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 | Acesso ao texto completo restrito à biblioteca da Embrapa Amazônia Ocidental. Para informações adicionais entre em contato com cpaa.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Amazônia Ocidental. |
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Data corrente: |
25/04/2018 |
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Data da última atualização: |
02/05/2018 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
AIRES, P. S. R.; GAMBARRA-NETO, F. F.; COUTINHO, W. M.; ARAUJO, A. E. de; SILVA, G. F. da; GOUVEIA, J. P. G.; MEDEIROS, E. P. de. |
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Afiliação: |
Priscila S.R. Aires, Federal University of Campina Grande; Francisco F. Gambarra-Neto, Federal University of Paraiba; WIRTON MACEDO COUTINHO, CNPA; ALDERI EMIDIO DE ARAUJO, CNPA; GILVAN FERREIRA DA SILVA, CPAA; Josivanda P.G. Gouveia, Federal University of Campina Grande; EVERALDO PAULO DE MEDEIROS, CNPA. |
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Título: |
Near infrared hyperspectral images and pattern recognition techniques used to identify etiological agents of cotton anthracnose and ramulosis. |
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Ano de publicação: |
2018 |
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Fonte/Imprenta: |
Journal of Spectral Imaging, v. 7, a8, p. 1-17, 2018. |
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DOI: |
https://doi.org/10.1255/jsi.2018.a8 |
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Idioma: |
Inglês |
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Conteúdo: |
Hyperspectral imaging near infrared (HSI-NIR) has the potential to be used as a non-destructive approach for the analysis of new microbiological matrices of agriculture interest. This article describes a new method for accurately and rapidly classifying the etiological agents Colletotrichum gossypii (CG) and C. gossypii var. cephalosporioides (CGC) grown in a culture medium, using scattering reflectance HSI-NIR and multivariate pattern recognition analysis. |
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Palavras-Chave: |
Cotton crop; Fungal identification; Fungal taxonomy; Non-destructive analysis. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 01304naa a2200253 a 4500
001 2090854
005 2018-05-02
008 2018 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1255/jsi.2018.a8$2DOI
100 1 $aAIRES, P. S. R.
245 $aNear infrared hyperspectral images and pattern recognition techniques used to identify etiological agents of cotton anthracnose and ramulosis.$h[electronic resource]
260 $c2018
520 $aHyperspectral imaging near infrared (HSI-NIR) has the potential to be used as a non-destructive approach for the analysis of new microbiological matrices of agriculture interest. This article describes a new method for accurately and rapidly classifying the etiological agents Colletotrichum gossypii (CG) and C. gossypii var. cephalosporioides (CGC) grown in a culture medium, using scattering reflectance HSI-NIR and multivariate pattern recognition analysis.
653 $aCotton crop
653 $aFungal identification
653 $aFungal taxonomy
653 $aNon-destructive analysis
700 1 $aGAMBARRA-NETO, F. F.
700 1 $aCOUTINHO, W. M.
700 1 $aARAUJO, A. E. de
700 1 $aSILVA, G. F. da
700 1 $aGOUVEIA, J. P. G.
700 1 $aMEDEIROS, E. P. de
773 $tJournal of Spectral Imaging$gv. 7, a8, p. 1-17, 2018.
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Registro original: |
Embrapa Amazônia Ocidental (CPAA) |
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Cutter |
Registro |
Volume |
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Voltar
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Registro Completo
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Biblioteca(s): |
Embrapa Agroenergia; Embrapa Café. |
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Data corrente: |
24/06/2021 |
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Data da última atualização: |
24/06/2021 |
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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: |
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. |
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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. |
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Título: |
Multiple-trait model through Bayesian inference applied to Jatropha curcas breeding for bioenergy. |
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Ano de publicação: |
2021 |
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Fonte/Imprenta: |
PLOS ONE , v. 16, n. 3, e0247775, Mar. 2021. |
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Volume: |
16 |
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ISSN: |
1932-6203 |
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DOI: |
https://doi.org/10.1371/journal.pone.0247775 |
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Idioma: |
Inglês |
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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. |
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Thesagro: |
Bioenergia. |
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Thesaurus NAL: |
Bioenergy; Biofuels; Genetic polymorphism; Petroleum; Vegetable oil. |
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Categoria do assunto: |
-- |
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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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Registro original: |
Embrapa Agroenergia (CNPAE) |
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