Predictive Modeling and Categorizing Likelihoods of Quarantine Pest Introduction of Imported Propagative Commodities...

Area of Science:

• Agricultural Science
• Risk Assessment
• Plant Pathology

Background:

• Quarantine pests pose a significant threat to agricultural biosecurity.
• Effective risk assessment is crucial for managing imported plant materials.
• Existing methods may not adequately capture the complexities of pest interception probabilities.

Purpose of the Study:

• To estimate the probability of quarantine pests on imported propagative plant materials.
• To develop a risk-based methodology for ranking country-commodity combinations.
• To categorize these combinations into compliance levels based on pest interception data.

Main Methods:

• Analysis of U.S. Department of Agriculture inspection records from the Agricultural Quarantine Activity System database (October 2014 - January 2016).
• Development and validation of predictive models using generalized linear models (GLM) with Bayesian inference and generalized linear mixed effects models (GLMM).
• Application of K-means clustering analysis to categorize country-commodity combinations into risk levels based on estimated pest interception probabilities and confidence intervals.

Main Results:

• Generalized linear mixed effects models demonstrated superior prediction ability compared to generalized linear models.
• A risk-based categorization system was successfully developed, assigning "High," "Medium," "Low," and "Poor/Unacceptable" compliance levels to country-commodity combinations.
• The methodology effectively incorporated both the probability of pest interception and the uncertainty associated with the assessment.

Conclusions:

• The developed risk-based categorization provides a valuable tool for prioritizing agricultural inspection and quarantine efforts.
• The study highlights the importance of advanced statistical modeling in enhancing biosecurity measures for imported plant materials.
• This approach can inform policy decisions and improve the management of potential pest introductions.