Genotype x environment interaction in cowpea by mixed models

Detalhes bibliográficos
Autor(a) principal: Carvalho,Leonardo Castelo Branco
Data de Publicação: 2017
Outros Autores: Damasceno-Silva,Kaesel Jackson, Rocha,Maurisrael de Moura, Oliveira,Giancarlo Conde Xavier
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Revista ciência agronômica (Online)
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1806-66902017000500872
Resumo: ABSTRACT Several methods have been proposed to measure effects of genotype × environment interaction (G×E) on various traits of interest of plant species, such as grain yield. Among these methods, mixed models using the Restricted Maximum Likelihood (REML)-Best Linear Unbiased Prediction (BLUP) procedure with random genotype effects have been reported as advantageous, since they allow the obtaining of actual genotypic values for cultivation and use. The objective of this work was to evaluate the response of grain yield to different locations and years, and the effects of G×E on the performance of cowpea genotypes by the methodology of mixed models. Twenty genotypes were evaluated under rainfed conditions in 47 locations in 2010, 2011 and 2012 using randomized block design. After joint analysis, the genotypes adaptability and stability patterns within and between years were tested by the Harmonic Mean of Relative Performance of Genetic Values (HMRPGV) statistics. The analysis within the years showed highly significant effects of the genotype × location interaction in all the years evaluated. The results of the joint analysis presented highly significant effects (. ≤0.01) of the genotype, and triple interaction (genotype × location × year) (. ≤0.001), denoting a strong effect of the G×E on the genotype performances. The HMRPGV analysis was adequate to identify superior genotypes, highlighting the MNC02-676F-3, MNC03-737F-5-1, MNC03-737F-5-9, BRS-Tumucumaque, and BRS-Guariba as the genotypes with best stability and highest grain yield. The selection of these genotypes resulted in a new average yield (1,402 kg ha-1) which is higher than that obtained by selection based only on the phenotype (1,230 kg ha-1).
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spelling Genotype x environment interaction in cowpea by mixed modelsVigna unguiculataG × E interactionBLUPABSTRACT Several methods have been proposed to measure effects of genotype × environment interaction (G×E) on various traits of interest of plant species, such as grain yield. Among these methods, mixed models using the Restricted Maximum Likelihood (REML)-Best Linear Unbiased Prediction (BLUP) procedure with random genotype effects have been reported as advantageous, since they allow the obtaining of actual genotypic values for cultivation and use. The objective of this work was to evaluate the response of grain yield to different locations and years, and the effects of G×E on the performance of cowpea genotypes by the methodology of mixed models. Twenty genotypes were evaluated under rainfed conditions in 47 locations in 2010, 2011 and 2012 using randomized block design. After joint analysis, the genotypes adaptability and stability patterns within and between years were tested by the Harmonic Mean of Relative Performance of Genetic Values (HMRPGV) statistics. The analysis within the years showed highly significant effects of the genotype × location interaction in all the years evaluated. The results of the joint analysis presented highly significant effects (. ≤0.01) of the genotype, and triple interaction (genotype × location × year) (. ≤0.001), denoting a strong effect of the G×E on the genotype performances. The HMRPGV analysis was adequate to identify superior genotypes, highlighting the MNC02-676F-3, MNC03-737F-5-1, MNC03-737F-5-9, BRS-Tumucumaque, and BRS-Guariba as the genotypes with best stability and highest grain yield. The selection of these genotypes resulted in a new average yield (1,402 kg ha-1) which is higher than that obtained by selection based only on the phenotype (1,230 kg ha-1).Universidade Federal do Ceará2017-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1806-66902017000500872Revista Ciência Agronômica v.48 n.5spe 2017reponame:Revista ciência agronômica (Online)instname:Universidade Federal do Ceará (UFC)instacron:UFC10.5935/1806-6690.20170103info:eu-repo/semantics/openAccessCarvalho,Leonardo Castelo BrancoDamasceno-Silva,Kaesel JacksonRocha,Maurisrael de MouraOliveira,Giancarlo Conde Xaviereng2017-10-23T00:00:00Zoai:scielo:S1806-66902017000500872Revistahttp://www.ccarevista.ufc.br/PUBhttps://old.scielo.br/oai/scielo-oai.php||alekdutra@ufc.br|| ccarev@ufc.br1806-66900045-6888opendoar:2017-10-23T00:00Revista ciência agronômica (Online) - Universidade Federal do Ceará (UFC)false
dc.title.none.fl_str_mv Genotype x environment interaction in cowpea by mixed models
title Genotype x environment interaction in cowpea by mixed models
spellingShingle Genotype x environment interaction in cowpea by mixed models
Carvalho,Leonardo Castelo Branco
Vigna unguiculata
G × E interaction
BLUP
title_short Genotype x environment interaction in cowpea by mixed models
title_full Genotype x environment interaction in cowpea by mixed models
title_fullStr Genotype x environment interaction in cowpea by mixed models
title_full_unstemmed Genotype x environment interaction in cowpea by mixed models
title_sort Genotype x environment interaction in cowpea by mixed models
author Carvalho,Leonardo Castelo Branco
author_facet Carvalho,Leonardo Castelo Branco
Damasceno-Silva,Kaesel Jackson
Rocha,Maurisrael de Moura
Oliveira,Giancarlo Conde Xavier
author_role author
author2 Damasceno-Silva,Kaesel Jackson
Rocha,Maurisrael de Moura
Oliveira,Giancarlo Conde Xavier
author2_role author
author
author
dc.contributor.author.fl_str_mv Carvalho,Leonardo Castelo Branco
Damasceno-Silva,Kaesel Jackson
Rocha,Maurisrael de Moura
Oliveira,Giancarlo Conde Xavier
dc.subject.por.fl_str_mv Vigna unguiculata
G × E interaction
BLUP
topic Vigna unguiculata
G × E interaction
BLUP
description ABSTRACT Several methods have been proposed to measure effects of genotype × environment interaction (G×E) on various traits of interest of plant species, such as grain yield. Among these methods, mixed models using the Restricted Maximum Likelihood (REML)-Best Linear Unbiased Prediction (BLUP) procedure with random genotype effects have been reported as advantageous, since they allow the obtaining of actual genotypic values for cultivation and use. The objective of this work was to evaluate the response of grain yield to different locations and years, and the effects of G×E on the performance of cowpea genotypes by the methodology of mixed models. Twenty genotypes were evaluated under rainfed conditions in 47 locations in 2010, 2011 and 2012 using randomized block design. After joint analysis, the genotypes adaptability and stability patterns within and between years were tested by the Harmonic Mean of Relative Performance of Genetic Values (HMRPGV) statistics. The analysis within the years showed highly significant effects of the genotype × location interaction in all the years evaluated. The results of the joint analysis presented highly significant effects (. ≤0.01) of the genotype, and triple interaction (genotype × location × year) (. ≤0.001), denoting a strong effect of the G×E on the genotype performances. The HMRPGV analysis was adequate to identify superior genotypes, highlighting the MNC02-676F-3, MNC03-737F-5-1, MNC03-737F-5-9, BRS-Tumucumaque, and BRS-Guariba as the genotypes with best stability and highest grain yield. The selection of these genotypes resulted in a new average yield (1,402 kg ha-1) which is higher than that obtained by selection based only on the phenotype (1,230 kg ha-1).
publishDate 2017
dc.date.none.fl_str_mv 2017-01-01
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=S1806-66902017000500872
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1806-66902017000500872
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.5935/1806-6690.20170103
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 Universidade Federal do Ceará
publisher.none.fl_str_mv Universidade Federal do Ceará
dc.source.none.fl_str_mv Revista Ciência Agronômica v.48 n.5spe 2017
reponame:Revista ciência agronômica (Online)
instname:Universidade Federal do Ceará (UFC)
instacron:UFC
instname_str Universidade Federal do Ceará (UFC)
instacron_str UFC
institution UFC
reponame_str Revista ciência agronômica (Online)
collection Revista ciência agronômica (Online)
repository.name.fl_str_mv Revista ciência agronômica (Online) - Universidade Federal do Ceará (UFC)
repository.mail.fl_str_mv ||alekdutra@ufc.br|| ccarev@ufc.br
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