Fireworks Fly! Time to Think about White Mold Management in Soybeans in Wisconsin
Damon Smith, Extension Field Crops Pathologist, Department of Plant Pathology, University of Wisconsin-Madison
Shawn Conley, Extension Soybean and Small Grains Agronomist, Department of Agronomy, University of Wisconsin-Madison
Roger Schmidt, Nutrient and Pest Management Program, University of Wisconsin-Madison
Paul Mitchell, Extension Economist, Agricultural and Applied Economics, University of Wisconsin-Madison
In Wisconsin, the first week of July brings us a heightened awareness of white mold in soybeans, and its management. The 2020 season in Wisconsin resulted in just pockets of white mold in the state. However, now is not the time to be complacent. If the weather becomes conducive in 2021, the situation could be much different than last year.
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.
While conditions have been hot and dry in parts of the state, we are seeing cooler and wetter conditions over the last week. The white mold situation can change rapidly based on weather, thus anticipating favorable conditions for white mold, can help you protect your soybean crop.
Predicting White Mold
The flowering growth stages are a critical time to manage white mold in-season. You can view a fact sheet and new 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 virtually available weather data, predictions can be made in most soybean growing regions. To facilitate precise predictions and make the model user-friendly, we use Sporecaster smartphone application for Android and iPhone.
The purpose of the smartphone app is to assist farmers in making early season management decisions for white mold in soybean. The best time to spray fungicides for white mold is during flowering (R1 and R3 growth stages) when apothecia are present on the soil surface.
Sporecaster uses university research to turn a few simple taps on a smartphone screen into an instant forecast of the risk of apothecia being present in a soybean field, which helps growers predict the best timing for white mold treatment during the flowering period.
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 Sporecaster prediction tool 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 now programmed in the app. We have made additional improvements for 2021, to further refine accuracy. So if you have used Sporecaster before, you might want to check the version in the “Help and Info” button to be sure you have version 1.4 of the Sporecaster. If you want to learn more about the science of Sporecaster, check out the embedded video below.
Not only can users run predictions of risk during the soybean bloom period for any field, you can also set up visual maps to look at multiple sites simultaneously. An example for the state of Wisconsin can be found in figure 2, which represents risk for June 29, 2021 for non-irrigated soybeans. Currently, if soybeans are flowering, risk is moderate to low in much of Wisconsin for non-irrigated soybeans, due to the recent hot and dry weather. In the north-eastern portions of the state, risk for flowering soybeans is higher due to more frequent rain events that have occurred there. Check back to this blog regularly as I will post maps like these with interpretation of risk for Wisconsin as we move through the season.
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 between the R1 and R2 growth stage performs well. We have also observed that the fungicide Aproach applied at 9 fl oz at R1 and again at R3 also performs comparably to the Endura treatment. Other fungicide options also include Omega and Proline. You can view results of past fungicide evaluations for Wisconsin by CLICKING HERE.If you would like to run tailored estimations of return on investment for various fungicide programs, you can use another smartphone application called Sporebuster.
What is Sporebuster?
When a fungicide application is needed to control white mold in soybeans, Sporebuster can help determine a profitable program. You enter your expected soybean price, expected yield, and treatment cost. Sporebuster instantly compares ten different treatment plans at once to determine average net gain and breakeven probability of each. You can mark, save and share by email, the best plans for your farming operation.
The purpose of Sporebuster is to assist soybean farmers in making a fungicide program decision that is profitable for their operation. Sporebuster is meant to complement Sporecaster. Once Sporecaster recommends a fungicide application, Sporebuster can be used to determine a profitable program.
Information that drives Sporebuster is based on research from 2009-2016 from across the upper Midwestern US. Statistical models were developed based on these data that included white mold pressure and yield response from fungicide for 10 common fungicide programs. Details about the research behind Sporebuster can be found by CLICKING HERE.
Helpful Smartphone Application Links
- Click here to download the Android version of Sporecaster.
- Click here to download the iPhone version of Sporecaster.
- Click here to download the Android version of Sporebuster.
- Click here to download the iPhone version of Sporebuster.
- Here is a video on how to use Sporebuster and interpret the output.
- To watch an in-depth video on white mold management, CLICK HERE.
- To find more information and download a fact sheet on white mold from the Crop Protection Network, CLICK HERE.
- 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
- 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 sclerotiorumapothecial models to predict Sclerotinia stem rot in soybean (Glycine max) fields. Plant Disease. https://doi.org/10.1094/PDIS-02-18-0245-RE.
- 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.
- 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.