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

Bayesian inference reveals that key parameters of C3 photosynthesis can be reliably estimated from a single A-Ci curve, but others require expanded light response sampling.

• Why it matters: Understanding how model structure and data choice influence parameter accuracy is crucial for improving steady-state photosynthesis modeling, which impacts ecological and agricultural research.
• What they did: The study systematically evaluated nine steady-state models and various gas exchange measurement levels using synthetic response curves and Bayesian inference to assess parameter uncertainty and model performance.
• The result: Findings suggest that simultaneous estimation of all parameters is advantageous, and parameter consistency across models depends on both model formulation and data type, guiding better model selection and phenotyping strategies.