朱中华, 韩拓, 柳金权, 朱高峰. 基于贝叶斯方法的光合作用生化模型参数估计及其在干旱区葡萄上的应用[J]. 中国生态农业学报(中英文), 2017, 25(6): 876-883. DOI: 10.13930/j.cnki.cjea.160967
引用本文: 朱中华, 韩拓, 柳金权, 朱高峰. 基于贝叶斯方法的光合作用生化模型参数估计及其在干旱区葡萄上的应用[J]. 中国生态农业学报(中英文), 2017, 25(6): 876-883. DOI: 10.13930/j.cnki.cjea.160967
ZHU Zhonghua, HAN Tuo, LIU Jinquan, ZHU Gaofeng. Biochemically-based model for photosynthetic parameter estimation using Bayesian method and its application in grapes in arid region[J]. Chinese Journal of Eco-Agriculture, 2017, 25(6): 876-883. DOI: 10.13930/j.cnki.cjea.160967
Citation: ZHU Zhonghua, HAN Tuo, LIU Jinquan, ZHU Gaofeng. Biochemically-based model for photosynthetic parameter estimation using Bayesian method and its application in grapes in arid region[J]. Chinese Journal of Eco-Agriculture, 2017, 25(6): 876-883. DOI: 10.13930/j.cnki.cjea.160967

基于贝叶斯方法的光合作用生化模型参数估计及其在干旱区葡萄上的应用

Biochemically-based model for photosynthetic parameter estimation using Bayesian method and its application in grapes in arid region

  • 摘要: 以无核白葡萄为试材,测定了其在不同季节(6—9月)、不同胞间CO2浓度下的净光合速率,根据贝叶斯方法,结合蒙特卡罗马尔科夫链算法对光合生化模型参数进行估算,以期获得不同季节的模型参数值,并与最小二乘法所得结果对比,探讨贝叶斯方法在解决高维度复杂模型参数估计问题中的可行性和葡萄光合作用关键参数季节变化规律。结果表明,最大羧化速率(Vcmax)、最大电子传递速率(Jmax)、磷酸丙糖利用速率(TPU)均有明显的季节变化特性,出现先增后减的趋势,8月达最高,分别为54.30 μmol·m-2·s-1、88.45 μmol·m-2·s-1和6.56 μmol·m-2·s-1;9月最小,分别为34.66 μmol·m-2·s-1、58.86 μmol·m-2·s-1和4.38 μmol·m-2·s-1。叶肉导度(gm)在各个月份波动不大,6—9月分别为5.16 μmol·m-2·s-1·Pa-1、5.29 μmol·m-2·s-1·Pa-1、5.39 μmol·m-2·s-1·Pa-1和5.41 μmol·m-2·s-1·Pa-1。与传统的最小二乘法相比,贝叶斯方法估算的Vcmax值偏小,Jmax、TPU和gm无明显差异。同时贝叶斯方法估计出的模型参数是在考虑参数先验信息的基础上获得的,生化意义更加显著。试验表明,光合作用生化模型(FvCB模型)在应用于光合作用模拟时,应充分考虑其参数的季节变化性;结合蒙特卡罗马尔科夫链算法的贝叶斯参数估计能更有效解决FvCB模型中参数估计问题。

     

    Abstract: The response of photosynthesis to CO2 concentration can provide a number of important parameters related to environmental factors. Using white seedless grape as the tested material in this study, net photosynthetic rates of leaves were measured for different intercellular CO2 concentrations during two typical growing seasons from June to September in 2014 and 2015. A widely used biochemical model (FvCB model) in the simulation of CO2 and H2O gas exchange at the leaf scale was parameterized using data obtained from situ leaf-scale observations. In order to obtain the photosynthetic parameters values, to explore seasonal variations in the photosynthetic parameters in different seasons and to discuss the feasibility and advantage of the Bayesian method in solving high dimensional and complex model parameters estimation, the Bayesian approach was used to estimate the parameters of the FvCB model. In order to generate the Bayesian posterior probability distribution, a version of the Markov Chain Monte Carlo (MCMC) technique was used. In contrast, the least square procedure was used in the application of the same set of observational data. The results showed that maximum ribulose 1.5-bisphosphate carboxylase/oxygenase (Rubisco) carboxylation rate (Vcmax), potential light-saturated electron transport rate (Jmax) and the rate of use of triose-phosphates utilization (TPU) had evident seasonal variations which increased from June to August, and then decreased in September. The maximum values were observed in August (54.30 μmol·m-2·s-1, 88.45 μmol·m-2·s-1 and 6.56 μmol·m-2·s-1, respectively) and minimum values in September (34.66 μmol·m-2·s-1, 58.86 μmol·m-2·s-1 and 4.38 μmol·m-2·s-1, respectively). The trend in mesophyll conductance (gm) was relatively stable in different months, with respective values of 5.16 μmol·m-2·s-1·Pa-1, 5.29 μmol·m-2·s-1·Pa-1, 5.39 μmol·m-2·s-1·Pa-1, 5.41 μmol·m-2·s-1·Pa-1 from June to September. In comparison with traditional least square method, the values of Vcmax estimated by the Bayesian method were relatively small and those of Jmax, TPU and gm had no obvious difference. Also because the estimated parameters by the Bayesian method were obtained after adequate consideration of prior information, each parameter was in biological sense obviously more meaning. As a consequence, it indicated that the Bayesian approach combined with Markov Chains and Monte Carlo (MCMC) sampling algorithm was an effective way of estimation of the parameters in the FvCB model. As the parameters in the FvCB model were different in different seasons, it was necessary to consider these variations in using the parameters in the FvCB model.

     

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