Weather based forewarning model for cotton pests using zero-inflated and hurdle regression models

Abstract

Early forewarning of crop pest based on weather variables provides lead time to manage impending pest attacks that minimize crop loss, decrease the cost of pesticides and enhance the crop yield. This paper is an attempt to forewarn incidence of Cotton pests using weather variables. The pest incidence data from 2015 to 2023 for Aphids, Jassids, Thrips, and Whiteflies has been used for the study. The pest incidence being count variable, different count regression models such as zero inflated Poisson & negative binomial, hurdle Poisson & negative binomial, negative binomial and generalized Poisson regression models have been developed for forewarning of pests. Results indicated that zero inflated Poisson regression model outperformed the other models with improved performance of nearly 30 to 75%. Thus, the zero inflated Poisson regression model is a reliable tool in prediction of cotton pests, thereby aiding towards better pest management strategies.

Keywords

• Pest Management
• Crop Yield
• Climate Change
• Forecasting
• Agriculture
• Regression