Spatial statistical analysis and selection of genotypes in plant breeding

Detalhes bibliográficos
Autor(a) principal: Duarte, João Batista
Data de Publicação: 2005
Outros Autores: Vencovsky, Roland
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Pesquisa Agropecuária Brasileira (Online)
Texto Completo: https://seer.sct.embrapa.br/index.php/pab/article/view/6930
Resumo: The objective of this study was to evaluate the efficiency of spatial statistical analysis in the selection of genotypes in a plant breeding program and, particularly, to demonstrate the benefits of the approach when experimental observations are not spatially independent. The basic material of this study was a yield trial of soybean lines, with five check varieties (of fixed effect) and 110 test lines (of random effects), in an augmented block design. The spatial analysis used a random field linear model (RFML), with a covariance function estimated from the residuals of the analysis considering independent errors. Results showed a residual autocorrelation of significant magnitude and extension (range), which allowed a better discrimination among genotypes (increase of the power of statistical tests, reduction in the standard errors of estimates and predictors, and a greater amplitude of predictor values) when the spatial analysis was applied. Furthermore, the spatial analysis led to a different ranking of the genetic materials, in comparison with the non-spatial analysis, and a selection less influenced by local variation effects was obtained.
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spelling Spatial statistical analysis and selection of genotypes in plant breedingSeleção de genótipos e análise estatística espacial no melhoramento de plantasaugmented design; mixed model; information recovery; autocorrelation; correlated data; geostatisticsdelineamento aumentado; modelo misto; recuperação de informação; autocorrelação; dados correlacionados; geoestatísticaThe objective of this study was to evaluate the efficiency of spatial statistical analysis in the selection of genotypes in a plant breeding program and, particularly, to demonstrate the benefits of the approach when experimental observations are not spatially independent. The basic material of this study was a yield trial of soybean lines, with five check varieties (of fixed effect) and 110 test lines (of random effects), in an augmented block design. The spatial analysis used a random field linear model (RFML), with a covariance function estimated from the residuals of the analysis considering independent errors. Results showed a residual autocorrelation of significant magnitude and extension (range), which allowed a better discrimination among genotypes (increase of the power of statistical tests, reduction in the standard errors of estimates and predictors, and a greater amplitude of predictor values) when the spatial analysis was applied. Furthermore, the spatial analysis led to a different ranking of the genetic materials, in comparison with the non-spatial analysis, and a selection less influenced by local variation effects was obtained.O objetivo deste trabalho foi avaliar a eficiência da análise estatística espacial na seleção de genótipos de plantas num programa de melhoramento. Buscou-se demonstrar os benefícios potenciais dessa abordagem quando as observações experimentais não são espacialmente independentes. O material consistiu de um ensaio de competição de linhagens de soja, com cinco cultivares testemunhas (de efeitos fixos) e 110 novos genótipos (de efeitos aleatórios), delineado em blocos aumentados. O ajuste espacial foi feito pelo modelo linear de campo aleatório (RFLM), com função de autocovariância estimada a partir dos resíduos da análise sob erros independentes. Os resultados apontaram uma autocorrelação residual de magnitude e alcance significativos, o que garantiu à abordagem espacial uma melhoria considerável na discriminação dos tratamentos genéticos – aumento do poder dos testes estatísticos, redução nos erros padrão de estimativas e de preditores e alargamento na amplitude das predições genotípicas. A análise espacial levou a um diferente ordenamento das linhagens em relação à análise não espacial e, finalmente, a uma seleção menos influenciada por efeitos da variação local.Pesquisa Agropecuaria BrasileiraPesquisa Agropecuária BrasileiraDuarte, João BatistaVencovsky, Roland2005-02-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://seer.sct.embrapa.br/index.php/pab/article/view/6930Pesquisa Agropecuaria Brasileira; v.40, n.2, fev. 2005; 107-114Pesquisa Agropecuária Brasileira; v.40, n.2, fev. 2005; 107-1141678-39210100-104xreponame:Pesquisa Agropecuária Brasileira (Online)instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa)instacron:EMBRAPAenghttps://seer.sct.embrapa.br/index.php/pab/article/view/6930/3978info:eu-repo/semantics/openAccess2010-09-09T18:38:37Zoai:ojs.seer.sct.embrapa.br:article/6930Revistahttp://seer.sct.embrapa.br/index.php/pabPRIhttps://old.scielo.br/oai/scielo-oai.phppab@sct.embrapa.br || sct.pab@embrapa.br1678-39210100-204Xopendoar:2010-09-09T18:38:37Pesquisa Agropecuária Brasileira (Online) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa)false
dc.title.none.fl_str_mv Spatial statistical analysis and selection of genotypes in plant breeding
Seleção de genótipos e análise estatística espacial no melhoramento de plantas
title Spatial statistical analysis and selection of genotypes in plant breeding
spellingShingle Spatial statistical analysis and selection of genotypes in plant breeding
Duarte, João Batista
augmented design; mixed model; information recovery; autocorrelation; correlated data; geostatistics
delineamento aumentado; modelo misto; recuperação de informação; autocorrelação; dados correlacionados; geoestatística
title_short Spatial statistical analysis and selection of genotypes in plant breeding
title_full Spatial statistical analysis and selection of genotypes in plant breeding
title_fullStr Spatial statistical analysis and selection of genotypes in plant breeding
title_full_unstemmed Spatial statistical analysis and selection of genotypes in plant breeding
title_sort Spatial statistical analysis and selection of genotypes in plant breeding
author Duarte, João Batista
author_facet Duarte, João Batista
Vencovsky, Roland
author_role author
author2 Vencovsky, Roland
author2_role author
dc.contributor.none.fl_str_mv

dc.contributor.author.fl_str_mv Duarte, João Batista
Vencovsky, Roland
dc.subject.por.fl_str_mv augmented design; mixed model; information recovery; autocorrelation; correlated data; geostatistics
delineamento aumentado; modelo misto; recuperação de informação; autocorrelação; dados correlacionados; geoestatística
topic augmented design; mixed model; information recovery; autocorrelation; correlated data; geostatistics
delineamento aumentado; modelo misto; recuperação de informação; autocorrelação; dados correlacionados; geoestatística
description The objective of this study was to evaluate the efficiency of spatial statistical analysis in the selection of genotypes in a plant breeding program and, particularly, to demonstrate the benefits of the approach when experimental observations are not spatially independent. The basic material of this study was a yield trial of soybean lines, with five check varieties (of fixed effect) and 110 test lines (of random effects), in an augmented block design. The spatial analysis used a random field linear model (RFML), with a covariance function estimated from the residuals of the analysis considering independent errors. Results showed a residual autocorrelation of significant magnitude and extension (range), which allowed a better discrimination among genotypes (increase of the power of statistical tests, reduction in the standard errors of estimates and predictors, and a greater amplitude of predictor values) when the spatial analysis was applied. Furthermore, the spatial analysis led to a different ranking of the genetic materials, in comparison with the non-spatial analysis, and a selection less influenced by local variation effects was obtained.
publishDate 2005
dc.date.none.fl_str_mv 2005-02-01
dc.type.none.fl_str_mv
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://seer.sct.embrapa.br/index.php/pab/article/view/6930
url https://seer.sct.embrapa.br/index.php/pab/article/view/6930
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv https://seer.sct.embrapa.br/index.php/pab/article/view/6930/3978
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Pesquisa Agropecuaria Brasileira
Pesquisa Agropecuária Brasileira
publisher.none.fl_str_mv Pesquisa Agropecuaria Brasileira
Pesquisa Agropecuária Brasileira
dc.source.none.fl_str_mv Pesquisa Agropecuaria Brasileira; v.40, n.2, fev. 2005; 107-114
Pesquisa Agropecuária Brasileira; v.40, n.2, fev. 2005; 107-114
1678-3921
0100-104x
reponame:Pesquisa Agropecuária Brasileira (Online)
instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa)
instacron:EMBRAPA
instname_str Empresa Brasileira de Pesquisa Agropecuária (Embrapa)
instacron_str EMBRAPA
institution EMBRAPA
reponame_str Pesquisa Agropecuária Brasileira (Online)
collection Pesquisa Agropecuária Brasileira (Online)
repository.name.fl_str_mv Pesquisa Agropecuária Brasileira (Online) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa)
repository.mail.fl_str_mv pab@sct.embrapa.br || sct.pab@embrapa.br
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