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

编号 030026203

推送时间 20201026

研究领域 森林经理 

年份 2020 

类型 期刊 

语种 英语

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

来源期刊 JOURNAL OF SUSTAINABLE FORESTRY

期 第262期

发表时间 20200725

关键词 Allometry;  Aquilaria malaccencis;  biomass;  biomass expansion factor; 

摘要 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).

服务人员 付贺龙

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