# The International Heat Stress Genotype Experiment for modeling wheat response to heat: field experiments and AgMIP-Wheat multi-model simulations

Pierre Martre, Matthew P. Reynolds, Senthold Asseng, Frank Ewert, Phillip D. Alderman, Davide Cammarano, Andrea Maiorano, Alexander C. Ruane,

Pramod K. Aggarwal, Jakarat Anothai, et al.

► To cite this version:

Pierre Martre, Matthew P. Reynolds, Senthold Asseng, Frank Ewert, Phillip D. Alderman, et al. The International Heat Stress Genotype Experiment for modeling wheat response to heat: field experiments and AgMIP-Wheat multi-model simulations. Open Data Journal for Agricultural Research, 2017, 3 (17), pp.23-28. 10.18174/odjar.v3i1.15766. hal-03902125

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<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
The International Heat Stress Genotype Experiment
for modeling wheat response to heat: field
experiments and AgMIP-Wheat multi-model
simulations

Pierre Martre<fnref n="1" />*, Matthew P. Reynolds<fnref n="2" />, Senthold Asseng<fnref n="3" />, Frank Ewert<fnref n="4" />*, Phillip D. Alderman<fnref n="2" />*, Davide Cammarano<fnref n="3" />‡, Andrea Maiorano<fnref n="1" />, Alexander C. Ruane<fnref n="5" />,<fnref n="11" /> Pramod K. Aggarwal<fnref n="6" />, Jakarat Anothai<fnref n="7" />, Bruno Basso<fnref n="8" />, Christian Biernath<fnref n="9" />, Andrew J. Challinor<fnref n="10" />,<fnref n="11" />, Giacomo De Sanctis<fnref n="12" />, Jordi Doltra<fnref n="13" />, Benjamin Dumont<fnref n="8" />||, Elias Fereres<fnref n="14" />,<fnref n="15" />, Margarita Garcia-Vila<fnref n="14" />,<fnref n="15" />, Sebastian Gayler<fnref n="16" />, Gerrit Hoogenboom<fnref n="7" />§, Leslie A. Hunt<fnref n="17" />, Roberto C. Izaurralde<fnref n="18" />,<fnref n="19" />, Mohamed Jabloun<fnref n="20" />, Curtis D. Jones<fnref n="18" />, Belay. T. Kassie<fnref n="3" />, Kurt C. Kersebaum<fnref n="21" />, Ann-Kristin Koehler<fnref n="10" />, Christoph Müller<fnref n="22" />, Soora Naresh Kumar<fnref n="23" />, Bing Liu<fnref n="24" />, David B. Lobell<fnref n="25" />, Claas Nendel<fnref n="21" />, Garry O'Leary<fnref n="26" />, Jørgen E. Olesen<fnref n="20" />, Taru Palosuo<fnref n="27" />, Eckart Priesack<fnref n="9" />, Ehsan Eyshi Rezaei<fnref n="4" />, Dominique Ripoche<fnref n="28" />, Reimund P. Rötter<fnref n="27" />, Mikhail A. Semenov²⁹, Claudio Stöckle³⁰, Pierre Stratonovitch²⁹, Thilo Streck<fnref n="16" />, Iwan Supit³¹, Fulu Tao³²,<fnref n="27" />, Peter Thorburn³³, Katharina Waha<fnref n="22" />¶, Enli Wang³⁴, Jeffrey W. White³⁵, Joost Wolf³¹, Zhigan Zhao³⁴,³⁶, and Yan Zhu<fnref n="24" />

<footnote n="1">¹ UMR LEPSE, INRA, Montpellier SupAgro, 2 place Viala, 34 060, Montpellier, France.</footnote>

<footnote n="2">² Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT) Apdo, 06600 Mexico, D.F., Mexico.</footnote>

<footnote n="3">³ Agricultural & Biological Engineering Department, University of Florida, Gainesville, FL 32611, USA.</footnote>

<footnote n="4">⁴ Institute of Crop Science and Resource Conservation (INRES), University of Bonn, 53115, Germany.</footnote>

<footnote n="5">⁵ NASA Goddard Institute for Space Studies, New York, NY 10025, USA.</footnote>

<footnote n="6">⁶ CGIAR Research Program on Climate Change, Agriculture and Food Security, International Water Management Institute, New Delhi 110012, India.</footnote>

<footnote n="7">⁷ AgWeatherNet Program, Washington State University, Prosser, WA 99350-8694, USA.</footnote>

<footnote n="8">⁸ Department of Geological Sciences and W.K. Kellogg Biological Station, Michigan State University East Lansing, MI 48823, USA.</footnote>

<footnote n="9">⁹ Institute of Soil Ecology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, 85764, Germany.</footnote>

<footnote n="10">¹⁰ Institute for Climate and Atmospheric Science, School of Earth and Environment, University of Leeds, Leeds LS29JT, UK.</footnote>

<footnote n="11">¹¹ CGIAR-ESSP Program on Climate Change, Agriculture and Food Security, International Centre for Tropical Agriculture (CIAT), A.A. 6713, Cali, Colombia.</footnote>

<footnote n="12">¹² European Commission Joint Research Centre, via Enrico Fermi, 2749 Ispra, 21027, Italy.</footnote>

<footnote n="13">¹³ Cantabrian Agricultural Research and Training Centre (CIFA), 39600 Muriedas, Spain.</footnote>

<footnote n="14">¹⁴ Dep. Agronomia, University of Cordoba, Apartado 3048, 14080 Cordoba, Spain.</footnote>

<footnote n="15">¹⁵ IAS-CSIC, Cordoba 14080, Spain.</footnote>

<footnote n="16">¹⁶ Institute of Soil Science and Land Evaluation, University of Hohenheim, 70599 Stuttgart.</footnote>

<footnote n="17">¹⁷ Department of Plant Agriculture, University of Guelph, Guelph, ON N1G 2W1, Canada.</footnote>

<footnote n="18">¹⁸ Department of Geographical Sciences, Univ. of Maryland, College Park, MD 20742, USA.</footnote>

<footnote n="19">¹⁹ Texas A&M AgriLife Research and Extension Center, Texas A&M University, Temple, TX 76502, USA.</footnote>

<footnote n="20">²⁰ Department of Agroecology, Aarhus University, 8830 Tjele, Denmark.</footnote>

<footnote n="21">²¹ Institute of Landscape Systems Analysis, Leibniz Centre for Agricultural Landscape Research, 15374 Müncheberg, Germany.</footnote>

<footnote n="22">²² Potsdam Institute for Climate Impact Research, 14473 Potsdam, Germany.</footnote>

<footnote n="23">²³ Centre for Environment Science and Climate Resilient Agriculture, Indian Agricultural Research Institute, IARI PUSA, New Delhi 110 012, India.</footnote>

<footnote n="24">²⁴ College of Agriculture, Nanjing Agricultural University, Nanjing, Jiangsu, 210095, China.</footnote>

<footnote n="25">²⁵ Department of Environmental Earth System Science and Center on Food Security and the Environment, Stanford University, Stanford, CA 94305, USA.</footnote>

<footnote n="26">²⁶ Landscape & Water Sciences, Department of Economic Development, Jobs, Transport and Resources, Horsham 3400, Australia.</footnote>

<footnote n="27">²⁷ Natural Resources Institute Finland (Luke), -00790 Helsinki, Finland.</footnote>

<footnote n="28">²⁸ INRA, US1116 AgroClim, F-84 914 Avignon, France.</footnote>
²⁹ Computational and Systems Biology Department, Rothamsted Research, Harpenden, Herts, AL5 2JQ, UK.

³⁰ Biological Systems Engineering, Washington State University, Pullman, WA 99164-6120, USA.

³¹ Plant Production Systems and Plant Production Systems and Water Systems & Global Change Group, Wageningen University, 6700AA Wageningen, The Netherlands.

³² Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Science, Beijing 100101, China.

³³ CSIRO Agriculture, St Lucia, Queensland 4067, Australia

³⁴ CSIRO Agriculture, Black Mountain ACT 2601, Australia.

³⁵ USDA, Agricultural Research Service, U.S. Arid-Land Agricultural Research Center, Maricopa, AZ 85138, USA.

³⁶ College of Agronomy and Biotechnology, China Agricultural University, Beijing, 100193, China.

* e-mail: pierre.martre@supagro.inra.fr

**Abstract:** The data set contains a portion of the International Heat Stress Genotype Experiment (IHSGE) data used in the AgMIP-Wheat project to analyze the uncertainty of 30 wheat crop models and quantify the impact of heat on global wheat yield productivity. It includes two spring wheat cultivars grown during two consecutive winter cropping cycles at hot, irrigated, and low latitude sites in Mexico (Ciudad Obregon and Tlaltizapan), Egypt (Aswan), India (Dharwar), the Sudan (Wad Medani), and Bangladesh (Dinajpur). Experiments in Mexico included normal (November-December) and late (January-March) sowing dates. Data include local daily weather data, soil characteristics and initial soil conditions, crop measurements (anthesis and maturity dates, anthesis and final total above ground biomass, final grain yields and yields components), and cultivar information. Simulations include both daily in-season and end-of-season results from 30 wheat models.

**Keywords:** wheat, field experimental data, heat stress, simulations.

**1 ORIGINAL PURPOSE:** The original purpose of this data set was to support model intercomparisons and improvements (Asseng et al., 2015; Maiorano et al., 2017) as part of the Agricultural Model Intercomparison and Improvement Project (AGMIP, http://www.agmip.org/; Rosenzweig et al., 2013). The field experimental data were selected from the four-year International Heat Stress Genotype Experiment (IHSGE) to cover the full range of temperature of the global network of field experiments. The IHSGE was carried out as part of a collaboration between CIMMYT and key national agricultural research system partners in warm wheat growing environments to identify important physiological traits that have value as predictors of wheat yield at high temperatures (Reynolds et al. 1994b).

<footnote>† Authors after A.C. Ruane are listed in alphabetical order.</footnote>

<footnote>‡ Present address: Leibniz Centre for Agricultural 36 Landscape Research (ZALF), 15374 Müncheberg, Germany.</footnote>

<footnote>¥ Present address: Department of Plant and Soil Sciences, Oklahoma State University, Stillwater, OK 74078-6028, USA.</footnote>

<footnote>‡‡ Present address: James Hutton Institute, Invergowrie, Dundee, DD2 5DA, Scotland, UK.</footnote>

<footnote>|| Present address: Department of Agronomy, Bio-Engineering and Chemistry, University of Liege, Gembloux 5030, Belgium.</footnote>

<footnote>§ Present address: Institute for Sustainable Food Systems, University of Florida, Gainesville, FL 32611, USA.</footnote>

<footnote>‡‡ Present address: Department of Crop Sciences, Division Crop Production Systems in the Tropics, Georg-August-Universität Göttingen, 37077 Göttingen, Germany.</footnote>

<footnote>¶ Present address: CSIRO, Queensland Biosci Precinct St Lucia, St Lucia, Qld 4067, Australia.</footnote>
<table><caption>**Table 1.** Location and characteristics of the six sites of the IHSGE experiments selected for the AgMIP-Wheat study on heat. Growth cycle duration, average daily mean temperatures, number of days with daily maximum air temperature ($T_{max}$) > 31°C, and heat thermal time are averages across the growth cycle for the two growing seasons and two varieties.</caption><thead><tr><th rowspan="2">Country</th><th rowspan="2">Site name</th><th rowspan="2">Coordinates</th><th rowspan="2">Elevation (m)</th><th rowspan="2">Sowing month</th><th rowspan="2">Days from crop emergence to maturity</th><th rowspan="2">Type of heat stress</th><th colspan="2">Average daily mean temperature (°C)</th><th rowspan="2">Number of days with $T_{max}$ > 31°C</th><th rowspan="2">Heat thermal time (°days)<sup>a</sup></th></tr><tr><th>Sowing to anthesis</th><th>Anthesis to maturity</th></tr></thead><tbody><tr><td>Mexico</td><td>Obregon</td><td>27°N 109°W</td><td>38</td><td>Nov/Jan-Mar</td><td>118/92</td><td>Hot, dry</td><td>20/22.7</td><td>23.3/26.4</td><td>41/66</td><td>112/279</td></tr><tr><td>Mexico</td><td>Tlaltizapan</td><td>18°N 99°W</td><td>940</td><td>Dec/Feb-Mar</td><td>102/84</td><td>Hot, dry</td><td>22.7/23.8</td><td>26.4/27.6</td><td>66/88</td><td>279/313</td></tr><tr><td>Egypt</td><td>Aswan</td><td>24°N 32°E</td><td>200</td><td>Dec</td><td>123</td><td>Hot, dry</td><td>16.8</td><td>24.1</td><td>29</td><td>155</td></tr><tr><td>India</td><td>Dharwar</td><td>16°N 76°E</td><td>638</td><td>Dec</td><td>85</td><td>Hot, dry</td><td>23.8</td><td>27.1</td><td>56</td><td>157</td></tr><tr><td>Sudan</td><td>Wad Medani</td><td>14°N 33°E</td><td>411</td><td>Nov</td><td>94</td><td>Very hot, dry</td><td>24.5</td><td>24.2</td><td>74</td><td>267</td></tr><tr><td>Bangladesh</td><td>Dinajpur</td><td>25°N 88°E</td><td>29</td><td>Dec</td><td>109</td><td>Hot, humid</td><td>18.4</td><td>25.8</td><td>28</td><td>97</td></tr></tbody></table>

<footnote><sup>a</sup> Heat thermal time was calculated by summing $T_{max}$ above 31°C between crop emergence and maturity.</footnote>
**2 FIELD EXPERIMENTS:** Experimental locations were selected based on a classification of air temperature and relative humidity during the wheat growing cycle. "Hot" and "very hot" locations were defined as having mean temperatures above 17.5 and 22.5°C, respectively, during the coolest month. "Dry" and "humid" locations were defined as having mean vapor pressure deficits above and below 1.0 kPa, respectively. The present data set includes data from six of the original 12 locations that were selected to represent a large range of temperatures (Table 1). At Obregon (first year only) and Tlaltizapán (both years), Mexico normal and late sowing dates were used to provide contrasting temperature regimes at the same location. Of the 16 cultivars originally included in the first two-years of the IHSGE, two (Bacanora 88 and Nesser) were selected for the AgMIP-Wheat model intercomparison. Bacanora 88 is a high-yielding Mexican cultivar (Sayre et al., 1997) and Nesser is considered heat and drought tolerant (Okuyama et al., 2005). Both cultivars have low photoperiod sensitivity and low vernalization requirements. Crop growth cycle durations ranged from 80 to 127 days. Mean temperature for the crop growth cycle ranged between 20.1°C and 30.1°C, and grain yield from 1.9 ± 0.28 to 6.3 ± 0.38 t DM ha⁻¹ (Fig. 1).

All data were collected on experimental plots consisting of eight rows, 15 cm apart and 6-m long, arranged in α-lattice designs with three replicates. All experiments were well-watered and -fertilized with temperature being the most important variable. Seeding rate was approximately 120 kg ha⁻¹ at all sites, while the specific amount of N and P fertilizer applied and the irrigation varied from site to site. No irrigation or fertilizer data are available. Details of these factors, as well as information on soil type and weather necessary to run a wheat crop model are reported herewith.

<figure><img src="image_2.png" /><figcaption>**Figure 1.** Final grain yield versus mean growing season temperature for six locations, two growth cycles, and two cultivars of the IHSGE experiment reported here. Data are for individual plots with 2 to 3 replicated plots for each cultivar / site / year / sowing date combination.</figcaption></figure>

Mean of crop measurements have been reported in tabular format in CIMMYT internal reports (Reynolds et al., 1992, 1994a), with the exception of the experiment in Egypt, which has not been previously reported and it is here reported for the first year of the IHSGE. Measured crop variables include crop emergence date, anthesis date, maturity date, anthesis and final total above ground biomass, grain yield, plant number per square meter, spikes per square meter, grains per square meter, final average single grain dry mass, and plant height. In the first year, maturity dates for the late sown treatments for both cultivars at Obregon, Mexico were not available and were estimated as the average growing degree-days from anthesis to maturity of all other treatments. Similarly, anthesis date for Bacanora 88 in Egypt was not available and was estimated as the average growing degree-days from crop emergence to anthesis of all other treatments. Biomass harvests at anthesis were made on a single date for the whole experiment. Missing data are indicated by "NA".

Correlations between replicates overall and the distribution within each cultivar across replicates were calculated. In cases where the cultivar x replicate effect was high and two replicates were much closer to each other than the third, the outlying replicate was excluded. Data are reported as mean and standard deviation. Figure 1 illustrates the range of yield and mean growing season temperature. Seasonal (defined based on observed phenological stages) mean air temperature was calculated from daily air temperature, which was derived from the sum of eight contributions of a cosine variation between maximum and minimum daily air temperatures as described in Weir et al. (1984).
**3 SIMULATION OF FIELD EXPERIMENTS:** Simulations for the 28 locations/years/sowing dates/cultivars combinations were carried out by 30 wheat model (see Supplementary of Asseng et al., 2015). Fifteen of these 30 wheat models also participated in a crop model improvement study (Maiorano et al., 2017) where the IHSGE data set was used as a validation data set (blind). For these 15 models, simulation results are given for both the original and the improved versions.

Model outputs include crop emergence date, anthesis date, maturity date, and grain number per square meter. They also include outputs of in-season time series and end-of-the-season for total above ground and grain biomass, leaf area index, cumulative evapotranspiration, and cumulative transpiration. Not all models simulated all variables. Variables not simulated are indicated by "NA". Simulation results are reported for each individual model and for the multi-model ensemble median (e.median).

Simulations were carried out using a standardized protocol and one step of calibration. All sowing dates, anthesis and maturity dates, soil type characteristics and weather data were supplied to the modelers to simulate the experiments. Other crop measurements were provided to the modelers for Obregon, Mexico only. Detailed soil information were not available for each individual experiment. Therefore, as water and nitrogen were managed to limit any stress effect, a unique set of soil parameters and initial conditions was used to simulate all 11 year/location/sowing date combinations. Some models used an unlimited water and nitrogen mode for simulating these experiments. Weather data were obtained from the AgMIP climate forcing data set based on the NASA Modern-Era Retrospective Analysis for Research and Applications (AgMERRA; Ruane et al., 2015):

http://data.giss.nasa.gov/impacts/agmipcf/agmerra/

**4 DATA FORMAT, STRUCTURE, AND AVAILABILITY:** An overview of the main tables from the data set is given in Table 2. Experimental (mean and standard deviation of crop measurements) and simulation (model output) data are provided in tab delimited text files. The names of the variables (key) are explained in companion text files with their correspondence and conversion factors in the International Consortium for Agricultural Systems Applications (ICASA) standard (White et al., 2013): http://research.agmip.org/display/dev/ICASA+Master+Variable+List

Model input (cultivar information and crop management), soil description and initial conditions for simulation set up are provided in a Microsoft Excel book in XML format following the AgMIP format for model input and in tabulation delimited text files. Daily weather data (global solar radiation, daily maximum and minimum air temperature, rainfall, wind run, dew point temperature, vapor pressure, and relative humidity) are provided in the ICASA format in space delimited text files. All text files are UTF-8 encoded.

All data are available in Harvard Dataverse data repository (https://dataverse.harvard.edu/) with the identifiers "doi:10.7910/DVN/ECSFZG".

<table><caption>**Table 2.** Overview of the main data set tables. All files are provided in space (weather data) or tabulation (all others) delimited text format. The crop management husbandry parameters, the soil description and initial conditions are also provided in the Microsoft Excel xml format used in AgMIP.</caption><thead><tr><td>Table name</td><td>Content</td></tr></thead><tbody><tr><td>XXXX0001.WTH</td><td>Space delimited file of weather data. XXXX is the site name.</td></tr><tr><td>ISGHE_AgMIPMeasurement_key.txt</td><td>Name, definition, and units of the measured variables with ICASA standard and conversion factor</td></tr><tr><td>ISGHE_AgMIPMeasurements_ave_sd.txt</td><td>Tab delimited file of all available crop measurements (means and standard deviations)</td></tr><tr><td>ISGHE_AgMIP_site_soil_crop_management.xml</td><td>XML (Microsoft Excel 2003) file with crop management, cultivar description, site description and initial conditions</td></tr><tr><td>ISGHE_AgMIP_model_names.txt</td><td>Tab delimited file with the full name, version, and two-letter code of the 30 wheat models</td></tr><tr><td>ISGHE_AgMIPSimulation_key.txt</td><td>Name, definition, and units of the simulated variables with ICASA standard and conversion factor</td></tr><tr><td>ISGHE_AgMIP_summary_simulations.txt</td><td>Tab delimited file of the summary model outputs</td></tr><tr><td>ISGHE_AgMIP_daily_simulations.txt</td><td>Tab delimited file of the summary model outputs</td></tr></tbody></table>
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