Legume Genomics and Genetics 2015, Vol.6, No.6, 1-9
8
ߝ
ଵ
= error term
ܺ
=vector of explanatory variables
ܺ
ଵୀ
Gender of the farmers (male =1, 0 otherwise)
ܺ
ଶୀ
Age of the farmers head in years
ܺ
ଷ
ൌ
Farm size in hectare
ܺ
ସ
ൌ
Household’s size in number
ܺ
ହ
= Farm experience in years
ܺ
ൌ
Education in years
ܺ
ൌ
Cooperative membership (1 if a member, 0
otherwise)
ܺ
଼ ୀ
Access to credit in naira (1 if farmer has access
to credit, 0 otherwise)
ܺ
ଽ
ൌ
Early maturity (1 if seed matures early, 0
otherwise)
ܺ
ଵ
ൌ
Distance to market in kilometers
ܺ
ଵଵ ୀ
Access to extension agent (Yes =1, 0 otherwise)
ܺ
ଵଶ
ൌ
Dependency ratio
ܺ
ଵଷ
= Pattern of Land ownership (Yes =1, 0 otherwise).
First a selection equation is estimated using a probit
model. This model predicts the probability that a
farmer adopt or does not adopt, and the inverse Mills
ratio is obtained.
h
∗ ൌ ܺ
ଶூ
ߚ
ଶ
ߝ
ଶூ
(5)
Where
h
is farmer’s adoption of improved soybean
variety.
E
ሼ
ݕ
݄
⁄ ൌ 1
} =
(6)
where
ߪ
ଵଶ
is the covariance between the two error
terms, the term is the inverse mill’s ratio
called the Heckman’s lambda. The second step of the
model as developed by Heckman J.J, (1979) is the
OLS estimation corrected by the inclusion of Heckman’s
lambda among the regressors and is indicated as follow:
(7)
Then the OLS regression equation including the inverse
Mills ratio (λ) as a regressor is estimated for the
quantity of soybean yield produced.
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