Harvesting crop residue needs to be managed to protect agroecosystem health and productivity. DAYCENT, a process-based modeling tool, may be suited to accommodate region-specific factors and provide regional predictions for a broad array of agroecosystem impacts associated with corn stover harvest. Grain yield, soil C, and N2O emission data collected at Corn Stover Regional Partnership experimental sites were used to test DAYCENT performance modeling the impacts of corn stover removal. DAYCENT estimations of stover yields were correlated and reasonably accurate (adjusted r2 = 0.53, slope = 1.18, p << 0.001, intercept = 0.36, p = 0.11). Measured and simulated average grain yields across sites did not differ as a function of residue removal, but the model tended to underestimate average measured grain yields. Modeled and measured soil organic carbon (SOC) change for all sites were correlated (adjusted r2 = 0.54, p << 0.001), but DAYCENT overestimated SOC loss with conventional tillage. Simulated and measured SOC change did not vary by residue removal rate. DAYCENT simulated annual N2O flux more accurately at low rates (≤2-kg N2O-N ha−1 year−1) but underestimated when emission rates were >3-kg N2O-N ha−1 year−1. Overall, DAYCENT performed well at simulating stover yields and low N2O emission rates, reasonably well when simulating the effects of management practices on average grain yields and SOC change, and poorly when estimating high N2O emissions. These biases should be considered when DAYCENT is used as a decision support tool for recommending sustainable corn stover removal practices to advance bioenergy industry based on corn stover feedstock material.