Legume Genomics and Genetics 2015, Vol.6, No.6, 1-9
5
Table 3 Sources of awareness about improved soybean variety
Source
Frequency
Percentage
Extension agents
100
42.55
Media(radio/television)
45
19.15
Friends/neighbors’
60
25.53
Non-governmental organization(NGO)
30
12.77
Total
235
100
with the first stage being the probit model which
identifies the factors that affect adoption of soybean is
presented using the Heckman two-stage model as of
adoption on yield. The diagnostic statistics from the
estimation revealed that the rho (0.2581) indicates
absence of correlation between the error term and the
quantity of soybean produced. The lambda is significant
and shows that there is selectivity bias in the sample.
The correction of this bias implies that the covariates
that condition the soybean yield operate conditional
on the probability to adopt the improved variety.
Four variables significantly explained the probability
of adoption and they include age of the farmers, gender,
dependency ratio and early maturity of the variety.
The age of the farmer was significant at 5% and
negatively related with the probability of adoption of
improved soybean variety. This implies that the
probability of adopting improved soybean variety
decreases as the farmer gets older. In support of this
finding, Caswell et al. (2001) opined that the effect of
age of farmers on adoption cannot be pre-determined
because older farmers are sometimes considered to be
risk-averse and thus less willing to try new innovations
than younger farmers. On the contrary, Tjornhom
(1995) considers older farmers as experienced and,
therefore, in a better position to make sound judgment
regarding the adoption of new technologies,
suggesting that older farmers will be quick to adopt
improved technologies that offer better returns than
younger and inexperience farmers
The gender of the farmer was significant at 1% and
positively related to the probability of adoption of
improved soybean variety implying that being a male
farmer increases the likelihood of adoption of the
improved soybean variety.
Dependency ratio was significant at 5% and increases
the probability of adoption.
Knowledge on the early maturing characteristic of the
soybean variety increases the probability of adoption.
This implies that if farmers are aware that the soybean
variety introduced is early maturing, it will increase
the likelihood of adoption of such variety. This is
expected as early maturity gives the crop an advantage,
especially in the study area which is prone to drought
which gives the farmers an advantage of preventing
pod shattering during the harmattan period as opined
by Sanginga et al. (1999).
1.6 Effect of adoption on soybean yield
Five variables significantly explained the effect of
adoption on yield. They are household size, farm
experience, cooperative membership, education and
access to credit.
The household size was significant and positively
related to the yield of soybean at 1% level. The
implication of this finding is that as the number of
persons in the household increases there is the
tendency that yields of farmer’s increase. This follows
that as the number of household increases, the cost of
hiring labour reduces and the yield increases.
Education showed significant and positive relationship
with the yield of soybean at 5%, implying that as level
of education increases yield also increases. Education
increases exposure to useful information such as
increase in yield which will assist farmer in decision
making (Waller et al., 1998). Caswell et al. (2001)
opined that education is thought to create a favorable
mental attitude for the acceptance of new practices
especially of information-intensive and management-
intensive practices.
Farming experience had a significant negative
relationship with yield of soybean. This means that the
yield of the soybean farmer reduces with the age of
the farmer. This is so because more experienced farmers
