Evaluation of silver birch diameter increment model based on data of the Czech National Forest Inventory

In the Czech Republic, the silver birch (Betula pendula Roth.) is considered as a pioneer and a soil preparing tree species. It occurs mainly on clearcutting areas after disturbances. The aim of this study was to fit breast height diameter increment model for birch with respect to tree age, share of birch trees and forest site type (ecological series – ES and forest vegetation zones – FVZ). We used data of both cycles of National Forest Inventory of the Czech Republic. We evaluated production potential of this species. We tested Korf and Michailoff increment models in variant of nonlinear least squares model (NLS) and nonlinear mixed effects model (NLME). Michailoff models performed better. We found seven statistically significant and practically applicable models. The greatest influence on increment of diameter at breast height have forest vegetation zone and ecological series whereas the influence of the share of birch in forest stand is smaller. The highest absolute values of diameter increment were on gleyed or enriched with water sites in the fourth forest vegetation zone.

An individual tree diameter increment model for white spruce in Alberta

Based on a data set from 164 permanent sample plots, an age-independent individual tree diameter increment model is presented for white spruce (Piceaglauca (Moench) Voss) grown in the boreal mixed-species stands in Alberta. The model is age independent in that it does not explicitly require tree or stand age as input variables. Periodic diameter increment is modelled as a function of tree diameter at breast height, total tree height, relative competitiveness of the tree in the stand, species composition, stand density, and site productivity. Because data from permanent sample plots are considered time series and cross sectional, diagnostic techniques were applied to identify the model's error structure. Appropriate fit based on the identified error structure was accomplished using weighted nonlinear least squares with a first-order autoregressive process. Results show that (1) all model parameters are significant at α = 0.05 level, and (2) the plot of studentized residuals against predicted diameter increment shows no consistent underestimate or overestimate for diameter increment. The model was also tested on an independent data set representing the population on which it is to be used. Results show that the average prediction biases are not significant at α = 0.05 level, indicating that the model appropriately describes the data and performs well when predictions are made.