Abstract:

Abstract: Forest growth data are generally repeatedly observed with hierarchical structure, which result in lack of independence among observations and produce biased parameter estimation if ordinary regression analysis was used. Mixed model with random parameters could solve the problem. Individual basal area growth models for larch, spruce and fir, Korean pine and two deciduous groups were developed using linear mixed models in semi-natural larix-spruce-fir forest in northeast China. The data came from 20 permanent sample plots with 10 756 observations, of which 8 034 observations from 15 plots are randomly used for model development and 2 722 observations from rest 5 plots for model validation. These models were independent of age and site index. They may have wide use in that initial diameter at breast height, stand basal area, site factors and distance-dependent competition index were included in them which are easily accessible in forest inventory. Random effects within plots showed significant in all models, and the effects among plots not besides larch model, however. The inclusion of random parameters in these models greatly improved the fixed models. The coefficients of determination reached0.85～0.89 from 0.38～0.64. Errors and RMSEs were also significantly decreased. These models are biologically and statistically reliable.

Key words: words: competition index, stand density, individual tree basal area growth, mixed effect model