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SOIL An interactive open-access journal of the European Geosciences Union
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Original research article
25 Jan 2018
Review status
This discussion paper is a preprint. It is a manuscript under review for the journal SOIL (SOIL).
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 1University of Delaware, Department of Pland and Soil Sciences, Newark DE, USA. 19713
2INTA EEA Mendoza, San Martín 3853, Luján de Cuyo, Mendoza, Argentina, M5507EVY
3FAO, Vialle de Terme di Caracalla, Rome, Italy
4Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias, Mexico
5Instituto Nacional de Estadísitica y Geografía, Aguascalientes, Mexico
6Zamorano University of Honduras and Asociación Hondureña de la Ciencia del Suelo
7National Commission for the Knowledge and Use of Biodiversity, Mexico City, Mexico
8Subdirección Agrología, Instituto Geográfico Agustín Codazzi, Colombia
9Oficina Regional de la FAO para América Latina y el Caribe, Chile
10Servicio Agrícola y Ganadero, Chile
11Direccion General de Recursos Naturales, Ministerio de Ganaderia, Agricultura y Pesca, Uruguay
12Facultad de Ciencias Agrarias de la Universidad Nacional de Asunción, Paraguay
13Land Viceministry, Ministry of Rural Development and Land, Bolivia
14Universidad Autónoma Agraria Antonio Narro Unidad Laguna, Mexico
15Ministerio de Agricultura y Ganaderia, Quito, Ecuador
16Universidad Nacional Agraria, Nicaragua
17Representación de FAO en Colombia
18Instituto de Investigación Agropecuaria de Panamá, Panama
19Sociedad Venezolana de la Ciencia del Suelo, Venezuela
20Ministerio de Medio Ambiente, Republica Dominicana
21Department of Natural and Life Sciences, COSTAATT, Port-of Spain, Trinidad and Tobago
22University of the West Indies, St Augustine Campus, Trinidad and Tobago
23Instituto de Innovación en Transferencia y Tecnología Agropecuaria, Costa Rica
24Ministerio de Ambiente y Recursos Naturales de Guatemala
25INTA CNIA, Buenos Aires, Argentina
26International Centre for Environmental and Nuclear Sciences, University of the West Indies, Jamaica
27Embrapa Solos, Rio de Janeiro, Brazil
Abstract. Country-specific soil organic carbon (SOC) maps are the baseline for the Global SOC Map of the Global Soil Partnership (GSOCmap-GSP). This endeavor 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, random forest, kernel weighted nearest neighbors, partial least squares regression, and regression-Kriging based on stepwise multiple linear models. Country-specific training data and SOC predictors (5 × 5 km pixel resolution) were obtained from ISRIC-World-Soil-Information-System. In general, 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 data scenarios and were able to explain ~ 53 % of SOC variability (range < 1 % and 80 %) with no universal predictive algorithm among countries. Overall, 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 setting unreliable (excessive or low) model prediction limits can have important effects (under or overestimating) for predicting SOC; thus expert opinion is needed to set boundary prediction limits. Selection of predictive algorithms should consider density and variability of country-specific available SOC data and country-specific environmental gradients to maximize explained variance while minimizing prediction bias. To progress with country-specific SOC mapping, we call for improvements on quality and quantity of country-specific SOC measurements and associated predictors. This study highlights the large degree of spatial heterogeneity of SOC across Latin America, and provides a reproducible framework that could be used for building DSM capacity to improve country-specific SOC estimates.

Citation: 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., Davila, 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., Prieto, P., Alexis Ramos, I., 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. M., Vargas, R. R., and Vargas, R.: No Silver Bullet for Digital Soil Mapping: Country-specific Soil Organic Carbon Estimates across Latin America, SOIL Discuss.,, in review, 2018.
Mario Guevara et al.
Mario Guevara et al.
Mario Guevara et al.


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Short summary
– We provide a reproducible multi-modeling approach for SOC mapping across Latin America in 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 in a quantifiable basis.
– We provide a reproducible multi-modeling approach for SOC mapping across Latin America in a...