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Ethiop. J. Agric. Sci. 33(3) 90-107 (2023)
Determinants of Participation and Extent of
Participation in Contract Farming Among
Smallholder Malt Barley Farmers in Oromia
Region, Ethiopia: A Double Hurdle Approach
Addisu Bezabeh Ali1* and Fekadu Beyene Kenee2
1 Ethiopian Institute of Agricultural Research, P.O.Box 2003, Addis Ababa, Ethiopia
2College of Agriculture and Environmental Sciences, Africa Center of Excellence for Climate Smart
Agriculture and Biodiversity Conservation, P.O.Box 161, Haramaya, Ethiopia
*Corresponding author’s E-mail: addisu.bz@gmail.com
Abstract
The study examined contract farming participation intensity determinants among
small-scale malt barley farmers in the Arsi Highlands, Oromia Region, Ethiopia.
Data was gathered from 384 sample respondents using a multistage sampling
procedure. Age, livestock ownership, crop output, price, advice service, cooperative
membership, and credit were found to be major determinants of probability of
contract farming participation. However, total land size and farming experience
negatively determined the likelihood of participation in contract farming. The
contract participation intensity was defined by educational level, landholding size,
production selling price, amount of fertilizer applied, and off-farm income. It is
discovered that smallholder producers of malt barley are increasingly drawn to
contract farming. It is anticipated that the trend will continue, bringing about more
awareness of the advantages of contract farming as well as better access to and
utilization of agricultural input supplies.
Keywords: Contract farming, participation, intensity, Oromia, Ethiopia
Introduction
Poverty alleviation and economic growth drive transformation of subsistence
agriculture in Ethiopia (Admassie et al., 2016; Atakilte, 2018). Agriculture
development flagship programs that include agricultural commercialization
clusters; irrigated wheat production; ten in ten; ye lemat tirufat; the green legacy;
and integrated soil fertility management practices put in place to achieve food
system transformation.
Initiatives for the commercial transformation of subsistence agriculture attract the
involvement of private companies in diverse sectors, including multinational beer
companies, including Heineken, Diageo, Bavaria, and Soufflet in Ethiopia
(Holtland, 2017; Tefera and Bijman, 2021). These multinational breweries
renovated and upgraded old breweries, which led to soaring malt demand in the
country. To bridge the gaps in malt demand, actors in the upstream (malt barley
farmers) and midstream (malt factories and breweries) and downstream (retailers)
Addisu Bezabeh and Fekadu Beyene [91]
began collaborating more closely through contractual agreements to create a local
malt barley supply chain in Ethiopia. The malt barley supply focuses on
organizing farmers in such a way as to provide technology like seeds of improved
varieties for malt barley production, training, and other support so that farmers, in
return, are expected to supply quality products deemed necessary for malting on a
contractual basis.
The barley subsector in Ethiopia employs 3,630,719 barley farm holders who
manage 799,127.84 hectares of land as one of their primary sources of income
(CSA, 2022). Nevertheless, the barley sub-sector has been characterized by a
number of intricate production, marketing, and financial limitations, such as high
transaction costs associated with gaining access to markets, new technology,
information, and inputs (Shiferaw et al., 2022; Dagnew et al., 2024). On account
of these, the barley sub-sector is defined by strong demand and limited supply of
the malt raw material and the grain as a whole. The upgrading of breweries and
malt mills, along with a shift in consumer behavior toward higher beer
consumption, is driving up demand for malted barley. Breweries and malt makers
must consequently expand the scope of their malt procurement programs to fulfill
the increased demand for malted barley. Despite widespread promotion of malt
barley contract farming in major barley producing regions, farmers' engagement as
net sellers in the output market remains relatively low (Gebru et al., 2019;
Dagnew et al., 2024). In light of this, the study set out to look into the variables
that affect farmers' involvement in contract farming in the research areas, as well
as the extent of participation towards the national plan of import substitution and
saving foreign currency expenditures.
Research Methodology
Descriptions of the Study Area
The study was carried out in the Oromia Region of Ethiopia in the Tiyyo and
Limu Bilbilo districts of the Arsi zone and the Kofele and Shashemene districts of
the west Arsi zone. From an astronomical perspective, the zones are located
between 7°08'58" N and 8°49'00" N latitude and 38°41'55" E and 40°43'56" E
longitude. The research areas experience an annual mean rainfall ranging from
1020 mm to 1300 mm. The research region offers ideal edaphic and climatic
conditions for agricultural output. Wheat, barley (food and malt), beans, peas,
maize, teff, sorghum, oats, chickpeas, noug, linseed millet, potatoes, and other
vegetables are among the principal annual crops farmed in the two zones (Oromia
Finance and Economic Development Bureau (OFEDB), 2019).
Determinants of Participation and Extent of Participation in Contract Farming [92]
Figure 1. Map of the study area (Arsi and West Arsi zones)
Sampling Methods and Sample Size
The potential for malt barley production, the existence of contract farming
practices, and the scheme's potential for scaling up were the factors taken into
account while selecting the sample. Thus, the Arsi and West Arsi zones were
purposively selected owing to their current potential for producing malt barley and
presence of contract farming. Additionally, from each zone two districts, Lemu-
Bilbilo and Tiyyo as well as Kofele and Shashemene were selected at random,
respectively. The number of kebeles producing malt barley and their marketing
profiles were used for randomly selecting two kebeles from each district. Then
lists of malt barley producer households obtained from the respective kebeles and
a simple random sampling technique was employed to determine the appropriate
number of respondents in each kebele.
Accordingly, a representative sample size for the study was determined using the
population size of malt barley producers obtained from district agricultural offices.
A representative sample size was determined employing the formula provided by
Kothari (2004):
Addisu Bezabeh and Fekadu Beyene [93]
Where n is the required sample size, e is the desired level of precision, Z is the
inverse of the standard cumulative distribution that corresponds to the confidence
level, p is the estimated proportion of an attribute that is present in the population,
and q = 1-p. The statistical Table, which includes the area under the normal curve
at a 95% confidence level and p = 0.5, given by Kothari (2004), is where the value
of Z can be determined. On the basis of this, 384 homes in total were chosen for
the study from four districts; q = 1-p; N is the size of the whole population from
which the sample was obtained; and a 95% confidence level and ± 5% precision
were assumed. Finally, a sample of 384 (190 contract and 194 non-contract)
household heads was drawn from eight Kebeles using simple random sampling
with probability proportionate to size (see Table 1).
Table 1. Selected study districts,
1
Kebele and household sizes
District
Sampled Kebele
Household size
Sample
households
Contract farming
CF
NCF
Limu Bilbilo
Chiba Michael
1323
90
23
23
Limu Dima
1261
21
21
Tiyyo
Haro Bilalo
1,233
84
19
19
Dosha
1,358
23
23
Kofele
Gurmicho
1,203
92
22
22
Alkaso
1,249
24
24
Shashemene
Hursa Simbo
1037
118
31
32
Gonde Karso
946
28
29
Total
9,610
384
190
194
CF = contract farming participants, while NCF is the farmers’ producer for open market
Source: Agriculture Office
Sources and Methods of Data Collection
Both primary and secondary data were utilized for this study. A total of 384 malt
barley farmers were contacted as the primary data source through face-to-face
personal interviews. The respondents to the household survey were 190 contract
participants. For comparison purposes, 194 non-contract malt barley farmers were
1
Kebele is the smallest administrative division in Ethiopia.
Determinants of Participation and Extent of Participation in Contract Farming [94]
randomly selected from the study area based on households’ lists available at the
respective study Kebeles. The interview schedule consisted of both open and
closed-ended types of questions so as to collect information pertinent to the
purposes of the study. Detailed household and plot-level data were gathered
accordingly to allow statistical analysis of the quantitative data, given the
available time and resources.
Methods of Data Analysis
In this study, descriptive and inferential statistical methods were utilized where
deemed appropriate for the data attribute. Analytical methods that address each
study objective are presented below.
Descriptive Analysis
Statistical inference and descriptive analysis were used in this research data
analysis. Inferential statistical techniques, including the χ2-test and t-test were
employed to compare household and farm characteristics between contract
farming participants and non-participants. The household and farm profiles of the
research areas are characterized using descriptive statistical methods such as
averages, ratios, percentages, and frequencies.
Model specification for determinants of
contract farming participation
In empirical studies on the determinants of farmers’ participation in contract
farming, diverse econometric models have been employed. The Probit, Tobit,
Heckman, and double hurdle models have all been alternatively used to test
determinants of involvement in contract farming (Komarek, 2010; Wang et al.,
2011; Abebe et al., 2013; Ochieng et al., 2016). The econometric approach to be
used depends on the nature of the response variable. Participation in contract
farming is a dependent variable that was analyzed in this study. Where contract
farming participation status was denoted by 1 if the household head participated in
contract farming in previous cropping period and 0 otherwise.
But the biggest issue with utilizing survey data to evaluate participation decisions
is the sizable fraction of households reporting no adoption or participation.
Traditionally, zero observations have been handled using the Tobit model, which
was first developed by Tobin in 1958. However, it has limitations because the
same variables and parameters that influence the probability of participation also
control the levels of involvement. This assumption implies that the direction (sign)
of a particular determinant's marginal effect is the same for smallholder farmers'
involvement as well as the intensity of their engagement when they make the
decision to participate (Burke, 2009). When modeling smallholder farmers'
involvement in contract farming, this isn't a reasonable assumption to make.
Addisu Bezabeh and Fekadu Beyene [95]
Therefore, more adaptable models are needed to enable distinct procedures for
identifying the variables that influence farmers’ decisions on involvement as well
as the degree of involvement. A binary variable is used to denote contract farming
participation. Participants could not, however, get all of their barley sales through
the program. The double-hurdle (DH) model (Cragg, 1971) that we employed
suggests that the choice of whether to sell through the contract and the choice of
how much to sell are independent choices. These choices are selected one after the
other and may go through two distinct decision-making processes (Bellamare and
Barrett, 2006). Therefore, the double hurdle model combines a zero-truncated
continuous distribution to forecast non-zero values with a binary model to predict
zero values. The amount of barley sold through contract farming is used for the
second obstacle. According to Green (2003), a generalization of the Tobit model,
the double-hurdle model features two independent stochastic processes that decide
whether to participate and how intensely. For example, Hailemariam et al. (2006)
found that a variety of factors affect the choice and rate of adoption of poultry
technology. Further studies employing the model include the fertilizer adoption in
Ethiopia study by Gebregziabher and Holden (2011), Kehinde and Adeyemo
(2017), and Shiferaw (2008), which determined the reasons behind farmers'
reluctance to adopt suggested technologies in the production systems of cocoa and
pigeon pea in Nigeria and Tanzania, respectively.
While there are some similarities between the Heckman procedure and the double-
hurdle model's parameterization in that both methods yield two distinct sets of
parameters, the double-hurdle model is thought to be less constrictive. This is so
because non-participants in the Heckman model will never, ever participate. On
the other hand, non-participants in the double-hurdle model are viewed as a
cornerstone solution in a utility-maximizing model (Yami et al., 2013). The
double-hurdle model for contract participation assumes that smallholder farmers
who voluntarily choose not to engage in contract farming are the source of the
zero values reported in the first hurdle, while smallholder farmers who voluntarily
choose to sell malt barley through contractual arrangements are the source of the
values reported in the second hurdle.
The double-hurdle model, which assumes that the decision to engage in contract
farming and the level of participation were controlled by two distinct stochastic
processes, calls for the combined use of the Probit and the truncated regression
models. A normal Probit model is used to estimate the formal model of the first
hurdle, which is the participation choice given below:
Determinants of Participation and Extent of Participation in Contract Farming [96]
is a latent variable that takes the value 1 if the farmer sells malt barley via
contractual arrangements and zero otherwise; and α is a vector of parameters. Z is
a vector of explanatory variables that include demographic, socio-economic, and
governance-related factors, while is a vector of error terms. The formal model of
the second hurdle or intensity of participation equation is presented as:
are the observable and hidden involvement levels in contractual
agreements, respectively. The percentage of malted barley sold to each contracting
partner was used to evaluate the level of involvement in the agreements.is a
vector of parameters to be estimated and is a vector of variables impacting the
household intensity of participation in contractual arrangements, including
governance-related, socioeconomic, and demographic aspects, while is a vector
of error terms.
Addisu Bezabeh and Fekadu Beyene [97]
Table 2. Summary of explanatory variables
Dependent variables
Measure
First hurdle: Contract farming participation status
Dummy: Yes = 1, otherwise =0
Second hurdle: Extent of contract farming participation
Continuous variable denoted by quantity of
malt barley sold in quintals
Explanatory variables
Hypothetical sign of influence on dependent
variables
Code
Descriptions
Measurement
(unit)
Participation
decision making in
CF
Extent of contract
farming participation
GENDER
Sex of the respondent
1 for male respondents
married, and 0 for female
respondent
Positive or negative
Positive or negative
RESPAGE
Respondent’s age
Year
+/－
+/－
EDUCS
Educational level of the
household head
Number of school years
completed
+/－
+/－
MASTS
Marital status
1 married, 0 otherwise
+
+
HHSIZ
Number of family
members
Number
+
+/－
FASIZ
Cultivated farm size
Hectare
+
+
LIVES
Livestock ownership
TLU
+
+
COPMEM
Cooperative membership
Yes = 1, otherwise =0
+
+
CRACS
Credit access
Yes = 1, otherwise =0
－
+/－
ADVIS
Access to advisory
service
Yes = 1, otherwise =0
+
+
OFINC
Off/non-farm income
Birr/year
+/－
+
MBFEX
Barley farming
experience
Year
+
+
YIELD
Yield
Q/ha
+
+
DMKT
Distance to market
Number of minutes
+
－
PRICE
Price for 1 quintal barley
Birr/Qt
+
+
ACSIV
Access to imp. varieties
Yes = 1, otherwise =0
+
+
ACSTR
Access to training
Yes = 1, otherwise =0
+
+
NUMCC
Number of crops
cultivated
Number
－
+
CFP
Contract participation
Yes = 1, otherwise =0
+
+
LEVCOM
Level of
commercialization
Number
+
+/－
Source: Literature review
Results and Discussion
Descriptive Statistics
The study comprises a sample of 384 farm household heads. From the sample,
about half, i.e., 49.50%, were contract farmers, while 50.50% were non-contract
malt barley farmers. The average age of the total sample household heads is 45
years old, which indicates that farm household heads were at their productive
Determinants of Participation and Extent of Participation in Contract Farming [98]
ages. The average schooling level of the total sample household heads is 6th
grade. Only 20.05% of the respondents have completed secondary education.
Generally, the level of education among malt barley farmers is basically
considered to be low. The average family size of the total sample household heads
is 7 persons, which is higher than the national average family size of 4.6 members.
A larger family size guarantees that family labor is available when needed for
agricultural tasks and lowers the expense of hiring labor, which could be one
cause.
Analysis of socio-economic status of the households’ indicated that the average
landholding size for sampled household heads was 1.84 hectares. The livestock
holding size in Tropical Livestock Unit was 7.26 for sample households. Distance
travel to get various inputs and services determine information access and
adoption decisions, accordingly the average travel time taken to reach the main
road for the total sampled of household heads is 26.74 minutes. Mekonnen and
Alamirew (2017) found that farmers near all-weather roads had a 19%
commercialization index, while their remotely located counterparts recorded a
16% commercialization index. On average the farm size allocated for malt barley
production by the total sample household heads was 0.74 hectares. Also, the
average farm size allocated for malt barley production by contract and non-
contract farmers was 0.83 ha and 0.66 ha, respectively. The t-test mean
comparison indicated a significant difference: farmers with larger malt barley farm
sizes are more involved in contract farming. Research indicates that the size of a
land holding affects both the amount and the participation in contract farming, as
demonstrated by studies conducted on rice and maize contract farming in Ghana
and Benin, respectively, by Olounlade et al. (2020) and Ragasa et al. (2018).
Of the total sample household heads, 94.79% were male, while 93.68% and 6.32%
were male and female-headed farm households that participated in malt barley
contract farming, respectively. Descriptive statistics show that a household headed
by male is in a better position to participate in contract farming than a household
headed by female. The majority, or 83.59%, participated in improved crop
production and protection technique trainings at least once in the previous
cropping period. Participation in agro-industrial supply chains such as breweries
or malt factories heavily requires production of quality, volume, and timely supply
of products, which can be realized through getting training to be knowledgeable to
that end. Access to and use of quality inputs such as improved varieties have been
one of the most blamed constraints to realizing the maximum crop production
potential in general and barley production in particular. About 86% of contract
farmers utilized high-yielding improved malt barley seeds as compared with
79.38% of non-contract farmers. Literature documents the positive role of
cooperative membership for farmers in information access, input and output markets,
technology, etc. Out of the sampled households, only 53% were cooperative
Addisu Bezabeh and Fekadu Beyene [99]
members. Access to credit and other extension services are expected to attract and
link farmers with coordinated market chains and ease liquidity and input supply.
Only 25% of the sample households had a chance of using financial services.
Comparing malt barley farmers in terms of their access to and use of financial
services, significant differences were observed: farmers selling their malt barley
production through formal agreements were in a better position in terms of access
to and use of financial services. It was found that farmers selling their malt barley
production through formal agreements were 37 percent, while their counterparts
were only 12 percent.
Table 3. Summary of demographic and socioeconomic variables
Item
Non-contract farmers
Contract farmers
T-Test
Average
SD
Average
SD
Respondent age
43.40
11.10
46.00
11.10
-2.02**
Family members
6.88
3.00
7.77
3.02
-2.90***
Educational level of the household head (grade
completed)
6.07
3.37
6.13
3.59
-0.18
Landholding (ha)
1.70
1.66
1.98
1.46
-1.77*
Malt barley farm size (ha)
0.66
0.60
0.83
0.52
-2.88***
Household income (Birr/year)
48045
51292
86203
58317
-6.80***
Distance to main road (Min.)
30.03
18.17
23.53
18.76
3.44***
Livestock size (TLU)
6.84
4.28
7.69
4.31
-1.94*
Amount of credit used (Birr)
179.28
472.79
707.96
946.92
-6.90***
Variable
Item
NCF
Percent
CF
Percent
Gender
Female
Male
Total
8
186
194
4.12
95.88
100
12
178
190
6.32
93.68
100
0.93
Marital status
Unmarried
Married
Total
9
185
194
4.64
95.36
100
12
178
190
6.32
93.68
100
0.52
Participation of training
Yes
No
Total
140
54
194
72.16
27.84
100
181
9
190
95.26
4.74
100
37.34***
Association in cooperative
Yes
No
Total
57
137
194
29.38
70.62
100
146
44
190
76.84
23.16
100
86.77***
Getting to credit
Yes
No
Total
24
170
194
12.37
87.63
100
70
120
190
36.84
63.16
100
31.09***
Having improved seeds
Yes
No
Total
154
40
194
79.38
20.62
100
164
26
190
86.32
13.68
100
3.24*
Note: NCF: Non-contract farming; CF: Contract farming; ***, **, and * represent significant t-test results at 1%, 5%, and
10% levels, respectively.
Source: Estimated from survey data
Determinants of Participation and Extent of Participation in Contract Farming [100]
Table 4. Demographic and social networks
Item
List
Number of
respondents
Non-contract
farmers
Contract farmers
No.
Percent
No.
Percent
No.
Percent
Gender
Female
Male
Total
20
360
384
5.21
94.79
100
8
186
194
4.12
95.88
100
12
178
190
6.32
93.68
100
0.93
Marital status
Unmarried
Married
Total
21
363
384
5.47
94.53
100
9
185
194
4.64
95.36
100
12
178
190
6.32
93.68
100
0.52
Participation of
training
Yes
No
Total
321
63
384
83.59
16.41
100
140
54
194
72.16
27.84
100
181
9
190
95.26
4.74
100
37.34***
Association in
cooperative
Yes
No
Total
203
181
384
53
47
100
57
137
194
29.38
70.62
100
146
44
190
76.84
23.16
100
86.77***
Getting to credit
Yes
No
Total
94
290
384
24.48
75.52
100
24
170
194
12.37
87.63
100
70
120
190
36.84
63.16
100
31.09***
Having improved
seeds
Yes
No
Total
318
66
384
82.81
17.19
100
154
40
194
79.38
20.62
100
164
26
190
86.32
13.68
100
3.24*
Note: *** and ** represent significant t-test results at 1%, 5%, and 10% levels respectively.
Source: Estimated from survey data
Results of Econometric Analysis
Determinants of contract farming participation and its intensity
Factors that affect smallholder farmers’ participation decision and intensity of
participation in malt barley contract farming were examined using a double hurdle
model. The first hurdle (Probit model) results on the determinants of malt barley
contract participation decision, the Likelihood ratio (LR) of 107.94 is significant
at p<1%. This suggests the combined importance of the explanatory factors
contained in the model. Similarly, outcomes of the reduced regression models that
were computed and displayed in Table 5 below shows that LR of 356.14 of the
fitting models for information produced by malt barley contract farming was
significant at p<1%. This indicates the joint significance of the explanatory
variables in influencing the intensity of participation in contract farming. Yet, the
results expose that there is some variation in the outcomes of the Probit and
truncated regression models, and the factors that determined the variables that
affected the farmers' choice to engage in contract barley farming were not quite
the same as those that affected their level of involvement. This explains why the
double-hurdle model is appropriate for analyzing farmers' involvement in malt
barley contract farming. Below are brief discussions of the significant focal points.
Addisu Bezabeh and Fekadu Beyene [101]
Table 5. Determinants of contract farming participation and its intensity
Variables
Probit Regression
(1st Hurdle)
Truncated Regression
(2nd Hurdle)
Coef.
RStd. Err
dy/dx
Variables
Coef.
RStd.Err
dy/dx
Sex
0.145
0.399
0.056
Sex
-2.878
1.958
1.96
Age
0.020*
0.010
0.008
Age
0.046
0.063
0.06
Educational
0.011
0.030
0.004
Educational
0.322**
0.161
0.16
Household size
0.008
0.033
0.003
Household size
-0.485**
0.200
0.20
Land holding size
-0.173**
0.076
-0.067
Land holding size
24.810***
2.407
2.41
Livestock ownership (TLU)
0.061**
0.026
0.024
Livestock ownership (TLU)
-0.444*
0.245
0.25
Malt barley farming experience
-0.044**
0.019
-0.017
Malt barley price
0.012***
0.003
0.00
Distance to market
0.012**
0.005
0.005
Access to advisory service
-2.545*
1.556
1.56
Access to advisory service
0.981***
0.282
0.329
Access to chemical fertilizers
0.135***
0.034
0.03
Access to chemical fertilizers
0.005
0.004
0.002
Cooperative membership
3.327**
1.103
1.10
Cooperative membership
1.097***
0.175
0.408
Access to credit
-3.532***
1.283
1.28
Access to credit
0.889***
0.208
0.343
Off/non-farm income
0.000**
0.000
0.00
Off/non-farm income
0.000
0.000
0.000
_cons
-14.184
4.326
Constant
-3.116
0.738
/sigma
9.674
0.731
Number of obs = 303
Log pseudo-likelihood = -147.02137
Wald chi2(15) = 83.75
Prob > chi2 = 0.000
Pseudo R2 = 0.2932
Number of obs = = 303
Log pseudolikelihood = -1117.5661
Wald chi2(12) = 363.20
Prob > chi2 = 0.0000
Significance: *** p<0.01, ** p<0.05, * p<0.1
Source: Analysis of survey data
Age of the household head: Age was statistically significant and positively
influenced farmer’s contract farming participation decision but was insignificant
in influencing intensity of contract farming participation. The implication is that as
the age of a farmer increases, he/she is more likely to participate in contract
farming. This result is found to be inconsistent with the study of Alene et al.
(2008), which revealed that as one grows older, risk-taking decreases, which could
also decrease chances of contract farming participation. This could be because
older farmers would have developed greater experience, networks, and trust that
would allow them to participate in contract farming.
Educational level of the household head: Education had a significant and
positive coefficient at p<5% in influencing farmer’s malt barley contract farming
participation intensity but was insignificant in determining participation decision.
The implication is that farmers with higher levels of education were more likely to
raise the volume of malt barley sales in contract farming. This result is in line with
findings of Awotide et al. (2016) and Nhan (2019), who found that education
positively determines the rice market's participation intensity in Nigeria and
Vietnam, respectively.
Landholding size: The estimated coefficient of total land size significantly
determined both contract farming participation and its intensity negatively and
Determinants of Participation and Extent of Participation in Contract Farming [102]
positively at p<1%, respectively. This shows an inverse relationship between farm
size and the likelihood of a decision to participate in contract farming. But once a
farmer made the choice to engage in contract farming, the intensity of sales
volume relied on farm size. This contradicts the theory and the findings of a prior
study conducted by Khan et al. (2019), which indicated that farmers allocated their
land for crops that responded to market signals in proportion to their land
ownership. However, Rao et al. (2017) could not find a significant difference in
farm size between contracted and decision-contracted farmers in India.
Livestock holding size (TLU): The livestock holding size (TLU) of a household
has shown a significant influence on contract farming participation decisions
positively, but the extent of participation negatively, at p<5% and p<10%,
respectively. The size of the livestock holding had a positive and significant
coefficient, at less than five percent, influencing the farmer's contract farming
participation decision. A large herd of animals demonstrates status and serves a
variety of social and economic purposes. Farmers may find it easier to finance the
investment necessary to enter contract farming if they have enough cattle. Studies
show the mixed influence of livestock holdings on contract farming participation.
For instance, Khan et al. (2019) did not find any significant relationship between
livestock holding and farmers’ maize and potato contract farming participation
decisions in Pakistan. In contrast, Muroiwa et al. (2018) found a favorable
correlation between farmers' decisions to engage in contract farming in Zimbabwe
and their ownership of cattle.
Malt barley farming experience: Malt barley farming experience was
statistically significant but negatively determined both contract farming
participation decision at <5%. That is a 1 year increase after 6 years of average
malt barley farming experience; the probability of participation decision
decreases, keeping other covariates unchanged. But studies present mixed results;
for instance, Maertens and Velde (2017) observe that farmers with previous
experience in cotton farming are more likely to participate in contracts; Ruml et
al. (2022) find no significant influence of experience on participation and its
intensity between rice contract and non-contract farmers. Azumah et al. (2016)
revealed that rice farming experience negatively affects farmers’ contract farming
participation and intensity.
Access to advisory service: The variable access to agricultural advice was
statistically significant and exerted a positive influence on contract farming
participation decisions at <5% and but with significant negative influence on
intensity of participation at <10%. The implication is that with improved access to
advisory services, so does contract farming participation inclination but not the
intensity. In line with this study, Abebe et al. (2013) attested that farmers
Addisu Bezabeh and Fekadu Beyene [103]
participate in contract farming as they seek various supports that enhance farmers’
knowledge about improved production systems.
Access to chemical fertilizers: The variable quantity of fertilizer applied
showed strong insignificant influence on intensity of contract farming
participation at <1%. This implies that quantity of fertilizer applied is directly
associated with quantity of malt barley sales, as marketable surplus production is a
function of input use, including improved varieties and chemical fertilizers.
Consistent with the finding, Shiferaw et al. (2014) underline the importance of
adoption of improved varieties, fertilizers, and pesticides to increase quantity and
quality of product sales.
Cooperative membership: Both the decision to participate in contract farming
and the extent of participation were statistically significantly influenced by the
cooperative membership variable. The marginal effect indicates that, as a farmer
who is a member of an effective cooperative, the probability of participation in
malt barley contract farming increases by 41%. The results are in line with those
of Mishra et al. (2018), who discovered that cooperatives function as a middleman
to facilitate transactions between farmers and major food processors. This is
because these associations are adept at mediating conflicts of power between
smallholder farmers and big enterprises. Moreover, farmer cooperatives are
believed to facilitate agricultural service delivery, raise productivity, and link
farmers to better markets (Ahmed and Mesfin, 2017).
Access to credit: Access to credit positively and significantly determined
contract farming participation decisions, but against the expected signs and
influence on intensity of participation. The study indicated that, if one changes
from no credit access to credit access, the predicted probability of contract
farming participation increases by 41%. As expected, farmers with access to
credit, either in monetary terms or in kind, are likely to be motivated to participate
in contract farming. Also, Mishra et al. (2018) support that contract farming offers
incentives to boost a commodity's output through improved seeds, technical
support, credit availability, and input access. It also improves the coordination of
activities along value chains, the perceived favorability of transactions,
dependability, and capacity to deliver on commitments.
Off/non-farm income: Off/non-farm income positively and significantly
influenced intensity of contract participation at p<1%. Studies show mixed effects
on contract farming participation. It is considered that high- and low-income
earners can easily mobilize productive resources, devise more diversified
enterprises and likely exit from farming (Randela et al., 2008). Bellemare and
Bloem (2018) provide further evidence that off-farm income alleviates financial
Determinants of Participation and Extent of Participation in Contract Farming [104]
limitations, especially for resource-poor farmers, allowing them to buy items that
increase production. Farmers who earn more income from off-farm or non-farm
activities could seek to maximize that income, giving less attention to their farm
activities, including malt barley production. Although this finding is inconsistent
with many studies, a study by Osmani and Hossain (2015) found that they had
reported similar findings.
Conclusion and Policy Implications
Participation in contract farming has been found to be determined by demographic
characteristics, including age, gender, and household size. In addition to
institutional factors like access to extension services, market information,
cooperative membership, credit availability, and price and off-farm income,
households' decisions to participate in contract farming have also been influenced
by socioeconomic factors like educational attainment, the size of their
landholding, and the number of livestock they own.
The following policy implications are emphasized for wider contract farming
participation based on the evidence presented in this study:
Farmers who engage in contract farming reveal that contract farming is as a
platform that enables them to secure equitable marketing price for their production
and boost sales volume that sustain malt industry and producer relationships.
Landholding size strongly affects the extent of contract farming performance, so
formulation of policies that encourage efficient land use and right transfer that
ensure efficiency in allocation of factors of production, which is landholding.
By encouraging farmers to share knowledge and experiences in their their
cooperatives, malt barley farmers shall be able to increase their prospects of
contract farming participation and extent of participation by selling their
production in the scheme.
Enhance access to rural financial services to motivate farmers’ investment and
participation in coordinated agri-food supply chains further.
Access to inputs, including chemical fertilizers, is positively correlated with
contract farming participation and involvement level. This shows that contract
farming should be promoted by providing chances for input access. As a result,
there will be more people involved in contract farming.
Contract farming participation decision and its extent of participation will also be
enhanced by efforts to devise off-income-generating interventions in the areas.
Declaration of competing interest
The authors declare that they have no competing interests.
Data availability
Data will be made available on request.
Addisu Bezabeh and Fekadu Beyene [105]
Funding
This research work was financed by Ethiopian Institute of Agricultural Research
(EIAR)
Contributions
The corresponding author contributed to survey design, data collection, cleaned
the data, analyzed the data, and wrote the first draft of the manuscript. The other
authors contributed to reading, editing, and structuring the manuscript. All authors
read and approved the final version of the manuscript.
Availability of data and materials
Data used for the analyses in this article are available from the corresponding
author upon request.
References
Abebe, G.K., Bijman, J., Kemp, R., Omta, O., and Tsegaye, A. (2013). Contract farming
configuration: smallholders’ preferences for contract design attributes. Food Policy,
40: 14–4.
Ahmed, M. and Mesfin, H. (2017). The impact of agricultural cooperatives membership
on the wellbeing of smallholder farmers: empirical evidence from eastern Ethiopia.
Agricultural and Food Economics, 5:6.
Alene, A. D., Manyong, V.M., Omanya, G., Mignouna, H. D., Bokanga, M., and
Odhiambo, G. (2008). Smallholder market participation under transactions cost:
Maize supply and fertilizer demand in Kenya. Food Policy, 33: 318–328.
Awotide, B.A., Aziz, A.K., and Diagne, A. (2016). Agricultural technology adoption,
commercialization and smallholder rice farmers’ welfare in Rural Nigeria.
Agricultural and Food Economics, 4:3.
Azumah S.B., Donkoh S.A, Ehiakpor D.S. (2016). Examining the determinants and
effects of contract farming on farm income in the northern region of Ghana. Ghana
Journal of Science, Technology and Development, 4(1): 2343-6727.
Bellemare, M. F., and Bloem, J. R. (2018). Does Contract Farming Improve Welfare? A
Review. World Development, 112: 259-271.
Bellemare, M.F.; Barrett, C.B. (2006). An ordered Tobit model of market participation:
Evidence from Kenya and Ethiopia. American Journal of Agricultural Economics,
88: 324–337.
Burke, W.J. (2009). Fitting and interpreting Cragg’s Tobit alternative using Stata. Stata
Journal, 9(4): 584–92.
Cafiero, C. (2008). Agricultural Producer Risk Management in a Value Chain Context:
Implications for Developing Countries Agriculture. Food and Agriculture
Organization of the United Nations, AAACP Paper Series - No. 4.
Cragg, J. G. (1971). Some statistical models for limited dependent variables with
application to the demand for durable goods. Econometrica, 39(5): 829-844.
Determinants of Participation and Extent of Participation in Contract Farming [106]
Dorward, A., S. Fan, J. Kydd, H. Lofgren, J. Morrison, C. Poulton, and N. Rao. (2004).
Institutions and policies for pro-poor agricultural growth. Development Policy
Review, 22(6): 611–22.
Gebregziabher, G. and Holden, S. (2011). Does irrigation enhance and food deficits
discourage fertilizer adoption in a risky environment? Evidence from Tigray,
Ethiopia. Journal of Development and Agricultural Economics, 3(10): 514-28.
Green, W.H. (2003). Econometric analysis. 5th Edition, Prentice Hall, Bergen County, NJ.
Hailemariam, T., Legesse, D., Alemu, Y. and Dana. N. (2006). Determinants of adoption
of poultry technology: A double-hurdle approach. Livestock Research for Rural
Development, 18(3):1-14.
Kehinde, A.D. and Adeyemo, R. (2017). A probit analysis of factors affecting improved
technologies dis-adoption in cocoa-based farming systems of southwestern Nigeria.
International Journal of Agricultural Economics, 2(2): 35–41.
Khan, M.F., Nakano, Y. and Kurosaki, T., (2019). Impact of contract farming on land
productivity and income of maize and potato growers in Pakistan. Food Policy, 85,
pp.28-39.
Khapayi, M., Van Niekerk, P. and Celliers, P.R. (2018). Agribusiness challenges to
effectiveness of contract farming in commercialization of small-scale vegetable
farmers in Eastern Cape, South Africa. Journal of Agribusiness and Rural
Development, (4 [50]).
Komarek, A. (2010). The Determinants of banana market commercialization in western
Uganda. African Journal of Agriculture Resource economics, 5 (9): 775-784.
Kothari, C.R. (2004). Research Methodology: Methods and Techniques, 2nd Edition.
New Age International, New Delhi, India.
Maertens, M. and Vande velde, K. (2017). Contract-farming in Staple Food Chains: The
Case of Rice in Benin. World Development, 95: 73–87.
Mekonnen, N. and Alamirew, B. (2017). The Effect of Rural Road Transport on Crop
Commercialization: Evidence from the Ethiopian Living Standard Measurement
Survey. Ethiopian Journal of the Social Sciences and Humanities, 13(2): 81-108.
Michelson, H.C. (2013). Small farmers, NGOs, and a Walmart world: Welfare effects of
supermarkets operating in Nicaragua. American Journal of Agricultural Economics,
95(3): 628–649.
Mishra, A.K., Kumar, A., Joshi, P.K., and D’Souza, A. (2018b). Impact of contract
farming on yield, costs and profitability in low-value crop: Evidence from a low-
income country. Australian Journal of Agricultural and Resource Economics, 62:
589–607.
Muroiwa, J., Mushunje, A. and Musitini, T., (2018). Factors influencing smallholder
farmer participation in tobacco contract farming arrangements in mount Darwin
district of Zimbabwe. International Journal of Development and
Sustainability, 7(12), pp.2986-3001.
Nhan, T.Q. (2019). Effect of private-led contract farming on rice growers’ yield, cost,
selling price and return: evidence from Vietnam’s central Mekong Delta.
International Food and Agribusiness Management Review, 22: 731-746.
Ochieng Justus, Beatrice Knerr, George Owuor & Emily Ouma. (2016).
Commercialization of Food Crops and Farm Productivity: Evidence from
Smallholders in Central Africa. Agrekon, 55(4): 458-482.
Addisu Bezabeh and Fekadu Beyene [107]
Olounlade, O. A., Li, G. C., Kokoye, S. E. H., Dossouhoui, F. V., Akpa, K. A. A.,
Anshiso, D., & Biaou, G. (2020). Impact of participation in contract farming on
smallholder farmers’ income and food security in rural Benin: PSM and LATE
parameter combined. Sustainability, 12(3), 901.
Osmani, A. Gani and Hossain, E. (2015). Market participation decision of smallholder
farmers and its determinants in Bangladesh. Economics of Agriculture, 62: 163-179.
Ragasa, C., Lambrecht, I., & Kufoalor, D. S. (2018). Limitations of contract farming as a
pro-poor strategy: The case of maize outgrower schemes in upper west
Ghana. World Development, 102, 30-56.
Randela, R, Z.G., Alemu. and J.A., Groenewald. (2008). Factors enhancing market
participation by small-scale cotton Farmers, Agrekon, 47: 4.
Rao, C., Sutradhar, R. and Reardon, T. (2017). Disruptive Innovations in Food Value
Chains and Small Farmers in India. Indian Journal of Agricultural Economics,
72(1): 22-48.
Ruml, A., Ragasa, C. and Qaim, M. (2022). Contract farming, contract design and
smallholder livelihoods. Australian Journal of Agricultural and Resource
Economics, 66(1), pp.24-43.
Shiferaw, B., Kassie, M., Jaleta, M. and Yirga, C. (2014). Adoption of improved wheat
varieties and impacts on household food security in Ethiopia. Food Policy, 44: 272–
284.
Tefera, D.A. and Bijman, J. (2021). Economics of contracts in African food systems:
evidence from the malt barley sector in Ethiopia. Agricultural and Food
Economics, 9(1), pp.1-21.
Teimoury, E., Fesharaki, M. and Bazyar, A. (2010). The relationship between mediated
power asymmetry, relational risk perception, and governance mechanisms in new
product development relationships. Journal of Research in Interactive Marketing,
4(4): 296-315.
Tobin, J. (1958). Estimation on Relationship for Limited Dependent Variables.
Econometrica, 26(1): 24-36.
Verhofstadt, E. and Maertens, M. (2014). Smallholder cooperatives and agricultural
performance in Rwanda: do organizational differences matter? Agricultural
economics, 45(S1), pp.39-52.
Wang, H.H., Zhang, Y. and Wu, L. (2011). Is contract farming a risk management
instrument for Chinese farmers? China Agricultural Economic Review, 3(4), 489–
504.
Yami, M., Teklu, T. Adam, B. (2013). Determinants of farmers’ participation decisions
on local seed multiplication in Amhara region, Ethiopia: A double hurdle approach.
International Journal of Science and Research, 2: 423–430.
