International Journal of Bio-resource and Stress Management
  • Year: 2026
  • Volume: 16
  • Issue: 6

Impact of Weather Factors on the Severity of Rhizoctonia Aerial Blight Caused by Rhizoctonia solani in Soybean (Glycine max L.)

  • Author:
  • Gurpreet Kaur Bhamra1✉, Munmi Borah2
  • Total Page Count: 7
  • Page Number: 01 to 07

1ICFRE-Rain Forest Research Institute, Sotai, Jorhat, Assam (785 010), India

2Dept. of Plant Pathology, Assam Agricultural University, Jorhat, Assam (785 014), India

Corresponding✉ gurpreetkaurbhamra50@gmail.com

Abstract

The experiment was conducted during kharif (July-November), 2020 and 2021 at Instructional cum Research Farm, Assam Agricultural University, Jorhat, Assam, India to evaluate ten soybean cultivars to screen for resistance to Rhizoctonia aerial blight and to analyze disease severity in relation to weather variables. The onset of the disease in both years occurred from September 10th to 16th, coinciding with the flowering stage. In both the years of study, the highest PDI was recorded in the cultivar Punjab 1 (Moderately susceptible) and PK472 (Resistant) recorded the lowest PDI. Maximum temperature, minimum temperature, and afternoon relative humidity showed a negative correlation with Rhizoctonia aerial blight disease development, whereas morning relative humidity and rainfall exhibited a positive correlation. During two years of research, the minimum temperature of the current week and rainfall and bright sunshine hours of the preceding week were substantially associated with severity of the disease. The scatter chart plotted showed a negative correlation between minimum temperature (current week), rainfall (preceding week) and mean PDI with R2 value of 0.63 and 0.59 respectively, while a positive correlation was observed between BSSH (preceding week) and mean PDI (R2=0.57). Stepwise multiple regression analysis was conducted between pooled PDI (2020-2021) and weekly average weather variables showed an R2 of 57.2-68.4% between minimum temperature (current week) and PDI. Furthermore, the preceding week’s BSSH explained 66.9-58.4% variability in certain cultivars, while rainfall explained 60.5-69.2% variability in others.

Keywords

Percent disease index, correlation, regression, prediction model, epidemiology