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首页> 外文期刊>The New Phytologist >Optimizing the statistical estimation of the parameters of the Farquhar-von Caemmerer-Berry model of photosynthesis
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Optimizing the statistical estimation of the parameters of the Farquhar-von Caemmerer-Berry model of photosynthesis

机译:优化Farquhar-von Caemmerer-Berry光合作用模型参数的统计估计

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摘要

The model of Farquhar, von Caemmerer and Berry is the standard in relating photosynthetic carbon assimilation and concentration of intercellular CO(2). The techniques used in collecting the data from which its parameters are estimated have been the object of extensive optimization, but the statistical aspects of estimation have not received the same attention. The model segments assimilation into three regions, each modeled by a distinct function. Three parameters of the model, namely the maximum rate of Rubisco carboxylation (V(c max)), the rate of electron transport (J), and nonphotorespiratory CO(2) evolution (R(d)), are customarily estimated from gas exchange data through separate fitting of the component functions corresponding to the first two segments. This disjunct approach is problematic in requiring preliminary arbitrary subsetting of data into sets believed to correspond to each region. It is shown how multiple segments can be estimated simultaneously, using the entire data set, without predetermination of transitions by the investigator. Investigation of the number of parameters that can be estimated in the two-segment model suggests that, under some conditions, it is possible to estimate four or even five parameters, but that only V(c max), J, and R(d), have good statistical properties. Practical difficulties and their solutions are reviewed, and software programs are provided.
机译:Farquhar,von Caemmerer和Berry的模型是光合作用碳同化作用和细胞间CO(2)浓度相关的标准。广泛用于优化收集参数参数所依据的数据的技术,但是估算的统计方面并未受到同样的关注。该模型将同化划分为三个区域,每个区域通过不同的函数进行建模。通常通过气体交换估算该模型的三个参数,即Rubisco羧化反应的最大速率(V(c max)),电子传输速率(J)和非光呼吸性CO(2)释放速率(R(d))。通过分别拟合与前两个段相对应的组件函数来获得数据。这种分离的方法在要求将数据进行初步的任意子集化成据信与每个区域相对应的集合方面存在问题。它显示了如何使用整个数据集同时估计多个片段,而无需研究人员预先确定转换。对可在两段模型中估计的参数数量的研究表明,在某些条件下,可以估计四个甚至五个参数,但是只有V(c max),J和R(d) ,具有良好的统计特性。审查了实际困难及其解决方案,并提供了软件程序。

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