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Registro Completo |
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Biblioteca(s): |
Embrapa Milho e Sorgo. |
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Data corrente: |
20/08/2020 |
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Data da última atualização: |
20/12/2020 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
KRAUSE, M. D.; DIAS, K. O. das G.; SANTOS, J. P. R. dos; OLIVEIRA, A. A. de; GUIMARAES, L. J. M.; PASTINA, M. M.; MARGARIDO, G. R. A.; GARCIA, A. A. F. |
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Afiliação: |
Matheus Dalsente Krause, Iowa State University; Kaio Olímpio das Graças Dias, Escola Superior de Agricultura "Luiz de Queiroz"; Jhonathan Pedroso Rigal dos Santos, Escola Superior de Agricultura "Luiz de Queiroz"; Amanda Avelar de Oliveira, Escola Superior de Agricultura "Luiz de Queiroz"; LAURO JOSE MOREIRA GUIMARAES, CNPMS; MARIA MARTA PASTINA, CNPMS; Gabriel Rodrigues Alves Margarido, Escola Superior de Agricultura "Luiz de Queiroz"; Antonio Augusto Franco Garcia, Escola Superior de Agricultura "Luiz de Queiroz". |
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Título: |
Boosting predictive ability of tropical maize hybrids via genotype-by-environment interaction under multivariate GBLUP models. |
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Ano de publicação: |
2020 |
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Fonte/Imprenta: |
Crop Science, v. 60, n. 6, p. 3049-3065, 2020. |
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DOI: |
10.1002/csc2.20253 |
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Idioma: |
Inglês |
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Conteúdo: |
Genomic selection has been implemented in several plant and animal breeding programs and it has proven to improve efficiency and maximize genetic gains. Phenotypic data of grain yield was measured in 147 maize (Zea mays L.) singlecross hybrids at 12 environments. Single-cross hybrids genotypes were inferred based on their parents (inbred lines) via single nucleotide polymorphism (SNP) markers obtained from genotyping-by-sequencing (GBS). Factor analytic multiplicative genomic best linear unbiased prediction (GBLUP) models, in the framework of multienvironment trials, were used to predict grain yield performance of unobserved tropical maize single-cross hybrids. Predictions were performed for two situations: untested hybrids (CV1), and hybrids evaluated in some environments but missing in others (CV2). Models that borrowed information across individuals through genomic relationships and within individuals across environments presented higher predictive accuracy than those models that ignored it. For these models, predictive accuracies were up to 0.4 until eight environments were considered as missing for the validation set, which represents 67% of missing data for a given hybrid. These results highlight the importance of including genotype-by-environment interactions and genomic relationship information for boosting predictions of tropical maize single-cross hybrids for grain yield. |
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Thesagro: |
Genótipo; Melhoramento Genético Vegetal; Milho. |
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Categoria do assunto: |
G Melhoramento Genético |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/219490/1/Boosting-predictive.pdf
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Marc: |
LEADER 02201naa a2200253 a 4500
001 2124456
005 2020-12-20
008 2020 bl uuuu u00u1 u #d
024 7 $a10.1002/csc2.20253$2DOI
100 1 $aKRAUSE, M. D.
245 $aBoosting predictive ability of tropical maize hybrids via genotype-by-environment interaction under multivariate GBLUP models.$h[electronic resource]
260 $c2020
520 $aGenomic selection has been implemented in several plant and animal breeding programs and it has proven to improve efficiency and maximize genetic gains. Phenotypic data of grain yield was measured in 147 maize (Zea mays L.) singlecross hybrids at 12 environments. Single-cross hybrids genotypes were inferred based on their parents (inbred lines) via single nucleotide polymorphism (SNP) markers obtained from genotyping-by-sequencing (GBS). Factor analytic multiplicative genomic best linear unbiased prediction (GBLUP) models, in the framework of multienvironment trials, were used to predict grain yield performance of unobserved tropical maize single-cross hybrids. Predictions were performed for two situations: untested hybrids (CV1), and hybrids evaluated in some environments but missing in others (CV2). Models that borrowed information across individuals through genomic relationships and within individuals across environments presented higher predictive accuracy than those models that ignored it. For these models, predictive accuracies were up to 0.4 until eight environments were considered as missing for the validation set, which represents 67% of missing data for a given hybrid. These results highlight the importance of including genotype-by-environment interactions and genomic relationship information for boosting predictions of tropical maize single-cross hybrids for grain yield.
650 $aGenótipo
650 $aMelhoramento Genético Vegetal
650 $aMilho
700 1 $aDIAS, K. O. das G.
700 1 $aSANTOS, J. P. R. dos
700 1 $aOLIVEIRA, A. A. de
700 1 $aGUIMARAES, L. J. M.
700 1 $aPASTINA, M. M.
700 1 $aMARGARIDO, G. R. A.
700 1 $aGARCIA, A. A. F.
773 $tCrop Science$gv. 60, n. 6, p. 3049-3065, 2020.
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Registro original: |
Embrapa Milho e Sorgo (CNPMS) |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Pantanal. Para informações adicionais entre em contato com cpap.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Pantanal. |
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Data corrente: |
28/10/2015 |
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Data da última atualização: |
17/03/2016 |
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Tipo da produção científica: |
Resumo em Anais de Congresso |
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Autoria: |
PELISSARO, G. E. H.; ZANELLA, M. S.; FLORES FILHO, E.; SANTOS, S. A.; BRASIL, S. M. |
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Afiliação: |
UFMS/CPAN; UFMS/CPAN; EMÍLIO FLORES FILHO, UFMS/CPAN; SANDRA APARECIDA SANTOS, CPAP; UFMS/CPAN. |
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Título: |
Microbial biomass with indicator of soil quality in area of the sub-region of the Nhecolandia, Pantanal Sul Mato Grossense. |
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Ano de publicação: |
2015 |
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Fonte/Imprenta: |
In: CONGRESSO BRASILEIRO DE MICROBIOLOGIA, 28., 2015, Florianópolis. Anais... Florianópolis: Sociedade Brasileira de Microbiologia, 2015. |
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Páginas: |
1 p. |
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Idioma: |
Inglês |
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Notas: |
Ref. R.1360-1. |
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Palavras-Chave: |
Estimates of carbon. |
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Thesagro: |
Biomassa; Carbono; Solo. |
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Thesaurus NAL: |
Biomass; Microbial activity; Pantanal. |
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Categoria do assunto: |
K Ciência Florestal e Produtos de Origem Vegetal |
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Marc: |
LEADER 00819nam a2200253 a 4500
001 2027519
005 2016-03-17
008 2015 bl uuuu u00u1 u #d
100 1 $aPELISSARO, G. E. H.
245 $aMicrobial biomass with indicator of soil quality in area of the sub-region of the Nhecolandia, Pantanal Sul Mato Grossense.
260 $aIn: CONGRESSO BRASILEIRO DE MICROBIOLOGIA, 28., 2015, Florianópolis. Anais... Florianópolis: Sociedade Brasileira de Microbiologia$c2015
300 $a1 p.
500 $aRef. R.1360-1.
650 $aBiomass
650 $aMicrobial activity
650 $aPantanal
650 $aBiomassa
650 $aCarbono
650 $aSolo
653 $aEstimates of carbon
700 1 $aZANELLA, M. S.
700 1 $aFLORES FILHO, E.
700 1 $aSANTOS, S. A.
700 1 $aBRASIL, S. M.
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Embrapa Pantanal (CPAP) |
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