Wheat blast, caused by the Triticum pathotype of Magnaporthe oryzae, is an emerging disease considered to be a limiting factor to wheat production in various countries. Given the importance of wheat blast as a high-consequence plant disease, weather-based infection models were used to estimate the probabilities of M. oryzae Triticum establishment and wheat blast outbreaks in the United States. The models identified significant disease risk in some areas. With the threshold levels used, the models predicted that the climate was adequate for maintaining M. oryzae Triticum populations in 40% of winter wheat production areas of the United States. Disease outbreak threshold levels were only reached in 25% of the country. In Louisiana, Mississippi, and Florida, the probability of years suitable for outbreaks was greater than 70%. The models generated in this study should provide the foundation for more advanced models in the future, and the results reported could be used to prioritize research efforts regarding the biology of M. oryzae Triticum and the epidemiology of the wheat blast disease.

There is copious literature on development and validation of models to forecast risk to crops from arthropods and diseases; however, there is little published on causes of failure associated with these models. This manuscript provides mechanistic model builders and users with a list of likely problems, potential causes, possible solutions, and associated references. The problems are divided into four categories: environmental inputs, model construction and parameterization, validation, and implementation. The list is based on the authors’ extensive experiences developing and running mechanistic modeling systems. A multidisciplinary approach involving researchers with expertise in pest biology, crop management, meteorology, and information technology is recommended for delivering the most effective pest forecast models.