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Risk analysis and economic optimization of late blight management tactics
Ian Small: University of Florida; Yangxuan Liu: Dept. of Agriculture, Eastern Kentucky University; Michael Langemeier: Dept. of Ag. Economics, Purdue University; Laura Joseph: Cornell University; William Fry: Cornell University
<div>Calendar-based fungicide application schedules are often employed to manage late blight regardless of existing disease severity, disease-resistance level of the potato cultivar, or prevailing weather. Such strategies may not be economically or environmentally efficient. BlightPro decision support system (DSS)-based fungicide application schedules are influenced by prevailing weather and host resistance. Objectives of this study were to assess the economic value of information created by the DSS and to optimize forecasting rules in the DSS for disease suppression and profitability. Three fungicide scheduling strategies were evaluated: calendar-based strategy, DSS-based strategy, and unsprayed. Using results from simulation experiments for several locations in the United States, we constructed distributions of the net return to all costs excluding fungicide cost and application cost (net return per acre) for calendar-based and DSS-based strategies at each location. These distributions were then compared using risk management methods: stochastic dominance with respect to a function and stochastic efficiency with respect to a function. The DSS-based strategy was identified as the most effective approach in terms of disease suppression, net return per acre, and risk-adjusted net return. The value of the information created by the DSS varied by cultivar resistance, producers’ risk-aversion level, and production location.</div>

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