Climate-disease integrated modeling of plant disease environment on sub-seasonal scale

The 42,600 ha in corns and soybeans in the Midwest represents 35% of all U.S. cultivated cropland and the Midwest accounts for a large portion of the $200 billion yearly U.S. agricultural production. Projecting meteorological environment on weekly-monthly scales during growing season is crucial to optimization of farming operation. We have integrated a regional climate mode (WRF), a dispersion model (HYSLPIT), and a disease model into an agricultural forecasting system. The integrated model has been used to forecast soybean rust and other plan diseases in past years over the U.S. main agricultural regions. This presentation reports the model skills in forecasting meteorological environment along with soybean rust forecast applications.

Thirty-day forecast are generated each week, projecting the key parameters for disease occurrence, including precipitation amount and frequency, temperature, and moisture. We found that the model captures overall pattern of rainfall distribution although it has a overall positive bias compared with observations. Both rainfall amount and rainy day forecasts match well with observations throughout the growing season except for around May, the spring barrier when seasons are in transit. These forecasted meteorological variables are then used to predict movements of soybean rust, a potentially devastating disease. The model seems to have reasonably predicted rust spread from coastal states towards North Central Region. These forecasts have provided a useful guidance for the early detection of the disease for soybean producers.