Preprints
https://doi.org/10.5194/egusphere-2026-4010
https://doi.org/10.5194/egusphere-2026-4010
16 Jul 2026
 | 16 Jul 2026
Status: this preprint is open for discussion and under review for Biogeosciences (BG).

A process-based framework for national-scale estimation of agricultural soil N₂O emissions under variable climate and management

Andrew Smerald, Hannes Imhof, Edwin Haas, David Kraus, Lioba Martin, Kathrin Fuchs, John Akubia, Ali Sakhaee, Cora Vos, Roland Fuß, Clemens Scheer, and Ralf Kiese

Abstract. Agricultural soils are the dominant source of anthropogenic N2O emissions, yet their high spatial and temporal heterogeneity provides a major challenge for accurately quantifying emissions and evaluating mitigation options. Most national greenhouse gas inventories rely on empirical Tier-1 or Tier-2 emission-factor approaches and therefore do not fully capture the effects of climate variability, soil properties, or management practices. Here, we present a transferable, process-based modelling framework based on the biogeochemical model LandscapeDNDC for determining direct and indirect N2O emissions from major crops cultivated on mineral soils at the national scale. We apply the method to Germany making use of high-resolution input data provided by the national reporting agencies, estimating N2O emissions of 35 (29–44) kt N yr-1(2017–2022 average). This is 28 % higher than the national inventory report (submission 2025), but well within the uncertainty range. In contrast to conventional inventory methods, the framework explicitly accounts for interannual climate variability and can be spatially disaggregated at high resolution, taking into account local variations in soil type, weather and agricultural management practices. Because the model simulates coupled carbon and nitrogen cycling, it also quantifies multiple nitrogen loss pathways and potential changes in carbon stocks simultaneously, providing a consistent basis for evaluating mitigation strategies and their potential trade-offs. Our results demonstrate that process-based modelling can substantially improve the spatial and temporal resolution of agricultural N₂O emissions and provide a platform for developing next-generation national greenhouse gas inventories. While further work is required before the framework fully satisfies all IPCC Tier-3 requirements, it offers a pathway towards a more mechanistic and policy-relevant assessment of agricultural greenhouse gas emissions.

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Andrew Smerald, Hannes Imhof, Edwin Haas, David Kraus, Lioba Martin, Kathrin Fuchs, John Akubia, Ali Sakhaee, Cora Vos, Roland Fuß, Clemens Scheer, and Ralf Kiese

Status: open (until 27 Aug 2026)

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Andrew Smerald, Hannes Imhof, Edwin Haas, David Kraus, Lioba Martin, Kathrin Fuchs, John Akubia, Ali Sakhaee, Cora Vos, Roland Fuß, Clemens Scheer, and Ralf Kiese
Andrew Smerald, Hannes Imhof, Edwin Haas, David Kraus, Lioba Martin, Kathrin Fuchs, John Akubia, Ali Sakhaee, Cora Vos, Roland Fuß, Clemens Scheer, and Ralf Kiese
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Short summary
Countries produce a yearly greenhouse gas inventory that is used to monitor and design climate policies. Agricultural N2O emissions are especially uncertain, due to measurement difficulties. Using a process-based model combined with high resolution management data we show that, in comparison with current methods, N2O emissions can be mapped with high spatial and temporal resolution.
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