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<p>Taper models are widely used to estimate log assortments and, consequently, forest yield from inventory data. However, for <i>Pinus taeda</i>, few studies have employed mixed-effects taper models that explicitly account for the hierarchical structure of forestry data and heterogeneity of variances. This study addresses this gap by developing and evaluating mixed-effects taper models based on modified versions of Kozak’s (1969) equation. The models incorporate random effects at the farm/forest region, stand, and tree levels and allow for different variance structures, enabling them to capture the heterogeneity commonly observed in <i>P. taeda</i> stands. Diagnostic procedures using least confounded residuals were applied to assess model adequacy. Compared with traditional fixed-effects taper models, the selected mixed-effects model achieved superior performance, including reduced bias, improved fit across stem sections, and better predictive accuracy. Additionally, in Appendices, we provide a tutorial outlining the computational procedures in R software for statistical modeling of data related to this species within the mixed-effects model framework.</p>

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Modeling Pinus tree taper data using mixed-effects models

  • 期刊论文

  • 发表日期：

    2026年2月21日

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  • Breno Gabriel da Silva,
  • Clarice Garcia Borges Demétrio,
  • Renata Alcarde Sermarini,
  • Alexandre Behling,
  • Geert Molenberghs,
  • Geert Verbeke,
  • Eduardo Resende Girardi Marques,
  • Yuri Accioly,
  • Marco Aurélio Figura

摘要

Taper models are widely used to estimate log assortments and, consequently, forest yield from inventory data. However, for Pinus taeda, few studies have employed mixed-effects taper models that explicitly account for the hierarchical structure of forestry data and heterogeneity of variances. This study addresses this gap by developing and evaluating mixed-effects taper models based on modified versions of Kozak’s (1969) equation. The models incorporate random effects at the farm/forest region, stand, and tree levels and allow for different variance structures, enabling them to capture the heterogeneity commonly observed in P. taeda stands. Diagnostic procedures using least confounded residuals were applied to assess model adequacy. Compared with traditional fixed-effects taper models, the selected mixed-effects model achieved superior performance, including reduced bias, improved fit across stem sections, and better predictive accuracy. Additionally, in Appendices, we provide a tutorial outlining the computational procedures in R software for statistical modeling of data related to this species within the mixed-effects model framework.