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| 9. |  | SHAPIRO, J. L.; WIEGERS, A.; DULAC, G. C.; BOUFFARD, A.; AFSHAR, A.; MYERS, D. J.; DUBUC, C.; MARTIN, M. W.; KOLLER, M. A survey of cattle for antibodies against bluetongue and epizootic hemorrhagic disease of deer viruses in British Columbia and Southwestern Alberta in 1987. Canadian Journal of Veterinary Research, v.55, n.2, p.203-204, 1991.| Biblioteca(s): Embrapa Pantanal. |
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| 10. | | GRUNWALD, S.; MYERS, D.; VASQUES, G. de M.; XIONG, X.; ROSS, C.; CHAIKAEW, P.; STOPPE, A.; KNOX, N.; COMERFORD, N.; HARRIS, W. Spatially-explicit and spectral soil carbon modeling in Florida. In: ASA, CSSA AND SSSA INTERNATIONAL ANNUAL MEETINGS, 2011, San Antonio. Abstracts... San Antonio: ASA/CSSA/SSSA, 2011.| Biblioteca(s): Embrapa Solos. |
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Registro Completo
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Biblioteca(s): |
Embrapa Solos. |
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Data corrente: |
05/11/2012 |
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Data da última atualização: |
11/11/2021 |
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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: |
VASQUES, G. de M.; GRUNWALD, S.; MYERS, D. B. |
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Afiliação: |
GUSTAVO DE MATTOS VASQUES, CNPS; University of Florida; Department of Agriculture, Columbia, Missouri, USA. |
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Título: |
Influence of the spatial extent and resolution of input data on soil carbon models in Florida, USA. |
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Ano de publicação: |
2012 |
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Fonte/Imprenta: |
Journal of Geophysical Research Geosciences, v. 117, n. G4, 2012. |
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DOI: |
https://doi.org/10.1029/2012JG001982 |
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Idioma: |
Inglês |
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Conteúdo: |
Understanding the causes of spatial variation of soil carbon (C) has important implications for regional and global C dynamics studies. Soil C predictive models can identify sources of C variation, but may be influenced by scale parameters, including the spatial extent and resolution of input data. Our objective was to investigate the influence of these scale parameters on soil C spatial predictive models in Florida, USA. We used data from three nested spatial extents (Florida, 150,000 km2; Santa Fe River watershed, 3,585 km2; and University of Florida Beef Cattle Station, 5.58 km2) to derive stepwise linear models of soil C as a function of 24 environmental properties. Models were derived within the three extents and for seven resolutions (30?1920 m) of input environmental data in Florida and in the watershed, then cross-evaluated among extents and resolutions, respectively. The quality of soil C models increased with an increase in the spatial extent (R2 from 0.10 in the cattle station to 0.61 in Florida) and with a decrease in the resolution of input data (R2 from 0.33 at 1920-m resolution to 0.61 at 30-m resolution in Florida). Soil and hydrologic variables were the most important across the seven resolutions both in Florida and in the watershed. The spatial extent and resolution of environmental covariates modulate soil C variation and soil-landscape correlations influencing soil C predictive models. Our results provide scale boundaries to observe environmental data and assess soil C spatial patterns, supporting C sequestration, budgeting and monitoring programs. MenosUnderstanding the causes of spatial variation of soil carbon (C) has important implications for regional and global C dynamics studies. Soil C predictive models can identify sources of C variation, but may be influenced by scale parameters, including the spatial extent and resolution of input data. Our objective was to investigate the influence of these scale parameters on soil C spatial predictive models in Florida, USA. We used data from three nested spatial extents (Florida, 150,000 km2; Santa Fe River watershed, 3,585 km2; and University of Florida Beef Cattle Station, 5.58 km2) to derive stepwise linear models of soil C as a function of 24 environmental properties. Models were derived within the three extents and for seven resolutions (30?1920 m) of input environmental data in Florida and in the watershed, then cross-evaluated among extents and resolutions, respectively. The quality of soil C models increased with an increase in the spatial extent (R2 from 0.10 in the cattle station to 0.61 in Florida) and with a decrease in the resolution of input data (R2 from 0.33 at 1920-m resolution to 0.61 at 30-m resolution in Florida). Soil and hydrologic variables were the most important across the seven resolutions both in Florida and in the watershed. The spatial extent and resolution of environmental covariates modulate soil C variation and soil-landscape correlations influencing soil C predictive models. Our results provide scale boundaries to observe environmental data and... Mostrar Tudo |
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Palavras-Chave: |
Soil carbon; Soil carbon models. |
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Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/69367/1/Vasques-et-al.-Influence-of-the-spatial-extent-and-resolution-of-input-data-on-soil-carbon-models-in-Florida-USA-2.pdf
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Marc: |
LEADER 02188naa a2200181 a 4500
001 1938847
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008 2012 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1029/2012JG001982$2DOI
100 1 $aVASQUES, G. de M.
245 $aInfluence of the spatial extent and resolution of input data on soil carbon models in Florida, USA.$h[electronic resource]
260 $c2012
520 $aUnderstanding the causes of spatial variation of soil carbon (C) has important implications for regional and global C dynamics studies. Soil C predictive models can identify sources of C variation, but may be influenced by scale parameters, including the spatial extent and resolution of input data. Our objective was to investigate the influence of these scale parameters on soil C spatial predictive models in Florida, USA. We used data from three nested spatial extents (Florida, 150,000 km2; Santa Fe River watershed, 3,585 km2; and University of Florida Beef Cattle Station, 5.58 km2) to derive stepwise linear models of soil C as a function of 24 environmental properties. Models were derived within the three extents and for seven resolutions (30?1920 m) of input environmental data in Florida and in the watershed, then cross-evaluated among extents and resolutions, respectively. The quality of soil C models increased with an increase in the spatial extent (R2 from 0.10 in the cattle station to 0.61 in Florida) and with a decrease in the resolution of input data (R2 from 0.33 at 1920-m resolution to 0.61 at 30-m resolution in Florida). Soil and hydrologic variables were the most important across the seven resolutions both in Florida and in the watershed. The spatial extent and resolution of environmental covariates modulate soil C variation and soil-landscape correlations influencing soil C predictive models. Our results provide scale boundaries to observe environmental data and assess soil C spatial patterns, supporting C sequestration, budgeting and monitoring programs.
653 $aSoil carbon
653 $aSoil carbon models
700 1 $aGRUNWALD, S.
700 1 $aMYERS, D. B.
773 $tJournal of Geophysical Research Geosciences$gv. 117, n. G4, 2012.
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