Measurements of nitrous oxide (N2O) emissions from agriculture are essential for understanding the complex soil–crop–climate processes, but there are practical and economic limits to the spatial and temporal extent over which measurements can be made. Therefore, N2O models have an important role to play. As models are comparatively cheap to run, they can be used to extrapolate field measurements to regional or national scales, to simulate emissions over long time periods, or to run scenarios to compare mitigation practices. Process-based models can also be used as an aid to understanding the underlying processes, as they can simulate feedbacks and interactions that can be difficult to distinguish in the field. However, when applying models, it is important to understand the conceptual process differences in models, how conceptual understanding changed over time in various models, and the model requirements and limitations to ensure that the model is well suited to the purpose of the investigation and the type of system being simulated. The aim of this paper is to give the reader a high-level overview of some of the important issues that should be considered when modeling. This includes conceptual understanding of widely used models, common modeling techniques such as calibration and validation, assessing model fit, sensitivity analysis, and uncertainty assessment. We also review examples of N2O modeling for different purposes and describe three commonly used process-based N2O models (APSIM, DayCent, and DNDC).
Funding for this publication was provided by the New Zealand Government to support the objectives of the Livestock Research Group of the Global Research Alliance on Agricultural Greenhouse Gases. Individual authors work contribute to the following projects for which support has been received: Climate smart use of Norwegian organic soils (MYR, 2017-2022) project funded by the Research Council of Norway (decision no. 281109); Scottish Government's Strategic Research Programme, SuperG (under EU Horizon 2020 programme); DEVIL (NE/M021327/1), Soils-R-GRREAT (NE/P019455/1) and the EU H2020 project under Grant Agreement 774378—Coordination of International Research Cooperation on Soil Carbon Sequestration in Agriculture (CIRCASA); to project J-001793, Science and Technology Branch, Agriculture and Agri-Food Canada; and New Zealand Ministry of Business, Innovation and Employment (MBIE) core funding. Thanks to Alasdair Noble and the anonymous reviewers for helpful comments on a draft of this paper and to Anne Austin for editing services.