Predicting Pest Outbreaks with a Climate-Insect Interaction Model Based on Degree-Day Accumulation

Year 2025, Volume: 10 Issue: 2, 315 - 325, 01.09.2025

Hyba Abduljaleel , Feruza Umirqulova M.a. Bruno Maqsad Matyakubov Prabakaran Paranthaman D Kalidoss

https://doi.org/10.28978/nesciences.1763850

Abstract

Pest outbreaks continue to present a global challenge to the sustainability of agriculture, threatening crop yields, food security, and economic stability. From a financial standpoint, sustaining or augmenting food production systems has increasingly relied upon fossil fuels, the availability and cost of which are subject to geopolitical turmoil. Understanding climate change impacts on the timings of seasonal events (phenology) adds a new layer to the complex problem of forecasting pest emergence and population growth. This study provides a new approach to predicting agricultural pests by modeling the 'life cycles' of major agricultural insect pests as functions of climate using degree day accumulation, a unit of measure of 'heat' for insects. A model was constructed and calibrated using historical pest occurrence data in conjunction with local temperature records. Results indicate significant relationships between time (thermal time) and pests' development, enabling outbreak estimation by timing and intensity.
Furthermore, the analysis demonstrated how changing temperature and rainfall patterns could worsen pest impacts in more temperate regions where they did not exist before. The presented analysis enhanced planning by offering warnings for integrated pest management of agriculture via one-way climate–insect interaction models. This research focuses on the frame of climate-influenced pest control strategies seeking an essential adaptive response to global warming. It underscores the need to factor climate-related risks into pest management structures.

Keywords

Degree-day accumulation , Pest phenology modeling , Climate-responsive pest forecasting , Insect development thresholds , Integrated pest management (IPM)

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