植物生态学报 ›› 2017, Vol. 41 ›› Issue (6): 693-706.DOI: 10.17521/cjpe.2016.0283

• 综述 • 上一篇    

FvCB生物化学光合模型及A-Ci曲线测定

梁星云, 刘世荣*   

  1. 中国林业科学研究院森林生态环境与保护研究所, 国家林业局森林生态环境重点实验室, 北京 100091
  • 收稿日期:2016-09-09 修回日期:2017-06-18 出版日期:2017-06-10 发布日期:2017-07-19
  • 通讯作者: 刘世荣
  • 基金资助:

    国家自然科学基金(31290223)。

A review on the FvCB biochemical model of photosynthesis and the measurement of A-Ci curves

LIANG Xing-Yun and LIU Shi-Rong*   

  1. Key Laboratory of Forest Ecology and Environment, China’s State Forestry Administration, Institute of Forest Ecology, Environment and Protection, Chinese Academy of Forestry, Beijing 100091, China
  • Received:2016-09-09 Revised:2017-06-18 Online:2017-06-10 Published:2017-07-19
  • Contact: LIU Shi-Rong

摘要:

由Farquhar、von Caemmerer和Berry提出的生物化学光合模型(以下简称FvCB模型)是一个基于光合碳反应过程的CO2响应模型。此模型认为C3植物叶片光合速率(A)由3个生物化学过程速率中的最低者——核酮糖-1,5-双磷酸羧化酶/加氧酶(Rubisco)所能支持的羧化速率、电子传递所能支持的核酮糖-1,5-双磷酸(RuBP)再生速率和磷酸丙糖(TP)利用速率决定。利用改进的FvCB模型对光合速率-胞间CO2浓度(A-Ci)曲线进行拟合, 能有效地估计最大羧化速率、最大电子传递速率、TP利用速率、明呼吸速率、叶肉细胞导度等生化参数, 促进我们对植物光合生理及其响应环境变化的理解和预测。该文首先详细地描述了FvCB模型, 并分析了此模型分段性和过参数化的特点。然后介绍利用FvCB模型对A-Ci曲线进行拟合, 从而估计叶片光合生化参数的研究进展。光合生化参数估计经历了主观分段、分段拟合到客观分段、整体拟合几个阶段, 目标函数的最小化方法也从传统的最小二乘法为主转向基于现代计算机技术的迭代算法(如遗传算法、模拟退火算法)。然而, 如要进一步提高参数估计的可靠性和精确性, 还需加强Rubisco动力学属性和温度依赖性方面的研究。最后, 为了获取能更有效地进行参数估计的光合数据, 根据目前对FvCB模型拟合的认知, 整合并改进了A-Ci曲线的测定方法。

Abstract:

The biochemical model of photosynthesis proposed by Farquhar, von Caemmerer and Berry is a CO2 response model based on photosynthetic processes. It hypothesizes that leaf CO2 assimilation rate (A) of C3 plants is decided by the minimum of three biochemical processes: the carboxylation rate supported by ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco), the ribulose-1,5-bisphosphate (RuBP) regeneration rate supported by electron transport and the triose-phosphate (TP) use rate. Fitting leaf CO2 assimilation rate versus intercellular CO2 concentration (A-Ci) curves with the modified FvCB model could provide several important biochemical parameters, including maximum Rubisco carboxylation rate, maximum rate of electron transport, TP use rate, day respiration rate and mesophyll conductance. The FvCB model has greatly improved our understanding and prediction of plant photosynthetic physiology and its response to environmental changes. In this review, we firstly described the FvCB model, and analysed the characteristics of this model: segmentation and overparameterization. We reviewed the estimation of biochemical parameters which by fitting A-Ci curves with the FvCB model. The biochemical parameters were estimated previously by segmenting subjectively and fitting each limitation state separately, whereas now by segmenting objectively and fitting all limitation simultaneously. In comparison to the previously conventional ordinary least squares (OLS), terativgorithms (eg. Genetic Algorithm, Simulated Annealing Algorithm) based on the modern computer technology are now in common use. However, to further improve the reliability and the precision of the parameters estimation, more studies about Rubisco kinetics parameters and their temperature dependence are needed. In the end, to obtain efficient photosynthetic data for biochemical parameters estimation, we integrated and modified methods concerning the measurement of A-Ci curves according to current knowledge about FvCB model fitting. We expect this review would advance our understanding and application of the FvCB model and A-Ci curves.