TY - GEN A1 - Martre, Pierre A1 - Dueri, Sibylle A1 - Guarin, Jose Rafael A1 - Ewert, Frank A1 - Webber, Heidi A1 - Calderini, Daniel A1 - Molero, Gemma A1 - Reynolds, Matthew A1 - Miralles, Daniel A1 - Garcia, Guillermo A1 - Brown, Hamish A1 - George, Mike A1 - Craigie, Rob A1 - Cohan, Jean‐Pierre A1 - Deswarte, Jean-Charles A1 - Slafer, Gustavo A. A1 - Giunta, Francesco A1 - Cammarano, Davide A1 - Ferrise, Roberto A1 - Gaiser, Thomas A1 - Gao, Yujing A1 - Hochman, Zvi A1 - Hoogenboom, Gerrit A1 - Hunt, Leslie A. A1 - Kersebaum, Kurt C. A1 - Nendel, Claas A1 - Padovan, Gloria A1 - Ruane, Alex C. A1 - Srivastava, Amit Kumar A1 - Stella, Tommaso A1 - Supit, Iwan A1 - Thorburn, Peter A1 - Wang, Enli A1 - Wolf, Joost A1 - Zhao, Chuang A1 - Zhao, Zhigan A1 - Asseng, Senthold T1 - Global needs for nitrogen fertilizer to improve wheat yield under climate change T2 - Nature Plants Y1 - 2024 U6 - https://doi.org/10.1038/s41477-024-01739-3 SN - 2055-0278 VL - 10 IS - 7 SP - 1081 EP - 1090 PB - Springer Science and Business Media LLC ER - TY - GEN A1 - Nóia Júnior, Rogério de S. A1 - Deswarte, Jean-Charles A1 - Cohan, Jean‐Pierre A1 - Martre, Pierre A1 - van der Velde, Marijn A1 - Lecerf, Remi A1 - Webber, Heidi A1 - Ewert, Frank A1 - Ruane, Alex C. A1 - Slafer, Gustavo A. A1 - Asseng, Senthold T1 - The extreme 2016 wheat yield failure in France T2 - Global Change Biology N2 - France suffered, in 2016, the most extreme wheat yield decline in recent history, with some districts losing 55% yield. To attribute causes, we combined the largest coherent detailed wheat field experimental dataset with statistical and crop model techniques, climate information, and yield physiology. The 2016 yield was composed of up to 40% fewer grains that were up to 30% lighter than expected across eight research stations in France. The flowering stage was affected by prolonged cloud cover and heavy rainfall when 31% of the loss in grain yield was incurred from reduced solar radiation and 19% from floret damage. Grain filling was also affected as 26% of grain yield loss was caused by soil anoxia, 11% by fungal foliar diseases, and 10% by ear blight. Compounding climate effects caused the extreme yield decline. The likelihood of these compound factors recurring under future climate change is estimated to change with a higher frequency of extremely low wheat yields. KW - compounding factors KW - extreme weather KW - food security KW - grain number KW - grain size KW - temporally and multivariate events Y1 - 2023 U6 - https://doi.org/10.1111/gcb.16662 SN - 1354-1013 VL - 29 IS - 11 SP - 3130 EP - 3146 PB - Wiley ER - TY - GEN A1 - Stella, Tommaso A1 - Webber, Heidi A1 - Rezaei, Ehsan Eyshi A1 - Asseng, Senthold A1 - Martre, Pierre A1 - Dueri, Sibylle A1 - Guarin, Jose Rafael A1 - Pequeno, Diego A1 - Calderini, Daniel A1 - Reynolds, Matthew A1 - Molero, Gemma A1 - Miralles, Daniel A1 - Garcia, Guillermo A1 - Slafer, Gustavo A. A1 - Giunta, Francesco A1 - Kim, Yean-Uk A1 - Wang, Chenzhi A1 - Ruane, Alex C. A1 - Ewert, Frank T1 - Wheat crop traits conferring high yield potential may also improve yield stability under climate change T2 - in silico Plants N2 - Increasing genetic wheat yield potential is considered by many as critical to increasing global wheat yields and production, baring major changes in consumption patterns. Climate change challenges breeding by making target environments less predictable, altering regional productivity and potentially increasing yield variability. Here we used a crop simulation model solution in the SIMPLACE framework to explore yield sensitivity to select trait characteristics (radiation use efficiency [RUE], fruiting efficiency and light extinction coefficient) across 34 locations representing the world’s wheat-producing environments, determining their relationship to increasing yields, yield variability and cultivar performance. The magnitude of the yield increase was trait-dependent and differed between irrigated and rainfed environments. RUE had the most prominent marginal effect on yield, which increased by about 45 % and 33 % in irrigated and rainfed sites, respectively, between the minimum and maximum value of the trait. Altered values of light extinction coefficient had the least effect on yield levels. Higher yields from improved traits were generally associated with increased inter-annual yield variability (measured by standard deviation), but the relative yield variability (as coefficient of variation) remained largely unchanged between base and improved genotypes. This was true under both current and future climate scenarios. In this context, our study suggests higher wheat yields from these traits would not increase climate risk for farmers and the adoption of cultivars with these traits would not be associated with increased yield variability. KW - climate change KW - climate risk KW - genetic yield potential KW - wheat KW - yield variability Y1 - 2023 U6 - https://doi.org/10.1093/insilicoplants/diad013 SN - 2517-5025 VL - 5 IS - 2 PB - Oxford University Press (OUP) 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 -