Weather based forewarning of whitefly (Bemisia tabaci) in cotton in southwestern district of Punjab

A three-year field investigation (2021–2023) was conducted in Faridkot district of Punjab to elucidate phase-wise weather dynamics governing whitefly [Bemisia tabaci (Gennadius)] abundance in Bt cotton. Pronounced inter-annual variation in whitefly population was observed, with the highest peak population during 2021 (15.4 adults leaf⁻¹), followed by 2022 (12.2 adults leaf⁻¹) and 2023 (7.2 adults leaf⁻¹). Peak infestation occurred earlier during 2021 [29th Standard Meteorological Week (SMW)] compared with 33rd and 36th SMW during 2022 and 2023, respectively. During the establishment phase of 2021, higher maximum (37.8 °C) and minimum temperatures (30.4 °C), lower rainfall (65.6 mm) and higher sunshine hours (6.6 h day⁻¹) accelerated early population build-up. Peak population phase was characterized by comparatively lower temperatures, elevated morning (83.4%) and evening relative humidity (66.1%) and cloudy weather conditions that favoured whitefly proliferation, whereas moderate to heavy rainfall events (> 20 mm) caused abrupt population decline. A clear sequential pattern of Thermal Humid Index (THI) was observed across crop phases, where higher THI values during the establishment phase, followed by gradual decline during peak and declining phases, favoured whitefly build-up and persistence. Correlation analysis showed a strong positive association of whitefly population with minimum temperature during 2021 (r = 0.80***) and 2023 (r = 0.61***), indicating the dominant role of nighttime temperature in regulating population dynamics. Bright sunshine hours exhibited significant negative association during 2021 (r = -0.53*) and 2023 (r = -0.47**), suggesting suppressive effects of prolonged sunshine on whitefly multiplication. The regression models identified minimum temperature, relative humidity and bright sunshine hours as the major meteorological drivers regulating whitefly population dynamics, with the 2021 model explaining 78% variation with low prediction error (RMSE = 13.3% of mean) in whitefly abundance. The developed phase-wise weather-whitefly framework can substantially improve early warning advisories, optimize intervention timing and support sustainable cotton pest management in Punjab.