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| Registros recuperados : 3 | |
| 1. |  | MALIKOUSKI, R. G.; PEIXOTO, M. A.; FERREIRA, F. M.; MORAIS, A. L. de; ALVES, R. S.; ZUCOLOTO, M.; BARBOSA, D. H. S. G.; BHERING, L. L. Genotypic diversity and genetic parameters of 'Tahiti' acid lime using different rootstocks. Pesquisa Agropecuária Brasileira, v. 58, e02768, 2023. Título em português: Diversidade genotípica e parâmetros genéticos de lima ácida 'Tahiti' com uso de diferentes porta-enxertos.| Biblioteca(s): Embrapa Mandioca e Fruticultura; Embrapa Unidades Centrais. |
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| 2. | | FERREIRA, F. M.; EVANGELISTA, J. S. P. C.; CHAVES, S. F. da S.; ALVES, R. S.; SILVA, D. B.; MALIKOUSKI, R. G.; RESENDE, M. D. V. de; BHERING, L. L.; SANTOS, G. A. Multivariate bayesian analysis for genetic evaluation and selection of eucalyptus in multiple environment trials. Bragantia, v. 81, e2922, 2022. 11 p.| Biblioteca(s): Embrapa Café. |
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| 3. | | FERREIRA, F. M.; LEITE, R. V.; MALIKOUSKI, R. G.; PEIXOTO, M. A.; BERNARDELI, A.; ALVES, R. S.; MAGALHAES JUNIOR, W. C. P. de; ANDRADE, R. G.; BHERING, L. L.; MACHADO, J. C. Bioenergy elephant grass genotype selection leveraged by spatial modeling of conventional and high-throughput phenotyping data. Journal of Cleaner Production, v. 363, 132286, 2022.| Biblioteca(s): Embrapa Gado de Leite. |
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| Registros recuperados : 3 | |
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Registro Completo
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Biblioteca(s): |
Embrapa Café. |
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Data corrente: |
10/01/2023 |
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Data da última atualização: |
10/01/2023 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
B - 1 |
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Autoria: |
FERREIRA, F. M.; EVANGELISTA, J. S. P. C.; CHAVES, S. F. da S.; ALVES, R. S.; SILVA, D. B.; MALIKOUSKI, R. G.; RESENDE, M. D. V. de; BHERING, L. L.; SANTOS, G. A. |
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Afiliação: |
FILIPE MANOEL FERREIRA, UNIVERSIDADE FEDERAL DE VIÇOSA; JENIFFER SANTANA PINTO COELHO EVANGELISTA, UNIVERSIDADE FEDERAL DE VIÇOSA; SAULO FABRÍCIO DA SILVA CHAVES, UNIVERSIDADE FEDERAL DE VIÇOSA; RODRIGO SILVA ALVES, UNIVERSIDADE FEDERAL DE LAVRAS; DANDÁRA BONFIM SILVA, UNIVERSIDADE ESTADUAL PAULISTA JULIO DE MESQUITA FILHO; RENAN GARCIA MALIKOUSKI, UNIVERSIDADE FEDERAL DE VIÇOSA; MARCOS DEON VILELA DE RESENDE, CNPCa; LEONARDO LOPES BHERING, UNIVERSIDADE FEDERAL DE VIÇOSA; GLEISON AUGUSTO SANTOS, UNIVERSIDADE FEDERAL DE VIÇOSA. |
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Título: |
Multivariate bayesian analysis for genetic evaluation and selection of eucalyptus in multiple environment trials. |
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Ano de publicação: |
2022 |
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Fonte/Imprenta: |
Bragantia, v. 81, e2922, 2022. |
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Páginas: |
11 p. |
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DOI: |
https://doi.org/10.1590/1678-4499.20210347 |
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Idioma: |
Inglês |
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Conteúdo: |
Forest plantations are strong allies in preserving natural resources, providing social and economic benefits. The plantations carried out in the coming years will be vital to meet the growing demand for forest products. To ensure the continuity of genetic progress and the good results achieved with the improvement of forest species, statistical methods that accurately selects superior genotypes are desirable. Multi-trait multi-environment trials are preferred over single-trait single-environment trials, since they can exploit the covariance between traits and environments, increasing the analysis?s prediction power. The Bayesian multi-trait multi-environments approach (BMTME) combines the cited advantages with the parsimony of Bayesian statistics promoting a more informative data analysis. Thus, the aims of this study were to estimate genetic parameters, evaluate genetic variability, and select eucalyptus clones through BMTME models. To this end, a data set with 215 eucalyptus clones evaluated in four environments for diameter at breast height and Pilodyn penetration was used. The Markov Chain Monte Carlo algorithm was applied to estimate the variance components and genetic parameters and to predict the genotypic values. The Smith-Hazel index was used to simultaneously achieve gains with selection for both traits. The BMTME approach provided high accuracies, being a good strategy to the evaluation of multiple environmental trials of Eucalyptus for breeding purposes. |
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Thesaurus NAL: |
Bayesian theory; Eucalyptus; Forest trees; Multivariate analysis; Quantitative genetics; Tree breeding. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1150839/1/Multivariate-Bayesian.pdf
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Marc: |
LEADER 02429naa a2200313 a 4500
001 2150839
005 2023-01-10
008 2022 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1590/1678-4499.20210347$2DOI
100 1 $aFERREIRA, F. M.
245 $aMultivariate bayesian analysis for genetic evaluation and selection of eucalyptus in multiple environment trials.$h[electronic resource]
260 $c2022
300 $a11 p.
520 $aForest plantations are strong allies in preserving natural resources, providing social and economic benefits. The plantations carried out in the coming years will be vital to meet the growing demand for forest products. To ensure the continuity of genetic progress and the good results achieved with the improvement of forest species, statistical methods that accurately selects superior genotypes are desirable. Multi-trait multi-environment trials are preferred over single-trait single-environment trials, since they can exploit the covariance between traits and environments, increasing the analysis?s prediction power. The Bayesian multi-trait multi-environments approach (BMTME) combines the cited advantages with the parsimony of Bayesian statistics promoting a more informative data analysis. Thus, the aims of this study were to estimate genetic parameters, evaluate genetic variability, and select eucalyptus clones through BMTME models. To this end, a data set with 215 eucalyptus clones evaluated in four environments for diameter at breast height and Pilodyn penetration was used. The Markov Chain Monte Carlo algorithm was applied to estimate the variance components and genetic parameters and to predict the genotypic values. The Smith-Hazel index was used to simultaneously achieve gains with selection for both traits. The BMTME approach provided high accuracies, being a good strategy to the evaluation of multiple environmental trials of Eucalyptus for breeding purposes.
650 $aBayesian theory
650 $aEucalyptus
650 $aForest trees
650 $aMultivariate analysis
650 $aQuantitative genetics
650 $aTree breeding
700 1 $aEVANGELISTA, J. S. P. C.
700 1 $aCHAVES, S. F. da S.
700 1 $aALVES, R. S.
700 1 $aSILVA, D. B.
700 1 $aMALIKOUSKI, R. G.
700 1 $aRESENDE, M. D. V. de
700 1 $aBHERING, L. L.
700 1 $aSANTOS, G. A.
773 $tBragantia$gv. 81, e2922, 2022.
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Embrapa Café (CNPCa) |
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