A system approach towards prediction of food safety hazards: Impact of climate and agrichemical use on the occurrence of food safety hazards. (February 2020)

• Record Type: Journal Article
• Authors: Marvin, Hans J.P.; Bouzembrak, Yamine
• Is Part Of: Agricultural systems. Volume 178(2020)
• Publication Date: 2020-02
• Subjects: Food supply chain -- Bayesian Networks -- Dairy and milk -- Feed -- Machine learning; Agricultural systems -- Periodicals; Agriculture -- Environmental aspects -- Periodicals; 338.16
• DOI: 10.1016/j.agsy.2019.102760
• Languages: English
• ISSNs: 0308-521X

Abstract

Abstract: In this study, we aimed to demonstrate the aptness of a system approach to predict the level of contamination in a given agricultural product. As a showcase, the impact of climate and agrichemical use on the occurrence of food safety hazards in feed of dairy cows in the Netherlands was used. Data on chemical hazards in dairy cows' feed in the Netherlands for the years 2000 to 2013 were retrieved from the Dutch monitoring program KAP (Quality Program for Agricultural Products). Climate data (17 variables) and agrichemical usage figs. (6 variables) for the Netherlands were obtained from the NOAA's National Centers for Environmental Information, the European Commission Joint Research Center's Agri4Cast database, and FAO's FAOSTAT. A Bayesian Network (BN) was constructed with this data and optimized for the prediction of the contamination level. The overall accuracy of prediction of the level of contamination in feed was 90.3%. Sensitivity analysis demonstrated that many climate and agrichemical variables contributed to the prediction; however, their individual contribution was generally small. The applicability of the BN was demonstrated in more detail for grass and maize as feed components. The observed trends in contamination of these crops were accounted for by climate and agrichemical variables, with the impact varying amongst the specific variables and commodities. The variables with the highest impact were "days of precipitations in a month with ≥ 2.5 mm" and "annual use of herbicides". The results demonstrate that data-driven BNs can capture complex interactions, thereby enabling high-accuracy predictions. Whilst the applicability of this approach to the safety of dairy cows' feed in the Netherlands has thus been demonstrated, it can also be applied to other areas of food safety when a systems approach is needed. Such models can support risk assessors and risk managers in their understanding of the impacts of a given factor on food and feed safety, and inform the latter's decisions to mitigate potential risks. Highlights: Bayesian Network (BN) model predicted contamination level with high accuracy. Impact of climate and agrichemical on the contamination level was demonstrated. BNs allow addressing food safety and food security problems in a holistic manner.