Epsilon Archive for Student Projects

Abstract

The study compared quality distribution in deliveries of pine sawlogs to SCA's sawmill Bollsta in 2019. The purpose was to investigate whether there were any differences depending on region, origin, season for harvesting and diameter and to create a picture of how the current quality classification meets the sawmill's own sorting. This was done by analyzing data on measured volumes. In order to investigate how the current quality classification meets Bollsta's own quality sorting, an interview was performed.
Jämtland was the region that delivered the largest share of class 1 sawlogs. However, Medelpad and Ångermanland were not far behind. Västerbotten, on the other hand, had the smallest share of class 1 and the largest share of class 4. Medelpad and Ångermanland had the highest average topdiameter, followed by Jämtland. These three regions were relatively even, while Västerbotten had a clearly lower average topdiameter. It was found that there was a greater share of class 1 from individual forest owners than from SCA's own forest holdings. It was also possible to see trends of more class 1 sawlogs being delivered during spring and autumn. The proportion of class 1 sawlogs increased in the coarser diameter classes. The wreckage proportion increased exponentially with the diameter. However, there was no connection between quality distribution and average site productivity. The total wreckage share for all deliveries to Bollsta sawmill in 2019 was 2.1 percent, which is a very good level.
In the sorting of sawlogs at the sawmill, information from the quality classification carried out by Biometria is not used because it is not fully adapted to the products produced at Bollsta. Instead, the wood is sorted using a three-dimensional measuring frame and an X-ray equipment. However, the payment to the seller is based on Biometria's quality rating.
In order to create a better picture of the raw material delivered to Bollsta sawmill, the results from the sawmill's analysis equipment linked to deliveries would need to be analyzed. Then clearer correlations can be made on how the distribution of wood properties looks in deliveries depending on region, origin, season and diameter.