Ontogeny partly explains the apparent heterogeneity of published biomass equations for Fagus sylvatica in central Europe

Genet, Astrid; Wernsdörfer, Holger; Jonard, Mathieu; Pretzsch, Hans; Rauch, Mathieu; Ponette, Quentin; Nys, Claude; Legout, Arnaud; Ranger, Jacques; Vallet, Patrick; Saint‐André, Laurent

doi:10.1016/j.foreco.2010.12.034

Cited by 75 publications

(59 citation statements)

References 64 publications

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“…In this purely statistical approach, we started from the following reference equation (Genet et al 2011):…”

Section: Empirical Approachmentioning

confidence: 99%

“…For each approach, the selected biomass models were simultaneously adjusted to satisfy the biomass compartment additivity constraint (Parresol 1999(Parresol , 2001Návar et al 2002;Carvalho and Parresol 2003;Bi et al 2004;Genet et al 2011). Seemingly unrelated regression method was used to solve the simultaneous equation system through the PROC MODEL procedure in SAS (SAS Institute Inc. 2011), and the parameter values were derived using the maximum likelihood method.…”

Section: Model Fittingmentioning

confidence: 99%

“…The parameter α represents the biomass of the tree before it attains a height of 1.30 m, the parameter β represents the scaling factor, and the parameter γ represents the allometric exponent governing the proportionality between biomass and volume increments (Genet et al 2011). It was selected as it proved to be quite efficient to model the different biomass compartments of beech (Fagus sylvatica L.) over a wide range of stands located in Central Europe (Genet et al 2011). Sessile and pedunculate oaks are indeed expected to behave similarly to beech in terms of allometric relationships (André et al 2010).…”

Section: Empirical Approachmentioning

confidence: 99%

“…While biomass studies often relied on limited numbers of sites and trees in the past, some biomass data from various sources were recently pooled together to obtain large data sets, which therefore broadened the validity range of the biomass equations established based on these data (Wutzler et al 2008). In addition, when a lot of data are available, a subset can be devoted to validation in order to independently evaluate model performances (Saint-André et al 2005;Wutzler et al 2008;Genet et al 2011). The use of covariate models is another way to improve the validity range of biomass equations; this approach consists in fitting biomass equations site by site (or stand by stand) and to relate equation parameters to site/stand variables (Saint- André et al 2005;António et al 2007;Genet et al 2011).…”

Section: Introductionmentioning

confidence: 99%

“…In addition, when a lot of data are available, a subset can be devoted to validation in order to independently evaluate model performances (Saint-André et al 2005;Wutzler et al 2008;Genet et al 2011). The use of covariate models is another way to improve the validity range of biomass equations; this approach consists in fitting biomass equations site by site (or stand by stand) and to relate equation parameters to site/stand variables (Saint- André et al 2005;António et al 2007;Genet et al 2011). Finally, the problem of tree component additivity is now commonly solved by using joint-generalized least square regression, also called seemingly unrelated regression (Parresol 2001;Lambert et al 2005).…”

Section: Introductionmentioning

confidence: 99%

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Improving the robustness of biomass functions: from empirical to functional approaches

Hounzandji

Jonard

Nys

et al. 2014

Annals of Forest Science

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Abstract& Key message We developed precise, consistent, generic, and robust biomass equations for seven aboveground tree components of sessile and pedunculate oaks. These equations can be used to accurately estimate carbon stocks and fluxes in and out of the forest. & Context Large uncertainties still persist when using existing biomass equations for larger scale applications. & Aims The objective of this study was to test two contrasting modeling approaches to obtain biomass estimates of various components (stem, stem wood, stem bark, crown, and three branch categories) for Quercus petraea and Quercus robur and to compare them in terms of predictive capacity, genericity, consistency, and robustness. & Methods All models were calibrated on a total of 117 oak trees sampled over a wide range of sites and stands and further tested on an independent data set of 33 trees. The "empirical" approach consisted in declining a common allometric equation based on two variables (diameter at breast height and total height) into all its possible forms and selecting the final model on purely statistical performances; the "structural" method was based on the fitting of a priori dedicated model forms for each component to allow a clear interpretation of the model parameters. & Results For the stem components, both approaches resulted in similar statistical performances despite difference in model forms and number of parameters. Although equally performant on the validation data set for the total crown, only the structural model gave satisfactory results when applied to the independent data set. Both approaches failed to accurately predict the branch fractions on the validation data set. & Conclusion Using physically based model forms increased the robustness of the biomass equations.

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“…In this purely statistical approach, we started from the following reference equation (Genet et al 2011):…”

Section: Empirical Approachmentioning

confidence: 99%