Publication Abstracts
Guarin et al. 2023
, P. Martre, F. Ewert, H. Webber, S. Dueri, D. Calderini, M. Reynolds, G. Molero, D. Miralles, G. Garcia, G. Slafer, F. Giunta, D. Pequeno, T. Stella, M. Ahmed, P. Alderman, B. Basso, A. Berger, M. Bindi, G. Bracho-Mujica, D. Cammarano, Y. Chen, B. Dumont, E. Eyshi Rezaei, E. Fereres, R. Ferrise, T. Gaiser, Y. Gao, M. Garcia-Vila, S. Gayler, Z. Hochman, G. Hoogenboom, L. Hunt, K. Kersebaum, C. Nendel, J. Olesen, T. Palosuo, E. Priesack, J. Pullens, A. Rodriguez, R. Rotter, M. Ruiz Ramos, M. Semenov, N. Senapati, S. Siebert, A. Srivastava, C. Stockle, I. Supit, F. Tao, P. Thorburn, E. Wang, T. Weber, L. Xiao, Z. Zhang, C. Zhao, J. Zhao, Z. Zhao, Y. Zhu, and S. Asseng, 2023: A high-yielding traits experiment for modeling potential production of wheat: Field experiments and AgMIP-Wheat multi-model simulations. Open Data J. Agric. Res., 9, 26-33, doi:10.18174/odjar.v9i0.18573.
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.
Export citation: [ BibTeX ] [ RIS ]
BibTeX Citation
@article{gu04200y,
author={Guarin, J. and Martre, P. and Ewert, F. and Webber, H. and Dueri, S. and Calderini, D. and Reynolds, M. and Molero, G. and Miralles, D. and Garcia, G. and Slafer, G. and Giunta, F. and Pequeno, D. and Stella, T. and Ahmed, M. and Alderman, P. and Basso, B. and Berger, A. and Bindi, M. and Bracho-Mujica, G. and Cammarano, D. and Chen, Y. and Dumont, B. and Eyshi Rezaei, E. and Fereres, E. and Ferrise, R. and Gaiser, T. and Gao, Y. and Garcia-Vila, M. and Gayler, S. and Hochman, Z. and Hoogenboom, G. and Hunt, L. and Kersebaum, K. and Nendel, C. and Olesen, J. and Palosuo, T. and Priesack, E. and Pullens, J. and Rodriguez, A. and Rotter, R. and Ruiz Ramos, M. and Semenov, M. and Senapati, N. and Siebert, S. and Srivastava, A. and Stockle, C. and Supit, I. and Tao, F. and Thorburn, P. and Wang, E. and Weber, T. and Xiao, L. and Zhang, Z. and Zhao, C. and Zhao, J. and Zhao, Z. and Zhu, Y. and Asseng, S.},
title={A high-yielding traits experiment for modeling potential production of wheat: Field experiments and AgMIP-Wheat multi-model simulations},
year={2023},
journal={Open Data Journal for Agricultural Research},
volume={9},
pages={26--33},
doi={10.18174/odjar.v9i0.18573},
}
[ Close ]
RIS Citation
TY - JOUR ID - gu04200y AU - Guarin, J. AU - Martre, P. AU - Ewert, F. AU - Webber, H. AU - Dueri, S. AU - Calderini, D. AU - Reynolds, M. AU - Molero, G. AU - Miralles, D. AU - Garcia, G. AU - Slafer, G. AU - Giunta, F. AU - Pequeno, D. AU - Stella, T. AU - Ahmed, M. AU - Alderman, P. AU - Basso, B. AU - Berger, A. AU - Bindi, M. AU - Bracho-Mujica, G. AU - Cammarano, D. AU - Chen, Y. AU - Dumont, B. AU - Eyshi Rezaei, E. AU - Fereres, E. AU - Ferrise, R. AU - Gaiser, T. AU - Gao, Y. AU - Garcia-Vila, M. AU - Gayler, S. AU - Hochman, Z. AU - Hoogenboom, G. AU - Hunt, L. AU - Kersebaum, K. AU - Nendel, C. AU - Olesen, J. AU - Palosuo, T. AU - Priesack, E. AU - Pullens, J. AU - Rodriguez, A. AU - Rotter, R. AU - Ruiz Ramos, M. AU - Semenov, M. AU - Senapati, N. AU - Siebert, S. AU - Srivastava, A. AU - Stockle, C. AU - Supit, I. AU - Tao, F. AU - Thorburn, P. AU - Wang, E. AU - Weber, T. AU - Xiao, L. AU - Zhang, Z. AU - Zhao, C. AU - Zhao, J. AU - Zhao, Z. AU - Zhu, Y. AU - Asseng, S. PY - 2023 TI - A high-yielding traits experiment for modeling potential production of wheat: Field experiments and AgMIP-Wheat multi-model simulations JA - Open Data J. Agric. Res. JO - Open Data Journal for Agricultural Research VL - 9 SP - 26 EP - 33 DO - 10.18174/odjar.v9i0.18573 ER -
[ Close ]
