Legume Genomics and Genetics 2015, Vol.6, No.6, 1-9
7
farmer, gender, dependency ratio and early maturity of
the variety influence the probability of adoption of
improved soybean variety. Also, factors that determine
the effect of adoption on soybean yield were
household size, education, farming experience,
membership of cooperative and access to credit.
It is therefore recommended that farmers should be
exposed to adult education programs that will change
the attitude and orientation of the farmers towards
adoption of innovation and modernized agricultural
practices so as to improve farmer’s productivity.
Farmers should be encouraged to form cooperatives or join
existing ones by government and non-governmental
organizations to enhance their access to improved
seeds and inputs. Extension service should be
strengthened so as to expose farmers to modern
farming techniques and improved technologies.
3 Methodology
3.1 Study area
The study was conducted in Benue State, Nigeria. The
state is popularly referred to as the “Food Basket of
the Nation” on the basis that agriculture is the main
economic activity, Benue state has an estimated
population of 2.8 million people; made up of 413,159
farm families, majority of whom are rural dwellers
and are directly involved in subsistence agriculture
characterized by small farm holdings with an average
farm size of 1.5-2.0 ha (NPC 1996). Soybean is
mainly produced in the Northern and Eastern Zones of
the State.
3.2 Sampling procedure and sample size
A multi-stage sampling procedure was employed in
selecting farmers for the study. The first stage
involved the random selection of 5 local governments
out of 23 local governments in the state. The second
stage involved the random selection of one ward from
the local government areas selected. The third stage
involved the purposive selection of 6 villages from the
selected ward. The fourth stage involved the random
selection of 10 soybean farmers to give a total of 300
respondents. However, due to incomplete response,
only 267 questionnaires were used for the analysis.
3.3 Type of data and data collection
Primary data was collected through the use of
structured questionnaires administered to the selected
farmers in the study area. Data were collected on the
socioeconomic characteristics (age, gender, education,
etc), awareness of seed variety. Data on production
input cost such as cost of seed, cost of fertilizer, cost
of agrochemicals and operational inputs cost such as
cost of land preparation, cost of weeding, cost of
planting, cost of spraying, cost of fertilizer application
and transportation cost were collected.
3.4 Analytical technique
Descriptive statistics was used to describe the socio-
economic characteristics of soybean farmers.
3.4.1 Gross margin analysis
The gross margin analysis was used to estimate the
profitability of the improved soybean production.
Following Olukosi and Erhabor (2005), it is given as:
GM = GI – TVC (N/ha)
(1)
GI = TVP = TPP. Py (N/ha)
(2)
GM = TPP. Py – TVC (N/ha)
(3)
Where,
GM= the gross margin
Py = the price of a unit product of soybean
TVC = the total variable cost of inputs
TVP = Revenue from soybean production
Inputs considered were: production inputs such as cost
of seed, cost of fertilizer, cost of agrochemicals;
operational inputs which are cost of land preparation,
weeding, planting, spraying and fertilizer application)
and transportation cost.
3.4.2 The heckman two-step model
Following the work of Shephard et al. (2010), the
heckman procedure is a relatively simple procedure
for correcting sample selectivity bias and was used to
examine the determinants of adoption and effect of
adoption on productivity of soybean farmers.
It consists of two steps.
ݕ
∗
ൌ
ߚ
+
ܺ
ଵ
ߚ
ଵ
+
ߝ
ଵ
(4)
Where
ݕ
∗
= quantity of soybean produced. These
quantities are observed only for those farmers that
adopt.
ߚ
ଵ
= parameters to be estimated
