Sporecaster: New white mold risk prediction smartphone app now live
Sporecaster was developed to predict when the white mold apothecia (white mold mushrooms) are present in a soybean field.
May 16, 2018 - Author: Martin Chilvers, and Jaime Willbur, Michigan State University, Department of Plant, Soil and Microbial Sciences
Fungicides for soybean white mold management should be applied at or between the R1 (beginning flowering) and R3 (beginning pod development) growth stages. However, we have noted that in some years, later or earlier applications within this window favor disease management. The variation in responses to fungicide timing from year to year is driven by the presence of apothecia.
Sporecaster is designed to predict the probability of white mold apothecial presence. However, fields still need to be scouted to determine if the soybean crop meets thresholds such as canopy closure and reproductive stages.
To use the Sporecaster app, download it onto your phone from the Apple Store or Google Play. The app allows the user to locate and setup multiple fields and run the apothecial risk prediction model using weather data from a third-party provider (Dark Sky API).
Once opened, you can create multiple fields to determine their apothecial risk. The app will prompt the user for information, such as field name, row spacing, if the field is irrigated and the field location. Then the risk of apothecial presence can be calculated. The model will only run if it is told that flowers are present and if canopy closure meets threshold (for 30-inch row spacing only). A forecast risk expressed in percentage units is then shown, with red being above the 40 percent action threshold for a fungicide application. It is possible to rerun the model as desired and even go back to previous years to examine previous risk.
The model has been validated in commercial fields and small test plots in Michigan, Wisconsin and Iowa. However, we very much want to hear about your experiences with the model. Jaime Wilbur, potato and sugarbeet pathologist at Michigan State University, is very familiar with the model, as this was her PhD thesis project under the guidance of Damon Smith at University of Wisconsin-Madison.
It is our hope that the model will facilitate fungicide application decisions and lead to greater disease management. We are also hopeful that the app can be modified and applied for use in other white mold-susceptible crops such as dry beans and potatoes.
The app is available for download on both iPhone and Android devices:
For more information on the app and video tutorials, see “New smartphone app: Sporecaster, The Soybean White Mold Forecaster” from the University of Wisconsin.
Funding for studies that contributed to the model were sponsored in part by the Michigan Soybean Promotion Board.