Cotton Leaf Curl Virus Disease Predictive Model Based on Environmental Variables

Abstract

Cotton leaf curl virus disease (CLCuVD), caused by a geminivirus, is a major threat to the cotton industry particularly in the province of Punjab. It has devastated cotton production during the past couple of decades or so causing serious problems in its management. This study, therefore, was initiated to develop a disease predictive model to characterize epidemiological factors conducive for disease spread/severity. Five years’ data of CLCuVD severity, whitefly population density, and environmental conditions were collected for the development of a predictive model from different districts of cotton-growing areas of Punjab. A close relationship was observed between CLCuVD severity and whitefly population. The disease severity was high (54.06–55.31 %) when whitefly population attack was at its maximum (8.48–10.97/leaf) and low (18.37–36.30) when its infestation fell down (2.97–3.78/leaf). The maximum temperature conducive for the development of disease ranged from 37.16 to 37.78 °C contributing 58.3–61.4 % influence on the development of CLCuVD. The role of minimum temperature remained relatively low (33.6–30.3 %). The range of 58.35–60.39 % of relative humidity was found to be more conducive for the development of disease. The contribution remained poor so far as the role of rainfall and wind speeds was concerned. A predictive model based on 5 years’ data (2002–2006) of CLCuV disease severity, whitefly population density, and environmental conditions was developed (Y = 145 + 4.47x ₁ − 0.151x ₂ − 0.490x ₃ − 1.83x ₄ + 1.58x ₅ − 4.84x ₆; R ² = 0.79). The model statistically justified (R ² = 0.79) at P < 0.05 was calibrated and then validated using 2 years’ data (2007–08) collected from the experiment in the research area of the Department of Plant Pathology, University of Agriculture, Faisalabad. A close relationship was observed between the two models. It was envisaged that the model would be helpful in forecasting the disease to decide the correct timing of pesticide application.