Forecast of maize dwarf mosaic using growth model forecasting method

Maize dwarf mosaic (MDM) is an important maize viral disease in the world. Disease forecast plays a vital role in controlling it. In this study, three growth models including Logistic growth model, Gompertz model and Weibull model, were used to fit seven groups of MDM data obtained in Chengde, Hebei Province in China. Residual sum of square test showed that Gompertz model had the smallest residual sum of square and regression error and the biggest correlation ratio of curve for each data group, which indicated that it was the best model to describe the seasonal epidemics of MDM. Forecast of MDM using Gompertz model indicated its ability for short-term and medium-term forecast of MDM based on field survey data from at least 4 different time points in the early stage of disease development. This study provided a method for the forecast of plant viral diseases.