Cost-effective allocation of resources for monitoring dioxins along the pork production chain

Abstract

The pork chain has been seriously affected by dioxin incidents in recent decades. Hence, monitoring dioxins is crucial for detecting contaminations in the pork chain. This study aims to develop a decision support tool (optimization model) to determine cost-effective monitoring schemes for detecting and tracing a dioxin contamination over multiple control points along the pork production chain.The optimization model considers four control points (i.e. at the supplier of fatty feed ingredients, the feed mill, the slaughterhouse and the fat melting facility) and a weekly monitoring period. It was applied to several hypothetical contamination scenarios involving contaminated animal fatty feed ingredients.The cost-effective allocation of resources for detecting and tracing the dioxin contamination from an integrated chain approach (i.e. considering all control points) focuses on monitoring at the feed mill, followed by the supplier of fatty feed ingredients and – to a lesser extent – by the slaughterhouse. The number of contaminated feed mills, the frequency of dioxin contaminations, the required level of effectiveness, and the cost of screening are main factors driving the total monitoring costs.Sharing the responsibility of monitoring dioxins within control points along the chain largely reduces the total monitoring costs. In each of the evaluated scenarios, the total costs of monitoring dioxins at individual control points are larger than the costs resulting from an optimal allocation of resources among all control points integrated in one overarching chain monitoring scheme. These results elicit the economic benefits of a chain approach to monitoring dioxins over an approach where each chain actor independently monitors dioxins. The developed model can be used by decision makers in the feed and food industry for determining optimal schemes for monitoring dioxins in the pork chain focusing on preventing specific contaminations.