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Arthur Villordon, Craig Roussel, and Tad Hardy

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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Arthur Villordon*, Craig Roussel, and Tad Hardy

The Louisiana Dept. of Agriculture and Forestry (LDAF) conducts sweetpotato weevil [SPW, Cylas formicarius (Fabricius)] monitoring in support of the statewide SPW quarantine program. The monitoring activity primarily involves a statewide pheromone-based trapping process that generates trap data for sweetpotato beds and production fields. We conducted GIS analysis of SPW trap data, collected over three years, to assess the potential use of GIS tools in managing and interpreting the data. The LDAF has already generated shapefiles for all beds and fields in each of three years, facilitating GIS analysis. However, trap data was manually collected and statewide data was compiled and stored in spreadsheet files. Trap data was mapped to specific beds and fields in each of three years, generating layers that clearly showed fields and parishes that reported high trap counts. GIS analysis showed potential SPW “hotspots” in each year, indicating that certain beds or fields are more prone to SPW infestation than others. This information can be useful in planning SPW management strategies by growers and other stakeholders. The GIS database also provides the foundation for the development of descriptive and predictive models of SPW occurence in Louisiana. Compiling the SPW trap data into a GIS database allows the data to be distributed over the Internet, facilitating real-time access by stakeholders.

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Arthur Villordon, Craig Roussel, and Tad Hardy

The Louisiana Department of Agriculture and Forestry (LDAF) conducts sweetpotato weevil (SPW) (Cylas formicarius Fabricius) monitoring as part of the statewide SPW quarantine program. This activity involves a statewide pheromone-based trapping program that monitors sweetpotato beds and production fields. We conducted GIS analysis of SPW trap data, collected over three years, to assess the potential use of publicly available GIS tools in managing and interpreting the data. Trap data was mapped to specific beds and fields in each of three years, generating layers that clearly showed fields and parishes that reported high trap counts. GIS analysis showed potential SPW hotspots in each year, indicating that certain beds or fields are predisposed to SPW infestation than others. This information can be useful in planning SPW management strategies by growers and other stakeholders. The GIS database also provides the foundation for the development of descriptive and predictive models of SPW occurence not only in Louisiana, but in other states where SPW is a potential pest. For example, using presence data for Louisiana and Genetic Algorithm for Rule Set Prediction (GARP), a GIS-based ecological niche modelling tool, we were able to generate predicted distribution using mean minimum temperature for January as the predictor variable. Although additional work is needed to identify other predictor variables and verify the models, the results demonstrate the potential use of GIS-based tools for generating warnings or advisories related to SPW.