Open AccessApplicability of a food chain analysis on aquaculture of Atlantic salmon to identify and monitor vulnerabilities and drivers of change for the identification of emerging risks
Author(s) -
Marvin Hans,
Bouzembrak Yamine,
Asselt Esther,
Meijer Nathan,
Kleter Gijs,
Lorentzen Grete,
Johansen LillHeidi
Publication year - 2019
Publication title -
efsa supporting publications
Language(s) - English
Resource type - Journals
ISSN - 2397-8325
DOI - 10.2903/sp.efsa.2019.en-1619
Subject(s) - identification (biology) , delphi method , supply chain , process management , business , risk analysis (engineering) , bayesian network , focus group , baseline (sea) , environmental resource management , computer science , marketing , fishery , environmental science , biology , botany , artificial intelligence
The objective of Aquarius project was to test the applicability of a food chain analysis on Atlantic salmon farmed in Norway to identify and monitor vulnerabilities and drivers of change for the identification of emerging risks. To this end, a comprehensive literature review was conducted complemented with expert views collected by on‐line questionnaires and in‐depth interviews. This review provided an overview of the various stages in the supply chain from farm to consumer and the identification of i) existing and emerging human and animal health hazards, ii) vulnerabilities, iii) control measures, iv) drivers of change, and v) related indicators. Next step in the study was to i) complement the list of vulnerabilities followed by a prioritisation using focus group discussions and Failure Mode and Effect Analysis (FMEA) method and ii) link drivers of change acting upon the (prioritised) vulnerabilities and identification of associated indicators and data sources by means of a Delphi method. Based on the experiences obtained with the different methodologies during this project, the Aquarius team considers literature review, and a direct face‐to‐face interaction with experts (in‐depth interviews, focus group discussions and FMEA in a workshop setting) as being most effective regarding output and effort. The comprehensive information collected formed the basis for the methodology to baseline key indicators of the selected vulnerabilities. The methodology proposed comprised a Bayesian Network (BN) that links data sources for indicators and drivers of change to the prioritised vulnerabilities and was demonstrated for salmon health. A Decision Support System (DSS) with such BN integrated was designed. The current BN and DSS could flag an increased exposure of known hazards that may occur due to changes in indicators but new hazards are not identified. The proposed methodology could be used instead to show trends in vulnerabilities, indicators, increased exposure of known hazards and for scenario studies.