Uncertainty of biomass stocks in Spanish forests: a comprehensive comparison of allometric equations

Ameztegui, A. ; Rodrigues, M. (Universidad de Zaragoza) ; Granda, V.

Resumen: Biomass and carbon content are essential indicators for monitoring forest ecosystems and their role in climate action, but their estimation is not straightforward. A typical approach to solve these limitations has been the estimation of tree or stand biomass based on forest inventory data, using either allometric equations or biomass expansion factors. Many allometric equations exist, but very few studies have assessed how the calculation methods used may impact outcomes and how this impact depends on genera, functional group, climate or forest structural attributes. In this study we evaluate the differences in biomass estimates yielded by the most widely used biomass equations in Spain. We first quantify the discrepancies at tree level and among the main forest tree species. We observed that the divergences in carbon estimations between different equations increased with tree size, especially in the case of hardwoods and for diameters beyond the range used to calibrate the equations. At the plot level, we found considerable differences between the biomass values predicted using different methods (above 25% in one out of three plots), which constitutes a warning against the uncritical choice of equations to determine biomass or carbon values. The spatial representation of the differences revealed geographical patterns related to the dominance of fast-growing species such as Eucalyptus or Pinus pinaster, with a minor effect of forest structure, and almost no effect of climate. Finally, we observed that differences were mostly due to the data source rather than the modelling approach or equation used. Based on our results, BEF equations seem a valid and unbiased option to provide nation-level estimations of carbon balance, although local equations should preferably be used if they are available for the target area. © 2022, The Author(s).

Idioma: Inglés

DOI: 10.1007/s10342-022-01444-w

Año: 2022

Publicado en: European Journal of Forest Research 141 (2022), 395–407
ISSN: 1612-4669

Factor impacto JCR: 2.8 (2022)

Categ. JCR: FORESTRY rank: 19 / 69 = 0.275 (2022) - Q2 - T1

Factor impacto CITESCORE: 5.1 - Agricultural and Biological Sciences (Q1)

Factor impacto SCIMAGO: 0.705 - Plant Science (Q1) - Forestry (Q1)

Tipo y forma: Artículo (Versión definitiva)

Área (Departamento): Área Análisis Geográfico Regi. (Dpto. Geograf. Ordenac.Territ.)