INTRODUCTION
Rice is a source of food, feed, employment
and a source of raw materials for a variety of industries. About half of the
world’s population (more than 3 billion people) depends on rice for their
staple food. Rice provides about 20% of direct human calorie intake worldwide,
making it the most important food crop (FAO, 2012).
The failure of
Nigeria’s rice economy to match its domestic demand raises a number of
questions both among the policy makers and researchers. A key reason to this concern
is that of productivity and efficiency of the rice farmers in the use of
resources. Average yield of upland and lowland rainfed
rice in Nigeria is 1.8 tons//ha while that of the irrigation system is 3.0t ha-1
(Singh et al., 1997). This is low
when compared with 3.0 t ha-1 from rainfed
upland and lowland systems and 7.0 t ha-1 from irrigation system in
places like Cote d’ Ivoire and Senegal (WARDA, 2003).
It therefore implies that rainfed rice farmers in
Nigeria are not getting commensurate returns from the resources they commit to
their enterprise. Sustainable national food security and economic development
in Nigeria will continue to depend upon farmers’ continued ability to sustain
high yields in rainfed rice ecologies (Singh et
al., 1997). Considering the risk and uncertainty in which crop production
takes place most especially in developing countries like Nigeria, farmers’
resources need to be organized and used efficiently in such a way as to produce
maximum output (Yahaya, 2013). The problem of
inefficient use of resources in rice production has been the greatest obstacle
to increased production (Udoh and Oluwatoyin,
2006).
It is in consideration of the inefficient use
of resources that characterised rice production in
Nigeria that this study stemmed to identify socio economic determinants of farmers’
efficiencies in rainfed rice production in Sokoto State, Nigeria. This is with a view to advancing
policy considerations for agricultural development in Nigeria
METHODOLOGY
Sokoto State is
located between latitude 13o 03’ N and longitude 5o 14’ E
with a land area of 28,232.37 Square kilometers. It is bordered in the north by
Niger Republic, Zamfara State to the east and Kebbi State to the south and west (SOSG, 2015). In terms of
vegetation, the State falls within the Sudan savannah zone. Rainfall starts
late May and ends late September or early October with an annual mean rainfall
ranging between 500mm – 700m. Over 80% of the inhabitants of Sokoto State practice one form of agriculture or the other.
They produce such crops as millet, guinea corn, rice, cassava, potatoes,
groundnuts and beans for subsistence and produce wheat, cotton, and vegetable
for cash (SOSG, 2015).
The sampling frame
was established by obtaining a list of all rainfed
rice producing Local Governments Areas and the respective rainfed
rice producing villages from the Ministry of Agriculture, Sokoto.
The names of rainfed lowland rice producing farmers
in the respective villages were obtained from the village heads and leaders of
cooperative associations. This provided the bases for sampling. A 3-stage
multi-stage random sampling technique was used to draw the sample. The first
stage involved a purposive selection of six leading Local Government Areas
noted for rainfed rice production in Sokoto state; these included Wurno,
Goronyo, Rabah, Kware, Kebbe and Silame local government areas. The second stage involved a
random selection of two rainfed rice producing
villages in each of the selected Local Government Areas. The third stage was a
random selection of 25 rainfed rice farmers from each
of the sampled communities. A total of 300 rainfed
rice farmers were sampled and interviewed. Primary data were collected using interview schedule administered by trained
enumerators, while secondary data were sourced from text books, journals, CBN
bulletins, past project works, and other relevant materials. Type of data
collected included socio-economic characteristics such as age, farming
experience, level of education, household size, etc,
and production data such as farm size (ha), quantity and cost of utilized
production inputs, output quantity, price, etc.
The tools of data
analysis used were descriptive statistics and stochastic frontier analysis
(SFA). The descriptive statistics used were frequency distribution and mean to
describe data collected on socio-economics characteristics and level of
resources utilization. The parametric Stochastic Frontier Analysis (SFA) (Aigner, 1977; Amaza &
Maurice, 2005; Tijjani (2006); Ogundare,
2008), also referred to as the econometric frontier approach, which specifies
the relationship between output and input levels and decomposes the error term
into two components (the random error and the inefficiency component) was used
to determine resource productivity and to capture the effects of socio-economic
factors (inefficiency variables) on technical efficiency of the farmers. The
specification of the models is given as follows:
Stochastic
Frontier Analysis (SFA) model:
The functional form of the stochastic
frontier was specified under the Cobb-Douglass specification model. The
frontier model is defined as:
Ln(Yi ) = α + ∑βijXij+
Vi - Ui……………………..(1)
Where the subscript, ij refers to the jth observation of the ithfarmer
Yi = Output of the farmer
(kg),
X1 = Farm size (ha)
X2 = Seeds (kg)
X3 = Inorganic Fertilizer
(kg)
X4 =Labour (mandays)
X5 = Agro-chemicals
(liters)
X6 = Variety (improved =
1, otherwise 0)
Vi = error term
Ui= Farmer
specific characteristics related to production efficiency
It is assumed that technical inefficiency
effects are independently distributed and assumed to be independent of Vi. Vis
are assumed to be independently and identically
distributed normal random errors, having zero means and unknown variance, q2. Uij
is defined as:
I
UijI = δ0 +δ1Z1 +δ2Z2+δ3Z3+δ4Z4+δ5Z5
………(2)
Where:
Ui= Technical inefficiency of the ith farmer
Z1 = Farming experience
(years)
Z2 =Level of Education
(years)
Z3 = Household size (No.)
Z4 = Off-farm income
(Dummy: Yes = 1, otherwise 0)
Z5 = Contact with
extension agent (Dummy: Yes = 1, otherwise 0)
The maximum likelihood estimates of the
parameters in the Cobb-Douglass stochastic frontier production model defined by
(1) given the specification of the technical inefficiency effects defined in
(2) will be simultaneously obtained using a computer program (frontier 4.1).
RESULTS AND
DISCUSSION
Socio-Economic
Characteristics of the Rainfed Rice Farmers:
The result of the study on socio-economic
characteristics is presented in Table 1. The result shows that rainfed lowland rice production in the study area was
dominated by middle aged (31-40 years) and ageing males (41-50 years) with a
family size of between 6 and 10 members. These are the economically active age
brackets and people in this age brackets are usually self- motivated and
innovative. The result shows that majority (59.67 percent) of the rainfed rice farmers had non-formal (Qur’anic)
education and only 33 percent had formal education. Responses on farming
experience shows that 41 percent of the rainfed rice
farmers in the study area had been cultivating rice for a period of 16 - 25
years. This implied that rainfed rice farmers in the
study area have been in farming profession for quite some period of time and
are not novices in rainfed rice farming. The result
further shows that 51.67 percent of the rainfed rice
farmers were non-members of any cooperative society. This finding may be
attributed to a minimal or absence of awareness campaign and/or sensitizations
on the importance of cooperative societies to farmers in the study area. Result
of the study also shows that majority (55.33 percent) of the farmers had no
contact in whatever form with agricultural extension agents, however, 44.67%
were at least contacted once.
Table 1: Distribution of the rainfed rice farmers by personal and socio-economic
characteristics
|
Variable
|
Frequency
|
Percentage
|
Age (Years)
20 – 30
31 - 40
41 - 50
51 - 60
61 Above
Household
size
1 – 5
6 – 10
Above 11
Education
Non-formal
Formal
Farming
Experience
6 – 15
16 – 25
26 – 35
36 Above
Members
of Coop-society
Members
Non-members
Contact
with Extension Agents
Contacted
Not contacted
|
30
89
91
52
38
58
154
88
199
101
75
125
49
51
145
155
134
166
|
10
29.67
30.33
17.33
12.67
19.30
51.40
29.30
66.33
33.67
25.00
41.67
16.33
17.00
48.33
51.67
44.67
55.33
|
Determinants
of Technical Efficiency
The study used multiple regression based on
stochastic production frontier to determine technical efficiency, assuming a
Cobb-Douglass functional form. Result of the inefficiency determinants and the
associated diagnostic statistics are presented in Table 2. As confirmed by the diagnostic statistics,
the result indicates the presence of production inefficiency among rainfed rice farmers in the study area. The Sigma Squared
(0.175) is statistically different from zero (p<0.01). This indicates a good
fit and the correctness of the specified distributional assumption of the
composite error term. This simply implies that a one-sided random inefficiency
component dominates the composite error term included in the model.
The result shows that
all the coefficients of the inefficiency variables included in the model have
the expected negative signs except the coefficient of household size. However,
only the coefficients of farming experience (p<0.01), off-farm income (p<0.05)
and extension contact (p<0.01) consistently and significantly influenced
technical efficiencies of the rainfed rice farmers in
the area.
Table 2:
Maximum likelihood estimates of the Cobb-Douglass stochastic frontier model
|
Variable ML estimate T- value
|
|
Production Factors
Intercept 0.158
4.963***
Farm Size (X1) 0.841 3.105***
Seed (X2) 0.172 1.391
Fertilizer (X3) 0.413 2.407***
Labour (X4) 0.328 2.132**
Agro-Chemical (X5) 0.006 1.259
Variety (X6) 0.036 1.961
|
|
Inefficiency factors
Intercept 0.401 1.635
F/ Experience (Z1) -0.932 -2.519***
Education (Z2) -0.348 -0.722
Household Size (Z3) 0.491 0.893
Off-farm Income (Z4) -0.588 -2.276**
Ext. Contact (Z5) -0.773 -2.914***
|
|
Diagnostic Statistics
Sigma Squared 0.175 2.837***
Gamma 0.683 3.926***
Ln likelihood (X2) 8.037
Likelihood ratio test (LR) 22.749
Mean technical efficiency 0.732
|
*** = Significant at 1 percent, ** = Significant at 5 percent
Farming
experience: Farming experience had a negative and significant
(p<0.01) MLE of 0.932. This implies that as farmers in the study area
advance in farming experience, inefficiency in resource use decreases and
technical efficiency increases proportionately. A high level of farming
experience is known to boost the farmer’s technical knowledge more effectively;
therefore, the farmer becomes more knowledgeable and skillful gradually which
eventually leads to improved method of production and subsequent improvement on
the TE of the farmer. Experience is a risk management factor and Ridler and Hishamunda (2001)
confirmed that new farmers in agriculture are at a higher risk compared to
experienced farmers. This conforms to the findings of Coelli
and Battese (1996) who reported negative production
elasticity between farming experience and technical inefficiency for farmers in
Pakistan, thus suggesting that older farmers are relatively more efficient.
Off-farm
income:
off-farm income was found to significantly (p<0.05) and negatively influence
the technical inefficiency of the rainfed rice
farmers in the study area. This implies that as farmers diversify their income
and earn more money from off-farm activities, inefficiency in resource use
decreases and their technical efficiency increases. Off-farm income buttresses
the farmers’ farm income and affords them the opportunity to access enough
production inputs which make them move closer to the frontier output. This
finding is in line with earlier finding by Obamiro et al., (2003) who opined that farm
families with limited access to productive resources such as capital and inputs
required for attaining physical efficiency will face low productivity.
Extension
contact:
Extension contact is negative and significant (p<0.01). This means that a
unit increase in extension contact will subsequently increase the technical
efficiency of the rainfed rice farmers by 0.77. The
main concern of agricultural extension is to provide farmers the necessary
education and technical information to enable them take
effective farm management decisions that enhance their farm productivity (Ani, 2007). Along this line, Benor
et al., (1984) cited in Yilkat and Murtala (2009)
reported that groundnut production has increased significantly in Gujarat,
India as a result of extension services. The implication of this finding is
that increased number of contacts with extension agents can considerably bridge
the gap between efficient and inefficient rice farmers in the study area.
Farmers’
specific technical efficiency
The distribution of farmers’ specific
technical efficiencies as shown in Table 3 indicates a wide variation of
efficiency index across the rainfed rice farms which
demonstrate a potential for efficiency improvement in the rainfed
rice production system.
The distribution of rainfed rice farmers’ specific technical efficiencies
(Table 3) revealed a predicted technical efficiency of between 27.4 percent for
the least efficient farmer and 98.1 percent for the most efficient farmer in
the study area. The result further reveals that 35 percent of the rice farmers
operated on a TE of 70-79 percent. Others were 29 percent and 15.33 percent who
operated on a technical efficiency of 80-89 percent and 90-99 percent,
respectively. On the overall, the result indicates that 79.33 percent of the
rice farmers operated on a technical efficiencies of 70 percent and above. The
mean TE in rainfed rice production system in the area
was 73.2 percent suggesting that about 26.8 percent chances exist for
increasing output without additional resources in the existing rice production
system. In other words, rice production falls 26.8 percent short of the maximum
(frontier) possible output.
The mean TE (0.732)
operational in the rainfed rice ecology in the area
is similar to that obtained by Udoh and Oluwatoyin (2006) but higher than that obtained by Igbekele (2006) who analyzed and linked the level of TE of
Nigerian small scale farmers to specific farmers’ socio economic and policy variables.The result demonstrates that for the average rainfed rice farmers in the study area to achieve the TE
level of the most efficient farmer they would have to realize about 24.8
percent cost savings, while the least technically efficient farmer will realize
about 70.6 percent cost saving if they are to attain the level of the most
efficient farmer in the area.
Table 3:
Distribution of rainfed rice farmers’ specific
technical efficiencies
|
Variable Frequency Percentage
|
|
< 0.3 11 3.67
|
|
0.30 – 39 18 6.00
0.40 – 0.49 12 4.00
0.50 – 0.59 7 2.33
0.60 – 0.69 14 4.67
0.70 – 0.79 105
35.00
0.80 – 0.89 87
29.00
0.90 – 0.99 46
15.33
Total 300 100
|
|
Minimum TE 0.274
Maximum TE 0.981
Mean TE 0.732
|
Source: Computed from frontier 4.1 output
CONCLUSION
AND RECOMMENDATIONS
The result of the inefficiency factors shows
that the farming experience, off-farm income and extension contact significantly
influenced technical efficiencies of the rainfed rice
farmers in Sokoto. The study shows that farmers were
not maximally technically efficient in rainfed rice
production in the study area, as such, rainfed rice production in the study area had not reached
the frontier threshold. Therefore, within the context of efficiency of
production, rainfed rice production can still be
increased by adopting the technology of the most efficient farmers in the study
area. Farmers need to exploit their farming experiences and should strive to
acquire more knowledge and skills in their production activities. The finding
also recommends that rainfed rice farmers under study
could employ strategic income diversification to improve their technical
efficiencies. The low level of agricultural extension activities in the area
calls for a decisive policy option. It is not an overstatement to claim that
without an effective agricultural extension delivery system, the goal to attain
agricultural development and food security is a mirage. Agricultural extension
is an essential ingredient for continued food supply and food security. It is
hence recommended that there should be a strong and functional partnership
between farmers, research organizations and extension institutions. This
linkage will no doubt help to remove the challenges that research results do
not reach farmers and that research results do not reflect farmers felt needs.
Moreover, fresh and qualified youths (preferably NCE/Degree holders in
agriculture) should be recruited and trained to serve as extension agents,
while incentives such as transport facilities and commensurate remuneration be given to motivate them for greater effectiveness.
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