Regression models for prediction of downy mildew progression in pearl millet var. HB 3 based on weather parameters

Abstract

The downy mildew constitutes an important group of plant diseases affecting plant species. The data of 10 years (1991-2000) of downy mildew in pearl millet var. HB 3 recorded at the experimental field of CCS Haryana Agricultural University, Hisar were taken to develop the regression models for prediction of downy mildew progression in the crop based on weather parameters. Weather data for the same period (maximum, minimum and mean temperature, morning and evening relative humidity, sunshine hours, rainfall, rainy days, wind speed and rate of evaporation) were used for epidemiological study of downy mildew in pearl millet. Disease intensity values were transformed using Logistic, Gompertz, Monomolecular and Von Bertalanffy-Richard disease progression models. Growing degree days and vapour pressure deficit were computed using daily weather data. Disease intensity (%) and transformed values by different models were correlated with weather parameters. Among the disease progression models, disease intensity transformed by Logistic model showed the best association with weather parameters. Mean temperature, wind speed and growing degree day were collectively explained upto 72% variability in downy mildew disease progression.