Forecasting of Wheat Diseases: Insights, Methods and Challenges

Abstract

In wheat the incidence and severity of diseases may vary with season, region, variety, weather, inoculum load and resistance level of the host cultivars. This variation leads to the varied yield losses even up to 100 percent under most severe conditions. Thus effective disease management strategy is to be followed for timely and successful management of wheat diseases. This may involve chemical control, Host plant resistance, cultural and biological control etc. Out of all these, methods chemical control gave a quick response with maximum benefits. But this method is not a preferable choice for sustainable agriculture. Hence to avoid/minimize the dependency on fungicides, we can look for the option of use of epidemiological models to predict the time and place of occurrence or development of the disease. So, prediction of disease appearance in advance in space and time would help a lot to the farmers to have maximum cost benefit ratio by timely managing the diseases with minimum expenditure. Keeping this in view several models has been developed for the almost all the important wheat diseases namely rusts, Fusarium head blight, Septoria blight, Karnal Bunt, Powdery mildew and Blast etc. in different groups in different countries with varied accuracy like PROCULTURE a mechanistic model, developed in Belgium could accurately predicted epidemics of septoria blight with probability of detection (POD) of more than 0.90 Based on the predictions. Likewise weather based mechanistic model developed in Italy for predicting DON worked with 90 per cent accuracy in Netherlands and 60 per cent in Egypt, U. K., Mexico, Hungary and Russia. For the prediction of Karnal Bunt a multiregression Models has been also developed on the basis of maximum temperature (Tmax), sunshine duration (SSD), evening relative humidity (RHe) and rainy days (RD) and the equations derived from them are explained up to 89 percent of the disease variation. Similarly in rusts and other diseases there are successful examples of disease prediction models. Some of them have been used for developing the Decision support systems for the benefit of the farmers e.g. granoduro.net^® in Italy, FusaProg in Switzerland and Fusarium Risk Tool in USA, SeptoriaSim in Denmark, PUCREC/PUCTRI, Superconsultant etc. are some. Despite of so many models in use still there is a need to develop by following the interdisciplinary approach which can be practically more feasible globally.

If “technology transfer tool” can be defined as a way to get information into the hands of as many people as possible, weather-based disease forecasting models are the perfect example of how this works in practice.: Julienne Isaacs.