Parameterizing steady-state C3 photosynthesis models using photosynthesis response curves: which models to use and which parameter estimates to trust?

Photosynthesis sustains life on Earth, yet we still lack comprehensive understanding of the biochemical and environmental factors that affect this fundamental process. Steady-state models of C3 photosynthesis provide a powerful framework but rely on reliable estimation of numerous parameters from gas exchange data. Despite advances, how model structure and data choice influence parameter accuracy and consistency remains poorly explored. Here, we systematically evaluate parameterization across nine steady-state photosynthesis models and different levels of gas exchange measurements. Using synthetic photosynthesis response curves generated from the examined models with sampled parameter values, we applied Bayesian inference to quantify parameter uncertainty and estimation of performance for the considered models. We showed that while key parameters of C3 photosynthesis can be reliably estimated from a single A-Ci curve, others require expanded sampling of light response space. We also demonstrated the advantage of using simultaneous estimation of all model parameters over biasing the estimation by keeping some parameters fixed to prior values. Barley gas exchange data further demonstrated that parameter consistency across models can be evaluated comparing different levels of measurements and depends strongly on both model formulation and data type. Together, our study provides practical guidance for selecting photosynthesis models, designing phenotyping strategies, and choosing parameterization approaches for steady-state C3 photosynthesis.