Weather-driven Forecasting of Whitefly Populations in Tamil Nadu's Cotton Ecosystem
Sadhana V
Department of Agricultural Entomology, Tamil Nadu Agricultural University, Coimbatore- 641003, Tamil Nadu, India.
Senguttuvan K *
Department of Agricultural Entomology, Tamil Nadu Agricultural University, Coimbatore- 641003, Tamil Nadu, India.
Murugan M
Department of Agricultural Entomology, Tamil Nadu Agricultural University, Coimbatore- 641003, Tamil Nadu, India.
Suriya S
Division of Entomology, Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir, Srinagar-190025, Jammu and Kashmir, India.
Prithiva J.N.
Department of Agricultural Entomology, Tamil Nadu Agricultural University, Coimbatore- 641003, Tamil Nadu, India.
*Author to whom correspondence should be addressed.
Abstract
The current research aimed to investigate the correlation between the population dynamics of Bemisia tabaci and other invasive whitefly species, including Paraleyrodes bondari, Aleyrodicus dispersus, and Aleyrodicus rugioperculatus, and weather factors from January to August 2022, covering two seasons: winter and summer. The primary objectives were to monitor whitefly population trends and analyse their relationship with various meteorological parameters. The results revealed that the overall whitefly population peaked during the 9th Standard Meteorological Week (SMW) of January 2022, with 9.80 individuals per three leaves, and the 31st SMW of August 2022, with 8.93 individuals per three leaves. Pearson correlation analysis indicated a positive correlation between whitefly populations and minimum and maximum temperatures, as well as minimum relative humidity, while rainfall showed a negative correlation. Insights into population dynamics and their association with weather factors are crucial for developing weather-based pest forecasting systems. Accurately predicting peak pest activity periods can facilitate effective pest management strategies, thereby enhancing agricultural productivity.
Keywords: Whiteflies, cotton, population dynamics, correlation, weather factors, regression