Introduction
Durbin-Watson = 1.944
Background
of the study
Climate change is one of the threats
facing mankind worldwide. It affects agriculture in several ways, including its
direct impact on food production climate change, which is attributable to the
national climate cycle and human activities has adversely affected agricultural
productivity in Africa. Available evidence shows that climate change is global,
like wise its impacts, but the most adverse effects will be felt mainly by
developing countries, especially those in Africa due to their low level of
coping capabilities.
Climate change can be defined as any
change in climate over time, whether due to natural variability or as a result
of human activity. As the planet warms, rainfall patter shift, and extreme
events such as droughts, floods and forest fires become more frequent which
result in poor and unpredictable yield there by making farmers more vulnerable
particularly in Africa. Small scale farmers force prospects of tragic cassava
failure reduced agricultural productivity, increase hunger, malnutrition and
disease.
As people of Nigeria strive to
overcome poverty and adverse economic growth, this phenomenon threatens to
deepen vulnerabilities and seriously undermine prospects for development.
Climate change could be an increase
in rainfall in some area, which could o an increase of atmosphere humidity and
the duration of the wet season combined with high temperature.
Objective of the study
The broad objective of this study is
the determine the effect of climate charge on small-scale cassava production in
Ebonyi state.
The specific is to analyze the
effect of socio economic characteristics of the respondents on their level of
awareness of climate change in the
study area.
Multiple regression models
Multiple regression model were used
to estimate the coefficient necessary for the determination of the effect of
socio-economic characteristic of small-scale farmers and their level of
awareness of comate change.
The model is stated as follows:
Y = F(X1,
X2, X3, X4, X5) Implicit function
Y = ao
+ a1 x1+ a2x2 + a3 x3
+ a4x4 + a5x5 + et
+ et. Explicit function
Where;
Y = Level
of awareness of climate change
X1 = Sex
X2 =
Age of farmers
X3 = Educational
status
X4 = Occupation
X5 = Farm size (ha)
ao = Regression
constant.
Et = Stochastic
Error term.
Hypothesis testing
H1: The socio economic characteristics of
small-scale farmers have no significant on level awareness of climate change
Model
for hypothesis testing
F - test
model
F - cal = R2(N-K)
1-R2(K-1)
where;
R2 = Coefficient
of determination
N = Sample
size
K = Number
of variable
R2 = 0.83.5
x 100 = 83.5%
F-ratio = 2.979
Standard
Error Estimates (SEE) = 0.69254
Durbin-Weston
constant. = 1.844
The result of multiple regression
analysis as shown in table 2 showed that a coefficient of multiple
determination (R2) of 1 83.5% was obtained. This means that about
(83.5%) change in the explained variable was caused any changes in the
explanatory variables.
However, the coefficient of sex (X1)
was positively significance.
This means positive relationship
with the dependent variable showing that there was no gender bias in terms of
awareness of climate change among small-scale cassava farmers in the study
area. Both male and female small-scale cassava farmers observed significant
change in climate and were all aware of it.
Similarly, age (X2) has a
positive sign and is statistical significant
at 5% level of significance. This implies that the higher the age of
cassava farmers, the higher of their level of awareness of climate change. This
is true and conforms to apriori expectations because older farmers are expected
to be farmers are expected to be more aware of climate change due to their high
level of experience in farming.
Education status by the farmers (X3)
was positively signed and highly significant at 1% level of significance. This
means that the higher the level of education of farmers, the higher their level
of awareness of climate in the study area. This is true because educated
farmers are intelligent and have the ability of making quick observation in the
their surroundings.
Farmers occupation (X4)
was negatively signed, indicating negative relationship, but was not
statistical significant. This showed that farmers who involved in other
occupation were les aware of climate change and its effects on agricultural
activities. This is true and conforms to the aprior expectation because their
involvement in other income generating activities does not allow them to
concentrate on agricultural practices in order to make critical observation on
climate changes.
Farm
size (X7)
The coefficient of farm size and
household size has a positive coefficient and was statistical significant at 5%
level of significance. This means that the higher the farm size of the
respondent the higher their level of awareness of climate change in the study
area. This is true because farmers with increased farm size diversified into
different agricultural practice where they easily observed change in climate.
Testing of Hypothesis
Decision Rule
If
f-cal is greater than F-tab reject null hypothesis otherwise accept alternative
hypothesis.
F-cal= R2(N-K)
1-R2(K-1)
where
R2 = Coefficient
of determination
N = Sample
size
K = Number
of variance
F-cal = 0.835(40-6)
1-0.835(6-1)
F-cal = 36.74
0.825
F-cal = 44.53
F-cab
at 5% level of significance
F-tab = 2.25
Since,
the f-cal is greater than the F-tab, the null hypothesis is rejected while
alternative accepted. This implies that the socioeconomic characteristics of
small-scale farmer have significant effect on level of awareness of climate
change on cassava production is the study area.
Variable
|
Variable name
|
Regression coefficient
|
Std error
|
T-value
|
Sign
|
Consistent
|
-
|
-0.107
|
0.854
|
-0.126
|
0.901
|
X1
|
Sex
|
0.58
|
0.070
|
-00.820
|
0.43
|
X2
|
Age
|
0.078
|
0.265
|
0.296
|
0.048
|
X3
|
0.016
|
0.035
|
0.464
|
0.646
|
|
X4
|
occupation
|
0.457
|
0.175
|
2.606
|
0.90
|
X5
|
Farm size
|
0.402
|
0.119
|
3.376
|
0.002
|
X6
|
House hold size
|
-0.98
|
0.123
|
-0.800
|
0.004
|
R2
(square) = 0.835
Adjusted
R2 = 0.812
Standard
Error of the estimates = 0.69254
Durbin-Watson = 1.944