Objective This paper aims to explore the effects of tree competition on growth and biomass of Cunninghamia lanceolata by comparing comprehensive competition index (CCI) with the Hegyi competition index (HCI), and to further elucidate the role of CCI in biomass model of Cunninghamia lanceolata.

Method (1) This study was conducted in Cunninghamia lanceolata plantations of Jiangle State Forest Farm in Fujian Province of eastern China. The HCI was calculated based on DBH, while CCI was constructed by integrating DBH, tree height and crown width. The relationship between two competition indices and growth as well as biomass of Cunninghamia lanceolata were tested using Kendall’s tau and Spearman’s rho correlation coefficients. The effects of tree competition on growth factors and biomass were analyzed through fitting scatter plots. A compatible biomass model for Cunninghamia lanceolata was developed by incorporating DBH, tree height and CCI using a three-level joint control scheme, and a systematic study was conducted on biomass models of Cunninghamia lanceolata trees and their individual components.

Result (1) Correlation analysis revealed that both HCI and CCI exhibited significantly negative correlations (Kendall’s tau and Spearman’s rho) with growth and biomass of Cunninghamia lanceolata (P < 0.01). Moreover, the absolute values of correlation coefficients for CCI were greater than those for HCI. (2) Scatter plot fitting showed significant power function relationships between two competition indices and DBH, tree height, crown width, and biomass components (bole, branches, leaves and roots) of Cunninghamia lanceolata. As competition index increased, the biomass of DBH, tree height, bole, branches, leaves, and roots all decreased. Notably, CCI outperformed HCI in fitting growth factors and biomass of Cunninghamia lanceolata with higher coefficients of determination. (3) In the process of developing biomass model for Cunninghamia lanceolata, CCI was introduced and a three-level joint control scheme was applied. A compatible biomass model was successfully constructed. This model, centered on the whole-tree biomass, effectively coordinated compatibility between whole-tree biomass and its individual components, as well as among different components.

Conclusion Tree competition significantly inhibits the growth of Cunninghamia lanceolata. Compared with HCI, CCI provides a more comprehensive and accurate assessment of tree competition intensity. Incorporating CCI into biomass model of Cunninghamia lanceolata enhances the model’s fitting and prediction accuracy. This indicates that CCI not only has theoretical value but also demonstrates its applicability in practical use.