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Registros recuperados : 52 | |
4. | | MORAIS, O. P. de; GUIMARÃES, C. M.; CASTRO, A. P.; MORAIS JÚNIOR, O. P. de. Avaliação da população de arroz de terras altas CNA6, quanto ao seu potencial de melhoramento visando tolerância à deficiência hídrica. In: CONGRESSO BRASILEIRO DE MELHORAMENTO DE PLANTAS, 8., 2015, Goiânia. O melhoramento de plantas, o futuro da agricultura e a soberania nacional: anais. Goiânia: UFG: SBMP, 2015. Biblioteca(s): Embrapa Arroz e Feijão. |
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7. | | GUIMARÃES, C. M.; STONE, L. F.; CASTRO, A. P. de; MORAIS JUNIOR, O. P. de. Physiological parameters to select upland rice genotypes for tolerance to water deficit. Pesquisa Agropecuária Brasileira, Brasília, DF, v. 50, n. 7, p. 534-540, jul. 2015. Biblioteca(s): Embrapa Arroz e Feijão; Embrapa Unidades Centrais. |
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9. | | MORAIS JÚNIOR, O. P. de; GUIMARÃES, P. H. R.; MORAIS, O. P. de; PEREIRA, J. A.; MELO, P. G. S. Análise dialélica parcial generalizada com linhagens de arroz vermelho e branco em dois ambientes. In: CONGRESSO BRASILEIRO DE MELHORAMENTO DE PLANTAS, 8., 2015, Goiânia. O melhoramento de plantas, o futuro da agricultura e a soberania nacional: anais. Goiânia: UFG: SBMP, 2015. Biblioteca(s): Embrapa Meio-Norte. |
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10. | | MORAIS JÚNIOR, O. P. de; GUIMARÃES, P. H. R.; MORAIS, O. P. de; PEREIRA, J. A.; MELO, P. G. S. Análise dialélica parcial generalizada com linhagens de arroz vermelho e branco em dois ambientes. In: CONGRESSO BRASILEIRO DE MELHORAMENTO DE PLANTAS, 8., 2015, Goiânia. O melhoramento de plantas, o futuro da agricultura e a soberania nacional: anais. Goiânia: UFG: SBMP, 2015. Biblioteca(s): Embrapa Arroz e Feijão. |
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11. | | GUIMARÃES, C. M.; BRESEGHELLO, F.; CASTRO, A. P. de; STONE, L. F.; MORAIS JÚNIOR, O. P. de. Avaliação de arroz de terras altas do grupo indica, sob condições de irrigação adequada e de deficiência hídrica. In: CONGRESSO BRASILEIRO DE ARROZ IRRIGADO, 6., 2009, Porto Alegre. Estresses e sustentabilidade: desafios para a lavoura arrozeira: anais. Porto Alegre: Palotti, 2009. 1 CD-ROM. Biblioteca(s): Embrapa Arroz e Feijão. |
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14. | | MORAIS JÚNIOR, O. P. de; MORAIS, O. P. de; BRESEGHELLO, F.; RANGEL, P. H. N.; MAGALHÃES JUNIOR, A. M. de. Comparação de índices de seleção aplicados em seleção recorrente de arroz irrigado. In: CONGRESSO BRASILEIRO DE ARROZ IRRIGADO, 9., 2015, Pelotas. Ciência e tecnologia para otimização da orizicultura: anais. Brasília, DF: Embrapa; Pelotas: Sosbai, 2015. Biblioteca(s): Embrapa Arroz e Feijão; Embrapa Clima Temperado. |
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15. | | MORAIS JÚNIOR, O. P.; BRESEGHELLO, F.; DUARTE, J. B.; MORAIS, O. P.; RANGEL, P. H. N.; COELHO, A. S. G. Effectiveness of recurrent selection in irrigated rice breeding. Crop Science, v. 57, n. 6, p. 3043-3058, Nov./Dec. 2017. Biblioteca(s): Embrapa Arroz e Feijão. |
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16. | | BARROS, M. S.; MORAIS JÚNIOR, O. P.; MELO, P. G. S.; MORAIS, O. P.; CASTRO, A. P.; BRESEGHELLO, F. Effectiveness of early-generation testing applied to upland rice breeding. Euphytica, v. 214, n. 4, article 61, Apr. 2018. Biblioteca(s): Embrapa Arroz e Feijão. |
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17. | | MORAIS JÚNIOR, O. P. de; MELO, P. G. S.; MORAIS, O. P. de; COLOMBARI FILHO, J. M. Genetic variability during four cycles of recurrent selection in rice. Pesquisa Agropecuária Brasileira, Brasília, DF, v. 52, n, 11, p. 1033-1041, nov. 2017. Biblioteca(s): Embrapa Arroz e Feijão; Embrapa Unidades Centrais. |
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Registros recuperados : 52 | |
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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
Biblioteca(s): |
Embrapa Arroz e Feijão. |
Data corrente: |
18/09/2018 |
Data da última atualização: |
18/09/2018 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Circulação/Nível: |
A - 1 |
Autoria: |
MORAIS JÚNIOR, O. P.; DUARTE, J. B.; BRESEGHELLO, F.; COELHO, A. S. G.; MORAIS, O. P.; MAGALHÃES JÚNIOR, A. M. |
Afiliação: |
ODILON PEIXOTO MORAIS JUNIOR, UFG; JOAO BATISTA DUARTE, UFG; FLAVIO BRESEGHELLO, CNPAF; ALEXANDRE S. G. COELHO, UFG; ORLANDO PEIXOTO DE MORAIS, CNPAF; ARIANO MARTINS DE MAGALHAES JUNIOR, CPACT. |
Título: |
Single-step reaction norm models for genomic prediction in multienvironment recurrent selection trials. |
Ano de publicação: |
2018 |
Fonte/Imprenta: |
Crop Science, v. 58, n. 2, p. 592-607, Mar./Apr. 2018. |
ISSN: |
0011-183X |
DOI: |
10.2135/cropsci2017.06.0366 |
Idioma: |
Inglês |
Conteúdo: |
In recurrent selection programs, progeny testing is done in multienvironment trials, which generates genotype × environment interaction (G × E). Therefore, modeling G × E is essential for genomic prediction in the context of recurrent genomic selection (RGS). Developing single-step, best linear unbiased prediction-based reaction norm models (termed RN-HBLUP) using data from nongenotyped and genotyped progenies, can enhance predictive accuracy. Our objectives were to evaluate: (i) a class of RN-HBLUP models accommodating combined relationship of pedigree and genomic data, environmental covariates, and their interactions for prediction of phenotypic responses; (ii) the predictive accuracy of these models and the relative importance of main effects and interaction components; and (iii) the influence of different grouping strategies of genetic?environmental data (within selection cycles or across cycles) on prediction accuracy of the merit for untested progenies. The genetic material comprised 667 S1:3 progenies of irrigated rice (Oryza sativa L.) and six check cultivars. These materials were evaluated in yield trials conducted in 10 environments during three selection cycles. Genomic information was derived from single-nucleotide polymorphism markers genotyped on 174 progenies in the third cycle. We evaluated six predictive models. Environmental covariates and G × E interaction explained a significant portion of the phenotypic variance, increasing accuracy and decreasing the bias of phenotypic prediction. Within-cycle data were sufficient for accurate prediction of untested progenies, even in untested environments. We concluded that the RN-HBLUP model, with the comprehensive structure, could be useful in improving the prediction accuracy of quantitative traits in RGS programs. MenosIn recurrent selection programs, progeny testing is done in multienvironment trials, which generates genotype × environment interaction (G × E). Therefore, modeling G × E is essential for genomic prediction in the context of recurrent genomic selection (RGS). Developing single-step, best linear unbiased prediction-based reaction norm models (termed RN-HBLUP) using data from nongenotyped and genotyped progenies, can enhance predictive accuracy. Our objectives were to evaluate: (i) a class of RN-HBLUP models accommodating combined relationship of pedigree and genomic data, environmental covariates, and their interactions for prediction of phenotypic responses; (ii) the predictive accuracy of these models and the relative importance of main effects and interaction components; and (iii) the influence of different grouping strategies of genetic?environmental data (within selection cycles or across cycles) on prediction accuracy of the merit for untested progenies. The genetic material comprised 667 S1:3 progenies of irrigated rice (Oryza sativa L.) and six check cultivars. These materials were evaluated in yield trials conducted in 10 environments during three selection cycles. Genomic information was derived from single-nucleotide polymorphism markers genotyped on 174 progenies in the third cycle. We evaluated six predictive models. Environmental covariates and G × E interaction explained a significant portion of the phenotypic variance, increasing accuracy and decreasing the bi... Mostrar Tudo |
Palavras-Chave: |
Multienvironment prediction. |
Thesagro: |
Arroz; Melhoramento Genético Vegetal; Oryza Sativa; Progênie; Seleção Recorrente. |
Thesaurus NAL: |
Genomics; Plant breeding; Recurrent selection; Rice; Variety trials. |
Categoria do assunto: |
G Melhoramento Genético |
Marc: |
LEADER 02811naa a2200337 a 4500 001 2095887 005 2018-09-18 008 2018 bl uuuu u00u1 u #d 022 $a0011-183X 024 7 $a10.2135/cropsci2017.06.0366$2DOI 100 1 $aMORAIS JÚNIOR, O. P. 245 $aSingle-step reaction norm models for genomic prediction in multienvironment recurrent selection trials.$h[electronic resource] 260 $c2018 520 $aIn recurrent selection programs, progeny testing is done in multienvironment trials, which generates genotype × environment interaction (G × E). Therefore, modeling G × E is essential for genomic prediction in the context of recurrent genomic selection (RGS). Developing single-step, best linear unbiased prediction-based reaction norm models (termed RN-HBLUP) using data from nongenotyped and genotyped progenies, can enhance predictive accuracy. Our objectives were to evaluate: (i) a class of RN-HBLUP models accommodating combined relationship of pedigree and genomic data, environmental covariates, and their interactions for prediction of phenotypic responses; (ii) the predictive accuracy of these models and the relative importance of main effects and interaction components; and (iii) the influence of different grouping strategies of genetic?environmental data (within selection cycles or across cycles) on prediction accuracy of the merit for untested progenies. The genetic material comprised 667 S1:3 progenies of irrigated rice (Oryza sativa L.) and six check cultivars. These materials were evaluated in yield trials conducted in 10 environments during three selection cycles. Genomic information was derived from single-nucleotide polymorphism markers genotyped on 174 progenies in the third cycle. We evaluated six predictive models. Environmental covariates and G × E interaction explained a significant portion of the phenotypic variance, increasing accuracy and decreasing the bias of phenotypic prediction. Within-cycle data were sufficient for accurate prediction of untested progenies, even in untested environments. We concluded that the RN-HBLUP model, with the comprehensive structure, could be useful in improving the prediction accuracy of quantitative traits in RGS programs. 650 $aGenomics 650 $aPlant breeding 650 $aRecurrent selection 650 $aRice 650 $aVariety trials 650 $aArroz 650 $aMelhoramento Genético Vegetal 650 $aOryza Sativa 650 $aProgênie 650 $aSeleção Recorrente 653 $aMultienvironment prediction 700 1 $aDUARTE, J. B. 700 1 $aBRESEGHELLO, F. 700 1 $aCOELHO, A. S. G. 700 1 $aMORAIS, O. P. 700 1 $aMAGALHÃES JÚNIOR, A. M. 773 $tCrop Science$gv. 58, n. 2, p. 592-607, Mar./Apr. 2018.
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