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

Ablo Paul Igor Hounzandji, Mathieu Jonard, Claude Nys , Laurent Saint-André   and Quentin Ponette

Earth and Life Institute, Environmental Sciences, UCL, Croix du Sud, 2, box L7.05.09, 1348 Louvain-la-Neuve, Belgium; UR1138, Unité Biogéochimie des Ecosystèmes Forestiers (BEF), Centre INRA de Nancy, INRA, 54280 Champenoux, France; CIRAD, UMR ECO&SOLS, 34000 Montpellier, France

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.


Large uncertainties still persist when using existing biomass equations for larger scale applications. 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. 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. 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. Using physically based model forms increased the robustness of the biomass equations.

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