Applicability 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

Abstract

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.

Digitalisation and Artificial Intelligence for sustainable food systems

“…Many 'drivers' that affect the food supply chain have been identified, among them (i) the growing world population and changing diets, (ii) scarcities in resources needed for agricultural production (e.g. fertile land, fresh water, energy), (iii) climate change, (iv) diminishing biodiversity, (v) inadequate governance, and (vi) competing agricultural systems (GO-Science, 2011;Marvin et al, 2019).…”

Determination and Metrics for Emerging Risks Identification DEMETER: Final Report

“…For all indicators shown in Figure 22, data sources were available (Table 4). Data sources identified were assessed for quality as described before (Marvin et al 2019). The data sources are assessed for relevance, timeliness, updated frequency, accuracy, accessibility, clarity, comparability and coherence.…”