Multi-model evaluation of nitrous oxide emissions from an intensively managed grassland

Kathrin Fuchs*, Lutz Merbold, Nina Buchmann, Daniel Bretscher, Lorenzo Brilli, Nuala Fitton, CFE Topp, Katja Klumpp, Mark Lieffering, Raphaël Martin, Paul CD Newton, RM Rees, Susanne Rolinski, Pete Smith, Valerie Snow

*Corresponding author for this work

Research output: Contribution to journalArticle

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 number16646846
JournalJournal of Geophysical Research
Early online date18 Dec 2019
DOIs
Publication statusFirst published - 18 Dec 2019

Fingerprint

grasslands
nitrous oxides
Nitrous Oxide
nitrous oxide
grassland
evaluation
greenhouses
Greenhouse gases
Fluxes
fertilization
carbon footprint
greenhouse gas
climate
management practice
gases
microorganisms
greenhouse gas emissions
in situ measurement
Gas emissions
farming system

Bibliographical note

This article is protected by AGU copyright. All rights reserved.

Keywords

  • Model validation
  • process-based modeling;
  • DayCent
  • APSIM
  • PaSim
  • biogeochemical modeling
  • eddy covariance

Cite this

Fuchs, K., Merbold, L., Buchmann, N., Bretscher, D., Brilli, L., Fitton, N., ... Snow, V. (2019). Multi-model evaluation of nitrous oxide emissions from an intensively managed grassland. Journal of Geophysical Research, [16646846]. https://doi.org/10.1029/2019JG005261
Fuchs, Kathrin ; Merbold, Lutz ; Buchmann, Nina ; Bretscher, Daniel ; Brilli, Lorenzo ; Fitton, Nuala ; Topp, CFE ; Klumpp, Katja ; Lieffering, Mark ; Martin, Raphaël ; Newton, Paul CD ; Rees, RM ; Rolinski, Susanne ; Smith, Pete ; Snow, Valerie. / Multi-model evaluation of nitrous oxide emissions from an intensively managed grassland. In: Journal of Geophysical Research. 2019.
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Fuchs, K, Merbold, L, Buchmann, N, Bretscher, D, Brilli, L, Fitton, N, Topp, CFE, Klumpp, K, Lieffering, M, Martin, R, Newton, PCD, Rees, RM, Rolinski, S, Smith, P & Snow, V 2019, 'Multi-model evaluation of nitrous oxide emissions from an intensively managed grassland', Journal of Geophysical Research. https://doi.org/10.1029/2019JG005261

Multi-model evaluation of nitrous oxide emissions from an intensively managed grassland. / Fuchs, Kathrin ; Merbold, Lutz; Buchmann, Nina; Bretscher, Daniel; Brilli, Lorenzo; Fitton, Nuala; Topp, CFE; Klumpp, Katja; Lieffering, Mark; Martin, Raphaël; Newton, Paul CD; Rees, RM; Rolinski, Susanne; Smith, Pete; Snow, Valerie.

In: Journal of Geophysical Research, 18.12.2019.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Multi-model evaluation of nitrous oxide emissions from an intensively managed grassland

AU - Fuchs, Kathrin

AU - Merbold, Lutz

AU - Buchmann, Nina

AU - Bretscher, Daniel

AU - Brilli, Lorenzo

AU - Fitton, Nuala

AU - Topp, CFE

AU - Klumpp, Katja

AU - Lieffering, Mark

AU - Martin, Raphaël

AU - Newton, Paul CD

AU - Rees, RM

AU - Rolinski, Susanne

AU - Smith, Pete

AU - Snow, Valerie

N1 - This article is protected by AGU copyright. All rights reserved.

PY - 2019/12/18

Y1 - 2019/12/18

N2 - 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 emissionsunder 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.

AB - 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 emissionsunder 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.

KW - Model validation

KW - process-based modeling;

KW - DayCent

KW - APSIM

KW - PaSim

KW - biogeochemical modeling

KW - eddy covariance

U2 - 10.1029/2019JG005261

DO - 10.1029/2019JG005261

M3 - Article

JO - Journal of Geophysical Research

JF - Journal of Geophysical Research

SN - 0148-0227

M1 - 16646846

ER -