干旱区生态环境知识资源中心(Arid): 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

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	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
其他题名	Biochemically-based model for photosynthetic parameter estimation using Bayesian method and its application in grapes in arid region
	朱中华 1; 韩拓 2; 柳金权 3; 朱高峰 2
来源期刊	中国生态农业学报
ISSN	1671-3990
出版年	2017
卷号	25 期号:6 页码:876-883
中文摘要	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CO_2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(V_(cmax))、最大电子传递速率(J_(max))、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 mumol·m~(-2)·s~(-1)、88.45 mumol·m~(-2)·s~(-1)和6.56 mumol·m~(-2)·s~(-1); 9月最小,分别为34.66 mumol·m~(-2)·s~(-1)、58.86 mumol·m~(-2)·s~(-1)和4.38 mumol·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 mumol·m~(-2)·s~(-1)·Pa~(-1)、5.29 mumol·m~(-2)·s~(-1)·Pa~(-1)、5.39 mumol·m~(-2)·s~(-1)·Pa~(-1)和5.41 mumol·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V_(cmax)值偏小, J_(max)、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。
英文摘要	The response of photosynthesis to CO_2 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 CO_2 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 CO_2 and H_2O 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 (V_(cmax)), potential light-saturated electron transport rate (J_(max)) 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 mumol·m~(-2)·s~(-1), 88.45 mumol·m~(-2)·s~(-1) and 6.56 mumol·m~(-2)·s~(-1), respectively) and minimum values in September (34.66 mumol·m~(-2)·s~(-1), 58.86 mumol·m~(-2)·s~(-1) and 4.38 mumol·m~(-2)·s~(-1), respectively). The trend in mesophyll conductance (gm) was relatively stable in different months, with respective values of 5.16 mumol·m~(-2)·s~(-1)·Pa~(-1), 5.29 mumol·m~(-2)·s~(-1)·Pa~(-1), 5.39 mumol·m~(-2)·s~(-1)·Pa~(-1), 5.41 mumol·m~(-2)·s~(-1)·Pa~(-1) from June to September. In comparison with traditional least square method, the values of V_(cmax) estimated by the Bayesian method were relatively small and those of J_(max), 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.
中文关键词	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
英文关键词	Arid region Grape Bayesian parameter estimation Biochemical photosynthesis model Photosynthetic parameter Seasonal variation
语种	中文
国家	中国
收录类别	CSCD
WOS类目	PLANT SCIENCES
WOS研究方向	Plant Sciences
CSCD记录号	CSCD:5991040
来源机构	兰州大学
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/237170
作者单位	1.甘肃省水利水电学校, 兰州, 甘肃 730021, 中国; 2.兰州大学资源环境学院, 西部环境教育部重点实验室, 兰州, 甘肃 730000, 中国; 3.华亭县水务局, 华亭, 744100
推荐引用方式 GB/T 7714	朱中华,韩拓,柳金权,等. 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]. 兰州大学,2017,25(6):876-883.
APA	朱中华,韩拓,柳金权,&朱高峰.(2017).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.中国生态农业学报,25(6),876-883.
MLA	朱中华,et al."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".中国生态农业学报 25.6(2017):876-883.