Multi-Criteria Decision Analysis for Designing Optimal Aflatoxin Monitoring Schemes in the Dairy Supply Chain.

Designing effective food safety monitoring schemes is a complex task involving multiple, often conflicting, criteria. This study applied Multi-Criteria Decision Analysis (MCDA) to evaluate and identify optimal aflatoxin monitoring schemes along a Dutch dairy supply chain-a critical context where aflatoxin B1 (AFB1) contamination in feed can lead to aflatoxin M1 (AFM1) in milk, potentially posing public health concerns and economic losses. Monitoring schemes differed in detection intensity at feed mills and dairy farms, defined as the probability of identifying contaminated batches (low: 50%, medium: 80%, high: 90%) and determined by the number of monitoring batches and corresponding sample sizes used for AFB1/AFM1 sampling and analysis. Performance scores for each monitoring scheme were derived from quantitative models, scientific evidence, and expert consultation, while preference weights for criteria were elicited separately from representatives of the feed industry, dairy industry, and from a combined supply chain perspective. Results revealed that all stakeholder groups prioritized public health, but differed in their weighting of monitoring costs, production losses, customer trust, and implementation complexity. The feed industry preferred high-intensity detection at both control points, while the dairy industry preferred medium-intensity at feed mills and high-intensity at farms. Overall, the MCDA framework facilitated a transparent and evidence-based approach to identify an optimal monitoring scheme, highlighting the importance of stakeholder engagement in designing programs that are not only scientifically robust but also socially responsive and aligned with the WHO Global Strategy for Food Safety and the Sustainable Development Goals.