This review evaluates current challenges with biogeochemical modeling of soil carbon dynamics, trace gas fluxes, and drought and age-related impacts on bioenergy crop productivity. We propose coupling a microbial process-based soil organic carbon and nitrogen model with DayCent, or other Century-based biogeochemical models, to improve representation of soil carbon dynamics. We describe recent improvements to DayCent for simulating unique plant structural and physiological attributes of perennial bioenergy grasses. Finally, we propose a method for using machine learning to identify key parameters for simulating N2O emissions.