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Destructive sampling-based allometric equations for biomass and carbon estimation in Acacia hybrid plantations in Southeastern Vietnam
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Abstract. Ha NT, Bao TQ, Tuan NT, Rodríguez-Hernández DI, Dung NT, Ngoan TT. 2025. Destructive sampling-based allometric equations for biomass and carbon estimation in Acacia hybrid plantations in Southeastern Vietnam. Nusantara Bioscience 16: 203-217. This study developed accurate allometric equations for estimating aboveground and belowground biomass, as well as carbon stocks, for Acacia hybrid (Acacia mangium × Acacia auriculiformis) plantations in Southeastern, Vietnam. A dataset of 45 destructively sampled trees with varying ages and diameter classes was used to validate the models. The fresh biomass of the four tree components (stem, branches, leaves, and roots) was measured for a total of 180 samples. Samples were oven-dried at 105°C for stems and branches, and 80°C for leaves, to determine their biomass. Linear and non-linear equations were employed to model both individual tree and stand-level dry biomass (AGB: aboveground biomass, BGB: belowground biomass, TGBG: total biomass), and carbon stocks (AGC: aboveground carbon, BGC: belowground carbon, TGC: total carbon). Diameter at breast height (DBH), tree height (H), stand density (SD), and stand age (A) were included as predictor variables. The best-fitting models were selected based on coefficients of determination (R²), sum of squared errors (SEE), mean absolute error (MAE), sum of squared residuals (SSR), correction factors (CF), mean absolute percentage error (MAPE), and root mean square error (RMSE), with R² values greater than 0.895 and RMSE values less than 0.363. The results revealed strong relationships between aboveground and belowground biomass, and logarithmic functions of DBH and tree height were found to be good predictors for all biomass components. The key equations are: ln(AGB) = -3.03805 + 0.586847*ln(DBH*H) + 1.58329*ln(DBH); ln(BGB) = -0.597955 + 0.485409*ln(DBH)2; ln(TGB) = -2.65453 + 2.11674*ln(DBH) + 0.57522*ln(H). Among the variables, DBH was found to be particularly effective in estimating BGB. At the stand level, total biomass (TSB) has a significant correlation with stand density, mean diameter, and stand height, as shown in the following equation: Ln(TSB) = -9.85561 + 1.09128*ln(SD) + 1.96789*ln(Ds) + 0.608831*ln(Hs). These models provide foresters with valuable tools for estimating biomass and carbon accumulation in Acacia hybrid plantations. The total carbon stock of the Acacia hybrid population in the study area ranged from 29.0 tons/ha to 313.3 tons/ha. This information can support carbon accounting efforts and contribute to Vietnam's initiatives for carbon reduction and climate change mitigation.
Title: Destructive sampling-based allometric equations for biomass and carbon estimation in Acacia hybrid plantations in Southeastern Vietnam
Description:
Abstract.
Ha NT, Bao TQ, Tuan NT, Rodríguez-Hernández DI, Dung NT, Ngoan TT.
2025.
Destructive sampling-based allometric equations for biomass and carbon estimation in Acacia hybrid plantations in Southeastern Vietnam.
Nusantara Bioscience 16: 203-217.
This study developed accurate allometric equations for estimating aboveground and belowground biomass, as well as carbon stocks, for Acacia hybrid (Acacia mangium × Acacia auriculiformis) plantations in Southeastern, Vietnam.
A dataset of 45 destructively sampled trees with varying ages and diameter classes was used to validate the models.
The fresh biomass of the four tree components (stem, branches, leaves, and roots) was measured for a total of 180 samples.
Samples were oven-dried at 105°C for stems and branches, and 80°C for leaves, to determine their biomass.
Linear and non-linear equations were employed to model both individual tree and stand-level dry biomass (AGB: aboveground biomass, BGB: belowground biomass, TGBG: total biomass), and carbon stocks (AGC: aboveground carbon, BGC: belowground carbon, TGC: total carbon).
Diameter at breast height (DBH), tree height (H), stand density (SD), and stand age (A) were included as predictor variables.
The best-fitting models were selected based on coefficients of determination (R²), sum of squared errors (SEE), mean absolute error (MAE), sum of squared residuals (SSR), correction factors (CF), mean absolute percentage error (MAPE), and root mean square error (RMSE), with R² values greater than 0.
895 and RMSE values less than 0.
363.
The results revealed strong relationships between aboveground and belowground biomass, and logarithmic functions of DBH and tree height were found to be good predictors for all biomass components.
The key equations are: ln(AGB) = -3.
03805 + 0.
586847*ln(DBH*H) + 1.
58329*ln(DBH); ln(BGB) = -0.
597955 + 0.
485409*ln(DBH)2; ln(TGB) = -2.
65453 + 2.
11674*ln(DBH) + 0.
57522*ln(H).
Among the variables, DBH was found to be particularly effective in estimating BGB.
At the stand level, total biomass (TSB) has a significant correlation with stand density, mean diameter, and stand height, as shown in the following equation: Ln(TSB) = -9.
85561 + 1.
09128*ln(SD) + 1.
96789*ln(Ds) + 0.
608831*ln(Hs).
These models provide foresters with valuable tools for estimating biomass and carbon accumulation in Acacia hybrid plantations.
The total carbon stock of the Acacia hybrid population in the study area ranged from 29.
0 tons/ha to 313.
3 tons/ha.
This information can support carbon accounting efforts and contribute to Vietnam's initiatives for carbon reduction and climate change mitigation.
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