Soybean rust: forecasting its risk for Minnesota
What is soybean rust and what is the concern?
Soybean rust is a destructive disease of soybeans that can spread quickly and cause considerable yield losses. The fungal pathogen that causes soybean rust cannot overwinter in the central U.S. because of the cold climate, Thus, the spores that are needed to initiate the disease during the summer must be transported from the southern U.S. and deposited in Minnesota before soybean rust can develop. After spore deposition, appropriate weather consisting of wet and cloudy conditions is also needed before this disease will develop to damaging levels in soybeans. Soybean rust has not been found in Minnesota, although the spores have been detected in Minnesota and the disease has been reported in northern Iowa.
Scouting for and managing soybean rust
Soybean rust causes small brown spots and blisters (pustules) on leaves of infected plants. Scouting and close examination of leaves with a hand lens, especially in the lower canopy, is required for early detection of soybean rust Once rust is detected in a field, or area, it is managed most effectively with foliar fungicide sprays.
Forecasting the risk of soybean rust in Minnesota
A soybean rust risk prediction model has been developed for Minnesota. The primary purpose of this forecasting system is to minimize crop loss and fungicide use by enabling more efficient and targeted scouting efforts for soybean rust and to provide an early warning system to enable timely fungicide applications. The model includes a long-range atmospheric spore transport and deposition module coupled to a leaf wetness module. Predictions are made on a daily basis for up to seven days in advance using forecast data from the US National Weather Service.
Use and interpretation of the Minnesota risk forecasting model
The overall prediction of soybean rust risk is based on combinations of daily relationships of atmospheric spore transport from confirmed soybean rust or source regions (www.sbrusa.net/) to the potential receptor locations in Minnesota soybean growing counties, and canopy wetness at the receptor locations. The risk scores are presented on a color-coded map for each day of the seven-day forecast period as shown below. The likelihood of soybean rust indicated by color coding on the map progresses with increasing probability from no known risk to the highest risk.
The four possible levels are:
1 (green) = no spore transport and no current/past spore deposition
2 (yellow) = spore transport but no current/past deposition with right leaf conditions or current/past spore deposition without right leaf conditions for disease
3 (orange) = current/past spore deposition with right leaf conditions
4 (red) = current and past spore deposition with right leaf conditions
Soybean rust risk forecast maps
The University of Minnesota Soybean Rust Forecasting system consists of three components: (1) regional weather conditions (supplied by the National Weather Service) found to be suitable for potential transport of the rust spores through the atmosphere from upwind source areas in the U.S. (e.g., Texas) into Minnesota; (2) conditions associated with deposition of rust spores onto the soybean canopy with rainfall or dry deposition processes in Minnesota; and (3) local weather conditions in Minnesota that favor leaf wetness for at least six hours which enables the spores to germinate. Once the spores germinate, it may take up to two weeks or more for infection and disease development. Note that because of that lengthy duration after spore germination before disease development and the uncertainties associated with daily weather forecasts into the future, predictions of disease development are not part of the Soybean Rust Forecasting System.
The Forecasting System in its present form predicts the risk for the arrival of rust spores into Minnesota and the potential for spore germination for up to seven days ahead of the time from any given day (one to seven days). Because of the uncertainties associated with weather forecasting, the accuracy of the predictions of the rust model progressively declines as the period following the forecast increases (e.g., 24, 48, 72 hours etc.). In other words, a 72-hour rust forecast will be less accurate than a 48-hour and a 48-hour forecast will be less accurate than a 24-hour prediction. Therefore, the forecasted information for the rust must be taken purely as an advisory and the user must treat the information with caution. The University of Minnesota cannot be held liable for any misuse or misinterpretation of the forecasted results.