Managing Global Resources for a Secure Future

Process-based models are increasingly used as tools for studying complex agroecosystem interactions and, specifically, N₂O emissions from agricultural fields. The widespread use of these models to conduct research and inform policy benefits from periodic model comparisons that assess the state of agroecosystem modeling and indicate areas for model improvement. This work provides an evaluation of simulated N₂O flux from three process-based models: DayCent, DNDC, and EPIC. The models were calibrated and validated using data collected from two research sites over five years that represent cropping systems and nitrogen fertilizer management strategies common to dairy cropping systems. With the exception of DNDC, calibration for daily N₂O flux produced positive Nash-Sutcliffe evaluations of individual model efficiency (E = 0.30 and 0.42 for DayCent and EPIC, respectively), though efficiency over mean observation in validation was reduced (E = 0.03 for both DayCent and EPIC). We also evaluated the use of a multi-model ensemble strategy, which outperformed individual models in validation (E = 0.10) but not calibration (E = 0.15). Regression analysis indicated a cross-model bias to underestimate high magnitude flux events, high cumulative fluxes, and mean cumulative flux. Model estimations of observed soil temperature and water content did not sufficiently explain model bias, and we found significant variation in model estimates of heterotrophic respiration, denitrification, soil NH₄⁺, and soil NO₃^-, which may indicate that additional types of observed data are required to evaluate and improve model performance. Our results suggest a bias in the modeling of N₂O flux from agroecosystems that limits the extension of models beyond calibration and as instruments of policy development. This highlights a growing need for the modeling and measurement communities to collaborate in the collection and analysis of the data necessary to improve models and coordinate future development.