A disease predictive model based on epidemiological factors for the management of bacterial leaf blight of rice

Abstract

Rice is a widely consumed staple food for a large part of the world’s human population. Approximately 90% of the world’s rice is grown in Asian continent and constitutes a staple food for 2.7 billion people worldwide. Bacterial leaf blight (BLB) caused by Xanthomonas oryzae pv. oryzae is one of the devastating diseases of rice. A field experiment was conducted during the year 2016 and 2017 to investigate the influence of different meteorological parameters on BLB development as well as the computation of a predictive model to forecast the disease well ahead of its appearance in the field. The seasonal dataset of disease incidence and environmental factors was used to assess five rice varieties/ cultivars (Basmati-2000, KSK-434, KSK-133, Super Basmati, and IRRI-9). The accumulated effect of two year environmental data; maximum and minimum temperature, relative humidity, wind speed, and rainfall, was studied and correlated with disease incidence. Average temperature (maximum & minimum) showed a negative significant correlation with BLB disease and all other variables; relative humidity, rainfall, and wind speed had a positive correlation with BLB disease development on individual varieties. Stepwise regression analysis was performed to indicate potentially useful predictor variables and to rule out incompetent parameters. Environmental data from the growing seasons of July to October 2016 and 2017 revealed that, with the exception of the lowest temperature, all environmental factors contributed to disease development throughout the cropping season. A disease prediction multiple regression model was developed based on two-year data (Y = 214.3-3.691 Max T-0.508 Min T + 0.767 RH + 2.521 RF + 5.740 WS), which explained 95% variability. This disease prediction model will not only help farmers in early detection and timely management of bacterial leaf blight disease of rice but may also help reduce input costs and improve product quality and quantity. The model will be both farmer and environmentally friendly.

Smart farming: modeling distribution of Xanthomonas campestris pv. oryzae as a leaf blight-causing bacteria in rice plants

“…Fewer bacteria founded in the area with the highest rainfall. This finding matches the study by Rehman et al [35] that moisture and precipitation were positively related to leaf-blight-causing bacteria. However, the bacteria are primarily distributed in places with high moisture and have less temperature change.…”

Multiple interval mapping of QTLs and epistasis for iron toxicity tolerance in segregating population of Indica rice

“…Rice is a primary food crop for the world. Approximately 90% of world rice is cultivated in Asia (Rehman et al, 2022). Rice is the main crop grown in more than 100 countries worldwide and is harvested in an area of approximately 158 million hectares (Banik et al, 2020).…”