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Liu et al. 2023, Open Data Journal for Agricultural Research, vol. 9, p. 10-25.
10
AgMIP-Wheat multi-model simulations on climate
change impact and adaptation for global wheat
Bing Liu1, Pierre Martre2,*, Frank Ewert3,4, Heidi Webber3,4, Katharina Waha5, Peter J. Thorburn5, Alex
C. Ruane6, Pramod K. Aggarwal7,†, Mukhtar Ahmed8,9,10, Juraj Balkovič11, Bruno Basso12,13, Christian
Biernath14, Marco Bindi15, Davide Cammarano16, Weixing Cao1, Andy J. Challinor17,18, Giacomo De
Sanctis19, Benjamin Dumont20, Mónica Espadafor21, Ehsan Eyshi Rezaei3,22, Elias Fereres21, Roberto
Ferrise15, Margarita Garcia-Vila21, Sebastian Gayler23, Yujing Gao24, Heidi Horan5, Gerrit
Hoogenboom24,25, Roberto C. Izaurralde26,27, Mohamed Jabloun16, Curtis D. Jones26, Belay T. Kassie24,
Kurt C. Kersebaum4, Christian Klein14, Ann-Kristin Koehler17, Andrea Maiorano2,28, Sara Minoli29,
Manuel Montesino San Martin30,31, Christoph Müller29, Soora Naresh Kumar32, Claas Nendel4,33, Garry
J. O’Leary34, Jørgen Eivind Olesen16, Taru Palosuo35, John R. Porter30,36, Eckart Priesack23,47,
Dominique Ripoche37, Reimund P. Rötter38,39, Mikhail A. Semenov40, Claudio Stöckle8, Pierre
Stratonovitch40, Thilo Streck23, Iwan Supit41, Fulu Tao35,42, Marijn Van der Velde43, Enli Wang44, Joost
Wolf45, Liujun Xiao1,24, Zhao Zhang46, Zhigan Zhao45, Yan Zhu1,*, and Senthold Asseng47
1 National Engineering and Technology Center for Information Agriculture, Nanjing Agricultural
University, Nanjing, Jiangsu 210095, P. R. China
2 LEPSE, Univ Montpellier, INRAE, Institut Agro Montpellier, Montpellier, France
3 Institute of Crop Science and Resource Conservation INRES, University of Bonn, 53115, Germany
4 Leibniz Centre for Agricultural Landscape Research, 15374 Müncheberg, Germany
5 CSIRO Agriculture and Food, St Lucia, Brisbane Qld 4067, Australia
6 NASA Goddard Institute for Space Studies, New York, NY 10025, USA
7 CGIAR Research Program on Climate Change, Agriculture and Food Security, BISA-CIMMYT, New
Delhi-110012, India
8 Biological Systems Engineering, Washington State University, Pullman, WA 99164-6120
9 Department of Agronomy, PMAS Arid Agriculture University Rawalpindi Pakistan
10 Department of Agricultural Research for Northern Sweden, Swedish University of Agricultural
Sciences, P.O. Box 7070, SE-750 07 Uppsala, Sweden
11 International Institute for Applied Systems Analysis, Biodiversity and Natural Resources Program,
A-2361 Laxenburg, Austria
12 Department of Earth and Environmental Sciences, Michigan State University East Lansing,
Michigan 48823, USA
13 W.K. Kellogg Biological Station, Michigan State University East Lansing, MI 48823, USA
14 Institute of Biochemical Plant Pathology, Helmholtz Zentrum München - German Research Center
for Environmental Health, Neuherberg, D-85764, Germany
15 Department of agriculture, food, environment and forestry (DAGRI), University of Florence, I-50144
Firenze, Italy
16 Department of Agroecology, Aarhus University, 8830 Tjele, Denmark17 Institute for Climate and
Atmospheric Science, School of Earth and Environment, University of Leeds, Leeds LS29JT, UK
17 Institute for Climate and Atmospheric Science, School of Earth and Environment, University of
Leeds, Leeds LS29JT, UK
18 Collaborative Research Program from CGIAR and Future Earth on Climate Change, Agriculture and
Food Security (CCAFS), International Centre for Tropical Agriculture (CIAT), A.A. 6713, Cali,
Colombia.
19 European Food Safety Authority, GMO Unit, Via Carlo Magno 1A, Parma, IT-43126, Italy
20 Gembloux Agro-Bio Tech - University of Liege, TERRA teaching and research centre & Plant
Sciences Axis, Gembloux 5030, Belgium
21 IAS‐CSIC, Department of Agronomy, University of Cordoba, 14071 Cordoba, Spain
22 Department of Crop Sciences, University of Göttingen, Von-Siebold-Strasse 8, 37075, Göttingen,
Germany
23 Institute of Soil Science and Land Evaluation, University of Hohenheim, 70599 Stuttgart, Germany
24 Agricultural & Biological Engineering Department, University of Florida, Gainesville, FL 32611, USA,
25 Global Food Systems Institute, University of Florida, Gainesville, FL 32611, USA
26 Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA
27 Texas A&M AgriLife Research and Extension Center, Texas A&M Univ., Temple, TX 76502, USA.
28 European Food Safety Authority, via Carlo Magno 1A, 43126 Parma PR, Italy
Liu et al. 2023, Open Data Journal for Agricultural Research, vol. 9, p. 10-25.
11
29 Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, 14473
Potsdam, Germany
30 University Copenhagen, Plant & Environment Sciences, DK-2630 Taastrup, Denmark
31 Department of Statistics, Computer Science and Mathematics, Public University of Navarre, 31006
Pamplona, Spain.
32 Centre for Environment Science and Climate Resilient Agriculture, Indian Agricultural Research
Institute, IARI PUSA, New Delhi 110 012, India
33 University of Potsdam, Institute of Biochemistry and Biology, 14476 Potsdam, Germany
34 Grains Innovation Park, Agriculture Victoria, Department of Jobs, Precincts and Regions, Horsham
3400, Australia
35 Natural Resources Institute Finland (Luke), FI-00790 Helsinki, Finland
36 Montpellier SupAgro, INRAE, CIHEAM–IAMM, CIRAD, University Montpellier, Montpellier, France
37 AgroClim, INRAE, Avignon, France
38 University of Göttingen, Tropical Plant Production and Agricultural Systems Modelling (TROPAGS),
Grisebachstraße 6, 37077 Göttingen, Germany
39 University of Göttingen, Centre of Biodiversity and Sustainable Land Use (CBL), Buesgenweg 1,
37077 Göttingen, Germany
40 Rothamsted Research, Harpenden, Herts, AL5 2JQ, UK
41 Water Systems & Global Change Group and WENR (Water & Food), Wageningen University,
6700AA Wageningen, The Netherlands
42 Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Science,
Beijing 100101, China
43 European Commission, Joint Research Centre, Via Enrico Fermi, 2749 Ispra, 21027 Italy
44 CSIRO Agriculture and Food, Black Mountain, ACT 2601, Australia
45 Plant Production Systems, Wageningen University, 6700AA Wageningen, The Netherlands
46 State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical
Science, Beijing Normal University, Beijing, China
47 Technical University Munich, School of Life Sciences, Digital Agriculture, HEF World Agricultural
Systems Center, 85354 Freising, Germany.
* e-mail: pierre.martre@inrae.fr and yanzhu@njau.edu.cn
Abstract: The climate change impact and adaptation simulations from the Agricultural Model
Intercomparison and Improvement Project (AgMIP) for wheat provide a unique dataset of multi-model
ensemble simulations for 60 representative global locations covering all global wheat mega
environments. The multi-model ensemble reported here has been thoroughly benchmarked against a
large number of experimental data, including different locations, growing season temperatures,
atmospheric CO2 concentration, heat stress scenarios, and their interactions. In this paper, we describe
the main characteristics of this global simulation dataset. Detailed cultivar, crop management, and soil
datasets were compiled for all locations to drive 32 wheat growth models. The dataset consists of 30-
year simulated data including 25 output variables for nine climate scenarios, including Baseline (1980-
2010) with 360 or 550 ppm CO2, Baseline +2oC or +4oC with 360 or 550 ppm CO2, a mid-century climate
change scenario (RCP8.5, 571 ppm CO2), and 1.5°C (423 ppm CO2) and 2.0oC (487 ppm CO2) warming
above the pre-industrial period (HAPPI). This global simulation dataset can be used as a benchmark
from a well-tested multi-model ensemble in future analyses of global wheat. Also, resource use
efficiency (e.g., for radiation, water, and nitrogen use) and uncertainty analyses under different climate
scenarios can be explored at different scales. The DOI for the dataset is 10.5281/zenodo.4027033
(AgMIP-Wheat, 2020), and all the data are available on the data repository of Zenodo (doi:
10.5281/zenodo.4027033). Two scientific publications have been published based on some of these data
here.
Keywords: wheat, CO2, carbon dioxide, climate change, climate change scenario, multi-model
ensemble, future crop yields, crop growth modeling.
1 BACKGROUND: As one of the largest staple crops, wheat (Triticum aestivum L.) plays an important
role in ensuring global food security. Global wheat production, which covers tremendously diverse
environments, is facing unprecedented climate change challenges (Lobell et al., 2011). Quantifying
potential climate change impacts on global and regional crop production (including quantity and quality)
Liu et al. 2023, Open Data Journal for Agricultural Research, vol. 9, p. 10-25.
12
accurately can provide valuable support for policy-making in mitigating climate change and for adapting
local wheat production for future scenarios (IPCC, 2014).
Daily wheat development and growth dynamic at 60 global locations during a 30-year period were
simulated as part of the Agricultural Model Intercomparison and Improvement Project (AgMIP;
Rosenzweig et al., 2013) for wheat under different climate change scenarios with 32 wheat growth
models. The multi-model ensemble reported here has been thoroughly benchmarked against a large
number of experimental data, including different locations, growing season temperatures, atmospheric
CO2 concentration, heat stress scenarios, and their interactions (Asseng et al., 2015; Asseng et al.,
2013; Asseng et al., 2019; Martre et al., 2015). The 60 global locations covered contrasting conditions
across all global wheat mega environments and included 30 high-rainfall or irrigated wheat-growing
locations and 30 low-rainfall wheat-growing locations (Reynolds and Braun, 2013). Each location
represents an important wheat-growing area worldwide (Fig. 1). The climate scenarios considered here
include Baseline (1980-2010) with a carbon dioxide concentration ([CO2]) of 360 or 550 ppm, Baseline
+2oC or +4oC with 360 or 550 ppm CO2, 2050s under representative concentration pathway (RCP) 8.5,
and 1.5oC and 2.0oC warming above the pre-industrial period from the Half a degree Additional
warming, Prognosis and Projected Impacts project (referred to as 1.5oC HAPPI and 2.0oC HAPPI).
These different climate scenarios represent different global warming levels (Ruane et al., 2017). Five
global climate models (GCMs) were used to produce the future climate change scenarios to consider
the uncertainty in climate projections in RCP8.5 and HAPPI scenarios. Instead of using regional-
averaged model inputs, detailed cultivar, crop management, and soil datasets were compiled for the 60
locations. In addition, the effects of possible genetic adaptation with delayed anthesis date and
increased potential grain filling rate were explored to quantify the impact of trait adaptation on global
wheat production under baseline and RCP8.5.
2 METHODS: Following AgMIP protocols, all modelling teams who joined the AgMIP-Wheat activities
were provided with the same modelling protocols. The protocols were developed by the AgMIP team to
standardize all critical steps to configure the simulations of the modelling experiments. Each individual
modelling group executed the full set of model simulations. The datasets of AgMIP-Wheat global
simulations consists of the model outputs of 32 wheat models for 60 global wheat-producing locations
under up to nine different climate scenarios. The protocol for running the global simulations under
Baseline scenarios is provided as an example in the supplementary information.
2.1 Global locations: The 60 global locations were selected by two steps. In the AgMIP-Wheat Phase
2, simulations for 30 high rainfall / irrigated locations (Locations 1 to 30) were conducted. And, in the
AgMIP-Wheat Phase 3, another 30 locations for rainfed/low input wheat regions (Locations 31 to 60)
were added (Table S1). Each location within each mega environment was selected based on
consultations with the global community of wheat crop modelers, to be representative and to have
quality data available. The 30 high-rainfall or irrigated wheat-growing locations represent about 68% of
current global wheat production and the 30 low-rainfall wheat-growing locations with wheat yields below
4 t DM ha-1 represent about 32% of current global wheat production (Reynolds and Braun, 2013). The
Liu et al. 2023, Open Data Journal for Agricultural Research, vol. 9, p. 10-25.
13
60 locations cover all major wheat-growing mega-environment types worldwide (Gbegbelegbe et al.,
2017) (Fig. 1).
Figure 1. The thirty locations representing high rainfall and irrigated wheat regions (blue) and thirty
locations representing low rainfall/low input regions (red) of the world used in the global simulations,
after Asseng et al. (2019) and Liu et al. (2019). The thirty high rainfall and irrigated locations include
locations which have low rainfall during the wheat growing season but have irrigation facilities. Wheat
areas came from Monfreda et al. (2008)
2.2 Process-based wheat crop models: Table 1 lists the 32 wheat crop models used. Most of these
models have been evaluated with detailed experiments (e.g., different growth locations, sowing dates,
chronic warming, heat stress, FACE), and have been encouraged to improve their models in recent
AgMIP simulation activities (Maiorano et al., 2017; Wang et al., 2017). All models can be downloaded
on the Internet or requested from the corresponding person. For the two HAPPI scenarios, only 31
models participated in the global simulations, all 32 models were used in the simulations for the other
climate scenarios.
Among the 32 models, the wheat models even with similar names, used here still have different model
structures and parameters. For example, the 3 different Expert-N wheat models use different algorithms
to simulate wheat growth and yield, even they have similar framework in simulating soil dynamics.
According to our previous study (Wallach et al., 2018), it’s currently hard to conclude which models
would perform better, as different model performance were observed under different modelling
experiments and conditions. Appling a multi-model ensemble approach by adding more models would
decrease the uncertainty significantly (Martre et al., 2015).Therefore, the modelling results from the 32
models were reported here.
Table 1. List of the 32 wheat crop models used in the AgMIP Wheat study §
Code
Name (version)
Reference
Documentation
AE
APSIM-E*
(Chen et al., 2010; Keating et al.,
2003; Wang et al., 2002)
http://www.apsim.info/Wiki
AF
AFRCWHEAT2*
(Porter, 1984; Porter, 1993; Weir et
al., 1984)
Request from John Porter: jrp@plen.ku.dk
AQ
AQUACROP (V.4.0)
(Steduto et al., 2009)
http://www.fao.org/nr/water/aquacrop.html
AW
APSIM-Wheat (V.7.3)*
(Keating et al., 2003)
http://www.apsim.info/Wiki
CS
CropSyst (V.3.04.08)
(Stockle et al., 2003)
http://modeling.bsyse.wsu.edu/CS_Suite_4/
CropSyst/index.html
DC
DSSAT-CERES-Wheat
(V.4.0.1.0)*
(Jones et al., 2003; Ritchie et al.,
1985; Ritchie et al., 1998)
http://dssat.net/
DN
DSSAT-Nwheat*
(Asseng, 2004; Kassie et al., 2016)
http://dssat.net/
Liu et al. 2023, Open Data Journal for Agricultural Research, vol. 9, p. 10-25.
14
Table 1. List of the 32 wheat crop models used in the AgMIP Wheat study (Continued) §
Code
Name (version)
Reference
Documentation
DR
DSSAT-CROPSIM
(V4.5.1.013)*
(Hunt and Pararajasingham, 1995;
Jones et al., 2003)
http://dssat.net/
DS
DAISY (V.5.24)*
(Hansen et al., 2012; Hansen et al.,
1991)
http://daisy.ku.dk
EI
EPIC-I (V0810)
(Balkovič et al., 2013; Balkovič et
al., 2014; Kiniry et al., 1995;
Williams, 1995; Williams et al.,
1989)
http://epicapex.tamu.edu/epic
EW
EPIC-Wheat(V1102)
(Izaurralde et al., 2012; Izaurralde
et al., 2006; Kiniry et al., 1995;
Williams, 1995; Williams et al.,
1989)
http://epicapex.brc.tamus.edu
GL
GLAM (V.2 updated)
(Challinor et al., 2004; Li et al.,
2010)
https://www.see.leeds.ac.uk/research/icas/r
esearch-themes/climate-change-and-
impacts/climate-impacts/glam
HE
HERMES (V.4.26)*
(Kersebaum, 2007; Kersebaum,
2011)
https://www.zalf.de/en/forschung_lehre/soft
ware_downloads/Pages/default.aspx
IC
INFOCROP (V.1)
(Aggarwal et al., 2006)
https://www.iari.res.in/infoCrop_v2/InfoCrop-
Registration.php
LI
LINTUL4 (V.1)
(Shibu et al., 2010; Spitters and
Schapendonk, 1990)
http://models.pps.wur.nl/node/950
L5
SIMPLACE<Lintul-5*
SlimWater3, FAO-56,
CanopyT,
HeatStressHourly
(Gaiser et al., 2013; Shibu et al.,
2010; Spitters and Schapendonk,
1990; Webber et al., 2016)
http://www.simplace.net/Joomla/
LP
LPJmL (V3.2)
(Beringer et al., 2011; Bondeau et
al., 2007; Fader et al., 2010;
Gerten et al., 2004; Müller et al.,
2007; Rost et al., 2008)
https://www.pik-
potsdam.de/research/projects/activities/bios
phere-water-modelling/lpjml
MC
MCWLA-Wheat (V.2.0)
(Tao et al., 2009a; Tao and Zhang,
2010; Tao and Zhang, 2013; Tao et
al., 2009b)
Request from taofl@igsnrr.ac.cn
MO
MONICA (V.1.0)*
(Nendel et al., 2011)
http://monica.agrosystem-models.com
NC
Expert-N (V3.0.10)
CERES (V2.0)*
(Biernath et al., 2011; Priesack et
al., 2006; Ritchie et al., 1987)
https://expert-n.uni-hohenheim.de/en
NG
Expert-N (V3.0.10)
GECROS (V1.0)*
(Biernath et al., 2011; Yin and van
Laar, 2005)
https://expert-n.uni-hohenheim.de/en
NP
Expert-N (V3.0.10)
SPASS (2.0)*
(Biernath et al., 2011; Priesack et
al., 2006; Wang and Engel, 2000;)
https://expert-n.uni-hohenheim.de/en
NS
Expert-N (V3.0.10)
SUCROS (V2)
(Biernath et al., 2011; Goudriaan
and Van Laar, 1994; Priesack et
al., 2006)
https://expert-n.uni-hohenheim.de/en
OL
OLEARY (V.8)*
(Latta and O'Leary, 2003;
Maiorano et al., 2017; O'Leary and
Connor, 1996a; O'Leary and
Connor, 1996b; O'Leary et al.,
1985)
Request from gjoleary@yahoo.com
S2
Sirius (V2014)*
(Jamieson and Semenov, 2000;
Jamieson et al., 1998; Lawless et
al., 2005; Semenov and Shewry,
2011)
https://sites.google.com/view/sirius-wheat/
Liu et al. 2023, Open Data Journal for Agricultural Research, vol. 9, p. 10-25.
15
Table 1. List of the 32 wheat crop models used in the AgMIP Wheat study (Continued) §
Code
Name (version)
Reference
Documentation
SA
SALUS (V.1.0)*
(Basso et al., 2010; Senthilkumar
et al., 2009)
http://salusmodel.glg.msu.edu
SP
SIMPLACE<Lintul-2
CC,Heat,CanopyT,Re-
Translocation
(Angulo et al., 2013)
http://www.simplace.net/Joomla/
SQ
SiriusQuality (V3.0)*
(Ferrise et al., 2010; He et al.,
2010; Maiorano et al., 2017; Martre
et al., 2006)
http://www1.clermont.inra.fr/siriusquality
SS
SSM-Wheat
(Soltani et al., 2013)
Request from afshin.soltani@gmail.com
ST
STICS (V.1.1)*
(Brisson et al., 2003; Brisson et al.,
1998)
http://www6.paca.inra.fr/stics_eng
WG
WheatGrow (V3.1)
(Cao et al., 2002; Cao and Moss,
1997; Hu et al., 2004; Li et al.,
2002; Pan et al., 2007; Pan et al.,
2006; Yan et al., 2001)
Request from yanzhu@njau.edu.cn
WO
WOFOST (V.7.1)
(de Wit et al., 2020)
http://www.wofost.wur.nl
§ After Asseng et al. (2019) and Liu et al. (2019)
*Models that have routines to simulate crop and grain nitrogen dynamics leading to grain protein and have been tested
with field measurements before. These 18 models have been used in the grain protein analysis.
2.3 Model inputs
2.3.1 Climate scenarios
Nine climate scenarios were considered, including Baseline (1980-2010) with 360 or 550 ppm [CO2],
Baseline +2oC or +4oC with 360 or 550 ppm [CO2], 2050s climate projections under RCP8.5, and 1.5oC
and 2.0oC warming above the 1861-1880 pre-industrial period from HAPPI, which correspond to ~ 0.6°C
and 1.1°C above current global mean temperature (Table 2). Five GCMs were used to produce the
future climate change scenarios in order to allow one to consider the uncertainty of climate projections
for both the RCP8.5 and HAPPI scenarios.
The Baseline (1980-2010) climate data are from the AgMERRA climate dataset (Ruane et al., 2015a),
which combines observations, data assimilation models, and satellite data products to provide daily
maximum and minimum temperatures, solar radiation, precipitation, wind speed, vapor pressure, dew
point temperatures, and relative humidity corresponding to the maximum temperature time of day for
each location. These data correspond to 360 ppm [CO2]. The Baseline+2oC and Baseline+4°C
scenarios were created by adjusting each day’s maximum and minimum temperatures upward by that
amount and then adjusting vapor pressure and related parameters to maintain the original relative
humidity at the maximum temperature time of day. Observations and projections of climate change
indicate that relative humidity is relatively stable even as this implies increases in specific humidity as
temperatures increase (commensurate with the Clausius-Clapeyron equation (Allen and Ingram, 2002).
The values 360 and 550 ppm [CO2] were used for the simulations in Baseline and Baseline+2oC and
Baseline+4°C scenarios.
The RCP8.5 scenarios used here represents a relatively high emission scenario for the middle of the
21st century (RCP8.5 for 2040-2069, 571 ppm [CO2] in 2055). Projections for RCP8.5 were taken from
five GCMs that are representative of the CMIP5 multi-climate model ensemble (HadGEM2-ES,
MIROC5, MPI-ESM-MR, GFDL-CM3, GISS-E2-R) (Ruane and McDermid, 2017), with historical
conditions modified to reflect projected changes in mean temperatures and precipitation along with
shifts in the standard deviation of daily temperatures and the number of rainy days. These scenarios
were created using the “Enhanced Delta Method” (Ruane et al., 2015b), and GCMs were selected to
include models with relatively large and relatively small global sensitivity to the greenhouse gases that
drive climate changes to account for the uncertainty of the fifth coupled model intercomparison project
(CMIP5) GCMs ensemble (Ruane and McDermid, 2017). Solar radiation changes from GCMs introduce
uncertainties that can at times overwhelm the impact of temperature and rainfall changes. Therefore,
as in previous AgMIP assessments, changes in solar radiation were not considered here other than
small radiation effects associated with changes in the number of precipitation days (Ruane et al.,
2015b).
Liu et al. 2023, Open Data Journal for Agricultural Research, vol. 9, p. 10-25.
16
The 1.5oC and 2.0oC HAPPI scenarios here are consistent with the AgMIP Coordinated Global and
Regional Assessments (CGRA) 1.5 and 2.0oC World Study (Rosenzweig et al., 2016; Ruane et al.,
2018), using the methods fully described by Ruane et al. (2018). In brief, climate changes from large
(83-500 members for each model) climate model ensemble projections of the +1.5 and +2.0oC
scenarios from HAPPI (Mitchell et al., 2017) were combined with the local AgMERRA baseline to
generate driving climate scenarios from five GCMs (MIROC5, NorESM1-M, CanAM4 [HAPPI], CAM4-
2degree [HAPPI], and HadAM3P) for each location (Ruane et al., 2018). Specifically, the HAPPI
ensemble changes in monthly mean climate, the number of precipitation days, and the standard
deviation of daily maximum and minimum temperatures were imposed upon the historical AgMERRA
daily series using quantile mapping that forces the observed conditions to mimic the future distribution
of daily events (Ruane et al., 2018; Ruane et al., 2015b). This results in climate scenarios that maintain
the characteristics of local climate while also capturing major climate changes. HAPPI anticipates [CO2]
for the 1.5°C and 2.0°C scenarios of 423 and 487 ppm, respectively. As the HAPPI project
(www.happimip.org/) was designed specifically to represent a stable climate in a +1.5 and +2.0 world,
not for a specific time period. Therefore, there is no indication for the time period for scenarios 18-27 in
Table 2.
Table 2 Outline of the baseline and climate change scenarios considered in the global simulations §
Scen
ario
id
Period
Climate scenario
Global Climate model
CO2
Adaptation
Years
Name
Code
Name
Code
ppm
Code
Name
Code
01
1981-2010
Baseline
B0
-
0
360
C360
None
N
02
1981-2010
Baseline
B0
-
0
360
C360
2-traits
combination
T
03
1981-2010
Baseline+2°C
B2
-
0
360
C360
None
N
04
1981-2010
Baseline+4°C
B4
-
0
360
C360
None
N
05
1981-2010
Baseline
B0
-
0
550
C550
None
N
06
1981-2010
Baseline+2°C
B2
-
0
550
C550
None
N
07
1981-2010
Baseline+4°C
B4
-
0
550
C550
None
N
08
2040-2069
RCP8.5
85
HadGEM2-ES
K
571
C571
None
N
09
2040-2069
RCP8.5
85
MIROC5
O
571
C571
None
N
10
2040-2069
RCP8.5
85
MPI-ESM-MR
R
571
C571
None
N
11
2040-2069
RCP8.5
85
GFDL-CM3
1
571
C571
None
N
12
2040-2069
RCP8.5
85
GISS-E2-R
2
571
C571
None
N
13
2040-2069
RCP8.5
85
HadGEM2-ES
K
571
C571
2-traits
combination
T
14
2040-2069
RCP8.5
85
MIROC5
O
571
C571
2-traits
combination
T
15
2040-2069
RCP8.5
85
MPI-ESM-MR
R
571
C571
2-traits
combination
T
16
2040-2069
RCP8.5
85
GFDL-CM3
1
571
C571
2-traits
combination
T
17
2040-2069
RCP8.5
85
GISS-E2-R
2
571
C571
2-traits
combination
T
18
-
1.5oC HAPPI
15
NorESM1-M
T
423
C423
None
N
19
-
1.5oC HAPPI
15
MIROC5
O
423
C423
None
N
20
-
1.5oC HAPPI
15
CanAM4
4
423
C423
None
N
21
-
1.5oC HAPPI
15
CAM4-
2degree
5
423
C423
None
N
22
-
1.5oC HAPPI
15
HadAM3P
8
423
C423
None
N
23
-
2.0oC HAPPI
20
NorESM1-M
T
487
C487
None
N
24
-
2.0oC HAPPI
20
MIROC5
O
487
C487
None
N
25
-
2.0oC HAPPI
20
CanAM4
4
487
C487
None
N
26
-
2.0oC HAPPI
20
CAM4-
2degree
5
487
C487
None
N
27
-
2.0oC HAPPI
20
HadAM3P
8
487
C487
None
N
§ After Asseng et al. (2019) and Liu et al. (2019)
2.3.2 Soil: Locations 1 to 30 were simulated using soil information from Maricopa, USA (location 1), as
no water or N limitations were considered (Table S1). Soil information for locations 31 to 60 were
obtained from a global soil database (Romero et al., 2012). The soil closest to a location was used, but
for locations 39 and 59, soil carbon was decreased after consulting local experts. Initial soil nitrogen
was set to 25 kg N ha-1 NO3-N and 5 kg N ha-1 NH4-N per 100 cm soil depth and reset each year for
locations 31 to 60. Initial plant available soil water for spring wheat sown after winter at locations 31 to
Liu et al. 2023, Open Data Journal for Agricultural Research, vol. 9, p. 10-25.
17
60 was set to 100 mm, starting from 10 cm depth until 100 mm was filled in between drained lower limit
(LL) and drained upper limit (DUL). The first 10 cm were kept at LL and reset each year. If wheat was
sown after summer, initial plant available soil water was set to 50 mm, starting from 10 cm depth until
50 mm was filled in between LL and DUL. The first 10 cm were kept at LL and reset each year. The
details of soil for all 60 locations can be found in data archive. In general, the soil data used for locations
31-60 were representative for the selected mega environment, as local experts were consulted when
compiling the soil data.
2.3.3 Crop management: For locations 1 to 30 sowing dates were fixed at a specific date. For locations
31 to 60, sowing windows were defined and a sowing rule was used. The sowing window was based
on sowing dates reported in literature. For locations 41, 43, 46, 53, 54, and 59, sowing dates were not
reported in literature and estimates from a global cropping calendar were used (Portmann et al., 2010).
The cropping calendar provided a month (the 15th of the month was used) in which wheat is usually
sown in the region of the location. The start of the sowing window was the reported sowing date and
the end of the sowing window was set two months later. Sowing was triggered in the simulations on the
day after cumulative rainfall reached or exceeds 10 mm over a 5-day period during the predefined
sowing window. Rainfall from up to 5 days before the start of the sowing window was considered. If
these criteria were not met by the end of the sowing window, wheat was sown on the last day of the
sowing window. Sowing dates were left unchanged for future scenarios.
Locations 1 to 30 were simulated without N or water limitation, therefore no inputs for crop water and N
management were supplied. No irrigation was applied for the 30 low-rainfall wheat-growing locations.
For locations 31 to 60, fertilizer rates were determined based on a FAO database (FAO, 2013) and
expert knowledge, which can be found in Gbegbelegbe et al. (2017). Fertilizer rates were set low (20
to 50 kg N ha-1) at locations 31, 32, 48, 51, 53, and 60; medium (60 kg N ha-1) at locations 33 to 43, 45
to 47, 49, 50, 52, 54, and 57 to 59; and relatively high (100 to 120 kg N ha-1) at locations 44, 55, and
56. All fertilizer was applied at sowing.
2.3.4 Cultivars: To carry out the global impact assessment and exclusively focus on climate change,
region-specific cultivars were used in all 60 locations. Detailed information were available on cultivars
grown in locations 1 to 30, whereas they were only limited in locations 31 to 60. Therefore, in these
sites cultivar characteristics were defined by selecting the most presumably suitable cultivars from the
first set of locations. Observed local mean sowing, anthesis, and maturity dates were supplied to
modelers with qualitative information on vernalization requirements and photoperiod sensitivity for each
cultivar (Table S1).
For locations 35, 39, 47, 49, and 55 to 57 (Table S1), anthesis dates were reported in the literature. For
the remaining sites from 31 to 60, anthesis dates were estimated with the APSIM-Wheat model. Maturity
dates were estimated from a cropping calendar for sites 31, 32, 37, 38, 41 to 46, 49 to 54, and 58 to 59
(Table S1) where no information from literature was available. For locations 31 to 60, observed grain
yields from the literature (Table S1) were provided to modelers with the aim to set up wheat models to
have similar yield levels, as well as similar anthesis and maturity dates. No yields were reported for
sites 49 and 56, so APSIM-Wheat yields were estimated and used as a guide.
2.3.5 Cultivar adaptation: The RCP8.5 scenario and Baseline were examined with current
management as well as under one possible trait adaptation, which is a cultivar combining delayed
anthesis and an increased potential grain filling rate.
To consider the diversity of model approaches of the 32 participating wheat models and allow all
modelers to incorporate the trait adaptations in their models, we proposed a simple but yet
physiologically sound trait combination. The proposed traits were simulated in full combination only, to
quantify the impact of such a trait combination. The aim of these simulations was not to analyze the
contribution of various individual traits, nor to explore the full range of traits that could possibly assist in
an adaptation strategy. The proposed simple trait combination that aimed to minimize the impact of
future increased temperatures on global yield production included:
1. Delayed anthesis by about 2 weeks under the Baseline scenario via increased temperature sum
requirement, photoperiod sensitivity, or vernalization requirement. No change in the temperature
requirement for grain filling duration was considered.
2. Increased rate (in amount per day) of potential grain filling by 20% (escape strategy).
It should be noted that this trait combination is currently available in wheat breeding lines and was
shown to be associated with significant yield increases in warm environments (Asseng et al., 2019).
Liu et al. 2023, Open Data Journal for Agricultural Research, vol. 9, p. 10-25.
18
2.4 Model configuration
Before conducting global simulations, modelers were asked to use the supplied sowing dates and
calibrate their cultivar parameters against the observed anthesis and maturity dates by considering the
qualitative information on vernalization requirements and photoperiod sensitivity. In the global
simulations for locations 1 to 30, no water or nitrogen stress was considered.
The trait adaptation was simulated by adjusting the cultivar parameters for each location (Table S2). In
30 of the 32 models, anthesis date was delayed by increasing the thermal time requirement between
emergence and anthesis, and for five models also by increasing the vernalization requirement and/or
the photoperiod sensitivity. In two models (AE and DN) anthesis date was delayed without changing
the thermal time requirement.
For the adaptation of grain filling trait, the 32 models were classified into five group according to how
models implemented the adaptation to increase grain filling rate.
Group 1: Sixteen models with increased rate of grain filling (or harvest index change), including AE,
AF, AW, DN, EW, IC, LI, NC, NP, NS, OL, SA, GL, MC, SS, ST, WG;
Group 2: Five models with increased potential grain size (or final harvest index), including DC, DR, CS,
EI, and LP;
Group 3: Two models with increased fraction of vegetative biomass remobilization, including L5 and
SP;
Group 4: One model with decreased grain filling duration (AQ);
Group 5: Seven models with no parameter change to increase the rate of grain filling, including DS,
HE, MO, NG, S2, SQ, and WO.
Table 2 shows the combination of climate scenarios, CO2 concentration, and trait adaptation for all 27
scenarios.
2.5 Model outputs: Table 3 shows the 25 output variables from each model that were requested.
Output data for 30 growing seasons under the same scenario were bind into the same text file as 30
line records. Results for variables that some models do not simulate are indicated with “na”.
Table 3. Definitions of model output variables
Variable
Unit
Definition
Model
-
2-letter model code
Year
YYYY
Year of harvest
Yield
Mg DM ha-1
Final grain yield at 0% moisture
Sowing
YYYY-MM-DD
Sowing date
Emergence
YYYY-MM-DD
Crop emergence date - Zadoks 10
Anthesis
YYYY-MM-DD
Anthesis date - Zadoks 65
Maturity
YYYY-MM-DD
Physiological maturity date - Zadoks 89
GNumber
grain m-2
Grain number per unit ground area
Haun
Leaf mainstem-1
Decimal number of emerged leaves per main stem (Haun index)
Biom-an
Mg DM ha-1
Cumulative total above ground dry biomass at anthesis
Biom-ma
Mg DM ha-1
Cumulative total above ground biomass at physiological maturity (including
grain)
MaxLAI
m2 m-2
Maximum leaf area index (green)
WDrain
mm
Cumulative (sowing to maturity) water drained below 150 cm at physiological
maturity
CumET
mm
Cumulative evapotranspiration (sowing to maturity) at physiological maturity
SoilAvW
mm
Plant available soil water content (soil water minus plant lower limit) in soil
profile (0-150 cm) at physiological maturity a
Runoff
mm
Cumulative runoff at physiological maturity
Transp
mm
Cumulative crop transpiration (sowing to maturity) at physiological maturity
CroN-an
kg N ha-1
Cumulative total above ground N mass (crop N uptake) at anthesis
CroN-ma
kg N ha-1
Cumulative total above ground N mass (crop N uptake including grain) at
physiological maturity
Nleac
kg N ha-1
Cumulative soil N leached (sowing to physiological maturity) below 150 cm at
physiological maturity a
GrainN
kg N ha-1
Grain N mass at physiological maturity
Nmin
kg N ha-1
Cumulative N mineralization (sowing to physiological maturity) in soil profile
(0-150 cm) at physiological maturity a
Liu et al. 2023, Open Data Journal for Agricultural Research, vol. 9, p. 10-25.
19
Table 3. Definitions of model output variables (Continued)
Variable
Unit
Definition
Nvol
kg N ha-1
Cumulative soil N volatilization (sowing to physiological maturity) at
physiological maturity
Nimmo
kg N ha-1
Cumulative soil N immobilization (sowing to physiological maturity) in soil
profile (0-150 cm) at physiological maturity a
SoilN
kg N ha-1
Inorganic soil N (NO3-N + NH4-N) in soil profile (0-150 cm) at physiological
maturity a
Nden
kg N ha-1
Cumulative soil N denitrification (sowing to physiological maturity) in soil profile
(0-150 cm) at physiological maturity a
ETo
mm
Cumulative potential evapotranspiration (sowing to physiological maturity) at
physiological maturity
GPC
%
Grain protein concentration
a For locations with root depth less than 150 cm, only the soil available water content or nitrogen
variables from root growth layers was reported.
3 DATA RECORDS
3.1 Data format: Data are provided in 27 “TAB” limited text files. Each file contains all annual output
variables for all 30 growing seasons from the 32 (scenarios 1 to 17 in Table 2) or 31 (scenarios 18 to
27 in Table 2) wheat models for the 60 global locations in one scenario combination. Files are named
following the convention below:
[Scenario id]-[Climate scenario]-[GCM]-[CO2]-[Adaptation].txt
In the data archive (AgMIP-Wheat, 2020), codes for the scenario id, climate scenario, GCM, CO2 and
adaptation in the file name are given in Table 2. In each text file, the 2-Letter model code is the
abbreviation for crop models in Table 1, location number is the two-digits number from Table S1, and
definitions of other output variables are given in Table 3. In the simulation output files, the years were
kept to 1981-2010 in the future climate scenarios, instead of using the time periods in Table 2. This was
simply because the future climate data were developed based on baseline (1980-2010) climate date,
and the years in the future climate were left unchanged.
3.2 Data availability: The DOI for the dataset is 10.5281/zenodo.4027033 (AgMIP-Wheat, 2020). Data
are available on the data repository of Zenodo (http://doi.org/10.5281/zenodo.4027033). All global
simulation data are published under the Creative Commons Attribution 4.0 International (CC BY 4.0)
license.
3.3 Technical Validation: All global simulation data submitted to the AgMIP-Wheat team were tested
by using a custom made R script for quality checking. Data were tested for compliance with data
formats, checking units, variable naming, and file naming. Errors in data formatting, data ranges, and
time coverage were reported to modelling groups, so that they could check and fix the simulation data.
3.4 Code Availability: The data of the AgMIP-Wheat global simulation dataset were produced by the
individual modelling groups using different wheat crop models. The source code of these models is
subject to different distribution policies and needs to be requested from the individual groups.
4 SUMMARY: The primary idea of these global simulations was to quantify global impacts on wheat
production under different climate change scenarios. Local climate change impacts on wheat grain yield
and protein (only for RCP8.5) were aggregated to global scale with a multi-model ensemble approach
(Asseng et al., 2019; Liu et al., 2019). The simulated yields and protein can be used as a benchmark
from a well-tested multi-model ensemble in future analyses. The multi-model outputs provide a
comprehensive dataset for investigating resource use efficiency (e.g., for radiation, water, and nitrogen
use) under different climate scenarios proposed recently (Porter et al., 2019). Also, the dataset can be
used to explore how to increase different resource use efficiencies while maintaining high yield and
grain quality for different global wheat cropping systems in the future (Porter et al., 2019). Another
potential use of this global simulation dataset is for uncertainty analysis, including comparison of
different modelling approaches at different scales, different sources of uncertainties, and inter-annual
variability.
As these datasets were developed mostly for assessing future temperature and CO2 impacts on wheat
production, several adaptation measures (e.g., changing sowing dates, improving fertilisation) were not
considered. However, sowing dates would change due the changing rainfall patterns in future climate
Liu et al. 2023, Open Data Journal for Agricultural Research, vol. 9, p. 10-25.
20
scenarios, especially for low rainfall locations. This could limit the use of these simulations to explore
climate change impact for these environments. Improving fertilisation, which could also increase wheat
production for adapting to climate change, was not considered in the current datasets. Therefore,
exploring the wheat yield increase potential by improving fertilisation at global scale should be
considered in the next AgMIP-Wheat activities.
5 ACKNOWLEDGEMENTS: We thank the Agricultural Model Intercomparison and Improvement
Project (AgMIP) for support. B.L., W.C., L.X. and Y.Z. were supported by the National Key Research
and Development Program of China (2019YFA0607404), the National Natural Science Foundation of
China (32021004, and 41961124008), and the Natural Science Foundation of Jiangsu province
(BK20220146). S.A. and B.K. received support from the International Food Policy Research Institute
(IFPRI) through the Global Futures and Strategic Foresight Project, the CGIAR Research Program on
Climate Change, Agriculture and Food Security (CCAFS) and the CGIAR Research Program on Wheat.
A.M. received support from the EU Marie Curie FP7 COFUND People Programme, through an
AgreenSkills fellowship under grant agreement no. PCOFUND-GA-2010-267196. P.M. and D.R.
acknowledge support from the FACCE JPI MACSUR Project (031A103B) through the metaprogram
Adaptation of Agriculture and Forests to Climate Change (AAFCC) of the French National Institute for
Agricultural Research (INRA). A.C.R. contributions were supported by the NASA GISS Climate Impacts
Group funded through the NASA Earth Science Division. F.T. and Z.Z. were supported by the National
Natural Science Foundation of China (Nos. 42061144003, 41901127, and 41571493) and the National
Key Research and Development Program of China (Nos. 2018YFA0606500 and 2019YFA0607401).
R.R. acknowledges support from the German Federal Ministry for Research and Education (BMBF)
through the ‘Limpopo Living Landscapes’ Project (SPACES program; grant number 01LL1304A).
Rothamsted Research receives grant-aided support from the Biotechnology and Biological Sciences
Research Council (BBSRC) Designing Future Wheat Project [BB/P016855/1]. M.J. and J.E.O. were
supported by Innovation Fund Denmark through the MACSUR project. L.X. and Y.G. acknowledge
support from the China Scholarship Council. M.B and R.F. were funded by JPI FACCE MACSUR2
through the Italian Ministry for Agricultural, Food and Forestry Policies and thank A. Soltani from Gorgan
Univ. of Agric. Sci. & Natur. Resour for his support. K.C.K. and C.N. received support from the German
Ministry for Research and Education (BMBF) within the FACCE JPI MACSUR project. S.M. and C.M.
acknowledge financial support from the MACMIT Project (01LN1317A) funded through BMBF. G.J.O’L.
acknowledge support from the Victorian Department of Jobs, Precincts and Regions, the Australian
Department of Agriculture and Water Resources, The University of Melbourne and the Grains Research
Development Corporation, Australia as part of the AGFACE project. P.K.A. was supported by multiple
donors contributing to the CGIAR Research Program on Climate Change, Agriculture and Food Security
(CCAFS). B.B. received financial support from USDA NIFA-Water Cap Award 2015-68007-23133. F.E.
acknowledges support from the FACCE JPI MACSUR project through the German Federal Ministry of
Food and Agriculture (2815ERA01J) and from the German Science Foundation (Project EW 119/5-1).
J.R.P. acknowledges the support of the Labex Agro (Agropolis no. 1501-003). T.P. and F.T.
acknowledges support from the Academy of Finland through the project DivCSA (decision no. 316215)
and the project AI-Crop (decision no. 316172), and from Natural Resources Institute Finland through
the project BoostIA.
Author contributions: S.A., P.M., F.E. motivated and coordinated the study, P.M., B.L., D.C., S.A.,
F.E., and A.M. analyzed data, G.J.O’L., P.M., P.J.T., K.W., H.H. and A.C.R. helped to prepare the input
data, P.K.A., M.A., J.B., B.B., C.B., M.B., D.C., W.C., A.J.C., G.D.S., B.D., M.E., E.E.R., E.F., R.F.,
M.G-V., S.G., Y.G., H.H., G.H., R.C.I, M.J., C.D.J., B.T.K., K.C.K., C.K., A-K.K., A.M., B.L., S.M.,
M.M.S.M., C.M., S.N.K., C.N., G.J.O’L., J.E.O., T.P., J.R.P., E.P., D.R., R.P.R., M.A.S., C.S., P.S., T.S.,
I.S., F.T., M.V.V., E.W., H.W., J.W., L.X., Z.Z., Z.Z. and Y.Z. carried out crop model simulations and
edited the manuscript, S.A., P.M., F.E., and B.L. wrote the paper.
Competing interests: The authors declare that there are no competing interests.
Disclaimer: The views expressed in this paper are the views of the authors and do not necessarily
represent the views of the organizations or institutions with which they are currently affiliated. This is
particularly true for author Giacomo De Sanctis.
Liu et al. 2023, Open Data Journal for Agricultural Research, vol. 9, p. 10-25.
21
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