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Registro Completo |
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Biblioteca(s): |
Embrapa Amazônia Ocidental. |
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Data corrente: |
18/10/2018 |
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Data da última atualização: |
05/12/2018 |
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Tipo da produção científica: |
Capítulo em Livro Técnico-Científico |
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Autoria: |
MARTINS, G. C.; MATSCHULLAT, J.; GUSTAV, K.; HEYER, K.; LIMA, R. M. B. de; FROMM, S. von; ERASMI, S.; DRAUSCHKE, T. |
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Afiliação: |
GILVAN COIMBRA MARTINS, CPAA; ROBERVAL MONTEIRO BEZERRA DE LIMA, CPAA. |
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Título: |
Methodology and quality control EcoRespira-Amazon. |
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Ano de publicação: |
2017 |
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Fonte/Imprenta: |
In: ECORESPIRA-AMAZON. Oficina research into use workshop. Manaus, 2017. |
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Páginas: |
p. 27-36. |
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Idioma: |
Inglês |
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Palavras-Chave: |
Metodologia; Projeto EcoRespira-Amazon. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/187826/1/digitalizar0004.pdf
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Marc: |
LEADER 00632naa a2200229 a 4500
001 2097680
005 2018-12-05
008 2017 bl uuuu u00u1 u #d
100 1 $aMARTINS, G. C.
245 $aMethodology and quality control EcoRespira-Amazon.
260 $c2017
300 $ap. 27-36.
653 $aMetodologia
653 $aProjeto EcoRespira-Amazon
700 1 $aMATSCHULLAT, J.
700 1 $aGUSTAV, K.
700 1 $aHEYER, K.
700 1 $aLIMA, R. M. B. de
700 1 $aFROMM, S. von
700 1 $aERASMI, S.
700 1 $aDRAUSCHKE, T.
773 $tIn: ECORESPIRA-AMAZON. Oficina research into use workshop. Manaus, 2017.
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Registro original: |
Embrapa Amazônia Ocidental (CPAA) |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Arroz e Feijão. Para informações adicionais entre em contato com cnpaf.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Arroz e Feijão. |
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Data corrente: |
26/01/2018 |
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Data da última atualização: |
26/01/2018 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 1 |
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Autoria: |
MORAIS JÚNIOR, O. P.; BRESEGHELLO, F.; DUARTE, J. B.; MORAIS, O. P.; RANGEL, P. H. N.; COELHO, A. S. G. |
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Afiliação: |
ODILON PEIXOTO MORAIS JUNIOR; FLAVIO BRESEGHELLO, CNPAF; JOAO BATISTA DUARTE, UFG; ORLANDO PEIXOTO DE MORAIS, CNPAF; PAULO HIDEO NAKANO RANGEL, CNPAF; ALEXANDRE S. G. COELHO, UFG. |
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Título: |
Effectiveness of recurrent selection in irrigated rice breeding. |
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Ano de publicação: |
2017 |
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Fonte/Imprenta: |
Crop Science, v. 57, n. 6, p. 3043-3058, Nov./Dec. 2017. |
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DOI: |
10.2135/cropsci2017.05.0276 |
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Idioma: |
Inglês |
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Conteúdo: |
Plant breeding for quantitative traits is a complicated task; thus, the recurrent selection method has been used in the rice (Oryza sativa L.) breeding program at the Brazilian Agricultural Research Corporation (Embrapa). Our general objective was to assess the effectiveness of this method in achieving genetic progress, maintaining genetic variability, and increasing the potential for selection of superior lines. A genetically broad-based population of irrigated rice, CNA12S, submitted to three selection cycles was used in this study. The dataset comprised 10 yield trials, in which 667 S1:3 progenies and six check cultivars were assessed for grain yield, plant height, and days to flowering. We measured effective population size in each cycle, using standard and linkage disequilibrium methods, and Nei's genetic diversity in the third cycle. Such analyses were performed using data of single-nucleotide polymorphism markers from progenies of the third cycle. For estimating the genetic gain, we adapted a generalized linear regression method to the Bayesian approach. This approach was also used to estimate variance and covariance components, according to the multivariate linear mixed model. Magnitudes of genetic and relative variation coefficients, as well as Nei?s genetic diversity, indicated maintenance of genetic variability over cycles. Mean genetic gain per year was 1.98% for grain yield and -1.29% for days to flowering. Genetic potential of the population for extraction of superior lines was increased, considering single-, two-, or three-trait selection. Our results show the effectiveness of the recurrent selection method when applied in rice breeding, although some refinements in the selection strategy could further improve its efficiency. MenosPlant breeding for quantitative traits is a complicated task; thus, the recurrent selection method has been used in the rice (Oryza sativa L.) breeding program at the Brazilian Agricultural Research Corporation (Embrapa). Our general objective was to assess the effectiveness of this method in achieving genetic progress, maintaining genetic variability, and increasing the potential for selection of superior lines. A genetically broad-based population of irrigated rice, CNA12S, submitted to three selection cycles was used in this study. The dataset comprised 10 yield trials, in which 667 S1:3 progenies and six check cultivars were assessed for grain yield, plant height, and days to flowering. We measured effective population size in each cycle, using standard and linkage disequilibrium methods, and Nei's genetic diversity in the third cycle. Such analyses were performed using data of single-nucleotide polymorphism markers from progenies of the third cycle. For estimating the genetic gain, we adapted a generalized linear regression method to the Bayesian approach. This approach was also used to estimate variance and covariance components, according to the multivariate linear mixed model. Magnitudes of genetic and relative variation coefficients, as well as Nei?s genetic diversity, indicated maintenance of genetic variability over cycles. Mean genetic gain per year was 1.98% for grain yield and -1.29% for days to flowering. Genetic potential of the population for extraction of s... Mostrar Tudo |
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Thesagro: |
Arroz irrigado; Melhoramento genético vegetal; Oryza sativa; Seleção recorrente. |
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Thesaurus NAL: |
Recurrent selection; Rice. |
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Categoria do assunto: |
G Melhoramento Genético |
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Marc: |
LEADER 02553naa a2200265 a 4500
001 2086482
005 2018-01-26
008 2017 bl uuuu u00u1 u #d
024 7 $a10.2135/cropsci2017.05.0276$2DOI
100 1 $aMORAIS JÚNIOR, O. P.
245 $aEffectiveness of recurrent selection in irrigated rice breeding.$h[electronic resource]
260 $c2017
520 $aPlant breeding for quantitative traits is a complicated task; thus, the recurrent selection method has been used in the rice (Oryza sativa L.) breeding program at the Brazilian Agricultural Research Corporation (Embrapa). Our general objective was to assess the effectiveness of this method in achieving genetic progress, maintaining genetic variability, and increasing the potential for selection of superior lines. A genetically broad-based population of irrigated rice, CNA12S, submitted to three selection cycles was used in this study. The dataset comprised 10 yield trials, in which 667 S1:3 progenies and six check cultivars were assessed for grain yield, plant height, and days to flowering. We measured effective population size in each cycle, using standard and linkage disequilibrium methods, and Nei's genetic diversity in the third cycle. Such analyses were performed using data of single-nucleotide polymorphism markers from progenies of the third cycle. For estimating the genetic gain, we adapted a generalized linear regression method to the Bayesian approach. This approach was also used to estimate variance and covariance components, according to the multivariate linear mixed model. Magnitudes of genetic and relative variation coefficients, as well as Nei?s genetic diversity, indicated maintenance of genetic variability over cycles. Mean genetic gain per year was 1.98% for grain yield and -1.29% for days to flowering. Genetic potential of the population for extraction of superior lines was increased, considering single-, two-, or three-trait selection. Our results show the effectiveness of the recurrent selection method when applied in rice breeding, although some refinements in the selection strategy could further improve its efficiency.
650 $aRecurrent selection
650 $aRice
650 $aArroz irrigado
650 $aMelhoramento genético vegetal
650 $aOryza sativa
650 $aSeleção recorrente
700 1 $aBRESEGHELLO, F.
700 1 $aDUARTE, J. B.
700 1 $aMORAIS, O. P.
700 1 $aRANGEL, P. H. N.
700 1 $aCOELHO, A. S. G.
773 $tCrop Science$gv. 57, n. 6, p. 3043-3058, Nov./Dec. 2017.
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Embrapa Arroz e Feijão (CNPAF) |
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