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dc.contributor.authorDiez-Olivan, Alberto
dc.contributor.authorAverós, Xavier
dc.contributor.authorSanz, Ricardo
dc.contributor.authorSierra, Basilio
dc.contributor.authorEstevez, Inma
dc.date.accessioned2018-05-02T09:18:56Z
dc.date.available2018-05-02T09:18:56Z
dc.date.issued2019-06
dc.identifier.citationDiez-Olivan, Alberto, Xavier Averós, Ricardo Sanz, Basilio Sierra, and Inma Estevez. “Quantile Regression Forests-Based Modeling and Environmental Indicators for Decision Support in Broiler Farming.” Computers and Electronics in Agriculture 161 (June 2019): 141–150. doi:10.1016/j.compag.2018.03.025.
dc.identifier.issn0168-1699en
dc.identifier.urihttp://hdl.handle.net/11556/540
dc.description.abstractAn efficient and sustainable animal production requires fine-tuning and control of all the parameters involved. But this is not a simple task. Animal farming is a complex biological system in which environmental parameters and management practices interact in a dynamic way. In addition, the typical non-linear response of biological processes implies that relationships across parameters that are critical to assure animal welfare and performance are difficult to determine. In this paper a novel decision support system based on environmental indicators and on weights, leg problems and mortality rates is proposed to address this issue. The data-driven modeling process is performed by a quantile regression forests approach that allows estimating growth, welfare and mortality parameters on the basis of environmental deviations from optimal farm conditions. Resulting models also provide confidence intervals able to deal with uncertainty. They are deployed in farm, offering an accessible tool for farmers, veterinarians and technical personnel. Experimental results involving 20 flocks of broiler meat chickens from different farms show the validity of the system, obtaining robust prediction intervals and high accuracy, namely over 81% for every model. The in-field use of the proposed approach will facilitate an efficient and animal welfare-friendly production management.en
dc.description.sponsorshipThis project was funded by the Spanish Ministry of Economy and Competitivity, General Directorate for Science and Technology, National Research Program ’Retos de la Sociedad’ Project #AGL2013-49173-C2-1-R P.I. Inma Estevez and #AGL2013-49173-C2-2-R. The authors wish to thank to AN and the farmers for facilitating access to their farms for data collection.en
dc.language.isoengen
dc.publisherElsevier B.V.en
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.titleQuantile regression forests-based modeling and environmental indicators for decision support in broiler farmingen
dc.typearticleen
dc.identifier.doi10.1016/j.compag.2018.03.025en
dc.rights.accessRightsembargoedAccessen
dc.subject.keywordsData processingen
dc.subject.keywordsRandom forestsen
dc.subject.keywordsBroiler meat chickenen
dc.subject.keywordsEfficient productionen
dc.subject.keywordsAnimal welfareen
dc.subject.keywordsMachine learningen
dc.journal.titleComputers and Electronics in Agricultureen
dc.page.final150
dc.page.initial141
dc.volume.number161


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