Evaluation of the mixed-effects model and quantile regression approaches for predicting tree height in larch (<i>Larix olgensis</i>) plantations in northeastern China

Tree height (H) is one of the most important tree variables and widely used in growth yield models, its measurement often time-consuming costly. Hence, height–diameter (H–D) models have become a great alternative, providing easy-to-use accurate tools for H prediction. In this study, H–D were developed Larix olgensis A. Henry northeastern China. The Chapman–Richards function with three predictors (diameter at breast height, dominant relative size individual trees) performed best. Nonlinear mixed-effects (NLME) nonlinear quantile regressions (NQR9, nine quantiles; NQR5, five NQR3, quantiles) further improved generalized model, successfully predictions. addition, predictions calibrated using several measurements from subsamples, which obtained different sampling designs sizes. results indicated that predictive accuracy was higher when by any number NLME model more than NQR9 models. best strategy NQR involved medium-sized trees. Overall, newly can provide highly L. olgensis.