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Registro Completo |
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Biblioteca(s): |
Embrapa Amazônia Oriental. |
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Data corrente: |
18/01/2024 |
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Data da última atualização: |
18/01/2024 |
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Tipo da produção científica: |
Resumo em Anais de Congresso |
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Autoria: |
PAULETTO, D.; ARCO-VERDE, M. F.; MARTORANO, L. G.; PACO, N. V.; SAULESLEJA, B.; QUIROZ, F. |
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Afiliação: |
DANIELA PAULETTO, UNIVERSIDADE FEDERAL DO OESTE DO PARÁ; MARCELO FRANCIA ARCO VERDE, CNPF; LUCIETA GUERREIRO MARTORANO, CPATU; NÁDIA VANESA PACO, UNIVERSIDAD NACIONAL DE JUJUY; BÁRBARA SAULESLEJA, MINISTERIO DE AMBIENTE Y DESARROLLO, ARGENTINA; FREDDY QUIROZ, PONTIFICIA UNIVERSIDAD CATÓLICA, EQUADOR. |
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Título: |
Análise financeira de sistema agroflorestal em Belterra, Pará. |
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Ano de publicação: |
2023 |
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Fonte/Imprenta: |
In: CONFERÊNCIA IUFRO 2023 AMÉRICA LATINA, 2023, Curitiba. Anais... Colombo: Embrapa Florestas, 2023. p. 180. (Embrapa Florestas. Eventos técnicos & científicos, 2). |
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Idioma: |
Português |
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Palavras-Chave: |
Análise financeira; Indicador de rentabilidade; Sistema agroflorestal. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1161045/1/Eventos-Anais-Iufro-AL-23-final-180.pdf
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Marc: |
LEADER 00717nam a2200193 a 4500
001 2161045
005 2024-01-18
008 2023 bl uuuu u00u1 u #d
100 1 $aPAULETTO, D.
245 $aAnálise financeira de sistema agroflorestal em Belterra, Pará.$h[electronic resource]
260 $aIn: CONFERÊNCIA IUFRO 2023 AMÉRICA LATINA, 2023, Curitiba. Anais... Colombo: Embrapa Florestas, 2023. p. 180. (Embrapa Florestas. Eventos técnicos & científicos, 2).$c2023
653 $aAnálise financeira
653 $aIndicador de rentabilidade
653 $aSistema agroflorestal
700 1 $aARCO-VERDE, M. F.
700 1 $aMARTORANO, L. G.
700 1 $aPACO, N. V.
700 1 $aSAULESLEJA, B.
700 1 $aQUIROZ, F.
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Registro original: |
Embrapa Amazônia Oriental (CPATU) |
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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
005 2021-11-11
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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Embrapa Solos (CNPS) |
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