Global wheat production has faced, and will persist in encountering many challenges. Therefore, developing a dynamic cultivation approach generated through modeling is crucial to coping with the challenges in specific districts. The modeling can contribute to achieving global objectives of farmers’ financial independence and food security by enhancing the cropping systems. The current study aims to assess the effects of cultivars and sowing windows intricately on irrigated wheat production using the two models from Coupled Model Intercomparison Project Phase 6 (CMIP6), including ACCES-CM2 and HadGEM31-LL under two shared socioeconomic pathways (SSP245, and SSP585). A two-year on-farm experiment was conducted for parametrization and validation of the APSIM-Wheat model at two locations. The model reasonably simulated the days to anthesis, maturity, biomass production, and yield within all cultivars. The normalized root-mean-square error (RMSE) of the phenological stages was simulated and measured values were 5% and 2–4%, while the index of agreement (IOA) was in the range of 0.84–0.88 and 0.95–0.97. An acceptable agreement of the simulated biomass (RMSE = 5–7% and 0.91–0.78) and yield (RMSE = 6–11% and IOA = 0.70–0.94) was identified in the model. Afterward, the LARS-WG model generated the baseline (2000–2014) based on the weather data at the sites and projected the models for the near (2030–2049) and remote future (2050–2070). The models revealed that not only the average maximum and minimum temperatures will rise by 1.85 °C and 1.62 °C which will exacerbate the reference evapotranspiration (ET₀), but also the precipitation and solar radiation will reach + 58%, and + 0.25 Mj m⁻². Our results clearly showed that precipitation volume over the growing seasons would elevate approximately two times as much as the baseline in the future, while there is a significant decrease in water productivity (WP) and yield from the intensive ET₀. Based on the wheat simulation, the short-duration cultivar (Kalate) combined with the postponed planting (16-Dec) was determined as a practical alternative; nonetheless, both WP and yield significantly decreased by 40% and 7%, respectively (p < 0.05). In conclusion, identifying and analyzing future farming conditions (e.g., agro-climate, soil and crop management data) would provide a perception of the forthcoming scenarios. When applied, this knowledge can potentially mitigate the adverse impacts of climate change on global wheat production.

全球小麦生产已经并将继续面临许多挑战。因此，开发通过建模产生的动态培育方法对于应对特定地区的挑战至关重要。该模型可以通过加强耕作系统，有助于实现农民经济独立和粮食安全的全球目标。当前的研究旨在使用耦合模型比较项目第六阶段（CMIP6）的两个模型，包括 ACCES-CM2 和 HadGEM31-LL 在两个共享的社会经济路径（SSP245 和SSP585）。为了对两个地点的 APSIM-小麦模型进行参数化和验证，进行了为期两年的农场实验。该模型合理地模拟了所有品种的开花天数、成熟期、生物量产量和产量。模拟物候期归一化均方根误差（RMSE），实测值为5%和2%~4%，一致性指数（IOA）范围为0.84~0.88和0.95~0.97。模型中确定了模拟生物量（RMSE = 5–7% 和 0.91–0.78）和产量（RMSE = 6–11% 和 IOA = 0.70–0.94）的可接受的一致性。随后，LARS-WG模型根据现场的天气数据生成了基线（2000-2014年），并预测了近期（2030-2049年）和遥远的未来（2050-2070年）的模型。_{模型显示，不仅平均最高和最低气温将上升1.85 °C和1.62 °C，参考蒸散量（ET 0} ）加剧，而且降水量和太阳辐射将达到+ 58%和+ 0.25 Mj m ^-2。我们的结果清楚地表明，未来生长季节的降水量将增加约两倍于基线，而集约型ET 0 导致的水生产率（WP）和产量显着_下降。根据小麦模拟，确定短期品种（Kalate）与推迟播种（12 月 16 日）相结合作为可行的替代方案；尽管如此，工作效率和产量均显着下降，分别下降了 40% 和 7% ( p  < 0.05)。总之，识别和分析未来的农业条件（例如农业气候、土壤和作物管理数据）将提供对未来情景的认识。如果应用的话，这些知识可能会减轻气候变化对全球小麦生产的不利影响。