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 becau
se 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
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