Effect of abiotic factors on progress and severity of sheath rot (Sarocladium oryzae) in rice

Abstract

AbstractSheath rot, caused by Sarocladium oryzae, is an emerging serious threat in rice cultivating regions especially in Union Territory of Jammu & Kashmir, causing yield losses of 20%–85%. Little and scattered information is available on the epidemiology of the disease. Hence, the field experiments were conducted during the cropping seasons of 2019 and 2020 to find out the effect of weather factors on the development of the disease as well as computation of predictive model for its initiation and spread in the field in five popular and commercially grown rice varieties viz. PB‐1509, PB‐1401, PR‐114, PB‐1121 and Arize 6444 Gold. The higher mean percent disease index (PDI) of 37.3% was recorded during 2019, whereas it was 33.8% during 2020. The effect of cropping seasons (years) on PDI, area under disease progress curve (AUDPC) and infection rate (r) was significant. Significant and negative correlation was observed between PDI and selected meteorological parameters, that is maximum and minimum temperature and relative humidity in the evening during both the cropping seasons The PDI had positive and significant correlation with relative humidity in the morning during the cropping season 2020. Rainfall showed significant and negative correlation with PDI in all the varieties during 2019 whereas it was non‐ significant and positive during 2020 in all the varieties except PR‐114 where, it was positive and significantly correlated. Minimum temperature, morning and evening relative humidity and sunshine hours resulted in ~92%–95% and 97%–99% variation for PDI in all the varieties during the cropping seasons 2019 and 2020, respectively. Sheath rot was the maximum recorded at minimum temperature (11.5–20.0°C), high morning relative humidity (74.0%–94.0%) and moderate evening relative humidity (26.0%–75.0%) and sunshine hours of 2.8–9.3 h, respectively. All the assumptions related to the developed model were acceptable as the global stat, skewness, kurtosis, link function and heteroscedasticity tests had the least p‐values in all the varieties during both the cropping seasons. Hence the model may be used for the prediction of sheath rot, which would be helpful in the early adoption, precise and judicious management practices.