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Quantification of plant biomass and carbon in ecosystems is critical for establishing climate change mitigation potential. For large trees in various ecosystems, allometric models for estimating biomass have been developed but few biomass equations exist for small trees and shrubby vegetation. Allometric above-ground biomass (AGB) models are needed for small trees and shrubs in order to improve the quantification of biomass, particularly for savanna ecosystems, where small trees and shrubs comprise a significant portion of the biomass. In this study we have developed species-specific and multi-species allometric models for biomass estimation of small tree species and shrubs in the savanna ecosystem of Lake Mburo National Park in South Western Uganda. For our models we selected 27 small tree species (N = 403 individuals) and 12 shrub species (N = 177) common in savanna ecosystems for destructive sampling. We developed species-specific and multi-species allometric AGB models to provide estimates of AGB using specific biometric variables recorded for the small trees (i.e. species, DBH, height and crown area), and shrubs (species, height and crown area). We found that crown area was the best single predictor of species-specific AGB for small trees and for species-specific and multi-species models for shrubs. Species-specific models had the best predictive capacity of AGB compared to multi-species biomass models for small trees and shrubs. Multiple-variable models had the best predictive capacity of AGB in both species-specific and multi-species modeling compared to single-variable models. Based on these findings we conclude that the evaluation of carbon stocks of tropical savanna ecosystems should use multi-variable species-specific models for AGB estimation at the individual level, and multi-species models for AGB at the ecosystem level.

Biomass measurements are central to understanding and monitoring carbon stocks in the forests. They also assists in assessing the growing stock of a species and biomass energy. Efforts in allometric equation development have grown. This compiled information is an important tool for assessing biomass and associated carbon stocks, the growing stock of a species and biomass energy. Despite this, except for a few attempts, the compilation of available equations has received little attention, particularly in Ethiopia. The uncertainty involved in biomass estimation is also associated with the choice and errors of the equations. The aim of the present paper is thus to review and compile available biomass and volume allometric equations and to critically synthesise quality control procedures for the equations. A total of 767 equations for both volume and biomass were collected from 47 articles, theses and dissertations, representing 47 sites in Ethiopia. Of these, 49% of the equations were developed for the Dry Afromontane biome, 22% for the Acacia-Commiphora biome, 15% for the Combretum-Terminalia biome and 14% for the Moist Afromontane biome. Among the compiled equations, biomass equations comprised 86.7% while the remainder 13.3% were for volume. Although a wide range of natural variations resulted in diverse species in Ethiopia, the already available equations were constrained by the size of individual samples, tree diameter range, tree species and spatial coverage. Moreover, variability in equation presentation and the missing of some relevant statistical parameters or inconsistent statistical analysis were among the problems observed. A quality control procedure revealed that 45 allometric equations had verification problems and were inappropriate for application. Invalid numeric values that may over- or underestimated values were predicted in 42% of the equations. Moreover, seven equations predicted “unrealistic” values, which were low values (4 equations) or both negative and low estimates (3 equations) within their interval of calibration. Altogether, 174 equations (22.7%) were statistically credible equations. These missing information affected the use of the equations and led to the preference of the generalised equations to assess forest biomass stocks. This highlighted important research gaps. The compiled database will also be useful to minimise duplication of efforts and enable efficient utilisation of limited resources, particularly in developing countries such as those in Eastern Africa and beyond in the tropics.

In order to assess the contribution to the overall carbon stock and generate carbon credits under REDD+, it is essential to have an accurate estimation of biomass of different forest components. Forest cover in India is gradually increasing due to the active restoration of degraded land and plantation on waste and barren lands leading to an increase in the abundance of small diameter woody species. However, these were not included in biomass studies due to the non- availability of adequate allometric equations and low carbon stocks compared to mature individuals. We have harvested 589 individuals belonging to 23 woody species at the seedling and sapling stage from a tropical dry deciduous forest and developed species specific allometric equation and general allometric equation for aboveground biomass estimation. Further, the belowground biomass equation of 9 species were also developed using above ground biomass and root to shoot ratio as predictor variable. In the case of general equation, the combination of diameter with height and diameter, height and wood specific gravity exhibited highest adjusted R2 value. In case of species-specific allometric equations, combination of diameter with height predicts above ground biomass more precisely as compared to the diameter and wood specific gravity. Since the estimation of wood specific gravity requires destruction of lower diameter individuals, usage of diameter and height for biomass estimation would help protecting regeneration as both methods yield same results. All the equations developed in the present study for below ground biomass predicts biomass precisely. We suggest use of species specific allometric model developed with diameter and height for estimation of biomass. Further, general models consisting of height and diameter may be used for biomass estimation in case of non-availability of species specific equations without destroying the regeneration.

Woody-biomass is an important indicator for good vegetation cover, and a healthy environment that provides ecosystems services such as supplying, provisioning, regulating, esthetic, and cultural values. Moreover, woody-biomass stores a very good amount of carbon dioxide as about fifty percent of living organisms are built from carbon. The objective of this study is to estimate the above and belowground woody biomass and carbon stock of Kunzila watershed, Northwest Ethiopia as a piece of baseline information so as to gage the changes after the intervention. The watershed covers about 11,200 ha of land with undulating topographic landscapes. More than 230 sample plots of 30 m by 30 m were surveyed and every woody plant (shrub/bush and tree) was measured. Allometric equations were applied to calculate biomass and carbon stock potential at the plot level. The Random forest algorithm was applied to upscale plot values to watershed level. The results indicate that Kunzila watershed offers maximum biomass of 1,756(±249) Mt year−1/watershed and corresponding carbon dioxide equivalent (CO2e) of 3,022(±429) Mt C year−1 /watershed with the current vegetation status; while the average woody biomass and CO2e were estimated about 244(±249) Mt ha−1 year−1 and 425(±429) Mt C ha−1 year−1, respectively. These results indicate that there are high variabilities among different ecosystem types and also the watershed generally offers below its potential. However, there is a high potential to achieve up to triples of biomass and CO2e by implementing recommended intervention plans developed out of the current study. The current approach using machine learning algorithm to upscale from sample plot-level to watershed scale is a very good one and can be applied to other watersheds at scale.

Savannah woodlands of South Africa are dominated by fruit tree species that have a potential for commercial harvest from the wild. However, allometric equations that enable the quantification of fruit biomass of trees are non-existent. The aim of this study was to develop fruit-based allometric equations for Strychnos madagascariensis and S. spinosa species. A total of 80 trees were selected by applying a stratified sampling approach according to four stem diameter classes during fruit ripening period. For each tree, the following parameters were measured: fruit biomass, diameter at breast height (DBH), canopy diameter, and total height. Six forms of the allometric models were fitted to the data using ordinary least squares method. The Akaike information criterion was used to select the best models and the Root Mean Squared Error (RMSE) was used to evaluate the quality of the predictions. DBH was the only appropriate variable in the prediction of the fruit biomass and explained 99.9% of the variation in fruit biomass. The simple linear regressions linking the DBH (in cm) to the total fresh fruit biomass (FB; in kg) were the best models and were expressed by (1) FB = 1.0243 × DBH1.1841; and (2) FB = 1.0297 × DBH1.1956; respectively for Strychnos madagascariensis and Strychnos spinosa. These equations provided realistic predictions of fresh fruit biomass. They induced on average a prediction error of 5.4 kg on the total fresh fruit biomass of a tree. Larger trees (DBH > 25 cm for S. madagascariensis; DBH > 11 cm for S. spinosa) were more susceptible to fruit biomass prediction errors than smaller trees. This study showed that simple linear regression basing on DBH is the best approach to estimate fresh fruit biomass of trees in savannah woodlands.