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| Registros recuperados : 8 | |
| 1. |  | FREIRE, A. I.; DIAS, K. O. G.; OLIVEIRA, L. B. V.; NALIN, R. S.; GUEDES, F. L.; SOUZA, J. C. Genetic control of the number of leaves above the ear in maize. Genetics and Molecular Research, Ribeirao Preto, v. 14, n. 1, p. 1318-1323, Mar. 2015.| Biblioteca(s): Embrapa Caprinos e Ovinos. |
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| 3. | | DIAS, K. O. G.; SANTOS, J. P. R. dos; KRAUSE, M. D.; PIEPHO, H.-P.; GUIMARAES, L. J. M.; PASTINA, M. M.; GARCIA, A. A. F. Leveraging probability concepts for cultivar recommendation in multi?environment trials. Theoretical and Applied Genetics, v. 135, n. 4, p. 1385-1399, 2022.| Biblioteca(s): Embrapa Milho e Sorgo. |
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| 4. | | CHAVES, S. F. S.; EVANGELISTA, J. S. P. C.; ALVES, R. S.; FERREIRA, F. M.; DIAS, L. A. S.; ALVES, R. M.; DIAS, K. O. G.; BHERING, L. L. Application of linear mixed models for multiple harvest/site trial analyses in perennial plant breeding. Tree Genetics & Genomes, v. 18, n. 6, Article number: 44, Dec. 2022.| Biblioteca(s): Embrapa Amazônia Oriental. |
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| 5. | | DIAS, K. O. G.; PIEPHO, H. P.; GUIMARAES, L. J. M.; GUIMARAES, P. E. de O.; PARENTONI, S. N.; PINTO, M. de O.; NODA, R. W.; MAGALHAES, J. V. de; GUIMARÃES, C. T.; GARCIA, A. A. F.; PASTINA, M. M. Novel strategies for genomic prediction of untested single-cross maize hybrids using unbalanced historical data. Theoretical and Applied Genetics, v. 133, p. 443-455, 2020. Publicado online em 22 nov. 2019.| Biblioteca(s): Embrapa Milho e Sorgo. |
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| 6. | | CHAVES, S. F. S.; EVANGELISTA, J. S. P. C.; TRINDADE, R. dos S.; DIAS, L. A. S.; GUIMARAES, P. E. de O.; GUIMARAES, L. J. M.; ALVES, R. S.; BHERING, L. L.; DIAS, K. O. G. Employing factor analytic tools for selecting high-performance and stable tropical maize hybrids. Crop Science, v. 63, n. 3, p. 1114-1125, 2023.| Biblioteca(s): Embrapa Milho e Sorgo. |
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| 7. | | FERREIRA, F. M.; CHAVES, S. F. S.; BHERING, L. L.; ALVES, R. S.; TAKAHASHI, E. K.; SOUSA, J. E.; RESENDE, M. D. V. de; LEITE, F. P.; GEZAN, S. A.; VIANA, J. M. S.; FERNANDES, S. B.; DIAS, K. O. G. A novel strategy to predict clonal composites by jointly modeling spatial variation and genetic competition. Forest Ecology and Management, v. 548, Article 121393, 2023. 10 p.| Biblioteca(s): Embrapa Café. |
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| 8. | | ARAÚJO, M. S.; CHAVES, S. F. S.; DIAS, L. A. S.; FERREIRA, F. M.; PEREIRA, G. R.; BEZERRA, A. R. G.; ALVES, R. S.; HEINEMANN, A. B.; BRESEGHELLO, F.; CARNEIRO, P. C. S.; KRAUSE, M. D.; COSTA-NETO, G.; DIAS, K. O. G. GIS-FA: an approach to integrating thematic maps, factor-analytic, and envirotyping for cultivar targeting. Theoretical and Applied Genetics, v. 137, 80, Mar. 2024.| Biblioteca(s): Embrapa Arroz e Feijão. |
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| Registros recuperados : 8 | |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Amazônia Oriental. Para informações adicionais entre em contato com cpatu.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Amazônia Oriental. |
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Data corrente: |
01/12/2022 |
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Data da última atualização: |
01/12/2022 |
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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: |
CHAVES, S. F. S.; EVANGELISTA, J. S. P. C.; ALVES, R. S.; FERREIRA, F. M.; DIAS, L. A. S.; ALVES, R. M.; DIAS, K. O. G.; BHERING, L. L. |
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Afiliação: |
SAULO F. S. CHAVES, UNIVERSIDADE FEDERAL DE VIÇOSA; JENIFFER S. P. C. EVANGELISTA, UNIVERSIDADE FEDERAL DE VIÇOSA; RODRIGO S. ALVES, UNIVERSIDADE FEDERAL DE LAVRAS; FILIPE M. FERREIRA, UNIVERSIDADE FEDERAL DE VIÇOSA; LUIZ A. S. DIAS, UNIVERSIDADE FEDERAL DE VIÇOSA; RAFAEL MOYSES ALVES, CPATU; KAIO O. G. DIAS, UNIVERSIDADE FEDERAL DE VIÇOSA; LEONARDO L. BHERING, UNIVERSIDADE FEDERAL DE VIÇOSA. |
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Título: |
Application of linear mixed models for multiple harvest/site trial analyses in perennial plant breeding. |
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Ano de publicação: |
2022 |
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Fonte/Imprenta: |
Tree Genetics & Genomes, v. 18, n. 6, Article number: 44, Dec. 2022. |
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DOI: |
https://doi.org/10.1007/s11295-022-01576-5 |
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Idioma: |
Inglês |
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Conteúdo: |
The optimization of perennial plant breeding necessarily involves the evaluation of multi-harvest and/or multi-site trials. In these situations, modeling covariance structures can elevate accuracy. This study aimed to evaluate different covariance structures for multi-harvest and multi-site trial analyses, using two datasets (D1 and D2). In D1, 25 hybrids of Theobroma grandiflorum were evaluated in a complete randomized block design, during twelve consecutive harvest years. In D2, 215 clones of Eucalyptus spp. were evaluated in a complete randomized block design, in four sites. For both datasets, the covariance structures of the random effects were modeled, and their adequacy was tested by the Akaike and Bayesian information criteria. From the selected model, the variance components and genetic parameters were estimated. We also compared the expected genetic gains and the rankings of genotypes based on the genotypic values provided by the basic and the selected models. For D1, the third-order factor analytic model was the most suitable for genetic effects, while for D2, the unstructured model showed the best fit for such effects. The models provided a better insight into the variances dynamics over the harvest years/sites. The genetic gains were 3.52 percentage points higher in D1 and did not change in D2. Despite similar results, the standard model, modeled with covariance structures that assume homogeneity of co-variances, was not the most statistically appropriate model for D2 according to the information criteria. Therefore, the modeling of covariance structures can and should be used in the genetic evaluation of perennial plants MenosThe optimization of perennial plant breeding necessarily involves the evaluation of multi-harvest and/or multi-site trials. In these situations, modeling covariance structures can elevate accuracy. This study aimed to evaluate different covariance structures for multi-harvest and multi-site trial analyses, using two datasets (D1 and D2). In D1, 25 hybrids of Theobroma grandiflorum were evaluated in a complete randomized block design, during twelve consecutive harvest years. In D2, 215 clones of Eucalyptus spp. were evaluated in a complete randomized block design, in four sites. For both datasets, the covariance structures of the random effects were modeled, and their adequacy was tested by the Akaike and Bayesian information criteria. From the selected model, the variance components and genetic parameters were estimated. We also compared the expected genetic gains and the rankings of genotypes based on the genotypic values provided by the basic and the selected models. For D1, the third-order factor analytic model was the most suitable for genetic effects, while for D2, the unstructured model showed the best fit for such effects. The models provided a better insight into the variances dynamics over the harvest years/sites. The genetic gains were 3.52 percentage points higher in D1 and did not change in D2. Despite similar results, the standard model, modeled with covariance structures that assume homogeneity of co-variances, was not the most statistically appropriate model f... Mostrar Tudo |
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Thesagro: |
Árvore Frutífera; Floresta; Melhoramento. |
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Categoria do assunto: |
K Ciência Florestal e Produtos de Origem Vegetal |
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Marc: |
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100 1 $aCHAVES, S. F. S.
245 $aApplication of linear mixed models for multiple harvest/site trial analyses in perennial plant breeding.$h[electronic resource]
260 $c2022
520 $aThe optimization of perennial plant breeding necessarily involves the evaluation of multi-harvest and/or multi-site trials. In these situations, modeling covariance structures can elevate accuracy. This study aimed to evaluate different covariance structures for multi-harvest and multi-site trial analyses, using two datasets (D1 and D2). In D1, 25 hybrids of Theobroma grandiflorum were evaluated in a complete randomized block design, during twelve consecutive harvest years. In D2, 215 clones of Eucalyptus spp. were evaluated in a complete randomized block design, in four sites. For both datasets, the covariance structures of the random effects were modeled, and their adequacy was tested by the Akaike and Bayesian information criteria. From the selected model, the variance components and genetic parameters were estimated. We also compared the expected genetic gains and the rankings of genotypes based on the genotypic values provided by the basic and the selected models. For D1, the third-order factor analytic model was the most suitable for genetic effects, while for D2, the unstructured model showed the best fit for such effects. The models provided a better insight into the variances dynamics over the harvest years/sites. The genetic gains were 3.52 percentage points higher in D1 and did not change in D2. Despite similar results, the standard model, modeled with covariance structures that assume homogeneity of co-variances, was not the most statistically appropriate model for D2 according to the information criteria. Therefore, the modeling of covariance structures can and should be used in the genetic evaluation of perennial plants
650 $aÁrvore Frutífera
650 $aFloresta
650 $aMelhoramento
700 1 $aEVANGELISTA, J. S. P. C.
700 1 $aALVES, R. S.
700 1 $aFERREIRA, F. M.
700 1 $aDIAS, L. A. S.
700 1 $aALVES, R. M.
700 1 $aDIAS, K. O. G.
700 1 $aBHERING, L. L.
773 $tTree Genetics & Genomes$gv. 18, n. 6, Article number: 44, Dec. 2022.
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