Soil Organic Carbon Across Mexico and the Conterminous United States (1991–2010)

Abstract

Soil organic carbon (SOC) information is fundamental for improving global carbon cycle modeling efforts, but discrepancies exist from country‐to‐global scales. We predicted the spatial distribution of SOC stocks (topsoil; 0–30 cm) and quantified modeling uncertainty across Mexico and the conterminous United States (CONUS). We used a multisource SOC dataset (>10 000 pedons, between 1991 and 2010) coupled with a simulated annealing regression framework that accounts for variable selection. Our model explained ~50% of SOC spatial variability (across 250‐m grids). We analyzed model variance, and the residual variance of six conventional pedotransfer functions for estimating bulk density to calculate SOC stocks. Two independent datasets confirmed that the SOC stock for both countries represents between 46 and 47 Pg with a total modeling variance of ±12 Pg. We report a residual variance of 10.4 ±5.1 Pg of SOC stocks calculated from six pedotransfer functions for soil bulk density. When reducing training data to define decades with relatively higher density of observations (1991–2000 and 2001–2010, respectively), model variance for predicted SOC stocks ranged between 41 and 55 Pg. We found nearly 42% of SOC across Mexico in forests and 24% in croplands, whereas 31% was found in forests and 28% in croplands across CONUS. Grasslands and shrublands stored 29 and 35% of SOC across Mexico and CONUS, respectively. We predicted SOC stocks >30% below recent global estimates that do not account for uncertainty and are based on legacy data. Our results provide insights for interpretation of estimates based on SOC legacy data and benchmarks for improving regional‐to‐global monitoring efforts.Key PointsMultisource topsoil organic carbon prediction and prediction variance in Mexico and the conterminous United StatesCalculated stocks of 46–47 Pg of SOC (0‐ to 30‐cm depth, years 1991–2010) using a simulated annealing regression frameworkPredicted stocks >30% below recent global estimates that are largely based on legacy data