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 | Acesso ao texto completo restrito à biblioteca da Embrapa Semiárido. Para informações adicionais entre em contato com cpatsa.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Semiárido. |
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Data corrente: |
11/05/2011 |
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Data da última atualização: |
18/05/2011 |
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Autoria: |
MOURA, R. M. de. |
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Afiliação: |
ROMERO MARINHO DE MOURA, Academia Pernambucana de Ciência Agronômica, Recife. |
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Título: |
Nematóides de interesse agrícola assinalados pelo laboratório de fitonematologia da UFRPE, no Nordeste do Brasil (1967-2004). |
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Ano de publicação: |
2005 |
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Fonte/Imprenta: |
Anais da Academia Pernambucana de Ciência Agronômica, Recife, v. 2, p. 166-171, 2005. |
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Idioma: |
Português |
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Conteúdo: |
São reportados fitonematóides de interesse agrícola assinalados em solo e tecido de plantas no nordeste do Brasil, pelo Laboratório de Fitonematologia da Universidade Federal Rural de Pernambuco, no período de 1967-2004. Na relação, foi incorporado o nematóide do vinagre, Turbatrix aceti, problema sanitário de real gravidade para as industrias que utilizam ácido acético, a exemplo do vinagre e picles. Esses produtos quando contaminados são inapropriados para o consumo. |
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Palavras-Chave: |
Fitonematóides; Lista; Nematóide do vinagre. |
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Thesagro: |
Nematóide; Solo. |
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Thesaurus Nal: |
Turbatrix aceti. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 01098naa a2200193 a 4500
001 1888296
005 2011-05-18
008 2005 bl uuuu u00u1 u #d
100 1 $aMOURA, R. M. de
245 $aNematóides de interesse agrícola assinalados pelo laboratório de fitonematologia da UFRPE, no Nordeste do Brasil (1967-2004).
260 $c2005
520 $aSão reportados fitonematóides de interesse agrícola assinalados em solo e tecido de plantas no nordeste do Brasil, pelo Laboratório de Fitonematologia da Universidade Federal Rural de Pernambuco, no período de 1967-2004. Na relação, foi incorporado o nematóide do vinagre, Turbatrix aceti, problema sanitário de real gravidade para as industrias que utilizam ácido acético, a exemplo do vinagre e picles. Esses produtos quando contaminados são inapropriados para o consumo.
650 $aTurbatrix aceti
650 $aNematóide
650 $aSolo
653 $aFitonematóides
653 $aLista
653 $aNematóide do vinagre
773 $tAnais da Academia Pernambucana de Ciência Agronômica, Recife$gv. 2, p. 166-171, 2005.
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Embrapa Semiárido (CPATSA) |
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Biblioteca(s): |
Embrapa Milho e Sorgo. |
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Data corrente: |
24/09/2018 |
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Data da última atualização: |
05/02/2019 |
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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: |
OLIVEIRA, A. A. de; PASTINA, M. M.; SOUZA, V. F. de; PARRELLA, R. A. da C.; NODA, R. W.; SIMEONE, M. L. F.; SCHAFFERT, R. E.; MAGALHAES, J. V. de; DAMASCENO, C. M. B.; MARGARIDO, G. R. A. |
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Afiliação: |
Amanda Avelar de Oliveira; MARIA MARTA PASTINA, CNPMS; Vander Filipe de Souza; RAFAEL AUGUSTO DA COSTA PARRELLA, CNPMS; ROBERTO WILLIANS NODA, CNPMS; MARIA LUCIA FERREIRA SIMEONE, CNPMS; ROBERT EUGENE SCHAFFERT, CNPMS; JURANDIR VIEIRA DE MAGALHAES, CNPMS; CYNTHIA MARIA BORGES DAMASCENO, CNPMS; Gabriel Rodrigues Alves Margarido. |
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Título: |
Genomic prediction applied to high-biomass sorghum for bioenergy production. |
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Ano de publicação: |
2018 |
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Fonte/Imprenta: |
Molecular Breeding, v. 38, n. 49, p. 1-16, 2018. |
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DOI: |
10.1007/s11032-018-0802-5 |
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Idioma: |
Inglês |
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Conteúdo: |
The increasing cost of energy and finite oil and gas reserves have created a need to develop alternative fuels from renewable sources. Due to its abiotic stress tolerance and annual cultivation, high-biomass sorghum (Sorghum bicolor L. Moench) shows potential as a bioenergy crop. Genomic selection is a useful tool for accelerating genetic gains and could restructure plant breeding programs by enabling early selection and reducing breeding cycle duration. This work aimed at predicting breeding values via genomic selection models for 200 sorghum genotypes comprising landrace accessions and breeding lines from biomass and saccharine groups. These genotypes were divided into two sub-panels, according to breeding purpose. We evaluated the following phenotypic biomass traits: days to flowering, plant height, fresh and dry matter yield, and fiber, cellulose, hemicellulose, and lignin proportions. Genotyping by sequencing yielded more than 258,000 single-nucleotide polymorphism markers, which revealed population structure between subpanels. We then fitted and compared genomic selection models BayesA, BayesB, BayesC?, BayesLasso, Bayes Ridge Regression and random regression best linear unbiased predictor. The resulting predictive abilities varied little between the different models, but substantially between traits. Different scenarios of prediction showed the potential of using genomic selection results between sub-panels and years, although the genotype by environment interaction negatively affected accuracies. Functional enrichment analyses performed with the marker-predicted effects suggested several interesting associations, with potential for revealing biological processes relevant to the studied quantitative traits. This work shows that genomic selection can be successfully applied in biomass sorghum breeding programs. MenosThe increasing cost of energy and finite oil and gas reserves have created a need to develop alternative fuels from renewable sources. Due to its abiotic stress tolerance and annual cultivation, high-biomass sorghum (Sorghum bicolor L. Moench) shows potential as a bioenergy crop. Genomic selection is a useful tool for accelerating genetic gains and could restructure plant breeding programs by enabling early selection and reducing breeding cycle duration. This work aimed at predicting breeding values via genomic selection models for 200 sorghum genotypes comprising landrace accessions and breeding lines from biomass and saccharine groups. These genotypes were divided into two sub-panels, according to breeding purpose. We evaluated the following phenotypic biomass traits: days to flowering, plant height, fresh and dry matter yield, and fiber, cellulose, hemicellulose, and lignin proportions. Genotyping by sequencing yielded more than 258,000 single-nucleotide polymorphism markers, which revealed population structure between subpanels. We then fitted and compared genomic selection models BayesA, BayesB, BayesC?, BayesLasso, Bayes Ridge Regression and random regression best linear unbiased predictor. The resulting predictive abilities varied little between the different models, but substantially between traits. Different scenarios of prediction showed the potential of using genomic selection results between sub-panels and years, although the genotype by environment interaction n... Mostrar Tudo |
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Palavras-Chave: |
Genotipagem. |
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Thesagro: |
Bioenergia; Biomassa. |
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Categoria do assunto: |
G Melhoramento Genético |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/183412/1/Genomic-prediction.pdf
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Marc: |
LEADER 02655naa a2200277 a 4500
001 2096210
005 2019-02-05
008 2018 bl uuuu u00u1 u #d
024 7 $a10.1007/s11032-018-0802-5$2DOI
100 1 $aOLIVEIRA, A. A. de
245 $aGenomic prediction applied to high-biomass sorghum for bioenergy production.$h[electronic resource]
260 $c2018
520 $aThe increasing cost of energy and finite oil and gas reserves have created a need to develop alternative fuels from renewable sources. Due to its abiotic stress tolerance and annual cultivation, high-biomass sorghum (Sorghum bicolor L. Moench) shows potential as a bioenergy crop. Genomic selection is a useful tool for accelerating genetic gains and could restructure plant breeding programs by enabling early selection and reducing breeding cycle duration. This work aimed at predicting breeding values via genomic selection models for 200 sorghum genotypes comprising landrace accessions and breeding lines from biomass and saccharine groups. These genotypes were divided into two sub-panels, according to breeding purpose. We evaluated the following phenotypic biomass traits: days to flowering, plant height, fresh and dry matter yield, and fiber, cellulose, hemicellulose, and lignin proportions. Genotyping by sequencing yielded more than 258,000 single-nucleotide polymorphism markers, which revealed population structure between subpanels. We then fitted and compared genomic selection models BayesA, BayesB, BayesC?, BayesLasso, Bayes Ridge Regression and random regression best linear unbiased predictor. The resulting predictive abilities varied little between the different models, but substantially between traits. Different scenarios of prediction showed the potential of using genomic selection results between sub-panels and years, although the genotype by environment interaction negatively affected accuracies. Functional enrichment analyses performed with the marker-predicted effects suggested several interesting associations, with potential for revealing biological processes relevant to the studied quantitative traits. This work shows that genomic selection can be successfully applied in biomass sorghum breeding programs.
650 $aBioenergia
650 $aBiomassa
653 $aGenotipagem
700 1 $aPASTINA, M. M.
700 1 $aSOUZA, V. F. de
700 1 $aPARRELLA, R. A. da C.
700 1 $aNODA, R. W.
700 1 $aSIMEONE, M. L. F.
700 1 $aSCHAFFERT, R. E.
700 1 $aMAGALHAES, J. V. de
700 1 $aDAMASCENO, C. M. B.
700 1 $aMARGARIDO, G. R. A.
773 $tMolecular Breeding$gv. 38, n. 49, p. 1-16, 2018.
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