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Registro Completo |
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Biblioteca(s): |
Embrapa Trigo. |
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Data corrente: |
17/12/2019 |
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Data da última atualização: |
17/12/2019 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
CORASSA, G. M.; SANTI, A. L.; AMADO, T. J. C.; REIMCHE, G. B.; GAVIRAGHI, R.; BISOGNIN, M. B.; PIRES, J. L. F. |
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Afiliação: |
JOAO LEONARDO FERNANDES PIRES, CNPT. |
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Título: |
Performance of soybean varieties differs according to yield class: a case study from Southern Brazil. |
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Ano de publicação: |
2019 |
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Fonte/Imprenta: |
Precision Agriculture, v. 20, p. 520-540, 2019. |
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DOI: |
10.1007/s11119-018-9595-0 |
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Idioma: |
Inglês |
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Thesagro: |
Glycine Max; Soja; Variedade. |
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Thesaurus Nal: |
Cultivars; Seed yield; Soybeans. |
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Categoria do assunto: |
F Plantas e Produtos de Origem Vegetal |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/207339/1/ID44837-2019v20p520PrecisionAgriculture.pdf
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Marc: |
LEADER 00765naa a2200265 a 4500
001 2117148
005 2019-12-17
008 2019 bl uuuu u00u1 u #d
024 7 $a10.1007/s11119-018-9595-0$2DOI
100 1 $aCORASSA, G. M.
245 $aPerformance of soybean varieties differs according to yield class$ba case study from Southern Brazil.$h[electronic resource]
260 $c2019
650 $aCultivars
650 $aSeed yield
650 $aSoybeans
650 $aGlycine Max
650 $aSoja
650 $aVariedade
700 1 $aSANTI, A. L.
700 1 $aAMADO, T. J. C.
700 1 $aREIMCHE, G. B.
700 1 $aGAVIRAGHI, R.
700 1 $aBISOGNIN, M. B.
700 1 $aPIRES, J. L. F.
773 $tPrecision Agriculture$gv. 20, p. 520-540, 2019.
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Registro original: |
Embrapa Trigo (CNPT) |
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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: |
18/09/2018 |
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Data da última atualização: |
18/09/2018 |
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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.; DUARTE, J. B.; BRESEGHELLO, F.; COELHO, A. S. G.; MORAIS, O. P.; MAGALHÃES JÚNIOR, A. M. |
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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. |
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Título: |
Single-step reaction norm models for genomic prediction in multienvironment recurrent selection trials. |
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Ano de publicação: |
2018 |
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Fonte/Imprenta: |
Crop Science, v. 58, n. 2, p. 592-607, Mar./Apr. 2018. |
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ISSN: |
0011-183X |
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DOI: |
10.2135/cropsci2017.06.0366 |
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Idioma: |
Inglês |
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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 |
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Palavras-Chave: |
Multienvironment prediction. |
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Thesagro: |
Arroz; Melhoramento Genético Vegetal; Oryza Sativa; Progênie; Seleção Recorrente. |
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Thesaurus NAL: |
Genomics; Plant breeding; Recurrent selection; Rice; Variety trials. |
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Categoria do assunto: |
G Melhoramento Genético |
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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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Embrapa Arroz e Feijão (CNPAF) |
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