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HONEY VALUE CHAIN ANALYSIS WITH ESPECIAL EMPHASIS TO
ADA’A WOREDA, EAST SHOA ZONE OF ETHIOPIA
M.Sc. Thesis
BETSELOT MESFIN
May, 2012
Haramaya University
HONEY VALUE CHAIN ANALYSIS WITH ESPECIAL EMPHASIS TO
ADA’A WOREDA, EAST SHOA ZONE OF ETHIOPIA
A Thesis Submitted to College of Agriculture and Environmental Sciences, School
of Agricultural Economics and Agri-Business, School of Graduate Studies
HARAMAYA UNIVERSITY
In Partial Fulfillment of the Requirements for the Degree of
MASTER OF SCIENCE IN AGRICULTURE
(AGRICULTURAL ECONOMICS)
By
Betselot Mesfin
May, 2012
Haramaya University
ii
SCHOOL OF GRADUATE STUDIES
HARAMAYA UNIVERSITY
As member of the Examining Board of the Final M.Sc. Open Defense, we certify that we have
read and evaluated the thesis prepared by: Betselot Mesfin entitled: Honey Value Chain
Analysis with Especial Emphasis to Ada’a Woreda, East Shoa Zone of Ethiopia and
recommended it to be accepted as fulfilling the thesis requirement for the degree of Master of
Science in Agricultural Economics.
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DEDICATION
This thesis is dedicated to my Beloved Family especially Dad, whose love remained a source
of potency and motivation throughout!
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STATEMENT OF THE AUTHOR
First of all, I declare that this thesis is my work and that all sources of materials used for this
thesis have been duly acknowledged. This thesis has submitted in partial fulfillment of the
requirements for M.Sc. degree in Agricultural Economics at the Haramaya University and is
deposited at the University library to be made available to borrowers under rules of the
library.
The author also declares that this thesis is not submitted to any other institution anywhere for
the award of any academic degree, diploma or certificate. Brief quotations from this thesis are
allowable without the special permission provided that accurate acknowledgment of source is
made. Requests for permission for extended quotation from or reproduction of this manuscript
in whole or in part may be granted by the head of the major department or the Dean of the
School of Graduate Studies when in his or her judgment the proposed use of the material is in
the interests of scholarship. In all other instances, however, permission must be obtained from
the author.
Name: Betselot Mesfin Admassu
Date of Submission: _______________
Place: Haramaya University
Signature: _______________
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BIOGRAPHICAL SKETCH
The author was born in June, 1984 in Bushoftu town (the then Debre-Zeit) .She attended
elementary and high school in the same town and joined Haramaya University in 2003. She
graduated in 2006 with BA in Economics. After serving Development Bank of Ethiopia for
three years at different positions she joined Haramaya University to pursue her Msc in
Agricultural Economics in 2010.
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ACKNOWLEDGEMENTS
This study would not have been possible without the support of many people. I wish to
express my gratitude to my advisor, Dr.Beyene Tadesse who was abundantly helpful and
offered invaluable assistance, support and guidance starting from idea provision. Deepest
gratitude is also due to Dr.Fitsum Hagos, Dr.Jemma Haji, Misrak, Ato Ebrahim, and Ato
Embaye Kidanu; without whose knowledge and assistance this study would not have been
successful.
I would also like to thank Ada’a Woreda Office of Agriculture and Rural Development staffs
specially Ato Mohamed, Ato Ararsa and Ato Negatu Alemayehu from IPMS project Office.
Special thanks is also to all my friends, particularly to my life time friend Melat Work Ketema
for her encouragement and tireless assistance and Meseret Getahun, Martha Yilma, Azeb,
Feven Tadesse, Ermiyas Engida,Aklilu Desta and all who encouraged me till the last minute. I
also thank my fiancé whose support has always been my source of strength and inspiration. It
is great pleasure to extend my appreciation to my family who has always been there for me
whenever I need them, the encouragement they give to keep me going and their love to
empower me that never fails all the time.
Above all, I would like to thank our Heavenly Father. He who was and is to come; Him who
is giving high hopes; for being my source of strength; for being true to what He promised me.
To God be the glory.
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ABBREVATION AND ACRONYMS
AWAPHO
Ada’a Woreda Animal Production and Health Office
CIDA
Canadian International Development Agency
CODIT
Institute
of
Community
and
Organizational
Development
CTA
Technical Centre for Agricultural and Rural Cooperation
CSA
Central Statistical Agency
EARO
Ethiopian Agricultural Research Organization
ESBKA
East Shoa Beekeepers Association
FIAS
Foreign Investment Advisory Service
GDP
Gross Domestic Product
GTZ
German Technical Cooperation
ha
Hectare
HBRC
Holeta Bee Research Center
HH
Household
IIRR
International Institute of Rural Reconstruction
ILRI
International Livestock Research Institute
IPMS
Improving Productivity and Market Success
Kg
Kilo gram
M4P
Making Markets Work for the Poor
MEDEP
Micro Enterprise Development Program
ML
Maximum Likelihood
MM
Marketing Margin
mm
millimeter
MT
Metric ton
m2
Meter square
NGOs
Non Governmental Organizations
NMM
Net Marketing Margin
OoARD
Office of Agriculture and Rural Development
PAs
Peasant Associations
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ABBREVATION AND ACRONYMS (Continued)
TLU
Tropical Livestock Unit
TGMM
Total Gross Marketing Margin
UNCTAD
United Nations Conference on Trade and Development
UNIDO
United Nations Industrial Development Organization
USAID
United States Agency for International Development
VC
Value Chain
VCA
Value Chain Analysis
VIF
Variance Inflation Factor
WB
World Bank
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TABLE OF CONTENTS
STATEMENT OF THE AUTHOR
v
BIOGRAPHICAL SKETCH
vi
ACKNOWLEDGEMENTS
vii
ABBREVATION AND ACRONYMS
viii
LIST OF TABLES
xii
LIST OF FIGURES
xiii
LIST OF TABLES IN THE APPENDICES
xiv
LIST OF FIGURS IN THE APPENDICES
xv
ABSTRACT
xvi
1.
2.
INTRODUCTION
1
1.1.
Background
1
1.2.
Statement of the Problem
3
1.3. Objectives of the Study
4
1.4.
Scope and Limitations of the Study
4
1.5.
Significance of the Study
5
1.6.
Organization of the Thesis
5
LITRATURE REVIEW
6
2.1. Definitions and Basic Concepts of Agricultural Value Chain
3.
6
2.1.1. Agricultural value chain
6
2.1.2. Value addition
7
2.1.3. Value chain development and up-grading
7
2.1.4. Value chain actors
8
2.1.5. Value chain governance
8
2.1.6. Marketing costs and margins
9
2.2.
Mapping the Value Chain
10
2.3.
Supply Chain Vs Value Chain
11
2.4.
Value Chain Analysis and its Importance
12
2.5.
Review of Empirical Studies
14
METHODOLOGY
16
x
TABLE OF CONTENTS (Continued)
3.1. Description of the Study Area
16
3.2. Data Requirements and Sources
17
3.3. Sample Size and Method of Sampling
17
3.4. Methods of Data Analysis
18
3.4.1. Descriptive analysis
18
3.4.2. Value chain analysis
20
3.4.3. Econometric Analysis
21
3.5. Hypothesis and Definition of Variables
4.
3.5.1.
Dependent variables
25
3.5.2.
Independent variables
25
RESULTS AND DISCUSSION
4.1.
30
Descriptive Analysis
30
4.1.1.
Demographic and Socio-Economic characteristics of sample households
30
4.1.2.
Household income and its sources
32
4.1.3.
Access to services
33
4.1.4.
Inputs, production and marketing of honey
35
4.2.
Value Chain Analysis
40
4.2.1.
Actors in honey value chain and their marketing functions
40
4.2.2.
Marketing Costs and Margins of honey market
44
4.2.3
Opportunities of honey sub-sector in the woreda
50
4.2.4
Major constraints of the woreda’s apiculture development
51
4.3.
5.
25
Results of Econometrics Model
52
4.3.1.
Determinants of farmers’ honey marketing decision
53
4.3.2.
Determinants of volume of honey marketed
56
SUMMARY, CONCLUSION AND RECOMMENDATION
60
5.1.
Summary
60
5.2.
Conclusion and Recommendations
62
6.
REFERENCES
64
7.
APPENDICES
70
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LIST OF TABLES
Table 1: Description of the dependent and independent variables used in the model.
29
Table 2: Demographic and socio-economic characteristics of sample households
31
Table 3: Sources of income by sample farmers (Birr/HH)
32
Table 4: Access to credit and market information
34
Table 5: Extension service on honey by sample households
35
Table 6: Number of beehives per household
36
Table 7: Frequency of honey harvest per year per household
37
Table 8: Volume of annual honey production per beehive (kg)
38
Table 9: Seasonal variation in production
38
Table 10: Problem of inputs in honey production
39
Table 11: Unit cost of items used in modern beekeeping
44
Table 12: Estimated cost and marketing margin for honey market in channel I.
46
Table 13: Market share of actors in honey marketing through channel I
49
Table 14: Heckman Maximum likelihood estimates of honey market participation and their
Marginal Effect
56
Table 15: Heckman Maximum likelihood estimation of volume of honey sold
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58
LIST OF FIGURES
Figure 1: Honey value chain map.
43
xiii
LIST OF TABLES IN THE APPENDICES
Appendix Table
1: VIF for multi co-linearity diagnosis
71
2: Conversion factor of tropical livestock unit (TLU)
71
xiv
LIST OF FIGURES IN THE APPENDICES
Appendix Figure
1: Map of Ada’a woreda.
72
xv
HONEY VALUE CHAIN ANALYSIS WITH ESPECIAL EMPHASIS TO
ADA’A WOREDA, EAST SHOA ZONE OF ETHIOPIA
ABSTRACT
This study was initiated to analyze honey value chain with especial emphasis to Ada’a
woreda, East Shoa zone of Ethiopia. The main objectives of the study were to identify the
actors, activities, the distribution of costs and benefits among them and to identify factors
affecting farmers’ participation in honey marketing and volume marketed in the study area.
Both primary and secondary data were used and a total of 160 honey producing sample
households from four potential honey producing kebeles of the woreda were surveyed. Value
chain analysis approach was used to describe actors’ activities and the rules governing the
activities in the honey value chain. Heckman Maximum likelihood procedure was also applied
to identify factors affecting the farmers’ participation decision in honey marketing and
volume marketed in the study area. The value chain analysis reveals that the major actors in
the woreda’s honey sector are beekeepers, collectors, processors, local brewery houses and
retailers. Results from Heckman’s procedure shows among fourteen explanatory variables
hypothesized to affect honey market participation decision sex of the household head, number
of beehives owned, market information, household’s beekeeping experience, tropical livestock
unit (TLU), and type of beehive used were found to be significant. Four variables, sex of the
household head, number of beehives owned, credit access for honey production, type of
beehive used were also found to be significantly influence the volume of honey sold by the
participants of honey marketing. As a concluding remark, raising awareness and capacity
building of both farmers and woreda’s agricultural development agents through provision of
appropriate training on how to manage bees and incorporate new technologies, and
formation of beekeeper unions and cooperatives to address problems like lack of access to
credit, market information and modern inputs are the actions to be taken to strengthen the
sector’s contribution to the woreda’s development.
xvi
1. INTRODUCTION
1.1. Background
Ethiopia, whose economy is mainly based on agriculture, has a favorable natural resource
endowment for the production of various types of agricultural outputs. Owing to its varied
ecological and climatic conditions, Ethiopia is home to some of the most diverse flora and
fauna in Africa. Its forests and woodlands contain diverse plant species that provide surplus
nectar and pollen to foraging bees. Beekeeping is one of the oldest farming practices in the
country. There is an ancient tradition for beekeeping in Ethiopia which stretches back into the
millennia of the country's early history (Girma, 1998). Of all countries in the world probably
no country has a longer tradition of beekeeping than Ethiopia (Hartmann, 2004).
Despite the long tradition of beekeeping in Ethiopia, having the highest bee density and being
the leading honey producer as well as one of the largest beeswax exporting countries in
Africa, the share of the sub-sector in the GDP has never been commensurate with the huge
numbers of honeybee colonies and the country's potentiality for beekeeping. Productivity has
always been low, leading to low utilization of hive products domestically, and relatively low
export earnings. Thus, the beekeepers in particular and the country in general are not
benefiting from the sub-sector (Nuru, 2002).
Beekeeping as a business is a recent development in the country. Presently, honey is a cash
generating activity for almost all beekeeping households. Households consume less than 10%
of their total harvest at home (mainly for medicinal, ritual or cultural ceremonies), and the
remaining is available for sale. The large portion of the marketed honey goes to the
production of local beverage called (tej) and small portion is used as a table honey (Beyene
and Phillips, 2007).
Honey is mostly produced at household level by beekeepers that are often the poorest and
most marginalized in society, and these people are highly disadvantaged in the market place.
1
Poor roads, remote locations, lack of knowledge of the final market, lack of containers and
infrequent interactions with traders mean the potential of the honey trade to bring income
benefits to producers remains unexploited (Bradbear, et al., 2002).
Besides all these, gender disparity in agricultural markets also remains a big issue. Men and
women are often engaged in different products, activities and markets, and women
smallholders frequently lack access to services and control over resources. Rural women in
relation to beekeeping activities, their participation is very low because of many reasons such
as fear of bee stings, cultural practices, lack of experience and lack of access to services and
control over resources, etc (Tessega, 2009). However, beekeeping holds promise for women
because it needs relatively little capital, does not rely on land or expensive inputs.
Production and supply of honey by regions shows that Oromia accounts for over 55% of the
bee colonies and 53% of the Honey production, followed by Amhara which accounts for
about 20% of the colonies and 21% of the honey production. The Southern Nations,
Nationalities Peoples Regional State, on the other hand, accounts for about 15% of the bee
colonies and 17% of the honey production. Tigray and Benshangul accounts for 4.5% and
3.6% of the total bee colonies and 5.5% and 3% of the total honey production respectively
(Ayalew, 2008).
Despite the fact that the quantity of honey product in Oromia takes the major share in the
country, the region has been unable benefit from the sector. Ada’a woreda is one of the areas
that have considerable potential of honey production in Oromia. Though many governmental
and nongovernmental organizations have been introducing beekeeping as one of marketable
commodities and tried to improve the existing traditional apiculture production system in the
woreda, lack of institutional linkages and lack of organized markets for honey still hinder the
development of the sector in that area. Besides the major constraints of the sector, particularly
in the woreda are lack of beekeeping knowledge, shortage of trained manpower, shortage of
beekeeping equipments, pests and predators, and inadequate research and extension services
to support apiculture development program (Melaku et al., 2008).
2
1.2.Statement of the Problem
Even if apiculture presents an opportunity for small producers, for many African beekeepers
the potential to create a significant livelihood from selling honey remains out of reach. Some
of the issues facing small honey producers are similar to those facing other small commodity
producers, while some aspects are specific to the honey trade. Beekeeping is often promoted
as being a pro-poor income generating activity because it is accessible to marginalized
members of communities, has low start up costs and requires little land or labor. However,
without access to a market, these benefits cannot be utilized (UNCTAD, 2006).
Ethiopia is a leading honey producer in Africa and one of the ten largest honey producing
countries in the world. Despite the favorable agro-ecology for honey production and the
number of bee colonies the country is endowed with, the level of honey production and
productivity in the country in general and in Oromia in particular is still low (Tilahun et al.,
2010). Despite its considerable potential of honey production in the region, farmers couldn’t
optimize the benefit from the sector.
Though there is a step by step approach that integrates apiculture potential, problem
identification and intervention processes that recognize farmers’ indigenous knowledge, and
includes capacity building, improvement of the production system, cooperative formation and
market and institutional linkages need to be strengthened for apiculture development in the
woreda (Melaku et al., 2008).
Even though many parts of the country are well known for fruits, horticulture and floriculture
production and integration of apiculture development in the agriculture production system has
huge advantage for pollination, there is no compiled and rigorous analysis on honey value
chains in the area. The set of actors and activities, and organizations and the rules governing
those activities in the honey production system of the areas are also not well known.
Thus, the purpose of this study is to investigate honey value chain actors and their role in the
chain and factors affecting farmers’ participation in honey marketing in Ada’a woreda.
3
Besides it is intended to narrow the information gap on the area of interest by drawing
attention to answer the questions like: who are the actors in the honey value chain and what
are their activities in the system? What are the characteristics of the actors? What does the
profit and cost structures look like in the chain? And does honey has the potential to be an
attractive development option in the study area?
1.3. Objectives of the Study
The general objective of this study is to analyze and illustrate honey value chain in the study
area.
The specific objectives of the study
1. To identify the actors, activities, and the rules governing the activities in the chain;
2. To identify the distribution of costs and benefits of the actors in the chain and;
3. To identify factors affecting farmers’ participation in honey marketing and volume
marketed in the study area.
1.4. Scope and Limitations of the Study
Value chain analysis includes from producers to the end users covering wide range of
geographical areas stretching from local to global markets. However, in this study the value
chain analysis focuses only on Ada’a woreda (East Shoa zone of Oromia) as a case of
reference.
Regarding the limitation of the study, due to shortage of logistics the study doesn’t represent
the whole value chain of honey in the country and only focus on the honey value chain that
originates from major honey producing peasant associations (PAs) in the woreda. Hence, the
generalizations of the finding are limited to the study area and locations with similar socioeconomic characteristics.
4
1.5. Significance of the Study
The smallholder producers have currently limited access to market due to low level of
productivity; poor product quality and market barriers, such as poor infrastructure, lack of
favorable trade policy and shortage of finance and lack of collective bargaining power. Thus,
there is a strong need to help small producers in Ethiopia to achieve sustainable and fair
access to honey market in order to increase their income and secure their livelihoods.
The implication is that there is a need to undertake research and generate information to
identify alternative mechanisms in which the honey producers and other actors can overcome
the trade barriers, improve and add value to their products, and become stronger negotiators in
local, regional, and international markets, thereby improving their income. The information
generated from this research can be used by local practitioners and be used as input in the
formulation of honey development strategies and policies.
1.6. Organization of the Thesis
The thesis is organized into five chapters. The next chapter reviews theoretical and empirical
works related to the study. Chapter three discusses the research methodology used in the
study. Results and discussions are presented in chapter four. Chapter five summarizes the
findings of the study and presents recommendations.
5
2. LITRATURE REVIEW
This chapter gives theoretical highlights for the study. It is intended to provide insights on
definition and concept of value chain, literatures on value chain analysis and review of recent
empirical findings on honey value chain analysis.
2.1. Definitions and Basic Concepts of Agricultural Value Chain
2.1.1. Agricultural value chain
An agricultural value chain is usually defined by a particular finished product or closely
related products and includes all firms and their activities engaged in input supply,
production, transport, processing and marketing (or distribution) of the product or products.
Kaplinsky (2000) defines the value chain as ‘the full range of activities which are required to
bring a product or service from conception, through the intermediary phases of production,
delivery to final consumers, and final disposal after use.’
An agricultural value chain can, therefore, be considered as an economic unit of analysis of a
particular commodity or group of commodities that encompasses a meaningful grouping of
economic activities that are linked vertically by market relationships. The emphasis is on the
relationships between networks of input suppliers, producers, traders, processors and
distributors (UNCTAD, 2000).
The value chain concept entails the addition of value as the product progresses from input
suppliers to producers to consumers. A value chain, therefore, incorporates productive
transformation and value addition at each stage of the value chain. At each stage in the value
chain, the product changes hands through chain actors, transaction costs are incurred, and
generally some form of value is added. Value addition results from diverse activities such as
bulking, cleaning, grading, and packaging, transporting, storing and processing. Value chains
encompass a set of interdependent organizations, and associated institutions, resources, actors
and activities involved in input supply, production, processing, and distribution of a
6
commodity. In other words, a value chain can be viewed as a set of actors and activities, and
organizations and the rules governing those activities (Anandajayasekeram and Berhanu,
2009).
2.1.2. Value addition
Value-addition is a measure for the wealth created in the economy. Referring to the definition
used in systems of national accounting, total value-added is equivalent to the total value of all
services and products produced in the economy for consumption and investment (the gross
domestic product - GDP), net of depreciation. To arrive at the value-added generated by a
particular value chain, the cost of bought-in materials, components and services has to be
deducted from the sales value (GTZ, 2007).
2.1.3. Value chain development and up-grading
A first step to chain development is to support chain actors /farmers to improve their farming
skills. This helps them produce higher yields of higher, more consistent quality, and produce
which is better suited to the market. This enables them to make more money and improve
their livelihoods (KIT et al., 2006).
In developing a growth strategy for the sub-sector under analysis, it is important to distinguish
between product and labor markets. It may not always be optimal or feasible to upgrade ‘en
masse’, but rather it is important to take into account that when zooming in on a particular
sub-sector, that growth strategies will likely involve “winners” who create jobs for “losers”,
either directly or indirectly (through increased need for service firms and the multiplier
effect).
According to USAID (2008), upgrading can be classified in to different types:
Process upgrading: Increasing the nature of internal processes such that these are
significantly better (differentiated) or more cost-efficient than those of rivals, both within
7
individual links in the chain (for example, increased inventory turns, lower scrap), and
between the links in the chain (for example, more frequent, smaller and on-time deliveries).
Product upgrading: Introducing new products or improving old products, with increased
value to end-consumers, faster than rivals. This involves changing new product development
processes both within individual links in the value chain and in the relationship between
different chain links;
Functional upgrading: Increasing value added by changing the mix of activities conducted
within the firm (for example, taking responsibility for, or outsourcing accounting, logistics
and quality functions) or moving the locus of activities to different links in the value chain
(for example from manufacturing to design).
Channel upgrading: Moving existing products into a new pathway leading to a new endmarket (for example, moving from domestic markets to export markets).
Chain upgrading: Moving to a new value chain for the production of a different product.
2.1.4. Value chain actors
According to GTZ (2007), the term “value chain actors” summarizes all individuals,
enterprises and public agencies related to a value chain, in particular the value chain
operators, providers of operational services and the providers of support services. In a wider
sense, certain government agencies at the macro level can also be seen as value chain actors if
they perform crucial functions in the business environment of the value chain in question.
2.1.5. Value chain governance
Governance in a value-chain refers the structure of relationships and coordination
mechanisms that exist between actors in the value-chain. Governance is important from a
policy perspective by identifying the institutional arrangements that may need to be targeted
8
to improve capabilities in the value-chain, remedy distributional distortions, and increase
value-added in the sector (M4P, 2008).
In Kaplinsky and Morris (2001) as cited by Anandajayasekeram and Berhanu (2009)
governance implies that interactions between firms along a value chain reflect organization,
rather than randomness. The various activities in the chain, within firms and between firms,
are influenced by chain governance. Value chains are characterized by repetitiveness of
linkage interactions. The governance of value chains emanate from the requirement to set
product, process, and logistic standards, which then influence upstream or downstream chain
actors and results in activities, actors, roles and functions. Therefore, power asymmetry is
central in value chain governance. In other words, some key actors in the chain shoulder the
responsibility to allocate roles (inter-firm division of labor) and improve functions (Kaplinsky
and Morris, 2001).
Power in value chain governance can be categorized into three: setting basic rules for
participation in the chain, monitoring the performance of chain actors in complying with the
basic rules, and assistance to help chain actors adhere to the basic rules (Kaplinsky and
Morris, 2001). It must, however, be noted that some value chains may exhibit very little
governance at all, or very thin governance. In most value chains, there may be multiple points
of governance, involved in setting rules, monitoring performance and/ or assisting producers.
The powers of governance may be vested within the chains themselves, in local communities,
or in business associations (Evans and Wurster, 2000).
2.1.6. Marketing costs and margins
Marketing Costs: all marketing activities generate costs. These costs vary widely across
agricultural commodities, depending for example on the extent of processing or the distance
between production areas and consumption centers. Agricultural marketing costs are costs
incurred between the moment an agricultural product leaves the farm and the moment it is
purchased by end users of consumers. This includes market research and promotion, product
9
preparation, packaging, handling, transport, product losses, storage, processing, and fees and
unofficial payments (Wandschneider and Yen, 2006).
Marketing margin: Marketing margin is the difference between the value of a product or a
group of products at one stage in the marketing process and the value of an equivalent product
or group of products at another stage. Measuring this margin indicates how much has been
paid for the processing and marketing services applied to the product(s) at that particular stage
in the marketing process (Smith, 1992).
2.2. Mapping the Value Chain
Mapping a value chain facilitates a clear understanding of the sequence of activities and the
key actors and relationships involved in the value chain. This exercise is carried out in
qualitative and quantitative terms through graphs presenting the various actors of the chain,
their linkages and all operations of the chain from pre-production (supply of inputs) to
industrial processing and marketing.According to UNIDO (2009), the mapping diagrams are
prepared through an iterative process which can be divided into two stages: First, an initial
map is drawn which depicts the structure and flow of the chain in logical clusters: the main
actors and the activities carried out at the local level, their links to activities at other domestic
or foreign locations, the supporting services and their interactions, the links to the final
market, and some initial indications of size and importance. The second stage is quantifying
the value chain. This involves adding detail to the basic maps drawn initially (structure and
flow). Depending on the level of detail needed for the research entry point, this exercise may
focus on elements such as size and scale of main actors; production volume; number of jobs;
sales and export destinations and concentration.
A value chain map also allows one to understand the sourcing, production, and delivery
segments of an industry at micro levels. This process of obtaining disaggregated information
about a firm (or a farm) or about a number of firms (or farms) and subsequent extrapolation to
an industry or sector allows one to better understand how a firm (or a farm) is linked to its
industry, region, country, and global chain, thus facilitating an analysis of the opportunities
10
that the firm (or farm) faces in upgrading its processes and strategically positioning itself in
the value chain (WB, 2007).
Value chain map generally helps to understand the functional levels of the chain and the
operators associated with the levels including the linkage at different levels of the chain, thus
facilitating the analytical study of the chain with visual representation (MEDEP, 2010).
2.3. Supply Chain Vs Value Chain
A supply chain is an integrated manufacturing process where in raw materials are converted
in to final products, then delivered to customers, at its highest level, a supply chain is
comprised of two basic integrated process: 1) the production and inventory control process, 2)
the distribution and logistics process (Beamon, 1998).
In value chain the production stages entail a combination of physical transformation and the
participation of various producers and services, and the chain includes the product’s disposal
after use. As opposed to the traditional exclusive focus on production, the concept stresses the
importance of value addition at each stage, thereby treating production as just one of several
value-adding components of the chain (UNIDO, 2009).
A supply chain consists of all parties involved, directly or indirectly, in fulfilling a customer
request. The supply chain not only includes the manufacturer and suppliers, but also
transporters, warehouses, retailers, and customers themselves. Within each organization, such
as manufacturer, the supply chain includes all functions involved in receiving and filling a
customer request. These functions include, but are not limited to, new product development,
marketing, operations, distribution, finance, and customer service (Chopra et al., 2004). The
basic concept of a supply chain is similar to the value chain. The difference is that the supply
chain refers to sequence of (upstream) sourcing and (downstream) marketing functions of
individual enterprises, mostly of lead companies. Therefore, supply chain management is a
business management tool rather than a development concept. It is concerned with logistics
rather than market development (GTZ, 2007).
11
In common parlance, a supply chain and a value chain are complementary views of an
extended enterprise with integrated business processes enabling the flows of products and
services in one direction, and of value as represented by demand and cash flow in the other.
Both chains overlay the same network of companies. Both are made up of companies that
interact to provide goods and services. When we talk about supply chains, however, we
usually talk about a downstream flow of goods and supplies from the source to the customer.
Value flows the other way. The customer is the source of value, and value flows from the
customer, in the form of demand, to the supplier. That flow of demand, sometimes referred to
as a “demand chain”, is manifested in the flows of orders and cash that parallel the flow of
value, and flow in the opposite direction to the flow of supply. Thus, the primary difference
between a supply chain and a value chain is a fundamental shift in focus from the supply base
to the customer. Supply chains focus upstream on integrating supplier and producer processes,
improving efficiency and reducing waste, while value chains focus downstream, on creating
value in the eyes of the customer (Feller, et al., 2006).
2.4. Value Chain Analysis and its Importance
Value chain analysis disaggregates the international structure of production, trade and
consumption of commodities and allows for identification of actors and geographical division
(Tuvhag, 2008).
Value chain analysis also reveals the dynamic flow of economic, organizational and coercive
activities involving actors within different sectors. It shows that power relations are crucial to
understand how entry barriers are created, and how gain and risks are distributed. It analyses
competitiveness in a global perspective. By revealing strengths and weaknesses, value chain
analysis helps participating actors to develop a shared vision of how the chain should perform
and to identify collaborative relationships which will allow them to keep improving chain
performance. The latter outcome is especially relevant in the case of new manufacturers –
including poor producers and poor countries – that are seeking to enter global markets in ways
that can ensure sustainable income growth (UNIDO, 2009).
12
A value chain analysis is important to assess the existing vertical and horizontal linkages
within the sub-sector as well as functions and roles of actors from input supply to the final
consumers. It also gives a clear picture of the actors, activities and existing relationships
across the board (SNV, 2009).
In sum, the concept of value chain provides a useful framework to understand the production,
transformation and distribution of a commodity or group of commodities. With its emphasis
on the coordination of the various stages of a value chain, value chain analysis attempts to
unravel the organization and performance of a commodity system. The issues of coordination
are especially important in agricultural value chains, where coordination is affected by several
factors that may influence product characteristics, especially quality (Anandajayasekeram and
Berhanu, 2009).
According to GTZ (2007), value chain concepts, there are four levels; namely, micro, meso,
macro and meta levels in which relevant survey topics for the analysis of a value chain are
embedded.
At the Micro level, value chain operators perform basic functions in the value chain be it as
input suppliers, primary producers, processors or distributors (wholesalers, retailers,
transporters, exporters).
At the Messo level, one finds public and private service providers’ e.g. regional associations,
rural banks, agricultural government institutions, local civil society organizations.
At the Macro level such as national, policymakers, regulatory bodies, federations of
associations provide enabling framework conditions for businesses that may be pro-poor.
This may relate to legislation, standards, infrastructure etc.
Finally, the Meta level describes Socio-cultural factors facilitating or hindering business
linkages, business attitudes and trust among the value chain actors.
13
2.5. Review of Empirical Studies
GTZ (2008) used value chain analysis to identify the constraints hindering the growth of the
honey subsector and the opportunities in Nepal. The key issue during the analysis stage was to
find the most pressing bottlenecks for sub sector growth first and address them in a systemic
manner. Referring the key findings of the study, large number of people is already involved in
beekeeping, honey collection, processing and marketing of honey and other bee products.
However, honey entrepreneurs in Nepal cannot harness that niche market due to Nepal’s
inability to meet legal requirements for export. There is limited support for addressing market
and quality related issues and value-adding activities. Assurance of quality is the first
prerequisite for enhancing export opportunities and improving access to international markets.
A honey value chain analysis study made by SNV (2005) in Kenya (as stated in KIT et al.,
2006) found that a large, complex distribution network, dominated by middlemen, moves
honey to the market from distant areas, especially during periods of scarcity. The markets are
both formal and informal, though the informal market is larger. Most of the few existing
producer groups are not organized properly. Most buyers are unable to meet the demands and
volumes required by the supermarkets.
In their study of Ensuring Small Scale Producers in Ethiopia to Achieve Sustainable and Fair
Access to Honey Markets, Beyene and Phillips (2007) identified that beekeepers (small scale
farmers), local honey collectors, cooperatives, tej houses, wholesalers, honey processors,
beeswax processors, retailers, input suppliers and exporters are the major actors in the
apiculture sub-sector. The methodology used in their research was based on sub-sector and
value chain analytical framework. The overall objective of their research was to significantly
increase the understanding of the constraints and opportunities facing honey sub-sector in
order to identify at what stages of the honey value chains and what kind of policy,
technological, institutional, infrastructural, organizational and management interventions are
needed in order to make the sector more competitive in the domestic and export markets, and
thereby improve the livelihood of, particularly, the rural poor. And they concluded that
development of marketing structure, expansion of knowledge based extension services for an
14
improved supply to the domestic and export market and standardization of products are the major
areas of intervention required to ensure the small scale farmer (beekeepers) to benefit from
apiculture.
Rehima(2006) used Heckman two-stage procedure to identify factors affecting marketable
supply of pepper in Alaba and Siltie woredas and found out that pepper production is the most
important and significant variable influencing the decision to participate in pepper market
positively. However, food crop yield adversely affected pepper market participation.
Moreover, pepper production and extension contacts are the significant determinant factors of
the quantity of pepper supplied positively. However, non farming income and number of
livestock are the significant determinants of the quantity of pepper supplied negatively. The
coefficient associated with the inverse Mill’s ratio was significant, indicating that the
influence of unobservable factors in the farmers’ decisions to participate was significant.
The result of Heckman’s procedure in Embaye, et al., (2010) , Analysis of butter supply
chain the case of Atsbiwenberta and Alamata woredas, clearly indicates that a marginal
increase in butter output increases both market participation and level of supply. This is
because farmers’ decision to participate in the market and to increase their level of
participation is normally driven by the availability of surplus produce.
15
3. METHODOLOGY
3.1. Description of the Study Area
Ada’a woreda, one of the 10 woredas in East Shoa zone of Oromia region, is located about 45
kms south-east of the capital Addis Ababa and is very close to the other major urban centers.
The woreda covers a total area of 96,600m2, stretching east of the Bole International Air Port
to the North West of the Koka dam. The human population in Addis Ababa, Adama and
Bishoftu towns creates a large market for most agricultural commodities of the woreda.
The altitude of Ada’a woreda ranges from 1500 and 2000 meters above sea level. The woreda
is classified in to rift valley, mountain and highland zones covering about 34%, 9% and 57%
of the total area of the woreda respectively. Annual temperature and rainfall vary between
7.9ºc to 28ºc; and 900mm and 1300mm, respectively. Belg (short rainy season) from March to
April and meher (main rainy season) from June to September are the two cropping seasons in
the area. The dominant soil types of the woreda are black clay and red light soils (ILRI,
2005). According to the woreda’s Agriculture and Rural Development Office, total population
of the woreda was about 131,273 out of which agricultural household accounts for the year
2010 was estimated at 21,320 and the total number of honey producing households was 1630.
A total of 28 PAs are available in the woreda.
Regarding the farming system and land use system of the area, the high altitude area has been
identified as greatest agricultural and market potential area and the farms in the area are small
in size (1-2.5 ha) and farming is operated with the help of ox power. Farms are mixed in terms
of crops and livestock. Major crop components are teff, wheat (mainly bread variety), pulses
of which the main one is chick peas which grows at mid altitude, followed by field peas and
faba beans (at higher altitudes). Livestock on the farms in the mid/high altitude zone consists
of cattle/oxen, poultry and small ruminants. There are also a number of rivers and creator
lakes that are being used for irrigated agriculture, particularly for horticultural crops
production.
16
Honey production (Apiculture) is another occupation of farmers in specific sites of the
woreda. Most of the beekeepers use traditional method of honey production while few use the
modern hives. Availability of over 23 different kinds of trees that flower at different times of
the year guarantees adequate supply of bee forage in the woreda (Melaku et al., 2008).
3.2. Data Requirements and Sources
In order to get the overall picture of honey value chain in the study area, the study used both
primary and secondary data. Primary data was collected through administering a structured
questionnaire to sample respondents and participatory data collection tools like, group and
individual discussions and key informant interview was utilized. The key informants’
interviewed includes: collectors, retailers, processors, and end users, the staffs of NGOs
working in the study area, local staff of Office of Agriculture and Rural Development,
association of honey processors.
Secondary data was also collected from relevant governmental and non-governmental offices
as deemed necessary. Moreover, different and relevant published and unpublished reports,
bulletins and websites were reviewed to strengthen and secure the study.
3.3. Sample Size and Method of Sampling
To select representative honey producing households in Ada’a woreda, two stages sampling
method was conducted. In the first stage, four major honey producing PAs (kebeles) found in
the study woreda (Denkaka, Godino, Ude and Yerer Silase) were selected purposively based
on information obtained from the woreda’s Agriculture and Rural Development Office. In the
second stage using probability proportional to size technique, producers of honey were
selected from each selected PA. A total of 160 sample honey producers were selected from
the four PAs.
In addition to farm households, sample respondents were also selected from the other value
chain actors on the basis of their size and availability and interviewed based on their
17
respective functions in the chain. By preparing checklist six collectors, processors (table
honey and local brewery makers), four retailers, and supporting actors were interviewed in the
study area. A checklist was also used to guide the informal discussions conducted at different
places with processors and retailers out of study area.
3.4. Methods of Data Analysis
This study used different categories of data analysis; namely descriptive, value chain and
econometric analyses.
3.4.1. Descriptive analysis
Descriptive statistics was used to analyze and explain different characteristics of the sample
households and used to clearly compare and contrast the role and functions of chain actors
along with the econometric model. Tests like chi-square and t-test statistics were also used to
complement or testify significance of results obtained from the model specified.
Marketing margin
Once the basic structure of a marketing channel is established, it is relatively easy to collect
information on the price at which the product is bought and sold at each stage in the
production process (Smith, 1992). Knowledge of marketing costs and margins in a chain will
enable us to identify how revenues and margins are distributed over the actors in the value
chain in order to conclude whether they can increase margins in a value chain.
Total gross marketing margin (TGMM) is the final price of the produce paid by the end
consumer minus farmers’ price divided by consumers’ price and expressed as a percentage.
𝐶𝑜𝑛𝑠𝑢𝑚𝑒𝑟𝑠 ′ 𝑃𝑟𝑖𝑐𝑒 − 𝐹𝑎𝑟𝑚𝑒𝑟𝑠 ′ 𝑃𝑟𝑖𝑐𝑒
𝑇𝐺𝑀𝑀 =
× 100
𝑃𝑟𝑖𝑐𝑒 𝑃𝑎𝑖𝑑 𝑏𝑦 𝑡ℎ𝑒 𝐶𝑜𝑛𝑠𝑢𝑚𝑒𝑟
18
(1)
The Net Marketing Margin (NMM) is the percentage over the final price earned by the
marketing middleman as his/her net income once his/her marketing and transaction costs are
deducted. From this measure, it is possible to see the allocative efficiency of markets. Higher
NMM or profit of the marketing intermediaries reflects reduced downward and unfair income
distribution, which depresses market participation of the smallholder. An efficient marketing
system is where the marketing costs are expected to be closer to transfer costs and the net
margin is near to normal or reasonable profit.
𝑁𝑀𝑀 =
𝑇𝐺𝑀𝑀 − 𝑀𝑎𝑟𝑘𝑒𝑡𝑖𝑛𝑔 𝑎𝑛𝑑 𝑇𝑟𝑎𝑛𝑠𝑎𝑐𝑡𝑖𝑜𝑛 𝐶𝑜𝑠𝑡
× 100
𝑃𝑟𝑖𝑐𝑒 𝑝𝑎𝑖𝑑 𝑏𝑦 𝑡ℎ𝑒 𝐶𝑜𝑛𝑠𝑢𝑚𝑒𝑟
(2)
Where: TGMM = Total Gross Marketing Margin
NMM = Net Marketing Margin
It is useful to introduce here the idea of “producer participation”, “farmer’s portion” or
“producer’s gross margin” (GMM) which is the portion of the price paid by the end consumer
that belongs to the farmer as a producer. It should be emphasized that growers that as
middlemen also receive an additional marketing margin. The producer’s margin or share in
the consumer price (GMMp) is calculated as:
𝐺𝑀𝑀𝑝 =
𝐸𝑛𝑑 𝐵𝑢𝑦𝑒𝑟 𝑃𝑟𝑖𝑐𝑒 − 𝑀𝑎𝑟𝑘𝑒𝑡𝑖𝑛𝑔 𝐺𝑟𝑜𝑠𝑠 𝑀𝑎𝑟𝑔𝑖𝑛
× 100
𝐸𝑛𝑑 𝐵𝑢𝑦𝑒𝑟 𝑃𝑟𝑖𝑐𝑒
(3)
Where GMMp is the producer’s share price.
The consumer price share/portion of market intermediaries is calculated as:-
𝑀𝑀 =
𝑆𝑒𝑙𝑙𝑖𝑛𝑔 𝑃𝑟𝑖𝑐𝑒 − 𝐵𝑢𝑦𝑖𝑛𝑔 𝑃𝑟𝑖𝑐𝑒
× 100
𝐹𝐶𝑃
Where: MM = Marketing margin (%)
SP = Selling price at each level
BP = Buying price
19
(4)
FCP = Final consumer price
3.4.2. Value chain analysis
Value chain analysis is the process of breaking a chain into its constituent parts in order to
better understand its structure and functioning. The analysis consists of identifying chain
actors at each stage and discerning their functions and relationships; determining the chain
governance, or leadership, to facilitate chain formation and strengthening; and identifying
value adding activities in the chain and assigning costs and added value to each of those
activities (UNIDO, 2009).
The study used value chain analysis which is very effective in tracing product flows, showing
the value adding stages, identifying key actors and the relationships with other actors in the
chain. For honey value chain analysis, the following four steps of value chain analysis,
summarized by M4P (2008) were adopted:
Mapping the value chain: This is to understand the characteristics of the chain actors and the
relationships among them, including the study of all actors in the chain; the flow of goods
through the chain; of employment features; and of the destination and volumes of domestic
and foreign sales. This information can be obtained by conducting surveys, interviews and
participatory workshops as well as by collecting secondary data from various sources.
Identifying the distribution of actors’ benefits and costs in the chain: This involves
analyzing the margins and profits within the chain and therefore determining who benefits
from participating in the chain and how much; who would need support to improve
performance and gains.
Defining upgrading needs within the chain: By assessing profitability within the chain and
identifying chain constraints, upgrading solutions can be defined.
20
Emphasizing the governance role: within the concept of value chain, governance is defined
as the structure of relationships and coordination mechanisms that exist among the chain
actors. By focusing on governance, the analysis will identify institutional actors that may
require support to improve capabilities in the value chain, increase value added in the sector
and correct distributional distortions. Thus, governance constitutes a key factor in defining
how the upgrading objectives can be achieved.
3.4.3. Econometric Analysis
Econometric model was used to identify the factors that affect farmers’ participation decision
in honey marketing in one hand and determinants of the volume of honey marketed in the
other hand. Most recent literatures adopt ‘Tobit and Heckman’s two-stage models’ to identify
factors that affect producers to participate in the marketing of honey (sale of honey) or not
and also identify factors that determine the quantity of honey marketed. Ideally, the Ordinary
Least Square (OLS) model is applicable when all households participate in the market. In
reality not all households participate in a specific commodity market. Some households may
not prefer to participate in a particular market in favor of another, while others may be
excluded by market conditions. If the OLS regression is estimated excluding the
nonparticipants from the analysis, a sample selectivity bias is introduced into a model. Such a
problem can be overcome by following a two-step procedure as suggested by Heckman
(1979). Tobit model can also be used to address the above mentioned problem; but its
assumption that both the participation decision and level of supply determined by the same
variable in the same way introduces inconsistency bias into the model. But in reality all
producers may not be potential suppliers of a product and a variable that affect participation
decision may or may not have similar effect on the volume of a produce supplied to the
market. Hence, Heckman’s procedure will be used in this study.
Heckman has developed a two-step estimation procedures model that corrects for sample
selectivity bias. The first stage of the Heckman model a ‘participation equation’, attempts to
capture factors affecting market participation decision. This equation is used to construct a
selectivity term known as the ‘inverse Mills ratio’ which is added to the second stage
21
‘outcome’ equation that explains factors affecting value of honey sales. The inverse Mill’s
ratio is a variable for controlling bias due to sample selection (Heckman, 1979). The second
stage involves including the Mills ratio to the value of honey sales equation and estimating the
equation using Ordinary Least Square (OLS). If the coefficient of the ‘selectivity’ term is
significant then the hypothesis that an unobserved selection process governs the participation
equation is confirmed. Moreover, with the inclusion of extra term, the coefficient in the
second stage ‘selectivity corrected’ equation is unbiased.
Specification of the Heckman two-equation procedure, which is written in terms of the
probability of Honey Market Participation (HMP), and Volume of Honey Marketed (VHM),
is:
The participation equation/the binary probit equation
𝑌1𝑖 = X1i β1 + u1i ;
𝑢1𝑖 ~N (0,1)
i = 1,2, … , N
(5)
𝐻𝑀𝑃 = 1 𝑖𝑓 𝑌1𝑖 > 0
𝐻𝑀𝑃 = 0 𝑖𝑓 𝑌1𝑖 ≤ 0
Where: 𝑌1𝑖 is the latent dependant variable, which is not observed.
𝑋1𝑖 are vectors that are assumed to affect the probability of sampled household honey
market participation.
β1 is a vector of unknown parameter in participation equation.
u1 are residuals that are independently and normally distributed with zero mean and
constant variance.
22
The observation equation
𝑉𝐻𝑀 = Y2i = 𝑋2𝑖 𝛽2 + 𝛼𝑖 + 𝑢2𝑖 ;
u2 ~ 𝑁(0, 𝛿 2 )
i = 1,2, … , N
(6)
𝑌2 is observed if and only if HMP=1. The variance of 𝑢1 is normalized to one because only
HMP, not 𝑌1 is observed. The error terms, 𝑢1 𝑎𝑛𝑑 𝑢2 are assumed to be bivariat and normally
distributed.
𝑌2 is regressed on the explanatory variables,X2i , and the vector of inverse Mills ratios (𝑖 )
from the selection equation by ordinary least Squares(OLS).
Where: 𝑌2 is the observed dependent variable.
𝑋2𝑖 is factors assumed to affect the volume of honey marketed.
𝛽2 is vector of unknown parameter in the volume of honey marketed equation
𝑢2𝑖 is residuals in the observation equation that are independently and normally
distrusted with zero mean and variance 𝛿 2 .
Mills ratios (𝑖 ) =
𝑓(𝑋1 𝛽1 )
1 − 𝐹(𝑋1 𝛽1 )
(7)
𝑓(𝑋𝛽) is a density function and 1 − F(X1 β1 ) is distribution function.
However, even if Heckman’s two-step procedure is widely used, it has problems like; the
estimators cannot be calculated if x1i contains all variables that belong to x2i and the estimator
is not efficient even if it can be calculated. The absolute values of the t-values of the
simultaneous maximum likelihood (ML) estimators were generally larger than those obtained
by Heckman’s two-step estimator. The reason for this finding is that the simultaneous ML
estimator is asymptotically efficient, suggesting usefulness of the simultaneous ML estimators
(Nawata, 1993). In general the two-step estimator will not be efficient, but computationally
simple and consistent (Verbeek, 2000). Therefore it is reasonable to use the Heckman’s ML
estimators to estimate the model. As Nawata and Nagase (1996) stated that Heckman's ML
procedure combines the estimation of the selection (binary) and outcome equation in a single
23
system. In this case, Heckman’s ML procedure estimates equations (5) and (6) simultaneously
and present consistent estimates of  and δ by numerically maximizing the log-likelihood
function and estimates (5) and (6) as a system of equations using maximum likelihood
estimation methods, and allows us to directly interpret the estimate of ρ.
Heckman (1974) proposed ML estimation as an appealing procedure to account for sample
selection bias. He stated the following assumption:
𝑢1 and 𝑢2 are independent of 𝑋1𝑖 , and independently and identically distributed (iid) over the
entire population (participants and non-participants) with the bivariate Normal distribution
N(0, Σ), where:
∑=[
𝜎12
𝜎21
𝜎12
]
𝜎22
(8)
The phrase “over the entire population”, inserted in the assumption is crucial. Basically, it
discriminates the selection models from the mixture-distribution models where the
distribution of u1i ; i
= 1, …, N, is defined only for a sub-population of the sample
(participants).Under the assumption the parameters of the model can be estimated by
Maximum Likelihood method. The log-likelihood to be maximized is:
L
N
∞
1
= ∑ {Y1 × ln [∫
∅u1 u2 (Y2 − X2i β2 , u1i )𝑑u1 ] + (1 − Y1 )
N
−X1iβ1
i=1
∞
× [ln ∫
∞
∫ ∅u1 u2 (u1 , u2 )𝑑u2i 𝑑u1i ]}
(9)
−X1i β1 −∞
where ∅𝑢1 𝑢2 denotes the probability density function for the bivariate normal distribution of
(𝑢1𝑖 , 𝑢2𝑖 ). Maximum Likelihood method is easy to be implemented while it yields consistent
and fully efficient parameter estimates given the assumption (Vella, 1998).
24
3.5. Hypothesis and Definition of Variables
In the course of identifying factors influencing honey supply, the main task is to analyze
which factor influences and how? Therefore, potential variables, which are supposed to
influence honey market participation and volume of honey marketed, need to be explained.
Accordingly, the major variables expected to have influence on both the farmers’ participation
decision and quantity supply are explained as follows:
3.5.1. Dependent variables
Market Participation Decision (MPD): is the dummy variable that represents the market
participation of the household in the market that is regressed in the first stage of two stages
estimation procedure. For the respondents who participate in market take the value of one
where as it takes the value of zero for the respondent who did not participate in market.
Volume of Honey Marketed (VHM): It is continuous dependant variable in the second step
of Heckman selection equation. It is measured in kilogram and represents the actual volume
of honey marketed by farm households which is selected for regression analysis takes of
positive value.
3.5.2. Independent variables
Age of Household Head (AGH): It is a continuous variable and measured in years. Age is a
proxy measure of farming experience of household head. Aged households are believed to be
wise in resource use, on the other hand young household heads have long investment horizon
and it is expected to have either positive or negative sign effect on market participation and
volume of honey marketed.
Sex of the Household Head (SHH): This is dummy variable (takes a value of 1 if the
household head is male and 0 otherwise). The variable is expected to have a positive relation
with honey market entry decision and volume of honey marketed.
25
Family Size (FS): This variable is a continuous explanatory variable and refers to the total
number of family in the household. In this study it is assumed that any family member might
decide to participate in honey production and marketing. Hence it is expected to have positive
relationship with the dependent variable.
Education status of the Household Head (EDH): This is a dummy variable with a value of
one if a household head is literate and zero otherwise. Education plays an important role in the
adoption of innovations/new technologies. Literate beekeepers are expected to be early
adopters. Therefore, in this specific study, education is hypothesized to affect market
participation decision and volume of honey marketed positively. Holloway et al. (1999)
observed that education and visits by an extension agent had significant and positive effect on
quantity of milk marketed in Ethiopian highlands.
Distance to Nearest Market (DNM): It is the location of the beekeeping household from the
nearest honey market and is measured in kilometer. The closer the honey market to
beekeeping household, the lesser would be the transportation charges, loss due to handling
and better access to market information and facilities. This improves return to labour and
capital; increases farm gate price and the incentives to participate in economic transaction. A
study conducted by Embaye, et al., (2010) on butter supply chain in the case of Atsbiwenberta
and Alamata woredas reveals that distance to market was negatively related to market
participation decision. Therefore, in this study, distance from nearest honey market is
hypothesized to be negatively related to market participation decision and marketable honey
surplus.
Land size in hectare (LAND): This is the total cultivated land holding measured in hectares.
No sign could be expected with regard to this variable it can have either direct or inverse
relationship.
Market Information (MI): It is a dummy variable. Farmers marketing decisions are based
on current information available on the market. Therefore, it is hypothesized that access to
current and updated market information is positively related to honey market participation and
26
volume of honey marketed. Study conducted by Goetz (1992) on food marketing behavior
identified better information significantly raises the probability of market participation.
Access to information, provided through mass media or from extension agents, reduces risk
perceptions of farmers (Siziba et al, 2011).
Credit Access (CA): This is a dummy variable, which indicates credit taken for honey
production. Access to credit would enhance the financial capacity of the farmer to purchase
the bee colony and the beehives. A study conducted by Bradbear (2003) states that in poor
societies, lack of credit is a major constraint to everyone concerned with selling and buying
honey. Therefore, it is hypothesized that access to credit would have positive influence on
level of production and sales.
Access to Honey production Extension service (ACCEXT): This variable is measured as a
dummy variable taking a value of one if the beekeeper has access to honey production
extension service and zero otherwise. It is expected that extension service widens the
household’s knowledge with regard to the use of improved honey production technologies
and has positive impact on honey market participation decision and volume of honey
marketed (Holloway et al., 2000). Number of extension visits improves the household’s
intellectual capitals, which improves honey production. Therefore, frequency of extension
visits is hypothesized to impact beekeeper market entry decision and marketed volume of
honey positively.
Number of Beehives Owned (NBHO): It is continuous variable measured in number of
beehives owned. The number of beehives kept is expected to have positive relation to market
participation and marketable surplus. The larger the number of hives owned, the higher the
quantity of honey harvested hence the participation in value addition and vice versa (Berem et
al., 2010). As the beehives owned increases, the probability to participate in market and sales
will increase. Hence, this variable is expected to influence market participation and volume of
honey marketed positively.
27
Tropical livestock Unit owned (TLU): It is the number of live animals measured in tropical
livestock unit. Households with larger TLU size are supposed to be less concerned about the
bees as they can sell their livestock to meet household needs; consequently, negative sign was
expected to this factor. A study conducted by Rehima (2006) on red pepper marketing reveals
that TLU influenced the quantity of pepper supply negatively.
Financial Income other than Beekeeping (FIOBK): It is continuous variable measured in
Ethiopian Birr (ETB). The variable represents income originating from different sources other
than beekeeping obtained by household head and other household members. Through
improving liquidity, this income makes the household to expand production and/ or purchase
from market. It also strengthens the household position in coping with different forms of
risks. Thus, income from non beekeeping source is hypothesized to affect honey market entry
decision by household and volume of honey marketed positively.
Years in Beekeeping (YBK): It is a continuous variable; measured in the number of years
that the household head spend in beekeeping business. Higher experience in beekeeping
business may favor beekeeping activity. Hence, this variable is expected to have positive
impact on the participation and volume of honey supplied to the market.
Type of Beehive used (TBH): This variable is a dummy variable indicating the beehive type
that the household owned. Modern beehive is more productive in honey production. But due
to financial, knowledge and other problems farmers may prefer the traditional beehive. A
study conducted by Crane (1990) on bees and beekeeping states that modern beehives give
higher yield and quality of honey. Therefore, this variable has been hypothesized to take
positive sign on market participation and marketable surplus. The households owning modern
and /or transitional beehives = 1 and 0, otherwise.
28
Table 1: Description of the dependent and independent variables used in the model.
Variable
Description
Type
Value
Dependant Variables
MPD
Market Participation Decision
Dummy
0=No 1=Yes
VHM
Volume of honey sold
Continuous
volume in Kg
Independent Variables
AGH(+)
Age of Household Head
Continuous
number of years
SHH(+)
Sex of the Household Head
Dummy
1=male,2=female
FS(+)
Family Size
Continuous
number of families
Dummy
1= literate 0=illiterate
EDH(+)
Education status of the Household
Head
DNM (-)
Distance to Nearest Market
Continuous
distance in Km
LAND
Land size in hectare
Continuous
size in hectare
MI(+)
Market Information
Dummy
0=no 1=yes
CA(+)
Credit Access
Dummy
0=no 1=yes
Dummy
0=no 1=yes
number of beehives
ACCEXT(+)
Access to Honey production
Extension service
NBHO(+)
Number of Beehives Owned
Continuous
TLU(-)
Tropical livestock Unit owned
Continuous
FIOBK(+)
Financial Income other than
Beekeeping
TLU
Continuous
number in Birr
number of years
YBK(+)
Years in Beekeeping
Continuous
TBH(+)
Type of beehive used
Dummy
29
number of livestock in
1=modern/transitional,
0= otherwise
4. RESULTS AND DISCUSSION
This section of the thesis discusses the findings of the study such as results of descriptive,
value chain and econometrics analyses that are found in relation to the research questions and
objectives. The descriptive analysis was used to describe the general socio-economic and
demographic characteristics of the sample farm households, the characteristics of honey
production and marketing in the study area, and the costs and benefits of honey marketing
channels in the area. Mean, percentage, standard deviations and marketing margins were
employed to obtain the results. In the value chain analysis description of major actors and
their functions were done. Econometric model was also employed to identify the factors
affecting farmers’ participation in honey marketing and volume marketed in the study area.
4.1. Descriptive Analysis
For the descriptive statistics, sample households were divided into participants and nonparticipants of honey marketing. The objective is to assess the differences and similarities
among participant and non-participants of honey producers in terms of their demographic and
socio-economic characteristics.
4.1.1. Demographic and Socio-Economic characteristics of sample households
The number of sample respondents handled during the survey was 160. The age of the sample
respondents ranges from 20 to 80 years and the average age of sample respondents was 42.47
years. Honey market participants were on average 42.84 years of age, while non-participants
were 41.14 years old. Thus there is no statistically significant difference between the two
groups with regards to age of household head. As Table 2 indicates that the average family
size per sample household was 5.71 and 6.72 for participants and non-participants
respectively.
30
Table 2: Demographic and socio-economic characteristics of sample households
Participants
Non- Participants
Total sample
t/x2
(N =125)
(N =35)
(N =160)
value
Mean/number
Std/%
Mean
Std
Mean
Std
Age (yrs)
42.84
12.14
41.14
8.51
42.47
12.02
0.738
Family size(no)
5.71
2.55
6.72
2.43
5.64
12.02
0.664
Male
76
56.3
21
60
97
60.63
Female
49
39.2
14
40
63
39.38
3
2.4
1
2.85
4
2.5
Married
116
92.8
33
94.28
149
93.13
Divorced
6
4.8
1
2.85
7
4.38
Illiterate
63
50.4
21
60
84
52.5
Primary
55
44
11
31.43
66
41.25
Secondary
7
5.6
3
8.57
10
6.25
2.36
1.37
2.24
1.2
2.33
1.33
Sex
0.932
Marital Status(no)
Single
0.876
Education(no)
Land size(ha)
0.384
0.484
N=Sample Size
Source: Survey result, 2011.
Of the total sample farm households 60.6% were male-headed and the remaining 39.4% were
female-headed implying that more of the sample households were male. From honey market
participants of sample households, 60.80%, and 39.20% were male and female headed
respectively. Regarding their marital status, majority of them were married (93.1%) and few
were single (2.5%) and divorced (4.4%). Referring Table 2, around 50% of the sample
households were illiterate. During the survey, there were no households in the sample who
has educational background above secondary level. With respect to land holding of the
respondents, an average size of land holding per household is 2.33ha with no statistically
significant mean difference between participant and non-participants. The survey result
31
depicts that there was no statistically significant difference between participant and nonparticipant sample households’ demographic characteristics.
4.1.2. Household income and its sources
Rural income generating activities encompass agricultural production (mainly crops and
animal husbandry), agricultural and non-agricultural wage employment, non-farm enterprises,
transfers and non-labor income sources. The people of the study area practice various
livelihood and income generating activities mainly crop production in addition to animal
husbandry, honey production, petty trading and daily labor. Crop production plays a major
role in income generation in the area and cereals such as teff, wheat, maize and barley, pulse
crops such as bean, pea, lentil and chickpea are the major crops grown. Especially, the area is
known for its quality teff production nationally. For the total sampled households, the average
annual income generated from selling of crops, livestock and other income sources (salary,
pension, petty trade, remittance, etc) was Birr 11,051.4, Birr 2263.43 and Birr 2,740.88,
respectively. The total income that was obtained from all sources including income from
selling of honey has statistically significant difference between the participants and nonparticipants of the sampled households at less than 5% level of significant.
Table 3: Sources of income by sample farmers (Birr/HH)
Income
Participants
Non- Participants
Total sample
sources
(N =125)
(N =35)
(N =160)
t-value
Mean
Std
Mean
Std
Mean
Std
Crops
11014.4
6570.55
11190.2
6181.5
11051.4
6470.92
0.136
Livestock
2149.12
2229.33
2671.68
2401.37
2263.43
2270.68
1.205
Honey
10851.26 25022.63
-
-
-
-
Others
2985.12
9203.61
1868.57
2568.16
2740.88
8227.1
-0.709
Total income
24814.5
26177.2
12915.2
6507.64
22309.4
23925.2
-2.545**
**Significant at less than 5% significant level, N= sample size.
Source: Survey result, 2011.
32
Ada’a woreda’s honey is used for table honey and local brewery (tej) making. With the given
number of beehives, for those who participate in honey marketing, the average annual income
from honey was 10,851.26 Birr with an average price of 80 Birr/kg. However, around 63% of
the market participants have got annual income that ranges between Birr1000-Birr5000 from
selling of honey.
4.1.3. Access to services
Access to services like credit, agricultural extension and market information has vital
importance to promote agricultural households’ production and productivity which thereby
increase marketable surplus and ultimately farm income. For farmers, knowing where and
when to sell their output is one of the most difficult challenges. If they have no knowledge of
current market prices, they can easily be exploited. But gathering current information about
markets may not be easy, especially for people living in very remote areas (CTA, 2008).
Addressing new challenges requires extension to play an expanded role with a diversity of
objectives, which include linking farmers more effectively and responsively to domestic and
international markets; enhancing crop diversification; coupling technology transfer with other
services relating to input and output markets; poverty reduction and environmental
conservation; viewing agriculture as part of a wider set of rural development process that
includes enterprise development and non-farm employment; and capacity development in
terms of strengthening innovation process, building linkages between farmers and other
agencies, and institutional development to support the bargaining position of farmers
(Sulaiman et al, 2006).
Respondents were also interviewed whether or not they have access for services like credit
and market information and only 15% of the total respondents replied as they have the access
for credit services for their beekeeping and around 55% of the total respondents have an
access for current and updated market information. From those who have the access, the
major sources of the credit facilities are NGOs and government organizations though NGOs
33
took the higher share in providing the service (58.33%). The main purpose why they took the
money was for fertilizer and honey production.
As depicted in Table 4, from the total sample respondents 55.6% get current market
information on honey from different sources. Among the groups, large proportion of honey
traders has better access to current and updated market information than non-traders. The
result also depicts that the major source of updated information for farm households includes
personal observation (35.6%), other honey traders (27.6%), telephone (26.4%) and others
(10.3%).
There is also statistically significant difference between participants and non-
participants’ access to current market information at less than 1% significant level.
Table 4: Access to credit and market information
Access to
Services
Credit
Market
information
Participants
Non-Participants
(N=125)
(N=35)
2
Total sample
(N=160)
value
Yes (%)
No (%)
Yes (%)
No (%)
17.6
82.4
5.71
94.29
65.6
34.4
20
60
3.03
Yes (%)
No (%)
15
85
15.497*** 55.6
40
***Significant at less than 1% significant level, N= sample size.
Source: Own survey, 2011.
Ideally, current market information should be the starting point for any decision regarding
next production, post-harvest handling, processing and marketing. However, access to market
information is important but far from sufficient. Farmers often find it difficult to interpret
market information and to understand its implications to their farming business. Therefore,
extension officers can work with farmers to process and interpret market information as a step
towards production and marketing decisions. As Table 5 depicts 37.14% of non-participants
in honey marketing and 29.75% of the total sample respondents has no access for honey
extension services. From sampled households who participate in honey marketing, 27.2%
have got no extension services for their honey production.
34
Table 5: Extension service on honey by sample households
Extension on honey
Participant
Non-Participants
Total
(N=125)
(N=35)
(N=160)
%
%
%
Regularly
27.20
22.86
26.58
Sometimes
16.00
8.57
14.56
Rarely
28.00
31.43
29.11
27.20
37.14
29.75
No-extension on
honey
N= sample size
Source: Survey result, 2011
4.1.4. Inputs, production and marketing of honey
According to the respondents, the major inputs and equipments used in the process of honey
production in the study area includes bee colony, beehive, supplementary feed, sanitation
materials (like ash), honey container and protective wears. In producing honey, those who
participate in honey marketing have an average of 7.67 traditional beehives and nonparticipants have an average of 6.62 beehives per household. As presented in Table 6, there is
significant difference on the mean of number of traditional and transitional beehives per
household between participants and non participants at less than 5% level of significance.
35
Table 6: Number of beehives per household
No. of
beehives
Participants
Non- Participants
Total sample
(N =125)
(N =35)
(N =160)
t-value
Mean
Std
Mean
Std
Mean
Std
Traditional
7.67
10.96
2.86
2.46
6.62
9.95
-2.57**
Transitional
0.78
1.73
0.17
0.51
0.65
1.57
-2.06**
Modern
0.32
1.02
0.11
0.40
0.28
0.92
-1.17
**Significant at less than 5% significant level, N= sample size
Source: Survey result, 2011
Honey is harvested in the study area from October through December and from May to June
(peak periods) every year. Considering the whole sample, most farmers (83.8%) owned less
than 10 traditional beehives, 12.5% owned 10-20, and only 3.8% owned traditional beehives
around 20-60 indicating beekeeping is practiced in small scale in the woreda. Out of the
sampled households only 31.3% and 11.3% have adopted the transitional and modern
beehives, respectively. From the total number of beehives owned by the sampled households
around 87.7% is traditional and 8.6% transitional and the remaining 3.6% is modern showing
that majority of the households are engaged in traditional beekeeping.
Among those who use traditional beehive and participate in honey marketing, 56.8% of them
harvest honey twice in a year, whereas 30.4% of participant traditional beehive users respond
that they harvest three times in a year. It was investigated from the survey that harvesting of
honey twice a year is a common practice in the study area (Table 7). It was also reported that
while harvesting of honey, farmers leave some part of it in the beehive and any production
obtained in the non-pick periods of the year would also be left as supplementary food for the
colony to strengthen it for the next harvest.
36
Table 7: Frequency of honey harvest per year per household
Type of Beehives
Frequency
of harvest/
year
Traditional beehive
(n=1059)
Participant
Nonparticipant
%
Transitional beehive
Modern beehive
(n=104)
(n=44)
Participant
NonParticipant
Nonparticipant
participant
%
%
1
6.4
0
1.6
0
0
0
2
56.8
85.7
66.4
94.3
87.2
88.6
3
30.4
14.3
19.2
5.7
11.2
11.4
4
6.4
0
1.6
0
1.6
0
n = number of beehives
Source: Survey result, 2011
Honey yield was markedly different for the traditional and modern hives and between
participants and non-participants. On average, it was about 9.77 kg/hive and 18.49 kg/hive
from the traditional and modern hives respectively (Table 8). High variability in yield was
also observed between honey market participant and non-participant sample farmers. This
might be due to differences in management of bees, and lack of honey business concern.
There is a significant difference between the mean yield obtained annually between
participants and non-participants in traditional hives. As compared with the national average
yield of honey per hive (kg/hive), 5, 13, and 15-20 for traditional, transitional and traditional
beehives respectively (Beyene and Phillips, 2007), the woreda has good potential of honey
productivity.
37
Table 8: Volume of annual honey production per beehive (kg)
(N =125)
Mean
Std
NonParticipants
(N =35)
Mean Std
(N =160)
Mean
std
Traditional
9.77
7.58
4.61
2.65
8.61
7.12
3.941***
Transitional
10.59
5.98
4.50
2.12
10.30
5.98
1.42
Modern
18.49
13.78
8.67
1.15
16.85
13.07
1.20
Participants
Production/Beehive type
Total sample
t-value
*** Significant at less than 1% significant level, N= sample size
Source: Survey result, 2011
Sample households were interviewed whether there is seasonal variation in the quantity of
honey produced and about 80.2% of honey market participants and 80.6% non-participants
responded that there is seasonal variation and the highest production season ranges from
October to December. Production season that ranges from May to June is the lowest
production period for 83.80% and 87.1% of honey market participants and non- participants
respectively.
Table 9: Seasonal variation in production
Participant
Non-participant
Level of production
Highest (%)
Lowest (%)
Highest (%)
Lowest (%)
Oct-Dec
80.2
16.20
80.60
12.9
May-June
19.8
83.80
19.40
87.1
Seasons
Source: Survey result, 2011
Out of the total honey market participants 52% of them responded that they faced problems in
accessing inputs for honey production and the remaining 48% replied they don’t have any
problem in accessing inputs. Main problems of accessibility of inputs for honey production
are associated with lack of access to feed, modern beehive and services like extension and
credit services. There is statistically significant difference between the two groups in
38
accessing beehive and services at less than 10% level of significant and less than 5%
significant level in affording beehives.
Table 10: Problem of inputs in honey production
Participants (%)
Problems
Access to feed
Access to modern
beehive
Affording modern
beehive
Access to extension
& Credit)
Non- Participants
(%)
(N =125)
(N =35)
Total sample (%)
(N =160)
2
value
Yes
No
Yes
No
Yes
No
40.80
59.20
25.71
74.29
37.50
62.50
2.655
38.40
61.60
22.86
77.14
35.00
65.00
2.904*
40.00
60.00
17.14
82.86
35.00
65.00
6.279**
44.80
55.20
28.57
71.43
41.30
58.80
2.972*
** and * Significant at less than 5% and 10% significant level respectively, N= sample size
Source: Survey result, 2011
39
4.2. Value Chain Analysis
This part discusses the structure and composition of honey value chain. The objective is to
understand and describe the function of honey value chain actors, opportunities of honey
production and major constraints of the sector in the study area and to identify the costs and
benefits of the actors in the chain.
4.2.1. Actors in honey value chain and their marketing functions
The focus of value chain framework is developing an effective way of coordinating the
hierarchical stages in the value chain to meet consumer demand in an efficient manner.
Effective vertical coordination of value chain stages requires partnership, actor interactions,
information flow along the chain and coordination of the activities of chain actors. Hence, the
competitiveness of a value chain is greatly influenced by the partnership and collaboration for
innovation that can be realized by chain actors. Moreover, the development and operation of
enabling and supportive business development services (e.g. market information, transport,
credit) play critical role in how well the value chain responds to consumer demands.
(Anandajayasekeram and Berhanu, 2009).
The actors participating in the honey value chain include Beekeepers (small scale farmers),
local honey collectors, cooperatives, local brewery (tej) houses, wholesalers, honey
processors, beeswax processors, retailers, input suppliers and exporters. On the woreda (study
area) level the main actors participating in honey value chain are small scale farmers
(Beekeepers), local honey collectors, local brewery houses, retailers and supportive actors.
Beekeepers: These are the first actors in value chain of honey and the basis of market
participant in honey markets. Traditionally, beekeepers work as integrated actors and perform
two or more functions of value chain. They make their hives out of available local materials,
catch and hive swarms, manage bees, harvest and process honey (for home consumption),
package and sell to the consumers. In the study area traditional hives for honey production are
mostly produced by the farmers (beekeepers) themselves and its price is 200Birr for those
40
who buy it. The improved hives and their accessories are usually supplied by other supportive
actors. The beekeepers sell crude honey and only in few instances undertake some form of
intermediate processing; that is, separating wax from crude honey. They sell crude honey to
tej brewers (which is a major outlet) and/or to collectors and transit consumers at the local
market.
Local honey collectors: as a second link in the honey value chain, they are engaged in buying
of honey from farmers and sell it to traders, retailers, and consumers who come from different
areas. Collectors play important roles of bulking, grading and sending the products to the
various market outlets. The collectors (particularly the association) undertake processing to
just separate honey from wax, and produce honey jelly and crude wax and store independently
for sale. Besides the processing activities, the collectors add value to honey by making spatial
and temporal differences (i.e., collecting from distant location to make easily available to the
user and storing for future use for long). However, the honey collectors found in the study
area purchase honey produced directly from farmers and use it for their own processing (tej
making or table honey) and hence become double agent as collector and processor in the
value chain.
East Shoa Beekeepers Association (ESBKA) which was established in 2000 G.C collects
honey from farmers located in the East Shoa zone at its collection site located in Adama. It
has got a minimum of 90 honey supplier farmers and provides capacity building activities,
like trainings and wax, to farmers. The association collects honey from farmers and processes
it for table honey and retails it to the consumers.
Local brewery houses: tej brewers are apparently processors of honey and beeswax and
thereby add value to it. Their primary function is to produce local brewery (tej) out of crude
or semi processed honey by collecting honey from farmers or traders in the area. They also
perform double tasks in the chain as collector and processor. Wax is the by-product of tej. As
also discussed in Beyene and Phillips (2007), tej producers remove the crude beeswax (called
sefef) from the tej and allow drying. Tej brewers produce partially refined beeswax (called
keskis) by melting the crude beeswax (sefef) with sufficient water and then straining using
41
sisal sacks. Therefore, in addition to tej, these actors produce a significant amount of wax.
These actors normally sell the crude or refined wax to wax collectors.
Retailers: Retailers comprise some of the integrated suppliers who sell both honey and wax
products to local consumers. These include supermarkets (especially in big towns), small
shops in rural villages and urban centers. Collectors and wholesalers do also act as retailers
since they sell small quantities of honey directly to consumers. In the study area honey
retailers are very few since most of the time the farmers themselves sale their honey to next
users (tej breweries or other processors or even to consumers). However, table honey
(processed honey) is found in big supermarkets of the woreda though there is only one retailer
shop for the woreda’s honey distribution that is processed by ESBKA.
Supportive actor: value chain supporters or enablers provide support services and represent
the common interests of the value chain operators. They remain outsiders to the regular
business process and restrict themselves to temporarily facilitating a chain upgrading strategy.
Typical facilitation tasks include creating awareness, facilitating joint strategy building and
action and the coordination of support activities (like training, credit, input supply, etc). The
main supporters of the honey value chain in the study area are woreda’s Office of Agricultural
and Rural Developemnt (OoARD), Improving Productivity and Market Success (IPMS), East
Shoa Beekeepers Association (ESBKA), Ada’a Woreda Animal Production and Health Office
(AWAPHO), Ratsun (NGO), and Holeta Bee Research Center (HBRC).
42
Inputs
Beekeeping
Collection
& Processing
Functions
Supply bee colonies,
Beehives,
Beehive equipments,
Skill and Knowledge,
Finance and credit
Tradingwholesale/retail
Place beehives,
Collect honey,
Distribute & sell
Attend bee colonies, separate from wax,
extract honey
Process and pack
Consumption
Consume as food &/
medicine, use it for tej
Consumers
Beekeepers
Bee
Associations
breeder
(ESBKA)
Actors
Carpenters
Beekeepers
Processor/
(beehive
Collectors/
maker)
Retail
shops/super
markets
Brewery
houses
Brewery Houses
Hotels and
Bakeries
Resource/E
Consumers
quipment
supplier
VC
enablers/
Supporters
OoARD,
AWAPHO,
HBRC
IPMS,
Ratsun
ESBKA,
(NGO),
Persistent
On-spot
Figure 1: Honey value chain map.
43
4.2.2. Marketing Costs and Margins of honey market
The costs and returns of actors playing various market functions are affected by differences in
enterprise size and location, vertical integration of functions, the internal organization of
enterprise operations and the nature of horizontal and exchange relations, particularly where
the latter are linked with credit (Scarborough and Kydd, 1992).
To start from the Beekeepers of the study area, they incur costs mostly during the production
periods rather than marketing their product. Traditional beekeepers make beehives by
themselves with very cheap materials even from the residues of other agricultural activities
and use it for ten years on average. However, for those who purchase the traditional beehive,
its average price in the study area is 200 Birr. But those who use the modern hives incur an
investment cost of Birr 1,110/beehive including the accessories that are introduced with the
modern hive (Table 11). Farmers of the area do not use modern equipments like smokers and
honey extractors. The most commonly used smoking material in the area is burning of dried
cow dung. This makes the woreda’s beekeeping activity less costly and requires very
minimum initial capital.
Table 11: Unit cost of items used in modern beekeeping
Items
Unit
Price per unit(Birr)
Hive(with two partition)*
pc
780
Protective wears
pc
Glove
pc
70
Veil
pc
130
Cloth
pc
130
Total
1110
*Most widely used type of modern beehive in the study area.
Source: Survey result, 2011.
44
There are three marketing channels of the woreda’s honey:
Channel I: Producer (Beekeeper) Collector &/Processor (Beekeeping association)
Retailer Consumer (32.29%)
Channel II: Producer (Beekeeper) Collector &/processor (local brewery houses)
Consumer (16.13%)
Channel III: Producer (Beekeeper)  Consumers. (55.65%)
In the study area 95.2% of the sample farmers who participate in honey marketing sell their
honey as honey comb. The reason behind of selling the honey as honey comb is that this kind
of selling brought them a better market for their product and also people prefer it as it is taken
out of the hive thinking that it is good for health. Producers sell their honey through one or
more marketing channels available in the woreda.
In channel I of honey marketing the farmers sell their product to the processors (beekeeping
associations) directly by transporting it to the collection centers. The East Shoa Beekeepers
Association is located in Adama town, Oromia Regional State, about 45kms away from
Bishoftu town. The major activities of the association are honey collection, honey and bee
wax processing and supply. The association collects honey from the whole East Shoa zone
beekeepers and mostly from Kechemo (one of the Adama woredas), Awash, Ada’a,
Batu(Alemtena), Meki, Ziway, Dukem, Mojo, and Welenchiti. During the survey, the total
number of suppliers of honey to the association was 350 and it reaches its minimum level, 90
farmers on average, during off seasons. The price of the honey at the collection center of the
association in 2010/11 was 70 Birr /kg. Besides, it provides honey processing service to its
members with minimum charge. After the processing of the collected honey, the association
distributes it to its retailing shops, to Welela Animal and Animal Husbandry Cooperative
(sister company of the association that perform the marketing activity) and to supermarkets.
The price of the company’s processed honey is 80Birr/kg.
45
Table 12: Estimated cost and marketing margin for honey market in channel I.
Producer
Operating Costs
Birr/Kg
Percentage of operating costs
Depreciation cost of investment items
4.36
19.99%
Labor cost
15.10
69.30%
Transportation cost
0.40
1.84%
Marketing cost
0.35
1.60%
Miscellaneous
1.58
7.27%
Total operating cost
21.79
Selling price
Farmer's profit
Collector(ESBKA)
Purchasing price
70
48.21
Birr/Kg
70
Percentage of operating costs
Labor cost
0.2
2.71%
Transportation cost
0.4
5.42%
Electricity
0.72
9.76%
Water
0.36
4.88%
3
40.65%
0.7
9.49%
2
27.10%
Operating Cost
Honey Container(Packaging)
Label
Distribution Cost
Total Operating Cost
7.38
Total Cost of production
77.38
Selling Price
80
Gross Profit
10
Net Profit
2.62
46
Retailer
Purchasing price
Birr/Kg
80
Percentage of operating costs
Labor cost
0.4
28.44%
Transportation cost
0.25
17.78%
Tax
0.15
10.67%
Shop Rent
0.11
7.56%
Miscellaneous
0.5
35.56%
Total Operating Cost
1.41
Total Cost of production
81.41
Selling Price
85
Gross Profit
5
Net Profit
3.59
Source: ESBKA and own computation, 2012.
Producers incur Birr 21.79/kg as an operating cost and sale their product with Birr 70/kg to
the collectors. As compared with other actors in the woreda’s honey value chain, the cost of
honey producers’ is much higher and the major share of the operating cost goes to labor
cost(69.30%) followed by depreciation cost of investment items(19.99%).
Since ESBKA works as collector and processor and mostly collect honey at its own collection
site located in the factory, its collection costs are almost nil. Therefore, the costs presented as
operating costs referred as the costs of processing the collected honey. The result of Table 12
shows that honey processors earn a profit of Birr 2.65/1kg of honey. This indicates that the
performance of marketing of honey collectors & processors for the specified year 2011 was
showing positive figure even though the amount of profit was small per kg basis. Table 12
also shows container (packaging) costs take the major proportion (40.65%) of the operating
costs followed by distribution (27.10%) and electricity costs (9.76%) respectively.
With regard to the cost and profitability analysis of the sample honey retailer’s in the woreda,
as Table 12 clearly presents, they were found to be profitable. This indicates that a retailer can
obtain a profit of 3.59 Birr/ kg which was higher than the profit of processors by 0.97 Birr/kg.
47
Regarding cost of operation of retailers’, miscellaneous costs like loading unloading,
commission payments and other costs took the largest share of the operating costs (35.56%)
and labor cost comes to the second level of being a higher cost (28.44%) in honey retailing
stage of the value chain.
Out of the available honey marketing channels in the study area, honey in channel II mainly
goes to local brewery houses that collect from the farmers and process it to their product, tej.
During the survey tej makers in the study area were interviewed about the source, quality, and
price of their input (honey). As explained by the local brewery owners, they mostly use honey
that is produced in other places than the study area because of its unattractive price for their
tej business though its good quality is doubtless. As a result they purchase very few amount of
the study area’s honey from the farmers with price of 70-80Birr/kg when they have special
orders and mix it with honey that comes from other parts of the country to get the good test of
the brewery they make. Therefore, due to lack of information on the cost of making local
brewery from one kg of the woreda’s honey, the researcher faced problem in computing the
marketing margins of the actors in the second channel of the honey value chain.
The III channel which took the lion share of honey flow (55.65%) in the study area includes
producers and consumers (both in the nearby towns; Bishoftu and Dukem) and those who
travel in the Bishoftu –Addis Ababa road. Here the honey mainly flows from beekeepers to
consumers and the beekeepers took 100% share of the consumer price. There were no
observed operational brokers in the honey marketing channel during the survey period. The
consumers will use the honey for different purposes like table honey, medicine, or brewery
making for home consumption. According to the survey result almost all beekeepers who use
the road side markets from Addis to Bishoftu (95.2%) sell their honey with its comb and that
creates good market for them. As the price of a product can make a difference between
success and failure of a business, setting a price for their honey has crucial importance for
their income and the survey result reveals that about 41.5% of the beekeepers set the price of
their honey by themselves using the current information available and about 28.5% of them
set it through bargaining with the purchasers. The main source of information used to set the
price of honey is personal observation followed by information from other traders in the area,
48
and telephone. The level of price in channel III of the honey marketing ranges from 80Birr/kg
to 90Birr/kg during the survey period.
Table 13: Market share of actors in honey marketing through channel I
Market Actors
Selling
Price( Birr)
Operating
Gross
Costs(Birr/kg)
Profit(Birr/kg)
Gross
Marketing
Margin (%)
Beekeepers
70
21.79
48.21
82.35
Collectors/processors
80
7.38
10
12.5
Retailers
85
1.41
5
5.88
Source: Survey result, 2011.
TGMM_I (Complete distribution of channel I) = 17.65%
GMMp (Producers share) = 82.35%
GMMc/p (Collectors/processors share) =12.5%
GMMr (Retailers share) = 5.88%
Where TGMM= Total gross marketing margin
GMMp= Gross marketing margin for producer (beekeepers)
GMMc/p= Gross marketing margin of collector/processor
GMMr= Gross marketing margin of retailers
Table 13 shows the summary of selling price, operating costs, gross profit and marketing
margins of honey marketing participants in channel I. As indicated in the table 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
(like ESBKA). Though the cost of beekeepers is higher than the rest of the chain actors, their
market share is still in the better position.
49
4.2.3 Opportunities of honey sub-sector in the woreda
Based on the survey conducted on the status of the Ada’a woreda’s beekeeping sub sector, it
was inspected that it has lots of opportunities and constraints. The opportunities refer to the
external favorable conditions that are in favor of honey production and marketing in the
woreda. Some of these include availability and diversity of bee forage, availability of strong
colonies and good yield, and market access.
Availability and diversity of bee forage: The forage sources for honey bees are an important
consideration for beekeepers. In order to determine where to locate hives for
maximum honey production one must consider the off-season. If there are no honey flows the
bees may have to be fed. However, as also stated by Melaku et al, (2008) Ada’a woreda is
very special for its diversified acacia and shrubs species and there are also different kind of
forage trees that flower at different seasons of a year which assures a constant supply of feed
for bees.
Availability of strong colonies and good yield: During the survey it was noted that the
average number of beehives per sampled household was 6.62 for traditional beehives with full
of strong bee families indicating that the woreda has a good potential for bee business
development. The survey also shows that production of honey per traditional beehive is
8.61kg and 16.85 kg per modern beehive and mostly harvested twice a year. All these sum up
to give insight of the available opportunity of apiculture development in the woreda.
Market Access: Out of the sampled beekeepers who participate in honey marketing about
52% of them stated that they don’t have market problem in selling their honey. In fact 85.5%
of them sale their honey on the road side markets with better price (80-90 Birr/kg) than selling
it at village level and for associations (70-75 Birr/kg).
50
4.2.4 Major constraints of the woreda’s apiculture development
Major constraints of the honey sub-sector in the study area were identified through review of
literature and thorough discussions with key informants such as representatives of concerned
government and non-government institutions, collectors, processors, retailers, and
professionals. Accordingly, some of the principal constraints and problems are discussed
below.
Lack of knowledge and skill on beekeeping: During the survey, it was noticed that the
average years of beekeeping experience per house hold is 7.02 and 53.8% of the sampled
households engaged in the sector have been keeping bees for at least 5 years. Though they
have been engaged in the sector for long, their knowledge of how to keep them well and get
better pay back is very low which results in lack of proper management of the beehives. Some
of the problems observed were poor/no shades for hives, poor sanitation in the process of
production (only 35% of the sampled households clean the apiary regularly), harvesting,
storing and transporting of honey.
Lack of institutional linkage: There were few trained beekeeping experts or extension
workers who can provide important advisory services to the farmers. The beekeepers have no
relationships with other beekeeping associations and marketing institutions, which hinders
them from promoting their production systems and market their products.
Lack of organized marketing channel: There is no well organized market channel for honey
in the woreda and these results in lack of grading and standardizing of the product, poor
quality control, and inadequate and inconsistent supply to the next users in the chain. Distant
markets, unreliable transport and inadequate joint efforts in marketing make it difficult for
timely delivery of the required volume. In this regard the farmers who use the road side
market for their output stay long on the street holding the honey with open containers like
tray. Due to dusty nature of the roads, the longer they stayed on the street searching for
travelers to sell their honey, the more the quality of honey deteriorates.
51
Agricultural chemicals: Farmers in the woreda primarily produce teff, wheat, chickpeas and
horticultural crops and for this they use various types of herbicides and pesticides without due
considerations to the damages caused on bee colonies. The woreda’s farmers highlighted that
a number of bee colonies either die or escape their hives due to the agro-chemicals used on
their forage.
Lack of appropriate extension service: As well explained in the descriptive part of this
study, only 26.58% of the sampled beekeepers get regular extension visits for their honey
bees which lead farmers to misinterpret the information available on the production and
marketing systems of the sub sector.
Little or no product promotion: Beekeeping as a sector is overlooked and neglected and
attracts very little attention and support in the woreda. This means, for example, extension
advisers know little about the product and micro-finance institutions do not give credit for this
business. This undermines the potential of the sector.
4.3. Results of Econometrics Model
The Heckman’s procedure results for both outcome and selection variables are presented and
discussed in the next subsection. Moreover, it is important to check multi co-linearity problem
before running the model for both the continuous as well as the dummy variables. The usual
measure of multi co-linearity among variables is Variance Inflation Factor (VIF). The values
of variance inflation factor for the variables were in the ranges of 1.1 and 1.96. To check the
multi co-linearity problem STATA 12 was employed and the VIF result shows that multi colinearity was not a problem among the hypothesized variables (Appendix Table 1).
52
4.3.1. Determinants of farmers’ honey marketing decision
The hypothetical underpinnings of why farm households participate in agricultural markets
can be found in trade theories. According to the theories farmers are essentially driven to
enter into trade or markets so that they can enjoy a diverse consumption bundle. They can
exploit welfare gains from trading by concentrating in the production of goods they have
comparative advantage, and exchange for those they have no comparative advantage, mostly
manufactures (Siziba et al, 2011).
In order to examine what factors mainly affect Ada’a woreda farmers’ decision to sell or not
to sell of their honey in the study area, fourteen variables which are age of the household
head, sex of the household head, education status of the household head, family size, size of
land holding(ha),distance to nearest market, market information, credit access, access to
honey production extension service, number of beehives owned, tropical livestock unit(TLU),
financial income other than beekeeping, years in beekeeping and type of beehive used were
hypothesized. Based on the Heckman’s selection assumption three variables, market
information, total livestock unit (TLU), and years in beekeeping were taken as exclusion
restriction variables and included in the participation equation but not in the observation
equation. Among the hypothesized variables, six of them influenced market participation
decision significantly (Table 14).
Sex of the household head (SHH) has a significant and positive effect (less than 10%) on the
farmers’ honey market participation decision. The marginal effect implies that being male
headed household would increase the probability of that family to supply honey to the market
by 4.4%. The probable reason for this result might be that even if female-headed households
keep bees, they may lack good management practices; this in turn would reduce amount of
production. As a result, they may not participate in honey market and use the produced honey
for home consumption.
53
Number of Beehives owned (NBHO) influenced farmers’ honey market participation decision
positively and statistically significant at less than 1% level of significance. The number of
beehives owned acts to represent the amount of honey harvested or the amount that a farmer
anticipates to harvest come the harvesting season. The larger the number of hives owned, the
higher the quantity of honey harvested hence the participation in honey marketing and vice
versa. Farmers with larger quantities of honey are more likely to engage in selling as they see
it as profitable unlike their colleagues who harvest smaller quantities. This factor was
identified as a major constraint to market participation decision with those who harvested
little amounts reporting that they could not participate in honey marketing majorly because
they viewed it as a waste of time.
Market information (MI), as expected, was positively associated with the probability of
entering in honey market with statistical significant level of less than 1%. Farmers constantly
make production and marketing decisions and current market information can help them make
choices, from the very first stages of the production planning process up until the moment
when the product is actually sold. Updated or current market information accessed through
different sources like radio programs, telephone services, personal observations, other traders
or from extension agents, reduces risk perceptions and encourages market participation
decision of farmers.
The household’s beekeeping experience (YBK) was rather surprisingly negatively associated
with probability to sell honey and statistically significant at less than 1%. Explanation for this
unexpected outcome may be perhaps more beekeeping experience could be associated with
older farmers who are less inclined to be engaged in honey business and their risk avert
behavior which results in less flexible in adopting new technologies and thereby boost
production for marketable surplus.
Tropical livestock unit (TLU) influenced the farmers honey market participation decision
negatively. This is mainly due to the fact that farmers with more number of livestock tend to
specialize in livestock production by disregarding the importance of beekeeping as means of
cash generating activity. Besides as explained by the sampled households, bees may sting
54
livestock and result in loss of the livestock so that households prefer to keep their “valuable
assets”; livestock than bees. According to the marginal effects computed, as TLU increased
by one unit, the probability of a beekeeper household to participate in honey marketing will
reduce by 0.1
Type of beehive owned (TBH) is another key factor which influences the farmers’ decision to
participate in honey marketing. Ownership of modern beehive is a significant variable that
was positively associated with increased probability of household’s participation in honey
market. The more the number of modern beehives owned by a household, the better the
volume of production and marketable surplus, that encourages selling. The marginal effect
also indicates that as the type of beehive owned increased by one unit, the probability of a
beekeeper to be engaged in honey marketing increases by 5.5%.
55
Table 14: Heckman Maximum likelihood estimates of honey market participation and their
Marginal Effect
Variable
Coefficient
Constant
-2.690
AGH
0.004
SHH
Marginal effects
z
P>|z|
-2.87
0.004
0.001
0.47
0.641
0.343
0.044
1.83
0.068*
EDH
0.009
0.001
0.06
0.955
FS
0.009
0.001
0.2
0.842
ACCEXT
0.069
0.009
0.84
0.402
DNM
-0.019
-0.002
-0.32
0.749
LAND
0.099
0.013
1.15
0.251
FIOBK
0.000
0.000
-0.85
0.394
NBHO
0.267
0.035
13.94
0.000***
TBH
0.583
0.055
2.15
0.032**
YBK
-0.001
0.000
-14.82
0.000***
MI
0.050
0.007
15.74
0.000***
CA
0.015
0.006
0.26
0.732
TLU
-0.007
-0.001
-15.78
0.000***
*, ** and *** are statistical significant level of 10%, 5% and 1% respectively
Source: Own computation, 2012
4.3.2. Determinants of volume of honey marketed
In the observation equation of Heckman’s ML procedure, eleven variables were hypothesized
to influence volume of honey marketed. These variables are age of the household head, sex of
the household head, education status of the household head, family size, size of land
holding(ha),distance to nearest market, credit access, access to honey production extension
service, number of beehives owned, financial income other than beekeeping, and type of
beehive used. Out of these, four variables were found to influence volume of honey sold
significantly (Table 15).
56
The number of beehives owned (NBHO) by the household, just like in the decision to
participate in honey marketing, influences the volume of sale of honey positively with
statistical significant level of less than 1%. This indicates that farmers with more number of
beehives can harvest more volume of honey and not only having of better marketable surplus
but will able to sell in bulk. This puts them in a position where they can negotiate for better
prices as well as contracts with major buyers in which case therefore, are assured of a constant
market.
Credit access (CA) for honey production also has positive influence on volume of honey sold
and statistically significant at less than 1%. A study conducted by Bradbear, (2003) states that
in poor societies, lack of credit is a major constraint to everyone concerned with selling and
buying honey. Beekeepers with honey to sell expect to receive cash from honey-collection
centers or private-sector traders; otherwise they prefer to sell their honey in small quantities in
markets to obtain an instant but low cash return. Lack of credit access leads to insignificant
volumes of honey being available for sale; no interest from traders and a stagnant industry.
The decision to sell how much of the produce is also influenced positively by the type of
beehive used (TBH) to produce the honey and statistically significant at less than 5%. This
can be explained as farmers possessing modern beehives produce better volume than those
who use the traditional one and the more they produce, the more they tend to supply to the
market. According to Crane (1990), modern beehives allow honey bee colony management
and use of a higher-level technology with larger colonies and can give higher yield and
quality of honey.
Sex of the household head (SHH) also significantly (less than 10%) and positively affects the
volume of honey sold. Male headed households tend to sell more volume than female one and
this can be related with the weight carrying capacity of female and usage of honey for home
consumption.
57
Table 15: Heckman Maximum likelihood estimation of volume of honey sold
Variable
Coefficient
z
P>|z|
Constant
-260.36
-2.93
0.003
AGH
0.51
0.58
0.563
SHH
30.36
1.67
0.095*
EDH
1.48
0.1
0.922
FS
2.26
0.5
0.619
ACCEXT
6.29
0.78
0.433
DNM
-2.76
-0.48
0.630
LAND
4.54
0.54
0.589
FIOBK
0.00
-0.41
0.685
NBHO
26.67
30.71
0.000***
TBH
62.85
2.4
0.017**
CA
84.01
3.35
0.001***
athrho
16.01
0.11
0.91
lnsigma
4.58
72.3
0.00
rho(𝜌)
1.00
sigma
97.16
lambda
97.16
LR test of indep. eqns. (rho = 0): chi2 (1) = 67.51 Prob > chi2 = 0.0000,
Log likelihood = -719.2357
*, ** and *** are statistical significant level of 10%, 5% and 1%, respectively
Source: Own computation, 2012
Rho(𝜌) is the correlation between the error terms of the substantive and selection models.
Rho has a potential range between -1 and +1 and can give some indication of the likely range
of selection bias. A correlation with an absolute value of 1 would occur if the regression
coefficients of the selection model and the regression coefficients of the substantive model
were estimated by identical processes (i.e., potential selection bias). Conversely, a value of
rho closer to zero would suggest that data are missing randomly or the regression coefficients
58
of the selection model and the regression coefficients of the substantive model were estimated
by unrelated processes (i.e., less evidence of selection bias) (Cuddeback et al.,2004).
59
5. SUMMARY, CONCLUSION AND RECOMMENDATION
5.1. Summary
The study was aimed at analyzing value chain of honey with especial emphasis to Ada’a
woreda, East Shoa Zone of Oromia. The specific objectives of the study include identifying of
actors, activities, and the rules governing the activities in the chain, identifying the
distribution of costs and benefits of the actors in the chain and identifying factors affecting
farmers’ participation decision in honey marketing and volume marketed in the study area.
The data were collected from 160 beekeeper households’ interview using semi-structure
questionnaire and checklist. The households are from four potential honey producing kebeles
of the woreda namely Denkaka, Godino, Ude and Yerer Silase. The analysis was made using
descriptive statistics and econometric model using SPSS and STATA software. All the
sampled household heads were beekeepers. Market participation decision and volume of
honey sales are found to be important elements in the study of honey value chain. Therefore,
Heckman’s ML procedure was used to identify factors influencing market participation
decision of honey and volume of honey sales of the sample households in the study area. The
main findings of this research are summarized as follows.
Of the 160-interviewed honey-producing households, 60.63% are male-headed and the rest
39.38 % were female-headed households. The average age of the sampled respondents was
42.47 years and the average family size was 5.64. The overall educational status of the
sampled households was composed of 52.50% illiterate, 41.25% primary school and 6.25%
secondary school level.
Honey value chain analysis of the study area reveals that the main actors in the chain of
woreda’s honey are beekeepers, local honey collectors, local brewery houses, retailers and
supportive actors. Beekeepers work as integrated actor and perform two or more functions of
value chain. They make their hives out of available local materials, catch and hive swarms,
manage bees, harvest and sell to the consumers. Honey collectors found in the study area
60
purchase honey directly from farmers and use it for their own processing (table honey or tej
making) and hence act as double agents in the chain. East Shoa beekeepers association is the
main collector of the woreda’s honey and processes it for table honey. The main function of
local brewery houses in the honey value chain is further processing of crude honey to their
main output called tej and in addition to tej, these actors produce a significant amount of wax.
In the study area honey retailers are very few since most of the time the farmers themselves
sale their honey to next users (tej breweries or other processors or even to consumers). Value
chain supporters or enablers provide facilitation tasks like creating awareness, facilitating
joint strategy building and action and the coordination of support activities. The main
supporters of the honey value chain in the study area are Woreda’s OoARD, IPMS, ESBKA,
AWAPHO, Ratsun (NGO), and HBRC.
The market channel of honey shows that the study area has three honey marketing channels
and the major share of honey marketing goes to the III channel (Beekeeper to consumer,
55.65%). This is mainly due to the beekeepers prefer to sell their honey directly to consumers
and selling it as honey comb to consumers brought better payback for their investment.
Regarding the costs of the chain actors, beekeepers in the study area incur costs mostly during
the production periods rather than marketing their produce. They incur more costs as
compared to other honey value chains actors of the study area; Birr 21.79/kg as operating cost
however they enjoy a profit of Birr 48.21/kg. This makes the woreda’s beekeeping sector
more attractive business activity with minimum initial capital. As depicted in this study,
collectors’ and or processors’ major cost is packing cost which is about 40.65% of the
operating cost incurred per one kg of honey processed. Retailers enjoy better profit than
honey processors in the woreda’s honey market channel.
Heckman’s ML procedure was used to analyze factors affecting farmers honey market
participation decision(selection equation) and volume marketed (observation equation) in the
study area and three variables, market information, total livestock unit (TLU), and years in
beekeeping were taken as exclusion restriction variables and included in the participation
equation but not in the observation equation.
61
Honey market participation decision were significantly and positively affected by sex of the
household, number of beehives owned, market information, and type of beehive owned while
household’s beekeeping experience(years in beekeeping) and tropical livestock unit (TLU)
were the variables that significantly and negatively influence farmers’ honey market
participation decision. Volume of honey marketed was also influenced by sex of the
household, number of beehives owned, type of beehive owned, and credit access positively.
5.2. Conclusion and Recommendations
A careful assessment and analysis of the production environment is required in order to
formulate apiculture development strategies that will lead to better use of local resources,
improve the living standards of poor farmers and ensure the sustainable development of the
sub-sector. In order to provide some insights to the sector’s development strategists, this study
has made a careful assessment on the Ada’a woreda’s beekeeping sector opportunities and
major constraints. From the findings of the study it emerges that the woreda’s apiculture
sector requires minimum initial capital to be engaged in and has a good prospect of being a
development practice for the rural poor if some of the demerits of the sector are resolved.
Some of the demerits of the sector in the woreda include the honey value chain actors in the
study area and the channels of honey marketing are few as compared to other agricultural
outputs. Most beekeepers sell their honey comb directly to consumers at the road side of the
nearby towns implying that there is lack of organized marketing channel. Lack of knowledge
and skill on beekeeping, lack of institutional linkage, little or no product promotion, lack of
appropriate extension service and inappropriate application of agricultural chemicals were
identified as the major constraints that the sector is facing in the woreda. Besides, as the result
of the econometric model reveals, number and type of beehives owned, access to credit and
market information have significant impact on farmers’ honey market participation decision
and volume of honey sale.
Based on the abovementioned points, the following recommendations could be given to
promote value chain development and upgrading strategies of honey in the study area.
62
Most of the beekeepers in the woreda have been using traditional beekeeping technique that
result in low hive products. Raising awareness and capacity building of beekeepers for quality
production is one of the many ways to assist beekeepers to build on their resources to create
more income by managing their apiary skillfully, and fetch a good price in the market. Hence
all concerned organizations (chain enablers) should focus on the provision of appropriate
training for both farmers and woreda’s agricultural development agents on how to manage
beehives and incorporate new technologies profitably in to farm level production strategies.
The major constraints to exploit the untapped potential of beekeeping activity in the woreda
were lack of access to credit, current or updated market information and modern inputs. This
is due to their fragmented production units, which makes collective action in input
acquisition, production planning and output marketing difficult. These problems can be
addressed via formation of beekeeper unions and cooperatives and through governmental or
non-governmental organizations intervention that improve possibilities for strong and
successful collective marketing of their hive products.
The other issue that needs the attention of chain supporters is organizing of honey collection
centers. These are centers where beekeepers can bring their products and be certain of a
market. When significant volumes of good quality honey is available in one place, traders will
be interested to travel to remote areas, being certain of the volume and quality they will be
able to collect. This in turn has an impact on improving farmers’ production and marketing
capacity since they feel secured for the market of their product.
The woreda farmers use agricultural chemicals like herbicides and insecticides for their cereal
production. This is a potential hindrance for beekeeping development in the study area.
Though it is difficult to completely prevent the effects of agrochemicals on honeybees, their
effect can be reduced through integrated pest management programs like application of the
chemicals when bees are less active or by using insecticides of relatively low toxicity with
proper methods of application. This requires the government or concerned bodies to launch
and strengthen extensive awareness creation program for the woreda’s farmers.
63
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7. APPENDICES
70
Appendix Table 1: VIF for multi co-linearity diagnosis
Variable
VIF
1/VIF
AGH
1.73
0.578742
SHH
1.11
0.904437
EDH
1.12
0.891301
FS
1.96
0.511255
ACCEXT
1.27
0.78981
DNM
1.12
0.892417
LAND
1.78
0.563112
FIOBK
1.62
0.616765
NBHO
1.43
0.699232
TBH
1.12
0.892724
CA
1.15
0.871885
YBK
1.37
0.732322
MI
1.22
0.820279
TLU
1.40
0.713022
Source: Own computation, 2012.
Appendix Table 2: Conversion factor of tropical livestock unit (TLU)
Livestock Category
Heifer
Cow or Ox
Horse/Mule
Donkey adult
Donkey young
Camel
Sheep or Goat adult
Sheep or Goat young
Chicken
Bull
Source: Storck, et al., 1991.
Conversion factor
0.75
1
1.1
0.7
0.35
1.25
0.13
0.06
0.013
0.75
71
Appendix Figure 1: Map of Ada’a woreda.
72
Survey Questionnaire
I. General Information
Questionnaire number: _______________
Name of Enumerator: _______________
Telephone: _________________________
Date __ __/__ __/2011
Wereda/District/PA ______________ Kebele_____________________
II. Household Demographic information
a. Name of Household__________________________________
b. Sex of the household, 1. Male 2. Female
c. Age of the house hold __________ years
d. Education level of the household head
1. Illiterate
2. 10 education
3. 20 education
4. Others (specify)
_________________
e. Marital status of the household head
1. Single 2. Married
3.
Divorced
f. Total Family size_________________
g. Family size with age of 12-65_____________________
III. Wealth and income other than beekeeping
a. .Size of land holding (ha) __________
b. Area of land allocated to ( in 2011).
1. Cropping_______(ha)
2. Fallow_________(ha)
4.Unproductive land______(ha)
3. Pasture_________(ha)
5.Others( specify)_______________
73
Sr.No
c. Off-farm activities and their incomes
Off-farm activities( excluding honey
Yes=1, No=2
If yes, any monthly
production)
1
Charcoal
2
local brewery
income
3
4
Total
d. Do you have any other income source?
1. Yes
2. No
e. If yes, 1. Salary 2. Pension 3. Remittance 4. thers(specify)_______________
f. If yes, how much do you get per month?____________Birr.
g. Income of other family member per month?____________Birr.
h. Present livestock possession
Type of livestock
IV.
No .of animals
Type of livestock
owned
Cattle
Sheep
Lactating cows
Goat
Dry cows
Donkey
Calves
Horse
Heifer
Mule
Bull
Poultry
Oxen
Others(specify)
No .of animals
owned
Honey production Issues
4.1.Do you produce honey?
1. Yes
2. No
4.2.If yes, how long have you been in honey production business? _____________Years.
4.3.If your answer for 3.1 is yes, how many beehives do you have?
1. Traditional beehives_________
74
2. Transitional beehives__________
3. Modern beehives____________
4.4.
How many times and how much do you harvest honey per year?
Frequency
Volume (kg)
1.
Traditional beehives
___________
_____________
2.
Transitional beehives
____________
____________
3.
Modern beehives
_____________
______________
4.5.
What materials do you use for the construction of beehive shade? 1. Grass 2. Wood
3. Corrugated iron
4. Others (specify)______________
4.6.
Do you provide supplementary food to your bee colonies?
1.Yes
2.No
4.7.
If yes, 1. Sugar 2. Some honey left there 3.others(specify)____________
4.8.
Do you process your honey? 1. Yes
4.9.
If yes, why you decide to process?
2. No
1. No market for honey with the comb
2. Processing earns more market price
3. To earn more money from its byproducts
4. Consumers prefer processed honey products
5. Others (specify) ___________________
4.10. If no, why?
1. No market for honey separated from the comb
2. Processing earns not that much market price
3. Don’t have knowledge of how to separate it
4. Consumers prefer honey with the comb
5. Others (specify)__________________
4.11. What problems do you face in accessing the inputs for beekeeping?( 1=yes, 2=no)
Feed
Service
Sanitation materials
Accessibility
Affordability
Price instability
75
Others
4.12. Are there seasonal variations in honey production?
1. Yes
2. No
4.13. If yes, in which months is honey production highest and lowest production?
Highest production
Lowest production
Month
4.14. Give reasons for the variation in seasonal production
4.15.
Reason for high production
Reason for low production
1.Surplus feed
1. Food shortage
2. Good climate
2. Unfavorable climate
3. Surplus water
3. water shortage
4. Good input market
4. Lack of input market
5.strong extension supervision
5. weak extension supervision
6. Others(specify)__________
6. Others(specify)_________
Do you observe some quality measure on your honey production process?
1. Yes
2. No
4.16. If yes, describe the measures.
1. Surrounding cleanness
2. Workers health and hygiene
3. Containers cleanness 4.others (specify) _________________
4.17. How often do you clean the apiary for the bees?
1. Regularly
2. Sometimes
3. Rarely
V. Credit and Extension Service issues
5.1. Do you have access to credit? 1. Yes
5.2. If yes, who is the service provider?
Relatives
5. Money lenders
2. No
1. Gove’t organizations
2. NGO
6. Others (specify)_______________
5.3. If your answer for 5.1 is yes, for what purpose do you take the credit?
76
3. Friends 4.
1. For honey production
2. To purchase fertilizer
4.to purchase food grains
3. To purchase animals
5. To purchase grain seed
6.Others
(specify)____________
5.4. How is the repayment schedule?
1. Monthly,
money
2. Quarterly
3.Semi annually
4. Annually
5.when you get
6. Others (specify)__________
5.5. How often do extension service providers meet you?
1. Regularly 2. Some times
3. Rarely
5.6. How often you get technical advice on honey production/marketing by the extension
service providers?
1. Regularly 2. Some times
3. Rarely
VI. Honey Marketing issues
6.1. Do you sell your honey at your farm? 1. Yes
2. No
6.2. If no,
1. Transport it to the collection point 2. Sell at the main road
3.use it for home
consumption 4. Sell it to processors 5. Others (specify)___________
6.3.If transport, how far is the market place from your residential area?
___________kms
6.4. How
far
is
the
_____________ walking hours.
main
road
(all
weather)
from
your
residential
area?
___________Kms,__________walking hrs.
6.5. How much kg did you sell from your last year honey production (2010/11)?______kg.
6.6. Do you buy honey for resell? 1. Yes
2. No
6.7. If yes, how much did you buy in the last year?_____________kg.
6.8. How much of it is re-sold? ______________kg.
6.9. What means of transport do you use to sell your honey?
1. Animals 2. Vehicles 3. Manpower 4. Others (specify)____________
6.10.
At which market do you sell your honey?
1. cooperatives 2. Supermarkets 3. Hotels/restaurants 4. Neighbors 5. Processors
6. Shops 7. Local nearby markets 8. Directly to consumers 9. Distant markets in
bigger towns.
77
6.11. What equipment do you use for honey container?
1. Plastic bags 2. Skin and hide
3. Others ( specify)____________
6.12. What problems do you face in using your containers?
1. Difficulity of cleaning
reduction
3. High price
2. Change of odur of the honey whch results in quality
4.others (specify)____________
6.13. How did you get current information about supply, demand & price of honey on
markets?
4. Radio
1. Other honey traders
5. TV
6. News paper
2. Personal observation
7. Broker
3. Telephone
8. Others(specify)________
6.14. Who decide the price of your honey?
1. Myself 2. Bargaining between me and buyers
Consumer
3. Collector /Processor
4.
5. Others (specify)__________
6.15. Are your customers concerned about the quality of honey you sold? 1. Yes 2. No
6.16. Are your customers willing to pay more for better quality of honey? 1. Yes
2. No
6.17. What marketing problems for your honey and other beehive products do you face?
1. Price variation
2. Lack of fair market
(specify)_________
78
3. Lack of demand
4. Others