Climate Change and Rice Production in Bangladesh: Finding the Best Prospective Factors Using Multiple Linear Regression Modeling Techniques

Abstract

Climatic factors like temperature, rainfall, humidity, CO2 and solar radiation significantly impact agricultural production. Bangladesh is primarily an agriculture-based developing country. Rice (Oryza sativa L.), the main food of Bangladeshi people also provides a significant percentage of their regular, balanced diet. Many studies have been conducted to determine the effects of climate variability and change on rice productivity in Bangladesh. This study aimed to investigate the relationship between rice crop production and climate variables (namely, average temperature, rainfall, CO2, and humidity) and find out the best model that has an actual impact on rice production. Selecting 'potential predictors' from numerous possible variables to influence the forecast variable and investigating the most appropriate model with a subset of the potential predictors are two major difficulties of fitting the multiple linear regression model. Best subset regression and stepwise regression were used to fit the model using R software. Our results revealed that temperature and CO2 were statistically significant for rice production at 5% and 1% levels of significance respectively. From Adjusted R2, climatic parameters account for 17.39 percent of the variation in rice production. Temperature and CO2 are the best predictors, according to model Cp and AIC values, and stepwise regression also supports this finding. The model that had been so successfully fitted was considered to be highly significant, demonstrating its potential for use in reality by the concerned planners and policymakers.