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| Registros recuperados : 445 | |
| 82. |  | SANTANA, J. C. F. de; BELTRAO, N. E. de M.; SILVA, J. G. de; SANTANA, J. C. da S.; PEREIRA, J. R. Indice de fibras flutuantes e de fibras curtas e uniformidade de comprimento em algodoeiro herbaceo da America, Africa e Europa. Revista de Oleaginosas e Fibrosas, v.3, n.2, p.121-126, 1999.| Biblioteca(s): Embrapa Algodão. |
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| Registros recuperados : 445 | |
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Registro Completo
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Biblioteca(s): |
Embrapa Solos. |
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Data corrente: |
31/08/2018 |
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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: |
C - 0 |
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Autoria: |
GUEVARA, M.; OLMEDO, G. F.; STELL, E.; YIGINI, Y.; AGUILAR DUARTE, Y.; ARELLANO HERNÁNDEZ, C.; ARÉVALO, G. E.; ARROYO-CRUZ, C. E.; BOLIVAR, A.; BUNNING, S.; BUSTAMANTE CAÑAS, N.; CRUZ-GAISTARDO, C. O.; DAVILLA, F.; DELL ACQUA, M.; ENCINA, A.; FIGUEREDO TACONA, H.; FONTES, F.; HERNÁNDEZ HERRERA, J. A.; IBELLES NAVARRO, A. R.; LOAYZA, V.; MANUELES, A. M.; MENDOZA JARA, F.; OLIVERA, C.; OSORIO HERMOSILLA, R.; PEREIRA, G.; PIETRO, P.; RAMOS, I. A.; REY BRINA, J. C.; RIVERA, R.; RODRÍGUEZ-RODRÍGUEZ, J.; ROOPNARINE, R.; ROSALES IBARRA, A.; ROSALES RIVEIRO, K. A.; SCHULZ, G. A.; SPENCE, A.; VASQUES, G. de M.; VARGAS, R. R.; VARGAS, R. |
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Afiliação: |
MARIO GUEVARA, University of Delaware; GUILLERMO FEDERICO OLMEDO, INTA EEA Mendoza/FAO; EMMA STELL, University of Delaware; YUSUF YIGINI, FAO; YAMELI AGUILAR DUARTE, Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias, Mérida, Mexico; CARLOS ARELLANO HERNÁNDEZ, Instituto Nacional de Estadísitica y Geografía, Aguascalientes, Mexico; GLORIA E. ARÉVALO, Zamorano University of Honduras and Asociación Hondureña de la Ciencia del Suelo, Tegucigalpa, Honduras; CARLOS EDUARDO ARROYO-CRUZ, National Commission for the Knowledge and Use of Biodiversity, Mexico City, Mexico; ADRIANA BOLIVAR, Subdirección Agrología, Instituto Geográfico Agustín Codazzi, Bogotá, Colombia; SALLY BUNNING, Oficina Regional de la FAO para América Latina y el Caribe, Santiago de Chile, Chile; NELSON BUSTAMANTE CAÑAS, Servicio Agrícola y Ganadero, Santiago de Chile, Chile; CARLOS OMAR CRUZ-GAISTARDO, Instituto Nacional de Estadísitica y Geografía, Aguascalientes, Mexico; FABIAN DAVILLA, Ministerio de Ganaderia, Agricultura y Pesca, Montevideo, Uruguay; MARTIN DELL ACQUA, Ministerio de Ganaderia, Agricultura y Pesca, Montevideo, Uruguay; ARNULFO ENCINA, Facultad de Ciencias Agrarias de la Universidad Nacional de Asunción, Asunción, Paraguay; HERNÁN FIGUEREDO TACONA, Land Viceministry, Ministry of Rural Development and Land, La Paz, Bolivia; FERNANDO FONTES, Ministerio de Ganaderia, Agricultura y Pesca, Montevideo, Uruguay; JOSÉ ANTONIO HERNÁNDEZ HERRERA, Universidad Autónoma Agraria Antonio Narro Unidad Laguna, Torreón, Mexico; ALEJANDRO ROBERTO IBELLES NAVARRO, Instituto Nacional de Estadísitica y Geografía, Aguascalientes, Mexico; VERONICA LOAYZA, Ministerio de Agricultura y Ganaderia, Quito, Ecuador; ALEXANDRA M. MANUELES, Zamorano University of Honduras and Asociación Hondureña de la Ciencia del Suelo, Tegucigalpa, Honduras; FERNANDO MENDOZA JARA, Universidad Nacional Agraria, Managua, Nicaragua; CAROLINA OLIVERA, Oficina Regional de la FAO para América Latina y el Caribe, Bogotá, Colombia; RODRIGO OSORIO HERMOSILLA, Servicio Agrícola y Ganadero, Santiago de Chile, Chile; GONZALO PEREIRA, Ministerio de Ganaderia, Agricultura y Pesca, Montevideo, Uruguay; PABLO PIETRO, Ministerio de Ganaderia, Agricultura y Pesca, Montevideo, Uruguay; IVÁN ALEXIS RAMOS, Instituto de Investigación Agropecuaria de Panamá, Panamá; JUAN CARLOS REY BRINA, Sociedad Venezolana de la Ciencia del Suelo, Caracas, Venezuela; RAFAEL RIVERA, Ministerio de Medio Ambiente, Santo Domingo, Dominican Republic; JAVIER RODRÍGUEZ-RODRÍGUEZ, National Commission for the Knowledge and Use of Biodiversity, Mexico City, Mexico; RONALD ROOPNARINE, Department of Natural and Life Sciences, COSTAATT, Port of Spain, Trinidad an Tobago/University of the West Indies, St. Augustine Campus, Trinidad and Tobago; ALBÁN ROSALES IBARRA, Instituto de Innovación en Transferencia y Tecnología Agropecuaria, San José, Costa Rica; KENSET AMAURY ROSALES RIVEIRO, Ministerio de Ambiente y Recursos Naturales de Guatemala, Ciudad Guatemala, Guatemala; GUILLERMO ANDRÉS SCHULZ, INTA CNIA, Buenos Aires, Argentina; ADRIAN SPENCE, International Centre for Environmental and Nuclear Sciences, University of the West Indies, Kingston, Jamaica; GUSTAVO DE MATTOS VASQUES, CNPS; RONALD R. VARGAS, FAO, Vialle de Terme di Caracalla, Rome, Italy; RODRIGO VARGAS, University of Delaware, Department of Plant and Soil Sciences, Newark, DE, USA. |
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Título: |
No silver bullet for digital soil mapping: country-specific soil organic carbon estimates across Latin America. |
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Ano de publicação: |
2018 |
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Fonte/Imprenta: |
Soil, v. 4, n. 1, p. 173-193, 2018. |
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DOI: |
https://doi.org/10.5194/soil-4-173-2018 |
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Idioma: |
Inglês |
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Conteúdo: |
Country-specific soil organic carbon (SOC) estimates are the baseline for the Global SOC Map of the Global Soil Partnership (GSOCmap-GSP). This endeavor is key to explaining the uncertainty of global SOC estimates but requires harmonizing heterogeneous datasets and building country-specific capacities for digital soil mapping (DSM).We identified country-specific predictors for SOC and tested the performance of five predictive algorithms for mapping SOC across Latin America. The algorithms included support vector machines (SVMs), random forest (RF), kernel-weighted nearest neighbors (KK), partial least squares regression (PL), and regression kriging based on stepwise multiple linear models (RK). Country-specific training data and SOC predictors (5 x 5 km pixel resolution) were obtained from ISRIC - World Soil Information. Temperature, soil type, vegetation indices, and topographic constraints were the best predictors for SOC, but country-specific predictors and their respective weights varied across Latin America. We compared a large diversity of country-specific datasets and models, and were able to explain SOC variability in a range between ~ 1 and ~ 60 %, with no universal predictive algorithm among countries. A regional (n = 11 268 SOC estimates) ensemble of these five algorithms was able to explain ~ 39% of SOC variability from repeated 5-fold cross-validation.We report a combined SOC stock of 77.8 +- 43.6 Pg (uncertainty represented by the full conditional response of independent model residuals) across Latin America. SOC stocks were higher in tropical forests (30 +- 16.5 Pg) and croplands (13 +- 8.1 Pg). Country-specific and regional ensembles revealed spatial discrepancies across geopolitical borders, higher elevations, and coastal plains, but provided similar regional stocks (77.8 +- 42.2 and 76.8 +- 45.1 Pg, respectively). These results are conservative compared to global estimates (e.g., SoilGrids250m 185.8 Pg, the Harmonized World Soil Database 138.4 Pg, or the GSOCmap-GSP 99.7 Pg). Countries with large area (i.e., Brazil, Bolivia, Mexico, Peru) and large spatial SOC heterogeneity had lower SOC stocks per unit area and larger uncertainty in their predictions. We highlight that expert opinion is needed to set boundary prediction limits to avoid unrealistically high modeling estimates. For maximizing explained variance while minimizing prediction bias, the selection of predictive algorithms for SOC mapping should consider density of available data and variability of country-specific environmental gradients. This study highlights the large degree of spatial uncertainty in SOC estimates across Latin America. We provide a framework for improving country-specific mapping efforts and reducing current discrepancy of global, regional, and country-specific SOC estimates. MenosCountry-specific soil organic carbon (SOC) estimates are the baseline for the Global SOC Map of the Global Soil Partnership (GSOCmap-GSP). This endeavor is key to explaining the uncertainty of global SOC estimates but requires harmonizing heterogeneous datasets and building country-specific capacities for digital soil mapping (DSM).We identified country-specific predictors for SOC and tested the performance of five predictive algorithms for mapping SOC across Latin America. The algorithms included support vector machines (SVMs), random forest (RF), kernel-weighted nearest neighbors (KK), partial least squares regression (PL), and regression kriging based on stepwise multiple linear models (RK). Country-specific training data and SOC predictors (5 x 5 km pixel resolution) were obtained from ISRIC - World Soil Information. Temperature, soil type, vegetation indices, and topographic constraints were the best predictors for SOC, but country-specific predictors and their respective weights varied across Latin America. We compared a large diversity of country-specific datasets and models, and were able to explain SOC variability in a range between ~ 1 and ~ 60 %, with no universal predictive algorithm among countries. A regional (n = 11 268 SOC estimates) ensemble of these five algorithms was able to explain ~ 39% of SOC variability from repeated 5-fold cross-validation.We report a combined SOC stock of 77.8 +- 43.6 Pg (uncertainty represented by the full conditional response of i... Mostrar Tudo |
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Palavras-Chave: |
Carbono orgânico do solo; Mapeamento digital do solo. |
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Thesagro: |
Carbono. |
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Thesaurus NAL: |
Soil map; Soil organic carbon. |
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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/182258/1/2018-032.pdf
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Marc: |
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001 2094880
005 2021-11-11
008 2018 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.5194/soil-4-173-2018$2DOI
100 1 $aGUEVARA, M.
245 $aNo silver bullet for digital soil mapping$bcountry-specific soil organic carbon estimates across Latin America.$h[electronic resource]
260 $c2018
520 $aCountry-specific soil organic carbon (SOC) estimates are the baseline for the Global SOC Map of the Global Soil Partnership (GSOCmap-GSP). This endeavor is key to explaining the uncertainty of global SOC estimates but requires harmonizing heterogeneous datasets and building country-specific capacities for digital soil mapping (DSM).We identified country-specific predictors for SOC and tested the performance of five predictive algorithms for mapping SOC across Latin America. The algorithms included support vector machines (SVMs), random forest (RF), kernel-weighted nearest neighbors (KK), partial least squares regression (PL), and regression kriging based on stepwise multiple linear models (RK). Country-specific training data and SOC predictors (5 x 5 km pixel resolution) were obtained from ISRIC - World Soil Information. Temperature, soil type, vegetation indices, and topographic constraints were the best predictors for SOC, but country-specific predictors and their respective weights varied across Latin America. We compared a large diversity of country-specific datasets and models, and were able to explain SOC variability in a range between ~ 1 and ~ 60 %, with no universal predictive algorithm among countries. A regional (n = 11 268 SOC estimates) ensemble of these five algorithms was able to explain ~ 39% of SOC variability from repeated 5-fold cross-validation.We report a combined SOC stock of 77.8 +- 43.6 Pg (uncertainty represented by the full conditional response of independent model residuals) across Latin America. SOC stocks were higher in tropical forests (30 +- 16.5 Pg) and croplands (13 +- 8.1 Pg). Country-specific and regional ensembles revealed spatial discrepancies across geopolitical borders, higher elevations, and coastal plains, but provided similar regional stocks (77.8 +- 42.2 and 76.8 +- 45.1 Pg, respectively). These results are conservative compared to global estimates (e.g., SoilGrids250m 185.8 Pg, the Harmonized World Soil Database 138.4 Pg, or the GSOCmap-GSP 99.7 Pg). Countries with large area (i.e., Brazil, Bolivia, Mexico, Peru) and large spatial SOC heterogeneity had lower SOC stocks per unit area and larger uncertainty in their predictions. We highlight that expert opinion is needed to set boundary prediction limits to avoid unrealistically high modeling estimates. For maximizing explained variance while minimizing prediction bias, the selection of predictive algorithms for SOC mapping should consider density of available data and variability of country-specific environmental gradients. This study highlights the large degree of spatial uncertainty in SOC estimates across Latin America. We provide a framework for improving country-specific mapping efforts and reducing current discrepancy of global, regional, and country-specific SOC estimates.
650 $aSoil map
650 $aSoil organic carbon
650 $aCarbono
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653 $aMapeamento digital do solo
700 1 $aOLMEDO, G. F.
700 1 $aSTELL, E.
700 1 $aYIGINI, Y.
700 1 $aAGUILAR DUARTE, Y.
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700 1 $aARROYO-CRUZ, C. E.
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700 1 $aDELL ACQUA, M.
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700 1 $aFIGUEREDO TACONA, H.
700 1 $aFONTES, F.
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700 1 $aIBELLES NAVARRO, A. R.
700 1 $aLOAYZA, V.
700 1 $aMANUELES, A. M.
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700 1 $aPEREIRA, G.
700 1 $aPIETRO, P.
700 1 $aRAMOS, I. A.
700 1 $aREY BRINA, J. C.
700 1 $aRIVERA, R.
700 1 $aRODRÍGUEZ-RODRÍGUEZ, J.
700 1 $aROOPNARINE, R.
700 1 $aROSALES IBARRA, A.
700 1 $aROSALES RIVEIRO, K. A.
700 1 $aSCHULZ, G. A.
700 1 $aSPENCE, A.
700 1 $aVASQUES, G. de M.
700 1 $aVARGAS, R. R.
700 1 $aVARGAS, R.
773 $tSoil$gv. 4, n. 1, p. 173-193, 2018.
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