Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/18174
Appears in Collections:Aquaculture Journal Articles
Peer Review Status: Refereed
Title: A multi-disciplinary framework for bio-economic modeling in aquaculture: a welfare case study
Author(s): Noble, Chris
Berrill, Iain
Waller, Bob
Kankainen, Markus
Setala, Jari
Honkanen, Pirjo
Mejdell, Cecilie M
Turnbull, James
Damsgard, Borge
Schneider, Oliver
Toften, Hilde
Kole, Adriaan P W
Kadri, Sunil
Contact Email: j.f.turnbull@stir.ac.uk
Keywords: aquaculture
bio-economic modeling
framework
multi-disciplinary
welfare
Issue Date: 2012
Date Deposited: 6-Jan-2014
Citation: Noble C, Berrill I, Waller B, Kankainen M, Setala J, Honkanen P, Mejdell CM, Turnbull J, Damsgard B, Schneider O, Toften H, Kole APW & Kadri S (2012) A multi-disciplinary framework for bio-economic modeling in aquaculture: a welfare case study. Aquaculture Economics and Management, 16 (4), pp. 297-314. https://doi.org/10.1080/13657305.2012.729250
Abstract: This article summarizes the framework that translated data from multiple disciplines into a bio-economic decision tool for modeling the costs and benefits of improving fish welfare in commercial aquaculture. This decision tool formed the basis of a recent EU research project, BENEFISH which was funded via the European Commission's Sixth Framework (FP6) initiative. The bio-economic decision model can incorporate biological data, productivity data, micro (farm) and macro (industry) level economic data, and consumer marketing and business to business data. It can identify areas for potential added value that might be achieved by improving fish welfare across a range of species and husbandry systems within European aquaculture. This article provides a brief overview of the minimum data requirements for successfully modeling the bio-economic impacts of improvements in farmed fish welfare using the model developed during the BENEFISH project. It also highlights potential bottlenecks and the minimum prerequisites for each potential data set to be used for successful modeling.
DOI Link: 10.1080/13657305.2012.729250
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