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Volume 4, issue 3 | Copyright

Special issue: Regional perspectives and challenges of soil organic carbon...

SOIL, 4, 173-193, 2018
https://doi.org/10.5194/soil-4-173-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Original research article 01 Aug 2018

Original research article | 01 Aug 2018

No silver bullet for digital soil mapping: country-specific soil organic carbon estimates across Latin America

Mario Guevara1, Guillermo Federico Olmedo2,3, Emma Stell1, Yusuf Yigini3, Yameli Aguilar Duarte4, Carlos Arellano Hernández5, Gloria E. Arévalo6, Carlos Eduardo Arroyo-Cruz7, Adriana Bolivar8, Sally Bunning9, Nelson Bustamante Cañas10, Carlos Omar Cruz-Gaistardo5, Fabian Davila11, Martin Dell Acqua11, Arnulfo Encina12, Hernán Figueredo Tacona13, Fernando Fontes11, José Antonio Hernández Herrera14, Alejandro Roberto Ibelles Navarro5, Veronica Loayza15, Alexandra M. Manueles6, Fernando Mendoza Jara16, Carolina Olivera17, Rodrigo Osorio Hermosilla10, Gonzalo Pereira11, Pablo Prieto11, Iván Alexis Ramos18, Juan Carlos Rey Brina19, Rafael Rivera20, Javier Rodríguez-Rodríguez7, Ronald Roopnarine21,22, Albán Rosales Ibarra23, Kenset Amaury Rosales Riveiro24, Guillermo Andrés Schulz25, Adrian Spence26, Gustavo M. Vasques27, Ronald R. Vargas3, and Rodrigo Vargas1 Mario Guevara et al.
  • 1University of Delaware, Department of Plant and Soil Sciences, Newark, DE, USA
  • 2INTA EEA Mendoza, San Martín 3853, Luján de Cuyo, Mendoza, Argentina
  • 3FAO, Vialle de Terme di Caracalla, Rome, Italy
  • 4Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias, Mérida, Mexico
  • 5Instituto Nacional de Estadísitica y Geografía, Aguascalientes, Mexico
  • 6Zamorano University of Honduras and Asociación Hondureña de la Ciencia del Suelo, Tegucigalpa, Honduras
  • 7National Commission for the Knowledge and Use of Biodiversity, Mexico City, Mexico
  • 8Subdirección Agrología, Instituto Geográfico Agustín Codazzi, Bogotá, Colombia
  • 9Oficina Regional de la FAO para América Latina y el Caribe, Santiago de Chile, Chile
  • 10Servicio Agrícola y Ganadero, Santiago de Chile, Chile
  • 11Direccion General de Recursos Naturales, Ministerio de Ganaderia, Agricultura y Pesca, Montevideo, Uruguay
  • 12Facultad de Ciencias Agrarias de la Universidad Nacional de Asunción, Asunción, Paraguay
  • 13Land Viceministry, Ministry of Rural Development and Land, La Paz, Bolivia
  • 14Universidad Autónoma Agraria Antonio Narro Unidad Laguna, Torreón, Mexico
  • 15Ministerio de Agricultura y Ganaderia, Quito, Ecuador
  • 16Universidad Nacional Agraria, Managua, Nicaragua
  • 17Oficina Regional de la FAO para América Latina y el Caribe, Bogotá, Colombia
  • 18Instituto de Investigación Agropecuaria de Panamá, Panamá, Panama
  • 19Sociedad Venezolana de la Ciencia del Suelo, Caracas, Venezuela
  • 20Ministerio de Medio Ambiente, Santo Domingo, Dominican Republic
  • 21Department of Natural and Life Sciences, COSTAATT, Port of Spain, Trinidad and Tobago
  • 22University of the West Indies, St. Augustine Campus, St. Augustine, Trinidad and Tobago
  • 23Instituto de Innovación en Transferencia y Tecnología Agropecuaria, San José, Costa Rica
  • 24Ministerio de Ambiente y Recursos Naturales de Guatemala, Ciudad Guatemala, Guatemala
  • 25INTA CNIA, Buenos Aires, Argentina
  • 26International Centre for Environmental and Nuclear Sciences, University of the West Indies, Kingston, Jamaica
  • 27Embrapa Solos, Rio de Janeiro, Brazil

Abstract. 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 × 5km 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 = 11268 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±43.6Pg (uncertainty represented by the full conditional response of independent model residuals) across Latin America. SOC stocks were higher in tropical forests (30±16.5Pg) and croplands (13±8.1Pg). 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±45.1Pg, respectively). These results are conservative compared to global estimates (e.g., SoilGrids250m 185.8Pg, the Harmonized World Soil Database 138.4Pg, or the GSOCmap-GSP 99.7Pg). 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.

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We provide a reproducible multi-modeling approach for SOC mapping across Latin America on a country-specific basis as required by the Global Soil Partnership of the United Nations. We identify key prediction factors for SOC across each country. We compare and test different methods to generate spatially explicit predictions of SOC and conclude that there is no best method on a quantifiable basis.
We provide a reproducible multi-modeling approach for SOC mapping across Latin America on a...
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