Multivariate approach in the selection of superior soybean progeny which carry the RR gene
Autor(a) principal: | |
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Data de Publicação: | 2014 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Revista ciência agronômica (Online) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1806-66902014000300021 |
Resumo: | Efficiency in the use of genetic variability, whether existing or created, increases when properly explored and analysed. Incorporation of biotechnology into breeding programs has been the general practice. The challenge for the researcher is the constant development of new and improved cultivars. The aim of this experiment was to select progenies with superior characteristics, whether or not carriers of the RR gene, derived from bi-parental crosses in the soybean, with the help of multivariate techniques. The experiment was carried out in a family-type experimental design, including controls, during the agricultural year 2010/2011 and 2011/2012 in Jaboticabal in the Brazilian State of São Paulo. From the F3 generation, phenotypically superior plants were selected, which were evaluated for the following traits: number of days to flowering; number of days to maturity; height of first pod insertion; plant height at maturity; lodging; agronomic value; number of branches; number of pods per plant; 100-seed weight; number of seeds per plant; grain yield per plant. Given the results, it appears possible to select superior progeny by principal component analysis. Cluster analysis using the K-means method links progeny according to the most important characteristics in each group and identifies, by the Ward method and by means of a dendrogram, the structure of similarity and divergence between selected progeny. Both methods are effective in aiding progeny selection. |
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Multivariate approach in the selection of superior soybean progeny which carry the RR geneGlycine maxGenetic variabilityMultivariate analysisEfficiency in the use of genetic variability, whether existing or created, increases when properly explored and analysed. Incorporation of biotechnology into breeding programs has been the general practice. The challenge for the researcher is the constant development of new and improved cultivars. The aim of this experiment was to select progenies with superior characteristics, whether or not carriers of the RR gene, derived from bi-parental crosses in the soybean, with the help of multivariate techniques. The experiment was carried out in a family-type experimental design, including controls, during the agricultural year 2010/2011 and 2011/2012 in Jaboticabal in the Brazilian State of São Paulo. From the F3 generation, phenotypically superior plants were selected, which were evaluated for the following traits: number of days to flowering; number of days to maturity; height of first pod insertion; plant height at maturity; lodging; agronomic value; number of branches; number of pods per plant; 100-seed weight; number of seeds per plant; grain yield per plant. Given the results, it appears possible to select superior progeny by principal component analysis. Cluster analysis using the K-means method links progeny according to the most important characteristics in each group and identifies, by the Ward method and by means of a dendrogram, the structure of similarity and divergence between selected progeny. Both methods are effective in aiding progeny selection.Universidade Federal do Ceará2014-09-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1806-66902014000300021Revista Ciência Agronômica v.45 n.3 2014reponame:Revista ciência agronômica (Online)instname:Universidade Federal do Ceará (UFC)instacron:UFC10.1590/S1806-66902014000300021info:eu-repo/semantics/openAccessDallastra,AndersonUnêda-Trevisoli,Sandra HelenaFerraudo,Antonio SergioDi Mauro,Antonio Orlandoeng2014-05-26T00:00:00Zoai:scielo:S1806-66902014000300021Revistahttp://www.ccarevista.ufc.br/PUBhttps://old.scielo.br/oai/scielo-oai.php||alekdutra@ufc.br|| ccarev@ufc.br1806-66900045-6888opendoar:2014-05-26T00:00Revista ciência agronômica (Online) - Universidade Federal do Ceará (UFC)false |
dc.title.none.fl_str_mv |
Multivariate approach in the selection of superior soybean progeny which carry the RR gene |
title |
Multivariate approach in the selection of superior soybean progeny which carry the RR gene |
spellingShingle |
Multivariate approach in the selection of superior soybean progeny which carry the RR gene Dallastra,Anderson Glycine max Genetic variability Multivariate analysis |
title_short |
Multivariate approach in the selection of superior soybean progeny which carry the RR gene |
title_full |
Multivariate approach in the selection of superior soybean progeny which carry the RR gene |
title_fullStr |
Multivariate approach in the selection of superior soybean progeny which carry the RR gene |
title_full_unstemmed |
Multivariate approach in the selection of superior soybean progeny which carry the RR gene |
title_sort |
Multivariate approach in the selection of superior soybean progeny which carry the RR gene |
author |
Dallastra,Anderson |
author_facet |
Dallastra,Anderson Unêda-Trevisoli,Sandra Helena Ferraudo,Antonio Sergio Di Mauro,Antonio Orlando |
author_role |
author |
author2 |
Unêda-Trevisoli,Sandra Helena Ferraudo,Antonio Sergio Di Mauro,Antonio Orlando |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Dallastra,Anderson Unêda-Trevisoli,Sandra Helena Ferraudo,Antonio Sergio Di Mauro,Antonio Orlando |
dc.subject.por.fl_str_mv |
Glycine max Genetic variability Multivariate analysis |
topic |
Glycine max Genetic variability Multivariate analysis |
description |
Efficiency in the use of genetic variability, whether existing or created, increases when properly explored and analysed. Incorporation of biotechnology into breeding programs has been the general practice. The challenge for the researcher is the constant development of new and improved cultivars. The aim of this experiment was to select progenies with superior characteristics, whether or not carriers of the RR gene, derived from bi-parental crosses in the soybean, with the help of multivariate techniques. The experiment was carried out in a family-type experimental design, including controls, during the agricultural year 2010/2011 and 2011/2012 in Jaboticabal in the Brazilian State of São Paulo. From the F3 generation, phenotypically superior plants were selected, which were evaluated for the following traits: number of days to flowering; number of days to maturity; height of first pod insertion; plant height at maturity; lodging; agronomic value; number of branches; number of pods per plant; 100-seed weight; number of seeds per plant; grain yield per plant. Given the results, it appears possible to select superior progeny by principal component analysis. Cluster analysis using the K-means method links progeny according to the most important characteristics in each group and identifies, by the Ward method and by means of a dendrogram, the structure of similarity and divergence between selected progeny. Both methods are effective in aiding progeny selection. |
publishDate |
2014 |
dc.date.none.fl_str_mv |
2014-09-01 |
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=S1806-66902014000300021 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1806-66902014000300021 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/S1806-66902014000300021 |
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 do Ceará |
publisher.none.fl_str_mv |
Universidade Federal do Ceará |
dc.source.none.fl_str_mv |
Revista Ciência Agronômica v.45 n.3 2014 reponame:Revista ciência agronômica (Online) instname:Universidade Federal do Ceará (UFC) instacron:UFC |
instname_str |
Universidade Federal do Ceará (UFC) |
instacron_str |
UFC |
institution |
UFC |
reponame_str |
Revista ciência agronômica (Online) |
collection |
Revista ciência agronômica (Online) |
repository.name.fl_str_mv |
Revista ciência agronômica (Online) - Universidade Federal do Ceará (UFC) |
repository.mail.fl_str_mv |
||alekdutra@ufc.br|| ccarev@ufc.br |
_version_ |
1750297487450570752 |