MAIZE AND FABA BEAN VALUE CHAINS: THE CASE OF BAKO
TIBE AND GOBU SEYO DISTRICTS IN CENTRAL WESTERN
ETHIOPIA
M.Sc. Thesis
BEZA ERKO
OCTOBER 2014
HARAMAYA UNIVERSITY
MAIZE AND FABA BEAN VALUE CHAINS: THE CASE OF BAKO
TIBE AND GOBU SEYO DISTRICTS IN CENTRAL WESTERN
ETHIOPIA
A Thesis submitted to School of Agricultural Economics and Agribusiness.
School of Graduate Studies
HARAMAYA UNIVERSITY
In Partial Fulfillment of the Requirements for the Degree of Masters of
Science in Agriculture (Agricultural Economics)
By
Beza Erko
October 2014
Haramaya University
APPROVAL SHEET
SCHOOL OF GRADUATE STUDIES
HARAMAYA UNIVERSITY
As Thesis Research advisors, we hereby certify that we have read and evaluate this thesis
prepared, under our guidance, by Beza Erko entitled- “Maize and Faba bean Value Chains:
The Case of Bako Tibe and Gobu Seyo Districts in Central Western Ethiopia. We
recommended that it be submitted as fulfilling the thesis requirement.
_Degye Goshu (Ph.D)__
Major Advisor
_Moti Jaleta (Ph.D)___
Co-Advisor
_________________
Signature
__________________
Signature
________________
Date
________________
Date
As member of the Board of Examiners of the M.Sc. Thesis Open Defense Examination, we
certify that we have read, evaluate the thesis prepared by Beza Erko and examined the
candidate. We recommended that the thesis be accepted as fulfilling the Thesis requirement
for the Degree of Master of Science in Agricultural Economics.
___________________
Chairperson
___________________
Internal Examiner
___________________
External Examiner
__________________
Signature
__________________
Signature
__________________
Signature
_______________
Date
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Date
________________
Date
DEDICATION
I dedicate this work to the fond memories of my late Mum, Lense Kefena.
ii
STATEMENT OF AUTHOR
By my signature below, I declare and affirm that this thesis is my own work. I have followed
all ethical principles of scholarship in the preparation, data collection, data analysis and
completion of this thesis. All scholarly matter that is included in the thesis has been given
recognition through citation. I affirm that I have cited and referenced all sources used in this
document. Every serious effort has been made to avoid any plagiarism in the preparation of
this thesis.
This thesis is submitted in partial fulfillment of the requirement for a degree from the School
of Graduate Studies at Haramaya University. The thesis is deposited in the Haramaya
University Library and is made available to borrowers under the rules of the library. I
solemnly declare that this thesis has not been submitted to any other institution anywhere for
the award of any academic degree, diploma or certificate.
Brief quotations from this thesis may be used without special permission provided that
accurate and complete acknowledgement of the source is made. Requests for permission for
extended quotations from, or reproduction of, this thesis in whole or in part may be granted
by the Head of the School or 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 interest of scholarship. In all
other instances, however, permission must be obtained from the author of the thesis.
Name: Beza Erko
Signature: ______________________
Place: Haramaya University
Date of Submission: October 2014
iii
BIOGRAPHY
The author was born on March 7, 1975 in Ambo town, West Shewa Zone of Oromia
Regional State, Ethiopia. He attended his elementary and secondary school education at
Ambo primary and comprehensive secondary school.
He joined Jimma Teachers Training Institute and graduated in Certificate in 1990 He also
joined the then called Ambo College of Agriculture in continuing education program in 1996
and graduated in general agriculture in Diploma. Then in the year 2009, he joined Jimma
University in continuing education program and graduated in B-A Degree in Economics. He
joined the school of Graduate Studies of Haramaya University in the year 2012 for his MSc
studies in Agricultural Economics.
The author has five years of experience in primary school teaching and more than ten years of
experience in Ethiopian Institute of Agricultural Research (EIAR). He worked at Haru (West
Wollega), Jimma and Ambo research centers. He was technical assistance for more than eight
years on coffee research at Wollega and Jimma. He has worked for three years as junior
researcher in agricultural economics and research extension department at Ambo research
center.
iv
ACKNOWLEDGEMENTS
This research is a culminating effort of many individuals and institutions. I would like to
thank many people and organizations who supported me in accomplishing this thesis work.
First, my appreciation and gratitude goes to my research major advisor Dr. Degye Goshu and
co-advisor Dr. Moti Jaleta for their constant instruction, guidance, offering constructive
comments and valuable suggestions in both proposal development and thesis writing. The
finalization of this thesis report would have been hardly possible without their guidance and
heart full support. I’m very appreciative and grateful to International Maize and Wheat
Improvement Center (CIMMYT) for having offered me especially good opportunity to the
thesis research fund and accommodation. Without their assistance, the completion of this
paper would have been hardly possible. Special thanks to Dr. Moti Jaleta for organizing and
facilitating the fund and close supervision in data organizing and analysis with timely
comments and suggestions.
I want to express my deepest gratitude and appreciation to the Ethiopian Institute of
Agricultural Research (EIAR) for permitting me to join the School of Graduate Studies. My
sincere acknowledgment goes to Dr Dawit Alemu Coordinator of Agricultural Economics,
Research Extension and Farmers’ Linkage (AEREFL) of EIAR for his facilitating to join my
second-degree study. I would like to thank Alemu Tolemariam in providing necessary data
and information on time. I would also like to thank Agricultural Development Offices,
primary cooperative offices and market development offices of Bako Tibe and Gobu Seyo
districts. I would also like to thank Nekemte city, Ambo and Dendi market development
offices for their contribution in assisting the survey and providing necessary information and
data about farmers, traders and input suppliers.
Finally yet importantly, I would like to make special mentioned of my wife Genet Mamo and
my father, brothers and sisters Erko Erge, Tejitu Erko, Meti Erko, Fosia Ali, Deressa Erko,
Aster Kebede and Teshome Zewde for their lovely assistance, care and encouragement
during my stay at Haramaya University.
v
LIST OF ABBREVIATIONS AND ACRONYMS
ACDI
Agricultural Cooperative Development International
BMGF
Bill and Melinda Gate Foundation
BTDAO
Bako Tibe District Agricultural Office
CIMMYT
International Maize and Wheat Improvement Center
CSA
Central Statistics Agency
EGTE
Ethiopian Grain Trade Enterprises
EIAR
Ethiopian Institute of Agricultural Research
ETB
Ethiopian Birr
FAO
Food and Agriculture Organization
FEWSNET
Famine Early Warning System Network
GDP
Gross Domestic Product
GMM
Gross Marketing Margin
GSDAO
Gobu Seyo District Agricultural Office
IFPRI
International Food Policy Research Institute
M4P
Making Markets Work Better for the Poor
MoFED
Ministry of Finance and Economic Development
MSU
Michigan State University
NGO
Non- Governmental Organization
NMM
Net Marketing Margin
OECD
Organization for Economic Cooperation and Development
RATES
Regional Agriculture Trade Expansion Support Program
SIFSIA
Sudan Integrated Food Security Information for Action
SIMLESA
Sustainable Intensification of Maize-Legume Cropping System for Food
Security in Eastern and Southern Africa
SNNP
Southern Nations Nationalities Peoples
TGMM
Total Gross Marketing Margin
UNIDO
United Nations Industrial Development Organization
USAID
United States Agency for International Development
VCA
Value Chain Analysis
VOCA
Volunteers in Overseas Cooperative Assistance
WDR
World Development Report
vi
TABLE OF CONTENTS
APPROVAL SHEET
i
DEDICATION
ii
STATEMENT OF AUTHOR
iii
BIOGRAPHY
iv
ACKNOWLEDGEMENTS
v
LIST OF ABBREVIATIONS AND ACRONYMS
vi
LIST OF TABLES
x
LIST OF FIGURES
xii
LIST OF TABLES IN THE APPENDIX
xiii
ABSTRACT
xiv
1. INTRODUCTION
1
1.1. Background
1
1.2. Statement of the Problem
3
1.3. Objectives of the Study
5
1.4. Significance of the Study
5
1.5. Scope and Limitations of the Study
6
1.6. Organization of the Thesis
7
2. LITERATURE REVIEW
8
2.1. Basic Concept of Value Chains
8
2.2. Approaches and Frameworks to Value Chain Analysis
11
2.2.1. Approaches to value chain analysis
11
2.2.2. Framework to value chain analysis
12
2.3. Agro–value Chains and Pro-Poor Growth
15
2.4. Approaches to the Study of Agricultural Marketing Problems
17
2.4.1. Functional approach
17
2.4.2. Institutional approach
17
2.4.3. Commodity approach
18
2.4.4. Structure – Conduct - Performance
18
vii
TABLE OF CONTENTS (Continued)
2.5. Marketing Costs and Margins
21
2.6. Maize and Faba Bean Production, Consumption and Marketing in Ethiopia
22
2.6.1. Maize and faba bean production in Ethiopia
22
2.6.2. Maize and faba bean consumption in Ethiopia
23
2.6.3. Maize and pulse marketing in Ethiopia
24
2.7. Empirical Evidence on Value Chain Analysis of Maize and Faba bean
3. RESEARCH METHODOLOGY
26
32
3.1. Description of the Study Area
32
3.2. Types and Sources of Data
34
3.3. Sample Size and Sampling Technique
34
3.4. Methods of Data Collection
36
3.5. Methods of Data Analysis
36
3.5.1. Descriptive analysis
36
3.5.2. Econometrics Model
38
3.6. Variables Definition and Measurement
4. RESULTS AND DISCUSSION
44
4.1. Descriptive Results
44
4.1.1. Household characteristics
44
4.1.2. Maize production and marketing
46
4.1.3. Faba bean production and marketing
48
4.1.3. Producers’ characteristics by market outlets
49
4.2. Value Chain Analysis
4.3.
40
50
4.2.1. Mapping maize value chain
50
4.2.2. Mapping faba bean value chain
57
4.2.3. Service providers in the value chains
60
4.2.4. Value chain governance
61
Marketing Channels and Performance Analysis
65
4.3.1. Maize market channels
65
4.3.2. Faba bean market channels
67
4.3.3. Maize market performance
68
viii
TABLE OF CONTENTS (Continued)
4.3.4. Faba bean market performance
72
4.4. Econometrics Analysis
74
4.5. Challenges and Opportunities in the Value Chains
78
4.5.1. Constraints and opportunities of input suppliers
78
4.5.2. Constraints and opportunities producers
80
4.5.3. Constraints and opportunities of traders
82
5. SUMMARY, CONCLUSION AND RECOMMENDATIONS
84
5.1. Summary and Conclusion
84
5.2. Recommendations
87
6. REFERENCES
90
7. APPENDIX
95
ix
LIST OF TABLES
Table 1: Area, production and yield of maize for different regions in 2010/11 ......................22
Table 2: Area, production and yield of faba bean in different regions in 2010/11 ..................23
Table 3: Crop production and percent of utilization in 2011/12 ..............................................24
Table 4: Distribution of sample kebeles and households across districts ................................35
Table 5: Hypothesis of explanatory variables ..........................................................................43
Table 6: Households characteristics (categorical variables) ....................................................44
Table 7: Households characteristics (continuous variables) ....................................................45
Table 8: Production, yield and marketed surplus of maize in the two districts .......................47
Table 9: Production, yield and marketed surplus of faba bean in the two districts .................48
Table 10: Households' characteristics to maize buyers (categorical variables) .......................50
Table 11: Agricultural inputs supplied by sample cooperatives to their respective kebeles ...53
Table 12: Agricultural inputs used by sample households.......................................................54
Table 13: Maize storage period and end stock .........................................................................55
Table 14: Price information sources for actors in the value chains .........................................62
Table 15: Maize storage period and stock in the study areas ...................................................63
Table 16: Current number of competitors on maize and faba bean trade in the study areas ...63
Table 17: Importance of quality attributes when purchasing maize and faba bean .................65
Table 18: Marketing margin and gross profit of actors in maize value chain. ........................69
Table 19: Marketing margins of actors along maize market channels.....................................71
Table 20: Faba bean marketing costs, margins and gross profit shares of actors ....................72
Table 21: Marketing margins of actors along different faba bean market channels ................74
Table 22: Tobit model results for maize marketed surplus ......................................................75
Table 23: Agricultural inputs quantity restriction and availability ..........................................78
Table 24: Lead-time and quality complaints of inputs ............................................................79
x
LIST OF TABLES (Continued)
Table 25: Production and marketing constraints of maize and legume ...................................80
Table 26: Maize and legume traders marketing constraints.....................................................82
xi
LIST OF FIGURES
Figure 1: A simplified value chain .............................................................................................9
Figure 2: Business models for inclusive agricultural value chains ..........................................14
Figure 3: Geographical location of the study areas ..................................................................33
Figure 4: Share of maize buyers directly purchased from producers.......................................49
Figure 5: Map of maize value chain in the study areas ............................................................51
Figure 6: Map of faba bean value chain in the study areas ......................................................58
Figure 7: Maize market channels in the study areas ................................................................66
Figure 8: Faba bean market channels in the study areas ..........................................................67
Figure 9: Percentage share of maize marketing costs ..............................................................70
Figure 10: Percentage share of faba bean marketing costs ......................................................73
xii
LIST OF TABLES IN THE APPENDIX
Appendix 1: Conversion factors used to compute Tropical livestock unit (TLU)...................95
Appendix 2: Agricultural inputs supplied by cooperatives to their respective kebeles ...........95
Appendix 3: Test for normality of residuals ............................................................................96
Appendix 4: Marginal effect of determinants of marketed surplus of maize ..........................97
Appendix 5: Household questionnaire .....................................................................................97
Appendix 6: Maize and legume grain traders’ questionnaire ................................................111
xiii
ABSTRACT
This research attempted to generate useful information on maize and faba bean value chains,
which help governmental institutions and NGOs to assess their activities and redesign their
operations in Bako Tibe and Gobu Seyo districts. This was done by identifying actors and
mapping their interactions, evaluating the governance, incentives and cost structure,
estimating determinants of maize marketed surplus and identifying constraints and
opportunities in the value chains. Data was collected by CIMMYT from 199 randomly
selected households, 53 maize traders, 9 legume traders and 7 input suppliers that were used
in this study. For data analysis, both descriptive and econometrics analysis of Tobit model
were used. The result of the study showed that, maize average production, yield and marketed
surplus were 36.95 quintals, 31.44 quintals per hectare and 18.23 quintals, respectively. On
the other hand, faba bean production, yield and marketed surplus were very low which were
on average 1.52 quintals, 6.32 quintals per hectare and 0.42 quintals, respectively. Value
chain actors involved in the study areas were input suppliers, producers, traders and
consumers. No processors and exporters were identified that involved in the value chains.
The highest values added in maize and faba bean value chains were about ETB 49 and 45 per
quintal, respectively. Rural assemblers in maize value chain and urban retailers in faba bean
value chain obtained the highest share of gross profit next to producers. The econometric
model result showed that current price, district dummy, fertilizers used, marketing costs, land
allocation, distance to main market and non-farm income were significantly determining
maize marketed surplus. The hypothesis of the association between quantity of maize supplied
to market and their determinant factors were as expected except for marketing costs and nonfarm income, which was against the expected association. Governance was exerted through
market information gap, quality standards, credit access and competition. In all the cases,
traders were influential in quantity handled, price and quality assessments setting. The major
constraints identified in the value chains were the time taken between order and placement
and quality of inputs, high price of seed and fertilizers, low fertility of soil, absence of
standard quality assessment, large number of unlicensed traders in the markets and unstable
prices. The opportunities identified were ordering inputs in bulk and transporting them in
pool, different hybrid maize varieties supplied to the areas, and potential in maize production
that can supply for agro-processors. The study recommends the need to develop maize
varieties (open pollinated varieties) that farmers can use the seed repeatedly year after year,
strengthen cooperatives and EGTE for grain trade, encourage agro-processors be involved
in maize processing and promoting factors that determined marketed surplus of maize. It also
needs to promote improved faba bean varieties along with its modern agronomic practices in
the study areas.
xiv
1. INTRODUCTION
1.1. Background
Agriculture continues to be the dominant sector in Ethiopian economy until 2006/07. Its
contribution to the GDP was 47% in the year 2003/04, 44.3% in 2006/07 and 41.1% in
2010/11. After 2006/07, service sectors take over the dominancy. For example, in 2010/11,
the contribution to GDP was 41.1% and 46.6% agriculture and services sectors, respectively.
Average growth rate of agriculture was 10.2% during 2003/04 to 2010/11. The growth rate of
industry was 12.8% while service sectors grown by 10.8% during 2003/04 to 2010/11
(MoFED, 2010/11). Crop production contributes more than 60% to agricultural GDP, while
livestock represents about 30% and the other sub-sectors contributes about 10% to the
agricultural GDP.
Maize accounts for the largest share among cereals in total production and the total number
of farm holdings. In 2010/11, it accounted for 28% of the total cereal production, compared
to 20% for teff and 22% for sorghum, which are the second and third most cultivated crops.
Maize yield is the highest among cereal crops and it is the only crop with significant use of
commercial inputs. In 2008, about 37 % of maize farmers used fertilizer, compared to the
national average of 17 % for all cereal farmers. An estimated 26% of the maize growers used
improved seed, which is again about twice the national average for all cereal farmers (Rashid
et al., 2010).
The major surplus maize production zones in Ethiopia were West Gojam, Jimma, East Shoa,
East Wellega, West Wellega, Illubabor, Arsi, West Shoa, East Hararghe, Agewawi, West
Hararghe, and Sidama (USAID, 2010). Most of the marketed quantity of maize (or 94%)
comes from smallholders, and the rest is from commercial and state farms. The marketed
volume of maize passes successively through a number of channels before it reaches the final
consumer. These are producers, rural assemblers, wholesalers, Ethiopian Grain Trade
Enterprise (EGTE), Emergency Food Security Reserve Administration (EFSRA), cereal
exporters, processors and retailers (RATES, 2003).
Pulses contribute to smallholders’ income, as a higher-value crop than cereals, and to diet, as
a cost-effective source of protein that accounts for approximately 15 % of protein intake.
Moreover, pulses offer natural soil maintenance benefits through nitrogen fixing, which
improves yields of cereals through crop rotation, and can result in savings for smallholder
farmers from less fertilizer use. The producers of pulses are smallholders with small and
dispersed plots under rain fed conditions. Yield of faba bean during 2000 to 2008 increased
from 1.06 tons/ha to 1.3 tons/ha. The marketing system of pulse with the exception of haricot
beans is highly underdeveloped, and more or less similar to that of cereals (IFPRI, 2010b).
The most important pulse producing zones in Ethiopia are North Shoa, South Wello, East
Gojam, North Gondar, South Gondar, West Gojam, West Shoa, Southwest Shoa, East Shoa,
Arsi, and North Wello. The major pulse crops cultivated are horse beans (faba beans),
chickpeas, field peas, lentils and grass pea (USAID, 2010).
Ethiopian agricultural sector today is in need to strengthen all actors along the entire
agricultural value chain, from input research, supply and distribution, through aggregation of
smallholder production and trading, to downstream processing and export. Actors cover
public and private institutions including seed enterprises, farmer cooperatives and unions,
agricultural processors, traders, aggregators and rural credit providers and a favorable
environment to operate effectively. These actors are needed to realize the full potential of
Ethiopia’s natural endowments and to bring efficiency and quality to the value chain. The
majority of actors across the value chains are small and informal with limited resources and
gaps in funding and technical skills. This imposes barriers to agricultural growth, efficient
scale of activities, high transaction costs and inefficient information flows from end market to
producers (BMGF, 2010).
The central western Ethiopia such as Bako Tibe and Gobu Seyo districts are SIMLESA
project implementation areas which work on maize-legume cropping system. As the study
was funded by this project, the two districts and commodities were selected for the study. The
intervention of legume production along with maize starts to increase legume production in
the areas. Moreover, maize was the major surplus production and marketing commodity in
which a number of farmers and traders dominantly involved in these activities. However,
there was information gap in the flow of commodities, actors involved and their interaction,
incentives through the activities, opportunities and constraints in the value chains. Therefore,
2
this study intends to assess the characteristics of actors, crops flow, profit and cost structure,
distribution of benefits, role of governance in value chains, identify determinants of marketed
surplus of maize and finally recommend possible interventions for the study areas.
1.2. Statement of the Problem
Value chain analysis is essential to explain the connection between all the actors in a
particular chain of production and distribution and it shows who adds value and where, along
the chain. It helps to identify pressure points and make improvements in weaker links where
returns are low (Schmitz, 2005). Value chain involves multiple actors, including input
suppliers, producers, traders (local assemblers and wholesalers), retailers and processors, and
consumers. Majority of actors across the value-chains are small and informal, with limited
resources and gaps in funding and technical skills. This imposes myriad barriers to
agricultural growth: inefficient scale of activities, high transaction costs, and insufficient
information flow from end market to producer. This means that upstream (input suppliers and
distributers), the input needs of farmers are unmet, both in terms of volume and coverage.
Highly fragmented midstream (grain traders/wholesalers) marketing impairs the links
between farmers and markets (BMGF, 2010).
Analysis of the value chains of various staple foods in Ethiopia shows that the proportion of
marketed surplus commodities differs from crop to crop - maize (18%), wheat (21%),
sorghum (13%), millet (30%), rice (55%), pulses (33%), and ground nuts (55%). The
marketed quantity flows from producers to consumers through various marketing channels,
and the key market players are rural assemblers, wholesale traders, food processors, exporters
and retailers (USAID, 2010). Even if this study showed marketed surplus of different staple
foods, it did not identify factors that determine the quantity of staple food supplied to
markets. Therefore, maize and faba bean value chain analysis in the two districts tried to
identify determinant factors of maize marketed surplus in the study areas.
Production is the key activity in agricultural value chain. The value chain network
functioning around smallholder farmers comprises linkage among input suppliers, farmers,
co-operatives, extension service providers, credit service providers, and traders. Farmers face
a series of challenges that limit their overall production and income. Sixty percent of the total
3
marketed volume is sold during the first three months after the harvest. This implies that the
benefit from higher prices during the lean period does not accrue to smallholders. The maize
marketing chain is not only long and complex; the scale of operation at various stages is also
very small (IFPRI, 2010a). Studies on maize and pulse value chains potential in Ethiopia did
not describe the distribution of value added and incentives across value chains. Therefore, the
study in the two districts tried to evaluate cost structures and incentives along value chains of
the two commodities.
The pulse value chain in Ethiopia is far from efficient and fraught with several challenges.
Productivity is below potential due to low input usage, limited availability of seed and limited
familiarity with the variety of existing pulse types, and; limited usage of modern agronomic
practices, the complexity of the marketing system. The link between the producers and the
export markets is weak, due to the large number of ineffective intermediaries operating in the
value chain and the export market is underdeveloped. The small quantity produced, poor
quality supplied and sold coupled with high transportation and transaction costs incurred in
marketing reinforce the subsistence orientation of the smallholder farmers (IFPRI, 2010b).
Farmers in Ethiopia face a series of challenges that limit their overall production and income.
The key challenges they face include lower yields, majority of sales immediately after harvest
and high post-harvest losses. These are due to limited use of modern inputs, liquidity
constraints, lack of adequate storage and uncertainty in price variability, etc. Improving and
strengthening value chain has the latent possibility to generate significant benefits for smallscale producers. The benefits can be derived largely through productivity increases and
improvements in marketing. It can also be drawn from improvements in handling and
storage. A well developed marketing system creates the economic incentive for producers to
invest in production and productivity enhancing activities (IFPRI, 2010a)
Maize and/or legume value chain studies in Ethiopia indicate that the sector faces a number
of constraints to increase productivity and improve market performance. These are limited
input supply and use, limited market outlets, limited efforts in market linkage activities and
poor market information among actors. This entails a need for more comprehensive study
which rigorously examines the maize and faba bean value chains. Studies on determinants of
maize marketed surplus, cost structures and benefit share of different actors in the value
4
chains were not investigated in the study areas. So, this study was proposed to analyze the
value chain of maize and faba bean and identify determinants factors of maize marketed
surplus. Therefore, this study addressed the gaps and intended to answer the following
research questions:
(a). How different actors in the value chains interact and create value?
(b). How governance, incentive, and cost structures exist in value chains?
(c).What factors affect marketed surplus of maize of smallholder farmers?
(d).What are opportunities and constraints of all actors in value chains?
1.3. Objectives of the Study
The general objective of the study is analyzing value chains of maize and faba bean in
western central Ethiopia.
Specific objectives of the study are to:
1. Identify actors; map them and their interactions in maize and faba bean value chains.
2. Evaluate the governance, incentive, and cost structures of maize and faba bean value
chain actors.
3. Estimate marketed surplus of maize by smallholders and to identify their underlying
determinants in the study areas.
4. Identify opportunities and constraints of all actors in maize and faba bean value
chains.
1.4. Significance of the Study
Value chain approach aims at making agricultural production and marketing more efficient
through value addition and improved returns for the actors along the chain. It increases the
competitiveness of a product and the chain as a whole. Analysis of value chain assists in
identifying available opportunities that could be factored into value capturing and value
creation. It enhances the competitiveness of a producer group through upgrading of both
product and the chain leading to improved earnings for the chain actors.
5
This study will provide information on the determinants of maize marketed surplus,
marketing margin, product flows and actors’ interactions, benefit share of actors, and
identifies opportunities and constraints of all actors in value chains. The information obtained
from this research will assist producers and market participants to identify opportunities that
increase productivity and improve marketing performance.
Bako Tibe and Gobu Seyo districts are SIMLESA project implementation areas in central
western Ethiopia which work on maize-legume cropping system for food security. As the two
districts are project implementation areas and this study was funded by the project, study was
conducted in these districts. The intervention of this project started legume production in the
areas. Among legume, faba bean was relatively better than others in production and
marketing. Therefore, it was important to analyze faba bean value chain and identify
opportunities and constraints at different value chain functions such as input supply,
production, marketing and consumption. Maize was the major surplus production and
marketing commodity in the areas in which more farmers dominantly participated in
production and supply to market. Moreover, a number of grain traders involved in this
commodity business.
The primary beneficiary of this study is smallholder farmers (producers) because they take
part both in production and in marketing of the commodities. It also benefits other value
chain actors involved in input supply, trading, processing. The information generated will
also help research and development organizations, policy makers, extension service
providers, government and non-governmental organizations to assess their activities and
redesign their operations in the study areas. It also helps development institutions to intervene
to increase the benefits of smallholders in the study areas.
1.5. Scope and Limitations of the Study
Value chain analysis of maize and faba bean were undertaken in two districts namely Bako
Tibe and Gobu Seyo in central western Ethiopia. Due to time and resource limit, only two
commodities such as maize and faba bean were used for the study. As crops from the study
areas distributed to different parts of the country, interviewing end users/consumers was
hardly possible. Therefore, data was not collected from consumers or end users in the value
6
chains. The study covered only smallholder producers of maize and faba bean. Therefore, the
study is limited to smallholder farmers and it does not represent commercial farmers and state
farms. As the number of sampled faba bean producers was very few (25 respondents), it was
impossible to analyze the commodity with econometrics model to estimate marketed surplus
and its determinant factors. Therefore, the study lacks faba bean econometrics analysis.
1.6. Organization of the Thesis
With the above brief introduction of the study, the remaining part of the thesis is organized as
follows: Chapter 2 contains critical analysis of the existing knowledge on the study of value
chain analysis of maize and faba bean. Chapter 3 presents the course of the data, methods,
procedure of data collection and analysis with the research methodology employed in the
study. Chapter 4 presents results and discussion, including data presentation on demographic
characteristics of sample households, findings of descriptive statistics and economic model
statistics. Chapter 5 presents summarization of findings of the study and provides conclusion
and recommendations.
7
2. LITERATURE REVIEW
This chapter presents definition and concepts of value chains, approaches and frameworks of
value chain analysis, agro value chain and pro poor growth, approaches to the study of
agricultural marketing problems, production, consumption and marketing of maize and faba
bean in Ethiopia and various relevant empirical studies, which were conducted in different
parts of the country that are pertinent and directly linked with the topic of the research.
2.1. Basic Concept of Value Chains
The term value chain comprises of two concepts: value and chain. The term value chain is
synonym to “value added” in the value chain analysis (VCA) as it characterizes the
incremental value of a product. For agricultural products, value addition can also take place
through differentiation of a product based on food safety and food functionality. Price of the
resultant product shows its incremental value. The term chain refers to supply chain including
the processes and actors involved in life cycle of a product (Hawkes and Raul, 2011).
Kaplinsky and Morris (2001) define value chain analysis as the study of the full range of
activities that are required to bring a product or service from conception through the different
phases of production to delivery to final consumers and disposal after use (recycling). A
value chain exists when all of the actors in the chain operate in a way that maximizes the
generation of value along the chain.
Agricultural value chains are usually defined by a particular finished product or closely
related products and include all forms and their activities engaged in input supply,
production, transportation, processing, and marketing for distribution of the product or
products 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
including bulking, cleaning, grading, packaging, transporting, storing and processing
(Anandajayasekeram and Berhanu, 2009)
8
The flow of seed to farmers and grain to the market occurs along chains. These can be
referred to as value chains because as the product moves from chain actor to chain actor, for
example, from producer to intermediary to consumer, it gains value. The chain actors who
actually transact a particular product as it moves through the value chain include input
suppliers, farmers, traders, processors, transporters, wholesalers, retailers and final
consumers. A simplified version of a value chain is shown in Figure 1 below.
Source: Hellin and Madelon, 2006
Figure 1: A simplified value chain
In reality, value chains are more complex than the above example, in many cases, the input
and output chains comprise more than one channel and these channels can also supply more
than one final market. A comprehensive mapping therefore describes interacting and
competing channels and the variety of final markets into which these connect (Hellin and
Madelon, 2006).
A value chain map illustrates the way the product flows from raw material to end markets
and shows the type of actors involved. Value chain mapping is often used to locate actors in
the chain, understand interactions and identify constraints and possible solutions at its
different levels. 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 (UNIDO, 2009).
Chains composed of companies (or individuals) that interact to supply goods and services are
variously referred to as productive chains, value chains, filières, marketing chains, supply
chains, or distribution chains. These concepts vary mainly in their focus on specific products
or target markets, in the activity that is emphasized, and in the way in which they have been
applied. What they have in common, however, is that they all seek to capture and describe the
9
complex interactions of firms and processes that are needed to create and deliver products to
end users. Moreover, they all strive to identify opportunities for and constraints against
increasing productivity. Although it is impossible to draw clear distinctions among these
often-overlapping concepts, it is still worthwhile to provide some basic definitions and
highlight some of the differences (Kaplinsky and Morris 2002).
Value chains focus on value creation—typically via innovation in products or processes, as
well as marketing—and also on the allocation of the incremental value. By contrast, the term
“supply chain” is used internationally to encompass every logistical and procedural activity
involved in producing and delivering a final product or service, “from the supplier’s supplier
to the customer’s customer”. Since the primary focus of supply chains is efficiency, the main
objectives are usually to reduce “friction” (for example, delays, blockages, or imbalances),
reduce outages or overstocks, lower transaction costs, and improve fulfillment and customer
satisfaction (Webber and Patrick, 2009).
A value chain is a form of supply chain, but the value component imbues it with greater
meaning: a supply chain becomes a value chain when it is perceived as a process of value
addition. A value chain can thus be described by the series of activities and actors along the
supply chain and what and where value is added in the chain for and by these activities and
actors. “Value” refers to the value added to the product by activities at each step in the chain.
For example, maize sells for $X at the farmgate, but cleaning it makes it worth $X + 4, as
well as the value created by the product and activities and then captured by each of the actors
involved. In this case $X for the farmer and $X + 4 for the retailer. The “added” part means
the difference between the total revenue created by the product and the costs of the materials,
labor, and other inputs used to produce it, which can then be captured by the actors along the
chain. “Upgrading” refers to the various ways that actors can capture more value by changing
their products, processes, and functions (Hawkes and Ruel, 2011).
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
10
activities. The flows of goods, information and finance through the various stages of the
chain are evaluated in order to detect problems or identify opportunities to improve the
contribution of specific actors and the overall performance of the chain (UNIDO, 2009).
Value chain governance is the dynamic distribution of power and control among actors in a
value chain. Power refers to the degree that one firm or group of firms dominates the value
chain, and has a controlling influence on the quantity, quality and price of goods. Power
relationship among firms influence value chain competitiveness, opportunities for upgrading,
and access to finance. Governance can be characterized by four types of relationships. These
are: market relationship, balanced relationship, directed relationship and hierarchical
relationship. In a directed value chain, buyers exert significant influence over the quantity,
quality and price of goods traded in the market, and sellers have limited negotiation power
(Johnston and Meyer, 2007).
2.2. Approaches and Frameworks to Value Chain Analysis
2.2.1. Approaches to value chain analysis
Kaplinsky and Morris (2001) stress that there is no “correct” way to conduct a value-chain
analysis; rather, the approach taken fundamentally rests upon the research question that is
being asked. However, four aspects of value-chain analysis of agriculture are particularly
important.
Firstly, at its most basic level, a value-chain analysis systematically maps the actors
participating in the production, distribution, marketing, and sales of a particular product (or
products). This mapping assesses the characteristics of actors, profits, and cost structures,
flows of goods through the chain, employment characteristics and the destination and
volumes of domestic and foreign sales (Kaplinsky and Morris, 2001), Such details can be
gathered from a combination of primary survey works, focus groups, participatory rural
appraisal (PRAs), informal intervention and secondary data (M4P, 2008).
Second, value-chain analysis can play a key role in identifying the distribution of benefits
of actors in the chain. That is, through the analysis of margins and profits within the chain, It
is possible to determine who benefit from participation in the chain and which actors could
11
benefit from increased support or organization. This is particularly important in the context of
developing countries (and agriculture in particular), given concern that the poor in particular
are vulnerable to the process of globalization (Kaplinsky and Morris, 2001). One can
supplement this analysis by determining the nature of participations within the chain to
understand the characteristics of the participants.
Third, value-chain analysis can be used to examine the role of upgrading within the chain.
Upgrading can involve improvements in quality and product design that enable producers to
gain higher-value or through diversification in the product lines served, allowing producers to
gain higher value. An analysis of the upgrading process includes an assessment of the
profitability of actors within the value chain as well as information on limitation that
currently present. Governance issues play a key role in defining how such upgrading occurs.
In addition the structure of regulations, entry barriers, trade restrictions, and standards can
further shape and influence the environment in which upgrading can take place.
Finally, value-chain analysis can highlight the role of governance in the value-chain, which
can be internal or external. Governance within a value-chain refers to the structure of
relationships and coordination mechanisms that exist between actors in the value-chain.
Governance within the chain occurs when some actors in the chain work to criteria set by
other actors in the chain, for example, quality standards or delivery times and volumes set by
processing industries. Commercial rules that govern commercial relationships in global or
local value chains may constrain or restrict the role of the poor, but also may create important
leading and upgrading opportunities. Commercial rules can be very specific, example, clearly
set and described quality grades of agricultural produce with corresponding transparent prices
or pricing formula (M4P, 2008).
2.2.2. Framework to value chain analysis
A value chain is a “cradle-to-grave” model of the operations function. Value chain analysis
was first suggested by Porter (1995) as a way of presenting the construction of value as
related to end customer. It is development of a set of functional-level strategies that support a
firm’s business-level strategy and strengthen its competitive advantage. Good value-chain
management requires marketing managers to focus on defining the firm’s business in terms
of customer needs. Value chain analysis focuses on how a business creates customer value by
12
examining contributions of different internal activities to that value. It divides a business into
a set of activities within the business. It starts with inputs a firm receives and finishes with
firm’s products or services and after-sales service to customers. It allows for better
identification of a firm’s strengths and weaknesses since the business is viewed as a process.
Generally value chain analysis improves overall profitability by increase competitiveness,
reducing costs, and improving market share.
A supply chain is the portion of the value chain that focuses primarily on the physical
movement of goods and materials, and supporting flows of information and financial
transactions through the supply, production, and distribution processes. Many organizations
use the terms “value chain” and “supply chain” interchangeably. A value chain is broader in
scope than a supply chain, and encompasses all pre- and post- production services to create
and deliver the entire customer benefit package. A value chain views an organization from
the customer's perspective — the integration of goods and services to create value — while a
supply chain is more internally-focused on the creation of physical goods. The supply chain
focus is on understanding the impact of tightly coupling supply chain partners to integrate
information, physical material, product flow, and financial activities to increase sales, reduce
costs, increase cash flow, and provide the right product at the right time at the right price to
customers.
Figure 2 below depicts the framework of value chain in developing countries. The arrows
reflect a possible order of analysis of value chains: define constraints for the value chain
under study – study (redesign) opportunities for this value chain – define upgrading options,
taking into consideration value chain constraints (Trienekens, 2011). The main aim of a value
chain is to produce value added products or services for a market, by transforming resources
and by the use of infrastructures – within the opportunities and constraints of its institutional
environment. Therefore, constraints for value chain development are related to market access
(local, regional, international) and market orientation, available resources and physical
infrastructures and institutions (regulative, cognitive and normative) (Scott, 1995).
13
Source: Byerlee (2012) and Haggblade (2012).
Figure 2: Business models for inclusive agricultural value chains
The three components of value chain analysis are network structure, value added and
governance structure. A network structure has two dimensions: vertical and horizontal. The
vertical dimension reflects the flow of products and services from primary producer up to
end-consumer. The horizontal dimension reflects relationships between actors in the same
chain link (between farmers, between processors, etc).
Value added is created at different stages and by different actors throughout the value chain.
Value added may be related to quality, costs, delivery times, delivery flexibility,
innovativeness, etc. The size of value added is decided by the end-customer’s willingness to
pay. Opportunities for a company to add value depend on a number of factors, such as market
characteristics (size and diversity of markets) and technological capabilities of the actors.
Moreover, market information on product and process requirements is the key to being able
to produce the right value for the right market. In this respect finding value adding
opportunities is not only related to the relaxation of market access constraints in existing
14
markets but also to finding opportunities in new markets and in setting up new market
channels to address these markets. Value added capture can be divided into five major
categories (Kaplinsky, 2001): trade rents (forthcoming from production scarcities or trade
policies), technological rents (related to asymmetric command over technologies),
organizational rents (related to management skills), relational rents (related to inter-firm
networks, clusters and alliances), and branding rents (derived from brand name prominence).
2.3. Agro–value Chains and Pro-Poor Growth
In many parts of the world, agriculture continues to play a central role in economic
development and to be a key contributor to poverty reduction. However, agriculture alone
will not be sufficient to address the poverty and inequality that are so pervasive in today’s
world. It is becoming increasingly crucial for policy makers to focus immediate attention on
agro-industries. Such industries, established along efficient value chains, can increase
significantly the rate and scope of industrial growth. In developing countries, a significant
proportion of national funds are used to support agricultural production inputs – primarily
seeds, fertilizers and irrigation systems.
Traditionally, little attention has been paid to the value chains by which agricultural products
reach final consumers and to the intrinsic potential of such chains to generate value added
and employment opportunities. While high-income countries add nearly US$185 of value by
processing one tone of agricultural products, developing countries add approximately US$40
(United Nations Industrial Development Organization, 2009); (UNIDO, 2009).
Smallholders are the centerpiece of a “pro-poor” agricultural growth agenda. Empowered
through producer organizations and made more competitive by both institutional and technological innovations, small farmers can become greater market participants, both domestically
and globally. Globalization and telecommunications have created new opportunities for small
farmers to enhance their position in the international marketplace. Such opportunities,
although growing, are still only a fraction of the global market. Thus, the challenge facing
small farmers is how to gain greater access to markets, enhance their value chain position and
increase their value-added so as to boost incomes and reduce poverty (World Development
Report, 2008); (WDR, 2008).
15
The value-chain approach to pro-poor economic development has its roots in the
globalization of the development agenda. It was developed to encourage greater participation
by poor people in modern value chains. Enterprises in developing countries increasingly had
to compete with enterprises from all over the world in both local and international markets.
Yet traditional production systems proved inadequately equipped to compete in international
markets (Organization for Economic Cooperation and Development, 2007); (OECD 2007;
UNIDO 2009). The concern thus arose that poor people at the bottom of the value chain were
becoming excluded from the economic growth opportunities presented by more open
markets.
The agricultural value chain have tended to focus on some form of upgrading as a means of
increasing returns (and therefore incomes) of farmers, The means of increasing returns in
value chain include: (i). increasing marginal and technological efficiency of the relationship
between farmers and markets through increasing technological advances in input supplies,
improvements in postharvest handling, and the provision of price information to farmers
(ACDI/VOCA, 2009). (ii). Participation in commercial supply chains with food
manufacturers and retailers (the food-consuming industries). With the rise in consumption of
processed foods and large chain supermarkets, development agencies have focused on
helping farmers participate in what they perceive to be more organized, lucrative, and secure
markets (relative to wholesalers), (iii). Participation in export market are also perceived as
lucrative because they have a large number of consumers willing to pay for differentiated
foods with added value and large supermarkets keen to source them (Gereffi et al, 2005).
Value chains have thus been developed to enable farmers and microenterprises to participate
in these high-value markets, and (iv). Greater involvement in the process of value addition.
Developing countries have historically provided raw agricultural commodities to world
markets rather than becoming involved in value addition as processing. Moving into valueadding activities is thus another means of increasing returns (Hawkes and Ruel, 2011).
Agro-value chains encompass activities that take place at various levels (farm, rural and
urban), starting with input supply and continuing through product handling, processing,
distribution and recycling. As products move successively through the various stages,
transactions take place between multiple chain actors, money and information are exchanged
and value is progressively added (Silva and Filho, 2007).
16
The analysis of such value chains highlights the need for enterprise development,
enhancement of product quality and safety, quantitative measurement of value addition along
the chain, promotion of coordinated linkages among producers, processors and retailers, and
improvement of the competitive position of individual enterprises in the marketplace.
2.4.Approaches to the Study of Agricultural Marketing Problems
Increased production is not in itself a guarantee for increased welfare to producers. Goal
orientated marketing and more economical marketing strategies are also indispensable,
particularly over the longer term. The different circumstances involved in the demand and
supply of agricultural products, and the unique product characteristics, require a different
approach for agricultural marketing (Johan, 1988). There are many ways to analyze the
agricultural marketing; each approach has been developed theory which requires a particular
emphasis or focus. Examples include: Functional Approach, Commodity Approach,
Institutional Approach, and Structure-Conduct-Performance Paradigm.
2.4.1. Functional approach
The functional approach is studies of the basic functions of marketing such as buying, selling,
transporting, sorting, grading, financing, entrepreneurial risk taking, issuing marketing
information (SIFSIA, 2011). Marketing functions includes exchange functions (buying and
selling), physical functions (storage, transportation and processing) and facilitative functions,
(standardization, financing, risk bearing and market intelligence).
2.4.2. Institutional approach
The institutional approach concentrates on the independent institutions in marketing channel,
such as retailers and wholesalers, etc. It considers the nature and character of various
middlemen and related agencies and also the arrangement and organization of marketing
(SIFSIA, 2011). The value of the institutional approach stems mainly from a frequent need
to know why certain things happen, which in turn can be explained by looking at who
controls things. The institutional approach, therefore, prevents the neglect of personal
aspects. An institutional approach for the marketing of agricultural product should be
17
instrumental in solving the three basic marketing problems, namely consumers' demand for
agricultural products, the price system that reflects these demands back to producers and the
methods or practices used in exchanging title and getting the physical product from producers
to consumers in the form they require, at the time and place desired (Johan, 1988).
2.4.3. Commodity approach
The commodity approach is a basic approach in studying marketing which categorizes all
goods and services in to some kind of classification system and then suggests an effective
distribution system for each (SIFSIA, 2011). In the approach, a specific commodity or groups
of commodities are taken and the functions and institutions involved in the marketing process
are analyzed. This approach focuses on what is being done to the product after its transfer
from its original production place to the consumer (Kohls and Uhl, 1985).
2.4.4. Structure – Conduct - Performance
One important approach to the study of market performance is the Structure-ConductPerformance (SCP) framework. The SCP framework suggests that relationships exist
between structural characteristics of a market and the behavior of market participants and that
their behavior in turn influences the performance of the market (Scarborough and Kydd,
1992; Scott, 1995). Typical structure-conduct-performance (SCP) analysis tend to assess
market performance largely in terms of: (1) whether marketing margins charged by various
actors in the marketing system are consistent with costs, and (2) whether the degree of market
concentration is low enough (and the number of firms operating in a market is large enough)
to ensure competition, which is in turn assumed to drive down costs to their lowest level.
Structure of market refers to the relatively stable features that influence the rivalry among
the buyers and sellers operating in a market. Some examples of the elements of structure
include the number of buyers and sellers in the market, barriers to entry and exit, and the
vertical coordination mechanisms (Caves, 1992). For example, if the market structure is
characterized by high barriers to entry, it may result in only a few firms or traders profitably
maintaining business activities in, or even entering, certain markets. These few traders may
engage in non-competitive behavior such as collusion and exclusionary or predatory price
18
setting behavior. Such non - competitive behavior can result in higher profits and high
marketing margins for traders (Kizito, 2008).
The structural features of an industry determine the strength of competitive forces and its
profitability. The focus of industry structure is therefore on identifying the basic, underlying
characteristics of an industry rooted in its economics and technology that shape the arena in
which competitive strategy must be set. Firms in an industry will have their capacity in
dealing with the industry structure and perform well in the market. The position of a firm in a
market depends on the characteristics of the industry in which it operates. Market structure
includes number and size of firms (market concentration), degree of product differentiation,
and barrier to entry in the industry (Porter, 1980).
Scott (1995) suggest that, as a rule-of-thumb, a four largest enterprise concentration ratio of
50% or more is an indication of strongly oligopolistic industry, 33 -50% a weak oligopoly,
and less than that, an un concentrated industry. Oligopoly is a market structure in which there
are a few large firms and entry is difficult but not impossible. Oligopolies can produce
identical products or differentiated products. Oligopoly is different from other market
structures because firms are interdependent (i.e. any action taken by one firm usually
provokes a reaction by other firms).
Entry or the ease, with which an individual can join and leave business, is important to a
competitive market structure. This may refer to the process of setting a license or professional
qualification or skill or to the need of having a minimum amount of capital or other resources
in order to operate successfully. Lack of available capital could effectively restrict entry of
new firms if a large initial outlay is required (Staal, 1995).
Conduct of market refers to the patterns of behavior that market participants adopt to affect
or adjust to the markets in which they sell or buy goods and services. Examples of conduct
include price-setting behavior and buying and selling practices (Caves, 1992). For example,
in an environment where there are many buyers and sellers, the market tends to determine the
price. If one trader tries to increase his or her price, he or she sells nothing. In contrast, if
there are only a few sellers of food commodities in a market, these few traders can conspire
and charge consumers higher prices (Kizito, 2008).
19
Conduct focuses on how firms set prices (independently or in collusion), decide on
advertising and research budgets etc. Market conduct specifically included market sharing
and price setting policies, policies aimed at coercing rivals, and policies towards setting the
quality of products (Acharya and Agarwal, 1999). Market conduct in general deals with
marketing strategies that firms adopt particularity in their marketing mix decisions in order to
address the needs of the target market, influence market structure and competitors’ activities
thereby improve market performances. The market conducts can be reflected on a single firm
(a dominant firm) and among leading firms (collective agreement on creating conduct
variables).
Market performance refers to the extent to which markets result in outcomes that are
deemed good or preferred by society (Caves, 1992). It includes price levels and price stability
in the short and long term, profit levels, costs, efficiency, and quantities and quality of goods
sold or provided. For example, regular and predictable availability of basic food commodities
at affordable prices is generally considered a desirable outcome. Other desirable outcomes
would be that traders do not obtain excessive profits. In addition, prices paid by consumers
should not be excessively above the cost of marketing, processing and transaction costs for a
given commodity, and the prices received by farmers should cover their costs of production
(Kizito, 2008). Market performance consists of the achievements, outcomes, and answers
provided by the market. It reflects the economic achievements that flow from the industry as
each firm pursues its particular line of conduct. Market performance is the success of a
market in producing benefits for consumers (Carlton and Perloff, 1994).
There are many approaches to measure market performances of firms. One way of measuring
the performance the food marketing system is whether the firm is achieving its goals of
satisfying the requirements of the various participants such as consumers, food manufacturing
firms, farmers, and the society (Kohl and Uhl, 2002). Another approach to the market
performance assessment of firms is the profitability or price to cost relationship which
includes rate of return, price-cost margin (the difference between price and marginal cost),
Tobin’s q which is the ratio of the market value of a firm to its value based upon the assets
replacement cost (Carlton and Perloff, 1994).
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2.5.Marketing Costs and Margins
Marketing costs refers to those costs, which are incurred to perform various marketing
activities in the shipment of goods from producers to consumers. Marketing costs include:
handling cost (packing and unpacking, loading and unloading putting inshore and taken out
again), transport cost, product loss, storage costs, processing cost, capital cost (interest on
loan), market fees, commission and unofficial payments (Heltberg and Tarp, 2001).
Margins represent the price charged for one or a collection of marketing services. For
example, the difference between producer and consumer prices is the amount charged for all
the marketing services rendered between production and consumption, including buying,
bulking, transports, storage, processing, etc. Marketing margin is defined as the difference
between the price paid by consumers and that obtained by producers. It is also called the
‘Farm‐Retail Price Spread”. Margins can be calculated all along the market chain and each
margin reflects the value added at that level of the market chain (Bonard and Sheahan, 2009).
Theoretically, the analysis of marketing costs and margins would reveal how efficient pricing
in domestic markets is, and gives an indication of the importance of transaction costs facing
traders, farmers and intermediaries (middlemen) and help in identifying and solving
bottleneck thus assist in reducing marketing costs. Understanding the concept of market costs
and margins requires a priori understanding of the marketing chains or channels under
question and a prescription of how long is it. In practice, the flow of agricultural commodities
usually starts at the farm, (sometimes passed through a storage phase) and/or goes directly to
rural (tertiary) markets where stored or passed to secondary markets where intermediaries
(middlemen) and wholesalers start purchase sizable stocks and convey it to primary or main
markets and/or to storage. From this final marketplaces the goods usually sold for
retailers/wholesalers and big companies for export (SIFSIA, 2011).
The profit range accruable to the market participants gives an indication of market
performance (Achoga and Nwagbo, 2004). Marketing margin has remained an important tool
in analyzing the performance of marketing systems. Marketing costs and profit margins
which make up marketing margins can be both indicators of efficiency or inefficiency of
marketing systems. The benefits that accrue to the individual participants may be incentives
or disincentives to continue in the business. Proper computation, understanding and
21
interpretation of marketing margin value in relation to prevailing circumstances can reveal a
lot about performance in the marketing channels.
2.6.Maize and Faba Bean Production, Consumption and Marketing in Ethiopia
2.6.1. Maize and faba bean production in Ethiopia
Among crops, maize accounts for largest share in total production and constitute largest
number of farm households that take part in production. In 2010/11, maize production was
49.86 million quintals, which constitute 28% of total cereals production. The number of
households involved in maize production was about eight millions or 27% of total farm
households participated in cereal production. Among cereals, rice, maize and sorghum were
the three most productive crops, which yield 30.27, 25.4 and 20.87 quintals per hectare
respectively. The major maize producing regions in Ethiopia were Oromia (58%), Amhara
(24%) and SNNP (11%). They constituted for 93 percent of total maize production in the
country (CSA, 2011b).
Table 1: Area, production and yield of maize for different regions in 2010/11
Region
No of HHs producing
Area
Production
Yield
maize (millions)
(thousand ha)
(million Qt)
(Qt/ha)
Tigray
0.598
62.86
1.50
23.84
Amhara
2.444
472.26
12.15
25.72
Oromia
3.470
1,109.28
28.81
25.97
Somali
0.078
28.09
0.49
17.54
Benshangul Gumuz
0.178
43.86
1.17
26.75
SNNP
1.140
237.35
5.57
23.45
Gambela
0.030
6.05
0.10
16.93
Harari
0.011
0.81
0.02
20.04
Dire Dawa
0.008
0.32
0.01
15.77
1,591
392,176
9,964
25.41
Average
Source: Adapted from CSA agricultural sample survey, 2011
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Among pulses, faba bean accounts for largest share in total production and constituted largest
number of farm households that take part in production. In 2010/11, faba bean production
was 6.98 million quintals that constitute 36% of total pulse production. The second and third
largest crops in pulse production were haricot beans and chickpeas, which accounted for 3.4
and 3.23 million quintals respectively. The number of households involved in faba bean
production was about 3.45 million or 35% of total farm households participated in pulse
production. Haricot bean and field pea production were the second and third largest number
of farm households involved which constitute about 2.25 and 1.39 million households
respectively. Among pulses, faba bean, chickpeas and grass pea were the three most
productive pulses. They have almost equal yield, which was about 15 quintal per hectare. The
major faba beans producing regions in Ethiopia are Oromia (47%), Amhara (36%) and SNNP
(15%), which constituted 98 percent of total faba bean production in the country (CSA,
2011b).
Table 2: Area, production and yield of faba bean in different regions in 2010/11
Region
No of HHs
producing faba
bean(in millions)
0.193
Area
(thousand ha)
Production
(million Qt)
Yield
(Qt/ha)
12.75
0.176
13.78
Amhara
1.268
175.95
2.503
14.22
Oromia
1.199
199.06
3.245
16.30
Benshangul Gumuz
0.009
0.79
0.014
17.82
SNNP
0.782
70.62
1.041
14.73
0.690
91.834
1.3396
15.20
Tigray
Average
Source: Adapted from CSA agricultural sample survey, 2011
2.6.2. Maize and faba bean consumption in Ethiopia
In Ethiopia, most cereals produced were used for household consumption. According to
2011/12 CSA agricultural sample survey, 67 percent of total cereals produced were used for
household consumption, 14 percent for seed and 15 percent for sale. The remaining 4 per cent
was used for other purposes like in kind payment, animal feed, etc. Regarding maize, farm
households consumed 75.5 % of their produced and only 10.5 % the product used for sale.
23
The remaining 14 % used for other purposes. The share of maize was accounted for 36% of
total cereal consumption which followed by sorghum (23%) and teff (15%).
Pulses contribute to smallholder income, as a higher value crop than cereals, and to diet, as a
cost effective source of protein that account for approximately 15% of protein intake.
Moreover, pulse offer benefit through nitrogen fixation, which improves yields of cereals
through crop rotation and can also result in savings for smallholders farmers from less
fertilizer use (IFPRI, 2010b). Agricultural sample survey in 2011/12 showed that from 23.16
million quintals pulse production, 59.71 % used for household consumption, 15.94 % for
seed, 21.52 % for sale, 0.81 % in kind payment and the rest for other purposes. On other
hand, from 7.15 million quintals pulse production, 63.53 % used for household consumption,
16.62% for seed, 16.93% for sale, 0.82% for in kind payment and the rest for other purposes.
The share of faba bean in total pulse consumption accounted for 33.65 percent, which
followed by haricot bean (19.46 %) and chickpea (16.89%). Household field pea
consumption was accounted for 57.67 % of total production, 20.38% used for sale and
19.15% for seed. The share of the commodity constituted 11.25% in total pulse consumption
(CSA, 2012).
Table 3: Crop production and percent of utilization in 2011/12
production
crop
(million Qt)
Households
seed
Percent utilized
sale
in kind
animal
payment
feed
consumption
Maize
Faba bean
Others
60.694
75.54
9.59
10.5
0.84
0.94
2.58
7.148
63.53
16.62
16.93
0.83
0.04
2.05
Source: Adapted from CSA 2011/12 agricultural sample survey volume vii
2.6.3. Maize and pulse marketing in Ethiopia
In Ethiopia, many types of traders and processors of various size and scale operate in cereal
markets. These traders and processors can be grouped according to the four major market
functions they perform: aggregation, wholesaling, processing, and retailing. The bottom end
of the marketing chain is dominated by smallholder farmers and various buyers (petty traders,
farmer-cum-traders and more recently, primary cooperatives) that aggregate the small
24
volumes typically sold by individual farmers. At the second level of the chain are the
wholesalers, including the EGTE, who mainly perform the tasks of temporal and spatial
arbitrage. Wholesalers are also the main suppliers of raw materials to flour millers and other
processors. The final stage of the marketing chain is retailing to the consumers. Brokers
(traders who arrange cereal trades but do not buy or sell grain themselves) also play a key
role in the coordination of grain buying, selling, and transporting by matching buyers and
sellers, inspecting and witnessing transactions, and providing guarantees to enforce contracts
(Rashid and Negassa, 2011).
Maize producers have different market outlets, including rural assemblers and wholesalers.
Rural assemblers collected maize surpluses from the smallholders and their major sales
outlets are wholesalers in the surplus areas, EGTE and private companies, and wholesalers in
urban markets. Wholesalers collected surplus from smallholders, rural assemblers and
state/commercial farms. There are five types of wholesalers: wholesalers in surplus areas,
wholesalers in major terminal markets, wholesalers in deficit areas, private companies that
carry out diversified business activities, and the EGTE. They mostly receive grain from
farmers and rural assemblers at their stores (RATES, 2003). As a number of intermediaries in
the maize marketing system, the market is highly unreliable and the price differential is
enormous between rural markets and the final terminal urban markets. Intermediaries in
maize marketing include rural assemblers, producers’ agents, private wholesalers, rural
retailers, brokers, urban wholesalers, urban retailers and EGTE (ECX, 2009).
Pulse marketing is a complex process involving handling from multiple intermediaries. For
example, the intermediaries who buy broad bean from farmers are retailers, wholesalers,
assemblers, and processers or in some cases farmers who themselves are grain traders
(Gezahegn and Dawit, 2007), There are three separate levels of pulse marketing. These are:
Primary markets(buyers who buy directly from the producers) include rural retailers, rural
assemblers, brokers, and primary cooperatives, Secondary markets (buyers who purchase
products primarily from originators) include woreda retailers, woreda wholesalers, and
farmers unions and Tertiary markets include urban wholesalers, urban retailers, processors,
supermarkets, and grain exporters, and are located in cities (IFPRI, 2010b).
25
Trade takes place as a “cash-and-carry” transaction. Buyers and sellers meet personally,
negotiate price, inspect the grain on the spot and complete transaction with cash payment to
the seller/farmer. As there are no reliable market information and organized exchange
systems, buyers and sellers have to bargain and negotiate to arrive at mutually agreed prices
(Demeke, 2012). Cereal prices in 2011 have significantly increased in most markets and then
declined as harvesting operations of meher crops were progressing in main producing areas.
In September 2011, at the peak of lean season, maize was traded at the price of about ETB
630 per quintal registering an increase of about 120- 140 percent since the beginning of the
year. Prices of pulses increased significantly during 2011. For example, prices of horse beans
reached average price of ETB 1,300-1,400 per quintals in most markets, doubling the prices
that were prevailing at the beginning of the year. A similar situation was for prices of field
peas, while less pronounced increases (FAO, 2012).
In Ethiopia, most cereals are non-tradable – meaning they are neither exportable nor
importable. As a result, with the exception of food aid import, all major cereals are
domestically grown and consumed. Cereals are non-tradable due to high costs of transporting
cereals both from the main port in Djibouti to primary consumption areas and from the main
production areas to the port. Thus, the cost of transport is so high that it is not profitable to
import or export cereals. One way to further examine the tradability of a commodity is
through export and import parity prices, which represent prices at which a commodity will be
exportable or importable (Rashid et al., 2010). Pulses are the third most important crop as
measured by export earnings in the country. The most important export pulses include haricot
beans, chickpeas, faba beans, lentils and field peas. A large number of small-scale exporters
that operate in lower quality markets dominate the pulse export sector (IFPRI, 2010b).
2.7. Empirical Evidence on Value Chain Analysis of Maize and Faba bean
USAID (2010) study on staple foods value chain analysis in Ethiopia showed that marketed
quantity of maize from the smallholders represents about 18% of national production, but it
varies from area to area depending on their production potential. The actors in value chain
were input suppliers, smallholders’ producer, state/commercial farmers, rural assemblers,
cooperative unions, grain wholesalers, food processors, grain retailers, donors/NGOs,
consumers and Food Aid recipients. The market channel: producer → assembler→
26
wholesaler → retailer → consumer channel was the most important in terms of the magnitude
of the marketed maize that flows from producers to consumers. The total valued added along
this channel is estimated at US$ 186.7/metric ton, of which US$ 136.28 went to maize
producers, US$ 10.42/metric ton was that of assemblers, US$ 15.42 to wholesalers, and US$
24.58/metric ton went to retailers. The study also showed that from the total annual
production of various pulse crops by smallholders, about 33% or about 440,000 metric tons
was supplied to the market, while most of the crop is retained on-farm for various uses. The
actors in pulse value chain were input suppliers, smallholders’ producer, state/commercial
farmers, rural assemblers, cooperative unions, grain wholesalers, exporters, grain retailers
and consumers. The case of chick pea is used to demonstrate the costs incurred, the revenue
obtained and the value added by the principal actors in the producer→ assembler→
wholesaler → exporter channel. The total value added by producers, wholesalers and
exporters was about US$ 228.02/metric ton, of which 42% was by producers, 15% was by
wholesalers and 43% by exporters
IFPRI (2010a) study on maize value chain potential in Ethiopia showed that the maize value
chain involves multiple actors, including input suppliers, producers, traders (local assemblers
and wholesalers), retailers and processors, and consumers. The value chain network
functioning around smallholder farmers comprises linkage among input suppliers (private),
farmers, cooperatives, extension service providers, credit service providers, and traders.
Where cooperatives are well developed and organized, they tend to provide input supply and
product marketing services to smallholders. A typical local trader / assembler transacts about
one ton of maize (worth about USD 300) four times a month during the peak periods, which
goes up to three to five tons in the case of traders in the surplus areas, and to 10 tons a week
for the wholesalers in Addis Ababa. In terms of storage capacity and financial ability to store,
only traders in the large terminal markets can store maize for one to three months. Maize
farmers in the country face a series of challenges that limit their overall production and
income. According to the study, the key challenges can be broadly categorized into three
groups: (i) lower yields due to limited use of modern inputs; (ii) majority of sales
immediately after harvest; and (iii) high post-harvest losses (both on- and off-farm). Maize is
critical to strengthening the value chain as it provides farmers with reliable incentives to
boost productivity. Export markets, processing industries (poultry and animal feed and bio-
27
fuel production), domestic household consumption and procurement for food aid could
provide ample end-market opportunities for maize.
IFPRI (2010b) study on pulse value chain potential in Ethiopia showed that interaction
between the actors in markets was complex, which not only exemplified by the multitude of
players, but also by the many paths that pulses can take to reach end consumers. From
primary markets, produce may or may not pass through secondary and/or tertiary market
actors before reaching consumers. For example, actors involved in chickpea marketing chain
were smallholder farmers, rural retailer, rural assembler, broker, cooperative/union, wereda
wholesaler and retailer, urban wholesaler and retailer, processor, supermarkets, grain
exporter, urban and rural consumers. The pulse value chain in Ethiopia is far from efficient
and fraught with several challenges. The key challenges at value chain stages were low on
farm productivity, inefficient marketing system and inconsistent export supply. At the farm
level, productivity appears to be severely constrained by three major factors (i) limited or no
use of chemical fertilizers for pulses (e.g., phosphates); (ii) very limited availability of
improved seeds (most pulses are grown from unimproved cultivars with low genetic
potential); and (iii) the use of conventional agronomic practices (e.g., sub-optimal crop
rotations, poor seed bed preparation). However, there is significant potential for further
productivity gains. Traders typically operate in small geographic areas and trade relationships
are based heavily on social capital. The relatively large number of actors working in a highly
fragmented manner, coupled with poor road infrastructure and the inability for large-scale
international traders to track products, implies high transaction costs for aggregators and
traders.
Nedelcovych and David (2012) study on private sector perspectives for strengthening
agribusiness and value chains in Africa showed that actors involved in maize value chain in
Ethiopia were farmers (such as subsistence/smallholders – characterized by small land
ownership (less than 2ha) and low utilization of yield enhancing technologies, large farmers –
characterized by relatively more land holding (2 to 5 ha,), hire temporary labour, use
manually operated machinery and make more use of improved technology; and large scale
commercial farmers – operate at large scale i.e more than 50 ha of land, mechanized in all
production activities, have better storage facilities, use of intensive improved inputs and hired
laborers (IFPRI, 2010a)), aggregators and traders, EGTE, ECX, cooperatives, institutional
28
buyers (World Food Program) wholesalers, retailers, processors, exporters, rural and urban
consumers. Institutional buyers (such as WFP) purchased through recently established
Ethiopian commodity exchange (ECX). The government to lead price stabilization
established Ethiopian Grain Trade Enterprise (EGTE). Maize production is usually smallscale and of subsistence, nature and markets are typically local and are not well integrated
across the country. The fragmentation of farms in Ethiopia and the limited use of modern
inputs mean that relatively small volumes of grain are produced and even smaller amounts
are marketed. Trade of maize has only a few large buyers and limited maize processing
activities for food (i.e. underdeveloped maize agro-processing sector). Most-trading activity
is conducted within three to four months after harvest and is not consistently available in
market.
RATES (2003) study on maize market assessment and baseline study for Ethiopia showed
that marketed volume of maize passes successively through a number of channels before it
reaches the final consumer. Maize producers have different market outlets, including rural
assemblers (40%) and wholesalers (35%).The results of marketing margins analysis at the
different transaction levels depicted that the total gross marketing margin is birr 51.03/100 kg
or 59.1%. Out of the total gross marketing margin, the retailer in Addis Ababa has the highest
share-about 39.6% followed by the wholesaler in Addis Ababa (35.3%). The market
participants at the collection and assembly level get 25.1%, of which only 3.9% goes to the
rural assembler. The share of the producer of the amount spent by the consumer is about 40.9
percent.
Muhammed (2011) study on market chain analysis of tef and wheat indicated that producers’
share of tef and wheat prices to end user were 78.7% and 74.2% respectively. In this
marketing margin analysis, urban assemblers received smallest share of gross marketing
margin. The result also dipicted that sex of household head, quantity of tef produced, access
to market information and access to extension services significantly and positively influenced
marketable surplus of the tef while quantity of wheat produced and credit access influenced
marketed surplus of wheat significantly and positively on determinants of marketable surplus.
29
Kumar et al. (2012) study to understand the delivery system of maize seed in a value chain
perspective show that the major actors in the maize seed value chains are seed companies,
input suppliers (including manufacturers, wholesalers and retailers); producers; and
institutional setup of state and central governments. The study in private maize seed value
chain sector reveals that the procurement cost of the hybrid seed to the company was
estimated to be around 0.29 USD per kilogram. The company does in-house processing &
packaging, storage and marketing of the seed which cost around 0.26 USD per kilogram, 0.16
USD per kilogram and 0.21 USD per kilogram, respectively. Transportation cost was
estimated to be 0.12 USD per kilogram. In the process of sale of seeds, the distributor gets a
margin of 10 - 15 per cent, while the dealers get 20 - 30 per cent. In the whole process,
company has the margin of around 1.13 USD per kilogram. Finally, farmers purchase the
hybrid seed at 3.40 USD per kilogram from retailers.
Kirimi et al. (2011) study on value chain analysis of Kenya’s maize marketing system
indicated that there are seven major categories of actors in the maize value chain: farmers,
primary assemblers, wholesalers, the National Cereals and Produce Board (NCPB), millers,
large-scale millers, and retailers. The numbers of players at each node has increased
substantially, particularly at the assembly stage. Each group of players is diverse and
interactions /transactions among players at different chain nodes lead to a complex system
with many marketing channels. The results showed that farmers are receiving a higher
proportion of the final consumer price of maize meal over time. We also find that consumers
are benefiting from lower retail maize meal prices. A reduction in margins can occur for two
reasons: (1) reductions in the cost of doing business and (2) increased competition among
intermediaries. Marketing margins should reflect the cost of moving a good from surplus to
deficit areas as well as the costs of storage and processing from one stage to the next in the
value chain. When a reduction in margins is observed, this could naturally follow from a
reduction in the cost of transportation or transformation.
Minten et al. (2013) study on tef value chains in Ethiopia showed that the share of the
producer in the final retail prices increased from a level of between 74 and 78 percent in 2001
to between 76 and 86 percent in 2011. The shares of urban–rural marketing, urban
distribution, and milling in final retail prices have declined significantly during this period.
The results also showed that 85% of teff was supplied directly from farmers to rural grain
30
traders and urban retailers obtained 32% of supply directly from farmers, which make market
channels shorter. Mill owners and cereal shops obtained 77% of tef supply from rural grain
traders/brokers.
Rehima (2006) study on analysis of red pepper marketing used Tobit model to analyze factors
affecting market supply. Dependent variable has a censored value as 17.6% of households did
not supply pepper even if 250 sample households produced the commodity. The model
parameters are estimated by examining the Tobit likelihood function to decompose the effects
of explanatory variables into quantity supply and intensity effects. Thus, a change in
explanatory variables has two effects. These are the marginal effect of an explanatory
variable on the expected value of the dependent value and the change in intensity of quantity
supplied with respect to a change in an explanatory variable among sellers.
Taddese (2011) study on value chain analysis of vegetables used Tobit model to analyze
amount of vegetables supplied to market. Dependent variable was taken on positive and zero.
The model parameters were estimated by maximizing the Tobit likelihood function. Marginal
effect of explanatory variables on the expected value of amount of vegetables supplied to
market among the whole sample and the change in volume of vegetables with respect to
change in explanatory variables among participating households.
31
3. RESEARCH METHODOLOGY
In this chapter, description of the study areas, data types and sources, sample size and
sampling techniques, methods of data collection, methods of data analysis and hypothesis of
explanatory variables are presented.
3.1. Description of the Study Area
The study was conducted in two districts, namely, Bako Tibe and Gobu Sayo. Bako Tibe is
found in West Shoa zone where as Gobu Sayo is found in East Wollega zone of Oromia
region in Central West of Ethiopia.
Bako Tibe district is located 250 km west of Addis Ababa following the tarmac road that
passes through Ambo to Nekemte (main town of East Wollega zone). It is located at 150 km
west of its zonal town Ambo. The district has 26 kebeles (lower level structure of government
administration). It is characterized by topography ranging from 1600 to 2870 meters above
sea level and its annual rainfall varies between 800-1200 mm per year. It has temperature
ranging from 110c to 240c. The agro-ecological zones of the area are 88% mid-altitudes,
which range between 1500 to 2300 meters above sea level and 12% highland, which is
greater than 2300 meters above sea level (Hurni, 1998; BTDAO, 2011). The population of
the district is about 139,051 of which 49.6% are male and 50.4% are female. It has a total
area about 638 km2 and the population density of about 217 per square kilometer (CSA,
2011a).
Gobu Seyo district is located at about 265 km west of Addis Ababa following the tarmac
road that passes through Ambo to Nekemte. The town of the district is called Ano. The
district is contiguous with Bako Tibe district in the east. It is located at 65 km east of the
zonal town Nekemte. It has nine kebeles and characterized by topography ranging from 1200
to 1960 meters above sea levels. Its annual rainfall reach to 2000 mm per year and has
temperature ranging from 150c to 20 0c. The agro-ecological zones of the area are about 20%
is lowland, which is less than 1500 meters above sea level, and 80% is mid-altitudes (Hurni,
1998; GSDAO, 2011). The population of the district is about 46,166 of which 49.5% are
32
male and 50.5% are female. The district has a total area of 344-km2 and population density of
134 persons per square kilometer (CSA, 2011a).
Source: own sketch from CSA 2005 data using Global Information System (GIS), 2013.
Figure 3: Geographical location of the study areas
The livelihood of the people in the two districts is pre-dominantly dependant on mixed
farming. Cereal (maize, tef, wheat, sorghum, barley and finger millet), legume (horse
bean/faba bean, haricot bean, and soybean) and oil crops (noug, linseed, sesame and
groundnut) are grown in the areas. Among the cereals, maize is the dominant crop produced
in the areas. Cattle, sheep, goat, horse, mule, donkey and poultry are the common livestock
rearing in the two districts.
33
3.2. Types and Sources of Data
The study used both primary and secondary data. Primary data was collected from
households, traders and input suppliers. Household data was collected by International Maize
and Wheat Improvement Center (CIMMYT) in collaboration with Ethiopian Institute of
Agricultural Research (EIAR) from the two districts in 2013. Grain traders and input
suppliers data were collected by the researcher in collaboration with CIMMYT from the two
districts and additional grain traders from Nekemte, Ambo and Dendi districts. Input
suppliers data was collected from union, cooperatives and private seed agent. Data of grain
traders was collected from assemblers, cooperatives, wholesalers and retailers. As there were
no processors and exporters involved in maize and faba bean value chains, no data was
collected from these actors. Secondary data was collected from CIMMYT, EIAR, CSA, Bako
Tibe and Gobu Seyo Agricultural offices, farmers’ cooperatives development offices, trade
and market development offices in respective districts and kebeles in the study areas. This
study mainly used a quantitative approach.
3.3. Sample Size and Sampling Technique
This study was funded by CIMMYT (SIMLESA project). As Bako Tibe and Gobu Seyo were
the two SIMLESA project implementation districts in central western Ethiopia, the two
districts were selected purposively based on the aim of the project. Based on the objectives of
the project, it was implemented on ten kebeles in Bako Tibe and three kebeles in Gobu Seyo
districts. Therefore, this study was based on pre-selected 13 kebeles from the two districts.
CIMMYT used probability proportional to the size (PPS) sampling technique to select sample
households from each kebele. This sampling technique is most important when the sampling
unit (in this case household in each kebele) vary considerably in size. Based on this, a
proportion of 2.2% sample size was selected randomly from list of Kebele’s registration book
using sampling interval (dividing total population by sample size in that kebele) and random
start (randomly chosen number between 1 and sampling interval). Accordingly, 199 sample
households were selected from the two districts: 149 from Bako Tibe and 49 from Gobu Seyo
districts. The distribution of sample kebeles and households are described in Table 4 below.
34
Table 4: Distribution of sample kebeles and households across districts
Districts
Sample kebeles
Number of
Sampled households
households
Amerti-Gibe
765
17
Cheka-Dimtu
753
17
Dembi-Gobu
803
18
Guto-Meti
946
21
Oda-Haro
637
14
Oda-Korma
803
18
Seden-Yuko
305
7
Seden-Kite
594
13
Terkanfeta-Gibe
486
11
Tulu-Sengota
614
14
6706
149
Angobo-Bekenisa
895
20
Ganbela_Tare
308
7
Ubula Kejema
1023
23
Sub total
2226
50
Grand total
8932
199
Bako Tibe
Sub total
Gobu Seyo
Input suppliers in the study areas were dominantly farmers’ cooperatives and union. In Bako
Tibe district three cooperatives that supplied agricultural inputs for selected kebeles while in
Gobu Seyo district two cooperatives and one private maize seed agent supplied the inputs for
selected kebeles. Therefore, whole input suppliers that supplied inputs for selected kebeles
were selected for value chain analysis. The cooperatives are located in selected Kebeles and
dominantly supplied agricultural inputs such as seeds, fertilizers and plant protection
chemicals.
Grain traders were selected from both surplus and deficit production areas. Maize traders
from the surplus production areas were selected using proportional to the size (PPS) sampling
technique to select sample traders from assemblers, wholesalers and retailers. Those input
supplied cooperatives also participated in grain maize trade in the study areas. Accordingly,
35
31 grain traders randomly selected and the whole cooperatives participated in input supply
were also selected from Bako Tibe and Gobu Seyo districts. Grain traders in deficit
production areas were selected based on the flow of the commodities from the surplus
production areas. They were selected only if they purchased maize and faba bean from Bako
Tibe and Gobu Seyo districts. Most traders were specific to maize trade and others trade all
grains. Therefore, some traders that randomly selected were traders of both cereals and
pulses. As the number of pulse traders was very few, all those found in study areas were
taken for the study. Accordingly, 13 grain traders from Ambo, 8 grain traders from Dendi and
2 grain traders from Nekemte were selected. Depending on this, a total of 52 maize and 9
pulse traders were selected from Bako Tibe, Gobu Seyo, Ambo, Dendi and Nekemte. There
were no maize and faba bean processors and/or exporters identified in the value chains.
3.4. Methods of Data Collection
The data collected from value chain actors were cross sectional. The data collected from all
actors in the value chains using semi structured questionnaire in which the questions asked
were decided in advance. The list of questions made up of open ended and close ended.
Separate questionnaires were prepared for households, traders and input suppliers. The
survey instruments were designed to explore the market performance in the value chains. The
data was collected by interviewing households, traders, cooperative and union using the
questionnaires. The questionnaires were pre-tested and corresponding adjustments was made.
The core reasons for pre-testing the pre-field questionnaire are to decide whether to exclude
or modify (rephrase) some of the questions. The study considered the type and role of
different market actors, volume and flow pattern of the maize and faba bean and value change
along the value chains.
3.5. Methods of Data Analysis
3.5.1. Descriptive analysis
In this section, descriptive analysis of demographic and socio-economic data, mapping of
maize and faba-bean value chains, market channels and performance were undertaken. The
descriptive statistics tools such as mean, standard deviation, variance analysis, and percentage
36
were used to describe and to test characteristics of actors in the value chains. Statistical tests
such as t-test for continuous variables and chi square for discrete variables were used to
compute and test the mean difference between selected characteristics in the two districts.
Mapping value chains facilitates a clear understanding of the sequence of activities and the
key actors and relationships involved in the value chain. In mapping maize and faba bean, the
main actors in the value chains, the volume and the flow of commodities were expressed in
diagram. Activities of main actors, their relationships and linkages between actors, and value
of products at different level of value chain were discussed. Costs and/or value added at
different stages and incentives of actors in the value chains were computed to analyze cost
structures, value added and profits of actors in the value chains.
Market performance refers to the extent to which markets result in outcomes that are
considered good or preferred by society. Marketing margin is one of the approaches to
measure the market performance. Market margin is the difference between the price paid by
consumers and received by producers. Margins can be calculated all along the market chain
and each margin reflects the value added at that level of the market chain.
Total Gross Marketing Margin (TGMM) is the final price of the produce paid by the end
consumers minus farmers’ price divided by consumers’ price and expressed as the percentage
(Mendoza, 1995).
TGMM =
X 100
(1)
where, TGMM is total gross marketing margin
Pc is the consumer (or final) price
Pp is producer price
It is useful to introduce the idea of ‘farmer’s portion’, or ‘Producer’s Gross Margin’ (GMMp)
which is the share of the price paid by the consumer that goes to the producer. The producer’s
margin is calculated as:
GM =
X 100
Where, GMp is the producer's share in consumer price
37
(2)
The Net Marketing Margin (NMM) is the percentage of the final price earned by the
intermediaries as their net income after their marketing costs are deducted. The percentage of
net income that can be classified as profit (i.e. return on capital) depends on the extension to
such factors as the intermediaries’ own (working capital) costs. It is possible to see the
allocate efficiency of markets, contribution of chain segments to total value, profitability of
chain operator and total net value added. Higher NMM or profit of marketing intermediaries
reflects reduced downward and unfair income distribution, which depressed market
participants of smallholders.
An efficient marketing system is where the marketing cost are expected to be closer to
transfer costs and the net margin is near to normal or reasonable profit.
NMM =
X 100
(3)
where, NMM is net marketing margin
MC is marketing cost
−! "
# # $%"
=
GMMi =
&'(()*+ ,-).' /0 ) –,2-.34&)*+ ,-).' /0 )
5'64)()*+ /- ./*&27'- ,-).'
* 100
NMMi = GMMi - TMC
3.5.2. Econometrics Model
In this section, hypothesized explanatory variables and econometrics model used for analysis
are discussed. As the sample size of households that produced faba bean was small, the effect
of explanatory variables on marketed surplus of the commodity was not analyzed using
econometrics model. The effect of explanatory variables on marketed surplus of maize was
hypothesized and analyzed using Tobit model.
The Tobit model is a statistical model proposed by Tobin (1985) to describe the relationship
between a non-negative dependent variable (yi) and independent variables (Xi). The model
can be described in terms of latent variable y*. The model also called a censored regression
model, because some observation on yi* (yi* ≤ 0) are censored. In other words, the latent
38
variable y* is observed only if yi*>0. The model has been used in a large number of
applications where the dependent variable is observed to be zero for some individuals in the
sample. This model is for metric dependent variable and when it is “limited” above or below
some cut off level. This suggests that the model proposed by Tobin is appropriate for
analyzing happening or non-happening events.
The model enables one to estimate the likelihood and extents (intensity) of events. The
intensity of marketed surplus was estimated by the following Tobit model (Tobin, 1985;
Cameron and Trivedi, 2009; Greene, 2012).
8 ∗ = :; + =)
8) = >
:; + =)
0
(4)
? 8∗ > 0 B
? 8∗ ≤ 0
(5)
Where, yi is the marketed surplus of maize by households expressed as natural logarithm of
kilogram.
x is a vector of explanatory variables determining intensity of marketed surplus of
maize;
β is a vector of parameters to be estimated, and
=) is the error term assumed to be independently and normally distributed.
Maddala (1997) proposed the following techniques to decompose the effects of explanatory
variables into quantity supply and intensity effects. Thus, a change in explanatory variable
has the two effects. In this study, the marginal effect of explanatory variables on the expected
value of the maize marketed surplus (equation 6) and the change in intensity of marketed
surplus with respect to a change in an explanatory variable (equation 7) among sellers were
used to estimate marketed surplus of maize by smallholders in the study areas..
CD(FG )
C(IG )
= !(J)K)
(6)
Where, F(Z) is the value of the derivative of the normal curve at a given point
Z is the Z score for the area under normal curve, J =
σ is the standard error
39
LG IG
M
F
CDN GO ∗ PQ R
FG
CIG
= ;) S1 − J
0(T)
U(T)
−(
0(T) V
U(T)
) W
(7)
Where, F(Z) is the cumulative Normal distribution of Z
βi is a vector of Tobit maximum likelihood estimates.
3.6. Variables Definition and Measurement
Dependent variable of the model refers to marketed surplus of maize by farm households
which expressed as natural logarithm of transformed kilogram.
Explanatory Variables
Family size man equivalent: Rural households are dependent on their own production for
family food consumption. It is expected that the more the household members, the larger the
amount of consumption in the family. This in turn reduced the amount of marketed surplus.
Daniel (2006) showed that households with larger family size consumed more of what was
produced and small amount was left for market through the cooperatives. Therefore, family
size was hypothesized negatively affect marketed surplus.
Land allocated (ha): It refers to the current maize farm plots allocated for cultivation. It is
continuous variable, which is measured in hectare. If households allocated more land for
maize cultivation, then they produced and supplied more to market. Therefore, land size was
directly related to production, which in turn to marketed surplus. Tesfaye (2011) and Bosona
et al (2009) showed that an increase in land allocation also increased marketed surplus.
Livestock holding (TLU): It is a continuous variable measured in tropical livestock unit. It is
assumed that households with larger TLU have better economic strength and financial
position to purchase sufficient amount of inputs (Kinde, 2007). In a mixed crop-livestock
system, more livestock holding usually goes with more crop production due to the availability
of draft power for crop production and the use of crop residue for livestock production (Moti
and Brihanu, 2012). Therefore, it was hypothesized that livestock holding positively
influenced marketed surplus.
40
Contact with extension agents (log): This is a continuous variable, which was measured in
total number of contacts with extension agents during current year which transformed into
natural logarithm and expressed as percentage. The more the number of contacts, the higher
the information and knowledge acquired. Therefore, it was hypothesized that contact with
extension agents positively affect marketed surplus of the commodity. Rehima (2006) study
showed that contact with extension agents positively influence the quantity supplied to
market.
District dummy: This is dummy variable, takes a value “1” if the district is Bako Tibe and
“0” if the district is Gobu Seyo. The districts consist of a number of characteristics such as
production, credit access, land holding and land allocation, extension contact, etc. It was
expected that there is difference between the two districts in marketed surplus. Therefore, it
was hypothesized that either Bako Tibe or Gobu Seyo district supplied more to market.
Abreham (2013) on his study showed that difference between districts have significant effect
on volume of marketed surplus.
Distance from main market (hr): It refers to the time taken between farm gates to the main
market. It was measured in hour. The distances from the main market influence households in
buying inputs and selling outputs. The closer the market place to farm gate, the lesser would
be the transportation costs, transaction costs, time, etc. Moreover, they have better market
information and access to inputs. Therefore, distance to main market negatively affect
marketed surplus of the commodity. Abreham (2013) showed that distance to the nearest
market affected volume of sales negatively.
Credit used (log): This is a continuous variable, which was measured in total credit received
in ETB which transformed into natural logarithm and expressed as percentage. Households,
who received credit for crop production (to purchase improved seed and fertilizers), assumed
to produce more than those who did not get credit. The more the amount of credit received,
the higher the production and this in turn increased marketed surplus. Hence, it was expected
that credit used had positive effect on maize marketed surplus. Muhammed (2011) study
result showed that farmers who have access to credit had supplied more wheat to market than
those who had no access.
41
Current price (log): Producers are sensitive to market price. Market prices in the study areas
were set by negotiations between the buyers and sellers as well as by prevailing market price
as generally known in the market. Price is a continuous variable, which was measured in ETB
and transformed into natural logarithm that expressed as percentage. When selling price
increased from the prevailing market price, they were encouraged to supply more to the
market. Therefore, it was expected that prevailing market price positively affect marketed
surplus the commodity. Welelaw (2005) and Ayelech, (2011) showed that price had positive
and highly significant effect on the volume crops supplied to market.
Improved seed used (log): It is a continuous variable, which is measured total improved
seed used for maize production in kg and transformed into natural logarithm that expressed as
percentage. Maize productivity can be much higher due to the use of hybrid seeds and
fertilizers (Berihanu et al., 2007). The more the use of hybrid seeds, the higher the yield per
unit area. Yield increased in turn had significant and positive effect on the volume of maize
supplied to the market (Muhammed, 2011). Therefore, it was hypothesized that improved
seed used has positive effect on marketed surplus.
Fertilizer used (log): It is a continuous variable that is measured total fertilizers used for
maize production in kg which transformed into natural logarithm and expressed as
percentage. The rate of fertilizers used on maize has significant and positive effect on the
yield. These imply that maize is responsive to fertilizer at rate in which farmers are applying
it (Berihanu et al., 2007). The more the rate of fertilizers used, the higher the yield. An
increase in yield in turn had significant and positive effect on the volume of maize supplied to
the market (Muhammed, 2011). Therefore, it was hypothesized that use fertilizer positively
affect marketed surplus.
Marketing costs (log): Households incurred costs such as transportation, storage and sales
tax when they supplied commodities to market. Marketing cost is a continuous variable that
measured total marketing costs incurred in ETB which transformed into natural logarithm and
expressed as percentage. As the total marketing costs incurred was highly increased,
households discouraged to supply more to market. This implies that the more costs they
incurred, the less they supplied to the market. Therefore, it was hypothesized that marketing
costs negatively affect marketed surplus of the commodity. Ayele (2007) indicated that high
42
marketing costs encourage subsistence production but discourage production of marketable
food surplus.
Non-farm income (log): This variable was assumed to measure the amount of non-farm
income (such as working as daily laborers, petty trade, handcraft, etc) which helps
households in generating additional income. Non-farm income is a continuous variable that
measured farmer’s total annual income in ETB which was transformed into natural logarithm
and expressed as percentage. This additional income is assumed to increase households’
financial capacity and invest more on agricultural production. Hence, it was hypothesized that
non farm income has positive effect on marketed surplus of maize. Abreham (2013) study
result indicated that off farm income influenced the volume of commodities supplied to
market positively. The hypothesis of explanatory variables, types of data and their effect on
marketable surplus of maize are summarized in Table 5 below.
Table 5: Hypothesis of explanatory variables
Variables
Types of data
Expected effect
Dependent variable
Marketed surplus of maize
Continuous
Explanatory variables
Family size
Continuous
-
Size of land allocated for maize
Continuous
+
Livestock holding (TLU)
Continuous
+
Contact with extension agents (log)
Continuous
+
District dummy
dummy
Credit use (log)
Continuous
+
Current selling price (log)
Continuous
+
Improved seed used (log)
Continuous
+
Fertilizers used (log)
Continuous
+
Market costs (log)
Continuous
-
Distance from main market (hr)
Continuous
-
Non-farm income (log)
Continuous
+
43
-/+
4. RESULTS AND DISCUSSION
This chapter presents the major findings of the study. It has five main sections. The first
section deals with descriptive and inferential statistics of the sample households. The second
section presents value chain analysis of maize and faba bean, which includes value chain
map, actors and their roles, and value chain governance. The third section discusses about
marketing channel and performance analysis of the value chain, which includes marketing
channels, marketing costs and margins, and benefit shares of actors in the value chain. The
fourth section presents results of econometric analysis, which contains the determinants of
marketed surplus that analyzed using Tobit Model. The fifth section deals with the constraints
and opportunities of maize and faba bean input supply, production and marketing in the study
areas.
4.1. Descriptive Results
4.1.1. Household characteristics
Demographic characteristics of household include sex, age, education, family size and marital
status. The result showed that the proportion of female-headed households constituted for 5%
of the total sample households in the two districts. The proportions of illiterate and married
household head living with spouse were about 28% and 96% of the sample households
respectively. There was no statistically significant difference between the two districts in
number of female headed household, education and marital status (Table 6).
Table 6: Households characteristics (categorical variables)
Bako Tibe
Variables
Items
Sex
Female
Male
Education
Illiterate
Grade 1 or above
Gobu Seyo
Total
N
%
N
%
N
%
7
4.70
3
6.00
10
5.03
142
95.30
47
94.00
189
94.97
42
28.19
13
26.0
55
27.64
107
71.81
37
74.0
144
72.36
44
χ2
0.13
0.09
Marital
Married living
status
with spouse
Married but
separate
Widow/widower
143
95.97
49
98.00
192
96.48
3
2.01
0
0.00
3
1.51
3
2.01
1
2.00
4
2.01
1.02
Source: own computation from 2013 survey result
The result in Table 7 below showed that age of household heads was on average 43 in GobuSeyo and 39 years in Bako-Tibe districts. Average age of them in the study areas was 40
years. The average family size in Gobu Seyo was seven and in Bako Tibe six persons. Land
holding varies among sample households, which ranges from no holding to maximum of 9.5
hectares (38 timad1). About 5.5% of sample households did not have their own land for
cultivation. On average, the own land holding was 1.55 ha (six timad) which was on average
1.28 ha in Bako-Tibe and 2.3 ha in Gobu-Seyo districts. The two districts had significant
difference in mean family size.
Distance to the main market and walking time to sell their products varies from farmers to
farmers. In the study areas, the minimum walking hours to main market was 5 minutes and
the maximum was 4 hours. On average, time taken to the main market was 50.4 minute in
Bako Tibe and 93 minute in Gobu Seyo. There was highly significant difference (at 1%
significant level) in mean waking time to main markets between the two districts. This
showed that sample households in Gobu Seyo walked more hours to take their products to the
main market.
Livestock rearing was important occupation and source of income for farm households in the
study areas. The major livestock holding in the study areas were endogenous cow, exotic
cow, trained oxen, heifers, calves and chicken. The farm households also owned local and
modern beehives. More Tropical Livestock Unit (TLU) per household was owned in Gobu
Seyo (average 7.79 TLU) than Bako Tibe (5.22 TLU). The result showed that there was
highly significant difference (at 1% significance level) between the two districts. Beehives
1
timad is local measurement unit of land size in the study areas where 1ha = 4 timad
45
are also used as an additional income source for the households. On average, the numbers of
traditional beehives were 1.96 and 1.45 in Gobu Seyo and Bako Tibe, respectively. Sample
households in Gobu Seyo district did not have modern beehives (Table 7 above).
Table 7: Households characteristics (continuous variables)
Variables
Mean
Bako Tibe
Gobu Seyo
Total
(N=149)
(N=50)
(N=199)
Standard
Mean
deviation
Age
Standard
Mean
deviation
t-test
Standard
deviation
39.33
12.71
42.79
14.28
40.26
13.20
-1.64
Family size
6.36
2.43
7.04
2.59
6.54
2.49
-1.72*
Education
3.70
3.24
4.34
3.56
3.87
3.33
-1.21
Distance to main
market (min)
Own land (ha)
50.4
48
93
61.2
61.2
54.6 -4.60***
1.28
1.04
2.33
1.47
1.55
1.24 -4.61***
(TLU)
5.22
5.5.07
7.79
6.05
5.91
5.46 -3.00***
Local beehive
1.45
3.7
1.96
3.37
1.58
3.62
0.39
Modern beehive
0.04
0.42
0
0
0.03
0.36
-
Livestock holding
Source: own computation from 2013 survey result
Note: *, ** and *** show statistically significant difference between the districts at 10%, 5% and 1% level of
significance respectively
4.1.2. Maize production and marketing
Cereal crops produced in the areas were maize, teff, sorghum, barley, wheat and finger millet.
Maize was the most important and dominant cereals in production and productivity, which
constituted for 46% of cereals production in the areas, followed by teff 34% and sorghum
14%. Farming was the main occupation and source of livelihood for farm households in the
areas. Majority of respondents have been practicing mixed cropping (crop and livestock
production). Experience of maize farming was ranging from two to sixty-five years. On
46
average, the experience was 21 years in Gobu-Seyo and 17 years in Bako-Tibe districts
(Table 8)
Table 8: Production, yield and marketed surplus of maize in the two districts
Variables
Farming
experience
Land allocation
(ha)
Production (qt)
Bako Tibe
(N=146)
Mean
Sdt.
Dev.
17.18
10.66
Gobu Seyo
(N=50)
Mean Sdt. Dev.
Total (N=196)
Mean
20.94
12.12
t-test
18.14
Sdt.
Dev.
11.14
-2.08**
1.11
1.02
1.79
1.08
1.28
1.07
-3.99***
31.61
31.24
52.55
35.39
36.95
33.53
-3.95***
Yield (qt/ha)
30.83
16.86
33.20
17.61
31.44
17.04
-0.85
Marketed surplus
(qt)
14.00
23.87
30.56
29.75
18.23
25.77
-3.59***
Source: own computation from 2013 survey result
Note: *, ** and *** show statistically significant difference between the districts at 10%, 5% and 1% level of
significance, respectively
Regarding to production, maize producers used inputs such as improved seeds, fertilizers and
plant protection chemical. On average, land allocated They allocated on average 1.28 ha or
five “timad” of land for maize production. About 5% of sample households did not use
improved maize seeds and fertilizers (DAP and UREA). The allocation of land was 1.0 and
1.8 ha in Bako-Tibe and Gobu-Seyo districts respectively. More land was allocated in the
Gobu Seyo district. Average production and yield of maize was 36.95 quintals and 31.44
quintals per hectare. This is higher than the national average yield of maize, which was 25.4
quintal per hectare (CSA, 2011b). Marketed surplus of maize was higher in Gobu Seyo,
which on average, 14 quintals in Bako Tibe and 30.56 quintals in Gobu Seyo districts. More
maize was supplied in Gobu-Seyo than Bako Tibe. There was statistically high significant
difference (at 1% level of significance) in farming experience, land allocation, production and
marketed surplus between the two districts (Table 8 above).
47
4.1.3. Faba bean production and marketing
Legumes produced in the areas were faba bean, haricot bean and soybean. Among these,
faba bean constituted for 70% of legume production. However, the production of legume was
very low. None of the respondents produced field pea in the areas. The farming experience
for faba bean was 8 years. On average, the experience was 6 years in Gobu-Seyo and 9 years
in Bako-Tibe districts. The experience faba bean farming was relatively high in Bako Tibe
than Gobu Seyo district (Table 9).
Table 9: Production, yield and marketed surplus of faba bean in the two districts
Gobu Seyo
(N=7)
Bako Tibe (N=18)
variables
Mean
Mean
5.71
Sdt.
Dev.
3.82
Total
(N=25)
Mean
Sdt.
Dev.
8.12
5.59
t-test
Farming experience
9.06
Sdt.
Dev.
5.98
Land allocation (ha)
0.41
0.25
0.26
0.15
0.37
0.23
1.45*
Production (qt)
1.67
0.73
1.12
0.62
1.52
0.73
1.75**
Yield (qt/ha)
6.47
7.37
5.93
4.15
6.32
6.52
0.23
Marketed surplus (qt)
0.52
0.68
0.14
0.38
0.42
0.67
1.77**
1.37
Source: own computation from 2013 survey result
Note: *, ** and *** show statistically significant difference between the districts at 10%, 5% and 1% level of
significance, respectively
Regarding to production, farmers in the areas both used neither improved seeds nor fertilizer.
They produced on small plot of land. On average, they allocated 0.37 ha or 1.5 “timad” of
land for faba bean production. The allocation of land was 0.41 and 0.26 ha in Bako-Tibe and
Gobu-Seyo districts respectively. More land was allocated in the Bako Tibe district. Average
production and yield of the commodity was 1.52 quintals and 6.32 quintals per hectare. This
is very low compared with the national average yield of faba bean 15 quintal per hectare
(CSA, 2011b). The quantity of faba bean supplied to market was 0.52 and 0.14 quintals in
Bako Tibe and Gobu Seyo districts, respectively. More was supplied in Bako Tibe than Gobu
Seyo district. There was statistically significant difference in land allocation, production and
marketed surplus between the two districts (Table 9 above).
48
4.1.3. Producers’ characteristics by market outlets
The alternative buyers that purchased maize and faba bean directly from producers were six
and four in channels of distribution, respectively. These were rural assemblers, rural
wholesalers, cooperatives, urban wholesalers, urban retailers and consumers. Cooperatives
and rural wholesalers were not involved in purchasing of faba bean due to low quantity
produced and supplied to markets. Among buyers, rural assemblers were the most important
one, who purchased 58.8% and 39% of marketed surplus of maize and faba bean,
respectively. Rural wholesalers and urban wholesalers were the next more important buyers
of maize and faba bean directly from farmers, respectively (Figure 4).
Source: own sketch from 2013 survey data
Figure 4: Share of maize buyers directly purchased from producers
Households in the study areas mainly sold maize to rural assemblers. The result showed that
50.93% of male and 28.59% female household heads sold the commodity to rural assemblers.
The households in Bako Tibe (52.85%) and Gobu Seyo (41.67%) supplied the commodity to
the rural assemblers. About 29% of female and 17% of male household heads sold maize to
49
urban retailers and urban wholesalers, respectively. Urban wholesalers and rural wholesalers
were the next important buyers in Bako Tibe and in Gobu Seyo districts, respectively.
Table 10: Households' characteristics to maize buyers (categorical variables)
Variable
Items
Rural
Coops
Rural
Urban
Urban
wholesalers
wholesalers
retailers
Consumer
χ2
Total
assemblers
Sex
District
N
%
N
%
N
%
N
%
N
%
N
%
N
%
Female
2
28.57
1
14.29
1
14.29
1
14.29
2
28.57
0
0
7
4.17
Male
82
50.93
6
3.73
19
11.80
28
17.39
20
12.42
6
3.73
161
95.83
Bako
65
52.85
6
4.88
10
8.13
23
18.70
14
11.38
5
4.07
123
71.93
Gobu
20
41.67
2
11
22.92
6
12.50
8
16.67
1
48
28.67
4.12
2.08
Source: own computation from 2013 survey data
4.2. Value Chain Analysis
4.2.1. Mapping maize value chain
Mapping is a central element of value chain analysis. It is used to show the flow of
transactions from sourcing of raw materials and inputs, to production, processing, marketing
and final sale. It is made up of three inter-linked components namely value chain actors
enabling environment and service providers. The value chain actors are those directly
involved in value chain activities. These are input suppliers, producers, grain traders,
processors, exporters and consumers. The enabling environment is activities related to
infrastructure and policies that shape production and market environments. The service
providers are those who provide services such as transportation, extension service, credit,
information, etc that support the value chain. .
50
4.13
8.97
Source: own sketch from 2013 survey result
Figure 5: Map of maize value chain in the study areas
In maize value chain, three key actors that connected producers and consumers were
assemblers, wholesalers and retailers. As depicted in figure 5, the flow channel: producers
→assemblers → wholesalers → retailers → consumers was the longest channel and large
volume of marketed surplus flowed from producers to consumers. There were no actors that
used maize as a raw material for processing in flour form. Moreover, there were no exporters
identified that export the commodity in the value chain
Input suppliers
Agricultural input particularly seeds and fertilizers were dominantly supplied by
cooperatives. There was no competition in supplying seeds and fertilizers in the study areas.
The government institutions like agricultural development offices also distributed improved
seeds for farmers. Unions supplied and distributed the inputs to their respective cooperatives.
The unions were supplied maize seeds from Ethiopian seed enterprise Nekemte branch. Bako
Tibe union purchased fertilizers from well-organized farmers’ unions of Ambo and Holleta.
In addition, plant protection chemicals such as pesticide and herbicide were purchased from
chemical dealers in Addis Ababa.
The cooperatives supplied different varieties of maize seed to the study areas. They supplied
varieties such as BH-540, BH-543, BH-660, Limu and Shone to Bako Tibe district and
Shone, Homa and Agar to Gobu Seyo district. Limu was newly introduced maize variety in
Bako Tibe district which only 10 quintals distributed to the study areas and Homa was newly
introduced variety to Gobu Seyo district. About 150 quintals of this seed distributed by
private seed agent in the district. The cooperatives in the two districts distributed 1447
quintals of maize seed, 16,469 quintals of fertilizers (DAP and UREA) and 5359 liters of
plant protection chemicals (pesticide and herbicide) to their respective kebeles. The result
showed that there was significant difference between the districts in supplying Shone (maize
seed) and UREA at 10% level of significance (Table 11).
Table 11: Agricultural inputs supplied by sample cooperatives to their respective kebeles
Bako Tibe (N=3)
Inputs
Mean Std. dev.
Maize seed (Shone) (qt)
34.67
DAP (qt)
Gobu Seyo (N=2)
Total (N=5)
Mean
Std. dev
Mean
Std. dev
t-test
13.80
8.50
4.95
24.20
17.51
2.47*
2496.0
1356.55
1752.5
1900.00
2198.6
1410.12
0.52
UREA (qt)
3770.0
1041.11
1268.5
1293.30
2769.4
1684.44
2.42*
Pesticide (liter)
105.50
70.00
1713.0
1919.09
909.25
1445.90
-1.18
Herbicide (liter)
416.67
236.29
2226.0
2575.28
1140.4
1633.42
-1.32
Source: own computation from 2013 survey result
The quantity of seed supplied differs among the varieties. Among maize varieties, BH-540
and Shone were largely supplied in Bako Tibe and Gobu Seyo districts, respectively. Shone
and Agar were the most costly seed variety in the areas which on average costs ETB 3912.25
and ETB 3946 per quintal, respectively. Average selling prices of DAP was ETB 1564 per
quintal and UREA was 1255 per quintal. Average selling price of plant protection chemicals
such as pesticide and herbicide was ETB 148 per liter. From the total agricultural inputs
supplied to the study areas, sample households used 36 quintals of improved seeds, 408
quintals of fertilizers and 386 liters of chemicals (Appendix 2).
Producers
Production is the major function in value chain, which starts from input preparation to final
harvesting. The major activities in production are land preparation, sowing, fertilizer
application, cultivation, weeding, chemicals spraying, harvesting and post-harvest
managements. Sample households accessed improved seed, fertilizers and chemicals from
nearby cooperatives, district agricultural development offices and private seed vendors. They
did not have access to irrigation and most of them (98.5%) depend on rain fed. They used
mostly their family labour, own/hired oxen and land. Average family labour used was about
five adult equivalents and it was more in Gobu-Seyo district. About 21% of sample
households did not have an ox and average holding was 1.76 oxen. The maximum number
owned by sample households was eight trained oxen.
53
Table 12: Agricultural inputs used by sample households
Variables
Bako Tibe
Gobu Seyo
Total
(N=146)
(N=50)
(N=196)
Mean
Std.
Mean
Dev
Std.
Mean
Dev
t-test
Std.
Dev
Oxen
1.56
1.42
2.36
1.40
1.76
1.45 --3.45***
Labour (adult
equivalent)
Maize seed (kg)
4.90
1.92
5.51
2.17
5.05
2.00
-1.89*
16.10
15.69
26.19
18.95
18.68
17.11
-3.39***
182.18
145.25
301.78
193.34
212.69
166.77
-4.60***
Fertilizer for maize (kg)
Source: own computation from 2013 survey result
The allocation of land for maize was 1.28 hectares (Table 8). Sample households used on
average 18.68 kg of seed and 212.69 kg of fertilizers (DAP+UREA) for maize production,
Using these and other factors of production, sample households’ average production was
36.95 quintals. The yield was 31.44 quintals per hectare. The result showed that there was
significant difference between districts in land allocation, oxen own, seeds and fertilizers
used (Table 12 above).
Post-harvest management was the main activity in maize production because maize grain is
susceptible for weevil and pests, which easily damage the crop. Sample households used
different storage type namely traditional crib, improved granary, metal silo, poly bags, and
wooden store. More of the respondents (60%) used traditional crib and about 29% store
maize in metal silo. Storage pest was controlled by both traditional method (5%) and treating
by chemicals (43%). They stored the commodity on average for 8.8 months and at this
period; end stock was 2.8 quintals. There was no significant difference between the two
districts in storage period and end stock (Table 13).
54
Table 13: Maize storage period and end stock
Bako Tibe
Variables
Stored period
(month)
End stock (Qt)
N
Mean
144
148
Gobu Seyo
Total
N
Mean
9.22
Std.
Dev.
3.25
194
8.80
t-test
Std.
Dev.
4.84 -0.89
3.48
10.41
198
2.67
8.04 -0.82
N
Mean
8.65
Std.
Dev.
5.29
50
2.39
7.08
50
Source: own computation from 2013 survey result
Sample households produced total quantity of 6914 quintals of maize. From this, 51 % was
marketed surplus and the rest was on farm consumption which utilized for family food,
reserve for seed, gift and in kind payment. From the total production, 30% was purchased by
rural assemblers, 2% by cooperatives, 8% by rural wholesalers, 6% by urban wholesalers, 4%
by urban retailers, and only 1% by urban consumers. On other hand, from marketed surplus
(51% of total production or 3526 quintals), the share of rural assemblers was 58.8%,
cooperative 3.9%, rural wholesalers 15.17%, urban wholesalers 11.8%, urban retailers 7.8%
and urban consumers only 2%. The quality of maize supplied was medium and/or above
average. About 30% to 50% was medium quality. The quality that supplied to cooperatives
and urban wholesalers was above average.
Rural assemblers
Rural assemblers play a key role in collecting marketed surplus of maize from rural markets
and delivered to urban wholesalers, rural wholesalers, Ethiopian Grain Trade Enterprise
(EGTE) and urban-retailers shopkeepers. They purchased 2074.2 quintals or 30% of
production by sample households and sold to urban grain wholesalers (80%), rural grain
wholesalers (4%), EGTE (14%) and to urban-retailers shopkeepers (2%). This implies that
they supplied more to urban wholesalers. The quality of maize sold was medium to above
average. The medium quality constituted for 40% to 50% and the quality of the commodity
sold to EGTE was above average.
Cooperatives/unions
Cooperatives in addition to supplying agricultural inputs to smallholder farmers, they also
participated in maize grain trade. They are located in the producing “kebeles” of farm
55
households and engaged in the procurement of maize from their members. They purchased
138.28 quintals or 2% of sample households’ production and sold the whole to urban
wholesalers through auction. The sample cooperatives purchased 1566 quintals from
households in their respective kebeles/market including sample households. The quality of
maize supplied to urban wholesalers was above average.
Grain wholesalers
Wholesalers included in maize value chain were urban wholesalers in surplus and deficit
areas, rural wholesalers and EGTE. The urban wholesalers in surplus and deficit areas
operated in major grain markets and were connected by a network of urban brokers. The
result showed that wholesalers as a group directly from producers and indirectly from other
actors handled 3137.96 quintals or nearly 45.39% of production by sample households. Rural
wholesalers handled 636.09 quintals, of which 87% purchased from households and 13%
from rural assemblers and sold all to urban wholesalers. On other hand, urban wholesalers in
surplus production areas purchased 6% of maize production by sample households and the
rest from rural assemblers, cooperatives and rural wholesalers. They handled 2847.57
quintals. They sold to urban wholesalers in deficit areas (92%), urban open-air retailers (2%)
and supplied to grain traders in ECX market (6%). Urban wholesalers in deficit production
areas purchased from urban wholesalers in surplus areas and sold to urban retailers’
shopkeepers (96%) and open air retailers (4%). EGTE is a government organization
mandated to stabilize domestic prices of main staple cereals. They also play role in strategic
reserve for food security and emergency operation. The result showed that they purchased
from rural assemblers, reserved and supplied to Food Aid recipients. The quality of maize
supplied to the buyers was above average.
Grain retailers
Grain retailers such as urban-retailers-shopkeepers and open-air retailers, were the most
important actors in the maize value chains that supplied to consumers. Urban retailers’
shopkeepers handled directly from producers and other actors 2833.9 quintals. They
purchased 4% of production from sample households and the rest from urban wholesalers in
deficit areas and rural assemblers. They sold to urban consumers (63%), open-air retailers
(24%) and to rural consumers (13%). In other words, open-air retailers handled 841.9
quintals, of this quantity 7% was purchased from urban wholesalers, 12% from wholesalers
56
in deficit areas and 89% from urban retailer’s shopkeepers. Open air retailers sold all volume
to urban consumers. Urban retailers supplied medium to above average quality of maize to
their buyers, in which 50% to 60% was above average quality.
Consumers
Urban and rural consumers purchased maize mainly from retailers. The urban consumers
purchased 1% of maize production by sample households. They also purchased from urban
retailer shopkeepers, open air retailers and grain traders in ECX market. The quantity of
2867.35 quintals directly from producers or indirectly through other actors reached urban
consumers. On other hand, the rural consumers in deficit areas purchased 368.41 quintals
from retailer shopkeepers.
4.2.2. Mapping faba bean value chain
Actors involved in faba bean value chain were producers, assemblers, wholesalers, retailers
and consumers. There were no processors and exporters participated in the value chain.
Cooperatives and rural wholesalers were not participated in faba bean marketing.
Input suppliers
Input suppliers were not involved in supplying of particularly improved faba bean seed
varieties for faba bean production as the demand was very low. The cooperatives supplied
only plant protection chemicals for the production. Farmers in the areas did not apply
fertilizer for faba bean production. They accessed local seed from other farmers in the market
or neighbors.
57
Source: own sketch from 2013 survey result
Figure 6: Map of faba bean value chain in the study areas
Producers
The major activities in production of faba bean were land preparation, sowing, cultivation,
weeding and harvesting. Faba bean producers in the study areas almost did not use improved
seed and fertilizers. They used either own reserved or purchased local seeds. And family
labour, oxen used and allocation of land were similar to maize. A sample household allocated
0.18 hectares of land for faba bean production. Total production of the commodity by sample
households was 416 quintals, of this only 29% or 120.55 quintals was marketed surplus.
Storage types, pest and rodents control methods were similar to maize and almost none of the
respondents stored the commodity for a month. The results showed that sample households
supplied 11% of total production to rural assemblers, 8% to urban wholesalers, 6% to urban
retailers shopkeepers and 4% to urban consumers (Figure 6). The quality of faba bean
supplied to rural assemblers and urban retailers was medium to above average. However; the
quality the commodity to urban wholesalers was above average.
Rural assemblers
Rural assemblers collected marketed surplus of faba bean from rural markets and delivered to
urban wholesalers and urban-retailers shopkeepers. They handled 45.76 quintals of faba bean
or 11% of production by sample households. They sold 67% to urban grain wholesalers and
33% to urban-retailers shopkeepers. The result showed that rural assemblers were the major
buyers from producers. They supplied good quality to urban wholesalers and medium quality
to urban retailers.
Grain wholesalers
Wholesalers included in value chain were urban wholesalers in surplus and wholesalers in
deficit areas. Urban wholesalers in production areas handled 63.88 quintals or 8% of
production by sample households and 67% of total quantity handled by rural assemblers.
They sold 67% to urban wholesalers in deficit area and 33% to open air retailers. Urban
wholesalers in deficit areas obtained from urban wholesalers in production areas and sold the
whole to urban-retailers shopkeepers. Grain wholesalers supplied above average quality to
their buyers.
Grain retailers
Urban-retailers-shopkeepers and open-air retailers were the two retailers identified in the
study areas. The urban retailers-shopkeepers purchased 103.91 quintals or 6% of production
from producers, 33% of quantity handled by rural assemblers, 33% that handled by urban
wholesalers in production areas and all quantity handled by urban wholesalers in deficit areas
They sold 60% to urban consumers and 40% to open air retailers. The urban open-air retailers
purchased 62.35 quintals or 60% of quantity handled by urban retailer shopkeepers and sold
the whole to urban consumers. The quality of faba bean supplied to consumers was above
average.
Consumers
In faba-bean value chain, only urban consumers were identified. They purchased 4% or 16.64
quintals directly from producers. Marketed surplus of faba bean reached final consumers was
120.55 quintals. Of this about 52% of urban consumers purchase was from open air retailers,
34% urban retailer shopkeepers and 14% from sample households. This showed that open air
retailers were the most important suppliers of faba bean to the consumers.
4.2.3. Service providers in the value chains
There were service providers that support actors in the value chains at each stage. In maize
and faba bean value chains, the most important service providers were agriculture
development offices, marketing offices, farmers’ cooperatives offices and unions, financial
institutions, research centers, seed enterprises, etc. They provided extension services,
trainings, market information and credit.
Training was provided to smallholder farmers on crop production, marketing and credit
management by agricultural development offices and research centers. Extension agents also
provided training on new varieties, field pest and disease control, soil and water management,
storage pest control, input and output marketing. For example, a farm household contacted
with extension agents on average 6 times a year. Farmers’ cooperative offices provided
training for primary cooperatives on safe input handling, distribution of inputs, finance and
60
credit management, customer care and market management. The primary cooperatives
provided agricultural inputs and market information. They supplied inputs with no profit
except commission. They transmitted price information through announcement on meetings,
notices and through their committee members during peak supply season. Financial
institutions such as micro finances provided credit services for the farmers in the study areas.
Government, banks, transport services providers and marketing offices supported grain
traders in the areas. District marketing offices provided trainings, monitoring quality and
registering the traders. Commercial banks provided credit for them. Government provided
facilitated markets by constructing and/or maintaining roads, serving as sources of market
information and providing telecommunication services. Actors in the value chains accessed
market and price information from television, radio, price displayers, mobile phone,
government departments, etc. Cooperatives accessed price information of agricultural inputs
mostly from government departments and unions. This was the most effective sources of
information for them. The most important sources of market information for farmers were
primary cooperatives, neighbor farmers, relative farmers and private traders. Grain traders
accessed the information from radio, television, agents in terminal market, other traders and
monitoring prices of different market places.
4.2.4. Value chain governance
Governance is important as it relates to the ability of an actor to determine, control and/or
coordinate the activities of other actors in the value chain. The power and the ability to
control existed due to different factors such as access to market information, competitiveness,
price instability, limited cooperation, stocking products, etc. Actors in the value chains
accessed market information from different sources. The main sources of price information
for farmers, cooperatives and grain traders were neighbor/relative farmers, government and
personal contacts with other traders respectively (Table 14).
61
Table 14: Price information sources for actors in the value chains
Information sources
Producers
cooperatives
Grain traders
N
%
N
%
N
%
Traders/agents
28
22.95
1
14.29
5
9.09
Government department
13
10.06
4
57.14
0
0
Radio/ Television
7
5.74
1
14.29
2
3.64
Personal contacts
0
0
1
14.29
48
87.27
Farmers cooperatives
13
10.06
0
0
0
0
Neighbors/relative farmers
61
50.81
0
0
0
0
Source: own computation from 2013 survey result
Traders in addition to access the information from different sources, they regularly monitored
prices on average from two markets. The result showed that the second important source of
information for farmers was private traders. This implies that majority of farmers accessed
either from farmers or private traders, which may not be reliable sources. In other hand,
traders by themselves were the source of price information as well as main buyers of grains
from farmers. As they monitored market prices from terminal market, deficit and surplus
production areas, they have more price information than producers do. This price information
difference made traders more powerful when negotiating on prices with suppliers and
benefited from the information gap.
Prices speculation was one of the most important factors that benefited actors in the value
chains as prices in peak supply season is less than those in low supply season. In peak supply
months of maize (December, January and February), producers average selling price was
ETB 399 per quintal. The price reached ETB 556 per quintal after five months. The result
showed that farmers stocked on average 2.8 quintals of maize for 8.5 months. However, at
the end of these months, 74% of respondents had no stock while traders stocked on average
112 quintals for 1.52 months (Table 15). This implied that traders stored more volume of
maize than producers did. This may be due to problems in financial means to store more
quantity for months until the price reached maximum. The results showed that, 75.76% of
respondents did not access credit, 19.79% of them accessed but did not get credit and only
4.45% of them accessed and got credit. As a result, more farmers were forced to sell in peak
62
supplying months. Whereas, traders were able to store for months that made them benefited
more than producers.
Table 15: Maize storage period and stock in the study areas
Variables
Observation
Mean
Std.Dev
Min
Max
Producers’ maize stock (quintal)
192
2.80
8.16
0
57
Producers’ maize storage period (month)
195
8.54
3.14
0
12
Traders’ stock (quintal)
52
111.8
164.11
0
750
Traders’ storage period (month)
53
1.52
1.10
0
7
Source: own computation from 2013 survey result
Traders competed when buying and selling maize and faba bean in the market. The number
of competitors before ten years, before three years and currently showed an increasing trend.
Currently, a maize trader, for example, has on average 8.5 competitors when he trade maize
at his village/market level and 35.67 competitors at town level in Bako Tibe district. The
numbers were less before three or ten years ago. The result showed that the numbers of
competitors on maize trade in towns was on average 27.33 in Ambo, 35.67 in Bako Tibe,
23.88 in Dendi, and 17.75 in Gobu Seyo. In other hand, the number of competitors on faba
bean trade at town level was 8.5 in Ambo, 14.6 in Bako Tibe and 37.5 in Nekemte. The
number of sample grain traders was much less than the number of competitors in the study
areas. This was because other grain traders in the districts purchased from another surplus
production districts but sold in the same market/town (Table 16).
Table 16: Current number of competitors on maize and faba bean trade in the study areas
Crop
Districts
Market
Obs
Mean
Std.Dev.
Min
Max
Ambo
Village
13
11.92
13.54
0
50
Town
13
27.33
13.63
14
56
Village
20
8.50
9.02
0
40
Town
20
35.67
28.34
5
100
Village
8
11.75
8.40
3
25
Town
8
23.88
10.58
15
40
Bako Tibe
Maize
Dendi
63
Gobu Seyo
Ambo
Faba
Bako
bean
Nekemte
Village
11
9.82
2.23
6
14
Town
11
17.75
7.30
10
30
Village
2
8.5
9.19
2
15
Town
2
8.5
9.19
2
15
Village
5
5.2
5.07
0
11
Town
5
14.6
14.94
3
40
Village
2
12.5
3.53
10
15
Town
2
37.5
17.68
25
50
Source: own computation from 2013 survey result
Generally, the proportion of maize traders in the study districts was 28.3% of rural
assemblers, 5.7% of rural wholesalers, 18.9% of urban wholesalers and 47.1% of urban
retailers. When comparing the proportion of traders in the market and the quantity they
purchased from market (Figure 5), large volume were handled by urban wholesalers and
more other traders in the market supplied to them. As they were less in number and potential
buyers in the market, they had a decision-making power on quantity, quality and prices of the
commodities.
Traders in the study areas set different quality attributes when they purchased crops from
producers and/or other traders. The most important quality attributes were moisture content
of grain, size, insect or pest damage, grain brakeage, foreign matters, weight, and smell. In
contrast, nutrition, variety, shape and cooking traits of maize were minor important for
majority of buyers. They used different assessment methods to evaluate the quality of crop
supplied to market. These were visual inspection, smell, experience/trust with seller, bite,
shaking and weight (Table 17). The result depicted that no standard quality measurement
method for assessing the quality of crops rather common assessment methods, which were
not uniform evaluation techniques among the buyers were used. Producers were the only
affected actors from this non-standard quality measurement method as they are involved only
in selling activities. Moreover, they had no price incentive for supplying more quality
products. In other hand, traders are involved in both buying and selling activities. This
enabled them to set quality assessment when purchasing according to their buyers interest.
This showed that traders benefit more in the value chain.
64
Table 17: Importance of quality attributes when purchasing maize and faba bean
Quality attributes
Not important
Minor
Very
at all
important
important
Assessment method
N
%
N
%
N
%
Grain size
2
3.77
23
43.40
28
52.83
Visual inspection
Grain color
1
1.89
14
26.42
38
71.70
Visual inspection
Homogeneity
0
0
17
34.00
33
66.00
Visual inspection
Foreign matter
0
0
4
7.55
49
92.45
Visual inspection
Insect/pest
0
0
2
3.77
51
96.23
Visual inspection
Chemical residue
0
0
15
35.71
27
64.29
Smell/experience
Moisture contents
0
0
0
0
53
100
Weight
0
0
4
7.84
47
92.16
Weight
Grain breakage
0
0
7
13.46
45
86.54
Visual inspection
Smell
0
0
5
10.20
44
89.80
Smelling
Bite/ shaking
Source: own computation from 2013 survey result
4.3. Marketing Channels and Performance Analysis
4.3.1. Maize market channels
Marketing channels analysis describes the direction and volume of goods and services flow
from producers to consumers. Maize and faba bean marketing channels were analyzed based
on their direction and volume of flow. Ten maize channels were identified that pass the
commodity from producers to consumers. The major actors in the channels were producers,
cooperatives, rural assemblers, urban wholesalers, retailers and consumers. Through the
channels 3526 quintals were passed from producers to consumers. There were six alternative
buyers that purchased maize directly from sample households. From the marketed surplus,
2074 quintals or 58.8% was purchased by rural assemblers directly from producers. 3.9% by
cooperatives, 15.7% by rural wholesalers, 11.8% by urban wholesalers, 7.9% by urban
retailers and 1.9% by consumers. The rural assemblers sold 80% to urban wholesalers, 14%
to EGTE, 4% to rural wholesalers and 2% to urban retailers (figure: 7).
65
Source: own sketch from 2013 survey result
Figure 7: Maize market channels in the study areas
In the maize channels, the largest volume flowed through channel IV which was 1659
quintals and the smallest flow was through channel I (69 quintals). The market channels are
described as follow:
I.
Producers → consumers (69 quintals)
II.
Producers → urban retailers → consumers ( 277 quintals)
III.
Producers → rural assemblers → urban retailers → consumers (41 quintals)
IV.
Producers → urban wholesalers → urban retailers → consumers (245 quintals)
V.
Producers → Cooperatives → urban wholesalers →urban retailers → consumers
(138 quintals)
66
VI.
Producers → rural assemblers → urban wholesalers → urban retailers →
consumers (1659 quintals)
VII.
Producers → rural assemblers → rural wholesalers → urban wholesalers →
urban retailers → consumers (83 quintals)
VIII.
Producers → rural wholesalers → urban wholesalers →urban retailers →
consumers (553 quintals)
IX.
Producers → urban wholesalers in surplus areas→ grain traders in ECX market
→ consumers (171 quintals)
X.
Producers → Rural assemblers → EGTE → Food Aid recipients (290 quintals)
4.3.2. Faba bean market channels
Five faba bean channels were identified that pass the commodity from producers to
consumers. The major actors in the channels were producers, urban wholesalers, retailers and
consumers. There were four alternative buyers that purchased the commodity directly from
sample households. From the marketed surplus of 121 quintals, 37.8% was purchased by
rural assemblers, 27.5% by urban wholesalers, 20.6% by urban retailers and 1.9% by
consumers directly from producers. The rural assemblers sold 67% to urban wholesalers and
33% to urban retailers (Figure 8).
Source: own sketch from 2013 survey result
Figure 8: Faba bean market channels in the study areas
67
In the faba bean channels, the largest volume flowed through channel V which was 33
quintals and the smallest was when it flows from producers to consumers, which was 17
quintals. The market channels are described as follow:
I.
Producers → consumers (17 quintals)
II.
Producers → urban retailers → consumers (25 quintals)
III.
Producers → urban wholesalers → urban retailers → consumers (33 quintals)
IV.
Producers → rural assemblers → urban wholesalers → urban retailers →
consumers (31 quintals)
V.
Producers → rural assemblers → urban retailers → consumers (15 quintals)
4.3.3. Maize market performance
Value added structure was analyzed using costs (production and marketing costs), marketing
margins and returns. The analysis standardized unit of measurement into ETB per quintal.
Actors incurred marketing costs for transportation, storage, sorting, packing, cleaning,
loading, seller searching, commission, taxes and others. Marketing margin used to measure
the share of the final selling price that is captured by a particular actor in the value chain.
Marketing margins were computed for producers, rural assemblers, wholesalers and retailers.
Actors in the value chain add value through marketing costs such as .transportation, loading,
seller/buyer searching, cleaning, packaging, sorting, storage costs like rent, pest/rodent
control and weight loss. Production costs such as seeds, fertilizers, plant protection
chemicals, land, labour and oxen were computed. As most of households used their own
family labour, oxen and land, opportunity costs were used to compute costs of production.
Accordingly, average cost of maize production for a sample household was 181 ETB. The
major actors in maize value chain were producers, rural assemblers, urban wholesalers and
retailers. When the commodity flows from producers to consumers, actors in the value chain
add costs. The result showed that rural assemblers add more costs (ETB 48.77 per quintal)
than other actors. In the chain, Producers had the highest share of market margin (35.56) and
profit margin (75.45%). From traders, rural assemblers had 12.64% and 10.02% share of
market margin and profit margin, respectively (Table 18 below).
68
Table 18: Marketing margin and gross profit of actors in maize value chain.
Cost items (ETB/qt)
producers
-
Rural
assemblers
378
Urban
wholesalers
448
Urban
retailers
496
181
-
-
-
Transportation
6.25
15.75
19.65
14.85
Storage
24.5
11.35
3
5.86
Cleaning/packing
0
7.36
5.3
10.75
commission
0
0.75
4.55
2.03
Custom fee/tax
2
1.79
1.31
1.47
Loading/unloading
0
4.53
2.5
3.17
Other costs
5
7.0
0.67
0.55
37.75
48.77
36.98
38.68
218.75
429.8
484.98
534.68
Selling price
378
448
496
554
Market margin
197
70
48
58
35.56
12.64
8.66
10.47
159.25
21.47
11.02
19.32
75.45
10.17
5.22
9.15
Purchasing price
Production cost
Marketing costs
Total marketing cost
Total cost
% share of margin
Profit margin
% share of profit
Source: own computation from 2013 survey result
Value was added to the product when it passed from one actor to another. More value was
added as transportation, storage and cleaning/packing. Actors in the value chain incurred
34.9% of marketing costs for transportation, 27.6% for storage costs, 14.5% for cleaning and
packing, 6.3% for loading/unloading, 4.5% for commission, 4.1% custom fee/tax and the rest
for personal expenses such as transport, food, mobile card and other utilities. Storage costs
were incurred for storage rent, control storage pest and rodents, and weight loss during
stocking. Weight loss during cleaning was also considered as cost for traders (Figure 9).
69
Source: own sketch from 2013 survey result
Figure 9: Percentage share of maize marketing costs
Gross Marketing Margin (GMM) is the difference between what the consumers paid for the
commodity and what the farmers received. It is also calculated as the percentage share
received by each marketing intermediaries. There is a strong cumulative effect on the
marketing margin resulting from the increasing number of intermediaries involved in
marketing process. Gross Marketing Margins (GMM) and Net Marketing Margins (NMM)
were computed for the major actors in eight marketing channels.
The result showed that there was a difference in the consumers’ price spread along the market
channels. Total gross marketing margin was high in channel VI and low in channel II in
which 39.3% of Total Gross Marketing Margin (TGMM) added to maize price in the channel
when it reached the final consumers. Of this, rural assemblers received 23.4% and urban
retailers 15.9 %. In other words, the market channels with only one actor between producers
and consumers showed low TGMM. For instance, in channel II only 17.8% of maize price
was added when it reached final consumers. This implied that as the market margin becomes
wide, price becomes high for consumers and low to producers (Table 19 below).
70
Table 19: Marketing margins of actors along maize market channels
Marketing margin
I
II
III
IV
V
VI
VII
VIII
Total Gross Marketing Margin (%)
-
17.8
30.3
35.3
34.9
39.3
32.5
25.7
100
82.2
69.7
64.7
65.1
60.7
67.5
74.0
GMM of cooperatives (GMMcop)
-
-
10.8
-
-
-
-
-
GMM of rural assemblers (GMMra)
-
-
-
11.5
13.1
23.4
-
-
GMM rural wholesalers (GMMrw)
-
-
-
0.0
9.3
-
12.9
-
GMM of urban wholesalers
-
-
10.4
14.7
6.5
14.5
11.2
GMM urban retailers (GMMur)
-
17.8
8.1
9.1
8.4
15.9
5.2
14.4
Total Net Marketing Margin (%)
-
13.4
22.6
22.6
22.6
33.1
23.0
18.0
NMM of cooperatives (NMMcop)
-
-
6.7
-
-
-
-
NMM of rural assemblers (NMMra)
-
-
8.02
8.75
19.2
-
-
NMM rural wholesalers (NMMrw)
-
-
-
-
4.10
-
8.2
-
NMM of urban wholesalers
-
-
8.35
9.85
4.15
-
12.0
8.52
-
13.4
6.84
4.69
5.59
13.1
2.8
9.51
GMM of producers (GMMp)
-
(GMMuw)
(NMMuw)
NMM urban retailers (NMMur)
Source: own computation from 2013 survey result
Net marketing margin was computed from the difference between percentage shares of gross
marketing margin and total marketing costs as the percentage of retail prices in the channels.
Accordingly, channel VI was the highest NMM, which constituted for 33.1% of net income.
The minimum NMM was taken in channel II, which constituted for 13.41% of NMM in the
channel. Along the channel V, the highest share of NMM (8.75%) went to rural assemblers
and the lowest to rural/urban wholesalers. These marketing margins difference among market
chains and actors were evidence for the existence of market inefficiency, which arose due to
differences in marketing costs and price difference between producers and consumers. There
was relatively fair distribution of NMM among actors in the channels III and VIII.
71
4.3.4. Faba bean market performance
Faba bean market performance evaluated through marketing margins, costs and returns.
Value was added as costs incurred for transportation, storage, packing, commission, custom
fee/taxes, loading and other costs.
Households mainly used their own labour, land and oxen for production of the commodity.
As a result, production cost for these inputs used opportunity costs. Accordingly, average cost
of production was ETB 270 per quintal. Producers’ average selling price was on average ETB
588 per quintal. The major actors in faba bean value chain were producers, rural assemblers,
urban wholesalers and retailers. The result showed that urban retailers incurred more costs
(ETB 45.3 per quintal) than other actors. In the chain, producers had the highest share of
market (41.46%) and profit margins (76.3%). Among traders, urban retailers had 11.99% and
11.6% share of market margin and profit margin, respectively (Table 20).
Table 20: Faba bean marketing costs, margins and gross profit shares of actors
Rural
assemblers
Urban
wholesalers
Urban
retailers
Purchasing price (ETB)
588
Production cost
270
Marketing costs
Transportation
7.0
0.0
Storage
0.0
3.9
Packing
0.0
3.8
commission
0.0
0.4
Custom fee/tax
2.0
5.6
Loading
0.0
4.0
others
1.5
8.0
Total marketing cost
10.50
25.60
Total cost
280.50
650
Selling price
588
650
Market margin
318
62
% share of margin
41.46
8.08
Profit margin
307.5
36.3
% share of profit
76.3
9.0
Source: own computation from 2013 survey result
650
675
0.0
0.0
4.0
1.6
1.5
3.0
2.5
12.60
675
675
25
3.26
12.40
3.1
28.3
3.9
2.3
0.7
3.4
6.0
0.7
45.30
720.2
767
92
11.99
46.7
11.6
Cost items (ETB/qt)
producers
72
Value was added to the commodity when it passed from one actor to another. Actors in the
value chain incurred highest cost for transportation, which constituted for 38% of marketing
costs. for transportation and the smallest cost for commission (3%). There was also cost for
traders’ personal expenses such as transport, food, mobile card and other utilities (Figure 10).
Source: own sketch from 2013 survey result
Figure 10: Percentage share of faba bean marketing costs
Gross Marketing Margin (GMM) and Net Marketing Margins (NMM) were computed for
actors in the five market channels. Total Gross Marketing Margin (TGMM) was high in
channel III and low in channel II, which constituted for 21.9 % and only 6.7% respectively.
These indicated the share of faba bean price that added to farm gate price when it reached the
final consumers. Among actors in the channels, urban retailers received the largest share of
TGMM. As the market margin becomes wide, there is high price to consumers and low price
to producers (Table 21).
The Net Marketing Margin was computed for each actor in the channels by subtracting total
marketing costs (as the percentage of retail price in each channel) from gross marketing
margin of each actor. Accordingly, channel III had the highest NMM, which constituted for
10.18% compared with 3.74% of channel II. Within channel III, the share of NMM among
73
actors was 2.35 % for rural assemblers, 3.58% for urban wholesalers and 4.25% for urban
retailers. There was fair distribution of NMM between urban retailers and wholesalers in
channel V. Marketing margins difference between market channels and among actors were
evidence for the existence of market performance. These differences arose due to marketing
costs and prices added between producers and consumers (Table 21 below).
Table 21: Marketing margins of actors along different faba bean market channels
Marketing margin
I
II
III
IV
V
0.0
6.7
21.9
20.0
20.0
100.0
93.3
78.1
80.0
80.0
GMM of rural assemblers (GMMra)
-
-
5.0
6.7
-
GMM of urban wholesalers (GMMuw)
-
-
4.4
0.0
6.7
GMM urban retailers (GMMur)
-
6.7
12.5
13.3
13.3
3.74
10.18
7.52
9.03
3.02
-
Total Gross Marketing Margin (TGMM %)
GMM of producers (GMMp)
Total Net Marketing Margin (TNMM %)
NMM of rural assemblers (NMMra)
-
-
2.35
NMM of urban wholesalers (NMMuw)
-
-
3.58
NMM urban retailers (NMMur)
-
3.74
4.25
4.53
4.5
4.5
Source: own computation from 2013 survey result
4.4. Econometrics Analysis
In the study areas, from 199 randomly selected households, only twenty-five were
participated in faba bean production. As a result, only descriptive statistics was used to
analyze characteristics of faba bean production and marketing. It is hardly possible to analyze
determinants of faba bean marketed surplus using econometrics model as its size was too
small (less than thirty). Therefore, econometrics model was used to analyze marketed surplus
of maize.
The result showed that 51% of maize production or 84% of respondents supplied the
commodity to markets. On the other hand, among the 199 sample households, 31 of them did
not supply maize to the market. The values of dependent variable for these observations were
zero that censored to the left. If zero values of dependent variable were the result of rational
74
choice of farmers, a Tobit model would be appropriate for econometrics analysis. The
econometrics analysis2 of the Tobit model that analyzed determinants of maize marketed
surplus is shown in Table 22 below.
Table 22: Tobit model results for maize marketed surplus
Variable Description
Coefficient
Standard
Error
Marginal effect
Marginal effect
C(IG )
CIG
CD(FG )
CDXY∗ /YG∗ PQ [
Dummy Bako Tibe
-0.468
0.145
-0.468***
-0.468***
Family size
-0.013
0.026
-0.013
-0.013
Seed used (log)
-0.008
0.093
-0.008
-0.008
0.397
0.077
0.397***
0.397***
-0.116
0.071
-0.116*
-0.116*
Current price (log)
1.633
0.096
1.633***
1.633***
Marketing costs (log)
0.191
0.047
0.191***
0.191***
-0.005
0.017
-0.005
-0.005
Extension contact (log)
0.027
0.0546
0.027
0.027
Land allocated (ha)
0.198
0.0685
0.198***
0.198***
Livestock holding (TLU)
0.018
0.013
0.018
0.018
-0.035
0.016
-0.035**
-0.035**
--3.715
0.621
--3.715***
--3.715***
0.726
0.040
0.726
0.726
Fertilizers used (log)
Distance to main market (hour)
Credit used (log)
Non-farm income (log)
Constant
Sigma
Number of observation
196
LR chi2 (12)
Pseudo R
575.36***
2
0.610
Left censored observations
31
Right censored observations
0
Predicted value (log)
5.665
Source: Author analysis from 2013.survey result
Note: Dependent variable is quantity of maize supplied to market (log),
*, ** and *** show explanatory variables were significant at 10%, 5% and 1% level respectively
2
Test for normality of residuals showed that error term was normally distributed (Appendix 2) and computed
marginal effects of determinant factors after Tobit (Appendix 3).
75
Twelve explanatory variables that were expected to determine marketed surplus of maize
were hypothesized and analyzed using the Tobit model. The results showed that district
dummy, fertilizer used, current price, marketing costs, land allocated, distance to main
market and non-farm income significantly determined marketed surplus. The remaining five
variables were found to have no significant effect on maize marketed surplus. Among these
districts dummy, distance to main market and non-farm income affected negatively.
Dummy Bako Tibe: Study areas of the two districts differ in their total production and
volume supplied to market. The result showed that there was statistically significant
difference between the two districts in supplying to the market. Sample households in GobuSeyo supplied to market more than Bako-Tibe district. When compared with Gobu-Seyo
district, maize supplied to market in Bako Tibe less by 0.468 percent from 1% increase
supply in Gobu Seyo. The result is in agreement with Abreham (2013) who conducted study
on vegetable value chain, which showed that difference between districts have significant
effect on volume of marketed supply.
Fertilizer used (log): It is a continuous variable which is expressed as the percentage of total
amount of fertilizers applied to maize in kilogram. The rate of fertilizers used for maize
production has significant and positive effect on the yield. These imply that maize is
responsive to fertilizer at rate in which farmers are applying it (Berihanu et al., 2007). This
increase in yield in turn positively affects the volume of maize supplied to market
(Muhammed, 2011). The result showed that use of fertilizers had significant and positive
effect on marketed supply of maize. For 1% increased in use of fertilizer application,
increased marketed surplus of maize by 0.397 percent..
Maize current price (log): Producers are sensitive to market price. Markets price of maize in
the study areas were set by negotiations between the buyers and sellers as well as by
prevailing market price as generally known in the market. When the selling price increased
from the prevailing market price, they were encouraged to supply more to the market. The
result shows that selling price was highly affecting the marketed supply of maize at 1%
significant level. When the current market price is increased by 1%, maize supplied to market
also increased by 1.633 percent. The result of this study is similar to the findings of Welelaw
(2005) and Ayelech (2011) who conducted studies on supply of rice and fruit market chain
76
respectively. They showed that current price had positive influence on the quantity supplied
to market.
Maize marketing costs (log): Households incurred costs such as transportation, storage and
sales tax when they supplied maize to market. It is a continuous variable which is expressed
as the percentage of total marketing costs incurred in ETB. It was hypothesized that as
marketing costs increased, the quantity supplied to market decreased. However, the result
showed that total marketing costs significantly and positively affected marketed supply. It
implied that for 1% increase in marketing costs, the volume supplied to market also increased
by 0.191 percent, which was in contrast with the hypothesis. The contrast may be due to high
transportation costs incurred associated with transporting large volume of maize to main
market. That is, the larger the volume transported to distant market, the higher the
transportation cost incurred. Ayele (2007) indicated that high marketing costs encourage
subsistence production but discourage production of marketable food surplus which in turn
affected the quantity supplied to market.
Land allocated for maize (ha): As hypothesized, the more the allocation of land for maize,
the more increase in production. This in turn increased the volume of marketable supply. The
result showed that the more the land is allocated for maize, the higher the production that in
turn increased marketed supply of maize. It implied that as the land allocated increased by
one hectare, marketed supply also increased by 0.198%. But other variables remain constant.
Tesfaye (2011) in his study also showed that size of cultivated land has positive influence on
the production of food grain, which in turn increased marketable supply, and Bosona et al
(2009) study showed that an increase in land allocation increased marketable supply.
Non-farm income (log): It was hypothesized that as family non-farm income increase, it
help in generating additional income and invest more on production of maize which in turn
increase the quantity supplied to market. However, the result was in opposite direction which
indicated that as non-farm income increase by 1%, quantity of maize supplied to market
decrease by 0.035%. This may be due to the fact that households who generate more income
from nonfarm activities, tends to sell less and increase family food consumption. This result
is in agreement with Rehima (2006) who indicated that non-farm income of households
negatively affected quantity supplied to market.
77
Distance from main market (hr): The distances from the main market influence households
in buying inputs and selling outputs. The closer the market place to farm gate, the lesser
would be the transportation costs, transaction costs, time, and more access to market
information. Therefore, the time taken to market negatively affected quantity supplied to the
market. .The result showed that for a hour increase in time taken to nearest market, marketed
surplus of maize decrease by 0.116%. The result is in line with Abreham (2013) study
showed that distance to the nearest market affected volume of sales negatively.
4.5. Challenges and Opportunities in the Value Chains
A number of constraints challenged actors in the value chains and some opportunities were
identified to overcome these constraints. The major constraints and opportunities in input
supply, production and marketing for maize and legume are discussed as follow:
4.5.1. Constraints and opportunities of input suppliers
Almost all agricultural input suppliers in the study areas were primary farmers’ cooperatives
and unions except few private sector maize seed suppliers. The cooperatives obtained maize
seed, fertilizers and plant protection chemicals from their respective unions. The unions
purchased seed from seed enterprises, fertilizers from importers and herbicide/pesticide from
chemical dealers. The cooperatives obtained maize seed and fertilizers when needed with
almost no quantity restrictions. However, they were constrained in accessing plant protection
chemicals and were restricted in obtaining the quantity ordered (Table 23).
Table 23: Agricultural inputs quantity restriction and availability
Maize seed
Variables
Availability when needed
Quantity restrictions
Fertilizers
Chemicals
N
%
N
%
N
%
No
2
40
1
20
4
80
Yes
3
60
4
80
1
20
No
4
80
5
100
2
40
Yes
1
20
0
0
3
60
Source: own computation from 2013 survey result
78
They were challenged by time taken from placing an order to delivery of the inputs. On
average, the lead-time needed for maize seed, fertilizers and chemicals were 5.4, 8.6 and 21.8
days respectively. This implied that cooperatives lack timely supply of inputs particularly
plant protection chemicals. Moreover, farmers complained on the quality of maize seeds and
chemicals supplied. On average, 5.8% and 3% of buyers complained on the quality of maize
seed and chemicals supplied respectively. The complaint reached a maximum of 10% for the
seed and 15% for the chemicals (Table 24). The result implied that some farmers were
suffering from quality of the inputs supplied.
In 2012/13 cropping season, 1447 quintals of maize seeds, 16,469 quintals of fertilizers and
5359 liters of chemicals were distributed to member farmers in the study areas. As they
ordered and distributed in pool, it was good opportunity to reduce transportation costs.
Moreover, coming together in cooperatives made farmers accessed to market information,
credit, extension services and intervention on inputs subsidy and distribution were made
through cooperatives.
Table 24: Lead-time and quality complaints of inputs
Observation
Variables
Mean
(coops)
Standard
Minimum
Maximum
deviation
value
value
Lead time needed to maize seed (days)
5
5.40
5.50
2
15
Lead time needed to fertilizers (days)
5
8.60
12.29
1
30
Lead time needed to chemicals (days)
5
21.8
9.42
7
30
Percentage of farmers complained on
5
5.80
4.02
2
10
5
0.00
0.00
0
0
5
3.00
6.71
0
15
quality of maize seed
Percentage of farmers complained on
quality of fertilizers
Percentage of farmers complained on
quality of chemicals
Source: own computation from 2013 survey result
79
4.5.2. Constraints and opportunities producers
There were different constraints that hindered production of maize and faba bean in the study
areas. These constraints were associated with inputs, market and nature. On other hand, there
were good opportunities for improving this production, which in turn increased marketable
surplus.
Constraints
Agricultural inputs such as improved seeds and fertilizers are the most important factors in
production, which in turn influenced marketable supply of crops. These constraints adversely
affect production and volume supplied to market. The major constraints of production and
marketing in the study areas were timely availability of inputs, prices of inputs and outputs,
availability of credit, access to market information, natural and environmental risk factors
such as drought, flood, pest, disease and soil fertility. Among these prices of fertilizers, prices
and quality of seed and fertility of soil were the most important constraints. They challenged
more than 80% of respondents. There were significant differences between the two districts
in facing constraints such as quality of seed, availability of credit, drought/flood and fertility
of soil (Table 25).
Table 25: Production and marketing constraints of maize and legume
Constraints?
Items
Timely availability of
Gobu Seyo
Bako Tibe
Total
(N=50)
(N=149)
(N=199)
N
%
N
%
N
%
No
16
32.00
34
22.82
50
25.13
inputs
Yes
34
68.00
115
77.18
149
74.87
Prices of seeds/
No
2
4.00
10
6..71
12
6.03
fertilizers
Yes
48
96.00
139
93.29
187
93.97
Availability of credit
No
32
64.00
66
44.30
98
49.25
to buy inputs
Yes
18
36.00
83
55.70
101
50.75
Quality of seeds
No
4
8.00
35
23.49
39
19.64
Yes
46
92.00
114
76.51
160
80.40
80
χ2
1.68
0.49
5.82**
5.70**
Access to market
No
25
50.00
59
39.60
84
42.21
information
Yes
25
50.00
90
60.40
115
57.79
Lack of reasonable
No
9
18
34
22.82
43
21.61
market price
Yes
41
82
115
77.18
156
78.39
Drought/flood
No
35
70
62
41.61
97
48.74
12.08**
Yes
15
30
87
58.39
102
51.26
*
No
20
40
55
36.91
75
37.69
0.15
Yes
30
60
94
63.09
124
62.31
26
15
10.07
28
14.07
74
134
89.93
171
85.93
pests/diseases
Fertility of soil
Yes
No
13
37
1.66
0.51
7.86***
Source: own computation from 2013 survey result
Note: ** and *** show statistically significant difference between the districts at 5% and 1% level of
significance respectively
The increased prices of seeds and fertilizers were the main problems of producers. As the
price of inputs increased, for example, some maize producers (14%) used partially/totally
local seeds and reduced the rate of fertilizers applied to maize. The result showed that
productivity of maize for local seed users was 20.71 quintals per hectare. It decreased by
about 12 quintals per hectare from those who used improved seeds in the areas. Therefore,
the increased in price of seeds and fertilizers decreased production. This in turn in turn
decreased the volume supplied to market. Moreover, poor quality of seeds also adversely
affected production and productivity of the crops.
Opportunities
Improved maize seed varieties such as BH-540, BH-543, BH-660, Agar, Homa, Limu and
Shone were supplied to farmers in the study areas. Fertilizers and plant protection chemicals
were also distributed through cooperatives. Using these inputs with modern agronomic
practices is a good opportunity for farmers to increase productivity. For example, in this case
it increased from 20 quintal per hectare to 32 quintal per hectare and reduced cost of input
transportation as they purchased from the nearby cooperatives.
81
Maize is a dominant crop is produced in the study areas; Agro-ecology in the two districts is
suitable for maize and other crops. The result indicated that maize yield was higher than the
national average of 25.4 quintals per hectare (CSA, 2011b). In other hand, almost no
respondents used improved faba bean seed and/or fertilizers. As a result, average production
and productivity of faba bean was 1.7 quintal per household and 6.3 quintals per hectare
respectively. This was very low from national average of 15 quintals per hectare. There was a
good opportunity to increase productivity at least to the national average using improved seed
varieties and other modern technologies.
One of the constraints of producers was access to market and price information. The sources
of the information varied among actors in the value chains. For the producers, the primary
sources of market information (52%) was neighbor and/relative farmers. Whereas, the main
source of market information (57%) for cooperatives was government department. It was
good opportunity for farmers to access inputs and output information from cooperatives as it
is reliable and timely available.
4.5.3. Constraints and opportunities of traders
Constraints
Marketing constraints affected the performance of market. The major market constraints
identified were prices, quality standards, market information, credit access, licensing,
taxation and infrastructures. Among these, being not all traders were licensed was the main
constraint of traders. The unlicensed traders in the market affected the performance of market
in creating unstable prices, poor in quality control; reduce government income, which in turn
affected development of market infrastructure.
Unstable price and weak demand for their products were the next important constraints and
more than half of the respondents also limited to credit access and weak market information
(Table 26 above). Grain traders’ main source of market information or 87% of their source
was from personal contacts with other traders, which was weak, not reliable and timely.
Table 26: Maize and legume traders marketing constraints
82
Are the following constraints?
No
Observations
Yes
Percent Observations
Percent
Unstable prices
17
32.08
36
67.92
Poor quality of grain
29
54.72
24
45.28
Absence of grade or standards
39
73.58
14
26.42
Multiple taxation
44
83.02
9
16.98
Non transparent taxation system
39
73.58
14
26.42
Difficulty in obtaining license
49
92.45
4
7.55
Not all traders are licensed
13
24.53
40
75.47
Weak access to market information
23
43.40
30
56.60
Limited access to credit
24
45.28
29
54.72
Weak demand for products
18
33.96
35
66.04
Source: own computation from 2013 survey result
Opportunities
In addition to supplying agricultural inputs, cooperatives took part in grain maize trade.
Accordingly, they purchased 138 quintals of maize from farmers as well as provided storage
services for their members. This was good opportunity for farmers in stabilizing market price
by competing with private trades. They could also be good sources of reliable and current
grain price information. Well-organized cooperatives protected their members from poor
price and created possibility of group/contract sales.
In the study areas, sample traders only purchased 22, 964 quintals of grain maize from
smallholder farmers. This indicated that the areas had high production potential to supply for
agro- industries. However, no agro-processors are found in maize value chain that is involved
in processing the crop. Hence, it is good opportunity for agro-processors to process and
distribute the commodity in flour form. Moreover, the presences of processors in the value
chain increase the demand for the commodity and in turn increase its price that benefits
producers.
83
5. SUMMARY, CONCLUSION AND RECOMMENDATIONS
5.1.Summary and Conclusion
The study aimed at analyzing maize and faba bean value chains in Bako Tibe and Gobu
Seyo districts in central west of Ethiopia. The specific objectives of the study include
mapping maize and faba bean value chains, evaluating governance, incentives and cost
structures in the value chains, estimating determinants of maize marketable surplus, and
identifying constraints and opportunities of actors in the value chain. The data was
collected from both primary and secondary sources. Primary data was collected from
households, grain traders and input suppliers. The data was analyzed using descriptive
statistics, market performance, and econometric analysis of Tobit model.
Results from demographic characteristics showed that more household heads were male
and completed primary school that can read and write. More household heads were adult
and had family size of national average. They had many years farming experience in maize
and few years in faba bean farming. The proportion of land allocated for maize was much
higher compared with faba bean. Farmers in the areas practiced rearing livestock which
supplemented crop farming.
The survey results showed that among cereals produced, maize was the most important
and dominant crop in the areas. Moreover, it was the commodity which used improved
seed and fertilizers intensively. Production and productivity of the commodity was much
higher than other crops produced in the areas. Its yield was more than the national average
by six quintals. From maize total production, households supplied about half to markets.
Legume crops produced in the areas were faba bean, haricot bean and soybean, which
produced in small quantity. Among these, faba bean was relatively produced by more
households on small plot of land. Farmers in the areas did not use improved seed varieties
and fertilizers for faba bean production. As a result the productivity was much less than
the national average 15 quintals per hectare. Moreover, the quantity supplied to market
was very low. As they did not use improved agricultural inputs and modern agronomic
practices, faba bean production and productivity was very low in the study areas.
84
Maize and faba bean passed from producers to final consumers through nine and five
distribution channels respectively. The major alternative buyers that purchased maize
directly from producers were rural assemblers, rural wholesalers, cooperatives, urban
wholesalers, urban retailers and consumers. The same is true for faba bean except
cooperatives and rural wholesalers, which were not involved in purchasing of faba bean
due to low quantity produced and supplied to markets. Rural assemblers were the main
buyers of maize and faba bean from farmers.
The main actors that participated in maize and faba bean value chains were input suppliers,
producers, assemblers, wholesalers, retailers and consumers. The flow of the commodities
was from producer → rural assembler → urban wholesaler → urban retailer → consumer.
High volume of maize and low quantity of faba bean passed through these actors from
producers to consumers. The quality of the commodities distributed through these
channels was medium to above average. In the value chains, more value was added as
transportation. Therefore, decreasing transportation costs could increase profit share of
actors in the value chains.
Governance in the value chains was exerted through market information gap, credit access,
number of competitors in market and quality standards. It exerted through access to price
information when grain traders were important price information source for producers and
had more information than producers. As a result, traders became influential when
negotiating on generally known prevailing market price. In other hand, producers sold
much of their product immediately after harvest because of their cash need and limited
access to credit. As a result, they were forced to sell at peak supply season when price was
low. This implies that credit access and financial capacity governed the seasons when to
supply to market.
Looking at competition in maize and faba bean markets, urban wholesalers handled large
volume of commodities in which they mainly purchased from other traders in the markets.
All grain traders except urban retailers sold their product to the wholesalers. As they had
fewer competitors and potential buyers in the markets, they had a decision-making power
on quantity, quality and prices. Moreover, grain traders set quality assessment methods,
which were not standardized as common measurements. Sellers had no room to convince
85
buyers that the quality of supplied commodities had good quality. Buyers paid depending
on the quality assessment they set. If they assessed the quality was poor, they paid less
price from generally known prevailing market price and if they thought quality was
average/above average, they paid generally known market price. Therefore, quality
assessments methods made potential buyers influential in deciding market price related to
the quality of commodities supplied to markets.
The performances of maize and faba bean markets were evaluated by considering costs,
returns and market margins. Regarding costs, more production costs were incurred for faba
bean than maize. However, maize had high marketing margin than faba bean, which
implies that farmers obtained more marketing margin from producing maize. Among
marketing costs, actors incurred high costs on transportation and reducing this cost
increase gross profit of actors in the value chains.
Producers obtained higher percentage share of profit when they sold their product directly
to consumers. Percentage shares of price spread received by intermediaries was higher in
the shorter channel which included rural assemblers. However, percentage shares of
producers from total marketing margin was highest in channels where only one
intermediary between producers and final consumers. Net marketing margin was highly
associated with gross marketing margin. The higher the share of gross marketing margin,
the more net marketing margin obtained.
Factors determining volume of maize supply to market were identified using econometrics
analysis of Tobit model. Twelve explanatory variables in maize that expected to determine
marketable surplus were hypothesized. Results showed that determinants of maize
marketed surplus were district dummy Bako Tibe, fertilizer quantity used in maize
production, current maize price, marketing costs, land allocated for maize cultivation,
distance to main market and non-farm income. They significantly determined volume of
maize supplied to market. Among these, district dummy Bako Tibe, non-farm income and
distance from main market determined negatively. The most important determinant factors
were location differences, current price and quantity of fertilizer used.
86
Constraints associated with input supply were limited access to inputs, long time taken
from order placement and delivery, quantity restriction and quality of plant protection
chemicals. This implies that cooperatives are constrained in supplying required quantity
and quality of improved seed and chemicals to their members. Concerning production, the
major constraints maize producers were price of seed and fertilizers, quality of seed, and
fertility of soil. As the price of input particularly price of seed and fertilizers increased,
farmers used local seed and reduce the rate of fertilizers application. As a result production
and productivity decreased which result in less supplied to markets. In addition to these,
there were constraints associated with marketing. The main constraint for traders was large
number of unlicensed traders in the market, which adversely affected the performance of
market.
The major opportunities in the value chains associated with input supply were ordered in
bulk, transporting and distributing in pool decreased transportation costs. Moreover, the
cooperatives are located in their kebeles or neighbor kebeles. This helps in saving their
time and reducing other costs related to purchasing inputs. As to production, a number of
improved maize seeds were available and supplied by cooperatives, which can increase
productivity. As the study areas have high maize production potential, it has the capacity
to supply more volume to markets. Therefore, it is good opportunity for agro-processors to
process and distribute the commodity.
5.2. Recommendations
The main costs of maize production were the costs incurred to purchase improved seed
and fertilizers. However, increase prices of these inputs were the main constraint of
production. As most maize seeds supplied to the study areas were hybrid which cannot be
used repeatedly year round. Even if productivity of these varieties was higher than nonhybrid, some farmers could not afford it. Moreover, the hybrid seed was not available at
peak time of sowing when farmers required. Therefore, researchers need to develop
productive maize varieties that could be recycled in seed use without much loss in yield.
The costs of fertilizers can be reduced by using organic fertilizers and modern agronomic
techniques such as crop rotation and intercropping which improve fertility of soil.
87
Producers primarily accessed price and market information mainly from other farmers
and/or grain traders. These might not be from primary and reliable sources. They sold their
product with market prices that heard from their sources. On other hand, cooperatives
mainly accessed the information from government marketing department/unions, which
was current and from reliable source. Therefore, it needs to develop market information
transferring system from cooperatives to farmers. This can be through posting current and
updated price of crops regularly and create strong communication linkage with agricultural
development agents and kebeles.
Cooperatives purchased maize grain mainly at their store, which are located in farmers’
“kebeles” or nearby kebele. They purchased relatively with better price than other traders
that directly purchased from the farmers. However, it was not long lasting because they
were constrained by financial capital. They could not accommodate the volume supplied to
them. As a result, farmers sold to private traders with low price compared with
cooperatives purchasing prices. Therefore, it needs to improve financial strength of
cooperatives by facilitating credit access from Farmers’ Cooperative Banks in peak grain
supply months. Moreover, government should strengthen Ethiopian Grain Trade
Enterprise to purchase large volume of maize from producers.
The quantity of maize supplied to markets was determined by production area differences,
chemical fertilizers used, current price, marketing costs, size of land allocated, distance to
main market and non-farm income. To improve the quantity supplied to markets, it needs
to promote the determinant factors. Accordingly, it needs to share experience between the
two districts, improve fertilizers use either chemical or organic fertilizers along with
modern agronomic practices that improve soil fertility, improve price by creating demand
through promoting to agro- processors and strengthen cooperatives’ financial capital.
Agro-processors need large volume of raw materials for processing and distributing. The
study areas have potential to supply large volume of maize to markets. However, there is
no maize processor or that uses maize as raw material in the maize value chain. Therefore,
it needs to promote agro-processors to be involved in processing and distributing maize in
flour form. This increases the demand for maize, which in turn increase the price of the
commodity which encourages producers.
88
Faba bean production and productivity can be improved by using improved seed varieties
and modern agronomic practices such as recommended cultivation, sowing time and
spacing, farm management, crop rotation, inter cropping etc. Farmers in the study areas
did not use improved faba bean varieties and other agricultural technologies for the
production of the commodity. However, the commodity in addition to production, it helps
in maintaining soil fertility. As maize is the dominant crop in the areas, practicing crop
rotation and/or inter cropping with faba bean improve fertility of the soil. Therefore, it
needs to promote improved faba bean varieties along with its modern agronomic practices.
This can be through on farm demonstration of the varieties, training and subsidize the
varieties for promotion.
89
6. REFERENCES
Abreham Tegegn, 2013. Value chain analysis of vegetables: The case of Habro and
Kombolcha woredas in Oromia region. M.Sc thesis submitted to School of Graduate Studies
of Haramaya University.
Acharya, S.S and N.L. Agarwal, 1999. Agricultural marketing in India, 3rd edition. Oxford
and IBH Publishing, New Delhi. p17.
Achoga, F.O. and E. C. Nwagbo, 2004. Economic assessment of the performance of private
sector marketing of fertilizer in Delta State. Annual conference of Nigeria Association of
Agricultural Economists on “Agricultural marketing and commercialization for sustainable
development”. Ahmadu Bello University. 2004, Samaru – Zaria
Agricultural Cooperative Development International/Volunteers in Overseas Cooperative
Assistance (ACDI/VOCA), 2009. Project Profile: Kenya Maize Development Program
(KMDP). Washington, DC: ACDI/VOCA.
Anandajayasekeram P. and Berhanu Gebremedhin. 2009. Integrating innovation systems
perspective and value chain analysis in agricultural research for development: Implications
and challenges. Improving Productivity and Market Success (IPMS) of Ethiopian Farmers
Project Working Paper 16. ILRI (International Livestock Research Institute), Nairobi, Kenya.
67 pp
Ayele Ulfata Gelan, 2007. Does food aid have disincentive effect on local production? A
general equilibrium perspective on food aid in Ethiopia. Food policy 32(2007): 436-458.
Ayelech Tadese, 2011. Market chain analysis of fruit for Gomma woreda, Jimma zone
Oromia, Ethiopia. M.Sc thesis submitted to School of Graduate Studies of Haramaya
University.
Bako Tibe Wereda Agricultural Office, 2011. Socio economic profile of Bako Tibe district
January, 2011.
Berhanu Gebremedhin, Fernandez-Rivera S, Mohammed Hassena, Mwangi W and Seid
Ahmed. 2007. Maize and livestock: Their inter-linked roles in meeting human needs in
Ethiopia. Research Report 6. ILRI (International Livestock Research Institute), Nairobi,
Kenya..
Bill and Melinda Gate Foundation (BMGF), 2010. Accelerating Ethiopian Agriculture
Development for Growth, Food Security, and Equity Synthesis of findings and
recommendations for the implementation of diagnostic studies in extension, irrigation, soil
health/fertilizer, rural finance, seed systems, and output markets (maize, pulses, and
livestock). July 2010.
Bonnard and Sheahan, 2009. Commodity Market Maps and Price Bulletins: Tools for Food
Security Analysis and Reporting Famine Early Warning System Network (FEWS NET).
Markets Guidance, No 4. July 2009.
90
Bosena D.T, F. Bekabil, G. Brihanu and H. Dirk, 2011. Factors affecting cotton supply at the
farmer level in Metema district of Ethiopia. Journal of agriculture, Biotechnology and
Ecology, 4(1), 41-51, 2011 ISSN:2006-3938.
Cameron C. and P.K. Trivedi, 2009. Micro econometrics using Stata. Stata press.
Carlton D. W. and J. M. Perloff, 1994. Modern Industrial Organization, Harper Collins
College Publishers, 2nd edition. New York. Pp 331-334.
Caves, E. R., 1992. American Industry; Structure, Conduct and Performance: Harvard
University, Prentice Hall.
Central Statistical Agency (CSAa), 2011. Population and Housing Projection Summary.
Statistical Report based on the 2007 Population and Housing Census. July 2011, Addis
Ababa, Ethiopia.
Central Statistical Agency (CSAb), 2011. Agricultural sample survey based on area and
production of major crops, Volume I. April 2011, Addis Ababa, Ethiopia.
Central Statistical Agency (CSA), 2012. Agricultural sample survey based on crop and
livestock product utilization. Volume VII. December, 2012, Addis Ababa, Ethiopia.
Daniel Belay, (2006). Performance of primary agricultural cooperatives and determinants of
members’ decision to use as marketing agent in Ada Liben and Lume districts. M.Sc thesis
submitted to School of Graduate Studies of Haramaya University.
Demeke M., 2012. Analysis of incentives and disincentives for maize in Ethiopia. Technical
notes series, MAFAP, FAO, Rome.
Ethiopia Commodity Exchange Authority (ECX), 2009. Understanding Maize: A Review of
supply and Marketing issues. Addis Ababa. January 2009.
Food and Agricultural Organization (FAO), 2012. FAO/WFP crop and food security
assessment mission to Ethiopia. FAO/WFP of the United Nations, Rome. April 2012.
Gereffi G., J. Humphrey and T. Sturgeon, 2005. The Governance of Global Value Chains.
Review of International Political Economy, 12(1): 78-104.
Gobus Seyo District Agricultural Office(GSDAO), 2011. Socio economic profile of Gobu
Seyo district. January, 2011.
Greene H. William, 2012. Econometrics analysis: Classical linear regression. 7th edition. The
Free press.
Haggblade S., V. Theriault, J. Staatz, N. Dembele and B. Diallo, 2012. A Conceptual
Framework for Promoting Inclusive Agricultural Value Chains. Improving the Inclusiveness
of Agricultural Value Chains in West Africa. Michigan State University (MSU).
Hawkes, C., and M. T. Ruel. 2011. Value Chains for Nutrition. Pp 2020 Conference Paper.
Washington, DC: International Food Policy Research Institute (IFPRI), 2011.
91
Hellin J., and Mandeln Meijer, 2006. Guidelines for value chain analysis, November 2006.
Heltberg R. and F. Tarp, 2001. Agricultural supply response and poverty in Mozambique.
The conference on “Growth and Poverty”. University of Copenhagen, Copenhagen. 25-26
May 2001. Institute of Economics.
Hurni H., 1986. Agro-ecological belts of Ethiopia. Soil Conservation Research Programme
Ethiopia. Research Report.
International Food Policy Research Institute (IFPRI), 2010a. Maize Value Chain Potential in
Ethiopia, Constraints and Opportunities for Enhancing the System. Technical Report, July
2010.
International Food Policy Research Institute (IFPRI), 2010b. Pulse value Chain Potential in
Ethiopia, Constraints and opportunities for enhancing the system Technical Report, July
2010.
Johan, V.Z., 1988. Institutional aspects of a marketing strategy for avocados. South African
avocado growers’ association yearbook. 11:11-15.
Johnston, C. and R.L Meyer, 2007. Value Chain Governance and access to finance. Maize,
Sugar cane and Sunflower oil in Uganda. micro REPORT #88, USAID.
Kaplinsky, R. and M. Morris, 2001. A Handbook for Value Chain Research. Brighton, United
Kingdom, Institute of Development Studies, University of Sussex.
Kinde Aysheshm, 2007. Sesame market chain analysis: The case of Metema woreda, North
Gondar Zone, Amhara National Regional State. M.Sc thesis submitted to the School of
Graduate Studies, Harmaya University. 102p
Kirimi, L.., N. Sitko, T.S. Jayne, F. Karin, M. Muyanga, M. Sheahan, J.Flock, and G. Bor ,
2011. A Farm Gate to Consumer Value Chain Analysis of Kenya’s Maize Marketing System.
MSU International Development working Paper No. 111 January 2011.
Kizito Andrew, 2008. Structure- Conduct- Performance and food security. Markets guidance,
No 2. Famine Early Warning System Network (FEWS NET) USAID, May 2008.
Kohls, R.L. and J.N. Uhl, 2002. Marketing of agricultural products, 9th edition Prentice
Hall, Upper Saddle River. Pp 33-34
Kumar, R., K. Alama, V. Krishnab and K. Srinivasa, 2012. Value chain analysis of maize
seed delivery system in public and private Sectors in Bihar. Agricultural Economics Research
Review. 25 (1): 387-398.
Maddala, GS., 1997. Limited dependent and qualitative variables in Econometrics.
Cambridge university press, Cambridge. Pp 175-181.
Making Markets Work Better for the Poor (M4P), 2008. Making value chains Work Better
for the Poor: a tool book for practitioners of value chain analysis. Version3.M4P project, UK
92
Mendoza, G., 1995. A primer on marketing channels and margins. Pp 257-275.In G.J. Scott
(ends). Prices, Products, and people; Analyzing Agricultural Markets in Developing
Countries. Lynne Rienner Publishers, Boulder, London.
Ministry of Finance and Economic Development (MoFED), 2010/11. Macro economic
development of Ethiopia. Annual Report 2010/11.
Ministry of Finance and Economic Development (MoFED), 2012. Growth and
Transformation Plan (2010/11-2014/15). Annual Progress Report for Fiscal Year 2010/11,
MoFED, June 2012, Addis Ababa.
Minten B., Seneshaw Tamru, Ermias Engida, and Tadesse Kuma, 2013. Using Evidence in
Unraveling Food Supply Chains in Ethiopia: The Supply Chain of Teff from Major
Production Areas to Addis Ababa. (Ethiopian Strategy support Program (ESSP). Working
paper 54, June 2013
Moti Jaleta and Berhanu Gebremedhin, 2012. Interdependence of smallholders’ net market
positions in mixed crop-livestock systems of Ethiopian highlands. Journal of Development
and Agricultural Economics. 4(7): 199-209.
Muhammed Urgesa, 2011. Market chain analysis of teff and wheat production in Halaba
woreda, Ethiopia. M.Sc thesis submitted to School of Graduate Studies of Haramaya
University.
Nedelcovych M. and David Shiferaw, 2012. Private sector perspectives for strengthening
agribusiness value chain in Africa. Case studies from Ethiopia, Gahana, Kenya, and Malawi.
Partnership to Cut Hunger and Poverty in Africa (PCHPA). May 2012.
Organization for Economic Cooperation and Development (OECD), 2007. Enhancing the
Role of SMEs in Global Value Chains. OECD global conference. May 31–June 1, 2007,
Tokyo.
Porter M. E., 1985. Competitive advantage: Creating and sustaining superior performance.
The Free Press, New York.
Rashid S., K. Getnet and S. Lemma 2010 Maize value chain potential in Ethiopia: Constraints
and opportunities for enhancing the system, IFPRI, Working Paper, July, 2010.
Rashid and Negassa. 2011. Policies and Performance of Ethiopian Cereal Markets.
Development Strategy and Governance Division, IFPRI – Ethiopia Strategy Support Program
II, Ethiopia. ESSP II Working Paper No. 21.May 2011.
Rehima Mussema (2006). Analysis of red pepper marketing: The case of Alaba and Siltie in
SNNP of Ethiopia, M.Sc thesis submitted to School of Graduate Studies of Haramaya
University.
Regional Agriculture Trade Expansion Support Program (RATES), 2003. Maize marketing
assessment and baseline study for Ethiopia. Technical Report, July, 2003, Nairobi, Kenya.
93
Scarborough, V. And J. Kydd, 1992. Economic Analysis of Agricultural Markets: A Manual.
Chatham, U.K. Natural Resources Institute.
Schmitz, H., 2005. Value chain analysis for policy makers and practitioners. International
Labour Office and Rockefeller Foundation, Geneva, Switzerland.
Scott Guy, 1995. "Agricultural Transformation in Zambia: Past Experience and Future
Prospects," paper presented at Agricultural Transformation Workshop, Abidjan (East
Lansing: Michigan State University, 1995.
Sudan Integrated Food Security Information for Action (SIFSIA),2011. Price and MarketStructure Analysis for Some Selected Agricultural Commodities: Marketing Costs and
Margins. May 2011.
Silva, C. D. and D S. Filho, 2007. Guidelines for rapid appraisal of agro-food chain
performance in developing countries, Agricultural Management, Marketing and Finance
Occasional Paper, No.20, FAO, Rome,
Staal, B. 1995. Marketing and Distribution System. Livestock Policy Analysis. ILRI Training
Manual.
Tadesse Negash (2011). Value chain analysis of vegetables in Daro Lebu district of western
Hararghe zone, Oromia region, Ethiopia. M.Sc thesis submitted to School of Graduate
Studies of Haramaya University.
Tesfaye Kumbi, 2011. Household food insecurity in Dodata-Sire district, Arsi zone: coping
strategies and policy options. M.Sc thesis submitted to School of Graduate Studies of
Haramaya University.
Tobin. J., 1985. Estimation of relationships for limited dependent variables. Econ., 26: 24-36.
Trienekens H. Jacques, 2011. Agricultural Value Chains in Developing Countries A
Framework for Analysis. International Food and Agribusiness Management Review. 14 (2):
51-76.
United Nations of Industrial Development Organization (UNIDO), 2009. Agro value chain
analysis and development. The UNIDO approach. Technical Report, 2009 Vienna, Austria,
United States Agency for International Development (USAID), 2010. Staple food value chain
analysis. Country report – Ethiopia, April 2010.
Webber C. Martin and Partick Labaste (2009). Building Competitiveness in Africa’s
Agriculture: A guide to value chain concept and applications. The World Bank, Washington
DC, 2009.
Welelaw Sendeku, 2005. Factors determining supply of rice: A study in Fogera district of
Ethiopia. M.Sc thesis submitted to School of Graduate Studies of Haramaya University.
World Development Report (WDR), 2008. “Agriculture for Development”, value chains and
small farmer integration. The World Bank - LCR series.
94
7. APPENDIX
Appendix 1: Conversion factors used to compute Tropical livestock unit (TLU)
Livestock category
Conversion factor
Calf
0.25
Weaned calf
0.34
Heifer
0.75
Cow or ox
1.0
Horse/mule
1.10
Donkey (adult)
0.7
Donkey (young)
0.35
Camel
1.25
Sheep/got (adult)
0.13
Sheep/got (young)
0.06
Chicken
0.013
Bull
0.75
Source: Storck et al., 1991
Appendix 2: Agricultural inputs supplied by cooperatives to their respective kebeles
Inputs
Maize seeds
Fertilizers
Chemicals
Variety/type
Quantity supplied
(qt or lit)
Average selling price
(ETB/qt or ETB/lit)
BH 540
622
2248.50
BH 543
187
1707.00
BH 660
496
1558.67
Shone
121
3912.25
Limu
8
3839.25
Agar
44
3946.00
Homa
150
2885.00
10614
1546.20
UREA
5855
1255.00
Pesticide
2642
146.00
Herbicide
2717
151.00
DAP
95
Appendix 3: Test for normality of residuals
Tobit regression
Number of obs
Log likelihood = -183.63508
Explanatory Variables
LR chi2(12)
= 575.36
Prob > chi2
= 0.0000
Pseudo R2
coef
Std.Err
t
= 196
p>/t/
= 0.6104
[95% Conf. Interval]
District dummy
-0.4680
0.1455
-3.22
0.002
-0.7550
-0.1810
Family size
-0.0130
0.0257
-0.51
0.613
-0.0636
0.0376
Maize seed used (log)
-0.0076
0.0927
-0.08
0.935
-0.1905
0.1753
0.3971
0.0774
5.13
0.000
0.2444
0.5499
-0.1163
0.0705
-1.65
0.101
-0.2554
0.0228
Current price (log)
1.6333
0.0960
17.01
0.000
1.4438
1.8227
Marketing costs (log)
0.1910
0.0474
4.03
0.000
0.0974
0.2846
Credit used (log)
0.0054
0.0183
-0.29
0.769
-0.0414
0.0307
Extension contact (log)
0.0269
0.0548
0.49
0.623
0.0808
0.1346
Land allocated for maize
(log)
0.1978
0.0675
2.93
0.004
0.0647
0.3309
Tropical Livestock Unit
(TLU)
0.0176
0.0129
1.37
0.171
0.0076
0.0428
Non-farm income (log)
-0.0349
0.0164
-2.13
0.035
-0.0673
0.0026
constant
-3.7154
0.6212
-5.98
0.000
-4.9409
-2.4899
0.7259
0.0400
0.6469
0.8050
Fertilizer used (log)
Distance to main market
Sigma
Chi-square(12)
p-value
Schwarz criterion
Akaike criterion
Hannan-Quinn
Left-censored observations
Right-censored observations
527.00
0.0001
441.16
395.27
413.85
31
0
Test for normality of residual Null hypothesis: error is normally distributed
Test statistic: Chi-square(2) = 2.02958
with p-value = 0.362479
96
Appendix 4: Marginal effect of determinants of marketed surplus of maize
1. Marginal effects after tobit
Y= E(marketed surplus of maize*/marketed surplus of maize>0) (predict, y*(0,.))
= 5.6648123
Explanatory Variables
Dy/dx
District dummy
‐0.4680
0.1455
‐3.22
0.001
‐0.7531
‐0.1829
0.7449
Family size
‐0.0130
0.0257
‐0.51
0.613
‐0.0633
0.0373
6.5561
Maize seed used (log)
‐0.0076
0.0927
‐0.08
0.935
‐0.1892
0.1741
2.5364
0.3971
0.0774
5.13
0.000
0.2454
0.5489
4.9395
‐0.1163
0.0705
‐1.65
0.099
‐0.2545
0.0219
1.0454
Current price (log)
1.6333
0.0960
17.01
0.000
1.4451
1.8214
4.3601
Marketing costs (log)
0.1910
0.0474
4.03
0.000
0.0980
0.2840
2.9800
Credit used (log)
0.0054
0.0183
‐0.29
0.768
‐0.0412
0.0304
2.1126
Extension contact (log)
0.0269
0.0548
0.49
0.622
0.0801
0.1338
2.9638
Land allocated for maize (log)
0.1978
0.0675
2.93
0.003
0.0656
0.3300
1.2814
Tropical Livestock Unit (TLU)
0.0176
0.0129
1.37
0.170
0.0075
0.0426
5.9805
‐0.0349
0.0164
‐2.13
0.033
‐0.0670
0.0028
3.5498
Fertilizer used (log)
Distance to main market (hr)
Non‐farm income (log)
Std.Err
z
p>/z/
[95% Conf. Interval]
X
(*) dy/dx is for discrete change of dummy variable from 0 to 1
2. Marginal effects after tobit
Y= E(marketed surplus of maize*/marketed surplus of maize>0) (predict, e(0,.))
= 5.6648123
Explanatory Variables
Dy/dx
District dummy
-0.4680
0.1455
-3.22
0.001
-0.7531
-0.1829
0.7449
Family size
-0.0130
0.0257
-0.51
0.613
-0.0633
0.0373
6.5561
Maize seed used (log)
-0.0076
0.0927
-0.08
0.935
-0.1892
0.1741
2.5364
0.3971
0.0774
5.13
0.000
0.2454
0.5489
4.9395
-0.1163
0.0705
-1.65
0.099
-0.2545
0.0219
1.0454
Current price (log)
1.6333
0.0960
17.01
0.000
1.4451
1.8214
4.3601
Marketing costs (log)
0.1910
0.0474
4.03
0.000
0.0980
0.2840
2.9800
Credit used (log)
0.0054
0.0183
-0.29
0.768
-0.0412
0.0304
2.1126
Extension contact (log)
0.0269
0.0548
0.49
0.622
0.0801
0.1338
2.9638
Land allocated for maize (log)
0.1978
0.0675
2.93
0.003
0.0656
0.3300
1.2814
Tropical Livestock Unit (TLU)
0.0176
0.0129
1.37
0.170
0.0075
0.0426
5.9805
-0.0349
0.0164
-2.13
0.033
-0.0670
0.0028
3.5498
Fertilizer used (log)
Distance to main market (hr)
Non-farm income (log)
Std.Err
z
p>/z/
(*) dy/dx is for discrete change of dummy variable from 0 to 1
97
[95% Conf. Interval]
X
Appendix 5: Household questionnaire
The following questionnaires were prepared by CIMMYT for Sustainable Intensification of
Maize-Legume Cropping Systems for Food Security in Eastern and Southern Africa
(SIMLESA) Project. As the CIMMYT is the sponsor on this thesis work and the study was
carried out in one of their project areas, the questionnaires were adapted from the SIMLESA
project in Ethiopia.
PART 0. INTERVIEW BACKGROUND
1.
2.
3.
4
Respondent’s name:...............................................................................................................................
Region..............................................
Zone..................................................................
District:…..…………..……..............................
5. Peasant Association …..…………….................
6. Village.........................................................................
7. GPS readings of Village: a) Altitude.......................; b) Latitude…………; c) Longitude…………………
PART 1. FARMERS IDENTIFICATION AND VILLAGE CHARACTERISTICS
Experience in growing maize (years)………………………………………………………….......
Experience in growing legumes (years) Haricot bean............... Soybean……........Pigeon pea……
Groundnut…..…Cowpea…… (Other, specify name) .................................Years of experience……....
Distance to the village market from residence (km) ............................minutes of walking time ...........................
What means of transport do you use mainly to get to the village market? (CodesA)………………
4
01
02
03
04
05
06
Purposively
98
Age
(years)A
3
Codes B
2
Marital
status
1
Codes A
Name of household member (start
with respondent)
Sex
Family code
Average single trip transport cost (per person) to the village market using this means of transport
(ETB/person).................
Education
(years)
5
6
Codes C
Own farm
labour
contribution
Codes D
10
07
08
09
10
Codes A
Codes B
Codes C
Codes D
0. Female
1. Married living with
spouse
0.
None/Illiterate
1. 100%
2. 75%
2. Married but spouse
away
1. Adult
education or 1
year of
education
3. 50%
* Give other
education in
years
6. Not a
worker
1. Male
3. Divorced/separated
4. Widow/widower
5. Never married
6. Other, specify……
4. 25%
5. 10%
Distance to the nearest main market from residence (km)……………minutes of walking time……...…
Average single transport cost (per person) to the main market using a car (ETB/person) ......................................
Distance to the nearest source of seed dealer from residence (km) .................minutes of walking time ............
Distance to the nearest source of fertilizer dealer from residence (km) ...........minutes of walking time ………
Distance to nearest source of herbicides and pesticides dealer from residence (km)…minutes of walking time ....
Distance to the nearest farmer cooperative from residence (km)………minutes of walking time ..............
…
Codes A: 1. Walking; 2. Bicycle; 3. Tractor; 4. Car ; 5. Cart, 6. Other, specify……………………
A/For the under 6 year olds, give age to the nearest 3 decimal places
PART 2: CURRENT HOUSEHOLD COMPOSITION AND CHARACTERISTICS
Section A: Land holding (kert) during the 2012/13 cropping year (last cropping year)
Long rain season
Land category
Cultivated
1
(annual + permanent crops)
Uncultivated (e.g.
grazing, homestead etc)
2
3
1. Own land used (A)
2. Rented in land (B)
3. Rented out land (C)
4. Borrowed in land (D)
5. Borrowed out land (E)
6. Total owned land (A+C+E)
7. Total operated land (A+B+D)
8. Bought land during long rain season
9. Sold land during long rain season
99
Section B: Main sources and quantity of seed for Maize, Haircot beans, faba bean, and other major legumes
grown last cropping year (2012/13)
Season
Codes
A
Quantity of seed and sources
Crop
(start
with
belg)
1
Crop
variety
Total
amount
of seed
(kg)
Codes
B
2
3
4
Codes A
Codes B
1. Belg
(residual
moisture)
1. Own saved
2. Gift from
family/neighbor
3. Farmer to farmer
seed exchange
4. On-farm trials
2. Meher
Source 1
Source 2
Source 3
Amount
(kg)
Codes B
Amount
(kg)
Codes
6
7
8
9
5
5. Extension demo plots
6. Farmer groups/Coops
7. Local seed producers
8. Local trader
B
9. Agro-dealers/agrovets
10. Bought from seed company
11. Provided free by
NGOs/govt
12. Govt subsidy program
Amount
(kg)
10
13.
Other
(specify)
PART 3. CROP PRODUCTION (2012/13 crop calendar)
Section A. Plot characteristics, investment and input use
plot size
(timad)
1
Crop(s)
grown
2
Crops
variety
3
(Sub)plot ownership
Soil fertility
Irrigation
Codes C
Codes E
(Codes J)
4
5
6
Codes C
Codes E
Codes J
1. Owned
4. Borrowed in
1. Good
1. Irrigated
2. Rented in
5.Borrowed out
2. Medium
2. Rainfed
3. Rented out
6. Other, specify….
3. Poor
100
Section B:Input use
1
Seed use (if intercropped, separate by comma)
Non-bought
Amount of DAP , etc
(Kg)
grown
Fertilizer (If not used, put Zero)
Total
cost
(ETB)
Amou
nt of
Urea
etc
(Kg)
Total
cost
(ETB
)
Main
seed
source
(Code
s A)
2
3
4
5
6
seed (own
saved, gift,
farmers to
farmers
exchange,
etc(kg/No.)
7
Number
of
seasons
own
saved
recycled
8
Bought
credit
Herbicides
including
Amoun
t (kg)
Total cost
(ETB)
9
10
Litre
s/kg
Total
cost
(ETB)
11
12
Codes A
1. Own saved
2. Gift from family/neighbor
3. Farmer to farmer seed exchange
4. On-farm trials
5. Extension demo plots
6. Farmer groups/Coops
7. Local seed producers
8. Local trader
9. Agro-dealers/agrovets
10. Bought from seed company
11. Provided free by NGOs/govt
12. Govt subsidy program
101
13. Other (specify)……………
Section C: Input use and crop harvested
(Serial number, plot code, sub-plotcode, and crop(s) grown in this Section should be in exactly the same order as in Section A above)
Intercrops: record harvesting and threshing/shelling separately (by
comma)
Oxen days/hand hoe
Plowing
Freq
Total
Plowing
days
Male
Female
Weeding
freq
Male
Female
Male
Female
Male
Female
Threshing or
shelling
Total cost (ETB)
5
Land
preparation &
planting
litres
Crop(s)
grown
6
7
8
9
11
12
13
14
15
16
17
18
19
Weed control
Harvesting
Cost of
hired
labour/sh
elling,
threshing
(ETB)
20
21
Total
harvested
per (sub)plot
Intercrops:
separate
comma
Codes A
Pesticides
Cost of
oxen
hired
(ETB)
Stress incidence on plot
Total labour (family and hired) use in person days
22
by
Fresh
or
green
(kg)
Dry (kg)
23
24
N/A
Codes A: 0. No stress; 1. Insect pests; 2. Diseases; 3. Water logging; 4. Drought; 5. Frost; 6. Hailstorm; 7. Animal trampling; 8. Other, specify……………………
102
Section D: Utilization of crop produced and household food security
Different from Sections A-C: one row per crop and season (e.g. add production from all maize plots together for season 1)
From the total available stock after 2012/13 harvest (column 6)…
Crop
(From
sectio
n C)
Form
Codes
A
Stock
before
2012/13
harvest
(kg)
Production
of 2012/13
(last
columns of
Section C)
(kg)
1
3
4
5
Total available
stock
after
2009/10
harvest
(kg)
6=4+5
Quantity
sold after
2012/13
harvest
(kg)
In-kind
payments
(labour, land &
others)
paid
during 2012/13
cropping
year
(kg)
Seed
used
during
2012/13
cropping year
(kg)
Gift,
tithe,
donations
given
out
during
2012/13
cropping year
(kg)
Consumpti
on during
2012/12
cropping
year (kg)
Ending
stock (Stock
before 2012/13 harvest)
(kg)
7
8
9
10
11
12=6-7-8-9-10-11
103
Section E: Marketing of crops
Crop
(From
Column
1 of
Section
D)
Form
(From
Column
3 of
Section
D)
1
3
Codes A
Quantity
sold (kg)
Market
type
Mont
h sold
Codes
A
4
Codes B
1. Farmgate
0. Female
2. Village
market
1. Male
3.
Main/district
market
2.both
Price
(ETB
Code
sC
(sum
should be
equal to
Column 7
of Section
D)
Who
sold
Codes
B
5
6
7
8
Codes C
1.
January
2.
February
3. March
4. April
5. May
6. June
/kg)
Buyer
Codes
D
9
Period
to
payment after
selling, weeks
(if immediate
write zero)
Relatio
n to
buyer
10
11
Codes D
7. July
8. August
9.
September
10. October
11.
November
12.
December
1. Farmer group
7. Rural wholesaler
2. Farmer Union or
Coop
8. Urban wholesaler
9. Urban grain trader
3. Consumer or
other farmer
10.Exporter,
4. Rural assembler
11. Other,
specify…….
5.
Broker/middlemen
Codes
E
Codes
F
Sales
tax or
charges
(ETB)
12
13
Quality
104
Mode
of
transpo
rt
15
16
14
Codes
G
Codes F
Codes G
1. No relation but not a long
time buyer
1. Below
average
1. Bicycle
2. No relation but a long term
buyer
2. Fair and
Average
3. Relative
3. Above
average
4. Friend
6. Other, specify……
Section G: Grain storage practices of 2012/13 season
(minutes
)
Time
taken to
get to
the
market
(minutes
)
Codes E
5. Money lender
6. Rural grain trader
Time
taken to
sell crop
Actual
transport
cost
(ETB)
17
2. Hired truck
3. Public transport
4. Donkey
5. Oxen/horse cart
6. Back/head load
7. Other, specify….
Main
storage
structure
Crop
Codes A
1
Amount
stored at
beginning
Length of
storage
(Kg)
Months
2
5
Amount
at end of
storage
period(kg
)
Amount lost
due to pest
or other
attacks
7
8
6
(kg)
Did quality
deteriorate
during storage
Codes D
If Yes in
column 9, %
of stored grain
affected
Storage loss
control measures
Codes E
Storage pests
seen
Rank 3
Rank 3
9
10
11
Codes G
13
1. Maize
2. Haircotbeans
3. Pigeonpea
4. Groundnut
Codes A
Codes D
Codes E
Codes F
Codes G
1. Traditional crib
0. No
1. None
1. Pest damage
1. Large Grain Borer (Osama/Scania/Nissan)
2. Improved granary
1. Yes
2. Actellic Super
2. Moisture loss
2. Weevil
3. Wooden store
3. Spindust
3. Rotting
3. Rodents
4. Metal silo
4. Scanner dust
4. Moulds
4. Fungal attack
5. Polythene bags
5. Ash
5. Theft
5. Bean bruchid
6. Other, specify…….
6. Smoking
6. Other, specify….
6. Others, specify…
7. Other, specify…
105
PART 4: LIVESTOCK PRODUCTION AND MARKETING
Section A: Livestock production activities during 2012/13 cropping year
Livestock type
Numberof livestock at end of 2012/13 cropping
season (including bought ones)
1
2
Cattle
1. Indigenous milking cows
2. Cross-bred milking cows
3.Exotic milking cows
4. Non milking cows (mature)
5. Trained oxen for ploughing
6. Bulls
7. Heifers
8. Calves
Goats
9. Mature female goats
10. Mature male goats
11. Young male goats
12.Young female goats
Sheep
13. Mature female sheep
14. Mature male sheep
15. Young female sheep
16. Young male sheep
Other livestock
17. Mature trained donkeys
18. Young donkeys
19. Horses
20. Mules
21. Mature chicken
22. Local Bee hives
23.Modern Bee hives
24.Pigs, mature
25.Pigs, young
106
PART 5: TRANSFER AND OTHER SOURCES OF INCOME DURING 2012/13 CROPPING YEAR
Total income (cash & inkind)
Sources
(ETB)
Payment in
kindCash
equivalent
2
3
Cash
1
1. Rented/sharecropped out land
2. Rented out oxen for ploughing
3. Salaried employment
4. Farm labour wages
5. Non-farm labour wages
6. Non-farm agribusiness NET income (e.g. grain
milling/trading)
7. Other business NET income (shops, trade, tailor,
sales of beverages etc)
8. Pension income
9. Drought/flood relief
10.Safety net or food for work
11. Remittances (sent from non-resident family and
relatives living elsewhere)
12. Marriage Gifts
13. Sales of firewood, brick making, charcoal making,
poles etc
14. Sale of maize crop residues
15. Sale of legumes crop residues
16. Sale of wheat crop residues
17. Sale of teff crop residues
18. Sale of other crop residues
19. Sale of hay
20. Quarrying stones
21. Sale of dung cake
22.Rental property (other than land and oxen)
23.Intrest from deposits
24.Land rent out in cash
25. Other, specify
107
Total income
(ETB)
4=2+3
PART 6: ACCESS TO FINANCIAL CAPITAL, INFORMATION AND INSTITUTIONS
Section A: Household credit need and sources during 2012/13 cropping year
Needed
credit?
Reason for loan
Codes A
1
2
If Yes in
column
2, then
did you
get it?
If Yes in column 3
Codes A
Source
of
Credit,
Codes
D
How
much
did you
get
(ETB)
Did you
get the
amount
you
requested
Codes A
Annual
interest
rate
charged
(%)
Debt
outstanding
including
interest rate
at end of
season
(ETB)
3
4
5
6
7
8
1. Buying seeds
2. Buying fertilizer
3. Buy herbicide and pesticides
4.
Buy
equipment/implements
farm
5. Invest in transport (bicycle
etc)
6. Buy oxen for traction
7. Buy other livestock
8. Invest in irrigation system
9. Invest in seed drill
minimum tillage system
or
10.Non-farm business or trade
11. To pay land rent
12. Buy food
13.
Consumption
needs
(health/education/travel/tax,)
Codes A
Codes D
0. No
1. Money lender
4. Microfinance
7. Relative
1. Yes
2. Farmer group/coop
5. Bank
8. AFC
3. Merry go round
6. SACCO
9. Other, specify..
108
Section B: Access to extension services
Received
training or
information
on […..]
during
2012/13?
Issue
Main information source
for 2012/13 Rank 3
(codes B)
Rank 1
Rank 2
Rank 3
Govt
extension
Nonprofit
NGOs
Private
Companies
4
5
6
7
8
9
(Codes A)
1
Number of contacts during 2012/13
(days/year)
3
1. New varieties of maize
2. New varieties of legumes
3. Field pest and disease control
4. Soil and water management
5. Crop rotation
6. Minimum tillage
7. Leaving crop residue in the
field
8. Adaptation to climate change
9. Irrigation
10. Crop storage pests
11. Output markets and prices
12. Input markets and prices
13. Collective
organization
action/farmer
14. Livestock production
15. Family health
16. Sanitation
17. Family planning
18. Tree planting
Codes
A
0. No
1. Yes
Codes B
1. Government
extension service
4. Seed
traders/Agrovets
7. Other private
trader
2. Farmer Coop or
groups
5. Relative
farmers
8. Private
Company
3. Neighbour farmers
6. NGOs
9. Research
center
109
10. School
11.
Radio/TV
12.
Newspaper
11. Mobile phone
12. Other, specify……
Section C. Market access
If yes in
column 2,
where did
you get the
information?
(code A)
Rank 3
Lack of
buyers
Poor price
Assemblers
or brokers
Wholesaler
s
Farmer
group or
coops
Consumers
Crop
Ever failed to sell
due to lack of
buyers or poor
price? Codes A
Did you get
market
information
before you
decided to sell
the crop?
2
3
4
5
6
7
8
9
1
(Code B)
No. of buyers who came to buy at farm
gate last season (2012/13)
1. Maize
2. H.beans
3Faba bean
Section D: Constraints in access key inputs and crop production
Maize
Input and production constraints
Constrai
nt?
Codes A
1
2
Haircot beans
Rank its
importanc
e
(only
those with
Yes
incolumn
2)
3
Socioeconomic
1. Timely availability of improved seed
2. Prices of improved seed
3. Quality of seed
4. Availability of credit to buy seed
5. Timely availability of fertilizer
6. Price of fertilizer
7. Availability of credit to buy fertilizer
8. Access to markets and information
9. Reasonable grain prices
Biophysical
10. Drought
11. Floods
12. Pests
13. Diseases
14. Soil fertility
110
Constrai
nt?
Codes A
4
Rank its
importanc
e
(only
those with
Yes
incolumn
4)
5
Faba bean
Constrai
nt?
Codes A
6
Rank its
importanc
e
(only
those with
Yes
incolumn
6)
7
Sept 9, 2013 version
Appendix 6: Maize and legume grain traders’ questionnaire
Sustainable Intensification of Maize-Legume Cropping Systems for Food Security in Eastern and Southern Africa
(SIMLESA) Project
[To be filled by enumerators with selected traders along the supply chain]
Section 1.0 Background Information
1.
2.
3.
4.
5.
6.
7.
8.
9.
Name of the Enumerator……………..………………………………………………..
Name of the respondent ………………………………………………………………..
Respondent postal address…………………….……………………………………….
Respondent tel. no. (mobile)…………………………………………………………….
Respondent tel. no. (fixed landline)…………………………………………………….
Sex of the respondent (Use codes)……………………….………1. Male 0. Female
Age of the respondent (years)…………………………………………………………..
Respondent’s level of formal education (completed years)…………………………….
Respondent’s main occupation (Use codes)……………………………………………
1. Farming 2.Business (this one) 3. Business (other) 4. Salaried employment 5. Other,
10. Respondent position in maize-legume business (Use codes)…………………………..
1. Owner manager 2. Hired manager 3. Other, specify………………………………………
11. Region/Province………………………………………….……………………………
12. District…………………………………………………………………………………..
13. Division/Ward…………………………………………………………………………..
14. Town/market/village where business is located .............……………………………….
15. Name of business enterprise……………………………………………………………
16. What year (YYYY) did you start this business?__________________________________
Section 2.0 Business/trader type identification
2.1 Fill the table below;
Business type (rank 3)
Product
Year
started
trading YYYY
Codes A
1st
2nd
1.Maize (dry)
2.Maize(green)
3.Haricot Beans
4.Faba bean
Business type:Codes A
7. Urban exporters
1. Rural open air retailers
8. Urban processors
2. Rural retail shopkeepers
9. Urban supermarkets
3. Rural assemblers
10. Urban retail shopkeepers
4. Rural brokers
11. Urban open air retailers
5. Rural wholesalers
12. Other, specify………….
6. Urban wholesalers
111
3rd
Sept 9, 2013 version
Section 3. Business peak seasons and competition
3.1: Peak seasons for the various crop products in the business
Buying
Crop
Peak months (use
codes)
Selling
No. of weeks in the
month
Peak months (use
codes)
No. of weeks in the
month
1.Maize (dry)
2.Maize(green)
3.Haricot beans
1.
Faba bean
5.Groundnuts
6.Soya beans
7.Pegion peas
8.Cowpeas
9.Other, specify
Peak month codes:1. January; 2. February; 3. March; -------------------12. December
3.2: Number of direct business competitors at different levels over time when buying
Main legume, specify name……………
Maize
Level of competition
No. at
the
start of
business
No.
about 3
years
ago
No. in
the year
2000
No. currently or
now
1.Market
2. Village
3. District
4.Region/Province
5.National
6.Other, specify….
112
No. at
the start
of
business
No.
about
3
years
ago
No.
in
year
2000
No.
currently or
now
Sept 9, 2013 version
3.3: Number of direct business competitors at different levels over time when selling
Maize
Level of competition
No. at the
start of
business
No.
about 3
years
ago
Main legume, specify name……………
No. in
the
year
2000
No.
currently
or now
No. at
the start
of
business
No.
about 3
years
ago
No. in
year
2000
No.
currently or
now
1.This Market
2. This Village
3. This District/Woreda
4.Region/Province
5.National
Section 4: Trading Activities
4.1: Quality attributes considered when buying
Maize/Faba bean
Attribute
Considered
when buying?
Use codes
Codes:
1.Yes 0.No
How important is this
attribute in affecting
the price of maize?
1.
2.
Not at all
Minor importance
3.Very important
1. Grain color
2. Grain shape
3. Grain size
4. Batch
homogeneity/uniformity of
size,
5. Foreign matter
6. Insect/pest damage
7. Chemical residual
8. Moisture content
9. Cooking traits
10. Nutrition (e.g. protein
content)
11. Weight
12. Variety
113
Three main assessment methods used –
Use codes
Rank 3
1st
2nd
3rd
Sept 9, 2013 version
13. Grain damage/breakage
14. Smell
15. Maturity
16. Age of product
Main method codes
1. Moisture meter 2. Visual inspection3.Feel 4. Smell 5.Taste 6.Weight 7. Bite 8. Shaking
9.
Lab analysis
10.
Sieve
11.
Experience
with
114
seller
12.
Other,
specify…………………….………..
Sept 9, 2013 version
4.2:Quantity, quality, and price of maize and legumes bought during the last 12 months (Convert quantities into kg equivalent).
SEASON
Mo
nth
bou
ght
Co
des
D
Cro
p
Sell
er
Co
des
A
Co
des
B
For
m
Co
des
C
Total
Quan
tity
boug
ht
kg
Price
paid
ETB
/kg
Qua
lity
of
the
grai
n
Cod
es E
Dista
nce
to
buyin
g
point
KM
mode of
transport to
warehouse/b
usiness
premises/
sale point
Codes F
If hired,
transport cost
to business
premises/ware
house/sale
point ETB/kg
Selle
r
searc
h
costs
ETB
/kg
Commis
sions
/fees to
buying
agents
ETB/kg
Cleaning/
grading
labour
ETB/kg
Wei
ght
loss
after
clean
ing
ETB
/kg
Stor
age
costs
ETB
/kg
Loadi
ng/
offloa
ding
charg
es
ETB/
kg
Peak Season
Normal/Avera
geSeason
Low Season
Codes A
Codes B
1.Maize (dry)
1. Farmers
2. Maize (green)
2. Rural open air
retailers
3. Common
beans
4. Groundnuts
5. Soya beans
6. Pigeon pea
3. Rural retail
shopkeepers
4. Rural assemblers
5. Rural brokers
Codes C
Codes D
Codes E
Codes F
8. Urban exporters
1. Unshelled
1. January
1. Above average
1. Train
9. Urban processors
2. Shelled
2. February
2. Medium
2. Truck
10. Urban supermarket s
3. March
3. Below average
3. Bicycle
11. Urban retail shopkeepers
-
4. Ox-cart
12. Urban open air retailers
-
5. Back/head lots
13. Other, specify…………
-
6. Other, specify
12. December
6. Rural wholesalers
7. Cowpea
8. Other, specify
7. Urban wholesalers
NB: *Storage costs include chemicals used in storage, labour, weight loss in storage due to moisture and or insect damage, refrigeration etc
115
Othe
r
costs
ETB
/kg
Sept 9, 2013 version
4.3: Quantity, quality, and price of maize and legumes sold during the last 12 months (Convert quantities into kg equivalent).
SEASON
Cod
es D
Total
Quant
ity
sold
Buy
er
Mon
th
sold
Cod
es B
Cro
p
For
m
Cod
es A
Cod
es C
Price
receivedET
B/kg
kg
Qual
ity of
the
grain
Cod
es E
Dista
nce to
marke
t/ sale
point
KM
Mode
of
transpor
t to
market/
sale
point
If
hired,
transp
ort
cost
Codes
F
Buyer
search
costs
(deliver
y
transpor
t, phone
costs,
discount
s,
advertis
ing)
Paym
ent to
sellin
g
agent
s
ETB/
kg
Processi
ng
costsET
B/kg
Packaging
labelingET
B/kg
Customs
clearing/
other
govt.
feesETB/
kg
Other
costs
ETB/
kg
ETB/kg
Peak Season
Normal/Average
Season
Low Season
Codes A
Codes B
1.Maize
1. Rural consumers
2.
beans
Common
3. Groundnuts
4. Soybeans
5. Pigeionpea
2. Rural
retailers
open
3.
Rural
shopkeepers
air
retail
4. Rural assemblers
5. Rural brokers
6. Cowpea
7.
Other,
specify…
6. Rural wholesalers
Codes C
Codes D
Codes E
Codes F
8. Urban wholesalers
1. Unshelled
1. January
1. Above average
1. Train
9. Urban exporters
2. Shelled
2. February
2. Medium
2. Truck
10. Urban processors
3. March
3. Below average
3. Bicycle
11. Urban supermarket s
-
4. Ox-cart
12. Urban retail shopkeepers
-
5. Back/head lots
13. Urban open air retailers
-
14. Other, specify…………
12. December
6.
specify…………
7. Urban consumers
NB:*Storage costs include chemicals used in storage, labour, weight loss in storage due to moisture and or insect damage, refrigeration etc.
116
Other,
Sept 9, 2013 version
Section 5: Agricultural input and output market price information
5.1 How many agricultural output markets do you monitor their prices regularly?...........................
5.2 How many agricultural input markets do you monitor their prices regularly? ...........................
5.3 Rank your three main sources of information on the price of the day in your main sales market
1st ………2nd……3rd…….
Codes:1. Extension agents 2. Press 3.Internet 4. Agents/Traders 5.Gov’t information services/departments 6.
Radio/TV 7. Banks 8. Personal contacts.9.Non-govt/non-profit information providers 10. Commercial marketing
agencies 11. Other, specify……………………………………….
5.4 Rank the three best or most effective methods in providing information on prices &markets to you 1st…….
2nd………3rd………..Codes:1. Radio 2.TV 3.Posted bulletin 4.Press 5.Internet 6.Telephone 7. Conversation 9.
Other, specify…………………………………………
Section 6: Marketing constraints
(Note to enumerators: Give a chance for the respondents to state the constraints and then if necessary, prompt them
using the list below
Constraints
Constraint?
0. No, 1. Yes (if 0,
move
to
next
constraint)
1.Prices are unstable
2. Poor quality of grains
3. Absence of grades or standards
4.Multiple taxes at different levels of government (between regions
districts, and provinces/zones)
5.Non-transparent (complicated) taxation system
6.Difficulties in obtaining license
7.Not all traders are licensed
8.Weak access to market information
9.Limited access to credit
10.Weak legal system for contract enforcement
11.Inadequate market infrastructure
12.Absence of government support to improve marketing
13.Weak demand for agricultural products
14. Other specify ………………………………………………..
15.Other, specify………………………………………………..
Codes A.
1.Very important 2. Important 3.Neutral 4. Little importance 5.not important at all
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Importance rank (only
those with Yes in column
2) (Codes A)
Sept 9, 2013 version
Section7: Product quality and price trends
Response (Use codes)
Question no.
Question
Maize
10.1
Legumes
What has been the trend of product quality provided by your supplier?
Code A: 1. Increasing 2. Same 3. Decreasing
10.2
What has been the trend of product purchasing prices?
Code A: Increased a lot 2. Increased slightly 3. Decreased a lot
Decreased slightly 6. No change 7. Do not know
10.3
5.
What has been the trend of product selling prices?
Code A: Increased a lot 2. Increased slightly 3. Decreased a lot
Decreased slightly 6. No change 7. Do not know
5.
Section 8
8.1 In your view, what three most important things do you suggest can be done to improve this crop/commodity
business? Give three major reasons
Maize
Suggestion 1
Suggestion 2
Suggestion 3
Beans
Suggestion 1
Suggestion 2
Suggestion 3
Soy beans
Suggestion 1
Suggestion 2
Suggestion 3
THANK YOU FOR PARTICIPATING IN THE INTERVIEW
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