Simulating wood quality in forest management models

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 the basis of 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.In forestry, understanding the development and variation of wood quality is important for different management and planning problems which can be broadly classif ied under (1) management for harvest operations, and (2) long-term silvicultural planning. This paper reviews approaches and models that allow us to study these two problems in quantitative terms. We start the review by discussing the concept of «wood quality model», then classify the approaches on the basis of their complexity, underlying principle and intended application. 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 forest management.Key words: wood quality; mixed models; empirical models; hybrids models. ResumenSimulación de la calidad de madera en los modelos de gestión forestal Las propiedades de la madera se desarrollan cuando crece el arbol, estableciendose las células de la madera con propiedades específicas, y formandose la estructura del tronco que es central para la calidad de la madera. Esta desarrollo se ve influido por factores genéticos y ambientales y por las prácticas de gestión forestal. Es deseable incluir en los modelos de crecimiento y producción destinados a la evaluación económica de las prácticas de gestión una indicación de la calidad de la madera y cómo se ve afectada por factores genéticos y ambientales y por las medidas silvícolas.En el sector forestal, la comprensión del desarrollo y la variación de la calidad de la madera es importante en distintos programas de gestión y planificación que se pueden clasificar en (1) la gestión de las operaciones de cosecha, y (2) la planificación silvícola a largo plazo. Este artículo discute los principales enfoques y modelos que nos permiten estudiar estos dos problemas en términos cuantitativos. Comenzamos la revisión discutiendo el concepto de «mo-delo de calidad de madera», y luego clasificamos los enfoques sobre la base de su complejidad, el principio subyacente y el uso previsto. Mostramos tres modelos dinámicos avanzados de calidad y sus aplicaciones con estudios de casos. Estos incluyen modelos empíricos, híbridos y mecánicistas aplicadaos a las predicciones tanto de la madera aserrada como a las propiedades de la fibra. Por último, consideramos los retos actuales para el modelado de la calidad de la madera...

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“…Wood quality models aim to predict the properties of timber based on measured forest inventory data or assumptions of stand development in response to scenarios of management or future climate. Following the references in Mäkelä et al (2010), their main development phase began in the late 1980s with the SYLVER growth and sawing simulation system (Mitchell 1988). As also presented by Mäkelä et al (2010), a spectrum of models and modules since has evolved that covers all main types of growth algorithms and may imply a high level of stem shape and structure detail description (Houllier et al 1995;Hann et al 1997;Di Lucca 1999;Todoroki et al 2005).…”

“…Therefore, they need to predict stem structure on the spatial scale of individual boards (Mäkelä et al 2010). The most central structure of the lower stem (butt log) in radial direction adheres to the juvenile growth phase that is complex and governed by strong competition on the individual tree level (Donato et al 2012) but has rarely been covered by individual tree models (e.g.…”

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