We have provided several examples of how predictive modeling and disease forecast systems are useful to growers to help make economic decisions about disease management. Plant disease forecasting systems help to determine the risk that a disease will occur, or that the intensity of the disease will increase (Campbell and Madden 1990).
If you would like more experience with R in the context of epidemiology, see our other exercises for the study of disease progress over time (Sparks et al. 2008), dispersal (Esker et al. 2007), and analysis of spatial pattern (Sparks et al. 2008). For other plant disease epidemiology exercises see Francl and Neher (1997) and for general exercises in ecology and epidemiology see Donovan and Weldon (2002).
Acknowledgments
This document was prepared as part of a course in the Ecology and Epidemiology of Plant Pathogens at Kansas State University. Esker and Sparks were the lead writers of the document, Garrett was faculty adviser, and student contributors appear in alphabetical order. We appreciate the very helpful comments of P. Garfinkel, S. Pethybridge, PHI reviewers, and members of the KSU course. It’s also a pleasure to acknowledge support by the U.S. National Science Foundation under Grants DEB-0130692, DEB-0516046, EF-0525712 (as part of the joint NSF-NIH Ecology of Infectious Disease program) and DBI-0630726, by the Ecological Genomics Initiative of Kansas through NSF Grant No. EPS-0236913 with matching funds from the Kansas Technology Enterprise Corporation, by the Office of Science (Program in Ecosystem Research), U.S. Department of Energy, Grant No. DE-FG02-04ER63892, by the U.S. Agency for International Development for the Sustainable Agriculture and Natural Resources Management Collaborative Research Support Program (SANREM CRSP) under terms of Cooperative Agreement Award No. EPP-A-00-04-00013-00 to the Office of International Research and Development at Virginia Tech and for the Integrated Pest Management CRSP, by USDA grant 2002-34103-11746, and by the NSF Long Term Ecological Research Program at Konza Prairie. This is Kansas State Experiment Station Contribution No. 08-100-J.
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