Models for optimizing selection based on adaptability and stability of cotton genotypes
Autor(a) principal: | |
---|---|
Data de Publicação: | 2021 |
Outros Autores: | , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Ciência Rural |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-84782021000500403 |
Resumo: | ABSTRACT: In multi-environment trials (MET), large networks are assessed for results improvement. However, genotype by environment interaction plays an important role in the selection of the most adaptable and stable genotypes in MET framework. In this study, we tested different residual variances and measure the selection gain of cotton genotypes accounting for adaptability and stability, simultaneously. Twelve genotypes of cotton were bred in 10 environments, and fiber length (FL), fiber strength (FS), micronaire (MIC), and fiber yield (FY) were determined. Model selection for different residual variance structures (homogeneous and heterogeneous) was tested using the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). The variance components were estimated through restricted maximum likelihood and genotypic values were predicted through best linear unbiased prediction. The harmonic mean of relative performance of genetic values (HMRPGV) were applied for simultaneous selection for adaptability, stability, and yield. According to BIC heterogeneous residual variance was the best model fit for FY, whereas homogeneous residual variance was the best model fit for FL, FS, and MIC traits. The selective accuracy was high, indicating reliability of the prediction. The HMRPGV was capable to select for stability, adaptability and yield simultaneously, with remarkable selection gain for each trait. |
id |
UFSM-2_e099f2370fcc43d68c87acc10ea2050c |
---|---|
oai_identifier_str |
oai:scielo:S0103-84782021000500403 |
network_acronym_str |
UFSM-2 |
network_name_str |
Ciência rural (Online) |
repository_id_str |
|
spelling |
Models for optimizing selection based on adaptability and stability of cotton genotypesBICGossypium hirsutumHMRPGVmulti-environment trialsREML/BLUP.ABSTRACT: In multi-environment trials (MET), large networks are assessed for results improvement. However, genotype by environment interaction plays an important role in the selection of the most adaptable and stable genotypes in MET framework. In this study, we tested different residual variances and measure the selection gain of cotton genotypes accounting for adaptability and stability, simultaneously. Twelve genotypes of cotton were bred in 10 environments, and fiber length (FL), fiber strength (FS), micronaire (MIC), and fiber yield (FY) were determined. Model selection for different residual variance structures (homogeneous and heterogeneous) was tested using the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). The variance components were estimated through restricted maximum likelihood and genotypic values were predicted through best linear unbiased prediction. The harmonic mean of relative performance of genetic values (HMRPGV) were applied for simultaneous selection for adaptability, stability, and yield. According to BIC heterogeneous residual variance was the best model fit for FY, whereas homogeneous residual variance was the best model fit for FL, FS, and MIC traits. The selective accuracy was high, indicating reliability of the prediction. The HMRPGV was capable to select for stability, adaptability and yield simultaneously, with remarkable selection gain for each trait.Universidade Federal de Santa Maria2021-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-84782021000500403Ciência Rural v.51 n.5 2021reponame:Ciência Ruralinstname:Universidade Federal de Santa Maria (UFSM)instacron:UFSM10.1590/0103-8478cr20200530info:eu-repo/semantics/openAccessPeixoto,Marco AntônioEvangelista,Jeniffer Santana Pinto CoelhoAlves,Rodrigo SilvaFarias,Francisco José CorreaCarvalho,Luiz PauloTeodoro,Larissa Pereira RibeiroTeodoro,Paulo EduardoBhering,Leonardo Lopeseng2021-03-10T00:00:00ZRevista |
dc.title.none.fl_str_mv |
Models for optimizing selection based on adaptability and stability of cotton genotypes |
title |
Models for optimizing selection based on adaptability and stability of cotton genotypes |
spellingShingle |
Models for optimizing selection based on adaptability and stability of cotton genotypes Peixoto,Marco Antônio BIC Gossypium hirsutum HMRPGV multi-environment trials REML/BLUP. |
title_short |
Models for optimizing selection based on adaptability and stability of cotton genotypes |
title_full |
Models for optimizing selection based on adaptability and stability of cotton genotypes |
title_fullStr |
Models for optimizing selection based on adaptability and stability of cotton genotypes |
title_full_unstemmed |
Models for optimizing selection based on adaptability and stability of cotton genotypes |
title_sort |
Models for optimizing selection based on adaptability and stability of cotton genotypes |
author |
Peixoto,Marco Antônio |
author_facet |
Peixoto,Marco Antônio Evangelista,Jeniffer Santana Pinto Coelho Alves,Rodrigo Silva Farias,Francisco José Correa Carvalho,Luiz Paulo Teodoro,Larissa Pereira Ribeiro Teodoro,Paulo Eduardo Bhering,Leonardo Lopes |
author_role |
author |
author2 |
Evangelista,Jeniffer Santana Pinto Coelho Alves,Rodrigo Silva Farias,Francisco José Correa Carvalho,Luiz Paulo Teodoro,Larissa Pereira Ribeiro Teodoro,Paulo Eduardo Bhering,Leonardo Lopes |
author2_role |
author author author author author author author |
dc.contributor.author.fl_str_mv |
Peixoto,Marco Antônio Evangelista,Jeniffer Santana Pinto Coelho Alves,Rodrigo Silva Farias,Francisco José Correa Carvalho,Luiz Paulo Teodoro,Larissa Pereira Ribeiro Teodoro,Paulo Eduardo Bhering,Leonardo Lopes |
dc.subject.por.fl_str_mv |
BIC Gossypium hirsutum HMRPGV multi-environment trials REML/BLUP. |
topic |
BIC Gossypium hirsutum HMRPGV multi-environment trials REML/BLUP. |
description |
ABSTRACT: In multi-environment trials (MET), large networks are assessed for results improvement. However, genotype by environment interaction plays an important role in the selection of the most adaptable and stable genotypes in MET framework. In this study, we tested different residual variances and measure the selection gain of cotton genotypes accounting for adaptability and stability, simultaneously. Twelve genotypes of cotton were bred in 10 environments, and fiber length (FL), fiber strength (FS), micronaire (MIC), and fiber yield (FY) were determined. Model selection for different residual variance structures (homogeneous and heterogeneous) was tested using the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). The variance components were estimated through restricted maximum likelihood and genotypic values were predicted through best linear unbiased prediction. The harmonic mean of relative performance of genetic values (HMRPGV) were applied for simultaneous selection for adaptability, stability, and yield. According to BIC heterogeneous residual variance was the best model fit for FY, whereas homogeneous residual variance was the best model fit for FL, FS, and MIC traits. The selective accuracy was high, indicating reliability of the prediction. The HMRPGV was capable to select for stability, adaptability and yield simultaneously, with remarkable selection gain for each trait. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-01-01 |
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=S0103-84782021000500403 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-84782021000500403 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/0103-8478cr20200530 |
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 de Santa Maria |
publisher.none.fl_str_mv |
Universidade Federal de Santa Maria |
dc.source.none.fl_str_mv |
Ciência Rural v.51 n.5 2021 reponame:Ciência Rural instname:Universidade Federal de Santa Maria (UFSM) instacron:UFSM |
instname_str |
Universidade Federal de Santa Maria (UFSM) |
instacron_str |
UFSM |
institution |
UFSM |
reponame_str |
Ciência Rural |
collection |
Ciência Rural |
repository.name.fl_str_mv |
|
repository.mail.fl_str_mv |
|
_version_ |
1749140555719245824 |