Leaf CO₂ response curve fitting method matters: cross-tool assumptions spread photosynthetic parameters and bias...

A_n-C_i curves are used to infer V_cmax,25 and J₂₅ for terrestrial biosphere models, but fitting-tool assumptions can alter both parameters; although V_cmax,25 varies more, J₂₅ differences can also shift inferred limitation regimes and thereby affect propagation from leaf to canopy simulations. We compared 11 widely used A_n-C_i fitting configurations and one optimal model configuration across four C₃ crops (soybean, perennial ryegrass, red clover, and winter wheat), ran two complementary sensitivities (one-at-a-time toggles relative to a baseline and variance-based decomposition), and used the resulting parameters in the Soil-Canopy Observation, Photochemistry and Energy (SCOPE) model to simulate A_n and gross primary productivity (GPP). V_cmax,25 differed by up to fourfold across tools, with consistent fingerprints across crops. Pooled sensitivities identified mesophyll conductance (g_m) and limiting-rate selection as the dominant drivers of between-tool spread. In SCOPE, using the same assumptions in simulation as in parameter estimation kept leaf A_n errors below 9%, while mismatched assumptions produced A_n deviations -68% to +81% across crops and soybean GPP shifts -12% to +20%. V_cmax,25 is transportable only with its assumptions. Users should align g_m, limiting-rate, and kinetic settings with intended simulations or treat catalogued values as priors unless recomputed from raw A_n-C_i data.