Estimating uncertainty: A Bayesian approach to modelling photosynthesis in C3 leaves.

The Farquhar‐von Caemmerer‐Berry (FvCB) model is extensively used to model photosynthesis from gas exchange measurements. Since its publication, many methods have been developed to measure, or more accurately estimate, parameters of this model. Here, we have created a tool that uses Bayesian statistics to fit photosynthetic parameters using concurrent gas exchange and chlorophyll fluorescence measurements whilst evaluating the reliability of the parameter estimation. We have tested this tool on synthetic data and experimental data from rice leaves. Our results indicate that reliable parameter estimation can be achieved whilst only keeping one parameter, Km, that is, Michaelis constant for CO2 by Rubisco, prefixed. Additionally, we show that including detailed low CO2 measurements at low light levels increases reliability and suggests this as a new standard measurement protocol. By providing an estimated distribution of parameter values, the tool can be used to evaluate the quality of data from gas exchange and chlorophyll fluorescence measurement protocols. Compared to earlier model fitting methods, the use of a Bayesian statistics‐based tool minimizes human interaction during fitting, reducing the subjectivity which is essential to most existing tools. A user friendly, interactive Bayesian tool script is provided. The uncertainties of estimating photosynthetic parameters in the Farquhar‐von Caemmerer‐Berry were systematically evaluated with a Bayesian approach using synthetic datasets. A more robust and reliable protocol of photosynthetic parameter estimation and corresponding data collection was developed and applied in rice leaves. [ABSTRACT FROM AUTHOR]