No silver bullet for digital soil mapping: country-specific soil organic carbon estimates across Latin America
Authors: Guevara Mario , Olmedo Guillermo Federico , Stell Emma , Yigini Yusuf , Aguilar Duarte Yameli , Arellano Hernández Carlos , Arévalo Gloria E. , Arroyo-Cruz Carlos Eduardo , Bolivar Adriana , Bunning Sally , Bustamante Cañas Nelson , Cruz-Gaistardo Carlos Omar , Davila Fabian , Dell Acqua Martin , Encina Arnulfo , Figueredo Tacona Hernán , Fontes Fernando , Hernández Herrera José Antonio , Ibelles Navarro Alejandro Roberto , Loayza Veronica , Manueles Alexandra M. , Mendoza Jara Fernando , Olivera Carolina , Osorio Hermosilla Rodrigo , Pereira Gonzalo , Prieto Pablo , Ramos Iván Alexis , Rey Brina Juan Carlos , Rivera Rafael , Rodríguez-Rodríguez Javier , Roopnarine Ronald , Rosales Ibarra Albán , Rosales Riveiro Kenset Amaury , Schulz Guillermo Andrés , Spence Adrian , Vasques Gustavo M. , Vargas Ronald R. , Vargas Rodrigo 
Published: August 2018

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 × 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.