Modeling nitrous oxide emissions from agricultural soil incubation experiments using CoupModel

Abstract

Abstract. Efforts to develop effective climate mitigation strategies for agriculture require methods to estimate nitrous oxide (N2O) emissions from soil. Process-based biogeochemical models have been often used for field- and large-scale estimates, while the sensitivity and uncertainty of model applications to incubation experiments are less investigated. In this study, a process-oriented model (CoupModel) was used to simulate N2O and CO2 fluxes and soil mineral nitrogen (N) contents in a short-term (43 d) factorial incubation experiment (16 treatments). A global sensitivity analysis (GSA) approach, “Morris screening”, was applied to quantify parameter sensitivity. The GSA suggested that a higher number of sensitive parameters was associated with N2O flux estimates and that inter-treatment variations in parameter sensitivities were distinguished by soil moisture levels or NO3- content and residue types. Important parameters regarding N2O flux estimates were linked to the decomposability of soil organic matter (e.g., organic C pool sizes) and the denitrification process (e.g., Michaelis constant and denitrifier respiratory rates). After calibration, the model better captured temporal variations and magnitude of gas fluxes and mineral N in unamended soils than in residue-amended soils. Low-magnitude daily and cumulative N2O fluxes were well simulated with mean errors (MEs) close to zero, but the model tended to underestimate N2O fluxes, as observed daily values increased by over 0.1 g N m−2 d−1, in which the major mismatch was due to limited success of the model to describe the high emissions during the first few days after crop residue addition. A larger uncertainty was also seen in the magnitude of pulse emissions by the posterior simulations. We also evaluated ancillary variables regarding N cycling, which indicated that more frequent measurements and additional types of observed data such as soil oxygen content and the microbial sources of emitted N2O are required to further evaluate model performance and biases. The major challenges for calibration were associated with high sensitivities of denitrification parameters to initial soil abiotic conditions and the instantaneous residue amendment. Model structure uncertainties and improved modeling practices in the context of incubation experiments were discussed.

Challenges of accounting nitrous oxide emissions from agricultural crop residues

“…The nonlinear dependencies of the factors influencing N turnover and N 2 O hotspots with residue management make the handling of spatiotemporal effects particularly important for model performance. This is still a considerable challenge because of strong spatial variations in soil structure and distribution of substrates and microorganisms (Chakrawal et al, 2019) as well as of crop residues at a smaller scale than the resolution of current field scale biogeochemical models (Zhang et al, 2022).…”

Modeling denitrification: can we report what we don’t know?

“…Some models (Del Grosso et al, 2000;Parton et al, 1996) have been partly parameterized with N 2 data that are no longer considered reliable (e.g., based on the acetylene inhibition technique (Weier et al, 1993)) and other model calibrations are simply incomplete. Given the lack of empirical data, approaches to describe soil N 2 are mostly process-oriented, with the sensitivity of both N 2 and N 2 O to controlling factors constrained based solely on N 2 O data (Grosz et al, 2021;Zhang et al, 2022). In these models, having data from frequent measurements (beyond the common weekly or fortnightly intervals) is crucial.…”

Modeling Denitrification: Can We Report What We Don't Know?

“…Ecosystem N cycling does not exist in isolation. Other factors, such as the soil oxygen availability and distribution (Zhang et al, 2022) and labile organic carbon (Philippot et al, 2007), also affect denitrification modeling. Whether a model relates transport functions to water-filled pore space or soil gas diffusivity in order to understand and model soil aeration, can have a significant effect on the simulated N 2 O and N 2 production (Balaine et al, 2013(Balaine et al, , 2016.…”

“…Some models (Del Grosso et al., 2000; Parton et al., 1996) have been partly parameterized with N 2 data that are no longer considered reliable (e.g., based on the acetylene inhibition technique (Weier et al., 1993)) and other model calibrations are simply incomplete. Given the lack of empirical data, approaches to describe soil N 2 are mostly process‐oriented, with the sensitivity of both N 2 and N 2 O to controlling factors constrained based solely on N 2 O data (Grosz et al., 2021; Zhang et al., 2022). In these models, having data from frequent measurements (beyond the common weekly or fortnightly intervals) is crucial.…”