Factor analysis applied to the G+GE matrix via REML/BLUP for multi-environment data
Autor(a) principal: | |
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Data de Publicação: | 2016 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Crop Breeding and Applied Biotechnology |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1984-70332016000100001 |
Resumo: | Abstract The genotype x environment interaction is frequently observed in many crops and studies on environmental stratification and genotype adaptability have been proposed to understand it. The aim of this study was to carry out factor analysis in data from multi-environment experiments by the mixed model approach (REML/BLUP). Instead of adjusted phenotypic means, a matrix containing the genotypic effects added to the effects of the genotype x environment interaction (G+GE) was used, predicted via REML/BLUP in joint analysis (designated as R-FGGE). In the study, data from 36 common bean lines evaluated in 15 environments were used. By this proposal, 46.7% of the environments were gathered in two groups, one with four and the other with three environments. The R-FGGE has the same characteristics as the previous proposals, that is, ease of identification of mega-environments and genotypes with broad adaptability, along with the advantages associated with the mixed model methodology. |
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Crop Breeding and Applied Biotechnology |
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Factor analysis applied to the G+GE matrix via REML/BLUP for multi-environment dataGenotype x environment interactionadaptabilityenvironmental stratificationmultivariate analysisAbstract The genotype x environment interaction is frequently observed in many crops and studies on environmental stratification and genotype adaptability have been proposed to understand it. The aim of this study was to carry out factor analysis in data from multi-environment experiments by the mixed model approach (REML/BLUP). Instead of adjusted phenotypic means, a matrix containing the genotypic effects added to the effects of the genotype x environment interaction (G+GE) was used, predicted via REML/BLUP in joint analysis (designated as R-FGGE). In the study, data from 36 common bean lines evaluated in 15 environments were used. By this proposal, 46.7% of the environments were gathered in two groups, one with four and the other with three environments. The R-FGGE has the same characteristics as the previous proposals, that is, ease of identification of mega-environments and genotypes with broad adaptability, along with the advantages associated with the mixed model methodology.Crop Breeding and Applied Biotechnology2016-03-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1984-70332016000100001Crop Breeding and Applied Biotechnology v.16 n.1 2016reponame:Crop Breeding and Applied Biotechnologyinstname:Sociedade Brasileira de Melhoramento de Plantasinstacron:CBAB10.1590/1984-70332016v16n1a1info:eu-repo/semantics/openAccessPeixouto,Leandro SantosNunes,José Airton RodriguesFurtado,Daniel Ferreiraeng2016-04-19T00:00:00Zoai:scielo:S1984-70332016000100001Revistahttps://cbab.sbmp.org.br/#ONGhttps://old.scielo.br/oai/scielo-oai.phpcbabjournal@gmail.com||cbab@ufv.br1984-70331518-7853opendoar:2016-04-19T00:00Crop Breeding and Applied Biotechnology - Sociedade Brasileira de Melhoramento de Plantasfalse |
dc.title.none.fl_str_mv |
Factor analysis applied to the G+GE matrix via REML/BLUP for multi-environment data |
title |
Factor analysis applied to the G+GE matrix via REML/BLUP for multi-environment data |
spellingShingle |
Factor analysis applied to the G+GE matrix via REML/BLUP for multi-environment data Peixouto,Leandro Santos Genotype x environment interaction adaptability environmental stratification multivariate analysis |
title_short |
Factor analysis applied to the G+GE matrix via REML/BLUP for multi-environment data |
title_full |
Factor analysis applied to the G+GE matrix via REML/BLUP for multi-environment data |
title_fullStr |
Factor analysis applied to the G+GE matrix via REML/BLUP for multi-environment data |
title_full_unstemmed |
Factor analysis applied to the G+GE matrix via REML/BLUP for multi-environment data |
title_sort |
Factor analysis applied to the G+GE matrix via REML/BLUP for multi-environment data |
author |
Peixouto,Leandro Santos |
author_facet |
Peixouto,Leandro Santos Nunes,José Airton Rodrigues Furtado,Daniel Ferreira |
author_role |
author |
author2 |
Nunes,José Airton Rodrigues Furtado,Daniel Ferreira |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Peixouto,Leandro Santos Nunes,José Airton Rodrigues Furtado,Daniel Ferreira |
dc.subject.por.fl_str_mv |
Genotype x environment interaction adaptability environmental stratification multivariate analysis |
topic |
Genotype x environment interaction adaptability environmental stratification multivariate analysis |
description |
Abstract The genotype x environment interaction is frequently observed in many crops and studies on environmental stratification and genotype adaptability have been proposed to understand it. The aim of this study was to carry out factor analysis in data from multi-environment experiments by the mixed model approach (REML/BLUP). Instead of adjusted phenotypic means, a matrix containing the genotypic effects added to the effects of the genotype x environment interaction (G+GE) was used, predicted via REML/BLUP in joint analysis (designated as R-FGGE). In the study, data from 36 common bean lines evaluated in 15 environments were used. By this proposal, 46.7% of the environments were gathered in two groups, one with four and the other with three environments. The R-FGGE has the same characteristics as the previous proposals, that is, ease of identification of mega-environments and genotypes with broad adaptability, along with the advantages associated with the mixed model methodology. |
publishDate |
2016 |
dc.date.none.fl_str_mv |
2016-03-01 |
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=S1984-70332016000100001 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1984-70332016000100001 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/1984-70332016v16n1a1 |
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.16 n.1 2016 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_ |
1754209187093544960 |