Photosynthetic CO2 response characteristics in canopy of Larix principis-rupprechtii Mayr. tree and practicability of three Models

Accurately predicting the crown photosynthesis of trees is important to understand the tree growth status and carbon circle in terrestrial ecosystem. However, modeling the photosynthetic carbon dioxide (CO2) response curves for individual tree are still challenging due to the complex canopy structure and changeable environmental conditions. Therefore, taking 16-old year Larix principis-rupprechtii Mayr. as the research material, the dynamic CO2 response models of photosynthesis, including rectangular hyperbolic model (RHM), the non-rectangular hyperbolic model (NRHM) and the modified rectangular hyperbolic model (MRHM), were used to simulate CO2 response curves of the crown. The fitting accuracy of the models depend on the comparison of determinants coefficients (R2), mean square errors (MSE) and Akaike information criterion (AIC). The results showed that the mean value of R2 (R2=0.9939 [~] 0.9964) of MRHM was the highest, whereas MSE value (MSE=0.2185[~]0.2627) and AIC value (AIC=-13.18[~]-8.03) were the lowest. The CO2-saturated gross photosynthetic rate (Amax) and the saturation point (CiSP) obtained by MRHM were closest to the measured value respectively. Therefore, the MRHM fitted the CO2 response data well, and calculated the photosynthetic parameters directly and accurately, the fitted result showed , Amax, CiSP, CiCP and RP were 0.04, 7.51 mol{middle dot}m-2s-1, 938.97 mol{middle dot}m-2, 67.54 mol{middle dot}m-2 and 0.60 mol{middle dot}m-2s-1, respectively. In addition, the difference on the photosynthetic CO2 response parameters values showed somewhat among different layers and orientations. In all, all data suggested that the modified rectangular hyperbolic model (MRHM) was an ideal model to fit the crown photosynthetic CO2 response curve of Larix principis-rupprechtii Mayr. These results are of great significance for parameter calibration of photosynthetic model and robust prediction of photosynthetic response in forest.