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.