Age Dynamics of Gray Alder Biomass in the Stands of the Arkhangelsk Region

The assessment of forest productivity in forestry and forest ecology has a long tradition, but in recent decades there has been a paradigm shift: the target function of forestry, consisting in wood cultivation, has shifted towards the biosphere-stabilizing function and the assessment of biomass and carbon sequestration capacity of forests. Equations and tables for assessing biomass at the stand level have an advantage as they can be used to characterize both the biomass of individual stands and their totality, as well as (when combined with the state forest inventory data) the situations in the forests throughout the country. The complexity of the work on assessing biomass of trees and stands has prompted researchers to use the existing tables of growth progress when compiling the tables of biological productivity of the stands using the recursive method. For the conditions of the northern and middle taiga of the Arkhangelsk Region, there are no tables of growth progress for gray alder biomass. The aim of this research is to study the growth progress of gray alder biomass in the stands of the Arkhangelsk Region. A series of sample plots has been laid to assess the above-ground biomass of the trees of this species. Based on the actual data from 50 model trees from 30 sample plots, the allometric models of the dependence of biomass fractions on the stem volume have been developed. The obtained models have been combined with the tables of growth progress for gray alder by quality class and the table of biological productivity of the species for the conditions of the Arkhangelsk Region has been drawn up. The results have been compared with the data on the biological productivity of gray alder in Belarus, Lithuania and Latvia. It has been shown that the biomass of the mature stands of gray alder of the 1st and 2nd quality classes in the Arkhangelsk Region is 3–9 % less than in Belarus and Lithuania. For the stands of the third and fourth classes, this difference increases to 29–48 %. Such differences in the biomasses of the compared regions can be explained by different zonal site conditions.