Predicting Monthly Heatwaves and Assessing their Impact on Summer Tomato Production

Background and Objective: The global mean temperature is gradually increasing, which has become a serious environmental concern. Prolonged periods of high temperature often lead to heatwaves (HWs), causing substantial damage to agricultural production. This study aims to predict monthly heatwave occurrences and assess their impact on summer tomato production by examining the influence of key climatic factors on crop yield and overall performance. Materials and Methods: This study applied the Zero-Inflated Poisson (ZIP) model to predict the monthly Heat Wave Count (HWC). The impact of heatwaves on the yield of summer tomatoes cultivated in Gazipur district was examined using simple linear regression (LR). Agro-climatological gridded data (1982-2023) were obtained from the assimilation model (MERRA-2) of the NASA POWER Project, while summer tomato yield data (2004-2023) were collected from the Olericulture Division of the Horticulture Research Center (HRC), Bangladesh Agricultural Research Institute (BARI). Results: The findings revealed that heatwaves occurred in Gazipur district from March to May, with the highest frequency recorded in April. The number of heatwave days (HWDs) also peaked in April, and the duration of HWDs showed an increasing trend over the years. The ZIP model demonstrated reliable performance with low Mean Squared Error (MSE) and Mean Absolute Error (MAE) during the validation period (2022-2023). Moreover, a significant negative association was observed between heatwaves and summer tomato yield at the 5% significance level (p = 0.029). Conclusion: The results highlight the increasing occurrence of heatwaves and their adverse effects on summer tomato production in Gazipur district. The ZIP model showed strong potential for reliable heatwave prediction under changing climatic conditions, which could support climate-resilient agricultural planning and crop management strategies.