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| Registros recuperados : 15 | |
| 8. |  | MIZUTA, K.; GRUNWALD, S.; CROPPER, W. P.; LEE, W.; VASQUES, G. M.; PHILLIPS, M. A. Prototype development of a new soil index using econometrics method: data envelopment analysis. In: ANNUAL SOIL AND WATER SCIENCES RESEARCH FORUM, 17., 2016, Gainesville. Judged poster presentation: abstracts. Gainesville: University of Florida, 2016.| Biblioteca(s): Embrapa Solos. |
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| 9. | | MIZUTA, K.; GRUNWALD, S.; PHILLIPS, M. A.; CROPPER JUNIOR, W. P.; LEE, W. S.; VASQUES, G. de M. New indication method using pedo-econometric approach. Data Envelopment Analysis Journal, v. 4, n. 2, p. 207-241, 2019.| Biblioteca(s): Embrapa Solos. |
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| 11. | | GRUNWALD, S.; CHAIKAEW, P.; CAO, B.; XIONG, X.; VASQUES, G. M.; KIM, J.; ROSS, C. W.; CLINGENSMITH, C. M.; XU, Y.; GAVILAN, C. The meta soil model: an integrative framework to model soil carbon across various ecosystems and scales. In: ZHANG, G.-L.; BRUS, D.; LIU, F.; SONG, X.-D.; LAGACHERIE, P. (Ed.). Digital soil mapping across paradigms, scales and boundaries. New York: Springer, 2016. cap. 14, p. 165-179.| Biblioteca(s): Embrapa Solos. |
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| 12. | | 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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| 13. | | HOOVER, B.; GRUNWALD, S.; MARTIN, T. A.; VASQUES, G. M.; KNOX, N. M.; KIM, J.; XIONG, X.; CHAIKAEW, P.; ADEWOPO, J.; CAO, B.; ROSS, C. W. The Terrestrial Carbon (Terra C) Information System to facilitate carbon synthesis across heterogeneous landscapes. In: INTERNATIONAL ANNUAL MEETINGS, 2011, San Antonio. Anais... San Antonio: ASA/CSSA/SSSA, 2011.| Biblioteca(s): Embrapa Solos. |
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| 14. | | MOURA-BUENO, J. M.; DALMOLIN, R. S. D.; HORST-HEINEN, T. Z.; CATEN, A. ten; VASQUES, G. de M.; DOTTO, A. C.; GRUNWALD, S. When does stratification of a subtropical soil spectral library improve predictions of soil organic carbon content? Science of The Total Environment, v. 737, 139895, Oct. 2020.| Biblioteca(s): Embrapa Solos. |
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| 15. | | DEMATTÊ, J. A. M.; DOTTO, A. C.; PAIVA, A. F. S.; SATO, M. V.; DALMOLIN, R. S. D.; ARAÚJO, M. do S. B. de; SILVA, E. B. da; NANNI, M. R.; CATEN, A. ten; NORONHA, N. C.; LACERDA, M. P. C.; ARAUJO FILHO, J. C. de; RIZZO, R.; BELLINASO, H.; FRANCELINO, M. R.; SCHAEFER, C. E. G. R.; VICENTE, L. E.; SANTOS, U. J. dos; SAMPAIO, E. V. de S. B.; MENEZES, R. S. C.; SOUZA, J. J. L. L. de; ABRAHÃO, W. A. P.; COELHO, R. M.; GREGO, C. R.; LANI, J. L.; FERNANDES, A. R.; GONÇALVES, D. A. M.; SILVA, S. H. G.; MENEZES, M. D. de; CURI, N.; COUTO, E. G.; ANJOS, L. H. C. dos; CEDDIA, M. B.; PINHEIRO, E. F. M.; GRUNWALD, S.; VASQUES, G. de M.; MARQUES JÚNIOR, J.; SILVA, A. J. da; BARRETO, M. C. de V.; NÓBREGA, G. N.; SILVA, M. Z. da; SOUZA, S. F. de; VALLADARES, G. S.; VIANA, J. H. M.; TERRA, F. da S.; HORÁK-TERRA, I.; FIORIO, P. R.; SILVA, R. C. da; FRADE JÚNIOR, E. F.; LIMA, R. H. C.; FILIPPINI ALBA, J. M.; SOUZA JUNIOR, V. S. de; BREFIN, M. de L. M. S.; RUIVO, M. de L. P.; FERREIRA, T. O.; BRAIT, M. A.; CAETANO, N. R.; BRINGHENTI, I.; MENDES, W. de S.; SAFANELLI, J. L.; GUIMARÃES, C. C. B.; POPPIEL, R. R.; SOUZA, A. B. e; QUESADA, C. A.; COUTO, H. T. Z. do. The Brazilian Soil Spectral Library (BSSL): a general view, application and challenges. Geoderma, v. 354, 113793, 2019. Na publicação: Gustavo M. Vasques.| Biblioteca(s): Embrapa Agricultura Digital; Embrapa Clima Temperado; Embrapa Cocais; Embrapa Meio Ambiente; Embrapa Milho e Sorgo; Embrapa Solos. |
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| Registros recuperados : 15 | |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Solos. Para informações adicionais entre em contato com cnps.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Solos. |
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Data corrente: |
25/08/2016 |
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Data da última atualização: |
25/08/2016 |
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Tipo da produção científica: |
Capítulo em Livro Técnico-Científico |
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Autoria: |
GRUNWALD, S.; CHAIKAEW, P.; CAO, B.; XIONG, X.; VASQUES, G. M.; KIM, J.; ROSS, C. W.; CLINGENSMITH, C. M.; XU, Y.; GAVILAN, C. |
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Afiliação: |
S. GRUNWALD, UNIVERSITY OF FLORIDA; P. CHAIKAEW, Chulalongkorn University; B. CAO, UNIVERSITY OF FLORIDA; X. XIONG, UNIVERSITY OF FLORIDA; GUSTAVO DE MATTOS VASQUES, CNPS; J. KIM, Chungnam National University; C. W. ROSS, UNIVERSITY OF FLORIDA; C. M. CLINGENSMITH, UNIVERSITY OF FLORIDA; Y. XU, UNIVERSITY OF FLORIDA; C. GAVILAN, UNIVERSITY OF FLORIDA. |
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Título: |
The meta soil model: an integrative framework to model soil carbon across various ecosystems and scales. |
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Ano de publicação: |
2016 |
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Fonte/Imprenta: |
In: ZHANG, G.-L.; BRUS, D.; LIU, F.; SONG, X.-D.; LAGACHERIE, P. (Ed.). Digital soil mapping across paradigms, scales and boundaries. New York: Springer, 2016. cap. 14, p. 165-179. |
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Idioma: |
Inglês |
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Conteúdo: |
Over the past decades, a changing climate, land use shifts, socioeconomic development, and political decisions have had a tremendous impact on the spatial and temporal variation of soil carbon. How soil carbon interacts with such changing natural environmental and anthropogenic forcings within various ecosystem domains and spatial and temporal scales is still poorly understood. We discern different paradigms to model soil carbon and explore the meaning of such diversity in soil carbon paradigms situated within digital soil mapping (DSM) and beyond. The Meta Soil Model offers a container to hold multiple modeling paradigms that generate a variety of soil carbon realizations. The term soil realization acknowledges that there is not only one ‘soil carbon map’ or ‘soil carbon model’, but also several possible ones that approximate reality. The Meta Soil Model allows integrating, fusing, and synthesizing various soil carbon observations/maps/models through laboratory, field, or proximal/remote methods and ensembles other integration methods aiming to create more holistic representations of soil carbon. Besides explicit integration of soil carbon data/maps/models, the Meta Soil Model also facilitates side-by-side comparisons in a consistent and coherent framework. Here, we present a multiplicity of different DSM and modeling approaches and how they are integrated into a Meta Soil Carbon Model. Each approach is exemplified by a coherent model that entails the full suite of classical steps adopted in DSM to: (1) identify research questions and model approach, (2) develop a sampling design, (3) collect soil carbon data, (4) collect ancillary data in environmental and human domains, (5) analyze data (modeling), (6) create soil carbon predictions, estimates, or simulations and their uncertainties, and (7) test and validate soil carbon models. We present the integration pathways to build each of the exemplified Meta Soil Carbon Models. In conclusion, soil carbon can be viewed through various lenses- from above (through remote and/or proximal sensing), below (a soil pit or petri dish in the laboratory), or sideways (i.e., in new ways integrating multiple approaches). DSM and modeling is shifted into a new phase that is pluralistic in nature embracing a multiplicity of pathways focused to integrate data, methods, and knowledge and to understand about soils and ecosystems. In that sense, it is becoming more and more inter- and transdisciplinary, and through multiple comparisons, adaptations and validations, more robust, reliable and useful. MenosOver the past decades, a changing climate, land use shifts, socioeconomic development, and political decisions have had a tremendous impact on the spatial and temporal variation of soil carbon. How soil carbon interacts with such changing natural environmental and anthropogenic forcings within various ecosystem domains and spatial and temporal scales is still poorly understood. We discern different paradigms to model soil carbon and explore the meaning of such diversity in soil carbon paradigms situated within digital soil mapping (DSM) and beyond. The Meta Soil Model offers a container to hold multiple modeling paradigms that generate a variety of soil carbon realizations. The term soil realization acknowledges that there is not only one ‘soil carbon map’ or ‘soil carbon model’, but also several possible ones that approximate reality. The Meta Soil Model allows integrating, fusing, and synthesizing various soil carbon observations/maps/models through laboratory, field, or proximal/remote methods and ensembles other integration methods aiming to create more holistic representations of soil carbon. Besides explicit integration of soil carbon data/maps/models, the Meta Soil Model also facilitates side-by-side comparisons in a consistent and coherent framework. Here, we present a multiplicity of different DSM and modeling approaches and how they are integrated into a Meta Soil Carbon Model. Each approach is exemplified by a coherent model that entails the full suite of classica... Mostrar Tudo |
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Palavras-Chave: |
Carbono orgânico do solo; Mapeamento digital de solos; Modelo meta solo; Modelos de solos; Paradigmas. |
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Thesagro: |
Fusão; Integração. |
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Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
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Marc: |
LEADER 03589naa a2200313 a 4500
001 2051680
005 2016-08-25
008 2016 bl uuuu u00u1 u #d
100 1 $aGRUNWALD, S.
245 $aThe meta soil model$ban integrative framework to model soil carbon across various ecosystems and scales.$h[electronic resource]
260 $c2016
520 $aOver the past decades, a changing climate, land use shifts, socioeconomic development, and political decisions have had a tremendous impact on the spatial and temporal variation of soil carbon. How soil carbon interacts with such changing natural environmental and anthropogenic forcings within various ecosystem domains and spatial and temporal scales is still poorly understood. We discern different paradigms to model soil carbon and explore the meaning of such diversity in soil carbon paradigms situated within digital soil mapping (DSM) and beyond. The Meta Soil Model offers a container to hold multiple modeling paradigms that generate a variety of soil carbon realizations. The term soil realization acknowledges that there is not only one ‘soil carbon map’ or ‘soil carbon model’, but also several possible ones that approximate reality. The Meta Soil Model allows integrating, fusing, and synthesizing various soil carbon observations/maps/models through laboratory, field, or proximal/remote methods and ensembles other integration methods aiming to create more holistic representations of soil carbon. Besides explicit integration of soil carbon data/maps/models, the Meta Soil Model also facilitates side-by-side comparisons in a consistent and coherent framework. Here, we present a multiplicity of different DSM and modeling approaches and how they are integrated into a Meta Soil Carbon Model. Each approach is exemplified by a coherent model that entails the full suite of classical steps adopted in DSM to: (1) identify research questions and model approach, (2) develop a sampling design, (3) collect soil carbon data, (4) collect ancillary data in environmental and human domains, (5) analyze data (modeling), (6) create soil carbon predictions, estimates, or simulations and their uncertainties, and (7) test and validate soil carbon models. We present the integration pathways to build each of the exemplified Meta Soil Carbon Models. In conclusion, soil carbon can be viewed through various lenses- from above (through remote and/or proximal sensing), below (a soil pit or petri dish in the laboratory), or sideways (i.e., in new ways integrating multiple approaches). DSM and modeling is shifted into a new phase that is pluralistic in nature embracing a multiplicity of pathways focused to integrate data, methods, and knowledge and to understand about soils and ecosystems. In that sense, it is becoming more and more inter- and transdisciplinary, and through multiple comparisons, adaptations and validations, more robust, reliable and useful.
650 $aFusão
650 $aIntegração
653 $aCarbono orgânico do solo
653 $aMapeamento digital de solos
653 $aModelo meta solo
653 $aModelos de solos
653 $aParadigmas
700 1 $aCHAIKAEW, P.
700 1 $aCAO, B.
700 1 $aXIONG, X.
700 1 $aVASQUES, G. M.
700 1 $aKIM, J.
700 1 $aROSS, C. W.
700 1 $aCLINGENSMITH, C. M.
700 1 $aXU, Y.
700 1 $aGAVILAN, C.
773 $tIn: ZHANG, G.-L.; BRUS, D.; LIU, F.; SONG, X.-D.; LAGACHERIE, P. (Ed.). Digital soil mapping across paradigms, scales and boundaries. New York: Springer, 2016. cap. 14, p. 165-179.
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