Prediction of N2O emissions under different field management practices and climate conditions

Mary E Foltz; Julie L Zilles; Sotiria Koloutsou-Vakakis

doi:10.1016/j.scitotenv.2018.07.364

Prediction of N₂O emissions under different field management practices and climate conditions

Sci Total Environ. 2019 Jan 1:646:872-879. doi: 10.1016/j.scitotenv.2018.07.364. Epub 2018 Jul 26.

Authors

Mary E Foltz¹, Julie L Zilles¹, Sotiria Koloutsou-Vakakis²

Affiliations

¹ Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, 205 N Mathews Ave, Urbana, IL 61801, USA.
² Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, 205 N Mathews Ave, Urbana, IL 61801, USA. Electronic address: sotiriak@illinois.edu.

PMID: 30064113
DOI: 10.1016/j.scitotenv.2018.07.364

Abstract

Due to the contributions of nitrous oxide (N₂O) to global climate change and stratospheric ozone destruction, it is important to understand how climate and agricultural management affect N₂O emissions. Although the process-based Denitrification Decomposition (DNDC) model is often used for quantifying emissions of N₂O, 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 N₂O 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 N₂O fluxes. Cumulative growing season N₂O 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 N₂O emission predictions at either site or time scale. Modeled and measured N₂O fluxes and model error were all strongly correlated with precipitation. Over-predictions of N₂O 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 N₂O fluxes, DNDC correctly identified the only field management (fertilizer application rate) that significantly influenced the measured N₂O fluxes.

Keywords: Agricultural management; Corn; DNDC; Denitrification; Fertilizer; N(2)O emissions.