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Oral: Risk Assessment


Validation and refinement of a predictive model for Sclerotinia sclerotiorum apothecial development in soybean fields
J. WILLBUR (1), H. Lucas (2), B. Mueller (2), S. Chapman (2), M. Kabbage (2), D. Smith (2) (1) University of Wisconsin - Madison, U.S.A.; (2) University of Wisconsin - Madison, U.S.A.

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Sclerotinia stem rot (SSR), caused by Sclerotinia sclerotiorum, is one of the most important yield-limiting diseases of soybean worldwide and is a significant concern in the Great Lakes region of the USA. Phenologically-based fungicide applications are often inefficient, and fungicide application is sometimes unnecessary when weather conditions are not conducive for infection. Weather-based risk assessment tools are sometimes used to more accurately predict the timing of fungicide applications, however, such tools are not currently available for soybeans. In 2014, a logistic regression model using virtual weather data as inputs was developed to predict apothecial germination. The model uses 30-day averages of mean air temperature (r = -0.59, P < 0.01) and maximum leaf wetness (r = 0.35, P < 0.01) to predict the probability of apothecial presence prior to and during the soybean flowering period. In 2015, model-based fungicide applications successfully decreased disease severity and prevented yield loss in field trials. An on-site weather station was also used to validate virtual weather data. Due to inaccuracies in virtual moisture variables, higher resolution virtual weather data was combined with data collected in Wisconsin and Michigan for model refinement. The model will be incorporated into a preliminary web-based user interface to assist growers in effectively timing fungicide applications for integrated SSR management.