Multimodel Evaluation of Nitrous Oxide Emissions From an Intensively Managed Grassland

Kathrin Fuchs* (Corresponding Author), Lutz Merbold, Nina Buchmann, Daniel Bretscher, Lorenzo Brilli, Nuala Fitton, Cairistiona F. E. Topp, Katja Klumpp, Mark Lieffering, Raphaël Martin, Paul C D Newton, Robert M. Rees, Susanne Rolinski, Pete Smith, Val Snow

*Corresponding author for this work

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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 (N2O) 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 N2O 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 multi‐model 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 N2O fluxes on annual timescales, while APSIM was most accurate for daily N2O fluxes. The multi‐model ensemble average reduced the error in estimated annual fluxes by 41% compared to an estimate using the 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 N2O 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 multi‐model ensembles are valuable for estimating the impact of climate and management on N2O emissions.
Original languageEnglish
Article numbere2019JG005261
Number of pages21
JournalJournal of geophysical research-Biogeosciences
Volume125
Issue number1
Early online date21 Jan 2020
DOIs
Publication statusPublished - Jan 2020

Bibliographical note

Funding Information:
We thank Charlotte Decock for kindly providing additional soil nitrate and ammonium data for the year 2013. This study was conducted under the Models4Pastures project within the framework of FACCE-JPI. Lutz Merbold and Kathrin Fuchs acknowledge funding for the Swiss contribution to Models4Pastures (FACCE-JPI project, SNSF funded contract: 40FA40_154245/1) and Kathrin Fuchs for the Doc.Mobility fellowship (SNSF funded project: P1EZP2_172121). Nina Buchmann acknowledges funding from GHG-Europe (FP7, EU contract 244122). The NZ coauthors acknowledge funding from the New Zealand Government Ministry of Primary Industries for supporting the Livestock Research Group of the Global Research Alliance on Agricultural Greenhouse Gases and from AgResearch's Strategic Science Investment Fund (the Forages for Reduced Nitrate Leaching (FRNL) Research Programme). The UK contributors acknowledge funding by DEFRA and the RCUK projects: N-Circle (BB/N013484/1), UGRASS (NE/M016900/1), and GREENHOUSE (NE/K002589/1) and further funding from the Scottish Government Strategic Research Programme. Lorenzo Brilli and Marco Bindi received funding from the Italian Ministry of Agricultural Food and Forestry Policies (MiPAAF). The FR partners acknowledge funding from the French National Research Agency for the CN-MIP project (ANR-13-JFAC-0001) and ADEME (12-60-C0023). Susanne Rolinski acknowledges financial support from the project Climasteppe (BMBF under grant 01DJ18012). Acknowledgment is made to the APSIM Initiative, which takes responsibility for quality assurance and a structured innovation program for APSIM. APSIM is provided free for research and development use (see www.apsim.info for details).

Keywords

  • model validation
  • process‐based modeling
  • biogeochemical modelling
  • eddy covariance
  • DayCent
  • APSIM
  • PaSim
  • biogeochemical modeling
  • GREENHOUSE-GAS EMISSIONS
  • UNCERTAINTIES
  • CO2
  • BIOGEOCHEMICAL MODELS
  • N2O
  • SIMULATION
  • CARBON STOCKS
  • SOIL
  • SYSTEMS
  • FLUXES

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