Development of Full Growth Cycle Crown Width Models for Chinese Fir (Cunninghamia lanceolata) in Southern China

This study focused on 16,101 Cunninghamia lanceolata trees across 133 plots in seven cities of Guangdong Province, China, to develop a comprehensive full growth cycle crown width (CW) model. We systematically analyzed the dynamic characteristics of CW and its multi-scale influencing mechanisms. A binary basic model, with the diameter at breast height (DBH) and height (H) as core predictor variables, effectively reflected tree growth patterns. The inclusion of age groups as dummy variables allowed the model to capture the dynamic changes in CW across different growth stages. Furthermore, the incorporation of a nested two-level nonlinear mixed-effects (NLME) model, accounting for random effects from the forest block- and sample plot-level effects, significantly improved the precision and applicability of the final model (R2 = 0.731, RMSE = 0.491). This model quantified both macro- and micro-level effects of region and plot on CW. Our findings showed that the two-level NLME model, incorporating tree age groups, optimally accounted for environmental heterogeneity and tree growth cycles, resulting in the best-fitting statistics. The proposed full growth cycle CW model effectively enhanced the model’s efficiency and predictive accuracy for Cunninghamia lanceolata, providing scientific support for the sustainable management and dynamic monitoring of plantation forests.