AutyAchimMacdonaldEtAl2016

Référence

Auty, D., Achim, A., Macdonald, E., Cameron, A.D., Gardiner, B.A. (2016) Models for predicting clearwood mechanical properties of scots pine. Forest Science, 62(4):403-413. (Scopus )

Résumé

Wood mechanical properties, such as modulus of elasticity (MOE) and modulus of rupture (MOR), are important determinants of solid lumber performance and value. These properties vary systematically at different scales owing to multiple, potentially confounding, factors. Therefore, a statistical modeling approach may be an effective way to predict the impact of silvicultural practices on mechanical properties. The aim of this study was to develop models for predicting MOE and MOR in Scots pine (Pinus sylvestris L.), as functions of cambial age, height in the stem, wood density, and microfibril angle (MFA). Thirty-six trees were sampled from four mature Scots pine plantations in Scotland, UK. Longitudinal MOE and MOR were determined in static bending on 513 small (300 × 20 × 20 mm) defect-free samples. Nonlinear mixed-effects models based on an exponential function of cambial age were developed to predict the within-stem patterns of variation. The best model for MOR included cambial age, height in the stem, and sample density as explanatory variables, whereas the best MOE model also included a density/MFA term in the predictors. In growth simulations over a range of typical scenarios, the largest effect of silvicultural interventions was on the proportion of juvenile wood in the stem, but these had a negligible impact on mean tree MOE and MOR. The models will be incorporated into a growth, yield, and wood quality simulation system. © 2016 Society of American Foresters.

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@ARTICLE { AutyAchimMacdonaldEtAl2016,
    AUTHOR = { Auty, D. and Achim, A. and Macdonald, E. and Cameron, A.D. and Gardiner, B.A. },
    TITLE = { Models for predicting clearwood mechanical properties of scots pine },
    JOURNAL = { Forest Science },
    YEAR = { 2016 },
    VOLUME = { 62 },
    NUMBER = { 4 },
    PAGES = { 403-413 },
    NOTE = { cited By 0 },
    ABSTRACT = { Wood mechanical properties, such as modulus of elasticity (MOE) and modulus of rupture (MOR), are important determinants of solid lumber performance and value. These properties vary systematically at different scales owing to multiple, potentially confounding, factors. Therefore, a statistical modeling approach may be an effective way to predict the impact of silvicultural practices on mechanical properties. The aim of this study was to develop models for predicting MOE and MOR in Scots pine (Pinus sylvestris L.), as functions of cambial age, height in the stem, wood density, and microfibril angle (MFA). Thirty-six trees were sampled from four mature Scots pine plantations in Scotland, UK. Longitudinal MOE and MOR were determined in static bending on 513 small (300 × 20 × 20 mm) defect-free samples. Nonlinear mixed-effects models based on an exponential function of cambial age were developed to predict the within-stem patterns of variation. The best model for MOR included cambial age, height in the stem, and sample density as explanatory variables, whereas the best MOE model also included a density/MFA term in the predictors. In growth simulations over a range of typical scenarios, the largest effect of silvicultural interventions was on the proportion of juvenile wood in the stem, but these had a negligible impact on mean tree MOE and MOR. The models will be incorporated into a growth, yield, and wood quality simulation system. © 2016 Society of American Foresters. },
    AUTHOR_KEYWORDS = { Models; Modulus of elasticity; Modulus of rupture; Nonlinear mixed-effects; Pinus sylvestris L; Simulation },
    DOCUMENT_TYPE = { Article },
    DOI = { 10.5849/forsci.15-092 },
    KEYWORDS = { Elastic moduli; Exponential functions; Forecasting; Graphical user interfaces; Mechanical properties; Models; Wood, Explanatory variables; Microfibril angles; Modulus of rupture; Nonlinear mixed effects; Pinus sylvestris L; Silvicultural practices; Simulation; Statistical modeling, Forestry },
    SOURCE = { Scopus },
    URL = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-84980622731&partnerID=40&md5=85c2c8270fabe539acd7343309162c80 },
}

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