Search

DeNitrification DeComposition (DNDC) model predictions of NH 3 fluxes following chemical fertilizer application were evaluated by comparison to relaxed eddy accumulation (REA) measurements, in Central Illinois, United States, over the 2014 growing season of corn. Practical issues for evaluating closure were addressed by accounting for fluxes outside the measurement site and differences in temporal resolution. DNDC modeled NH 3 fluxes showed no significant differences in magnitude (at p = 0.05) compared to measurements and replicated trends satisfactorily (r a 2 > 0.74), during the initial 33 days after fertilizer application, when measured fluxes were to the atmosphere, compared to later time periods when depositional fluxes were measured (r a 2 < 0.52). Among the model input parameters, NH 3 fluxes were most sensitive to air temperature, precipitation, soil organic carbon, field capacity, pH, and fertilizer application rate, timing, and depth. By constraining these inputs for conditions in Central Illinois, uncertainty in daily NH 3 fluxes was estimated to vary from 0% to 70% on a daily basis, during the corn growing season, with the highest uncertainty values estimated for the period of highest positive NH 3 fluxes. These results can guide future improvements in DNDC, which is a valuable tool to assist (1) in the development of NH 3 emission inventories with high spatial (constrained by the spatial resolution of input parameters) and temporal resolution (daily) and (2) in upscaling emissions from the site (farm) to the regional scale. [ABSTRACT FROM AUTHOR]

Abstract Due to the contributions of nitrous oxide (N 2 O) to global climate change and stratospheric ozone destruction, it is important to understand how climate and agricultural management affect N 2 O emissions. Although the process-based Denitrification Decomposition (DNDC) model is often used for quantifying emissions of N 2 O, the accuracy of these predictions remains in question, and it is not clear which input variables, environmental or field management, have the greatest effect on model performance. In this study, DNDC was evaluated for prediction of N 2 O fluxes from two climatically-different corn-field sites in the United States (a Colorado irrigated field and a Minnesota rainfed field). Besides climate, these sites offer the additional advantage that measurements are available for multiple field management practices, including fertilizer application, tillage, and crop rotation. This evaluation found that DNDC did not consistently, correctly predict daily-scale N 2 O fluxes. Cumulative growing season N 2 O fluxes were significantly under-predicted in Colorado and were both under- and over-predicted in Minnesota. Model calibration of four soil input parameters did not significantly improve N 2 O emission predictions at either site or time scale. Modeled and measured N 2 O fluxes and model error were all strongly correlated with precipitation. Over-predictions of N 2 O fluxes were associated with heavy precipitation and high modeled denitrification. Based on our results, model improvements to decrease model error for corn cropping systems in temperate climate zones should focus on better accounting for the effects of precipitation on denitrification. Despite discrepancies in daily and cumulative growing season N 2 O fluxes, DNDC correctly identified the only field management (fertilizer application rate) that significantly influenced the measured N 2 O fluxes. Graphical abstract Unlabelled Image Highlights • DNDC was evaluated for agricultural N 2 O under different management and climates. • DNDC correctly identified field management that significantly influenced N 2 O fluxes. • DNDC did not predict daily and cumulative N 2 O fluxes well, even after calibration. • N 2 O fluxes and model error correlated with precipitation frequency and intensity. • Model improvements should focus on denitrification surrounding precipitation events. [ABSTRACT FROM AUTHOR]