Objective A high-precision growth model system of Chinese fir (Cunninghamia lanceolata) plantations was developed based on the 3-PG model to predict growth, productivity, and biomass allocation, thereby providing a basis for studying the growth patterns in Chinese fir plantations.

Method Based on the data of 70 measured plots, soil data, historical meteorological data, and the biomass of each component of Chinese fir plantations, the 3-PG model was used to simulate the diameter at breast height (DBH), height and biomass of each component of the Chinese fir in the study area. Simultaneously, the effects of the main environmental factors on the photosynthetic production of Chinese fir plantations were examined. In addition, the measured values of the leaf area index were compared with the simulated values by fish-eye lens photography. Sensitivity analysis was conducted on two parameters: soil fertility rating (FR) and leaf biomass when DBH=20 cm (P₂₀).

Result (1) The model parameters of the Chinese fir plantation were obtained by calibration and validation. The model predicted values and measured values of the stand variables were consistent with each other, with R² ranging from 0.72 to 0.94, and the MRE of stand variables ranging from -1.01% to 13.86%. The results were also biologically reasonable. (2)The effects of temperature (f_T), atmospheric vapor pressure deficit (f_D), and available soil water (f_asw) on photosynthetic production of Chinese fir plantation were significant, with values ranging from 0.49 to 0.99, 0.40 to 0.91, and 0.29 to 0.48, respectively. (3)The LAI values of the middle-aged Chinese fir plantation (18 years old) simulated by the model were between 1.84 and 3.23 m²·m⁻². (4) The physiological parameters FR and P₂₀ showed high sensitivity in relation to leaf, root, stem, and aboveground biomass, indicating they are key parameters in the C. lanceolata 3-PG model.

Conclusion After parameter calibration, the 3-PG model effectively simulates the productivity of C. lanceolata plantations with relatively reliable prediction accuracy. It can serve as an effective tool for long-term growth predictions and forest management practices.