Multimodel Evaluation of Nitrous Oxide Emissions From an Intensively Managed Grassland

Abstract

Process-based models are useful for assessing the impact of changing management practices and climate on yields and greenhouse gas (GHG) emissions from agricultural systems such as grasslands. They can be used to construct national GHG inventories using a Tier 3 approach. However, accurate simulations of nitrous oxide (N 2 O) fluxes remain challenging. Models are limited by our understanding of soil-plant-microbe interactions and the impact of uncertainty in measured input parameters on simulated outputs. To improve model performance, thorough evaluations against in situ measurements are needed. Experimental data of N 2 O emissions under two management practices (control with typical fertilization versus increased clover and no fertilization) were acquired in a Swiss field experiment. We conducted a multimodel evaluation with three commonly used biogeochemical models (DayCent in two variants, PaSim, APSIM in two variants) comparing four years of data. DayCent was the most accurate model for simulating N 2 O fluxes on annual timescales, while APSIM was most accurate for daily N 2 O fluxes. The multimodel ensemble average reduced the error in estimated annual fluxes by 41% compared to an estimate using the Intergovernmental Panel on Climate Change (IPCC)-derived method for the Swiss agricultural GHG inventory (IPCC-Swiss), but individual models were not systematically more accurate than IPCC-Swiss. The model ensemble overestimated the N 2 O mitigation effect of the clover-based treatment (measured: 39-45%; ensemble: 52-57%) but was more accurate than IPCC-Swiss (IPCC-Swiss: 72-81%). These results suggest that multimodel ensembles are valuable for estimating the impact of climate and management on N 2 O emissions. Plain Language SummaryWe tested the performance of three dynamic simulation models against measured nitrous oxide (N 2 O) fluxes and its driver variables for a Swiss grassland. We showed that DayCent performed best in the prediction of annual N 2 O emissions but was outperformed by APSIM for daily N 2 O emissions. We identified particular strengths and weaknesses of each model. Further, we compared the individual models against the N 2 O flux estimate made with the Intergovernmental Panel on Climate Change (IPCC)-derived method for the Swiss agricultural greenhouse gas inventory (IPCC-Swiss). Most individual models were worse than IPCC-Swiss but the mean of all model predictions was much better than IPCC-Swiss. All methods overestimated the N 2 O mitigation effect of a clover-based N 2 O mitigation. IPCC-Swiss was worst and the model ensemble was best at estimating the effects of the mitigation. The findings highlight that using multiple models in an ensemble is beneficial for assessing management and climate impacts on N 2 O emissions.

Evaluating the Potential of Legumes to Mitigate N2O Emissions From Permanent Grassland Using Process‐Based Models

“…For N 2 O emissions, relative biases were −0.80-0.03 and RRMSE ranged between 0.13 and 0.81 (Ehrhardt et al, 2018; see Table S1a). A detailed validation of N 2 O emissions and its drivers at the Swiss site revealed that both models used here generally performed better than the Swiss IPCC calculations for that site (Fuchs et al, 2020).…”

“…In order to obtain the response variables on a homogeneous grid and exclude inter-annual variability, surfaces were fitted to model outputs (yield, N 2 O emissions) per model-site combination on the surface expanded by actual clover percentage and annual fertilizer amount. Ehrhardt et al, 2018) with respect to plant productivity, soil moisture and temperature, soil mineral N content, and model uncertainties of the respective models were evaluated in an international model intercomparison (Ehrhardt et al, 2018 ; Table S1) with further testing of yield and N 2 O emissions at two levels of pasture clover content in Fuchs et al (2020). Biological nitrogen fixation and yields in grass-clover mixtures were validated at one site described by Fitton et al (2019).…”

“…10.1029/2020GB006561 Ehrhardt et al, 2018) with respect to plant productivity, soil moisture and temperature, soil mineral N content, and model uncertainties of the respective models were evaluated in an international model intercomparison (Ehrhardt et al, 2018; Table S1) with further testing of yield and N 2 O emissions at two levels of pasture clover content in Fuchs et al (2020). Biological nitrogen fixation and yields in grass-clover mixtures were validated at one site described by Fitton et al (2019).…”

“…In this study, we use the biogeochemical models APSIM (Holzworth et al, 2014) and DayCent (Del Grosso et al, 2001; Parton et al, 1998, 2001). These models have the ability to simulate key processes of C and N cycling, including key processes of N 2 O production, nitrification, and denitrification and, importantly, have been validated with field data at the study sites used with respect to N symb (Fitton et al, 2019) and combinations of biomass yields and N 2 O emissions (Ehrhardt et al, 2018; Fuchs et al, 2020). The number of deployed models in this current study was lower than those in the publication cited above because most of the biogeochemical models were not able to simulate grass‐legume interaction explicitly, as required for this study.…”

Are there memory effects on greenhouse gas emissions (CO<sub>2</sub>, N<sub>2</sub>O and CH<sub>4</sub>) following grassland restoration?

“…The study by further elaborated the variation in management intensity and related variations in GHG exchange across sites, stressing the need for more case studies based on continuous GHG observations to improve existing knowledge and close remaining knowledge gaps. To complete the picture on factors driving ecosystem GHG exchange, irregularly occurring events such as dry spells or extraordinary wet periods can further lead to enhanced or reduced GHG emissions (Chen et al, 2016;Hartmann and Niklaus, 2012;Hopkins and Del Prado, 2007;Mudge et al, 2011;Wolf et al, 2013).…”