Modeling the Impact of Weather Variability on Paddy Yield in Malaysia using Copula Methods

Abstract

Paddy is an important crop in Malaysia, with the country producing more than 2.43 million tons of paddy annually, making it a key contributor to national food security and the economy. Rice, the staple food for most of the population, also serves as a primary source of income for many, particularly small-scale farmers. Despite its significance, extreme weather continues to negatively impact paddy yields and threatens future production. Traditional linear models often fail to capture the complex and nonlinear dependencies between weather extremes and agricultural output, resulting in limited predictive accuracy. To address this gap, this study aims to examine the effects of weather indices on paddy yield and rice production. It utilizes annual data from 1963 to 2022, focusing on temperature and rainfall as the primary indicators of weather that disrupts paddy cultivation. The copula method is employed to analyze the relationship between extreme weather and both paddy yield and rice production. This method is useful for measuring the dependency structure between the variables. The findings indicate that the Frank Copula provides the best fit among the tested copula families, producing the lowest Akaike Information Criterion (AIC) value, suggesting a significant dependence between weather extremes and paddy production. These results highlight the importance of accurately modelling weather and yield relationships to support food security planning in Malaysia.