that family size can have either a negative

or positive and significant effect on the quantity of soybean market supply.

Access to market information (MKTINFN): It is a dummy variable that took the value 2

if the farmer has access to market information and 1 for not accessed. Smallholder producers

cannot produce surplus outputs for the market as a result of poor access to market

information. Access to market information increases the quantity of potato supply to the

market (Wondim Awoke & Dessalegn Molla, 2018). Farmers also become price takers for

their products due to the absence of reliable market information. Therefore, the researcher

hypothesized that access to market information positively and significantly affects the

quantity of soybean market supply.

Soybean farming experience (SOYFAMEXP): It is a continuous variable measured in

terms of the number of years of the household head involving in soybean production. If the

number of years of experience increases in farming, farmers can have knowledge of

production as well as marketing activity by adding some value to the commodity. Farming

experience significantly and positively increases the quantity of sesame supplied to the

market (Tamirat Girma, 2017). In this study, the researcher hypothesized that experience

has a significant and positive effect on the quantity of soybean to the market.

Cultivated land (CLAND): A continuous variable measured in a hectare. It refers to the

total farmland that farmers cultivated during the 2018/19 production period. The quantity

of haricot bean supply increased by 2.03 quintals as the farmland size increased by a hectare

(Wogayehu Abele & Tewodros Tefera, 2015). This indicates that farmland size has a

positive and significant effect on the quantity of the commodity supplied to the market. So,

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it was hypothesized that cultivated land affects positively and significantly the quantity of

soybean market supply.

Table 3.4 Description of explanatory variables & hypothesis in MLR model

Variables Symbol Type Measurement Expected

sign

Productivity PRODUCTIVITY Continuous Quintal ha-1 +

Lagged price LAGGED PRICE Continuous Birr per kg-1 +

Education level EDLEVEL Categorical

1= unable to read

&write 2=read &

write 3=primary

school (1-8)

4=secondary

school (9-10)

5=preparatory

school (11-12)

6=TVET &

above

+

Age HH AGE Continuous Years +/-

Extension contact FEXTCONT Categorical

1=rarely

2=monthly

3=twice a month

4=weekly

5=daily

+

Market distance DISTANCE Continuous Walking hour +/-

Coops. membership COOPMB Dummy 2= Yes 1= No +

Credit utilization CREDIT Continuous Birr +

Sex HH SEX Dummy 1= Female 2=

Male +

Training TRAINING Dummy 2= Yes 1= No +/-

off/non-farm income OFF-NONFAM Dummy 2= Yes 1= No +

Family size FAMSIZ Continuous Years +

Market information MKTINFN Dummy 2= Yes 1= No +

Farm experience SOYFAMEXP Continuous Years +

Cultivated land CLAND Continuous Hectare +

b) Independent variables affecting value addition

Age of the household head (AGE): A continuous variable which is the number of years of

the household heads in the study area. Aged people are wise and have good experience in

the overall process of agricultural production and value addition to get a better price in the

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market. They have relatively good knowledge about how to increase the values of their

product in the market. Unlike the youngsters, aged households give more value for each

agricultural product to receive a good return. Aged people are also familiar with the market

requirements of the product and performing some tasks to increase the values of their

product by targeting the end return. So, it was hypothesized that aged households were more

likely to add values on soybean.

The education level of household head (EDLEVEL): It is a categorical variable that

ranges from illiterate to TVET and above education levels. Education improves the level of

knowledge for farmers to engage in production for marketable products to provide the

market with better quality and quantity by adding some values to their products in order to

receive a better price (Taye Melese, et al., 2018). Education is the key to improve farmers’

skills and knowledge on value addition (Badri, et al., 2017). As the results of this study, the

likelihood of value addition to the agriculture sector is increased by 1% as the expenditure

costs to improve education increased by 0.31%. This implies that educated households are

focusing on value addition in order to receive a good return by providing their quality

product to their valued customers. So, education was hypothesized to increase the likelihood

of farmers to add values on soybean.

Market distance (DISTANCE): It is a continuous variable that refers to the amount of time

taken to reach the nearest market measured in walking hours. Distance to the nearest urban

market affects negatively and significantly the likelihood of farmers to add values to their

soybean product. The finding by Sultan Usman (2016) found that the probabilities of

farmers to add values on wheat is decreased by 0.03% as the distance of the residence to the

nearest market increased by a kilometer. Therefore, it was hypothesized that the longer the

travel time to reach the nearest market the less likely to add values to soybean.

Family size (FAMSIZ): It is a continuous variable measured in terms of the number of

family members per household head in 2018/19 production season. The existence of more

family members per household reduces the likelihood of producers adding values to

soybean. The finding by Nyongesa et al. (2018) confirmed that there is an inverse

relationship between maize value addition and household family size. Because the product

is sold immediately after harvesting to cover student fees and different home expenditures

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instead of storing the product to add some values. So, it was hypothesized to have a negative

and significant influence on the likelihood of farmers to add values on soybean.

Livestock owned (TLU): It is a continuous variable that refers to the numbers of livestock

owned in 2018/19 production season measured in terms of tropical livestock unit.

Ownership of more livestock increases farmers’ capacity to purchase packaging materials

and to prepare suitable storage conditions for value addition. On the other hand, farmers

who own more livestock focus on their livestock raring and give less attention to increase

their soybean products. So, it was hypothesized that to have either a positive or negative

effect on farmers’ likelihood to add values on soybean.

The market price of the commodity (MKT PRICE): It is a continuous variable that refers

to the price of soybean in birr per kilogram in 2019 marketing year. A good market price of

the commodity has a positive influence on the improvement of product quality by

performing some value-adding activities. If there is a good market price, producers give

more attention to the quality of their product in order to get a good return in the market.

Because quality products are highly demanded by all the buyers and can establish good

customer relationships by building trust among each other. So, the researcher hypothesized

selling price influences the likelihood of farmers to add values on soybean.

Disease incidence: It a categorical variable range from very serious constraint to not

constraint for the farm households in the study area during the 2018/19 cropping season.

Disease incidence has a negative influence on the quantity as well as the quality of each

commodity in the overall agricultural production. If farmers are facing a disease incidence,

it makes difficult the process of value addition since it deteriorates the biological and

physical appearance of the crop. The study by Bandara et al. (2020) also found that diseases

negatively and significantly impacted value addition on soybean due to quality deterioration

and loss of production. Therefore, it was hypothesized that diseases reduce farmers’

likelihood to add values on soybean.

Training (TRAINING): It is a dummy variable and assigned 2 (yes) for households who

took training in the 2018/19 cropping year on soybean pre- and post-harvest handling

techniques and 1 (no) for those not took the training. Technical training improves the skill

of farmers about the way how to increase their level of production with significant value

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addition. So, it was hypothesized to have a positive and significant effect on farmers’

likelihood to add values.

Quantity of soybean produced (QUPROD): It is a continuous variable that refers to the

amount of soybean produced in 2018/19 production season and measured in quintals. The

quantity of soybean produced in quintals affects the decision of farmers to participate in

value addition positively and significantly. Farmers who produce more yields of soybean

emphasized on the quality of their product by performing some value-adding activities like

cleaning, Packaging, storing, and transporting to sell with a better price for their customers

as well as to provide directly their product to ECX. Since soybean is an export commodity

and passes through ECX by considering some standards, farmers give more attention to

perform some value-adding tasks to meet the requirements of their buyers further to increase

the value of their soybean product. So, it was hypothesized that the quantity of soybean

produced influences farmers’ likelihood of value addition to soybean positively and

significantly.

Improved seed (IMPSEED): It is a dummy variable that represents 2 for those households

use improved soybean seed and 1 otherwise in 2018/19 production season. The seed is one

of the major inputs that the farm households use in the production process. In order to get

the required yield with better quality, farmers highly recommended using improved variety

seed. Producers can produce surplus outputs with better quality that maximizes the value of

their product if they are applying improved seed with other extension packages. Therefore,

it was hypothesized that the use of improved seed influences farmers’ likelihood to add

values on soybean.

Packaging material (PACKMT): Dummy variable and assigned 2 (yes) for households

who accessed appropriate packaging materials and 1(no) for those not accessed. After the

soybean has been threshed, it has to be packed appropriately to keep its quality for a long

time. Appropriately packed soybeans can be stayed for a long time without losing the quality

until sold (Obute, et al., 2019). The researcher hypothesized that this variable can increase

the value of soybean with time positively and significantly.

Storage problem (STORAGE): Dummy variable and assigned 2 (yes) for households who

have problems of storage and 1 (no) for those not facing storage problems. Good storage is

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one of the post-harvest technologies that increase the value of crops by keeping their quality

a long time. Safe storage keeps soybean for a long period of time without deteriorating

nutrient composition (Prabakaran, et al., 2018). Storage is a great problem even at the

country level which causes yield loss due to quality deterioration as a result of poor storage.

So, it was hypothesized that the storage problem negatively and significantly influences

soybean value addition.

Table 3.5 Description of explanatory variables in the Probit model

Variables Symbol Type Measurement Expecte

d sign

Age AGE Continuous Years +

Education level EDLEVEL Categorical

1 = illiterate 2 = read &

Write 3 = Primary

school (1-8) 4 =

secondary school (9-10)

5 = Preparatory school

(11-12) 6 = TVET &

above

+

Market distance DISTANCE Continuous Walking Hr. -

Family size FAMSIZ Continuous Number -

Livestock TLU Continuous TLU +/-

Market price MKTPRICE Continuous Birr kg-1 +

Disease incidence DISEACON Categorical

1 = very serious 2 =

serious 3 = moderately

serious 4 = not serious 5

= not constraint at all

-

Training TRAINING Dummy 2= Yes 1 = No +

Quantity produced QUPROD Continuous Quintal +

Improved seed IMPSEED Dummy 2= Yes 1 = No +

Packaging material PACKMT Dummy 2= Yes 1 = No +

Storage problem STORAGE Dummy 2= Yes 1 = No -

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Chapter 4. RESULTS AND DISCUSSION

This chapter presents the major findings of the study. Descriptive and inferential statistics

were employed to analyze the demographic and socioeconomic characteristics of

households, traders, and consumers. The determinant factors that affect the quantity of

soybean supply to the market and value addition in the study area were analyzed through

econometric analysis. Soybean value chain was analyzed through marketing margin analysis

which includes a map of the value chain, actors and their respective roles, marketing

margins, marketing channels, and benefit shares of each actor along the value chain were

discussed.

4.1 Descriptive and Inferential Statistics

4.1.1 Household heads characteristics

Household characteristics include age, sex, training, education level, family size, and farm

experience of the household head. The results of the study revealed that all of the households

are producing soybean mainly for marketing purposes since direct consumption of soybean

as a food is not habited. All households have been provided their soybean product to the

market after reserving seed for next year production. The study result indicated that 204

sample households participated in some value-adding activities whereas 24 households

didn’t participate in any value-adding activities. Although most of them participated in

value-adding activities, the estimated value-added was not significant as compared to other

developing countries even as compared to the neighboring country with Kenya. Because

Kenya was added 30-290 Kenyan shillings (8.19-79.21 ETB) value by processing 1-

kilogram soybean seed (Nyongesa, et al., 2018).

Sex of household head: Of the total interviewed households, 3.95% were female-headed

households and 96.05% were males. The survey result indicated that none of the female-

headed households participated in any value-adding activities. This may be due to either

inclusion of few female respondents in the sampling due to random sampling or low

awareness of female-headed households about value-addition as compared to their

counterparts.

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Training of household head: The survey result indicated that 58.33% of participant

households took training on pre-and post-harvest handling techniques and 31.14 not took

the training. All non-participants did not take the training of pre-and post-harvest handling

techniques on soybean. The chi-square result (χ2 = 37.55***) is evidence for the presence

of a significant association between the two groups at less than a 1% level of significance.

The finding concurs with Roy et al. (2013) who found a significant association between

training and tomato value addition. The result is also similar to Opolot et al. (2018) who

confirmed the existence of a significant association between training of farmers and soybean

value-addition.

Education level: As indicated in Table 4.1 below, 40.35% of participant households were

completed primary school education while only 5.26% of non-participants completed their

primary school education. Although the chi-square test (χ2 = 3.046) is not evidence for the

presence of a significant association between the two groups, relatively more educated

households are involved in value-addition.

Table 4.1 Socio-demographic characteristics of soybean producers

Participation on value addition

Variables Category Participant

(N=204)

Non-participant

(N=24)

All cases

(N=228) χ2

Sex of HH Male 195 (85.53) 24 (10.53) 219 (96.05) 1.1

Female 9 (3.95) 0 (0.00) 9 (3.95) Training of HH Yes 133 (58.33) 0(0.00) 133 (58.33) 37.55***

No 71(31.14) 24(10.53) 95 (41.67)

Education level Illiterate 57 (25.00) 6 (2.63) 63 (27.63) 3.05

Read & Write 36 (15.79) 6 (2.63) 42 (18.42)

Primary (1-8) 92 (40.35) 12 (5.26) 104 (45.61)

Secod. (9-10) 13 (5.70) 0 (0.00) 13 (5.70) Prep. (11-12) 3 (1.32) 0 (0.00) 3 (1.32) TVET & above 3 (1.32) 0 (0.00) 3 (1.32)

Source: Own survey result, 2020

Family size: The average family size of participant and non-participant households on value

addition was 5.71 and 6.13 respectively.

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The mean age of participant and non-participant households was 45.28 and 42.46 years

respectively. Age reflects the productivity of the population since it has a bearing on the

overall situation of health within the community. Aged members of the society are more

prone to diseases particularly in developing countries and thus leads to less productive. It

implies the employment pattern, spatial mobility, and quality of work done. Age plays an

important role in any type of business, especially in agriculture. The t-test (-1.36) indicates

that there was no significant mean difference between the two groups in terms of age.

Farm experience: The survey result indicated that average overall farming and soybean

farming experience of the total sampled households was 23.20 and 9.28 years respectively.

Soybean farming experience of participant households was 9.49 years and 7.5 years for non-

participants. The t-test (-1.75*) is evidence for the presence of a significant mean difference

between the two groups in terms of farming experience on soybean. The negative sign for

t-value indicates that households who did not participate in value-addition were less

experienced on soybean production. The finding is similar to Regasa Dibaba et al. (2019)

who confirmed that farm experience of soybean significantly influences the knowledge and

skills of farmers to increase their technical efficiency of value addition on soybean.

Experienced households have the knowledge and skill on the overall processes of the

agricultural production system and can produce more outputs from a small amount of inputs

used and can supply more output to the market with significant value addition.

Table 4.2 Socio-demographic characteristics of sampled household (t-test)

Variables Category Participant

(N=204)

Non-participant

(N=24)

All cases

(N=228) t-value

Family size Mean 5.71 6.13 5.75 1.02

SD 1.92 1.78 1.91 Age Mean 45.28 42.46 44.99 -1.36

SD 9.87 7.24 9.59 Farm experience Mean 23.45 21.08 23.20 -1.26

SD 8.79 7.63 8.67

Soybean experience Mean 9.49 7.5 9.28 -1.75*

SD 5.29 5.08 5.27

Source: Own survey result, 2020

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The results depicted in Table 4.3 show that the average quantity of soybean market supply

by a household who had access and not access market information was 20.55 and 5.55

quintals respectively. The t-test (7.31***) indicates that there was a significant mean

difference between the two groups in terms of the quantity of soybean market supply at less

than 1% significant level. The positive sign of t-value implies that farmers who have

accessed market information supplied more out puts to the market. The result is in line with

Wondim Awoke and Dessalegn Molla (2018) who confirmed the existence of a significant

correlation between potato market supply and market information. As a result of FGDs,

friends/neighbors were the major sources of market information for most of the households

in the study area although traders, development agents, radio & television were additional

sources. The existence of reliable market information helps farmers to sell their products a

better price since farmers can choose a profitable mode of transaction.

As the results indicated in Table 4.3 below, 57.46% of households were cooperative

members and 42.54% of them were not a member of a cooperative. On average, 19.57 and

10.28 quintals of soybean were supplied to the market by cooperative members and non-

members respectively. The t-test (4.47***) is evidence for the presence of statistical mean

difference among cooperative members and non-members regarding the quantity of soybean

products delivered to the market. Positive sign of t-value implies cooperative delivered more

soybean to the market as compared to non-members. The finding is similar to Kumilachew

Achamyelh et al. (2020) who found that cooperative membership significantly affects

quantity of sesame market supply. Cooperatives are crucial in the study area particularly for

input supply and to buy the products in bulk. However, the local communities were not

satisfied by the cooperatives as well as unions in regarding to marketing as the data collected

from FGDs & KI interviews. Even cooperative members sold their soybean and other

products to local traders instead of supplying to the cooperative. Because cooperatives were

not able to pay money on time for their products and not able to provide different services

as expected. On the other hand, local traders can give sacks free for their soybean product

and give money and other inputs like seed in credit in time of scarcity and this attracts most

producers to supply their products to those traders.

The average quantity of soybean supplied to the market by households who took and not

took training last year on soybean production and management was 21.07 and 13.88 quintals

respectively. The t-test (2.92***) implies that there was a significant mean difference

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between the two groups in terms of quantity of soybean market supply at less than 1%

significant level. The finding agrees with Opolot et al. (2018) who confirmed that training

significantly influenced the quantity of soybean marketed. The result is also in line with

Banda et al. (2017) who found that training of framers significantly influences surplus

production of soybean to supply more to the market.

Table 4.3 Relationship of quantity of soybean market supply with categorical variables

Quantity supplied (qt)

Variables Category Response Mean SD t-value

Market information Yes 153 20.55 17.69 7.31***

No 75 5.55 2.11 All cases 228 15.62 12.57 Coops. membership Yes 131 19.57 18.89 4.47***

No 97 10.28 9.16 All cases 228 15.62 14.75 Training of HH Yes 55 21.07 23.68 2.92***

No 173 13.88 12.48 All cases 228 15.62 15.19

Source: Own survey result, 2020

4.1.2 Institutional characteristics of soybean producers

The socio-economic development in general and the well-being of individuals in particular,

can be enhanced through the provision of adequate services to the communities by different

institutions. It has a positive contribution regarding improving production and productivity

and this leads to an increase the supply level of marketable crops further to increase the

income level of smallholder farmers. The most important services that are expected to

deliver for users to promote the production and marketing of soybean in the study area are

explained below.

The survey results indicated in Table 4.4 showed that 63.16% of participant households on

value-addition had access to market information whereas 26.32% of them couldn’t access

the information about their product. From the non-participants, 3.95% accessed market

information and 6.58% of households not accessed the information. The chi-square test (χ2

= 10.65***) indicates that there was a significant association between access to market

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information and value addition at less than a 1% level of significance. The finding is

consistent with Magesa et al. (2020) who found a significant association between the use of

market information and value addition.

As depicted in Table 4.4 below, 72.37% of the households responded that there was a price

difference due to value addition whereas 27.63% were responded no price difference at all

as a result of value addition. The chi-square test (χ2 =70.25***) is evidence for the presence

of a significant association between the two groups at less than 1% level of significance.

The finding by Kyomugisha et al. (2018) also confirmed that existence of significant

association between selling price and value addition of potato.

The survey result indicated that 54.39% and 35.09% of households were cooperative

members from participants and non-participants respectively. From the non-participant

respondents, 3.07% of them were cooperative members and 7.46% were not members. The

chi-square test (χ2 = 8.78***) shows that there was a significant association between

cooperative membership and value addition at less than 1% significant level. The finding is

similar to Kolade and Harpham (2014) who found a significant association between

cooperative membership and technological adoption for value addition.

As the results indicated below, 49.12% of participant households were used improved

soybean seed for production, and 40.35% of them were not used improved seed. From the

non-participants, 6.14% of them used improved seed, and 4.39% not used improved seed.

The chi-square result (χ2 = 0.10) shows there was no significant relationship between use

of improved seed and value addition.

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Table 4.4 Socio-economic characteristics of soybean producers

Variables Category Participant

(N=204)

Non-participant

(N=24)

All cases

(N=228) χ2

Market information Yes 144 (63.16) 9 (3.95) 153 (67.11) 10.65***

No 60 (26.32) 15 (6.58) 75 (32.89)

Price difference Yes 165 (72.37) 0 (0.00) 165 (72.37) 70.25***

No 39 (17.11) 24 (10.53) 63 (27.63)

Coops. membership Yes 124 (54.39) 7 (3.07) 131 (57.46) 8.78***

No 80 (35.09) 17 (7.46) 97 (42.54)

Use of improved seed Yes 112 (49.12) 14 (6.14) 126 (55.26) 0.1

No 92 (40.35) 10 (4.39) 102 (44.74)

Source: Own survey result, 2020

The study result revealed that average travel time for participant and non-participant

households was 0.41 and 0.69 hours respectively to reach the nearest market. The t-value (-

2.78***) is evidence for the presence of a significant mean difference between the two

groups in terms of travel time at less than 1% significant level. The negative sign of t-value

implies that participants traveled less time as compared to non-participants due to their

closeness of the market. The finding agrees with Orinda et al. (2017) who affirmed a

significant association between distance and sweet potato value addition. On average,

participant and non-participant households paid 18.67- and 17.00-birr qt-1 respectively to

transport their soybean product. The t-test (-0.69) indicates that there was no significant

mean difference between the two groups regarding to transportation cost of their product.

The study result showed that 47.4% of respondents had access to credit service but only

3.9% of them took the credit. Microfinance, local money lenders, and saving and credit

associations were the main credit sources for farmers in the study area. The mean credit

utilization of participant and non-participant households was 139.89 and 83.33 birr with a

standard deviation of 892.57 and 408.25 respectively. The result of the t-test (-0.31)

indicates that there was no significant mean difference between the two groups in terms of

credit utilization. As the data collected from FGDs, religious taboo, high-interest rate,

complex process, and no need for credit due to self-sufficiency were the main factors that

hinder farmers from utilizing credit.

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The survey result showed that the overall average selling price of participants of value

addition was 1141.19 birr per quintal whereas 987.50 birr per quintal for non-participants.

The t-test ( 3.63***) revealed that there was a significant mean difference between the two

groups in terms of selling price at less than a 1% level of significance. The positive sign of

t-value implies that participant households in value-addition sold their product with better

price as compared to their counter parts. The finding agrees with Kyomugisha et al. (2018)

who confirmed that selling price significantly influences producers to add values to potato.

The study result shows that 1.75 birr per kilogram value was added by participant

households with a standard deviation of 1.68. The t-test (5.09***) indicates that there was

a significant mean difference at less than 1% significant level among the two groups

regarding estimated value addition.

The amount of livestock owned by the household was measured in terms of tropical

livestock unit. The mean livestock holding of participants and non-participants in terms of TLU

was 6.04 and 4.60 respectively. The result of t-test (-1.59) showed that there is no evidence for

the presence of a significant mean difference between the two groups in terms of TLU.

Table 4.5 Socio-economic characteristics of sampled households (t-test)

Variables Category Participant

(N=204)

Non-participant

(N=24)

All cases

(N=228) t-value

Travel time (hrs.) Mean 0.41 0.69 0.44 -2.78***

SD 0.47 0.44 0.47

Product transport cost Mean 18.67 17.00 18.49 -0.69

SD 11.46 8.32 11.13

Credit utilization Mean 139.89 83.33 133.93 0.31

SD 892.57 408.25 841.59

Selling price Birr Qt Mean 1141.19 987.5 1125.01 3.63***

SD 202.58 126.19 194.54

Value addition Mean 1.75 0.00 1.57 5.09***

SD 1.68 0.00 1.51

livestock in TLU Mean 6.04 4.60 5.89 -1.59

SD 4.28 3.23 4.17

Source: Own survey result, 2020

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As the results of this study, 39.5% of soybean producers confirmed that they rarely contacted

with extension agents and 60.5% had contacts that ranges from daily to monthly. The result

of the analysis of variance ANOVA (F = 16.006, p = 0.000) is evidence for the presence of

a statistical mean difference between the groups regarding to quantity of soybean supplied

to the market. The finding by Dagnaygebaw Goshme et al. (2018) also confirmed that the

frequency of extension contact significantly affects the quantity of sesame market supply at

less than 1% significant level. The provision of appropriate agricultural extension service

takes a lion share in the overall journey of improving the living standards of farmers. It

provides assistance for farmers to improve their production and productivity by applying

scientific knowledge. Scientific findings can be put into practice by farmers with close

assistance of DAs and experts. Agricultural extension service is crucial to convince farmers

on the adoption of new agricultural technologies by taking risks. It enables farmers to be

aware of and get a better understanding of the research findings that increase their level of

production and productivity. It also plays a significant role to promote and disseminate

improved technologies to the majority of farmers.

The government of Ethiopia has been assigned five development agents (DAs) per each

kebele administration and building one farmers’ training center (FTC) to fill the knowledge

gaps of farmers and poverty reduction. Development agents are the major source of

extension service for farmers and they are expected to support farmers in their day to day

farming activities. Different organizations like the development group and one to five

groups have been established to enhance the provision of extension service and knowledge

transfer among groups. However, as the data collected from FGDs & KIs, farmers’

organization is not functioning according to the objectives. Although DAs have been

assigned to assisting the farmers for only agricultural activities, they are forced to perform

different political and other missions out of their professions and this affects the

performance of farmers for their production and productivity. Extension agents are expected

to give support for farmers by contacting frequently in their everyday life. However, more

frequently extension contact of farmers with DAs doesn’t necessarily give the expected

result, rather it is better to deliver appropriate extension services through service providers.

The education level of sample household heads in the study area ranges from illiteracy to

TVET and above levels. The proportion of household heads were illiterate (27.6%), read

and write (18.4%), primary school (45.6%), secondary school (5.7%), preparatory school

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and TVET, and above each 1.3%. The ANOVA result (F = 0.536) indicates that there was

no statistical evidence for the presence of significant difference between the groups in terms

of the quantity of soybean supplied to the market.

Table 4.6 Extension contact and education level of soybean producers

Source of variation Sum of squares df Mean squares F Sig.

Frequency of extension contact

Between groups 13217 4 3304.26 16.006 0.000

Within groups 46036.4 223 206.441

Total 59253.4 227

Education level of household head

Between groups 706.651 5 141.33 0.536 0.749

Within groups 58546.8 222 263.724

Total 59253.4 227

Source: Own computation from survey result, 2020

4.1.3 In put utilization

DAP fertilizer, seed, and herbicides are the major agricultural inputs used by farmers in the

study area for soybean production. Very few farmers have used biofertilizer by mixing with

DAP and sugar that has been supplied by PARC and mama union. These inputs were

supplied to farmers through cooperatives/unions, bureau of agriculture, and private traders.

Unions and cooperatives are the major suppliers of fertilizer for producers in the study area.

The average quantity of improved soybean seed applied by participant and non-participant

households was 0.76 and 0.74 quintal per hectare respectively. The t-test (0.10) indicates

that there was no significant mean difference among the two groups in regarding to seed

rate. On average, participants applied 0.35 quintal DAP fertilizer per hectare and 0.32

quintal for non-participants. Statistically, there was no significant mean difference between

the two groups in terms of fertilizer application. The mean herbicides applied by participants

and non-participants were 2.16 and 1.60 liters per hectare respectively. Similarly, the t-test

indicates that there was not significant mean difference between the two groups in terms of

herbicide application. Round up and 2-4D are the two types of herbicides that farmers are

applying before sowing. These herbicides were completely supplying to farmers through

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private traders at Pawe and Gilgelbeles town and any pesticide is not recommended to use

for soybean commodity.

To get the required production and marketable supply, farmers recommended to use these

agricultural inputs with the recommended rate given by PARC. PARC recommends

applying 100kg DAP ha-1 but applying UREA fertilizer for soybean is not recommended

since the crop itself replaces UREA. However, farmers in the study area were not applying

each input as the recommendation and that is why they received the yield per hectare that

was far from nation productivity. This finding is similar to Afework Hagos and Adam

Bekele (2018) who obtained that soybean yield per hectare was far away from the national

productivity due to limited use of improved seed and other recommended packages. Some

farmers also perceived that their plot is fertile and no need of applying any artificial fertilizer

for soybean production which is a great problem in the study area.

Table 4.7 Utilization of agricultural inputs for soybean production

Variables Category Participant

(N=204)

Non-participant

(N=24)

All cases

(N=228) t-value

Improved seed Qt ha-1 Mean 0.76 0.74 0.76 0.10

SD 0.17 0.19 0.18

DAP fertilizer Qt ha-1 Mean 0.35 0.32 0.35 0.526

SD 0.40 0.43 0.41

Herbicide applied lit ha-1 Mean 2.16 1.60 2.10 0.104

SD 2.52 3.05 2.57

Source: Own computation from survey result, 2020

4.1.4 Soybean production and marketing

Ethiopia is one of the potential countries in soybean production in East Africa as described

in Chapter 1. Similarly, Metekel is the leading zone of Ethiopia in soybean production and

contributes 65.7% of the region and 24.69% of the national production. Soybean is the

dominant legume crop-producing in Metekel zone as well as the region. It is not the legume

crop only, but also the known oil crop in the study area next to sesame and groundnut. In

Pawe district, all farmers (100%) are soybean producers and allocated a large proportion of

their lands for soybean production. According to Birhanu Ayalew et al. (2018), soybean

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took a 35% share of the land among other crops produced in Pawe in the 2015/16 cropping

season.

Land is an important measure of wealth and the most significant factor of production in the

study area. It is the major source of income and improves the status of people in the

community. The survey result showed that the mean cultivated land of participant and non-

participant households in the 2018/19 cropping season was 3.47 and 2.73 hectares

respectively. The t-test (t = 2.30**) is evidence for the presence of a significant mean

difference between the two groups in terms of cultivated land at a 5% level of significance.

Positive sign of t-value indicates that participants in value-addition owned more cultivated

land as compared to non-participants. The finding is similar to Orinda et al. (2017) who

found a significant association between land size and value addition to sweet potato.

Similarly, the average land size allocated for soybean production by participants and non-

participants was 1.40 and 0.85 hectares respectively for the same production season. The t-

test (3.01***) indicates that there was a significant mean difference among the two groups

regarding allocating lands for soybean at less than 1% significant level. Similarly, the

positive sign of t-value implies that participants allocated more proportion of land for

soybean production. Overall, out of the total cultivated lands, 39.53% (1.34 ha) of their

lands were used for soybean in the 2018/19 cropping season since the area is known for its

soybean potential. This shows a 4.53% increment as compared to the 2015/16 cropping

season in terms of lands allocated for soybean.

As the results of this study, 3823.21 quintal soybean was produced by sample households

in the 2018/19 cropping season. The average quantity of soybean produced by participant

and non-participant households was 17.74 and 8.52 quintal respectively. The t-test (2.55**)

indicates that there was a significant mean difference between the two groups regarding

soybean production at a 5% level of significance. The positive t-value implies that

participants produced more outputs as compared to their counter parts. This finding agrees

with Surni et al. (2019) who confirmed that quantity of farmers production significantly

influences value addition at less than 1% level of significance. Households who participated

in value addition was produced 12.26 quintals per hectare and 10.26 quintals for non-

participants. The t-test (1.54) indicates that there was no significant mean difference

between the two groups in terms of productivity. Overall productivity of soybean in the

study area was 12.05 quintals per hectare which is far from the national productivity of 21.5

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quintals. As the data collected from FGDs & KIs, low use of improved variety seed, limited

use of fertilizer, improper application of agrochemicals, and other agronomic practices

without the recommended rate were the major inhibiting factors for productivity. This result

is in line with Afework Hagos and Adam Bekele (2018) who affirmed that soybean

productivity was far from the national productivity due to limited use of improved soybean

varieties and other recommended packages.

The survey result revealed that 93.11% (3559.95 qt) of soybean was provided to the market

out of the total volume of 3823.21quintal produced and 6.89% (263.26qt) was reserved for

seed in the next season production and local consumption. As indicated in Table 4.8, the

mean quantity of soybean sold by participant and non-participant households was 16.50 and

8.13 quintals respectively. The t-value (2.43**) is evidence for the presence of a significant

mean difference between the two groups regarding to quantity of soybean sold. The positive

sign of t-value indicates that paricipants were provided and sold more soybean product as

compared to non-participants households.

Table 4.8 Soybean production and marketing in 2018/19 cropping season

Variables Category Participant

(N=204)

Non-participant

(N=24)

All cases

(N=228) t-value

Cultivated land (ha.) Mean 3.47 2.73 3.39 2.30**

SD 1.50 1.38 1.49

Soybean land size (ha.) Mean 1.40 0.85 1.34 3.01***

SD 0.86 0.55 0.83

Soybean production (qt.) Mean 17.74 8.52 16.77 2.55**

SD 17.52 7.11 16.43

Productivity qt. ha-1 Mean 12.26 10.26 12.05 1.54

SD 6.09 5.30 6.01

Sold soybean (qt.) Mean 16.50 8.13 15.62 2.43**

SD 16.71 6.83 15.62

Source: Own survey result, 2020

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4.2 Description of Sample Traders and Consumers

4.2.1 Household characteristics of sampled traders

All sampled traders in the study area were males. Out of the total 14 traders interviewed, 13

were married and one trader was single. The majority (42.86%) of traders can read and write

and 21.43% of them had an education level of primary school and TVET and above. The

result showed that 57.14% and 42.86% of traders are Orthodox and Muslim religious

followers respectively. The mean age and family size of traders were 41.86 and 4.14 with a

standard deviation of 6.57 and 1.03 respectively. On average, traders have 5.93 years of

experience in soybean trading.

Table 4.9. Socio-demographic characteristics of sample traders

Variable (N = 14) Frequency Percent

Sex Male 14 100

Marital status Married 13 92.86

Single 1 7.14

Education Read and write 6 42.86

Primary 3 21.43

Secondary 1 7.14

Preparatory 1 7.14

TVET and above 3 21.43

Religion Orthodox 8 57.14

Muslim 6 42.86

Mean Std. Deviation

Age 41.86 6.57

Family size 4.14 1.03

Experience of trade on soybean 5.93 2.02

Source: Own survey result, 2020

4.2.2 Price setting strategies of traders for soybean purchasing

Most traders (85.71%) have been purchased soybean during the pick periods of the year.

Few (14.29%) traders have purchased soybean throughout the year although there are few

months in which soybean is scarce. Terms of payment for purchasing are in cash and credit

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in the study area. The study result showed that 64.29% of traders purchased soybean both

in cash and credit whereas 35.71% of traders purchased only in cash. According to the

information obtained from FGDs, most of the local traders have been purchased soybean

through credit from their long-standing customers. Most (85.71%) of the traders responded

that the purchase price of soybean was set by the market and 2 traders responded that it was

set through negotiation and by the traders. Out of the total 14 traders, 64.29% of them said

that the purchase price is set early in the market during the market day. 14.29% and 21.43%

of traders responded that the purchase price is set one day before the market day and at the

time of purchase respectively. However, the responses of traders contradict with farmers in

regarding to price setting strategies. Because most farmers responded that the selling price

of soybean was set by traders and they couldn’t influence the selling price.

Table 4.10 Time of soybean purchasing and price setting strategies

Variables (N = 14) Frequency Percent

Time of purchase year-round 2 14.29

During pick period 12 85.71

Terms of payment Cash 5 35.71

Both cash & credit 9 64.29

Price setting strategy Negotiation 1 7.14

By the market 12 85.71

By trader 1 7.14

Time of price setting one before market day 2 14.29

Early in the morning during market day 9 64.29

At the time of purchase 3 21.43

Source: Own computation from survey result, 2020

4.2.3 Initial and working capital of traders

The average initial and working capital of traders in the study area were 288,751.43 and

441,071.43 birr respectively. The majority (78.57%) of traders have their own saved sources

of capital for starting the business. Three traders took credit from banks, private money

lenders, and other traders as a starting capital for the trading business. The study result

indicated that most traders in the study area started their trading business on soybean and

other trading items by using their own saved capital.

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Table 4.11 Initial, working capital & credit source

Variable (N= 14) Frequency Percent

Source of capital Own saved 11 78.57

Credit 3 21.43

Mean Std. Deviation

Initial capital 288751.43 276895.07

Working capital 441071.43 332566.82

Source: Own survey result, 2020

4.2.4 Soybean oil production

Soybean can be processed as feed and food for animal feeding and human consumption with

an excellent nutrient composition. The estimated annual consumption of oil in Ethiopia is

about 394 million kg which indicates that around 11 billion ETB is spending per annum

(Sopov & Sertse, 2014). Three-fourths of the edible oil demand has been covered through

import. Most of the soybean and sunflower edible oils have been covered through import

since there is only one soybean edible oil processing industry (health care food manufacturer

PLC) in the country (Sopov & Sertse, 2014). The imported soybean edible oil costs US$ 4

per litter whereas it cost US$ 3 per litter for domestic processed oil. This implies that US$1

can be saved if the domestic soybean oil production substitutes the imported oil.

Health care food manufacture PLC: It is the only soybean oil-producing plant in Ethiopia

and produces and distributes the produced soybean oil and soybean meal and hulls to the

end-users through its distributors. The byproducts of soybean meal and hulls were selling

directly to consumers after the oil has been extracted. As the data obtained from the factory,

extracted oil was distributed through distributors/whole-sellers but not directly sold to

retailers and consumers, unlike byproducts. The processing plant bought soybean grain from

Addis Ababa whole sellers and producers before the crop being an ECX commodity and

that was so challenging to get soybean in the required quantity as well as quality. Currently,

soybean becomes an ECX commodity starting from 2019 and the factory has been bought

from ECX. Now, the factory can get relatively a quality soybean from ECX even though

still the quantity is limited. In 2019, the processing plant bought about 75,000 quintals

soybean and produced 900,000-937,500 litters of soybean oil. As the data collected from

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the factory, to produce 1 litter oil, 8kg soybean grain is needed. This indicates that from

100kg soybean grain, 12.5 litters of oil can be produced. After oil production, the oil is

delivered to whole sellers with a price of 67.77 birr per litter. Whole sellers also sold to

retailers at the price of 76.07 birr per litter and finally consumers purchased by 85 birr per

litter.

Table 4.12 Purchase price of soybean oil by traders and consumers

Actors Purchase price of soybean oil per litter

Whole sellers 67.77

Retailors 76.07

Consumers 85.00

Source: Own survey result, 2020

Palm, soybean, and sunflower oils are the major imported oils in Ethiopia (Sopov & Sertse,

2014). According to the data collected from the Ministry of Trade and Industry and health

care food manufacturer plant, the quantity of domestically produced soybean oil shows

some increment although the existing soybean oil-producing plant is still one. As indicated

in Figure 4.1, the country imported 8115.46kg soybean oil besides to other oils in 2019

marketing year. At the same time, the domestic soybean oil processor produced 781,250kg

soybean oil. This implies that the establishment of additional processing plants and capacity

improvement of the existing processing plant needs great attention to substitute the imported

oils as well as to save foreign currency.

Source: Ministry of Trade & Industry & Health care food manufacturer plant, 2019

Figure 4.1 Quantity of soybean oil imported and domestically produced, 2019

Imported

soybean oi l

Domest ic

product ion

soybean oi l

8115.46 781250.00

Volume of soybean oi l (kg)

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Exporters: Exporters are those traders who buy soybean from central ECX and export the

commodity abroad. According to the data collected from the ministry of trade and industry

report (2019), 137 exporters were involved in soybean export in the 2019 fiscal year. In

2019 marketing season, 942,038.10 quintal of soybean was exported by those 137 exporters.

Quantity of soybean grain exported increased by 37.37% as compared to 590,042 quintals

exported in the 2017/18 production season (Ministry of Trade and Industry & Byrne, 2018,

2019). As the data indicated below in Figure 4.2, from the total volume provided to the

market, the majority (92.63%) of soybean grain was exported and 7.37% was used for

domestic processing. This implies that still now, the country exports soybean grain without

adding significant values and import back it again the processed products by spending huge

money. Even though the government of Ethiopia promotes export of the commodity, more

attention has to be given to processing to meet the domestic consumption of the byproducts

as well as to promote export by adding significant values to the product instead of exporting

soybean grain. Because viable economic growth can be realized through the addition of

significant values to each agricultural product and this enhances the competitiveness in the

domestic and international markets.

Source: Ministry of Trade & Industry & Health care food manufacturer plant, 2019

Figure 4.2 Quantity of soybean grain exported & domestic consumption for processing

80%

90%

100%

Volume (Qt) Percent

942,038.10 92.63

75,000 7.37

Soybean exported & domestically used for processing

Quantity exported quantity used for domestic processing

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4.2.5 Household characteristics of consumers

Out of the total interviewed consumers, 66.67% were females and 33.33% of consumers

were males. These consumers are households that are consuming soybean oil which were

found in Addis Ababa city and Pawe town. Five consumers were from Addis Ababa and 10

of them were from Pawe. The education level of consumers ranges from illiterate to

certificate holder and above levels. The result showed that 86.67% of consumers were

married and 13.33 % were single households. Government employment, trading, and daily

labor were the main livelihoods for 46.67%, 40%, and 13.33% of consumers respectively.

The mean age and family size of consumers were 32.13 years and 3.67 family members

respectively. Soybean oil is a recently produced consumption oil in which most people are

not habited to consume it. The average soybean oil consumption experience of the

respondent was 1.60 years. The oil has been provided from Addis Ababa whole sellers to

retailors in each corner of the country. Finally, consumers purchased the oil from there

nearby retailors.

Table 4.13 Socio-demographic characteristics of soybean oil consumers

Variables (N = 15) Frequency Percent

Sex Male 5 33.33

Female 10 66.67

Education Illiterate 4 26.67

Read and Write 3 20.00

Certificate & above 8 53.33

Marital Status Married 13 86.67

Single 2 13.33

Means of income for consumers Trade 6 40.00

Employment 7 46.67

Daily labor 2 13.33

Mean Std. Deviation

Age 32.13 6.61

Family size 3.67 1.50

soy oil consumption experience 1.60 0.63

Purchase price of soy oil (birr/lit) 85.00 1.36

Source: Own survey result, 2020

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4.3 Main Value Chain Actors and Functions

4.3.1 Primary value chain actors and their functions

Table 4.14 Primary actors and supporters along soybean value chain in the study area

Primary Actors Supportive Actors

Cooperative Development agents

Unions Experts

Agriculture office Pawe Agricultural Research Center

Pawe Agricultural Research Center Ethiopian Commodity Exchange

Private Traders/agro chemical suppliers District agriculture office

MBI/Menagesha Bio-technology Institute Zone agriculture bureau

Farmers District trade and industry office

Local traders/village level traders N2-Africa project

District whole sellers AGRA project

Ethiopian Commodity Exchange BG saving credit association

Processor

Exporters

Oil whole sellers

Retailors

Consumers

Input suppliers, producers, local traders, cooperatives/unions, district whole-sellers,

Ethiopian commodity exchange, Addis Ababa wholesalers, processors, exporters, retailers,

and consumers were the primary actors in soybean value chain in the study area.

Input suppliers: This segment of the value chain consists of the actors that provide the

starting materials for the proper functioning of the subsequent soybean value chain. The

actors include in this segment were seed supplier, fertilizer supplier, herbicide supplier, and

extension service providers as well as other technical and financial supporters.

Cooperatives, Mama union, private traders, MBI, and Pawe Agricultural Research Center

were the main input suppliers in the study area. Cooperatives were the major suppliers of

fertilizer, seeds, and agrochemicals. Cooperatives also collected soybean and other products

from cooperative members and delivered to Mama union. On the other hand, Mama union

distributes fertilizer, improved seeds, agrochemicals, oil, sugar, and other sanitary goods to

basic primary cooperatives. The union received the collected soybean and other products

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from basic cooperatives and delivered the bulk products to Ethiopian commodity exchange

at Almu branch. Private traders from Pawe and Gilgelbeles town were the main

agrochemical suppliers. Inoculants (mar-1495) were supplied through Menagesha

Biotechnology Institute (MBI) and PARC. PARC provides basic and pre-basic initial seeds

and technical trainings with full packages to seed producers, DAs, experts, and other

stakeholders in the overall process of production and marketing.

Producers: Soybean producers are the major value chain actors following input suppliers

in soybean value chain. All soybean producers in the study area were smallholder farmers

having different land size. The smallholder producers in Pawe district were providing their

soybean product to village level traders, basic cooperatives, and to district whole sellers.

Farmers in the study area were producing soybean only for marketing purposes. The

production process of soybean undergoes through the following basic farming operations

from land preparation to final post-harvest handling. As the data obtained from FGDs and

household surveys, Seed preparation, chemical application, and storing were the basic

farming operations which are performed through family labor only. However, land

preparation, w plowing, sowing, weeding, harvesting, damping, and threshing farming

operations were performed by both family and daily labor. Cleaning, packaging,

transportation, storing, and by very few farmers sorting were the post-harvest handling tasks

performed by farmers along soybean value chain. Finally, producers provided their

produced soybean to local traders, district whole-sellers, cooperatives, ECX, and other

producer farmers for seed and investors outside the district. The study result indicated that

the majority (77.97%) of the total produced was sold to local traders.

Local/village traders: Were the major soybean buyers among all traders in the study area.

Local traders had a strong linkage with farmers. According to the FGDs and key informants’

interviews in the four sampled kebeles, local traders give different inputs and food grains as

well as money for farmers in time of scarcity as a means of attraction. Some farmers also

received sacks free from the local traders for soybean packaging. Due to such attraction

mechanisms, 83.77% of sampled farmers were sold their soybean production to local

traders. After collection of the product from producers, all local traders finally delivered to

ECX at Almu branch since all of them have a trading license.

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Pawe whole sellers: These whole sellers can be received soybean products from farmers,

local traders, and even from Mama union and delivered it to Addis Ababa whole sellers and

processors before the crop is becoming an ECX commodity. Currently, soybean becomes an

export ECX commodity starting 2019 and Pawe whole sellers were forced to buy soybean

only from farmers and delivered it to ECX at Almu branch. As the data obtained from those

whole sellers during the interview, they complain about the existence of ECX and not

willing to expand soybean trading business since the market alternative was limited and

monopolized by ECX. Such an issue was not a problem of whole sellers only, but also to

local traders and producers due to misperceptions and low awareness about the roles of

ECX.

Ethiopian Commodity Exchange: ECX is a national multi-commodity exchange to

provide market integrity by guaranteeing the product grade and quality. It is designed to be

a marketplace where buyers and sellers meet to trade, assured of quality, delivery, and

payment (Meijerink G & Dawit Alemu, 2014). ECX is a modern trading system based on

standard crop contracts by establishing standard parameters for commodity grades,

transaction, size, payment, and delivery as well as trading order matching. The main role of

ECX is to bring buyers and sellers together to trade at the trading floor more efficiently and

transparently (Bizualem Assefa & Saron Mebratu, 2018). ECX has a positive impact on the

existing marketing system and for the development of the agricultural value chain in

Ethiopia through a more reliable way to connect buyers and sellers efficiently. It delivers

timely market price information to all marketing actors. Central ECX received the collected

soybean production from each branch across the country and stored in the warehouse until

the bulk collection to sell to buyers. After bulk collection, the product was sold to exporters

and domestic processors with clear bid competition on the trading floor. As data collected

from the Ministry of Trade and Industry and health care food manufacturer, 92.63% soybean

production was sold to exporters, and the rest 7.37% soybean sold to processors in 2019

marketing year.

Exporters: According to the data collected from the Ministry of Trade and Industry, there

were about 137 soybean exporters in the 2019 marketing season. These exporters bought

soybean from central ECX and exported the crop abroad.

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Processors: Buy soybean from central ECX and process soybean into food and feed

products. After processing, soybean oil is delivered to distributers/whole-sellers to reach to

final consumers through retailers across the country. Although soybean oil is the main

product in soybean oil production, byproducts of soybean meal and hulls are the main

driving force for soybean oil production since it contributes 60-62% of the total revenues

received by the processor. Soybean meal and hulls can be sold directly to poultry production

centers, beef, and dairy cattle producers at a better price.

Processed product whole sellers: There were three whole-sellers/distributers from Addis

Ababa to receive the soybean oil from health care food manufacturer plant and to sell the

oil to retailers across the country.

Retailors: Received soybean oil from the three whole sellers and delivered to consumers.

Consumers: Rural and urban dwellers, poultry production centers, beef, and dairy cattle

producers were the main consumers after soybean has been processed. Soybean oil can be

received from retailers in different corners of the country. However, soybean meal and hulls

were directly received from the processor by poultry production centers, beef and dairy

cattle producers for direct consumption as well as ration formulation.

4.3.2 Support service providers and their functions

Along soybean value chain in the overall process of this study, the following are the lead

organizations to provide support to primary actors at different stages of the value chain.

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Table 4.15 Soybean value chain supporters and their functions

Indirect/supportive actors Functions

Development agents Delivery of advisory service and follow up

Training of farmers

Field day preparation on model farmers’ farm & FTC

Agriculture offices Provision of advisory service

Training of farmers and development agents

Field supervision and follow up

Technical support and facilitation to cooperatives and

union

Trade and industry office Provision of trading license

Kebele administration Community mobilization and facilitation

District administration Facilitation and controlling

Mama union Provides market information to basic cooperatives

Orientation to basic cooperative and members on the

quality of soybean product and packaging systems

Pawe Agricultural

Research Center

Development and adaptation of improved soybean varieties

On farm demonstration of improved soybean varieties

Establishment of CBSM to alleviate seed shortage

Provision of technical training and advisory service to DAs,

experts, cooperatives/unions and different stakeholders

Field day event preparation and experience sharing

programs

Government

market

regulators

(Export

marketing)

Ministry of

trade

Providing trading license to exporters

Ministry of

agriculture

Provides quality, safety and healthy certification for export

Ministry of

revenue

Clearing and documentation

National

Bank of

Ethiopia

NBE provides export permits through commercial banks

and control hard currency repatriation

facilitate & provide guarantee to exporter payment systems

Transport

service

providers

Cross

boarder

transporting

companies

Transporting the products to markets in the neighboring

country or to ports

Local

transporters

Transport the products from farmers plot to local markets

Transport the products from local markets to Almu ECX

and

to Central ECX Addis Ababa

Financial

service

providers

Saving and

credit

association

and Banks

Credit services

Solve the financial problems of farmers, traders and

exporters

Source: Own survey result, 2020

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As indicated in Figure 4.3, primary actors connect with their primary functions. The Figure

also shows the roles of support services providers and the types of support for primary actors

at each stage of the value chain.

Source: Adapted from Almaz Giziew, 2018

Figure 4.3 Value Chain Actors, Functions and Support service providers

Input Supplier Producer Transporter Consumer Trader

Coops. /union Private traders, Research centres & Farmers

MBI

Farmers

Workers

Vehicle owners

Drivers

Cart drivers

Exporters, Whole sellers, Local traders, Cooperatives/unions, Processors, retailors & shops

Rural dwellers & Urban dwellers

Poultry centres

Beef & dairy cattle producers

Inputs transport

ation Production consum

ption

Trading/whole selling

exporting, processing & retailing,

processing,

Fertilizer, inoculants, chemicals & improved seeds, Technical trainings

Loading,

unloading, take

the produce to

Almu ECX,

central ECX,

plants, ports on

cart, vehicle &

aircraft

Land preparation, chemical application, Ploughing, Sowing, Weeding, harvesting, Threshing

Sorting,

Packaging,

Grading,

Transporting,

buying, selling

to the local

market or

abroad

Buying the product for consumption and ration formulation

GOs, Pawe Agricultural Research

Centre, BoARD, District trade &

industry office, Mama union, &

MBI, N2-Africa and AGRA projects,

BG saving and credit association

Transport

service

providers

GOs, Mama union& Almu and

central Ethiopian Commodity

Exchanges, Commercial Bank

of Ethiopia, Ministry of trade &

Industry, Ministry of Revenue

Value chain functions

Value chain supporters

Value chain operators

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4.3.3 Map of soybean value chain

Value chain mapping is the first step to conduct value chain analysis. It is a system of

sketching to show the product flow from producers up to final consumers bypassing

different stages. The map indicates the activities of actors, their relationship, and value-

added at each stage of the value chain.

Source: Own sketch from survey result, 2020

Figure 4.4 Map of soybean value chain

Producers

Cooperatives Local traders

Consumers

Investors/

other

farmers

Mama

union

Pawe whole sellers

Ethiopian Commodity Exchange

Exporters Processor

Oil whole sellers Oil retailors

Production

Value chain actors Value Chain supporters Value Chain functions

Agriculture

office

Cooperatives Traders

Consumption

Input

supply

GOs, PARC, BoARD,

Trade & industry

office, Mama union, &

MBI, BG saving and

credit association

GOs, NGOs, BoARD,

Cooperatives/Mama

union, PARC, DAs

and experts, BG

saving and credit

association

GOs, Mama

union& Almu and

central Ethiopian

Commodity

Exchanges,

Commercial Bank

of Ethiopia,

Ministry of trade

& Industry,

Ministry of

Revenue,

Ministry of

agriculture &

Transport service

providers

Marketing

Trading/wh

ole selling

Exporting

Processing

Retailing

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4.3.4 Marketing channels along soybean value chain

A marketing channel is a pathway in which the product is moving from the point of

production origin to the final consumption destination. In the study area, producers were

providing their soybean product to five major buyers with varying degrees of volumes. All

soybean products were collected by Pawe whole-sellers, local traders, and cooperatives and

then delivered to ECX at Almu branch except the first channel in which producers directly

sold to other farmers or investors for production purposes. After the product has been

collected by ECX, it was sold to exporters and processors at Addis Ababa by central ECX

based on its marketing rules. According to the data collected from traders and farmers, the

local traders and district whole sellers sold their collected product to Exporters and

processors through ECX. The rest value chain actors sold the product to exporters only

through ECX.

In the overall process of this study, nine marketing channels were identified. A total volume

of 3823.21 quintal soybean was produced by sample households in the 2018/19 cropping

season. From this production volume, 93.11% (3559.95qt) was provided to the market and

sold along the following nine marketing channels. The rest 6.89% (263.26qt) was reserved

for next season production and local consumption by the households.

Channel I: Farmer _consumer/investor: This channel was the shortest among nine

marketing channels when producers directly sold to other farmers and investors for

production purpose. In this channel, producers sold the produced quality product which can

be used as a seed for further production and a good price was received by producers. This

channel represents a 4.94% share among all other channels.

Channel II: Farmer_ ECX_ Exporter: It is also the second shortest channel in which the

farmer directly provides to ECX and then to the exporter. Only 1.40% of the product volume

was sold in this channel. This channel was considered as a good channel for producers since

it has good return due to no intermediaries in the chain. Because farmers can directly provide

to ECX by considering some standard quality parameters.

Channel III: Farmer _ Local trader _ ECX _ Exporter: On this channel, the local traders

were collected soybean from farmers on each kebele and delivered to ECX and then sold to

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exporters. This channel was the main marketing channel in which the largest volume of

soybean was sold on it which represents 71.68% of share from all marketing channels.

Channel IV: Farmer _ WSPawe _ ECX_ Exporter: Along this channel, farmers were

provided their soybean to Pawe district whole sellers. On this marketing channel, 13.26%

of the total volume was sold which represents the second-largest volume next to channel III.

Then Pawe whole sellers sold the collected product to exporters through ECX.

Channel V: Farmer _ Coops. _ Union _ ECX _ Exporter: Only 2.42% of soybean

production was sold along this marketing channel.

Channel VI: Farmer _Local trader _ ECX_ Processor _ Consumer: On this marketing

channel, the local traders purchased soybean from producers and resale it to a processor

through ECX. After the oil has been extracted by the processor, soybean meals and hulls

were directly sold to poultry production centers, beef and dairy cattle producers for direct

consumption as well as ration formulation. This channel represents 4.41% of the volume

sold.

Channel VII: farmer _ WSPawe _ ECX _ Processor _ Consumer: Pawe whole sellers

purchased soybean from farmers and resale it to a processor through ECX as channel VI.

Byproducts of soybean meal and hulls were directly sold to poultry production centers, beef,

and dairy producers and represent 0.75% of share among other channels.

Channel VIII: Farmer _ Local trader _ ECX _ Processor _ WSoil _ RToil _ Consumer:

This channel is the largest and represents 1.89% share along the nine channels. In this

channel, the processor has been extracted soybean oil and delivered this oil to whole-

sellers/distributers from Addis Ababa further to provide to retailers in different corners of

the country. Finally, consumers purchased soybean oil from their nearby retailers.

Channel IX: Farmer _ WSPawe _ ECX _ Processor _ WSoil _ RToil _ Consumer: On this

channel also, a processor purchased soybean from central ECX delivered by Pawe whole

sellers and extracted soybean oil further to distribute the oil to whole sellers. Retailers

received the oil from whole sellers and then sold to consumers. This marketing channel

accounts only 0.32% share along all marketing channels.

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Channel I: Farmer consumer/investor (4.94%)

Channel II: Farmer ECX exporter (1.4%)

Channel III: Farmer L. trader ECX Exporter (71.68%)

Channel IV: Farmer WSPawe ECX Exporter (13.26%)

Channel V: Farmer Coops. Union ECX Exporter (2.42%)

Channel VI: Farmer L. trader ECX Processor Consumer (4.41%)

Channel VII: Farmer WSPawe ECX Processor Consumer (0.75%)

Channel VIII: Farmer L. trader ECX Proc WSoil RToil Consumer (1.89%)

Channel IX: Farmer WSPawe ECX Proc. WSoil RToil consumer (0.32%)

Figure 4.5 Soybean value chain marketing channels

Producers

Consumers

Investors/oth

er farmers

(4.94%)

Mama union

Local traders (77.68%) Cooperatives

(2.42%)

Pawe whole

sellers (13.26%)

Ethiopian Commodity Exchange

(95.06%)

Exporters (92.63%)

3134.55 qt

Processor (7.37%) 249.4 qt

Oil whole sellers (30%) or

3117.5 liters

Oil retailors (30%)

70% (SBM & hulls)

1.4%

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4.4 Marketing Margin Analysis

4.4.1 Production cost of soybean in the study area

Cost identification and profit estimation is one of the tasks of value chain analysis. Before

calculating the profit shares of each actor, each cost type has to be identified. As the survey

result indicated in Table 4.16 below, the total production cost of the sample households was

9264.28-birr ha-1. On average, sampled households obtained 12.05 qt yield ha-1. As the result

depicted in Table 4.16, the production cost of the household per quintal was 768.82 birr.

Households were received a total revenue of 13,556.37 birr per hectare with a gross profit

of 4292.09 birr by considering family labor. By excluding family labor in the production

stream, farmers can receive a gross profit of 6985.30 birr per hectare. Weeding, harvesting,

and land preparation-sowing are ranked 1st, 2nd, and 3rd in terms of costs incurred for

soybean producers in the study area. The highest cost was incurred at the time of weeding

followed by harvesting. As the results of this study, 1607.47- and 1580.60-birr were incurred

per hectare for labor and chemical costs respectively to control weeds. Households incurred

1069.91 birr per hectare for labor cost at the time of harvesting and 701.99-birr was incurred

during land preparation to sowing.

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Table 4.16 Production cost of soybean producers

List of cost types Cost in Birr per ha Percent

Seed purchase 1352.38 14.60

Fertilizer purchase 1369.42 14.78

Chemical purchase 1580.60 17.06

Family Labor 2693.21 29.07

Land preparation – sowing 97.20 1.05

Weeding 1071.66 11.57

Harvesting 599.49 6.47

Threshing 59.00 0.64

Damping 73.27 0.79

Cleaning 1.25 0.01

Product transportation 222.80 2.40

Packaging Material 144.00 1.55

Total cost in birr per hectare 9264.28 100.00

Average selling price in birr per quintal 1125.01 Yield in quintal per hectare 12.05 Revenue per hectare of production 13556.37 Gross Profit in birr per hectare 4292.09

Source: Own survey result, 2020

In the study area, labor was one of the main factors of production for soybean, and family

labor took the lion share. As indicated in Table 4.17, the total cost of family and daily labor

was 2693.21- and 1901.87-birr ha-1 respectively. Households were incurred 4595.08-birr ha-

1 for labor cost only which implies that more than half of the production cost for soybean

was labor in the study area.

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Table 4.17 Labor cost of soybean production for producers

Cost type Unit Family labor cost Daily labor cost Total cost

Land preparation-sowing ETB/ha 604.79 97.20 701.99

Weeding ETB/ha 535.80 1071.66 1607.46

Chemical application ETB/ha 307.08 0.00 307.08

Harvesting ETB/ha 470.42 599.49 1069.91

Damping ETB/ha 306.28 73.27 379.55

Threshing ETB/ha 358.41 59.00 417.41

Cleaning ETB/ha 110.43 1.25 111.68

Total labor cost ETB/ha 2693.21 1901.87 4595.08

Source: Own survey result, 2020

The overall average selling price of soybean was 1125.01 birr per quintal by incurring an

average production cost of 768.82 birr. The majority (83.77%) of respondents were provided

their soybean to local traders and received the least price when they were supplied to them.

The finding by Toure and Wang (2013) also confirmed that producers received the least

price from their Potato product when they sold at the farm gate to village level traders.

Farmers received a profit margin of 356.19 birr per quintal by considering family labor.

However, they can receive a profit margin of 579.69 birr per quintal by excluding the cost

of family labor from soybean production. Farmers received a gross profit of 4292.09- and

6985.30 Birr ha-1 with including and excluding of family labor cost respectively. This

indicates that soybean production is profitable for smallholder farmers in Pawe as well as

the region although the expected return cannot be realized due to insignificant value addition

and less productivity. The finding is similar to Afework Hagos and Adam Bekele (2018)

who found that farmers received a gross profit of 3931.952 Birr ha-1 from soybean in the

2016 marketing season. The result is also in line with Hichaambwa et al. (2014) who

confirmed that soybean production is quite profitable for smallholder producers even with

below-recommended levels of inoculant and fertilizer utilization.

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Table 4.18 Value addition and margin of producers

Attribute Unit Amount

Yield Qt/ha 12.05

Total production cost ETB/ha 9264.28

Cost Per quintal (Value Addition) including family labor ETB/Qt 768.82

Cost Per quintal (Value Addition) excluding family labor ETB/Qt 545.32

Average Selling Price ETB/Qt 1125.01

Farmer's Margin including family labor ETB/Qt 356.19

Gross Profit including family labor ETB/ha 4292.09

Farmer's Margin excluding family labor ETB/Qt 579.69

Gross Profit excluding family labor ETB/ha 6985.30

Source: Own computation from survey result, 2020

4.4.2 Marketing margin and profit shares of actors in the value chain

The survey result indicated in Table 4.19 shows that the processor received the highest

marketing margin which is 669.26 Birr qt-1 followed by local traders and district grain whole

sellers with the respective marketing margin of 431.56- and 423.25-Birr qt-1. Producers and

cooperatives also received the respective marketing margin of 386.63 and 392.38 Birr qt-1.

Even though the processor received the highest marketing margin, its profit share was the

least (6.64%) among grain traders due to incurring the highest processing and marketing

cost. The finding is consistent with Wondim Awoke and Dessalegn Molla (2018) who found

that potato processors were incurred the highest cost among traders since processors perform

different value adding activities. The highest profit share was received by local traders

which is 377.75 Birr qt-1. This finding agrees with Esayas Negasa and Mustefa Bati (2019)

and Kumilachew Achamyelh et al. (2020) who confirmed that profit margin of traders is

more than that of soybean producer farmers. Producers, Pawe whole sellers and cooperatives

received 23.15%, 23.13% and 22.53% profit shares respectively.

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Table 4.19 Margin & profit shares of actors along soybean value chain

Cost Items Producers Local

traders WSPawe Cooperatives Processor

Sale price (Birr/qt) 1125.01 1523.00 1571.25 1496.67 2229.26

GMM (Birr/qt) 386.63 431.52 423.25 392.38 669.26

% share Market margin 16.79 18.74 18.38 17.04 29.06

Gross cost (Birr/qt) 768.82 1145.25 1215.48 1149.99 2127.17

Gross profit (birr/qt) 356.19 377.75 355.78 346.68 102.09

% share Gross profit 23.15 24.55 23.13 22.53 6.64

Source: Own computation from survey result, 2020

Along the nine marketing channels in the overall process of soybean value chain, channel 3

was selected to estimate value addition and total costs incurred among actors. Channel 3

was selected due to 71.68% of soybean production volume sold along this channel. The

maximum profit margin was received by local or village level traders which is 377.75 birr

per quintal following channel 3. Producers received a profit margin of 356.19 birr per quintal

which indicates that producers were price takers since they were chain actors in the value

chain. Because chain actors cannot influence the selling price within the value chain.

Table 4.20 Gross margin following marketing channel 3

Actors Cost incurred per quintal Sales price in birr per quintal Gross margin

Producers 768.82 1125.01 356.19

Local traders 1145.25 1523.00 377.75

Source: Own computation from survey result, 2020

The survey result indicated in Table 4.21 shows that local traders and producers were added

a gross value of 3.7775 and 3.5619 birr per kilogram respectively. The result revealed that

producers were not benefited from their effort and that is why they were exploited by traders

particularly village-level traders. Local traders received relatively good returns from

soybean without exerting more effort as compared to other actors in the value chain. A total

value of 7.3394 birr per kilogram was added by local traders and smallholder producers

along this marketing channel.

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Table 4.21 Distribution of value addition among major actors

Value chain

Sales price in birr per kg 11.2501 15.23

Cost of raw material birr per kg 7.6882 11.4525

Gross value added in birr per kg 3.5619 3.7775

% of total value-added 48.5312 51.4648

Source: Own computation from survey result, 2020

Total value added = 7.3394 Birr per kilogram

Health Care Food Manufacturer PLC: It is the only soybean oil processing plant in

Ethiopia located at Addis Ababa around Kaliti. The plant is producing oil from soybean

besides to other oils and sales the residues of soybean after the oil has been extracted. The

hexane extraction process is the most commonly used approach in soybean oil processing

due to its high oil recovery and lower production cost. A well-adjusted interest rate and high

annual soybean oil production capacity can realize the profitability of soybean oil

production industries (Cheng & Rosetrater, 2017). According to the data obtained from the

factory, soybean oil is the main product, and soybean meal (SBM) and hulls are the

byproducts in the overall process of soybean oil production. However, out of the total

revenues received from soybean processing, soybean oil contributes only 38-39%, and the

majority (60-62%) of the revenues received from the byproducts/residues of soybean meal

and hulls. As the result indicated from Table 4.22, from 100kg of soybean grain, 12.5 litters

of soybean oil can be extracted since 8kg soybean grain is needed to produce a litter of

soybean oil. At the same time after the oil has been extracted, SBM is produced and about

1382.14 birr can be received by selling the byproducts to poultry production centers, beef,

and dairy cattle producers with a better price. This implies that the processor can receive a

total revenue of 2229.26 birr per 100kg soybean grain after processing by incurring a gross

cost of 2127.16 birr. The processor received a marketing margin and gross profit of 669.26

and 102.10 birr per 100kg soybean grain respectively.

Soybean purchase price, selling price of soybean oil, and soybean meal are the determinant

factors in the overall process of soybean oil extraction. The finding is similar to Cheng &

Rosentrater (2017) who found that soybean meal is regarded as an important driving force

for soybean oil production. Even though soybean oil is the main product for soybean oil

Producer Local trader

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processors, soybean meal is considered as the main driving force for soybean oil production

industry due to its higher productivity and higher revenues than soybean oil.

Table 4.22 Production cost of soybean oil processor

Cost items to process 100kg soybean grain Total cost incurred in birr

Soybean purchase price 1560.00

Labor cost 9.13

Chemical cost 96.63

Gas oil cost 36.25

Utilities cost 31.13

Bottles and labels 126.50

Packaging 188.13

Transport cost 20.00

Loading/unloading 12.00

Tax and fee 40.00

License fee 6.67

Telephone cost 0.74

Total cost 2127.16

Sale of 12.5 litters soybean oil 847.12

Sale of Soybean meal & hulls 1382.14

Total sale in birr 2229.26

Market margin 669.26

Gross profit 102.10

Source: Own survey result, 2020

Perfuming different value-adding activities increases the value of the product in the market

and can be received a good return. Products with significant value addition can well

competitive in domestic as well as international markets. The survey result indicated in

Table 4.23 showed that the highest value was added by the processor which is 567.17 birr

by processing 100kg soybean. Pawe whole-sellers, local traders, and cooperatives added

67.48, 53.77, and 45.70 values respectively on soybean from marketing of 100kg soybean.

Accordingly, most of the value-adding activities performed by the processor was form

values and to some extent place and time value after processing. However, other actors

perform time and place value by storing and transporting the product respectively. The only

form value activity performed by these actors is packing which was not significant.

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Table 4.23 Value addition & margin by soybean grain traders & processor

Cost Items Unit Local traders WSPawe Cooperatives Processor

Purchase price ETB/qt 1091.48 1148.00 1104.29 1560.00

Production cost ETB/qt 0.00 0.00 0.00 299.63

Transport ETB/qt 13.89 22.00 20.00 20.00

Loading/unloading ETB/qt 8.11 7.80 10.00 12.00

Packaging cost ETB/qt 12.49 12.38 12.00 188.13

Tax and fee ETB/qt 10.00 15.00 0.00 40.00

License fee ETB/qt 5.08 6.52 0.00 6.67

Storage cost ETB/qt 0.00 0.00 0.00 0.00

Telephone cost ETB/qt 0.75 0.33 0.25 0.74

Warehouse rent ETB/qt 3.45 3.45 3.45 0.00

Total cost ETB/qt 1145.25 1215.48 1149.99 2127.17

Sales price ETB/qt 1523.00 1571.25 1496.67 2229.26

Market margin ETB/qt 431.52 423.25 392.38 669.26

Value added ETB/qt 53.77 67.48 45.70 567.17

Source: Own computation from survey result, 2020

After processing, the processor distributes the soybean oil to consumers through its

distributers/whole sellers. The factory has three distributors/whole-sellers at Addis Ababa.

The whole sellers received the soybean oil from the factory at the price of 67.77 birr per

litter and sold to retailers by 76.07 birr per litter. Finally, consumers purchased the oil with

an average price of 85 birr per litter. Whole sellers and retailors added a value of 3.30 and

3.71 birr per litter and 8.30 and 8.93 birr per litter marketing margin respectively.

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Table 4.24 Value addition and margin by soybean oil traders

Cost Items Unit Oil whole seller Retailor

Purchase price ETB/lit 67.77 76.07

Processing cost ETB/lit 0.00 0.00

Transport ETB/lit 0.28 0.50

Loading/unloading ETB/lit 0.32 0.32

Packaging material ETB/lit 0.00 0.00

Tax and fee ETB/lit 2.20 2.00

License fee ETB/lit 0.25 0.20

Storage cost ETB/lit 0.00 0.56

Telephone cost ETB/lit 0.25 0.13

Warehouse rent ETB/lit 0.00 0.00

Total cost ETB/lit 71.07 79.78

Sales price ETB/lit 76.07 85.00

Market margin ETB/lit 8.30 8.93

Value added ETB/lit 3.30 3.71

Source: Own computation from survey result, 2020

4.5 Econometrics Analysis

4.5.1 The determinant factors affecting soybean market supply

Multiple linear regression model (OLS) was employed to analyze the determinant factors

affecting the quantity of soybean supply to the market. As the result indicated in Table 4.25,

all the coefficients of the independent variables were indicated which shows that the amount

of change in the quantity of soybean supply for a unit change of the listed independent

variables. The coefficient of determination (R2) that shows the explanatory power of the

model also indicated. According to the result of the OLS model, the coefficient of

determination (R2) was 0.8937. This implies that 89.37% of the variation on the dependent

variable i.e. quantity of soybean supply to the market is due to the independent variables

that are included in the model. The F-statistics calculated value (F = 118.84, p > 0.0000)

indicated that the overall model is significant at less than 1% level of significance.

Multicollinearity problem was checked through variance inflation factor (VIF) for

continuous explanatory variables and results obtanined ranges from 1.01 to 1.97 which

shows that there is no problem of multicollinearity among explanatory variables.

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Multicollinearity for dummy/categorical variables was tested through contingency

coefficient (CC) and results show that no problem of multicollinearity between the

explanatory variables that are included in the model. Similarly, heteroscedasticity problem

was tested by using Breusch-Pagan/Cook-Weisberg test. Since the result showed the

existence of heteroscedasticity, it was corrected through robust regression.

Table 4.25 Regression results of factors affecting quantity of soybean market supply

Variables Coefficients Robust Std. Err. t-value P>t

PRODUCTIVITY 0.415*** 0.054 7.64 0.000

LAGGED PRICE 2.539*** 0.256 9.91 0.000

EDLEVEL 0.029 0.036 0.82 0.413

AGE -0.131 0.111 -1.19 0.237

FEXTCONT 0.065* 0.037 1.79 0.076

DISTANCE -0.036* 0.018 -1.93 0.055

COOPMB 0.096 0.060 1.59 0.113

CREDIT 0.232*** 0.087 2.66 0.008

SEX 0.128 0.227 0.56 0.573

TRAINING 0.085 0.056 1.50 0.136

OFF-NONFAM 0.036 0.065 0.56 0.577

FAMSIZ -0.117*** 0.043 -2.74 0.007

MKTINFN 0.415*** 0.071 5.84 0.000

SOYFAMEXP 0.089** 0.036 2.48 0.014

CLAND 0.361*** 0.056 6.42 0.000

Constant -16.863*** 1.818 -9.28 0.000

N = 228, R2 = 0.8937, ***, **, * are significant levels at 1%, 5% and 10% respectively

Productivity (PRODUCTIVITY): It is a continuous variable which refers to the quantity

of soybean produced in quintals per hectare in 2018/19 production season. Productivity

influences the quantity of soybean supply to the market positively and significantly at less

than 1% level of significance as hypothesized. The amount of soybean supplied to the

market can be increased if the households produce more yield per hectare. The result of

multiple linear regression (OLS) model indicated that quantity of soybean market supply is

increased by 0.415 quintal as the productivity increased by 1 quintal. This implies that

increment of productivity positively enforces producers to produce soybean intensively and

this leads to increase quantity market supply. The finding is similar to Mengistu Berhe et al.

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(2019) who found that quantity of sesame market supply increased by 3.499 quintals for a

unit increase of sesame productivity.

Lagged price (LAGGED PRICE): It is a continuous variable measured in terms of birr per

quintal. The lagged price of the commodity affects positively and significantly the quantity

of soybean supply to the market at less than 1% level of significance as expected. Good

price of soybean in the previous year attracts farmers to produce more further to supply

more outputs to the market in the next year by allocating more of their lands for soybean.

As the results of the OLS model, the quantity of soybean supply is increased by 2.539

quintals as the market price of the crop in the previous year increased by 1 birr per kilogram.

The finding is similar with Tadie Mirie and Lemma Zemedu (2018) and Nugusa Abajobir

(2018) who confirmed that teff and maize marketed surplus positively and significantly

correlated with the lagged price.

Frequency of extension contact (FCONTACT): It is a categorical variable which refers

to the frequency of contacts of sampled farmers with development agents during 2018/19

cropping season. Frequency of extension contact positively and significantly affected the

quantity of soybean market supply at 10% significant level as hypothesized. This might be

due to knowledge and skill improvement of farmers on farming operations and this leads to

increase production and productivity. Farmers who frequently contact with development

agents can able to get different information on different soybean varieties with full

recommended packages and they can be convinced to apply these technologies. Use of

improved soybean varieties with recommended packages increases farmers’ production and

productivity and this leads to increase the quantity soybean market supply. The results of

multiple linear regression model indicates that quantity of soybean market supply is

increased by 0.065 quintal as frequency extension contact increased by one. The finding

agrees with Sultan Usman (2016) who found that the existence of positive relationship

between wheat market supply and extension service. The result is also in line with

Kumilachew Achamyelh et al. (2020) who confirmed positive correlation of extension

contact and market participation of soybean producer farmers with surplus outputs. The

findings of Nugusa Abajobir (2018) also confirmed that the quantity of maize market supply

is increased by 1.404 quintals as the number of extension contact increased by one.

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Market distance (DISTANCE): It is a continuous variable that refers to the length of time

taken to reach the nearest market for the household measured in hours. Distance to the

nearest market affects the quantity of soybean supply negatively and significantly at 10%

significant level as expected. Results of the OLS model indicated that the quantity of

soybean market supply is decreased by 0.036 quintal as the travel time to reach the nearest

market increased by an hour. Households who are far from markets forced to pay more costs

for input and product transportation as well as forced to travel for long hours on foot and

this reduces their level of participation on production. This might be the reason for limited

supply of soybean for those distant farmers. The finding is consistent with Rehima Mussema

et al. (2013) who confirmed that a minute decrease in walking distance to reach the nearest

market increases households’ market participation by 0.97%. The finding by Amare Tesfaw

(2013) and Tadele Melaku and Ashalatha (2016) confirmed that distance to reach the nearest

market affects quantity of pepper and teff market supply negatively and significantly. The

finding is also in line with Dagnaygebaw Goshme et al. (2018) who found that quantity of

sesame market supply is decreased by 0.24 quintal as distance to the nearest market

increased by a kilometer.

Credit utilization (CREDIT): It is a continuous variable that refers to the quantity of credit

utilized by a household in 2018/19 cropping season measured in birr. The amount of credit

utilization affects quantity of soybean market supply positively and significantly at less than

1% level of significant as expected. Credit can improve farmers’ purchasing power of

agricultural inputs and this leads to increase surplus production of the crop for the market

due to improvement of productivity. Results of OLS model indicated that quantity of

soybean market supply is increased by 0.232 quintal as credit utilization increased by one

birr. The result is consistent with Ali and Awade (2019) who confirmed that having a full

amount of credit positively and significantly influences surplus production of soybean and

market return. The result is also similar to Seven and Tumen (2020) who found that credit

utilization increases farmers’ productivity and this leads to an increase quantity of marketed

surplus.

Family size (FAMSIZ): It was hypothesized that to have either a positive or negative

influence on the quantity of soybean market supply. The model result indicated that family

size affects the quantity of soybean market supply negatively and significantly at less than

1% significant level. As a result of OLS model, the quantity of soybean supply to the market

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is decreased by 0.117 quintal as the number of family size increased by one. This implies

that even though consumption of soybean in the study area is not habited, currently, PARC

promotes soybean dishes by giving training on the nutritional values of soybean and way of

preparing soy foods like porridge, soy milk, bread, and kookis for local consumption. This

opportunity increases the level of soy foods consumption in the area and those households

who have more family members use more soybean for consumption and this may be the

reason for the reduction of quantity of soybean market supply. The finding is similar to

Nugusa Abajobir (2018) and Sultan Usman (2016) who confirmed the quantity of maize

and wheat supply is decreased by 0.379 and 0.05 quintal respectively as the family size

increased by one. The result is also in line with Edosa Tadesa (2018) who found negative

and significant relationships between the quantity of teff marketed and family size of the

household.

Access to market information (MKTINFN): Access to market information is an important

factor for farmers to produce surplus outputs for marketing purposes. Access to market

information influences the quantity of soybean market supply positively and significantly at

less than 1% level of significance as expected. This implies that those farmers who cannot

able to get market price information on a certain commodity, they are not producing more

outputs and cannot allocate more lands for production and this reduces market supply. As a

result of the OLS model, the quantity of soybean supply to the market is increased by 0.415

quintal when farmers have got market information. The finding is consistent with Wondim

Awoke and Dessalegn Molla (2018) who confirmed that the quantity of potato market

supply increased by 7.316 quintals for a household who has accessed market information.

Zamasiya et al. (2014) also found a positive correlation between access to market

information and soybean marketed surplus.

Soybean farm experience (SOYFAMEXP): It is a continuous variable that refers to the

number of years of the households involving in soybean production. Soybean farm

experience of the household influences positively and significantly the quantity of soybean

market supply at less than 5% level of significance as hypothesized. The model result

indicated that the quantity of soybean market supply is increased by 0.089 quintal as the

farm experience of soybean producers increased by a year. This implies that experienced

households have the knowledge and skills of applying agronomic practices and other

farming operations very well. This leads to increase their production and productivity and

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this might be the reason why experienced households supplied more outputs to the market.

The finding is consistent with Modeste et al. (2018) and Tadele Melaku and Ashalatha

(2016) who found that significant and positive relationships between the quantity of market

supply and soybean and teff farm experience. The finding is also similar to Almaz Giziew

(2018) who obtained that onion market supply is increased by 0.0185 quintal as the farm

experience of onion producers increased by a year. This result also agrees with the findings

of Ali et al. (2015) and Tamirat Girma et al. (2017) who found a strong and positive

relationship between farm experience and quantity of sesame and haricot bean marketed.

Landholding size (CLAND): It is a continuous variable which refers to the total lands that

the household cultivated during 2018/19 cropping season measured in hectares. The

availability of more cultivated land affects the quantity of soybean supply to the market

positively and significantly at less than 1% level of significance as hypothesized. Farmers

can allocate more lands for soybean production if they have more cultivated land. Because

farmers are producing different food grains for home consumption besides to cash crop and

cannot allocate lands for soybean if the owned small cultivated land. This might be the

reason why producers supplied less outputs to the market due to small size cultivated land.

The quantity of soybean supply to the market is increased by 0.361 quintal as the amount of

cultivated land increased by a hectare. The finding is similar to Shewaye Abera et al. (2016)

and Wogayehu Abele and Tewodros Tefera (2015) who confirmed that the quantity of

haricot bean market supply increased by 2.1 and 2.03 quintals respectively for a hectare

increase of landholding size for the farm households. The finding by Dagnaygebaw Goshme

et al. (2018) also confirmed that sesame market supply increased by 6.8 quintals for a

hectare increase of land size. The finding of Besufekad Belayneh et al. (2018) indicated that

quantity of common bean market supply increased by 2.97 quintals as cultivated land

increased by a hectare. The finding is also in line with (Edosa Tadesa, 2018, Regasa Dibaba

& Degye Goshu, 2018, Falmata Gezachew, 2018 and Yegon et al., 2015) who confirmed

that positive and significant correlation of land size and quantity of (teff, wheat, and

soybean) market supply.

4.5.2 Factors affecting farmers’ participation on value addition

Probit model was employed for the estimation of factors affecting the probabilities of

sampled households to add values to soybean as indicated in Table 4.26. The Table below

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also contains the marginal effects that are evaluated at the means of all other independent

variables. Marginal effects indicate a unit change in an exogenous variable on the

probability that an individual farmer adds value to his/her soybean product. Pseudo R2

indicated that the explanatory variables included in the Probit model explain a significant

proportion of the variation in soybean producer farmers’ likelihood to add values to soybean.

As the result indicated in Table 4.26, the Probit model explains 40.54% of the variations in

the likelihood of soybean producer farmers to add values on their product.

Table 4.26 Probit estimation of factors influencing value addition

Variables Coefficients Std. Error Marginal effects

(dy/dx) P>|z|

AGE 0.061*** 0.0216 0.0011 0.005

EDLEVEL 0.249 0.1607 0.0047 0.122

DISTNCE -0.486* 0.2902 -0.0091 0.094

FAMSIZ -0.091 0.0866 -0.0017 0.294

TLU -0.053 0.0506 -0.001 0.291

MKTPRICE 0.005*** 0.0014 0.0001 0.001

DISEACON -0.354** 0.1501 -0.0067 0.018

TRAINING 0.348 0.4298 0.0065 0.419

QUPROD 0.087*** 0.0296 0.0016 0.003

IMPSEED 0.307 0.3437 0.0058 0.372

PACKMT 0.379*** 0.133 0.0071 0.004

STORAGE -0.309* 0.1693 -0.0058 0.068

Constant -7.135*** 2.4522 0.004

N = 228, Pseudo R2 = 0.4054, LR Chi2 = 62.21, P > Chi2 = 0.0000, ***, **, * are significant

levels at 1%, 5% and 10% respectively

Age of the household head (AGE): A continuous variable which is the number of years of

the sampled households in the study area. The Probit model indicated in the above table

shows the age of the household head influenced positively and significantly the likelihood

of farmers to add values on soybean at 1% significant level. Aged people are wise and have

good experience with age in the overall process of agricultural production and marketing.

They know relatively better than the youngsters how to increase the value of their products

by performing some value-adding activities to increase the qualities of their product to get

a better price in the market. The result indicates that the likelihood of farmers to add values

to soybean is increased by 0.11% as the age of the household head increased by a year. The

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finding agrees with Kyomugisha et al. (2018) who found that the age of the household

positively and significantly influenced the probabilities of farmers to add values to the

potato at less than 1% significant level.

Market distance (DISTANCE): Distance to the nearest urban market affects negatively

and significantly the likelihood of farmers to add values to soybean product as expected.

This implies that those producers who are far from their nearby markets are less likely to

add values on soybean due to unable to get value adding materials and high cost product

transportation. The result indicates that the likelihood of farmers to add values to soybean

is decreased by 0.91% as the travel time to reach the nearest urban center increased by an

hour. The reason behind here is that those farmers who are near to the urban centers can be

able to get the packaging materials, can prepare relatively good silos/storage conditions for

their product, and have good know-how about the value-adding activities. The finding

concurs with Sultan Usman (2016) who confirmed that the likelihood of farmers to add

values on wheat products is decreased by 0.3% as the distance to the nearest market

increased by a kilometer.

Market price of the commodity (MKTPRICE): It is a continuous variable that refers to

the price of soybean in birr per kilogram in 2019. Selling price affects positively and

significantly the likelihood of farmers to add values to soybean at 1% level of significance

as hypothesized. A good selling price of the commodity has positive implication for the

improvement of product quality by performing some value adding activities. According to

the results of the Probit model indicated above, the probabilities of farmers’ likelihood to

add values on soybean is increased by 0.01% as the selling price of soybean increased by

one birr per kilogram. The finding agrees with Kyomugisha et al. (2018) who confirmed

that selling price positively and significantly influences potato value addition at less than

1% significant level.

Disease constraint (DISEACON): The incidence of diseases on different agricultural

products in general and soybean in particular negatively influences the returns received due

to yield loss and quality deterioration. Disease negatively and significantly affects the

likelihood of soybean producers to add values to soybean at less than 5% level of

significance as expected. The model result indicated that the probabilities of farmers’

likelihood to add values to soybean is decreased by 0.67% as the level of disease incidence

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increased by one level. The finding by Minyahil Kebede and Assefa Gidesa (2016) also

confirmed that leaf blotch and brown spot soybean diseases highly reduce the quality and

quantity of soybean produced. This finding is also similar with Bandara et al. (2020) and

Murithi et al. (2015) who found that diseases negatively and significantly impacted value

addition on soybean due to high deterioration of yield quality and loss of production.

Quantity of soybean produced (QUPROD): The amount of soybean produced in quintals

affects the decision of farmers to participate in value addition positively and significantly at

1% level of significance as expected. As the results of the Probit model, the probability of

farmers to add values on soybean is increased by 0.16% as the quantity of soybean produced

increased by one quintal. This indicates that farmers who produce more outputs of soybean

give more attention to the quality of their product by performing some value-adding

activities like cleaning, Packaging, storing and transporting to sell with a better price for

their customers and to provide directly to ECX. Because soybean is an export commodity

and passes through ECX by considering some standards. To provide the product directly to

ECX, it needs a minimum of 50 quintals with some quality parameters. The finding is

consistent with Orinda et al. (2017) who found that positive and significant relationships

between the quantity of potato produced and farmers’ participation in value addition. The

result also agrees with the findings of Surni et al. (2019) who confirmed that the production

quantity of farmers influences value addition positively and significantly at less than 1%

level of significance.

Packaging material (PACKMT): Packaging material influences the likelihood of soybean

producers to add values to soybean at less than a 1% level of significance as hypothesized.

The availability of appropriate packaging materials is crucial to increase the value of a

product by storing and transporting to different places without quality deterioration. As the

results of the Probit model, the probabilities of farmers to add values to soybean is increased

by 0.71% when farmers have accessed packing materials. The result is in line with

Kyomugisha et al. (2018) who confirmed the existence of a positive and significant

association between packaging materials and potato value addition. This finding is also

similar to Obute et al. (2019) who found that packaging materials have a significant effect

on soybean to keep its quality for a long period of time.

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Storage (STORAGE): The absence of appropriate storage/silos negatively and

significantly influenced farmers’ likelihood to add values on soybean as expected. Storage

plays a significant role to store the products for a long period by keeping the qualities and

this maximizes the time value of the product. The model result depicted that farmers’

likelihood to add values to soybean is decreased by 0.58% when farmers have faced storage

problems. This is in line with Afework Hagos and Adam Bekele (2018) and Prabakaran et

al. (2018) who confirmed that suitable storage condition increases the time value of soybean

products by storing for a long time without losing the nutrient composition. This finding

also agrees with Esayas Negasa and Mustefa Bati (2019) who found that soybean producers

were not maximized the time value of their soybean product due to poor storage conditions.

As the data collected from FGDs, most producers had not good storage conditions and they

were forced to deliver their product immediately after harvest to local traders and this leads

farmers to be a price taker.

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Chapter 5. CONCLUSION AND RECOMMENDATIONS

5.1 Conclusion

The overall findings of this study concluding that producers, local-traders, whole-sellers,

cooperatives/unions, exporters, processors, retailers, and consumers were the primary value

chain actors with assistance of different supporters. Soybean production is a profitable crop

for smallholder farmers in the study area although productivity is far from the national

average that is why 39.53% of cultivated land was allocated for soybean. The contribution

of this soybean sub-sector in the overall economic growth is not as expected since most of

the product was exported as a grain without adding significant values. Local-traders

received the highest profit margin since all producers were chain actors and cannot influence

the selling price that is why they are price takers. More than half of the production costs for

soybean producers was labor and family labor took the lion share. Soybean meal is an

important driving force for soybean oil production due to its higher productivity and return

than soybean oil. According to the results of multiple linear regression (OLS) model,

productivity, lagged price, distance, family size, market information, soybean farm

experience, size of cultivated land, credit utilization and extension contact were the main

determinant factors affecting the quantity of soybean market supply. Results of the Probit

model also indicating age, distance, quantity produced, selling price, disease incidence,

packaging material, and storage were the determinant factors that influence the probabilities

of farmers’ likelihood to add values on soybean in the study area.

5.2 Recommendations

Based on the findings of this study, the following recommendations are relevant to improve

and develop a sustainable and viable soybean value chain that is locally adaptable and

expected to increase competitiveness.

Ethiopia imported large volume of byproducts of soybean oil and others annually by

spending huge money to cover domestic consumption. Because there is only one soybean

oil processing plant in the country at Addis Ababa. Therefore, the government has to give

more emphasis on the establishment of additional soybean oil processing plants on the

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potential producing parts of the country to satisfy domestic consumption and to save foreign

currency through import substitution.

All producers in the study area are small-scale farmers and most of them provided their

product to local traders with the least price since they are chain actors. Actors have poor

linkages and the product passes different stages to reach processors which are not viable for

both actors. Therefore, producers have to be directly linked to processors through unions to

get the expected return from soybean and to keep sustainable production.

Currently, edible oil factories are under establishment and they designed to cover the

domestic oil consumption by substituting the imported oil and soybean is considered as the

main crop for oil production. However, the current production status is so limited and this

is the main threat to the factories. Therefore, unrestricted and unreserved effort is needed

from producers, experts, researchers and other concerned bodies to increase the status of

soybean production to ensure sustainable domestic oil production.

The result of multiple linear regression analysis indicates that productivity, lagged price,

market information, credit utilization, extension contact, soybean farming experience, and

size of cultivated land influence the quantity of soybean market supply positively and

significantly. Therefore, to increase the volume of soybean market supply, these variables

should get more attention and has to be promoted. Increasing surplus production can be

realized by improving the production and productivity of soybean through the use of

improved varieties with full packages and other recommended agronomic practices with

close assistance of development agents, experts and other concerned bodies.

The decision of farmers to participate in value addition to soybean was influenced by the

quantity of soybean produced, age, market price, diseases, distance, packaging material, and

storage condition of the farm households. Therefore, more emphasis has to be given to each

significant variable by concerned bodies to enhance the contribution of what is expected

from this subsector to ensure sustainable economic growth through surplus soybean

production with significant value-addition that can be competitive in the domestic and

international markets.

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This study focused on soybean value chain analysis from producers to exporters and

soybean oil processor. The profit and marketing margins of each actor along this value chain

was investigated except exporters. However, there are different soy foods and feed

processing plants besides soybean oil in the country. Therefore, further investigation is

needed to know the processes of feed and food processing and way of delivering the

processed products to consumers as well as to estimate value addition of the processing

industries. Also, future investigation is required to estimate the profit margins of soybean

exporters in the international markets.

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7. APPENDICES

Appendix Table 1. Livestock conversion factors

Livestock category Conversion factors

Ox 1.1

Cow 0.8

Bull 1.1

Heifer 0.5

Calf 0.2

Sheep 0.09

Goat 0.09

Mule 0.8

Donkey 0.36

Hen 0.01

Source: ILRI (International Livestock research Institute)

Appendix Table 2. Test of multicollinearity for continuous explanatory variables

Variable VIF 1/VIF

LAGPRICE 1.97 0.51

PRODUCTIVITY 1.82 0.55

CLAND 1.45 0.69

SOYFAMEXP 1.23 0.82

FAMSIZ 1.17 0.85

AGE 1.11 0.90

TTIME 1.07 0.93

CREDIT 1.01 0.99

Mean VIF 1.35

Source: Own computation from survey result, 2020

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Appendex Table 3. Contigency coefficient for dummy/categorical variables

Mkt_inf

n

Ed_

level

Ex_conta

ct

Coops.m/shi

p

sex trainin

g

off/non-

farm

Mkt_infn 1

education

level

0.045 1

Ex_contact 0.178 0.026 1

Coops.m/ship 0.266 -0.065 0.211 1

sex -0.050 0.221 -0.080 -0.099 1

training 0.220 -0.138 0.110 0.084 -

0.018

1

off/non-farm 0.050 0.009 0.230 -0.025 0.004 -0.080 1

Appendix Table 4. ANOVA table for F-statistics

Source SS df MS F Sig. value

Model 127.945 15 8.5296 118.84 0.0000

Residual 15.216 212 0.0718

Total 143.161 227

Appendix 5. Questionnaires and interview guides for different stakeholders

Questionnaire developed for Farmer’s Survey to conduct a study on value chain

analysis of soybean in Pawe district of Metekel Zone /2018/19

Name of district_________________________________

Name of Kebele ________________________________

Name of Household head_________________________

Name of respondent_____________________________

Name of Enumerator ____________________________

Date of Interview _________Date _________Month ____________Year

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I. Household/respondents’ general information

1. Sex of household head 1= Male 0 = Female

2. Age of household head in years _______________

3. Religion of household head 1 = Orthodox 2 = Muslim 3 =Protestant 4=

Catholic 5 = others (specify ___________)

4. Education level of household head 1 = Illiterate 2 = Read and Write 3 =

primary (1-8) 4 = Secondary (9-10) 5 =

preparatory (11-12) 6 = TVET and above

5. Marital status 1 = married 2 = single 3 = Divorced 4 =

widowed

6. Distance of your residence to reach the nearest market ___Km, time taken on foot in

min__

7. Total family size of the household _______________

Sex category <15 years 15 to 30 years 31 to 65 years >65 years

Male

Female

Total

II. Resource ownership: Land holding size and farming characteristics

1.Total land size operated in ha ________________

Land ownership description Amount in hectare

Owen land

Cultivated land

Grazing land

Fallow land

Land rented in

Land rented out

Land share in

Land shared out

2. Farming experience in years___________________________

3. Soybean farming experience in years__________________

4. Did you use improved soybean varieties in your soybean farming experience? 1 = Yes 2=

No

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5. If no for Q4. Why? 1 = not available 2 = Lack of awareness 3 = the seed is expensive 4

= not productive 5 = others

6. If yes in question No. 4, what type of varieties that you have used?

1 =TGX 2 = Gishama 3 = Belesa-95

4 = Pawe -1 5 = Pawe-2 6 =Pawe-3 7 = others

7. From question No. 6 which variety is best for you? _____ Why? _________________

8. If yes for Q4 from above, what is the source of seed? 1 = government 2 = PARC 3 = other

farmers 4 = own saved 5 = Others

9. Have you used Inoculant for soybean production? 1= Yes 2 =No, if yes time of use

____E.C

10. If yes for question No.9, what is the source of Inoculant? 1 = government 2 = PARC 3

= agro dealers 4 = unions 5 = Others

11. Did you produce soybean in 2018/19 cropping season? 1 = Yes 2 = No

12. If yes in question No. 11, the total land allocated for soybean production is _______ha.

13. Crops produced in 2018/19 cropping season

Type of

crop

Variety Area

covered

(ha)

Amount

produced

(Qnt.)

Amount

consumed

(Qnt.)

Amount

sold (Qnt.)

Income

received

from sold

(Birr)

1 = Local

2 =

Improved

3 = Both

Maize

Soybean

Sorghum

Millet

Ground

nut

Sesame

Rice

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14. Livestock ownership

Livestock type Number owned

Ox

Cow

Bull

Heifer

Calf

Sheep

Goat

Mule

Hen

Donkey

III. Sources of Income

1. What are your major sources of income? 1 = sales of crops 2 = sales of livestock/ products

3 = off/non-farm income 4 = others

2. Estimation of annual cash income received from:

a) sales of crops ______________Birr/year

b) sales of livestock __________Birr/year

c) sales of livestock products (Butter, milk and egg…) ______________Birr/year

d) off/non-farm income ________________Birr/year) others

3. Are you involved the following off/non-farm activities? 1 Yes 2 = No

Source of income 1 = Yes 2 = No Estimated income received per year (Birr)

Daily labour

Handicraft

Petty trade

Fire wood

Employment

Remittance

Other (specify)

Total income received

4. What are the crops that you sold frequently? _____________________ (put in the order

of importance from the given crops) 1 = soybean 2 = maize 3 = sorghum 4 = rice 5 = ground

nut 6 = sesame 7= Others

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IV. Soybean production

Input supply

1. Have you used agricultural inputs (fertilizer, improved seed, agro chemicals etc.) for

soybean production in 2018/19 cropping season? 1 = Yes 2 = No

Input type Did you use for

soybean?

1 = Yes

2 = No

Pri

ce/Q

t.

/lit

ter/

Pac

ks

Am

ount

use

d /

ha

Tota

l am

ount

use

d

(Qt/

lit/

pac

ks)

Tota

l co

st i

ncu

rred

(Bir

r)

Source

1 = own saved

2 = government

3 =cooperative

4 = PARC

5 = private

traders

6= other farmers

Improved seed

Fertilizer DAP

Inoculant

Herbicide

Pesticides

Others (specify__)

Credit services

1. Did you have access to credit for different purposes? 1 = Yes 2 = No

2. If your answer for Q1 yes, did you take credit in cash last year? 1 = Yes 2 = No

3. If yes for Q 2, how much you took in Birr _____________________?

4. If yes for Q2, for what purpose you took the credit? 1 = house hold consumption 2 = to

purchase farm inputs = 3 = livestock purchase 4 = student fee 5 = land rent fee 6 = others

5. where is the source of your credit? 1 = micro finance 2 = NGOs 3 = local money lenders

4 = saving and credit association 6 = Banks 7 = others

6. If the answer for Q2 is No, why? 1 = high interest rate 2 = no need credit 3 = fear of

repayment due to in ability 4 = no service 5 = lack of awareness about the service 6 = others

7. What was the precondition to get credit service? 1 = personal guarantee 2 = membership

3 = land holding 4 = partial saving 5 = others

Extension and information services

1. Did you have an extension contact? 1 = Yes 2 = No

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2. If yes for Q1, frequency of contact with the extension agents per year? 1 = daily 2 =

weekly 3 = twice a month 4 = monthly 5 = rarely

Did you participate in the following trainings?

No. Type training 1 = yes

2 = no

By whom

1 = Research center

2 = Bureau of agriculture

3 = NGO

4 = University

5 = Others

(specify_____)

How many

times per

year

1 Crop

management/protection

2 Input use

3 Use of credit service

4 Marketing of agri. products

5 Pre and postharvest

handling

6 About seed production

7 Field day and

demonstration

Farming activities and associated costs

1. What is your means of cultivation? 1= hand tool 2= Own oxen/donkeys 3= rented oxen 4

= rented donkeys 4= rented tractor

2. If your answer is rented oxen, how much the cost of rented oxen per day in ETB

_______total oxen days used _________total cost paid for oxen rent in ETB____________

If rented donkeys, cost per day ________ETB, Total cost Paid _________ETB.

If rented tractor, how much the cost of the rented tractor per hectare in ETB? ___________

Total cost paid for tractor rent in ETB____________

3. Are you weeding your soybean manually? 1= Yes 2= No

4. Frequency of weeding? 1 = once only 2 = two times 3 = three times

5. If yes for Q3. what is/are your source of labor? 1= family labor 2= hired labor

3= daily labor 4 = family labor & hired labor 5 = family labor & daily labor

6. If Q5 is hired labor, how much you pay for him/her per month? _______ETB.

7. If Q5 is daily labor, numbers of labors used _______how much you pay for him/her per

man per day ________ETB, total cost paid for daily labors in ETB ___________

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8. Did you use daily labor for spraying chemical? 1= Yes 2= No

9. If yes for Q8, how much you pay per hectare? ___ETB, total cost paid for spraying in

ETB___

10. How did you harvest your soybean? 1= through family labor 2= daily labor 3= both

11. If through daily labor for Q10, how much you pay per man per day in ETB _____total

cost paid for harvesting in ETB ______________

12. What are the major soybean production constraints?

No. Constraints Rank of constraints

1= very serious

2= serious

3= moderate

4= not serous

5= not a constraint

Remark

1 diseases

2 Lack of improved varieties

3 Lack of inoculants and late arrival of

fertilizers

4 Weeds

5 High Cost of inputs

6 High cost of labor

7 Low productivity

8 Shortage of improved seeds

9 Lack of market facilities

10 Low awareness about agronomic practices

11 Low price of the commodity

12 Erratic rainfall and hill storm

V. Marketing

1. Did you sell soybean last year/ 2019? 1= Yes 2 = No

2. If yes for Q1, for whom you prefer to sell your product MRAP? 1= local traders 2= broker

3= district whole sellers 4= unions/cooperatives 5 = ECX 6= processors

3. Why you prefer for selling the selected actor? 1= price difference from others 2=

closeness of the buyer in distance 3= transport availability 4= customer relationship 5=

others

4. If you sold your product to more than one actor for Q2, please estimate the volume of sell

for each actor in quintals. _____________________________________

5. For how many months you store your soybean product for sale? on average ____months.

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6. The selling price of your soybean product immediately after harvesting _______

ETB/100kg

7. Where did you sell your soybean product? 1 = Farm gate 2= village market 3 = district

market 4 = outside district market 5 = ECX

8. Is there price difference for soybean in different places and to different buyers? 1 Yes 2

= No

9. If yes for Q8, indicate the price of sale in different places and to different buyers.

Place of sale Price when the product is sold to (Birr/100kg) in 2019

Village traders District whole sellers cooperatives

Farm gate

Village market

District market

Outside district market

11. What is your means of transportation for transporting soybean to the market? 1=

Donkey 2= cart 3= vehicle 4= others (specify________________)

12. Do you owned means of transportation? 1= Yes 2= No

13. If no for Q12. How much you cost for transporting ETB per 100kg? ___________

Marketing Association

1. What type of relation you have with buyer/s? 1= customer relation 2 = no relation 3=

friend 4= relative 5 = others (specify_____________________)

2. Do you have long standing customer (buyer)? 1= Yes 2= No

3. Have you sold your soybean product in credit basis? 1 =Yes 2= No

4. If yes for Q3, how long you wait for the payment? ______________

5. To decide for whom to sell, what factors you consider?

____________________________

Price information

1. Selling price of soybean in birr/100kg in 2018 _______

2. What is the trends of soybean price for the last five years? 1 = increasing 2= decreasing

3= no change

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3. Who is the decision maker for the price of soybean during selling? 1= Traders 2 = brokers

3= negotiation between farmers and traders 4 = others

4. If broker/middle men negotiate on price, who will pay for him? 1= trader 2 = farmer

5. If farmer pay, how much you pay per quintal ____ETB. Total cost for broker ______ETB.

Supply information

1. When did you sell your soybean product last year? 1 = immediately after harvest 2= one

month later 3 = more than two months later

2. If you sell immediately after harvest, why you did sell immediately? 1= storage problem

2= better price 3= fear of price fall 4= financial problems for home expenditure 5= others

3. What do you consider while supplying your soybean product to the market? 1= When we

need money, supply to the market 2= assessing market price information and supply if it has

better price 3 = others

Value addition

4. Are you keep the quality of your product? 1 = Yes 2 = No

5. If yes for Q4., what value adding activities you perform?

1= cleaning, cost per quintal _______________ETB.

2. storing, cost per quintal for storage ____________ETB.

3. Packaging, cost per quintal __________________ETB

4. transporting, cost per quintal for sale of transportation ____________ETB.

5. Sorting, cost per quintal/sacks __________________ETB

6. others

6. Is there price difference due to value addition? 1 = Yes 2 = No

7. If yes for Q6., what is your estimate of price difference due to value addition?

___ETB/Kg.

VI. Sources of market information

1. Do you get market information before providing your soybean product to the market? 1

= Yes 2= No

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Information source category Sources of information 1= Yes 2 = No

Professional/ personal networks Traders

Friends/neighbor

Development Agent

Others

(specify_________)

Public information system From market bulletins

Radio

Television

Message blackboards at

market

places/ECX board

Others (specify ______)

VII. Average return of soybean

Selling

price in

ETB/qt

Total cost ETB/qt

Packaging

material

Loading

/unloading

Transportation Storage

rent

Weight

loss

Revenue

VIII. Membership in cooperatives

1. Are you a member of farmers’ cooperatives? 1 = Yes 2= No

3. What is the advantage of being a member of a cooperative?

1 =The cooperative provides better price

2 = The cooperative tries to hold the cost down

3 = Provide guaranteed outlet (market)

4 = Give field service or technical assistance __

5 = It makes timely payment _____

6 = gives an input through credit

7 = gives oil and sugar

4. Who is the member of cooperative from your family? 1. Husband 2. Wife 3 = Children

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Marketing constraints

No. Constraints 1 =Yes

2 = No

Rank according to importance

1 = very serious

2 = series

3 = Moderately

4 = not series

5 = not constraint at all

1 Low price

2 Less/no market information

3 Price fluctuation

4 No buyer or lack of market

5 Lack of transport facility

6 Problem of packing

7 Poor linkage of actors

8 Quality problem

9 Storage problem

Questionnaires for Traders

General information

Name of organization ____________________________________

Name of enumerator: __________________ Signature: _____________

Date: ___________/____________/_____________

Address: ___________Region: __________Zone: ________Woreda: _______Town:

______

Demographic characteristics of Traders

1. Name of the Trader

2. Sex of the Trader 1 = male 2 = female

3. Marital status of the trader 1 = married 2 = single 3 = divorced 4 =

widowed

4. Education level of the trader 1 = illiterate 2 = primary school (1-8) 2 =

secondary school (9-10) 3 = preparatory (11-

12) 4 = TVET and above

5. Religion of the trader 1 = Orthodox 2= Muslim 3 = Protestant 4 =

Catholic 5 = Other

6. Business type of trader 1 = Retailor 2 = Whole seller 3 = Collector 4 =

Broker 5 = Cooperative 6 = union 7 = Exporter

8 = processor

7.Position of respondent on the

business

1 = Owner 2 = Spouse 3 = Employed manager

4 = relative of business owner 5 = others

8. Trade type 1 = Alone 2 = Partnership 3 = other

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9. If your answer is partnership for

Q8.

Number of members involved in joint

venture___

Number of women_________

Number of men__________

10. Year of involvement on trade

11. Time of trading 1 = Year-round 2 = When purchase price is low

3 = During high supply 4 = Other

12. Number of market days you

purchase soybean per week

1 = once a week 2 = two times 3 = throughout

the week 4 = others

13. Are you involved in trading other

than soybean?

1 = Yes 2 = No

14. If yes for Q13, what is that?

15. What was the amount of your

initial working capital when you start

soybean trading business in ETB?

___________Birr.

16. Sources of your initial starting

capital for trading

1 = Own saved 2 = Credit

17. If your source is credit, what is

the sources of your credit?

1= Relative 2 = Bank 3 = Micro finance 4 =

private money lenders 5 = Friends 6 = Other

traders 7 = Others

18. Reasons of credit 1= To extend soybean trading 2 = To purchase

soybean transporting vehicles 3 = To extend the

items of trading crop 4 = Others.

19. Is/Are there entry barriers for

trading?

1 = Yes 2 = No

20. If yes for Q20., What type of

entry barriers you face?

1 = Social barriers 2 = Legal barriers 3 =

Political barriers 4 = Financial barriers 5 =

administrative problems 6 = Coopetition of

unlicensed traders 7 = Discrimination 8 =

Others

21. With whom do you have a

linkage?

1 = Farmers 2 = Retailors 3 = Whole sellers 4 =

Consumers 5 = Collectors 6 = Brokers 7 =

Others

Purchasing part

22. What is/are your means of attracting your suppliers? 1= By giving credit to purchase

inputs and other expenditures 2 = By giving better price relative others 3 = By fair Weighing

4 = By giving other food items and seed through credit 5 = Others

23. How do you attract your buyer/s? 1 = By providing a quality product 2 = By giving fair

price relative to others 3 = By giving bonus 4 = Others

24. From which market you bought soybean in 2019? 1= Village market 2 = Pawe woreda

market 3 = Jawe woreda market 4 = Zonal market 5 = Addis Ababa market

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25. From whom you bought soybean in 2019? 1 = Purchased from sellers 2 = Farmers 3 =

Retailors 4 = Whole sellers 5 = from rural collectors 6 = cooperatives/unions

26. How do you purchase soybean? 1 = Year-round 2 = During pick periods 3 = Others

27. How many quintals of soybean you purchased per month? _________________

28. How many quintals of soybean you purchased totally in 2019? ______price

birr/kg______

29. Terms of payment for the purchase: 1 = Cash 2= Credit 3 = Both

30. Who sets the purchase price? 1 = Negotiation 2 = By the market 3 = By the trader 4 =

By seller 5 = others

31. If you decide the purchase price by yourself, how do you set the price? 1 = Individually

2 = By agreeing with other traders 3 = By considering the current situation of the market 4

= Others

32. When did you set the purchase price? 1 = one day before the market day 2 = a week

before the market day 3 = Early in the morning during the market day 4= At the time of

purchase

33. Did you use brokers for soybean purchase? 1 = Yes 2 = No

34. If brokers used, what problems you face? 1 = Brokers cheat the quality 2 = Wrong price

information 3 = Cheating scaling (weighing) 4 = Charged high brokerage 5 = Others

35. Did you use brokers for selling your collected soybean? 1= Yes 2 = No

36. What problems you face during selling through your brokers? 1 = Wrong price

information 2 = Cheating scaling (weighing) 3 = Charged high brokerage 4 = Others

37. What is the preferable season of the year for purchasing soybean in terms price? Lowest

price of soybean ________________months.

38. How do you measure your purchase? 1= By weighing (quintals) 2 = By weighing

through traditional weighing materials 3 = others

39. Have you ever stopped purchasing soybean due to lack of supply? 1 = Yes 2 = No

40. If yes for Q38., for how long? ________________________.

41. Indicate the average costs incurred per quintal in trading activities

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Activities Cost/quintal, Selling price and revenue

Purchase price

Transportation

Labor for packing

Loading/unloading

fee Sorting

Storage cost

Telephone cost

Material cost

License and taxes

Total cost

Selling price

Revenue

42. What is your estimation of price difference due to value addition? _________ETB/100kg

43. What are the prices of soybean during scarce and abundant seasons? Fill below table

Price Soybean during scarce ETB/kg Soybean during abundant ETB/kg

Maximum Minimum Maximum Minimum

Selling price

Purchase

44. Is soybean needs trade license in your locality? 1 = Yes 2 = No

45. If yes for Q42, how do you see the license procedures? 1 = Complicated 2 = Easy

46. Did you have soybean trade license? 1 = Yes 2 = No

47. How much did you pay for soybean trade license for the beginning? _________ETB.

48. Are there any trade restrictions for unlicensed soybean traders? 1 = Yes 2 = No

49. Are there charges (taxes) imposed by the government or community officials at the

market? 1 = Yes 2 = No

50. If yes for Q47, _________amount (ETB) based on sales volumes of products? And what

is the basis of payment? ____________________________________________________

51. Do you expand soybean trading? 1 = Yes 2 = No

52. If yes for Q49, why? ___________________________________________________

53. If No for Q49, why? ___________________________________________________

54. Are there problems in soybean marketing? 1 = Yes 2 = No

55. If yes for Q52, what are the problems? 1 = Credit and capital shortage 2 = Supply

shortage 3 = Storage problem 4 = Lack of demand 5 = Inadequate information 6 = Quality

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problem 7 = Government, Telephone cost, Absence of government support, Problem of road

access, High competition with unlicensed traders

56. What do you think the causes of the problems and what interventions is needed to solve

this problem in your opinion? ____________________________________________

Interview schedule for healthcare food manufacture plant

1. Quantity of soybean bought for processing in 2019? ___________quintals

2. Average purchase price of soybean in 2019 in birr per quintal ________

3.How many litters of soybean oil produced from one quintal soybean grain? ______

4. Average selling price of one litter soybean oil in Birr for whole seller ________for

retailor____ for consumer_________

5. How many whole sellers you have for soybean oil? ________

6.How many retailors you have for soybean oil? ________

7. Estimated cost of labor to produce one litter oil including hired labor _______Birr

8. Do you have commission agents? 1 = Yes 2 = No

9. If your answer is yes, how much you pay per unit for commission agents? _______Birr

Table 1: List of major ingredients and associated costs for producing one litter oil in 2019

No. Type inputs used to produce

one litter oil from soybean

Unit Amount used Cots per unit

1 Soybean

2

3

4

Table 2: List of major buyers of soybean oil and byproducts in 2019

No. Buyers Unit Amount Price of oil per unit

1 Whole sellers

2 Retailors

3 Consumers

10. Average return received from selling of soybean meal and hulls/byproducts from

100kg soybean grain after the oil has been extracted in birr_________________

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Questionnaires for Ministry of Trade and Industry

1. Total numbers of exporters involved for soybean export in 2011 E.C? ___________

2. Total volume of soybean grain exported in 2011E.C_____________ quintal

3. Average selling price of soybean exported in birr per quintal _____

4. Quantity of soybean grain imported to Ethiopia in 2011 E.C ________quintals.

5. Average import price of soybean grain in birr per quintal __________

Table 1: List of major imported soybean processed byproducts

No. Type of soybean byproduct Unit Amount Import price in birr per unit

1 Soybean oil

2 Soy milk

3

Table 2: List of major exported soybean processed byproducts

No. Type of soybean byproduct Unit Amount Export price in birr per unit

1 Soybean oil

2 Soy milk

3

Consumers Interview Schedule

1. Name of respondent _________________________

2. Zone_________Woreda___________Kebele__________Village_____________

3. Sex of consumer: 1 = Male 2 = Female

4. Age of consumer in years ______________________

5. Marital status of consumer: 1= married 2 = single 3 = divorced 4 = widowed

6. Education level of consumer: 1 = illiterate 2 = read and write 3 = primary school (1-

8) 4 = secondary school (9- 12) 5 = TVET and above

6. Religion of consumer: 1 = Orthodox 2 = Muslim 3 = Protestant 4 = Catholic 5 =

others

7. Means of income for consumers: 1 = farming 2 = trade 3 = employment 4= daily

laborer

8. What type of soybean byproduct you consume? 1 = oil 2 = soy milk 3 = bread 4 =

testy soya 5 = processed feed 6 = others

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9. If oil for Q.No.7, have you used other types of oil other than soybean oil? 1 = Yes 2 = No

10. Types other oils used: 1 = sunflower oil 2 = nug oil 3 = sesame oil 4 = vegetable oil

11. If yes for Q. No. 8, how do you evaluate the cost and taste of that oil as compared to

soy-oil. 1 = cheap and has good taste 2 = expensive and has good taste 3 = cheap but has

not good taste and not suitable for health 4 = others

12. What is your preference from soy-oil and others type of oils? 1 = soy-oil 2 = others

13. If for Q. No. 10 is soy-oil, why? __________if others type of oil, why? _____________

14. Source of soybean byproducts you consume: 1 = village retailors 2 = district retailors 3

= zonal whole sellers/retailors 4 = cooperatives/unions 5 = processors 6 = supper markets

14. How much the cost soybean byproducts? 1 = Soy oil ___________ETB/litter

2 = Processed feed _________ETB/kg

3 = Soy milk __________ETB/litter.

15. Consumers linkage with commercial soybean value chain actors: MRAP. 1 = byproduct

retailors 2 = brokers 3 = cooperatives/unions 4 = processors 5 = whole sellers 6 = others

16. Do you think soybean value chain is complex & has many intermediaries? 1 =Yes 2 =

No

17. Do you think that retailors and whole sellers of soybean byproducts marketing are

efficient and fair? 1 = Yes 2 = No

18. If No for Q. No. 16, what are the problems in regarding to soybean byproduct marketing?

1 = high price of the byproducts 2 = unfair distribution of the byproducts for consumers 3 =

unfair price set by sellers 4 = lack of clear information about the exact price of soybean

byproducts 5 = existence of many intermediaries in the market 6 = cheating by sellers and

brokers due to weak follow up of the government at different levels 7 = others

19. If yes for Q. No. 16, how do you evaluate the overall process of soybean byproduct

marketing? 1= very good 2 = good 3 = fair according to the existing situation.

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AUTHOR BIOGRAPHICAL SKETCH

The author was born in North Mecha Woreda, West Gojam zone of Amhara Regional State

in May 1989. He attended his primary education at Wotet Ber primary school and his junior

at Densabata junior school. He attended his secondary school education at Adet secondary

high school and his preparatory at Merawi preparatory school in West Gojam zone. After

completion of his preparatory school education, he joined Bahir Dar University College of

Agriculture in October 2008 and graduated with BSc. Degree in Rural Development in

2010. Soon after his graduation, he was employed by Menge agriculture and rural

development office and served as an agricultural extension and communication expert for

about five years. Starting from 2016, the author joined the Ethiopian Institute of Agricultural

Research at Pawe Agricultural Research Center and served as an assistant researcher in the

Agricultural extension and communication research directorate. Finally, he joined Bahir Dar

University in 2019 to Pursue his MSc. Degree in Rural Development Management in the

regular program.