Exploring the Impact of Weather Parameters on Cotton Leaf Curl Disease Progression and Whitefly Population Dynamics: A Decadal Analysis (2011–2020)

ABSTRACT Cotton leaf curl disease (CLCuD) has become a potential menace to the production of cotton ( Gossypium hirsutum ) in Africa and South Asia. Whitefly ( Bemisia tabaci ) is the potential vector of this virus. Due to the dearth of resistant cultivars and effective management strategies of whitefly, yield loss in cotton is witnessd regularly. To ensure the timely application of management practices, there is a dire need for a reliable regression model that can forecast the CLCuD with high speed and accuracy. Keeping this in view, long‐term studies were conducted during 2011–2020 at Cotton Research Station, Sirsa. The influence of weather parameters on the modulation of the disease progression and whitefly dynamics was recorded and analysed through correlation and regression. A prediction equation for disease incidence and vector population was developed through regression. Minimum temperature, maximum temperature and evening relative humidity (RH) significantly influenced the disease development, with the former two having negative significant effects. The coefficient of determination ( R 2 ) ranged from 0.19 to 0.90 for disease development with the weather parameters. The best fitted regression equation based on the decadal study for prediction of CLCuD incidence was Y = −12.913 Tmax + 2.489 Tmin + 0.242 RHm − 0.197 RHe − 0.890 Rf + 459.368 and for percent disease intensity (PDI) of CLCuD was Y = −8.962 Tmax + 2.608 Tmin + 0.232 RHm − 0.567 RHe − 0.570 Rf + 306.433. Additionally, a significant positive correlation between the CLCuD incidence, PDI and population of whiteflies was observed, as reflected by high values of the correlation coefficient ( r ) for most of the years during the period of study. The vector, whitefly ( B. tabaci ) population was also significantly influenced by the ambient weather conditions. High relative humidity favoured the whitefly population. Regression analysis revealed 25%–62% variability in whitefly population due to weather parameters, and the best fitted regression model for whitefly incidence is Y = −0.194 Tmax − 1.610 Tmin − 0.439 RHm + 0.911 RHe + 0.020 Rf + 44.733. On the basis of these equations, the main meteorological factors, such as temperature, relative humidity and rainfall, have a substantial impact on the emergence of CLCuD over the years.