Improving the robustness of biomass functions: from empirical to functional approaches
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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.
KeywordsQuercus robur Quercus petraea Covariate models Allometric equations Seemingly unrelated regression Generic models
- André F (2007) Influence of the heterogeneity of canopy structure on the spatio-temporal variability of atmospheric deposition within a mixed oak-beech stand. PhD thesis, Université catholique de LouvainGoogle Scholar
- Cienciala E, Apltauer J, Exnerová Z, Tatarinov F (2008) Biomass functions applicable to oak trees grown in Central-European forestry. J For Sci 54:109–120Google Scholar
- Dagnelie P (1975) Analyse statistique à plusieurs variables. Presses Agronomiques de Gembloux, GemblouxGoogle Scholar
- Dagnelie P, Palm R, Rondeux J (1999) Tables de cubage des arbres et des peuplements forestiers. Presses Agronomiques de Gembloux, GemblouxGoogle Scholar
- FAO (2010) Global Forest Resources Assessment 2010—main report. FAO Forestry Paper No. 163. http://www.fao.org/docrep/013/i1757e/i1757e00.htm. Accessed 12 June 2013
- Genet A, Wernsdörfer H, Jonard M, Pretzsch H, Rauch M, Ponette Q, Nys C, Legout A, Ranger J, Vallet P, Saint-André L (2011) Ontogeny partly explains the apparent heterogeneity of published biomass equations for Fagus sylvatica in central Europe. For Ecol Manag 261:1188–1202. doi:10.1016/j.foreco.2010.12.034 CrossRefGoogle Scholar
- Huet S, Bouvier A, Poursat M-A, Jolivet E (2004) Statistical tools for nonlinear regression: a pratical guide with S-PLUS and R examples. Springer, New YorkGoogle Scholar
- Husch B, Miller C, Beers T (1982) Forest mensuration. Wiley, New YorkGoogle Scholar
- IPCC (2003) Good practice guidance for land use, land-use change and forestry. Institute for Global Environmental Strategies, HayamaGoogle Scholar
- Nepveu G (1984) Contrôle héréditaire de la densité et de la rétractibilité du bois de trois espèces de Chêne (Quercus petraea, Quercus robur et Quercus rubra). Silvae Genet 33:110–115Google Scholar
- Parresol BR (1999) Assessing tree and stand biomass: a review with examples and critical comparisons. For Sci 45:573–593Google Scholar
- Sabine CL, Heimann M, Artaxo P, Bakker DC, Chen C-TA, Field CB, Gruber N, Le Quéré C, Prinn RG, Richey J (2004) Current status and past trends of the global carbon cycle. Scope-Sci Commitee Probl Environ Int Counc Sci Unions 62:17–44Google Scholar
- Sakamoto Y, Ishiguro M, Kitagawa G (1986) Akaike information criterion statistics. Reidel, Dordrecht, HollandGoogle Scholar
- SAS Institute Inc (2011) SAS/ETS OnlineDoc® 9.3. SAS Institute Inc, Cary, NCGoogle Scholar
- Seifert T, Schuck J, Block J, Pretzsch H (2006) Simulation von Biomasse-und Nährstoffgehalt von Waldbäumen. Beiträge zur Jahrestagung vom 29:208–223Google Scholar
- Spurr SH (1952) Forest inventory. Ronald Press, New YorkGoogle Scholar
- Zanetto A, Roussel G, Kremer A (1994) Geographic variation of inter-specific differentiation between Quercus robur L. and Quercus petraea (Matt.) Liebl. For Genet 1:111–123Google Scholar