Prediction of genetic gains by mixed models in conilon coffee progenies

Detalhes bibliográficos
Autor(a) principal: Carias, Cíntia Machado de Oliveira Moulin
Data de Publicação: 2016
Outros Autores: Gravina, Geraldo Amaral, Ferrão, Maria Amélia Gava, da Fonseca, Aymbiré Francisco Almeida, Ferrão, Romário Gava, Vivas, Marcelo, Viana, Alexandre Pio
Tipo de documento: Artigo
Idioma: por
Título da fonte: Coffee Science (Online)
Texto Completo: https://coffeescience.ufla.br/index.php/Coffeescience/article/view/961
Resumo: The objective of this study was to evaluate the genetic gains predicted through different selection indices by REML / BLUP methodology in five traits of interest to the breeding program of conilon coffee Incaper. Eight half-sib progenies of early maturing cycle were evaluated regarding to average of two harvests with three replications, totaling 1368 observations. Indices of classic selection, multiplicative and based on the sum of the ranks, were used. During harvest, it were evaluated the characteristics: grain size (GS), productivity (PRO), size (S), vegetative vigor (VIG) and degree of slope (GI). The population was evaluated at the Experimental Farm of Marilândia, in Northwest region of Espírito Santo. The genetic-statistical analyzes were performed by Selegen program - REM / BLUP. It was verified by the analysis of genetic parameters, an excellent selective potential among families for all evaluated characteristics. The Mulamba and Mock index showed greater efficiency for the selection of half-sib families of conilon coffee.
id UFLA-4_c77d9616d8f3f7793ec525608d6a8791
oai_identifier_str oai:coffeescience.ufla.br:article/961
network_acronym_str UFLA-4
network_name_str Coffee Science (Online)
repository_id_str
spelling Prediction of genetic gains by mixed models in conilon coffee progeniesPredição de ganhos genéticos via modelos mistos em Progênies de café conilonPredicted genotypic valuesCoffea canephoramixed linear modelsValores genotípicos preditosCoffea canephoramodelos lineares mistosThe objective of this study was to evaluate the genetic gains predicted through different selection indices by REML / BLUP methodology in five traits of interest to the breeding program of conilon coffee Incaper. Eight half-sib progenies of early maturing cycle were evaluated regarding to average of two harvests with three replications, totaling 1368 observations. Indices of classic selection, multiplicative and based on the sum of the ranks, were used. During harvest, it were evaluated the characteristics: grain size (GS), productivity (PRO), size (S), vegetative vigor (VIG) and degree of slope (GI). The population was evaluated at the Experimental Farm of Marilândia, in Northwest region of Espírito Santo. The genetic-statistical analyzes were performed by Selegen program - REM / BLUP. It was verified by the analysis of genetic parameters, an excellent selective potential among families for all evaluated characteristics. The Mulamba and Mock index showed greater efficiency for the selection of half-sib families of conilon coffee.Objetivou-se, neste trabalho, avaliar os ganhos genéticos preditos por meio de diferentes índices de seleção pela metodologia REML/BLUP, em cinco caracteres de interesse ao programa de melhoramento do café conilon do Incaper. Foram avaliadas 8 progênies de meios-irmãos, de ciclo de maturação precoce, média de duas safras, com três repetições, o que totalizou 1368 observações, utilizados os índices de seleção clássico, multiplicativo e com base na soma de postos. Avaliaram-se, na época de colheita, as características tamanho dos grãos (TG), produtividade (PRO), porte (PT), vigor vegetativo (VIG) e grau de inclinação (GI). A população foi avaliada na Fazenda Experimental de Marilândia, região Noroeste do estado do Espírito Santo. As análises genético-estatísticas foram realizadas pelo programa Selegen - REM/BLUP. Verificou-se, a partir da análise dos parâmetros genéticos, um excelente potencial seletivo entre famílias, para todas as características avaliadas. O índice Mulamba e Mock foi o que mostrou maior eficiência de seleção entre famílias de meios-irmãos de café conilon.Editora UFLA2016-03-22info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/zipapplication/zipapplication/ziphttps://coffeescience.ufla.br/index.php/Coffeescience/article/view/961Coffee Science - ISSN 1984-3909; Vol. 11 No. 1 (2016); 39 - 45Coffee Science; Vol. 11 Núm. 1 (2016); 39 - 45Coffee Science; v. 11 n. 1 (2016); 39 - 451984-3909reponame:Coffee Science (Online)instname:Universidade Federal de Lavras (UFLA)instacron:UFLAporhttps://coffeescience.ufla.br/index.php/Coffeescience/article/view/961/pdf_5https://coffeescience.ufla.br/index.php/Coffeescience/article/view/961/1491https://coffeescience.ufla.br/index.php/Coffeescience/article/view/961/1492https://coffeescience.ufla.br/index.php/Coffeescience/article/view/961/1493Copyright (c) 2016 Coffee Science - ISSN 1984-3909https://creativecommons.org/info:eu-repo/semantics/openAccessCarias, Cíntia Machado de Oliveira MoulinGravina, Geraldo AmaralFerrão, Maria Amélia Gavada Fonseca, Aymbiré Francisco AlmeidaFerrão, Romário GavaVivas, MarceloViana, Alexandre Pio2016-03-23T02:36:31Zoai:coffeescience.ufla.br:article/961Revistahttps://coffeescience.ufla.br/index.php/CoffeesciencePUBhttps://coffeescience.ufla.br/index.php/Coffeescience/oaicoffeescience@dag.ufla.br||coffeescience@dag.ufla.br|| alvaro-cozadi@hotmail.com1984-39091809-6875opendoar:2024-05-21T19:53:54.069779Coffee Science (Online) - Universidade Federal de Lavras (UFLA)true
dc.title.none.fl_str_mv Prediction of genetic gains by mixed models in conilon coffee progenies
Predição de ganhos genéticos via modelos mistos em Progênies de café conilon
title Prediction of genetic gains by mixed models in conilon coffee progenies
spellingShingle Prediction of genetic gains by mixed models in conilon coffee progenies
Carias, Cíntia Machado de Oliveira Moulin
Predicted genotypic values
Coffea canephora
mixed linear models
Valores genotípicos preditos
Coffea canephora
modelos lineares mistos
title_short Prediction of genetic gains by mixed models in conilon coffee progenies
title_full Prediction of genetic gains by mixed models in conilon coffee progenies
title_fullStr Prediction of genetic gains by mixed models in conilon coffee progenies
title_full_unstemmed Prediction of genetic gains by mixed models in conilon coffee progenies
title_sort Prediction of genetic gains by mixed models in conilon coffee progenies
author Carias, Cíntia Machado de Oliveira Moulin
author_facet Carias, Cíntia Machado de Oliveira Moulin
Gravina, Geraldo Amaral
Ferrão, Maria Amélia Gava
da Fonseca, Aymbiré Francisco Almeida
Ferrão, Romário Gava
Vivas, Marcelo
Viana, Alexandre Pio
author_role author
author2 Gravina, Geraldo Amaral
Ferrão, Maria Amélia Gava
da Fonseca, Aymbiré Francisco Almeida
Ferrão, Romário Gava
Vivas, Marcelo
Viana, Alexandre Pio
author2_role author
author
author
author
author
author
dc.contributor.author.fl_str_mv Carias, Cíntia Machado de Oliveira Moulin
Gravina, Geraldo Amaral
Ferrão, Maria Amélia Gava
da Fonseca, Aymbiré Francisco Almeida
Ferrão, Romário Gava
Vivas, Marcelo
Viana, Alexandre Pio
dc.subject.por.fl_str_mv Predicted genotypic values
Coffea canephora
mixed linear models
Valores genotípicos preditos
Coffea canephora
modelos lineares mistos
topic Predicted genotypic values
Coffea canephora
mixed linear models
Valores genotípicos preditos
Coffea canephora
modelos lineares mistos
description The objective of this study was to evaluate the genetic gains predicted through different selection indices by REML / BLUP methodology in five traits of interest to the breeding program of conilon coffee Incaper. Eight half-sib progenies of early maturing cycle were evaluated regarding to average of two harvests with three replications, totaling 1368 observations. Indices of classic selection, multiplicative and based on the sum of the ranks, were used. During harvest, it were evaluated the characteristics: grain size (GS), productivity (PRO), size (S), vegetative vigor (VIG) and degree of slope (GI). The population was evaluated at the Experimental Farm of Marilândia, in Northwest region of Espírito Santo. The genetic-statistical analyzes were performed by Selegen program - REM / BLUP. It was verified by the analysis of genetic parameters, an excellent selective potential among families for all evaluated characteristics. The Mulamba and Mock index showed greater efficiency for the selection of half-sib families of conilon coffee.
publishDate 2016
dc.date.none.fl_str_mv 2016-03-22
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://coffeescience.ufla.br/index.php/Coffeescience/article/view/961
url https://coffeescience.ufla.br/index.php/Coffeescience/article/view/961
dc.language.iso.fl_str_mv por
language por
dc.relation.none.fl_str_mv https://coffeescience.ufla.br/index.php/Coffeescience/article/view/961/pdf_5
https://coffeescience.ufla.br/index.php/Coffeescience/article/view/961/1491
https://coffeescience.ufla.br/index.php/Coffeescience/article/view/961/1492
https://coffeescience.ufla.br/index.php/Coffeescience/article/view/961/1493
dc.rights.driver.fl_str_mv Copyright (c) 2016 Coffee Science - ISSN 1984-3909
https://creativecommons.org/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Copyright (c) 2016 Coffee Science - ISSN 1984-3909
https://creativecommons.org/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/zip
application/zip
application/zip
dc.publisher.none.fl_str_mv Editora UFLA
publisher.none.fl_str_mv Editora UFLA
dc.source.none.fl_str_mv Coffee Science - ISSN 1984-3909; Vol. 11 No. 1 (2016); 39 - 45
Coffee Science; Vol. 11 Núm. 1 (2016); 39 - 45
Coffee Science; v. 11 n. 1 (2016); 39 - 45
1984-3909
reponame:Coffee Science (Online)
instname:Universidade Federal de Lavras (UFLA)
instacron:UFLA
instname_str Universidade Federal de Lavras (UFLA)
instacron_str UFLA
institution UFLA
reponame_str Coffee Science (Online)
collection Coffee Science (Online)
repository.name.fl_str_mv Coffee Science (Online) - Universidade Federal de Lavras (UFLA)
repository.mail.fl_str_mv coffeescience@dag.ufla.br||coffeescience@dag.ufla.br|| alvaro-cozadi@hotmail.com
_version_ 1799874920397668352

Registros relacionados