@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{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} }