DETERMINANTS FOR THE ADOPTION OF TECHNOLOGY AND THE CHOICE OF MARKETING
CHANNEL FOR RICE SMALLHOLDER FARMERS IN SOUTHEAST NIGERIA
A. B. Ezeibe1, P. I. Opata2 and C. O. Ume2
1Center for
Entrepreneurship and Development Research, University of Nigeria, Nsukka
2Agricultural Economics Department, University of
Nigeria, Nsukka, Nigeria
*Corresponding
author’s E-mail: patience.opata@unn.edu.ng
ABSTRACT
This
article examines the factors that unravel how the smallholder rice farmers were
influenced by contractual engagement and cooperative participation against spot
marketing channels for the adoption of technology and market access using a
sample of 420 rice farmers in south-east Nigeria. The multinomial logistic
regression model was used and the findings of which reveal that age, land size,
distance to market, the volume of long grain rice, fertilizer applied,
agrochemicals, access to extension agent, distance to the asphalt road and belonging
to farmers’ association werefound as significant factors influencing the choice
of marketing channels. Spot market predominates as the majority of farm
households are still using spot market and there are still high transaction
costs and other constraints. Creation of market linkages, technology and access
to input and output markets by smallholders was found to be areas of
intervention to improve the rice value chain and to mitigate high transaction
costs and technology constraints.
Keywords:
smallholders,
cooperative, rice, market access, contract, technology.
https://doi.org/10.36899/JAPS.2020.5.0143
Published online
June 25, 2020
INTRODUCTION
Rice
(Oryza sativa) is generally regarded as one of the most important staple
cereal crops in sub-Saharan Africa (SSA) with a huge potential for addressing
the challenge of food insecurity (Saito et al., 2015; Niang et al., 2017). Rice is eaten by
almost everybody in Nigeria and it represent basic food for more than 750
million persons in SSA in 2016 (Arouna et al.,
2017).
In 2016, demand for milled rice was 6.3 million metric tons while the supply
was 2.3 million (Federal Ministry
of Agriculture and Rural Development, 2016; Abbas etal.,2018). This suggests
that Nigeria has to make up for rice production deficiency through the
importation of at least 4 million tons of rice annually. The heavy reliance of
Nigeria on food import leaves the local populations vulnerable to increased
prices and volatility from international markets during crises as well as
depletion of the nation’s foreign reserves (Blanc et al.,
2016; Abbas et al., 2018; Harris, 2018). Nigeria's Government spent a
whooping sum of $2.41 billion on rice importation between January 2012 and May
2015(Abbas et al.,
2018).
However, smallholder sector in Nigeria and other sub-Saharan Africa lack access
to technology such as chemical fertilizers, improved seeds, agro-chemicals,
technical guidance, other inputs and market outlets along the rice value chain,
from production to consumption and these constraints increase the gap in rice
supply and demand due to low productivity of rice (Saito et al.,
2015; Niang et al., 2017). Low productivity of rice also resulted
from lack of information about how to use these technologies resulting in low
adoption rate (Bernard et al.,
2017).Other
limiting factors in the rice value chains are lack of access to credit and
choice of market outlet. Choice of marketing channels for rice is affected by information
asymmetry, transaction costs and other institutional factors (Alemu, 2015;
Jagwe and Machethe, 2016; Ebata et al., 2017). Thus lack of
input and output markets affect rice producers' value chains and prevent them
from market-oriented production(Alemu, 2015; Du et
al., 2016; Ebata et al., 2017).Various constraints of rice farmers along
the value chain could be reduced by choosing among contract, cooperative or spot
markets.
Spot marketing are
traditional methods of business relationships that involve a large number of
buyers and sellers who meet at a certain time and place. Spot market
transactions offer independence to producers in decision making and they
involve no costs or profit-sharing as all production costs incurred or profits
generated through the production and sale are borne by the producer. A contract
involves an agreement between a seller and a buyer which stipulates prices,
quantity and quality for a commodity to be delivered at a later time (Mishra et al.,
2018).This
can be a resource-providing contract which involves the provision of farm
technology and credit by contractors to the farmers. It can also be a marketing
contract which help farmers to reduce price and income risks and improve access
to markets(Ochieng et
al., 2017).
Cooperatives carry out collection, storing, cooling, processing, and
distribution of agricultural produce the aim is to add value to products and
improve gains to famer members. Besides, farmers favor and trust cooperatives,
and they facilitate technology adoption (Chagwiza et
al., 2016; Wossen et al., 2017)
Within the promotion of
the performance of the rice marketing system in Nigeria, a proper understanding
of the factors influencing the adoption of technology and the choice of
marketing channels between contract, cooperative and spot market is necessary
for research, development and policy. Several studies have investigated factors
affecting the selection of marketing channels or those affecting volume of
sales through the use of spot market as well as structure conduct and
performance of spot marketing system (Jagwe and
Machethe, 2016; Bernard et al., 2017; Ebata and Hernandez, 2017; Chandio
and Jiang, 2018; Opata, 2018). Some point to the problems of structure conduct and
performance of various staple food markets while others show significant effect
of personal, asset capital, institutional, technological and socio-economic
factors on volume of sale or choice of marketing channels in spot market.
Existing research echoes the question of why spot marketing systems of rice are
not performing well pointing to low input use and stagnated agricultural
productivity (Jagwe and Machethe, 2016; Bernard et al., 2017). Effective
marketing channels and adoption of technology require an appropriate solution
to the various constraints encountered by farmers along the rice value chains
from production to consumption in Nigeria. Cooperative and contract engagement
could be the solution to technology adoption and access to markets since they
assist farmers in input and output markets. Unlike previous empirical studies
in southeast Nigeria, our interest and objective in this paper is to understand
the determinants for adoption of technology and choice of marketing channel in
the context of contract and cooperative channels compared to spot marketing for
rice smallholder farmers.This is the focus of this paper. This is essential for
identifying the factors that can transform rice sector to market oriented
production.
MATERIALS AND METHODS
Study
area: The
study area is South-east geopolitical zone of Nigeria. Five states constitute
this zone: Abia, Anambra, Ebonyi, Enugu, and Imo, covering latitude
40 50’N to 70 10’ N and longitudes 60 40’E to
80 30’E. The zone spreads over a total area of 78,618 km2,
representing 8.5% of the nation’s total land area. The area has
aprojected total population of 16381729 (World Meters, 2019).
Sampling,
the validity of measurement and data collection: There
are four-stage simple random sampling techniques. The first stage was the
selection of statesthrough random sampling techniques and this gave rise to two
states Enugu and Abia. The second stage was the selection of Local government
Areas (LGAs), six LGAs areas were selected from each state to give a random sample
of twelve LGAs. The third stage was the selection of communities. Two
communities were randomly selected from each LGAs to give a sample of
twenty-four communities while the fourth stage was the selection of respondents
from the communities. A total of four hundred and twenty households engaged in
rice farming were randomly selected from the communities for the study.
Cross-sectional
data were generated from a farmed survey conducted during 2017/18 crop year,
and primarily involved administration of sets of structured questionnaires to
respondents, including using open-ended questionnaires for focus group
discussions. The survey questionnaire was duly pre-tested on a randomly
selected sample of 26 respondents, in January 2017. This was subsequently
followed by a pilot survey of 100 farmers (50 men and 50 women), exploring the
potentials and limitations of the study. The result of the pre-test and pilot
study was used in validating the survey questionnaire coverage, timing, and
administration techniques adopted.
Analytical framework and Empirical Model: Smallholder market behavior in the context of rice
produce marketing can be understood by examining choices of a marketing channel
for output market in a constraint utility optimization framework where an
individual is assumed to maximize market participation in terms of market
channel selection subject to a set of socio-economic and institutional
constraints. The framework is couched around market channels (channel 1,
channel 2, and channel 3) in a market environment where transactions on varying
quantities of rice take place. Producers in this context are categorized based
on their selection for either channel 1 denoting contract or channel 2 denoting
cooperatives or channel 3 spot market, and also depending on their inherent
marketing goals, including socio-economic and institutional characteristics
that influence their choices. Market channel choice decision is presented in
the frame of a Multinomial logit regression model. Here the dependent variables
are defined to have three possible conditional (choice) probabilities across
channel 1, channel 2 and channel 3 (i.e. the three-channel choice used in this
study). The conceptual foundation for choice models is often appropriate for
modeling discrete choice decisions such as the case of this study. In the
implementation model, market channel choices are modeled with a three equation
system. Thus a set of coefficients b(1), b(2), b(3), corresponding to three possible options step 1, 2, 3
in marketing channels can be estimated as:
The
model, however, is unidentified in the sense that there is more than one
solution of b(1), b(2), b(3), that
lead to the same probabilities for Z=1, Z=2, and Z=3. To identify the model,
one of b(1), b(2), b(3) is
arbitrarily set to zero. That is if we set b(3) = 0 the remaining coefficient b(1), b(2), would
measure the change in relative to the Z = 3 group. In other words, we would be
comparing the most vertically differentiated channel (3) with the less
differentiated ones (1 and 2). Then setting b(3) = 0, the above equations (1) to (3) become:
The
relative probability of Z = 1 to the base category is given as:
If
we call this the relative likelihood and assume that X and bk(1) are vectors equal to (X1, X2, X3…
Xk)and (b1(1) b2(1) ……. bk(1) respectively. The ratio of relative likelihood for
one unit change in X1 relative to the base category is then:
Therefore the exponential
value of a coefficient is the relative likelihood ratio for a unit change in
the corresponding variable as reported by Opata, (2018)considering that
producers make decisions regarding which buyers for selling their production
(for example contracts in channel step 1; cooperatives in channel step 2 or
spot market in channel step 3). Thus if Z1, Z2, Z3
are the dependent variables representing (channel step 1), which denotes
channels where the farmer sells directly to the contractor; (channel step 2),
denoting channels where farmer sell directly to the cooperatives; and (channel
step 3), which denotes channels where the farmer sell his or her product in the
spot market. The latter category is where the smallholder incurs transaction
costs and the marketing channel has vertically differentiated into specialized
functions like wholesale and retail. Then Multinomial logit model will be
fitted to test how dependent variables: step1, step 2 and step 3 channels can
be explained by some independent variable xjs.
The empirical model: The independent variables are the following
socio-economic factors that were hypothesized as possible determinants of the
producers’ choice of contract engagement, cooperatives or spot market channel.
Z1=F(X1,
X2, X3, X4, X5, X6, X7……
X16.) + µ …(9)
Z2=F(X1,
X2, X3, X4, X5, X6, X7…….
X16) + µ…(10)
Z3=F(X1,
X2, X3, X4, X5, X6, X7………
X16) + µ …(11)
The variables and expected signs are as
follows:
Independent
variables and the expected sign: The choice of cooperative and contract
channel options for sale of output of farmers compared to spot market
transactions is expected to improve the technology and market access of
smallholders. These private institutions (contractors or cooperatives) assist
the smallholders by providing high-quality rice seeds variety such as long rice
grain or Nerica, a variety developed specifically for Africa, credit access,
agrochemicals and pre and post-emergence herbicides to them as well as buying
back their output of rice.
The contractors engage
smallholders to provide input and output market and therefore the income of
farmers from crop production is expected to increase as a result of contract
engagement. An increase in income is from a reduction in transaction costs resulting
from searching for market information as they have already secured market.
Welfare of smallholders in terms of human and asset capital of the farmer (sex,
age, education, active family size, land size), market characteristics (variety
of rice for the market, distance to market), institutions and infrastructure
(access to asphalt roads and participation in rural institutions), technology
requirement (pre-emergence herbicides, postemergence herbicides, insecticides,
fungicides, rodenticides, 75 Hp tractor, knapsack sprayer, boom sprayer,
fertiliser, use of Faro 44, 52, Nerica or long-grain rice varieties,
herbicides), and networks/social capital (knowledge of extension agents (EAs),
membership of farmers' associations) is also expected to improve by selection
of contract engagement or cooperative marketing channels.
Table 1. Summary of
independent variables and the expected sign.
Variables
|
Expected sign in
relation to spot market is negative when contract or cooperative is positive
|
Contract
|
Cooperative
|
Human and asset capital
Gender of the household head (GHH)
Age of the household head (AHH)
Education of the household head (EHH)
Number of the active family member (FS)
Land size in Ha (LS)
Market
Distance to the market in km (MKD)
The volume of long grain rice in kg
(VOL)
Technology
Fertilizer
Faro 44 or Faro 52
Nerica or long grain rice
Seed treatment chemical
Herbicides (Pre-emergence,
post-emergence)
Other agrochemicals (insecticides,
fungicides, rodenticides)
Institutional infrastructure and
network
Distance to asphalt road km (RDD)
Access to extension agent (EXA)
Farmers association (FG)
|
+/-
+
+
+
+
+
+
-
+
+
+
+
+
+
+
|
+
+
+
-
-
+
+
+
+
+
+
+
+
+
+
+
|
Field
survey, 2017 (Characters
in parenthesis are the symbols of the variables)
No sign could be
expected apriorito gender concerning contract engagement compare to the
spot market, however, gender is expected to be positively related to
cooperative participation concerning spot market transaction. Gender is
allocated dummy value where the gender of households took the value of one if
the household head is a male and zero otherwise The expected effect of years of
formal education is that those educated household head will have better
knowledge and skill in making an informed decision especially concerning choice
of cooperatives than those with less education. Education is also expected to
be positively related to contract engagement as the contractors will more
likely relate to those who are educated and skilled in keeping the terms of the
agreement. Education is captured with the number of years of formal schooling
and it is a continuous variable. The age of the household head is measured in
years. This variable has the ambiguous expectation, on one hand, we expect that
as farmer ages he or she has more experience and skills in the problems
affecting the marketing of output and thus will join cooperative or operate
under contract. On the other hand, younger farmers may operate under
cooperative to get the loan and other input or output more than old farmers.
Family size is captured by the number of adult members in the house and it is a
continuous variable. This variable will positively influence more volume of
production and this will likely influence farmers preference for contract or
cooperative for the assured market for rice output relative to spot market
transaction. Land size is measured in hectares and is a continuous variable and
is expected to be positively related to contract or cooperative and negative
with the spot market.
The market situation is
captured in terms of the amount of long rice grain or Nerica variety produced
and the distance the household is located from the district market. Those with
large quantities of long rice grain FARO 66 and 67 or Nerica and who are
located at a far distance from the market may be affected by market access and
will prefer cooperative and contract for selling their output compared to spot
market transactions. The variables that were used to capture institutional
infrastructure, as well as social capital, contact with extension agent for
accessing technical information, membership of farmers group and distance to
the asphalt road. Technology requirement is captured in terms of households’
demands for farm technology such as fertilizer, herbicides, pesticides, and
long grain rice and other input. Those who need more technology are expected to
enter into contracts or engage in cooperatives.
RESULTS AND DISCUSSION
Farm and sample
household characteristics: Table 2 shows that there are three types
of marketing channels from where producers of rice could select to market their
rice output (spot market, contract and cooperatives). Spot market predominates
as most (83.34%) of the rice producers engage in spot markets while 8.56% and
8.10% selected contracts and cooperatives respectively.
Table 2.
Coordination mechanisms used by rice producer in marketing.
Coordination
types
|
Rice producers
|
Number
|
Percentage
|
Spot
market
Contract
Cooperatives
Total
|
350
36
34
420
|
83.34
8.56
8.10
100.00
|
Field
survey, 2017.
Summary statistics of rice
producers’ demographic and economic characteristics: Tables 3 shows the
characteristics of farmers who engage in contract, participate in cooperative
and or transact through spot market in terms of mean and standard deviation of
age, gender, education, active family members, land size, distance to market, volume
of latest improved rice seed, fertilizers as well as other institutional
factors.
The table shows that rice
farmers that are engaged in contract used a higher volume of fertilizer (550kg)
while those that participate in cooperative applied 450kg and the minimum was
used by those involved in spot market transactions (350kg) fertilizer. Looking
at the distance from the asphalt road from the farmer, it reveals that those
involved in cooperative and contract were located further from the asphalt
road. The distance is 28.56 for cooperatives, 25.50 km for contact and 18.56 km
for spot market showing that those engaged in spot market transaction has the
highest access to the market. About (67%) of the cooperative rice producers
have access to extension service; 73% of the contracting rice producers have
access to extension service and 45% of the spot operating rice producers have
access to extension service. Similarly, about 61% of the cooperative rice
producers are members of farmers' organization while 81% of those that are
engaged in the contract are members of farmers group and 69 per cent of those
that use spot market are members of farmers group.
Table 3. Summary
statistics of rice producers’ demographic and economic characteristics.
Variables
|
Cooperatives
|
Contract
|
Spot market
|
Mean
|
SD
|
Mean
|
SD
|
Mean
|
SD
|
Human and asset capital
Gender of the household head (GHH)
Age of the household head (AHH)
Education of the household head (EHH)
Number of the active family member (FS)
Land size in Ha (LS)
|
0.73
45.87
10.89
3.34
2.15
|
0.41
7.61
8.10
1.34
2.31
|
0.91
39.12
12.03
2. 09
1.89
|
0.19
5.05
1.98
0.91
0.71
|
0.91
48.21
9.10
2.01
2.55
|
0.12
6.94
3.10
0.56
1.23
|
Market
Distance to the market in km (MKD)
The volume of long grain rice in tons (VOL)
|
18.96
2.13
|
9.66
0.73
|
24.78
1.95
|
9.91
0.43
|
16.23
1.98
|
3.55
5.44
|
Technology
Fertilizer in kg
Faro 44 or Faro 52 in tons
Nerica or long grain rice in tons
Seed treatment chemical in mg
Herbicides (Pre-emergence,
post-emergence)
Other agrochemicals (insecticides,
fungicides, rodenticides) in liters
|
450
1.25
0.90
250
8.00
10.00
|
250
0.89
1.52
150
0.12
0.50
|
550
1.36
0.49
375
8.00
9.50
|
150
0.56
0.13
156
0.15
0.10
|
350
1.75
0.20
55.08
0.78
12.25
|
200
0.23
0.09
17.0
0.20
1.67
|
Institutional infrastructure and
network
Distance to asphalt road km (RDD)
Access to extension agent (EXA)
Farmers association (FG)
|
28.56
0.67
0.61
|
12.2
0.18
0.23
|
25.5
0.73
0.81
|
6.55
0.11
0.21
|
18.56
0.45
0.69
|
3.46
0.21
0.17
|
Field survey 2017
Diagnostic test for Multinomial
Logit and correlation coefficient of some variables: Before subjecting
data for multinomial logit analysis, several econometric issues needed to be
addressed before estimation. The pair-wise correlations among independent
variables were examined to find out those variables that will affect the model.
The independent variables that can affect the model were removed from the
model. This also eliminates potential multicollinearity among explanatory
variables. An analysis of the variance inflation factor (VIF) did not also show
any problem.
The assumption of
independence is critical and leads to substantial computational difficulties
involving in the computation of multivariate integrals. If there is a change in
the characteristics of any other alternative in the choice set, this property
requires that the two probabilities must adjust precisely to preserve their initial
ratio, that is, the percentage change in each probability must adjust precisely
to preserve their initial ratio, that is, the percentage change in each
probability should be equal. The independence of irrelevant alternatives (IIA)
specification test for models was conducted to check whether the ratio of the
probabilities of choosing any two alternatives is independent of the attributes
of any other alternative in the choice set. The test indicates that the
difference in the coefficients is not systematic and the ratio of the
probability of choosing contracts from spot markets is independent of the
attributes of cooperatives and therefore no need of using nested logit as an
alternative.
Multinomial
Logistic Regression Analysis: A multinomial logistic regression model
was run to determine the significant variables that drive farmers to engage in
contracts or cooperatives against spot market transactions. The variables in
Table 4 were considered and tested for their significance. The multinomial logistic
results of contract (channel step 1) i.e. farmers engaged with a contractor for
output market before going into production; (channel step 2) i.e. producers are
participating in a cooperative that sponsored selling and distribute input
before going into production; as compared with spot market (channel step 3) i.e
rice farmers sell rice in the spot market and incur transaction costs and this
is the baseline group as presented in Table 4. Table 4 shows the estimated
coefficients (b values), standard
error, and significant values (P) of independent variables in the model.
The estimated coefficients
(b values) measure
the expected change in the logit for a unit change in each independent
variable, all other independent variables being constant. The sign of the
coefficient shows the direction of the influence of the variable on the logit.
It follows that a positive value indicates an increase in the likelihood that a
household will change to the alternative option for the baseline group. A
negative value shows that it is less likely that a household will consider the
alternative. Therefore, in this study, a positive value in contract implies an
increase in the likelihood of remaining in contract engagement.
The significant values
(also known as the p-value) show whether a change in the independent variable
significantly influences the logit at a given level. In other words, the degree
to which choosing channel step 1, step 2 or 3 can be explained by household
heads' human and asset capital, market characteristics, technology, and
institutional infrastructure and network. In this study, the variables were
tested at 1%, 5% and 10% significant levels. Thus, if the significant value is
greater than 0.01, 0.05, and 0.1 then it shows that there is insufficient evidence
to support that the independent variable influences a change away from the
baseline group. If the significant value is equal or less than 0.01 or 0.05
and 0.10, then there is enough evidence to support a claim presented by the
coefficient value. The standard error in the value measures the standard
deviation of the error in the value of a given variable.
The marginal effects of
the multinomial logistic regression model are presented in Table 4. Rice
producers’ choice of contracting or cooperative engagements or spot market
depends on market, technology, infrastructural, human asset capital and
network-related factors.
From the results in Table
4, based on the multinomial logit model used, the R2 value of the
model is 0.873 implying that the independent variables in the model explained
about 87% of the variability in the choice of marketing channels.
The model showed that in
comparison with contract engagement against spot market transaction, human and
asset capital, specifically the age of household head positively influenced
farmers in contract engagement. One year increase in the age of farmers
increased the probability of contracting by 3 percent and it is significant at
5% level. Similarly, age positively influenced farmers' participation in cooperative
against spot market transaction and it is 4.3 percent and 1% significant level.
This implies that a year increase in farmers' age increases the probability to
be a member of cooperative by 4.3 percent. This is contrary to aprioriexpectation
as younger farmers were expected to participate in cooperative and to engage in
contract compare to the spot market.
Land size also positively influencing
farmers to engage in the spot market against contract engagement and 1ha
increase in the size of land increases the probability to transact through spot
market against contract by 9 percent and it is significant at 1%. The numbers
of active family members are negative and significant factors influencing
cooperative engagements of farmers. Thus if the number of active family
members increases by one, then there will be 6.8% increase in the number of
farmers that will change to spot market and it is significant at 5% level.
In comparison with
contract engagement against transaction through the spot market, market
characteristics, specifically, a longer distance to market positively
influenced the volume of rice marketed by farmers. The results indicate that
being one kilometer farther away from the market increases the probability of
rice farmers to contract by 6.3 percent and significant at the 5% level.
However, the distance to the market negatively influences cooperative
engagement and the farmers are found changing to the baseline if the distance
increases i.e. to sports market transactions. One kilometer increase in
distance increases spot market transactions by 4.1 percent and significant at
the 5% level. Many of the farmers are smallholders who lack market power as
they offer small amounts to the market. Thus they pool rice and sell
collectively, it may reduce competition among them and give market power to
influence prices. The volume of rice produced by rice farmers also positively
influenced him or her to contract engagement. Farmers secure the market before
going into the large volume of rice production. A one-kilogram increase in the
volume of rice for sale in the market increases the probability of rice farmers
to contract by 9.6 percent at the 1% significance level. However, an increase
in the volume of rice negatively influenced the rice farmers to cooperative
engagement by 3.8 percent significant at the 1% level. This implies that a high
volume of rice products influenced farmers towards spot transactions rather
than cooperatives participation.
Table 4. Multinomial
Logistic regression result for determinants of rice market choice.
Variables
|
Contract participation
|
Cooperative engagement
|
Coefficient
|
(p)
|
Coefficient
|
(p)
|
Human and asset capital
Gender of the household head (GHH)
Age of the household head (AHH)
Education of the household head (EHH)
Number of the active family member (FS)
Land size in Ha (LS)
|
5.023(10.08)
0.035(0.01)
0.030(0.06)
0.007(0.015)
-0.09(0.023)
|
0.251
0.001***
0.103
0.234
0.002***
|
-0.12(0.054)
0.043(0.01)
0.663(0.004)
-0.068(0.02)
0.034(0.45)
|
-1.03
0.002***
0.321
0.011**
0.105
|
Market
Distance to the market in km (MKD)
The volume of long grain rice in tons
(VOL)
|
0.0633(0.01)
0.096(0.003)
|
0.012**
0.001***
|
-0.041(0.01)
0.038(0.002)
|
0.043**
0.008***
|
Technology
Fertilizer in kg (FERT)
Faro 44 or Faro 52 in tons (FARO)
Nerica or long-grain F 66 in tons (NER)
Seed treatment chemical in mg (SEED)
Herbicides (Pre-emergence,
post-emergence)
Other agrochemicals (insecticides,
fungicides, rodenticides) in liters (CHEM)
|
0.081(0.002)
-0.095(0.01)
0.145(0.02)
0.234(0.34)
0.025 (0.35)
0.045 (0.56)
|
0.004***
0.002***
0.056*
0.74
0.45
0.035**
|
0.076(0.003)
-0.05(0.003)
0.134(0.05)
0.045(0.05)
0.045(0.34)
0.043(0.01)
|
0.009***
0.007***
0.73
0.45
0.32
0.051*
|
Institutional infrastructure and network
Distance to asphalt road km (RDD)
Access to extension agent (EXA)
Farmers association (FG)
Cons
|
0.056 (0.003)
0.076(0.125)
0.075(0.003)
2. 938(11.04)
|
0.001***
0.167
0.056*
0.006***
|
0.065(0.023)
0.053(0.013)
-0.04(0.015)
6.78(0.873)
|
0.034**
0.043**
0.032**
0.262
|
Value in brackets are standard errors;
chi-square < 0.001; No of obs = 420; R2 = 0.873
***, **, *,
Significant at the1, 5, 10% significant level
In comparison with spot
market transactions, technology requirements of rice farmers also drive them to
contract and cooperative engagement. The need for fertilizer influence farmers
to contract and participate in the cooperative. One kilogram increase in the
need for fertilizer by rice farmers would increase the probability to contract
by 8.1 percent, significant at the 1% level compared with spot market
transactions. The demand for fertilizer will also increase the probability of
cooperative participation by 7.6% and significant at 1%. This implies that
contract and cooperative engagement can serve as a source of technology
acquisition for smallholders in rural Nigeria. Longer grain FARO 66 and 67 and
NERICA are positively related to contract engagement against spot market
transactions by 14.5% and significant at 10%. The longer grain rice variety is
positive but not significant in cooperative engagement. Other improved
varieties FARO 44 and 52 are negatively related to cooperative and contract
engagement and therefore those varieties increase the probability of farmers to
engage in spot market transactions against cooperative and contract
transactions. They increase the probability to engage in spot market
transactions by 9.5 percent compared with the contract engagement and 5%
compared with cooperatives participation and they are both significant at the
1% level. Using pesticides and fertilizer may imply financial constraint rice
farmers, as fertilizer and pesticides are expensive and hence producers may prefer
to engage in cooperatives as cooperatives facilitate access to credit to use
pesticides and fertilizers.
In comparison with spot
market transactions, institutional infrastructure and network specifically
distance to asphalt roads influences farmers to participate in cooperative as
well as contract engagement. The results showed that a one-kilometer increase
in distance from farm to asphalt road increases the probability to sell through
the cooperative by 6.5% and this is significant at the 5% level. Memberships of
farmers group also influence farmers to engage in contracts and also to
participate in cooperatives. Farmers' group membership increases the
probability of selecting contract engagement as an option for marketing channel
by 7.5 percent and significant at 10% level. Membership of farmers group
increases the probability of participation in spot market transactions against
cooperative by 4 percent and 10% significant level. The transfer of knowledge
about how to use technology is influenced by the formation of the cooperative.
This is because extension agents train farmers through cooperatives. The
results show that technology transfer by extension agent increases the
probability for a farmer to participate in the cooperative channel by 4.7 percent
and this is significant at 10% level.
Conclusion: This article
examines the factors that unravel how the smallholder rice farmers were
influenced by contract engagement and cooperative participation against spot
market channels for technology acquisition and market access using a sample of
420 rice farmers in southeast Nigeria. The study concluded that the use of
agricultural technologies such as chemical fertilizer, improved seeds,
agro-chemicals and technical guidance to markets remains low in developing
countries due to high transaction costs and other constraints. Technology
factors influencing the selection of marketing channels were the use of
fertilizer, use of rice seed variety FARO 44, 52, NERICA, long-grain FARO 66,
67. Creation of market linkages, technology and access to credit to
smallholders was found to be areas of intervention to improve the rice value
chain and to mitigate high transaction costs and technology constraints through
contract and cooperatives.
Acknowledgement: The authors sincerely thank the two
anonymous reviewers for their helpful comments and suggestions.
REFERENCE
- Abbas, A. M., I. G. Agada, and O. Kolade (2018). Impacts of rice importation on Nigeria’s economy. Journal of Sci. Agri.2 (1): 71–75. https://doi.org/10.25081/jsa.2018.v2.901
- Alemu, A. E. (2015). Technology and market access via contracts and cooperatives for smallholders : Evidence from honey producers in Ethiopia. Afri. J. of Sci. Tech. and Dev., 7(6): 420 - 428. https://doi.org/10.1080/20421338.2015.1096512
- Arouna, A., J. C. Lokossou, M. C. S. Wopereis, S. Bruce-oliver, and H. Roy-macauley (2017). Contribution of improved rice varieties to poverty reduction and food security in sub-Saharan Africa. Glob. Food Sec. 14(August 2016): 54 -60. doi: 10.1016/j.gfs.2017.03.001.
- Bernard, T., A. D. Jvanvry, S. Mbaye, and E. Sadoulet (2017). Expected product market reforms and technology adoption by Senegalese. Am. J. of Agri. Econ. 1(1): 1- 20. https://doi.org/10.1093/ajae/aax033
- Blanc, B. E., A. Lepine, and E Strobl (2016). Determinants of crop yield and profit of river valley. Exp. Agr., 52(1): 110 -136. https://doi.org/10.1017/S0014479714000581
- Chagwiza, C., R. Muradian, and R. Ruben (2016). Cooperative membership and dairy performance among smallholders in Ethiopia. Food Policy. 59(1):165 -173. https://doi.org/10.1016/j.foodpol.2016.01.008
- Chandio, A. A., and J. Yuansheng (2018). Determinants of Adoption of Improved Rice Varieties in Northern Sindh, Pakistan. Rice Science.25(2): 103–110. https://doi.org/10.1016/j.rsci.2017.10.003
- Du, X., Lu, L., Reardon, T., and D. Zilberman (2016). Economics of agricultural supply chain design : a portfolio selection approach. Am. J. of Agri. Econ.0(0): 1-12. https://doi.org/10.1093/ajae/aaw074
- Ebata, A., P. A. V. Pacheco, and S. V. Cramon-taubadel (2017). The influence of proximity to market on bean producer prices in Nicaragua. Agr. Econ. 00(0): 1-9. https://doi.org/10.1111/agec.12347
- Ebata, A., and M. A. Hernandez (2017). Linking smallholder farmers to markets on extensive and intensive margins : Evidence from Nicaragua. Food Policy.73(September): 34-44. https://doi.org/10.1016/j.foodpol.2017.09.003
- Federal Ministry of Agriculture and Rural Development (2016). The Agricultural Promotion Policy2016-2020. Abuja: Federal Ministry of Agriculture and Rural Development. Retrieved fromhttps://fscluster.org/sites/default/files/documents/2016-nigeria-agric-sector-policy-roadmap_june-15-2016_final1.pdf
- Harris, B. D. (2018). Intensification benefit index : how much can rural households benefit from agricultural intensification? Exp. Agr. 55(2): 273-287 https://doi.org/10.1017/S0014479718000042
- Jagwe, J. N., and C. Machethe (2016). Effects of Transaction Costs on Choice of Selling Point : A Case of Smallholder Banana Growers in the Great Lakes Region of Central Africa of selling point : a case of smallholder. Agr. Ekon.50(3): 19-123. https://doi.org/10.1080/03031853.2011.617866
- Mishra, A. K., A. Kumar, P. K. Joshi, A. D. Souza, and , G.Tripathi (2018). How can organic rice be a boon to smallholders ? Evidence from contract farming in India. Food Policy. 75(January): 147-157. https://doi.org/10.1016/j.foodpol.2018.01.007
- Niang, A., M. Becker, F. Ewert, I. Dieng, T. Gaiser, A. Tanaka, K. Saito (2017). Field Crops Research Variability and determinants of yields in rice production systems of West Africa. Field Crops Res.207(1): 1–12. https://doi.org/10.1016/j.fcr.2017.02.014
- Ochieng, D. O., P. C. Veettil, and M. Qaim (2017). Farmers ’ preferences for supermarket contracts in Kenya. 68, 100–111. https://doi.org/10.1016/j.foodpol.2017.01.008
- Opata, P. I. (2018). Determinants of the choice of marketing channel among cocoyam farmers in south-east Nigeria. The J. Anim. Plant Sci. 28(4): 1142-1151. Retrieved from http://www.thejaps.org.pk/
- Saito, K., I. Dieng, A. A. Toure, E. A. Somado, and M. C. S. Wopereis (2015). Rice yield growth analysis for 24 African countries over 1960 – 2012. Glob. Food Sec.5(1): 62–69. https://doi.org/10.1016/j.gfs.2014.10.006
- Wossen, T., T. Abdoulaye, A. Alene, M. G. Haile, S. Feleke, A. Olanrewaju, and V. Manyong (2017). Impacts of extension access and cooperative membership on technology adoption and household welfare. J. of Rural Stud. 1(54): 223–233. https://doi.org/10.1016/j.jrurstud.2017.06.022
- World Meters (2019). Nigerian population, state by state analysis Retrieved from https://www.worldometers.info/world-population/nigeria-population/.
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