A high-yielding traits experiment for modeling potential production of wheat: field experiments and AgMIP-Wheat multi-model simulations - INRAE - Institut national de recherche pour l’agriculture, l’alimentation et l’environnement Access content directly
Journal Articles (Data Paper) Open Data Journal for Agricultural Research Year : 2023

A high-yielding traits experiment for modeling potential production of wheat: field experiments and AgMIP-Wheat multi-model simulations

1 UF - University of Florida [Gainesville]
2 CCSR - Center for Climate Systems Research [New York]
3 LEPSE - Écophysiologie des Plantes sous Stress environnementaux
4 ZALF - Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research
5 INRES - Institut für Nutzpflanzenwissenschaften und Ressourcenschutz
6 BTU - Brandenburgische Technische Universität = Brandenburg Technical University
7 Universidad Austral de Chile
8 CIMMYT - International Maize and Wheat Improvement Center
9 KWS Momont SAS
10 Universitad de Buenos Aires = University of Buenos Aires [Argentina]
11 Universitat de Lleida
12 ICREA - Institució Catalana de Recerca i Estudis Avançats
13 UNISS - Università degli Studi di Sassari = University of Sassari [Sassari]
14 AAUR - Pir Mehr Ali Shah Arid Agriculture University = PMAS-Arid Agriculture University Rawalpindi
15 SLU - Swedish University of Agricultural Sciences
16 OSU - Oklahoma State University [Stillwater]
17 Michigan State University [East Lansing]
18 INIA - Instituto Nacional de Investigación Agropecuaria
19 DAGRI - Department of Agriculture, Food, Environment and Forestry
20 Georg-August-University = Georg-August-Universität Göttingen
21 Aarhus University [Aarhus]
22 IGSNRR - Institute of geographical sciences and natural resources research [CAS]
23 Gembloux Agro-Bio Tech [Gembloux]
24 Universidad de Córdoba = University of Córdoba [Córdoba]
25 CSIC - Consejo Superior de Investigaciones Cientificas = Spanish National Research Council
26 University of Hohenheim
27 CSIRO - CSIRO Agriculture and Food
28 University of Guelph
29 CAS - Czech Academy of Sciences [Prague]
30 University of Potsdam = Universität Potsdam
31 LUKE - Natural Resources Institute Finland
32 Helmholtz Zentrum München = German Research Center for Environmental Health
33 UCLM - Universidad de Castilla-La Mancha = University of Castilla-La Mancha
34 CBL - Centre for Biodiversity and Sustainable Land-use [University of Göttingen]
35 UPM - Universidad Politécnica de Madrid
36 Rothamsted Research
37 WSU - Washington State University
38 WUR - Wageningen University and Research [Wageningen]
39 University of Kassel
40 Zhejiang University
41 NAU - Nanjing Agricultural University
42 BNU - Beijing Normal University
43 CAU - China Agricultural University
44 TUM - Technische Universität München = Technical University of Munich
Gemma Molero
  • Function : Author
Phillip Alderman
Benjamin Dumont
Yujing Gao
Sebastian Gayler
Gerrit Hoogenboom
Leslie Hunt
  • Function : Author
Johannes Pullens
Margarita Ruiz Ramos
Mikhail Semenov
Nimai Senapati
Peter Thorburn
Zhigan Zhao


Grain production must increase by 60% in the next four decades to keep up with the expected population growth and food demand. A significant part of this increase must come from the improvement of staple crop grain yield potential. Crop growth simulation models combined with field experiments and crop physiology are powerful tools to quantify the impact of traits and trait combinations on grain yield potential which helps to guide breeding towards the most effective traits and trait combinations for future wheat crosses. The dataset reported here was created to analyze the value of physiological traits identified by the International Wheat Yield Partnership (IWYP) to improve wheat potential in high-yielding environments. This dataset consists of 11 growing seasons at three high-yielding locations in Buenos Aires (Argentina), Ciudad Obregon (Mexico), and Valdivia (Chile) with the spring wheat cultivar Bacanora and a high-yielding genotype selected from a doubled haploid (DH) population developed from the cross between the Bacanora and Weebil cultivars from the International Maize and Wheat Improvement Center (CIMMYT). This dataset was used in the Agricultural Model Intercomparison and Improvement Project (AgMIP) Wheat Phase 4 to evaluate crop model performance when simulating high-yielding physiological traits and to determine the potential production of wheat using an ensemble of 29 wheat crop models. The field trials were managed for non-stress conditions with full irrigation, fertilizer application, and without biotic stress. Data include local daily weather, soil characteristics and initial soil conditions, cultivar information, and crop measurements (anthesis and maturity dates, total above-ground biomass, final grain yield, yield components, and photosynthetically active radiation interception). Simulations include both daily in-season and end-of-season results for 25 crop variables simulated by 29 wheat crop models.

Dates and versions

hal-04187862 , version 1 (25-08-2023)





Jose Guarin, Pierre Martre, Frank Ewert, Heidi Webber, Sibylle Dueri, et al.. A high-yielding traits experiment for modeling potential production of wheat: field experiments and AgMIP-Wheat multi-model simulations. Open Data Journal for Agricultural Research, 2023, 9, pp.26-33. ⟨10.18174/odjar.v9i0.18573⟩. ⟨hal-04187862⟩
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