In recent decades, forest plantations have emerged as pivotal atmospheric carbon sinks, with biomass equations playing a crucial role in quantifying their carbon sequestration potential. This research delineates biomass equations for E. globulus and E. nitens. The methodology proposed for the model fitting uses the maximum likelihood method in a multivariate equation system fitting simultaneously. The fit strategy incorporates additivity constraints in the estimation functions and variance functions to address the heteroskedasticity of biomass. Two structures of the variance–covariance matrix were evaluated to assess the dependence of the different components of tree biomass. Trees from both species were sourced from varied forest plantations and assessed for their volume with-bark (<InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="44391_2025_14_Article_IEq1.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="27" /> </InlineMediaObject> <EquationSource Format="TEX">${V}_{wb}$</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mi>V</mi> <mrow> <mi mathvariant="italic">wb</mi> </mrow> </msub> </math></EquationSource> </InlineEquation>) and diameter at breast height (D). Notably, <InlineEquation ID="IEq2"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="44391_2025_14_Article_IEq1.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="27" /> </InlineMediaObject> <EquationSource Format="TEX">${V}_{wb}$</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mi>V</mi> <mrow> <mi mathvariant="italic">wb</mi> </mrow> </msub> </math></EquationSource> </InlineEquation> exhibited the minimal Root Mean Square Error (RMSE) and bias. For E. globulus, the RMSE was 29.8 kg/tree with a bias of -3.3 kg/tree, while for E. nitens, the metrics were an RMSE of 56.7 kg/tree and a bias of 1.4 kg/tree. This study furnishes robust biomass estimation equations, optimizing the management of forest plantations as carbon sinks and laying the groundwork for advanced forest management practices.

Modeling Above-Ground Biomass in Eucalyptus Globulus and Eucalyptus Nitens Incorporating Explicit Multivariate Uncertainty

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发表日期：

2025年2月18日

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Simón Sandoval,
Cristián R. Montes,
Pablo Mena-Quijada,
Eduardo Acuña,
Guillermo F. Olmedo

摘要

In recent decades, forest plantations have emerged as pivotal atmospheric carbon sinks, with biomass equations playing a crucial role in quantifying their carbon sequestration potential. This research delineates biomass equations for E. globulus and E. nitens. The methodology proposed for the model fitting uses the maximum likelihood method in a multivariate equation system fitting simultaneously. The fit strategy incorporates additivity constraints in the estimation functions and variance functions to address the heteroskedasticity of biomass. Two structures of the variance–covariance matrix were evaluated to assess the dependence of the different components of tree biomass. Trees from both species were sourced from varied forest plantations and assessed for their volume with-bark ( ${V}_{wb}$ $V_{wb}$ ) and diameter at breast height (D). Notably, ${V}_{wb}$ $V_{wb}$ exhibited the minimal Root Mean Square Error (RMSE) and bias. For E. globulus, the RMSE was 29.8 kg/tree with a bias of -3.3 kg/tree, while for E. nitens, the metrics were an RMSE of 56.7 kg/tree and a bias of 1.4 kg/tree. This study furnishes robust biomass estimation equations, optimizing the management of forest plantations as carbon sinks and laying the groundwork for advanced forest management practices.