通过当前的米模型,改善在短期热应激期间和之后的叶子光合作用预测
Ting Sun1,2, Xiaohu Zhang1, Suyu Lv1
1National Engineering and Technology Center for Information Agriculture, Engineering Research Center of Smart Agriculture, Ministry of Education, Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture, Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, College of Agriculture, Nanjing Agricultural University, Nanjing, Jiangsu, China.
Plant, cell & environment
|August 14, 2023
概括
极端的热量会影响米的光合作用,但模型却面临诸如蒸汽压力缺陷 (VPD) 和叶子等因素. 这项研究通过调整热应激条件的关键参数来改善光合作用建模.
科学领域:
- 植物生理学 植物生理学
- 农业科学 农业科学
- 气候变化生物学 气候变化生物学
背景情况:
- 全球变暖和极端的热应激显著影响植物的光合作用生产.
- 以前关于热应激对光合作用的影响的研究往往忽略了诸如蒸汽压力缺陷 (VPD),叶子和水槽限制反等关键因素.
研究的目的:
- 评估和提高七种大米生长模型在极端热应激下计算光合作用过程的准确性.
- 研究VPD,叶子和沉积限制在热应激事件期间和之后对光合作用预测的影响.
主要方法:
- 评估了七种米生长模型,使用从多年的受控环境实验中观察到的最大光合作用率 (Pmax).
- 生物化学 (FvCB类型) 和光响应曲线 (LRC类型) 模型的比较.
- 纳入了环境变量,如温度,光合作用活性辐射,细胞间二氧化碳度 (Ci) 和叶子.
主要成果:
- 生物化学模型在包括关键输入变量时,表现优于LRC类型模型.
- 温度和Ci变化是热应激期间预测不确定性的主要来源.
- 修改FVPD以取决于温度,显著提高了Ci预测的准确性.
- 校准的叶子反应功能和改进的水槽限制反减少了后热应激预测错误.
结论:
- 在热应力下准确的光合作用建模需要考虑VPD,叶子动态和沉积限制.
- 有了改进的参数化 (例如,温度依赖的FVPD) 的生物化学模型,可以更好地预测植物对极端高温的反应.
- 进一步完善作物模型对于在未来气候情景下预测农业生产率至关重要.
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