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Abstract: 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. [ABSTRACT FROM AUTHOR]

Agricultural emissions account for 53% of 2010 global non-CO2emissions and are projected to increase substantially over the next 20 years, especially in Asia, Latin America and Africa. While agriculture is a substantial source of emissions, it is also generally considered to be a potential source of cost-effective non-CO2GHG abatement. Previous “bottom-up” analyses provided marginal abatement cost (MAC) curves for use in modeling these options within economy-wide and global mitigation analyses. In this paper, we utilize updated economic and biophysical data and models developed by the US Environmental Protection Agency (EPA) to investigate regional mitigation potential for major sources of agricultural GHG emissions. In addition, we explore mitigation potential available at costs at or below the estimated benefits of mitigation, as represented by the social cost of carbon. Key enhancements over previous regional assessments include incorporation of additional mitigation options, updated baseline emissions projections, greater spatial disaggregation, and development of MAC curves through 2030. For croplands and rice cultivation, biophysical, process-based models (DAYCENT and DNDC) are used to simulate yields and net GHG emissions under baseline and mitigation scenarios while the livestock sector is modeled by applying key mitigation options to baselines compiled by EPA. MAC curves are generated accounting for net GHG reductions, yield effects, livestock productivity effects, commodity prices, labor requirements, and capital costs where appropriate. MAC curves are developed at the regional level and reveal large potential for non-CO2GHG mitigation at low carbon prices, especially in Asia. [ABSTRACT FROM AUTHOR]