TY - GEN A1 - Nóia-Júnior, Rogério de S. A1 - Martre, Pierre A1 - Deswarte, Jean-Charles A1 - Cohan, Jean-Pierre A1 - Van der Velde, Marijn A1 - Webber, Heidi A1 - Ewert, Frank A1 - Ruane, Alex C. A1 - Ben-Ari, Tamara A1 - Asseng, Senthold T1 - Past and future wheat yield losses in France’s breadbasket T2 - Field Crops Research N2 - Context or problem: In recent decades, compounding weather extremes and plant diseases have increased wheat yield variability in France, the largest wheat producer in the European Union. Objective or research question: How these extremes might affect future wheat production remains unclear. Methods: Based on department level wheat yields, disease, and climate indices from 1980 to 2019 in France, we combined an existing disease model with machine learning algorithms to estimate future grain yields. Results: This approach explains about 59% of historical yield variability. Projections from five CMIP6 climate models suggest that extreme low wheat yields, which used to occur once every 20 years, could occur every decade by the end of this century, but elevated CO2 levels might lessen these events. Conclusions: Heatwave-related yield losses are expected to double, while flooding-related yield losses will potentially decline by one third, depending on the representative concentration pathway. Ear blight disease is projected to contribute to 20% of the expected 400 kg ha-1 average yield losses by the end of the century, compared with 12% in the historical baseline period. These projections depend on the timing of anthesis, currently between late May and early June in most departments. Anthesis advancing to early May would shift losses primarily to heavy rainfall and low solar radiation. Implications or significance: French wheat production must adapt to these emerging threats, such as heat stress, which until recently had little impact but may become the primary cause of future yield losses. KW - Compounding factors KW - Extreme weather KW - Machine learning KW - Plant diseases KW - Wheat KW - Yield failure Y1 - 2025 U6 - https://doi.org/10.1016/j.fcr.2024.109703 SN - 0378-4290 VL - 322 PB - Elsevier BV ER - TY - GEN A1 - Nóia-Júnior, Rogério de S. A1 - Stocca, Valentina A1 - Martre, Pierre A1 - Shelia, Vakhtang A1 - Deswarte, Jean-Charles A1 - Cohan, Jean-Pierre A1 - Piquemal, Benoît A1 - Dutertre, Alain A1 - Slafer, Gustavo A. A1 - Zhang, Zhentao A1 - Van Der Velde, Marijn A1 - Kim, Yean-Uk A1 - Webber, Heidi A1 - Ewert, Frank A1 - Palosuo, Taru A1 - Liu, Ke A1 - Harrison, Matthew Tom A1 - Hoogenboom, Gerrit A1 - Asseng, Senthold T1 - Enabling modeling of waterlogging impact on wheat T2 - Field crops research N2 - Most crop simulation models do not consider the effect of waterlogging despite its importance for crop performance. Here, we reviewed the impact of waterlogging during different wheat phenological stages on grain number per unit area, average grain size, and grain yield. Episodes of waterlogging from the onset of tillering to anthesis result in fewer, and during grain filling in lighter grains. To simulate such impacts, we implemented a new waterlogging module into the wheat crop simulation model DSSAT-NWheat, accounting for the effects of waterlogging on wheat root growth, biomass growth, and potential average grain size. The model incorporating the new waterlogging routine was tested using data from a controlled experiment, and it reasonably reproduced wheat yield responses to pre-anthesis waterlogging. A sensitivity analysis showed that the simulated impact of waterlogging on above ground biomass and roots, as well as leaf area index, grain number, and grain yield varied with phenological stages. The simulated crop was most sensitive to pre-anthesis waterlogging, consistent with experimental studies. The new waterlogging-enabled crop model is an initial attempt to consider the impact of excess rainfall and waterlogging on crop growth and final grain yield to reduce model uncertainties when projecting climate change impacts with increasing rainfall intensity. KW - NWheat KW - DSSAT KW - Excess of water KW - Grain number KW - Grain size KW - Wheat crop simulation model Y1 - 2025 U6 - https://doi.org/10.1016/j.fcr.2025.110090 SN - 0378-4290 VL - 333 SP - 1 EP - 13 PB - Elsevier BV CY - Amsterdam ER - TY - GEN A1 - Nóia-Júnior, Rogério de S. A1 - Ruane, Alex C. A1 - Athanasiadis, Ioannis N. A1 - Ewert, Frank A1 - Harrison, Matthew Tom A1 - Jägermeyr, Jonas A1 - Martre, Pierre A1 - Müller, Christoph A1 - Palosuo, Taru A1 - Salmerón, Montserrat A1 - Webber, Heidi A1 - Maccarthy, Dilys Sefakor A1 - Asseng, Senthold T1 - Crop models for future food systems T2 - One earth N2 - Global food systems face intensifying pressure from climate change, resource scarcity, and rising demand, making their transformation toward resilience and sustainability urgent. Process-based crop growth models (CMs) are critical for understanding cropping system dynamics and supporting decisions from crop breeding to adaptive management across diverse environments. Yet, current CMs struggle to capture extreme events, novel production systems, and rapidly evolving data streams, limiting their ability to inform robust and timely decisions. Here, we outline CM structure, identify key knowledge gaps, and propose six priorities for next-generation CMs: (1) expand applications to extremes and to diverse systems; (2) support climate-resilient breeding; (3) integrate with machine learning for better inputs and forecasts; (4) link with standardized sensor and database networks; (5) promote modular, open-source architectures; and (6) build capacity in under-resourced regions. These priorities will substantially enhance CM robustness, comparability, and usability, reinforcing their role in guiding sustainable food system transformation. Y1 - 2025 U6 - https://doi.org/10.1016/j.oneear.2025.101487 SN - 2590-3322 VL - 8 IS - 10 SP - 1 EP - 7 PB - Elsevier BV CY - Amsterdam ER -