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Research Project: Enhanced System Models and Decision Support Tools to Optimize Water Limited Agriculture

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Title: Evaluating four N2O emission algorithms in RZWQM2 in response to N rate on an irrigated corn field

Author
item Fang, Q.x. - Qingdao Agricultural University
item Ma, Liwang
item Halvorson, Ardell - Collaborator
item Malone, Robert - Rob
item Ahuja, Lajpat
item Del Grosso, Stephen - Steve

Submitted to: Journal of Environmental Modeling and Software
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/14/2015
Publication Date: 7/14/2015
Citation: Fang, Q., Ma, L., Halvorson, A.D., Malone, R.W., Ahuja, L.R., Del Grosso, S.J. 2015. Evaluating four N2O emission algorithms in RZWQM2 in response to N rate on an irrigated corn field. Journal of Environmental Modeling and Software. 72:56-70. doi:10.1016/j.envsoft.2015.06.005.

Interpretive Summary: Correctly assessing the effects of the interactions between agricultural practices and environmental factors on N2O emissions is required for better crop and nitrogen (N) management. We used an enhanced RZWQM2 (Root Zone Water Quality Model) to simulate the observed responses of N2O emissions to different N application rates (0, 67, 134, and 168-246 kg N ha-1) on an irrigated corn field from 2003 to 2006, in eastern Colorado, USA. Four different algorithms from the literature were coupled with RZWQM2 and compared for simulating N2O emissions during nitrification and denitrification processes. The RZWQM2 was calibrated for corn yield, N uptake, soil water (0-10 cm) and soil Nitrate-N content (0-180 cm), with relative root mean square error (RMSE/Mean) values of 13.9%, 24.6%, 11.5% and 71.3%, respectively, from 2003 to 2006. Better simulations of N2O emissions were obtained by using the algorithms from DAYCENT and NOE (Nitrous Oxide Emissions) models than from WNMM (Water and Nitrogen Management Model) and FASSET models. The best N2O simulations were obtained when the fraction of N2O release from nitrification was modified by water filled pore space (WFPS) as in the NOE model and the fraction N2O release from denitrification was adopted from the DAYCENT model. As a result of this study, the RZWQM2 has now adopted the algorithms from NOE model for N2O emission during nitrification and from the DAYCENT model for N2O emission during denitrification.

Technical Abstract: Nitrous oxide (N2O) emissions from agricultural soils are major contributors to greenhouse gases. Correctly assessing the effects of the interactions between agricultural practices and environmental factors on N2O emissions is required for better crop and nitrogen (N) management. We used an enhanced RZWQM2 (Root Zone Water Quality Model) to simulate the observed responses of N2O emissions to different N application rates (0, 67, 134, and 168-246 kg N ha-1) on an irrigated corn field from 2003 to 2006, in eastern Colorado, USA. Four different algorithms from the literature were coupled with RZWQM2 and compared for simulating N2O emissions during nitrification and denitrification processes. The RZWQM2 was calibrated for corn yield, N uptake, soil water (0-10 cm) and soil Nitrate-N content (0-180 cm), with relative root mean square error (RMSE/Mean) values of 13.9%, 24.6%, 11.5% and 71.3%, respectively, from 2003 to 2006. Better simulations of N2O emissions were obtained by using the algorithms from DAYCENT and NOE (Nitrous Oxide Emissions) models than from WNMM (Water and Nitrogen Management Model) and FASSET models. The best N2O simulations were obtained when the fraction of N2O release from nitrification was modified by water filled pore space (WFPS) as in the NOE model and the fraction N2O release from denitrification was adopted from the DAYCENT model. Average observed and simulated seasonal N2O emissions were 1006 g N ha-1 and 978 g N ha-1, with r2 of 0.45 (P < 0.001). N2O emissions were under-simulated when soil water content was under-simulated (such as in 2003). Reducing N application rates from 202 kg N ha-1 to 134 kg N ha-1 resulted in reductions in N2O emissions by 40% (measured) and 57% (simulated) and grain yield reductions by 5% (measured) and 0% (simulated). As a result of this study, the RZWQM2 has now adopted the algorithms from NOE model for N2O emission during nitrification and from the DAYCENT model for N2O emission during denitrification.