Tools to accurately estimate tree volume and biomass are scarce for most forest types in East Africa, including Tanzania. Based on a sample of 142 trees and 57 shrubs from a 6,065 ha area of dry miombo woodland in Iringa rural district in Tanzania, regression models were developed for volume and biomass of three important species,
Standing volume and aboveground biomass (AGB) are the two main measures of forest stocking that are typically considered within the framework of sustainable forest management and for carbon accounting purposes [
However, direct measurement of volume and AGB is time consuming, costly, and usually destructive by nature. Therefore, the general practice is to estimate volume and AGB from tree dendrometric characteristics such as diameter and height, using established, general, or site-specific allometric equations [
In eastern, central, and southern Africa, where miombo woodland is the principal vegetation type, only few studies of this nature have been conducted and tools for accurate estimation of forest volume and biomass and for carbon accounting purposes are generally lacking [
Miombo woodlands consist of open, light stands of a deciduous or semideciduous nature, with up to three vegetation strata, upper canopy trees, secondary layer (including the shrub layer, <8 m tall), and herbaceous layer which consists chiefly of grasses up to 2 m tall [
Although with some limitations due to the large number of species in tropical forests, species-specific volume and biomass models are often preferred for accurate estimation of forest volume and biomass and for carbon accounting purposes [
Wood density is a key variable in the estimation of tree biomass [
Accordingly, the objectives of this study were to (1) develop volume and AGB allometric equations for three dominant species (
Gangalamtumba Village Land Forest Reserve (GVLFR) is located in central-southern Tanzania (7°35′ S, 35°35′ E), about 30 km northwest of Iringa Municipality, the administrative capital of Iringa Region (Figure
Map of the study area and its location in Iringa Region, Tanzania.
The survey was conducted in August 2009 and September-October 2010 and involved harvesting of 142 individual trees (28 species) and 57 individual shrubs (16 species) from a 30 m wide boundary zone of 35 permanent circular sample plots with a radius of 50 m (0.7854 ha) that were distributed across the entire area of the GVLFR (Figure
Based on their estimated relative basal area, 44 species (both trees and shrubs) were selected as a representative sample of the most common species. This selection was meant to cover the widest possible range of diameter classes as recommended by, for example, Brown [
In the field, small branches with diameter less than 5 cm were first removed, tied into bundles (piles), and weighed (fresh weight). Subsamples of these piles were selected and brought to the laboratory for dry weight determination. For each tree/shrub, five disks with a thickness of about 2-3 cm were sampled from the remaining part of the tree/shrub (≥5 cm diameter). These were used for basic density estimation and their green/fresh weight was measured in the field. Selection of the five disks was based on importance sampling, for example, [
In the laboratory, all subsamples for twigs/branches <5 cm (crown wood) and disks extracted from stem and branch sections were oven dried at
For each tree, the stump volume was calculated using the cylinder formula and the volume of stem sections and branches with diameter ≥5 cm was calculated using Newton’s formula. The volume of twigs/branches <5 cm in diameter was estimated using their estimated biomass and the estimated basic density [
Biomass (kg) was calculated as the product of density (kg m−3) and volume (m3) for stem and branch sections [
Like in other studies [
For species-specific volume and biomass models, we used ordinary least squares estimation, assuming that errors were normally and independently distributed, whereas models for groups of species (i.e., trees, shrubs, and both groups combined) were prepared using the following mixed linear model formulation:
In all models,
Furthermore, to test the contribution of wood basic density in explaining the variation of biomass, the logarithmic version of the following power model
Prior to the regression analysis, dependent variables (volume and biomass) were plotted against each of the explanatory variables to examine the range and shape of the functional relationship and to assess the heterogeneity of the variance. Relationships were tested after transforming the variables. We selected our final models based on high adjusted
Finally, the average percentage error of the models prepared in this study was compared with that of ten previously published models when applied to the datasets from GVLFR. Correction for logarithmic bias was made in cases where dependent variables had been log-transformed. All analyses were made in Excel spreadsheets and R version 2.13.0 (
Selected models for total and stem volume and biomass of the three most dominant species are presented in Table
Volume and biomass equations† for estimating total aboveground and stem (≥5 cm) volume and biomass of three dominant species in the Gangalamtumba Village Land Forest Reserve. All parameter estimates are significantly different from zero (
Species | Component | Equation |
Regression parameters | Dbh range (cm) | Adj. |
RMSE | Avg. error (%) | ||
---|---|---|---|---|---|---|---|---|---|
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| |||||||
|
Total volume |
3 | −9.3188 |
1.9663 |
0.9118 |
1.2–54.3 | 0.997 | 0.142 | 1.99 |
(df = 38) | 4 | −8.5018 |
2.4142 |
— | 1.2–54.3 | 0.995 | 0.185 | 3.49 | |
Stem volume |
3 | −11.7673 |
2.0335 |
1.6858 |
5–54.3 | 0.990 | 0.209 | −0.02 | |
(df = 32) | 4 | −9.8909 |
2.7460 |
— | 5–54.3 | 0.981 | 0.286 | 2.14 | |
Total biomass |
3 | −2.6071 |
2.0638 |
0.7847 |
1.2–54.3 | 0.998 | 0.127 | 1.60 | |
(df = 38) | 4 | −1.9040 |
2.4492 |
— | 1.2–54.3 | 0.996 | 0.162 | 2.67 | |
(df = 38) | 6 | −9.3309 |
0.9827 |
— | 1.2–54.3 | 0.998 | 0.127 | 1.66 | |
Stem biomass |
3 | −5.0668 |
2.1424 |
1.5563 |
5–54.3 | 0.992 | 0.189 | −0.57 | |
(df = 32) | 4 | −3.3345 |
2.8002 |
— | 5–54.3 | 0.985 | 0.262 | 1.17 | |
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|
Total volume |
3 | −9.0504 |
1.9212 |
0.7712 |
2.2–26.5 | 0.987 | 0.171 | 2.90 |
(df = 39) | 4 | −8.5247 |
2.2972 |
— | 2.2–26.5 | 0.981 | 0.204 | 4.24 | |
Stem volume |
3 | −10.3990 |
2.0420 |
1.0908 |
5–26.5 | 0.977 | 0.146 | 4.64 | |
(df = 31) | 4 | −9.8028 |
2.6237 |
— | 5–26.5 | 0.954 | 0.206 | 4.98 | |
Total biomass |
3 | −2.4539 |
1.9685 |
0.7545 |
2.2–26.5 | 0.987 | 0.170 | 2.88 | |
(df = 39) | 4 | −1.9395 |
2.3364 |
— | 2.2–26.5 | 0.982 | 0.203 | 4.20 | |
(df = 39) | 6 | −8.8347 |
0.9371 |
— | 2.2–26.5 | 0.987 | 0.170 | 2.98 | |
Stem biomass |
3 | −3.7737 |
2.1112 |
1.0410 |
5–26.5 | 0.976 | 0.151 | 4.41 | |
(df = 31) | 4 | −3.2047 |
2.6663 |
— | 5–26.5 | 0.956 | 0.205 | 4.66 | |
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|
Total volume |
3 | −9.3147 |
1.9701 |
0.8854 |
1.8–21.5 | 0.991 | 0.144 | 2.04 |
(df = 35) | 4 | −8.3253 |
2.2413 |
— | 1.8–21.5 | 0.982 | 0.200 | 3.90 | |
Stem volume |
3 | −13.8803 |
2.0113 |
2.7732 |
5–21.5 | 0.871 | 0.355 | 12.97 | |
(df = 30) | 4 | −10.5743 |
2.7785 |
— | 5–21.5 | 0.715 | 0.528 | 32.87 | |
Total biomass |
3 | −2.7097 |
1.9900 |
0.9035 |
1.8–21.5 | 0.990 | 0.150 | 1.22 | |
(df = 35) | 4 | −1.7001 |
2.2667 |
— | 1.8–21.5 | 0.981 | 0.206 | 3.00 | |
(df = 35) | 6 | −9.3782 |
0.9823 |
— | 1.8–21.5 | 0.990 | 0.148 | 1.37 | |
Stem biomass |
3 | −7.3073 |
2.0138 |
2.8503 |
5–21.5 | 0.869 | 0.362 | 11.53 | |
(df = 30) | 4 | −3.9094 |
2.8023 |
— | 5–21.5 | 0.708 | 0.541 | 30.54 | |
(df = 30) | 6 | −14.5150 |
1.2961 |
— | 5–21.5 | 0.823 | 0.421 | 17.00 |
Note: numbers in brackets indicate standard errors of the parameter estimates. Height (range):
Volume and biomass equations for estimating total aboveground and stem (≥5 cm) volume and biomass of shrubs, trees, and woody plants in general (shrubs and trees) in Gangalamtumba Village Land Forest Reserve. A few estimates are not significantly different from zero (
Category | Component | Equation |
Regression parameters | Dbh range (cm) |
|
RMSE |
|
Avg. error (%) | ||
---|---|---|---|---|---|---|---|---|---|---|
|
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Shrubs |
Total volume |
3 | −8.3287 |
2.2340 |
0.0507ns |
1.8–21.5 | 0.979 | 0.251 | 0.00 | 18.48 |
(df = 28) | 4 | −8.2844 |
2.2517 |
— | 1.8–21.5 | 0.979 | 0.247 | 0.00 | 18.57 | |
Stem volume |
3 | −13.4822 |
1.7917 |
2.8547 |
5–21.5 | 0.865 | 0.533 | 0.00 | 30.34 | |
(df = 24) | 4 | −10.8001 |
2.6587 |
— | 5–21.5 | 0.792 | 0.482 | 0.636 | 57.79 | |
Total biomass |
3 | −1.8567 |
2.2145 |
0.1591ns |
1.8–21.5 | 0.977 | 0.267 | 0.00 | 21.90 | |
(df = 28) | 4 | −1.7179 |
2.2697 |
— | 1.8–21.5 | 0.977 | 0.265 | 0.00 | 22.43 | |
(df = 28) | 6 | −8.6142 |
0.9417 |
— | 1.8–21.5 | 0.968 | 0.266 | 0.184 | 18.93 | |
Stem biomass |
3 | −6.8855 |
1.9676 |
2.6083 |
5–21.5 | 0.916 | 0.390 | 0.276 | 29.50 | |
(df = 24) | 4 | −4.3236 |
2.6903 |
— | 5–21.5 | 0.799 | 0.470 | 0.676 | 56.02 | |
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Trees |
Total volume |
3 | −9.5238 |
1.8067 |
1.1940 |
1.4–62 | 0.997 | 0.129 | 0.00 | 1.66 |
(df = 70) | 4 | −8.4800 |
2.3351 |
— | 1.4–62 | 0.986 | 0.219 | 0.179 | 6.46 | |
Stem volume |
3 | −11.0643 |
1.9316 |
1.5360 |
5–62 | 0.994 | 0.162 | 0.00 | 4.55 | |
(df = 35) | 4 | −10.9307 |
3.0134 |
— | 5–62 | 0.975 | 0.317 | 0.119 | 13.10 | |
Total biomass |
3 | −3.1399 |
1.7586 |
1.2934 |
1.4–62 | 0.992 | 0.118 | 0.278 | 2.47 | |
(df = 70) | 4 | −2.0667 |
2.3561 |
— | 1.4–62 | 0.979 | 0.213 | 0.392 | 9.96 | |
(df = 70) | 6 | −9.1880 |
0.9668 |
— | 1.4–62 | 0.996 | 0.131 | 0.052 | 1.32 | |
Stem biomass |
3 | −5.6032 |
2.1420 |
1.7090 |
5–62 | 0.965 | 0.294 | 0.221 | 6.30 | |
(df = 38) | 4 | −4.4622 |
2.9972 |
— | 5–62 | 0.928 | 0.387 | 0.375 | 10.60 | |
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Combined |
Total volume |
3 | −9.0339 |
1.9637 |
0.7737 |
1.4–62 | 0.989 | 0.193 | 0.131 | 6.54 |
(df = 101) | 4 | −8.4554 |
2.3236 |
— | 1.4–62 | 0.983 | 0.248 | 0.140 | 9.89 | |
Stem volume |
3 | −12.2617 |
2.1378 |
1.7695 |
5–62 | 0.977 | 0.271 | 0.153 | 18.50 | |
(df = 43) | 4 | −11.1929 |
3.0514 |
— | 5–62 | 0.935 | 0.413 | 0.327 | 49.22 | |
Total biomass |
3 | −2.6896 |
1.9041 |
0.9377 |
1.4–62 | 0.983 | 0.182 | 0.317 | 8.96 | |
(df = 101) | 4 | −1.9564 |
2.3260 |
— | 1.4–62 | 0.974 | 0.242 | 0.327 | 12.60 | |
(df = 101) | 6 | −8.8819 |
0.9497 |
— | 1.4–62 | 0.988 | 0.187 | 0.162 | 6.40 | |
Stem biomass |
3 | −5.9897 |
2.0894 |
1.9457 |
5–62 | 0.970 | 0.290 | 0.230 | 13.70 | |
(df = 43) | 4 | −4.8335 |
3.0979 |
— | 5–62 | 0.924 | 0.383 | 0.488 | 43.10 | |
(df = 43) | 6 | −13.5156 |
1.2284 |
— | 5–62 | 0.971 | 0.314 | 0.153 | 25.10 |
Note: numbers in brackets indicate standard errors of the estimates. Height range: shrubs 2.5–8 m; trees: 2.5–18.2 m; and combined: 2.5–18.2 m. df is degrees of freedom; RMSE is standard error of the residuals;
Model 4 for total volume (a) and biomass (b) of the three species groups: shrubs, trees, and shrubs and trees. First three columns: circles indicate observed values, unbroken lines show expected values, and dashed lines are 95% prediction intervals; fourth column: all three models (see legend).
To some extent the overall performance of the volume and biomass models can be judged from the RMSE and
The new volume and biomass models for individual species and for broader species groups (shrubs, trees, and both) provide a comprehensive range of tools for estimation of standing volume, aboveground biomass, and carbon stock of dry miombo vegetation in Tanzania. Since the number of sample trees and shrubs for the general model is relatively large and includes a large number of species (44 different species) compared to site-specific models reported elsewhere [
Not surprisingly, the species-specific volume and biomass models were superior to models prepared for species groups in terms of average percentage error. Similarly, models prepared for individual species groups (trees and shrubs separately) were superior to models prepared for the combined dataset including both trees and shrubs. Thus, the precision and accuracy of the predictions tends to increase from general (all species, trees, and shrubs) to species-specific (single species) models.
To assess the increase in accuracy achieved in GVLFR by the new models, 10 different previously published volume and biomass models for miombo woodlands were tested on the datasets prepared in this study. The models include five volume functions and five biomass functions and the calculated average percentage error obtained for each combination of model and dataset is shown in Table
Average error in percent of measured values as observed for previously published volume and biomass equations for miombo woodlands when applied to species-specific and mixed-species datasets prepared in this study. Models developed in this study are included for comparison. Symbols:
Model |
Source | Name of species | Average error in percent for the datasets prepared in this study | |||||
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Shrubs | Trees | Combined | |||
Model (3) = total volume | This study |
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(7) |
Abbot et al. [ |
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−10.73 | 21.76 | 12.01 | 29.98 | 8.44 | 15.34 |
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(8) |
Temu [ |
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−6.03 | 17.12 | 0.12 | 2.82 | 10.48 | 7.45 |
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(9) |
Malimbwi and Temu [ |
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−21.16 | 4.45 | −6.86 | −1.11 | −4.45 | −3.64 |
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(10) |
Abbot et al. [ |
Mixed species (17 spp.) | −15.36 | 8.77 | −5.7 | −2.66 | 1.06 | −0.20 |
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(11) |
Malimbwi et al. [ |
Mixed species (13 spp.) | −20.91 | −1.94 | −11.0 | −7.45 | −12.82 | −11.26 |
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Model (3) = total biomass | This study |
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(12) |
Malimbwi et al. [ |
Mixed species (13 spp.) | −38.56 | −23.44 | −30.61 | −20.51 | −8.07 | −11.68 |
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(13) |
Chamshama et al. [ |
Mixed species (20 spp.) | −28.17 | −33.17 | −36.28 | −41.81 | −16.29 | −24.46 |
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(14) |
Mugasha et al. [ |
Mixed species (49 spp.) | −24.21 | 4.52 | −6.81 | 8.85 | 21.19 | 17.39 |
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(15) |
Mugasha et al. [ |
Mixed species (49 spp.) | −20.38 | −1.27 | −11.09 | −3.35 | 15.57 | 9.75 |
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(16) |
Brown [ |
Mixed species (not indicated) | −50.02 | −30.29 | −35.91 | −19.27 | −19.04 | −19.26 |
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Considering the high species diversity found in the miombo woodlands, the variation of species composition from site to site, and the impact of site conditions on the shape of trees, the use of mixed-species regression models calibrated on data from sites with similar site conditions and species composition is a logical choice. By contrast, general allometric equations developed in other regions with different species composition should be used with caution and only if local mixed-species models are not available. Locally abundant species would usually not be represented in the databases used for development of such general allometric models and they therefore may not accurately reflect the true biomass of trees in a given forest area [
The large percentage errors observed for the shrub dataset are presumably caused by the large variation of physical shapes characterising this category of woody plants. The dataset included 16 different shrub species, and some of them are characterised by very special stem and crown shapes and very low (or high) wood basic densities. This could explain the large variation observed in the volume and biomass for stems.
The value of species-specific models as a way to minimise variation and increase the accuracy of models should not go unmentioned. However, as explained, the high diversity of species in miombo woodlands and in most other tropical forest types combined with frequent challenges of correct botanical identification enhances the cost of preparing species-specific models with a view to improve the accuracy of standing volumes and biomass estimates at the forest level. Mixed-species models are, therefore, still useful but they should be applied with due consideration of the improved quality of volume/biomass estimates that can be obtained by the application of species-specific models.
This study for the first time provides a comprehensive pool of different allometric equations for estimating total and stem volume and biomass, for (i) selected individual species and (ii) for mixed-species groups found in the dry miombo woodlands of Tanzania. Since most of the models have higher
The authors declare that there is no conflict of interests regarding the publication of this paper.
Special thanks are due to the people of Mfyome village for their invaluable assistance during the field work: