Genetic progress in upland rice breeding program for grain yield and plant height

Detalhes bibliográficos
Autor(a) principal: Moura,Amanda Mendes de
Data de Publicação: 2021
Outros Autores: Botelho,Flávia Barbosa Silva, Tomé,Laís Moretti, Rodrigues,Cinthia Souza, Silva,Camila Soares Cardoso da, Silva,Marcos Paulo da
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Ciência e Agrotecnologia (Online)
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1413-70542021000100238
Resumo: ABSTRACT In the context of plant breeding programs, it is necessary to evaluate the efficiency of genotype selection over successive years. However, evaluating the genotype selection efficiency is not an easy task, since there is not just a single way to precede it. Besides that, the programs need to be dynamic; that is, they should be able to track the introduction and discard of genotypes each year. As a result, the available data is quite unbalanced, leading to difficulties in certain analyses. Thus, the present study aims to propose some approaches to verify the genetic progress in the preliminary trial of the Federal University of Lavras (UFLA) upland rice breeding program. We utilized mixed models for grain yield and plant height. Trials were performed with a total of 120 genotypes in seven environments, defined by the interaction between locations and years. Due to the imbalance in the available data, the mixed model approach, i.e., Restricted Maximum Likelihood/Best Linear Unbiased Prediction (REML/BLUP), was adopted for the joint analysis. Besides the genetic and phenotypic parameters, the expected gains were also obtained with the selection, genetic progress, renewal rate (RR%), and dynamism of preliminary trials. The efficiency of the selection of superior genotypes per year was verified, with genetic progress favorable for reducing the medium-sized plants associated with high yield.
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spelling Genetic progress in upland rice breeding program for grain yield and plant heightOryza sativa Lvariance componentsmixed models.ABSTRACT In the context of plant breeding programs, it is necessary to evaluate the efficiency of genotype selection over successive years. However, evaluating the genotype selection efficiency is not an easy task, since there is not just a single way to precede it. Besides that, the programs need to be dynamic; that is, they should be able to track the introduction and discard of genotypes each year. As a result, the available data is quite unbalanced, leading to difficulties in certain analyses. Thus, the present study aims to propose some approaches to verify the genetic progress in the preliminary trial of the Federal University of Lavras (UFLA) upland rice breeding program. We utilized mixed models for grain yield and plant height. Trials were performed with a total of 120 genotypes in seven environments, defined by the interaction between locations and years. Due to the imbalance in the available data, the mixed model approach, i.e., Restricted Maximum Likelihood/Best Linear Unbiased Prediction (REML/BLUP), was adopted for the joint analysis. Besides the genetic and phenotypic parameters, the expected gains were also obtained with the selection, genetic progress, renewal rate (RR%), and dynamism of preliminary trials. The efficiency of the selection of superior genotypes per year was verified, with genetic progress favorable for reducing the medium-sized plants associated with high yield.Editora da UFLA2021-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1413-70542021000100238Ciência e Agrotecnologia v.45 2021reponame:Ciência e Agrotecnologia (Online)instname:Universidade Federal de Lavras (UFLA)instacron:UFLA10.1590/1413-7054202145010421info:eu-repo/semantics/openAccessMoura,Amanda Mendes deBotelho,Flávia Barbosa SilvaTomé,Laís MorettiRodrigues,Cinthia SouzaSilva,Camila Soares Cardoso daSilva,Marcos Paulo daeng2021-12-06T00:00:00Zoai:scielo:S1413-70542021000100238Revistahttp://www.scielo.br/cagroPUBhttps://old.scielo.br/oai/scielo-oai.php||renpaiva@dbi.ufla.br|| editora@editora.ufla.br1981-18291413-7054opendoar:2022-11-22T16:31:46.210073Ciência e Agrotecnologia (Online) - Universidade Federal de Lavras (UFLA)true
dc.title.none.fl_str_mv Genetic progress in upland rice breeding program for grain yield and plant height
title Genetic progress in upland rice breeding program for grain yield and plant height
spellingShingle Genetic progress in upland rice breeding program for grain yield and plant height
Moura,Amanda Mendes de
Oryza sativa L
variance components
mixed models.
title_short Genetic progress in upland rice breeding program for grain yield and plant height
title_full Genetic progress in upland rice breeding program for grain yield and plant height
title_fullStr Genetic progress in upland rice breeding program for grain yield and plant height
title_full_unstemmed Genetic progress in upland rice breeding program for grain yield and plant height
title_sort Genetic progress in upland rice breeding program for grain yield and plant height
author Moura,Amanda Mendes de
author_facet Moura,Amanda Mendes de
Botelho,Flávia Barbosa Silva
Tomé,Laís Moretti
Rodrigues,Cinthia Souza
Silva,Camila Soares Cardoso da
Silva,Marcos Paulo da
author_role author
author2 Botelho,Flávia Barbosa Silva
Tomé,Laís Moretti
Rodrigues,Cinthia Souza
Silva,Camila Soares Cardoso da
Silva,Marcos Paulo da
author2_role author
author
author
author
author
dc.contributor.author.fl_str_mv Moura,Amanda Mendes de
Botelho,Flávia Barbosa Silva
Tomé,Laís Moretti
Rodrigues,Cinthia Souza
Silva,Camila Soares Cardoso da
Silva,Marcos Paulo da
dc.subject.por.fl_str_mv Oryza sativa L
variance components
mixed models.
topic Oryza sativa L
variance components
mixed models.
description ABSTRACT In the context of plant breeding programs, it is necessary to evaluate the efficiency of genotype selection over successive years. However, evaluating the genotype selection efficiency is not an easy task, since there is not just a single way to precede it. Besides that, the programs need to be dynamic; that is, they should be able to track the introduction and discard of genotypes each year. As a result, the available data is quite unbalanced, leading to difficulties in certain analyses. Thus, the present study aims to propose some approaches to verify the genetic progress in the preliminary trial of the Federal University of Lavras (UFLA) upland rice breeding program. We utilized mixed models for grain yield and plant height. Trials were performed with a total of 120 genotypes in seven environments, defined by the interaction between locations and years. Due to the imbalance in the available data, the mixed model approach, i.e., Restricted Maximum Likelihood/Best Linear Unbiased Prediction (REML/BLUP), was adopted for the joint analysis. Besides the genetic and phenotypic parameters, the expected gains were also obtained with the selection, genetic progress, renewal rate (RR%), and dynamism of preliminary trials. The efficiency of the selection of superior genotypes per year was verified, with genetic progress favorable for reducing the medium-sized plants associated with high yield.
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=S1413-70542021000100238
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1413-70542021000100238
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/1413-7054202145010421
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 Editora da UFLA
publisher.none.fl_str_mv Editora da UFLA
dc.source.none.fl_str_mv Ciência e Agrotecnologia v.45 2021
reponame:Ciência e Agrotecnologia (Online)
instname:Universidade Federal de Lavras (UFLA)
instacron:UFLA
instname_str Universidade Federal de Lavras (UFLA)
instacron_str UFLA
institution UFLA
reponame_str Ciência e Agrotecnologia (Online)
collection Ciência e Agrotecnologia (Online)
repository.name.fl_str_mv Ciência e Agrotecnologia (Online) - Universidade Federal de Lavras (UFLA)
repository.mail.fl_str_mv ||renpaiva@dbi.ufla.br|| editora@editora.ufla.br
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