The goal of the research is also to establish a strong relationship
between different farmers of sugarcane in Sindh. We required a perfect
prediction of the link without using the independent variable
coefficient to predict Y, which is usually the case in time series data.
The coefficient of the independent variables in this investigation,
which justifies removing constant interception value, is useless due to
the cross-sectional data obtained by the survey. Another reason for
eliminating the intercept value is that we have not changed the dataand
have ensured that all independent variables are used during
theregression, while Y-intercept is used to remove the data distortion
when the researcher deletes some variable. Intercept value also help
define the path of independent variable curves in the slope graph,which
is not used in this study due to a smaller sample size (55 observations)
and the nature of the research. After removing the intercept value, the
model shows perfect results for the relationship between the selected
variables. The p -values for independent variables (Table 5) show
that irrigation, rain, and soil are in particular issues for farmers in
Sindh. At the same time, other factors that also influence climate
change factors in Sindhcan be examined in a further study, but they do
not affect the sugarcane production in Sindh. The p -values of
other factors are higher than 0.05,meaning they are not significant as
per the survey data, which includes intense heat, floods, climate change
diseases, droughts, and increase in water demand.
Table 5. p -Values of independent variables.