Methods for assessing uncertainty of growth and yield predictions

Abstract

Good estimates of the precision and accuracy of stand growth and yield predictions are needed in the decision-making process. Future growth and yield are often projected with a complex simulation system, so that these assessments are not easy. The precision of long-term growth and yield predictions has often been estimated through Monte Carlo simulation, combining several error sources with different variances. A simple method to assess the uncertainty of stand growth and yield predictions is to model the observed (past) errors obtained by comparing the observed stand characteristic with the characteristic predicted using the simulation system. Using this model, the uncertainty of future predictions can then be anticipated. Another possibility is to form an elementary model for stand growth (or yield) and use its variance as an assessment of the uncertainty for the simulation system. In this paper, the above-mentioned three methods were used for assessing the uncertainty of a simulation system. These assessments were then compared with the empirical estimates of uncertainty. The three methods were also compared with respect to their data requirements and the capabilities of the methods.