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Underestimation of N2O emissions in a comparison of the DayCent, DNDC, and EPIC models
- Gaillard, Richard K., Jones, Curtis D., Ingraham, Pete, Collier, Sarah, Izaurralde, Roberto Cesar, Jokela, William, Osterholz, William, Salas, William, Vadas, Peter, Ruark, Matthew D.
- Ecological applications 2018 v.28 no.3 pp. 694-708
- agricultural land, agroecosystems, ammonium, cropping systems, data collection, denitrification, greenhouse gas emissions, issues and policy, model validation, models, nitrates, nitrogen fertilizers, nitrous oxide, regression analysis, soil temperature, water content
- Process‐based models are increasingly used to study agroecosystem interactions and 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. We also evaluated the use of a multi‐model ensemble strategy, which inconsistently outperformed individual model estimations. Regression analysis indicated a cross‐model bias to underestimate high magnitude daily and cumulative N₂O flux. Model estimations of observed soil temperature and water content did not sufficiently explain model underestimations, 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 model performance and possible biases. Our results suggest a bias in the model estimation 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.
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