The use of artificial intelligence for the prediction of productivity parameters in swine culture
Autor(a) principal: | |
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Data de Publicação: | 2016 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFRGS |
Texto Completo: | http://hdl.handle.net/10183/185851 |
Resumo: | In similar conditions of food handling and genetics, there are large differences in the final productivity of farms, resulting from inherent factors of the production system. This fact predisposes the need of studies on optimizing the rearing conditions of the farms, in order to verify the main limitations for the producers. Therefore, the present study aims to generate predictions of the swine productivity in the finishing phase, using variables related to their profiles and the production results achieved. 107 farmers belonging to a swine cooperative were considered in the study, located in 47 counties at the Taquari valley region, Brazil. Predictions were generated through the aid of neural networks, and the findings show that Artificial Neural Networks (ANN) can predict the productivity variables Feed Conversion, Mortality and Average Daily Gain for the proposed case. |
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Sangoi, Luiz FernandoKessler, Alexandre de MelloNeuenfeldt Junior, Álvaro LuizSiluk, Julio Cezar MairesseRibeiro, Andrea Machado LealSoliman, Marlon2018-12-05T02:43:38Z20160101-7438http://hdl.handle.net/10183/185851001079163In similar conditions of food handling and genetics, there are large differences in the final productivity of farms, resulting from inherent factors of the production system. This fact predisposes the need of studies on optimizing the rearing conditions of the farms, in order to verify the main limitations for the producers. Therefore, the present study aims to generate predictions of the swine productivity in the finishing phase, using variables related to their profiles and the production results achieved. 107 farmers belonging to a swine cooperative were considered in the study, located in 47 counties at the Taquari valley region, Brazil. Predictions were generated through the aid of neural networks, and the findings show that Artificial Neural Networks (ANN) can predict the productivity variables Feed Conversion, Mortality and Average Daily Gain for the proposed case.application/pdfengPesquisa Operacional. Rio de Janeiro, RJ. Vol. 36, n. 1 (jan./abr. 2016), p. 67-79SuinoculturaProdutividadeCompetitividadeInteligência artificialSwine cultureArtificial neural networksCompetitivenessThe use of artificial intelligence for the prediction of productivity parameters in swine cultureinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/otherinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFRGSinstname:Universidade Federal do Rio Grande do Sul (UFRGS)instacron:UFRGSTEXT001079163.pdf.txt001079163.pdf.txtExtracted Texttext/plain28618http://www.lume.ufrgs.br/bitstream/10183/185851/2/001079163.pdf.txt2df973f31712f017fc6a0f5d201ee61bMD52ORIGINAL001079163.pdfTexto completo (inglês)application/pdf342497http://www.lume.ufrgs.br/bitstream/10183/185851/1/001079163.pdfb32ff4b5c529c33b6c8b4d3a99457b41MD5110183/1858512022-02-22 05:01:11.280305oai:www.lume.ufrgs.br:10183/185851Repositório de PublicaçõesPUBhttps://lume.ufrgs.br/oai/requestopendoar:2022-02-22T08:01:11Repositório Institucional da UFRGS - Universidade Federal do Rio Grande do Sul (UFRGS)false |
dc.title.pt_BR.fl_str_mv |
The use of artificial intelligence for the prediction of productivity parameters in swine culture |
title |
The use of artificial intelligence for the prediction of productivity parameters in swine culture |
spellingShingle |
The use of artificial intelligence for the prediction of productivity parameters in swine culture Sangoi, Luiz Fernando Suinocultura Produtividade Competitividade Inteligência artificial Swine culture Artificial neural networks Competitiveness |
title_short |
The use of artificial intelligence for the prediction of productivity parameters in swine culture |
title_full |
The use of artificial intelligence for the prediction of productivity parameters in swine culture |
title_fullStr |
The use of artificial intelligence for the prediction of productivity parameters in swine culture |
title_full_unstemmed |
The use of artificial intelligence for the prediction of productivity parameters in swine culture |
title_sort |
The use of artificial intelligence for the prediction of productivity parameters in swine culture |
author |
Sangoi, Luiz Fernando |
author_facet |
Sangoi, Luiz Fernando Kessler, Alexandre de Mello Neuenfeldt Junior, Álvaro Luiz Siluk, Julio Cezar Mairesse Ribeiro, Andrea Machado Leal Soliman, Marlon |
author_role |
author |
author2 |
Kessler, Alexandre de Mello Neuenfeldt Junior, Álvaro Luiz Siluk, Julio Cezar Mairesse Ribeiro, Andrea Machado Leal Soliman, Marlon |
author2_role |
author author author author author |
dc.contributor.author.fl_str_mv |
Sangoi, Luiz Fernando Kessler, Alexandre de Mello Neuenfeldt Junior, Álvaro Luiz Siluk, Julio Cezar Mairesse Ribeiro, Andrea Machado Leal Soliman, Marlon |
dc.subject.por.fl_str_mv |
Suinocultura Produtividade Competitividade Inteligência artificial |
topic |
Suinocultura Produtividade Competitividade Inteligência artificial Swine culture Artificial neural networks Competitiveness |
dc.subject.eng.fl_str_mv |
Swine culture Artificial neural networks Competitiveness |
description |
In similar conditions of food handling and genetics, there are large differences in the final productivity of farms, resulting from inherent factors of the production system. This fact predisposes the need of studies on optimizing the rearing conditions of the farms, in order to verify the main limitations for the producers. Therefore, the present study aims to generate predictions of the swine productivity in the finishing phase, using variables related to their profiles and the production results achieved. 107 farmers belonging to a swine cooperative were considered in the study, located in 47 counties at the Taquari valley region, Brazil. Predictions were generated through the aid of neural networks, and the findings show that Artificial Neural Networks (ANN) can predict the productivity variables Feed Conversion, Mortality and Average Daily Gain for the proposed case. |
publishDate |
2016 |
dc.date.issued.fl_str_mv |
2016 |
dc.date.accessioned.fl_str_mv |
2018-12-05T02:43:38Z |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/other |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10183/185851 |
dc.identifier.issn.pt_BR.fl_str_mv |
0101-7438 |
dc.identifier.nrb.pt_BR.fl_str_mv |
001079163 |
identifier_str_mv |
0101-7438 001079163 |
url |
http://hdl.handle.net/10183/185851 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.ispartof.pt_BR.fl_str_mv |
Pesquisa Operacional. Rio de Janeiro, RJ. Vol. 36, n. 1 (jan./abr. 2016), p. 67-79 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
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UFRGS |
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Repositório Institucional da UFRGS |
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Repositório Institucional da UFRGS |
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