TY - GEN A1 - Martre, Pierre A1 - Dueri, Sibylle A1 - Brown, Hamish A1 - Asseng, Senthold A1 - Ewert, Frank A1 - Webber, Heidi A1 - George, Mike A1 - Craigie, Rob A1 - Guarin, Jose Rafael A1 - Pequeno, Diego A1 - Stella, Tommaso A1 - Ahmed, Mukhtar A1 - Alderman, Phillip A1 - Basso, Bruno A1 - Berger, Andres A1 - Bracho Mujica, Gennady A1 - Cammarano, Davide A1 - Chen, Yi A1 - Dumont, Benjamin A1 - Rezaei, Ehsan Eyshi A1 - Fereres, Elias A1 - Ferrise, Roberto A1 - Gaiser, Thomas A1 - Gao, Yujing A1 - Garcia-Vila, Margarita A1 - Gayler, Sebastian A1 - Hochman, Zvi A1 - Hoogenboom, Gerrit A1 - Kersebaum, Kurt C. A1 - Nendel, Claas A1 - Olesen, Jørgen A1 - Padovan, Gloria A1 - Palosuo, Taru A1 - Priesack, Eckart A1 - Pullens, Johannes A1 - Rodríguez, Alfredo A1 - Rötter, Reimund P. A1 - Ruiz Ramos, Margarita A1 - Semenov, Mikhail A1 - Senapati, Nimai A1 - Siebert, Stefan A1 - Srivastava, Amit Kumar A1 - Stöckle, Claudio A1 - Supit, Iwan A1 - Tao, Fulu A1 - Thorburn, Peter A1 - Wang, Enli A1 - Weber, Tobias A1 - Xiao, Liujun A1 - Zhao, Chuang A1 - Zhao, Jin A1 - Zhao, Zhigan A1 - Zhu, Yan T1 - Winter wheat experiments to optimize sowing dates and densities in a high-yielding environment in New Zealand: field experiments and AgMIP-Wheat multi-model simulations T2 - Open Data Journal for Agricultural Research N2 - This paper describes the data set that was used to test the accuracy of twenty-nine crop models in simulating the effect of changing sowing dates and sowing densities on wheat productivity for a high-yielding environment in New Zealand. The data includes one winter wheat cultivar (Wakanui) grown during six consecutive years, from 2012-2013 to 2017-2018, at two farms located in Leeston and Wakanui in Canterbury, New Zealand. The simulations were carried out in the framework of the Agricultural Model Intercomparison and Improvement Project for wheat (AgMIP-Wheat). Data include local daily weather data, soil profile characteristics and initial conditions, crop measurements at maturity (grain, stem, chaff and leaf dry weight, ear number and grain number, grain unit dry weight), and at stem elongation and anthesis (total above ground dry biomass, leaf number per stem and leaf area index). Several in-season measurements of the normalized difference vegetation index (NDVI) and the fraction of intercepted photosynthetically active radiation (FIPAR) are also available. The crop model simulations include both daily in-season and end-of-season results from twenty-nine wheat models. KW - field experimental data KW - multi-crop model ensemble KW - sowing date KW - sowing density KW - winter wheat KW - yield potential Y1 - 2024 U6 - https://doi.org/10.18174/odjar.v10i0.18442 SN - 2352-6378 VL - 10 SP - 14 EP - 21 PB - Wageningen University and Research ER - TY - GEN A1 - Webber, H. A1 - Cooke, D. A1 - Wang, C. A1 - Asseng, S. A1 - Martre, P. A1 - Ewert, F. A1 - Kimball, B. A1 - Hoogenboom, G. A1 - Evett, S. A1 - Chanzy, A. A1 - Garrigues, S. A1 - Olioso, A. A1 - Copeland, K.S. A1 - Steiner, J.L. A1 - Cammarano, D. A1 - Chen, Y. A1 - Crépeau, M. A1 - Diamantopoulos, E. A1 - Ferrise, R. A1 - Manceau, L. A1 - Gaiser, T. A1 - Gao, Y. A1 - Gayler, S. A1 - Guarin, J.R. A1 - Hunt, T. A1 - Jégo, G. A1 - Padovan, G. A1 - Pattey, E. A1 - Ripoche, D. A1 - Rodríguez, A. A1 - Ruiz-Ramos, M. A1 - Shelia, V. A1 - Srivastava, A.K. A1 - Supit, I. A1 - Tao, F. A1 - Thorp, K. A1 - Viswanathan, M. A1 - Weber, T. A1 - White, J. T1 - Wheat crop models underestimate drought stress in semi-arid and Mediterranean environments T2 - Field crops research N2 - Under climate change and increasingly extreme weather, projections of water demand and drought stress from process-based crop models can inform risk management and adaptation strategies. Previous studies investigating maize crop models demonstrated considerable error in the simulation of water use, and no similar evaluation of wheat crop models exists. The aims of this study were to (1) evaluate wheat crop models’ performance in reproducing observed daily evapotranspiration (ET) for Mediterranean and semi-arid environments, and (2) identify factors and processes associated with model error and uncertainty. These were assessed with an ensemble of wheat crop models for two experiments, one conducted in Bushland, Texas, USA (three seasons, deficit and full irrigation) and another in Avignon, France (four rainfed seasons) with winter bread and durum wheat, respectively. Models were calibrated with all observed data for crop growth. The model ensemble median underestimated water use in all environments evaluated, suggesting a systematic bias. The relative error in underestimating daily ET was constant across levels of atmospheric evaporative demand; therefore, the absolute error was greater for days with larger evaporative demand. This implies errors in the soil water balance increase more rapidly under high evaporative demand conditions. Using a potential versus reference crop evapotranspiration approach did not explain relative model performance. However, the sensitivity analysis indicated that simulation of atmospheric evaporative demand terms explained much more uncertainty in seasonal water use than terms related to soil depth or root growth. Errors in simulated leaf area index were associated with errors in daily simulated ET, but the relationship varied with the growth stage. Collectively, the results suggest the need to improve simulation of atmospheric ET demand to avoid underestimating projected impacts of drought or required water resource availability for viable production systems. KW - Crop models KW - Evapotranspiration KW - Wheat KW - Drought stress KW - Climate risk Y1 - 2025 U6 - https://doi.org/10.1016/j.fcr.2025.110032 SN - 0378-4290 VL - 332 SP - 1 EP - 18 PB - Elsevier BV CY - Amsterdam ER -