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Registro Completo |
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Biblioteca(s): |
Epagri-Sede. |
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Data corrente: |
12/09/2011 |
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Data da última atualização: |
12/09/2011 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
HICKEL, E. R.; DUCROQUET, J. P. H. J. |
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Afiliação: |
Epagri |
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Título: |
Pragas da goiabeira serrana (Feijoa sellowiana): Tripes (Phrasterothrips sp. e Liothrips sp.)(Thysanoptera:Phlaeothripidae). |
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Ano de publicação: |
1993 |
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Fonte/Imprenta: |
Anais da Sociedade Entomológica do Brasil, Londrina, SC, v. 22, n. 2, p. 381-384, 1993. |
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Idioma: |
Português |
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Notas: |
ISSN, 0301-8059 |
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Palavras-Chave: |
Fruticultura; Goiaba-da-serra; Levantamento faunístico; Myrtaceae. |
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Categoria do assunto: |
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Marc: |
LEADER 00572naa a2200169 a 4500
001 1080327
005 2011-09-12
008 1993 bl uuuu u00u1 u #d
100 1 $aEpagri
245 $aPragas da goiabeira serrana (Feijoa sellowiana)$bTripes (Phrasterothrips sp. e Liothrips sp.)(Thysanoptera:Phlaeothripidae).
260 $c1993
500 $aISSN, 0301-8059
653 $aFruticultura
653 $aGoiaba-da-serra
653 $aLevantamento faunístico
653 $aMyrtaceae
773 $tAnais da Sociedade Entomológica do Brasil, Londrina, SC$gv. 22, n. 2, p. 381-384, 1993.
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Epagri-Sede (Epagri-Sede) |
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Biblioteca(s): |
Epagri-Sede. |
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Data corrente: |
15/03/2022 |
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Data da última atualização: |
15/03/2022 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
MENDES, W. S.; DEMATTÊ, J. A.; ROSIN, N. A.; TERRA, F. S.; POPPIEL, R. R.; URBINA-SALAZAR, D. F.; BOECHAT, L.; SILVA, E. B.; CURI, N.; SILVA, S. H. G.; SANTOS, U. J.; VALLADARES, G. S. |
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Título: |
The Brazilian soil Mid-infrared Spectral Library: The Power of the Fundamental Range. |
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Ano de publicação: |
2022 |
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Fonte/Imprenta: |
Geoderma, AMSTERDAM, n. 415, p. 1-12, 2022. |
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Idioma: |
Inglês |
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Conteúdo: |
Brazil plays an important role in global agricultural production and environmental politics, and thus a national spectral library can bring light to new initiatives on soil monitoring. The middle infrared spectral range (MIR, 4000?600 cm− 1) includes the fundamental reflectance of soil organic and mineral components and thus can bring important information on soil characterization. This work aimed to build a national soil spectral library from the MIR, relate its descriptive and quantitative potential to soil assessment. We characterized up to 4309 soil samples from different depths (0?20, 40?60, 80?100, and 100?120 cm) including particle size distribution and chemical analyses (2963?4309 samples), and mineralogical analyses (278?861 samples). Unsupervised classification distinguished five soil spectra classes with distinct absorption shapes and features. The modeling results showed that MIR can accurately predict soil physicochemical attributes and revealed a strong association with environmental and geographical variables. There were no significant differences between raw and pre processed MIR spectral data in predicting soil attributes. In general, local spectral models presented better results than national ones, as expected. Consistent results were achieved for several soil attributes, such as mineralogy (R2 = 0.68?0.87), clay fraction (R2 = 0.55?0.91), organic carbon (R2 = 0.77?0.94), and base saturation (R2 = 0.68?0.87). It was possible to create a MIR spectral classification based only on the spectra and assess its relation with environmental and geographical variables. The information generated in this work may be the basis for future research as MIR spectra contain fundamental soil signatures that can be practically used for agronomic and environmental decisions. MenosBrazil plays an important role in global agricultural production and environmental politics, and thus a national spectral library can bring light to new initiatives on soil monitoring. The middle infrared spectral range (MIR, 4000?600 cm− 1) includes the fundamental reflectance of soil organic and mineral components and thus can bring important information on soil characterization. This work aimed to build a national soil spectral library from the MIR, relate its descriptive and quantitative potential to soil assessment. We characterized up to 4309 soil samples from different depths (0?20, 40?60, 80?100, and 100?120 cm) including particle size distribution and chemical analyses (2963?4309 samples), and mineralogical analyses (278?861 samples). Unsupervised classification distinguished five soil spectra classes with distinct absorption shapes and features. The modeling results showed that MIR can accurately predict soil physicochemical attributes and revealed a strong association with environmental and geographical variables. There were no significant differences between raw and pre processed MIR spectral data in predicting soil attributes. In general, local spectral models presented better results than national ones, as expected. Consistent results were achieved for several soil attributes, such as mineralogy (R2 = 0.68?0.87), clay fraction (R2 = 0.55?0.91), organic carbon (R2 = 0.77?0.94), and base saturation (R2 = 0.68?0.87). It was possible to create a MIR spectral ... Mostrar Tudo |
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Thesagro: |
MIR; Proximal soil sensing; Soil analysis; Soil classification; Soil quality; Soil spectroscopy. |
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Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
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Marc: |
LEADER 02708naa a2200325 a 4500
001 1131891
005 2022-03-15
008 2022 bl uuuu u00u1 u #d
100 1 $aMENDES, W. S.
245 $aThe Brazilian soil Mid-infrared Spectral Library$bThe Power of the Fundamental Range.$h[electronic resource]
260 $c2022
520 $aBrazil plays an important role in global agricultural production and environmental politics, and thus a national spectral library can bring light to new initiatives on soil monitoring. The middle infrared spectral range (MIR, 4000?600 cm− 1) includes the fundamental reflectance of soil organic and mineral components and thus can bring important information on soil characterization. This work aimed to build a national soil spectral library from the MIR, relate its descriptive and quantitative potential to soil assessment. We characterized up to 4309 soil samples from different depths (0?20, 40?60, 80?100, and 100?120 cm) including particle size distribution and chemical analyses (2963?4309 samples), and mineralogical analyses (278?861 samples). Unsupervised classification distinguished five soil spectra classes with distinct absorption shapes and features. The modeling results showed that MIR can accurately predict soil physicochemical attributes and revealed a strong association with environmental and geographical variables. There were no significant differences between raw and pre processed MIR spectral data in predicting soil attributes. In general, local spectral models presented better results than national ones, as expected. Consistent results were achieved for several soil attributes, such as mineralogy (R2 = 0.68?0.87), clay fraction (R2 = 0.55?0.91), organic carbon (R2 = 0.77?0.94), and base saturation (R2 = 0.68?0.87). It was possible to create a MIR spectral classification based only on the spectra and assess its relation with environmental and geographical variables. The information generated in this work may be the basis for future research as MIR spectra contain fundamental soil signatures that can be practically used for agronomic and environmental decisions.
650 $aMIR
650 $aProximal soil sensing
650 $aSoil analysis
650 $aSoil classification
650 $aSoil quality
650 $aSoil spectroscopy
700 1 $aDEMATTÊ, J. A.
700 1 $aROSIN, N. A.
700 1 $aTERRA, F. S.
700 1 $aPOPPIEL, R. R.
700 1 $aURBINA-SALAZAR, D. F.
700 1 $aBOECHAT, L.
700 1 $aSILVA, E. B.
700 1 $aCURI, N.
700 1 $aSILVA, S. H. G.
700 1 $aSANTOS, U. J.
700 1 $aVALLADARES, G. S.
773 $tGeoderma, AMSTERDAM$gn. 415, p. 1-12, 2022.
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