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.

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.

• 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.

Update:
FY 2024 Mid-Year Report North Dakota Soybean Council
November 2023

Richard Wade Webster

Project Title: Validation of Sclerotinia sclerotiorum Apothecial Prediction Models in North Dakota and
Evaluation of Soybean Resistance to White Mold
Project dates: July 1, 2022 to June 30, 2023.
Objectives:
Objectives:
1. The accuracy of predictive models (Sporecaster) for predicting white mold of soybean 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


Completed work:
Beginning in August of 2023, a M.S. graduate student, Sarita Poudel, joined the Soybean Pathology program and is primarily responsible for managing this project.
2023 experienced very localized development of white mold across the state with the heaviest pockets being identified in the central and southeast portion of the state under dry land conditions and areas with heavy irrigation. These pockets were primarily driven by very timely rainfall events around the flowering periods which allowed the pathogen to successfully infect, leading to severe epidemics. During these flowering periods (R1-R3), fields across Central and Eastern North Dakota were scouted for the development of apothecia, the primary source of inoculum. Similarly, between the R5 and R6 growth stages (pod fill) fields were scouted for the development of white mold and assessment of disease incidence (%). In total, 28 fields were scouted for apothecial presence or white mold development. From our scouting for apothecial presence, only irrigated locations near Oakes, ND had apothecial presence from our scouting. However, when scouting for development of white mold, high incidence of white mold was identified between Griggs and Stutsman counties ranging from 34% to 91% field incidence. For each of these locations, the GPS coordinates were recorded. Currently weather data is being pulled from each of these GPS coordinates from IBM weather services, data is being aggregated, and validation exercises are being performed on of the Sporecaster predictive models. This validation will be completed during the spring of 2024.
Additionally, work has begun on the screening of soybean germplasm lines for resistance to white mold. Due to environmental conditions, greenhouse inoculation assays can only be performed during the winter months and has been started in October of 2023. First, we have begun screening eight soybean lines from each of the maturity groups 00, 0, and 1 that come from diverse backgrounds. These lines were accessed through USDA-GRIN services. Alongside these 24 lines, four soybean check lines were included which represent susceptibility ratings of resistant, moderately resistant, moderately susceptible, and susceptible. These were included so that we can then compare against other lines and determine their resistance rating. From this initial experiment, white mold symptoms were able to develop after initial inoculation with a highly aggressive isolate, but disease development was slowed due to low levels of humidity that were present early in the infection present. To mitigate, we have installed a robust humidity chamber to improve environmental conditions for this assay. However, despite these challenges our four soybean check lines were ranked in their expected order indicating that resistance was being evaluated. Further a single line, PI548601, was identified as being highly susceptible from this experiment (Fig. 1). In order to evaluate the resistance, plants were inoculated using the cut petiole technique and a highly aggressive isolate of Sclerotinia sclerotiorum. At 7, 10, and 14 days after inoculation lesion length measurements were taken using a digital caliper. These lesion length measurements were then used to calculate an area under the disease progress curve (AUDPC) values which are represented below. With these values, a lower AUDPC represents greater resistance and a higher AUDPC represents greater susceptibility. These lines are being tested again currently to represent another experimental run. At the completion of this experiment, additional germplasm lines will be evaluated for white mold resistance. This data will assist in selecting parental lines for future breeding efforts of improved white mold resistance into agronomically favorable soybean lines with the potential for public release.

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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.