Combining Tree- and Stand-Level Models: A New Approach to Growth Prediction

Abstract

Abstract Approaches to combining information generated from different models are recognized for improving forecast quality. However, such an approach has not been developed for forest growth predictions. Therefore, a study was carried out to investigate the potential of combining models to forecast forest growth. A growth prediction system was developed for Norway spruce (Picea abies [L.] Karst.) by combining growth information generated from tree- and stand-level growth models parameterized from an identical data source. The tree-level model predicts relative tree diameter growth rates of a cumulative diameter distribution. The stand-level models predict absolute stand basal area growth. The combined prediction system consists of three basic steps: preliminary prediction, combination, and feedback modification. Preliminary predictions of stand basal area growth are generated annually from both types of growth models. The preliminary predictions are then combined on the basis of a variance and covariance method, and the combined estimator is used to update the growth models in a feedback procedure. In the tree-level growth model, the updated stand basal area is subsequently disaggregated to individual trees. On the basis of a validation data set, forecasts of stand basal area with the combined prediction system were shown to gain in efficiency from 14 to 43% compared with forecasts with the tree-level model alone and from 4 to 16% compared with projections with the stand-level model, depending on the length of the projection (5–30 years).