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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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Biblioteca |
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Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
URL |
Voltar
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Registro Completo
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Biblioteca(s): |
Embrapa Meio Ambiente; Embrapa Roraima. |
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Data corrente: |
05/11/2012 |
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Data da última atualização: |
05/11/2012 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 2 |
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Autoria: |
MAGALHAES, C. A. de S.; LIMA, J. M. de; COSTA, A. L. da; SILVA, E. G. da; SILVA, C. A.; BETTIOL, W.; NOBREGA, J. C. A.; GUIMARES, P. T. G. |
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Afiliação: |
CIRO AUGUSTO DE SOUZA MAGALHAES, CPAF-RR; WAGNER BETTIOL, CNPMA. |
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Título: |
Sorção e lixiviação de tiametoxam em solos tratados com lodo de esgoto e cultivados com cafeeiro. |
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Ano de publicação: |
2012 |
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Fonte/Imprenta: |
Quimica Nova, v. 35, n. 5, p.871-875, 2012. |
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Idioma: |
Português |
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Conteúdo: |
Abastract: The objective of this study was to evaluate the sorption and leaching of thiamethoxam in dystrophic Red-Yellow Latosol (LVAd), dystroferric Red Latosol (LVdf) and Red-Yellow Argisol (PVAd) with coffee under the effect of sewage sludge doses. There was thiamethoxam sorption decreasing at higher doses of sewage sludge in LVAd and LVdf. In the PVAd, sorption was higher in samples that received the highest dose of sewage sludge. At 150 days after the application of thiamethoxam, the compound began to be detected in the effluent, in all soils. Dissolved organic carbon had no influence on the thiamethoxam leaching. |
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Palavras-Chave: |
Biosolid. |
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Thesagro: |
Adubo de esgoto; Inseticida; Lixiviação; Matéria orgânica; Sorção. |
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Thesaurus NAL: |
Biosolids; Dissolved organic carbon; Insecticides; Leaching; Sewage sludge; Soil pollution; Sorption. |
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Categoria do assunto: |
--
P Recursos Naturais, Ciências Ambientais e da Terra |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/69352/1/sorcao-e-lixiviacao.pdf
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/72725/1/2012AP57.pdf
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Marc: |
LEADER 01628naa a2200361 a 4500
001 1938832
005 2012-11-05
008 2012 bl uuuu u00u1 u #d
100 1 $aMAGALHAES, C. A. de S.
245 $aSorção e lixiviação de tiametoxam em solos tratados com lodo de esgoto e cultivados com cafeeiro.$h[electronic resource]
260 $c2012
520 $aAbastract: The objective of this study was to evaluate the sorption and leaching of thiamethoxam in dystrophic Red-Yellow Latosol (LVAd), dystroferric Red Latosol (LVdf) and Red-Yellow Argisol (PVAd) with coffee under the effect of sewage sludge doses. There was thiamethoxam sorption decreasing at higher doses of sewage sludge in LVAd and LVdf. In the PVAd, sorption was higher in samples that received the highest dose of sewage sludge. At 150 days after the application of thiamethoxam, the compound began to be detected in the effluent, in all soils. Dissolved organic carbon had no influence on the thiamethoxam leaching.
650 $aBiosolids
650 $aDissolved organic carbon
650 $aInsecticides
650 $aLeaching
650 $aSewage sludge
650 $aSoil pollution
650 $aSorption
650 $aAdubo de esgoto
650 $aInseticida
650 $aLixiviação
650 $aMatéria orgânica
650 $aSorção
653 $aBiosolid
700 1 $aLIMA, J. M. de
700 1 $aCOSTA, A. L. da
700 1 $aSILVA, E. G. da
700 1 $aSILVA, C. A.
700 1 $aBETTIOL, W.
700 1 $aNOBREGA, J. C. A.
700 1 $aGUIMARES, P. T. G.
773 $tQuimica Nova$gv. 35, n. 5, p.871-875, 2012.
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Registro original: |
Embrapa Roraima (CPAF-RR) |
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