supply analysis in wheat industry: contributions of value...
TRANSCRIPT
Supply analysis in wheat industry: contributions
of value chain analysis in Ethiopia: Cases from
Arsi and East Shewa Zones in Oromia National
and Regional State
Zewdie Habte, Belaineh Legesse, Jima Haji and Moti Jeleta
Invited paper presented at the 5th International Conference of the African Association of
Agricultural Economists, September 23-26, 2016, Addis Ababa, Ethiopia
Copyright 2016 by [authors]. All rights reserved. Readers may make verbatim copies of this
document for non-commercial purposes by any means, provided that this copyright notice
appears on all such copies.
1
Supply analysis in wheat industry: contributions of value chain analysis in Ethiopia: Cases from
Arsi and East Shewa Zones in Oromia National and Regional State
Zewdie Habte*, Belaineh Legesse**, Jima Haji*** and Moti Jeleta****
Haramaya University*,**, ***
International Maize and Wheat Improvement Center (CIMMYT), Addis Ababa, Ethiopia****
Corresponding author: Zewdie Habte, Email: [email protected], Mobile phone:
0911776446
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Abstract
In this paper an attempt is made to analyse factors affecting supply issues at different functional
nodes of Wheat Value Chain (WVC), flow of commodities and roles of cooperatives and other
institutions in supply issues. Sources of primary data include input suppliers, service providers,
wheat producers, traders, cooperatives, wheat processing industries. Interview schedules,
informal group discussions and observations were used to collect primary data from actors in
wheat value chain. The sample design was multiple sampling stages, zones as first stage
sampling unit, districts as second stage sampling unit, kebeles as third stage sampling unit and
key WVC actors as fourth stage sampling unit. Factors of supply issues in wheat industry has
been analyzed with the help of descriptive statistics, qualitative methods and stepwise multiple
regression (OLS). The result indicates that cooperative as actor has failed to supply adequate
input, namely pesticide and herbicide caused input retailers to manifest their opportunistic
behavior and exploit asymmetric information on input quality at small shops and spot market,
which in turn, declined wheat productivity. Wheat producer’s marketed surplus significantly
increased with land size, fertilizer, extension service, and distance from main road, producer’s
WVC function, and decreased with crop rotation. About 90% of wheat processing industries
ranked shortage of raw materials as the number one barrier for wheat product supply. Concerned
body should work on technology and extension service supply and coordination to address low
raw materials and final products supply at each functional node of wheat value chain.
Keywords: wheat marketed surplus, wheat product supply, wheat value chain
1. INTRODUCTION
Gap between wheat demand and supply has been increasing from time to time in Ethiopia (Mary
et al., 2012) due to changes in population size, wheat processing industry capacity and dietary
composition of wheat product which has made the country net importer of wheat and still
incapable to fill the gap despite its tremendous potential for wheat production and productivity
improvement (Rashid, 2010). Country has imported wheat on average 40% of aggregation of
wheat demanded to narrow demand and supply gap since 1991(Mary et al., 2012), but still
wheat processing industries has been working under capacity, for instance, capacity utilization
was 40.4% for flour mills and 42% for macaroni and spaghetti (Dendena, 2009). Particularly,
dearth of wheat supply is also serious problem in Oromia region (Mohammed, 2009; Dendena,
2009), average wheat marketed surplus in Arsi and East showa zone was about 23% and 28%
respectively (CSA, 2014) and 47% of wheat marketed surplus in Ada’a, Alaba and Fogera
3
districts (Berhanu and Hoekstra, 2007). Variation in wheat marketed surplus among wheat
producers was another great challenge in WVC (Berhanu and Hoekstra, 2007). Thus, Ethiopian
government has generically given a great room for industry chains to coordinate supply and
demand issues at different functional nodes of value chain to ensure continuous economic
growth.
Commonly, industry chains are streamlined as either supply or value chains is deemed to mean
the physical flow of commodities which include input suppliers, service providers, producers,
traders, processors and traders. The wheat industry has many sectors which are strongly
interlinked each other that means the failure of one sector leads to a failure in another. For
instance, if upstream actors fail to deliver the right quality and quantity of inputs at a right time
to wheat producers, they can not deliver the right quality and quantity of wheat demanded in
downstream sector. This implies that inadequate input supply in input market has direct adverse
effect on wheat productivity and supply, also indirect adverse effect on wheat product supply.
Thus, supply chains rely on coordination between actors (Bryceson and Kandampully 2004).
Value chain analysis can used to address supply issues such as raw product supply, quality and
consistency of raw product in the chains to shrink gap between demand and supply (Bryceson,
2008). Input supply is highly inconsistent (ibid). Policy such as import bans or tariffs and market
failures create scarcity of input in input market because of high costs and poor quality of inputs,
(FIAS, 2007). Protectionist policies may act as barrier to enter into foreign input market for
newer firms that may increase the price of inputs because it favours the existence of high market
concentration (FIAS, 2006b).
On farm commodity supply side, the basic supply theory for farm commodities argues that
commodity price, the existence and extent of production alternatives have an effect on quantity
supply for some specified time period (Cochrane, 1944 and Nerlove and Bachman, 1960).
Others argue that changes in weather, market structure, government policies, demographics and
technology (Nyairo and Backman, 2009); prices of production inputs (Yevdokimov, 2012)
influence aggregate farm commodity supply. A dynamic land allocation model assumes that
dynamic land allocation process leads to high crop yield whereas monoculture results in low land
productivity because of depletion of nitrogen and accumulation of crop-specific insects and
worms, diseases in soil which has direct impact on crop yield.
Household farm model which is against the mainstream microeconomic theory argues that prices
of staple crop do not have significant effect on quantity supplied in rural area of Japan (Kuroda
and Pan, 1978).
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This study is going to test concepts, namely changes in agricultural technology, dynamic land
allocation and WVC function which are expected to have positive effects on quantity supply,
identify factors affecting input and wheat product supply that the earlier authors did not include
these concepts in their studies of factors determining wheat marketed surplus (e.g. Berhanu and
Hoekstra, 2007 and Muhammed, 2011). Furthermore, WVC’s constraints were studied by
USAID (2010); Mohammed (2009) and Mary et al. (2012), but these studies lack detail
information on constraints of input, institutions and wheat processing industry supply. Moreover,
value chain analysis in this paper is used as heuristic or analytical framework to identify supply
issues in wheat industry which was not used in earlier studies. Because one of the aims of value
chain analysis is to enhance the quantity of supply at different functional nodes of a value chain
(Anandajayasekeram and Gebremedhin, 2009). Moreover, it overcomes the weakness of sector
analyses which focus on various economic aspects of production and examines dynamic linkages
between productive activities that go beyond that particular sector (Kaplinsky and Morris, 2000).
It will add new knowledge to existing theoretical knowledge with regarding to supply issue links
between upstream and downstream actors. Thus, the result is useful to generate useful
information and bridge the existing knowledge gaps in marked areas. Thus, study is going to
look at supply chain issues in input, domestic wheat and wheat product market.
2. CONCEPTUAL FRAMEWORK
In this study, institutional environments are expected to have an effect on input quantity, quality
supply and incentives (i.e., prices, costs), which also have an effect on level of natural resources,
namely diseases and weeds, and quantity wheat supply. Also actor’s attributes are influenced by
incentives and actor’s attributes such as quantity supply interacts each other. Industries and its
policy interlink with these attributes because they may have positive or negative effect on the
actors’ attributes which depends on the existing institutional environments. Technology, resource
and socio-economic attributes have interactions with actors’ attributes. Particularly, WVC
function, technology, resource and socio-economic attributes determine the actors’ quantity
wheat supply. Actors’ attributes interrelate with industries. Industries demand wheat as raw
material, which induce farmers’ technology utilization, which in turn, lead to high quantity wheat
supply. Industrial policy, working capital, age and size of technology associated with amount of
wheat product supply. Thus, this study will explore hypotheses which are formulated on the
basis of theoretical literature reviews and researcher’s experiences. They are clearly reported in
the conceptual frame in figure 1.
5
Figure 1. Conceptual framework
Source: Own construction
3. METHODS
Sampling technique used was multiple sampling stages, zones as the first stage sampling unit,
districts as the second stage sampling unit, kebeles as the third stage sampling units and key
WVC actors as the fourth stage sampling unit. In Oromiya region, Arsi and East shewa zones
were purposely selected and stratified into wheat producing and non-producing districts. All
wheat producing districts were listed and classified into upper 50% wheat producing and lower
50% wheat producing on the basis of area of wheat coverage. Only three districts, one from East
shewa zone and two from Arsi zone, from upper 50% wheat producing districts in both zones
were randomly selected. And then two kebeles were randomly drawn from each selected district;
a number of households were determined based on probability proportional to size of total
households in each selected kebele. Finally, the households were randomly selected from the
land ownership register to be obtained from the Office of land administration from each district.
Traders in markets could not be selected randomly for the interviews because the complete list of
them was not available and their numbers with warehouse were a few. We visited 6 markets in
the study districts and input suppliers (retailers) at small retailer’s shops and spot market at
different times of the day (morning, afternoon and evening) to interview all traders present.
Input quantity
supply
Wheat quantity
supply
Wheat product
quantity
Incentives In
stit
uti
onal
envir
onm
ents
Physical and natural resources, socio-economic
and wheat value chain function
Indu
stri
al
poli
cy,
work
ing
capit
al,
age
and
si
ze
of
tech
nolo
gy
an
d,
mar
ket
str
uct
ure
6
Wholesaler input suppliers were visited in Addis Ababa for interviews. All firms such as
bakeries, flour and food complex industries were interviewed with the help of fresh list of wheat
processing industries. In addition to this, we visited traders and firms purposely in Adama,
Assela and Bishoftu towns and Addis Ababa. To carry out formal survey, census was applied to
collect data from indirect actors such as supporting business service providers, input distributors.
There is no common consensus on formula or rule of thumb that yields optimal sample size to
run a regression model and the controversy is still unsettled. So, scholars have failed to reach
common consensus, which leads various researchers to use various methods to determine sample
size. However, most statisticians and econometricians deem independent variables to determine
sample size (i.e., sample size (m) is 10 or more times the number of relevant independent
variables) in a given model (Edriss, 2013). Sample size determination for other actors such as
bakeries, flour and food complex firms and wholesalers in WVC relies on numbers of these
actors in the study area. Thus, based on the above justifications, data used in this paper were
extracted from 220 randomly selected wheat producers, a census of 50 wholesalers, a census of
30 wheat industries and a census of 25 institutions, namely 13 cooperatives, 2 Agricultural
Research Centers, 2 Seed Enterprises, 4 Agricultural extension organization, 1 investment and
industry bureau, 3 Oromia International Cooperative, Banks, development Banks in the study
districts survey carried out in 2015/16 in Arsi and East shewa zones. Moreover, data were
extracted from 20 input suppliers (retailers) at small retailer’s shops and spot market and visited
spot market and village shop at different times of the day ( morning, afternoon and evening) to
interview all traders present. 5 wholesaler input suppliers were visited in Addis Ababa for
interviews and tried to collect data on constraints of input supply and its distribution. In addition
to these, 20 traders, 15 wheat processing industries were purposely selected from Adama, Assela
and Bishoftu towns and Addis Ababa.
This paper demanded single round quantitative and qualitative primary data from key WVC
actors and secondary data. Sources of primary data include input suppliers, wheat producers,
traders, cooperatives, wheat processing industries and institutions. Unstructured and structured
interviews, informal group discussion and observations were used as data collection techniques
to collect primary data from actors in wheat value chain.
7
Quantitative analysis such as descriptive analysis, correlation analysis and stepwise multiple
regression was used to address quantitative part of the objective whereas qualitative analysis
such as data reduction and data display (mapping) and conclusion drawing was applied to
address qualitative part of the objective.
4. EMPIRICAL RESULTS
This chapter looks at flows of commodities, wheat producer’s socio-economic profile,
technology utilization patterns, factors impeding actor’s wheat product supply in WVC.
4.1. Flows of Commodities in Wheat Value Chain
Typical actors in wheat value chains are input suppliers, wheat producers, assemblers,
cooperatives, grain wholesalers, grain retailers, and wheat and wheat product consumers, WPI,
baking industry, wholesalers and retailers of processed food and service providers. Services
include storages, rented tractors, combiners and oxen, supervision of production, market
information, technical expertise and business advice, training, fumigation (outsourcing) and
credit and savings. The value of services is estimated with the help of labor input method and
direct price of rented tractor, combiners and oxen per hectare and then estimated value of
services for total wheat farm for both private and governmental enterprises. These services are
delivered by private enterprises, government and non governmental organizations.
8
Imp
ort
ed w
hea
t
100%
Fer
tili
zers
(1
00
%)
AISE
SE
Res
earc
h
cente
rs
Seed producers
Ch
emic
als
(35
%)
See
d (
70
%)
Seed (90%)
5%
Imported and local manufactured inputs Chemicals
(74%)
See
d (
96
%)
Seed (10%)
Chemicals (26%)
Res
earc
h c
ente
r Seed producers
Seed (90%)
Imported and local manufactured inputs Chemicals
(74%)
Seed (4%)
Producers (wheat) and dairy farm
Assembler
Wholesalers
Retailers
Cooperatives
WPI
Urban and rural consumers
Processed food wholesalers
Processed food retailers
Bakeries
1.5%
80%
4%
10%
80
63%
10%
100% 90%
16%
30% 70%
3%
1.5%
4%
80%
20%
100%
14%
23% (barn)
100%
PCIS
Dea
ler
Ser
vic
e p
rov
ider
s
See
d
(25
%
) See
d (
5%
)
Retailer
78%
14%
3%
0.4
0.6%
Inputs (100%)
30%
65%
100%
100%
Imp
ort
ed w
hea
t 100%
Fer
tili
zers
(1
00
%)
AISE
SE
Ch
emic
als
(10
0%
)
See
d (
70
%)
5% 4%
See
d (
96
%)
Seed (10%)
Chemicals (26%)
Res
earc
h c
ente
r
Seed producers
Seed (90%)
Imported and local manufactured inputs Chemicals
(74%)
Seed (4%)
9
Source: own survey data (2015)
Figure 2: Wheat input-output flows in WVC,
Wheat producer value chain function (WPWCF)
Each activity in wheat producer value chain function associated with its costs, namely land
preparation, planting activities, fertilizer application, weeding and harvesting. The result
indicates that WPWCF demands about birr 10345 per ha or birr 2530 per ton to produce on
average 4.1 tons of wheat per ha. Also, it partially demands tractors and combine harvester for
land preparations and harvesting in Hetosa and Tiyo districts.
Table 1: Distribution of households by output, marketed surplus and consumption in quintal
Gimibichu Hetosa Tiyo Total
Mean S.D Mean S.D Mean S.D Mean S.D
Output 71.62 46.15 72.37 61.49 78.37 53.58 74.5 54.11
Yield per ha 38.69 8.80 41.82 12.19 40.32 10.56 40.27 10.81
Marketed
surplus
62.36 45.77 57.3 58.52 67.95 52.31 63 52.57
consumption 9.26 4.11 14.45 17.78 10.44 4.17 11.59 10.77
This study indicates that 15.55% and 7.11% of wheat output was consumed at home and used for
seed respectively and 77.43% was sold in the market which was significantly higher than 47% of
wheat marketed surplus in Ada’a, Alaba and Fogera districts (Berhanu and Hoekstra, 2007), and
23% and 28% of wheat marketed surplus in Arsi and East showa zones respectively (CSA,
2014). Producers secured higher average yield (i.e., 40.27 quintals of wheat per ha) as compared
to national and regional average yield (CSA, 2014). In general, wheat is consumed by the people
of the zones or transported to other parts of the country and consumed by others.
Actors in wheat value chain
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Seed producing agencies — Kulumsa, Debrezeit and other agricultural research centers generate
and supply new wheat varieties to Seed Enterprises. They multiply and distribute the seeds to
end consumers through unions and direct seed marketing. Fore example, Oromia Seed Enterprise
produced 88155 quintals of certified seed and distributed 58155 quintals of certified seed to
wheat producers through direct seed marketing and unions. 14000 quintals were distributed
through direct seed marketing and leftover one was distributed to wheat producers through union
cooperatives. Oromia seed enterprise supplied about 10% of the certified seed to Amahara,
SNNP and Tigray regions, and about 90% to Oromia region.
The Seed Enterprises supply basic seed with cost and extension services without cost to farmers.
They multiply basic seed and sell 90% of product to Seed Enterprise and use 10% of it for
themselves. Seed Enterprises supply certified seed as per the prior demand to their registered
distributors. The challenges were lack of basic seed and reliable demand for some seed variety
due to mismatching demand report. Farmers claimed low seed quality supply (i.e., poor cleaning,
low germination rate, and mixed seeds) and ineffective and inefficient coordination to ensure that
the varieties distributed are matching to farmer’s demand.
Input suppliers import inputs such as chemicals, equipments, pesticides and herbicides from
Germany and China, and supply them to unions, small wholesalers and retailers and even smaller
retail shops that sell herbicides, pesticides and other chemicals to farmers. Input retailers operate
at small shops and spot market in the villages and towns to sell inputs to farmers. Combinations
of different technologies like DAP, UREA, Pallas, Tilt, 2-4D and Grandstar (Richway-
750WDG) are widely used in the production of wheat in the study areas.
The enabling environment
Organizations and institutions create the enabling environment for economic actors in the value
chain, which may have a positive effect on the entire value chain. For instance, industrial policy
(i.e., investment incentives) increases capital supply and actual working capacity from 5,694
tones to 34,602 tones per years in Arsi zone and export incentives such as free from sales and
value added taxes increase wheat products export by 50% at national level, an increase in the
final demand for wheat of 34,602 tons has caused higher price of wheat which demand more
farm technology to increase both production and yield of wheat. There is not policy environment
that facilitates implementation of wheat and wheat product quality standard which leads to weak
quality-based pricing system that awards almost equal incentive for suppliers with higher and
lower quality wheat and wheat product.
11
Service providers
GOs, NGOs and private enterprises support actors to transact wheat and render various services
such as input supplies (seeds, fertilizers, pesticide and herbicide, tractor and combine harvester),
trainings, market information (prices, buyers, and suppliers), financial services (such as credit
and savings). However, all value chain actors do not always get these services consistently and
timely.
Wheat producers
They sell wheat to downstream actors such as assemblers, WPI, wholesalers, retailers and end
users. About 80% of wheat marketed surplus was sold to wholesalers at farm gate, warehouses
and spot market.
Wholesalers
Wheat processing industries distribute wheat products to wholesalers and retailers through their
agencies throughout all regions.
Cooperatives
Unions provide limited amount of money to basic cooperatives in form of credit. They purchase
wheat with help of this credit from farmers at spot market and cooperative offices for only two
months because they cannot rotate the limited amount of capital and sell it to union for on
average of birr 35 profit per quintal and then unions sell it to potential actors during peak period
through auction. Basic cooperatives do not have self-governing authority to rotate money, sell
the wheat to any actors and purchase inputs directly from companies. They stick to blue print
approach which takes away their input and output market decision power. Thus, they were
limited to purchase only 25,074.17 quintals of wheat per annum from farmers at spot markets
and cooperative offices in Gimbichu district, 13,792.36 quintals of wheat in Hetosa district and
782.29 quintals of wheat in Tiyo district.
Unions purchase and distribute inputs to basic cooperative at predetermined prices which lead
them not to solve excess and/or under input supply. These procedures are really against
principles of cooperative. Above obstacles and long chain, inadequate finance, lack of storage
facilities and offices, limited experts in quality and quantity lead to poor performance of basic
12
cooperatives. Basic cooperatives supply insufficient inputs, namely Pallas and Rexdou, and
distribute to only few members of cooperative which force other members to purchase inputs
from private traders at relatively birr 50-200 higher prices per liter. On the contrary, private
traders sell the inputs at relatively birr 50-100 lower prices per liter when the inputs were
available in basic cooperatives which create excess input in stores of basic cooperatives.
Corruption or bribe weakens the power of institutions and discourages control experts (i.e.
agricultural experts who assigned to control quality of inputs) which also lead to existence of
unapproved chemicals at the input market. Weak institutions in input market lead to lower wheat
production due to ineffectiveness of inputs. That is, 30% of farmers who used low quality (i.e.
adulterated or expired) pesticide and herbicide harvest on average 17 quintals of wheat per ha
lower than 70% of farmers who used approved inputs. In general, cooperative as actor has failed
to accessing markets for output and input (pesticide and herbicide) which result in high
transaction risks, costs and existence of expired or adulterated pesticide and herbicide in the
input market. It is possible to conclude that weak institution has a negative implication on
Ethiopian growth transformation plan.
Wheat processing industries (WPI)
Wheat goes through different sectors and activities with significant value addition before it reach
final consumers. Wheat processing industries convert wheat into wheat flour and barn, flour into
biscuits, pasta, macaroni and bread that add value to the product and to satisfy market
requirement. Wheat processing industries purchase domestically produced wheat at market price
from traders and farmers, and imported wheat at subsidized price from government. They sell
former one to wholesalers and retailers at market price and distribute later one to bakeries at
subsidized fixed price. Specifically, WPI purchase about 80% of raw materials from wholesalers,
10% from government quota wheat, and 10% directly from farmers.
The result indicates that the capacity utilization of wheat processing industries increase from
41% to72% for flour, 42% to 80% for macaroni and biscuits and 5 to 19 in numbers because of
industrial policy, but until now they have been working under capacity because of inadequate
supply of raw material which leads to low supply of wheat products. Specifically, the total
capacity was 156 tons per day before 2010 year. Total capacity utilization grew from 156 tons to
1104 tons per day after industrial policy and current average capacity utilization was about 797
tons (72%) per day in Arsi zone. This study covered only 10 wheat processing industries in
Adama town which used about 65% of their full capacity.
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Bakeries
Baking industry is processing wheat flour into final wheat products (bread) and delivering it to
local institutions, final consumers and retailers. They purchase government quota flour from
flour factories at fixed price. Amount of quota wheat flour varies across places and number of
industries in particular area because of the size of consumers. On average, 12 bakeries in the
study areas baked 5.5 quintals of flour per day in Assela town and 2.4 in two districts.
Milling
A number of milling provider services were found in Hetosa and Tiyo districts which milled on
average 8 quintals of wheat per day and 15 quintals of wheat per day in Chefedonsa town for
urban and rural consumers
4.2. Socioeconomic Profile of Sample Wheat Producers
It is hypothesized that wheat marketed surplus declines with farmer’s age, on average, 46.45,
44.04 and 43.85 years old in Gimbich, Hetosa and Tiyo districts respectively (Table 2). On the
contrary, the result indicates that age had a positive, but insignificant effect on wheat marketed
surplus at the 5% level because older one secured more land as compared to younger one (r =
0.35ns
). Family size is assumed to decline marketed surplus because of high share of home
consumption, on average family size was 5.1, 5.3 and 5.5 persons in Gimbich, Hetosa and Tiyo
districts respectively. But the result shows that family size had positive but insignificant effect on
wheat marketed surplus at 5% level (r = 0.28ns
). According to theoretical justification, male
household heads supply more wheat to market than that of female because of better exposure to
crop production, 85% were male and 15% were female. There was no significant difference
between male and female-headed households at the 5% level (r =0.009ns
). Theories argue that
family productive labor improves production, which in turn, increases wheat marketed surplus
and has positive but insignificant effect on wheat marketed surplus at the 5% level (r = 0.33ns
).
The average family labor force supply per household was 4.12, 3.99 and 4.17 persons in
Gimbich, Hetosa and Tiyo districts respectively. The conversion coefficients developed by
Storck et al. (1991) have been used to convert woman and minor labor into man units. It is
supposed that family education makes difference in wheat marketed surplus. On average, family
education was 3.5, 4.3 and 5.4 grades in Gimbich, Hetosa and Tiyo districts respectively, which
consider only male and female members of the respondents above 15 years old.But the result
14
indicates that family education negatively associated with wheat marketed surplus but
insignificant at 5% level (r=.-0.06ns
).
Table 2: Distribution of wheat producers by scio-economic profiles
Gimibichu Hetosa Tiyo Total
Mean S.D Mean S.D Mean S.D Mean S.D
Age 46.45 11.94 44.04 13.35 43.85 11.96 43.47 12.36
Family size 5.10 1.53 5.3 2.16 5.49 1.77 5.32 1.84
Labor supply 4.12 1.32 3.99 1.49 4.17 1.44 3.10 1.42
Land size 2.59 1.33 1.98 1.05 2.21 1.23 2.07 1.22
TLU 5.98 2.60 4.74 3.064 5.66 2.89 5.53 2.93
Oxen 2.55 1.03 2.14 1.37 2.36 1.08 2.34 1.18
Di from road 2.025 1.65 2.93 2.11 2.33 1.95 2.43 1.95
Di from factory 38.35 8.01 6.76 10.28 24.93 16.11 23.05 17.52
Extension 3.48 2.49 3.67 3.91 3.05 3.30 3.37 3.30
% Yes
(%)
No
(%)
Yes (%) No
(%)
Yes
(%)
No
(%)
Yes
(%)
No (%)
Crop rotation 78.13 21.88 38.57 61.43 54.65 45.35 56.36 43.64
credit 42.19 57.81 32.86 67.14 36.05 63.95 36.82 63.18
Non-farm
income
38.37 61.63 47.14 52.86 46.88 53.13 43.64 56.36
Source: own survey data (2015)
Theoretical justifications say that livestock number associated with more marketed surplus and
agree with this finding that it has positive and significant effect on wheat marketed surplus at 5%
level (r= 0.43ns
), on average, farmers secured 5.98, 4.74 and 5.66 TLU in Gimbichu, Hetosa and
Tiyo districts respectively (Table 2). The conversion factor has been recommended by Jahnke
(1982) has been used to convert livestock number into TLU in this study. It is hypothesized that
marketed surplus increases with land size, agrees with this finding that land size has positive and
significant effect on wheat marketed surplus at 1% level (r=0.8***). The average land size was
found to be highest in Gimbichu district that was 2.59 hectares. Average land size was 1.98
15
hectares was the lowest in Hetosa district, was relatively the second lowest (2.21 hectares) in
Tiyo district. It is assumed that extension services improve wheat marketed surplus because it
augments farmer’s farm, pest and disease management skills which lead to higher efficiency.
Farmer’s participation in extension events correlated significantly and positively with marketed
surplus at 5% level (r=0.24**); on average, were involved 3.48, 3.67 and 3.05 times in
Gimbichu, Hetosa and Tiyo districts respectively. Almost 63.18% of farmers were non-credit
users for three reasons: adequate money, no access to credit and fear of risk. More than half of
the wheat producers did not borrow inputs and money from cooperatives, local government
offices, microfinance institutions because fertilizers and other inputs were not delivered to
farmers on credit, so has insignificant but positive effect on wheat marketed surplus at 5% level
(r=0.08ns
). 43.64% of farmers engaged and 56.36% not engaged in non-farm income generating
activities, specifically 38.37%, 47.14% and 46.88% of them involved in non-farm income
activities to generate additional income to meet their social and economic needs in Gimbich,
Hetosa and Tiyo districts respectively, has negative but insignificant effect on wheat marketed
surplus at 5% level (r=-0.22ns
).
4.3. Technology Utilization Pattern of Wheat Producers
Average Pallas use per household across the study areas was 0.49 liter, particularly 0.53, 0.48
and 0.47 liter in Gimbichu, Hetosa and Tiyo districts respectively. The average Rexduo use in
two districts such as Hetosa and Tiyo was 0.325 and 0.333 liter respectively. But, farmers did not
use Rexduo in Gimbichu due to missing Rexduo market. The average Topic use in two districts
such as Hetosa and Tiyo was 0.225 and 0.233 liter respectively. The average fertilizer use for
wheat production was 169 and 98.23 kilograms of DAP and UREA per hectare per annum
respectively. The highest amount of fertilizer was used in Gimbichu district (185.45 kg of DAP
and 178.5 kg of UREA per ha per year) and the lowest amount of fertilizer was consumed in
Hetosa district (157.2 kg of DAP and 64.82 kg of UREA per ha). In Tiyo district, average
fertilizer use was 166.63 kg of DAP and 65.68 kg of UREA per ha per year which was relatively
the second lowest. There was significant variation in UREA utilization between Gimbichu and
other districts, variation in DAP consumption has significant and positive impact on wheat
marketed surplus at 1% level (r=0.84***). Though the average fertilizer use, found in the study
area was higher than regional and national. About 90% of farmers had used rented tractor for
first tillage and 100% had used combine harvester to harvest wheat in Hetosa district, and nearly
61% of farmers had used tractor for first tillage and 90% had used combine harvester to harvest
wheat in Tiyo district. Both technologies have not been practiced in Gimbichu district because
topography is not convenient for mechanized farm. About 91.36% of farmers had their own
16
oxen, specifically 96.88%, 85.71% and 91.86% in Ginbichu, Hetosa and Tiyo districts
respectively.
Reasons for non-optimal application of technologies
Farmer’s reasons for over utilization of seed were to increase yield, reduce weeds and
compensate low seed germination percentage due to heavy vertisols. Farmer’s justifications for
over utilization of DAP were to maximize yield and under utilization of UREA were to
overcome lodging of wheat in Hetosa and Tiyo districts.
Table 3. Agricultural technologies utilization pattern
Gimibichu Hetosa Tiyo Total
Mean S.D Mean S.D Mean S.D Mean S.D
Seed 454.04 280 535.85 811 633.89 781 550.8 691
Seed per ha 221.5 64 341.67 729.7 320 405 298 485
DAP 296 190 230 179. 290 202 273 193
DAP Per ha 185 57 157 60 167 53 169 58
UREA 285 193 161 158 201 188 222 188
UREA Per ha 178 55 65 71 66 75 98 85
Pallas .0.53 .46 0.48 .56 .0.47 .625 0.53 .515
Rexduo 0 0 0.63 .689 .633 .216 .862 .627
Topic 0 0 .475 .532 .43 .404 .494 .569
Tilt 1 1 .9 .22 1.06 .416 1.025 .338
Grandster 13.90 7.44 14.5 9.46 21.91 15.83 16.96 12.090
PWVCF 13385 9302 9231 7042 9958 7401 10724 8049
Tractor
utilization%
0 100 90 10 61.63 38.37 52.73 47.27
Combine
harvester%
0 100 100 0 90 10 67.73 32.27
Owner of oxen
%
96.88 3.13 85.71 14.29 91.86 8.14 91.36 8.64
Source: own survey data (2015)
4.4 Multiple regression Analysis
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Assumptions of the multiple regression analysis are tested to check the healthiness of the model.
The assumptions are briefly discussed and tested here below.
Test for normal distribution
A normal distribution curve is symmetrical and bell-shaped. A symmetrical curve is one which
theoretically has zero coefficient of skewness. A bell-shaped curve is mesokurtic with zero
coefficient of kurtosis. For practical purposes, a distribution may be regarded as normal if its
coefficient of skewness and kurtosis do not deviate significantly from the theoretical values
(zero). If the calculated Z is less than 1.96, it is treated as not significantly different from zero at
5% level. For a normal distribution following hypotheses are tested. As the Z values of skewness
and kurtosis for wheat marketed surplus and other variables are less than the critical value, 1.96,
the distribution of these variables are deemed as normal.
Test for linearity of regression
Besides normal distribution of data, linearity is tested for multiple regression models. The result
indicates that the calculated F values are less than the corresponding critical values of F. Thus, it
is possible to conclude that linear regression model is appropriate choice.
Multicollinearity
Multicollinearity leads to spurious relationships, biased estimates and larger standard error of
regression coefficient. For these reasons, multicollinearity has been checked during regression
analysis with the help of zero-order correlations matrix and found high multicollinearity between
improved seed and land size. The problem is settled by omitting improved seed having high
intercorrelations with land size.
Test for endogeniety
High relationship between any explanatory variable and error terms lead to biased and
inconsistent estimates. For these reasons, endogeniety test has been carried out, and the result
indicates that there was not endogeniety problem in the model.
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Test for hetroscedasticity
Hetroscedasticity problem causes inefficient estimations and turns out important variable to be
insignificant which leads to a wrong conclusion on the basis of the usual standard error. The test
detected the existence of hetroscedasticity. So, weighted least square has been applied to convert
multiple regression equation with hetroscedasticity into homoscedasticity.
Test for fitness of model
R-squared or coefficient of determination, adjusted R-squared, standard errors and F-test are
used as criteria to appraise the best fit of the model in OLS regression. The result indicates that
the model is fit because all these indices surpass the criteria.
Eight independent variables with high predictive value have been chosen and entered into
stepwise regression analysis to generate the predictive model of wheat marketed surplus. The
coefficient of multiple determinations (R2) of the eight variables was 0.82 which explain about
82% of variations in the wheat marketed surplus in aggregate term.
Table 4: Multiple regression analysis result
Marketed surplus Coefficient Std. Err. t P>|t|
Livestock number 0.56ns
0.47 1.18 0.2201
Distance from main road 2.21 ***
0.81 2.72 0.0071
producer’s wheat value chain function 0.77 ***
0.187 4.12 5.51e-05
Total land size 2.87 ***
0.58 4.91 1.78e-06
Crop rotation -14.11 ***
3.48 -4.05 7.06e-05
Extension participation 1.83 ***
0.48 3.77 0.0002
Fertilizer use 0.11 ***
0.016 6.58 3.54e-010
Tractor utilization 7.45 **
3.28 2.27 0.0242
Constant -17.80 ***
4.43 -4.02 8.25e-05
** Statistically significant at the 0.05 level; ***statistically significant in at the 0.01 level;
ns=non significant at the 0.05 level
Indicators of fitness of the model: R-squared = 0.82, Adjusted R-squared = 0.81, F (8, 210) =
117.55, P-value (F) =3.11e-73, S.E. of regression = 90.54
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It was hypothesized that distance from main road negatively correlates with wheat marketed
surplus, but the relationship between wheat marketed surplus and distance from main road were
positive and significant at the 1% level because farmers have access to weather road and can
communicate, negotiate price and arrange farm gate transaction with wholesalers on phone
mobile because farm gate transaction is more attractive for both of them. Producer’s WVC
function have significant and positive effect on wheat marketed surplus at the 1 % level because
total wheat output significantly increases with producer wheat value chain function. There was a
positive but non-significant relationship between livestock number and wheat marketed surplus
at the 5% level because livestock may not have significant and direct effect on wheat marketed
surplus. It is possible to conclude that weak relationship found in this study reveals that livestock
number is not a crucial factor to explain the variation in the marketed surplus. There was a
positive and significant relationship between rented tractor utilization and wheat marketed
surplus at the 5% level because tractor tills the soil well it may have an effect on yield of wheat.
There was a positive and significant relationship between extension participation and wheat
marketed surplus at 1% level because it may increase wheat productivity due to higher farmer’s
farm, pest, rust and disease management skills. There was a negative and significant relationship
between crop rotation and wheat marketed surplus at the 1% level because practice of crop
rotation downscales share of wheat farm land. It does not mean that crop rotation declines yield.
A strong positive relationship, significant at the 1% level, was found between land size and
marketed surplus because farmers with more land size allocate more land for wheat cultivation as
compared to farmers with fewer land size. Theoretical justification for the observed relationship
is that resource (large land size), generally, increases output. Fertilizer use was found to be
positively associated with wheat marketed surplus at the 1% level of significance because
technology increases the productivity which, in turn, increase marketed surplus.
4.4. Factors impeding Key Actor’s Wheat Product supply in WVC
Actors in wheat value chain vary in amount of wheat product supply to the market because of
working capital, size and age of technologies, relationship with other actors, demands, raw
material supply, existing market structure, hard currency and credit accessibility. Actors in WVC
were asked to point out major barriers that act against wheat product supply. They point out that
working capital, credit accessibility, raw material supply, size and age of technology; networks
with actors, market structure are highly interlinked with the volume of output and working
capacity utilization. Wheat product supply of flour largely associated with working capital,
wheat supply, size and age of milling machine and market structure. About 90% of wheat
20
processing industries ranked shortage of raw materials as the number one barrier for wheat
product supply. A few number of WPI addressed number one challenge by purchasing 90% of
wheat directly from farmers through their own agencies and themselves. So that, they could
utilize above 90% of their full capacity, but electricity and water supply act as barrier not to
realize their full potential. About 60% of WPI claimed that capital is the second impediment to
supply more wheat product to market because it acts as barrier not to create strong linkage with
many wholesalers and farmers that ensure reliable wheat supply. 30% and 40% are size and age
of milling machine and access to credit as the reason for low wheat product supply. Almost
100% bakeries justified low consumer’s demand as first reason, government intervention (fixed
flour quota and fixed bread price) as the second and high utility for home made bread as the third
reason impede bread supply. Input supply companies justified consumer’s seasonal demand;
working capital and hard currency as major reasons for low chemical supply such as herbicide
and pesticide. Cooperatives supply inadequate and/or excess inputs because incorrect demand
request reports.
4. CONCLUSIONS
This study look at the effect of wheat producer’s socio-economic characteristics, wheat value
function and technology utilization on wheat marketed surplus, and effect of other factors on
WVC actor’s wheat products. The result indicates that cooperative as actor has failed to supply
adequate input, namely pesticide and herbicide caused input retailers to manifest their
opportunistic behavior and exploit asymmetric information on input quality at small shops and
spot market, which in turn, declined wheat productivity. Econometric analysis result indicates
that wheat producer’s marketed surplus significantly increased with land size, fertilizer,
extension service, and distance from main road, producer’s WVC function, and decreased with
crop rotation. Working financial capital and network with wheat processing industries were
found to be constraints for trader’s wheat supply for wheat processors. About 90% of wheat
processing industries ranked shortage of raw materials as the number one barrier for wheat
product supply. About 16% of WPI addressed number one challenge by purchasing 90% of
wheat directly from farmers through their own agencies and themselves. Almost 100% bakeries
justified low consumer’s demand as first reason, government intervention (fixed flour quota and
fixed bread price) as the second and high utility for home made bread as the third reason impede
bread supply.
Concern body should establish strong coordination mechanisms among WVC actors to ensure
reliable technology supply with technical trainings, wheat and wheat products supply. It should
attempt to improve land productivity, existing ineffective and inconsistent extension services;
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provide training to wheat producers on WVC function management, and particularly ensure
stainable vertical linkages between wheat producers and WPI.
6. REFERENCES
Anandajayasekeram P. and B. Gebremedhin. 2009. Integrating Innovation System
perspective Value Chain Analysis in Agricultural Research for
Development: Implication and Challenge Improving Productivity and Market success
(IPMS) of Ethiopian Farmers Project Working papert 16. ILRI 67pp.
Berhanu,Gebremedihn. and Hoekstra, D. 2007. Cereal marketing and household market
participation in Ethiopia: The case of teff, wheat and rice. AAAE conference proceedings
(Pp. 243-252).
Cochrane, W. 1944. Conceptualizing the supply relation in agriculture. Journal of Farm
Economics, 37(5): 1161-1176.
CSA. 2014. Agricultural sample survey report on area and production of major crops. Statistical
Bulletin(532), Volume VI, Addis Ababa, Ethiopia.
Dendena Chemeda. 2009. Keynote address. Pp.26-34.In: Dendena Chemeda. (ed.), Proceedings
of the value chain seminar, UNECA conference room Addis Ababa, Ethiopia, 24
November 2009. UNECA conference room Addis Ababa, Ethiopia
DIIDRC. 2007. Markets and rural poverty: upgrading in value chains / edited by
Jonathan Mitchell and Christopher Coles.
Bryceson KP and Kandampully J. 2004. ‘The Balancing Act: “E” Issues in the Australian Agri-
Industry Sector’. Proceedings of the McMaster World Congress on The Management
of Electronic Business, Hamilton, Ontario, 14–16 Jan 2004.
Bryceson KP. 2008. Value chain analysis of bush tomato and wattle seed products,
DKCRC Research Report 40. Desert Knowledge Cooperative Research Centre, Alice
Springs.
22
Edriss, A.K. 2013. Pears of applied statistics. Theory and STATA applications with real
data. BEECSI Series Publications, International I-Publishers.
Jahnke, H.E. 1982. Livestock Production Systems and Livestock Development in
Tropical Africa. Keil Wisenschaftsverlag Vauk, Keil.
Kumar, A., Singha, H., Kumara, S. and Surabhi, M. 2011. Value chains of agricultural
commodities and their role in food security and poverty alleviation: A synthesis.
Agricultural Economics Research Review, 24: 169-181.
Kuroda, Y. and Pan, Y. 1978. A microeconomic analysis of production behavior of the farm
household in Japan: A profit function approach, The Economic Review, 29:115- 129.
Mohammed Hassena. 2009. Keynote address. Pp.26-34.In: Mohammed Hassena. (ed.),
Proceedings of the value chain seminar, UNECA conference room Addis Ababa,
Ethiopia, 24 November 2009. UNECA conference room Addis Ababa, Ethiopia.
Mary, K.G. and Anderson C. L, Kathryn, B., and Chew A. 2012. Wheat value chain: Ethiopia.
Evan school policy analysis and research.
Muhammed Ummer. 2011. Market chain analysis of teff and wheat production in Halaba special
woreda, southern Ethiopia. M.Sc. Thesis, Haramaya University, Haramaya
Nerlove, M. and K. L. Bachman. 1960. The analysis of changes in agricultural supply: problems
and approaches. Journal of Farm Economics, 42: 531-554.
Nyairo, N. and Bäckman, S. 2009. Analysis factors affecting supply of agricultural products:
Market liberalization, agricultural policies, bioenergy policies, population growth, input
price development, trade policies and other relevant factors. Discussion Papers N:O 36.
Rashid, S., 2010. Staple Food Prices in Ethiopia. A paper prepared for the COMESA policy
seminar on Variation in staple food prices: Causes, consequence, and policy options‖,
Maputo, Mozambique, 25-26 January 2010, under the African Agricultural Marketing
Project (AAMP).
Storck, H., Brehanu Adnew, Bezabih Emana, A. Borowiecki and Shemelis Weldehawariat, 1991.
Farming system and farm management practices of smallholders in the Hararge
highlands. Farming system and resource economics in the topics, vol 11. Kiel:
Wirtschaftsverlag Vauk.
USAID. 2010. Staple foods value chain analysis country report - Ethiopia
Yevdokimov, Y. 2012. Practical guide to contemporary economics, ISBN 978-87-403-0238-2:
pp 21-28.