Additive tree biomass equations for Betula platyphylla Suk. plantations in Northeast China

  • Xiuwei WangEmail author
  • Dehai Zhao
  • Guifen LiuEmail author
  • Chengjun Yang
  • R. O. Teskey
Research Paper


Key message

A new system of additive tree biomass equations was developed for juvenile white birch plantations based on tree diameter at breast height (DBH) and tree height (HT). Compared with previous equations developed for natural white birch forests, the new system included one more biomass component and provided more accurate predictions.


Accurate estimates of tree component and total biomass are necessary for evaluating alternative forest management strategies for biomass feedstock, carbon sequestration, and products. Previous biomass equations developed for white birch trees in natural stands provided substantially biased predictions for white birch plantations.


A new system of additive tree biomass equations was developed for juvenile white birch plantations in the northeastern China.


With destructive biomass sampling data from 501 trees sampled from white birch provenance and family trails at ages 7, 9, 10, and 13 in three provinces, a system of nonlinear additive tree biomass equations based on DBH and tree height was developed using the nonlinear seemingly unrelated regressions (NSUR) approach.


Compared with previously published equations developed for natural white birch forests, the new system provided more accurate predictions of white birch tree component and aboveground and total biomass, especially of branch, foliage, and root biomass.


The new system extended the applicability of biomass equations to white birch plantations in the northeastern China.


Biomass additivity Destructive sampling White birch 


Funding information

This research was financially supported by the National Natural Science Foundation of China (31670476) and the Fundamental Research Funds for the Central Universities (2572016CA02).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


  1. Affleck DLR, Dieguez-Aranda U (2016) Additive nonlinear biomass equations: a likelihood-based approach. For Sci 62:129–140Google Scholar
  2. Albaugh T, Allen HL, Fox TR, Carlson CA, Rubilar RA (2009) Opportunities for fertilization of loblolly pine in the sand hills of the Southeastern United States. South J Appl For 33:129–136Google Scholar
  3. Bi H, Turner J, Lambert MJ (2004) Additive biomass equations for native eucalypt forest trees of temperate Australia. Trees 18:467–479CrossRefGoogle Scholar
  4. Bi H, Long Y, Turner J, Lei Y, Snowdon P, Li Y, Harper RJ, Zerihun A, Ximenes F (2010) Additive prediction of aboveground biomass for Pinusradiata (D.Don) plantations. For Ecol Manag 259:2301–2314CrossRefGoogle Scholar
  5. Bi H, Murphy S, Volkova L, Weston CJ, Fairman T, Li Y, Norris J, Lei X, Caccamo G (2015) Additive biomass equations based on complete weighing of sample trees for open eucalypt forest species in south-eastern Australia. For Ecol Manag 349:106–121CrossRefGoogle Scholar
  6. Castedo-Dorado F, Gómez-García E, Diéguez-Aranda U, Barrioanta M, Crecente-Campo F (2012) Aboveground stand-level biomass estimation: a comparison of two methods for major forest species in northwest Spain. Ann For Sci 69:735–746CrossRefGoogle Scholar
  7. Daryaei A, Sohrabi H (2016) Additive biomass equations for small diameter trees of temperate mixed deciduous forests. Scand J For Res 31:394–398CrossRefGoogle Scholar
  8. Dong LH, Li FR, Jia WW (2013) Compatible tree biomass models for natural white birch (Betulaplatyphylla) in northeast China forest area. Sci Silvae Sin 49:75–85Google Scholar
  9. Dong L, Zhang L, Li F (2015) Developing additive systems of biomass equations for nine hardwood species in Northeast China. Trees 29:1–15CrossRefGoogle Scholar
  10. Fang J, Wang Z, Tang Z (2011) Atlas of Woody plants in China. Higher Education Press, Beijing and Springer-Verlag Berlin HeidelbergGoogle Scholar
  11. Gerbing DW, Anderson JC (1985) The effects of sampling error and model characteristics on parameter estimation for maximum likelihood confirmatory factor analysis. Multivar Behav Res 20:255–271CrossRefGoogle Scholar
  12. Kuang KR, Zheng SX, Li PQ, Lu AM (1979) Flora of China. Science Press, BeijingGoogle Scholar
  13. Li P, Fang G, Sun C (1995)Wood characteristics of pulpwood. Chemistry & Industry of Forest ProductsGoogle Scholar
  14. Mensah S, Kakaï RG, Seifert T (2016) Patterns of biomass allocation between foliage and woody structure: the effects of tree size and specific functional traits. Ann For Res 59:49–60CrossRefGoogle Scholar
  15. Parresol BR (1999) Assessing tree and stand biomass: a review with examples and critical comparisons. For Sci 45:573–593Google Scholar
  16. Parresol BR (2001) Additivity of nonlinear biomass equations. Can J For Res 31:865–878CrossRefGoogle Scholar
  17. SAS INSTITUE INC (2011) Publishing S., SAS/ETS® 9.3 user’s guide. SAS Institute Inc., CaryGoogle Scholar
  18. Satoo T, Madgwick HAI (1982) Forest biomass. MartinusNijhoff/Dr. W. Junk Publishers, The Hague. 152ppCrossRefGoogle Scholar
  19. Vaughn N (2007) An individual-tree model to predict the annual growth of young stands of Douglas-fir (Pseudotsugamenziesii (Mirbel) Franco) in the Pacific Northwest, MS thesis, University of WashingtonGoogle Scholar
  20. Wang XW, Weng YH, Liu GF, Krasowski MJ, Yang CP (2015) Variations in carbon concentration, sequestration and partitioning among Betulaplatyphylla provenances. For Ecol Manag 358:344–352CrossRefGoogle Scholar
  21. Zeng J, Zou Y, Bai J, Zheng H (2003) RAPD analysis of genetic variation in natural populations of Betulaalnoides from Guangxi, China. Euphytica 134:33–41CrossRefGoogle Scholar
  22. Zhang K, Wang D, Yang C, Liu G, Zhang H, Lian L, Wei Z (2012) Linkage map construction and QTL analysis for Betulaplatyphylla Suk using RAPD, AFLP, ISSR and SSR. Silvae Genet 61:1–9CrossRefGoogle Scholar
  23. Zhao D, Kane M, Markewitz D, Teskey R, Clutter M (2015) Additive tree biomass equations for midrotation loblolly pine plantations. For Sci 61:613–623Google Scholar
  24. Zheng C, Mason EG, Jia L, Wei S, Sun C, Duan J (2015) A single-tree additive biomass model of Quercusvariabilis Blume forests in North China. Trees 29:705–716CrossRefGoogle Scholar
  25. Zhu X, Liu GF, Yang C, Liu ZX, Yuan GH, Liu JC, Li JY (2001) Provenance division and optimal provenance selection of Betulaplatyphylla. J Northeast For Univ 29:11–14Google Scholar

Copyright information

© INRA and Springer-Verlag France SAS, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of ForestryNortheast Forestry UniversityHarbinChina
  2. 2.Warnell School of Forestry and Natural ResourcesUniversity of GeorgiaAthensUSA

Personalised recommendations