Rice Genomics and Genetics 2015, Vol.7, No.1, 1-10
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Table 4 Determinants of adoption of improved rice varieties
Explanatory variable
Coefficients
Standard Error
T-value
First Stage: Probability of Adoption
Dependent variable: whether a farmer adopt IRV or not
Gender
-0.724
0.599
-1.21
Years of formal education
0.017
0.032
0.54
Farm size
-0.09
0.062
-1.44
Access to credit
0.911
0.464
1.96**
Access to media
0.948
0.474
2.00**
Membership in association
-0.307
0.365
-0.84
Ownership of mobile phone
-0.447
0.431
-1.04
Main occupation
0.3
0.575
0.52
Household size
-0.05
0.05
-1.01
Agricultural income
3.28E-06
8.77E-07
3.73***
Second Stage: Intensity of Adoption:
Dependent variable: Proportions of area under IRV
Gender
0.526
0.280
1.88*
Years of formal education
0.022
0.034
1.18
Farm size
0.949
0.062
28.11***
Access to credit
-0.190
0.191
-1.00
Age
0.012
0.008
1.44
Membership in association
0.041
0.179
0.23
Off-farm Employment
0.120
0.201
0.60
Main occupation
0.227
0.301
0.76
Household size
-0.041
0.024
-1.71*
Agricultural income
5.36e-07
2.08e-07
2.58***
Mills lambda
-0.885
0.456
-1.94**
Rho
-0.905
Sigma
0.978
Lambda
-0.885
Wald chi2(10)
845.29
Prob>chi2
0.0000
Note: *, ** and *** Significant at 1%, 5% and 10% respectively
Similarly, household size had a negative and
significant (p≤0.1) influence on the level of adoption
which means that an additional member to the
family will reduce the area cultivated to improved
rice variety intensity by 4.1%. This is in agreement
with Alene et al.
(2008) that household size explains
the family labour supply for prediction and household
consumption levels. A positive sign implies that a
larger household provides cheaper labour while a
negative sign on the other hand means that a larger
household is labour inefficient hence will rely on
hired labour which will eventually increase the cost
of production. Agricultural income also had a
positive and significant (p≤0.1) influence on the
intensity of adoption, this implies that proportion of
area cultivated to improved rice variety increases
with an increase in agricultural income. This could
be an incentive for the farmers to produce more,
create wealth, boost their output and ultimately
increase household income.
Although years of formal education did not influence
adoption significantly, however, its influence was
positive, which implies that highly educated farmers
are better adopters, one cogent reason for this is that
with an increase in the number of years of education,
the ability of farmers to use resources efficiently
increases. Allocative effect of education also enhances
farmer’s ability to obtain, analyze and interpret
information. Several studies reviewed by Feder et al.
(1985) indicate that education level enhances farmers’
ability to acquire, interpret and use information,
including information about agricultural technologies,
and hence leads to earlier and faster adoption.
