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Quality and Price of Spruce Logs, Determined Conventionally and by Dendrochronological and NDE Techniques
We examined valuable log assortments of Norway spruce (Picea abies) from a traditional auction in Slovenia where spruce growth on many sites is affected by climate change. From 6620 logs, we selected 817 that obtained the highest prices. Factors including log dimensions and geometry, tree-ring characteristics, quality grades according to the standard, properties measured by NDE stress wave testing, and their combined effect on price were modelled. The results showed that half of the auctioned logs were of highest quality (Q1, Q2), with diameters over 60 cm. These logs were more expensive than the thinner logs of lower quality (Q3, Q4). The quality class of the logs, determined by their external features and geometry, was associated with tree-ring and acoustic characteristics. The artificial neural network model (ANN) with feed-forward backpropagation using tree-ring data, longitudinal stress wave velocity, and damping showed that more than 75% of the logs could be accurately classified into quality classes. On the other hand, tree-ring data and acoustic characteristics could not adequately explain the price offered at auction, which probably also depends on unidentified individual requirements and the needs of the buyer.
Quality and Price of Spruce Logs, Determined Conventionally and by Dendrochronological and NDE Techniques
We examined valuable log assortments of Norway spruce (Picea abies) from a traditional auction in Slovenia where spruce growth on many sites is affected by climate change. From 6620 logs, we selected 817 that obtained the highest prices. Factors including log dimensions and geometry, tree-ring characteristics, quality grades according to the standard, properties measured by NDE stress wave testing, and their combined effect on price were modelled. The results showed that half of the auctioned logs were of highest quality (Q1, Q2), with diameters over 60 cm. These logs were more expensive than the thinner logs of lower quality (Q3, Q4). The quality class of the logs, determined by their external features and geometry, was associated with tree-ring and acoustic characteristics. The artificial neural network model (ANN) with feed-forward backpropagation using tree-ring data, longitudinal stress wave velocity, and damping showed that more than 75% of the logs could be accurately classified into quality classes. On the other hand, tree-ring data and acoustic characteristics could not adequately explain the price offered at auction, which probably also depends on unidentified individual requirements and the needs of the buyer.
Quality and Price of Spruce Logs, Determined Conventionally and by Dendrochronological and NDE Techniques
Aleš Straže (author) / Klemen Novak (author) / Katarina Čufar (author)
2022
Article (Journal)
Electronic Resource
Unknown
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