Development of weather-based prediction models for leaf rust in wheat in the Indo-Gangetic plains of India

Abstract

Weather based prediction models for leaf rust were developed using disease severity and weather data recorded at four locations viz. Ludhiana, Kanpur, Faizabad and Sabour of the All India Wheat and Barley Improvement Project. Weeks 7–9 of the crop growing season at Ludhiana, Faizabad and Sabour and weeks 10–12 at Kanpur were identified as critical periods for relating weather variables to disease. Highly significant correlation coefficients were found between disease severity and a greater number of weather variables in these critical 3-week periods than at other times. The correlation coefficients were greatest for the Humid Thermal Ratio (HTR), Maximum Temperature (MXT) and Special Humid Thermal Ratio (SHTR), and these three weather variables were selected as predictor variables. Linear regressions with these predictor variables (individually) during the critical periods, and a multiple regression with MXT and relative humidity (RH), serve as four disease prediction models, with sufficient lead-time to take control measures. Validation of these prediction models with independent disease severity data showed that the regression equation with MXT (Model-1) was the best among the prediction models, with four out of six simulations matching observed disease severity classes and also having lowest residual sum of squares (SSE) value of 2727. Models 4 (multiple regression), 2 (HTR) and 3 (SHTR) with SSE values of 2881, 3092 and 3732, respectively are in order of decreasing accuracy of prediction. The model using MXT can be used to predict the disease severity in the Indo-Gangetic Plains and provide the basis for efficient disease control.