4.6 Article

Dissecting Variation in Biomass Conversion Factors across China's Forests: Implications for Biomass and Carbon Accounting

Journal

PLOS ONE
Volume 9, Issue 4, Pages -

Publisher

PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pone.0094777

Keywords

-

Funding

  1. Forestry Public Welfare Programme of China [201304205]
  2. Strategic Priority Programme of Chinese Academy of Sciences [XDA05060102, XDA05050602]
  3. Fujian Provincial ST Programme [2013Y0083, 2011Y0052]
  4. National Natural Science Foundation of China [31200363, 31270588]
  5. International Partnership Programme for Creative Research Teams [KZCX2-YW-T08]

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Biomass conversion factors (BCFs, defined as the ratios of tree components (i.e. stem, branch, foliage and root), as well as aboveground and whole biomass of trees to growing stock volume, Mg m(-3)) are considered as important parameters in large-scale forest biomass carbon estimation. To date, knowledge of possible sources of the variation in BCFs is still limited at large scales. Using our compiled forest biomass dataset of China, we presented forest type-specific values of BCFs, and examined the variation in BCFs in relation to forest type, stand development and environmental factors (climate and soil fertility). BCFs exhibited remarkable variation across forest types, and also were significantly related to stand development (especially growing stock volume). BCFs (except Stem BCF) had significant relationships with mean annual temperature (MAT) and mean annual precipitation (MAP) (P<0.001). Climatic data (MAT and MAP) collectively explained 10.0-25.0% of the variation in BCFs (except Stem BCFs). Moreover, stronger climatic effects were found on BCFs for functional components (i.e. branch, foliage and root) than BCFs for combined components (i.e. aboveground section and whole trees). A general trend for BCFs was observed to decrease and then increase from low to high soil fertility. When qualitative soil fertility and climatic data (MAT and MAP) were combined, they explained 14.1-29.7% of the variation in in BCFs (except Stem BCFs), adding only 4.1-4.9% than climatic data used. Therefore, to reduce the uncertainty induced by BCFs in forest carbon estimates, we should apply values of BCFs for a specified forest type, and also consider climatic and edaphic effects, especially climatic effect, in developing predictive models of BCFs (except Stem BCF).

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