Abstract The presence and development of many food safety risks are driven by factors within and outside the food supply chain, such as climate, economy and human behaviour. The interactions between these factors and the supply chain are complex and a system or holistic approach is needed to reveal cause-effect relationships and to be able to perform effective mitigation actions to minimise food safety risks. In this study, we demonstrate the potential of the Bayesian Network (BN) approach to identify and quantify the strength of relationships and interactions between the presence of food safety hazards as reported in Rapid Alert System for Food and Feed (RASFF) for fruits and vegetables on one hand, and climatic factors, economic and agronomic data on the other. To this end, all food safety notifications in RASFF (i.e. 3781 notifications) on fruits and vegetables originating from India, Turkey and the Netherlands were collected for the period 2005–2015. In addition, climatic factors (e.g. temperature, precipitation), agricultural factors (e.g. pesticide use, fertilizer use) and economic factors (e.g. price, production volumes) were collected for the countries of origin of the product concurrent with the period of food safety notification in RASFF. A BN was constructed with 80% of the collected data using a machine-learning algorithm and optimised for each specific hazard category. The performance of the developed BN was determined in terms of accuracy of prediction of the hazard category in the evaluation set comprising 20% of the total data. The accuracy was high (95%) and the following factors contributed most: product category, notifying country, yearly production, number of notification, maximal residue level (MRL) ratio, country of origin, and the annual agricultural budget of a country. The assessment of the impact of interactions within the BN showed a significant interaction between the presence and level of a hazard as reported in RASFF and several drivers of change but at present, no definite conclusions can be drawn regarding the climatic factors and food safety hazards.

中文翻译：

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包括气候因素在内的变化驱动因素对水果和蔬菜中化学食品安全危害发生的影响：贝叶斯网络方法

摘要 许多食品安全风险的存在和发展是由食品供应链内外的因素驱动的，如气候、经济和人类行为。这些因素与供应链之间的相互作用很复杂，需要一种系统或整体方法来揭示因果关系，并能够采取有效的缓解措施以最大限度地降低食品安全风险。在这项研究中，我们展示了贝叶斯网络 (BN) 方法在识别和量化水果和蔬菜食品和饲料快速警报系统 (RASFF) 中报告的食品安全危害之间的关系和相互作用强度方面的潜力一方面是气候因素，另一方面是经济和农艺数据。为此，RASFF 中的所有食品安全通知（即 2005-2015 年期间收集了来自印度、土耳其和荷兰的水果和蔬菜的 3781 份通知。此外，在食品安全通报期间收集了产品原产国的气候因素（如温度、降水）、农业因素（如农药使用、化肥使用）和经济因素（如价格、产量）。在 RASFF。BN 是使用机器学习算法使用 80% 的收集数据构建的，并针对每个特定的危险类别进行了优化。开发的 BN 的性能是根据评估集中危险类别的预测准确性来确定的，该评估集中包括总数据的 20%。准确率高（95%），以下因素贡献最大：产品类别、通知国、年产量、通报数量、最大残留水平 (MRL) 比率、原产国和一个国家的年度农业预算。对 BN 内相互作用影响的评估表明，RASFF 中报告的危害的存在和水平与若干变化驱动因素之间存在显着的相互作用，但目前，无法得出关于气候因素和食品安全危害的明确结论。