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| Registros recuperados : 80 | |
| 9. |  | SILVA, F. L. da; FELIPE, S. H. S.; LEAO, N. V. M.; SHIMIZU, E. S. C. Teste de germinação de sementes de cumarú (Dipteryx odorata (Aubl.) Willd.) sob diferentes substratos e temperaturas. In: SEMANA DE INTEGRAÇÃO, CIÊNCIA, ARTE E TECNOLOGIA, 1., 2010, Castanhal. [Educação, trabalho, cultura e inovação tecnológica para a sustentabilidade da Amazônia]. [Castanhal]: Instituto de Educação, Ciência e Tecnologia, 2010. SICAT 2010.| Biblioteca(s): Embrapa Amazônia Oriental. |
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| 16. | | FELIPE, S. H. S.; SILVA, F. L. da; LEAO, N. V. M.; SHIMIZU, E. S. C. Comportamento germinativo de sementes de morototó armazenadas sob câmara fria analisadas em diferentes temperaturas e substratos. In: SEMANA DE INTEGRAÇÃO, CIÊNCIA, ARTE E TECNOLOGIA, 1., 2010, Castanhal. [Educação, trabalho, cultura e inovação tecnológica para a sustentabilidade da Amazônia]. [Castanhal]: Instituto de Educação, Ciência e Tecnologia, 2010. SICAT 2010.| Biblioteca(s): Embrapa Amazônia Oriental. |
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| 17. | | SILVA, F. L. da; CARVALHO, H. G. de; MARTIN, V. G.; LAMPERT, V. do N. Desenvolvimento de uma aplicação do tipo glossário com os principais indicadores utilizados na produção de bovinos de corte. In: SIMPÓSIO DE INICIAÇÃO CIENTÍFICA DA EMBRAPA PECUÁRIA SUL, 11., 2021, Bagé, RS. Resumos... Bagé: Embrapa Pecuária Sul, 2021. p. 8. (Embrapa Pecuária Sul. Eventos técnicos & científicos, 4). Ana Cristina Mazzocato, editora técnica.| Biblioteca(s): Embrapa Pecuária Sul. |
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| 18. | | BASSO, V. M.; JACOVINE, L. A. G.; ALVES, R. R.; SILVA, F. L. da. Contribuição da certificação florestal no cumprimento da legislação em plantações florestais. In: CONGRESSO BRASILEIRO SOBRE FLORESTAS ENERGÉTICAS, 1., 2009, Belo Horizonte. Anais. Colombo: Embrapa Florestas, 2009. 1 CD-ROM. (Embrapa Florestas. Documentos, 178). Seção: Sustentabilidade Ambiental, Social e Econômica.| Biblioteca(s): Embrapa Florestas. |
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Registro Completo
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Biblioteca(s): |
Embrapa Florestas. |
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Data corrente: |
05/07/2019 |
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Data da última atualização: |
30/10/2019 |
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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: |
VOLPATO, L.; ALVES, R. S.; TEODORO, P. E.; RESENDE, M. D. V. de; NASCIMENTO, M.; NASCIMENTO, A. C. C.; LUDKE, W. H.; SILVA, F. L. da; BORÉM, A. |
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Afiliação: |
Leonardo Volpato, Universidade Federal de Viçosa; Rodrigo Silva Alves, Universidade Federal de Viçosa; Paulo Eduardo Teodoro, Universidade Federal de Mato Grosso do Sul; MARCOS DEON VILELA DE RESENDE, CNPF; Moysés Nascimento, Universidade Federal de Viçosa; Ana Carolina Campana Nascimento, Universidade Federal de Viçosa; Willian Hytalo Ludke, Universidade Federal de Viçosa; Felipe Lopes da Silva, Universidade Federal de Viçosa; Aluízio Borém, Universidade Federal de Viçosa. |
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Título: |
Multi-trait multi-environment models in the genetic selection of segregating soybean progeny. |
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Ano de publicação: |
2019 |
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Fonte/Imprenta: |
PLoS ONE, v. 14, n. 4, e0215315, Apr. 2019. 22 p. |
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DOI: |
10.1371/journal.pone.0215315 |
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Idioma: |
Inglês |
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Conteúdo: |
At present, single-trait best linear unbiased prediction (BLUP) is the standard method for genetic selection in soybean. However, when genetic selection is performed based on two or more genetically correlated traits and these are analyzed individually, selection bias may arise. Under these conditions, considering the correlation structure between the evaluated traits may provide more-accurate genetic estimates for the evaluated parameters, even under environmental influences. The present study was thus developed to examine the efficiency and applicability of multi-trait multi-environment (MTME) models by the residual maximum likelihood (REML/BLUP) and Bayesian approaches in the genetic selection of segregating soybean progeny. The study involved data pertaining to 203 soybean F2:4 progeny assessed in two environments for the following traits: number of days to maturity (DM), 100-seed weight (SW), and average seed yield per plot (SY). Variance components and genetic and non-genetic parameters were estimated via the REML/BLUP and Bayesian methods. The variance components estimated and the breeding values and genetic gains predicted with selection through the Bayesian procedure were similar to those obtained by REML/BLUP. The frequentist and Bayesian MTME models provided higher estimates of broad-sense heritability per plot (or heritability of total effects of progeny; h2 prog) and mean accuracy of progeny than their respective single-trait versions. Bayesian analysis provided the credibility intervals for the estimates of h2 prog. Therefore, MTME led to greater predicted gains from selection. On this basis, this procedure can be efficiently applied in the genetic selection of segregating soybean progeny. MenosAt present, single-trait best linear unbiased prediction (BLUP) is the standard method for genetic selection in soybean. However, when genetic selection is performed based on two or more genetically correlated traits and these are analyzed individually, selection bias may arise. Under these conditions, considering the correlation structure between the evaluated traits may provide more-accurate genetic estimates for the evaluated parameters, even under environmental influences. The present study was thus developed to examine the efficiency and applicability of multi-trait multi-environment (MTME) models by the residual maximum likelihood (REML/BLUP) and Bayesian approaches in the genetic selection of segregating soybean progeny. The study involved data pertaining to 203 soybean F2:4 progeny assessed in two environments for the following traits: number of days to maturity (DM), 100-seed weight (SW), and average seed yield per plot (SY). Variance components and genetic and non-genetic parameters were estimated via the REML/BLUP and Bayesian methods. The variance components estimated and the breeding values and genetic gains predicted with selection through the Bayesian procedure were similar to those obtained by REML/BLUP. The frequentist and Bayesian MTME models provided higher estimates of broad-sense heritability per plot (or heritability of total effects of progeny; h2 prog) and mean accuracy of progeny than their respective single-trait versions. Bayesian analysis provided... Mostrar Tudo |
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Palavras-Chave: |
Bayesian-inference; Breeding values; Genomic selection; Inferência Bayesian; Mixed models; Modelo misto; Seed protein; Seleção genômica. |
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Thesagro: |
Soja. |
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Thesaurus NAL: |
Agronomic traits; Prediction; Soybeans. |
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Categoria do assunto: |
G Melhoramento Genético |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/199239/1/2019-M.Deon-PO-Multi-trait.pdf
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Marc: |
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024 7 $a10.1371/journal.pone.0215315$2DOI
100 1 $aVOLPATO, L.
245 $aMulti-trait multi-environment models in the genetic selection of segregating soybean progeny.$h[electronic resource]
260 $c2019
520 $aAt present, single-trait best linear unbiased prediction (BLUP) is the standard method for genetic selection in soybean. However, when genetic selection is performed based on two or more genetically correlated traits and these are analyzed individually, selection bias may arise. Under these conditions, considering the correlation structure between the evaluated traits may provide more-accurate genetic estimates for the evaluated parameters, even under environmental influences. The present study was thus developed to examine the efficiency and applicability of multi-trait multi-environment (MTME) models by the residual maximum likelihood (REML/BLUP) and Bayesian approaches in the genetic selection of segregating soybean progeny. The study involved data pertaining to 203 soybean F2:4 progeny assessed in two environments for the following traits: number of days to maturity (DM), 100-seed weight (SW), and average seed yield per plot (SY). Variance components and genetic and non-genetic parameters were estimated via the REML/BLUP and Bayesian methods. The variance components estimated and the breeding values and genetic gains predicted with selection through the Bayesian procedure were similar to those obtained by REML/BLUP. The frequentist and Bayesian MTME models provided higher estimates of broad-sense heritability per plot (or heritability of total effects of progeny; h2 prog) and mean accuracy of progeny than their respective single-trait versions. Bayesian analysis provided the credibility intervals for the estimates of h2 prog. Therefore, MTME led to greater predicted gains from selection. On this basis, this procedure can be efficiently applied in the genetic selection of segregating soybean progeny.
650 $aAgronomic traits
650 $aPrediction
650 $aSoybeans
650 $aSoja
653 $aBayesian-inference
653 $aBreeding values
653 $aGenomic selection
653 $aInferência Bayesian
653 $aMixed models
653 $aModelo misto
653 $aSeed protein
653 $aSeleção genômica
700 1 $aALVES, R. S.
700 1 $aTEODORO, P. E.
700 1 $aRESENDE, M. D. V. de
700 1 $aNASCIMENTO, M.
700 1 $aNASCIMENTO, A. C. C.
700 1 $aLUDKE, W. H.
700 1 $aSILVA, F. L. da
700 1 $aBORÉM, A.
773 $tPLoS ONE$gv. 14, n. 4, e0215315, Apr. 2019. 22 p.
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