Assessing uncertainties in crop and pasture ensemble model simulations of productivity and N2O emissions
Fiona Ehrhardt
(1)
,
Jean-François Soussana
(1)
,
Gianni Bellocchi
(2)
,
Peter Grace
(3)
,
Russel Mcauliffe
(4)
,
Sylvie Recous
(5)
,
Renata Sandor
(5)
,
Pete Smith
(6)
,
Val Snow
(4)
,
Massimiliano de Antoni Migliorati
(3)
,
Bruno Basso
(7)
,
Arti Bhatia
(8)
,
Lorenzo Brilli
(9)
,
Jordi Doltra
(10)
,
Christopher D. Dorich
(11)
,
Luca Doro
(12)
,
Nuala Fitton
(6)
,
Sandro J. Giacomini
(13)
,
Brian Grant
(14)
,
Matthew T. Harrison
(15)
,
Stephanie K. Jones
(16)
,
Miko U. F. Kirschbaum
(17)
,
Katja Klumpp
(2)
,
Patricia Laville
(18)
,
Joël J. Léonard
(19)
,
Mark Liebig
(20)
,
Mark Lieffering
(4)
,
Raphaël Martin
(2)
,
Raia Silvia Massad
(18)
,
Elizabeth Meier
(21)
,
Lutz Merbold
(22)
,
Andrew D. Moore
(21)
,
Vasileios Myrgiotis
(16)
,
Paul Newton
(4)
,
Elizabeth Pattey
(14)
,
Susanne Rolinski
(23)
,
Joanna Sharp
(24)
,
Ward N. Smith
(14)
,
Lianhai Wu
(25)
,
Qing Zhang
(26)
1
CODIR -
Collège de Direction
2 UREP - Unité Mixte de Recherche sur l'Ecosystème Prairial - UMR
3 QUT - Queensland University of Technology [Brisbane]
4 Agresearch Ltd
5 FARE - Fractionnement des AgroRessources et Environnement
6 University of Aberdeen
7 Michigan State University [East Lansing]
8 IARI - Indian Agricultural Research Institute
9 UniFI - Università degli Studi di Firenze = University of Florence = Université de Florence
10 CIFA - Catabrian Agricultural Research and Training Center
11 CSU - Colorado State University [Fort Collins]
12 UNISS - Università degli Studi di Sassari = University of Sassari [Sassari]
13 UFSM - Universidade Federal de Santa Maria = Federal University of Santa Maria [Santa Maria, RS, Brazil]
14 AAFC - Agriculture and Agri-Food
15 TIA - Tasmanian Institute of Agriculture
16 SRUC - Scotland's Rural College
17 Manaaki Whenua – Landcare Research [Lincoln]
18 ECOSYS - Ecologie fonctionnelle et écotoxicologie des agroécosystèmes
19 AgroImpact - Agroressources et Impacts environnementaux
20 USDA-ARS : Agricultural Research Service
21 CSIRO - Commonwealth Scientific and Industrial Research Organisation [Canberra]
22 ETH Zürich - Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich]
23 PIK - Potsdam Institute for Climate Impact Research
24 New Zealand Institute for Crop and Food Research
25 Rothamsted Research
26 CAS - Chinese Academy of Sciences
2 UREP - Unité Mixte de Recherche sur l'Ecosystème Prairial - UMR
3 QUT - Queensland University of Technology [Brisbane]
4 Agresearch Ltd
5 FARE - Fractionnement des AgroRessources et Environnement
6 University of Aberdeen
7 Michigan State University [East Lansing]
8 IARI - Indian Agricultural Research Institute
9 UniFI - Università degli Studi di Firenze = University of Florence = Université de Florence
10 CIFA - Catabrian Agricultural Research and Training Center
11 CSU - Colorado State University [Fort Collins]
12 UNISS - Università degli Studi di Sassari = University of Sassari [Sassari]
13 UFSM - Universidade Federal de Santa Maria = Federal University of Santa Maria [Santa Maria, RS, Brazil]
14 AAFC - Agriculture and Agri-Food
15 TIA - Tasmanian Institute of Agriculture
16 SRUC - Scotland's Rural College
17 Manaaki Whenua – Landcare Research [Lincoln]
18 ECOSYS - Ecologie fonctionnelle et écotoxicologie des agroécosystèmes
19 AgroImpact - Agroressources et Impacts environnementaux
20 USDA-ARS : Agricultural Research Service
21 CSIRO - Commonwealth Scientific and Industrial Research Organisation [Canberra]
22 ETH Zürich - Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich]
23 PIK - Potsdam Institute for Climate Impact Research
24 New Zealand Institute for Crop and Food Research
25 Rothamsted Research
26 CAS - Chinese Academy of Sciences
Jean-François Soussana
- Fonction : Auteur
- PersonId : 1207476
- ORCID : 0000-0002-1932-6583
Gianni Bellocchi
- Fonction : Auteur
- PersonId : 744818
- IdHAL : gianni-bellocchi
- ORCID : 0000-0003-2712-7979
- IdRef : 176945385
Peter Grace
- Fonction : Auteur
- PersonId : 782598
- ORCID : 0000-0003-4136-4129
Sylvie Recous
- Fonction : Auteur
- PersonId : 17288
- IdHAL : sylvie-recous
- ORCID : 0000-0003-4845-7811
- IdRef : 034117628
Pete Smith
- Fonction : Auteur
- PersonId : 763978
- ORCID : 0000-0002-3784-1124
- IdRef : 153069570
Massimiliano de Antoni Migliorati
- Fonction : Auteur
- PersonId : 784191
- ORCID : 0000-0002-5651-5834
Luca Doro
- Fonction : Auteur
- PersonId : 773546
- ORCID : 0000-0003-1404-2255
Katja Klumpp
- Fonction : Auteur
- PersonId : 1203897
Patricia Laville
- Fonction : Auteur
- PersonId : 1203988
Joël J. Léonard
- Fonction : Auteur
- PersonId : 7619
- IdHAL : joel-leonard
- ORCID : 0000-0002-9907-9104
- IdRef : 089427548
Raphaël Martin
- Fonction : Auteur
- PersonId : 183620
- IdHAL : raphael-martin
- ORCID : 0000-0001-8778-7915
Raia Silvia Massad
- Fonction : Auteur
- PersonId : 739023
- IdHAL : raia-silvia-massad
- ORCID : 0000-0002-1296-1744
- IdRef : 136415660
Lutz Merbold
- Fonction : Auteur
- PersonId : 774820
- ORCID : 0000-0003-4974-170X
Andrew D. Moore
- Fonction : Auteur
- PersonId : 1023435
Qing Zhang
- Fonction : Auteur
- PersonId : 770376
- ORCID : 0000-0002-6595-8995
Résumé
Simulation models are extensively used to predict agricultural productivity and greenhouse gas emissions. However, the uncertainties of (reduced) model ensemble simulations have not been assessed systematically for variables affecting food security and climate change mitigation, within multi-species agricultural contexts. We report an international model comparison and benchmarking exercise, showing the potential of multi-model ensembles to predict productivity and nitrous oxide (N2O)emissions for wheat, maize, rice and temperate grasslands. Using a multi-stage 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 N2O emissions. Results showed that across sites and crop/grassland types, 23%–40% of the uncalibrated individual models were within two standard deviations (SD) of observed yields, while 42 (rice) to 96% (grasslands) of the models were within 1SD of observed N2O emissions. At stage 1, ensembles formed by the three lowest prediction model errors predicted both yields and N2O emissions within experimental uncertainties for 44% and 33% of the crop and grass-land growth cycles, respectively. Partial model calibration (stages 2–4) 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 N2O emissions. Yield-scaled N2O emissions (N2O emissions divided by crop yields) were ranked accurately by three-model ensembles across crop species and field sites. The potential of using process-based model ensembles to predict jointly productivity and N2O emissions at field scale is discussed.