Estratificação de ambientes visando otimização da rede de ensaios de híbridos de milho para primeira e segunda safras
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Tipo de documento: | Tese |
Idioma: | por |
Título da fonte: | Repositório Institucional da UFG |
Texto Completo: | http://repositorio.bc.ufg.br/tede/handle/tede/10027 |
Resumo: | The environmental stratification process consists of subdividing heterogeneous regions into more uniform sub-regions with similar ecological characteristics, so that the genotype by environment interactions (GE) it is predominantly of single nature. Environmental stratification procedures can be used to verify whether information generated at the testing locations used for breeding is complementary or redundant. This study was carried out with the objective of stratifying environments used to select corn hybrids in the final stage of breeding in the Central region of Brazil, to optimize the multi environment yield trials, in the first and second growing seasons. Yield data were obtained of 99 and 125 corn hybrids, tested across four years, in 55 and 49 locations, in the first (normal season) and in the second (“safrinha”) seasons, respectively. All yield trials were conducted in a randomized complete block design with two replicates. The yield data were submitted to individual and joint analyzes of the experiments, within each year. The components of variance were estimated and were tested through the Likelihood Test Ratio. The effect of GE interaction it was significant in all evaluated years, except for “safrinha” 2013. For the stratification process, two univariate approaches were used; intraclass correlation and prediction model based on reaction norm; and two multivariate approaches, GGE biplot and Factor Analysis. The most efficient method for eliminating redundant locations it was one chosen for the final stratification. Genetic correlation between pairs of location it was estimated by the univariate approaches. These correlations were used in cluster analysis, applying a SAHN algorithm (Sequential, Agglomerative, Hierarquic, Nonoverlapping clustering), associated with complete linkage method, which established the environmental stratification for the both planting seasons. In the multivariate approaches the environmental stratification were established according to the “who-won-where” approach and the factor loading established within each factor, for GGE Biplot and Factor Analysis, respectively. The verification of the established environmental stratification allowed to identify pairs of locations that were grouped in the same environmental strata, indifferent years of tests, which means redundancy. To optimize the target population of environments, the GGE Biplot method it was chosen which eliminated 12,5% of yield test locations in the both seasons. |
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Melo, Patrícia Guimarães Santoshttp://lattes.cnpq.br/1508679345970114Morais Júnior, Odilon Peixoto dehttp://lattes.cnpq.br/5190558264625516Duarte, João BatistaSilva Filho, João Luís daCoelho, Alexandre Siqueira GuedesSantos, Rodrigo Sampaio dosMelo, Patrícia Guimarães Santoshttp://lattes.cnpq.br/4632291802774249Lepre, André Luiz2019-09-19T13:47:44Z2019-07-11LEPRE, André Luiz. Estratificação de ambientes visando otimização da rede de ensaios de híbridos de milho para primeira e segunda safras. 2019. 114 f. Tese (Doutorado em Genética e Melhoramento de Plantas) - Universidade Federal de Goiás, Goiânia, 2019.http://repositorio.bc.ufg.br/tede/handle/tede/10027ark:/38995/00130000083krThe environmental stratification process consists of subdividing heterogeneous regions into more uniform sub-regions with similar ecological characteristics, so that the genotype by environment interactions (GE) it is predominantly of single nature. Environmental stratification procedures can be used to verify whether information generated at the testing locations used for breeding is complementary or redundant. This study was carried out with the objective of stratifying environments used to select corn hybrids in the final stage of breeding in the Central region of Brazil, to optimize the multi environment yield trials, in the first and second growing seasons. Yield data were obtained of 99 and 125 corn hybrids, tested across four years, in 55 and 49 locations, in the first (normal season) and in the second (“safrinha”) seasons, respectively. All yield trials were conducted in a randomized complete block design with two replicates. The yield data were submitted to individual and joint analyzes of the experiments, within each year. The components of variance were estimated and were tested through the Likelihood Test Ratio. The effect of GE interaction it was significant in all evaluated years, except for “safrinha” 2013. For the stratification process, two univariate approaches were used; intraclass correlation and prediction model based on reaction norm; and two multivariate approaches, GGE biplot and Factor Analysis. The most efficient method for eliminating redundant locations it was one chosen for the final stratification. Genetic correlation between pairs of location it was estimated by the univariate approaches. These correlations were used in cluster analysis, applying a SAHN algorithm (Sequential, Agglomerative, Hierarquic, Nonoverlapping clustering), associated with complete linkage method, which established the environmental stratification for the both planting seasons. In the multivariate approaches the environmental stratification were established according to the “who-won-where” approach and the factor loading established within each factor, for GGE Biplot and Factor Analysis, respectively. The verification of the established environmental stratification allowed to identify pairs of locations that were grouped in the same environmental strata, indifferent years of tests, which means redundancy. To optimize the target population of environments, the GGE Biplot method it was chosen which eliminated 12,5% of yield test locations in the both seasons.O processo de estratificação ambiental consiste na subdivisão de regiões heterogêneas em sub-regiões mais uniformes, com características ecológicas semelhantes, de modo que a interação genótipos com ambientes (GA) seja predominantemente simples. Procedimentos de estratificação ambiental podem ser utilizados para verificar se informações geradas nos locais detestes utilizados para o melhoramento genético são complementares ou redundantes. Este estudo foi realizado com o objetivo de estratificar ambientes utilizados para selecionar híbridos de milho em fase final de melhoramento, na região Central do Brasil, visando otimizar a rede de ensaios, na primeira e segunda safras. Dados de produtividade de grãos foram obtidos de 99 e 125 híbridos de milho, testados ao longo de quatro anos, em 55 e 49 locais, na primeira safra (safra de verão) e na segunda safra (“safrinha”), respectivamente. Todos os ensaios foram conduzidos no delineamento de blocos completos casualizados, com duas repetições. Os dados de produtividade foram submetidos a análises individuais e conjuntas dos experimentos. Foram estimados componentes de variância, os quais foram testados por meio do teste da razão de verossimilhança (Likelihood Test Ratio). Verificou-se o efeito significativo da interação GA em todos os anos, exceto para a safrinha de 2013. Para o processo de estratificação, foram utilizadas duas abordagens univariadas, sendo correlação intraclasse e modelo de predição baseado em norma de reação; e duas abordagens multivariadas, GGE biplot e Análise de Fatores (AF). O método mais eficiente na eliminação de locais redundantes foi o escolhido para a estratificação final. Com as abordagens univariadas obteve-se correlações genéticas entre pares de locais. Essas foram utilizadas em análise de agrupamento, aplicando um algoritmo SAHN (“Sequencial, Agglomerative, Hierarquic, Nonoverlapping clustering”), associado ao método de ligação completa, que estabeleceu os estratos ambientais, nas duas épocas de plantio. Nas abordagens multivariadas os estratos ambientais foram estabelecidos conforme a abordagem “quem-venceu-onde” e as cargas fatoriais estabelecidas dentro de cada fator, para os métodos GGE Biplot e AF, respectivamente. A verificação dos estratos ambientais estabelecidos permitiu identificar pares de locais que foram agrupados nos mesmos estratos ambientais, em diferentes anos de testes, o que significa redundância. Para otimizar a rede de ensaios, escolheu-se o método GGE Biplot, que permitiu eliminar 12,5% dos locais utilizados para avaliação de genótipos de milho, tanto na safra verão quanto na safrinha.Submitted by Luciana Ferreira (lucgeral@gmail.com) on 2019-09-19T13:43:07Z No. of bitstreams: 2 Tese - André Luiz Lepre - 2019.pdf: 2325636 bytes, checksum: 8c8090921e5379f18bd2446288806798 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5)Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2019-09-19T13:47:44Z (GMT) No. of bitstreams: 2 Tese - André Luiz Lepre - 2019.pdf: 2325636 bytes, checksum: 8c8090921e5379f18bd2446288806798 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5)Made available in DSpace on 2019-09-19T13:47:44Z (GMT). No. of bitstreams: 2 Tese - André Luiz Lepre - 2019.pdf: 2325636 bytes, checksum: 8c8090921e5379f18bd2446288806798 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2019-07-11application/pdfporUniversidade Federal de GoiásPrograma de Pós-graduação em Genética e Melhoramento de Plantas (EA)UFGBrasilEscola de Agronomia - EA (RG)http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessZea maysEstratificação de ambientesMelhoramento de plantasMilho safrinhaNorma de reaçãoEnvironment stratificationPlant breedingSafrinha seasonReaction normZea maysCIENCIAS AGRARIAS::AGRONOMIAEstratificação de ambientes visando otimização da rede de ensaios de híbridos de milho para primeira e segunda safrasEnvironmental stratification to optimize corn multi-environment yield trials in the first and second seasonsinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesis-6265679607231828330600600600-6046953723502374070-3091138714907603907reponame:Repositório Institucional da UFGinstname:Universidade Federal de Goiás (UFG)instacron:UFGLICENSElicense.txtlicense.txttext/plain; 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dc.title.eng.fl_str_mv |
Estratificação de ambientes visando otimização da rede de ensaios de híbridos de milho para primeira e segunda safras |
dc.title.alternative.por.fl_str_mv |
Environmental stratification to optimize corn multi-environment yield trials in the first and second seasons |
title |
Estratificação de ambientes visando otimização da rede de ensaios de híbridos de milho para primeira e segunda safras |
spellingShingle |
Estratificação de ambientes visando otimização da rede de ensaios de híbridos de milho para primeira e segunda safras Lepre, André Luiz Zea mays Estratificação de ambientes Melhoramento de plantas Milho safrinha Norma de reação Environment stratification Plant breeding Safrinha season Reaction norm Zea mays CIENCIAS AGRARIAS::AGRONOMIA |
title_short |
Estratificação de ambientes visando otimização da rede de ensaios de híbridos de milho para primeira e segunda safras |
title_full |
Estratificação de ambientes visando otimização da rede de ensaios de híbridos de milho para primeira e segunda safras |
title_fullStr |
Estratificação de ambientes visando otimização da rede de ensaios de híbridos de milho para primeira e segunda safras |
title_full_unstemmed |
Estratificação de ambientes visando otimização da rede de ensaios de híbridos de milho para primeira e segunda safras |
title_sort |
Estratificação de ambientes visando otimização da rede de ensaios de híbridos de milho para primeira e segunda safras |
author |
Lepre, André Luiz |
author_facet |
Lepre, André Luiz |
author_role |
author |
dc.contributor.advisor1.fl_str_mv |
Melo, Patrícia Guimarães Santos |
dc.contributor.advisor1Lattes.fl_str_mv |
http://lattes.cnpq.br/1508679345970114 |
dc.contributor.advisor-co1.fl_str_mv |
Morais Júnior, Odilon Peixoto de |
dc.contributor.advisor-co1Lattes.fl_str_mv |
http://lattes.cnpq.br/5190558264625516 |
dc.contributor.referee1.fl_str_mv |
Duarte, João Batista |
dc.contributor.referee2.fl_str_mv |
Silva Filho, João Luís da |
dc.contributor.referee3.fl_str_mv |
Coelho, Alexandre Siqueira Guedes |
dc.contributor.referee4.fl_str_mv |
Santos, Rodrigo Sampaio dos |
dc.contributor.referee5.fl_str_mv |
Melo, Patrícia Guimarães Santos |
dc.contributor.authorLattes.fl_str_mv |
http://lattes.cnpq.br/4632291802774249 |
dc.contributor.author.fl_str_mv |
Lepre, André Luiz |
contributor_str_mv |
Melo, Patrícia Guimarães Santos Morais Júnior, Odilon Peixoto de Duarte, João Batista Silva Filho, João Luís da Coelho, Alexandre Siqueira Guedes Santos, Rodrigo Sampaio dos Melo, Patrícia Guimarães Santos |
dc.subject.por.fl_str_mv |
Zea mays Estratificação de ambientes Melhoramento de plantas Milho safrinha Norma de reação |
topic |
Zea mays Estratificação de ambientes Melhoramento de plantas Milho safrinha Norma de reação Environment stratification Plant breeding Safrinha season Reaction norm Zea mays CIENCIAS AGRARIAS::AGRONOMIA |
dc.subject.eng.fl_str_mv |
Environment stratification Plant breeding Safrinha season Reaction norm Zea mays |
dc.subject.cnpq.fl_str_mv |
CIENCIAS AGRARIAS::AGRONOMIA |
description |
The environmental stratification process consists of subdividing heterogeneous regions into more uniform sub-regions with similar ecological characteristics, so that the genotype by environment interactions (GE) it is predominantly of single nature. Environmental stratification procedures can be used to verify whether information generated at the testing locations used for breeding is complementary or redundant. This study was carried out with the objective of stratifying environments used to select corn hybrids in the final stage of breeding in the Central region of Brazil, to optimize the multi environment yield trials, in the first and second growing seasons. Yield data were obtained of 99 and 125 corn hybrids, tested across four years, in 55 and 49 locations, in the first (normal season) and in the second (“safrinha”) seasons, respectively. All yield trials were conducted in a randomized complete block design with two replicates. The yield data were submitted to individual and joint analyzes of the experiments, within each year. The components of variance were estimated and were tested through the Likelihood Test Ratio. The effect of GE interaction it was significant in all evaluated years, except for “safrinha” 2013. For the stratification process, two univariate approaches were used; intraclass correlation and prediction model based on reaction norm; and two multivariate approaches, GGE biplot and Factor Analysis. The most efficient method for eliminating redundant locations it was one chosen for the final stratification. Genetic correlation between pairs of location it was estimated by the univariate approaches. These correlations were used in cluster analysis, applying a SAHN algorithm (Sequential, Agglomerative, Hierarquic, Nonoverlapping clustering), associated with complete linkage method, which established the environmental stratification for the both planting seasons. In the multivariate approaches the environmental stratification were established according to the “who-won-where” approach and the factor loading established within each factor, for GGE Biplot and Factor Analysis, respectively. The verification of the established environmental stratification allowed to identify pairs of locations that were grouped in the same environmental strata, indifferent years of tests, which means redundancy. To optimize the target population of environments, the GGE Biplot method it was chosen which eliminated 12,5% of yield test locations in the both seasons. |
publishDate |
2019 |
dc.date.accessioned.fl_str_mv |
2019-09-19T13:47:44Z |
dc.date.issued.fl_str_mv |
2019-07-11 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/doctoralThesis |
format |
doctoralThesis |
status_str |
publishedVersion |
dc.identifier.citation.fl_str_mv |
LEPRE, André Luiz. Estratificação de ambientes visando otimização da rede de ensaios de híbridos de milho para primeira e segunda safras. 2019. 114 f. Tese (Doutorado em Genética e Melhoramento de Plantas) - Universidade Federal de Goiás, Goiânia, 2019. |
dc.identifier.uri.fl_str_mv |
http://repositorio.bc.ufg.br/tede/handle/tede/10027 |
dc.identifier.dark.fl_str_mv |
ark:/38995/00130000083kr |
identifier_str_mv |
LEPRE, André Luiz. Estratificação de ambientes visando otimização da rede de ensaios de híbridos de milho para primeira e segunda safras. 2019. 114 f. Tese (Doutorado em Genética e Melhoramento de Plantas) - Universidade Federal de Goiás, Goiânia, 2019. ark:/38995/00130000083kr |
url |
http://repositorio.bc.ufg.br/tede/handle/tede/10027 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.program.fl_str_mv |
-6265679607231828330 |
dc.relation.confidence.fl_str_mv |
600 600 600 |
dc.relation.department.fl_str_mv |
-6046953723502374070 |
dc.relation.cnpq.fl_str_mv |
-3091138714907603907 |
dc.rights.driver.fl_str_mv |
http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
http://creativecommons.org/licenses/by-nc-nd/4.0/ |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Universidade Federal de Goiás |
dc.publisher.program.fl_str_mv |
Programa de Pós-graduação em Genética e Melhoramento de Plantas (EA) |
dc.publisher.initials.fl_str_mv |
UFG |
dc.publisher.country.fl_str_mv |
Brasil |
dc.publisher.department.fl_str_mv |
Escola de Agronomia - EA (RG) |
publisher.none.fl_str_mv |
Universidade Federal de Goiás |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional da UFG instname:Universidade Federal de Goiás (UFG) instacron:UFG |
instname_str |
Universidade Federal de Goiás (UFG) |
instacron_str |
UFG |
institution |
UFG |
reponame_str |
Repositório Institucional da UFG |
collection |
Repositório Institucional da UFG |
bitstream.url.fl_str_mv |
http://repositorio.bc.ufg.br/tede/bitstreams/0912ed4e-b971-4d86-83e8-f0dd27e915f2/download http://repositorio.bc.ufg.br/tede/bitstreams/a009462b-4904-4c4b-982b-32676691026c/download http://repositorio.bc.ufg.br/tede/bitstreams/b2ee53af-0078-4701-8bee-7f953a58d38d/download http://repositorio.bc.ufg.br/tede/bitstreams/2623c1f6-f156-4948-9af8-6d0e459be002/download http://repositorio.bc.ufg.br/tede/bitstreams/bdc25fa6-16f7-4ecf-91bc-d359ddb8f47d/download |
bitstream.checksum.fl_str_mv |
bd3efa91386c1718a7f26a329fdcb468 4afdbb8c545fd630ea7db775da747b2f d41d8cd98f00b204e9800998ecf8427e d41d8cd98f00b204e9800998ecf8427e 8c8090921e5379f18bd2446288806798 |
bitstream.checksumAlgorithm.fl_str_mv |
MD5 MD5 MD5 MD5 MD5 |
repository.name.fl_str_mv |
Repositório Institucional da UFG - Universidade Federal de Goiás (UFG) |
repository.mail.fl_str_mv |
tasesdissertacoes.bc@ufg.br |
_version_ |
1811721445234966528 |