Photosynthetic parameter estimations by considering interactive effects of light, temperature and CO2 concentration

Biochemical leaf photosynthesis models are evaluated by laboratory results andhave been widely used at field scale for quantification of plant production,biochemical cycles and land surface processes. It is a key issue to search forappropriate model structure and parameterization, which determine modeluncertainty. A leaf photosynthesis model that couples the Farquhar-vonCaemmerer-Berry (FvCB) formulation to four different leaf temperature models isused to investigate the photosynthetic characteristics across a range of temperaturegradients using both light (A-Q) and CO2 response curves (A-Ci). We used theBayesian approach to fit the model to trial data of C3 crop plants (soybean, wheat)in the North China Plain and estimated key photosynthetic parameters, such as themaximum carboxylation rate of Rubisco (Vcmax25), the potential electron transportrate (Jmax25), leaf dark respiration in the light (Rd25), mesophyll conductance (gm25)and the kinetic parameter of Rubisco (Г*25) at a reference temperature of 25 °C.The results showed that 1) the model with moderate complexity showed the bestgoodness of fit, while conversely the simpler and more complex models wereunder and over fitting with their corresponding data, respectively; 2) the nonpeaked Arrhenius temperature response, which including both light and CO2responses data gave the best estimates for the key parameters among the fourmodels; and 3) the temperature gradient used to verify the model has greatlyimproved the estimation of six key parameters (Jmax25, Vcmax25, Rd25, Г*25, Kc25, gm25)with relatively more narrow confidence intervals (CIs) and showing regular variation on temperature gradient. Overall, this method offers an accurate basis forestimating leaf photosynthesis parameters and may enhance the accuracy ofcanopy, ecosystem and even global vegetation models.