Development of Two Early Forecasting Models for Predicting Incidence of Rice Panicle Blast in China

Abstract

Early forecasting of rice panicle blast is critical to the management of rice blast. To develop early forecasting models for rice panicle blast, the relationship between the seasonal maximum incidence of rice panicle blast ( PBx) and the PBx in the preceding crop, weather conditions, location, and acreage of susceptible varieties was analyzed. Results revealed that PBx in the preceding crop, acreage of the susceptible varieties in class ( SVC), altitude, weather conditions 120 to 180 days before the PBx date (dbPBx) and 30 to 90 dbPBx were significantly correlated with the PBx. Subsequently, a logistic model and a two-step hurdle model were developed to predict rice panicle blast. The logistic model was developed to predict whether the PBx was 0 or not based on the preceding PBx, altitude, acreage of susceptible varieties, the longest stretch of days with soil temperatures between 16 and 24°C for the period 120 to 150 dbPBx, and the longest stretch of rainy days in the period 120 to 180 dbPBx. The hurdle model predicted if the PBx was greater than 0 at the first step, and if the prediction was greater than 0, then a regression model was developed for predicting PBx based on the preceding PBx, SVC, altitude, and weather data 180 to 30 dbPBx. Validation with the test datasets showed that the logistic model could correctly predict whether PBx was 0 at a mean test accuracy of 78.39% and that the absolute prediction error of PBx by the two-step hurdle model was smaller than 6.16% for 90% of the records. The model developed in this study will be helpful in management decisions for rice growers and policy makers and offer a useful basis for further studies on the epidemiology and forecasting of rice panicle blast.