A CASE STUDY
OF OHAUKWU LGA
Objective of the Study
The
broad objective of this study is to determine socioeconomic and cultural
factors affecting adoption of yam production technology among farmers in
Ohaukwu Local Government Area of Ebonyi State. The specific objective is to
analyse the relationship between the socio-economic characteristics of rice
producers and level of adoption of new technology in the study area.
Hypothesis test
There
is no significant difference between socioeconomic characteristics and level of
adoption of new technology in the study area.
Data analysis
Model specification
Multiple regression model is shown
Y = F(X1,
X2, X3, X4, X5 -------Xn)
Implicit function
Y = bo
+ b1 x1+ b2x2 + b3 x3
+ b4x4 + b5x5 + et
Explicit function.
Where;
Y = Level
of adoption of new technology
X1 = Sex
X2 = Age
(Years)
X3 = Marital
status
X4 = Educational
status
X5 = Farm size
Effect
of socio-economic characteristics of the respondents on level of adoption of
new technology.
Multiple regression analysis was
carried out to determine the effect of socioeconomic characteristics of
respondents on level of adoption of new technology.
Variable
|
Variable name
|
Regression coefficient
|
Std error
|
T-value
|
Sign
|
Consistent
|
-
|
4.564
|
0.835
|
5.468
|
0.000
|
X1
|
Sex
|
0.214
|
0.158
|
-1.354
|
0.185
|
X2
|
Age
|
0.042
|
0.250
|
0.167
|
0.069
|
X3
|
Marital status
|
0.270
|
0.180
|
-1.604
|
0.142
|
X4
|
Educational status
|
0.543
|
0.171
|
-3.171
|
0.00
|
X5
|
Farm size
|
0.337
|
0.206
|
1.637
|
0.001
|
R2
(square) = 0.759
Adjusted
R2 = 0.603
Standard
Error of the estimates = 0.73287
Durbin-Watson = 1.849
Source:
survey, 2013
From the result of multiple
regression model showed a coefficient of multiple determination R2
of 0.759. This shows that about 75.9% change in the dependent variable was
caused by changes in the independent variables (socio-economic characteristics
of the respondents).
This is quite high indicating that
the socio-economic characteristics of the respondents had significant influence
on their level of adoption of new technology in the study area.
The coefficient of Age(X1)
had a positive sign and was not statistically significant at beyond 10% level
of the significance in regression model. This implies that there is a positive
relationship existing the between the Age (X1) and level of adoption
of new technology among the yam producer in the study area. The apirori
expectation was met because increase in age will increase the level of adoption
of new technology and at was as result high level of farming experience.
Sex X2 and marital status
had a positive sign in the regression
coefficient and statistically significant in the regression model of the study.
And this concerns with the apriori expectation of the study.
Furthermore, the coefficients of the
Educational status (X5) and farm size Educational status (X5)
and farm size (X6) bore a positive sign and statistically
significant at 5% and 1% at level of significance in the regression model in
the study.
It means that there is a positive
relationship existing between the educational status (X4), farm size
(X5) and level of adoption of new technology in the study area.
Educational and the farm size
increases the output. The apriori expectation was met.
The
regression model equations as follows;
Y 4.564 + 0.214
+ 0.042 + 0.270
+ 0.543
(0.835) (0.158)
(0.250) (0.180) (0.171)
+ 0.337 + et
(0.206)
Testing of hypothesis
F-cal = R2
(N-K)
1-R2(K-1)
where;
R2 = multiple determination
N = sample
size
K = Number
of variables
f-cal
?
F-cal = 0.759(40-5)
1-0.75(5-1)
F-cal = 43.155
0.964
F-cal = 35.430
F-tab
at 0.05 level of significance
F-tab
= 2.61
Since,
the F-cal is greater than F-tab, the null hypothesis was regretted while the
alternative hypothesis was accepted. This implies that there is significance
difference between socio-economic characteristics and level of adoption of new
technology in the study area.