Global analysis of N2O emissions from agricultural soil surfaces considering non-linearity effects for the GAINS model

Nitrous oxide (N₂O) emissions from agricultural soils vary greatly in time and space, rendering detailed quantification challenging. Process models that have been used for that purpose are challenged by high computing resources and duration of model runs while providing modest improvement over data-driven approaches. In addition to the well-confirmed dependence on nitrogen input that also provides the basis for the IPCC emission factor to be applied to agricultural soils, multiple studies denoted a non-linear effect, such that excess fertilization provides over-proportionally high emissions. Directed towards effect-based consideration of sources and in order to better reflect mitigation measures, we have revised the N₂O emissions calculation methodology for IIASA’s GAINS model to cover such non-linearities, which requires spatially explicit accounting of inputs and emissions. In a first step, emission sources (mineral N fertilizer application and manure N application taken from GAINS) were distributed on a 5’ grid globally using harvested areas from the M3 crop map and the gridded livestock of the world dataset, both updated using annual EUROSTAT data on NUTS2 level (for Europe) and FAOSTAT data for the rest of the world. In a second step, a data-driven approach was chosen reflecting enhanced emissions based on excessive nitrogen application to calculate N₂O emissions. The spatially explicit representation of emissions allows to discern sub-regional hot spots of particularly high impact of this non-linearity such as the Indo-Gangetic plain in South Asia, Egypt’s Nile delta, the Yangtse river delta in China, with Northern France or also the Brazilian North-East tip to follow. Automatizing the calculations facilitates the development of a time series as well as the analysis of individual sources of nitrogen and different scenarios. Scenario analysis identifies the value of efficient N abating measures even before applying specific N₂O reduction technology. These improvements in depicting N₂O emissions in GAINS enhance the analysis of sub-regional emission patterns. Furthermore, they offer to cost-effectively address emission hotspots in more focused emission reduction policies and provide the foundation for fully assessing the impact of N₂O abatement policies, both retroactively and in emission projections.