作物模型是研究作物响应环境变化的重要工具，被广泛用于作物产量预测和气候风险评估。然而，复杂的模型结构和参数化方案是作物产量预测不确定性的主要来源。自然植被模拟的最新研究表明，基于生态演化最优性原理构建的模型约束条件，不仅能简化模型结构、减少参数，还能将植物对环境的适应纳入模型。为可靠评估全球小麦产量对气候变化的响应，本研究将生态演化最优性原理应用于小麦生长模拟，探讨气候变化对全球小麦产量和小麦播种日期的影响。本研究首先以生态演化最优性和质量平衡等基本原理为建模约束，在生产力模型P model的基础上，发展了结构简洁、参数少、考虑光合能力和叶面积指数环境适应性的小麦生长新模型PC model；然后应用PC model模拟了全球小麦潜在产量，量化了全球小麦产量亏缺，分析了近四十年间（1981-2016）气候和大气CO2浓度变化对全球小麦产量的影响；最后借助PC model，预测了最大小麦产量对应的最优播种日期，探究了小麦播种行为对全球气候态的适应。

As the crucial tool for understanding responses of crops to environmental changes, crop models are widely used in predicting crop yields and assessing risks of climate change. However, the complex model structure and parametrizations are major sources of uncertainty in predicting crop yield. Recent studies have shown that the principle of Eco-Evolutionary Optimality (EEO) can be used as a model constraint, which not only simplifies the structure and parameters of natural vegetation models, but also naturally accounts for the acclimation/adaptation processes of plants. To reliably assess the responses of global wheat yields to climate change, this study developed a new model for wheat constrained by the EEO principle, then, applied it to explore the effects of climate change on global wheat yields and sowing dates.In this study, starting from an EEO-based productivity model (P model), we firstly developed a new model for wheat growth, named PC model - the Productivity model for Crop. The PC model considers the acclimation/adaptation of photosynthetic capacity and leaf area index, and predicts wheat yield parsimoniously. This PC model was then applied to predict the potential yield of wheat globally, quantify the yield gap and diagnose the impacts of climate change and elevated atmospheric CO2 concentration over the last four decades (1981-2016). Finally, we applied the PC model to predict the optimal sowing date corresponding to maximum wheat yield and investigated the long-term adaptation of sowing activity to climatology at the global scale.