TY - GEN T1 - Soil Organic Carbon Stock Estimates with Uncertainty across Latin America AU - GUEVARA, M. AU - OLMEDO, G.F. AU - STELL, E. AU - YIGINI, Y. AU - HERNANDEZ, C.A. AU - AREVALO, G. AU - ARROYO-CRUZ, C.E. AU - BOLIVAR, A. AU - BUNNING, S. AU - CANAS, N.B. AU - CRUZ-GAISTARDO, C.O. AU - DAVILA, F. AU - ACQUA, M.D. AU - ENCINA, A. AU - FONTES, F. AU - HERRERA, J.A.H. AU - NAVARRO, A.R.I. AU - LOAYZA, V. AU - MANUELES, A.M. AU - JARA, F.M. AU - OLIVERA, C. AU - PEREIRA, G. AU - PRIETO, P. AU - RAMOS, I.A. AU - BRINA, J.C.R. AU - RIVERA, R. AU - RODRIGUEZ-RODRIGUEZ, J. AU - ROOPNARINE, R. AU - ROSALES, A. AU - RIVERO, K.A.R. AU - SCHULZ, G.A. AU - SPENCE, A. AU - VASQUES, G.M. AU - VARGAS, R.R. AU - VARGAS, R. DO - 10.3334/ORNLDAAC/1615 UR - https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1615 AB - This dataset provides 5 x 5 km gridded estimates of soil organic carbon (SOC) across Latin America that were derived from existing point soil characterization data and compiled environmental prediction factors for SOC. This dataset is representative for the period between 1980 to 2000s corresponding with the highest density of observations available in the WoSIS system and the covariates used as prediction factors for soil organic carbon across Latin America. SOC stocks (kg/m2) were estimated for the SOC and bulk density point measurements and a spatially explicit measure of the SOC estimation error was also calculated. A modeling ensemble, using a linear combination of five statistical methods (regression Kriging, random forest, kernel weighted nearest neighbors, partial least squared regression and support vector machines) was applied to the SOC stock data at (1) country-specific and (2) regional scales to develop gridded SOC estimates (kg/m2) for all of Latin America. Uncertainty estimates are provided for the two model predictions based on independent model residuals and their full conditional response to the SOC prediction factors. PY - 2019 PB - ORNL Distributed Active Archive Center LA - en ER -