Predicting the Extent of Root and Butt rot in Stems of Norway Spruce (Picea abies)
Commercially, Norway spruce (Picea abies (L.) Karst) is the most important tree species in Norway, representing 72% of the industrial timber volume sold in 2019. However, Norway spruce is particularly prone to infection of root and butt rot pathogens, degrading the value of the resource. Studies usi...
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Format: | Dissertation |
Sprache: | eng |
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Zusammenfassung: | Commercially, Norway spruce (Picea abies (L.) Karst) is the most important tree species in Norway, representing 72% of the industrial timber volume sold in 2019. However, Norway spruce is particularly prone to infection of root and butt rot pathogens, degrading the value of the resource. Studies using National Forest Inventory Data (NFI) show that the nationwide rot frequency of Norway spruce is somewhere between 7.9-9.5%. Other studies have indicated that the presence of root and butt rots are in proportions of approximately 1 out of 5 Norway spruce trees. Root and butt rots cause substantial annual economic losses estimated at NOK 100 millions. The losses are related to degradation of timber resources, per instance, culled by the harvester operator. However, the extent of which the root and butt rots develops up the stem is not known by the harvester operator. Therefore, developing a practical prediction model for rot heights in Norway spruce, to guide the operator in decision making, was addressed in this thesis.
The data sampling was carried out on three different occasions. In the first sampling, root and butt rot infected spruce trees were identified. Secondly, the trees were cut to examine the extent of rot in the stems. At last, tree-specific measurements were gathered.
The prediction model could explain approximately 21% of the variance in the dataset. Individually, the independent variables diameter at root, proportion of rot and tree height, showed a positive relationship to the prediction of rot height, with increasing values increasing the prediction of rot height. In the evaluation of the prediction model using optimal bucking, a total utilization grade of 76% was discovered. On tree level, the utilization grade was 74% with a standard error of 18%. |
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