Abstract Stripe rust, caused by Puccinia striiformis f. sp. tritici , significantly reduces wheat ( Triticum aestivum L.) yields when uncontrolled. This study developed predictive models for stripe rust severity using weather variables. Field experiments were conducted from 2017 to 2019 at the Research Farm, Chatha, using susceptible wheat variety PBW 343 in a randomized block design with four replications. Disease severity was assessed weekly using the modified Cobb scale. Additional historical weather and disease data from 2005 to 2017 were incorporated to develop comprehensive forecasting models. Strong positive correlations were observed between disease severity and maximum temperature ( r = 0.89–0.91) and minimum temperature ( r = 0.75–0.91), while morning relative humidity showed negative correlation ( r = −0.80). Rainfall exhibited no significant correlation with disease progression. Two multiple linear regression models were developed: for 2005–2017, Y = −502.14 + 0.64 X 1 + 8.57 X 2 + 3.04 X 3 + 1.42 X 4 + 0.58 X 5 ; and for 2017–2019, Y = 322.57 + 9.41 X 1 − 4.14 X 2 − 2.56 X 3 − 0.71 X 4 + 0.26 X 5 , where Y represents disease severity and X 1 – X 5 represent weather parameters. Both models explained approximately 89%–91% of variation in disease severity. Spore concentration increased 84% from 51st to seventh standard meteorological week during 2017–2019. These models provide quantitative tools for predicting stripe rust severity using readily available weather data, enabling timely disease management decisions and supporting sustainable wheat production.
Khushboo et al. (Sun,) studied this question.