Greener Journal of Agricultural Sciences Vol. 9(2), pp. 155162, 2019 ISSN: 22767770 Copyright ©2019, the copyright of this article is
retained by the author(s) DOI Link: http://doi.org/10.15580/GJAS.2019.2.032619050 http://gjournals.org/GJAS 

Comparative
Analysis of the Technical Efficiency of Beneficiary and NonBeneficiary Rice
Farmers of the Anchor Borrowers’ Programme in Benue
State, Nigeria
*Okeke, A.M^{1}; Mbanasor,
J.A^{2}; Nto, P.O^{2}
^{1}Department
of Agribusiness, University of Agriculture, Makurdi,
Benue State, Nigeria.
^{2}Department
of Agribusiness and Management, Michael Okpara
University of Agriculture, Umudike, Abia State, Nigeria.
^{ }
ARTICLE INFO 
ABSTRACT 
Article No.: 03261950 Type: Research DOI: 10.15580/GJAS.2019.2.032619050 
Comparative
analysis of the technical efficiency of beneficiary and nonbeneficiary rice
farmers of the Anchor Borrowers’ Programme in Benue State, Nigeria was
investigated. Data for the study were collected with the aid of
wellstructured questionnaire from 768 rice farmers consisting of 388
beneficiaries and 380 nonbeneficiaries from 18 communities and 18 Local
Government Areas using multistage sampling technique. The collected data
were analysed using descriptive statistics, multiple regression analysis,
and stochastic frontier production function. The findings revealed that the
beneficiary rice farmers achieved lower levels of technical efficiency
compared to the nonbeneficiary rice farmers and that seed (0.483) and
agrochemical (1.60) used, increased technical efficiency more among
beneficiary rice farmers than the nonbeneficiary rice farmers while
fertilizer (1.285) used, decreased technical efficiency of beneficiary rice
farmers more compared to the nonbeneficiary rice farmers. The results also
showed that rice production among the beneficiaries was in stage I of the
production curve and that gender (1.249), educational level (0.045), age
(0.058), membership of cooperative (0.250), extension visit (0.126),
marital status (2.633), and household size (0.059) significantly influenced
their technical inefficiency. The study recommended policies and programmes
targeted at reallocation and redistribution of farm production inputs and
taking cognizance the incorporation of farmers’ socioeconomic
characteristics in their formulation. 
Submitted: 26/03/2019 Accepted: 28/03/2019 Published: 23/04/2019 

*Corresponding Author Okeke,
A.M. Email: anayomichaelokeke@ gmail.com 

Keywords: 



INTRODUCTION
Rice has continued to
play a significant role in the sociocultural and economic lives of the
Nigerian population. Its demand in Nigeria has been increasing at a much faster
rate than other West African countries since the mid1970s. As a core staple
food among the Nigerian population, it has been subjected to a combination of
measures in an effort to boost local production and reduce the country’s large
dependence on imports (FEWS NET, 2017). In spite of efforts by successive
governments to make Nigeria selfsufficient in rice production, this objective
seems not to have been met.
PwC Analysis (2017)
revealed that domestic production of rice in Nigeria has never been able to
meet domestic demand thereby leading to considerable imports which as at the
year 2017 stood at 3.7 million tons with domestic consumption estimated to be
6.4 million tons leaving a huge gap of 2.7 million tons. This situation has
continued to encourage dependence on importation. Since this rice import is
paid in foreign currency, this has led to the precarious balance of payment
position of the country.
Among the several
explanations for the poor performance of the Nigerian rice subsector are low
level of improved farm inputs usage among the small scale rice farmers in the
country, high cost of inputs, diversion of subsidized farm inputs, soil
degradation, annual bush burning which destroys the soil organic matter, land
issues, lack of capital, neglect of the agricultural sector, inadequate
extension agents, market failures, insufficient technical knowhow in the area
of fertilizer application and improved seeds, and inadequate essential inputs
for rice farmers (Osanyinlusi and Adenegan,
2016; Ahmed, Xu, Yu and Wang, 2017). These have
resulted in inefficiency in the allocation of production resources and hence,
low productivity.
Thus, every effort
aimed at improving the productivity of rice farmers cannot overlook identifying
and addressing these key factors. It is also important to note that knowledge
of the technical efficiency status and its determinants, in addition to the key
drivers of productivity of rice farms are relevant from policy perspective in a
country where new technologies are scarce and productive resources are
inadequate. This is because, gains in the efficiency and productivity of rice
farms are essential for increasing the farm income of small scale producers of
rice in the country.
Although plethora of
technical efficiency studies on Nigeria’s agricultural production exist in the
literature, no study has been done using additive multiplicative dummy approach
to compare the technical efficiency of beneficiary and nonbeneficiary rice farmers
of the Anchor Borrowers’ programme. Thus, the broad
objective of this study was to compare the technical efficiency of beneficiary and
nonbeneficiary rice farmers of the Anchor Borrowers’ Programme
(ABP) in Benue State, Nigeria. The specific objectives were to:
i.
Compare the technical efficiency level of
beneficiary and nonbeneficiary rice farmers of the ABP in the study area;
ii.
Estimate the determinants of technical
inefficiency among beneficiary rice farmers of the ABP in the study area; and
iii.
Describe the technical efficiency level of
beneficiary rice farmers of the ABP in the study area.
METHODOLOGY
The Study
Area:
The study was conducted in Benue State, Nigeria. The State is situated between
latitudes 6^{0}25^{/}N and 8^{0}8^{/}N and
longitudes 7^{0}47^{/}E and 10^{0}E. Benue State is the
nation’s acclaimed food basket of the nation because of the abundance of its
agricultural resources. The State is a major producer of food and cash crops
(BNARDA, 2004). Smallholder farmers who are involved in arable crop production
like rice, yam, cassava, sweet potato, maize, vegetables, soya bean, as well as
livestock like poultry, goat, sheep, piggery, cattle, and fish abound in the
State.
Sampling
Technique and Data Collection: Multistage sampling technique was
employed to select a sample size of 768 rice farmers consisting of 388
beneficiary and 380 nonbeneficiary rice farmers of the Anchor Borrowers’ Programme selected from 18 communities and 18 Local
Government Areas.
The data for the study were collected using a
wellstructured questionnaire. Data were collected on the socioeconomic
characteristics of the respondents; costs and returns of rice production in the
study area; farm output, income and productive assets acquired by the
respondents; credit demanded and level of utilization of such credit among the
respondents; and challenge to credit demand and utilization among respondents.
Analytical
Techniques: The data collected were subjected to descriptive and
econometric analyses.
The
Additive Multiplicative Dummy Variable Approach: The additive
multiplicative dummy variable approach was employed to compare the technical
efficiency among beneficiary and nonbeneficiary rice farmers.
This approach was
used rather than the traditional method of fitting separate models and testing
the equality of coefficients between them.
This approach was
suggested by Gujarati (1970) and Maddala (1988) and
has been used widely by researchers (Nwaru, 2003; Nwaru and Iheke, 2010).
The CobbDouglas functional
form was adopted in this comparison as in most cases, it satisfies statistical,
economic and econometric conditions better (Sankhayan,
1998).
Furthermore, Nwaru and Iheke (2010) revealed
that the CobbDouglas functional form has been found by economists to be most
suitable in analyzing production problems of industries and agriculture.
The CobbDouglas
functional form using the additive multiplicative dummy variable approach is
given as:
lnY = lnA_{0}
+ β_{0}D + A_{1}lnX_{1} + β_{1}DlnX_{1}
+ A_{2}lnX_{2} + β_{2}DlnX_{2} +…+ A_{n}lnX_{n}+ β_{n}DlnX_{n}
+µ_{i} …..(1)
Where:
Y= dependent variable
Ln = natural logarithm
A_{0} = intercept or constant term
β_{0} = coefficient of the
intercept shift dummy
D = dummy variable which takes the value of
unity for beneficiary rice farmers and zero for nonbeneficiary rice farmers.
X_{1}D, X_{2}D, … , X_{n}D = slope shift
dummies for the independent variables
X_{1} X_{n}
= independent variables
β_{1} β_{n}
= coefficients of the slope shift dummies
A_{1}A_{n} = coefficients of
the independent variables
µ_{i} = stochastic error term assumed
to satisfy all the assumptions of the classical linear regression model.
If the coefficient of
the dummy variable, D (in the additive) is significant, it means that there is
a difference in the technical efficiency of the farmer groups. If it is
positive, it implies that the specified function for the rice farmer groups
denoted as unity has a higher technical efficiency than the group denoted as
zero and vice versa.
If β_{0}
= 0 and all β_{i} (i = 1,2,…n) = 0, then
the two farmer groups are represented by the same function. If β_{0 }=0
and β_{i }≠ 0, then the two groups of farmers face neutral
function. If at least one of the β_{i} ≠ 0, then the two
groups of farmers are facing factor biased or nonneutral function (Nwaru and Iheke, 2010).
The
Stochastic Frontier Production Function: The stochastic
frontier production function model of CobbDouglas functional form was employed
to estimate the farm level technical efficiency of beneficiary rice farmers of
the ABP. The CobbDouglas functional form was used because the functional form
meets the requirement of being selfdual, it allows the examination of economic
efficiency and it has been applied in many empirical studies (Battese and Coelli, 1988; Bivan_et al., 2015).
The CobbDouglas production functional form
is specified as:
Y_{i} = f(X_{i}
; β) exp V_{i}µ_{i} ……………………………..(2)
The technical
efficiency of individual rice farmers is defined in terms of the ratio of
observed output to the corresponding frontier output conditioned on the level
of input used by the farmers. Hence, the technical efficiency (TE) of the
farmer is expressed as:
TEi = = = exp (µ_{i})
……… (3)
Where:
Y_{i} = the observed output
Y_{i}* = the frontier output
The production
technology of the beneficiary rice farmers was specified by the CobbDouglas
frontier production function defined as follows:
LnY_{i} = β_{0} +
β_{1}lnX_{1i} + β_{2}lnX_{2i} +
β_{3}lnX_{3i} + β_{4}lnX_{4i} +
β_{5}lnX_{5i} + V_{i} – U_{i}
…………………(4)
Where:
Y_{i} = output of rice (Kg)
β_{0} = constant or
intercept of the model
β_{1} – β_{5} =
regression coefficients
X_{1} = quantity of seeds (Kg)
X_{2} = quantity of fertilizer
used (Kg)
X_{3} = quantity of agrochemicals
used (Litres)
X_{4} = quantity of labour used (mandays)
X_{5} = farm size (ha)
V_{i} = random variability
in the production that cannot be influenced by the farmer. V_{i}
is assumed to be independent and identically distributed random errors having
normal distribution and independent of µi.
µ_{i} = deviation from maximum
potential output attributed to technical inefficiency. The µ is assumed to be
nonnegative truncation of the halfnormal distribution.
i = 1, 2, 3 . . . n farms
The technical inefficiency effect, µ_{i}
is defined as:
U_{i}_{ }= δ_{0}
+ δ_{1}Z_{1} + δ_{2}Z_{2} + δ_{3}Z_{3}
+ δ_{4}Z_{4} + δ_{5}Z_{5} + δ_{6}Z_{6}
+ δ_{7}Z_{7} + δ_{8}Z_{8} ……………………..
(5)
Where:
U_{i} =
technical inefficiency effect
Z_{1} = age of the farmer (years)
Z_{2} = farming experience (years)
Z_{3} = educational level of the
farmer (years)
Z_{4} = household size (number)
Z_{5} = sex of the farmer (dummy; 1 =
male and 0 = female)
Z_{6} = extension visit (number of
visit in 2016/2017 farming season)
Z_{7} = membership of cooperative
society (years of participation for members and 0 for nonmembership)
Z_{8} = marital status (dummy; married
=1 and single =0)
δ_{0} and δ_{i}
coefficients are unknown parameters to be estimated along with the variance
parameters δ^{2} and γ. The δ^{2} indicates the
goodness of fit and the correctness of the distributional form assumed for the
composite error term. The γ indicates the total variation of output from
the frontier which can be attributed to technical inefficiency.
The a priori expectation was that the
coefficients of age of the farmer, and household size will be positive while
those of farming experience, educational level of the farmer, sex of the
farmer, extension visit, membership of cooperative society, and marital status
will be negative.
The estimates of all
the parameters of the stochastic frontier production function and the
inefficiency model were simultaneously obtained using the programme,
frontier 4.1 developed by Coelli (1994).
Generalized
LikelihoodRatio Tests: The generalized likelihoodratio test was
employed to test the null hypotheses pertaining to the appropriateness of the
specified frontier function, the presence of inefficiency effects, and the
relevance of farmspecific and socioeconomic factors in explaining the
inefficiency of the rice farms studied.
The generalized likelihoodratio test
statistic was specified as:
λ = 2 [ln L
(H_{0}) – ln L (H_{1})]……………………… …
(6)
Where L (H_{0}) and L (H_{1})
denote the values of the likelihood functions under the specification of the
null (H_{0}) and the alternative (H_{1}) respectively. The test
statistic (λ) has a chisquare (χ2) distribution with degrees of
freedom equal to the difference between the parameters involved in H_{0}
and H_{1}.
RESULTS
AND DISCUSSION
Comparative
Analysis of the Technical Efficiency of Beneficiary and NonBeneficiary Rice
Farmers
The estimated production function for
beneficiary and nonbeneficiary rice farmers are summarized and presented in
Table 1.
Table 1:
Estimated production function for the beneficiary and nonbeneficiary rice
farmers
Variable 
Parameter 
Coefficient 
tratio 
Intercept 
A_{0} 
2.522 
6.716*** 
Seeds 
A_{1} 
0.107 
0.985^{NS} 
Hired labour 
A_{2} 
0.014 
0.763^{NS} 
Fertilizer 
A_{3} 
0.904 
7.027*** 
Agrochemical 
A_{4} 
0.485 
2.357** 
Farm size 
A_{5} 
0.063 
0.531^{NS} 
(Intercept dummy)D 
β_{0} 
2.930 
4.210*** 
(Seed)D 
β_{1} 
0.483 
3.089*** 
(Hired labour)D 
β_{2} 
0.107 
1.188^{NS} 
(Fertilizer)D 
β_{3} 
1.285 
4.787*** 
(Agrochemical)D 
β_{4} 
1.603 
4.558*** 
(Farm size)D 
β_{5} 
0.067 
0.409^{NS} 

R^{2} 

0.283 

Ṝ^{2} 

0.273 

Fratio 

27.188*** 
Source: Field survey
data, 2018. ***, **, = statistically significant at 1 and 5 percent
respectively. NS = Not significant
The coefficient of
multiple (R^{2}) was 0.283 which implies that 28.3 percent of the
variation in rice output is accounted for by the independent variables. The
Fratio was significant at 1% which attests to the overall significance of this
estimated function.
The coefficient of
fertilizer was positive and significant at 1%. The implication is that increase
in fertilizer utilization would lead to increase in rice output. The
coefficient of agrochemical was negative and significant at 5%. The implication
is that increase in agrochemical utilization would lead to a decrease in rice
output.
The intercept dummy
was statistically significant implying that a shift in technology existed
between the beneficiary and nonbeneficiary rice farmers. In other words, both
groups of farmers had unequal technical efficiency and production function.
The coefficient of
the intercept dummy was negative. The implication is that there was a shift in
neutral technical efficiency parameter to a lower level for the beneficiary
rice farmers. The beneficiary rice farmers therefore had lower technical efficiency
than the nonbeneficiary rice farmers and hence, achieved a lower level of
output per unit of input.
The slope dummies for
seed and agrochemical used were significant and positively related to technical
efficiency. This implies that seed and agrochemical used have stronger positive
relationship to technical efficiency among beneficiary rice farmers as compared
to the nonbeneficiary rice farmers. Specifically, a 1% increase in seed and
agrochemical used would lead to 0.48% and 1.60% respectively more technical
efficiency among beneficiary rice farmers than among the nonbeneficiary rice
farmers.
The slope dummy for
fertilizer was significant and negatively related to technical efficiency. This
implies that fertilizer used have stronger negative relationship to technical
efficiency among beneficiary rice farmers as compared to the nonbeneficiary
rice farmers. Specifically, a 1% increase in fertilizer used would lead to
1.29% less technical efficiency among beneficiary rice farmers than among the nonbeneficiary
rice farmers.
Hypotheses
Test for Beneficiary Rice Farmers
The result of the
generalizedlikelihood ratio tests of hypotheses involving the parameters of
the stochastic frontier and inefficiency model for beneficiary rice farmers in
Benue State is presented in Table 2.
Table 2 presents the
results of the null hypotheses of interest. The first null hypothesis which
states that production function is not CobbDouglas was rejected as the
computed test statistic (λ) is greater than the tabulated χ2. Hence,
CobbDouglas frontier model was an adequate representation of the data.
The second hypothesis
which states that technical inefficiency effects are absent from the model was
rejected indicating that there is presence of technical inefficiency effects in
the production of rice among beneficiaries. Confirming this result further is
the value of gamma (γ) which is 99.92% and very close to one and
significantly different from zero, thereby establishing the fact that high
level of inefficiencies exist among the sampled farmers.
The third hypothesis
which states that farmers’ socioeconomic characteristics considered in the
inefficiency model do not have significant influence on their level of
technical inefficiency was rejected. This means that the determinants of the
technical inefficiency significantly contribute to the differences in the
farmers’ technical efficiencies.
The null hypothesis
which specifies that technical inefficiency effects are not stochastic was
rejected. This implies that the traditional mean response function is not an
adequate representation for farm production among the respondents given the
specification of the stochastic frontier and inefficiency models defined by
equations 4 and 5 respectively.
Table 2:
Generalizedlikelihood ratio tests of hypotheses involving the parameters of
the stochastic frontier and inefficiency model for beneficiary rice farmers in
Benue State
S/N 
Null hypotheses 
L(Ho) 
L(Ha) 
λ 
Degree of freedom 
χ2 Critical 
Decision 

Stochastic frontier 






1 
Production function is not CobbDouglas (Ho: βi = 0) 
7.52 
407.23 
829.5* 
5 
15.09 
Reject Ho 

Inefficiency model 






2 
Absence of inefficiency (Ho: γ = δ0= δ1= δ2 =
δ3 = δ4 = δ5 = δ6 = δ7 = δ8 = 0 
31.28 
407.23 
751.9* 
10 
23.21 
Reject Ho 
3 
No technical effect (Ho: δ1 =
δ2 = δ3 = δ4 = δ5 = δ6 = δ7 = δ8 = 0 
94.46 
407.23 
625.54* 
8 
20.09 
Reject Ho 
4 
Technical inefficiency not stochastic (Ho: γ = 0) 
188.19 
407.23 
438.08* 
2 
9.21 
Reject Ho 
Source:
Field survey data, 2018. * = Test
statistic exceeds the 99^{th} percentile for the corresponding χ2
distribution: so the null hypothesis is rejected.
Maximum Likelihood
Estimates of the Production Function of Beneficiary Rice Farmers
The maximum
likelihood estimates (MLE) for the stochastic production function used in
explaining the influence of production inputs on farm output among
beneficiaries of ABP, and also in determining the effect of farmer specific
characteristics on technical inefficiency is presented in Table 3.
The value of the
sigmasquared (δ^{2}) was 0.71 and was statistically significant
at 1% level. This indicates a good fit and correctness of the distributional
form assumed for the composite error term in the model.
The gamma value
(γ) was 0.9992 and it was statistically significant at 1%, implying that
99.92% of the total deviation from the efficient rice frontier output is due to
inefficiencies arising from the production process while the random effects
constitute 0.08%. This further means that technical inefficiency effects
dominate the noise effect in explaining the total variation in rice output.
Table 3:
Stochastic frontier production function results for the beneficiary rice
farmers
Variable 
Coefficient 
Standard error 
tratio 
Production function 



Constant 
0.7459 
0.0692 
10.78*** 
Seeds (X_{1}) 
0.5068 
0.00846 
59.94*** 
Labour (X_{2}) 
0.00167 
0.0101 
1.65^{NS} 
Fertilizer
(X_{3}) 
1.4964 
0.0341 
43.88*** 
Agrochemical
(X_{4}) 
2.3002 
0.0305 
75.30*** 
Farm size
(X_{5}) 
0.0083 
0.0139 
0.5999^{NS} 




Inefficiency model 



Constant 
2.7563 
0.4967 
5.55*** 
Gender (Z_{1}) 
1.2485 
0.1624 
7.69*** 
Educational
level (Z_{2}) 
0.0453 
0.00995 
4.56*** 
Age (Z_{3}) 
0.0579 
0.007599 
7.63*** 
Membership
of cooperative (Z_{4}) 
0.2500 
0.0333 
7.50*** 
Extension
visit (Z_{5}) 
0.1260 
0.0332 
3.79*** 
Marital
status (Z_{6}) 
2.6327 
0.2212 
11.90*** 
Household
size (Z_{7}) 
0.05851 
0.01856 
3.15*** 
Experience
(Z_{8}) 
0.00396 
0.008861 
0.45^{NS} 
Diagnostic statistics 



Sigmasquared
(δ^{2}) 
0.70696 
0.06702 
10.55*** 
Gamma
(γ) 
0.9992 
0.0001931 
5175.21*** 
Source:
Field survey data, 2018. *** = Significant at 1%; NS = Not significant
The estimated
elasticity parameters of seed (0.5068) and agrochemical (2.3002) were positive
and significantly influenced output of farmers (p < 0.01). This implies that
increasing these factors will increase the output of rice among the
beneficiaries in the study area. It also means that a 10% increment in these
inputs will increase rice output by 5.068 and 23.002 percent respectively.
The estimated
elasticity parameter of fertilizer (1.4964) was negative and significantly
influenced output of farmers (p < 0.01). This implies that increasing this
factor will decrease the output of rice in the study area. It also means that a
10% increment in this input will decrease rice output by 14.964 percent.
However, the
coefficients of labour (0.0167) and farm size (0.0083)
were not significant at all conventional levels. Furthermore, the sum of
coefficients (b_{i}) in CobbDouglas production model gives the return
to scale. The return to scale (RTS) was 1.319, indicating an increasing return
to scale and that rice production among beneficiary farmers was in stage I of
the production curve. Therefore, farmers are encouraged to continue increasing
their inputs especially seeds and agrochemical for a better output.
The inefficiency
parameters were specified as those relating to farmers’ specific socioeconomic
characteristics. A positive coefficient indicates that the variable increases
technical inefficiency in rice production while a negative coefficient
indicates that the variable decreases technical inefficiency in rice
production.
Analysis of Table 3
shows that the estimated coefficient of gender was significant at 1% and
positively related to technical inefficiency. The positive sign of the
coefficient is at variance with the a
priori expectation, implying that if a farmer is male, the farmer’s level
of technical inefficiency in rice production increases. Female farmers owing to
the challenges they face compared to the male farmers in terms of access to
information and resources and also due to their responsibilities in the home,
are less likely to be technically efficient compared to male farmers. However,
male farmers who are technically inefficient are those that are older and had
no contact with extension agents. According to Sibiko_et al. (2012), older farmers are risk averse making them late
adopters of better agricultural technologies. The study by Sibiko_et al. (2012) also revealed that access to extension agent services
enable farmers to obtain information on crop diseases or pests and their
control methods as well as insights on innovative farming techniques that
guarantee higher productivity. This finding is at variance with Ojehomon_et al. (2013) who revealed that female
farmers are technically inefficient compared to the male farmers.
The coefficient of educational
level was significant at 1% and negatively related to technical inefficiency.
The negative sign of the coefficient agrees with the a priori expectation, implying that as years of formal education
increases, technical inefficiency decreases. Farmers with formal schooling tend
to be more efficient in food crop production due to their enhanced ability to
acquire technical knowledge which makes them closer to the frontier output.
This finding agrees with Girei_et al. (2013) who
revealed education increases efficiency in food crop production.
The coefficient of
age was significant at 1% and positively related to technical inefficiency. The
positive sign of the coefficient conforms to the a priori expectation, implying that as age increases, technical inefficiency
increases. Older farmers are risk averse making them late adopters of better
agricultural technologies. This finding is consistent with Itam_et al. (2015) who revealed that older farmers because of their
conservative attitudes will be less willing to adopt improved technology and
hence, have low levels of technical efficiency.
The coefficient of
membership of cooperative was significant at 1% and negatively related to
technical inefficiency. The negative sign of the coefficient is consistent with
the a priori expectation, implying
that if a farmer is a member of cooperative, the farmer’s level of technical
inefficiency decreases. Group membership helps farmers to mitigate problems
associated with market imperfections and reduces transaction costs, hence
increasing technical efficiency. The
finding agrees with Sibiko_et al. (2012) who
revealed that farmers who are members of producer organizations tend to benefit
shared knowledge with respect to modern farming methods, economies of scale in
accessing input markets as a group and hence, more technically efficient in
production.
The coefficient of
extension visit was significant at 1% and positively related to technical
inefficiency. The positive sign of the coefficient is at variance with the a priori expectation, implying that if a
farmer had contact with extension agents, the farmer’s level of technical
inefficiency increases. Access to extension services enable farmers to obtain
information on crop diseases or pests and their control methods, as well as
insights on innovative farming techniques that guarantee higher productivity.
However, farmers who had contact with extension agents and are technically
inefficient are older farmers. Older farmers due to their conservative
attitudes will be less willing to adopt improved technology and hence, less
efficient compared to younger farmers. This is consistent with Sibiko_et al. (2012) who observed that older
farmers are relatively more reluctant to take up better technologies, as they
prefer to hold on to the traditional farming methods and thus, more technically
inefficient compared to their younger counterpart.
The coefficient of
marital status was significant at 1% and negatively related to technical
inefficiency. The negative sign of the coefficient concurs with the a priori expectation, implying that if a
farmer is married, the farmer’s level of technical inefficiency decreases.
Marriage brings about an increase in family size which makes labour readily available and reduce high cost of hired labour. Since rice farming requires a lot of farm hand and
given the fact that farming is still at the subsistent level in the study area,
rice farmers who are married are more likely to be technically efficient than
unmarried farmers. This finding agrees with Bivan_et al. (2015) that revealed a negative relationship between marital
status and technical inefficiency.
The coefficient of
household size was significant at 1% and positively related to technical
inefficiency. The positive sign of the coefficient agrees with the a priori expectation, implying that as
household size increase, the level of technical inefficiency increases.
Increase in family size would decrease the level of technical inefficiency if
only the household is constituted of adults who make labour
readily available as well as reduce the cost of hired labour.
However, rice farmers who had large household size and are technically
inefficient are those whose household were made of children which increases the
farmer’s cost of hired labour and hence, making the
farmer more technically inefficient. This finding is at variance with Itam_et al. (2015) which revealed that an increase
in family size would result in increased levels of technical efficiency.
Efficiency
Analysis of Beneficiary Rice Farmers
The frequency
distribution of the technical efficiency estimates of beneficiary rice farmers
in the Anchor Borrowers’ Programme (ABP) are
presented in Table 4.
Analysis of Table 4
shows that majority (81.2%) of beneficiary rice farmers had technical
efficiency above 0.783 with a mean technical efficiency of 0.854. The mean
technical efficiency of beneficiary rice farmers implies there is room for
improvement by 14.6%.
Table 4:
Distribution of technical efficiency estimates of beneficiary rice farmers
Efficiency class 
Frequency 
Percentage 
≤0.150000 
7 
1.8 
0.1500010.361000 
9 
2.3 
0.3610010.572000 
36 
9.3 
0.5720010.783000 
21 
5.4 
≥0.783001 
315 
81.2 
Total 
388 
100 
Mean 
0.854 

Minimum 
0.123 

Maximum 
0.995 

Source:
Field survey data, 2018
CONCLUSION
The findings revealed
that the beneficiary rice farmers achieved lower levels of technical efficiency
compared to the nonbeneficiary rice farmers and also that seed and
agrochemical used increased technical efficiency more among beneficiary rice
farmers than the nonbeneficiary rice farmers while fertilizer used decreased
technical efficiency of beneficiary rice farmers more compared to the
nonbeneficiary rice farmers.
The result also
showed that rice production among beneficiaries of the Anchor Borrowers’ Programme was in stage I of the production curve. Thus, the
beneficiary rice farmers are encouraged to continue increasing their inputs
especially seeds and agrochemical for a better output. The findings further
revealed that socioeconomic characteristics of the beneficiary rice farmers
significantly influenced their level of technical inefficiency.
Based on these
findings, it was advocated that the Benue State government should come up with
policies and programmes targeted at reallocation and
redistribution of farm production inputs for increased farm productivity and
efficiency. Such policies should include increasing rice farmers’ access to
farm land that will enable them employ the use of more farm resources since
there is increasing return to scale.
Also, policies geared
towards increasing the resource use efficiency of rice farmers in the State and
hence their farm income should include farmers’ specific efficiency factors
such as gender, educational level, age, membership of cooperative, extension
visit, marital status, household size, and experience in their formulation.
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Cite this Article: Okeke, AM; Mbanasor, JA; Nto, PO (2019).
Comparative Analysis of the Technical Efficiency of Beneficiary and NonBeneficiary
Rice Farmers of the Anchor Borrowers’ Programme in Benue State, Nigeria.
Greener Journal of Agricultural Sciences 9(2): 155162,
http://doi.org/10.15580/GJAS.2019.2.032619050. 