Predicting the green density as a means to achieve the volume of Norway spruce

Abstract

All roundwood that arrives at pulp mills in Sweden is measured manually to obtain the volume. Shortened times between the harvest of the wood and its transportation to the mill have decreased the variation in the green density of wood and have increased the appeal of a method that combines the prediction of green density and weight scaling of the trucks with wood to obtain the volume. The aim of this article is to create a statistical model that can predict the green density of Norway spruce (Picea abies (L.) Karst.) pulpwood with the help of density data from earlier years for a certain mill and meteorological data from the wood supply area of the mill. To create the model to predict the green density, stepwise regression was used. The results showed that the model can explain the variation in green density by 89%, and can predict the density with a mean error of 0–0.019 ton/m3. The average standard deviation of the ratio between the measured observations and the predicted density was 7% on a yearly basis with variations over the year. From October to March, the model showed results that were at the same level to the result for the volume measurement performed in Sweden today.