Canadian Forest Service Publications
Modeling wood fibre length in black spruce Picea mariana (Mill.) BSP based on ecological land classification. 2015. Townshend, E.; Pokharel, B.; Groot, A.; Pitt, D.; Dech, J.P. Forests 6:3369-3394.
Year: 2015
Issued by: Great Lakes Forestry Centre
Catalog ID: 36393
Language: English
Availability: PDF (request by e-mail)
Available from the Journal's Web site. †
DOI: 10.3390/f6103369
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Abstract
Effective planning to optimize the forest value chain requires accurate and detailed information about the resource; however, estimates of the distribution of fibre properties on the landscape are largely unavailable prior to harvest. Our objective was to fit a model of the tree-level average fibre length related to ecosite classification and other forest inventory variables depicted at the landscape scale. A series of black spruce increment cores were collected at breast height from trees in nine different ecosite groups within the boreal forest of northeastern Ontario, and processed using standard techniques for maceration and fibre length measurement. Regression tree analysis and random forests were used to fit hierarchical classification models and find the most important predictor variables for the response variable area-weighted mean stem-level fibre length. Ecosite group was the best predictor in the regression tree. Longer mean fibre-length was associated with more productive ecosites that supported faster growth. The explanatory power of the model of fitted data was good; however, random forests simulations indicated poor generalizability. These results suggest the potential to develop localized models linking wood fibre length in black spruce to landscape-level attributes, and improve the sustainability of forest management by identifying ideal locations to harvest wood that has desirable fibre characteristics.
Plain Language Summary
Effective planning to optimize the forest value chain requires accurate and detailed information about the resource. We wanted to determine whether ecosite classification and other forest inventory variables at the landscape scale could be used to predict average tree fibre length. We found that ecosite type was the best predictor, as more productive sites supported faster growth and associated longer fibres. Our results show there is potential to develop localized models linking wood fibre length in black spruce to landscape-level attributes. This could improve the sustainability of forest management by identifying ideal locations to harvest wood that has desirable fibre characteristics