%0 Research article
%T Linear prediction application for modelling the relationships between a large number of stand characteristics of Norway spruce stands
%A Siipilehto, Jouni
%D 2006
%J Silva Fennica
%V 40
%N 3
%R doi:10.14214/sf.334
%U https://silvafennica.fi/article/334
%X The aim was to produce models for a large number of stand  characteristics of Norway spruce dominated stands. A total of 227  national forest inventory based permanent stand plots, dominated by  Norway spruce (Picea abies), were used in modelling eight stand  variables as a function of the stand mean biological age and site  characteristics. The basic models were able to characterize the average  development of the modelled stand variables, but resulted in a  relatively high RMSE. Basal area (G) and stem number (N) were the most  inaccurate, having a RMSE of 34–41%, while that of mean diameter and  height characteristics varied between 16–20%. The expectations and error  variances of the basic models were calibrated with known stand  variables using linear prediction theory. The best linear unbiased  predictor (BLUP) with a single stand variable used for calibration  proved to be ineffective for unknown G and N, but relatively effective  for the unknown mean characteristics. However, calibration with one sum  and one mean characteristic proved to be effective, and additional  calibration variables enhanced the precision only marginally. The BLUP  method provided a flexible approach when characterizing the  relationships between a large number of stand variables, thus enabling  multiple use of these models because they were not fixed to a specific  inventory system.