Improving allometry models to estimate the above-and belowground biomass of subtropical forest, China

Yaozhan Xu, Jiaxin Zhang, Scott B. Franklin, Junyi Liang, Peng Ding, Yiqi Luo, Zhijun Lu, Dachuan Bao, Mingxi Jiang

Research output: Contribution to journalArticlepeer-review

27 Scopus citations


Subtropical forests are important carbon sinks and have a huge potential for mitigating climate change. However, few studies have developed biomass models to give robust estimates of subtropical forest aboveground and belowground biomass. Although wood density (WD) can greatly reduce the uncertainty in aboveground biomass (AGB) estimates in tropical forest, it has never been applied in other ecosystems. In addition, crowns hold a large component of tree biomass and vary among forest types, so crown dimensions as new variables have been recommended for AGB models. To test the role of wood density and crown dimensions and to select the best AGB model in subtropical forest, we harvested and weighted dry mass of 147 trees from 41 dominant species in subtropical forest. In order to account the belowground biomass (BGB) of these forests, 23 roots systems were excavated following aboveground harvest. Models with wood density performed better than all those without wood density, and models with height performed better than those without height, indicating wood density and tree height (H) are crucial factors in AGB models of subtropical forest. Adding crown radius (CR) did not improve the model performance. The BGB models with diameter at breast (DBH) in power form were significant (p, 0.001). The new AGB models presented here, with wood density and tree height, and BGB models substantially improve biomass estimates in subtropical forest.

Original languageEnglish (US)
Article number289
Issue number12
StatePublished - Dec 1 2015
Externally publishedYes


  • Aboveground biomass
  • Allometry
  • Belowground biomass
  • Subtropical forest

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Ecology


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