With the occurrence of extreme events projected to increase under climate change, it is critical to assess the risk they pose to food security and identify suitable adaptation options. While mechanisms and impacts of climatic stressors (e.g. frost, drought, heat or flooding) have been studied individually, little is known their combined impacts on crops to be expected under actual production conditions. This lack of process knowledge is reflected in the few instances of crop models considering multiple stressors. Here we provide an overview of the representation of single stressors in process based crop models. From this basis, a framework to consider multiple stressors in current models is presented, defining four stressor combination types: 1. Single exposure; 2. No direct interaction; 3. Known interaction; and 4. Unknown interaction. An analytical framework from ecological sciences is then presented as an approach to consider when formulating algorithms for the 4th type of unknown interactions. In a final section, we discuss new data driven and model based exploration options to support understanding multiple stressor interactions in recognition of the challenges of experimentation around multiple stressors. We assert that process based modeling has a large and largely untapped potential to support scientific investigations of the underlying mechanisms driving crop response to multiple stressors.

随着极端事件的发生预计在气候变化下会增加，评估它们对粮食安全构成的风险并确定合适的适应方案至关重要。虽然已经单独研究了气候压力源（例如霜冻、干旱、高温或洪水）的机制和影响，但人们对它们在实际生产条件下对作物的预期综合影响知之甚少。这种过程知识的缺乏反映在考虑多重压力源的作物模型的少数实例中。在这里，我们概述了基于过程的作物模型中单个压力源的表示。在此基础上，提出了一个考虑当前模型中多个压力源的框架，定义了四种压力源组合类型：1、单次曝光；2.没有直接互动；3. 已知交互；和 4. 未知交互。然后提出了一个生态科学的分析框架，作为在为第 4 类未知相互作用制定算法时要考虑的一种方法。在最后一节中，我们讨论了新的数据驱动和基于模型的探索选项，以支持理解多个压力源的相互作用，以认识到围绕多个压力源进行实验的挑战。我们断言，基于过程的建模具有巨大且很大程度上尚未开发的潜力，可以支持对推动作物对多种压力源做出反应的潜在机制的科学研究。