Allometric Biomass Model for Aquilaria Malaccensis Lam. in Bangladesh: A Nondestructive Approach

ABSTRACT

Aquilaria malaccensis: Lam. is an important commercial tree species of Bangladesh. This species is widely planted for the increased demand for an essential oil locally knows as “Agar”. A nondestructive method was adopted to derive the allometric biomass model for A. malaccensis. Stem volume of 254 trees and the model of biomass expansion factor (BEF) were used to estimate the total above-ground biomass (TAGB). A total of five allometric equations with natural logarithm were tested to derive best-fit biomass models for crown, stem, and total above-ground biomass (TAGB). The best-fit allometric model was selected based on the lowest value of akaike information criteria (AIC), residual standard error (RSE), and the highest value of the coefficient of determination (R²) and akaike information criteria weighted (AICw). The best-fit model of BEF was BEF = exp(2.112318 – (DBH*TH)^0.1066121). The best-fit allometric biomass models for crown, stem and TAGB were crown biomass = exp(−0.6031 + 0.4279*Ln(DBH^2*TH), steam biomass = exp(−3.2483 + 1.7910*Ln(DBH) + 0.7881*Ln(TH) and TAGB = exp(−1.9121 + 1.5937*Ln(DBH) + 0.6152*Ln(TH). The best-fit TAGB model showed the highest efficiency in biomass estimation compared to commonly used pan-tropical biomass models in terms of model prediction error (MPE), model efficiency (ME).

KEYWORDS:

• Allometry
• Aquilaria malaccencis
• biomass
• biomass expansion factor

Acknowledgments

We greatly acknowledge the financial support of the Food and Agriculture Organization of the United Nations (FAO) through GCP/BGD/058/USA (LOA Code: FAOBGDLOA 2017-008) to accomplish the field and laboratory work. We also greatly acknowledge the Bangladesh Forest Research Institute for the sharing of raw volume data of the studied tree species. Finally, we like to thank Forestry and Wood Technology Discipline, Khulna University for their logistical supports.

Additional information

Funding

This work was supported by the Food and Agricultural Organization of the United Nations [GCP/BGD/058/USA (LOA Code: FAOBGDLOA 2017-008)].