Damon Smith, Extension Field Crops Pathologist, Department of Plant Pathology, University of Wisconsin-Madison

It is time for my annual reminder about white mold in soybeans, and its management. The 2024 season in Wisconsin was hot and reasonably dry. So far, the 2025 season has been wet with low to moderate temperatures. Weather only recently has turned off hot and less conducive for white mold in the southern portion of Wisconsin. As we approach the beginning of white mold flowering it will be important to pay attention to the weather in coming weeks.

Remember that the white mold fungus infects soybeans through open and senescing flowers, by spores that are born from small mushroom-like structures called apothecia (Fig. 1). Remember that if the bloom period gets extended due to cool weather, this can lead to an extended window for infection by the fungus. Often cool weather is a double whammy as it is good for the white mold fungus and slows down soybean crop development, thereby extending bloom. This could be at play this season, stay on top of your game!

Given the moderate temperatures and moisture we have been getting the risk for white mold is currently moderate to high for the central to northern tiers of the state, while the far southern tier is at generally low risk (Fig. 2).

Predicting White Mold 

The flowering growth stages are a critical time to manage white mold in-season. You can view a video on the subject. As you probably know, timing in-season fungicide sprays at the correct time during the soybean bloom period can be extremely difficult. To help solve this decision-making issue, models were developed at the University of Wisconsin-Madison in conjunction with Michigan State University and Iowa State University to identify at-risk regions which have been experiencing weather favorable for the development of white mold apothecia. These models predict when apothecia will be present in the field using combinations of 30-day averages of maximum temperature, relative humidity, and wind speed. Using weather data, predictions can be made in most soybean growing regions. To facilitate precise predictions and make the model user-friendly, we use the Wisconsin Agricultural Forecasting and Advisory System or the Crop Protection Network Crop Risk Tool to calculate risk for specific locations.

University research has indicated that the appearance of apothecia can be predicted using weather data and a threshold of percent soybean canopy row closure in a field. Based on these predictions and crop phenology, site-specific risk values are generated for three scenarios (non-irrigated soybeans, soybeans planted on 15″ row-spacing and irrigated, or soybeans planted on 30″ row-spacing and irrigated). Though not specifically tested we would expect row-spacings of 22 inches or less to have a similar probability response to fungicide as the 15-inch row-spacing.

The Sclerotinia apothecial models that underlie the prediction tools cited above have undergone significant validation in both small test plots and in commercial production fields. In 2017, efficacy trials were conducted at agricultural research stations in Iowa, Michigan, and Wisconsin to identify fungicide application programs and thresholds for model implementation. Additionally, apothecial scouting and disease monitoring were conducted in a total of 60 commercial farmer fields in Michigan, Nebraska, and Wisconsin between 2016 and 2017 to evaluate model accuracy across the growing region. Across all irrigated and non-irrigated locations predictions during the soybean flowering period (R1 to early R4 growth stages) were found to explain end-of-season disease observations with an accuracy of 81.8% using the established probability thresholds at that time. We have made additional improvements for 2025, to further refine accuracy. These refinements have been built into both the Wisconsin Agricultural Forecasting and Advisory System or the Crop Protection Network Crop Risk Tool.

We also know that for highly susceptible soybean varieties, the action threshold should be adjusted down. Research in the upper Mid-west demonstrated that for most soybean varieties the default action threshold depicted in the tool when you set up a field is accurate. However, some varieties are highly susceptible and a lower action threshold should be used, moving down from 40% to 20% for varieties that are known to be highly susceptible. This can improve accuracy of the tool and recommendation for fungicide application.

What to Spray for White Mold?

If you have decided to spray soybeans for white mold, what are the best products to use? Over the last several years we have run numerous fungicide efficacy trials in Wisconsin and in conjunction with researchers in other states. In Wisconsin, we have observed that Endura applied as a single application at 8 oz at the R3 growth stage performs well. The window to spray runs from R1 to R3, but our recent data suggests waiting a bit into that window improves efficacy of the fungicide application vs. spraying at R1. If you do choose to spray at the R3 growth stage, be sure to focus on getting good canopy penetration with your fungicide spray. Soybean canopies at R3 can be dense and hauling more water, slowing your sprayer speed, and increasing spray pressure can all help improve spray penetration in those dense canopies. Other fungicide options also include Omega and Proline. You can view results of past fungicide evaluations for Wisconsin by CLICKING HERE. You can also use the Crop Protection Network Fungicide Efficacy Tool to find the best rated fungicides for white mold management.

Other Resources

1. To watch an in-depth video on white mold management, CLICK HERE.
2. To find more information on white mold, view a web book from the Crop Protection Network, CLICK HERE.

Scientific References

1. Willbur, J.F., Fall, M.L., Blackwell, T., Bloomingdale, C.A., Byrne, A.M., Chapman, S.A., Holtz, D., Isard, S.A., Magarey, R.D., McCaghey, M., Mueller, B.D., Russo, J.M., Schlegel, J., Young, M., Chilvers, M.I., Mueller, D.S., and Smith, D.L. Weather-based models for assessing the risk of Sclerotinia sclerotiorum apothecial presence in soybean (Glycine max) fields. Plant Disease. https://doi.org/10.1094/PDIS-04-17-0504-RE
2. Willbur, J.F., Fall, M.L., Byrne, A.M., Chapman, S.A., McCaghey, M.M., Mueller, B.D., Schmidt, R., Chilvers, M.I., Mueller, D.S., Kabbage, M., Giesler, L.J., Conley, S.P., and Smith, D.L. Validating Sclerotinia sclerotiorum apothecial models to predict Sclerotinia stem rot in soybean (Glycine max) fields. Plant Disease. https://doi.org/10.1094/PDIS-02-18-0245-RE.
3. Fall, M., Willbur, J., Smith, D.L., Byrne, A., and Chilvers, M. 2018. Spatiotemporal distribution pattern of Sclerotinia sclerotiorum apothecia is modulated by canopy closure and soil temperature in an irrigated soybean field. Phytopathology. https://doi.org/10.1094/PDIS-11-17-1821-RE.
4. Willbur, J.F., Mitchell, P.D., Fall, M.L., Byrne, A.M., Chapman, S.A., Floyd, C.M., Bradley, C.A., Ames, K.A., Chilvers, M.I., Kleczewski, N.M., Malvick, D.K., Mueller, B.D., Mueller, D.S., Kabbage, M., Conley, S.P., and Smith, D.L. 2019. Meta-analytic and economic approaches for evaluation of pesticide impact on Sclerotinia stem rot control and soybean yield in the North Central U.S. Phytopathology. https://doi.org/10.1094/PHYTO-08-18-0289-R.
5. Webster, R.W., Mueller, B., Conley, S.P., and Smith, D.L. 2023. Integration of soybean (Glycine max) resistance levels to Sclerotinia stem rot into predictive Sclerotinia sclerotiorum apothecial models. Plant Disease. https://doi.org/10.1094/PDIS-12-22-2875-RE.