中国生态农业学报2017，Vol.25Issue(6)：876-883,8.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
Abstract
The response of photosynthesis to CO2 concentration can provide a number of important parameters related to envi-ronmental 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 val-ues, to explore seasonal variations in the photosynthetic parameters in different seasons and to discuss the feasibility and ad-vantage 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 carboxy-lase/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 Sep-tember. 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 mes-ophyll 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 ofVcmax estimated by the Bayesian method were relatively small and those ofJmax, TPUandgm had no obvious difference. Also because the estimated parameters by the Bayesian method were obtained after adequate considera-tion 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.关键词
干旱区/葡萄/贝叶斯参数估计/光合作用生化模型/光合作用参数/季节变化Key words
Arid region/Grape/Bayesian parameter estimation/Biochemical photosynthesis model/Photosynthetic parameter/Seasonal variation分类
农业科技引用本文复制引用
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,2017,25(6):876-883,8.基金项目
国家自然科学基金(41571016)和中央高校基本科研业务费(861944)资助 This study was supported by the National Natural Science Foundation of China (41571016) and the National Higher-education Institution General Research and Development Project of China (861944). （41571016）
