Modeling Free Branch Growth with the Competition Index for a Larix principis-rupprechtii Plantation

Abstract

Competition among free branches in the tree canopy is an important factor influencing branch length growth. Therefore, there is a need to quantify this competition and to understand the impact of the regression technique on the predictive accuracy of the growth of free branch length (GFBL) model in a Larix principis-rupprechtii plantation. This study focused on an L. principis-rupprechtii plantation in Saihanba Mechanized Forest Farm. Five competition indices based on 2176-branch data points from 76 trees were used to quantify the branch competition, and three regression techniques (nonlinear least squares (NLS), nonlinear mixed-effects model (NLME), and nonlinear quantile regression (NQR)) were used to construct the GFBL model including the branch competition index. The results showed that the Chapman–Richards growth function, including the diameter at breast height (DBH) and depth of branch into crown (DINC), was the optimal equation for describing the GFBL in the studied L. principis-rupprechtii plantation. The branch competition index (CI) was found to be optimal for quantifying the branch competition when used with the maximum value parameter (a0) of the Chapman–Richards growth function. The three parameter estimation methods were compared, and the NLME, which included the CI, was found to have the highest predictive accuracy. The results of this study can act as a reference for improving the management, assessing the management effectiveness, and enhancing the quality of L. principis-rupprechtii plantations.