Nonlinear Quantile Mixed-Effects Models for Prediction of the Maximum Crown Width of Fagus sylvatica L., Pinus nigra Arn. and Pinus brutia Ten.

In the current study, a novel approach combining quantile regression with nonlinear mixed-effects (QR-NLME) modeling was applied to predict maximum crown width (cwmax) of three economically important forest species—the European beech (Fagus sylvatica L.), black pine (Pinus nigra Arn.), and Calabrian brutia Ten.) at tree level. A power QR-NLME model fitted first dataset including 1414 trees obtained from 29 randomly distributed sample plots, 770 25 1880 41 plots in Greece, cwmax Additionally, (NLME) same average level for all species. second stage, competition factor (CCF) estimated based on population response predictions. The proposed presented sound results when compared outcomes relevant models other regions open-grown data, therefore, it can be well implemented clustered data structures, cases absence data.