2024-03-29T07:00:06Z
https://nagoya.repo.nii.ac.jp/oai
oai:nagoya.repo.nii.ac.jp:00028355
2023-01-16T04:20:58Z
643:666:667
Allometric equations for estimating the aboveground biomass of bamboos in northern Laos
Xayalath, Singkone
Hirota, Isao
Tomita, Shinsuke
Nakagawa, Michiko
open access
This is an Accepted Manuscript of an article published by Taylor & Francis Group in Journal of Forest Research on 25/01/2019, available online: http://www.tandfonline.com/10.1080/13416979.2019.1569749.
Allometry
carbon stocks
culm
fallow
multi-species relationship
Bamboos are dominant plants in northern Laos, where they are closely associated with local people’s livelihoods. We developed species-specific allometric equations for estimating aboveground biomass from culm size parameters (diameter at breast height [DBH] and DBH^2H; H is a culm length) using 11 common bamboo species in the region. The applicability of multi-species allometric equations based on pooled data was also examined. Most species-specific allometric regressions showed significant correlations. In addition, the multi-species allometric relationships for culm biomass and aboveground biomass showed particularly high correlations (r^2 > 0.96), indicating the usefulness of multi-species allometric equations to estimate bamboo biomass in mixed-species bamboo forests with unknown bamboos and bamboos without species-specific allometric equations. The generally small differences in the fitness of aboveground biomass estimates between DBH and DBH^2H indicate that DBH is a practical explanatory variable for biomass estimation. These species-specific and multi-species allometric equations will help in developing future work on carbon stocks and cycles in bamboo forests in this region.
ファイル公開:2020-01-25
Taylor & Francis
2019-01-25
eng
journal article
AM
http://hdl.handle.net/2237/00030549
https://nagoya.repo.nii.ac.jp/records/28355
https://doi.org/10.1080/13416979.2019.1569749
1341-6979
Journal of Forest Research
24
2
115
119
https://nagoya.repo.nii.ac.jp/record/28355/files/XayalathEtAl2019FinalMS.pdf
application/pdf
272.3 kB
2020-01-25