Evaluation of sugarcane genotypes and production environments in Paraná by GGE biplot and AMMI analysis

Detalhes bibliográficos
Autor(a) principal: Mattos,Pedro Henrique Costa de
Data de Publicação: 2013
Outros Autores: Oliveira,Ricardo Augusto de, Bespalhok Filho,João Carlos, Daros,Edelclaiton, Veríssimo,Mario Alvaro Aloiso
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Crop Breeding and Applied Biotechnology
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1984-70332013000100010
Resumo: The purpose of this study was to evaluate sugarcane genotypes for the trait tons of sugar per hectare (TSH), stratifying five production environments in the state of Paraná. The performance of 20 genotypes and 2 standard cultivars was analyzed in three consecutive growing seasons by the statistical methods AMMI and GGE Biplot. The GGE Biplot grouped the locations into two mega-environments and indicated the best-performing genotypes for each one, facilitating the selection of superior genotypes. Another advantage of GGEBiplot is the definition of an ideal genotype (G) and environment (E), serving as reference for the evaluation of genotypes and choice of environments with greater GE interaction. Both models indicated RB006970, RB855156 and RB855453 as the genotypes with highest TSH and São Pedro do Ivai as the environment with the greatest GE interaction. Both approaches explained a high percentage of the sum of squares, with a slight advantage of AMMI over GGE Biplot analysis.
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spelling Evaluation of sugarcane genotypes and production environments in Paraná by GGE biplot and AMMI analysisSaccharum spp.adaptabilitystabilityenvironment stratificationThe purpose of this study was to evaluate sugarcane genotypes for the trait tons of sugar per hectare (TSH), stratifying five production environments in the state of Paraná. The performance of 20 genotypes and 2 standard cultivars was analyzed in three consecutive growing seasons by the statistical methods AMMI and GGE Biplot. The GGE Biplot grouped the locations into two mega-environments and indicated the best-performing genotypes for each one, facilitating the selection of superior genotypes. Another advantage of GGEBiplot is the definition of an ideal genotype (G) and environment (E), serving as reference for the evaluation of genotypes and choice of environments with greater GE interaction. Both models indicated RB006970, RB855156 and RB855453 as the genotypes with highest TSH and São Pedro do Ivai as the environment with the greatest GE interaction. Both approaches explained a high percentage of the sum of squares, with a slight advantage of AMMI over GGE Biplot analysis.Crop Breeding and Applied Biotechnology2013-03-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1984-70332013000100010Crop Breeding and Applied Biotechnology v.13 n.1 2013reponame:Crop Breeding and Applied Biotechnologyinstname:Sociedade Brasileira de Melhoramento de Plantasinstacron:CBAB10.1590/S1984-70332013000100010info:eu-repo/semantics/openAccessMattos,Pedro Henrique Costa deOliveira,Ricardo Augusto deBespalhok Filho,João CarlosDaros,EdelclaitonVeríssimo,Mario Alvaro Aloisoeng2013-05-16T00:00:00Zoai:scielo:S1984-70332013000100010Revistahttps://cbab.sbmp.org.br/#ONGhttps://old.scielo.br/oai/scielo-oai.phpcbabjournal@gmail.com||cbab@ufv.br1984-70331518-7853opendoar:2013-05-16T00:00Crop Breeding and Applied Biotechnology - Sociedade Brasileira de Melhoramento de Plantasfalse
dc.title.none.fl_str_mv Evaluation of sugarcane genotypes and production environments in Paraná by GGE biplot and AMMI analysis
title Evaluation of sugarcane genotypes and production environments in Paraná by GGE biplot and AMMI analysis
spellingShingle Evaluation of sugarcane genotypes and production environments in Paraná by GGE biplot and AMMI analysis
Mattos,Pedro Henrique Costa de
Saccharum spp.
adaptability
stability
environment stratification
title_short Evaluation of sugarcane genotypes and production environments in Paraná by GGE biplot and AMMI analysis
title_full Evaluation of sugarcane genotypes and production environments in Paraná by GGE biplot and AMMI analysis
title_fullStr Evaluation of sugarcane genotypes and production environments in Paraná by GGE biplot and AMMI analysis
title_full_unstemmed Evaluation of sugarcane genotypes and production environments in Paraná by GGE biplot and AMMI analysis
title_sort Evaluation of sugarcane genotypes and production environments in Paraná by GGE biplot and AMMI analysis
author Mattos,Pedro Henrique Costa de
author_facet Mattos,Pedro Henrique Costa de
Oliveira,Ricardo Augusto de
Bespalhok Filho,João Carlos
Daros,Edelclaiton
Veríssimo,Mario Alvaro Aloiso
author_role author
author2 Oliveira,Ricardo Augusto de
Bespalhok Filho,João Carlos
Daros,Edelclaiton
Veríssimo,Mario Alvaro Aloiso
author2_role author
author
author
author
dc.contributor.author.fl_str_mv Mattos,Pedro Henrique Costa de
Oliveira,Ricardo Augusto de
Bespalhok Filho,João Carlos
Daros,Edelclaiton
Veríssimo,Mario Alvaro Aloiso
dc.subject.por.fl_str_mv Saccharum spp.
adaptability
stability
environment stratification
topic Saccharum spp.
adaptability
stability
environment stratification
description The purpose of this study was to evaluate sugarcane genotypes for the trait tons of sugar per hectare (TSH), stratifying five production environments in the state of Paraná. The performance of 20 genotypes and 2 standard cultivars was analyzed in three consecutive growing seasons by the statistical methods AMMI and GGE Biplot. The GGE Biplot grouped the locations into two mega-environments and indicated the best-performing genotypes for each one, facilitating the selection of superior genotypes. Another advantage of GGEBiplot is the definition of an ideal genotype (G) and environment (E), serving as reference for the evaluation of genotypes and choice of environments with greater GE interaction. Both models indicated RB006970, RB855156 and RB855453 as the genotypes with highest TSH and São Pedro do Ivai as the environment with the greatest GE interaction. Both approaches explained a high percentage of the sum of squares, with a slight advantage of AMMI over GGE Biplot analysis.
publishDate 2013
dc.date.none.fl_str_mv 2013-03-01
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=S1984-70332013000100010
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1984-70332013000100010
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/S1984-70332013000100010
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 Crop Breeding and Applied Biotechnology
publisher.none.fl_str_mv Crop Breeding and Applied Biotechnology
dc.source.none.fl_str_mv Crop Breeding and Applied Biotechnology v.13 n.1 2013
reponame:Crop Breeding and Applied Biotechnology
instname:Sociedade Brasileira de Melhoramento de Plantas
instacron:CBAB
instname_str Sociedade Brasileira de Melhoramento de Plantas
instacron_str CBAB
institution CBAB
reponame_str Crop Breeding and Applied Biotechnology
collection Crop Breeding and Applied Biotechnology
repository.name.fl_str_mv Crop Breeding and Applied Biotechnology - Sociedade Brasileira de Melhoramento de Plantas
repository.mail.fl_str_mv cbabjournal@gmail.com||cbab@ufv.br
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