Skip to main content

The accuracy of species-specific allometric equations for estimating aboveground biomass in tropical moist montane forests: case study of Albizia grandibracteata and Trichilia dregeana



Application of allometric equations for quantifying forests aboveground biomass is a crucial step related to efforts of climate change mitigation. Generalized allometric equations have been applied for estimating biomass and carbon storage of forests. However, adopting a generalized allometric equation to estimate the biomass of different forests generates uncertainty due to environmental variation. Therefore, formulating species-specific allometric equations is important to accurately quantify the biomass. Montane moist forest ecosystem comprises high forest type which is mainly found in the southwestern part of Ethiopia. Yayu Coffee Forest Biosphere Reserve is categorized into Afromontane Rainforest vegetation types in this ecosystem. This study was aimed to formulate species-specific allometric equations for Albizia grandibracteata Tuab. and Trichilia dregeana Sond. using the semi-destructive method.


Allometric equations in form of power models were developed for each tree species by evaluating the statistical relationships of total aboveground biomass (TAGB) and dendrometric variables. TAGB was regressed against diameter at breast height (D), total height (H), and wood density (ρ) individually and in a combination. The allometric equations were selected based on model performance statistics. Equations with the higher coefficient of determination (adj.R2), lower residual standard error (RSE), and low Akaike information criterion (AIC) values were found best fitted. Relationships between TAGB and predictive variables were found statistically significant (p ≤ 0.001) for all selected equations. Higher bias was reported related to the application of pan-tropical or generalized allometric equations.


Formulating species-specific allometric equations is found important for accurate tree biomass estimation and quantifying the carbon stock. The developed biomass regression models can be applied as a species-specific equation to the montane moist forest ecosystem of southwestern Ethiopia.


The tropical forest ecosystem has been playing a significant role in mitigating the atmospheric carbon dioxide concentration and associated climate change impacts. Particularly, this ecosystem is known for its highest carbon pool when compared to other biomes of the world [1,2,3]. It is the most productive ecosystem accounting for over 60% of global terrestrial photosynthesis and one-third of global primary productivity [4]. Generally, the accumulation of a great deal of carbon stock in the aboveground biomass of tropical forests was verified [5, 6]. However, the biomass information is uncertain for many tropical forests due to the paucity of site-specific allometric equations [5, 7]. Therefore, applying a robust method for carbon stock estimation is a crucial step for the successful implementation of climate change mitigation strategies like REDD+ [8].

Allometric equations are important for their application to local and national forest carbon assessments, as well as for global carbon balance assessments [9]. Primarily, the current issue of global carbon cycles is the prominent factor for the formulation of biomass regression models [7, 10, 11]. As a result, generalized pantropical allometric equations were developed by many researchers [5, 7, 12, 13]. The development of a generalized allometric equation was approached by measuring multiple tree species and it was intended to be applied to a broad range of tropical forests [12]. However, a great error is generated related to adopting generic pantropical allometric equations for many forests [14, 15]. Biomass error that can be generated at individual tree level is also regularly propagated bias at forest stand and country-level during the assessment of biomass and carbon stock change when the appropriate allometric equation is not used.

Environmental variations among different forests are the ultimate factors for the variation of their biomass. Climatic regimes are the prominent factors that affect the growth of woody plants and biomass accumulation of different forest stands [16, 17]. Also, environmental variability in the context of physiographic and edaphic conditions plays a significant role in the variation of species composition and biomass difference among different forest sites [18, 19]. Within-stand variation of biomass for different tree species is related to tree architecture, growth strategies and its dynamic interplay with the biophysical environments [20,21,22]. The difference of TAGB across a forest landscape is mostly related to the variation in slope, elevation, and aspect [18, 23]. Generally, tropical forests are known for their high diversity of woody plants. The application of multispecies pan-tropical equations to individual tree species generates uncertainty of TAGB [9, 24]. Therefore, formulating a species-and site-specific biomass regression model was found the best approach to accurately quantify biomass and carbon storage of forests [25,26,27].

Yayu Coffee-Forest Biosphere has comprised Afromontane Rainforest Vegetation types, which is home for the endemic Coffea arabica in Southwestern Ethiopia [28]. This forest is known for storing a good deal of carbon stock and high species diversity. According to [29], the forest is categorized into Eastern Afromontane Biodiversity Hotspots which has global significance. In Ethiopia, forestry and agricultural sectors are the major sources of carbon dioxide CO2 emission, contributing more than 85% of the country. The forestry sector alone contributes a total emission of about 37% CO2 emission in the country. The total contribution of other sectors like power, transport, industry, and buildings is less than 15% [30, 31].

To reduce the current rate of carbon dioxide emission, the country has devised forestry-based strategies like REDD+ in potential forest areas. However, the estimation of biomass and carbon stock change depends on generic pan-tropical allometric equations, which generate bias when applied to individual tree species [28]. Species-specific equations were not formulated for many tree species in Ethiopia. Therefore, this study is intended to formulate species-specific TAGB allometric equations considering selected tree species (A. grandibracteata and T. dregeana). The selected tree species have a wide range of ecological distribution across different parts of Africa [32]. A. grandibracteata species has multiple socio-economic benefits and ecological services. It is fast-growing species on forest soils with high moisture-holding capacity. A. grandibracteata is a medium-sized deciduous tree with a straight trunk to 20 m, and a flattened or layered crown [33]; it can grow up to 30 m [34]. T. dregeana is also a very large evergreen tree to 30 m, with a large straight trunk dividing into large branches and a rounded crown [32]. These attributes of the selected species are important for storing a high amount of biomass and carbon. The semi-destructive method of data collection was found environmentally sound approach in the situation of the biosphere reserve. The core and buffer zones of the Yayu coffee-forest biosphere reserve were established mainly for scientific research, biodiversity conservation, and for monitoring the ecological processes.


Site description

The study was conducted in Yayu Coffee-Forest Biosphere Reserve which is located in Illubabor Zone, southwestern Ethiopia. The biosphere extends between latitude 8° 15′ 0′′–8° 35′ 0′′ N and longitude 35° 30′ 0′′–36° 0′ 0′′ E of zone 36 (Fig. 1). Detailed information regarding the study area extent, soil types, climatic condition, altitudinal range, and establishment of the biosphere reserve can be found in [28]. Celtis africana, Diospyros abyssinica, Albizia grandibracteata, Ehretia cymosa, Trichilia dregeana, Vangueria apiculata. Argomuellera macrophylla, Antiaris toxicaria, Millettia ferruginea, and Cordia africana are dominant tree species of the study forest. In addition, Albizia grandibracteata and Trichilia dregeana contributes significant amount of basal area (BA) M2 ha−1, which has great implication to storing high amount of biomass and carbon (Daba and soromessa: Species composition, stand structure and regeneration status of tree species in Yayu coffee forest biosphere reserve, Illubabore zone, southwestern Ethiopia, unpublished).

Fig. 1

Map of Ethiopia with Oromia region, and the study area. The study area is marked green and the lines with blue color are rivers

Species description

Albizia grandibracteata species belongs to the plant family Fabaceae; whereas, T. dregeana species belongs to the plant family Meliaceae. Both tree species have a wide range of ecological distributions [32]. A. grandibracteata species mostly grow in the upland rainforest and riverine forest areas, with preference in moist and wet sites. This tree species is known for its multiple provisional services [35]. It provides multiple socio-economic benefits (firewood, farm tools, medicine from the root, bee forage, ornamental, and soap from its bark); it is also known for nitrogen fixation which is one of its ecological services [32]. In Ethiopia, A. grandibracteata species grow in moist agroclimatic zones within an altitude range of 1200–1700 m asl. [33]. It is a medium-sized deciduous tree with a straight trunk to 20 m and flattened crown; it can also attain a maximum height of 30 m.

In Ethiopia, T. dregeana Sond. species occurs in the moist and wet montane rainforest of southwestern and eastern highland between 1100 and 2200 m asl. altitude ranges [33]. This tree species is well known for provisional services like firewood, timber (construction, furniture), and coffee shade under natural forest. The T. dregeana Sond. is a large evergreen tree grows up to 35 m height [36]. It has a straight trunk that can attain higher DBH size that divides into large branches and forms a rounded crown [33]. The selected tree species have a significant contribution in terms of stocking density and BA m2 ha−1; which indicates that these species have great potential in storing TAGB and carbon [37].

Sampling method

The procedures of semi-destructive methodology in “Manual for building tree volume and biomass allometric equations” prepared by Food and Agriculture Organization (FAO) [38] were followed. A random sampling technique where all individuals have an equal probability of being involved in the study was applied. Therefore, the selection of individuals from different diameters at breast height (DBH) classes was used. The random sampling technique was conducted with a quick screening of vegetation variability across the landscape or particular locality; to delimit the vegetation type in mind [20]. The guideline by [10] suggests the relevance of considering some biophysical factors of the forest stand to have representative sample sets. Detail information about sampling trees, field-based measurements, and laboratory analysis can be found in [28, 38, 39].

Tree biomass procedures

The biomass calculation has also followed the procedure in “Manual for building tree volume and biomass allometric equations” prepared by FAO [38]. During semi-destructive procedures for biomass, diameters of the trunk and large branches were measured at every 1 m length directly in the field. The fresh biomass of trimmed branches was also directly measured in the field. The volume and dry weight of the wood aliquots were measured in the laboratory which was later used for wood density calculation. Three small branches for every tree sampled (3 × 60 = 180) were trimmed for the determination of trimmed biomass. The fresh biomass of small untrimmed branches was calculated based on the relationship between BD and dry biomass of trimmed branches. The fresh biomass of large untrimmed branches and trunk was calculated from volume and wood density measurement. The tree sections were considered to be cylinder; whereas, density was considered to be the same for all compartments of the tree. The assumption is that along every 1 m length of a tree there is no tapering (variation in diameter is insignificant), and every section is considered to have cylinder shape [38].

Measuring trimmed and untrimmed fresh biomass

The diameter at the bases of each trimmed branch was measured and the leaves of each branch were fully harvested from the wood. Only basal diameter was measured for small untrimmed branches. A section about one-meter length was preferred for diameter measurement of the trunk and main branches [40]. Detail information regarding measurement of trimmed branches in the field, measurement of wood and leaf aliquots in the laboratory, measurement of small and large untrimmed branches, and trunk can be found in [28, 38].

Biomass calculations

All procedures of laboratory analysis related to wood and leaves aliquots and calculation of trimmed and untrimmed biomass can be found in the FAO manual [38] was used.

Comparison between species-specific and pan-tropical allometric equations

The species-specific equation and pan-tropical allometric equations were compared in this study for accuracy assessment. The pan-tropical allometric equations potentially applicable in tropical moist forests were used for the comparison. In Ethiopia, these equations have been most frequently used for biomass estimation in the montane moist forest ecosystem. The datasets for biomass comparisons were generated form: (1) Measured biomass- generated based on semi-destructive procedures; (2) Specific Equation-equation which was developed for A. grandibracteata and T. dregeana; (3) Equations developed by [5, 12, 13, 41] for tropical forests.

Data analysis and model selection

Points regarding the summarized data and statistical package used during the data analysis was can be found in [28]. Eight allometric equations were developed by evaluating the relationships among the considered variables i.e. TAGB against single predictor variables (D, H, ρ); TAGB against single compound variables (D2H, DH, ρDH); TAGB against multiple variables (D + H + ρ; D2H + ρ; D + H). These models were fitted based on log-transformed data and all have achieved model goodness of fit statistics. The relationship between TAGB and wood density was found statistically not significant (p > 0.05). Biomass regression models selections and evaluation were tested based on performance statistics including coefficient of determination (adj.R2), residual standard error (RSE), Akaike information criterion (AIC), and p-value. AIC is an estimator of the relative quality of statistical models for a given set of data. AIC estimates the quality of each model relative to each other [42]. All formulated models used natural logarithm transformation; to minimize the systematic bias during the back transformation a correction factor (CF) was calculated for each equation [43].


Allometric equations and their performance

The allometric equations relating the dependent variable TAGB against the predictor variables (D, H, and ρ) were formulated for A. grandibracteata and T. dregeana tree species. The descriptive summary of these main variables for TAGB regression models formulation was presented in (Table 1).

Table 1 Descriptive summary of dendrometric variables for A. grandibracteata and T. dregeana

The relationship between the predictor variables of A. grandibracteata and T. dregeana

The correlation coefficient between the predictor variables was calculated using Pearson's correlation coefficient at a 95% confidence interval. Pearson's correlation coefficient is the statistical measure of the strength of a linear relationship between the paired variable (D & H, D & ρ). The correlation coefficient between D and H is 0.92 indicating a strong relationship between the variables for A. grandibracteata and also was found statistically significant at (p < 0.05). The correlation coefficient between D & H is 74.3, and statistically significant at (p < 0.05) for T. dregeana. However, the correlation coefficient between predictor variables (D & ρ) was found statistically not significant at (p > 0.05) for both tree species. Scatter plot depicted in (Fig. 2a, b) verifies the relationship between D & H of A. grandibracteata and T. dregeana species respectively; indicating an increment of tree height for a unit increment of its DBH. The DBH versus tree height plot was constructed for the area of interest; also, it has a purpose to compare and determine how appropriate the biomass regression model for a given site.

Fig. 2

Scatter plot of Diameter-Height relationships for: a A. grandibracteata, b T. dregeana tree species

Selected allometric equations

The allometric equations were formulated by relating TAGB against independent variables individually and in combination. The selected allometric equations were tested for goodness of fit based on different performance statistics. The coefficients for all selected allometric equations were found statistically significant (p ≤ 0.001); indicating the strong relationships between the TAGB and its predictor variables (Table 2).

Table 2 Best fitted regression models for predicting aboveground biomass of A. grandibracteata and T. dregeana

Scatter plots of TAGB against dendrometric variables for A. grandibracteata

As depicted in (Fig. 2b, 3a, and 4a) that the dependent variable (TAGB) increases with a unit increase of independent variables (D, H, and D2H). This indicates the existence of a strong relationship between TAGB and the predictive variables. However, a significant relationship was not observed between TAGB and ρ (Fig. 4b). The relationship between wood density and tree dendrometric variables is quite complex for a tree species related to microsite variation and tree maturity as well as due to different site factors.

Fig. 3

Linear regression for log-transformed data: a aboveground biomass against D; b aboveground biomass against height

Fig. 4

Linear regression for log-transformed data: a aboveground biomass against D2H, b aboveground biomass against wood density

Scatter plots of TAGB against dendrometric variables for T. dregeana

The scatter plots displayed in (Fig. 5a, b and 6a) shows the increase of TAGB against a unit variation of the predictor variables (D, H, & D2H). The scatter plots of TAGB against D exhibits the linear relationship. The relationship between TAGB and H also shows an increment of TAGB for an increment of tree height. However, the relationship between TAGB and ρ has shown a weak association, due to the complex relationship of wood density with site factors and stand structure.

Fig. 5

Scatter plots for: a aboveground biomass against diameter, b aboveground biomass against height for T. dregeana

Fig. 6

Scatter plots for: a TAGB against D2H, b TAGB against wood density for T. dregeana

The scatter plot of TAGB against D2H shows linearity; however, a significant relationship was not observed between TAGB and ρ as displayed in (Fig. 5).

Selected biomass regression models for A. grandibracteata

The species-specific biomass regression models were developed for the selected tree species based on log-transformed data. All selected allometric equations have achieved the model goodness of fit. The best performing regression models of A. grandibracteata and T. dregeana were listed in decreasing order of importance based on AIC value (Table 2).

The formulated allometric equations were listed in decreasing order of importance from (AgEq1–AgEq8 and TdEq1–TdEq8) respectively for A. grandibracteata and T. dregeana to estimate TAGB (Table 2). The equations were ordered based on their AIC values, where the lower the AIC vale comparatively is the best equation and vice versa. Therefore, comparisons among the selected allometric equations of A. grandibracteata shows that AgEq1 was found best based on its AIC (− 79.49) value. This model shows strong relationships between the TAGB and the main predictive variables. The value of adj. R2 describes that 99.36% variation in TAGB was explained by the predictor variables in this model. The 2nd best-performing equation was (AgEq2) which was formulated by relating TAGB against D2H and wood density. It is the biomass regression model with a lower AIC value (− 50.96) based on statistical criteria for model selection. A strong relationship was observed between TAGB and the predictive variables. The value of adj. R2 also describes that the predictor variables explain 98.30% variation of the TAGB.

The 3rd best equation (AgEq3) was formulated by relating TAGB with D. The adj. R2 value for this biomass regression equation has shown (97.97%); indicating that D is a single tree dendrometric variable that best explains variation in TAGB of A. grandibracteata species. Other selected equations (AgEq4, 5, 6, 7, and 8) have also achieved models performance statistics and also listed in decreasing order of importance based on AIC value (− 44.49, − 32.89, 12.59, 16.97, 29.77) respectively. There is a lower value of adj.R2 and a higher value of RSE for these models compared to the above models (1, 2, and 3). Generally, a strong relationship was found between TAGB and the dendrometric variables for all models. However, H has explained (adj. R2 = 81.9%) variation in TAGB of A. grandibracteata tree species. TAGB against predictive variable (H) has achieved a strong relationship which is statistically significant at (p ≤ 0.001). In contrast, a model relating TAGB with ρ was found statistically insignificant and then rejected.

Trichilia dregeana biomass regression model (TdEq1) was found as the best model with the least AIC value (22.55). The predictor variables in this model have explained 97.5% of the variation in TAGB. Also, the lower value of RSE is the parameter that has proved the fitness of this model. The allometric equation (TdEq2) has also shown the model goodness of fit well among the selected biomass regression models. This model predicts the relationship of TAGB against the main variables (D2H + ρ) and each predictive variable was independently fitted. The predictive variables explain adj.R2 (97.3%) variation of TAGB for this tree species. The relationship between TAGB and the predictor variable was also found highly significant at (p ≤ 0.001).

The other equations (TdEq3, 4, 5, 6, 7, & 8) were listed in decreasing order of importance considering their AIV values (24.48, 24.74, 32.74, 35.87, 40.71, 79.71) respectively. Generally, these models have achieved the goodness of fit statistics considering the adj.R2, RSE, & AIV values. The single predictive variable D in (TdEq5) explains adj.R2 (96.2%) variation in TAGB. Tree diameter is among the dendrometric variable that can be accurately and easily measured in the field. However, tree height (H) as a single predictor variable in TdEq8 was found to explain adj. R2 (81.9%) of TAGB variation which is lowest compared to all other formulated equation. The highest value of RSE (0.8565) was also recorded for this equation when compared to the other selected models.

Comparison of species-specific with pan-tropical equations

The pan-tropical equations that have been used for estimation of forest biomass and carbon stocks in Ethiopia were compared with species-specific equations of A. grandibracteata and T. dregeana. Summary of the statistical parameters (paired t-test, mean difference of TAGB, percent bias, and root mean square error) for equations comparison was presented in (Table 3). The statistical parameters calculated were based on observed and predicted TAGB. The species-specific equation found better in accurately predicting TAGB of A. grandibracteata and T. dregeana. When compared to pan-tropical equation. The higher value of PBIAS shows the poor performance of pan-tropical equations in predicting TAGB of a specific species. This proofs the application of species specific allometric equation is fundamental to accurately estimate TAGB of tree species.

Table 3 Comparison of species-specific to pan-tropical equations in predicting biomass of A. grandibracteata and T. dregeana

The allometric equation TAGB = 0.0509(ρD2H) by [12] is potentially applicable equation in tropical moist forests. In addition, this equation has been most frequently used for biomass estimation in Afromontane rainforests of Ethiopia. Species-specific equation with input variable (ρD2H) was formulated (i.e. TAGB = 0.3274 × (ρD2H)0.759 for A. grandibracteata and TAGB = 0.0832 × (ρD2H)0.899 for T. dregeana) based on measured dataset of total aboveground biomass. Therefore, Chave’s equation was compared with the species-specific equation as depicted in (Fig. 7a, b).The power models were plotted by regressing tree DBH against TAGB (Measured and predicted biomass). The predicted TAGB was obtained using species-specific equations (Species Eq.) and generalized equation (Generalized Eq.).

Fig. 7

Species specific and pan-tropical allometric equations comparison for: a A. grandibracteata, b T. dregeana TAGB. Measured biomass: was obtained based on the semi-destructive methodology for allometric equation; Specific Equation: equation which was developed for A. grandibracteata and T. dregeana; Generalized Equation: was taken from [12] for pantropical tropical moist forest stands


The biomass regression model formulated by relating TAGB against multiple variables (D, H, and ρ) was found statistically the best performing equation for the selected regression models of A. grandibracteata and T. dregeana tree species. This model has considered important predictive variables that improve the accuracy of estimating TAGB. Several studies also suggest the importance of considering the dendrometric variables (D, H, and ρ) in formulating biomass regression models [25, 44]. Studies explain that TAGB estimation is inaccurate when tree height is not available as the predictor variable. Allometric equations are more likely to vary across vegetation types since diameter and height are influenced by the environmental condition.

The compound variable of diameter and height (D2H) as a single predictor or in combination with ρ is robust in prediction TAGB of the tree in this study. Allometric equations with such predictor variables are mostly proposed for its wide range of applications. The study of [45] also reports that the combination of predictor variables (D & H) is used to capture volume variation. The important predictor variables (D & H) can be directly modulated with climatic and physiographic factors, hence affect the biomass. For instance, the biomass regression model that relates TAGB against the compound variable (ρD2H) was found the best fit model in many studies [5, 12].

Wood density is the best dendrometric variable that converts tree volume into biomass. However, A significant relationship was not established when data of TAGB was regressed against the wood density of both A. grandibracteata and T. dregeana. Generally, wood density varies among individuals of the same tree species due to the variability of environmental conditions. Similarly, the study of [46] explains the variation of specific wood gravity of a tree species with spatial variation across a forest landscape. Also, the comprehensive study of [47] reveals the variation of tree wood density is most likely correlated with increasing elevation, the coarseness of soil texture, and drought stress. The importance of wood density as a single variable in yielding the best fitted TAGB model is a great point of debate in several recent studies. The comprehensive study of [48] explains that xylem density which is the physical property of wood varies between individuals, species, and environments. Since, it reflects the physiological strategies of trees that lead to growth and survival, wood density as a single predictor variable will not be correlated with TAGB. Several other studies confirm the regional variation of stand-level wood specific gravity that can significantly affect the variation of TAGB. Overall, the great importance of wood density is reported in carbon accounting of tropical forests. However, its variation among different species is correlated with morphological, mechanical, physiological and ecological properties [21, 49].

The biomass regression model relating TAGB against D has achieved the model goodness of fit and found statistically significant in this study. This has confirmed that there is a strong relationship between TAGB and D. Basically, D measurement is accurate and practical when compared to other dendrometric variables. Several studies have also reported the significance of D in predicting TAGB [9, 12, 50]. In this study, many of the biomass regression models formulated for TAGB against predictors (DH) or (D + H) were also found the best performing models. These equations are robust in predicting TAGB of the tree species considered in the study. Similar studies also clarify that the addition of diameter and height as a predictive variable in biomass measurement shows improvement in the TAGB variation.

Also, such models have anticipated to increases the accuracy at a multiregional scale [20, 51] related to its application. On the other hand, the relationship between D and H is modulated by multiple environmental factors of forests [52, 53]. The report of [5] also suggests the consideration of the diameter-height relationship for locally developed allometric equations. The ultimate reason is that the variation of the relationship between these predictive variables depends directly on the bioclimatic variables.

The existence of a few allometric equations for sub-Saharan Africa is reported by several studies [10, 54, 55]. Many of adopted generalized equations generate great uncertainty of biomass. The study of [14] reports that higher bias was observed related to the Chave’s model II largely overestimating by approximately 300% to 400% for two tropical forest sites. This confirms the significance of formulating species-and site-specific allometric equations for tropical forests. Such an approach avoids a systematic error generated related to the generalized equation which possibly propagates to the national and global carbon budget. Generally, in response to global climate change mitigation, the monitoring and assessment of carbon dioxide from forests is essential. Ethiopia is known for its diverse vegetation ecosystems and associated high diversity of woody plants. However, the assessment of biomass and carbon stock of forests has been practiced by adopting the generic pan-tropical allometric equations that cause great uncertainty. Therefore, the development and application of species-specific allometric equation is inevitable for accurate estimation of biomass. Formulating allometric equations for all woody plants in Ethiopia is quite desirable for accurately quantifying the biomass and carbon stock of forests to achieve accurate national and international reporting of carbon dioxide emission inventories.


Formulating an allometric equation is an important approach for the estimation of tree biomass. It has also an indirect role in contributing to the assessment and monitoring of the global carbon cycle. Adopting the generic pantropical allometric equation has the limitation of uncertainty in quantifying biomass of specific forest stand. This is a particularly serious problem for the assessment of carbon stock in tropical forests of Africa, related to ecological variability and diverse tree species. Species-specific allometric equations were formulated for A. grandibracteata and T. dregeana species following the procedure of semi-destructive methodology. The formulated equations are proposed as a species-specific equation particularly in the Afromontane rainforest as well as in the montane moist forest ecosystem of southwestern Ethiopia.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.


  1. 1.

    Riutta T, Malhi Y, Kho LK, Marthews TR, Huasco WH, Khoo M, et al. Logging disturbance shifts net primary productivity and its allocation in Bornean tropical forests. Glob Change Biol. 2018;24(7):2913–28.

    Article  Google Scholar 

  2. 2.

    Townsend AR, Cleveland CC, Houlton BZ, Alden CB, White JWC. Multi-element regulation of the tropical forest carbon cycle. Front Ecol Environ. 2011;9(1):9–17.

    Article  Google Scholar 

  3. 3.

    Foley JA, Asner GP, Costa MH, Coe MT, DeFries R, Gibbs HK, et al. Amazonia Revealed: Forest Degradation and Loss of Ecosystem Goods and Services in the Amazon Basin. Front Ecol Environ. 2007;5(1):25–322.

    Article  Google Scholar 

  4. 4.

    Moore S, Adu-Bredu S, Duah-Gyamfi A, Addo-Danso SD, Ibrahim F, Mbou AT, et al. Forest biomass, productivity and carbon cycling along a rainfall gradient in West Africa. Glob Change Biol. 2018;24(2):496–510.

    Article  Google Scholar 

  5. 5.

    Chave J, Rejou-Mechain M, Burquez A, Chidumayo E, Colgan MS, Delitti WB, et al. Improved allometric models to estimate the aboveground biomass of tropical trees. Glob Change Biol. 2014;20(10):3177–90.

    Article  Google Scholar 

  6. 6.

    Houghton RA. Aboveground forest biomass and the global carbon balance. Glob Change Biol. 2005;11(6):945–58.

    Article  Google Scholar 

  7. 7.

    Henry M, Picard N, Trotta C, Manlay RJ, Valentini R, Bernoux M, et al. Estimating tree biomass of sub-Saharan African forests: a review of available allometric equations. Silva Fennica. 2011;45(3B):477–569.

    Article  Google Scholar 

  8. 8.

    Saatchi SS, Harris NL, Brown S, Lefsky M, Mitchard ETA, Salas W, et al. Benchmark map of forest carbon stocks in tropical regions across three continents. Proc Natl Acad Sci. 2011;108(24):9899–904.

    CAS  Article  Google Scholar 

  9. 9.

    Basuki TM, van Laake PE, Skidmore AK, Hussin YA. Allometric equations for estimating the above-ground biomass in tropical lowland Dipterocarp forests. For Ecol Manag. 2009;257(8):1684–94.

    Article  Google Scholar 

  10. 10.

    Jara MC, Henry M, Réjou-Méchain M, Wayson C, Zapata-Cuartas M, Piotto D, et al. Guidelines for documenting and reporting tree allometric equations. Ann For Sci. 2014;72(6):763–8.

    Article  Google Scholar 

  11. 11.

    Wang C. Biomass allometric equations for 10 co-occurring tree species in Chinese temperate forests. For Ecol Manag. 2006;222(1–3):9–16.

    Article  Google Scholar 

  12. 12.

    Chave J, Andalo C, Brown S, Cairns MA, Chambers JQ, Eamus D, et al. Tree allometry and improved estimation of carbon stocks and balance in tropical forests. Oecologia. 2005;145:87–99.

    CAS  Article  Google Scholar 

  13. 13.

    Brown S. Estimating biomass and biomass change of tropical forests: a primer. Rome: FAO Forestry Pape; 1997. p. 134.

    Google Scholar 

  14. 14.

    Alvarez E, Duque A, Saldarriaga J, Cabrera K, de las Salas G, del Valle I, et al. Tree above-ground biomass allometries for carbon stocks estimation in the natural forests of Colombia. For Ecol Manag. 2012;267:297–308.

    Article  Google Scholar 

  15. 15.

    Ngomanda A, Obiang Obiang NL, Lebamba J, Moundounga Mavouroulou Q, Gomat H, Mankou GS, et al. Site-specific versus pantropical allometric equations: which option to estimate the biomass of a moist central African forest? For Ecol Manag. 2014;312:1–9.

    Article  Google Scholar 

  16. 16.

    Xu X, Medvigy D, Trugman AT, Guan K, Good SP, Rodriguez-Iturbe I. Tree cover shows strong sensitivity to precipitation variability across the global tropics. Glob Ecol Biogeogr. 2018;27(4):450–60.

    Article  Google Scholar 

  17. 17.

    Pfeifer M, Gonsamo A, Woodgate W, Cayuela L, Marshall AR, Ledo A, et al. Tropical forest canopies and their relationships with climate and disturbance: results from a global dataset of consistent field-based measurements. For Ecosyst. 2018;5(1):7.

    Article  Google Scholar 

  18. 18.

    Alves LF, Vieira SA, Scaranello MA, Camargo PB, Santos FAM, Joly CA, et al. Forest structure and live aboveground biomass variation along an elevational gradient of tropical Atlantic moist forest (Brazil). For Ecol Manag. 2010;260(5):679–91.

    Article  Google Scholar 

  19. 19.

    Laumonier Y, Edin A, Kanninen M, Munandar AW. Landscape-scale variation in the structure and biomass of the hill dipterocarp forest of Sumatra: Implications for carbon stock assessments. For Ecol Manag. 2010;259(3):505–13.

    Article  Google Scholar 

  20. 20.

    Ketterings QM, Coe R, van Noordwijk M, Ambagau Y, Palm CA. Reducing uncertainty in the use of allometric biomass equations for predicting above-ground tree biomass in mixed secondary forests. For Ecol Manag. 2001;146:199–209.

    Article  Google Scholar 

  21. 21.

    Muller-Landau HC. Interspecific and inter-site variation in wood specific gravity of tropical trees. Biotropica. 2004;36(1):20–322.

    Google Scholar 

  22. 22.

    Clark DB, Clark DA. Landscape-scale variation in forest structure and biomass in a tropical rain forest. For Ecol Manag. 2000;137:185–98.

    Article  Google Scholar 

  23. 23.

    SalinaS-Melgoza MA, Skutsch M, Lovett JC. Predicting aboveground forest biomass with topographic variables in human-impacted tropical dry forest landscapes. Ecosphere. 2018;9(1):1–20.

    Article  Google Scholar 

  24. 24.

    Djomo AN, Chimi CD. Tree allometric equations for estimation of above, below and total biomass in a tropical moist forest: case study with application to remote sensing. For Ecol Manag. 2017;391:184–93.

    Article  Google Scholar 

  25. 25.

    Litton CM, Kauffman JB. Allometric models for predicting aboveground biomass in two widespread woody plants in Hawaii. Biotropica. 2008;40(3):313–20.

    Article  Google Scholar 

  26. 26.

    van Breugel M, Ransijn J, Craven D, Bongers F, Hall JS. Estimating carbon stock in secondary forests: decisions and uncertainties associated with allometric biomass models. For Ecol Manag. 2011;262:1648–57.

    Article  Google Scholar 

  27. 27.

    Goussanou C, Guendehou S, Assogbadjo A, Kaire M, Sinsin B, Cuni-Sanchez A. Specific and generic stem biomass and volume models of tree species in a West African tropical semi-deciduous forest. Silva Fennica. 2016;50(2):1474.

    Article  Google Scholar 

  28. 28.

    Daba DE, Soromessa T. Allometric equations for aboveground biomass estimation of Diospyros abyssinica (Hiern) F. White tree species. Ecosyst Health Sustain. 2019;5:1–12.

    Article  Google Scholar 

  29. 29.

    Mittermeier RA, Gil PR, Hoffman M, Pilgrim J, Brooks T, Mittermeier CG, et al. Hotspots revisited. Mexico City: CEMEX. Sierra; 2004.

    Google Scholar 

  30. 30.

    CRGE. Ethiopia’s green economy strategy, Federal Democratic Republic of Ethiopia, Addis Ababa, Ethiopia, vol. 188; 2012.

  31. 31.

    Paul CJ, Weinthal E. The development of Ethiopia's Climate Resilient Green Economy 2011–2014: implications for rural adaptation. Clim Dev. 2019;11(3):193–202.

    Article  Google Scholar 

  32. 32.

    Katende AB, Birnie A, Tengnas B, Useful trees and shrubs for Uganda: identification propagation and management for Agricultural and Pastoral Communities Regional soil conservation unit (RSCU), Swedish International Development Authority (SIDA); 1995.

  33. 33.

    Bekele-Tesemma A. Useful trees of Ethiopia: identification, propagation and management in 17 agroecological zones. Technical Manual No 6. RELMA in ICRAF Project, Nairobi, Kenya. 552; 2007.

  34. 34.

    Muasya JM, Young TP, Okebiro DN. Vegetation map and plant checklist of Ol Ari Nyiro ranch and the Mukutan Gorge, Laikipia, Kenya. J East Afr Nat Hist. 1994;83(2):143–97.

    Article  Google Scholar 

  35. 35.

    Ojelel S, Otiti T, Mugisha S. Fuel value indices of selected woodfuel species used in Masindi and Nebbi districts of Uganda. Energy Sustain Soc. 2015;5(1):14.

    Article  Google Scholar 

  36. 36.

    Aine-Omucunguzi A, Byarugaba D, Rugunda GK. Population structure and regeneration status of Trichilia dregeana Sond. in heavily and less disturbed areas of kalinzu forest reserve, South Western Uganda. Open J For. 2015;5(7):662–7.

    Google Scholar 

  37. 37.

    Gole TW, Borsch T, Denich M, Teketay D. Floristic composition and environmental factors characterizing coffee forests in southwest Ethiopia. For Ecol Manag. 2008;255(7):2138–50.

    Article  Google Scholar 

  38. 38.

    Picard N, Saint-André L, Henry M. Manual for building tree volume and biomass allometric equations: from field measurements to prediction. Rome: Food and Agricultural Organization of the United Nations. 213; 2012.

  39. 39.

    Mahmood H, Abdullah SMR, Siddique MRH, Matieu H, Iqbal MZ, Islam SMZ, et al. Applicability of semi-destructive method to derive allometric model for estimating aboveground biomass and carbon stock in the Hill zone of Bangladesh. J For Res. 2019.

    Article  Google Scholar 

  40. 40.

    Segura M, Kanninen M. Allometric models for tree volume and total aboveground biomass in a tropical humid forest in Costa Rica. Biotropica. 2005;37(1):2–8.

    Article  Google Scholar 

  41. 41.

    Brown S, Gillespie AJR, Lugo AE. Biomass estimation method for tropical forests with application of forest inventory data. For Sci. 1989;35(4):881–902.

    Google Scholar 

  42. 42.

    Aho K, Derryberry D, Peterson T. Model selection for ecologists: the worldviews of AIC and BIC. Ecology. 2014;95(3):631–6.

    Article  Google Scholar 

  43. 43.

    Sprugel D. Correcting for bias in log-transformed allometric equations. Ecology. 1983;64(1):209–10.

    Article  Google Scholar 

  44. 44.

    Nogueira EM, Fearnside PM, Nelson BW, Barbosa RI, Keizer EWH. Estimates of forest biomass in the Brazilian Amazon: New allometric equations and adjustments to biomass from wood-volume inventories. For Ecol Manag. 2008;256(11):1853–67.

    Article  Google Scholar 

  45. 45.

    Ploton P, Barbier N, Momo ST, Réjou-Méchain M, Bosela FB, Chuyong G, et al. Closing a gap in tropical forest biomass estimation: taking crown mass variation into account in pantropical allometries. Biogeosciences. 2016;13(5):1571–85.

    Article  Google Scholar 

  46. 46.

    Baker TR, Phillips OL, Malhi Y, Almeida S, Arroyo L, Di Fiore A, et al. Variation in wood density determines spatial patterns in Amazonian forest biomass. Glob Change Biol. 2004;10(5):545–62.

    Article  Google Scholar 

  47. 47.

    Slik JWF, Aiba S-I, Brearley FQ, Cannon CH, Forshed O, Kitayama K, et al. Environmental correlates of tree biomass, basal area, wood specific gravity and stem density gradients in Borneo’s tropical forests. Glob Ecol Biogeogr. 2010;19:50–60.

    Article  Google Scholar 

  48. 48.

    Patino S, Lloyd J, Paiva R, Baker TR, Quesada CA, Mercado LM, et al. Branch xylem density variations across the Amazon Basin. Biogeosciences. 2009;6(4):545–68.

    Article  Google Scholar 

  49. 49.

    Chave J, Muller-Landau HC, Baker TR, Easdale TA, Steege H, Webb CO. Regional and phylogenetic variation of wood density across 2456 neotropical tree specie. Ecol Appl. 2006;16(6):2356–67.

    Article  Google Scholar 

  50. 50.

    Nelson BW, Mesquita R, Pereira JLG, de Souza SGA, Batista GT, Couto LB, et al. Allometric regressions for improved estimate of secondary forest biomass in the central Amazon. For Ecol Manag. 1999;117:149–67.

    Article  Google Scholar 

  51. 51.

    Yoona TK, Parka C-W, Leea SJ, Koa S, Kimb KN, Sonb Y, et al. Allometric equations for estimating the aboveground volume of five common urban street tree species in Daegu Korea. Urban For Urban Green. 2013;12:344–9.

    Article  Google Scholar 

  52. 52.

    Zhanga L, Bib H, Chenga P, Davisa CJ. Modeling spatial variation in tree diameter–height relationships. For Ecol Manag. 2004;189:317–29.

    Article  Google Scholar 

  53. 53.

    Wang X, Fang J, Tang Z, Zhu B. Climatic control of primary forest structure and DBH–height allometry in Northeast China. For Ecol Manag. 2006;234:264–74.

    Article  Google Scholar 

  54. 54.

    Fayolle A, Doucet J-L, Gillet J-F, Bourland N, Lejeune P. Tree allometry in Central Africa: Testing the validity of pantropical multi-species allometric equations for estimating biomass and carbon stocks. For Ecol Manag. 2013;305:29–37.

    Article  Google Scholar 

  55. 55.

    Henry M, Bombelli A, Trotta C, Alessandrini A, Birigazzi L, Sola G, et al. GlobAllomeTree: international platform for tree allometric equations to support volume, biomass and carbon assessment. iFor Biogeosci For. 2013;6(5):326–30.

    Article  Google Scholar 

Download references


The authors would like to acknowledge Oromia Forest and Wildlife Enterprise (OFWE) for their technical and material support during data collection. We also would like to acknowledge those individuals involved in data collection.


This research was supported by Addis Ababa University Thematic Research Fund.

Author information




DED, conceived the research; contributed to data analysis and wrote the draft manuscript; TS, edited and improved the manuscript. Both authors read and approved the final manuscript.

Corresponding author

Correspondence to Damena Edae Daba.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Authors give full permission for the publication, reproduction, and broadcast.

Competing interests

The authors declare no conflict of interest. The funding sponsors had no role in the design of the study; in the data collection, analyses, or interpretation of data; and in the decision to publish the results.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Daba, D.E., Soromessa, T. The accuracy of species-specific allometric equations for estimating aboveground biomass in tropical moist montane forests: case study of Albizia grandibracteata and Trichilia dregeana. Carbon Balance Manage 14, 18 (2019).

Download citation


  • Afromontane rainforest
  • Model comparisons
  • Scatter plots
  • Semi-destructive
  • Species-specific