Abstract
The raw material properties of wood develop as the tree grows, laying down wood cells with specific properties, and forming the stem structure that is focal for timber quality. This development is influenced by genetic and environmental factors and forest management practices. It is desirable in growth and yield models intended for the economic assessment of management practices to include some indication of wood quality and how it is affected by genetics, environmental factors and silvicultural measures. This paper reviews approaches and models that allow us to consider the development of wood quality in combination with tree growth, and thus to include wood quality in the assessment of the value of the yield. We present such models as classified into three categories based on their complexity and information needs: quality indicators, static quality models, and dynamic quality models. We illustrate three advanced dynamic quality models and their applications with example case studies. These include empirical, hybrid, and mechanistic models applied to predictions of both sawn timber and fibre properties. Finally, we consider the current challenges for wood quality modelling in connection with growth models.
Highlights
Wood is used as raw material for sawmills, pulpmills, panel production, and increasingly for bioenergy
The raw material properties of wood develop as the tree grows, laying down wood cells with specific properties, and forming the stem structure that is key for timber quality
Our objective is to demonstrate how this basic approach can be connected with either empirical or process-based growth models at different levels of detail, and how these models can be applied to variable forest management questions
Summary
Wood is used as raw material for sawmills, pulpmills, panel production, and increasingly for bioenergy. The objective is to predict wood quality characteristics from measured stem, stand and site properties through equations fitted to data This approach applies, in principle, to all levels of model complexity, and the inputs can be created using a growth model. The system applies different models that together characterise wood quality properties, including the branch cluster model BLOSSIM (Grace et al, 1999, 2006) and descriptive models for taper and wood density (Gordon et al, 2006) These data, together with a set of log grades and prices are used to estimate potential stand value. Economic optimisation has been carried out using PipeQual to determine the optimal thinnings and rotation lengths for Scots pine (Hyytiäinen et al, 2004) and to consider alternative uses of Norway spruce by sawmills and pulpmills (Cao et al, 2008)
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