Population parameters and selection of kale genotypes using Bayesian inference in a multi-trait linear model
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Acta Scientiarum. Agronomy (Online) |
Texto Completo: | http://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/30856 |
Resumo: | Variance components must be obtained to estimate genetic parameters and predict breeding values. This information can be obtained through Bayesian inference. When multiple traits are evaluated, Bayesian inference can be used in multi-trait models. The objective of this study was to obtain estimates of genetic parameters, gains with selection, and genetic correlations among traits. Likewise, we aim to predict the genetic values and select the best kale genotypes using the Bayesian approach in a multi-trait linear model. The following traits were evaluated: stem diameter, plant height, number of shoots, number of marketable leaves and fresh weight of leaves using Bayesian inference in 22 kale genotypes. The experiment consisted of a randomized block design with three replications and four plants per plot. Genetic effects predominated over environmental effects. The highest correlation estimates were found between the fresh weight of leaves and stem diameter and between the plant height and number of marketable leaves. The following commercial cultivars and genotypes are recommended for cultivation and to integrate into breeding programs: UFLA 11, UFLA 5, UFLA 6, UFVJM 3 and UFVJM 19. The estimates of the gain with selection indicate the potential for improvement of the studied population. |
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Acta Scientiarum. Agronomy (Online) |
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Population parameters and selection of kale genotypes using Bayesian inference in a multi-trait linear modelBrassica oleracea L. var. acephala DC.genetic parameterscrop breedingstatistical modelingcorrelationsMelhoramento genético de plantas Variance components must be obtained to estimate genetic parameters and predict breeding values. This information can be obtained through Bayesian inference. When multiple traits are evaluated, Bayesian inference can be used in multi-trait models. The objective of this study was to obtain estimates of genetic parameters, gains with selection, and genetic correlations among traits. Likewise, we aim to predict the genetic values and select the best kale genotypes using the Bayesian approach in a multi-trait linear model. The following traits were evaluated: stem diameter, plant height, number of shoots, number of marketable leaves and fresh weight of leaves using Bayesian inference in 22 kale genotypes. The experiment consisted of a randomized block design with three replications and four plants per plot. Genetic effects predominated over environmental effects. The highest correlation estimates were found between the fresh weight of leaves and stem diameter and between the plant height and number of marketable leaves. The following commercial cultivars and genotypes are recommended for cultivation and to integrate into breeding programs: UFLA 11, UFLA 5, UFLA 6, UFVJM 3 and UFVJM 19. The estimates of the gain with selection indicate the potential for improvement of the studied population. Universidade Estadual de Maringá2017-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionPesquisa de campoapplication/pdfapplication/ziphttp://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/3085610.4025/actasciagron.v39i1.30856Acta Scientiarum. Agronomy; Vol 39 No 1 (2017); 25-31Acta Scientiarum. Agronomy; v. 39 n. 1 (2017); 25-311807-86211679-9275reponame:Acta Scientiarum. Agronomy (Online)instname:Universidade Estadual de Maringá (UEM)instacron:UEMenghttp://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/30856/pdfhttp://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/30856/751375144530Copyright (c) 2017 Acta Scientiarum. Agronomyinfo:eu-repo/semantics/openAccessAzevedo, Alcinei MisticoAndrade júnior, Valter Carvalho deSantos, Albertir Aparecido dosSousa Júnior, Aderbal Soares deOliveira, Altino Júnior MendesFerreira, Marcos Aurélio Miranda2022-02-20T21:47:24Zoai:periodicos.uem.br/ojs:article/30856Revistahttp://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:2022-02-20T21:47:24Acta Scientiarum. Agronomy (Online) - Universidade Estadual de Maringá (UEM)false |
dc.title.none.fl_str_mv |
Population parameters and selection of kale genotypes using Bayesian inference in a multi-trait linear model |
title |
Population parameters and selection of kale genotypes using Bayesian inference in a multi-trait linear model |
spellingShingle |
Population parameters and selection of kale genotypes using Bayesian inference in a multi-trait linear model Azevedo, Alcinei Mistico Brassica oleracea L. var. acephala DC. genetic parameters crop breeding statistical modeling correlations Melhoramento genético de plantas |
title_short |
Population parameters and selection of kale genotypes using Bayesian inference in a multi-trait linear model |
title_full |
Population parameters and selection of kale genotypes using Bayesian inference in a multi-trait linear model |
title_fullStr |
Population parameters and selection of kale genotypes using Bayesian inference in a multi-trait linear model |
title_full_unstemmed |
Population parameters and selection of kale genotypes using Bayesian inference in a multi-trait linear model |
title_sort |
Population parameters and selection of kale genotypes using Bayesian inference in a multi-trait linear model |
author |
Azevedo, Alcinei Mistico |
author_facet |
Azevedo, Alcinei Mistico Andrade júnior, Valter Carvalho de Santos, Albertir Aparecido dos Sousa Júnior, Aderbal Soares de Oliveira, Altino Júnior Mendes Ferreira, Marcos Aurélio Miranda |
author_role |
author |
author2 |
Andrade júnior, Valter Carvalho de Santos, Albertir Aparecido dos Sousa Júnior, Aderbal Soares de Oliveira, Altino Júnior Mendes Ferreira, Marcos Aurélio Miranda |
author2_role |
author author author author author |
dc.contributor.author.fl_str_mv |
Azevedo, Alcinei Mistico Andrade júnior, Valter Carvalho de Santos, Albertir Aparecido dos Sousa Júnior, Aderbal Soares de Oliveira, Altino Júnior Mendes Ferreira, Marcos Aurélio Miranda |
dc.subject.por.fl_str_mv |
Brassica oleracea L. var. acephala DC. genetic parameters crop breeding statistical modeling correlations Melhoramento genético de plantas |
topic |
Brassica oleracea L. var. acephala DC. genetic parameters crop breeding statistical modeling correlations Melhoramento genético de plantas |
description |
Variance components must be obtained to estimate genetic parameters and predict breeding values. This information can be obtained through Bayesian inference. When multiple traits are evaluated, Bayesian inference can be used in multi-trait models. The objective of this study was to obtain estimates of genetic parameters, gains with selection, and genetic correlations among traits. Likewise, we aim to predict the genetic values and select the best kale genotypes using the Bayesian approach in a multi-trait linear model. The following traits were evaluated: stem diameter, plant height, number of shoots, number of marketable leaves and fresh weight of leaves using Bayesian inference in 22 kale genotypes. The experiment consisted of a randomized block design with three replications and four plants per plot. Genetic effects predominated over environmental effects. The highest correlation estimates were found between the fresh weight of leaves and stem diameter and between the plant height and number of marketable leaves. The following commercial cultivars and genotypes are recommended for cultivation and to integrate into breeding programs: UFLA 11, UFLA 5, UFLA 6, UFVJM 3 and UFVJM 19. The estimates of the gain with selection indicate the potential for improvement of the studied population. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017-01-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Pesquisa de campo |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/30856 10.4025/actasciagron.v39i1.30856 |
url |
http://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/30856 |
identifier_str_mv |
10.4025/actasciagron.v39i1.30856 |
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/30856/pdf http://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/30856/751375144530 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2017 Acta Scientiarum. Agronomy info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2017 Acta Scientiarum. Agronomy |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf application/zip |
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 39 No 1 (2017); 25-31 Acta Scientiarum. Agronomy; v. 39 n. 1 (2017); 25-31 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_ |
1799305909863710720 |