Synoptic Weather Forecasting and Web-Based Information Delivery Systems for Managing Crop Disease Risk in Multiple Regions of the U.S.

Non-Technical Summary: The project will create weather-based disease risk forecasts for crop diseases in various regions of the U.S. We will specifically focus on leaf spot of peanut in Georgia and northern Florida, Fusarium head blight of barley in the northern Great Plains, and late blight of potato in Michigan. The resulting forecasts will be delivered through web-based systems and will be available to growers on a daily update basis. Risk forecasts can improve crop quality while at the same time reducing fungicide use by allowing growers to improve the timing of fungicide applications. Our goals in reducing fungicide use are of increasing product quality, limiting expenditures, and reducing the amount of chemical released to the environment. We will examine and quantify the accuracy, economic and environmental impacts, and usability of crop disease risk forecasts at both the synoptic and mesoscale (different weather forecasting scales) in the various regions of the U.S. By selecting three very different crop species (peanut, barley, potato) each heavily dependent upon fungicide sprays for the prevention, avoidance, and management of disease in various regions of the country (southeast, northern Great Plains, Great Lakes region), we plan to capture the overall potential of such forecasting systems for widespread use in the U.S. New technologies in the form of improved National Weather Service forecasts (since 2004) and access to high performance computer workflows give us the opportunity to exploit these advances funded through large government technology grants (the LEAD project alone cost $11.5 million) for the benefit of both major and minor agricultural commodity groups. The ultimate goal of any forecast system is reduction of uncertainty that can negatively influence decision making by users. In the case of disease risk forecasting for agriculture, decisions by the target group of users can negatively impact our food supply, environment, and economy by increasing use of pesticides. Increasing accessibility to forecast information through free, thoroughly tested forecast services in the web environment has the potential to dramatically decrease uncertainty in a variety of crop systems. In this project we examine three crops in three regions of the country, but the implications of the project's results are much more far reaching. Many crop diseases have similar meteorological triggers based on temperature and moisture requirements that may be mined from available forecasts. For a number of years, growers, stakeholders , commodity associations, crop consultants and land grant specialists have been requesting disease risk prediction models for all the major crops in the U.S. through Pest Management Strategic Plans. The public availability of weather forecasts and the improved infrastructure to promote the use of these forecasts make this the time to examine usefulness of forecasting as a national priority in agriculture. <P> Approach: An artificial neural network (ANN) synoptic crop disease forecasting model developed by Baker and Kirk (2007) has proven successful in predicting potato late blight risk as estimated by potato late blight disease severity values. The initial phase of the current project will involve developing similar neural network models to interface with the daily NWS model output statistics for automatic updates of leaf spot of peanut and Fusarium head blight of barley grain values. While the usefulness and accuracy of synoptic scale predictions have been proven by our prior research, mesoscale forecasting is a newly available option to those entities who do not maintain their own networks of high performance computers. Linked Environments for Atmospheric Discovery (LEAD) makes meteorological data, forecast models, and analysis and visualization tools available to anyone who wants to interactively explore the weather as it evolves. The LEAD Portal brings together all the necessary resources at one convenient access point, supported by high-performance computing systems. This type of environment is ideal for gaining access to large amounts of data and computational power not traditionally associated with plant pathology. ANN models similar to those used at the synoptic scale will be incorporated with mesoscale LEAD outputs and spatially linked through a geographic information system (GIS). Initial validation of predictions made with both synoptic and mesoscale models will be examined through comparison of predicted disease risk with those computed based on the Unedited Local Climatological Data (ULCD) for 2005-08 growing seasons available in archived form from the National Weather Service. Spatial and temporal variability in risk predictions and associated model accuracy will be evaluated using standard statistical procedures and spatial statistics. Impacts of additional variables such as length of archive, regional climatic normals, and seasonal variation will also be explored. Field validation will be consistent with standard practices in plant pathology. Disease risk forecast results will be taken into account during field monitoring and fungicide spraying at research farms. Research sites and farmer's fields will be selected and monitored for disease. The data collected from these sites will be used to evaluate the accuracy of the disease model predictions. Economic and environmental impact assessments will be used to estimate the reduction of financial and chemical inputs to the various cropping systems as a result of access to model information. Spatial and temporal variability in weather patterns, as determined from regional climatic normals, will be examined with respect to spatial and temporal variability in forecast accuracy, resulting in uncertainty metrics for both the predicted and actual patterns for a region. A comparison of these metrics will allow some quantifiable measure of the reduction of uncertainty resulting from model use.