Denitrification is an important component of the nitrogen cycle in soil, returning reactive nitrogen to the atmosphere. Denitrification activity is often concentrated spatially in anoxic microsites and temporally in ephemeral events, which presents a challenge for modelling. The anaerobic fraction of soil volume can be a useful predictor of denitrification in soils. Here, we provide a review of this soil characteristic, its controlling factors, its estimation from basic soil properties and its implementation in current denitrification models. The concept of the anaerobic soil volume and its relationship to denitrification activity has undergone several paradigm shifts that came along with the advent of new oxygen and microstructure mapping techniques. The current understanding is that hotspots of denitrification activity are partially decoupled from air distances in the wet soil matrix and are mainly associated with particulate organic matter (POM) in the form of fresh plant residues or manure. POM fragments harbor large amounts of labile carbon that promote local oxygen consumption and, as a result, these microsites differ in their aeration status from the surrounding soil matrix. Current denitrification models relate the anaerobic soil volume fraction to bulk oxygen concentration in various ways but make little use of microstructure information, such as the distance between POM and air-filled pores. Based on meta-analyses, we derive new empirical relationships to estimate the conditions for the formation of anoxia at the microscale from basic soil properties and we outline how these empirical relationships could be used in the future to improve prediction accuracy of denitrification models at the soil profile scale.

Biogeochemical models simulate soil nitrogen (N) turnover and are often used to assess N losses through denitrification. Though models simulate a complete N budget, often only a subset of N pools/fluxes (i.e., N₂O, , NH₃, NO_x) are published since the full budget cannot be validated with measured data. Field studies rarely include full N balances, especially N₂ fluxes, which are difficult to quantify. Limiting publication of modeling results based on available field data represents a missed opportunity to improve the understanding of modeled processes. We propose that the modeler community support publication of all simulated N pools and processes in future studies.

Biogeochemical models are essential for the prediction and management of nitrogen (N) cycling in agroecosystems, but the accuracy of the denitrification and decomposition sub-modules is critical. Current models were developed before suitable soil N₂ flux data were available, which may have led to inaccuracies in how denitrification was described. New measurement techniques, using gas chromatography and isotope-ratio mass spectrometry (IRMS), have enabled the collection of more robust N₂, N₂O and CO₂ data. We incubated two arable soils – a silt-loam and a sand soil – for 34 and 58 d, respectively, with small field-relevant changes made to control factors during this period. For the silt-loam soil, seven treatments varying in moisture, bulk density and ${\mathrm{NO}}_{\mathrm{3}}^{-}$ contents were included, with temperature changing during the incubation. The sandy soil was incubated with and without incorporation of litter (ryegrass), with temperature, water content and ${\mathrm{NO}}_{\mathrm{3}}^{-}$ content changing during the incubation. The denitrification and decomposition sub-modules of DeNi, Coup and DNDC were tested using the data. No systematic calibration of the model parameters was conducted since our intention was to evaluate the general model structure or “default” model runs. Measured fluxes generally responded as expected to control factors. We assessed the direction of modeled responses to control factors using three categories: no response, a response in the same direction as measurements or a response in the opposite direction to measurements. DNDC responses were 14 %, 52 % and 34 %, respectively. Coup responses were 47 %, 19 % and 34 %, respectively. DeNi responses were 0 %, 67 % and 33 %, respectively. The magnitudes of the modeled fluxes were underestimated by Coup and DNDC and overestimated by DeNi for the sandy soil, while there was no general trend for the silt-loam soil. None of the models was able to determine litter-induced decomposition correctly. To conclude, the currently used sub-modules are not able to consistently simulate the denitrification and decomposition processes. For better model evaluation and development, we need to design better experiments, take more frequent measurements, use new or updated measurement techniques, address model complexity, add missing processes to the models, calibrate denitrifier microbial dynamics, and evaluate the anaerobic soil volume concept.

The prediction of liquid manure effects on N transformations in soils and the associated N₂O and N₂ fluxes is poor because previous investigations have mostly excluded N₂. The objectives of this study were thus to quantify N₂, N₂O and CO₂ fluxes, the source processes of N₂O, N₂O reduction and the depth distribution of moisture, NO₃⁻, NH₄⁺, water-extractable organic carbon concentration and pH in a laboratory incubation study with sandy arable soil using ¹⁵N tracing to quantify N processes and gaseous fluxes. The soil was amended with and without artificial slurry in various manure treatments (control, surface and injected) and incubated for 10 days at varying moisture levels, where the depth distribution of control parameters was determined twice during the experiment. Manure application was found to increase N₂ and N₂O fluxes from denitrification, with the highest fluxes occurring in the wet manure injection treatment (33 ± 32 mg N m⁻² d⁻¹ and 36.1 ± 39.1 mg N m⁻² d⁻¹, respectively), confirming that manure injection under wet conditions enhances denitrification and possibly also N₂O fluxes. This study concluded that the current dataset is suitable as a first step towards improving the capability of biogeochemical models to predict manure application effects, but further studies with more soils and refined experiments are needed.

Denitrification represents a major nitrogen loss pathway in agriculture, reducing plant N uptake, lowering crop N use efficiency, while emitting the potent greenhouse gas N2O. However, due to methodological challenges, field measurements of N2 emissions remain rare, leaving denitrification losses poorly characterised across most cropping systems and comprehensive N budgets lacking. This constrains parameterization and validation of biogeochemical models, impeding efforts to forecast denitrification losses under changing conditions and design effective mitigation strategies. This study (i) reviewed the current literature on field-based N2 measurements, (ii) tested the ability of five different models to accurately simulate denitrification losses, and (iii) simulated denitrification losses from agricultural soils in Germany using gridded and point-based modelling frameworks. Results show that field studies suitable for model calibration remain scarce and mostly limited to temperate systems. Simulated cumulative N2 emissions varied widely between models, ranging from 0.89 to 6.07 kg N ha⁻¹, far below the observed N2 emissions of 21±3 kg N ha⁻¹. The ratio of N2O to (N2O+N2) emitted (RN2O), a key determinant of the climate impact of denitrification, was not only strongly overestimated by all models but also exhibited considerable variations, ranging from 0.056 to 0.707, compared to the measured ratio of 0.006. Models also failed to capture expected responses of RN2O to environmental drivers. For Germany, simulated average N2 emissions from agricultural soils ranged from 1.7 to 14.5 kg N ha⁻¹yr-1, with corresponding RN2O ranging from 0.18 to 0.59. Spatial patterns of emissions also differed significantly across modelling frameworks, reflecting large structural uncertainties. These findings highlight that both N2 emissions and RN2O remain poorly represented in current models, severely limiting our ability to establish reliable N budgets and develop targeted N management strategies. Coordinated advancements in field-based measurements and modelling are urgently needed to improve the representation of denitrification in biogeochemical models.

Denitrification is a major component of the nitrogen cycle in soil that returns reactive nitrogen to the atmosphere. Denitrification activity is often concentrated spatially in anoxic microsites and temporally in ephemeral events, which presents a challenge for modelling. The anaerobic fraction of soil volume can be a useful predictor of denitrification in soils. Here, we provide a review of this soil characteristic, its controlling factors and its estimation from basic soil properties.The concept of the anaerobic soil volume and its link to denitrification activity has undergone several paradigm shifts that came along with the advent of new oxygen and microstructure mapping techniques. The current understanding is that hotspots of denitrification activity are partially decoupled from air distances in the wet soil matrix and are mainly associated with particulate organic matter (POM) in the form of fresh plant residues or manure. POM fragments harbor large amounts of labile carbon that fuels local oxygen consumption and, as a result, these microsites differ in their aeration status from the surrounding soil matrix.Current denitrification models link the anaerobic soil volume fraction to bulk oxygen concentration in different ways but take almost no account of microstructure information, such as the distance between POM and air-filled pores. Based on meta-analyses, we derive new empirical relationships to estimate conditions for the formation of anoxia at the microscale from basic soil properties and we outline how these empirical relationships could be used in the future to improve prediction accuracy of denitrification models at the soil profile scale.