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Allometric Equations for Aboveground Biomass Estimations of Four Dry Afromontane Tree Species

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Abstract Background: Tree species based developing allometric equations are important because they contain the largest proportion of total biomass and carbon stocks of forests. Studies on developing and validating the species-specific allometric models (SSAM) remain insufficient that may result to biomass estimation errors in the forests. The purpose of this study is to determine the wood density of four tree species and develop and validate the accuracy of allometry for biomass estimations. A total of 103 sample trees representing four species were harvested semi-destructively. The species specific allometric equations (SSAM) were developed using aboveground biomass (AGB in kg) as dependent variable, and three of the predictor’s variables: diameter at beast height (DBH in cm), height (H in m) and wood density (WD in g cm-3). The relation between dependent and independent variables were tested using multiple correlations (R2). The model selection and validation was based on statistical significance of model parameter estimates, Akaike Information Criterion (AIC), adjusted coefficient of determination (R2), residual standard error (RSE) and mean relative error (MRE). Results: The results showed that the AGB correlated significantly with diameter at breast height (R2 > 0.944, P < 0.001), and tree height (R2 > 0.742, P <0.001). The species-specific allometric models, which include DBH, H and WD predicted AGB with high-model fit (R2 > 93.6%, P < 0.001). These models for biomass estimations produced small MRE (1.50–3.40%) and AIC (-7.04 –12.84) compared to a single predictor (MRE:-0.4 – 20.1%; AIC: -7.25 – 35.29). The SSAM also predicted AGB against predictors with high-model fit (R2 > 93.6%, P < 0.001) and small MRE: 1.50 – 3.40% compared to existing general allometric models (MRE: - 31.3 – 11.31%). Conclusions: The research confirmed that the inclusion of DBH, H, and WD in the SSAM predicted AGB with small bias than a single or two predictors. The wood density values of those studied species can be used as the references for biomass estimations using general allometric equations. The study contributes to species-specific allometric models for understanding the total biomass estimation of species. Therefore, the application of species-specific allometric models should be considered in biomass estimations of forests.
Title: Allometric Equations for Aboveground Biomass Estimations of Four Dry Afromontane Tree Species
Description:
Abstract Background: Tree species based developing allometric equations are important because they contain the largest proportion of total biomass and carbon stocks of forests.
Studies on developing and validating the species-specific allometric models (SSAM) remain insufficient that may result to biomass estimation errors in the forests.
The purpose of this study is to determine the wood density of four tree species and develop and validate the accuracy of allometry for biomass estimations.
A total of 103 sample trees representing four species were harvested semi-destructively.
The species specific allometric equations (SSAM) were developed using aboveground biomass (AGB in kg) as dependent variable, and three of the predictor’s variables: diameter at beast height (DBH in cm), height (H in m) and wood density (WD in g cm-3).
The relation between dependent and independent variables were tested using multiple correlations (R2).
The model selection and validation was based on statistical significance of model parameter estimates, Akaike Information Criterion (AIC), adjusted coefficient of determination (R2), residual standard error (RSE) and mean relative error (MRE).
Results: The results showed that the AGB correlated significantly with diameter at breast height (R2 > 0.
944, P < 0.
001), and tree height (R2 > 0.
742, P <0.
001).
The species-specific allometric models, which include DBH, H and WD predicted AGB with high-model fit (R2 > 93.
6%, P < 0.
001).
These models for biomass estimations produced small MRE (1.
50–3.
40%) and AIC (-7.
04 –12.
84) compared to a single predictor (MRE:-0.
4 – 20.
1%; AIC: -7.
25 – 35.
29).
The SSAM also predicted AGB against predictors with high-model fit (R2 > 93.
6%, P < 0.
001) and small MRE: 1.
50 – 3.
40% compared to existing general allometric models (MRE: - 31.
3 – 11.
31%).
Conclusions: The research confirmed that the inclusion of DBH, H, and WD in the SSAM predicted AGB with small bias than a single or two predictors.
The wood density values of those studied species can be used as the references for biomass estimations using general allometric equations.
The study contributes to species-specific allometric models for understanding the total biomass estimation of species.
Therefore, the application of species-specific allometric models should be considered in biomass estimations of forests.

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