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Registro Completo |
Biblioteca(s): |
Embrapa Solos. |
Data corrente: |
12/08/2015 |
Data da última atualização: |
28/01/2016 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Autoria: |
HONG, J.; GRUNWALD, S.; VASQUES, G. M. |
Afiliação: |
JINSEOK HONG, EAST TENNESSEE STATE UNIVERSITY; SABINE GRUNWALD, UNIVERSITY OF FLORIDA; GUSTAVO DE MATTOS VASQUES, CNPS. |
Título: |
Soil phosphorus landscape models for precision soil conservation. |
Ano de publicação: |
2015 |
Fonte/Imprenta: |
Journal of Environmental Quality, Madison, v. 44, n. 3, p. 739-753, 2015. |
DOI: |
10.2134/jeq2014.09.0379 |
Idioma: |
Inglês |
Conteúdo: |
Phosphorus (P) enrichment in soils has been documented in the Santa Fe River watershed (SFRW, 3585 km2) in north-central Florida. Yet the environmental factors that control P distribution in soils across the landscape, with potential contribution to water quality impairment, are not well understood. The main goal of this study was to develop soil-landscape P models to support a "precision soil conservation" approach combining fine-scale (i.e., site-specific) and coarse-scale (i.e., watershed-extent) assessment of soil P. The specific objectives were to: (i) identify those environmental properties that impart the most control on the spatial distribution of soil Mehlich-1 extracted P (MP) in the SFRW; (ii) model the spatial patterns of soil MP using geostatistical methods; and (iii) assess model quality using independent validation samples. Soil MP data at 137 sites were fused with spatially explicit environmental covariates to develop soil MP prediction models using univariate (lognormal kriging, LNK) and multivariate methods (regression kriging, RK, and cokriging, CK). Incorporation of exhaustive environmental data into multivariate models (RK and CK) improved the prediction of soil MP in the SFRW compared with the univariate model (LNK), which relies solely on soil measurements. Among all tested environmental covariates, land use and vegetation related properties (topsoil) and geologic data (subsoil) showed the largest predictive power to build inferential models for soil MP. Findings from this study contribute to a better understanding of spatially explicit interactions between soil P and other environmental variables, facilitating improved land resource management while minimizing adverse risks to the environment. MenosPhosphorus (P) enrichment in soils has been documented in the Santa Fe River watershed (SFRW, 3585 km2) in north-central Florida. Yet the environmental factors that control P distribution in soils across the landscape, with potential contribution to water quality impairment, are not well understood. The main goal of this study was to develop soil-landscape P models to support a "precision soil conservation" approach combining fine-scale (i.e., site-specific) and coarse-scale (i.e., watershed-extent) assessment of soil P. The specific objectives were to: (i) identify those environmental properties that impart the most control on the spatial distribution of soil Mehlich-1 extracted P (MP) in the SFRW; (ii) model the spatial patterns of soil MP using geostatistical methods; and (iii) assess model quality using independent validation samples. Soil MP data at 137 sites were fused with spatially explicit environmental covariates to develop soil MP prediction models using univariate (lognormal kriging, LNK) and multivariate methods (regression kriging, RK, and cokriging, CK). Incorporation of exhaustive environmental data into multivariate models (RK and CK) improved the prediction of soil MP in the SFRW compared with the univariate model (LNK), which relies solely on soil measurements. Among all tested environmental covariates, land use and vegetation related properties (topsoil) and geologic data (subsoil) showed the largest predictive power to build inferential models for soil M... Mostrar Tudo |
Palavras-Chave: |
Modelo de precisão. |
Thesagro: |
Conservação do solo; Fósforo. |
Thesaurus Nal: |
Phosphorus; Precision. |
Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
Marc: |
LEADER 02383naa a2200217 a 4500 001 2021794 005 2016-01-28 008 2015 bl uuuu u00u1 u #d 024 7 $a10.2134/jeq2014.09.0379$2DOI 100 1 $aHONG, J. 245 $aSoil phosphorus landscape models for precision soil conservation.$h[electronic resource] 260 $c2015 520 $aPhosphorus (P) enrichment in soils has been documented in the Santa Fe River watershed (SFRW, 3585 km2) in north-central Florida. Yet the environmental factors that control P distribution in soils across the landscape, with potential contribution to water quality impairment, are not well understood. The main goal of this study was to develop soil-landscape P models to support a "precision soil conservation" approach combining fine-scale (i.e., site-specific) and coarse-scale (i.e., watershed-extent) assessment of soil P. The specific objectives were to: (i) identify those environmental properties that impart the most control on the spatial distribution of soil Mehlich-1 extracted P (MP) in the SFRW; (ii) model the spatial patterns of soil MP using geostatistical methods; and (iii) assess model quality using independent validation samples. Soil MP data at 137 sites were fused with spatially explicit environmental covariates to develop soil MP prediction models using univariate (lognormal kriging, LNK) and multivariate methods (regression kriging, RK, and cokriging, CK). Incorporation of exhaustive environmental data into multivariate models (RK and CK) improved the prediction of soil MP in the SFRW compared with the univariate model (LNK), which relies solely on soil measurements. Among all tested environmental covariates, land use and vegetation related properties (topsoil) and geologic data (subsoil) showed the largest predictive power to build inferential models for soil MP. Findings from this study contribute to a better understanding of spatially explicit interactions between soil P and other environmental variables, facilitating improved land resource management while minimizing adverse risks to the environment. 650 $aPhosphorus 650 $aPrecision 650 $aConservação do solo 650 $aFósforo 653 $aModelo de precisão 700 1 $aGRUNWALD, S. 700 1 $aVASQUES, G. M. 773 $tJournal of Environmental Quality, Madison$gv. 44, n. 3, p. 739-753, 2015.
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Embrapa Solos (CNPS) |
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Biblioteca(s): |
Embrapa Amazônia Ocidental. |
Data corrente: |
22/04/2020 |
Data da última atualização: |
14/01/2021 |
Tipo da produção científica: |
Resumo em Anais de Congresso |
Autoria: |
MESQUITA, M. da S.; FERREIRA, T. R.; VIEIRA, L. do R.; FRANÇA, L. P.; CHAVES, F. C. M. |
Afiliação: |
Mirian da Silva Mesquita; Thais Rodrigues Ferreira; Lídia do Rêgo Vieira; Leandro Pereira França; FRANCISCO CELIO MAIA CHAVES, CPAA. |
Título: |
Potencial antimicrobiano de óleos essenciais de Piper hispidum e Piper hostmanianum para controle de bactérias patogênicas. |
Ano de publicação: |
2019 |
Fonte/Imprenta: |
In: CONGRESSO NACIONAL E SEMANA ACADÊMICA DE BIOTECNOLOGIA DA UNIVERSIDADE DO ESTADO AMAZONAS - UEA, 4., 2019, Manaus. Anais... Manaus: UEA, 2019. |
Páginas: |
p. 42. |
Idioma: |
Português |
Conteúdo: |
O objetivo deste estudo foi avaliar a composição volátil dos óleos de P. hispidum e P. hostmanianum e seu potencial antimicrobiano para controle de bactérias patogênicas em condições de laboratório. |
Thesagro: |
Óleo Essencial. |
Categoria do assunto: |
-- |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/220301/1/Anais-Congresso-Nacional-IV-Semana-de-Biotecnologia-UEA-2019-p42.pdf
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Marc: |
LEADER 00894nam a2200181 a 4500 001 2121793 005 2021-01-14 008 2019 bl uuuu u00u1 u #d 100 1 $aMESQUITA, M. da S. 245 $aPotencial antimicrobiano de óleos essenciais de Piper hispidum e Piper hostmanianum para controle de bactérias patogênicas.$h[electronic resource] 260 $aIn: CONGRESSO NACIONAL E SEMANA ACADÊMICA DE BIOTECNOLOGIA DA UNIVERSIDADE DO ESTADO AMAZONAS - UEA, 4., 2019, Manaus. Anais... Manaus: UEA$c2019 300 $ap. 42. 520 $aO objetivo deste estudo foi avaliar a composição volátil dos óleos de P. hispidum e P. hostmanianum e seu potencial antimicrobiano para controle de bactérias patogênicas em condições de laboratório. 650 $aÓleo Essencial 700 1 $aFERREIRA, T. R. 700 1 $aVIEIRA, L. do R. 700 1 $aFRANÇA, L. P. 700 1 $aCHAVES, F. C. M.
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