Esker, P.D., A.H. Sparks, L. Campbell, Z. Guo, M. Rouse, S.D. Silwal, S. Tolos, B. Van Allen, and K.A. Garrett, 2008. Ecology and Epidemiology in R: Disease Forecasting. The Plant Health Instructor. DOI:10.1094/PHI-A-2008-0129-01.
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Plant disease forecasting systems (synonym: plant disease warning systems) have been developed to help growers make economic decisions about disease management (Agrios 2004). Plant disease forecasting systems may support a producer's decision-making process with regard to the costs and benefits of pesticide applications,
What defines a successful plant disease forecasting system? Campbell and Madden (1990) outlined several attributes, including:
For an excellent introduction to the epidemiological concepts behind plant disease management, including reducing initial inoculum and/or controlling apparent infection rate, see Arneson's simulation exercise in the Plant Health Instructor. Plant disease forecasting systems often provide information about how a grower's management decisions can help to avoid initial inoculum or to slow down the rate of an epidemic. These two concepts are important because they often differentiate the risk for a monocyclic disease (having only one cycle of infection) versus polycyclic disease, where there are multiple infection cycles, and a forecasting system can be used to time appropriate management tactics, such as a foliar fungicide application (Madden et al. 2007). It should be noted that some plant disease forecasts focus both on avoiding initial inoculum and also on reducing the rate of the epidemic during the season (see below). Many plant disease forecasting systems have emphasized forecasts based on the following principles (with examples) (Agrios 2004; Campbell and Madden 1990):
Forecasts based on measures of initial inoculum or disease, example: Stewart's disease of corn
Forecasts based on favorable weather conditions for development of secondary inoculum, example: Late blight of potato (description and simulation)
Forecasts based on both initial and secondary inoculum, example: Apple scab (description and simulation)
An example of a multiple disease/pest forecasting system is the EPIdemiology, PREdiction, and PREvention (EPIPRE) system developed in the Netherlands for winter wheat that focused on multiple pathogens (Reinink 1986).
Current examples of plant disease forecasting providing daily information on-line are available for two important plant diseases: Fusarium head blight of wheat (www.wheatscab.psu.edu) and Asian soybean rust (www.sbrusa.net). Both systems provide background information on the disease, current management recommendations, as well as disease forecast information based on host, pathogen, and environmental factors important for making an accurate forecast.
The successful development of a plant disease forecasting system also requires the proper validation of a developed model. There is increased interest among plant disease modelers and researchers to improve producer profitability through validation based on quantifying the cost of a model making false predictions (positive and/or negative). Yuen discusses this issue in his article, 'Deriving Decision Rules'. As pointed out by Yuen, this methodology is not necessarily a new concept, as historical systems, such as the Mills rules for apple scab, or those used for potato late blight and Alternaria leaf blight of carrots, were developed using prediction rules for plant disease management. An economic validation of a plant disease forecasting system requires the examination of two false predictions:
These two types of false predictions may have different economic effects for producers (Madden 2006).
Lastly, the range of disease forecasting models has expanded to include a Bayesian statistical approach. This information is beyond the scope of this exercise and the interested reader is referred to the discussions of Bayesian approaches found in Mila et al. (2003), Yuen (2006), and Madden (2006).
Throughout the rest of this document, an introduction to plant disease forecasting is presented through examples, many of which use the R programming environment (Garrett et al. 2007; R Development Core Team). A brief introduction to some of the mathematical/statistical approaches that have been used for developing plant disease forecasting systems is presented, followed by an introduction to how using rainfall and temperature may be applicable for developing a forecast model, and finally, four case studies are presented that highlight the following:
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