Fitting of the Light Response Model and Analysis of the Photosynthetic Characteristics of 12 Kinds of Subtropical Artificial Saplings

The photosynthetic-light response model is an important method to study the photosynthetic characteristics of tree species. Four common light response models including the rectangular hyperbolic model (RH), the non-rectangular hyperbolic model (NRH), the modified rectangular hyperbolic model (MRH) and the exponential model (EM) were used to fit the light response curves of 12 kinds of subtropical artificial saplings. The light response parameters and the goodness of fitting of each model were compared and analyzed to explore the optimal model and the photosynthetic characteristics of 12 kinds of subtropical artificial saplings. The results showed that: the coefficient of determination (R²) of the four models were all above 0.962 7. In the fitting of light compensation point (LCP), maximum net photosynthetic rate (P_nmax) and apparent quantum efficiency (AQE), the fitting effects of the four models were not much different, but NRH had the highest fitting accuracy, and MRH had the best fitting effect of light saturation point (LSP). At the same time, when fitting the light response curve, the data type should also be considered to observe whether the curve appeared the stationary state, rather than blindly using the model for fitting. The cluster analysis of photosynthetic parameters showed that, Mytilaria laosensis, Osmanthus fragrans and Pinus massoniana had the strongest tolerance to strong light, and Cinnamomum camphor had the strongest photosynthetic potential. According to the analysis of superior-inferior solution distance method, the shade tolerance capability of Cryptomeria japonica was the strongest, while that of Osmanthus fragrans was the weakest. The broad-leaved tree species with the strongest shade tolerance capability was Phoebe chekiangensis, which could be used for the broadleaf transformation of Pinus massoniana and Cunninghamia lanceolata plantations, thus to improve the productivity of plantations.