Weather based prediction model for forcasting cotton leaf curl disease in American cotton - J.S. GILL and J.S. BRAR, DALJEET SINGH*, PRITPAL SINGH (Indian Phytopathology, 2010)
Cotton is one of the most important cash crops of India . The productivity of cotton in the last decade has suffered to a great set back due to cotton leaf curl disease (CLCuD) in Punjab. Results indicated that the best fitted equations were Y=63.96+0.809Tmax - 4.550*Tmin +0.488RHmax +0.699*RHmin-0.453*RF of year 2002-2003 with R2 (78.0%) and showed 9.4% error (when validated with 2001 data) and 15.2% error (when validated with 2004 data) . Linear regression analysis also revealed that two week lag meteorological parameters i.e. temperature, relative humidity along with vector of CLCuD has played a significant role in the appearance of disease over the years as compared to one week lag and current week meteorological parameters. It can also be concluded that the previous week’s meteorological weather parameters played a significant role in the appearance of disease in subsequent weeks. As the value of R 2 was 0.68 for the best fitted linear regression equation for two week lags meteorological weather parameters, whereas the corresponding value of R2 for the current week meteorological weather parameters was 0.52. The disease intensity increases exponentially on an average up to first ten weeks from the appearance of disease (i.e. from 21st standard week to 30th standard week). The exponential equation was found to be Y= 0.081e 0.608X R2=0.98, where X=weeks. Therefore based on this equation the major meteorological parameters i.e. temperature, relative humidity and rainfall played a significant role in the appearance of CLCuD over the years. This prediction model gave fairly close estimation to the observed values.