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Transforming the food system while addressing climate change requires proactive measures based on quantitative projections of anticipated future conditions. A key component of the food system that must be considered during this transformation is food safety, which is the focus of this paper. Milk safety has been selected as a case study. Future milk contamination levels in Europe, in terms of total bacterial counts, are evaluated under various climate change scenarios. Projections from multiple climate models are integrated into a data-driven milk contamination model, validated using data from Malta, Spain, and Belgium. The modeling framework accounts for variability among dairy farms and the inherent uncertainties in climate projections. Results are presented through geographical heatmaps, highlighting coastal and southern areas such as Portugal, Western Spain, Southern Italy, and Western France as regions expected to face the highest bacterial counts. The analysis underlines the significant roles of humidity and wind speed, alongside temperature. It also examines compliance with the regulatory threshold for raw milk, revealing an increased frequency of summer weeks exceeding the threshold of 100,000 colony-forming units. Based on this analysis, regions are classified into low-risk, high-risk, and emerging-risk categories. This classification can guide the selection of farm strategies aimed at meeting future food safety standards. By informing these decisions with the anticipated impacts of climate change, the food system can be future-proofed.
Transforming the food system while addressing climate change requires proactive measures based on quantitative projections of anticipated future conditions. A key component of the food system that must be considered during this transformation is food safety, which is the focus of this paper. Milk safety has been selected as a case study. Future milk contamination levels in Europe, in terms of total bacterial counts, are evaluated under various climate change scenarios. Projections from multiple climate models are integrated into a data-driven milk contamination model, validated using data from Malta, Spain, and Belgium. The modeling framework accounts for variability among dairy farms and the inherent uncertainties in climate projections. Results are presented through geographical heatmaps, highlighting coastal and southern areas such as Portugal, Western Spain, Southern Italy, and Western France as regions expected to face the highest bacterial counts. The analysis underlines the significant roles of humidity and wind speed, alongside temperature. It also examines compliance with the regulatory threshold for raw milk, revealing an increased frequency of summer weeks exceeding the threshold of 100,000 colony-forming units. Based on this analysis, regions are classified into low-risk, high-risk, and emerging-risk categories. This classification can guide the selection of farm strategies aimed at meeting future food safety standards. By informing these decisions with the anticipated impacts of climate change, the food system can be future-proofed.
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