Regional Variability in Crop Specific Synoptic Forecasts

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1 Regional Variability in Crop Specific Synoptic Forecasts Kathleen M. Baker 1 1 Western Michigan University, USA, kathleen.baker@wmich.edu Abstract Under climate change scenarios, growing season patterns and variability may deviate from the expectations of even the most experienced growers. In such situations, early warning systems for plant disease can provide growers with disease risk information that has the potential to limit expenditures and the amount of chemical released to the environment. Crop specific synoptic forecasts were developed for 48 locations throughout the Great Lakes region of the United States. Because most synoptic forecast models are without a long archive record, it is important to assess spatial and temporal trends in accuracy when they are applied to plant disease. In this paper, regional patterns and variability of model results developed to predict potato late blight risk five days in advance are examined. Spatial patterns in accuracy and model usefulness are associated with patterns in climatic normals. Throughout the region, periods of greater potato late blight risk are associated with lower overall model accuracies; however, greater risk is also associated with a greater model skill when accuracy is compared with climatic normals. Keywords: weather, forecast, synoptic, artificial neural network, climatology Introduction Crop specific synoptic forecasts delivered through web-based information systems can allow growers to better implement integrated pest management (IPM) strategies, reducing pesticide use and the associated negative impacts on human health and environmental quality. Advanced crop disease modeling follows the recommendations of the United States Department of Agriculture (USDA) Economic Research Report 18 (2005) supporting the creation of early-warning systems for plant disease. Early warning systems can be especially useful to growers in the context of climate change scenarios. While growers are often perceived as reluctant to adopt new technologies, increasing awareness of changes in growing season patterns and variability have increased interest in grower communities for accurate, publically available risk prediction systems. The overall goal of this project is to enable growers to better manage crop disease risk by developing the use of synoptic weather forecasts for specific crop diseases. Because most synoptic forecast models are without a long archive record, it is important to assess spatial and temporal trends in accuracy when they are applied to plant pathology. In this paper, regional patterns and variability of model results developed to predict potato late blight risk five days in advance are discussed. The model was developed for 48 locations throughout the Great Lakes region of the United States (US). 359

2 Forecast Development Weather-based prediction models have been used to estimate environmental conditions that are favorable for epidemic risk, and fungicide recommendations appropriate to that risk for more than 50 years (Beaumont, 1947; Cook, 1949; Wallin, 1960). Potato late blight is just one of a wide range of crop diseases that result in high use of pesticides each year, especially in regions where conditions are sporadically favorable for disease development. Baker and Kirk (2007) created a method to derive hourly microclimate variables associated with potato late blight risk from the available forecast model output statistics produced by the National Weather Service. This data was then fed into an artificial neural network (ANN) computer algorithm which generated accurate predictions of late blight disease risk up to five days in the future. A pilot model was expanded for use with the 48 stations throughout the state of Michigan and was trained using growing season data. The 2006 and 2007 growing seasons were used for model validation. Accuracy of predictions for all stations and years was examined through comparison of predicted potato late blight disease severity values with those computed based on the Unedited Local Climatological Data (ULCD). Preliminary results with this potato late blight model in Michigan had an accuracy of approximately 79 percent for five day predictions at all locations during growing season months from (Baker et al., 2007). This accuracy is significantly higher than expected values based on climate normals (72 percent). Spatial and Temporal Variability To further improve model accuracies, spatial and temporal cluster variables were incorporated into the ANN model. Spatial clustering methods included hierarchical clustering based on latitude and longitude station coordinates, as well as association of each station with the nearest station used in the initial development of the forecasts by the NWS. Temporal clustering methods included a month designation to designate time within the growing season, as well as a site specific record of high and low risk time periods (greater than 50 percent risk vs less than 10 percent risk) as recorded in the archives of each station. Three models using different combinations of the spatial and temporal cluster variables proved most accurate in forecasting potato late blight. Results are shown in Table 1. Even when spatial and temporal cluster variables were incorporated into the ANN model, model results were not uniformly accurate for the entire region and the accuracy each model varied extensively from year to year. Table 1 Annual accuracy for ANN models with varying spatial and temporal cluster combinations. Accuracy Spatial Clusters Temporal Clusters No spatial clusters Site specific high/low risk periods Nearest long archive station Month Lat-long clusters Site specific high/low risk periods

3 While the model that combined lat/long clusters and site specific risk periods had the highest overall accuracy for the five years of model testing, one of the other two models was most accurate in each year individually. The model with highest accuracy also varied by stations and by months of the year. Since all stations had relatively short archive lengths, climatic normals (30 year averages) for each station and each month were used to determine long term trends in risk. When stations were clustered by whether their mean monthly temperature was above or below the regional mean, clear trends appeared in model accuracy. The model that incorporated lat-long clusters and site specific risk periods was most accurate in August, and also in September when station normal temperature was warmer than the regional mean. The model that incorporated no spatial clusters and site specific risk periods was most accurate in June when station normal temperature was warmer than the regional mean and in September when station normal temperature was cooler than the regional mean. The model that incorporated nearest long archive station and month clusters was most accurate in May, in July, and in June when station normal temperature was cooler than the regional mean. An expert system was created to integrate the models and vary spatial and temporal clusters by month. Average accuracy of the expert system for all stations and all years reached an accuracy of 0.81 percent. Monthly accuracy histograms for regional stations are shown in Figure 1. The model is most accurate in May. The model is significantly less accurate in June Fig. 1 Monthly accuracy of the final ANN crop specific synoptic forecast model for potato late blight in the Great Lakes region of the U.S. than in May, and significantly less accurate in July than in June. Accuracies in August are not statistically different from those in July, and accuracies in September are not statistically different from those in June. Accuracies are normally distributed in each month. Model accuracy is strongly correlated with the total number of risk days in the growing season (r=-0.693), as shown in Figure 2. When the accuracy is examined spatially, stations in southern Michigan typically have lower accuracies than those in northern Michigan (Figure 3). The lowest overall accuracies occur at stations lying near the coast of the Great Lakes. The difference between model accuracy and 361

4 Fig. 2 Negative correlation between percentage of growing season days forecasted accurately and percentage of days in the growing season with risk. climatology was used as a simple estimate of model skill. When examined spatially (Figure 3), model skill is much more variable than total accuracy (Figure 3). Highest model skill occurs at at individual southern Michigan stations and often along the lakeshore at the same locations which had lowest overall model accuracy. Fig. 3 Spatial patterns in model accuracy and model skill. 362

5 Discussion Southern Michigan has a warmer, moister growing season environment which is more conducive to the development of late blight risk than the northern Michigan environment. Weather patterns along the lakeshore of the Great Lakes are less predictable than inland locations. The significant negative statistical association between number of risk days and accuracy was evident spatially in Figure 3, as southern stations where higher risk would be expected overwhelmingly exhibited lower total accuracies. Conversely, these same locations were often the locations with higher model skill. 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. The relationship between lower model accuracy with higher skill at locations with higher risk justifies the further development of synoptic scale disease risk systems. Growers in higher risk areas receive the greatest reduction in uncertainty from model forecasts. However, the variability in model skill at relative small distances (5-10 percent within 30 km) is puzzling and warrants further analysis. In some cases lack of a long enough archive length at a particular location could have a negative impact on model skill. Future development of crop-specific synoptic forecasts and associated economic analysis has recently been funded by the USDA Food Quality Protection Act (FQPA) Risk Avoidance and Mitigation Program (RAMP) (Proposal number: ). The project will examine and quantify the accuracy, economic and environmental impacts, and usability of crop disease risk forecasts at both the synoptic and mesoscale in 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. This broader sample of stations will be used to assess and improve accuracy of the model regionally, providing a better understanding of both the strengths and limitations of such a system. Predictive systems should prove useful in the context of a myriad of diseases and regions through the US, especially as we experience changes in climate that make the personal experience of individual growers less likely to accurately reflect the growing season patterns and variability that we may experience in coming years. Acknowledgements This research was funded by USDA North Central Integrated Pest Management Program. The Western Michigan University Lucia Harrison Endowment Fund provided additional support for travel, publication and presentation of results. The author would like to thank Magdalena Wisniewska and Jeremy Bell for their research assistance. References Baker, K.M., and W.W. Kirk (2007) Comparative analysis of models for integration of extended range synoptic forecast data into potato late blight risk systems. Computers and Electronics in Agriculture 57: Baker, K.M., Wharton, P. and W. Kirk (2007) Inclusion of synoptic weather forecast models in decision support systems for agriculture, Proceedings of MODSIM 07: International Congress on Modeling and Simulation, Christchurch, New Zealand. Dec

6 Beaumont, A., The dependence on the weather of the dates of outbreak of potato late blight. Transactions of the British Mycological Society. 31; Cook, H Forecasting late blight epiphytotics of potatoes and tomatoes. Journal of Agricultural Research 78; United States Department of Agriculture [USDA] (2005) The Value of Plant Disease Early-Warning Systems: A case study of USDA s soybean rust coordinated network. Economic Research Report Number 18. Wallin, J.R., and Schuster, M.L Forecasting potato late blight in western Nebraska. Plant Disease Reporter 44;