Modelling nitrous oxide emissions: comparing algorithms in six widely used agro-ecological models
Hongtao Xing A B , Chris. J. Smith
A * , Enli Wang A , Ben Macdonald A and David Wårlind A C
+ Author Affiliations
- Author Affiliations
A CSIRO Agriculture, GPO BOX 1666, Canberra, ACT 2601, Australia.
B Present address: Department of Environment and Science, Queensland Government, Brisbane, Qld 4001, Australia.
C Institute for Physical Geography and Ecosystem Science, Lund University, Lund, Sweden.
Soil Research 61(6) 523-541 https://doi.org/10.1071/SR22009
Submitted: 12 January 2022 Accepted: 14 February 2023 Published: 6 March 2023
© 2023 The Author(s) (or their employer(s)). Published by CSIRO Publishing. This is an open access article distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND)
Abstract
Agricultural soils are the most important anthropogenic source of nitrous oxide (N2O) emissions. This occurs via two main pathways: (1) from microbial-mediated oxidation of ammonium to nitrite and nitrate; and (2) denitrification. Most agro-ecological models explicitly deal with these two pathways albeit with different degrees of process understanding and empiricism. Models that integrate the impact of multiple environmental factors on N2O emissions can provide estimates of N2O fluxes from complex agricultural systems. However, uncertainties in model predictions arise from differences in the algorithms, imperfect quantification of the nitrification and denitrification response to edaphic conditions, and the spatial and temporal variability of N2O fluxes resulting from variable soil conditions. This study compared N2O responses to environmental factors in six agro-ecological models. The comparisons showed that environmental factors impact nitrification and denitrification differently in each model. Reasons include the inability to apportion the total N2O flux to the specific N transformation rates used to validate and calibrate the simplifications represented in the model algorithms, and incomplete understanding of the multiple interactions between processes and modifying factors as these are generally not quantified in field experiments. Rather, N2O flux data is reported as total or net N2O emissions without attributing emissions to gross and/or net rates for specific N processes, or considering changes that occur between production and emissions. Additional measurements that quantify all processes understand the multiple interactions that affect N2O emissions are needed to improve model algorithms and reduce the error associated with predicted emissions.
Keywords: agriculture, agro-ecological models, algorithm comparison, denitrification, N2O emissions, nitrification, soil, soil-atmosphere flux.
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