Assessing uncertainties in crop and pasture ensemble model simulations of productivity and N2O emissions

Fiona Ehrhardt, Jean-François Soussana, Gianni Bellocchi, Peter Grace, Russel McAuliffe, Sylvie Recous, Renáta Sándor, Pete Smith, Val Snow, Massimiliano D. A. Migliorati, Bruno Basso, Arti Bhatia, Lorenzo Brilli, Jordi Doltra, Christopher D. Dorich, Luca Doro, Nuala Fitton, Sandro J. Giacomini, Brian Grant, Matthew T. Harrison & 20 others Stephanie K. Jones, Miko U. F. Kirschbaum, Katja Klumpp, Patricia Laville, Joël Léonard, Mark Liebig, Mark Lieffering, Raphaël Martin, Raia Silvia Massad, Elizabeth Meier, Lutz Merbold, Andrew D. Moore, Vasileios Myrgiotis, Paul Newton, Elizabeth Pattey, Susanne Rolinski, Joanna Sharp, Ward N. Smith, Lianhai Wu, Qing Zhang

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Abstract

Simulation models are extensively used to predict agricultural productivity and greenhouse gas (GHG) emissions. However, the uncertainties of (reduced) model ensemble simulations have not been assessed systematically for variables affecting food security and climate change mitigation, within multispecies agricultural contexts. We report an international model comparison and benchmarking exercise, showing the potential of multimodel ensembles to predict productivity and nitrous oxide (N2 O) emissions for wheat, maize, rice and temperate grasslands. Using a multistage modelling protocol, from blind simulations (stage 1) to partial (stages 2-4) and full calibration (stage 5), 24 process-based biogeochemical models were assessed individually or as an ensemble against long-term experimental data from four temperate grassland and five arable crop rotation sites spanning four continents. Comparisons were performed by reference to the experimental uncertainties of observed yields and N2 O emissions. Results showed that across sites and crop/grassland types, 23 to 40% of the uncalibrated individual models were within two standard deviations (s.d.) of observed yields, while 42 (rice) to 96% (grasslands) of the models were within one s.d. of observed N2 O emissions. At stage 1, ensembles formed by the three lowest prediction model errors (RRMSE) predicted both yields and N2 O emissions within experimental uncertainties for 44 and 33% of the crop and grassland growth cycles, respectively. Partial model calibration (stages 2 to 4) markedly reduced prediction errors of the full model ensemble E-median for crop grain yields (from 36% at stage 1 down to 4% on average) and grassland productivity (from 44 to 27%) and to a lesser and more variable extent for N2 O emissions. Yield-scaled N2 O emissions (N2 O emissions divided by crop yields) were ranked accurately by 3-model ensembles across crop species and field sites. The potential of using process-based model ensembles to predict jointly productivity and N2 O emissions at field scale is discussed. This article is protected by copyright. All rights reserved.

Original languageEnglish
Pages (from-to)603-616
Number of pages14
JournalGlobal Change Biology
Volume24
Issue number2
Early online date24 Nov 2017
DOIs
Publication statusPublished - Feb 2018

Fingerprint

Crops
pasture
Productivity
productivity
crop
simulation
grassland
Uncertainty
rice
Calibration
calibration
benchmarking
Nitrous Oxide
crop rotation
Benchmarking
prediction
food security
Gas emissions
nitrous oxide
Greenhouse gases

Keywords

  • Journal Article
  • greenhouse gases
  • climate change
  • agriculture
  • benchmarking
  • biogeochemical models
  • nitrous oxide
  • yield
  • soil

Cite this

Ehrhardt, F., Soussana, J-F., Bellocchi, G., Grace, P., McAuliffe, R., Recous, S., ... Zhang, Q. (2018). Assessing uncertainties in crop and pasture ensemble model simulations of productivity and N2O emissions. Global Change Biology, 24(2), 603-616. https://doi.org/10.1111/gcb.13965

Assessing uncertainties in crop and pasture ensemble model simulations of productivity and N2O emissions. / Ehrhardt, Fiona; Soussana, Jean-François; Bellocchi, Gianni; Grace, Peter; McAuliffe, Russel; Recous, Sylvie; Sándor, Renáta; Smith, Pete; Snow, Val; Migliorati, Massimiliano D. A.; Basso, Bruno; Bhatia, Arti; Brilli, Lorenzo; Doltra, Jordi; Dorich, Christopher D.; Doro, Luca; Fitton, Nuala; Giacomini, Sandro J.; Grant, Brian; Harrison, Matthew T.; Jones, Stephanie K.; Kirschbaum, Miko U. F.; Klumpp, Katja; Laville, Patricia; Léonard, Joël; Liebig, Mark; Lieffering, Mark; Martin, Raphaël; Massad, Raia Silvia; Meier, Elizabeth; Merbold, Lutz; Moore, Andrew D.; Myrgiotis, Vasileios; Newton, Paul; Pattey, Elizabeth; Rolinski, Susanne; Sharp, Joanna; Smith, Ward N.; Wu, Lianhai; Zhang, Qing.

In: Global Change Biology, Vol. 24, No. 2, 02.2018, p. 603-616.

Research output: Contribution to journalArticle

Ehrhardt, F, Soussana, J-F, Bellocchi, G, Grace, P, McAuliffe, R, Recous, S, Sándor, R, Smith, P, Snow, V, Migliorati, MDA, Basso, B, Bhatia, A, Brilli, L, Doltra, J, Dorich, CD, Doro, L, Fitton, N, Giacomini, SJ, Grant, B, Harrison, MT, Jones, SK, Kirschbaum, MUF, Klumpp, K, Laville, P, Léonard, J, Liebig, M, Lieffering, M, Martin, R, Massad, RS, Meier, E, Merbold, L, Moore, AD, Myrgiotis, V, Newton, P, Pattey, E, Rolinski, S, Sharp, J, Smith, WN, Wu, L & Zhang, Q 2018, 'Assessing uncertainties in crop and pasture ensemble model simulations of productivity and N2O emissions', Global Change Biology, vol. 24, no. 2, pp. 603-616. https://doi.org/10.1111/gcb.13965
Ehrhardt, Fiona ; Soussana, Jean-François ; Bellocchi, Gianni ; Grace, Peter ; McAuliffe, Russel ; Recous, Sylvie ; Sándor, Renáta ; Smith, Pete ; Snow, Val ; Migliorati, Massimiliano D. A. ; Basso, Bruno ; Bhatia, Arti ; Brilli, Lorenzo ; Doltra, Jordi ; Dorich, Christopher D. ; Doro, Luca ; Fitton, Nuala ; Giacomini, Sandro J. ; Grant, Brian ; Harrison, Matthew T. ; Jones, Stephanie K. ; Kirschbaum, Miko U. F. ; Klumpp, Katja ; Laville, Patricia ; Léonard, Joël ; Liebig, Mark ; Lieffering, Mark ; Martin, Raphaël ; Massad, Raia Silvia ; Meier, Elizabeth ; Merbold, Lutz ; Moore, Andrew D. ; Myrgiotis, Vasileios ; Newton, Paul ; Pattey, Elizabeth ; Rolinski, Susanne ; Sharp, Joanna ; Smith, Ward N. ; Wu, Lianhai ; Zhang, Qing. / Assessing uncertainties in crop and pasture ensemble model simulations of productivity and N2O emissions. In: Global Change Biology. 2018 ; Vol. 24, No. 2. pp. 603-616.
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abstract = "Simulation models are extensively used to predict agricultural productivity and greenhouse gas (GHG) emissions. However, the uncertainties of (reduced) model ensemble simulations have not been assessed systematically for variables affecting food security and climate change mitigation, within multispecies agricultural contexts. We report an international model comparison and benchmarking exercise, showing the potential of multimodel ensembles to predict productivity and nitrous oxide (N2 O) emissions for wheat, maize, rice and temperate grasslands. Using a multistage modelling protocol, from blind simulations (stage 1) to partial (stages 2-4) and full calibration (stage 5), 24 process-based biogeochemical models were assessed individually or as an ensemble against long-term experimental data from four temperate grassland and five arable crop rotation sites spanning four continents. Comparisons were performed by reference to the experimental uncertainties of observed yields and N2 O emissions. Results showed that across sites and crop/grassland types, 23 to 40{\%} of the uncalibrated individual models were within two standard deviations (s.d.) of observed yields, while 42 (rice) to 96{\%} (grasslands) of the models were within one s.d. of observed N2 O emissions. At stage 1, ensembles formed by the three lowest prediction model errors (RRMSE) predicted both yields and N2 O emissions within experimental uncertainties for 44 and 33{\%} of the crop and grassland growth cycles, respectively. Partial model calibration (stages 2 to 4) markedly reduced prediction errors of the full model ensemble E-median for crop grain yields (from 36{\%} at stage 1 down to 4{\%} on average) and grassland productivity (from 44 to 27{\%}) and to a lesser and more variable extent for N2 O emissions. Yield-scaled N2 O emissions (N2 O emissions divided by crop yields) were ranked accurately by 3-model ensembles across crop species and field sites. The potential of using process-based model ensembles to predict jointly productivity and N2 O emissions at field scale is discussed. This article is protected by copyright. All rights reserved.",
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author = "Fiona Ehrhardt and Jean-Fran{\cc}ois Soussana and Gianni Bellocchi and Peter Grace and Russel McAuliffe and Sylvie Recous and Ren{\'a}ta S{\'a}ndor and Pete Smith and Val Snow and Migliorati, {Massimiliano D. A.} and Bruno Basso and Arti Bhatia and Lorenzo Brilli and Jordi Doltra and Dorich, {Christopher D.} and Luca Doro and Nuala Fitton and Giacomini, {Sandro J.} and Brian Grant and Harrison, {Matthew T.} and Jones, {Stephanie K.} and Kirschbaum, {Miko U. F.} and Katja Klumpp and Patricia Laville and Jo{\"e}l L{\'e}onard and Mark Liebig and Mark Lieffering and Rapha{\"e}l Martin and Massad, {Raia Silvia} and Elizabeth Meier and Lutz Merbold and Moore, {Andrew D.} and Vasileios Myrgiotis and Paul Newton and Elizabeth Pattey and Susanne Rolinski and Joanna Sharp and Smith, {Ward N.} and Lianhai Wu and Qing Zhang",
note = "FE acknowledges support through a grant from ADEME (n° 12-60-C0023). This study was coordinated by the Integrative Research Group of the Global Research Alliance (GRA) on agricultural greenhouse gases and was supported by five research projects (CN-MIP, Models4Pastures, MACSUR, COMET-Global and MAGGNET) funded by a multi-partner call on agricultural GHGs with support of FACCE JPI. LM was supported by the Swiss National Science Foundation under the 40FA40_154245 / 1 grant agreement; JD participated in the framework of Red REMEDIA. The authors wish to thank Dr. Alex Ruane (NASA GISS) for provision of AgMERRA weather data; Benjamin Loubet (INRA) and Kathrin Fuchs (ETH-Z{\"u}rich) for their contribution to the consolidation of experimental datasets; Laura Cardenas (Rothamsted Research), Marco Carozzi (INRA) and DairyMod team (Karen Christie, Brendan Cullen, Rachel Meyer, Richard Eckard, Richard Rawnsley) for their modelling efforts; Marco Bindi (University of Florence), Rich Conant (Colorado State University), Heinrike Mielenz (Julius K{\"u}hn Institute) and Kairsty Topp (SRUC), for their help as supervisors.",
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TY - JOUR

T1 - Assessing uncertainties in crop and pasture ensemble model simulations of productivity and N2O emissions

AU - Ehrhardt, Fiona

AU - Soussana, Jean-François

AU - Bellocchi, Gianni

AU - Grace, Peter

AU - McAuliffe, Russel

AU - Recous, Sylvie

AU - Sándor, Renáta

AU - Smith, Pete

AU - Snow, Val

AU - Migliorati, Massimiliano D. A.

AU - Basso, Bruno

AU - Bhatia, Arti

AU - Brilli, Lorenzo

AU - Doltra, Jordi

AU - Dorich, Christopher D.

AU - Doro, Luca

AU - Fitton, Nuala

AU - Giacomini, Sandro J.

AU - Grant, Brian

AU - Harrison, Matthew T.

AU - Jones, Stephanie K.

AU - Kirschbaum, Miko U. F.

AU - Klumpp, Katja

AU - Laville, Patricia

AU - Léonard, Joël

AU - Liebig, Mark

AU - Lieffering, Mark

AU - Martin, Raphaël

AU - Massad, Raia Silvia

AU - Meier, Elizabeth

AU - Merbold, Lutz

AU - Moore, Andrew D.

AU - Myrgiotis, Vasileios

AU - Newton, Paul

AU - Pattey, Elizabeth

AU - Rolinski, Susanne

AU - Sharp, Joanna

AU - Smith, Ward N.

AU - Wu, Lianhai

AU - Zhang, Qing

N1 - FE acknowledges support through a grant from ADEME (n° 12-60-C0023). This study was coordinated by the Integrative Research Group of the Global Research Alliance (GRA) on agricultural greenhouse gases and was supported by five research projects (CN-MIP, Models4Pastures, MACSUR, COMET-Global and MAGGNET) funded by a multi-partner call on agricultural GHGs with support of FACCE JPI. LM was supported by the Swiss National Science Foundation under the 40FA40_154245 / 1 grant agreement; JD participated in the framework of Red REMEDIA. The authors wish to thank Dr. Alex Ruane (NASA GISS) for provision of AgMERRA weather data; Benjamin Loubet (INRA) and Kathrin Fuchs (ETH-Zürich) for their contribution to the consolidation of experimental datasets; Laura Cardenas (Rothamsted Research), Marco Carozzi (INRA) and DairyMod team (Karen Christie, Brendan Cullen, Rachel Meyer, Richard Eckard, Richard Rawnsley) for their modelling efforts; Marco Bindi (University of Florence), Rich Conant (Colorado State University), Heinrike Mielenz (Julius Kühn Institute) and Kairsty Topp (SRUC), for their help as supervisors.

PY - 2018/2

Y1 - 2018/2

N2 - Simulation models are extensively used to predict agricultural productivity and greenhouse gas (GHG) emissions. However, the uncertainties of (reduced) model ensemble simulations have not been assessed systematically for variables affecting food security and climate change mitigation, within multispecies agricultural contexts. We report an international model comparison and benchmarking exercise, showing the potential of multimodel ensembles to predict productivity and nitrous oxide (N2 O) emissions for wheat, maize, rice and temperate grasslands. Using a multistage modelling protocol, from blind simulations (stage 1) to partial (stages 2-4) and full calibration (stage 5), 24 process-based biogeochemical models were assessed individually or as an ensemble against long-term experimental data from four temperate grassland and five arable crop rotation sites spanning four continents. Comparisons were performed by reference to the experimental uncertainties of observed yields and N2 O emissions. Results showed that across sites and crop/grassland types, 23 to 40% of the uncalibrated individual models were within two standard deviations (s.d.) of observed yields, while 42 (rice) to 96% (grasslands) of the models were within one s.d. of observed N2 O emissions. At stage 1, ensembles formed by the three lowest prediction model errors (RRMSE) predicted both yields and N2 O emissions within experimental uncertainties for 44 and 33% of the crop and grassland growth cycles, respectively. Partial model calibration (stages 2 to 4) markedly reduced prediction errors of the full model ensemble E-median for crop grain yields (from 36% at stage 1 down to 4% on average) and grassland productivity (from 44 to 27%) and to a lesser and more variable extent for N2 O emissions. Yield-scaled N2 O emissions (N2 O emissions divided by crop yields) were ranked accurately by 3-model ensembles across crop species and field sites. The potential of using process-based model ensembles to predict jointly productivity and N2 O emissions at field scale is discussed. This article is protected by copyright. All rights reserved.

AB - Simulation models are extensively used to predict agricultural productivity and greenhouse gas (GHG) emissions. However, the uncertainties of (reduced) model ensemble simulations have not been assessed systematically for variables affecting food security and climate change mitigation, within multispecies agricultural contexts. We report an international model comparison and benchmarking exercise, showing the potential of multimodel ensembles to predict productivity and nitrous oxide (N2 O) emissions for wheat, maize, rice and temperate grasslands. Using a multistage modelling protocol, from blind simulations (stage 1) to partial (stages 2-4) and full calibration (stage 5), 24 process-based biogeochemical models were assessed individually or as an ensemble against long-term experimental data from four temperate grassland and five arable crop rotation sites spanning four continents. Comparisons were performed by reference to the experimental uncertainties of observed yields and N2 O emissions. Results showed that across sites and crop/grassland types, 23 to 40% of the uncalibrated individual models were within two standard deviations (s.d.) of observed yields, while 42 (rice) to 96% (grasslands) of the models were within one s.d. of observed N2 O emissions. At stage 1, ensembles formed by the three lowest prediction model errors (RRMSE) predicted both yields and N2 O emissions within experimental uncertainties for 44 and 33% of the crop and grassland growth cycles, respectively. Partial model calibration (stages 2 to 4) markedly reduced prediction errors of the full model ensemble E-median for crop grain yields (from 36% at stage 1 down to 4% on average) and grassland productivity (from 44 to 27%) and to a lesser and more variable extent for N2 O emissions. Yield-scaled N2 O emissions (N2 O emissions divided by crop yields) were ranked accurately by 3-model ensembles across crop species and field sites. The potential of using process-based model ensembles to predict jointly productivity and N2 O emissions at field scale is discussed. This article is protected by copyright. All rights reserved.

KW - Journal Article

KW - greenhouse gases

KW - climate change

KW - agriculture

KW - benchmarking

KW - biogeochemical models

KW - nitrous oxide

KW - yield

KW - soil

U2 - 10.1111/gcb.13965

DO - 10.1111/gcb.13965

M3 - Article

VL - 24

SP - 603

EP - 616

JO - Global Change Biology

JF - Global Change Biology

SN - 1354-1013

IS - 2

ER -