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Registro Completo |
Biblioteca(s): |
Embrapa Agroindústria de Alimentos. |
Data corrente: |
25/11/2002 |
Data da última atualização: |
25/11/2002 |
Autoria: |
BITTENCOURT, A. M.; FARIAS, A. X.; FREITAS-SILVA, O.; ROCHA, E. S.; CORREA, T. B. S. |
Título: |
Identificacao rapida de linhagens de Aspergillus flavus isoladas em graos de milho pos-colheita. |
Ano de publicação: |
2001 |
Fonte/Imprenta: |
In: CONGRESSO BRASILEIRO DE MICOLOGIA, 3., 2001, Aguas de Lindoia, SP. Micologia: desenvolvimento e perspectivas para o milenio. Sociedade Brasileira de Micologia, 2001. p.51, ref. AL.022. |
Idioma: |
Português |
Palavras-Chave: |
Graos. |
Thesagro: |
Aspergillus Flavus; Identificação; Milho; Pós-Colheita. |
Categoria do assunto: |
-- |
Marc: |
LEADER 00763naa a2200217 a 4500 001 1414104 005 2002-11-25 008 2001 bl --- 0-- u #d 100 1 $aBITTENCOURT, A. M. 245 $aIdentificacao rapida de linhagens de Aspergillus flavus isoladas em graos de milho pos-colheita. 260 $c2001 650 $aAspergillus Flavus 650 $aIdentificação 650 $aMilho 650 $aPós-Colheita 653 $aGraos 700 1 $aFARIAS, A. X. 700 1 $aFREITAS-SILVA, O. 700 1 $aROCHA, E. S. 700 1 $aCORREA, T. B. S. 773 $tIn: CONGRESSO BRASILEIRO DE MICOLOGIA, 3., 2001, Aguas de Lindoia, SP. Micologia: desenvolvimento e perspectivas para o milenio. Sociedade Brasileira de Micologia, 2001. p.51, ref. AL.022.
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Registro original: |
Embrapa Agroindústria de Alimentos (CTAA) |
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Registro Completo
Biblioteca(s): |
Embrapa Agroenergia; Embrapa Café. |
Data corrente: |
24/06/2021 |
Data da última atualização: |
24/06/2021 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Circulação/Nível: |
A - 1 |
Autoria: |
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. |
Afiliação: |
MARCO ANTÔNIO PEIXOTO, Universidade Federal de Viçosa; JENIFFER SANTANA PINTO COELHO EVANGELISTA, Universidade Federal de Viçosa; IGOR FERREIRA COELHO, Universidade Federal de Viçosa; RODRIGO SILVA ALVES, Universidade Federal de Viçosa; BRUNO GALVEAS LAVIOLA, CNPAE; FABYANO FONSECA E SILVA, Universidade Federal de Viçosa; MARCOS DEON VILELA DE RESENDE, CNPCa; LEONARDO LOPES BHERING, Universidade Federal de Viçosa. |
Título: |
Multiple-trait model through Bayesian inference applied to Jatropha curcas breeding for bioenergy. |
Ano de publicação: |
2021 |
Fonte/Imprenta: |
PLOS ONE , v. 16, n. 3, e0247775, Mar. 2021. |
Volume: |
16 |
ISSN: |
1932-6203 |
DOI: |
https://doi.org/10.1371/journal.pone.0247775 |
Idioma: |
Inglês |
Conteúdo: |
Multiple-trait model tends to be the best alternative for the analysis of repeated measures, since they consider the genetic and residual correlations between measures and improve the selective accuracy. Thus, the objective of this study was to propose a multiple-trait Bayesian model for repeated measures analysis in Jatropha curcas breeding for bioenergy. To this end, the grain yield trait of 730 individuals of 73 half-sib families was evaluated over six harvests. The Markov Chain Monte Carlo algorithm was used to estimate genetic parameters and genetic values. Genetic correlation between pairs of measures were estimated and four selective intensities (27.4%, 20.5%, 13.7%, and 6.9%) were used to compute the selection gains. The full model was selected based on deviance information criterion. Genetic correlations of low, moderate, and high magnitude were observed between pairs of harvests. Bayesian analyses provide robust inference of genetic parameters and genetic values, with high selective accuracies. In summary, the multiple-trait Bayesian model allowed the reliable selection of superior Jatropha curcas progenies. Therefore, we recommend this model to genetic evaluation of Jatropha curcas genotypes, and its generalization, in other perennials. |
Thesagro: |
Bioenergia. |
Thesaurus NAL: |
Bioenergy; Biofuels; Genetic polymorphism; Petroleum; Vegetable oil. |
Categoria do assunto: |
-- |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/224043/1/Multiple-trait-model-2021.pdf
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Marc: |
LEADER 02186naa a2200325 a 4500 001 2132550 005 2021-06-24 008 2021 bl uuuu u00u1 u #d 022 $a1932-6203 024 7 $ahttps://doi.org/10.1371/journal.pone.0247775$2DOI 100 1 $aPEIXOTO, M. A. 245 $aMultiple-trait model through Bayesian inference applied to Jatropha curcas breeding for bioenergy.$h[electronic resource] 260 $c2021 300 $a16 490 $v16 520 $aMultiple-trait model tends to be the best alternative for the analysis of repeated measures, since they consider the genetic and residual correlations between measures and improve the selective accuracy. Thus, the objective of this study was to propose a multiple-trait Bayesian model for repeated measures analysis in Jatropha curcas breeding for bioenergy. To this end, the grain yield trait of 730 individuals of 73 half-sib families was evaluated over six harvests. The Markov Chain Monte Carlo algorithm was used to estimate genetic parameters and genetic values. Genetic correlation between pairs of measures were estimated and four selective intensities (27.4%, 20.5%, 13.7%, and 6.9%) were used to compute the selection gains. The full model was selected based on deviance information criterion. Genetic correlations of low, moderate, and high magnitude were observed between pairs of harvests. Bayesian analyses provide robust inference of genetic parameters and genetic values, with high selective accuracies. In summary, the multiple-trait Bayesian model allowed the reliable selection of superior Jatropha curcas progenies. Therefore, we recommend this model to genetic evaluation of Jatropha curcas genotypes, and its generalization, in other perennials. 650 $aBioenergy 650 $aBiofuels 650 $aGenetic polymorphism 650 $aPetroleum 650 $aVegetable oil 650 $aBioenergia 700 1 $aEVANGELISTA, J. S. P. C. 700 1 $aCOELHO, I. F 700 1 $aALVES, R. A. 700 1 $aLAVIOLA, B. G. 700 1 $aSILVA, F. F. e 700 1 $aRESENDE, M. D. V. de 700 1 $aBHERING, L. L. 773 $tPLOS ONE$gv. 16, n. 3, e0247775, Mar. 2021.
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