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15. | | LOPES, F. R. F.; COSTA, P. G. da; HIRAGI, G. de O.; SANTOS, R. S. dos; SANTOS, G. A. dos; RODRIGUES, M. V. B. G. B. Busca personalizada de dados, uma nova ferramenta do ALELO Vegetal. Revista RG News, v. 4, n. 3, p. 491, 2018. Edição especial com os Anais do V Congresso Brasileiro de Recursos Genéticos. Biblioteca(s): Embrapa Recursos Genéticos e Biotecnologia. |
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16. | | CASTRO, C. A. de O.; NUNES, A. C. P.; SANTOS, O. P. dos; RESENDE, R. T.; SANTOS, G. A. dos; RESENDE, M. D. V. de; CRUZ, C. D. Comportamento da interação genótipos por locais aos três e nove anos em clones de eucalipto. Scientia Forestalis, Piracicaba, v. 46, n. 120, p. 594-605, dez. 2018. Biblioteca(s): Embrapa Florestas. |
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17. | | CASTRO, C. A. de O.; SOUZA, G. A. de; SANTOS, G. A. dos; RESENDE, M. D. V. de; TAKAHASHI, E. K.; LEITE, F. P. Aceleração do florescimento em genótipos autofecundados e florescimento ultra-precoce de mudas jovens de eucalyptus por top graftings. Boletim Técnico Sif, v. 1, n. 4, p. 1-6, 2021. Biblioteca(s): Embrapa Café. |
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20. | | NOGUEIRA, T. A. P. C.; NUNES, A. C. P.; SANTOS, G. A. dos; TAKAHASHI, E. K.; RESENDE, M. D. V. de; CORRADI, I. S. Estimativa de parâmetros genéticos em progênies de irmãos completos de eucalipto e otimização de seleção. Scientia Forestalis, v. 47, n. 123, p. 451-462, set. 2019. Biblioteca(s): Embrapa Florestas. |
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Registros recuperados : 38 | |
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Registro Completo
Biblioteca(s): |
Embrapa Café. |
Data corrente: |
08/12/2023 |
Data da última atualização: |
08/12/2023 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Circulação/Nível: |
A - 4 |
Autoria: |
FERREIRA, F. M.; CHAVES, S. F. da S.; PEIXOTO, M. A.; ALVES, R. S.; COELHO, I. F.; RESENDE, M. D. V. de; SANTOS, G. A. dos; BHERING, L. L. |
Afiliação: |
FILIPE MANOEL FERREIRA, UNIVERSIDADE FEDERAL DE VIÇOSA; SAULO FABRÍCIO DA SILVA CHAVES, UNIVERSIDADE FEDERAL DE VIÇOSA; MARCO ANTÔNIO PEIXOTO, UNIVERSIDADE FEDERAL DE VIÇOSA; RODRIGO SILVA ALVES, INSTITUTO NACIONAL DE CIÊNCIA E TECNOLOGIA DO CAFÉ; IGOR FERREIRA COELHO, UNIVERSIDADE FEDERAL DE VIÇOSA; MARCOS DEON VILELA DE RESENDE, CNPCa; GLEISON AUGUSTO DOS SANTOS, UNIVERSIDADE FEDERAL DE VIÇOSA; LEONARDO LOPES BHERING, UNIVERSIDADE FEDERAL DE VIÇOSA. |
Título: |
Multi-trait multi-environment models for selecting high-performance and stable eucalyptus clones. |
Ano de publicação: |
2023 |
Fonte/Imprenta: |
Acta Scientiarum. Agronomy, v. 45, e61626, 2023. |
Páginas: |
9 p. |
DOI: |
https://doi.org/10.4025/actasciagron.v45i1.61626 |
Idioma: |
Inglês |
Conteúdo: |
Multi-trait multi-environment (MTME) models were fitted to eucalyptus breeding trials data to assess residual variance structure, genetic stability and adaptability. To do so, 215 eucalyptus clones were evaluated in a randomized complete block design with 30 replicates and one plant per plot in four environments. At 36 months of age, tree diameter at breast height (DBH) and pilodyn penetration (PP) were measured. Two MTME models were fitted, for which residuals were considered homoscedastic and heteroscedastic, with the best MTME model selected using Bayesian information criterion. The harmonic mean of the relative performance of the genotypic values (HMRPGV) was used to determine stability and adaptability. Of the two models, the heteroscedastic MTME model had better fit and provided greater accuracy. In addition, genotype-by-environment interaction was complex, and there was low genetic correlation between DBH and PP. Rank correlation between the clones selected by the MTME models was high for DBH but low for PP. The HMRPGV facilitated clone selection through simultaneous evaluation of stability, adaptability, and productivity. Thus, our results suggest that heteroscedastic MTME model / HMRPGV can be efficiently applied in the genetic evaluation and selection of eucalyptus clones. |
Thesaurus NAL: |
Clones; Eucalyptus; Genetic variance; Genotype-environment interaction; Multivariate analysis; Quantitative genetics. |
Categoria do assunto: |
-- |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1159359/1/Multi-trait-multi-environment-models.pdf
|
Marc: |
LEADER 02224naa a2200301 a 4500 001 2159359 005 2023-12-08 008 2023 bl uuuu u00u1 u #d 024 7 $ahttps://doi.org/10.4025/actasciagron.v45i1.61626$2DOI 100 1 $aFERREIRA, F. M. 245 $aMulti-trait multi-environment models for selecting high-performance and stable eucalyptus clones.$h[electronic resource] 260 $c2023 300 $a9 p. 520 $aMulti-trait multi-environment (MTME) models were fitted to eucalyptus breeding trials data to assess residual variance structure, genetic stability and adaptability. To do so, 215 eucalyptus clones were evaluated in a randomized complete block design with 30 replicates and one plant per plot in four environments. At 36 months of age, tree diameter at breast height (DBH) and pilodyn penetration (PP) were measured. Two MTME models were fitted, for which residuals were considered homoscedastic and heteroscedastic, with the best MTME model selected using Bayesian information criterion. The harmonic mean of the relative performance of the genotypic values (HMRPGV) was used to determine stability and adaptability. Of the two models, the heteroscedastic MTME model had better fit and provided greater accuracy. In addition, genotype-by-environment interaction was complex, and there was low genetic correlation between DBH and PP. Rank correlation between the clones selected by the MTME models was high for DBH but low for PP. The HMRPGV facilitated clone selection through simultaneous evaluation of stability, adaptability, and productivity. Thus, our results suggest that heteroscedastic MTME model / HMRPGV can be efficiently applied in the genetic evaluation and selection of eucalyptus clones. 650 $aClones 650 $aEucalyptus 650 $aGenetic variance 650 $aGenotype-environment interaction 650 $aMultivariate analysis 650 $aQuantitative genetics 700 1 $aCHAVES, S. F. da S. 700 1 $aPEIXOTO, M. A. 700 1 $aALVES, R. S. 700 1 $aCOELHO, I. F. 700 1 $aRESENDE, M. D. V. de 700 1 $aSANTOS, G. A. dos 700 1 $aBHERING, L. L. 773 $tActa Scientiarum. Agronomy$gv. 45, e61626, 2023.
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