White mold is a major disease to soybean production across the Upper Midwest region of the United States. However, this disease is highly dependent on environmental conditions, and as a result is inconsistent in developing over the years. To manage white mold, producers will often use fungicide applications during the growing season. However, many of the most effective fungicide programs come at an excessive cost, and in years that are not conducive for the development of white mold, producers may be making unnecessary applications and wasting money. The use of the previously developed models has proven to be effective at controlling white mold in states such as Wisconsin, Iowa, and Michigan. However, the accuracy of these models in predicting white mold development across North Dakota has previously been unknown. By utilizing an accurate white mold predictive model, farmers can make informed decisions on fungicide application timing and potentially eliminate unnecessary fungicide applications. After validation, these models will be integrated into the North Dakota Agricultural Weather Network (NDAWN) to be used by farmers in the Northern Great Plains region.
Genetic resistance in soybean varieties is another effective tool for managing white mold. Many effective breeding efforts have been performed, identifying varieties with elevated levels of resistance. However, little is known about resistance levels in current breeding populations from NDSU. The research proposed here will help to understand the levels of resistance present in current breeding efforts and help to identify parental lines with levels of resistance for future crosses.

1. The accuracy of predictive models (Sporecaster) for predicting white mold of soybean in will be determined for North Dakota soybean production fields.
2. Incorporation of soybean white mold models into NDAWN web platform.
3. Soybean breeding lines and additional PI lines will be screened for resistance to Sclerotinia sclerotiorum.
a. A panel of soybean genotypes adapted to North Dakota will be identified with consistent resistance responses to Sclerotinia sclerotiorum for use as standard controls in future greenhouse and field experiments.
4. Development of New Extension Material for the Management of White Mold

• Understand the accuracy of these predictive models and improve the acceptance and integration of these predictive model tools in North Dakota soybean production.
• A new white mold risk map tool will be available utilizing the NDAWN platform for the creation of daily risk assessments during the growing season.
• Assess the levels of white mold resistance present in current soybean breeding lines and the identification of resistant parental lines for future breeding efforts.

To manage white mold of soybean, farmers use fungicide applications during the season to prevent the development of the disease. However, many of these products are expensive, and by utilizing this predictive model tool, unnecessary fungicide applications can be avoided, which would allow for cost savings. These models can be easily run from any smartphone device, are publicly available at no cost, and use localized weather data to provide spray recommendations to farmers on a field-by-field basis. By ensuring these models are appropriate for all North Dakota soybean growing regions, this effective tool will guide the decision-making process for when to make these high-cost fungicide applications. Further, the development and availability of soybean varieties with high levels of resistance to white mold will benefit farmers by giving them an additional management tool. The use of resistance could also allow for reduced use of fungicide applications and input costs.