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Biblioteca(s): |
Embrapa Solos. |
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Data corrente: |
12/08/2015 |
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Data da última atualização: |
28/01/2016 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
HONG, J.; GRUNWALD, S.; VASQUES, G. M. |
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Afiliação: |
JINSEOK HONG, EAST TENNESSEE STATE UNIVERSITY; SABINE GRUNWALD, UNIVERSITY OF FLORIDA; GUSTAVO DE MATTOS VASQUES, CNPS. |
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Título: |
Soil phosphorus landscape models for precision soil conservation. |
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Ano de publicação: |
2015 |
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Fonte/Imprenta: |
Journal of Environmental Quality, Madison, v. 44, n. 3, p. 739-753, 2015. |
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DOI: |
10.2134/jeq2014.09.0379 |
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Idioma: |
Inglês |
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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 |
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Palavras-Chave: |
Modelo de precisão. |
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Thesagro: |
Conservação do solo; Fósforo. |
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Thesaurus Nal: |
Phosphorus; Precision. |
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Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
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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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| 1. |  | GAZZIERO, D. L. P.; BRIGHENTI, A. M.; VOLL, E.; KAJIHARA, L. H.; NAKANO, M. T. Controle de biótipos de amendoim-bravo (Euphorbia heterophylla) e picão-preto (Bidens subalternans) resistentes aos herbicidas inibidores da enzima ALS. In: REUNIÃO DE PESQUISA DE SOJA DA REGIÃO CENTRAL DO BRASIL, 27., 2005. Cornélio Procópio. Resumos... Londrina: Embrapa Soja, 2005. p. 535-536. (Embrapa Soja. Documentos, 257). Organizado por Odilon Ferreira Saraiva, Janete Lasso Ortiz, Simone Ery Grosskopf.| Biblioteca(s): Embrapa Soja. |
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| 3. |  | UTIAMADA, C. M.; SATO, L. N.; YORINORI, J. T.; OUCHI, I.; KAJIHARA, L. H. Efeito de fungicidas na germinação e no controle de doenças transmitidas pelas sementes de soja (Glycine max (L.) Merrill). Informativo ABRATES, Curitiba, v. 9, n.1/2, p. 107, jul./ago. 1999. Número especial. Ref. 153. Resumo apresentado no XI Congresso Brasileiro de Sementes.| Tipo: Resumo em Anais de Congresso |
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| 4. |  | GAZZIERO, D. L. P.; BRIGHENTI, A. M.; VOLL, E.; PRETE, C. E. C.; SUMIYA, M.; KAJIHARA, L. Variabilidade no grau de resistência de campim-marmelada (Brachiaria plantaginea) aos herbicidas clethodim, tepraloxydim e sethoxydim. Planta Daninha, v. 22, n. 3, p. 397-402, 2004.| Tipo: Artigo em Periódico Indexado |
| Biblioteca(s): Embrapa Soja. |
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| 5. |  | GAZZIERO; D. L. P.; BRIGHENTI, A. M.; ADEGAS, F. S.; VOLL, E.; PRETE, C. E. C.; SUMIYA, M.; KAJIHARA, L. H. Variabilidade no grau de resistência de capim-marmelada a herbicidas inibidores da enzima ACCase. In: CONGRESSO BRASILEIRO DA CIÊNCIA DAS PLANTAS DANINHAS, 23., 2002, Gramado. Resumos... Londrina: SBCPD: Embrapa Clima Temperado, 2002. p.197.| Biblioteca(s): Embrapa Soja. |
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