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Research article - Peer-reviewed, 2022

Comparing basal area growth models for Norway spruce and Scots pine dominated stands

Goude, Martin; Nilsson, Urban; Mason, Euan; Vico, Giulia


Models that predict forest development are essential for sustainable forest management. Constructing growth models via regression analysis or fitting a family of sigmoid equations to construct compatible growth and yield models are two ways these models can be developed. In this study, four species-specific models were developed and compared. A compatible growth and yield stand basal area model and a five-year stand basal area growth model were developed for Scots pine (Pinus sylvestris L.) and Norway spruce (Picea abies (L.) Karst.). The models were developed using data from permanent inventory plots from the Swedish national forest inventory and long-term experiments. The species-specific models were compared, using independent data from long-term experiments, with a stand basal area growth model currently used in the Swedish forest planning system Heureka (Elfving model). All new models had a good, relatively unbiased fit. There were no apparent differences between the models in their ability to predict basal area development, except for the slightly worse predictions for the Norway spruce growth model. The lack of difference in the model comparison showed that despite the simplicity of the compatible growth and yield models, these models could be recommended, especially when data availability is limited. Also, despite using more and newer data for model development in this study, the currently used Elfving model was equally good at predicting basal area. The lack of model difference indicate that future studies should instead focus on model development for heterogeneous forests which are common but lack in growth and yield modelling research.


Picea abies; Pinus sylvestris; basal area; difference equation; long-term experiment; national forest inventory; regression

Published in

Silva Fennica
2022, Volume: 56, number: 2, article number: 10707