Weather Based Forewarning Model for Powdery Mildew Disease of Mustard

Abstract

Powdery mildew disease is the most important disease of mustard (Brassica Juncea) which is highly sensitive to weather condition particularly temperature and humidity. To ascertain the relationship between weather variables and powdery mildew disease of mustard crop at Sardarkrushinagar, weekly powdery mildew data of 12 years (2010-11 to 2021-22) was collected from Centre for Oilseed Research, S.D. Agricultural University, Sardarkrushinagar. The data were pooled over the year and correlated with corresponding weather parameters recorded in Agromet Observatory, S.D. Agricultural University, Sardarkrushinagar. The forewarning regression equation was developed with the help of weather variables to predict the disease incidence. The results revealed that the maximum and minimum temperature ranged 26.2 to 38.1oC and 8.7 to 18.5oC respectively found congenial for disease incidence. The weather variables like maximum temperature (Tmax) and minimum temperature (Tmin) correlated significantly positive with powdery mildew disease. While morning relative humidity (RH I) and evening relative humidity (RH II) correlated significantly negative with disease severity. The impact of rainfall (RF), sunshine hour (SSH) and wind speed (WS) found positive but non-significant on powdery mildew disease. The weather variables those have significant relationship with powdery mildew disease, were used to develop multiple regression model. The regression equation Y = 62.837 + 8.072*Tmax – 3.919*RH1 developed with maximum temperature and morning relative humidity caused up to 96 % variability in the powdery mildew disease. Hence the combined effect of Tmax and RH1 was more pronounced and most influencing weather parameters on powdery mildew disease. Therefore, model could be used for predicting the powdery mildew disease well in advance.