Tree and stand level estimations of Abies alba Mill. aboveground biomass
We provided a complete set of tree- and stand-level models for biomass and carbon content of silver fir Abies alba. This allows for better characterization of forest carbon pools in Central Europe than previously published models. The best predictor of biomass at the stand level is stand volume, and the worst are stand basal area and density.
Context Among European forest-forming tree species with high economic and ecological significance, Abies alba Mill. is the least characterized in terms of biomass production.
Aims To provide a comprehensive set of tree- and stand-level models for A. alba biomass and carbon stock. We hypothesized that (among tree stand characteristics) volume will be the best predictor of tree stand biomass.
Methods We studied a chronosequence of 12 A. alba tree stands in southern Poland (8–115 years old). We measured tree stand structures, and we destructively sampled aboveground biomass of 96 sample trees (0.0–63.9 cm diameter at breast height). We provided tree-level models, biomass conversion and expansion factors (BCEFs) and biomass models based on forest stand characteristics.
Results We developed general and site-specific tree-level biomass models. For stand-level models, we found that the best predictor of biomass was stand volume, while the worst were stand basal area and density.
Conclusion Our models performed better than other published models, allowing for more reliable biomass predictions. Models based on volume are useful in biomass predictions and may be used in large-scale inventories.
Keywords
Allometric equations, Biomass allocation, Biomass conversion and expansion factors, IPCC guidelines, Silver fir
Open-access Publication
Jagodziński, A.M., Dyderski, M.K., Gęsikiewicz, K. et al. Annals of Forest Science (2019) 76: 56. https://doi.org/10.1007/s13595-019-0842-y
Data availability
The datasets generated and analyzed during the current study are available in the FigShare repository (Jagodziński et al. 2019) at https://doi.org/10.6084/m9.figshare.7673651.
