Project Details:

Title:
Validation of Sclerotinia Sclerotiorum Apothecial Prediction Models in ND and Evaluation of Soybean Resistance to White Mold

Parent Project: This is the first year of this project.
Checkoff Organization:North Dakota Soybean Council
Categories:Soybean diseases, Technology, Breeding & genetics
Organization Project Code:NDSC 2024 Agr 16
Project Year:2024
Lead Principal Investigator:Richard Webster (North Dakota State University)
Co-Principal Investigators:
Samuel Markell (North Dakota State University)
Febina Mathew (North Dakota State University)
Carrie Miranda (North Dakota State University)
Keywords:

Contributing Organizations

Funding Institutions

Information and Results

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Project Summary

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 between 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 which 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 at predicting white mold development across North Dakota is currently unknown. By utilizing an accurate white mold predictive model, producers can make informed decisions on fungicide application timing and potentially eliminate unnecessary fungicide applications. 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.

Project Objectives

1. The accuracy of predictive models (Sporecaster) for predicting white mold of soybean in will be determined for North Dakota soybean production fields.
2. 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.

Project Deliverables

• Understand the accuracy of these predictive models and improve the acceptance and integration of this predictive model tool in North Dakota soybean production.
• Assess the levels of white mold resistance present in current soybean breeding lines and the identification of resistant parental lines for future breeding efforts.

Progress of Work

Final Project Results

Benefit to Soybean Farmers

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.

Performance Metrics

Project Years