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 - TY - GEN A1 - Heyman, Tom A1 - Pronizius, Ekaterina A1 - Lewis, Savannah C. A1 - Acar, Oguz A. A1 - Adamkovič, Matúš A1 - Ambrosini, Ettore A1 - Antfolk, Jan A1 - Barzykowski, Krystian A1 - Baskin, Ernest A1 - Batres, Carlota A1 - Boucher, Leanne A1 - Boudesseul, Jordane A1 - Brandstätter, Eduard A1 - Collins, W. Matthew A1 - Filipović Ðurđević, Dušica A1 - Egan, Ciara A1 - Era, Vanessa A1 - Ferreira, Paulo A1 - Fini, Chiara A1 - Garrido-Vásquez, Patricia A1 - Godbersen, Hendrik A1 - Gomez, Pablo A1 - Graton, Aurelien A1 - Gurkan, Necdet A1 - He, Zhiran A1 - Johnson, Dave C. A1 - Kačmár, Pavol A1 - Koch, Chris A1 - Kowal, Marta A1 - Kratochvil, Tomas A1 - Marelli, Marco A1 - Marmolejo-Ramos, Fernando A1 - Martínez, Martín A1 - Mattiassi, Alan A1 - Maxwell, Nicholas P. A1 - Montefinese, Maria A1 - Morvinski, Coby A1 - Neta, Maital A1 - Nielsen, Yngwie A. A1 - Ocklenburg, Sebastian A1 - Onič, Jaš A1 - Papadatou-Pastou, Marietta A1 - Parker, Adam J. A1 - Paruzel-Czachura, Mariola A1 - Pavlov, Yuri G. A1 - Perea, Manuel A1 - Pfuhl, Gerit A1 - Roembke, Tanja C. A1 - Röer, Jan P. A1 - Roettger, Timo B A1 - Ruiz-Fernandez, Susana A1 - Schmidt, Kathleen T1 - Crowdsourcing multiverse analyses to explore the impact of different data-processing and analysis decisions : a tutorial T2 - Psychological methods N2 - When processing and analyzing empirical data, researchers regularly face choices that may appear arbitrary (e.g., how to define and handle outliers). If one chooses to exclusively focus on a particular option and conduct a single analysis, its outcome might be of limited utility. That is, one remains agnostic regarding the generalizability of the results, because plausible alternative paths remain unexplored. A multiverse analysis offers a solution to this issue by exploring the various choices pertaining to data-processing and/or model building, and examining their impact on the conclusion of a study. However, even though multiverse analyses are arguably less susceptible to biases compared to the typical single-pathway approach, it is still possible to selectively add or omit pathways. To address this issue, we outline a novel, more principled approach to conducting multiverse analyses through crowdsourcing. The approach is detailed in a step-by-step tutorial to facilitate its implementation. We also provide a worked-out illustration featuring the Semantic Priming Across Many Languages project, thereby demonstrating its feasibility and its ability to increase objectivity and transparency. (PsycInfo Database Record (c) 2026 APA, all rights reserved). Y1 - 2025 U6 - https://doi.org/10.1037/met0000770 SN - 1939-1463 PB - American Psychological Association (APA) CY - Washington, DC ER -