Crop-weather based relation and severity prediction of aerial blight incited by Rhizoctonia solani Kuhn in soybean

Abstract


 Crop-weather based relation and other aspects of aerial blight incited by Rhizoctoniasolani Kuhn were investigated on two mega varieties (JS 335 and JS 97-52, now susceptible) under central Indian conditions during 2017, 2018 and 2019. It was found that aerial severity and sclerotial formation on affected leaves were varied significantly in all three season, and progress of disease was rapid between 63–84 days old crop {full pod (R4) to maturity initiation (R7) stage}.Increasing crop age was also significantly positively correlated with increasing severity (0.732*) and sclerotial formation (0.981**). Mean relative humidity and maximum temperature of current as well as previous week were found to be positively and negatively correlated with increasing severity of aerial blight, respectively. A regression based model with three explanatory variables (Mean RH, Rainfall and Minimum temperature) of current week was found to be most significant in prediction of disease severity (R2=0.946) of aerial blight. Whereas previous week weather variables i.e. rainy days and rainfall were also accounted 71.7 per cent variability (R2 = 0.717) in disease severity. Overall, weekly average maximum temp (27 to 30o C) and mean RH (80 to 90 per cent) in current week coupled with more rainfall and rainy days in previous week were found to be most conducive field condition for rapid progress of aerial blight disease in soybean.

Epidemiological study of soybean anthracnose and pod blight (Colletotrichum truncatum (Schw.) Andrus and Moore)

“…From the models, it can be inferred that weather parameters like, maximum and minimum temperature and evening relative humidity played crucial role in disease development during three years of investigation, and the regression equation formulated on the basis of these parameters appears to be the best fit. Amrate et al, (2021) also found best model with three significant weather variables (Mean Relative Humidity, Rainfall and Minimum temperature) for prediction of Rhizoctonia aerial blight of soybean.…”

Forecasting models for forewarning Anthracnose and web blight of mung bean (Vigna radiata) under Tarai zone of Uttarakhand

“…The findings are per the observations of other researchers in developing prediction models for disease assessment. Amrate et al, (2021) developed a disease forecasting model for the prediction of Rhizoctonia aerial blight of soybean with three significant weather variables (mean relative humidity, rainfall, and minimum temperature). Kulkarni and Raja (2019) studies indicate a negative correlation with temperature and a positive correlation with relative humidity and rainfall on per cent disease index and spore load of mung bean anthracnose.…”

Weather based prediction model for yellow mosaic disease of soybean (Glycine max (L.) Merrill)

“…It is more serious and widespread disease of soybean in the Northern and Central India and Pantnagar is considered as a hotspot for YMD. Understanding of weather factors and their role in disease incidence is a prerequisite for developing disease forewarning system (Amrate et al, 2021). Hence, a detailed investigation was undertaken to study the influence of weather parameters on the incidence of YMD and differential response of the soybean genotypes varying in YMD resistance under varying agro-climatic conditions.…”