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| Registros recuperados : 16 | |
| 4. |  | PEIXOTO, M. A.; EVANGELISTA, J. S. P. C.; ALVES, R. S.; FARIAS, F. J. C.; CARVALHO, L. P.; TEODORO, L. P. R.; TEODORO, P. E.; BHERING, L. L. Models for optimizing selection based on adaptability and stability of cotton genotypes. Ciência Rural, v. 51, n. 5, e20200530, p. 1-8, 2021. 8 p.| Biblioteca(s): Embrapa Algodão. |
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| 5. | | PEIXOTO, M. A.; EVANGELISTA, J. S. P. C.; COELHO, I. F.; CARVALHO, L. P. de; FARIAS, F. J. C.; TEODORO, P. E.; BHERING, L. L. Genotype selection based on multiple traits in cotton crops: the application of genotype by yield trait biplot. Acta Scientiarum. Agronomy, v. 44, e54136, 2022.| Biblioteca(s): Embrapa Algodão. |
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| 6. | | 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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| 7. | | EVANGELISTA, J. S. P. C.; PEIXOTO, M. A.; COELHO, I.; ALVES, R.; RESENDE, M. D. V. de; SILVA, F. F. e; LAVIOLA, B.; BHERING, L. L. Genetic evaluation and selection in jatropha curcas through frequentist and bayesian inferences. Bragantia, v. 81, 2022. 12 p.| Biblioteca(s): Embrapa Café. |
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| 8. | | SANTOS, I. G. DOS; PEIXOTO, M. A.; CRUZ, C. D.; FERREIRA, R. de P.; NASCIMENTO, M.; ROSADO, R. D. S.; SANT ANNA, I. DE C. A novel approach to determine tropical persistence on alfalfa germplasm. Agronomy Journal, v. 114, p. 3225-3233, 2022.| Biblioteca(s): Embrapa Pecuária Sudeste. |
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| 9. | | EVANGELISTA, J. S. P. C.; ALVES, R. S.; PEIXOTO, M. A.; RESENDE, M. D. V. de; TEODORO, P. E.; SILVA, F. L. da; BHERING, L. L. Soybean productivity, stability, and adaptability through mixed model methodology. Ciência Rural, v. 51, n. 2, e20200406, 2021. Título em português: Produtividade, estabilidade e adaptabilidade da soja por meio de metodologia de modelo misto.| Biblioteca(s): Embrapa Café. |
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| 10. | | 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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| 11. | | PEIXOTO, M. A.; EVANGELISTA, J. S. P. C.; COELHO, I. F; ALVES, R. A.; LAVIOLA, B. G.; SILVA, F. F. e; RESENDE, M. D. V. de; BHERING, L. L. Multiple-trait model through Bayesian inference applied to Jatropha curcas breeding for bioenergy. PLOS ONE , v. 16, n. 3, e0247775, Mar. 2021. 16| Biblioteca(s): Embrapa Agroenergia; Embrapa Café. |
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| 12. | | 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. Multi-trait multi-environment models for selecting high-performance and stable eucalyptus clones. Acta Scientiarum. Agronomy, v. 45, e61626, 2023. 9 p.| Biblioteca(s): Embrapa Café. |
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| 13. | | EVANGELISTA, J. S. P. C.; PEIXOTO, M. A.; COELHO, I. F.; ALVES, R. S.; SILVA, F. F. e; RESENDE, M. D. V. de; SILVA, F. L. da; BHERING, L. L. Environmental stratification and genotype recommendation toward the soybean ideotype: a Bayesian approach. Crop Breeding and Applied Biotechnology, v. 21, n. 1, e359721111, 2021.| Biblioteca(s): Embrapa Café. |
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| 14. | | ALVES, R. S.; RESENDE, M. D. V. de; ROCHA, J. R. do A. S. de C.; PEIXOTO, M. A.; TEODORO, P. E.; SILVA, F. F. e; BHERING, L. L.; SANTOS, G. A. dos. Quantifying individual variation in reaction norms using random regression models fitted through Legendre polynomials: application in eucalyptus breeding. Bragantia, v. 79, n. 4, 2020. p. 360-376.| Biblioteca(s): Embrapa Café. |
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| 15. | | GOMES JUNIOR, R. A.; BOARI, A. de J.; PEIXOTO, M. A. de A.; BENCHIMOL, R. L.; CUNHA, B. P. de O.; SOUSA, N. L. de; SILVA, J. P. A. da; SILVA, R. T. da; SOUSA, E. R. M. de. Seleção preliminar de linhagens de segundo ciclo de melhoramento de feijão-caupi do tipo manteiguinha na Amazônia Oriental. Belém, PA: Embrapa Amazônia Oriental, 2024. 17 p. (Embrapa Amazônia Oriental. Boletim de pesquisa e desenvolvimento, 164).| Biblioteca(s): Embrapa Amazônia Oriental. |
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| 16. | | EVANGELISTA, J. S. P. C.; PEIXOTO, M. A.; COELHO, I. F.; FERREIRA, F. M.; MARÇAL, T. de S.; ALVES, R. S.; CHAVES, S. F. da S.; RODRIGUES, E. V.; LAVIOLA, B. G.; RESENDE, M. D. V. de; DIAS, K. O. das G.; BHERING, L. L. Modeling covariance structures and optimizing jatropha curcas breeding. Tree Genetics & Genomes, v. 19, 21, 2023. 11 p.| Biblioteca(s): Embrapa Agroenergia; Embrapa Café. |
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| Registros recuperados : 16 | |
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Registro Completo
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Biblioteca(s): |
Embrapa Café. |
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Data corrente: |
20/01/2022 |
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Data da última atualização: |
20/01/2022 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 2 |
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Autoria: |
EVANGELISTA, J. S. P. C.; PEIXOTO, M. A.; COELHO, I. F.; ALVES, R. S.; SILVA, F. F. e; RESENDE, M. D. V. de; SILVA, F. L. da; BHERING, L. L. |
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Afiliação: |
JENIFFER SANTANA PINTO COELHO EVANGELISTA, UFV; MARCO ANTÔNIO PEIXOTO, UFV; IGOR FERREIRA COELHO, UFV; RODRIGO SILVA ALVES, UFV; FABYANO FONSECA E SILVA, UFV; MARCOS DEON VILELA DE RESENDE, CNPCa; FELIPE LOPES DA SILVA, UFV; LEONARDO LOPES BHERING, UFV. |
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Título: |
Environmental stratification and genotype recommendation toward the soybean ideotype: a Bayesian approach. |
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Ano de publicação: |
2021 |
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Fonte/Imprenta: |
Crop Breeding and Applied Biotechnology, v. 21, n. 1, e359721111, 2021. |
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DOI: |
https://doi.org/10.1590/1984-70332021v21n1a11 |
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Idioma: |
Inglês |
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Conteúdo: |
The genotype × environment (G×E) interaction plays an essential role in phenotypic expression and can lead to difficulties in genotypes recommendation. Thus, the objectives of this study were: i) propose the Multi-Environment Index Based on Factor Analysis and Ideotype-Design/Markov Chain Monte Carlo (FAI/MCMC index), and ii) apply it for soybean genotypes recommendation. To this end, a data set with 30 soybean genotypes evaluated in 10 environments for grain yield trait was used. Variance components, genetic parameters and genetic values were estimated through MCMC algorithm. Environmental stratification was conducted by factor analyses and the selection of soybean genotypes was performed using the FAI/MCMC index. The results indicated the existence of genotypic variability and G×E interaction. The environments were grouped into three factors. The predicted genetic gains from indirect selection was 4.81%. Thus, our results suggest that the FAI/MCMC index can be successfully used in soybean breeding. |
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Thesagro: |
Genótipo; Glycine Soja; Soja. |
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Thesaurus NAL: |
Bayesian theory; Genotype; Seed stratification; Soybeans. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/230409/1/environmental-stratification-and-genotype.pdf
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Marc: |
LEADER 01935naa a2200301 a 4500
001 2139213
005 2022-01-20
008 2021 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1590/1984-70332021v21n1a11$2DOI
100 1 $aEVANGELISTA, J. S. P. C.
245 $aEnvironmental stratification and genotype recommendation toward the soybean ideotype$ba Bayesian approach.$h[electronic resource]
260 $c2021
520 $aThe genotype × environment (G×E) interaction plays an essential role in phenotypic expression and can lead to difficulties in genotypes recommendation. Thus, the objectives of this study were: i) propose the Multi-Environment Index Based on Factor Analysis and Ideotype-Design/Markov Chain Monte Carlo (FAI/MCMC index), and ii) apply it for soybean genotypes recommendation. To this end, a data set with 30 soybean genotypes evaluated in 10 environments for grain yield trait was used. Variance components, genetic parameters and genetic values were estimated through MCMC algorithm. Environmental stratification was conducted by factor analyses and the selection of soybean genotypes was performed using the FAI/MCMC index. The results indicated the existence of genotypic variability and G×E interaction. The environments were grouped into three factors. The predicted genetic gains from indirect selection was 4.81%. Thus, our results suggest that the FAI/MCMC index can be successfully used in soybean breeding.
650 $aBayesian theory
650 $aGenotype
650 $aSeed stratification
650 $aSoybeans
650 $aGenótipo
650 $aGlycine Soja
650 $aSoja
700 1 $aPEIXOTO, M. A.
700 1 $aCOELHO, I. F.
700 1 $aALVES, R. S.
700 1 $aSILVA, F. F. e
700 1 $aRESENDE, M. D. V. de
700 1 $aSILVA, F. L. da
700 1 $aBHERING, L. L.
773 $tCrop Breeding and Applied Biotechnology$gv. 21, n. 1, e359721111, 2021.
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Embrapa Café (CNPCa) |
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