Models are key tools in our quest to better understand the impacts of soil waterlogging on plant growth and crop production. Here, we reviewed the state of the art of modeling approaches and compared the conceptual design of these models with recent experimental findings. We show that many models adopt an aeration stress (AS) principle where surplus water reduces air‐filled porosity, with implications for root growth. However, subsequent effects of AS within each model vary considerably. In some cases, AS inhibits biomass accumulation (e.g. AquaCrop), altering processes prior to biomass accumulation such as light interception (e.g. APSIM), or photosynthesis and carbohydrate accumulation (e.g. SWAGMAN Destiny). While many models account for stage‐dependent waterlogging effects, few models account for experimentally observed delays in phenology caused by waterlogging. A model intercomparison specifically designed for long‐term waterlogged conditions (APSIM‐Oryza) with models developed for dryland conditions with transient waterlogging would advance our understanding of the current fitness for purpose of exsiting frameworks for simulating transient waterlogging in dryland cropping systems. Of the point‐based dynamic models examined here, APSIM‐Soybean and APSIM‐Oryza simulations most closely matched with the observed data, while GLAM‐WOFOST achieved the highest performance of the spatial‐regional models examined. We conclude that future models should incorporate waterlogging effects on genetic tolerance parameters such as (1) phenology of stress onset, (2) aerenchyma, (3) root hydraulic conductance, (4) nutrient‐use efficiency, and (5) plant ion (e.g. Fe/Mn) tolerance. Incorporating these traits/effects into models, together with a more systematic model intercomparison using consistent initialization data, will significantly improve our understanding of the relative importance of such factors in a systems context, including feedbacks between biological factors, emergent properties, and sensitive variables responsible for yield losses under waterlogging.

中文翻译：

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对涝渍对植物的影响进行建模的最新技术：我们知道什么，我们需要知道什么

模型是我们寻求更好地了解土壤涝渍对植物生长和作物生产的影响的关键工具。在这里，我们回顾了建模方法的最新发展，并将这些模型与最新的实验结果进行了比较。我们表明，许多模型都采用了曝气应力（AS）原理，其中多余的水减少了空气填充的孔隙度，对根系的生长有影响。但是，每个模型中AS的后续影响差异很大。在某些情况下，AS会抑制生物量积累（AquaCrop），从而在生物量积累之前改变过程，例如光拦截（APSIM）或光合作用和碳水化合物积累（SWAGMAN Destiny）。尽管许多模型解释了阶段性的淹水效应，但很少模型解释了实验观察到的由淹水引起的物候延迟。针对长期涝灾条件而专门设计的模型比较（APSIM‐Oryza）与针对旱地条件而短暂涝灾而开发的模型将提高我们当前的建模能力。在基于点的动态模型中，APSIM-Soybean和APSIM-Oryza仿真与所观察到的数据最匹配，而GLAM-WOFOST在所研究的空间区域模型中表现出最高的性能。我们得出结论，未来的模型应将淹水效应纳入遗传耐受性参数中，例如（1）应激发作的物候，（2）气孔，（3）根系水力传导，（4）养分利用效率和（5）植物铁/锰公差。将这些特征/效果纳入模型，