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

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 (R-2) 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).

Automated summary

Aquilaria malaccensis Lam. is a significant commercial tree species in Bangladesh, primarily cultivated for the production of the locally known Agar essential oil. This study employed a nondestructive method to establish an allometric biomass model for A. malaccensis, utilizing stem volume data from 254 trees and the biomass expansion factor (BEF) model to estimate the total above-ground biomass (TAGB). Five allometric equations incorporating natural logarithm were tested to identify the best-fit biomass models for crown, stem, and total above-ground biomass (TAGB), with selection criteria based on lowest akaike information criteria (AIC), residual standard error (RSE), and highest coefficient of determination (R-2) and akaike information criteria weighted (AICw) values. The best-fit models for crown, stem, and TAGB were determined as 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), respectively. The TAGB model exhibited superior efficiency in biomass estimation compared to commonly used pan-tropical biomass models, as evidenced by lower model prediction error (MPE) and higher model efficiency (ME).