Estimating uncertainty: A Bayesian approach to modelling photosynthesis in <scp>C3</scp> leaves

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