Analysis of Honey Market Chain: the Case of Chena Woreda, Kaffa Zone, Southern Ethiopia

ANALYSIS OF HONEY MARKET CHAIN: THE CASE OF CHENA WOREDA, KAFFA ZONE, SOUTHERN ETHIOPIA

MSc THESIS

KASSA TAREKEGN EREKALO

APRIL 2017 HARAMAYA UNIVERSITY, HARAMAYA

Analysis of Honey Market Chain: The Case of Chena Woreda, Kaffa Zone, Southern Ethiopia

A Thesis Submitted to School of Agricultural Economics and Agribusiness, Postgraduate Program Directorate HARAMAYA UNIVERSITY

In Partial Fulfillment of the Requirements for the Degree of MASTER OF SCIENCE IN AGRICULTURAL ECONOMICS

Kassa Tarekegn

April 2017 Haramaya University, Haramaya

HARAMAYA UNIVERSITY POSTGRADUATE PROGRAM DIRECTORATE

We hereby certify that we have read and evaluated this Thesis entitled “Analysis of Honey Market Chain: The Case of Chena Woreda, Kaffa Zone, Southern Ethiopia” prepared under our guidance by Kasssa Tarekegn. We recommend that it be submitted as fulfilling the thesis requirement.

Jema Haji (PhD) ______Major Advisor Signature Date

Bosena Tegegne (PhD) ______Co-Advisor Signature Date

As members of the Board of Examiners of the MSc. Thesis Open Defense Examination, We certify that we have read and evaluated the Thesis prepared by Kassa Tarekegn and examined the candidate. We recommend that the Thesis be accepted as fulfilling the Thesis requirements for the degree of Masters of Science in Agricultural Economics.

______Chairperson Signature Date

______Internal examiner Signature Date

______External examiner Signature Date

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DEDICATION

I dedicated the manuscript of this thesis to whole members of my family especially my mother Abebach Ersumo, and my father Tarekegn Erekalo for their encouragement, prayer, support and partnership in the success of my academic career.

STATEMENT OF THE AUTHOR

By my signature below, I declare and affirm that this Thesis is my own work. I have followed all ethical and technical principles of scholarship in the preparation, data collection, data analysis and compilation of this Thesis. Any scholarly matter that is included in the Thesis has been given recognition through citation.

This thesis is submitted in partial fulfillment of the requirements for MSc degree at the Haramaya University. The Thesis is deposited in the Haramaya University library and is made available to borrowers under the rules of the Library. I solemnly declare that this Thesis has not been submitted to any other institution anywhere for the award of any academic degree, diploma or certificate.

Brief quotations from this Thesis may be made without special permission provided that accurate and complete acknowledgement of the source is made. Requests for permission for extended quotations from or reproduction of this Thesis in whole or in part may be granted by the Head of the School when in his judgment the proposed use of the material is in the interest of scholarship. In all other instances, however, permission must be obtained from the author of the Thesis.

Name: Kassa Tarekegn Signature: ______Date: ______School: Agricultural Economics and Agribusiness

BIOGRAPHICAL SKETCH

The author was born from his parents Tarekegn Erekalo and Abebach Ersumo on April 16, 1991 in Elefata kebele, in West Badewacho district of Hadiya zone Southern Ethiopia. He attended his elementary education at Elefata Primary and Junior School. The author attended his secondary and preparatory education in Durame comprehensive high school.

The author joined Hawassa University in October 2009 and graduated with Bachelor of Science Degree in Agricultural Resource Economics and Management in July 2011. Soon after his graduation, he was employed by Southern Agricultural Research Institute of Bonga Agricultural Research Center and served as socio-economics researcher until he joined Haramaya University in October 2015 for his MSc study in Agricultural Economics.

ACKNOWLEDGEMENTS

Almighty God deserves all praise for this work. His providence of love, mercy, grace, forgiveness, strength, health and the gift of life provided in me tolerance and endurance throughout the years of study from course work to research. I have always been protected and safely shielded under His Mighty Name.

I express my genuine gratitude to my major advisor Dr. Jema Haji, for his consistent guidance, encouragement and critical reviews while developing the proposal, and for giving me constructive and valuable comments and suggestions that shaped this thesis. It is my great pleasure to extend my appreciation and gratefulness to my co-advisor, Dr. Bosene Tegegne, for her positive influence on my thesis research, devoting her precious time, warm welcome at her office, and pertinent comments at each level of the analysis.

I owe thanks to Southern Agricultural Research Institute (SARI) for giving me chance to pursue MSc study and financing the research project. It is also a great pleasure to extend my appreciation to staff members of Bonga Agricultural Research Center (BARC) for their facilitation of the study process and encouragement.

I am very much indebted to all enumerators who assisted me during my data collection. I also would like to extend my thanks to all staff members of Chena district Livestock and Fishery office that helped me at various levels during my research work. I also feel great to express my thanks to the respondents who participated in the study for sparing their precious time and for responding positively to the lengthy interview schedule.

Finally, I am greatly indebted to my parents, my brothers and sisters for their special love and prayer throughout my life. My sincere appreciation and thanks also go to my colleague Kibreab, Mestafe, Zelelam, Engida and Assefa for the remarkable memories and constant moral support during the study period.

My God bless you all!!

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ACRONYMS AND ABBREVIATIONS

BARC Bonga Agricultural Research Center CIAT Centro International de Agricultural Tropical CR Concentration Ratio CSA Central Statistical Agency CDLFO Chena District Livestock and Fishery Office CDFEDO Chena District Finance and Economic Development Office DA Development Agent EBA Ethiopian Beekeeping Association GDP Gross Domestic Product ILRI International Livestock Research Institute GO Governmental Organization KZLFD Kaffa Zone Livestock and Fishery Department MoA Ministry of Agriculture MVP Multivariate Probit Model NBE National Bank of Ethiopia NGO Non-Governmental Organization OLS Ordinary Least Squares PPS Probability Proportional to Size RMA Rapid Market Appraisal SARI Southern Agricultural Research Institute SCP Structure Conduct Performance SNNPRS Southern Nation, Nationalities and Peoples Region State VIF Variance Inflation Factor

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TABLE OF CONTENTS

DEDICATION iv

STATEMENT OF THE AUTHOR v

BIOGRAPHICAL SKETCH vi

ACKNOWLEDGEMENTS vii

ACRONYMS AND ABBREVIATIONS viii

TABLE OF CONTENTS ix

LIST OF TABLES xii

LIST OF FIGURES xiii

LIST OF TABLES IN THE APPENDIX xiv

LIST OF FIGURES IN THE APPENDIX xv

ABSTRACT xvi

1. INTRODUCTION 1

1.1. Background of the Study 1

1.2. Statement of the Problem 2

1.3. Research Questions 4

1.4. Objective of the Study 4

1.5. Significance of the Study 4

1.6. Scope and Limitations of the Study 5

1.7. Organization of the Thesis 5

2. LITRATURE REVIEW 6

2.1. Definition of Terms and Basic Concepts of Market Chain Analysis 6

2.2. Honey Production and Marketing in Ethiopia 7

2.3. Approaches to the Study of Agricultural Marketing 9

2.4. Analytical Framework for the Study 11 2.4.1. Structure, conduct and performance (SCP) of market 11 2.4.1.1. Structure of the market 11 2.4.1.2. Conduct of the market 12 2.4.1.3. Performance of the market 13 ix

TABLE OF CONTENTS (CONTINUED)

2.4.2. Factors affecting volume of market supply 14 2.4.3. Determinants of market outlet choices 15

2.5. Empirical Evidences 15 2.5.1. Empirical literature on S-C-P 15 2.5.2. Empirical studies on the determinants of market supply 16 2.5.3. Empirical studies on the determinants of market outlets choices 18

2.6. Conceptual Framework of the Study 20

3. RESEARCH METHODOLOGY 20

3.1. Description of the Study Area 22

3.3. Sampling Procedure and Sample Size 24

3.4. Methods of Data Analysis 25 3.4.1. Descriptive analysis 26 3.4.2. Econometric analysis 28 3.4.2.1. Determinants of honey market supply 28 3.4.2.2. Determinants of honey producers’ market outlets choice 28

3.5. Definition of Variables and Hypothesis 30 3.5.1. Dependent variables 30 3.5.2. Independent variables 31 3.5.2.1. Independent variables for volume of honey marketed 31 3.5.2.2. Independent variables for honey market outlets choice 33

3.6. Model Diagnosis 35

4. RESULTS AND DISCUSSION 37

4.1. Descriptive Statistics Results 37 4.1.1. Demographic and socio-economic characteristics of producers 37 4.1.2. Honey production characteristics of the sample households 38 4.1.3. Institutional services 40 4.1.4. Market related issues 41 4.1.5. Demographic and socio-economic characteristics of sampled traders 41 4.1.6. Demographic characteristics of sampled consumers 43 4.2. Honey Market Chain Actors, their Roles and Marketing Channels 44

TABLE OF CONTENTS (CONTINUED)

4.2.1. Honey market chain actors and their roles 44 4.2.2. Honey marketing channels 45

4.3. Structure, Conduct and Performance of the Honey Market 47 4.3.1. Honey market structure 47 4.3.1.1. Degree of market concentration 47 4.3.1.2. Degree of market transparency and barriers to entry 48 4.3.2. Honey market conduct 50 4.3.2.1. Honey producers conduct 50 4.3.2.2. Conduct of honey traders 51 4.3.3. Marketing performance 52 4.3.3.1. Marketing costs 52 4.3.3.2. Structure of production costs and profitability of honey production 53 4.3.3.3. Marketing margin 54 4.4. Econometric Results 56 4.4.1. Determinants of honey market supply 57 4.4.2. Determinants of honey producers market outlets choices 60

5. SUMMARY, CONCLUSION AND RECOMMENDATIONS 66

5.1. Summary and Conclusion 66

5.2. Recommendations 67

6. REFERENCES 70

7. APPENDIXICS 78

LIST OF TABLES

Table Page

1. Sample distribution of honey producers in selected kebeles 25 2. Demographic characteristics of sampled honey producers 38 3. Sources of income by sampled honey producers per year 38 4. Experience, number of hives owned and quantity of honey produced 39 5. Frequency of extension contact and amount of credit received 40 6. Cooperative membership and average quantity of honey supplied in Kg 40 7. Access to market information, price of honey and distance to market 41 8. Demographic characteristics of sampled traders 42 9. Financial capital of sampled traders 42 10. Sources of working capitals and loans of sampled traders 43 11. Demographic characteristics of consumers 43 12. Concentration ratio of sample traders 47 13. Market information, lack of capital and licensing procedure 48 14. Production and selling strategies of producers 50 15. Buying and selling behavior of traders 51 16. Honey average marketing costs for different marketing agents 52 17. Structure of honey production costs and profitability by type of beehives used 54 18. Honey market margin for different channels 56 19. OLS estimate of determinants of honey market supply 59 20. Multivariate probit estimations for determinants of producers outlets choice 63

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LIST OF FIGURES

Figure Page

1. Conceptual framework of the study 21 2. Map of the study Area 23 3. Types of bee hives used by the sample honey producers 39 4. Honey marketing channels for different market actors 46

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LIST OF TABLES IN THE APPENDIX

Appendix Table Page

1. Test for multicollinearity of explanatory variables 79 2. Specification /omitted variable test result 79 3. Heteroscedasticity test result 79 4. Questionnaires 81

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LIST OF FIGURES IN THE APPENDIX

Appendix Figure Page

1. Norma probability plot for residuals 80 2. Boxplot for volume of honey supplied to check outliers 80

Analysis of honey Market Chain: The Case of Chena Woreda, Kaffa Zone, Southern Ethiopia ABSTRACT

This study was initiated to identify honey marketing channels, actors and their roles; to analyze the structure, conduct and performance of honey markets; to identify factors affecting volume of honey marketed and producers’ market outlets choices in Chena woreda of Kaffa zone, Southern Ethiopia. Data from a total of 154 sample honey producers from three randomly selected honey producing kebeles, 30 traders and 20 consumers were collected and analyzed. SCP model for structure conduct and performance analysis, multiple linear regression model for determinants of market supply and MVP model for outlet choice decision determinants analysis were used. The identified honey market chain actors in the study area include producers, cooperatives, collectors, retailers, wholesalers, processors and consumers. Seven honey marketing channels were identified in the study area with major share of volume of honey marketed goes to marketing through channel VII. The result from analysis of market concentration indicates that the structure of honey market in Chena district is weak oligopoly with four largest honey traders’ concentration ratio of 33.63%. In line with degree market of concentration, lacks of market information for producers, problems in licensing and unfair competition with the unlicensed traders are identified to be the major entry barriers to honey marketing; as the result honey market in the district shows some deviations from competitive market norms. Market conduct shows that price of honey was set by traders and producers are price takers. Market performance analysis based on marketing margins shows that all the actors generated positive gross profit. The result reveals that the total gross marketing margin is highest in channel V (38.55%) and lowest in channel II (22.67%). The results also showed that the maximum producers’ share from the total consumers’ price is highest in channel II (77.33%) and lowest in channel V (61.45%). Multiple linear regression model result reveals that beekeeping experience, hive types used, number of beehives owned, extension contact and cooperative membership positively and significantly affected honey market supply while distance from nearest market negatively and significantly affected it. The simulation result of MVP model shows that 69.05%, 73.4%, 61% and 46.9% probability for choice retailers, cooperatives, collectors and consumer outlets, respectively and with 10.96% probability of jointly choosing the four outlets simultaneously. The MVP result for honey producers’ market outlet choices also reveals that quantity of honey sold, frequency extension contact, beekeeping experience, distance to market, access to market information, cooperative membership and trust in buyers significantly determined honey producers market outlet choices in the study area. To enhance volume supplied with appropriate market outlet choices which in turn increase producers income generated from honey, all concerned bodies need to focus on experience sharing with experienced households, capacity building through training on improved honey production, increasing access to improved beehives, improving poor road facility, strengthen financial capacity of existing and establishment of additional beekeepers cooperatives.

Key words: Honey; Marketing margin; Multiple linear regression; Multivariate probit; Chena woreda

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1. INTRODUCTION

1.1. Background of the Study

Beekeeping is one of the oldest farming practices in Ethiopia as result of its forests and woodlands contain diverse plant species that provide surplus nectar and pollen to foraging bees (Workneh, 2011). The country has comparative advantage for beekeeping due to its favorable natural resource endowment for the production of honey and wax (MoA and ILRI, 2013). Owing to its varied ecological and climatic conditions, Ethiopia is home to some of the most diverse flora and fauna with the largest honey producer in Africa (Nuru, 2007).

Ethiopia is among the major producer of honey both in Africa and in the world. For instance in 2013 the country produced about 45 thousand tones which accounted about 27% and 3% of African and World honey production respectively which makes the country the largest producers in Africa and the tenth in the world (FAOSTAT, 2015). According to CSA (2015), the total volume of honey production in Ethiopia was about 49 thousand tones.

Apiculture is a promising off-farm enterprise, which directly and indirectly contributes to smallholder’s income in particular and it accounts 1.3% of agricultural GDP of the country (Demisew, 2016). It has been reported that annually an average of 420 million Ethiopian Birr is obtained from the sale of honey (MoA and ILRI, 2013). The subsector is also creating job opportunities in both rural and urban areas through organizing jobless urban and landless rural youth and women to involve in them in bee equipment production and beekeeping activities (Chagwiza, 2014).

According to USAID (2012), about 10% of the honey produced 2011/12 in the country is consumed by beekeeping households. The remaining 90% is sold for income generation; of this amount it is estimated that about 70% is used for brewing tej and the rest is consumed as table honey. Domestic honey consumption is increasing due to highly increasing demand for tej and birzi increased consumption of processed table honey in most urban areas and increased demand for honey in the local industries (Gemechis, 2015). 2

Despite the long tradition of beekeeping in Ethiopia, being the leading honey producer and the availability of huge potential, the production system of the sector is traditional (Miklyaev et al., 2014). According to CSA (2014), 96% of the hives are reported to be traditional and 91% of the total honey produced comes from traditional hives. This results in low productivity, which in turn result the low contribution of the sector to agricultural GDP of the country. Proper understanding of the performance of the market system apparently required for making market orientation of product (CIAT, 2004).

Southwestern part of Ethiopia has diversified types of forest vegetation suitable for beekeeping, as a result large volume of honey was produced annually. Despite the high honey production in the study area, due to poor infrastructural facility, poor market information and long market chain there is no ready market attracting beekeepers (Kassa et al., 2017). According to Kifle et al. (2015) knowledge on how marketing routes and systems could contribute to the household income and the implications of these for national and international trade in apiculture is the way to design any policy or institutional innovation to improve marketing for the benefit of the poor. Therefore, this study was conducted to analyze market chain of honey in Chena woreda.

1.2. Statement of the Problem

Southwestern part of Ethiopia has great potential for beekeeping activities; due to the presence of dense natural forest with different species of flora and fauna which are used as pollen and nectar source for bees and suitable environmental conditions for bee colony and the production of honey (Yoshimasa, 2014). Kaffa zone is highly suitable for beekeeping and large volume of honey is produced annually in Southwest part of the country (Nuru, 2007). However, sparsely populated rural areas, and poor infrastructural facility are physical barriers to accessing markets; lack of negotiating skills, lack of collective organizations and lack of market information are impediments to market access (Kassa et al., 2017).

Chena woreda is believed to have diversified types of vegetation;; and cultivated crops and expected to be one of the areas that have considerable potential for beekeeping activities and honey production in Kaffa zone (Awraris et al., 2012). However, honey production is very traditional which is practiced mainly by hanging traditional hives on tall trees in the dense forest far from human settlement areas. Beekeepers produce honey using traditional

3 methods and selling their honey products at the local market. Though the honey production is traditional, currently due some interventions by government and non- government organizations the beekeepers in the woreda are using improved beehives that boost volume of honey produced as the result the woreda is high honey producer in the zone (KZLFD, 2016).

Despite high honey production, the market supply of honey is low as compared its potentiality due to some socioeconomic, production, market and institution related factors. According to Kassa et al. (2017), honey producers in the study faced marketing problem due to remoteness of some kebeles, low farm-gate prices and long market chain which results low level market participation. Additionally, honey producers in Chena district are widely characterized by limited marketing linkage which emanates from physical barrier to accessing market, low bargaining power, faraway from weather road results inability to force local collectors and trader’s price setting and exploitation at farm get level. The market of the area is dominated by conventional system and honey producers are forced to sale directly for conventional transaction root like; collectors and unlicensed traders which they do not get premium price for their produce.

Improved information and marketing facility enables farmers to plan their production more in line with market demand, to schedule their harvest at the most profitable time, to decide which market to sell their produce to and negotiate on a more even footing with traders (CIAT, 2004). According to MoA and ILRI (2013), enhancing the ability of beekeepers to reach markets and actively engaging them is one of the most pressing development challenges. Without having convenient marketing conditions, the possible increment in output and rural incomes resulting from the introduction of improved production technologies could not be effective.

Even though honey is economically and socially important, the research on apiculture on the study area has largely focused on biophysical aspects such as yield enhancement, production system analysis. Besides, previous studies on honey in Kaffa zone have concentrated on production system (Nuru, 2007; Awraris et al., 2012; Gallmann and Thomas, 2012; Awraris et al., 2015), information on identification of honey market type and marketing systems look like; determinants of volume of supply to market and producers’ market outlets choices in the woreda is lacking where the great potential of honey production exists. Therefore, there is a need to employ a market chain approach to

4 fully understand and make an intervention to resolve the problem of honey marketing at all stages by identifying major honey market chain actors and marketing channels; analyzing structure, conduct and performance of honey market and by identifying determinants of honey marketed surplus and producers’ market outlets choice in Chena woreda.

1.3. Research Questions

The study attempted to answer the following research questions:

1. What are the major honey marketing channels and who are the honey market chain actors and their roles in Chena woreda? 2. How is the structure-conduct-performance of honey market in the study area? 3. What factors influence the volume of market supply of honey in the study area? 4. What factors influence honey producers’ market outlets choice in Chena woreda?

1.4. Objective of the Study

The general objective of this study is to analyze honey market chain in Chena woreda.

The specific objectives of the study are: 1. To identify the major honey marketing channels, market actors and their roles; 2. To analyze the structure, conduct and performance of honey market; 3. To identify factors that determine market supply of honey; and 4. To identify determinants of honey producers’ market outlets choice in Chena woreda.

1.5. Significance of the Study

Critical analysis of marketing is very important before launching and implementing market development issues. Improved information and marketing facilities enable farmers to plan their production more in line with market demand, to decide which markets to send their produce to and negotiate on a more even footing with traders. Also it enables traders to move produce profitably from a surplus to a deficit market and to make decisions about the economics of storage, where technically possible. Therefore, beekeepers (producers), traders, cooperatives, government and non-government organizations, which have interest in improving honey marketing system are expected to benefit from the results of this study.

1.6. Scope and Limitations of the Study

The study seeks only to examine honey market chain by analyzing market margins among the actors within the marketing chains, honey market structure and conduct, and factors that determine honey marketed supply and market outlet choice using cross-sectional data which are collected from three kebeles of Chena woreda. At woreda market levels, role of actors in the channel and bargaining characteristics of producers, buying and selling strategies of producers and traders in the marketing channel were assessed. However, this study was conducted only in Chena, one of the 11 woredas of Kaffa zone due to time and budget limitations. Therefore, the result and data obtained from this study cannot be generalized to other woredas of the zone because their socio-economic conditions different.

1.7. Organization of the Thesis

The thesis has been organized under five chapters. Chapter one pinpoints background, statement of the problem, research questions, objectives, significance of the study, scope and limitations of the study and organization of the thesis. Chapter two presents review of theoretical and empirical evidences related to the study. Chapter three discusses research methodology (description of the study area, data types and sources, methods of data collection, sampling techniques and methods of data analysis) of the study. Chapter four presents’ descriptive and econometric results and discussed in detail. Chapter five summarizes the main findings of the study and draws conclusion and recommendations.

2. LITRATURE REVIEW

This chapter highlights definitions of basic related terms, basic concepts of market chain, honey production and marketing in Ethiopia, analytical framework for the study, empirical evidences on the determinants of market supply and producers’ market outlets choice and conceptual framework for the study.

2.1. Definition of Terms and Basic Concepts of Market Chain Analysis

Beekeeping: It can be defined as the process of keeping honeybees to produce honey and bee wax for food, income generation and or medicinal purpose.

Honey producers: These are households who involved in beekeeping to produce honey for sale and consumption who owned minimum of five bee hives.

Market: It is the collection of buyers and sellers through their actual or potential interactions determine the price of a product or set of products. The concept of market is linked to the degree of communication among buyers and sellers and the degree of substitutability among goods. Market means a social institution that performs activities and provides facilities for exchanging commodities between buyers and sellers. Economically the term market refers not to a place but to commodities, buyers and sellers, hence they are freely interacted with one another (Kotler and Armstrong, 2003).

Marketing: It is the process of planning and executing as a social and managerial process by which individuals and groups obtain what they want and need through creating and exchanging products and value with others (Lunnd et al., 2004). Kohls and Uhl (1985) forwarded a broader definition that marketing is the set of economic and behavioral activities that are involved in coordination the various stages of economic activities from production to consumption pricing, promotion and distribution of idea, goods and services to create exchange that satisfy individual and organizational goals.

Marketing channels: It is the paths through which products pass from producers until it reaches on the hand of consumers (Mendoza, 1995). Collectors, wholesalers, processors, retailers and other sources used in getting the product to the hand of final consumers are in the classification of marketing channels. Marketing channel can be short or long,

7 depending on the type and quality of the product marketed, available marketing services, and prevailing social and physical environment (Islam et al., 2001).

Chain actors: These are individuals or groups who involve directly or indirectly in the chain in production and delivering the products.

Market chain: The term used to describe the various links that connect all the actors and transactions involved in the movement of agricultural goods from the producer to the consumer (CIAT, 2004). Market chain analysis, therefore, identifies and describes all points in the chain (producers, traders, transporters, processors, consumers), prices in and out at each point, functions performed at each point/ who does what?, market demand/ rising, constant, declining, approximate total demand in the channel, market constraints and opportunities for the products.

Market supply: It refers to the amount actually taken to the markets irrespective of the need for home consumption and other requirements where as the market surplus is the residual with the producer after meeting the requirement of seed, payment in kind and consumption by producers at source. In order to describe market supply words like marketable surplus and marketed surplus are usually used (Allen et al., 2008).

Marketable and marketed surplus: Marketable surplus is the quantity of the product left out after meeting the farmers’ consumption and utilization requirements for kind payments and other obligations such as gifts, donation or charity. Thus, marketable surplus shows the quantity left out for sale in the market. The marketed surplus shows the quantity actually sold after accounting for losses and retention by the farmers, if any and adding the previous stock left out for sale. Thus, marketed surplus may be equal to marketable surplus, it may be less if the entire marketable surplus is not sold out and the farmers retain some stock and if losses are incurred at the farm level or during transit (Kohls and Uhl, 1985).

2.2. Honey Production and Marketing in Ethiopia

Honey production in Ethiopia is characterized by traditional beekeeping practice exercised for more than thousands of years (Beyene et al., 2014). Traditional beekeeping is mostly practiced with different types of traditional hives that are very much diversified in shape, volume and the materials used depending on the cultural differences and the local

8 materials available for construction (Gemechis, 2014). The productivity of traditional hives is extremely low and the average yield is only about 5–8 kg per colony per annum (USID, 2012). However, with this existing practice the annual honey production in the country is increasing and has reached quite higher than 53 thousand tons in 2012 (CSA, 2013).

Currently, improved beehives and the locally made “chefeka” hives and frame box hives are being highly disseminated to the beekeepers by different GOs and NGOs (Gemachis, 2015). The annual average of the honey yield obtained from “chefeka” hive is about 17kg, while that of the frame hive is about 26kg (Demisew, 2016). On the other hand, in high potential areas of northern and southwestern parts of the country more than the average yield from well managed colonies is commonly reported (MoA and ILRI, 2013). In Ethiopia, there are generally two honey harvesting seasons: the major one that lasts from October to November and the secondary one from April to June. However, in addition to these major harvesting periods, there are many small harvesting periods which depend on the type of flowering plants and rainfall patterns in different agro ecologies (Nuru, 2007).

The domestic honey market starts at the smallholder beekeepers level, who mainly sell crude honey to collectors in the nearest town/village markets (Desalegn, 2011). According to MoA and ILRI (2013), beekeepers, honey collectors, retailers, tej brewers, processors and exporters are identified as the key actors in the value chain of the honey sub-sector. Three principal channels were identified in the value chain of the sub-sector in Ethiopia. (Chagwiza, 2014). These are tej brewery channels, honey processing and exporting channels and beeswax channels. These channels are complex and interconnected that implies absence of organized marketing channel and lack of formal linkages among the actors. Most of the harvested honey goes through tej brewery channels and the producers are indicated as price takers.

To strengthen the honey marketing chain, beekeepers form honey producing and marketing cooperatives to cope with the market challenge they face. The cooperatives collect crude honey from their members and sell the semi processed honey to processing companies and other intermediaries who buy in bulk and retail. However, in many cases the cooperatives lack proper collection, storage and transportation facilities and hence compromise the quality of the honey. They also have low business concept (market

9 information gathering and analysis, pricing, promotion) to be competitive (Chagwiza, 2014).

Despite severe deforestation throughout many regions of the country, the southwest part of Ethiopia still contains many nectar and pollen producing plants suitable for beekeeping (Chala et al., 2013). According to Yoshimasa (2014), beekeeping is dominated by traditional methods and the quality of honey is remaining poor regardless of the potential in this part of the country. The beekeepers hang their hives on the top of a tree in the forest far from their living home that is made from tree barks, reeds, logs, grounds and clay pots.

Regarding to honey marketing in south-western part of the country, the major buyers are cooperatives, local brewers, collectors, retailers, honey and wax processing industries in the nearby markets (Gallmann and Thomas, 2012). The producers of southwest Ethiopia do not process honey before sale. However, about 45 % of the farmers strain the crude honey by simple drainage to remove the beeswax and any floating impurities simply using their hands (Mullubrahan, 2014).

Despite all the benefits that honey can bring to the beekeepers in south western Ethiopia, the producers are tackling with a number of challenges and constraints that can potentially hamper the honey production and the economic contribution it brings to their livelihoods. Low price of honey, lack of access to credit, lack of support, private trader cheats on price and weight, lack of capital for an organization to buy all our honey, transport problem, fewer buyers and lack of access to timely information are some of the challenges that producers faces in southwest Ethiopia (Kassa et al., 2017).

2.3. Approaches to the Study of Agricultural Marketing

Different circumstances involved in the demand and supply of agricultural products, and the unique product characteristics, require a different approach for analyzing agricultural marketing problems (Kohls and Uhl, 1985). The major and most commonly used approaches are functional, institutional, commodity behavioral and SCP.

Functional approach: In this approach we took all the basic marketing activities (functions) that have to be performed in the agricultural commodities and at the marketing of inputs in to agricultural production. Functional approach studies marketing in terms of

10 the various activities that are performed in getting farm product from the producer to the consumer. These activities are called functions (Crammer et al., 1997).

Institutional approach: This approach focuses on the description and analysis of different organizations engaged in marketing (producers, wholesalers and retailers) and pays special attention to the operations and problems of each type of marketing institution. The institutional analysis is based on the identification of the major marketing channels and it considers the analysis of marketing costs and margins (Mendoza, 1995). An institutional approach for the marketing of agricultural product should be instrumental in solving the three basic marketing problems, namely consumers’ demand for agricultural products, the price system that reflects these demands back to producers and the methods or practices used in exchanging title and getting the physical product from producers to consumers in the form they require, at the time and place desired (Kotler and Armstrong, 2003).

Commodity approach: In this approach a specific commodity or groups of commodities are taken and the functions and institutions involved in the marketing process are analyzed. This approach focuses on what is being done to the product after its transfer from its original production place to the consumer (CIAT, 2004). It helps to pinpoint the specific marketing problems of each commodity as well as improvement measures. The approach follows the commodity along the path between producer and consumer and is concerned with describing what is done and how the commodity could be handled more efficiently.

Behavioral approach: In this approach either a particular marketing firm or an organization of firms, such as the marketing channel, can be viewed as a system of behavior. Each is composed of people who are making decisions in an attempt to solve particular problems. If these problems and their behavioral systems for solving them can be classified and a greater understanding of changes that may be forthcoming can be obtained. In either the firm or the organization of firms’ four major types of problems with their associated behavioral systems can be identified: input-output system, power system, communication system, and adaptive behavior system (Crammer et al., 1997).

Structure, conduct and performance (S-C-P) paradigm: The basic view of this approach is that, given certain basic conditions, the structure of an industry or market determines conduct of buyers and sellers which influence its performance. The basic

11 conditions refer to characteristics which are exogenous to the market, for example infrastructure, legal and policy environment and available technology. Efficiency factors can be evaluated by examining marketing enterprises for structure, conduct and performance (Abbott and Makeham, 1981). Both the functional and institutional approaches are useful in analyzing the existing marketing activities. However, the marketing process is continually changing in its organization and functional combinations are a major problem to understand and predict change. SCP approach is one of the most common methods to study marketing systems which analyzes the relationship between functionally similar firms and their market behavior as a group. It is mainly based on the nature of various sets of market attributes and relations between them and their performance (Scarborough and Kydd, 1992). Thus, SCP approach has been used for this study as a guideline to study honey marketing system in Chena district.

2.4. Analytical Framework for the Study

2.4.1. Structure, conduct and performance (SCP) of market

The Structure-conduct-performance approach was served as a tool to evaluate the performance of the marketing system. The approach distinguishes between three related levels; the structure, conduct and performance of the market (Cramer et al., 1997).

2.4.1.1. Structure of the market

Market structure is defined as characteristics of the organization of a market which seems to influence strategically the nature of competition by pricing behavior within the market (Kohls and Uhl, 1985). Structural characteristics may be used as a basis for classifying markets. The four salient aspects of market structures include the degree of seller concentration, the degree of buyer concentration, the degree of product differentiation, and the condition of entry (Kotler and Armstrong, 2003). Based on these aspects markets may be perfectly competitive, monopolistic, oligopolistic or monopoly.

Market concentration: It shows the number and relative size distribution of buyers/sellers in a market (Abbott and Makeham, 1981). It is generally believed that higher market concentration implies non-competitive behavior and thus inefficiency. The common measures are Concentration Ratio, Gini-coefficient and Hirshman Herfindahl Index.

Concentration Ratio (CR): It indicates the relative size of k-large firms in relation to their industry as a whole. It shows whether an industry is dominated by a few large firms or many small firms. CRk is also used as an indicator of the relative size of firms in relation to the industry as a whole (Kohls and Uhl, 1985). The problem associated with this index, there is no justification for focusing on the market shares of the top four firms is somewhat arbitrary.

Gini-Coefficient (GC): It is an alternative concentration measure that has some similarities to the concentration ratio. It is done based on Lorenz curve and the line at 45 degree thus represents perfect equality of market shares. Gini-coefficient ranges from zero (perfect equality) to one (perfect inequality). Gini-coefficients provide useful information based on Lorenz curve shapes, a problem arises when Lorenz curves cross. It is problematic whether we can in this special case claim that a higher coefficient means a more unequal distribution. The other problem associated with Gini-coefficients is that it favors equality of market shares without regard to the number of equalized firms.

Hirschman Herfindahl Index (HHI): The index considers the number and size distribution of all firms and also it takes into account all points on concentration curve. A very small index indicates the presences of many firms of comparable size, one or near one, suggests that the number is small and/ or that they have unequal shares in the market (Scarborough and Kydd, 1992). HHI includes all firms in the calculation. On the other hand, this needs more data to be collected. The problem of HHI is lack of information about small firms. Thus, it is difficult to obtain the market share of every firm that operates in a single market.

Unlike HHI and GC, CR4 ratio is measures relative market concentration for top four traders that influence the holy market of commodity (Abdulrazak et al., 2013). Thus, concentration ratio was used in the analysis to measure market concentration of honey market in the district.

2.4.1.2. Conduct of the market

Conduct of the market refers to the strategies that firms pursue with regard to price, product and promotions, and the linkages/relationships between and among firms. The use of regular partners, long-term relations with clients and suppliers, the use of intermediaries and trade within personalized networks are common the strategies (Abah et al., 2015).

According to Kohls and Uhl (1985), conduct is pattern of behavior which enterprises follow in adopting or adjusting to the market in which they sell or buy, in other words the strategies of the actors operating in the market. Market conduct deals with the behavior of firms that are price-searchers are expected to act differently than those in a price-taker type of industry (Cramer and Jensen, 1982).

However, there are no agreed upon procedures for analyzing the elements of market conduct. More specifically, it cover the following topics: the existence of formal and informal marketing groups that perpetuate such practice; formal and informal producer groups that affect bargaining power; the availability of price information and its impact on prevailing price; the distance from the major market and its impact on price; and the feasibility of utilizing alternative market outlets (Hugo et al., 2016). In this study, the availability of information about purchasing and selling strategies for producers and traders, adjusting to the market in which they sell or buy and practice of storage and processing were used to measure conduct of honey market.

2.4.1.3. Performance of the market

Market performance refers to economic results: product suitability in relation to consumer preferences (effectiveness); rate of profits in relation to marketing costs and margins; price seasonality and price integration between markets (Kohls and Uhl, 1985). One can imagine causal relations starting from the structure, which determine the conduct, which together determine the performance (Cramer et al., 1997). The performance of market for a particular commodity can be evaluated by analyzing costs and margins of marketing agents in different channels.

Marketing costs

Marketing costs are the expenditure incurred by various market intermediaries from the time when commodity leaves the farm until it reaches the consumers (Wisdom et al., 2014). It is cost of performing various marketing functions, which are required to transfer a commodity from the place of production to the ultimate consumers. Such costs are necessarily incurred to create form, time, place, and possession utilities in the products to make them marketable. To determine whether the marketing margins (amount received by the different marketing agencies for providing their services) were reasonable, it was essential to calculate the 'costs' of these agencies. The costs incurred by the producers and

14 other marketing intermediaries have impact on prices as well as on the margins of the market intermediaries (Wandschneider and Yen, 2006).

Marketing margins

A marketing margin is the percentage of the final weighted average selling price taken by each stage of the marketing chain (Mendoza, 1995). The total marketing margin is the difference between what the consumer pays and what the producer/farmer receives for his product. In other words it is the difference between retail price and farm gate price (Cramer et al., 1997).

The marketing margin in an imperfect market is likely to be higher than that in a competitive market because of the expected abnormal profit. A wide margin means usually high prices to consumers and low prices to producers. But marketing margins can also be high, even in competitive market due to high real market cost (Wolday, 1994). Marketing margin can be a useful descriptive statistics if it is used to show how consumers’ expenditure is divided among market participants at different levels of the marketing systems (Jema, 2008). It is commonly used measure for a performance of marketing system.

2.4.2. Factors affecting volume of market supply

Different models can be employed to analyze the determinants market supply. The commonly used ones are the well-known multiple linear regression, Tobit and Heckman’s sample selection models. If some households may not prefer to participate in a particular market in favor of another, while others may be excluded by market conditions Tobit or Heckman models were used to analyze market supply. By using Tobit model, the volume of market supply can be analyzed by clustering the respondents’ in to supplier and non- suppliers. If censored regression is applied, the model estimates are biased because of there is no clustering honey producers as all of households supply there product to market (Wooldridge, 2010).

Like Tobit model, sample selection model (Heckman) was used in some cases when sample selection biased occurred in addition to clustering of respondents. The first stage of the Heckman model a ‘participation equation’, used to construct a selectivity term known as the ‘inverse Mills ratio’ which is added to the second stage ‘outcome’ equation that explains factors affecting volume of product marketed and estimated by using ordinary

15 least square (Wooldridge, 2010). However, in the study area all honey producers participate in the market by supplying their produce and therefore there no clustering of the honey producers in honey market participant and non-participant. Thus, for this study multiple linear regression model was used to identify determinants of honey marketed supply.

2.4.3. Determinants of market outlet choices

A farmer’s decision to select a given market or not is made by evaluating the return in expected utility, taking into account the related investment and transaction costs (Urquieta, 2009). Thus, farmers will select the market outlets that show the most positive profit.

Econometric models such as multivariate probit/logit, multinomial probit/logit, conditional or mixed or nested logit are useful models for analysis of categorical choice dependent variables. The choice decision over the different groups of market outlet can be modeled in two ways; by either multinomial or multivariate regression analysis. One of the underlying assumptions of multinomial models is the independence of irrelevant alternatives that is error terms of the choice equations are mutually exclusive (Greene, 2012). Thus, multinomial models are appropriate when individuals can choose only one outcome from among the set of mutually exclusive, collectively exhaustive alternatives.

However, the choices among the market outlet are not mutually exclusive as honey producers are selling honey at more than one market outlets at the same time and therefore the random error components of the market outlets may be correlated (Arinloye et al., 2014). Therefore, the researcher considers using a multivariate probit model which allows for the possible contemporaneous correlation in the choice to access the four different market outlets simultaneously. Hence, multivariate probit model (mvprobit) was applied to identify determinants of honey producers’ market outlets choice.

2.5. Empirical Evidences

2.5.1. Empirical literature on S-C-P

Assefa (2009) conducted study on honey market chain analysis at Atsbi Wemberta District in Tigray region, Ethiopia. The study identified producers, honey collectors, retailers, processors and final consumers of the product as honey marketing participants in the study

16 area. In addition, by using marketing margin analysis he found that reveals that 17% of total gross marketing margin was added to honey price when it reaches the final consumer. The study result of sample market honey traders’ concentration ratio CR4 shows that 35.82 percent of honey was controlled by top four honey traders in the study area.

The study conducted by Betsalot (2012) in Ada’a woreda of East Shoa zone of Oromia region on honey value chain analysis used marketing margin analyze to evaluate honey market performance. The result reveals that producer’s marketing margin constitute about 82.35% of the final consumer’s price and 12.5% share of the marketing margin goes to the processors, who collect the honey for their own processing. The study also identified actors participating in the honey value chain such as beekeepers, local honey collectors, cooperatives, local brewery (tej) houses, wholesalers, honey processors, beeswax processors, retailers, input suppliers and exporters.

Samuel (2014) conducted a study using marketing margin analysis on performance of honey marketing system in southern Ethiopia with special emphasis on Sodo Zuria woreda and found that showed that, the total gross marketing margin was highest in longest channel which was 83.6% of the consumers’ price and from all honey traders, processors have got the highest gross marketing margin which accounted for 50%. Regarding honey market structure at the district, the CR4 measures of showed that the top four were controlled 58.84% of the honey market which implies strong oligopoly.

The study by Atsbaha (2015) on value chain analysis of honey in Ahferom Woreda, Central Zone of Tigray Regional state shows that the honey traders’ concentration ratio was found to be 76.86 percent which indicates the presence of strong oligopoly market structure in the study area. This implies that the market is controlled by few traders. Further the study reveals that without considering producers to consumers’ channel, producers share was highest in channel V(beekeeping cooperatives- enticho retailers- consumers) and lowest in channel III(Individual-beekeepers-local collectors-enticho – retailers-consumers) at the percent of 87.5 and 67.1, respectively.

2.5.2. Empirical studies on the determinants of market supply

A number of studies were conducted on the factors affecting supply of agricultural commodities to the market. For instance, Assefa (2009) employed multiple linear

17 regression model to identify factors affecting market supply of honey in Atsbi Wemberta district Tigray National Regional State. Accordingly, the study found that education level of household, experience in beekeeping, extension access, quantity honey of produce, price of honey, access to credit and distance to the nearest market significantly affect market supply of honey.

A study conducted by Getachew (2009) used Heckman two stage model to identify determinants of honey market supply in Burie woreda of West Gojjam zone, Ahmara National Regional State. The model result shows that income from farm and non-farm activities, beekeeping experience, beekeeping training, apiary visit, and access to improved beekeeping equipment’s are the major determinants of market supply of honey at household level significantly and positively. Similarly, Betselot (2012) employed Heckman maximum likelihood method to identify factors affecting volume of honey sale in Ada’a woreda, East Shoa zone of Oromia National Regional State. The result reveals that sex of the household, number of beehives owned, type of beehive used, and credit access positively and significantly influence volume of honey marketed in the study area.

Pandey et al. (2013) conducted study on an economic study of marketed surplus of chickpea in Rewa District of Madhya Pradesh using cross sectional data by adopted multiple linear regression. The study came up with the finding that yield/ha, size of family, production of chickpea, size of holding and income from other sources variables are significantly affected on marketed surplus.

Samuel (2014) used multiple linear regression model to identify factors that determine volume of honey marketed by the sample households in Sodo Zuria district, Southern Ethiopia. He found that age of household, previous year price, family size, beekeeping training, agro-ecology, literacy status of household, size of livestock holding and total numbers of modern hives used in production by household heads significantly determine volume of honey marketed.

A study by Nega et al. (2015) employed multiple linear regression model to identify factors that determine banana, mango and avocado market supply of the producers in Tembaro district of Kembata Tembaro zone, South Ethiopia. The model result indicates that price, access to extension service, distance to the market, access to market information and quantity produced affected mango and avocado market supply whereas active family

18 size, distance to the market, quantity produced, access to market information, and price for banana affected banana supply significantly.

Bizualem et al. (2015) used multiple linear regressions to identify determinants of marketed surplus of coffee by smallholder farmers in Jimma zone, Ethiopia. The result of OLS regression shows that sex, coffee farming experience, access to credit, adequacy of extension services, attractiveness of coffee price, cooperative membership and non and/or off farm income are significant positive factors affecting marketed surplus of coffee.

2.5.3. Empirical studies on the determinants of market outlets choices

A number of studies have been done that have revealed factors influencing marketing channel choice decisions. Anteneh et al. (2011) employed Tobit model and identified factors affecting marketing channels choice of coffee farmers in Sidama zone. The finding of their study revealed that younger coffee farmers, with better education, higher proportion of off-farm income to total income, and higher proportion of land allocated to coffee tend to diversify their market choices by selling to traders. Farmer delivering exclusively to the cooperatives seems to be the older ones, with a relative lower individual performance. Among non-members however, younger farmers with lower proportion of off-farm income are ones using the cooperative outlet channel through their relatives.

Kadigi (2013) employed multinomial logistic regression to identify factors influencing choice of milk outlets in Iringa and Tanga city, Tanzania. The result revealed that access to credit decreases the choice of neighbor milk market outlet. The probability of choosing to sell to milk vendors is positively influenced by the price per liter and gender. Milk vendors who offer better price are likely to increase dairy farmers’ willingness to market their milk produce through the milk vendor market outlet, which are more rewarding than milk collection centers.

Geoffrey (2015) conducted a study on factors affecting the choice of marketing outlets among small-scale pineapple farmers in Kericho country. The result of multinomial logistic regression revealed that gender, group marketing, pineapple produce, price information and vehicle ownership significantly influenced the choice of pineapple marketing outlets. The result confirmed that price information had a positive influence on the choice of local market outlet while vehicle ownership positively and significantly influenced the choice of both local and urban market outlets.

A study by Atsbaha (2015) used multinomial logit model in an attempt to determine factors affecting honey marketing channels in Ahferom woreda of Central zone, Tigray region. The model result indicated that the probability to choose the collector outlet was significantly affected by average monthly income, previous agreement with buyers and market information. Similarly variables such as age, beekeeping experience, market information and distance to nearest market affected the choice of retailers channel compared to the consumers’ channel.

Solomon et al. (2016) used multinomial logit model to analyze factors affecting farmers’ coffee market outlet preference in coffee potential districts of Jimma zone, South-western Ethiopia The model result revealed that age of the household has negative and significant effect on the preference of farmers for formal markets and brokers and farm experience of the household has positive and significant effect on the preference of the farmer for formal market and brokers as compared to informal markets. Distance to formal coffee market has positive and significant effect on the preference of the farmer to cooperatives and brokers and it has negative and significant effect on formal markets preference.

Addisu (2016) applied multivariate probit model to investigate factors influencing market outlets choice of vegetable producers in Ejere district West Shoa zone, Oromia National Regional state of Ethiopia. The result indicates that the correlations between the potato producers choice of wholesaler and consumer outlet was negative and statistically significant, and correlation between retailer and rural collector outlet was also negative and significant. The study also shows that the potato producers in the study area have made their choice of market outlets for their produce based on quantity of potato sold, education level of households, sex of the household head, family size, farmers’ experience, distance to nearest market, current farm gate price, access of off/non-farm income, trust in traders, ownership of motor pump and area of land allocated for potato.

Kifle et al. (2015) employed multinomial logit regression model to analyze determinants of the choice of marketing channels among small-scale honey producers in Tigray region of Ethiopia. The study revealed that beekeeping experience distance from market, access to market information, grading and access to credit significantly affects choice of local market channel; while household head age, volume of honey sold, average price and access to market information significantly influence trader channel choice.

Finally, Shewaye (2016) employed multivariate probit model to analyze factors affecting haricot bean market outlets choice. The model result indicated that the outlet choice of rural assemblers was negatively influenced by number of equine owned and use of credit and positively influenced by distance to the nearest district market and distance to all weather road. Whereas consumers outlet was positively and significantly affected by number of equine owned. Finally, urban traders market outlet was positively and significantly affected by number of equine owned, membership in cooperative, access to price information and use of credit, and also negatively affected by distance to the nearest market. Therefore, based on the empirical studies reviewed multiple linear regression and multivariate probit models were adopted for this study to identify factors that affect household level of honey market supply and producers market outlet choice decision, respectively.

2.6. Conceptual Framework of the Study

This study built on the assumption that market supply and market outlet choices are made in sequence where producers initially decide how much to sell and then for whom to sell. Quantity of honey supplied to market was affected by numerous factors. They are divided in to socio-economic factors like (education level, gender, household income and ownership of resources), institutional factors like (cooperative membership, credit availability, extension service and road infrastructure), production factors like (year of experience, size and types of production inputs) and market factors like (prices of output, market information and distance to the market) (Bestalot, 2012; Samuel, 2014; Jinanus and Tamiru, 2016). These factors could have positive or negative effects, which could either improve or cause a decline in the welfare of the farmers. The main approach is that greater market supply of farmers results in more commodities being traded and this may lead to more return being obtained by the farmers. This becomes an incentive to increase production and hence a positive supply response is achieved (Sigie et al., 2013).

Farmers’ decision to sell in a given markets derives from the maximization of expected utility from these markets (Djalalou et al., 2015). As this study focused on analyzing factors affecting selection of market outlets in addition to market supply, assuming that the selection of different honey marketing outlets, as well as their simultaneous choose which is led by producers’ willingness to maximize their profit and conditional to a socioeconomic, institutional, production and market related factors (Jari and Fraser, 2012;

Arinloye et al., 2014; Addisu, 2016; Shewaye, 2016). Following the literature, the researcher concluded that a honey producers’ decision to sell in two or more markets derives from the maximization of profit he or she expects to gain from this markets.

The conceptual framework in Figure 1 illustrates the interrelationships in the study, the key variables involved and how they are interrelated that is influence on honey market supply and market outlets choice decision. The increase in volume of honey supplied to market leads to producers’ to select alternative market outlets to sell their supply that maximizes their profit which in turn results in increased household income.

Production factors  Beekeeping Market related factors  Experience,  Distance to market,  Types of beehives,  Market information,  Number of beehives  Trust in buyers owned

Institutional factors Socio-economic  Extension service, factors Volume of honey  Credit availability,  Education level, supplied  Membership of  Gender, honey cooperatives  Total income

Honey market outlets choice decision

Increase household income

Figure 1: Conceptual framework of the study Source: Own sketch 2016.

3. RESEARCH METHODOLOGY

In this chapter, description of the study area, data types, sources and methods of data collection, sampling technique and methods of data analysis are presented.

3.1. Description of the Study Area

The study was conducted at Chena woreda, Kaffa zone of Southern Nations, Nationalities and Peoples Region of Ethiopia. The woreda was purposely chosen out of 11 woredas in the zone because of it is high honey production potential, which accommodates about 24% of the total honey production in Kaffa zone (KZLFD, 2016).

The woreda is found within the southwestern plateau of Ethiopia which is 510km and 785km far from Addis Ababa and Hawassa, respectively. The area is located at 07º18’48’’N Latitude and 036º16’25’’ E Longitude and at altitude of 2020 m.a.s.l. The district is bordered on the south by the Bench Majji zone, on the west by Bita, on the north by Gewata, on the northeast by Gimbo and on the east by Decha woredas (Belachewu et al., 2015). According to CWFEDO (2015), Chena woreda comprises of 42 of this 39 are rural kebles and with a total population of 158,449, of whom 78,150 are men and 80,299 women; 11,629 or 7.34% of its population are urban dwellers. The total area of Chena woreda is estimated to be 901.92 km2 that endowed with natural tropical rain forests with suitable climates that favour high honeybee population density and forest beekeeping is widely practiced (Nuru, 2007).

According to Chernet (2008), the woreda experiences long rainy season, lasting from March to October. The mean annual rainfall ranges from 1710 mm to 2000 mm. Over 85 % of the total annual rainfall, with mean monthly values in the range of 125 to 250 mm occurs in the 8 months long rainy season. The mean temperature ranges from 18.1ºC to 21.4ºC. Environmentally, it belongs to the sub-agro ecology tepid to midland and comprising of mixed arable farming and woodland, including much relict primary tropical forest. The topography is characterized by slopping and rugged areas with very little plain land (Tilahun and Kifle, 2015).

According to CWLFO (2016), the total households found in the woreda are 21685 of this households 7752 are honey producers. Total number beehives owned at district level from 2006E.C to 2008E.C in 2006 E.C about 40010 traditional, 4876 improved with the total of

44886; 43730 traditional, 6322 improved and a total of 50052 in 2007 E.C, whereas in 2008 E.C about 46140 traditional, 7932 improved with the total of 8118 beehives are owned by producers. In this woreda, there are two honey harvesting periods, April to June and September to October, of which the former is the major harvesting period contributing 95 % of the annual honey production.

Figure 2: Map of the study Area

3.2. Data Types, Sources and Methods of Data Collection

Both qualitative and quantitative type from both primary and secondary data sources were used for this study. Primary data were collected using two types of semi-structured questionnaires, one for honey producers and the other for honey traders. Primary data that were collected from beekeepers focused on factors affecting volume of honey supplied and market outlet choices, marketing channels actors and their roles, production costs, marketing costs, demographic and socioeconomic characteristics of the households. Moreover, the questionnaire for traders includes buying and selling strategies, buying and selling prices, cost of marketing, source of market information, demographic characteristics of the traders and other market chain related data were collected.

Enumerators who are working in the district rural kebles as development agents and technical assistants from Bonga agricultural research center were selected. Before data collection the enumerators were trained on the techniques of data collection and the questionnaire was pre-tested on ten households to evaluate the appropriateness of the design, clarity and interpretation of the questions, relevance of the questions and time taken for an interview. Hence, appropriate modifications were made on the questionnaire prior to conducting the survey.

In addition to the questionnaire, an informal survey in the form of focus group discussion and key informants’ interview was employed using checklists to obtain additional supporting information for the study. Secondary data were collected from different published and unpublished sources, government institutions and websites.

3.3. Sampling Procedure and Sample Size

Different types of questionnaires were prepared for formal survey of honey producers, traders and consumers to generate important information for the study.

Producers sampling: A mult-stage sampling technique was employed for this study. At the first stage, out of 39 rural kebeles in the woreda three kebeles were selected randomly as because of all of the rural kebles are honey producer. At the second stage, total households that produce honey during 2015/16 from three randomly selected kebeles were identified and stratified. Finally, based on the list of honey producers from the sampled kebeles, the intended sample size was selected by employing probability proportional to

25 size. Accordingly, a total of 154 honey producers was sampled randomly. For this study, sample size was determined from out of 7752 honey producers in the woreda based on the following formula given by Yamane (1967) at 8% level of precision: N 7752 n = = ~154 (1) 1+N(e2) 1+7752(0.082) Where: n = the sample size, N = is total size of the honey producers (7752) e= is the level of precision (8%)

From 154 selected households, 35.7% were from Dinbra-Woshi, 29.9% were from Wareta kebele and the remaining 34.4% were selected from Wanabola Keble.

Table 1: Sample distribution of honey producers in selected kebeles

No. Kebeles Total number of honey producers Number of sampled producers 1 Dinbra-woshi 396 55 2 Wareta 332 46 3 Wanabola 379 53 Total 1107 154 Source: Own computation 2016

Traders and consumers sampling: Data from traders, honey cooperatives and consumers were collected. Honey traders list was obtained from woreda office of trade and industry, a total 30 licensed honey traders existed. On the basis of flow of honey, four markets (Wacha, Sheshonde Woshi and Dibira) were selected purposively as the main honey marketing sites in the study area. Since, the number of traders in each market is very few; the all 30 licensed traders were interviewed based on the required information criteria. Consumers sampling was the very difficult task due to absence of list of consumers’ and large population of consumers. Hence, purposive sampling was employed to select 20 consumers during honey purchasing period in Wacha and Shishonde markets due to presence of large number of honey consumers.

3.4. Methods of Data Analysis

Two types of data analysis, namely descriptive statistics and econometric models were used for analyzing the data collected from producers, traders and consumers.

3.4.1. Descriptive analysis

Descriptive statistics such as percentages, frequencies, mean and standard deviation were used to analyze the characteristics of the sample households, traders and consumers. Furthermore, the structure conduct and performance (SCP) model was used to analyze type of honey market, how the honey market participants behave and the performance of honey market in Chena district.

Structure-Conduct-Performance (SCP) model analysis

The model examines the causal relationships between commodity market structure, conduct and performance.

Structure of market

Structural characteristics like market concentration, barriers to entry, and market transparency were some of the indictors to be considered in this study.

Market concentration refers to number and size distribution of sellers and buyers in the market, the firm’s objectives, barriers to entry, and assumptions about rival firm’s behaviors are relevant in determining the degree of concentration, behaviors and performance (Kolter and Armstrong, 2003). Market concentration can be measured with CR, HHI, and Gini coefficient. Concentration Ratio (CR) was used for this study to analyze relative degree of honey market concentration of sampled honey traders.

Concentration ratio: It indicates the relative size of k-large firms in relation to their industry as a whole. It shows whether an industry is dominated by a few large firms or many small firms. Concentration ratio measures the traded volume accounted for by a given four largest traders and is designated by the formula:

Vi Si  (2) Vi

Where:-푆푖- Market share of trader I, 푉푖- Amount of product handled by trader i

∑ 푉푖- Total amount of product handle

r CR   Si (3) i1 th Where: CR = concentration ratio, Si = the percentage market share of i trader, and r = the number of largest traders for which the ratio is going to be calculated.

Khols and Uhl (2002) suggest that as a rule of thumb, a four enterprise concentration ratio of 50 percent or more is indicative of a strong oligopolistic industry; of 33-50 per cent ratio denotes a weak oligopoly, and less than that an unconcentrated industry (competitive industry). The greater degree of concentration is the greater the possibility of non- competitive behavior existing in the market. In addition to market concentration, degree of market transparency by using market information, and licensing process, capital requirement and presence of unlicensed traders from entry barriers were assessed to judge the structure of honey market in the study area.

Market conduct

It is a systematic way to detect indication of unfair price setting practices and the conditions under which practices are likely to prevail. In this study, the availability information about purchasing and selling strategies for producers and traders, adjusting to the market in which they sell or buy and practice of storage and processing used to measure conduct of honey market.

Market performance

Market performance can be evaluated by analysis of costs and margins of marketing agents in different channels.

Marketing costs: It is a cost incurred by the honey producers and other honey marketing intermediaries have impact on prices as well as on the margins of the market intermediaries. According to Islam et al. (2014), marketing cost is the sum of transport cost, loading and unloading, storage cost, labour cost, market taxes and other costs associated with moving the commodity from the point of purchase to the customer or final consumer.

Marketing margin: According to Mendoza (1995), it can be analyzed using the price difference of the actors in the marketing channels. Total gross marketing margin (TGMM) is the final price paid by the end consumer, minus the producers’ price, divided by the consumers’ price and expressed as a percentage. TGMM is useful to calculate the producer’s gross margin (GMMp) and it is given by the formula:

End buyer price- first seller price TGMM  *100 (3) End buyer price

In order to gauge the level of equity in the distribution of benefits accrued along chain, producer’s gross margin (GMMP) which is the portion of the price paid by the end buyer that goes to the producer is calculated as: End buyer price- marketing gross margin (4) GMMP  *100 End buyer pricer Where GMMp - Producers’ participation (the producers’ share in consumer price)

3.4.2. Econometric analysis

The factors influencing honey market supply and honey producers’ market outlets choices were analyzed by using multiple linear regression and multivariate probit models, respectively.

3.4.2.1. Determinants of honey market supply

Multiple linear regression model was used to analyze factors affecting volume of sales as all sampled households producing honey in the study area participated in the marketing by supplying their produce to the market. The model is specified as Y=f(sex of household, household size, beekeeping experience, types of beehives used, number of beehives owned, frequency of extension contact, education level of household heads, total income excluding income from beekeeping, amount of credit received and cooperative membership).

In matrix form, the supply function can be specified as:

훾 = 훽푋 + 푈 (5)

Where, 훾 = the volume of honey supplied to the market 훽 = a vector of estimated coefficient of the explanatory variables 푋 = a vector of explanatory variables 푈 = disturbance term

3.4.2.2. Determinants of honey producers’ market outlets choice

As honey producers more likely choose different market outlets simultaneously in the study area, multivariate probit was employed to estimate its determinants. The model simultaneously capture the influence of the set of explanatory variables on each of the different outlets choice, while allowing for the potential correlations between unobserved

29 disturbances, as well as the relationships between the choices of different market outlets (Arinloye et al., 2014).

According to Djalalou et al. (2015), every producer is a rational decision maker in maximizing utility or profit relative to his choices. It is assumed that given producer i in making a decision considering not exclusive alternatives that constituted the choice set K th of honey marketing outlets, the choice sets may differ according to the decision maker. Consider the ith farm household (i=1, 2…...N), facing a decision problem on whether or not to choose available market outlets. Let U0 represent the benefits to the th farmer who chooses retailer, and let Uk represent the benefit of farmer to choose the K market outlet: where K denotes choice of retailer (Y1), cooperatives (Y2), collectors (Y3) th and consumers (Y4). The farmer decides to choose the K market outlet if

* * * Y ik U k U0  0. The net benefit (Y ik ) that the farmer derives from choosing a market outlets is a latent variable determined by observed explanatory variable (Xi) and the error term ( i ):

* ' Y ik  X i k  i (k  Y1,Y2 ,Y3 ,Y4 ) (6)

Using the indicator function, the unobserved preferences in equation (6) translates into the observed binary outcome equation for each choice as follows:

* 1 if Y ik  0 Yik   K  Y1 ,Y2 ,Y3 ,Y4 ) (7) 0 Otherwise

In multivariate model, where the choice of several market outlets is possible, the error terms jointly follow a multivariate normal distribution (MVN) with zero conditional mean and variance normalized to unity (for identification of the parameters) where

( x1 ,  x2 ,  x3 ,  x4 ) MVN ~ (0,) and the symmetric covariance matrix  is given by:-

1 x1x2 x1x3 x1x4    x2x1 1 x2x3 x2x4    (8) x3x1 x3x2 1 x3x4   x4x1 x4x2 x4x3 1 

Of particular interest are off-diagonal elements in the covariance matrix, which represent the unobserved correlation between the stochastic components of the different type of

30 outlets. This assumption means that equation (8) generates a MVP model that jointly represents decision to choice particular market outlet. This specification with non-zero off- diagonal elements allows for correlation across error terms of several latent equations, which represents unobserved characteristics that affect the choice of alternative outlets.

Following the form used by Cappellarri and Jenkins (2003), the log-likelihood function associated with a sample outcome is then given by;

N ln L  i ln(i ,) (9) i1

Where  is an optional weight for observation i, and i is the multivariate standard normal distribution with arguments  i and Ω, where  i can be denoted as;-

i  (ki11 X i1 ,ki2  2 ,ki33 xi3 ), While ik  1 for j  k and (10)

 jk  kj  kijkik  jk for j  k,k 1,2,3.....withkik  2yik 1 (11)

3.5. Definition of Variables and Hypothesis

The potential variables, which were supposed to influence volume of honey supplied and producers’ market outlets choice, need to be explained. Hence, the major explanatory variables expected to have influence on the two dependent variables are explained as follows:

3.5.1. Dependent variables

Volume of honey supplied (VHS): It is a continuous variable that represents the actual volume of honey supplied to the market by individual households in survey year, which is measured in kilograms.

Market outlets choice (MKTOUT): It is a binary categorical dependent variable used in Multivariate probit model and measured by the probability that producer sells honey to either of the alternatives market outlets. The dependent variable for the model is discrete variable taking a value of 1, 2, 3 and 4, representing the choices of selling through retailers

(Y1), cooperatives (Y2), collectors (Y3) and consumers (Y4), respectively.

3.5.2. Independent variables

In order to identify factors influencing honey volume sales and market outlets choice both continuous and discrete variables were hypothesized based on economic theories and the findings of different empirical studies. Accordingly, in order to investigate the determinants of market supply and market outlet choices, the following variables were constructed.

3.5.2.1. Independent variables for volume of honey marketed

The explanatory variables that were expected to influence volume of honey marketed are the following:

Sex of the household head (SHH): This is dummy variable that takes a value of 1 if the household head is 1 male and 0 otherwise. Tura et al. (2014) indicates that female-headed households have less access to improved technologies, land, and extension services as compared to male headed households. Therefore, in this study the variable was expected to have a positive influence on volume of honey marketed.

Household size (HSZ): It is a continuous variable which refers to the total number of individuals in the household. Samuel (2014), indicates that household with more number of family members supply less amount of honey to market than those households with relatively less number of family members because of the increase in consumption at household level. Hence, in this study household size was expected to influence the amount of honey supplied to the market negatively

Educational level of the household head (EDLEV): It is a continuous variable measured in the number of grade level the head of the household attended. According to Betselot (2012), education improves the beekeeping household ability to acquire new idea about production and market related information, and hence increases honey produced which in turn increase level of market supply. So, this variable was hypothesized to influence positively the marketed supply of honey.

Distance to nearest market (DNM): It is the location of the beekeeping household from the nearest honey market and is measured in kilometer. The study by Nega et al. (2015) indicated that distance from market discourages producers from selling high volumes of product. The closer the honey market to beekeeping household, the lesser would be the

32 transportation charges and loss due to handling. Therefore, distance from nearest honey market was hypothesized to be negatively related with volume of honey marketed.

Amount of credit received in 1000 (CREDIT): This is a continuous variable that represent the amount of credit taken by an individual household for honey production purposes. It enhances the financial capacity of the farmer to purchase the bee colony and the beehives. Betselot (2012) found that the amount of credit received affected level of honey marketed positively Hence, it was hypothesized to positively influence marketed surplus of honey.

Frequency of extension contact (EXTCONT): It is a continuous variable measured in number of extension contact per year on honey production and marketing extension service. Assefa (2009) stated that beekeepers who have contact with extension agents are more likely to have knowledge about production, quality, and price of inputs and information on markets and output prices that increasing the amount of honey market supply. Therefore, the variable was hypothesized to affect the volume honey marketed positively.

Number of beehives owned (NBHO): It is a continuous variable measured in number of beehives owned for honey production by households. Also, this variable was taken as proxy variable for quantity of honey produced. The more the number of beehives owned, the higher the quantity of honey harvested which in turn influences market supply of honey positively (Samuel, 2014). As a result, the number of beehives owned was expected to have positive relationship with honey marketed surplus.

Total income other than beekeeping in ln (CINBK): It is continuous variable that represents income generated from different sources excluding income from beekeeping activity and measured in Ethiopian Birr (ETB). Non-beekeeping income enables the beekeepers to purchase more number of improved beehives which can contribute to increased honey production per household per hive and then contribute positively to market supply (Betselot, 2012). Hence, total income from non-beekeeping activities is hypothesized to affect volume of honey marketed positively.

Beekeeping experience (BKEXP): It is a continuous variable and measured in the number of years that the farmer engaged in beekeeping activity. According to Getachew (2009), as farmers got more experience in beekeeping, there is probability of increasing

33 production and hence supply would be higher. In addition, farmers with longer farm experience will have a cumulative knowledge of the entire beekeeping this enables them to adopt the use of improved box beehives earlier that would results in higher quantity of honey produced (Belets and Birahanu, 2014). Thus, this variable was expected to have positive impact on volume of honey supplied.

Type of beehive used (TBH): This variable is a categorical variable taking value 1 if producers used traditional beehive only, 2 if producers used both improved and traditional beehive and 3 if producer used improved beehive only. Improved beehives include transitional and modern that is chefeka, kenaya top bar and zendar (Awraris et al. 2012). A study conducted by Awraris et al. (2015) shows that improved beehives give higher yield which in turn influences market supply of honey positively. Therefore, this variable is hypothesized to have positive influence on volume of honey supplied to market.

Cooperative membership (COOPM): It is measured as a dummy variable taking a value of 1 if beekeeper is member of honey cooperatives and 0 otherwise. According to Adeoti et al. (2014), being membership of cooperative could have better access of market information, inputs, extension services and/or technical advice, and access to credit facilities important to production and marketing decisions. As a result, the likelihood of households who involved in producers’ cooperative is more likely to supply than non- member. Hence, this variable was expected to influence honey market supply positively.

3.5.2.2. Independent variables for honey market outlets choice

The explanatory variables that were expected to influence honey producers’ choice of market outlets decision are the following:

Education level of household head (EDUC): It is a continuous variable and refers to the number of years of formal schooling a household attended. Educated person make better use of available information as the result producer would have possibility to choose appropriate outlets. Anteneh et al. (2011) in the study of coffee market outlet choice confirmed that level of education of household head significantly influenced traders’ outlet choice of coffee producers. Thus, it is expected in this study that this variable has positive influence on retailer and collectors marketing outlet choice.

Household size (HHSIZE): This variable is a continuous variable and refers to the total number of the household members. According to the study by Kadigi (2015) household size is positively related to the probability of the choice of cooperative as one of the milk marketing outlets. Hence, household size was hypothesized to have positive impact on honey producers’ market outlets choice.

Distance to the market (DISTM): It is a continuous variable measured in kilometer that producers are required to travel in order to sell their product in the market. In addition, those households who are close to market are assumed to have more probability of choosing better market outlets. Riziki et al. (2015) confirmed that distance to the market is negatively significant determinant of choice of marketing outlets. If the distance to the nearest market increases, the transportation cost will also increase. Thus, this variable was expected to have negative effect on honey market outlets choice.

Frequency of extension contact (EXT): It is a continuous variable which refers to the number of days that producers had contact with extension agent per year on honey production and marketing supervision. If producers have frequent contact with DAs, there is an opportunity of obtaining important related agricultural information which helps to increase the farmer’s ability to choose the best market outlets for his/her product. The study made by Addisu (2016) indicated the positive relationship between extension contact and choice of channels. Therefore, the variable was hypothesized to have positive influence on choice of honey market outlets.

Beekeeping experience (EXP): It is a continuous variable measured in number of years. Kifle et al. (2015) shows that an increase in household experience in beekeeping increases the probability of searching market which provide high price. This implies that increased in beekeeping experience allows households to use alternatives outlet choices. Hence, it is expected to have positive impact on honey marketing outlets choice.

Annual income of beekeepers (AI): It is an individual beekeepers average annual income excluding income beekeeping activities which was measured in birr for production year of 2015/16. The result by Atsbaha (2015) shows the existence of more income sources in the household decreases the probability of using unprofitable outlets as selling option. As the beekeepers’ average annual income increases the probability of producers’ choosing the profitable outlets will increase. Thus, this variable was expected to have positive influence on producers’ market outlets choice decision.

Volume of honey supplied to market (VHS): It is a continuous variable measured in kilograms and shows the volume of honey sold in a year 2015/16. A marginal increase in honey production has obvious and significant influence on market supply of honey. If the marketable supply of honey increases, the ability of farmers to choose market increases. Muthini (2015) shows that as the quantity to be sold is high the producers’ search of a market outlet which buys with relatively in higher volume with reasonable benefits. Therefore, the variable was expected to have positive impact on choosing honey market outlets choice with better price.

Access to market information (MRTINFO): It is dummy variable that takes a value 1 if farmers obtained purchase price offered by each outlet, demand and supply information and 0 otherwise. According to Geoffrey (2015), market information had positive influence on the choice of market outlet in the marketing of pineapple. Hence, access to market information was hypothesized to influence the honey marketing outlets choice positively.

Trust in buyers (TRUST): It is a dummy variable which takes a value of 1 if the outlet is trusted and 0 otherwise. Producer who have high trust in buyers are likely to spend less time screening their transacting partners or following up on payments and deliver their product to this outlet (Addisu, 2016). Trust in traders was hypothesized to have positive relation with producers’ decision to choose that market outlet.

Cooperative membership (COOPM): It is a dummy variable and takes the value 1 if the household is membership of honey cooperatives and 0 otherwise. Thus, cooperatives improve understanding of members about market and strengthen the relationship among the members. According to Berhanu et al. (2013), membership to cooperative positively and significantly affected accessing cooperative milk market outlet as compared with accessing individual consumer milk market outlet. Therefore, cooperative membership was expected to have positive impact on honey marketing outlet choice.

3.6. Model Diagnosis

An OLS estimates interpretation is possible if and only if the basic assumptions of multiple linear regression model are satisfied. There are many post-estimation tests used to check the satisfaction of the basic assumptions of multiple linear regression model. Thus,

36 it is necessary to test for heteroscedasticity, omitted variable, multicolliniarity and normality problems

Test of hetroscedasticity: It exists when the variances of error terms are not the same, leading to consistent but inefficient parameter estimates. The assumption of absence of heteroscedasticity was detected by using Breusch Pagen test.

Test for specification error or omitted variable: When a variable is omitted which causes endogenity problem hence violating the OLS assumptions and making our OLS estimates biased and inconsistence. Omitted variable test was carried out in the model using Ramsey omitted variable test to check whether there were omitted variables in the model.

Test of multicollinearity: The presence of multicollinearity among the variables seriously affects the parameter estimates of any regression model. The Variance Inflation Factor (VIF) technique was employed to detect the problem of multicollinearity for the continuous variables (Gujarati, 2004).

Normality test: In order to test the normality assumption, Shapiro-Wilk’s W test is recommended for small and medium samples of up to n=2000 (Garson, 2012). A very simple method of checking the normality assumption is to construct a normal probability plot of the residuals. Thus, to test normality predicted residuals a normal probability plot was used in this study.

4. RESULTS AND DISCUSSION

This chapter has four major sections. The first section presents the results of descriptive statistics of the demographic and socio-economic characteristics of the sampled households, traders and consumers; honey production, marketing and institutional related issues in the study area. The second section deals with description of marketing channels; actors and their role. The third section deals with the structure, conduct and performance of honey market and the final section of the chapter deals with econometric results on factors determining volume of honey marketed and producers’ market outlets choice in Chena district.

4.1. Descriptive Statistics Results

In this section, descriptive statistic of socio-demographic, honey production characteristics of the beekeeper, institutional services and market related issues are discussed as follows.

4.1.1. Demographic and socio-economic characteristics of producers

This section begins by discussing demographic characteristics sample respondents with regard to sex of household head, marital status of household head, household size, age and education level sampled households. It further discuses households’ income and its sources.

The survey result in table 2 shows that the average age of sample household heads is 38.62 years with a range of 28 to 62 years. About 78.6% of the sample households were male headed while 21.4% were female headed households. Regarding household size, the mean household size of the total sample households was 6.15 with the standard deviation of 2.55. With respect to educational level of the respondents, mean educational level of households 5.4 grades. This implies that majority of the beekeeping households are literate though they are with low educational status. Among the sample households considered, about 95.45% of them are married, 0.65% single, 1.3% widowed and 2.6% are divorced.

Table 2: Demographic characteristics of sampled honey producers

Variable Response Frequency Percent Sex Female 33 21.43 Male 121 78.57 Marital status Single 1 0.65 Married 147 95.45 Widow 2 1.3 Divorced 4 2.6 Observation Mean Std. Dev. Minimum Maximum Age 154 38.62 10.07 28 62 Education level 154 5.40 2.63 0 12 Household size 154 6.15 2.55 3 13 Source: Own computation (2016)

Household income and its sources: The beekeepers of the study area practice various livelihood and income generating activities mainly crop production in addition to animal husbandry, honey production, petty trade and daily labor. For the total sampled households, the average annual income generated from selling of crops, livestock, non/off- farm activity (pension, petty trade and remittance) and honey were 8433.51, 5955.35, 2100.39 and 2542.56 birr, respectively. The total mean income obtained from all sources including income from selling honey was birr 1923.81.

Table 3: Sources of income by sampled honey producers per year (birr)

Income sources Mean SD Min Max Crops 8433.51 3699.48 1756 24510 Livestock 5955.35 2165.15 1350 21500 Non/off-farm activity 2100.39 865.807 1850 7800 Total income without honey 16489.25 4357.23 2785 32580 Honey 2742.56 856.15 1480 19560 Total income 19231.81 4395.14 3750 38740 Source: Own computation (2016)

4.1.2. Honey production characteristics of the sample households

Types of beehive used by beekeepers

A type of hive used is one of the important factors which determine productivity of bees. Therefore, it is important to discuss different hive types that are used by sampled beekeepers in the district. According to the survey result, 27.38% of the respondents were using traditional beehives only by keeping bees in the forest by hanging the hive on long

39 trees in dense forests and about 23.9% used only improved beehives. While, the rest 48.7% of sample beekeepers were using both traditional and improved beehives in the district.

Traditional hive only (27.38%)

Improved hives only(23.9%)

Both traditionalBoth and improved hives(48.7%)

Figure 3: Types of bee hives used by the sample honey producers Source: Own computation (2016)

Beekeeping experience, number of hives owned and quantity of honey produced

The average years of beekeeping experience for the sampled households was about 11 years with minimum and maximum of 5 and 39 years, respectively (Table 4). Having cumulative knowledge on how to keep bees is a prerequisite to obtain process and use information related to the practice. With regard to the respondents’ number of hive possession (traditional or modern), the average holding was about 12 hives per household with minimum of 6 and maximum of 49.

Table 4. Experience, number of hives owned and quantity of honey produced

Variable Mean Std. Dev. Min Max Beekeeping experience(years) 10.89 3.951 5 39 Number of bee hives owned 12.25 5.0216 6 49 Quantity of honey produced/year/hh(kg) 141.52 97.82 62 672 Source: Own computation (2016)

The survey result in Table 4 indicates that the mean level of output per household in 2015/16 production year was 141.52 kg that ranges from 62 kg to 672 kg with a standard deviation of 97.82. This result indicates that there is large variation in quantity of honey produced among sampled households may be due to variation in types of hives used.

4.1.3. Institutional services

Extension services and amount of credit received

Table 5 depicts that the respondents had mean extension contact of 12.34 times per year in 2015/16 production year. The extension service providers in the study area were livestock and fishery office experts, DAs, NGOs and research institutions. Regarding credit service, the mean credit received was 1397.72 birr from informal sources (friends, relatives or village money lenders). Even if credit services enhance the productivity of farmers, there is lack of attention to access and availability of credit from formal institution.

Table 5: Frequency of extension contact and amount of credit received

Variable Obsv Mean Std. Dev Min Max Extension contact per year(days) 154 12.34 3.95 2 24 Amount of credit received(birr) 83 1397.72 496.76 500 5550 Source own computation (2016)

Cooperative membership The survey result in Table 6 indicates that majority (73.38%) of the respondents were members of honey cooperatives while the rest (26.62 %) were not. There is a significant difference between the mean annually supply of honey between those who are members of cooperatives (176.19kg) and those who are not (91.97kg). This might be due to differences in benefits of being membership of cooperatives in terms of training and technical support which can enhance honey production and in turn results in high market supply.

Table 6: Cooperative membership and average quantity of honey supplied in Kg

Coop member Observation Percentage Mean Std. Dev. t-value No 41 26.62 91.97 41.44 8.03*** Yes 113 73.38 176.19 57.03 Combined 154 132.60 55.39 Source: Own computation (2016)

4.1.4. Market related issues

Market related issues in Table 7 shows that about 55% of the sampled households had access to market information from different sources. The types of information provided were about output price information, buyers’ information, market place and market demand. According to sampled respondents, the major sources of market information were traders, cooperatives, friends/ relatives, and a combination of them.

Table 7. Access to market information, price of honey and distance to market

Variables Response Frequency Percent Access to market information No 71 46.10 Yes 83 54.90 Observation Mean SD Price of honey in 2015/16(birr/kg) 154 51.03 5.73 Distance from nearest market(km) 154 3.74 2.06 Source own compotation (2016)

The survey result in Table 7 also shows that the average distance needed for producers to reach to nearest market place was 3.74km. Regarding the price of honey, the survey result indicates that the mean selling price of honey in 2015/16 production year was about 51 birr per kg and ranges from birr 45 to birr 59 per kg.

4.1.5. Demographic and socio-economic characteristics of sampled traders

Table 8 summarizes the demographic characteristics of traders in terms of sex, marital status, religion, household size, education level and experience. The survey result indicates that 83.3% of the sample traders were male while 16.7% were female. About 40% of traders were Muslims while the remaining 33.33% and 26.67% were Orthodox Christians and Protestants, respectively. With regards to marital status, from total sample traders 86.7% were married and 13.3% of them were single. Business experience refers to the number of years that honey traders engaged in trading activity where their business experience plays crucial role in decision making activity. The survey result indicates that average of experience 6.74 years in honey trading ranging from 3 to 17 years.

Table 8: Demographic characteristics of sampled traders

Variable Response Frequency Percentage Sex Male 25 83.33 Female 5 16.67 Religion Orthodox 10 33.33 Muslim 12 40 Protestant 8 26.67 Marital status Married 26 86.67 Single 4 13.33 Observation Mean SD Minimum Maximum Household size 30 4.89 1.84 2 9 Education level 30 7.68 2.29 4 11 Experience 30 6.74 1.91 3 17 Source: Own computation (2016)

Socio-economic characteristics include financial assets such as initial capital, working capital, sources of capital and sources of loan. As depicted in Table 9, the average initial capital of the sampled honey traders was birr 3316.50 ranging of 1500 to 11500 birr. Furthermore, the survey result shows that the average working capital of sample honey traders was birr 13226.70 ranging of 5550 to 145000 birr in 2016.

Table 9: Financial capital of sampled traders

Variable Number Mean SD Minimum Maximum Initial capital 30 3316.50 1187.02 1500 11500 Working capital in 2016 30 13226.70 5415.04 5550 145000 Source: Own computation (2016)

As indicated in Table 10, about 36.67% of the sampled traders were using their own capital while about 20% use loan and 13.33% operate by share. For about 30% of traders, the source working capital was by combinations of own and loan. Furthermore, the survey results revealed that about 46.67% of traders borrowed working capital from relatives/family while about 26.66% borrowed working capital from micro-finance institutions, 20% from private money lenders, and 6.67% of traders borrowed from friends. This indicates that major source of loan for a trader was families /relatives.

Table 10: Sources of working capitals and loans of sampled traders

Source of working capital Frequency Percentage Own 11 36.67 Share 4 13.33 Loan 6 20 Own and loan 9 30 Total 30 100 Source of loan Family/relatives 7 46.67 Private money lenders 3 20 Friends 1 6.67 Micro-finance institutions 4 26.66 Total 15 100 Source: Own computation (2016)

4.1.6. Demographic characteristics of sampled consumers

The survey results in the Table11 show that about 67.5% of sampled consumers were males and the remaining 32.5% were females. The average household size of the consumers was 4.03 persons and ranges from 3 to 8. The consumers have an average of 14.42 years of experience in purchasing honey for consumption. Regarding marital status of the consumers, majority (92.5%) of the consumers were married, the rest 7.5% were single. Finally, the survey result reveals that the mean education level of sampled consumers was 8.84 with a minimum of 3 grades and a maximum certificate.

Table 11: Demographic characteristics of consumers

Variables Wacha town Shishonde town Total Frequency % Frequency % Frequency % Sex Male 14 70 13 65 27 67.5 Female 6 30 7 35 13 32.5 Marital status Married 18 90 19 95 37 92.5 Single 2 10 1 5 3 7.5 Education level Mean 7.89 9.78 8.84 SD 3.73 4.58 4.16 Household size Mean 4.57 3.48 4.03 SD 2.16 1.78 1.97 Experience Mean 11.3 17.54 14.42 SD 4.57 7.24 5.90 Source: Own computation (2016)

4.2. Honey Market Chain Actors, their Roles and Marketing Channels

In this section, major honey market chain actors with their roles and major honey marketing channels were presented and discussed.

4.2.1. Honey market chain actors and their roles

In this study, different honey market participants were identified. The major actors participating in honey market chain in the study area were beekeepers, cooperatives, local collectors, wholesaler, retailers, processors and final consumers of the product.

Producers: These are the first actors in market chain of honey and they sell their honey to different buyers involved in the market at farm gate, village or district market center. They make their hives out of available local materials, catch and hive swarms, manage bees, harvest and sell to the consumers. In the study area, traditional beehives for honey production are mostly produced by the beekeepers themselves. They sell crude honey to cooperatives, local collectors, retailers and consumers at the local market and farm gate

Honey collectors: They are those actors who buy cured honey directly from honey producers at the farm gate and local markets in the district. There are both legal collectors who have honey collecting license and illegal collectors who have no honey collecting license in the district. They sale collected honey to retailers, wholesalers and processors. Collectors play important roles of bulking and sending the products to the various market outlets. They add value to honey by making spatial and temporal differences (that is collecting from distant location to make easily available and storing for future sale).

Cooperatives: These are the major actors who directly participate in marketing of honey and also support honey producers in the district. These actors organize the beekeepers households to regularly supply honey to them and then sell it to processors and consumers. They process and pack honey by themselves and sell to the local consumers at their own retailing shop. In addition, cooperatives sale the crude honey which they bought directly from producers to Apinec agro-industry and Kaffa honey union that process and pack honey for export market by extracting liquid honey from the honeycomb. Cooperatives in the district are jointly working with beekeepers households and give trainings on bee forage development, queen rearing, harvesting and how to use processing materials.

Wholesaler: This is actor who receive honey directly from local collectors those who buy honey at local market and farm gate directly from beekeepers. The wholesaler has intimate relation with retailers who purchase for resale purpose and collectors who bring a bulk of honey for them. Sometimes, wholesaler gives money (advance payment) for some collectors in the morning on the market day in order to bring for them the honey they bought from producers.

Retailers: These are the link in the channel that delivered honey to end users. They are small shops that engaged in honey trading in the district that buying honey directly from producers, collectors and wholesaler in the form of semi processed or crude honey. Then, they process the honey and sell it to local consumers and passengers who pass through the district.

Processors: These are processors both in urban and rural areas who purchase crude honey from cooperatives and collectors then supply processed honey to consumers directly and indirectly in the form of brewery (tej and birzi). The well know honey processors in the district are Apinec agro-industry and Kaffa honey union that process, pack and export honey to different European countries and other countries in the world. The union purchases honey from cooperatives at a premium price.

Consumers: These are the final actors of the chain who buy honey for their own consumption purpose. They buy crude or processed honey directly from producers, retailers and processers to consume the honey produced in the study area. It also includes local communities who consume 'tej' and 'birz'. The honey produced in the district passes through different channels actors to reach the hands of final consumers.

4.2.2. Honey marketing channels

The analysis of marketing channels was intended to know the alternative routes that the product follows from the point of origin to final destination. This part discusses the major flow directions of honey among different actors in the market chain. From 21795Kg estimated volume of honey produced by sampled households in 2015/16, about 20703Kg of honey was supplied to market. The main honey marketing channels identified from the point of production until the product reaches to the final consumer were:

Channel I: Producers Consumers = 1652Kg=7.98% Channel II: Producers Retailers Consumers = 3488Kg (16.85%)

Channel III: Producers Cooperatives Consumers = 2407Kg (11.6%) Channel IV: Producers Cooperative Processors Consumers = 3710Kg (17.92%) Channel V: Producers Collectors Processors Consumers= 2699Kg (13.04%) Channel VI: Producers Collectors Retailers Consumers=2271Kg (10.97%) Channel VII: Producers Collectors Wholesaler Retailers Consumers = 4476 kg (21.62%)

The channel comparison was made based on the volume of honey that passed through each channel. Accordingly, from total amount of honey supplied to the market (20703kg) the largest volume of honey passed through channel VII which is about 4476kg of honey in a year 2015/16, which was 21.62 percent of the total volume. In channel IV about 17.92 percent of the total honey marketed which was second largest channel (Figure 4). This shows the flow of honey was more concentrated in these two channels and less in other channels.

Producers= 20703kg (100%)

%) 2407kg(11.6%)

16.85

Cooperatives 1652kg(7.98%) %

Local collectors 4476kg(21.62%

3488kg( %

) 3710kg(17.92%) 2271kg(10.97%)

% Whole 2699kg(13.04%) seller Processers

Retailers

Consumers

Where: Channel 1 Channel 3 Channel 5 Channel 2 Channel 4 Channel 6 Channel 7

Figure 4: Honey marketing channels for different market actors Source: Own sketch from survey result (2016)

4.3. Structure, Conduct and Performance of the Honey Market

In this section, the structure, conduct and performance of honey market were presented and discussed.

4.3.1. Honey market structure

The structure of honey market in the study area was described using market concentration, the degree of transparency (market information) and entry conditions (licensing procedure, capital requirement and business experience).

4.3.1.1. Degree of market concentration

Even though different types of honey traders were available in the study area, the number of traders at each market level was few. Therefore, district level market concentration ratio analysis was carried out for all sampled traders. In addition, the computation was performed by taking the total volume of honey purchased in 2015/2016 production year by sample traders at woreda level.

Table 12: Concentration ratio of sample traders (CR4)

Number Cumulative Woreda total traders of traders Quantity Total honey % share of % of (A) (B) purchased purchased in purchase cumulative (C) kg (D=A*C) (Si=D/86740) purchase 1 1 9720 9720 11.206 11.206 1 2 7420 7420 8.554 19.76 1 3 6980 6980 8.047 27.81 1 4 5050 5050 5.822 33.63 3 7 4130 12390 14.2841 47.91 6 13 3450 20700 23.8644 71.78 5 18 2390 11950 13.7768 85.55 1 19 1550 1550 1.7869 87.34 1 20 1200 1200 1.3834 88.72 3 23 1070 3210 3.7007 92.43 3 26 1030 3090 3.5624 95.99 4 30 870 3480 4.012 100.00 Total 86740 1 Source: Own computation (2016)

As indicated in table 12, result of CR4 shows that the top four honey traders handled 33.63% of the honey purchased by all sampled traders in the study area. Following the market structure criteria suggested by (Kohls and Uhl, 1985), the conclusion derived from CR4 is that honey markets in Chena woreda is weak oligopoly.

4.3.1.2. Degree of market transparency and barriers to entry

The degree of market transparency: It refers to the adequacy, timeliness and reliability of market information that the traders have for their marketing decision. In a transparent market, participants have adequate market information about their competitors regarding their source of supply and buying prices for better decisions. The traders in the study area had varieties of honey market information through mobile telephone contact (34.75%), through personal observations (43.25%) and other traders (22%) (Table13). The results showed that traders had more advantaged in information access than producers. Therefore, there was no market transparency in sampled markets since producers lack it.

Table 13: Market information, lack of capital and licensing procedure (%)

Acces to service Shishonde Wacha Woshe Bonga Total Market information Other traders 22 24 27 15 22 Personal observation 43 51 35 44 43.25 Mobile phone 35 25 38 41 34.75 Lack of capital Yes 24 52 18 57 37.75 No 76 48 82 43 62.25 Licensing procedure Difficult 38 63 23 71 48.75 Easy 62 37 77 29 51.25 Source: Own computation (2016)

Barriers to entry into the market: It reflects the competitive relationships between existing traders and potential entrants. If the barriers to entry are low, new traders can easily enter into honey markets and compete with established traders. In this study licensing procedure, capital requirement and presences of unlicensed traders were used to analyze barriers of honey market entry.

Capital requirement: Though capital is important to all market players, the degree of importance varies among actors. However, survey results in table 13 revealed that about 62.25% of honey traders reported that their lack of capital, whereas only 37.75% of the traders reported that they had their own capital to run the business. In addition, the sample traders reported that access to credit in the district has been the single most critical constraint in the start-up and expand the existing business. Some are not willing to get the service from the available formal credit sources due to collateral and other complicated processes. The above mentioned factors were reported as among the constraints to expand the scale of operations. This implied that, lack of capital discourages entry into honey trading.

Licensing procedure: The survey result in Table 13 shows that, 48.75% of the traders indicated that there difficulty in getting license while the remaining 51.25% of traders reported that it is easy to get honey trade license, so long as they fulfill the required initial capital which is a minimum of 7,000 birr and above. Some traders were relatively free to enter the market as far as they had the desired amount of capital and access and availability to different infrastructure that could facilitate their bargaining power. In addition, license procedure is not the case, as some of the traders operating in the study area had no honey-trade license; hence it seems, there was no as such strong restriction to enter in the honey markets with relation to honey trading license.

The existence of unlicensed traders: Unlicensed traders can purchase honey in different markets and transport it to the zonal markets around the district (Jimma and Bonga). The existing unlicensed traders in the district is one of the major bottlenecks to enter and expand trading activities. Weak mechanism of controlling unlicensed traders and the quality of crude honey have led to the supply of honey that is adulterated. This resulted in loses to some customers as a result of reduction in the amount of honey consumed as it was indicated by majority of honey traders in the study area.

Generally, lack market information for producers, problems in licensing and unfair competition with the unlicensed traders are identified to be the major entry barriers to honey marketing. Since the market concentration is to some extent concentrated and there are also entry barriers, the honey markets structure in Chena woreda had deviated from the norms of competitive market structure.

4.3.2. Honey market conduct

The conduct of the honey market was analyzed in terms of the availability information about purchasing and selling strategies, adjusting to the market in which they sell or buy and practice of storage and processing of producers and traders.

4.3.2.1. Honey producers conduct

According to the survey result presented in Table 14, about 95.5% of the sampled households produce honey for market. This indicates that honey production in study area is income generating activity for the majority of producers. With regard to honey price setting about 72.7% of the respondents reported price of honey was set by buyer only, about 8.3% of respondents reported price was set by negotiation, about 11% reported honey price was set by market and none of the producers set honey price.in the study area

Table 14: Production and selling strategies of producers

Activities Strategies Percent Activities Strategies Percent Purpose of Consumption 0 Market for sale Wacha 29.5 production Sale 75.5 Shishonde 32.5 Consumption 24.5 Wosh 27 and Sale Bonga 21 Buyer Collectors 71.43 Retailers 70.13 Purpose of High price 21.5 Cooperatives 72.73 selecting this Proximity 69.5 Consumers 53.9 market Fair scaling 9 Price setter Producer 0 Practice of Yes 27.5 Buyer 72.7 storage No 72.5 Negotiation 8.3 Practice of Yes 0 Market 11 processing No 100 Source: Own computation (2016)

The survey result in Table 14 indicates that 29.5%, 32.5%, 27% and 21%, of the producers selling their product at Wacha, Shishonde, Wosh and Bonga markets, respectively and producers selected the markets based on price change (21.5%), proximity to the residence (69.5%) and fair scaling (9%). Majority of sampled respondents (72.5%) sell their honey as soon as they harvest while only about 27.5% of sample producers store honey during harvesting season. None of the producers practices of honey processing at producers level during the survey period in the woreda.

4.3.2.2. Conduct of honey traders

The strategies of traders in maximizing profit and increase their bargaining power include the use of regular partner, long term relation with clients or suppliers and the use of collectors.

Traders’ purchasing strategy: Collectors usually purchase crude honey from producers and resale for wholesaler, retailers, processors and consumers. From the sampled honey traders, 21.5% purchase honey from Wacha, 27.5% from Shishonde, 46.5% from Wosh and 4.5% from Bonga market. They select these markets for purchase of honey due to high supply (38.5%), short distance (38%) and better quality (23.5%).

Table 15: Buying and selling behavior of traders

Activities Strategies Percent Activities Strategies Percent Market for Wacha 21.5 Market for sale Wacha 21.6 purchase Shishonde 27.5 Shishonde 12.5 Wosh 46.5 Wosh 8.2 Bonga 4.5 Bonga 57.7 Purpose of Better quality 23.5 Purpose of High price 37.5 selecting market High supply 38.5 selecting this High consumers 39.5 for purchase Short distance 38 market for selling Short distance 23 Buyer Retailers 41 Purchase price Market 36 Wholesalers 12 setter Buyers 45.5 Processors 27 Negotiation 18.5 Practice of Yes 56.7 Practice of Yes 67.6 storage No 43.3 processing No 32.4 Source: Own computation (2016)

Price setting strategy is crucial in honey trading activity. The survey result indicates that majority (45.5%) of the honey traders reported that purchase price of honey was set by their own in the study area; while 36% and 18.5% reported that honey purchase price is set by market and negotiation, respectively (Table 15).

Traders’ selling strategy: According to survey result, majority of traders prefer Bonga (57.7%) market to sale their honey, followed by Wacha (21.6%), Shishonde (12.5%) and Wosh (8.2%). The purposes of selecting these markets were based on high price (37.5%), high consumer (39.5%) and short distance (23%). About 56.7% of sample traders store honey during harvesting season and sold at higher price during slack season since there is less honey in the market. While 43.3% sell their honey as soon as they get the market

(buyer). One of selling strategy traders in the study area was purchase of crude honey from producers at low prices and they filtered and add value to the honey which in turn increases the price of honey. Accordingly, majority (67.6%) of the sample traders process honey while the remaining 33.4% did not process honey in the study area.

4.3.3. Marketing performance

According to Wisdom et al. (2014) to measure market performance a four parameters are required which are volume handled, producers share, marketing cost and marketing margin. Similarly, marketing costs, producers share and marketing margin were considered to measure honey market performance in the study area.

4.3.3.1. Marketing costs

Marketing costs are estimated to compute the share of profit captured by key actors in the marketing chain. Table 16 reveals that different types of costs incurred by different market actors in the transaction of honey. The highest cost was incurred by processors which was about 9.5 birr per kg of honey. This because of high storage and wastage cost due to processing of honey. The second highest cost is that of wholesaler and the relatively lowest marketing cost was incurred by cooperatives with 8.67 birr per kg of honey since they buy directly from producers in their localities thus in turn reduce transportation cost.

Table 16: Honey average marketing costs for different marketing agents (Birr/Kg)

Marketing costs Actors (Birr) Producer Cooperative Collector Retailer Wholesaler Processer Packing material 8 8 7.5 7.5 7.5 7.5 Load and unload 0.1 0.1 0.3 0.2 0.3 0.4 Transport 0.15 0.2 0.2 0.2 0.3 0.25 Storage cost 0.2 0.3 0.35 0.3 Wastage loss 0.05 0.07 0.1 0.15 0.1 0.5 Personal expense 0.1 0.05 0.2 0.5 0.05 0.2 Tax 0.15 0.15 0.2 0.25 0.25 Other costs 0.05 0.05 0.05 0.15 0.15 0.1 Total cost 8.6 8.67 8.8 8.9 9 9.5 Source: Own computation (2016).

Average marketing cost of producers was (8.6 birr/kg) when they sold honey to consumers and retailers. As the result of no transportation cost and tax, average marketing cost is

53 lower for producer when they sold to cooperative and collectors which was 8.3 birr/kg (Table 16).

4.3.3.2. Structure of production costs and profitability of honey production

In order to perform profitability analysis, major production costs for both traditional and improved beehive type are considered. Based on the survey data, the costs of production and returns at the prevailing prices were used to estimate the benefits. This section aims at identifying and quantifying the different costs, which are incurred by the beekeepers in production process. The purchase cost of bee colony was not considered because absence of practice of colony sale and purchase in the study area. The costs included were purchase cost of bee wax for foundation sheet preparation, labor cost during preparation of foundation sheet, harvesting and shed construction, bee keeping equipment (protective clothes, smoker, extractor and plastic container) cost, depreciation cost on beehives, feed cost and interest on input costs.

The average prices of beehives obtained from survey data were 90 and 780 birr for traditional and improved beehives, respectively. The labor cost was estimated based on the price or wage of labour in the locality per man-day for combs preparation and harvesting. Family labour was evaluated at the prevailing wage rates of hired labour at the village level. Interest for input costs (beehive and bee equipment) was calculated by assuming 5% interest rate. In the study area, improved beehive is estimated to serve for 10 years, while traditional beehive is estimated to serve for 5 years. Thus, depreciation costs of beehives were calculated using the straight-line method by considering the salvage value of 10% of its original price at 5 and 10 year service life for traditional and improved beehives, respectively.

Beekeepers obtained honey yield of 7 kg/hive/year form traditional beehive and 23kg/hive /year form improved beehive on average. Average hive output was valued at farm gate price which was on average about birr 51 per kg. The total costs for both improved and traditional beehive types were estimated to be 384.30 birr/hive and 86.95 birr/hive per year, respectively. Accordingly, the gross profits were 788.70 birr/hive and 270.05 birr/hive for improved and traditional beehives per year, respectively. That is, the gross profit from improved beehive is more than double of the gross profit from traditional beehive. Similarly, Belets and Birahanu (2014) found that the gross profit of improved

54 beehive was around two times higher than that of traditional beehive in Ahferom woreda of Tigray region, Ethiopia

Table 17: Structure of honey production costs and profitability by type of beehives used

Major items Type of beehive Traditional Improved Bee keeping equipment cost(Birr) 25 102 Bee wax(Birr) 43 Labour cost (Birr) 35 91 Feed cost (Birr) 5 34 Interest on input costs (Birr) 5.75 44.1 Deprecation cost on beehives (Birr) 16.2 70.2 Total cost of production per hive (Birr) 86.95 384.3 Average yield of honey per hive (Kg) 7 23 Total revenue from sale of honey per hive (Birr) 357 1173 Gross profit per hive (Birr) 270.05 788.7 Production cost per Kg (Birr) 12.4 16.7 Source: Own computation (2016).

4.3.3.3. Marketing margin

The survey results in Table 18 depicted differences between the total income from honey trading and the costs incurred in the process of honey trading which gives the gross profit of each actor. To do this, average production cost of 14.55 birr/kg for producers was taken by merging the average production cost of 1kg honey for traditional and improved beehives (Table 17). Accordingly, the honey producers’ gross profit was highest when they directly sale consumers in channel I which is 36.85birr/kg while they take lowest gross profit when they sale to collectors which accounts 28.15 birr/kg. This implies producers are more profitable if they sold directly to retailers and consumers. Processors from traders shared the highest profit 10.25 birr/kg when they made purchase from collectors in channel V and they sold directly to consumers. Cooperatives gained the second highest profit 5.33 birr/kg on channel III when they directly bought from producers and they sold to consumers. Honey collectors made a profit of 5.05 birr/kg in channel VI. While retailers and wholesalers get 2.5 birr/kg profit in channel VI and 1.5 birr/kg in

55 channel VII. This implies that processors and cooperatives received the highest profit from honey marketed in the study area while retailers and wholesalers capture the smallest profits shares from traders in honey market chain.

As indicated in Table 18, total gross marketing margin (TGMM) is highest in channel V which was 38.55% and lowest in channel II which was 22.67%. While without considering channel I where producer directly sold honey to consumers, the maximum producer’s share (GMMp) is highest in channel II which was 77.33% from the total consumers’ price and lowest in channel V which was 61.45%. This difference might support the theory that as the number of marketing agents increases the producers share decreases. The reason being, the more the number of middlemen in honey market, the more profit they retain for their services whether they add value to the item or not.

The survey result also shows that the lowest gross marketing margin was taken by wholesaler in channel VII which was 12.5%. While the highest gross marketing margin from traders was taken by processors which accounts 24.24% of the consumers’ price in channel V and followed by cooperatives which accounts 23.53% in channel III (Table 18). This implies share of market intermediaries in the consumer’s price was substantial and there was a need to reduce market intermediaries to minimize the marketing margins and thereby enhance the producers’ income.

Table 18: Honey market margin for different channels (Birr/kg)

Actors Honey marketing channels I. II. III. IV. V. VI. VII. Producers Production cost 14.55 14.55 14.55 14.55 14.55 14.55 14.55 Marketing cost 8.6 8.6 8.3 8.3 8.3 8.3 8.3 Selling price 60 58 52 52 51 51 51 Gross profit 36.85 34.85 29.15 29.15 28.15 28.15 28.15 GMMpr (%) 100 77.33 76.47 62.65 61.45 63.75 63.75 Collectors Purchase price 51 51 51 Marketing cost 8.8 8.95 8.5 Selling price 63 65 62 Gross profit 3.2 5.05 2.5 GMMcoll(%) 14.46 17.5 13.75 Retailers Purchase price 58 65 72 Marketing cost 8.9 8.5 6.15 Selling price 75 80 80 Gross profit 5.1 2.5 2.85 GMMret (%) 22.67 18.75 13.21 Cooperative Purchase price 52 52 Marketing cost 8.67 8.15 Selling price 68 65 Gross profit 5.33 4.85 GMMcoop (%) 23.53 15.66 Processors Purchase price 65 63 Marketing cost 9.5 9.75 Selling price 83 83 Gross profit 8.5 10.25 GMMprc (%) 21.68 24.09 Wholesaler Purchase price 62 Marketing cost 8.5 Selling price 72 Gross profit 1.5 GMMwh (%) 12.5 TGMM (%) 0 22.67 23.53 37.35 38.55 36.25 36.25 Source: Own computation (2016)

4.4. Econometric Results

This section presents the econometric models outputs of the study. Thus, the determinants of volume of honey marketed and honey producers’ market outlet choices decisions are discussed.

4.4.1. Determinants of honey market supply

Honey is produced mainly for market and is one of the most important cash commodities for Chena woreda farmers. Analysis of determinants of household level of honey supply was found to be important to identify factors constraining honey market supply. From the survey result, the variance of volume of honey supplied was found to be high and logarithmic transformation was implemented to reduce the variance (Appendix figure 2). Interpretation of OLS estimates is possible if and only if the basic assumptions of multiple linear regression model are satisfied. Thus, hypothesized explanatory variables were checked for existence of multicolliniarity, heteroscedasticity, omitted variable and normality problems.

The test for multicollinearity suggests that there is no serious problem of multicollinearity among explanatory variables because the mean VIF is about 1.40 (Appendix table 1). The omitted variable bias test, with Ramsey RESET test (F (3, 138) = 1.03; prob > F= 0.3831); the null that there is not omitted variable in the model is accepted suggesting that the model has no problem of omitted variable bias. Heteroscedasticity test was performed using Breusch-pagan/Cook-Weisberg (chi2 (1) = 0.07; prob > chi2 = 0.7923); suggests that the errors are of the same variance. The null that the errors have constant variance is accepted. In addition, error terms are normality distributed as the normal probability plot for residuals approaches to normality line (Appendix figure 1). The fitness of the model (Adjusted R2) was 0.8175 that passed the tests and indicating about 82% of the variation in volume of honey supplied to the market by households was explained by the variables included in this model.

Among the hypothesized eleven variables included in the regression model, six variables were found to be significantly affecting the market supply of honey at household level. These are experience in beekeeping, frequency of extension contact, number of beehives owned, type of beehives used, cooperative membership and distance to the nearest market (Table 19). The discussion about these variables is given below.

Beekeeping experience (EXPBK): The beekeeping experience of households affected honey market supply positively and significantly at 1% significance level. The model result implied that as beekeeping experience increase by one year, the quantity of honey supplied to the market increases by 3.9 %, keeping others factors constant. This means the beekeepers with more experience in honey production and marketing supplied more

58 honey to market than less experienced due to their having more knowledge in bee management and marketing network. This result is in line with finding of Samuel (2014) and Bestalot (2012) who illustrated the positive relationship between beekeeping experience and volume of honey supplied to the market.

Type of beehive used (TBH): It is categorical variable that affected market supply of honey positively as expected. The model result shows that as compared to base category (use of tradition beehives only), use of both traditional and improved beehives to produce honey affected quantity of honey supplied positively at 5% level of significance. While using only improved beehive affects volume of honey marketed positively at 1% level of significance. Thus, as compared to those households who use traditional beehives only, the volume of honey supplied to market increase by 15.3% for those households who use both traditional and improved beehives and 33.2% for those households who use improved beehives only. This can be explained as producers who used both traditional and improved at the simultaneously and those producers who used improved beehives only produce better volume than those producers who used the traditional beehives only. Hence, the more they produce the more they tend to supply to the market. Bestalot (2012) confirmed that improved beehives allow honey bee colony management and use of a higher-level technology with larger colonies easily and can give higher yield and quality of honey thus in turn increase the market supply.

The number of beehives owned (NBHO): It has positive influence on the volume of honey supplied to market at 1% significance level. The model result indicates that as the number of hives owned increased by one, the volume of honey marketed increased by 13.5%, keeping others factors constant. This indicates that producers with more number of beehives can harvest more volume of honey and as the result they supply more to the market. Samuel (2014) confirmed that the use of large number of hives was directly related with the amount supplied to the market and return earned by beekeeper. This result is also in line with finding of Getachew (2009).

Frequency of extension contact (EXTCONT): It was positively and significantly related to the volume of honey supplied to the market at 10% significance level. The positive and significant effect was mostly due to the reality that beekeepers who have frequent contact with extension worker concerning beekeeping particularly about modern honey production, harvesting and marketing methods which contribute to increasing the amount

59 of honey produced which leads to increased market supply. The model result predicts that increase in number of extension contacts per year by one in relation to beekeeping, increases the volume of honey supplied by 3.33%, holding others predictors constant. This suggests that access to extension service avails information regarding improved technology and market information which improves production that in turn increases the marketed surplus. The result is consistent with earlier results of Assefa (2009); Bestalot (2012); Samuel (2014).

Table 19: OLS estimate of determinants of honey market supply

Variables Coefficients Standard errors Sex of household head 0.049 0.038 Level of education 0.009 0.008 Household size -0.051 0.039 Total income 0.086 0.073 Beekeeping experience 0.039*** 0.007 Hive type(traditional and improved) 0.153** 0.069 Hive type(improved) 0.332*** 0.048 Number of hives 0.135*** 0.043 Distance from market -0.052*** 0.018 Amount of credit received 0.0132 0.009 Extension contact frequency 0.033* 0.019 Cooperative membership 0.284*** 0.050 Constant 0.93*** 0.125 Number of observations 154 F(12, 141) 58.130 Prob > F 0.000*** R-squared 0.832 Adjusted R-squared 0.818 Predicted value, volume supplied (ln) 125.21(4.83) Note: Dependent variable is volume of honey supplied (in natural logarithm) ***, **and * Significant at 1%, 5% and 10 probability level, respectively. Source: Own computation (2016).

Distance to the nearest markets (DNM): It affected volume of honey supplied to the market negatively and significantly at 1% significance level. The model result indicated

60 that other explanatory variables being constant, as the distance of the farmers’ residence from the nearest market increase by one kilometer, the volume of honey supplied decreased by 5.2%. This is due to the fact that as the farmers reside far from the nearest market the transport cost for selling their output and loss due to handling would be high which discourages producers from selling high volumes of honey. The result is consistent with the finding of Samuel (2014) and Efa et al. (2016).

Cooperative membership (COOPM): It influence positively and significantly the volume of honey marketed at 1% level of significance as expected. From model result of holding all other independent variables constant, as compared to those households who are not member of cooperative the volume of honey marketed increases by 28.4% for those household who are member of cooperative. Being a member of producer group motivates farmers to supply more by giving technical advice, input and up to date information provision to members. A study by Adeoti et al. (2014) confirmed that being membership of cooperative could have better access of market information, inputs, technical advice and access to credit facilities important to production and marketing decisions which grid towards increments of output that in turn increase volume of supply to market.

4.4.2. Determinants of honey producers market outlets choices

The Wald test (χ2(40)  111.49, p = 0.000) indicates that the subset of coefficients of the model is jointly significant and that the explanatory power of the factors included in the model is satisfactory thus the MVP model fits the data reasonably well. Likewise, the model is significant because the null that choice decision of the four honey market outlets is independent is rejected at 1% significance level. The results of likelihood ratio test in the model (LR χ 2(6)  38.663, χ2 > p= 0.0000) indicating the null that the independence between market outlets choice decision (21=31=41=32=42=43=0) is rejected at 1% significance level. There are significant joint correlations for three estimated coefficients across the equations in the model (Table 20). This verifies that separate estimation of choice decision of these outlets is biased and the decisions to choose the four honey market outlets are interdependent household decisions.

Hence, there are differences in market outlets selection behavior among producers, which are reflected in the likelihood ratio statistics. Separately considered, the ρ values (ij) indicate the degree of correlation between each pair of dependent variables. The 41

(correlation between the choice for retailer and consumer outlet), 32 (correlation between the choice for cooperative and collector outlet) and 42 (correlation between the choice for cooperative and consumer outlet) are negatively interdependent and significant at the 1%, 10% and 5% probability level. This finding leads us to the conclusion that beekeepers supplying to retailer outlet are less likely to supply to consumer (ρ41). Equally, those beekeepers who supply to cooperative market outlet are less likely to send their honey to the collector and consumer outlets (ρ32, ρ42). This indicates a competitive relationship of retailer with consumer outlet and cooperative with collector and consumer outlets (Table 20).

The Simulated Maximum Likelihood(SML) estimation results shows that the probability that honey producers choose retailers, cooperatives, collectors and consumers market outlets were 69.05%, 73.4%, 61% and 46.9%, respectively. This indicates the likelihood of choosing consumers outlet is relatively low (46.9%) as compared to the probability of choosing retailers (69%), cooperatives (73.4%) and collectors (61%). The joint probabilities of success or failure of the four outlets also suggest that households are more likely to jointly choose the four outlets. The likelihood of households to jointly choose the four outlets simultaneously is 10.96%, while their failure to jointly choose the four outlets is nearly zero.

Based on result of MVP model, as shown in Table 20 some of the variables used in the model were significant at more than one market outlets while one variable was significant in only one market outlet. Out of ten explanatory variables included in the model, three variables affected significantly retailer market outlet; five variables significantly affected cooperative outlet; four variables significantly affected collector outlet; and two variables significantly affected consumer market outlet choice at different probability levels.

Beekeeping experience (EXPBK): It is associated positively with likelihood of choosing cooperatives outlet at 5% levels of significance. The result showed that those households with a more number of years engagement in honey production and marketing are more likely to choose cooperatives outlet. This may be due to that experienced beekeepers had better knowledge of cost and benefits associated with various honey marketing outlets that give the producers desire to adjust their market links; trying alternative marketing outlets to increase sales volume so as to increase the profits. Consequently the likelihood of choosing it is high as the result of experience favour to choose cooperative outlet. The

62 finding of Kifle et al. (2015) showed that the number of years a household spent in beekeeping, positively and significantly affected using cooperative market outlet.

Volume of honey supply to market (VHS): It influenced positively the likelihood of choosing cooperative market outlet at 5% significance level and negatively influenced the likelihood of choosing consumer outlet at 1% level of significance. This result indicated that those households with large volume of honey were more likely to sell to cooperative. This is because of cooperative capacity to purchase large quantity of honey and its incentives like share dividend for those households who supply more honey. The implication is that if the quantity of honey to be sold is large, beekeepers search market outlets that buy large volume with reasonable price and incentive. This finding is in line with results of Muthini (2015).

Moreover, the negative influence of variable with likelihood of choosing consumer outlets was due the preference of other market outlets that purchase in large quantity. This implies that if the quantity to be sold is low, beekeepers are not forced to search incentives from other outlets. This result is consistent with study by Atsbaha (2015).

Distance from the market (DSTM): It is negatively associated with likelihood of producers selling to retailer 10% level of significance and positively associated with likelihood selling to cooperative and collector outlet at 10% and 1% level of significance, respectively. It reflects that household located far away from nearest market center faces difficulty in delivering honey to retailer outlet due to poor road facility to sell their product. Hence, they sold to available market outlets in their locality. As a result, supplying honey to retailers requires transporting the product to urban market to meet retailers. This is in line with the finding of Atsbaha (2015) that showed distance to nearest market was negatively and significantly related to the channel choice of retailers’ channel.

Moreover, the positive relation of distance and likelihood of choosing a cooperative and collectors was due to the fact that cooperatives have honey collection centers in each kebeles/nearby kebeles to collect honey at farm gate that reduces the transportation cost of beekeepers. Likewise, collectors purchase honey at farm gate from beekeepers by going door to door during harvesting season. This implied that with increase in distance to market, beekeepers preferred to sell to honey cooperatives in their kebeles or in nearby kebeles and collectors, rather than selling to other market outlets that associated with

63 incurring higher transportation costs. This result is similar to the finding by Bardhan et al. (2012).

Table 20. Multivariate probit estimations for determinants of producers’ outlets choice

Market outlets Variables Retailers(106) Cooperatives(113) Collectors(95) Consumers(72) Coeff(Se) Coeff(Se) Coeff(Se) Coeff(Se) Education level 0.0008(0.063) 0.054 (0.082) -0.039(0.072) 0.073(0.035) Household size 0.021(0.057) 0.053(0.079) 0.038(0.066) 0.064(0.057) Total income -0.002(0.003) 0.0014(0.0036) 0.003(0.004) -0.002(0.003) Experience -0.028(0.048) 0.125**(0.062) 0.0356(0.054) -0.067(0.052) Volume supplied -0.002(0.0017) 0.007**(0.003) 0.0057(0.0038) -0.005*** (0.002) Extens contact 0.320**(0.156) 0.516**(0.203) -0.06(0.142) 0.25(0.163) Distance to mrkt -0.216*(0.120) 0.352*(0.181) 0.13***(0.041) 0.05(0.121) Trust of buyer -0.194(0.250) 0.387(0.318) 0.94***(0.290) 0.19(0.232) Market info 0.04***(0.014) 0.015(0.013) -0.049*(0.024) 0.039*(0.020) Coop. member -0.019(0.340) 1.17***(0.420) -0.95**(0.470) -0.456 (0.38) Constant 1.980 (1.482) -1.55**(0.720) 1.368*(0.804) 1.81(1.331)

Predicted probability 0.690 0.734 0.610 0.469 Joint probability(success) 0.1096 Joint probability (failure) 0.00005 Number of draws (#) 15 Number of observations 154 Log Likelihood -265.268 Wald(흌2(40)) 111.49 Prob > 흌2 0.0000 Estimated correlation matrix

   

   

   

  -0.441* (0.226)  

    1

Likelihood ratio test of: 21= 31 = 41 = 32 = 42 = 43 =0:  2(6) = 38.663 2 Prob > 흌 = 0.0000*** Note: Coeff=Coefficient and Se=Standard errors in parentheses

*, ** and *** =significance level at 10, 5 and 1%, respectively. Source: Own computation (2016).

Frequency of extension contact (EXTCONT): It has positive and significant influence on retailer and cooperative outlets choice decision at 5% significance level. Extension services increases the ability of farmers to acquire important market information as well as enable the beekeepers to improve production methods hence leading to more output which in turn increases producers’ ability to choose the best market outlet for their product. Thus, households who were visited more by extension agents were more likely to deliver honey via retailer and cooperative outlets. This result is similar to study by Bardhan et al. (2012) that confirms regular contact with extension functionaries had a positive influence on the likelihood choice of cooperative outlet by milk producer in Uttarakhand.

Trust in buyers (TURST): The variable was positively and significantly associated with choice of collector outlet at 1% significance level. The positive and significant results showed that households who trust in buyers are more likely to deliver honey to collector outlet. A good reputation and trustworthiness of traders increase producers’ commitment to collector because it reduces opportunistic behavior and promotes cooperation and commitment in the relationship. This study is in line with Addisu (2016) who found trust in buyer is associated positively with collector outlet that farmers who trust in traders are more likely to choose rural collectors to sell their onion product.

Access to market information (MINFO): The variable was positively and significantly associated with the likelihood of choosing retailer and consumer outlets at 1%and 10% level of significance, respectively. Access to current market information improves producers selling price. Because market information helps producers to analyzing the price difference in their locality and the nearby main market that increases probability of choosing retailers and consumers which give relatively higher price to producers. The findings of Bezabih et al. (2015) confirmed that market information has a positive and significant effect on retailer channel choice decision of potato producers. Moreover, the variable was negatively and significantly associated with the choice of collector outlet at 1% significance level. The negative relation may be due to preference of other outlets that gives relatively higher price. This declines beekeepers preference to local collectors; rather they transport it to the nearest market. This is a line with the finding of Astabah (2015).

Cooperative membership (COOPM): The model result shows that membership to honey cooperative affected the likelihood of choosing cooperative outlet positively and significantly. This obvious that member of honey production and marketing cooperatives has responsibility to supply to its cooperative from their production as norm of cooperative. This may be also because of cooperative provides some technical assistance and training to its members and give share dividend at the end of each year. The variable is also associated negatively with collectors’ outlet choice at 5% probability level of significance. The negative relation indicates that beekeepers who were member of cooperative are less likely to choose collector outlet compared to those who are not member due to incentives of cooperative. This finding is similar with the finding by Bongiwe and Micah (2013).

5. SUMMARY, CONCLUSION AND RECOMMENDATIONS

This chapter has two sections. The first section presents summary of the study which briefly reflects the overall summary and conclusion of the findings and the second section forwards recommendations or policy implications emanated from the findings of the study.

5.1. Summary and Conclusion

This study was aimed at analyzing market chain of honey with especial emphasis to Chena woreda of Kaffa zone, Southern Ethiopia. The specific objectives of the study were identifying honey market channels, chain actors and their respective roles; analyzing structure conduct performance of honey market, analyzing the determinants of quantity supplied and market outlets choice decisions of honey producers. Data from a 154 honey producers, 30 traders and 20 consumers were collected. Mean, percentage, frequency, concentration ratio, market margin, multiple linear regression and multivariate probit models were used to analyze the collected data. The main findings of this study are summarized as follows.

The major actors involved in honey market chain include producers, cooperatives, collectors, wholesalers, retailers, processors (APINAC Plc, tej and birz makers) and consumers. Out of 21795kg estimated volume of honey produced by sampled households in 2015/16, about 20703Kg of honey was supplied to market through seven channels in the study area. From identified seven channels, major share of honey goes to marketing through channel VII (producers collectors wholesaler retailers consumers).

The four firms’ concentration ratio indicated that about 33.63 percent of the total volume of honey purchased in 2015/16 was handled by four largest traders in the study area. The result suggesting that the structure of the honey market was weak oligopoly. In addition to traders concentration, lack market information for producers and due some entry barriers to honey marketing (capital requirement and unfair competition with the unlicensed traders) honey market in the district deviates from norms of competitive market. Market conduct analysis shows that the price of honey was made by traders thus honey buyers have the power in setting price and producers are price takers.

The results of market margin analysis indicated that the honey producers’ gross profit was highest when they direct sale to consumers in channel I which was 36.85 birr/kg and

lowest when they sell to collectors which was 28.15 birr/kg. Processors from traders shared the highest profit (10.25 birr/kg) when they made purchase from collectors in channel V and they sold directly to consumers and wholesalers shared lowest profit (1.5 birr/kg) in channel VII. The total gross marketing margin was highest in channels V which was 38.55% and lowest in channel II which was 22.67%. The result also revealed that the maximum producer’s share was highest in channel II which was 77.33% from the total consumers’ price and lowest in channel V which was 61.45%.

Econometric result of the multiple linear regression model indicated that beekeeping experience, beehive types used, number of beehives owned, frequency of extension contact and cooperative membership were positively and significantly determining the quantity of honey supplied to the market. Moreover, distance to nearest market affected the quantity of honey supplied to market negatively and significantly.

Finally, multivariate probit result for honey producers’ outlets choice revealed a competitive relationship of retailer with consumer outlet, cooperative with collectors and consumer outlets in the study area. The simulation results shows that the probability of choosing retailers, cooperatives, collectors and consumers market outlet were 69.05%, 73.4%, 61% and 46.9%, respectively with 10.96% likelihood of choosing the four outlets simultaneously and unlikely fail to choose the four market outlets jointly. The model result also shows that volume of honey sold, extension contact, beekeeping experience, distance to market, access to market information, cooperative membership and trust in buyers determine significantly the alternative market outlets choices of honey producers in the study area.

5.2. Recommendations

The following policy recommendations are drawn based on the results of the study.

First, improving technical knowhow of beekeepers on using best practices of the experienced beekeepers as a point of reference would help setting targets in increasing market supply levels. Thus, facilitating knowledge share between the producers is recommended for improvement of honey production. This can be achieved by arranging field days, cross visits and creating forum for experience sharing with experienced households.

Second, use of improved beehive is found to have a significant effect on the volume of honey supplied. Thus, the concerned bodies should focus on increasing the production of honey per hive through promoting improved beehives. In addition, efforts should be made in order to increase access of improved beehives by introducing improved hives like chefeka that can be constructed from locally available material and reduce production cost.

Thirdly, the producer with more number of beehives can harvest more volume of honey with better marketed surplus. Nevertheless, simply increasing number of beehives cannot be an option to increase honey marketed supply since volume of honey harvested from traditional beehive is low. Hence, increasing number of improved beehives to increase volume of honey per hive is better alternative to increase market supply. However, the current number of improved beehives owned by beekeepers in the study area is relatively low. One of the primary reasons for operating using small number of improved hives is the initial cost of the hives. So, there is a need for intervention to increase the total number of beehives owned by increasing access to improved hives and access to credit services.

Fourth, the result of the study revealed that beekeepers with more extension contact supplying more. Beekeepers need to gain skill in improved honey production in order to increase volume of honey produced. Hence, given the existing technology at hand bringing beekeepers under more extension contact and rendering them the necessary advisory service can help beekeepers increase their level of honey market supply.

Fifth, distance to the nearest market places is one of determinant of honey market supply. Due to the fact that as the farmers resides far from the nearest market the transport cost for selling their output would be high that discourages producers from selling high volumes of honey. Therefore, the concerned bodies need to intervene in improving poor road facility and poor transport accessibility to supply their product and establishing honey collection points across rural areas would assist beekeepers for faster delivery of honey.

Sixth, honey cooperative membership is found to be important determinant of honey market supply. Cooperatives motivates producers to supply more by giving technical advice, input and up to date information to members which grid towards increments of output that in turn increase volume of supply and take an advantage of bargaining power in time selling their produce. Hence, strengthen of the existing by financial capacity building,

69 teaching non-members to become member and establishment of additional honey cooperatives is suggested.

Seventh, multivariate probit model results indicated that beekeepers have been influenced by different factors to choose appropriate marketing outlets and several ways in which beekeepers can actively market their produce. Initially, distance to the nearest market significantly affects market outlets choice decision. Therefore, establishing honey collection centers in potential production areas encourage honey producers. Further, expanding equal accessibility of road and transportation facilities needs government intervention to promote the effective marketing of honey through all outlets.

Eighth, membership of honey cooperative affects the likelihood of choosing cooperative outlet. This is because it provides technical assistance and training to its members and it also enhances the bargaining power of members. So, to sustain and distribute these benefits, capacitating the existing cooperatives and organizing beekeepers into honey cooperatives is suggested. In line with this, increasing extension contact during production season and equipping them with some technical skills and market information through extension education would help to increase supply of large volume of honey to appropriate outlets. Therefore, extension services should be strengthened its implementation strategies to train and qualify beekeepers to choice appropriate market outlets with reasonable price.

Finally, the findings indicated quantity of honey sold affecting choice of appropriate market outlets. Enhancing producers’ marketed surplus which could be attained through support in capacity building, increasing access to improved hives, strengthening financial capacity existing and establishment of additional honey cooperatives. Therefore, concerned bodies should focus more on improvement of the extension services to aware beekeepers to choose appropriate market outlets with higher returns resulting from quantity sold not from unit price.

6. REFERENCES

Abah, D., Abu, G. and Ater, P. 2015. Analysis of the structure and conduct of paddy rice marketing in Benue state, Nigeria. American Journal of Marketing Research, 1(2): 70-78. Abdulrazak, U.M., Zainalabidin, M., Mad, N.S. and Ismail, A.L. 2013. Structure-conduct performance of the Malaysian poultry industry. Australian Journal of Basic and Applied Sciences, 7(8): 170-177. Abott, J.C. and Makeham, J.P. 1981. Agricultural Economics and Marketing in the Tropics: Intermediate Tropical Agricultural Series, Longman, UK. Abraham Tegegn. 2013. Value chain analysis of vegetables: The case of Habro and Kombolcha woredas in Oromia region, Ethiopia. MSc Thesis, Haramaya University, Haramaya, Ethiopia. Addisu Hailu. 2016. Value chain analysis of vegetables: The case of Ejere district, West Shoa zone, Oromia national regional state of Ethiopia. MSc Thesis, Haramaya University, Haramaya, Ethiopia. Adeoti, A.I., Issack, B., Oluwatayo, S. and Raheem, O. 2014. Determinants of market participation among maize producers in Oyo state, Nigeria. British Journal of Economics, Management and Trade, 4(7): 1115-1127. Alene, A., Manyong, V., Omanya, G., Mignouna, H., Bokanga, M. and Odhiambo, G. 2008. Smallholder market participation under transactions costs: Maize supply and fertilizer demand in Kenya. Food Policy, 33: 318–328. Anteneh Amare, Muradian, R. and Ruben, R. 2011. Factors affecting coffee farmers market outlet choice in Ethiopia. Centre for International Development Issues Nijmegen, Radboud University, Netherland. Arinloye, D., Pascucci, S., Linnemann, A., Coulibaly, O., Hagelaar, G. and Omta, S. 2014. Marketing channel selection by smallholder farmers. Journal of Food Products Marketing, 20(1): 1–21. Assefa Abebe. 2009. Market chain analysis of honey production in Atsbi Wemberta district, Eastern zone of Tigray national regional state, Ethiopia. MSc Thesis, Haramaya University, Haramaya, Ethiopia. Atsbaha Mehari. 2015. Value chain analysis of movable frame hive honey: The case of Ahferom district,Tigray region. MSc Thesis, Aksum University, Aksum, Ethiopia.

Awraris Getachew, Yemisrach Getachew, Dejen Assefa, Nuru Adgaba, Gebeyehu Ganga and Workneh Abebe. 2012. Honey production systems in Kafa, Sheka and Bench- Maji zones of Ethiopia. Journal of Agricultural Extension and Rural Development, 4(19): 528-541. Awraris Getachew, Amenay Assefa, Hailemariam Gizaw, Nuru Adgaba, Dejen Assefa, Zerihun Tajebe and Asrat Tera. 2015. Comparative analysis of colony performance and profitability of different beehive types in Southwest Ethiopia: Global Journal of Animal Scientific Research, 3(1): 178-185. Bardhan, D., Sharma, M. and Raka, S. 2012. Market participation behavior of smallholder dairy farmers in Uttarakhand. Agricultural Economics Research Review, 25(2): 243-254. Belachew Kifle, Mebratu Gebremariam, Kumlachew Alemu. 2015. Integrated management of common bacterial blight (Xanthomonas axonopodis pv. Phaseoli) of common bean (Phaseolus vulgaries) in Chena district of Kaffa zone, Southwest Ethiopia. Malaysian Journal of Medical and Biological Research, 2(2): 147-152. Belets Gebremichael and Birahanu Gebremedhin. 2014. Perception of smallholder farmers on improved box hive technology and its profitability in Northern Ethiopia. Journal of Agricultural Extension and Rural Development, 6(12): 393-402. Berhanu Kuma, Derek Baker, Kindie Getnet and Belay Kassa. 2013. Factors affecting milk market outlet choices in Wolaita zone, Ethiopia. African Journal of Agricultural Marketing, 1(2): 024-031. Betselot Mesfin. 2012. Honey value chain analysis with especial emphasis to Ada’a woreda, East Shoa zone of Ethiopia. MSc Thesis, Haramaya University, Haramaya, Ethiopia. Beyene Taye, Gemechis Legesa and Verschuur, M. 2014. Assessment of constraints and opportunities of honey production and marketing in selected zones of Ethiopia. American Journal of Research Communication, 2(10): 342-353. Bezabih Emana, Mengistu Ketema, Jeffreyson, K. and Jemal Yousuf. 2015. Factors affecting market outlet choice of potato producers in Eastern Hararghe zone, Ethiopia. Journal of Economics and Sustainable Development, 6(15): 159-172. Bongiwe, G. X. and Micah, B. M. 2013. Factors affecting the choice of marketing channel by vegetable farmers in Swaziland. Sustainable Agriculture Research, 2(1): 112- 123.

Chagwiza, C. 2014. Engaging cooperative farmers in agricultural intensification: Case studies on honey, dairy and linseed value chains in Ethiopia. Doctoral Dissertation, Radboud University, Nijmegen. Chala Kinati, Taye Tolemariam. and Kebede Debele. 2013. Assessment of honey production and marketing system in Gomma district, Southwestern Ethiopia. Greener Journal of Business and Management Studies, 3(3): 099-107. Cappellari, L. and Jenkins, S. 2003. Multivariate probit regression using simulated maximum likelihood. The Stata Journal, 3(3): 278-297. Chernet Tessemma. 2008. Land resources and socio-economic report of Bonga, Boginda, Mankira and the surrounding areas in Kafa zone, Southern Ethiopia [WWW.document].URLhttp://www.kafabiosphere.com/assets/contentdocuments/ KafaLanduse- Survey-Final-Report.pdf [accessed on 13 January 2015]. CIAT (Centro International de Agricultural Tropical). 2004. Increasing the Competitiveness of Market Chains of Smallholder’s Producers. Manual, 3: Territorial Approach to Rural Agro Enterprise Development Project. Cramer, G. and Jensen, W. 1982. Agricultural Economics and Agribusiness, 2nd Edition. McGraw Hill Book Company, USA. Cramer, G., Jensen, W. and Douglas, D. 1997. Agricultural Economics and Agribusiness, 7th Edition. John Wiley and Sons Inc, USA. CSA (Central Statistical Agency). 2013. Summary and Statistical Report of the 2012/2013 Survey on Livestock and Livestock Characteristics, Volume II. CSA, Addis Ababa, Ethiopia. CSA (Central Statistical Agency). 2014. Agricultural Sample Survey Report of 2013/2014 (September–January 2013/2014). Volume VII: Report on Crop and Livestock Product Utilization (Private Peasant Holdings, Meher Season). CSA, Addis Ababa, Ethiopia. CSA (Central Statistical Agency). 2015. Agricultural Sample Survey Report of 2014/15. Volume II: Report on Livestock and Livestock Characteristics (Private Peasant Holdings). CSA, Addis Ababa, Ethiopia. CWFEDO (Chena Woreda Finance and Economic Development Office). 2015. Physical and Socio-economic Profile of the Woreda. CWLFO (Chena Woreda Livestock and Fishery Office). 2016. Livestock Extension Profile of the Woreda.

Demisew Wakjira. 2016. Beekeeping in Ethiopia: Country Situation Paper Presented to 5th Annually African Association of Apiculture Exporter, 21-26 September 2016. African Association of Apiculture Exporter, Kigali, Rwanda. Desalgne Paulos. 2011. Ethiopian honey: Accessing international markets with inclusive business and sector development, SNV, Ethiopia. Available at:[accessedon12/07/2016] Djalalou-Dine, A., Stefano, P., Anita, R., Linnemann, O., Coulibaly, N., Geoffrey, H. and Onno, S.W. 2015. Marketing channel selection by smallholder farmers. Journal of Food Products Marketing, 21(4): 337-357. Efa Gobena, Degye Goshub, Tinsae Demisiec and Tadesse Kenead. 2016. Determinants of market participation and intensity of marketed surplus of teff producers in Bacho and Dawo woredas of Oromia national regional state, Ethiopia. Journal of Economics and Sustainable Development, 7(1): 13-24. FAOSTAT (Food and Agriculture Organization of the United Nations). 2015. FAOSTAT (Database). (Latest update: 07 Mar 2014) viewed 15 Jul 2016, http://data.fao.org/ ref/262b79ca-279c-4517-93de-ee3b7c7cb553.html?version=1.0. Gallmann, P. and Thomas, H. 2012. Honey production and marketing in Southwestern Ethiopia:Online.http://www.learningforlife.ch/media//DIR_76001/76253729aa011 222ffff8015a426365.pdf. Garson, D. 2012. Testing Statistical Assumptions. North Carolina State University, Statistical Associates Publishing, Blue Book Series. Gemechis Legesa. 2014. Beeswax production and marketing in Ethiopia: Challenges in value chain. Journal Agriculture, Forestry and Fisheries, 3(6): 447-451. Gemechis Legesa. 2015. Honey production and marketing in Ethiopia. American Journal of Life Sciences, 3(1): 42-46. Geoffrey, S., Bett, K., Hillary, K., Lawrence, K. and Mutai, C. 2015. Determinants of market participation among small-scale pineapple farmers in Kericho County, Kenya. Journal of Economics and Sustainable Development, 4(19): 59-66. Getachew Nigussie. 2009. Honey market chain analysis: The case of Burie woreda, West Gojjam zone, Amhara national regional state. MSc Thesis, Haramaya University, Haramaya, Ethiopia. Greene, W. 2012. Econometric Analysis. 7th edition. NJ, Prentice Hall, Pearson. Gujarati, D. N. 2004. Basic Econometrics, 4th edition. McGraw Hill, New York.

Hugo, S.F., Miranda, P.M., Jair, A.F. and Alfons, G.J. 2016. Evaluating strategies for honey value chains in Brazil using a structure-conduct-performance framework. International Food and Agribusiness Management Review, 19(3): 225-250. Islam, M.S., Miah, T.H. and Haque, M.M. 2001. Marketing system of marine fish in Bangladesh. Bangladesh Journal of Agricultural Economics, 24(2): 127-142. Islam, M.S., Haque, M.M., Rabbani, M.G. and Sharmin, S. 2014. Marketing of Shrimp in Bangladesh: A Value Chain Analysis. Journal of Bangladesh Agricultural University, 12(2): 359–368. Jari, B. and Fraser, G. 2012. Influence of Institutional and Technical Factors on Market Choices of Smallholder Farmers in the Kat River Valley: Unlocking Markets for Smallholders. Lessons from South Africa (Vol. 10, pp. 59–89). Wageningen, the Netherlands: Wageningen Academic Publishers. Jemma Haji. 2008. Economic efficiency and marketing performance of vegetable production in Eastern and Central parts of Ethiopia. Doctoral Dissertation, Swedish University of Agricultural Sciences, Uppsala, Sweden. Jinanus Dinka and Tamiru Kumsa. 2016. Factors affecting honey production in Ambo district, West Shewa zone, Oromia regional state, Ethiopia. International Journal of Economics and Business Management. 2(2): 170-182. Lunndy, M., Gottret, M., Cifuentes, W., Ostertag, C., Best, R., Peters, D. and Ferris, S. 2004. Increasing the Competitiveness of Market Chains for Small-holder Producers. Manual 3: Territorial Approach to Rural Ggro-enterprise Development. International Centre for Tropical Agriculture. Colombia.117p. Kadigi, L.M. 2013. Factors influencing choice of milk outlets among smallholder dairy farmers in Iringa municipality and Tanga city. MSc Thesis, Sokeine University of Agriculture, Morogoro, Tanzania. Kassa Tarekegn, Gonche Girma and Amenay Assefa. 2017. Value Chain Analysis of Honey in Kaffa and Sheka Zones of SNNPR, Ethiopia. International Journal of Research in Agricultural Sciences, 4(3):142-148. Kerealem, E., Tilahun Gebure. and Preston, R. 2009. Constraints and Prospects for Apiculture Research and Development in Amhara region, Andassa Livestock Research Center, Bahir Dar, Ethiopia. Available at: [accessed on 23/08/2015]. Kifle Tesfamariam, Tekeste Berhanu and Abadi Afera. 2015. Determinants of the choice of marketing channel among small-scale honey producers in Tigray region,

Ethiopia. Journal of Business Management and Social Sciences Research, 4(4): 295-305. Kohl, R.L. and Uhl, J. 1985. Marketing of Agricultural Product, 5th Edition, Collier Macmiln, USA. Kolter, P. and Armstrong, G. 2003. Principle of Marketing, 10th Edition. Hall of India Pvt. Ltd. New Delhi. KZLFD (Kaffa Zone Livestock and Fishery Department). 2016. Livestock Extension Profile of the Zone. Maikasuwa, M.A. and Jabo, M.S. 2014. Analysis of sheep and goats marketing in Sokoto Metropolis, Sokoto state, Nigeria. International Journal of Agricultural Science and Veterinary Medicine, 2(1): 68-74. Mekonin Abera. 2015. Determinants of market outlet choice and livelihood outcomes of coffee producing farmers in Lalo Assabi woreda, Oromiya, Ethiopia. MSc Thesis, Haramaya University, Haramaya, Ethiopia. Mendoza, G. 1995. A Primer on Marketing Channels and Margins. In Price Products and People, eds. Scott, 254-275. International Potato Center, Lima, Peru. Miklyaev, M., Jenkins, G.P. and Barichello, R.R. 2014. Honey Production in Ethiopia: A Cost-Benefit Analysis of Modern versus Traditional Beekeeping Technologies. Cambridge Resources International Inc, UK. MoA and ILRI. 2013. Apiculture Value Chain Vision and Strategy for Ethiopia: Ministry of Agriculture and International Livestock Research Institute, Addis Ababa, Ethiopia. Mohammed Hassan. 2012. Coffee value chain analysis: The case of Nensebo district, West Arsi zone, Oromia region, Ethiopia. MSc Thesis, Haramaya University, Haramaya. Muthini, N.D. 2015. An assessment of mango farmers’ choice of marketing channels in Makueni, Kenya. MSc Thesis, University of Nairobi, Nairobi, Kenya. Mulubrihan Bayissa. 2014. Assessment of honey quality gap: The case of smallholder farmers of Sheka zone, Southwestern Ethiopia. MS.c Thesis, Van Hall Larenstein University of Applied Sciences, Wageningen, Netherlands. Nega Mateows, Teshale, Woldemanuel, Zebene Asfaw. 2015. Market chain analysis of fruit: The case of Tembaro district, Kembata Tembaro zone of Southern Ethiopia. International Journal of Business and Economics Research. 4(4): 201-216.

Nuri Lefebo. 2016. Value chain analysis of enset (ensete ventricosum) in Hadiya zone, Southern Ethiopia. Doctoral Dissertation, Haramaya University, Haramaya, Ethiopia. Nuru Adgaba. 2007. Atlas of Pollen Grains of Major Honey Bee Flora of Ethiopia: Sixth National Annual Conference of the Ethiopian Beekeepers Association (EBA), June 2007, Addis Ababa, Ethiopia. Pandey, K.K., Tiwari, D. and Upadhyay, S. 2013. An economic study on marketed surplus of chickpea in Rewa district of Madhya Pradesh, India. International Journal of Plant, Animal and Environmental science, 3(3): 223-241. Ramakumar, R. 2001. Costs and margins in coconut marketing: Some evidence from Kerala. Indian Journal of Agricultural Economics, 56(4): 668-680. Riziki, J.M., Mlongo, P.M., Mwanarusi, S., Oradu, A.N. and Ipomai, S. 2015. Determinants of choice of marketing outlets for African indigenous vegetables among the agro-pastoral Maasai of Narok and Kajiado counties of Kenya. Journal of Economics and Sustainable Development, 6(8): 222-229. Samuel Sarka. 2014. Market chain analysis of honey production in Sodo Zuria district, Southern Ethiopia. MSc Thesis, Haramaya University, Haramaya, Ethiopia. Scarborough, V. and Kydd. J. 1992. Economic Analysis of Agricultural Markets: A Manual of Marketing series 5, Natural Resource Research Institute, Chatham, UK. Shewaye Abera. 2016. Econometric analysis of factors affecting haricot bean market outlet choices in Misrak Badawacho district, Ethiopia. International Journal of Research Studies in Agricultural Sciences, 2(9): 6-12. Tilahun Mola and Kifle Belachew. 2015. Effect of weeding time on rice (Oryza sativa Linn) yield and yield components at Kaffa, Southwest Ethiopia. Journal of Biology, Agriculture and Healthcare, 5(1): 162-167. Tufa Aman, Adam Bekele. and Lemma Zemedu. 2014. Determinants of smallholder commercialization of horticultural crops in Gemechis district, West Hararghe zone, Ethiopia. African Journal of Agricultural Research, 9(3): 310-319. Urquieta, A. 2009. Effects of access to information on farmer’s market channel choice: The case of potato in Tiraque Sub-Watershed(Cochabamba-Bolivia). MSc Thesis, Virginia Polytechnic Institute and State University, Blacksburg, Virginia. USAID (United States Agency for International Development). 2012. Cost-Benefit Analysis of the Honey Value Chain in Ethiopia: Graduation With Resilience to

Achieve Sustainable Development - Grad Project Final Report. Universities of Maryland and Cambridge, USA. Wandschneider, T. and Yen, K.N. 2006. Guide to Agricultural Marketing Extension Module No.1, with special reference to Vietnam. Wisdom, M., Joseph, D. and Bonnet, K. 2014. Structure, conduct and performance of groundnuts markets in Northern and Central Malawi: Case of Mzimba and Kasungu districts. International Journal of Business and Social Science, 5(6): 130-139. Wolday Amaha. 1994. Food grain marketing development in Ethiopia after reform 1990: A case study of Alaba Siraro. Doctoral Dissertation, VerlagKoster University, Berlin. Workneh Abebe. 2011. Identification and documentation of indigenous knowledge of beekeeping practices in selected districts of Ethiopia. Journal of Agricultural Extension and Rural Development, 3(5): 82-87. Wooldridge, J.M. 2010. Econometric Analysis of Cross Section and Panel Data, 2nd Edition. Cambridge, MA: MIT Press. Yamane, T. 1967. Statistics: An Introductory Analysis, 2nd Edition. New York: Harper and Row. Yoshimasa Ito. 2014. Local honey production activities and their significance for local people: A case of mountain forest area of Southwestern Ethiopia. African Study Monographs, 48: 77–97.

APPENDIXICS

I. Appendix Tables

Appendix table 1: Test for multicollinearity of explanatory variables

Variables VIF Tolerance Sex of household head 1.20 0.83255 Level of education 1.14 0.87606 Household size 1.19 0.83745 Total income 1.06 0.94208 Beekeeping experience 1.29 0.77492 Hive type(traditional and improved) 2.33 0.42867 Hive type(improved) 1.60 0.62449 Number of hives 2.30 0.43518 Distance from market 1.10 0.90515 Amount of credit received(1000) 1.05 0.95564 Extension contact frequency 1.16 0.86553 Cooperative membership 1.33 0.75088 Mean VIF 1.40

Appendix table 2: Specification /omitted variable test result

. ovtest

Ramsey RESET test using powers of the fitted values of volume sold (ln) Ho: model has no omitted variables F(3, 138) = 1.03 Prob > F = 0.3831

Appendix table 3: Heteroscedasticity test result

. hettest

Breusch-Pagan / Cook-Weisberg test for heteroskedasticity Ho: Constant variance Variables: fitted values of volume sold(ln) chi2(1) = 0.07 Prob > chi2 = 0.7923

II. Appendix Figures

Appendix figure 1: Norma probability plot for residuals

1.00

0.75 normal probality line

0.50

Normal F[(r-m)/s] predicted residuals normal probablity line

0.25

0.00 0.00 0.25 0.50 0.75 1.00 Empirical P[i] = i/(N+1)

Appendix figure 2: Boxplot for volume of honey supplied to check outliers

600

400

200

Quantity of of Quantityhoney 2015/16 sold in

III. Questionnaires i. Household Survey Interview Schedule This questionnaire is prepared to collect data for analysis of honey market chain in Chena woreda to:- Identify the major honey marketing channels, market actor and their role; Analyze structure, conduct and performance of honey market; Identify the factors that affects volume of honey marketed; and Identify determinants of beekeepers’ market outlets choices in Chena woreda.

It will provide a major input for master’s thesis and it is purely conducted for academic purposes. Therefore, please fill the interview schedule according to the farmers reply (do not put your own feeling), ask each question clearly and patiently until the farmer gets your points and do not use technical terms and do not forget local units.

Questionnaire code______Household name ______Kebele ______Got ______Date of interview ______

I. Households Demographic and Socioeconomic Characteristics 1. Sex of the household head (1=Male; 0=female): ______2. Age of the household head (in years): ______3. Education level of the household head (in years of schooling): ______4. Total number of household members: ______

Hhsz in years Sex Male Female Below 10 years 10-18 years 18-30 years 30-45years Above 45

5. Wealth and income other than beekeeping a. Crop Production 1. Do you cultivate crops? 1. Yes 2. No 2. Please specify the types of major crop and amount of income earned from them in 2015/16 Type of major Amount of product per Amount of sell per Average Total crops year(kg) production(kg) Price per kg income

 Total income from crops sell in 2015/2016------(ETB) b. Present livestock possession 1. Do you have livestock? 1. Yes 2. No 2. How many of them do you own now? Type of livestock No. of animals Average value of livestock owned owned Average estimated Total price(ETB) Income(ETB) cows Calves Heifer Bull Oxen Sheep Goat Donkey Horse Poultry  The total income from livestock sell in 2015/16------(ETB) c. Off-farm/Non -farm activities and their incomes 1. Do you participate in non-farm income generating activities? (1=Yes; 0=No): 2. If yes what are the activities you participate? No Off-farm activities( excluding honey Yes=1, No=2 If yes, any monthly production)in 2015/16 income 1 Charcoal production 2 Petty trade 3 Remittance 4 Income of other family member 5 others Total  Total annual income of in 2015/16______(ETB)

II. Honey production issues 1. Do you produce honey? 1. Yes 2. No 2. If yes, how long have you been in honey production activity? ______in Years. 3. Why do you engage in honey production? 1. For home consumption 2. For sale 3. For consumption and sale 4. Others (specify) ______4. What type of beehive you use? 1. Traditional 2.improved 3.both 5. How many beehives do you have currently (2015/16) in number? 1. Traditional beehives______2. Improved______3. Total ______6. How many times and how much do you harvest honey per hive in 2015/16? Types of hive Frequency Volume (kg/harvest) 1 2 3 1 2 3 Traditional beehives Improved beehives Total volume of harvest in 2015/16 in kg

7. Where is the place where beehives are located? 1=within home compound/ Backyard 2=in the forest 3= constructed shade 4= other (specify- 8. For how long you use one beehive? ------years 1. Traditional------2. Improved------9. Would you construct shade for protection of beehives? 1. Yes 2. No 10. If yes, what is cost of shade construction______? 11. Do you provide supplementary food to your bee colonies? 1.Yes 2.No 12. If yes, to Q11, which is common supplementary feed you use? 1. Sugar 2. Some honey left there 3.others(specify)__ 13. What is the cost of supplementary feed______? 14. What is the labor source for honey production and marketing (more than one is possible)? 1.Family labor 2. Labor exchange 3. Hired labor 4. Cooperation

15. Honey production costs and revenues for profitability analysis per hive per year Items Traditional beehives Improved beehives Input Costs(Birr) Bee hive Bee wax for comb preparation Smoker Overall coat Veil Glove Extractor Plastic container Service costs(birr) Sugar for feeding Labour cost(harvesting and shed ) Transportation cost Deprecation cost on beehives Total cost of production Revenue from honey sales in 2015/16(birr) Net Profit(birr)

III. Honey Marketing issues 1. Did you sell honey in 2015/16 production season? 1. Yes 2. No 2. If your answer in question one is yes, how much produced amount did you sell? Quantity Produced (kg) ----- Quantity consumed (kg) ------Quantity sold (kg) ------Average selling price (Birr/kg) ------3. What are the major honey marketing actors? 1. Beekeepers 2.Coopretives 3. Local collectors 4. Wholesalers 5. Retailers 6. Processors 6. Final consumer 7. Others (specify- 4. Major duties and responsibility of actors in honey marketing Actors* roles linkage Beekeepers Cooperatives processors collectors retailers wholesalers Consumers Others(specify) *1.Honey supply 2.Price setting 3.Market searching 4.market information provision 5. Honey marketing cooperative formation 6.Facilitating marketing 7. Nothing 5. To sell your honey produced in 2015/16, which market outlets do you use? (Multiple responses are possible) 1. Local collectors 2. Cooperatives 3. Retailers

4. Processers (Tejji and birzi houses, APNIAC and honey union) 5. Wholesalers 6. Consumers 7.Others (specify----- 6. How much and to whom did you sell your production in 2015/16? Amount sold(qt) To whom Selling price per kg *Where (place of sale) i.e market you sell

*1. Local nearby markets 2.Woreda market 3. Distant markets in bigger towns 4. Directly to consumers 5.Other (specify) 7. In deciding to whom to sell, what factors do you consider? (Multiple responses are possible) (√) 1. Transport availability 2. Price 3. Fairness of scaling (Weighing) 4. Closeness in distance 5.Expectation of future benefits (dividend) 6 others______8. Farm gate lagged year (2008 E.C) average selling price of honey _____Birr/kg 9. Are you trust the buyers of your honey? 1. Yes 2. No 10. If your answer for Q.9 is yes, why do trust? (Multiple responses are possible) 1. Give fair price 2. Scaling fair (weighing) 3. Give cash as soon as you sold 4. [ ] Relatives/Friends 5. Others (specify) ______11. How far is the market place from your residential area? ______kms ______walking hours. 14. What equipment do you use for honey container? 1. Plastic bags 2. Pots 3. Glass 4. Sacks 5. Others (specify)______15. What problems do you face in using your containers? 1. Difficulty of cleaning 2. Change of odur of the honey which results in quality reduction 3. High price 4. Lower price 5. Others (specify)______16. Do you have marketing information in 2015/16? 1. Yes 2. No 17. If yes, what type of information did you get? 1. Price information 2.Market place information 3. Time of year to sell 4. Marketing channel options 5. Postharvest handling and value addition 6. Other (specify) ______18. How did you get current market information about supply, demand & price of honey on markets? 1. Other honey traders 2. Personal observation 3. Cooperatives 4. Radio/TV 5. DA workers 6. Others (specify)______19. At what time interval did you get the information? 1. Daily 2. Weekly 3. Monthly 4. Other (Specify) ______20. Who decide the selling price of your honey? 1. Producer 2. Buyers 3. Set by demand and supply 4. Negotiations between producer and buyer 5. Others (specify) _____ 21. What are the factors that govern price of the honey in your locality? 1. Seasons of the year 2. Quality 3.Colors of the honey 4. Distance from market 5. presence of marketing institutions (Cooperative, Apinec) 6. Others (specify): ____ 22. During sell of honey did you get market information (purchase price and demand) of

each market outlets? 1. Yes 2. No 23. If your answer for Q.22 is yes, is that information determining the choice decision to sell for it? 1. Yes 2. No 24. Are your customers concerned about the quality of honey you sold? 1. Yes 2. No 25. Are your customers willing to pay more for better quality of honey? 1. Yes 2. No 26. What marketing problems for your honey do you face? 1. Price variation 2. Lack of fair market 3.Lack of demand 4.Others (specify)______27. Do you buy honey for resell? 1. Yes 2. No 28. If yes, how much did you buy in the last year______kg? 29. Did you store honey before sale of it? 1. Yes 2. No 30. If your answer for Q.29 is yes, indicate total volume of the honey stored ------in kg 31. Amount of honey sold and cost of marketing Quantity Selling Total cost profit sold price processing Packing Transport Tax and personal Br/kg material expenses

IV. Institutional Related Services 1. Did you have extension contact in relation to honey production and marketing in the 2015/16? 1. Ye 2. No 2. If yes, how often do you get extension contact per year ____? 3. What was the extension advice specifically on honey production? (Multiple responses are possible) 1. Apiary sites preparation 2. Bee forage development 3.Use of Improved hive 4. Honey harvesting and processing 5. Storage and Post-harvest handling 6. Marketing of honey 7.others (specify) ______4. Who provides the advisory service? (Multiple responses are possible) 1. Development agents 2. NGOs (specify) 3. Woreda LF and COOP.M offices experts 4. Research institutions (specify) 5. Neighbors 6. Others (specify) _____ 5. Do have you received credit in 2015/16 for honey production purpose? 1. Yes 2. No 6. If yes for Q.5, how much did you take for honey production purpose? ______Birr 7. For what purpose did you take the credit in relation to honey production? (Multiple responses are possible) 1. To purchase additional bee hive for honey production 2. To purchase processing material of honey 3. To purchase colony 4. To pay for labour wage. 5. To purchase supplement feed for bees in dry season. 8. From whom did you get credit for honey production? (Multiple responses are possible) 1. Relative 2. Bank 3. Micro finance institution 4. Friends 5. Traders 6. NGO 7. Cooperatives 8. Others (specify) ______9. Are you a member of honey cooperative? 1. Yes 0. No 10. What is the nature of the cooperative? (1=Formal; 0=Informal): 11.What service(s) do you receive from the cooperative you belong to? (Multiple responses are possible) 1.Savings and Credit 2.profit division 3.Marketing 4.Training 5. If others others(specify)------Thank you!!

ii. Honey Traders Interview Schedule 1. Name of trader ______2. Age, sex, and educational level of traders Age Sex* Educational Religion** Marital status*** Household size

*1=Male 2=Female **1=Muslim 2= Orthodox 3=Protestant 4= Other (Specify) ------***1=Single 2= Married 3. Types of trading 1. Wholesaler 2.Retailer 3.collector 4.Broker 5. Processor 7. Others (specify) ______4. For how long have you been in this business? ______years 5. When do you often undertake your business? 1. Year round 2. When purchasing price is low (high supply) 3. When the demand for honey is high 4.Others (specify) 6. Did you have another occupation(s) before becoming honey trader? 1. Yes 2. No 7. If yes, what type of business and for how long? ______8. Distance from residence to the market------Km 9. Have you had any credit source? 1. Yes 2. No 10. If yes, what is the source (more than one choice is possible)? 1. Relative/family 2. Other traders 3. Private money lenders 4. Micro finance Institution 5. NGO 6. Bank 7. Friends 8. Others, (specify) 11. What was the reason behind securing the loan ______? 1. Working capital for honey trading 2. Working capital for other commodity trading 3. Others (specify) __ 12. Is there special treatment in accessing credit for honey trading? 1. Yes 2. No 13. If yes, what type of treatment? 1. Lower interest rate 2. High amount of credit 3.Other 24. Indicate your average cost incurred in the trading of honey in 2015/16. Cost of Marketing Birr/kg Purchas price per kg. License and Taxes Labor employed to collect and processing Load/ unload Storage cost Transportation fee Processing cost Telephone cost Loss in transport and storage Personal travel & other expenses Total cost Selling price profit

Purchasing activities 1. How do you attract your suppliers? 1. By giving credit to purchase inputs 2. By giving better price relative to others 3. By fair weighing 4.By visiting them 5.Other(specfy—

2. From which market and supplier did you buy honey in 2015/16? Purchased Market Purchased from Quantity purchased Average %age share of honey (Location name) on market month price per purchased from (KG) kg specific source Where (use code) 1. Farmers 1. Village market 2. Retailers 2. Woreda market 3. Wholesaler 3. Zonal market 4.Rural assembler 4. cooperatives 5.You don’t 5. Other (specify) Know 3. How do you measure your purchase? 1. by sack 2. By plastic box 3. By weighing (kg) 4. By pot 5. Others (specify) ______4. From which market (s) do you prefer to buy most of the time in 2015/16? ------5. Why do you prefer this market? 1. Better quality 2. High supply 3. Shortest distance 6. Who set the purchase price in 2015/6? 1. Negotiation 2. By the market 3. Your Self 4. The seller 4. Other (specify______7. If you decide on the purchasing price, how did you set it? 1. Agreeing with other traders 2. Individually 3.Other (specify) ______8. Is your purchasing price higher than your competitors? 1. Yes 2. No 9. If yes, what was the reason? 1. To attract suppliers 2. To buy more quantity 3. To kick Competitors 4.To get better quality 5. Others (specify) 10. Did you use brokers to purchase honey? 1. Yes 2.No 11. If brokers were used, what problems did they create? 1. Cheating quality 2. Wrong Price information 3. Cheating scaling (weighing) 4.Charged high brokerage 5.Other 12. What was the advantage of using brokers? 1. You could get buyers and sellers easily 2. Reduce transaction costs 3. Purchased at lower Price 4.Save your time 5. Sell at higher price 6.Other (specify) _____ 13. Which season of the year was preferable to purchase honey in terms of price? ______14. Do you process your purchase? 1. Yes 2. No 15. If your answer for Q.14 yes, what is the cost of packing? ______Birr/kg 16. How many regular suppliers do you have 2015/16? 1. Producer ___ 2. Rural collectors__ 3. Processors _____4. Wholesalers ______5. Retailers ___6.cooperatives------

Selling practices 1. To which market and to whom did you sell honey in 2015/16? Sold on Market Sold to** Quantity sold Average price %age share of (Location name)* on market day per kg buyers (KG) Where* ______*1. Village market 2. Woreda market 3. Zonal market 4. Cooperatives 6. Other(specify) **1. Processers 3. Wholesalers 5. Consumers 7. Processers including teji and birzi houses 2. Retailers 4. Cooperatives 6. Consumers 8. Others (specify)

2. How did you attract your buyers? 1. By giving better price relate to others 2. By fair scaling (weighing) 3. By providing credit selling 4. By improving quality of the product. 3. How many regular buyers do you have 2015/16? 1. Wholesalers___ . 2. Consumers___ 3. Processors ___ 4. Retailers ____ 5. Others (specify) _____ 4. Do you know the market prices in different markets (on farm, village market and other areas) before you sold your honey? 1. Yes 2. No 5. Did you store honey before you sold in 2015/16? 1. Yes 2. No 6. If yes, in question 5 for how long did you store honey in the store? ------days/month 7. Amount of honey lost due to storage ------Kgs. 8. Cost of storage------birr 9. What is your source of information? 1. TV/ Radio 2. Other trader 3. Personal observation 4. Other (specify) 10. Are there restrictions imposed on unlicensed honey traders? 1. Yes 2. No 11. Are there entry barriers for trading honey? 1. Yes 2. No 12. If yes, what type of entry barriers you observe? 1. Social barriers 2. Legal barriers 3.Political barriers 4.Financial barriers 5. Administrative problems 6.competition of unlicensed traders 7. Discrimination 13. Linkage with market chain actors 1. Farmers 2.Retailers 3.Whole sellers 4. Consumers 5.Collectors 6.others (specify) _

iii. Consumers Interview Schedule 1. Zone______: Woreda______: Kebele: ______Village: ______2. Age, sex, and educational level of consumers Age Sex* Educational Religion** Marital status*** family size

*1=male 2=female **1=Muslim 2= Orthodox 3=Protestant 4= Other (Specify) ------***1=Single 2= Married 3= Divorced 4= Widows 3. What is the distance to nearest town ------km? 4. What was the means of income generation? 5. What is the amount of income earned per monthly------birr? 6. How long do you experience in honey consumption------years? 7. What Proportion of your income spent on honey purchase ------birr? 8. Linkage with commercial honey market chain actors: (Multiple responses are possible). 1. Collectors 2.Beekeepers 3.Retailers 4.Wholesaler 5.Cooperatives 6. Others (specify) 9. Do you think honey market chain is complex and many intermediaries? 1. Yes 2. No 10. Do you think traders of honey marketing are efficient and competitive? (Multiple responses are possible). 1. Yes 2.No 11. If your answer for Q.10 is No, what is the problem of traders? 1. High competition with unlicensed traders 2. Supply poor quality 3. Cheat scaling weighting 4. Price setting problem 5. Government policy problem 6.Others (specify) ____

Purchase of honey 1. What amount of purchased for consumption? Please respond to the following questions. Quantity purchased------Low price paid (birr/kg) ------No. of months you may buy at lower price------High price paid (birr/kg) ------

No. of months you may buy at higher price------2. From whom do you buy honey most of the time? 1. Beekeepers 2.collectors 3. Retailers 4. Wholesalers 5.Cooperatives 6. Processers 3. As a buyer, do you have difficulty in obtaining sufficient supplies? 1. Yes 2. No 4. As a buyer, do you have a particular seller? (√) 1. Yes 2. No 5. If the answer to Q. 4 is yes, how many producers could be your potential sellers with respect to a honey? Approximate ______. 6. Do you consider any quality requirements to purchase honey? 1. Yes 2. No 7. If yes for Q.6, what quality requirement do you consider? _____

iv. Checklist for Key Informants Interview Schedule

Name of the office: ______1. What are the threats for apiculture extension service and input supply? 2. What are the most important constraining infrastructures affecting honey production? 3. What are the possible solutions to correct these problems? 4. Common honey Production system in the area? 1 forest 2 garden 5. What is the role of your organization in honey market chain in the study area? 6. What are the challenges and opportunities you faced in undertaking those roles assigned to your organization? __ 7. Linkage /interaction/ partnership/ coordination between honey market actors______8. Do you think market chain actors of honey are competitive and efficient? 9. Where does intervention needed in market chain of honey? 10. Please fill the table below for Secondary data List of Average Average % share of %cumulative Main traders quantity sold quantity sold purchase purchase destination in per week in per month

No. Honey marketing 2006E.C 2007E.C 2008E.C 1 Total Production of honey in the District 2 Number of farmers producing honey 3 Market price per kg 4 Number of honey traders 5 Total volume of honey marketed by traders 6 Total volume of honey marketed by farmers

v. Checklist for Focus Group Discussion

Participants: Honey producers in selected kebele; 1. Kebele ______2. What was a honey production trend in the district for last five years? 3. Problems related to inputs suppliers (availability/access, quality, and cost of inputs)? 4. Problems related to honey production (bee forage development, apiary site preparation, post-harvest loss, pests, disease, extension service, credit access and market access)? 5. How these problems can be solved? ______6. Could you identify the major chain actors who are actively involved in honey marketing in your areas with their functions? And which are more beneficial 7. Linkage /interaction/ partnership/ coordination between honey market actors______? 8. How do traders influence beekeepers participation in honey market chain? 9. What are the major problems in marketing of honey? 10. What are the major factors that affect producers‟ decision to participate honey market and the amount they are supplying to the market? 11. What is the quality trend of honey improving or deteriorating? Who is responsible for the problem? 12. What overall recommendations do you have in honey production in your areas and the overall activities that have to be taken in enhancing the benefits honey producers?

Thank you for your valuable time and patience for the focus group discussion made.