Equations for estimating belowground biomass of Silver Birch, Oak and Scots Pine in Germany

• Main Authors: Röhling S, Demant B, Dunger K, Neubauer M, Oehmichen K, Riedel T, Stümer W
• Format: Article
• Language: English
• Published: Italian Society of Silviculture and Forest Ecology (SISEF) 2019-04-01
• Series: iForest - Biogeosciences and Forestry
• Subjects: Belowground Biomass; Allometric Equations; National Greenhouse Gas Inventory; Betula pendula; Quercus robur; Quercus petraea; Pinus sylvestris
• Online Access: https://iforest.sisef.org/contents/?id=ifor2862-012

In this study we derived allometric functions for estimating the belowground biomass (BGB) of Silver Birch (Betula pendula Roth), Pedunculate Oak (Quercus robur L.), Sessile Oak (Quercus petraea (Matt.) Liebl.) and Scots Pine (Pinus sylvestris L.) in Germany. To assess the impact on German greenhouse gas (GHG) reporting, these new functions were further compared with BGB functions currently used in France and Sweden. For developing new BGB functions 48 Silver Birches, 39 Pedunculate and Sessile Oaks and 54 Scots Pines were destructively sampled. The sampled trees spanned a DBH range from 8.2 to 52.9 cm for Silver Birch, from 7.4 to 42.0 cm for Oak and from 7.2 to 53.2 cm for Scots Pine. After fitting the data, the following values of model efficiency were achieved: 0.81 for Silver Birch, 0.98 for Oak and 0.95 for Scots Pine. The model root mean square error varies between 5.2 kg for Oak, 13.7 kg for Scots pine and 26.9 kg for Silver Birch. Comparison with the currently applied BGB functions in the German GHG inventory from France and Sweden showed that the use of these functions results in systematically different estimates for the BGB of Silver Birch and Oak. Thus, our findings indicate that BGB functions recommended for other European countries (in particular France and Sweden) are not appropriate for estimating the BGB for the tree species concerned in Germany. Currently, the derived data-set for BGB of Silver Birch, Oak and Scots Pine is the largest in Germany and the developed functions are thus the best available for estimating national BGB stock and stock change in Germany at the moment.