Biogeochemical models are useful tools for integrating the effects of agricultural management on GHG emissions; however, their development is often hampered by the incomplete temporal and spatial representation of measurements. Adding to the problem is that a full complement of ancillary measurements necessary to understand and validate the soil processes responsible for GHG emissions is often not available. This study presents a rare case where continuous N 2 O emissions, measured over seven years using a flux gradient technique, along with a robust set of ancillary measurements were used to assess the ability of the DNDC model for estimating N 2 O emissions under varying crop-management regimes. The analysis revealed that the model estimated soil water content more precisely in the normal and wet years (ARE 3.4%) than during the dry years (ARE 11.5%). This was attributed to the model’s inability to characterize episodic preferential flow through clay cracks. Soil mineral N across differing management regimes (ARE 2%) proved to be well estimated by DNDC. The model captured the relative differences in N 2 O emissions between the annual (measured: 35.5 kg N 2 O-N ha −1 , modeled: 30.1 kg N 2 O-N ha −1 ) and annual-perennial (measured: 26.6 kg N 2 O-N ha −1 , modeled: 21.2 kg N 2 O-N ha −1 ) cropping systems over the 7 year period but overestimated emissions from alfalfa production and underestimated emissions after spring applied anhydrous ammonia. Model predictions compared well with the measured total N 2 O emissions (ARE −11%) while Tier II comparison to measurements (ARE −75%) helped to illustrate the strengths of a mechanistic approach in characterizing the site specific drivers responsible for N 2 O emissions. Overall this study demonstrated the benefits of having near continuous GHG flux measurements coupled with detailed ancillary measurements towards identifying soil process interactions responsible for regulating GHG emissions.
