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Sal (Shorea robusta Gaertn.) is a major tree species of Nepal, which plays a vital role in the socio-economic development of livelihoods through multi-purpose uses. Developing a tree stem volume model provides a fundamental tool for estimating forest biomass, carbon stock, and economic value of timber and is useful for modeling growth and yield and analysis of forest ecosystems. This study developed tree stem volume models using measurements from 503 S. robusta trees of different community-managed forests in both the Siwalik Hill and non-Siwalik hilly regions of Nepal. As significant differentiation of the stem volume was observed by region in the analysis, a common tree stem volume model applicable to S. robusta forests in both regions was developed by applying the dummy variable modeling approach. Among some versatile growth functions (power, fractional and exponential functions) considered for fitting data with diameter at breast height, total tree height and crown width used as predictors, the power function provided the best fits (R2adj = 0.9730; RMSE = 0.1427) with no systematic residual trends observed. The model simulation exhibited an increased volume with increasing tree height but decreasing crown width. The presented model was proved to be statistically flexible and biologically plausible and thus can be applied for a precise volume prediction of the species of interest. Model accuracy can be increased with the model recalibrated using additional predictor variables (e.g., site and climate variables) and more data collected in wider geographical ranges of the Siwalik and non-Siwalik hills of the Karnali province and beyond.

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.

Foresters rely on taper models to estimate total and merchantable volume for commercial species. While there is an abundance of literature in taper modeling for many softwood and hardwood species, little is known of teak tree stem form. Taper modeling is in early stage for the forestry sector in Nepal. Currently, no publicly available taper equations exist for teak trees in Nepal. Therefore, the main goal of this study was to develop a taper equation for valuable teak trees of lowland Nepal. Destructive sampling was carried out in teak plantations in the Sagarnath Forestry Development Project. Five common taper equations (simple, segmented, and variable exponent types) from the literature as well as one dynamic taper equation were considered as candidate models. Our results showed that the nonlinear dynamic taper equation performed best in terms of fit and cross-validation statistics with least error and bias. This new taper model can be considered as a step forward for hardwood forest management in Nepal. It is expected that this equation will assist forest managers to predict stem volume and diameters to any merchantable limit for teak growing in similar site conditions.

Dry woodland ecosystems play an important role in the global carbon balance and these need accurate biomass predictions that can be obtained from tree allometry. However, information on allometric models is limited for Combretum-Terminalia woodlands of the Amhara Region in Northwestern Ethiopia. The objective of the study was to develop an allometric model for the aboveground biomass (AGBind) of the tree species in the study areas. A total of 149 trees were considered to predict the AGBind (stems, branches and leaves) of the selected species of which 101 samples were collected from nine tree species in Metema district, and 48 sample trees were obtained from four species in Quara district. A new model was developed using log-transformed data with linear regression, and their performance was assessed with the mean absolute prediction error (MAPE%) and the mean prediction error (MPE%). The results of the study indicated that Diameter at breast height (DBH) was the best predictor variable of biomass components for all species. For site-specific data, combined DBH, tree height and wood density best explained the stem and AGBind with the MAPE ranged 18.6–19.2% compared with the DBH-alone model (MAPE ranging from 29.8 to 30.2%). A positive validation was observed on all model forms, but M3 and M4 provided highly precise predictions of AGBind for independent data. However, a large bias was observed in the branch and leaf biomass model for site-specific data with the MAPE ranged 66.6–72.8%. A little improvement was observed in the model performance of plant functional type, while general models developed from the compiled data performed well. For a given model form, a comparable performance was found between the site-specific, tree functional type and general model. Generally, the general model with DBH and wood density together followed by the one which includes three predictors was a better AGBind predictor over the site and tree functional type. The species and site-specific models are thus accurate for site-based prediction whereas the general model contributes to the reliable prediction of AGBind in the studied Combretum-Terminalia woodlands. The comparison with the published models showed that most models produced large errors with MPE ranging from 16.10 to 36.60%, but the best model of tropical dry forests performed much better with MPE ranging from -2.24 to 0.34% and these models can probably be used for Combretum-Terminalia woodland.