Artificial neural networks classify cotton genotypes for fiber length
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | , , , , |
Tipo de documento: | Relatório |
Idioma: | eng |
Título da fonte: | Crop Breeding and Applied Biotechnology |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1984-70332018000200200 |
Resumo: | Abstract Fiber length is the main trait that needs to be improved in cotton. However, the presence of genotypes x environments interaction for this trait can hinder the recommendation of genotypes with greater length fibers. The aim of this study was to evaluate the adaptability and stability of the fibers length of cotton genotypes for recommendation to the Midwest and Northeast, using artificial neural networks (ANNs) and Eberhart and Russell method. Seven trials were carried out in the states of Ceará, Rio Grande do Norte, Goiás and Mato Grosso do Sul. Experimental design was a randomized block with four replications. Data were submitted to analysis of adaptability and stability through the Eberhart & Russell and ANNs methodologies. Based on these methods, the genotypes BRS Aroeira, CNPA CNPA 2009 42 and CNPA 2009 27 has better performance in unfavorable, general and favorable environment, respectively, for having fiber length above the overall mean of environments and high phenotypic stability. |
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Artificial neural networks classify cotton genotypes for fiber lengthGenotype x environment interactionartificial intelligenceGossypium hirsutumAbstract Fiber length is the main trait that needs to be improved in cotton. However, the presence of genotypes x environments interaction for this trait can hinder the recommendation of genotypes with greater length fibers. The aim of this study was to evaluate the adaptability and stability of the fibers length of cotton genotypes for recommendation to the Midwest and Northeast, using artificial neural networks (ANNs) and Eberhart and Russell method. Seven trials were carried out in the states of Ceará, Rio Grande do Norte, Goiás and Mato Grosso do Sul. Experimental design was a randomized block with four replications. Data were submitted to analysis of adaptability and stability through the Eberhart & Russell and ANNs methodologies. Based on these methods, the genotypes BRS Aroeira, CNPA CNPA 2009 42 and CNPA 2009 27 has better performance in unfavorable, general and favorable environment, respectively, for having fiber length above the overall mean of environments and high phenotypic stability.Crop Breeding and Applied Biotechnology2018-04-01info:eu-repo/semantics/reportinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1984-70332018000200200Crop Breeding and Applied Biotechnology v.18 n.2 2018reponame:Crop Breeding and Applied Biotechnologyinstname:Sociedade Brasileira de Melhoramento de Plantasinstacron:CBAB10.1590/1984-70332018v18n2n28info:eu-repo/semantics/openAccessCarvalho,Luiz Paulo deTeodoro,Paulo EduardoBarroso,Lais Mayara AzevedoFarias,Francisco José CorreiaMorello,Camilo de LellisNascimento,Moyséseng2018-04-23T00:00:00Zoai:scielo:S1984-70332018000200200Revistahttps://cbab.sbmp.org.br/#ONGhttps://old.scielo.br/oai/scielo-oai.phpcbabjournal@gmail.com||cbab@ufv.br1984-70331518-7853opendoar:2018-04-23T00:00Crop Breeding and Applied Biotechnology - Sociedade Brasileira de Melhoramento de Plantasfalse |
dc.title.none.fl_str_mv |
Artificial neural networks classify cotton genotypes for fiber length |
title |
Artificial neural networks classify cotton genotypes for fiber length |
spellingShingle |
Artificial neural networks classify cotton genotypes for fiber length Carvalho,Luiz Paulo de Genotype x environment interaction artificial intelligence Gossypium hirsutum |
title_short |
Artificial neural networks classify cotton genotypes for fiber length |
title_full |
Artificial neural networks classify cotton genotypes for fiber length |
title_fullStr |
Artificial neural networks classify cotton genotypes for fiber length |
title_full_unstemmed |
Artificial neural networks classify cotton genotypes for fiber length |
title_sort |
Artificial neural networks classify cotton genotypes for fiber length |
author |
Carvalho,Luiz Paulo de |
author_facet |
Carvalho,Luiz Paulo de Teodoro,Paulo Eduardo Barroso,Lais Mayara Azevedo Farias,Francisco José Correia Morello,Camilo de Lellis Nascimento,Moysés |
author_role |
author |
author2 |
Teodoro,Paulo Eduardo Barroso,Lais Mayara Azevedo Farias,Francisco José Correia Morello,Camilo de Lellis Nascimento,Moysés |
author2_role |
author author author author author |
dc.contributor.author.fl_str_mv |
Carvalho,Luiz Paulo de Teodoro,Paulo Eduardo Barroso,Lais Mayara Azevedo Farias,Francisco José Correia Morello,Camilo de Lellis Nascimento,Moysés |
dc.subject.por.fl_str_mv |
Genotype x environment interaction artificial intelligence Gossypium hirsutum |
topic |
Genotype x environment interaction artificial intelligence Gossypium hirsutum |
description |
Abstract Fiber length is the main trait that needs to be improved in cotton. However, the presence of genotypes x environments interaction for this trait can hinder the recommendation of genotypes with greater length fibers. The aim of this study was to evaluate the adaptability and stability of the fibers length of cotton genotypes for recommendation to the Midwest and Northeast, using artificial neural networks (ANNs) and Eberhart and Russell method. Seven trials were carried out in the states of Ceará, Rio Grande do Norte, Goiás and Mato Grosso do Sul. Experimental design was a randomized block with four replications. Data were submitted to analysis of adaptability and stability through the Eberhart & Russell and ANNs methodologies. Based on these methods, the genotypes BRS Aroeira, CNPA CNPA 2009 42 and CNPA 2009 27 has better performance in unfavorable, general and favorable environment, respectively, for having fiber length above the overall mean of environments and high phenotypic stability. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-04-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/report |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
report |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1984-70332018000200200 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1984-70332018000200200 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/1984-70332018v18n2n28 |
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 |
Crop Breeding and Applied Biotechnology |
publisher.none.fl_str_mv |
Crop Breeding and Applied Biotechnology |
dc.source.none.fl_str_mv |
Crop Breeding and Applied Biotechnology v.18 n.2 2018 reponame:Crop Breeding and Applied Biotechnology instname:Sociedade Brasileira de Melhoramento de Plantas instacron:CBAB |
instname_str |
Sociedade Brasileira de Melhoramento de Plantas |
instacron_str |
CBAB |
institution |
CBAB |
reponame_str |
Crop Breeding and Applied Biotechnology |
collection |
Crop Breeding and Applied Biotechnology |
repository.name.fl_str_mv |
Crop Breeding and Applied Biotechnology - Sociedade Brasileira de Melhoramento de Plantas |
repository.mail.fl_str_mv |
cbabjournal@gmail.com||cbab@ufv.br |
_version_ |
1754209187571695616 |