Genotype plus genotype by-environment interaction biplot and genetic diversity analyses on multi-environment trials data of yield and technological traits of cotton cultivars
Autor(a) principal: | |
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Data de Publicação: | 2022 |
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=S0103-84782022000200201 |
Resumo: | ABSTRACT: Understanding the genetic diversity and overcoming genotype-by-environment interaction issues is an essential step in breeding programs that aims to improve the performance of desirable traits. This study estimated genetic diversity and applied genotype + genotype-by-environment (GGE) biplot analyses in cotton genotypes. Twelve genotypes were evaluated for fiber yield, fiber length, fiber strength, and micronaire. Estimation of variance components and genetic parameters was made through restricted maximum likelihood and the prediction of genotypic values was made through best linear unbiased prediction. The modified Tocher and principal component analysis (PCA) methods, were used to quantify genetic diversity among genotypes. GGE biplot was performed to find the best genotypes regarding adaptability and stability. The Tocher technique and PCA allowed for the formation of clusters of similar genotypes based on a multivariate framework. The GGE biplot indicated that the genotypes IMACV 690 and IMA08 WS were highly adaptable and stable for the main traits in cotton. The cross between the genotype IMACV 690 and IMA08 WS is the most recommended to increase the performance of the main traits in cotton crops. |
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Genotype plus genotype by-environment interaction biplot and genetic diversity analyses on multi-environment trials data of yield and technological traits of cotton cultivarsfiber qualitygenotype-by-environment interactionGossypium hirsutumprincipal component analysesABSTRACT: Understanding the genetic diversity and overcoming genotype-by-environment interaction issues is an essential step in breeding programs that aims to improve the performance of desirable traits. This study estimated genetic diversity and applied genotype + genotype-by-environment (GGE) biplot analyses in cotton genotypes. Twelve genotypes were evaluated for fiber yield, fiber length, fiber strength, and micronaire. Estimation of variance components and genetic parameters was made through restricted maximum likelihood and the prediction of genotypic values was made through best linear unbiased prediction. The modified Tocher and principal component analysis (PCA) methods, were used to quantify genetic diversity among genotypes. GGE biplot was performed to find the best genotypes regarding adaptability and stability. The Tocher technique and PCA allowed for the formation of clusters of similar genotypes based on a multivariate framework. The GGE biplot indicated that the genotypes IMACV 690 and IMA08 WS were highly adaptable and stable for the main traits in cotton. The cross between the genotype IMACV 690 and IMA08 WS is the most recommended to increase the performance of the main traits in cotton crops.Universidade Federal de Santa Maria2022-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-84782022000200201Ciência Rural v.52 n.2 2022reponame:Ciência Ruralinstname:Universidade Federal de Santa Maria (UFSM)instacron:UFSM10.1590/0103-8478cr20201054info:eu-repo/semantics/openAccessPeixoto,Marco AntônioMalikouski,Renan GarciaNascimento,Emanuel Ferrari doSchuster,AndreiaFarias,Francisco José CorreiaCarvalho,Luiz PauloTeodoro,Paulo EduardoBhering,Leonardo Lopeseng2021-08-18T00:00:00ZRevista |
dc.title.none.fl_str_mv |
Genotype plus genotype by-environment interaction biplot and genetic diversity analyses on multi-environment trials data of yield and technological traits of cotton cultivars |
title |
Genotype plus genotype by-environment interaction biplot and genetic diversity analyses on multi-environment trials data of yield and technological traits of cotton cultivars |
spellingShingle |
Genotype plus genotype by-environment interaction biplot and genetic diversity analyses on multi-environment trials data of yield and technological traits of cotton cultivars Peixoto,Marco Antônio fiber quality genotype-by-environment interaction Gossypium hirsutum principal component analyses |
title_short |
Genotype plus genotype by-environment interaction biplot and genetic diversity analyses on multi-environment trials data of yield and technological traits of cotton cultivars |
title_full |
Genotype plus genotype by-environment interaction biplot and genetic diversity analyses on multi-environment trials data of yield and technological traits of cotton cultivars |
title_fullStr |
Genotype plus genotype by-environment interaction biplot and genetic diversity analyses on multi-environment trials data of yield and technological traits of cotton cultivars |
title_full_unstemmed |
Genotype plus genotype by-environment interaction biplot and genetic diversity analyses on multi-environment trials data of yield and technological traits of cotton cultivars |
title_sort |
Genotype plus genotype by-environment interaction biplot and genetic diversity analyses on multi-environment trials data of yield and technological traits of cotton cultivars |
author |
Peixoto,Marco Antônio |
author_facet |
Peixoto,Marco Antônio Malikouski,Renan Garcia Nascimento,Emanuel Ferrari do Schuster,Andreia Farias,Francisco José Correia Carvalho,Luiz Paulo Teodoro,Paulo Eduardo Bhering,Leonardo Lopes |
author_role |
author |
author2 |
Malikouski,Renan Garcia Nascimento,Emanuel Ferrari do Schuster,Andreia Farias,Francisco José Correia Carvalho,Luiz Paulo Teodoro,Paulo Eduardo Bhering,Leonardo Lopes |
author2_role |
author author author author author author author |
dc.contributor.author.fl_str_mv |
Peixoto,Marco Antônio Malikouski,Renan Garcia Nascimento,Emanuel Ferrari do Schuster,Andreia Farias,Francisco José Correia Carvalho,Luiz Paulo Teodoro,Paulo Eduardo Bhering,Leonardo Lopes |
dc.subject.por.fl_str_mv |
fiber quality genotype-by-environment interaction Gossypium hirsutum principal component analyses |
topic |
fiber quality genotype-by-environment interaction Gossypium hirsutum principal component analyses |
description |
ABSTRACT: Understanding the genetic diversity and overcoming genotype-by-environment interaction issues is an essential step in breeding programs that aims to improve the performance of desirable traits. This study estimated genetic diversity and applied genotype + genotype-by-environment (GGE) biplot analyses in cotton genotypes. Twelve genotypes were evaluated for fiber yield, fiber length, fiber strength, and micronaire. Estimation of variance components and genetic parameters was made through restricted maximum likelihood and the prediction of genotypic values was made through best linear unbiased prediction. The modified Tocher and principal component analysis (PCA) methods, were used to quantify genetic diversity among genotypes. GGE biplot was performed to find the best genotypes regarding adaptability and stability. The Tocher technique and PCA allowed for the formation of clusters of similar genotypes based on a multivariate framework. The GGE biplot indicated that the genotypes IMACV 690 and IMA08 WS were highly adaptable and stable for the main traits in cotton. The cross between the genotype IMACV 690 and IMA08 WS is the most recommended to increase the performance of the main traits in cotton crops. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-01-01 |
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=S0103-84782022000200201 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-84782022000200201 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/0103-8478cr20201054 |
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.52 n.2 2022 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_ |
1749140556532940800 |