The USDA’s Plant Protection and Quarantine (PPQ) division is responsible for reducing pest threats to U.S. agriculture without overly restricting commerce. With limited resources, PPQ and the Department of Homeland Security (DHS) face the challenge of efficiently allocating inspection efforts for the large volume of people and cargo entering the U.S. to minimize pest-related damages. A current CBTS project with our partners from the University of Texas (titled “Sampling Design for Random Inspections”) is examining the current approaches and methods used for inspection. Their findings will provide mathematical tools, software, and results to help the USDA and DHS reduce the damages caused by pests imported into the U.S.
Year One Findings
The project team’s year one findings were outlined in a research paper titled, “Optimal Sampling Strategy for Probability Estimation: An Application to the Agricultural Quarantine Inspection Monitoring Program“. The paper has been accepted for publication in the journal Risk Analysis. The report addresses the risk of imported agricultural pests in the U.S. and the role of the Agricultural Quarantine Inspection Monitoring (AQIM) program in estimating these risks. Furthermore, the paper formulates an optimization model to improve AQIM’s sampling strategy, balancing whether to sample more containers with fewer boxes or fewer containers with more boxes per container. The goal is to minimize the mean squared error in probability estimates of pest presence.
The model is applied to a case study of maritime cargo sampling at the Port of Long Beach. The findings suggest that the optimal strategy would involve sampling more containers and fewer boxes per container than the current AQIM approach, particularly when the pest status of boxes within a container is highly correlated (if one box in a container is found to have pests, it is more likely that other boxes in the same container also have pests).
A copy of the final report can be found on the CBTS website.