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Wudpecker Journal of Agricultural Research ISSN 2315-7259
Vol. 2(2), pp. 043 - 048, February 2013
2013 Wudpecker Journals
Resource use efficiency and productivity of food crop
farmers in Idemili North of Anambra state Nigeria
Chinasaokwu Onyemauwa 1* , Chiedozie Eze 1 , Akujuobi Emenyonu 1 , Irenaeus Osugiri 1 ,
Nwabugo Nnadi 2 , Chimezie Tasie 3
1 Department of Agricultural Economics, Federal University of Technology, Owerri, Imo State, P.M.B.1526, Owerri,
Nigeria.
2 Department of Agricultural Extension, Federal University of Technology, Owerri, Imo State, P.M.B.1526, Owerri,
Nigeria.
3 Department of Agricultural Science, Ignatius Ajuru University of Education, Port Harcourt, P.M.B. Port Harcourt, Nigeria.
*Corresponding author E-mail: csonyemauwa@yahoo.com.
Accepted 12 January 2013
The study estimated the productivity of the farmers, resource use efficiency as well as their elasticity of
production. Data were collected from forty randomly selected farmers through the use of questionnaire
and interview schedule from four communities in Idemili area of Anambra State Nigeria. The information
collected was analyzed using descriptive and inferential statistics. The result indicated that the
respondents have favourable socioeconomic features in terms of age, household size, formal education,
farming experience and marital status. Farm size, labour and planting material were found to influence the
farm revenue in the food crop farms while there was positive partial and total factor productivity of the
resources used by the farmers in the area. There was inefficiency in the use of all the three productive
resources. Farm size has positive increasing function to scale while labour and planting material have
negative decreasing function to scale. Though all the resources used in production were found to be
productive, none was efficiently used on the farm.
Key words: Resource use, efficiency, productivity, elasticity, resource, optimum.
INTRODUCTION
Over the years, farmers have through the application of
science and technology evolved methods of increasing
agricultural productivity. Ukeje (2000) contends that
agricultural productivity which has been growing over the
years at different rates can be described as low.
Agriculture in Nigeria is largely at the subsistence level
and the main problem facing its development is how to
improve productivity thereby bridging the food gap” in
terms of the difference between the production and
demand for food. Most development economists
attributes this low productivity to the use of unimproved
technology and the difficulties associated with the
transfer and adoption of available improved technology in
subsistence agriculture (Mellor, 1986).
It is also attributed to scarcity and inefficient use of
production resources for this has resulted to the
importation of food items to supplement local production
(Olayide and Heady, 1982). Agricultural productivity is
defined as the index of the ratio of the value of total farm
output to the value of the total inputs in the farm (Olayide
and Heady, 1982). This study adopted this definition in
terms of measuring productivity and efficiency since
value of total farm output is synonymous with total farm
revenue while value of total inputs is synonymous with
total cost of production. Resource productivity, according
to them, is defined in terms of individual resource inputs
or in terms of a combination of them. Based on the above
therefore, labour productivity is defined as the ratio of
total farm output to the value of total labour inputs used.
Similarly, fertilizer, land, and planting materials
productivities can each be defined as the ratio of the
value of total output to the value of inputs of fertilizer,
land and planting materials respectively.
Ehui and Spencer (1990) called this ratio partial
productivity because it is a ratio of the value of total
outputs to a single input; otherwise it is total factor
productivity, which is the ratio of the value of total output
to the factor inputs combined. Maximum resource
productivity, in the words of Olayide and Heady (1982)
imply obtaining the maximum possible output from the
maximum possible set of inputs. Thus optimal
productivity of resources implies an efficient utilization of

Onyemauwa et al. 044
resources in the production process. In this context,
productivity and efficiency are synonymous. In line with
this, Ehui and Spencer (1990) assert that productivity is
generally defined in terms of the efficiency with which the
factor inputs are converted to output within the production
process.
According to the International Food Policy Research
Institute (IFPRI) (2002), the food production per head in
each country is decreasing and the case of Nigeria is not
likely to be different. Therefore, it is important that
efficient use of resources be increased to improve food
output and to ensure good and healthy living. This means
that the effort to achieve higher output per unit resource
is necessary. To effectively assess the activities of the
farmers, there is need to determine the efficiency and
productivity of their factor inputs. An intensified study
such as this is imperative. The study will analyze the
socioeconomic features of the farmers since studies have
shown that variables other than inputs affect the
productivity of farmers (Chidebelu, 1983; Nwosu, 1975).
The study will also estimate the productivity of the
farmers, resource use efficiency as well as their elasticity
of production.
MATERIALS AND METHODS
This research was carried out in Idemili North Local
Government Area of Anambra State. It is one of the
largest local government areas in Anambra State and has
a population estimate of 179,206 people (NPC, 2006).
The Local Government Area was created in September
22, 1998 out of the former Anaocha Local Government
Area (Omumu Idemili Brochure, 2000). Idemili North lies
in the rain forest zone and it is characterized by the
growth of tall trees like mango, cassava, palm trees etc.
The annual rainfall of the area is between 2045-2078mm,
while relative mean annual range of temperature is
ranged from 32 о C to 38 о C. The Local Government Area
is located on the north end of the State and is bounded
by Anaocha Local Government Area on the East, on the
West by Ogidi River and on the South by Idemili South
Local Government Area.
The Local Government was stratified according to 15
communities in which 2 communities namely Oraukwu
and Adazi-ani were selected randomly. The two
communities have a total of 13 villages and 4 villages,
namely Amaeze, Amada, Edeh and Umuru, were
selected. This is because food crop farmers are
predominantly found in the areas. A list of households
compiled by the national population commission for each
of the four villages was obtained and updated. The
updated list served as sampling frame from which
samples were drawn for the study. 10 respondents were
randomly selected from the sampling frame for each of
the four villages giving a sample size of 40 food crop
farmers.
Data for this study were obtained from two sources;
primary and secondary. The primary data were collected
through a well structured questionnaire which was
administered to literate respondents and personal
interview schedule administered by the researcher on
illiterate food crop farmers in the area. Extension agents
are the key informants that assisted in identifying the
selected farmers. Data were collected on socio-economic
characteristics of food crop farmers which include age,
sex, marital status, farming experiment, household size,
source of labour, educational level etc. Data were
collected also on the resources used by the food crop
farmers in the area such as land, labour planting
materials, farming implements and their value. Data on
total output of different crops produced by the farmers
and their respective value were collected too.
Data were analyzed by use of descriptive statistics
such as mean, frequency counts and percentages to
examine the farmers’ socioeconomic characteristics and
productivity index while inferential statistics such as the
ordinary least squares regression was used to compute
the factors that affect the value of their output. It is
explicitly specified thus;
Y = b 0 + b 1 X 1 + b 2 X 2 + b 3 X 3 + b 4 X 4 + b 5 X 5 + b 6 X 6 + (v i -μ i )
………….… (1)
Where;
Y = Value of output of food crop farmers (N)
b 0 = intercept
X 1 = Farm size (hectares)
X 2 = Cost of labour (N)
X 3 = Cost of planting materials (N)
X 4 = Depreciation of capital input (N)
X 5 = Household size (persons)
X 6 = Farming experience (years)
b i (where i = 1…6), σ 2 v, σ 2 μ,σ 2 are unknown scalar
parameters to be estimated
v i = a random error term or “white noise”, assumed
to be independent of μ i , identical and normally distributed
with zero mean and constant variance N (0, σ 2 v ),
intended to capture events beyond the control of the
farmers.
μ i = disturbance terms, which are assumed to be
independent of v i . They are non-negative truncations at
zero or half normal distribution with (0, σ 2 μ).
Different functional forms were fitted into the regression
model above and the form that best fits the regression
line was chosen and used for analysis.
Allocative efficiency ratio was computed using the
relation:
MVP
ri ……….… (2)
MFC
Where r i = Allocative efficiency ratio of i th

045 Wudpecker J. Agric. Res.
Table 1. Distribution of the respondents based on socioeconomic characteristics.
Variables Frequency Percentage
(i) Sex
Male 24 60.00
Female 16 40.00
Total 40 100.00
(ii) Age
Mean 53.75
21 – 40 01 2.50
41 – 60 31 77.50
61 – 80 8 20.00
Total 40 100.00
(iii) Years of Formal Education
Mean 7.50
1 – 6 13 32.50
7 – 12 16 40.00
13 – 18 11 27.50
Total 40 100.00
(iv) Household Size
Mean 7.50
1 – 6 4 10.00
7 – 11 32 80.00
12 – 16 4 10.00
Total 40 100.00
(v) Farming Experience
Mean 19.60
5 – 24 15 37.50
25 – 44 15 37.50
45 – 64 9 25.00
Total 40 100.00
(vi) Marital Status
Single 2 5.00
Married 31 77.50
Widowed 7 17.50
Total 40 100.00
Source: Field Survey, 2012
farmer in the area MVP i = Marginal value product
of i th farmer
MFC i = Marginal Factor Cost of i th farmer
RESULTS AND DISCUSSION
Result of the socioeconomic characteristics as presented
in Table 1 show that the food crop farmers in the area
had different characteristics. Table 1 show that majority
(60.00%) of the respondents are male relative to women
(40.00%). An average respondent in the area is about 54
years, with a household size of about 8 persons, and has
spent about 8 years to acquire formal education. About
78 % of the respondents are married and have had about
20 years experience in food crop production.
From the result of Table 2, the linear form best fits the
regression line based on economic, statistical and
econometric criteria and was adopted for analysis. The
linear form shows that farm size, investment on farm
labour and investment on planting materials are the
statistical significant resources, at 5% level, that affect
the value of food crop output in the area. Based on the
magnitude and direction of the coefficients, a 1 hectare
increase in farm size will increase their farm revenue by
N1,484,912, while N1.00 increase in investment in labour
will lead to a N3. 72 decrease in their farm revenue.
Similarly, a N1.00 increase in investment on planting
materials will result to a N3.22 decrease in their farm
revenue. Table 2 indicates also that the linear form gave
an R 2 value of 0.96. This implies that the exogenous
variables included in the model were able to explain

Onyemauwa et al. 046
Table 2. Ordinary least squares estimates of farmers value of food crop output.
Variables Linear Exponential Double Log Double Log
Constant
-11883.5 10.47
12.11 1073617.00
(-0.66) (42.57)* (7.25)* (3.02)*
X1
148.49 2.04
0.84 245153.3
(15.01)* (1.51)
(5.09)* (6.97)*
X2
-3.72 5.1E-06 0.03 -42865.40
(-5.33)* (0.54)
(0.23) (-1.47)
X3
-3.22 4.2E-05 0.11 7308.26
(-5.14)* (4.92)*
(4.41)* (1.42)
X4
-1.03 -1.4E-05 0.06 11300.42
(-0.23) (-0.23)
(0.60) (0.52)
X5
-1302 -0.01
0.10 24587.03
(-0.54) (-0.24)
(0.57) (0.65)
X6
-156.00 -0.01
-0.20 -39603
(-0.22) (-0.82)
(-1.35) (-1.24)
R 2 0.96 0.75 0.81 0.75
F 137.10 16.34 23.00 16.54
N 40 40 40 40
* = Significant at p ≤ 0.05 (5%) level
Figures in parentheses are the t-ratios
Source: Field Survey, 2012
Table 3. Partial and total factor productivities of the farmers in Anambra state.
Variables
Value (in Nigerian Naira)
Average Returns 189 148.13
Average Total Cost 101 109.83
Average Amount of Equity Capital used 54 890.56
Average Cost of Labour used 87 370.15
Average Cost of Fertilizer used 7 947.30
Average Cost of Planting Material used 1 484.38
Average Rent on Farm Land 3 322.38
Average Depreciation Cost of Implements used 985.38
Productivity of Equity Capital used 3.45
Productivity of Labour used 2.16
Productivity of Fertilizer used 23.79
Productivity of Planting Materials used 127.43
Productivity of Farm Holding 56.93
Productivity of Farm Implements used 191.94
Total Factor Productivity (TFP) 1.87
Standard Deviation of TFP 1.44
Variance of TFP 2.07
Sample Size (n) 40
Source: Field Survey, 2012
Table 4. Estimates of Efficiency of Resources used in Food Crop Production in the area.
Inputs MVP MFC r i Interpretation
Farm size (X 1) 1484912 3322.38 446.94 Under utilized resource
Cost of Labour (X 2) -3.72 1.00 -3.72 Over utilized resource
Cost of Planting
Materials (X 3) -3.22 1.00 -3.22 Over utilized resource
Source: Field Survey, 2012

047 Wudpecker J. Agric. Res.
Table 5. Elasticity of production and returns to scale of the food crop farmers.
Variables
Elasticity
Farm size 7.22
Labour -0.0031
Planting material -0.025
Return to scale 7.19
Source, Field Survey, 2012
about 96% of the variation in the farm revenue of the
respondents.
Result of the partial and total factor productivity of the
respondents as shown in Table 3 indicates that the
productivity of all the resources used by an average
farmer is 1.87. This implies that the revenue obtained by
the use of these resources is greater than the
expenditure incurred by the use of the resources. Table 3
show also that each of the inputs used on the farm for
food production was productive.
Marginal value product (MVP) and marginal factor cost
(MFC) are two important factors used to determine the
productivity and efficiency ratio (r) of resources used by
farmers. The Marginal value product of each significant
input was computed by taking the first partial derivative of
the revenue function with respect to the input (Mbabasor,
2002). Since the linear function produced the lead
equation, the coefficient of each input represents the
MVP of that input. That is;
y
i MVP ………………………….… (3)
x
The study adopted the unit price of the input as a proxy
for marginal factor cost (MFC) (Mbanasor, 2002). The
MFC of labour and planting materials were assigned the
value of 1 respectively since they were measured in
naira, and not in physical, terms (Onyenweaku, 1994;
Ohajianya, 2005) while that for farm size was absolute
quantity because it was measured in physical terms. The
marginal value product, marginal factor cost, and the
allocative efficiency ratio of the resource inputs of the
sampled farmers are presented in Table 4.
A production input is efficiently utilized if the ratio of the
MVP/ input price equates to unity, a ratio less than unity
indicates over-utilization of production inputs while a ratio
greater than unity shows that resources are underutilized.
Higher marginal value product indicates
increasing resource productivity while negative MVP is an
indication of unproductive resources (Utamakili, 1992,
Olayemi, 1998; Mbanasor and Obioha, 2003; Emokaro
and Erhabor, 2006a). Based on this theory, Table 4
shows that the farmers had farm size as the only
productive resource and was under-utilized with a ratio of
446.94. Table 4 shows also that labour and planting
material were over-utilized with a ratio of 3.72 and 3.22
respectively. These estimates indicate inefficiency in the
use of production resources by the food crop farmers in
the area.
The linear functional relationship is adopted in the
computation of elasticity of production and returns to
scale of the farmers and the result is presented in Table
5. Elasticity of production (e p ) was estimated using the
relation following Odii (2001):
e
p i
Where:
e
p i
i
xi
………………………..… (4)
y
= elasticity of production of i th input
i
= parameter estimate of i th input
x
i
= quantity or value of i th input used in production
y = quantity or value of output produced
Result of Table 5 show that farm size is the only positive
increasing function to the factors, indicating that the
allocation and utilization of the variable was in the stage
of economic relevance of the production function, i.e.
stage 1. The elasticity of labour and planting material
were negative decreasing functions to the factor which is
an indication that more than their profit maximizing levels
was used on the farm which characterizes stage 3 of the
production process.
Returns to scale of 7.19, as shown in Table 5, was
estimated. This signifies positive increasing returns to
scale and that the food crop farmers in the area are in
stage 1 of production. The productivity of the factors can
be improved by both reducing the amounts of labour and
planting materials or by increasing the amount of farm
size.
Conclusion
The study estimated the revenue determinants and the
partial and total factor productivities of the food crop
farmers in Anambra State. It also assessed the efficiency
of their resource use and production elasticity. Result

Onyemauwa et al. 048
from the study show that farm size, investments in labour
and planting material affects the revenue of the farmers.
The partial and total factor productivities of the farmers
are positive and greater than unity which is an indication
that they are productive. Result show also that less than
the economic optimum level of farm size was used while
more than the economic optimum levels of labour and
planting materials were used on the farm. Farm size was
a positive increasing function of the factor while labour
and planting material were negative decreasing functions
of the factor. Contrary to some previous studies, the
farmers had increasing returns to scale and operate in
stage 1 production. Though all the resources used in
production were found to be productive, none was
efficiently used on the farm.
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