@misc{StellaWebberRezaeietal., author = {Stella, Tommaso and Webber, Heidi and Rezaei, Ehsan Eyshi and Asseng, Senthold and Martre, Pierre and Dueri, Sibylle and Guarin, Jose Rafael and Pequeno, Diego and Calderini, Daniel and Reynolds, Matthew and Molero, Gemma and Miralles, Daniel and Garcia, Guillermo and Slafer, Gustavo A. and Giunta, Francesco and Kim, Yean-Uk and Wang, Chenzhi and Ruane, Alex C. and Ewert, Frank}, title = {Wheat crop traits conferring high yield potential may also improve yield stability under climate change}, series = {in silico Plants}, volume = {5}, journal = {in silico Plants}, number = {2}, publisher = {Oxford University Press (OUP)}, issn = {2517-5025}, doi = {10.1093/insilicoplants/diad013}, pages = {16}, abstract = {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.}, language = {en} } @misc{RezaeiWebberAssengetal., author = {Rezaei, Ehsan Eyshi and Webber, Heidi and Asseng, Senthold and Boote, Kenneth and Durand, Jean Louis and Ewert, Frank and Martre, Pierre and MacCarthy, Dilys Sefakor}, title = {Climate change impacts on crop yields}, series = {Nature Reviews Earth \& Environment}, volume = {4}, journal = {Nature Reviews Earth \& Environment}, number = {12}, publisher = {Springer Science and Business Media LLC}, issn = {2662-138X}, doi = {10.1038/s43017-023-00491-0}, pages = {831 -- 846}, language = {en} } @misc{KimAssengWebber, author = {Kim, Yean-Uk and Asseng, Senthold and Webber, Heidi}, title = {Spring frost risk assessment on winter wheat in South Korea}, series = {Agricultural and Forest Meteorology}, volume = {366}, journal = {Agricultural and Forest Meteorology}, publisher = {Elsevier BV}, address = {Amsterdam}, issn = {0168-1923}, doi = {10.1016/j.agrformet.2025.110484}, pages = {10}, abstract = {Spring frost remains a major climatic risk for winter wheat production. However, frost risk is often overlooked in climate change studies, especially those that rely on process-based crop models. This study assesses the spring frost risk for winter wheat in South Korea using observed trial data, a process-based crop model, and a large ensemble of climate data. Trial data from seven sites across South Korea suggest that the extreme yield loss in the 2019/20 season resulted from a combination of a warm winter, which accelerated phenology, and a cool April, which led to several frost events around heading. Projections with a calibrated DSSAT-Nwheat model and a large ensemble of climate data (HAPPI) suggest that the risk of yield loss due to spring frost will increase in the southern region of South Korea. However, this risk can be reduced by switching to later-maturing cultivars to avoid spring frost. In contrast, while the risk of yield loss due to spring frost in the central and northern regions is not expected to increase significantly, it will persist and can only be reduced by introducing frost-tolerant cultivars. Extending this analysis to include losses from other major stressors and linking it to socio-economic analyses will be needed for developing long-term strategies to boost wheat production, enhance self-sufficiency, and ensure food security.}, language = {en} } @misc{NoiaJuniorStoccaMartreetal., author = {N{\´o}ia-J{\´u}nior, Rog{\´e}rio de S. and Stocca, Valentina and Martre, Pierre and Shelia, Vakhtang and Deswarte, Jean-Charles and Cohan, Jean-Pierre and Piquemal, Beno{\^i}t and Dutertre, Alain and Slafer, Gustavo A. and Zhang, Zhentao and Van Der Velde, Marijn and Kim, Yean-Uk and Webber, Heidi and Ewert, Frank and Palosuo, Taru and Liu, Ke and Harrison, Matthew Tom and Hoogenboom, Gerrit and Asseng, Senthold}, title = {Enabling modeling of waterlogging impact on wheat}, series = {Field crops research}, volume = {333}, journal = {Field crops research}, publisher = {Elsevier BV}, address = {Amsterdam}, issn = {0378-4290}, doi = {10.1016/j.fcr.2025.110090}, pages = {1 -- 13}, abstract = {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.}, language = {en} } @misc{KimRuaneFingeretal., author = {Kim, Yean-Uk and Ruane, Alex C. and Finger, Robert and Webber, Heidi}, title = {Robust assessment of climatic risks to crop production}, series = {Nature food}, volume = {6}, journal = {Nature food}, number = {5}, publisher = {Springer Science and Business Media LLC}, address = {Berlin ; Heidelberg}, issn = {2662-1355}, doi = {10.1038/s43016-025-01168-1}, pages = {415 -- 416}, language = {en} } @misc{NoiaJuniorRuaneAthanasiadisetal., author = {N{\´o}ia-J{\´u}nior, Rog{\´e}rio de S. and Ruane, Alex C. and Athanasiadis, Ioannis N. and Ewert, Frank and Harrison, Matthew Tom and J{\"a}germeyr, Jonas and Martre, Pierre and M{\"u}ller, Christoph and Palosuo, Taru and Salmer{\´o}n, Montserrat and Webber, Heidi and Maccarthy, Dilys Sefakor and Asseng, Senthold}, title = {Crop models for future food systems}, series = {One earth}, volume = {8}, journal = {One earth}, number = {10}, publisher = {Elsevier BV}, address = {Amsterdam}, issn = {2590-3322}, doi = {10.1016/j.oneear.2025.101487}, pages = {1 -- 7}, abstract = {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.}, language = {en} } @misc{FayeMbayeWebberetal., author = {Faye, Babacar and Mbaye, Mamadou Lamine and Webber, Heidi and Dieye, Bounama and Diouf, Di{\´e}gane and Gaye, Amadou Thierno}, title = {Adaptation potential of alternate varieties and fertilization strategies for peanut and maize in Senegal under climate change}, series = {Regional environmental change}, volume = {25}, journal = {Regional environmental change}, number = {4}, publisher = {Springer Science and Business Media LLC}, address = {Berlin ; Heidelberg ; New York, NY}, issn = {1436-3798}, doi = {10.1007/s10113-025-02491-w}, pages = {1 -- 15}, abstract = {In Senegal, rising temperatures are projected to reduce maize yields due to a shortened growth duration, while elevated CO2 fertilization may increase peanut yields under climate change. However, there is limited evidence on climate change impacts if crop cultivars change and systems intensify, which is expected to occur in parallel with climate change. For climate-adapted agriculture, the performance of improved agronomy and varieties should be evaluated under current and future climate scenarios. This study assesses the impact of climate change on crop yields of two varieties of peanut and maize at each under current and intensified fertilization. Simulations were performed for mid-century (2045-2074) and end-century (2070-2099) relative to a baseline (1981-2010) using the SIMPLACE modeling framework at 0.5° resolution. Climate projections from nine global climate models (GCMs) were used under SSP2-4.5 and SSP5-8.5 scenarios. Soil data was derived from the Harmonized World Soil Database. The results indicate that the impacts of climate change on crop yields differed by crop. Peanut showed an increase in yield of up to 45\% and a decrease for maize of up to 25\% by the end of the century. Peanut yield gains were higher under the intensification fertilization case compared to the current fertilization case, whereas for maize, losses were high in the intensification case. Furthermore, yield losses are more substantial in the southern and western parts of the country for both crops. Additionally, for maize, yield losses were higher for the short cycle variety than the long cycle variety; there was little difference between varieties for peanut.}, language = {en} }