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Registro Completo |
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Biblioteca(s): |
Embrapa Soja. |
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Data corrente: |
18/07/2019 |
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Data da última atualização: |
28/10/2019 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
TAVARES, E. R.; SILVA, L. F. da; MOREY, A. T.; OLIVEIRA, A. G. de; ROCHA, S. P. D. da; RIBEIRO, R. A.; HUNGRIA, M.; THIHARA, I. R. T.; PERUGINI, M. R. E.; YAMAUCHI, L. M.; YAMADA-OGATTA, S. F. |
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Afiliação: |
UEL; UEL; IFRS, CANOAS, RS; UEL; UEL; RENAN AUGUSTO RIBEIRO, CNPSO; MARIANGELA HUNGRIA DA CUNHA, CNPSO; UEL; UEL; UEL; UEL. |
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Título: |
Draft Genome Sequence of Vancomycin-Resistant Enterococcus faecium UEL170 (Sequence Type 412), Isolated from a Patient with Urinary Tract Infection in a Tertiary Hospital in Southern Brazil. |
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Ano de publicação: |
2019 |
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Fonte/Imprenta: |
Microbiology Resource Announcements, v. 8, n. 7, e01365-18, 2019. |
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DOI: |
10.1128/MRA.01365-18 |
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Idioma: |
Inglês |
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Thesagro: |
Genoma; Infecção. |
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Thesaurus Nal: |
Enterococcus faecium; Genome; Infection. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/199684/1/aTavares-Microbiology-Resource-Announcements-2019-Tavares-e01365-18.full.pdf
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Marc: |
LEADER 00992naa a2200301 a 4500
001 2110716
005 2019-10-28
008 2019 bl uuuu u00u1 u #d
024 7 $a10.1128/MRA.01365-18$2DOI
100 1 $aTAVARES, E. R.
245 $aDraft Genome Sequence of Vancomycin-Resistant Enterococcus faecium UEL170 (Sequence Type 412), Isolated from a Patient with Urinary Tract Infection in a Tertiary Hospital in Southern Brazil.$h[electronic resource]
260 $c2019
650 $aEnterococcus faecium
650 $aGenome
650 $aInfection
650 $aGenoma
650 $aInfecção
700 1 $aSILVA, L. F. da
700 1 $aMOREY, A. T.
700 1 $aOLIVEIRA, A. G. de
700 1 $aROCHA, S. P. D. da
700 1 $aRIBEIRO, R. A.
700 1 $aHUNGRIA, M.
700 1 $aTHIHARA, I. R. T.
700 1 $aPERUGINI, M. R. E.
700 1 $aYAMAUCHI, L. M.
700 1 $aYAMADA-OGATTA, S. F.
773 $tMicrobiology Resource Announcements$gv. 8, n. 7, e01365-18, 2019.
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Registro original: |
Embrapa Soja (CNPSO) |
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Registro Completo
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Biblioteca(s): |
Embrapa Unidades Centrais. |
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Data corrente: |
03/04/2017 |
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Data da última atualização: |
16/05/2017 |
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Autoria: |
GUSSO, A.; ARVOR, D.; DUCATI, J. R. |
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Afiliação: |
ANIBAL GUSSO, UFRGS; DAMIEN ARVOR, CNRS; JORGE RICARDO DUCATI, UFRGS. |
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Título: |
Model for soybean production forecast based on prevailing physical conditions. |
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Ano de publicação: |
2017 |
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Fonte/Imprenta: |
Pesquisa Agropecuária Brasileira, Brasília, DF, v. 52, n. 2, p. 95-103, fev. 2017. |
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Idioma: |
Inglês |
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Notas: |
Título em português: Modelo para previsão da produção de soja baseado em condições físicas predominantes. |
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Conteúdo: |
The objective of this work was to evaluate the reliability of the physiological meaning of the enhanced vegetation index (EVI) data for the development of a remote sensing-based procedure to estimate soybean production prior to crop harvest. Time-series data from the moderate resolution imaging spectroradiometer (Modis) were applied to investigate the relationship between local yield fluctuations of soybean and the prevailing physically-driven conditions in the state of Mato Grosso, located in the south of the Brazilian Amazon. The developed methodology was based on the coupled model (CM). The CM provides production estimates for early January, using images from the maximum crop development period. Production estimates were validated at three different spatial scales: state, municipality, and local. At the state and municipality levels, the results obtained from the CM were compared with official agricultural statistics from Instituto Brasileiro de Geografia e Estatística and Companhia Nacional de Abastecimento, from 2001 to 2011. The coefficients of determination ranged from 0.91 to 0.98, with overall result of R2=0.96 (p?0.01), indicating that the model adheres to official statistics. At the local level, spatially distributed data were compared with production data from 422 crop fields. The coefficient of determination (R2=0.87) confirmed the reliability of the EVI for its applicability on remote sensing-based models for soybean production forecast. |
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Thesagro: |
Agricultura; Satélite; Sensoriamento remoto. |
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Thesaurus NAL: |
Moderate resolution imaging spectroradiometer; Remote sensing; Satellites. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/158533/1/Model-for-soybean-production.pdf
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Marc: |
LEADER 02255naa a2200229 a 4500
001 2068041
005 2017-05-16
008 2017 bl uuuu u00u1 u #d
100 1 $aGUSSO, A.
245 $aModel for soybean production forecast based on prevailing physical conditions.
260 $c2017
500 $aTítulo em português: Modelo para previsão da produção de soja baseado em condições físicas predominantes.
520 $aThe objective of this work was to evaluate the reliability of the physiological meaning of the enhanced vegetation index (EVI) data for the development of a remote sensing-based procedure to estimate soybean production prior to crop harvest. Time-series data from the moderate resolution imaging spectroradiometer (Modis) were applied to investigate the relationship between local yield fluctuations of soybean and the prevailing physically-driven conditions in the state of Mato Grosso, located in the south of the Brazilian Amazon. The developed methodology was based on the coupled model (CM). The CM provides production estimates for early January, using images from the maximum crop development period. Production estimates were validated at three different spatial scales: state, municipality, and local. At the state and municipality levels, the results obtained from the CM were compared with official agricultural statistics from Instituto Brasileiro de Geografia e Estatística and Companhia Nacional de Abastecimento, from 2001 to 2011. The coefficients of determination ranged from 0.91 to 0.98, with overall result of R2=0.96 (p?0.01), indicating that the model adheres to official statistics. At the local level, spatially distributed data were compared with production data from 422 crop fields. The coefficient of determination (R2=0.87) confirmed the reliability of the EVI for its applicability on remote sensing-based models for soybean production forecast.
650 $aModerate resolution imaging spectroradiometer
650 $aRemote sensing
650 $aSatellites
650 $aAgricultura
650 $aSatélite
650 $aSensoriamento remoto
700 1 $aARVOR, D.
700 1 $aDUCATI, J. R.
773 $tPesquisa Agropecuária Brasileira, Brasília, DF$gv. 52, n. 2, p. 95-103, fev. 2017.
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Registro original: |
Embrapa Unidades Centrais (AI-SEDE) |
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