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Published: 2013

Total Pages: 90

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Fibre length is one of the most important attributes influencing the quality of wood resources, and the ultimate value of forest products. Effective planning to optimize the forest value chain requires accurate and detailed information about the resource; however, information about the distribution of fibre properties on the boreal landscape is unavailable prior to harvest. Recent studies have shown that the growth rates of black spruce (Picea mariana (Mill)B.S.P.) are strongly linked to ecosite classifications, which represent standard combinations of substrate characteristics and canopy vegetation. The objective of this study was to create a model that links the microscopic cellular properties of wood (fibre length) to ecosite classification at the landscape scale. A series of black spruce increment cores were collected from nine different ecosite types within the boreal forest of northeastern Ontario and were processed using standard techniques for maceration and fibre length measurement. Hierarchical classification approaches including regression tree analysis and random forests were used to fit spatial classification models and find the most important predictor variables for four response variables; mean fibre length, standard deviation of fibre length, coefficient of variation (CV) and percentage area of stem containing ideal (≥ 3 mm) fibre. Stand basal area (BA) was the best predictor of mean fibre length, crown width class was found to be the best predictor of CV, and ecosite classification was the best variable for predicting the percentage of stem area containing ideal fibre lengths. The explanatory power of the above mentioned models ranged from 67-75%. By creating a model that links wood fibre length attributes to the landscape scale this research could be used to improve the sustainability of forest management by identifying ideal locations for harvest and silvicultural activities.