Inferring fruit infestation prevalence from a combination of pre-harvest monitoring and consignment sampling data

• Published in: Scientific reports Vol. 14; no. 1; pp. 13022 - 11
• Main Authors: Caley, Peter, Gladish, Daniel W., Kingham, Lloyd, van Klinken, Rieks D.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 06.06.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114/2401; 639/705/531; Animals; Bayes Theorem; Bayesian analysis; Fruit - parasitology; Fruits; Harvesting; Humanities and Social Sciences; Infestation; Inspection; International trade; Monitoring methods; multidisciplinary; Pests; Prevalence; Sampling; Science; Science (multidisciplinary); Surveillance
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-63569-9

International trade in horticultural produce happens under phytosanitary inspection and production protocols. Fruit inspection typically involves the sampling and inspection of either 600-pieces or 2% of packed product within a single consignment destined for export, with the purpose of certification (typically with 95% confidence) that the true infestation level within the consignment in question doesn’t exceed a pre-specified design prevalence. Sampling of multiple consignments from multiple production blocks in conjunction with pre-harvest monitoring for pests can be used to provide additional inference on the prevalence of infested fruit within an overall production system subject to similar protocols. Here we develop a hierarchical Bayesian model that combines in-field monitoring data with consignment sample inspection data to infer the prevalence of infested fruit in a production system. The results illustrate how infestation prevalence is influenced by the number of consignments inspected, the detection efficacy of consignment sampling, and in-field monitoring effort and sensitivity. Uncertainty in inspection performance, monitoring methods, and exposure of fruit to pests is accommodated using statistical priors within a Bayesian modelling framework. We demonstrate that pre-harvest surveillance with a sufficient density of traps and moderate detection sensitivity can provide 95% belief that the prevalence of infestation is below 1 × 10 - 6 . In the absence of pre-harvest monitoring, it is still possible to gain high confidence in a very low prevalence of infestation ( < 1 × 10 - 5 ) on the basis of multiple clean samples if the inspection sensitivity during consignment sampling is high and sufficient consignments are inspected. Our work illustrates the cumulative power of in-field surveillance and consignment sampling to update estimates of infestation prevalence.