Address: 17 Naberezhnaya Severnoy Dviny, Arkhangelsk 163002 Russian Federation. Northern (Arctic) Federal University named after M.V.Lomonosov. Office 1425

Phone / Fax: (818-2) 21-61-18



Reserves and Structure of Phytomass in Northern Taiga Pine Forest Stands in the Komi Republic. P. 25–38

Версия для печати
Creative Commons License
These works are licensed under a Creative Commons Attribution 4.0 International License.

Andrey F. Osipov, Ivan N. Kutyavin, Aleksey V. Manov, Mikhail A. Kuznetsov, Kapitolina S. Bobkova

Complete text of the article:

Download article (pdf, 0.6MB )






The research aims at estimating phytomass reserves of northern taiga green-moss and sphagnum pine forests growing in the Komi Republic. The study was carried out in pine forests at the Zelenoborsk forest research station of the Institute of Biology of Komi Science Centre of the Ural Branch of the Russian Academy of Sciences (IB Komi SC UB RAS) in 2016–2019. We analyzed sample trees data and derived power equations of the dependence of the individual fractions weight on the stem diameter at the breast height (1.3 m) for the main forest-forming species (pine, spruce, larch, and birch) of green-moss and sphagnum forest are characterized by rather large phytomass reserves, 136–211 t/ha. While in sphagnum pine forests there are 89–96 t/ha of phytomass. Despite the admixture of other wood species, the leading pool is represented by pine trees, and the main fraction (46–56 %) is wood of stem. The input of stem wood and stem as a whole (bark and wood) is greater in green-moss pine forests compared to sphagnum pine forests, while the share of roots in these two types is approximately the same. Participation of tree crowns (needles/leaves and branches) in the total phytomass reserves of pine stands on automorphic soils is significantly lower (17 % on average) than in communities on semihydromorphic and hydromorphic soils (22 % on average). The relatively greater mass of needles and leaves resulted in a high LAI, which varied from 8.8 to 17.8 and from 7.7 to 9.8 ha/ha, respectively, in green-moss and sphagnum pine forests. We found a high reliable (R = 0.88; p = 0.004) relationship between LAI and the tree basal areas sum, whereas it is statistically insignificant with density and wood supply. The conversion factors were calculated to convert timber volume into phytomass reserves of both individual fractions and species as a whole. The presented data are useful for assessing the productivity of pine ecosystems in different growing conditions during forest monitoring (including remote sensing methods) and also when planning forest management measures in order to increase the productivity of pine forests.


Andrey F. Osipov*, Candidate of Biology, Senior Research Scientist; ResearcherID: P-9583-2015, ORCID:
Ivan N. Kutyavin, Candidate of Agriculture, Research Scientist; ResearcherID: P-9829-2015, ORCID:
Aleksey V. Manov, Candidate of Agriculture; Research Scientist; ResearcherID: P-9089-2015, ORCID:
Mikhail A. Kuznetsov, Candidate of Biology, Research Scientist; ResearcherID: P-9870-2015, ORCID:
Kapitolina S. Bobkova, Doctor of Biology, Chief Research Scientist; ResearcherID: P-9476-2015, ORCID:


Institute of Biology of Komi Science Centre of the Ural Branch of the Russian Academy of Sciences, ul. Kommunisticheskaya, 28, GSP-2, Syktyvkar, Komi Republic, 167982, Russian Federation;*,,,


pine forest, phytomass, Komi Republic, green-moss forest type, sphagnum forest type, northern taiga, leaf area index, conversion factor


This work was carried out within the framework of the state assignment of the Institute of Biology Komi SC UB RAS for the research topic “Zonal Regularities of the Structure and Productivity Dynamics of Primary and Anthropogenically Modified Phytocenoses of Forest and Bog Ecosystems of European North-East Russia” (registration number: 1021051101417-8-1.6.19). The authors are grateful to N.V. Torlopova, A.I. Patov and S.I. Naymushina for their assistance in the fieldwork.

For citation

Osipov A.F., Kutyavin I.N., Manov A.V., Kuznetsov M.A., Bobkova K.S. Reserves and Structure of Phytomass in Northern Taiga Pine Forest Stands in the Komi Republic. Lesnoy Zhurnal = Russian Forestry Journal, 2022, no. 4, pp. 25–38. (In Russ.).


  1. Babich N.A., Merzlenko M.D., Evdokimov I.V. Phytomass of Pine and Spruce Plantations in the European Part of Russia. Arkhangelsk, 2004. 112 p. (In Russ.).

  2. Process of Bioproductivity in the North Forest Ecosystems. Ed. by K.S. Bobkova, E.P. Galenko. Saint Petersburg, Nauka Publ., 2001. 278 p. (In Russ.).

  3. Bobkova K.S. Biological Productivity of Coniferous Forests of the European North-East. Leningrad, Nauka Publ., 1987. 156 p. (In Russ.).

  4. Vakurov A.D. Productivity of Pine Forests in Northern Taiga Subzone. Productivity of Forest Organic Matter in Different Natural Zones. Moscow, 1973, pp. 7–25. (In Russ.).

  5. Grabovskii V.V., Zukert N.V., Korzukhin M.D. Leaf Area Index Estimate for the Russian Territory Based on the State Forest Inventory. Lesovedenie = Russian Journal of Forest Science, 2015, no. 4, pp. 255–259. (In Russ.).

  6. Zamolodchikov D.G., Utkin A.I., Korovin G.N. Determination of Carbon Reserves by Conversion-Volume Factors Related to the Age of Stands. Lesovedenie = Russian Journal of Forest Science, 1998, no. 3, pp. 84–93 (In Russ.).

  7. Klevtsov D.N., Tyukavina O.N., Adayi G.M. Bioenergy Potential of Aerial Phytomass of Scots Pine in the Middle Taiga Forest Region. Lesnoy Zhurnal = Russian Forestry Journal, 2018, no. 4, pp. 49–55. (In Russ.).

  8. Kutyavin I.N. Pine Forests of the Northern Cis-Urals: Structure, Growth, Productivity. Syktyvkar, IB Komi SC UB RAS Publ., 2018. 176 p. (In Russ.).

  9. Molchanov A.A. Productivity of Organic Matter in Forests of Different Zones. Moscow, Nauka Publ., 1971. 275 p. (In Russ.).

  10. Molchanov A.A., Polyakov A.F. Productivity of Organic Matter in Sphagnum Pine Forests. Productivity of Organic and Biological Matter of the Forest. Moscow, 1974, pp. 43–78. (In Russ.).

  11. Osipov A.F. Biological Productivity of Whortleberry-Sphagnum Pine Forests in Medium Boreal Taiga. Lesnoy Zhurnal = Russian Forestry Journal, 2013, no. 1, pp. 43–51. (In Russ.).

  12. Tarasov S.I., Pristova T.A., Bobkova K.S. Dynamics of Phytomass of a Tree Stand of the Deciduous-Coniferous Phytocenosis in Middle Taiga of Komi Republic. Sibirskij Lesnoj Zurnal = Siberian Journal of Forest Science, 2018, no. 1, pp. 50–58. (In Russ.).

  13. Usol’tsev V.A. Biological Productivity of Forests in Northern Eurasia: Methods, Database and Its Supplements. Yekaterinburg, UrB RAS Publ., 2007. 637 p. (In Russ.).

  14. Schepaschenko D.G., Shvidenko A.Z., Perger C., Dresel C., Fritz S., Lakyda P. I., Mukhortova L.V., Usoltsev V.A., Bobkova K.S., Osipov A.F., Martynenko O.V., Karminov V.N., Ontikov P.V., Shchepashchenko M.V., Kraxner F. Forest Biomass Observation: Current State and Prospective. Sibirskij Lesnoj Zurnal = Siberian Journal of Forest Science, 2017, no. 4, pp. 3–11 (In Russ.).

  15. Bukvareva E., Zamolodchikov D., Grunewald K. National Assessment of Ecosystem Services in Russia: Methodology and Main Problems. Science of the Total Environment, 2019, vol. 655, pp. 1181–1196.

  16. Calders K., Origo N., Disney M., Nightingale J., Woodgate W., Armston J., Lewis Ph. Variability and Bias in Active and Passive Ground-Based Measurements of Effective Plant, Wood and Leaf Area Index. Agricultural and Forest Meteorology, 2018, vol. 252, pp. 231–240.

  17. Ivanov A.V., Pokamestova V.Yu., Kasatkin A.S., Zamolodchikov D.G. Leaf Area Indices of Forest Stands in Natural and Disturbed Forests of Primorsky Krai. Russian Journal of Ecology, 2020, vol. 51, iss. 4, pp. 299–305.

  18. Lu D. The Potential and Challenge of Remote Sensing-Based Biomass Estimation. International Journal of Remote Sensing, 2006, vol. 27, iss. 7, pp. 1297–1328.

  19. Payne N.J., Allan Cameron D., Leblanc J.-D., Morrison I.K. Carbon Storage and Net Primary Productivity in Canadian Boreal Mixedwood Stands. Journal of Forestry Research, 2019, vol. 30, iss. 5, pp. 1667–1678.

  20. R Core Team. R: A Language and Environment for Statistical Computing. Vienna, R Foundation for Statistical Computing, 2020. Available at: (accessed 16.10.20).

  21. Reich P.B., Luo Y., Bradford J.B., Poorter H., Perry Ch.H., Oleksyn J. Temperature Drives Global Patterns in Forest Biomass Distribution in Leaves, Stems, and Roots. PNAS, 2014, vol. 111, no. 38, pp. 13721–13726.

  22. Schepaschenko D., Moltchanova E., Shvidenko A., Blyshchyk V., Dmitriev E., Martynenko O., See L., Kraxner F. Improved Estimates of Biomass Expansion Factors for Russian Forests. Forests, 2018, vol. 9, iss. 6, art. 312.

  23. Sheil D., Bongers F. Interpreting Forest Diversity-Productivity Relationships: Volume Values, Disturbance Histories and Alternative Inferences. Forest Ecosystems, 2020, vol. 7, art. 6.

  24. Shobairi S.O.R., Usoltsev V.A., Chasovskikh V.P. Vegetation Fractional Coverage (VFC) Estimation of Planted and Natural Zones Based on Remote Sensing. American Journal of Environmental Policy and Management, 2018, vol. 4, no. 1, pp. 21–31.

  25. Usoltsev V.A. Forest Biomass and Primary Production Database for Eurasia. Yekaterinburg, USFEU Publ., 2020.

  26. Usoltsev V.A., Chasovskikh V.P., Noritsina Yu.V., Kokh E.V. Methods and Results of Studying the Geographical Trends in the Structure of Single-Tree Biomass of Larches and Two-Needled Pines in Eurasia. Russian Journal of Ecology, 2016, vol. 47, pp. 442–452.

  27. Yemshanov D., McKenney D.W., Hope E., Lempriere T. Renewable Energy from Forest Residues – How Greenhouse Gas Emission Offsets Can Make Fossil Fuel Substitution More Attractive. Forests, 2018, vol. 9, iss. 2, art. 79.

  28. Zianis D., Muukkonen P., Mäkipää R., Mencuccini M. Biomass and Stem Volume Equations for Tree Species in Europe. Silva Fennica Monographs 4, 2005. 63 p.


Make a Submission


Lesnoy Zhurnal (Russian Forestry Journal) was awarded the "Seal of Recognition for Active Data Provider of the Year 2024"