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.
Bibliographical noteFunding Information:
Acknowledgments We gratefully acknowledge the support provided for this project by the Minnesota Soybean Research and Promotion Council and the Minnesota Rapid Agricultural Response Fund. We also thank Grace Anderson and Axelina Swenson for their assistance in analyzing filter samples, Carrie Harmon at the University of Florida for providing ASR spore samples, and Les Szabo, Charlie Barnes, and Jerry Johnson of the USDA-ARS Cereal Disease Laboratory, St. Paul, MN for their technical assistance. The views expressed in this paper are those of the authors and do not necessarily reflect the views of the Illinois State Water Survey or other agencies.
Copyright 2010 Elsevier B.V., All rights reserved.
- Atmospheric deposition
- Atmospheric model
- Risk prediction
- Source-receptor relationship
- Soybean rust