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: | Pesquisa operacional (Online) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-74382016000100067 |
Resumo: | ABSTRACT 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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THE USE OF ARTIFICIAL INTELLIGENCE FOR THE PREDICTION OF PRODUCTIVITY PARAMETERS IN SWINE CULTUREswine cultureArtificial Neural NetworkscompetitivenessABSTRACT 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.Sociedade Brasileira de Pesquisa Operacional2016-04-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-74382016000100067Pesquisa Operacional v.36 n.1 2016reponame:Pesquisa operacional (Online)instname:Sociedade Brasileira de Pesquisa Operacional (SOBRAPO)instacron:SOBRAPO10.1590/0101-7438.2016.036.01.0067info:eu-repo/semantics/openAccessSangoi,Luiz FernandoKessler,Alexandre de MelloNeuenfeldt Júnior,Alvaro LuizSiluk,Julio Cezar MairesseRibeiro,Andréa Machado LealSoliman,Marloneng2016-06-14T00:00:00Zoai:scielo:S0101-74382016000100067Revistahttp://www.scielo.br/popehttps://old.scielo.br/oai/scielo-oai.php||sobrapo@sobrapo.org.br1678-51420101-7438opendoar:2016-06-14T00:00Pesquisa operacional (Online) - Sociedade Brasileira de Pesquisa Operacional (SOBRAPO)false |
dc.title.none.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 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 Júnior,Alvaro Luiz Siluk,Julio Cezar Mairesse Ribeiro,Andréa Machado Leal Soliman,Marlon |
author_role |
author |
author2 |
Kessler,Alexandre de Mello Neuenfeldt Júnior,Alvaro Luiz Siluk,Julio Cezar Mairesse Ribeiro,Andréa 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 Júnior,Alvaro Luiz Siluk,Julio Cezar Mairesse Ribeiro,Andréa Machado Leal Soliman,Marlon |
dc.subject.por.fl_str_mv |
swine culture Artificial Neural Networks competitiveness |
topic |
swine culture Artificial Neural Networks competitiveness |
description |
ABSTRACT 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.none.fl_str_mv |
2016-04-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-74382016000100067 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-74382016000100067 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/0101-7438.2016.036.01.0067 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Sociedade Brasileira de Pesquisa Operacional |
publisher.none.fl_str_mv |
Sociedade Brasileira de Pesquisa Operacional |
dc.source.none.fl_str_mv |
Pesquisa Operacional v.36 n.1 2016 reponame:Pesquisa operacional (Online) instname:Sociedade Brasileira de Pesquisa Operacional (SOBRAPO) instacron:SOBRAPO |
instname_str |
Sociedade Brasileira de Pesquisa Operacional (SOBRAPO) |
instacron_str |
SOBRAPO |
institution |
SOBRAPO |
reponame_str |
Pesquisa operacional (Online) |
collection |
Pesquisa operacional (Online) |
repository.name.fl_str_mv |
Pesquisa operacional (Online) - Sociedade Brasileira de Pesquisa Operacional (SOBRAPO) |
repository.mail.fl_str_mv |
||sobrapo@sobrapo.org.br |
_version_ |
1750318017829404672 |