New smartphone app: Sporecaster, the soybean white mold forecaster
Sporecaster is a new smartphone application designed to help farmers predict the need for a fungicide application to control white mold in soybean. The app, which is free to use, was developed with support from the Wisconsin Soybean Association and Wisconsin Soybean Marketing Board. It was programmed by personnel in the UW-Madison Nutrient and Pest Management Program.
Here are the links to get the free app. Scroll down to watch tutorial videos.
The purpose of the 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 (small, mushroom-like structures) are present on the soil surface. Apothecia release spores which infect senescing soybean flowers, leading to the development of white mold.
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.