Predicting the risk of soybean rust in Minnesota based on an integrated atmospheric model

Zhining Tao, Dean Malvick, Roger Claybrooke, Crystal Floyd, Carl J. Bernacchi, Greg Spoden, James Kurle, David Gay, Van Bowersox, Sagar Krupa

Research output: Contribution to journalArticlepeer-review


To minimize crop loss by assisting in timely disease management and reducing fungicide use, an integrated atmospheric model was developed and tested for predicting the risk of occurrence of soybean rust in Minnesota. The model includes a long-range atmospheric spore transport and deposition module coupled to a leaf wetness module. The latter is required for spore germination and infection. Predictions are made on a daily basis for up to 7 days in advance using forecast data from the United States National Weather Service. Complementing the transport and leaf wetness modules, bulk (wet plus dry) atmospheric deposition samples from Minnesota were examined for soybean rust spores using a specific DNA test and sequence analysis. Overall, the risk prediction worked satisfactorily within the bounds of the uncertainty associated with the use of modeled 7-day weather forecasts, with more than 65% agreement between the model forecast and the DNA test results. The daily predictions are available as an advisory to the user community through the University of Minnesota Extension. However, users must take the actual decision to implement the disease management strategy.

Original languageEnglish (US)
Pages (from-to)509-521
Number of pages13
JournalInternational Journal of Biometeorology
Issue number6
StatePublished - Dec 2009


  • Atmospheric deposition
  • Atmospheric model
  • Risk prediction
  • Source-receptor relationship
  • Soybean rust

ASJC Scopus subject areas

  • Ecology
  • Atmospheric Science
  • Health, Toxicology and Mutagenesis


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