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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 Algodão. |
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Data corrente: |
12/02/2022 |
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Data da última atualização: |
24/02/2022 |
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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: |
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. |
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Afiliação: |
MARCO ANTÔNIO PEIXITO, UNIVERSIDADE FEDERAL DE VIÇOSA; JENIFFER SANTANA PINTO COELHO EVANGELISTA, UNIVERSIDADE FEDERAL DE VIÇOSA; RODRIGO SILVA ALVES, UNIVERSIDADE FEDERAL DE VIÇOSA; FRANCISCO JOSÉ CORREA FARIAS, CNPA; LUIZ PAULO DE CARVALHO, CNPA; LARISSA PEREIRA RIBEIRO TEODORO, UNIVERSIDADE FEDERAL DE MATO GROSSO; PAULO EDUARDO TEODORO, UNIVERSIDADE FEDERAL DE MATO GROSSO; LEONARDO LOPES BHERING, UNIVERSIDADE FEDERAL DE VIÇOSA. |
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Título: |
Models for optimizing selection based on adaptability and stability of cotton genotypes. |
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Ano de publicação: |
2021 |
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Fonte/Imprenta: |
Ciência Rural, v. 51, n. 5, e20200530, p. 1-8, 2021. |
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Páginas: |
8 p. |
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ISSN: |
1678-4596 |
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DOI: |
10.1590/0103-8478cr20200530 |
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Idioma: |
Inglês |
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Conteúdo: |
In multi-environment trials (MET), large networks are assessed for results improvement. However, genotype by environment interaction plays an important role in the selection of the most adaptable and stable genotypes in MET framework. In this study, we tested different residual variances and measure the selection gain of cotton genotypes accounting for adaptability and stability, simultaneously. Twelve genotypes of cotton were bred in 10 environments, and fiber length (FL), fiber strength (FS), micronaire (MIC), and fiber yield (FY) were determined. Model selection for different residual variance structures (homogeneous and heterogeneous) was tested using the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). The variance components were estimated through restricted maximum likelihood and genotypic values were predicted through best linear unbiased prediction. The harmonic mean of relative performance of genetic values (HMRPGV) were applied for simultaneous selection for adaptability, stability, and yield. According to BIC heterogeneous residual variance was the best model fit for FY, whereas homogeneous residual variance was the best model fit for FL, FS, and MIC traits. The selective accuracy was high, indicating reliability of the prediction. The HMRPGV was capable to select for stability, adaptability and yield simultaneously, with remarkable selection gain for each trait. |
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Palavras-Chave: |
Bayesian Information Criterion; BIC; Ensaios multi ambientes; Estabilidade; HMRPGV; Média Harmônica do Desempenho Relativo dos Valores Genéticos; Multi environment trials. |
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Thesagro: |
Algodão; Fibra Vegetal; Gossypium Hirsutum; Produtividade. |
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Thesaurus NAL: |
Cotton. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/231250/1/MODELS-FOR-OPTIMIZING-SELECTION-BASED-ON-ADAPTABILITY.pdf
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|
Marc: |
LEADER 02564naa a2200385 a 4500
001 2139988
005 2022-02-24
008 2021 bl uuuu u00u1 u #d
022 $a1678-4596
024 7 $a10.1590/0103-8478cr20200530$2DOI
100 1 $aPEIXOTO, M. A.
245 $aModels for optimizing selection based on adaptability and stability of cotton genotypes.$h[electronic resource]
260 $c2021
300 $a8 p.
520 $aIn multi-environment trials (MET), large networks are assessed for results improvement. However, genotype by environment interaction plays an important role in the selection of the most adaptable and stable genotypes in MET framework. In this study, we tested different residual variances and measure the selection gain of cotton genotypes accounting for adaptability and stability, simultaneously. Twelve genotypes of cotton were bred in 10 environments, and fiber length (FL), fiber strength (FS), micronaire (MIC), and fiber yield (FY) were determined. Model selection for different residual variance structures (homogeneous and heterogeneous) was tested using the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). The variance components were estimated through restricted maximum likelihood and genotypic values were predicted through best linear unbiased prediction. The harmonic mean of relative performance of genetic values (HMRPGV) were applied for simultaneous selection for adaptability, stability, and yield. According to BIC heterogeneous residual variance was the best model fit for FY, whereas homogeneous residual variance was the best model fit for FL, FS, and MIC traits. The selective accuracy was high, indicating reliability of the prediction. The HMRPGV was capable to select for stability, adaptability and yield simultaneously, with remarkable selection gain for each trait.
650 $aCotton
650 $aAlgodão
650 $aFibra Vegetal
650 $aGossypium Hirsutum
650 $aProdutividade
653 $aBayesian Information Criterion
653 $aBIC
653 $aEnsaios multi ambientes
653 $aEstabilidade
653 $aHMRPGV
653 $aMédia Harmônica do Desempenho Relativo dos Valores Genéticos
653 $aMulti environment trials
700 1 $aEVANGELISTA, J. S. P. C.
700 1 $aALVES, R. S.
700 1 $aFARIAS, F. J. C.
700 1 $aCARVALHO, L. P.
700 1 $aTEODORO, L. P. R.
700 1 $aTEODORO, P. E.
700 1 $aBHERING, L. L.
773 $tCiência Rural$gv. 51, n. 5, e20200530, p. 1-8, 2021.
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Embrapa Algodão (CNPA) |
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