Representing Grazing Disturbance–Recovery Processes Improves Simulation of Soil Carbon Dynamics in Grazed Systems

Grazing management strongly influences soil organic carbon (SOC) dynamics, yet existing versions of the widely used DayCent model represent grazing effects largely through biomass removal and manure return, without explicitly simulating post-grazing vegetation regrowth or trampling-driven redistribution of organic matter. To address this limitation, DayCent-IGM (Improved Grazing Module) was developed by incorporating two additional processes: climate- and residual biomass-constrained post-grazing regrowth and trampling-driven redistribution of biomass and litter into soil pools. Model performance was evaluated using SOC stocks and treatment-based SOC change rates from 18 grazing experiment sites. Compared with the original DayCent, DayCent-IGM improved simulations of both SOC stocks and change rates. For SOC change rates, RMSE decreased from 0.688 to 0.419 Mg C ha−1 yr−1, bias improved from −0.081 to −0.001 Mg C ha−1 yr−1, and simulated mean SOC change rates closely matched observations (0.099 vs. 0.100 Mg C ha−1 yr−1, simulated vs. observed), whereas DayCent underestimated the mean rate (0.019 Mg C ha−1 yr−1). These results demonstrate that explicit representation of grazing disturbance–recovery processes improves simulation of management-driven SOC responses. Because DayCent-IGM requires no additional inputs beyond standard DayCent data, it provides a practical framework for SOC modeling in grazing ecosystems.