The atmospheric concentration of nitrous oxide (N₂O), an important greenhouse gas and natural catalyst of stratospheric ozone decay, is increasing, and agricultural soils are the main source of this increase.  While factors regulating N₂O production by soil bacteria are well known, our understanding of agricultural management impacts on soil N₂O emissions remains limited due to complex interactions among proximal and distal factors regulating soil N₂O emissions.  It is commonly recognized that mathematical models are needed to predict soil N₂O emissions in light of this complexity.  Existing models have not been widely tested in diverse agricultural systems.  We compared soil N₂O emissions predicted using a process based model, Denitrification-Decomposition (DNDC), with N₂O emissions data collected between 2005 and 2010 from the long-term USDA-ARS Farming Systems Project (FSP) in Beltsville, Maryland.  We collected soil N2O emissions data from two conventional three-year corn-rye-soybean-wheat/soybean rotations, one under no-till (NT) management and one under chisel till (CT) management; and one three-year corn-rye-soybean-wheat/vetch rotation that receives poultry litter two of every three years.  Neither conventional system receives any manure applications.  We hypothesized that DNDC would better predict soil N₂O emissions from the two conventional systems than from the organic system since there are few long-term datasets from systems relying on organic nutrient sources available for calibrating process-based models.  We used local climate, soil and management information to run DNDC and will present results comparing DNDC predictions with observed values.  We anticipate that our results will help inform future improvements in the DNDC model.