Expert-driven methodology to assess and predict the effects of drivers of change on vulnerabilities in a food supply chain: Aquaculture of Atlantic salmon in Norway as a showcase

Hans J.P. Marvin*, Esther van Asselt, Gijs Kleter, Nathan Meijer, Grete Lorentzen, Lill Heidi Johansen, Rita Hannisdal, Veronika Sele, Yamine Bouzembrak

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

1 Citation (Scopus)

Abstract

Background: In the last decades, food produced by aquaculture has seen an impressive increase worldwide but maintaining high quality and safety is increasingly becoming a concern. It is apparent that changes in- and outside the aquaculture supply chain may act as driving forces for the introduction of food safety hazards. Knowledge on these drivers of change and their impact in the various steps in the food supply chain may help food producers to mitigate to potential risks and maintain high-quality food. Scope and approach: In this study, we analysed the use of expert driven methodologies to assess and predict the effect of drivers of change on selected food/feed safety vulnerabilities in the salmon aquaculture supply chain of Norway. The presented overview is based on the findings of the “Aquarius” project, which was funded by the European Food Safety Authority (EFSA). Key findings and conclusions: In this study, over 100 experts were involved and various expert elicitation methods were applied such as on-line questionnaires, interviews, Delphi and Failure Mode and Effect Analysis (FMEA). This approach resulted in a comprehensive overview of the Norwegian salmon supply chain. For each step in the supply chain, vulnerabilities for human and animal health were identified, which were prioritised by FMEA. For the two highest-ranked vulnerabilities in each step of the supply chain, drivers were identified and prioritised by expert elicitation in a Delphi study. Also, indicators and linked data sources were obtained for the highest-ranked drivers. The comprehensive information collected was integrated in a Bayesian Network (BN) model that links data sources for indicators and drivers of change. The applicability of the BN model was demonstrated for salmon health for four vulnerabilities and three steps in Atlantic salmon aquaculture. The accuracy of developed model was 81%.

Original languageEnglish
Pages (from-to)49-56
Number of pages8
JournalTrends in Food Science and Technology
Volume103
DOIs
Publication statusPublished - Sep 2020

Keywords

  • Bayesian network
  • Delphi and failure mode and effect analysis (FMEA)
  • Delphi study
  • Expert elicitation
  • Food safety hazards
  • Machine learning

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