干旱胁迫下植物气孔导度估算模型研究进展与展望
收稿日期: 2025-07-07
修回日期: 2025-08-07
网络出版日期: 2025-08-31
基金资助
国家自然科学基金面上项目(42271043);甘肃省优秀博士生项目(24JRRA109)
Review of Research Advances and Future Perspectives of Modeling Stomatal Conductance of Plants Under Drought Stress
Received date: 2025-07-07
Revised date: 2025-08-07
Online published: 2025-08-31
Supported by
the National Natural Science Foundation of China(42271043);Gansu Planning Projects on Science and Technology-Outstanding Ph. D. Student Program(24JRRA109)
干旱胁迫会通过土壤和大气两条路径影响植物气孔行为，气孔导度作为植物适应干旱胁迫的重要生理指标，受环境因素和植物内部机制双重调控。通过对国内外研究的分析，梳理了干旱胁迫情境下气孔导度模型的发展与应用，包括Jarvis类经验模型和Ball-Berry类半经验模型，以及基于水力学理论和气孔优化理论的两类机理模型，深入剖析了其各自的优势与不足。总体来说，经验与半经验模型缺乏生物物理机制，机理模型虽复杂，但能阐明干旱胁迫下气孔行为的内在规律，仍是未来研究的核心方向。此外，机器学习与稳定同位素等新兴技术的发展也为气孔导度模型改进提供了新途径，这些技术不仅拓宽了气孔导度模型的理论边界，也在一定程度上提高了对干旱胁迫下植物气孔导度的模拟能力。最后提出了未来研究的发展方向与建议，明确了未来需深化机理认知，并融合先进技术发展高精度、强机理气孔导度模型，为深入认识干旱胁迫下优化植物光合和蒸腾过程及气孔调节提供了更为坚实的理论支持和方法借鉴。
陈锐 , 吉喜斌 , 赵文玥 . 干旱胁迫下植物气孔导度估算模型研究进展与展望[J]. 地球科学进展, 2025 , 40(9) : 877 -889 . DOI: 10.11867/j.issn.1001-8166.2025.077
Drought stress affects plant stomatal behavior through both soil and atmospheric pathways. Stomatal conductance is an essential physiological parameter for plant adaptation to drought stress, regulated by both internal and external environmental factors. This review synthesizes findings from domestic and international studies on the development and application of stomatal conductance models under drought stress scenarios, including Jarvis-type empirical models, Ball-Berry-type semi-empirical models, and two types of mechanistic models based on stomatal hydraulic theory and optimization theory. The respective advantages and limitations of each model type are analyzed. Despite being straightforward and practical, empirical and semi-empirical models of stomatal conductance have a weak theoretical foundation and cannot adequately explain the biophysical mechanisms. In contrast, mechanistic models have greater biophysical explanatory power and adaptability. Despite their complexity, they can clarify the inherent patterns of stomatal behavior under drought stress. This makes them a universal predictive framework for simulating stomatal conductance in plants under complex drought conditions. Considering the numerous obstacles to the creation of mechanistic models of stomatal conductance, more research in this area is crucial. In addition, the development of emerging technologies such as machine learning and stable isotopes has provided new avenues for model improvement, which have not only broadened the theoretical boundaries of the models but have also improved the simulation ability of stomatal conductance in plants under drought stress. We conclude by outlining our prognosis and recommendations for future research directions, emphasizing the necessity of incorporating cutting-edge technology and expanding mechanistic knowledge to create robust and high-precision mechanistic stomatal conductance models. This study will offer a stronger theoretical foundation and methodological point of reference for a more thorough understanding of how to optimize transpiration, photosynthesis, and stomatal regulation in drought-stressed plants.
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