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dc.contributor.authorDavie, S.
dc.contributor.authorMinto, C.
dc.contributor.authorOfficer, R.
dc.contributor.authorLordan, C.
dc.contributor.authorJackson, E.
dc.date.accessioned2015-02-06T11:19:29Z
dc.date.available2015-02-06T11:19:29Z
dc.date.issued2015
dc.identifier.citationDavie, S., Minto, C., Officer, R., Lordan, C., Jackson, E. (2015). Modelling fuel consumption of fishing vessels for predictive use. ICES Journal of Marine Science, 72:708-719en_GB
dc.identifier.issn1095-9289
dc.identifier.urihttp://hdl.handle.net/10793/1061
dc.descriptionThis is an author produced pre-print pdf. The article has been accepted for publication in ICES Journal of Marine Science, published by Oxford University Press. Available at: ICES J. Mar. Sci. (January/February 2015) 72(2):708-719. doi:10.1093/icesjms/fsu084. First published online: June 10, 2014en_GB
dc.description.abstractFuel costs are an important element in models used to analyse and predict fisher behaviour for application within the wider mixed fisheries and ecosystem approaches to management. This investigation explored the predictive capability of linear and generalized additive models (GAMs) in providing daily fuel consumption estimates for fishing vessels given knowledge of their length, engine power, fleet segment (annual dominant gear type), and fuel prices. Models were fitted to half of the Irish fishing vessel economic data collected between 2003 and 2011. The predictive capabilities of the seven best models were validated against the remaining, previously un-modelled, data. The type of gear used by a fleet segment had an important influence on fuel consumption as did the price of fuel. The passive pot gear and Scottish seine gear segments indicated consistently lower consumptions, whereas dredge and pelagic gears showed consistently higher fuel consumptions. Furthermore, increasing fuel price negatively affected fuel consumption, especially for more powerful, larger vessels. Of the formulated models, the best fit to training data were a GAM with a gear main effect and two smooth functions; standardized vessel length and engine power interacting with fuel price. For prediction, overall, this model showed the closest predictions with the least bias, followed by three linear models. However, all seven models compared for predictive capability performed well for the most sampled segments (demersal and pelagic trawlers).en_GB
dc.language.isoenen_GB
dc.publisherICESen_GB
dc.relation.ispartofseriesICES Journal of Marine Science;72:708-719
dc.subjectfishing gearen_GB
dc.subjectfishing vesselsen_GB
dc.subjectfuel consumptionen_GB
dc.subjectfuel cost predictionsen_GB
dc.subjectfuel priceen_GB
dc.subjectGAMen_GB
dc.subjectmodellingen_GB
dc.titleModelling fuel consumption of fishing vessels for predictive useen_GB
dc.typeArticleen_GB
refterms.dateFOA2018-01-12T03:13:00Z


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