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

A - Papers appearing in refereed journals

Guarin, J. R., Martre, P., Ewert, F., Webber, H., Dueri, S., Calderini, D., Reynolds, M., Molero, G., Miralles, D., Slafer, G. A., Giunta, F., Pequano, D. N. L., Stella, T., Ahmed, M., Alerman, P. D., Basso, B., Berger, A. G., Bindi, M., Bracho-Mujica, G., Cammarano, D., Chen, Y., Dumont, B., Rezaei, E. .E, Fereres, E., Ferrise, R., Gaiser, T., Gao, Y., Garcia-Vila, M., Gayler, S., Hochman, Z., Hoogenboom, G., Hunt, L. A., Kersebaum, K. C., Nendel, C., Olesen, J. E., Palosuo, T., Priesack, E., Pullens, J. W. M., Rodriguez ,A., Rotter, R. P., Ruiz Ramos, M., Semenov, M. A., Senapati, N., Siebert,S., Srivastava, A. K., Stockle, C., Supit, I., Tao, F., Thorburn, P., Wang, E., Weber, T. K. D., Xiao, L., Zhang, Z., Zhao, C., Zhao, J., Zhao, Z., Zhu, Y. and Asseng, S. 2023. 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 (ODjAR). 9, pp. 26-33. https://doi.org/10.18174/odjar.v9i0.18573

AuthorsGuarin, J. R., Martre, P., Ewert, F., Webber, H., Dueri, S., Calderini, D., Reynolds, M., Molero, G., Miralles, D., Slafer, G. A., Giunta, F., Pequano, D. N. L., Stella, T., Ahmed, M., Alerman, P. D., Basso, B., Berger, A. G., Bindi, M., Bracho-Mujica, G., Cammarano, D., Chen, Y., Dumont, B., Rezaei, E. .E, Fereres, E., Ferrise, R., Gaiser, T., Gao, Y., Garcia-Vila, M., Gayler, S., Hochman, Z., Hoogenboom, G., Hunt, L. A., Kersebaum, K. C., Nendel, C., Olesen, J. E., Palosuo, T., Priesack, E., Pullens, J. W. M., Rodriguez ,A., Rotter, R. P., Ruiz Ramos, M., Semenov, M. A., Senapati, N., Siebert,S., Srivastava, A. K., Stockle, C., Supit, I., Tao, F., Thorburn, P., Wang, E., Weber, T. K. D., Xiao, L., Zhang, Z., Zhao, C., Zhao, J., Zhao, Z., Zhu, Y. and Asseng, S.
Abstract

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.

KeywordsWheat; Yield potential; Field experimental data; Crop model ensemble; Simulations
Year of Publication2023
JournalOpen Data Journal for Agricultural Research (ODjAR)
Journal citation9, pp. 26-33
Digital Object Identifier (DOI)https://doi.org/10.18174/odjar.v9i0.18573
Open accessPublished as bronze (free) open access
Publisher's version
Output statusPublished
Publication dates
Online26 Jul 2023
PublisherWageningen Academic Publishers

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