The sweetpotato weevil [SPW, Cylas formicarius (Fabricius)] is an important economic pest in “pink-tagged” or SPW-infested areas of Louisiana. From time to time, sweetpotato weevils are detected in “green-tagged” or SPW-free locations. When sweetpotato weevils are detected in “green'tagged” areas, the produce is quarantined and may not be shipped to locations that do not allow “pink-tagged” sweetpotatoes. As part of the statewide SPW monitoring program, the Louisiana Department of Agriculture and Forestry (LDAF) conducts a statewide pheromone-based trapping program to monitor SPW presence in beds and fields. We used SPW presence-absence data with a GIS-based logistic regression modeling tool to assess the feasibility of developing a model for predicting SPW risk in sweetpotato beds. Using pheromone trap data from 2001–03, we performed stepwise logistic regression experiments to assess the role of various weather variables (daily mean maximum and minimum temperature, rainfall) in the occurrence of SPW in beds. Our modeling experiments showed a strong relationship of mean daily minimum temperature during the winter months with SPW occurrence in beds. In particular, a logistic regression equation developed from 2003 trap data and mean April daily minimum temperature created a spatially accurate map of SPW risk for 2002. However, the same model did not accurately predict the 2001 SPW risk. These results indicate that additional variables are needed to improve the predictive ability of the model. Spatial risk mapping can be a potentially useful tool for decision makers in choosing between risk-averse and -prone decisions.
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