2018BioResourcesOpen access

Allometric Model for Predicting Aboveground Biomass and Carbon Stock of Acacia Plantations in Sarawak, Malaysia

Nur Syazni Adam, Ismail Jusoh

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Abstract

Allometric equations estimating biomass and carbon stock for Acacia mangiumin Malaysia have been developed. However,in previous studies they were obtained from small trials or experimental plots.In this study, models were proposed to quantify the aboveground biomass as well as the amounts of stored and sequesteredcarbonintwo planting variants,namely second generation Acacia mangium and Acaciahybrid plantations that wereapproximately 10 years old. Linear, power, exponential,and logarithmicfunctions were fitted for aboveground biomass using trunk diameter at breast height (DBH)as the independentvariable. The best fit model for estimation of total aboveground biomass was in theform of apower function y=aDb. Application of the developed model yielded total aboveground biomassesof Acacia hybrid and second generation Acacia mangium of113.3 Mha-1 and 178.9 Mha-1, respectively. This study indicated that 10-year-old second generation Acacia mangium and Acacia hybrid had sequestered30.8 Mha-1and 19.5 Mha-1, respectively, of CO2annually.This study showedthattheAcacia mangiumplantation playsarole as a carbon sink.Thus, these forest plantations benefit the economy and also mitigate carbon emissions.

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What this paper is about

Allometric equations estimating biomass and carbon stock for Acacia mangiumin Malaysia have been developed. However,in previous studies they were obtained from small trials or experimental plots.In this study, models were proposed to quantify the aboveground biomass as well as the amounts of stored and sequesteredcarbonintwo planting variants,namely second generation Acacia mangium and Acaciahybrid plantations that wereapproximately 10 years old. Linear, power, exponential,and logarithmicfunctions were fitted for aboveground biomass using trunk diameter at breast height (DBH)as the independentvariable. The best fit model for estimation of total aboveground biomass was in theform of apower function y=aDb. Application of the developed model yielded total aboveground biomassesof Acacia hybrid and second generation Acacia mangium of113.3 Mha-1 and 178.9 Mha-1, respectively. This study indicated that 10-year-old second generation Acacia mangium and Acacia hybrid had sequestered30.8 Mha-1and 19.5 Mha-1, respectively, of CO2annually.This study showedthattheAcacia mangiumplantation playsarole as a carbon sink.Thus, these forest plantations benefit the economy and also mitigate carbon emissions.

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Available abstract

Allometric equations estimating biomass and carbon stock for Acacia mangiumin Malaysia have been developed. However,in previous studies they were obtained from small trials or experimental plots.In this study, models were proposed to quantify the aboveground biomass as well as the amounts of stored and sequesteredcarbonintwo planting variants,namely second generation Acacia mangium and Acaciahybrid plantations that wereapproximately 10 years old. Linear, power, exponential,and logarithmicfunctions were fitted for aboveground biomass using trunk diameter at breast height (DBH)as the independentvariable. The best fit model for estimation of total aboveground biomass was in theform of apower function y=aDb. Application of the developed model yielded total aboveground biomassesof Acacia hybrid and second generation Acacia mangium of113.3 Mha-1 and 178.9 Mha-1, respectively. This study indicated that 10-year-old second generation Acacia mangium and Acacia hybrid had sequestered30.8 Mha-1and 19.5 Mha-1, respectively, of CO2annually.This study showedthattheAcacia mangiumplantation playsarole as a carbon sink.Thus, these forest plantations benefit the economy and also mitigate carbon emissions.

Key concepts: Acacia mangium, Acacia, Biomass (ecology), Carbon stock, Tree allometry, Allometry, Agroforestry, Environmental science

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Allometric Model for Predicting Aboveground Biomass and Carbon Stock of Acacia Plantations in Sarawak, Malaysia — Research Paper | ScholarLens