Estimating soil organic carbon deficits at the continental scale using legacy-data-driven dynamic baseline and attainable projections

Soil organic carbon (SOC) stocks vary temporally, across land use, climate, and soil type, making it challenging to model current stocks (SOC_cs). The attainable steady-state stock (SOC_at), the practically achievable under optimal soil, climate, and management conditions, remains dynamic because soil-loss processes continually modify the biophysical limits on maximum storage, causing SOC_at to shift over time. Robust methods are required to integrate temporal dynamics, model and extrapolate SOC_cs and SOC_at, and quantify current deficits (SOC_def), the unrealized sequestration capacity. We used legacy SOC observations along with time-adjustment and data-driven modeling to generate spatially explicit projections of 2024 SOC_cs. We estimated SOC_at by selecting the maximum SOC value from a spatially constrained similarity matrix of projected SOC_cs, then used both reference layers to derive location-specific SOC_def. The resulting maps revealed extensive heterogeneity among the land use types and across regions. Mean SOC_def of 3.46 kg m^–2 was noted for all croplands in continental United States. Larger deficits clustered on soil orders of Mollisols and Alfisols in the Midwest region, but were lower than anticipated, alluding to depleted SOC_cs and SOC_at. Degraded grasslands showed similarly reduced SOC_at, underscoring the need to recalibrate sequestration targets based on current projected steady states. The results underscore the framework’s novelty as it effectively integrates temporal dynamics of soil carbon stocks and converts soil-carbon storage processes into actionable, farm-level sequestration targets. By generating empirically derived, region-specific SOC_cs–SOC_at–SOC_def benchmarks, the framework offers precise reference points that align management incentives, climate-smart policies, and site-specific restoration strategies.