Use este identificador para citar ou linkar para este item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/5708
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dc.contributor.authorVALE, M. M.pt_BR
dc.contributor.authorMOURA, D. J. dept_BR
dc.contributor.authorNÄÄS, I. de A.pt_BR
dc.contributor.authorOLIVEIRA, S. R. de M.pt_BR
dc.contributor.authorRODRIGUES, L. H. A.pt_BR
dc.date.accessioned2017-04-11T15:52:42Z-
dc.date.available2017-04-11T15:52:42Z-
dc.date.created2008-12-17pt_BR
dc.date.issued2008pt_BR
dc.identifier.citationScientia Agricola, Piracicaba, v. 65, n. 3, p. 223-229, May/June 2008.pt_BR
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/5708pt_BR
dc.descriptionHeat waves usually result in losses of animal production since they are exposed to thermal stress inducing an increase in mortality and consequent economical losses. Animal science and meteorological databases from the last years contain enough data in the poultry production business to allow the modeling of mortality losses due to heat wave incidence. This research analyzes a database of broiler production associated to climatic data, using data mining techniques such as attribute selection and data classification (decision tree) to model the impact of heat wave incidence on broiler mortality. The temperature and humidity index (THI) was used for screening environmental data. The data mining techniques allowed the development of three comprehensible models for estimating specifically high mortality during broiler production. Two models yielded a classification accuracy of 89.3% by using Principal Component Analysis (PCA) and Wrapper feature selection approaches. Both models obtained a class precision of 0.83 for classifying high mortality. When the feature selection was made by the domain experts, the model accuracy reached 85.7%, while the class precision of high mortality was 0.76. Meteorological data and the calculated THI from meteorological stations were helpful to select the range of harmful environmental conditions for broilers 29 and 42 days old. The data mining techniques were useful for building animal production models.pt_BR
dc.language.isoengeng
dc.rightsopenAccesseng
dc.subjectITUpt_BR
dc.subjectDados ambientaispt_BR
dc.subjectMineração de dadospt_BR
dc.subjectAgropecuáriapt_BR
dc.subjectData miningpt_BR
dc.titleData mining to estimate broiler mortality when exposed to heat wave.pt_BR
dc.typeArtigo de periódicopt_BR
dc.date.updated2017-04-11T15:52:42Zpt_BR
dc.subject.thesagroFrango de Cortept_BR
riaa.ainfo.id5708pt_BR
riaa.ainfo.lastupdate2017-04-11pt_BR
dc.identifier.doihttp://dx.doi.org/10.1590/S0103-90162008000300001pt_BR
dc.contributor.institutionMARCOS MARTINEZ VALE, FEAGRI/UNICAMP; DANIELLA JORGE DE MOURA, FEAGRI/UNICAMP; IRENILZA DE ALENCAR NÄÄS, FEAGRI/UNICAMP; STANLEY ROBSON DE MEDEIROS OLIVEIRA, CNPTIA; LUIZ HENRIQUE ANTUNES RODRIGUES, FEAGRI/UNICAMP.pt_BR
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