Inference of population effect and progeny selection via a multi-trait index in soybean breeding

Detalhes bibliográficos
Autor(a) principal: Volpato, Leonardo
Data de Publicação: 2020
Outros Autores: Rocha, João Romero do Amaral Santos de Carvalho, Alves, Rodrigo Silva, Ludke, Willian Hytalo, Borém, Aluízio, Silva, Felipe Lopes
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Acta Scientiarum. Agronomy (Online)
DOI: 10.4025/actasciagron.v43i1.44623
Texto Completo: http://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/44623
Resumo: The selection of superior genotypes of soybean entails a simultaneous evaluation of a number of favorable traits that provide a comparatively superior yield. Disregarding the population effect in the statistical model may compromise the estimate of variance components and the prediction of genetic values. The present study was undertaken to investigate the importance of including population effect in the statistical model and to determine the effectiveness of the index based on factor analysis and ideotype design via best linear unbiased prediction (FAI-BLUP) in the selection of erect, early, and high-yielding soybean progenies. To attain these objectives, 204 soybean progenies originating from three populations were examined for various traits of agronomic interest. The inclusion of the population effect in the statistical model was relevant in the genetic evaluation of soybean progenies. To quantify the effectiveness of the FAI-BLUP index, genetic gains were predicted and compared with those obtained by the Smith-Hazel and Additive Genetic indices. The FAI-BLUP index was effective in the selection of progenies with balanced, desirable genetic gains for all traits simultaneously. Therefore, the FAI-BLUP index is an adequate tool for the simultaneous selection of important traits in soybean breeding.
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spelling Inference of population effect and progeny selection via a multi-trait index in soybean breedingInference of population effect and progeny selection via a multi-trait index in soybean breedingmixed-model methodology; best linear unbiased prediction; selection index; genotype x environment interaction; factor analysis; ideotype design.Genética Vegetalmixed-model methodology; best linear unbiased prediction; selection index; genotype x environment interaction; factor analysis; ideotype design.The selection of superior genotypes of soybean entails a simultaneous evaluation of a number of favorable traits that provide a comparatively superior yield. Disregarding the population effect in the statistical model may compromise the estimate of variance components and the prediction of genetic values. The present study was undertaken to investigate the importance of including population effect in the statistical model and to determine the effectiveness of the index based on factor analysis and ideotype design via best linear unbiased prediction (FAI-BLUP) in the selection of erect, early, and high-yielding soybean progenies. To attain these objectives, 204 soybean progenies originating from three populations were examined for various traits of agronomic interest. The inclusion of the population effect in the statistical model was relevant in the genetic evaluation of soybean progenies. To quantify the effectiveness of the FAI-BLUP index, genetic gains were predicted and compared with those obtained by the Smith-Hazel and Additive Genetic indices. The FAI-BLUP index was effective in the selection of progenies with balanced, desirable genetic gains for all traits simultaneously. Therefore, the FAI-BLUP index is an adequate tool for the simultaneous selection of important traits in soybean breeding.The selection of superior genotypes of soybean entails a simultaneous evaluation of a number of favorable traits that provide a comparatively superior yield. Disregarding the population effect in the statistical model may compromise the estimate of variance components and the prediction of genetic values. The present study was undertaken to investigate the importance of including population effect in the statistical model and to determine the effectiveness of the index based on factor analysis and ideotype design via best linear unbiased prediction (FAI-BLUP) in the selection of erect, early, and high-yielding soybean progenies. To attain these objectives, 204 soybean progenies originating from three populations were examined for various traits of agronomic interest. The inclusion of the population effect in the statistical model was relevant in the genetic evaluation of soybean progenies. To quantify the effectiveness of the FAI-BLUP index, genetic gains were predicted and compared with those obtained by the Smith-Hazel and Additive Genetic indices. The FAI-BLUP index was effective in the selection of progenies with balanced, desirable genetic gains for all traits simultaneously. Therefore, the FAI-BLUP index is an adequate tool for the simultaneous selection of important traits in soybean breeding.Universidade Estadual de Maringá2020-08-14info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionPesquisa Empírica de Campoapplication/pdfhttp://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/4462310.4025/actasciagron.v43i1.44623Acta Scientiarum. Agronomy; Vol 43 (2021): Publicação contínua; e44623Acta Scientiarum. Agronomy; v. 43 (2021): Publicação contínua; e446231807-86211679-9275reponame:Acta Scientiarum. Agronomy (Online)instname:Universidade Estadual de Maringá (UEM)instacron:UEMenghttp://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/44623/751375150513Copyright (c) 2021 Acta Scientiarum. Agronomyhttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessVolpato, LeonardoRocha, João Romero do Amaral Santos de CarvalhoAlves, Rodrigo SilvaLudke, Willian HytaloBorém, AluízioSilva, Felipe Lopes2022-02-16T21:47:05Zoai:periodicos.uem.br/ojs:article/44623Revistahttp://www.periodicos.uem.br/ojs/index.php/ActaSciAgronPUBhttp://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/oaiactaagron@uem.br||actaagron@uem.br|| edamasio@uem.br1807-86211679-9275opendoar:2022-02-16T21:47:05Acta Scientiarum. Agronomy (Online) - Universidade Estadual de Maringá (UEM)false
dc.title.none.fl_str_mv Inference of population effect and progeny selection via a multi-trait index in soybean breeding
Inference of population effect and progeny selection via a multi-trait index in soybean breeding
title Inference of population effect and progeny selection via a multi-trait index in soybean breeding
spellingShingle Inference of population effect and progeny selection via a multi-trait index in soybean breeding
Inference of population effect and progeny selection via a multi-trait index in soybean breeding
Volpato, Leonardo
mixed-model methodology; best linear unbiased prediction; selection index; genotype x environment interaction; factor analysis; ideotype design.
Genética Vegetal
mixed-model methodology; best linear unbiased prediction; selection index; genotype x environment interaction; factor analysis; ideotype design.
Volpato, Leonardo
mixed-model methodology; best linear unbiased prediction; selection index; genotype x environment interaction; factor analysis; ideotype design.
Genética Vegetal
mixed-model methodology; best linear unbiased prediction; selection index; genotype x environment interaction; factor analysis; ideotype design.
title_short Inference of population effect and progeny selection via a multi-trait index in soybean breeding
title_full Inference of population effect and progeny selection via a multi-trait index in soybean breeding
title_fullStr Inference of population effect and progeny selection via a multi-trait index in soybean breeding
Inference of population effect and progeny selection via a multi-trait index in soybean breeding
title_full_unstemmed Inference of population effect and progeny selection via a multi-trait index in soybean breeding
Inference of population effect and progeny selection via a multi-trait index in soybean breeding
title_sort Inference of population effect and progeny selection via a multi-trait index in soybean breeding
author Volpato, Leonardo
author_facet Volpato, Leonardo
Volpato, Leonardo
Rocha, João Romero do Amaral Santos de Carvalho
Alves, Rodrigo Silva
Ludke, Willian Hytalo
Borém, Aluízio
Silva, Felipe Lopes
Rocha, João Romero do Amaral Santos de Carvalho
Alves, Rodrigo Silva
Ludke, Willian Hytalo
Borém, Aluízio
Silva, Felipe Lopes
author_role author
author2 Rocha, João Romero do Amaral Santos de Carvalho
Alves, Rodrigo Silva
Ludke, Willian Hytalo
Borém, Aluízio
Silva, Felipe Lopes
author2_role author
author
author
author
author
dc.contributor.author.fl_str_mv Volpato, Leonardo
Rocha, João Romero do Amaral Santos de Carvalho
Alves, Rodrigo Silva
Ludke, Willian Hytalo
Borém, Aluízio
Silva, Felipe Lopes
dc.subject.por.fl_str_mv mixed-model methodology; best linear unbiased prediction; selection index; genotype x environment interaction; factor analysis; ideotype design.
Genética Vegetal
mixed-model methodology; best linear unbiased prediction; selection index; genotype x environment interaction; factor analysis; ideotype design.
topic mixed-model methodology; best linear unbiased prediction; selection index; genotype x environment interaction; factor analysis; ideotype design.
Genética Vegetal
mixed-model methodology; best linear unbiased prediction; selection index; genotype x environment interaction; factor analysis; ideotype design.
description The selection of superior genotypes of soybean entails a simultaneous evaluation of a number of favorable traits that provide a comparatively superior yield. Disregarding the population effect in the statistical model may compromise the estimate of variance components and the prediction of genetic values. The present study was undertaken to investigate the importance of including population effect in the statistical model and to determine the effectiveness of the index based on factor analysis and ideotype design via best linear unbiased prediction (FAI-BLUP) in the selection of erect, early, and high-yielding soybean progenies. To attain these objectives, 204 soybean progenies originating from three populations were examined for various traits of agronomic interest. The inclusion of the population effect in the statistical model was relevant in the genetic evaluation of soybean progenies. To quantify the effectiveness of the FAI-BLUP index, genetic gains were predicted and compared with those obtained by the Smith-Hazel and Additive Genetic indices. The FAI-BLUP index was effective in the selection of progenies with balanced, desirable genetic gains for all traits simultaneously. Therefore, the FAI-BLUP index is an adequate tool for the simultaneous selection of important traits in soybean breeding.
publishDate 2020
dc.date.none.fl_str_mv 2020-08-14
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Pesquisa Empírica de Campo
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/44623
10.4025/actasciagron.v43i1.44623
url http://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/44623
identifier_str_mv 10.4025/actasciagron.v43i1.44623
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv http://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/44623/751375150513
dc.rights.driver.fl_str_mv Copyright (c) 2021 Acta Scientiarum. Agronomy
https://creativecommons.org/licenses/by/4.0
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Copyright (c) 2021 Acta Scientiarum. Agronomy
https://creativecommons.org/licenses/by/4.0
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universidade Estadual de Maringá
publisher.none.fl_str_mv Universidade Estadual de Maringá
dc.source.none.fl_str_mv Acta Scientiarum. Agronomy; Vol 43 (2021): Publicação contínua; e44623
Acta Scientiarum. Agronomy; v. 43 (2021): Publicação contínua; e44623
1807-8621
1679-9275
reponame:Acta Scientiarum. Agronomy (Online)
instname:Universidade Estadual de Maringá (UEM)
instacron:UEM
instname_str Universidade Estadual de Maringá (UEM)
instacron_str UEM
institution UEM
reponame_str Acta Scientiarum. Agronomy (Online)
collection Acta Scientiarum. Agronomy (Online)
repository.name.fl_str_mv Acta Scientiarum. Agronomy (Online) - Universidade Estadual de Maringá (UEM)
repository.mail.fl_str_mv actaagron@uem.br||actaagron@uem.br|| edamasio@uem.br
_version_ 1822182776903827456
dc.identifier.doi.none.fl_str_mv 10.4025/actasciagron.v43i1.44623

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