The broad objective of this research
is to determine the role played by agriculture in the growth and reduction of
property among rural households in Abakaliki L.G.A of Ebonyi State. The specific objectives to determine
the effect of the socio economic characteristics of the rural farmers on their
income generated from agriculture.
Hypothesis
Ho: Socio-economic characteristics of the rural
farmers have no significant effect on their income generated from agriculture.
Data Analysis
The
specific objective was analyzed using multiple regression analysis. The model specifications
are as follows;
Multiple regression analysis.
Y = (FX1,
X2, X3, X4, X4, X5, X6)
Implicit form
Y = a0+
a1 + a1 + a2 + a2 + a3 +
a3 + a4 + a4 + a5 + a5+a6+a6+et
Explicit form
Where;
Y = Level
of income generated form Agriculture produce
X1 = Age
(yrs)
X2 = Educational
level
X3 = Farm
size
X4 = Farm
Experience
X5 = Farm Enterprise
X6 = Family size
a0 = Constant
a1-a6 = Coefficient.
et = Error term
Regression analysis of the
relationship between the socio-economic characteristics of the rural household
and their income generated from agriculture in Abakaliki L.G.A.
The result of the analysis is presented in table below.
The income generated by rural household form agricultural activities where
regressed against the exploratory variable (Socio-economic characteristics of
the households) which are age(X1), Educational level (X2)
farm size (X3), farming Experience (X4) farming
enterprise (X5) and household size (x6) including the
stochastic error term.
Variable
|
Variable
name
|
Regression
coefficient
|
Std
error
|
T-value
|
Sign
|
Consistent
|
-
|
1.757
|
0.727
|
2.416
|
0.21
|
X1
|
Age
|
0.112
|
0.074
|
-1.505
|
0.142
|
X2
|
Educational
level
|
0.007
|
0.229
|
0.030
|
0.976
|
X3
|
Farm
size
|
0.056
|
0.076
|
0.736
|
0.467
|
X4
|
Farm
Enterprise
|
0.373
|
0.184
|
2.026
|
0.051
|
X5
|
Farming
experience
|
0.023
|
0.065
|
-0.401
|
0.691
|
X6
|
Family
size
|
0.313
|
0.114
|
-2.747
|
0.010
|
R2
(square) = 0.873
Adjusted
R2 = 0.848
Standard
Error of the estimates = 0.73636
Durbin-Watson = 1.849
Source:
Computed data, 2013
A multiple regression was utilized to analyse the
relationship between the socio-economic characteristics of the rural household
and their income generated from agriculture. In the analysis of the
relationship between the socio-economic characteristics of the rural household
and their income generated form agriculture, the computed coefficient of
multiple determination (R2) 0.873 showed that 87.3% of total
vacation in the dependent variable (income generated from agriculture) was
caused by the combined effect of the the explanatory variables.
The high value of the adjusted R20.848
shows a good fit in the model specification. This indicates that the proportion
of changes accounted by the explanatory (independent) variables is high. Thus the
exploratory variables help to predict
the income generated from agriculture. The significant variables in the model
are farm size and farming experience from table above, it sowed that variable,
Age(X1) with a positive coefficient was not statistically
significant. This showed that coefficient of Age had a positive relationship
with the income generated from agriculture. Thus the apriori expectation not
met since the result showed that increase in the age of farmer increase output.
This finding refute that of Anele (2010) who claimed that the higher the age of
farmer, the lower the farm output and thus income generated form such. This
could due to lack of strength by some aged men who take agriculture as their
lucrative business.
It was further discovered that the coefficient of
educational level was positively signed related to the dependent variable (Y)
and was not statistically significant. This is implies that the level of
education of rural household increases their income generated from agriculture
in the study area. This is justified by Babatunde et al (2007). Who claimed
that educational level of rural households allows for flexibility in decision
making hereby affording them the opportunity to take risk through engagement in
multiple agricultural enterprises which can improve their aggregate income.
On the other hand, farm size (X3) had a
positive coefficient and is significant. This mean that as farm size increase
the income generated form agriculture increases. Thus, the higher the size of farm,
the higher the income generated from Agriculture. This result conforms to the
apriori expectation.
Farm enterprise (X4) also bore a positive
coefficient and was not statistically significant. This conforms to the a
priori expectation. It showed that the higher the farm enterprise and the level
of experience in farm operations by rural households the higher the income
generated from agriculture in the study area.
The result of regression analysis showed that the
coefficient of farming experience complied with the apriori experience of the
study. It shows that the higher the is a positive effect of farming experience
on the level of income generated form agriculture produce in the study area.
House
hold size (X6) which bore a positive coefficient did comply with the
a priori expectation. This revealed that the size of households in the study
area determines to a great extent the income generated from agriculture. This
findings is in line with the claims of Okunmadewa (2001). According to him
household size is a significant factor which contributes to increase in farm
income.
Y = 1.757 +
0.112 + 0.007 + 0.056 + 0.373
(0.727) (0.074)
(0.229) (0.076) (0.184)
(0.056) (0.114)
Hypothesis Testing
Decision
Rule
If
the F-cal is greater than the F-tab reject null hypothesis otherwise accept the
alternative hypothesis.
F-cal = R2(N-K)
1-R2(K-1)
where;
R2 = Coefficient determination
N = Number
of variable
F-Cal
?
F-cal
=
0.873(40.6)
1-0.873(6-1)
F-cal = 29.682
0.635
F-cal = 46.74
F-tab = 2.10
Since
the f-cal is greater the f-tab, null hypothesis was rejected while alternative
hypothesis was rejected while alternative hypothesis.
Therefore, socio-economic
characteristics of the rural farmers have significant effect on their level of
income generated for agriculture.