Biomass Equations Research Articles

Exploring influential factors on biomass and diversity of ancient trees in human-dominated regions: A case study in Guangdong Province, China

Ancient trees, which are vital for biodiversity and cultural heritage, maintain species richness, ecosystem resilience, and societal interconnectedness. However, escalating global climate change and human activities have led to widespread declines in ancient tree diversity and habitat degradation. Hence, understanding ancient tree dynamics is vital for precise conservation strategies, fostering harmony between society and ecology. This study addresses the gap in understanding the factors influencing ancient tree biomass and diversity in human-dominated areas. This study first collected biomass equations for ancient tree species. The biomass of ancient trees within human-influenced regions of Guangdong Province was subsequently computed in detail. Finally, by employing a structural equation model, we sought to quantitatively elucidate the influence of natural variables (encompassing geography, topography, soil composition, and climatic conditions) alongside anthropogenic variables (including population density, per capita GDP, the human footprint, ancient villages, and religious sites) on both the biomass and species richness of ancient trees. The species with the highest total biomass included Ficus microcarpa L.f. (2.45 × 105 t), Ficus concinna (Miq.) Miq. (4.07 × 104 t), and Litchi chinensis Sonn. (3.16 × 104 t). Moreover, the biomass presented a pronounced phylogenetic signal and evolutionary conservation. According to the results of the structural equation model, across the entire study area, soil characteristics emerged as the most influential predictor among the natural factors (β = −0.14), whereas population density stood out as the foremost predictor among the anthropogenic factors (β = 0.23). Notably, both climatic conditions and anthropogenic factors contributed positively to an increase in ancient tree biomass (β = 0.02) (β = 0.07), albeit concurrently leading to a decrease in species richness (β = −0.05) (β = −0.08). In regions characterized by high levels of disturbance, anthropogenic factors indirectly increase biomass by increasing the abundance of large-diameter ancient trees (β = 0.28) while concurrently decreasing species richness (β = −0.11). Conversely, in areas with low levels of disturbance, anthropogenic influences also indirectly increase biomass (β = 0.19) by fostering the growth of large-diameter ancient trees while simultaneously promoting species richness (β = 0.01). This study investigated biomass and diversity factors in ancient trees within human-dominated regions of Guangdong Province. This study provides insights into ecosystem determinants, informing conservation strategies and contributing to biodiversity preservation amid societal and environmental dynamics.

Trends in planted native tree biomass established in a tropical Andes city water basins

Studying tree growth processes helps us evaluate the success of restoration efforts in recovering ecosystem processes such as carbon accumulation. Here, we study aboveground biomass dynamics in four common‐endemic tree species from the tropical Andes (Quercus humboldtii, Retrophyllum rospigliosii, Citharexylum subflavescens, and Croton magdalenensis), established in the main water supply basins of Medellín, Colombia. We use measurements from 19 permanent plots from five censuses between 2010 and 2023 to develop biomass equations as a function of t for each species (B = f[t]) by combining tree growth models of diameter (D) as a function of time (t) and pre‐existing allometric equations of biomass (B) developed in the same site as a function of D and wood density (ρ). We found that, because of initially higher growth rates characteristic of species belonging to the fast‐growing end of the fast‐slow growth economic spectrum, C. magdalenensis and C. subflavescens were the species with higher biomass accumulation at 13 years (142 and 132 kg, respectively). However, the potential of the slow‐growing species (Q. humboldtii and R. rospigliosii) for carbon sequestration initiatives in the long term was highlighted. To our knowledge, this is the first study in the region examining the dynamics of aboveground biomass in Andean restoration programs. It combines growth models and allometric equations, which can be useful for measuring the success of the implementation of ecological restoration programs and evaluating the need to apply adaptive management strategies.

Allometric equations for biomass and carbon pool estimation in short rotation Pinus radiata stands of the Western Cape, South Africa

AbstractThe quantification of tree growth and carbon storage over time is an important task for sustainable forest management and carbon sequestration projects. For the South African short-rotation Pinus radiata (D. Don) forests, this knowledge is lacking. We developed allometric equations and compared the estimated weights to previously published biomass studies and we used Dirichlet Regression (DR) modelling to ensure additivity of the component proportions. The biomass components and their contribution to carbon storage depend strongly on forest structure and mean tree size but also on-site conditions and tree architecture. Our first two hypotheses were that the (1) best model for stemwood (SW), bark and total mass will include the combined variable DBH2H and (2) that the DR will yield statistically similar estimates for all components when compared to the best models. Our third hypothesis was that allometric equations developed for sites with high resource availability (e.g. wet, fertile sites) will yield biased estimates when extrapolated to sites with lower levels of resource availability (drier and/or infertile sites). The results indicated that DBH2H was the best variable to describe SW, bark and total mass and the DR yield similar estimates for all component proportions when compared to the best models. There were strong similarities in the SW and total mass of independent test sites in comparison to the SW and total mass of this study but greater variability in the bark, needle and branch mass. This can be associated to site and seasonal differences as well as variability in tree architecture brought about by different silvicultural operations on individual sites. Previously developed equations by other authors for sites with high resource availability overpredicted the SW and total mass of the models developed in this study. Our set of additive component equations performed well even when applied to sites of similar productivity over a climate gradient. The presented new equations bridge the gap in knowledge where allometric equations for short rotation Radiata pine stands are lacking.

Allometry and biomass dynamics in temperate mixed and monospecific stands: Contrasting response of Scots pine (Pinus sylvestris L.) and sessile oak (Quercus petraea (Matt.) Liebl.)

Mixed forests generally outperform monospecific forests in terms of productivity, stability, and resilience and are becoming increasingly important for sustainable forest management. However, accurate estimates of tree biomass allocation, as well as aboveground and component biomass in mixed forests, remain scarce. Our study addressed three different objectives to identify differences in biomass between mixed and monocultures and develop biomass models: (1) identification of biomass growth patterns in mixed and monoculture stands using analysis of covariance (ANCOVA), (2) investigation of the best fitting approach to modeling aboveground biomass using logarithmic regression and nonlinear mixed-effects models, and (3) fitting compartment biomass proportion models by Dirichlet regression, considering the additivity property. We analyzed 52 harvested trees from six plots within an experimental triplet in northern Spain, consisting of mixed and single-species stands of Scots pine (Pinus sylvestris L.) and sessile oak (Quercus petraea (Matt.) Liebl.). Moreover, diameter at breast height and tree height were used as covariate variables to determine the most accurate and unbiased models. The research findings showed that (i) allometric patterns of individual-tree biomass in mixed stands significantly differed from those in monospecific stands for sessile oak, while those in Scots pine did not change; (ii) nonlinear mixed-effect models demonstrated a better fit – indicated by lower Furnival index values – than logarithmic regression models in predicting aboveground biomass; and (iii) the fitted biomass equations provided good performance and accurate estimates of biomass component proportions compared to those of existing models. Consequently, our results offer a better understanding of biomass and carbon storage within mixed and monoculture forests in the context of climate change.

PID Controller Design for an E. coli Fed-Batch Fermentation Process System Using Chaotic Electromagnetic Field Optimization

This paper presents an optimal tuning of a proportional integral differential (PID) controller used to maintain glucose concentration at a desired set point. The PID controller synthesizes an appropriate feed rate profile for an E. coli fed-batch cultivation process. Mathematical models are developed based on dynamic mass balance equations for biomass, substrate, and product concentration of the E. coli BL21(DE3)pPhyt109 fed-batch cultivation for bacterial phytase extracellular production. For model parameter identification and PID tuning, a hybrid metaheuristic technique—chaotic electromagnetic field optimization (CEFO)—is proposed. In the hybridization, a chaotic map is used for the generation of a new electromagnetic particle instead of the electromagnetic field optimization (EFO) search strategy. The CEFO combines the exploitation capability of the EFO algorithm and the exploration power of ten different chaotic maps. The comparison of the results with classical EFO shows the superior behaviour of the designed CEFO. An improvement of 30% of the objective function is achieved by applying CEFO. Based on the obtained mathematical models, 10 PID controllers are tuned. The simulation experiments show that the designed controllers are robust, resulting in a good control system performance. The closed-loop transient responses for the corresponding controllers are similar to the estimated models. The settling time of the control system based on the third PID controller for all estimated models is approximately 9 min and the overshoot is approximately 15%. The proposed CEFO algorithm can be considered an effective methodology for mathematical modelling and achievement of high quality and better performance of the designed closed-loop system for cultivation processes.

Biomass equations and annual growth of various Eucalyptus clones in commercial plantations across Thailand

Plantation forestry is pivotal in meeting global timber and fiber demands while promoting sustainable practices. Eucalyptus plantations, renowned for their rapid growth and adaptability, have significantly expanded in tropical regions such as Thailand. Improved Eucalyptus clones have enhanced productivity, emphasizing the role of genetic improvement programs. Accurate biomass estimation in these plantations is crucial for sustainable management and bioenergy production. This study employs a nonlinear mixed model approach to evaluate tree variables combined with rotation, clones, and region on aboveground biomass (AGB) estimation. The results showed that the "rotation, clones, region" model emerged as the most precise, achieving the highest R² and the lowest SEE, ASE, and MPSE values. However, over-parameterization is a concern. The more straightforward "clone" model performed well, achieving a high R² and relatively low prediction error, with no systematic bias and comparable ASE, MPE, and MPSE values, making it a strong choice when fewer predictor variables are preferred. Our results revealed that clones H4 and K7 for the northeastern region and K58 for the eastern region show the highest annual productivity, with growth rates up to 20 t ha−1 year−1. The rapid AGB increment in clones K58, K62, and K7 during the first rotation suggests improved performance in subsequent rotations. As in eastern Thailand, selecting less arid sites can enhance these clones' AGB productivity. Additionally, intensive silvicultural practices could further boost their productive efficiency.

Productivity of hybrid sweetgum (Liquidambar formosana x styraciflua) plantations compared to native sweetgum (L. Styraciflua) in the Western Gulf Region of south-central U.S.

Sweetgum (Liquidambar styraciflua) is an excellent short-rotation woody crop species for producing hardwood pulp and biofuel feedstock on marginal sites. A recent development which may improve growth is the production of hybrid sweetgum (L. formosana x styraciflua) clones. We compared the productivity and attributes, e.g., wood specific gravity and stand growth, of operational plantations of hybrid sweetgum clones to improved native sweetgum half-sib families in 36 stands in Oklahoma, Texas, and Louisiana, U.S. The least square means (LSMean) of wood specific gravity was significantly greater for hybrids clones than the half-sibs (0.49 vs. 0.46 g/cm3), and the hybrid clones had less taper which necessitated developing genotype-specific volume and biomass equations. Despite having less fertilizer inputs on average, the hybrids had greater standing green stem biomass than the half-sibs at an age of 8.5 years (LSMean of 73.3 vs. 51.9 Mg/ha). The hybrids as a group also exhibited greater green biomass growth during the 2022 growing season when compared to the half-sibs at 8.5 years (LSMean of 15.8 vs. 11.4 Mg/ha/y). Therefore, the hybrids provide growth and wood quality advantages that may provide an opportunity to increase productivity and profitability of hardwood SRWC plantings.

A Lidar Biomass Index of Tidal Marshes from Drone Lidar Point Cloud

Accompanying climate change and sea level rise, tidal marsh mortality in coastal wetlands has been globally observed that urges the documentation of high-resolution, 3D marsh inventory to assist resilience planning. Drone Lidar has proven useful in extracting the fine-scale bare earth terrain and canopy height. Beyond that, this study performed marsh biomass mapping from drone Lidar point cloud in a S. alterniflora-dominated estuary on the Southeast U.S. coast. Three point classes (ground, low-veg, and high-veg) were classified via point cloud deep learning. Considering only vegetation points in the vertical profile, a profile area-weighted height (HPA) was extracted at a grid size of 50 cm × 50 cm. Vegetation point densities were also extracted at each grid. Adopting the plant-level allometric equations of stem biomass from long-term S. alterniflora surveys, a Lidar biomass index (Lidar_BI) was built to represent the relative quantity of marsh biomass in a range of [0, 1] across the estuary. Compared with the clipped dry biomass samples, it achieved a comparable and slightly better performance (R2 = 0.5) than the commonly applied spectral index approaches (R2 = 0.4) in the same marsh field. This study indicates the feasibility of the drone Lidar point cloud for marsh biomass mapping. More advantageously, the drone Lidar approach yields information on plant community architecture, such as canopy height and plant density distributions, which are key factors in evaluating marsh habitat and its ecological services.

UAV-RGB-image-based aboveground biomass equation for planted forest in semi-arid Inner Mongolia, China

The acquisition of high-resolution aboveground biomass (AGB) data cost-effectively and expeditiously represents a formidable challenge within the domain of current ecosystem surveillance. Plot-based inventory, the conventional approach for estimating and validating remote sensing data, is nonetheless costly and constrained in terms of spatial coverage. The expeditious advancements in unmanned-aerial-vehicle (UAV) technology furnish the potential to devise AGB equations that transcend traditional diameter-height-based equations alongside techniques for quantifying forest structural parameters through standard RGB aerial imagery. Since the canopy diameter (CD) and tree height (H) can be directly ascertained from UAV-derived datasets, biomass equations parameterized by CD and H may be more valuable. In the present investigation, we established AGB equations predicated on data procured from a UAV outfitted with a high-resolution RGB camera, specifically for planted sparsely Pinus sylvestris forest in central Inner Mongolia, China. Utilizing the aerial imagery, we generated the digital terrain model (DTM), digital surface model (DSM) and the digital orthophoto image (DOM). Then, the canopy height model (CHM) was obtained by subtracting DSM from DTM to extract the H and CD of individual trees. This methodology's CD (R2 = 0.85, RMSE = 0.203 m) and H (R2 = 0.77 and RMSE = 0.671 m) obtained closely mirrored the in-situ measurements. Six prospective AGB equations were constructed for the Pinus sylvestris forest, taking H and CD extracted from UAV aerial survey datasets as the dependent variables. The accuracy of the AGB estimation was appraised by employing extant allometric growth equations, which were parameterized using the ground-measured tree diameter at breast height (DBH) and H. The most efficacious biomass equation, predicated on H and CD data extracted from UAV aerial surveys, was delineated as W=2.3442CD∗H0.9057(R2 = 0.731, RMSE = 2.46 kg), thus presenting a convenient tool for estimating the AGB of sparse Pinus sylvestris forests in semi-arid locales.

Improving Volume and Biomass Equations for Pinus oocarpa in Nicaragua

We present a new set of equations for tree level volume and aboveground biomass estimation for ocote pine (Pinus oocarpa Schiede ex Schltdl). These equation systems are the first developed for this species in Nicaragua. The first system includes a taper function, a merchantable volume equation, and volume equations for stem, coarse branches, and whole trees. The second system estimates whole tree and individual tree component biomass (stem wood, bark, branches, and needles). Data from 112 sampled trees were used for models’ development. Seemingly Unrelated Iterative Regression and the Generalized Method of Moments were used to simultaneously fit the volume and biomass equations systems, respectively; both methods ensure additivity and compatibility between equations. Weighted regression and a second-order continuous autoregressive error structure were used to correct heteroscedasticity and autocorrelation within the hierarchical dataset. The predictive power of the new proposed equations is higher than the currently used models for P. oocarpa in the country. These equation systems represent a scientific advancement that will enhance forest inventories, optimize timber management of the species, and facilitate accurate monitoring of forest carbon dynamics. Additionally, the new equations will contribute to a more precise accounting of CO2 emissions from the country’s forestry sector.

Species-specific allometric models for reducing uncertainty in estimating above ground biomass at Moist Evergreen Afromontane Forest of Ethiopia

An allometric equation is used to convert easily measured tree variables into biomass. However, limited species-specific biomass equations are available for native tree species grown in various biomes of Ethiopia. The available pantropic generic equation has resulted in biases owing to the uncertainty of the generic model estimation due to the difference in tree nature and response to growth conditions. The objective of the study is, thus, to develop a species-specific allometric equation for reducing uncertainty in biomass estimation at the Moist Evergreen Afromontane Forest in south-central Ethiopia. Five tree species were selected for model development, these selected trees were harvested and weighed in the field. The measured above-ground biomass data related to easily measured tree variables: diameter at stump height, diameter at breast height (dbh), crown diameter, and total tree height. The developed model evaluated and compared with previously published model by using measures of goodness of fit such as coefficient of determination (R2), total relative error, mean prediction error, root mean square error, and Akaike information criteria. The analysis showed that a model with dbh as a single predictor variable was selected as the best model for the estimation of above-ground biomass. It gives the highest R2 for Syzygium guineense (0.992) and the lowest for Bersama abyssinica (0.879). The additions of other tree variables did not improve the model The pantropic model by Brown overestimates the biomass by 9.6–77.8% while both Chave models resulted in an estimation error of 12–50.3%. Our findings indicated that species-specific allometric equations outperformed both site-specific and pantropic models in estimating above-ground biomass by giving 0.1% up to 7.9% estimation error for the respective tree species.

Characterising the metabolic rewiring of extremely slow growing Komagataella phaffii.

Retentostat cultivations have enabled investigations into substrate-limited near-zero growth for a number of microbes. Quantitative physiology at these near-zero growth conditions has been widely discussed, yet characterisation of the fluxome is relatively under-reported. We investigated the rewiring of metabolism in the transition of a recombinant protein-producing strain of Komagataella phaffii to glucose-limited near-zero growth rates. We used cultivation data from a 200-fold range of growth rates and comprehensive biomass composition data to integrate growth rate dependent biomass equations, generated using a number of different approaches, into a K. phaffii genome-scale metabolic model. Here, we show that a non-growth-associated maintenance value of 0.65 and a growth-associated maintenance value of 108 lead to accurate growth rate predictions. In line with its role as energy source, metabolism is rewired to increase the yield of ATP per glucose. This includes a reduction of flux through the pentose phosphate pathway, and a greater utilisation of glycolysis and the TCA cycle. Interestingly, we observed activity of an external, non-proton translocating NADH dehydrogenase in addition to the malate-aspartate shuttle. Regardless of the method used for the generation of biomass equations, a similar, yet different, growth rate dependent rewiring was predicted. As expected, these differences between the different methods were clearer at higher growth rates, where the biomass equation provides a much greater constraint than at slower growth rates. When placed on an increasingly limited glucose diet, the metabolism of K. phaffii adapts, enabling it to continue to drive critical processes sustaining its high viability at near-zero growth rates.

Species-specific and generalized allometric equations for improving aboveground biomass estimations of 33 understory woody species in northeastern China forest ecosystems

Understory small trees and shrubs play a crucial role in the biogeochemical cycles in forest ecosystems. However, their biomass in northeastern China is still uncertain and has been heavily disregarded due to the limited number of available allometric equations. For this study, 782 plants from 33 species obtained by the destructive method were used to develop species-specific and generalized aboveground allometric biomass equations based on collar diameter ( D) and height ( H) using weighted nonlinear seemingly unrelated regression. Each biomass component was shown to be well predicted by D alone, with R2 adj. values mostly greater than 0.80. The majority of species performed better in the models for wood and aboveground biomass when H was included as D2 H. Furthermore, generalized equations for the two components showed a comparatively large coefficient of variation but comparable Bias to species-specific equations, especially for small trees. It is recommended to estimate biomass using generalized equations for mixed species only when species-specific equations are unavailable at a given site. However, in the event when precision is not the primary concern, generalized equations are also suggested. The developed equations will help to improve the accuracy of biomass assessment of understory woody plants in northeastern China forest ecosystems.

The above-ground biomass model of mangrove forest in Panai Hilir, Indonesia

Mangrove forest is one of the forests with high biomass storage in the tropics besides mainland forests. The mangrove ecosystem has an ecological function as a carbon reducer through a sequestration process (C-sequestration). One technique that can be used to calculate biomass is the use of remote sensing technology using satellite imagery and the application of transformation of the vegetation index. This study aims to obtain the best biomass equation model from several vegetation indices, analyze the estimated biomass value from Landsat 8 imagery in 2022, and map the distribution of the estimated distribution of average above-ground biomass of mangrove forest in Panai Hilir using Landsat 7 imagery and Landsat 8 in 2002 and 2022. This study used a systematic sampling method with a random start with 70 plots. Biomass calculations are carried out using allometric equations. Modeling uses the dependent variable (biomass) and the independent variable (vegetation index). The vegetation indices used are NDVI, GNDVI, and TDVI. The results of this study showed that the chosen equation model was the GNDVI (Green Normalized Difference Vegetation Index) linear equation model y = 11, 479x – 2,5569 with r2 of 0,404. The average distribution of above-ground biomass of mangrove vegetation at research sites 2002 was 109,66 tons/ha, with the highest biomass distribution being 200,48 tons/ha. The average above-ground biomass content of mangrove vegetation at the research location in 2022 is 98,28 tons/ha, with the highest biomass distribution of 213,10 tons/ha. In calculations using the selected model and index, in 2022, a total estimated average biomass was obtained of 405,52 tons/ha.

Biomass allocation and equations for silver birch (Betula pendula) and downy birch (Betula pubescens) in Estonia

ABSTRACT The values of the fractional distribution of aboveground (AGB) and belowground biomass (BGB), root/shoot ratios (R/S) and stemwood density were estimated for silver (Betula pendula) and downy birch (Betula pubescens) along site index and age gradients for Estonia. Biomass models were used, in which the diameter at breast height (D1.3, measured above root collar), tree height, age and site index served as independent variables. The models for AGB and BGB were elaborated based on the data of 117 and 30 model trees, respectively. Strong allometric relationships were revealed between D1.3 and total AGB (with foliage), stem mass and BGB. For trees with D1.3 < 15 cm, individual models predicted similar biomass patterns for both species, for larger trees, species-specific models are suggested. The relative share of stem mass in AGB was 80–90% for all site index and age classes, being significantly higher for silver birch. The average relative share of stembark, branches, leaves and the R/S, were significantly higher for downy birch. Mean oven-dry mass stemwood density for silver and downy birch was 0.573 g cm−3, there was no statistically significant difference between the species. The mean R/S ratio was 0.29 and 0.43 for silver and downy birch, respectively.

New aboveground biomass equations by components for small black spruce in peatland ecosystems of Western Canada

Black spruce is the most common tree species in Canada, dominating treed peatlands where they are usually stunted. We used 495 destructively sampled trees from 56 plots to develop allometric models of aboveground biomass by components (stem, branches, and needles) for small (&lt;5 m tall) black spruce from peatlands in the Taiga Plains and Boreal Plains Ecozones of Western Canada, for which there were no specific models available of biomass by components. We used leave-one-plot-out cross-validation to assess transferability and compare our models with existing national and ecozone-specific equations. Our models predicted total tree biomass with better accuracy and less biased estimates than the national model (relative RMSE: 30% versus 35% national; relative bias: +1% versus –12% national). Similar results were obtained in other external datasets. Existing ecozone equations performed worse than either our models or the national ones. When we applied the models at the plot level to predict aboveground biomass density (Mg·ha−1), our models outperformed the national model again (relative RMSE: 15.9% versus 18.6% national, relative bias: +3.5% versus –13.6% national). These results indicate that at least for peatlands of Western Canada, our models provide better aboveground biomass estimates for small black spruce trees than existing models.

Utilizing LiDAR to quantify aboveground tree biomass within an urban university

Universities, often situated at the heart of metropolitan areas, have the unique opportunity to leverage effective urban forestry methods to promote ecological and economic conservation. Simply identifying what trees to plant where can have major effects such as reducing temperature, flood impacts, habitat fragmentation, and carbon emissions in urban areas. Detailed mapping of tree biomass allows researchers to spatially identify carbon sinks and analyze the associated ecological benefits at an urban or intra-urban level. This study utilizes non-destructive field measurements and aerial Light Detection and Ranging (LiDAR) remote sensing to estimate biomass on the University of Alabama in Huntsville (UAH) campus. A field survey of campus trees was performed to calculate the observed biomass through allometry. These values were then used as an input in a regression analysis along with LiDAR-derived canopy metrics to evaluate LiDAR’s ability to estimate biomass. The result yielded biomass equations specific to 14 tree species found on the UAH campus. It was found that the regression models showed a high fit (R2 = 0.62–0.98) for most species. Some trees such as the Pinus taeda, Pinus echinata, and Lagerstroemia indica had lower R2 values (0.26–0.43) most likely due to overlapping tree canopies; a known limitation in urban biomass studies that can be solved with more intricate segmentation methods. Nonetheless, this methodology is an avenue for urban planners to estimate biomass without intensive and costly field surveys, particularly in north Alabama where there is a lack of urban biomass research.

Species-Specific Allometric Equations, Biomass Expansion Factor, and Wood Density of Native Tree Species in the Dry Afromontane Forest of Ethiopia

A forest is a storehouse of carbon released from different sources when the activities of sustainable forest management, planting, and rehabilitation exist. However, few allometric equations are present to determine its contribution to carbon reduction. The target of the study was to develop species-specific allometric equations and establish a database for biomass expansion factor and wood density for five tree species grown in the dry Afromontane forest of Ethiopia. A direct or destructive sampling method was used on 62 trees from different diameter classes. The diameter at breast height and the total height of selected trees ranged from 7 to 48 cm and 6.7 to 23.4 m, respectively. Trees were felled and divided into various biomass sections. Stem and big branch discs were sampled to determine the wood density and volume of the trees. Sample wood and foliage were oven-dried for three days and two days at 105°C and 70°C, respectively, to get their dry weight. Total above-ground biomass was regressed using diameter at breast height, total height, wood density, and average crown diameter as independent variables. R software version 4.0.1 was used to fit the biomass equations. The best biomass models were determined to have lower AIC and RSE and highest adj. R2. The biomass expansion factor and wood density of five tree species ranged from 1.19 to 1.40 and 0.53 to 0.74 g/cm−3, respectively. Species-specific allometric equations were better than both mixed species and pan tropical models for the assessment of above-ground biomass in the Chilimo dry Afromontane forest of Ethiopia.

Comparison of Parameter Estimation Methods Based on Two Additive Biomass Models with Small Samples

Accurately estimating tree biomass is crucial for monitoring and managing forest resources, and understanding regional climate change and material cycles. The additive model system has proven reliable for biomass estimation in Chinese forestry since it considers the inherent correlation among variables based on allometric equations. However, due to the increasing difficulty of obtaining a substantial amount of sample data, estimating parameters for the additive model equations becomes a formidable challenge when working with limited sample sizes. This study primarily focuses on analyzing these parameters using data extracted from a smaller sample. Here, we established two additive biomass model systems using the independent diameter and the combined variable that comprises diameter and tree height. The logarithmic Nonlinear Seemingly Uncorrelated (logarithmic NSUR) method and the Generalized Method of Moments (GMM) method were applied to estimate the parameters of these models. By comparing four distinct approaches, the following key results were obtained: (1) Both the GMM and logarithmic NSUR methods can yield satisfactory goodness of fit and estimation precision for the additive biomass equations, with the root mean square error (RMSE) were significantly low, and coefficients of determination (R2) were mostly higher than 0.9. (2) Comparatively, examining the fitted curves of predicted values, the GMM method provided better fitting than the NSUR method. The GMM method with the combined variable is the most suggested approach for the calculation and research of single-tree biomass models with a small sample size.

Developing biomass allometric equations for small trees in mixed-species forests of tropical rainforest ecozone

Allometric equations for the small trees that dominate many forests recovering from disturbance, such as fire, are relatively rare, increasing the uncertainty of aboveground biomass (AGB) estimates in young regrowth forests. In this study we sampled 516 small trees (diameter <5 cm at 1.3 m height) from 105 species within Indonesian mixed forests to develop multispecies biomass allometric models and root to shoot ratio. Small trees were sampled across three ecosystems: peat swamp forests, heath forests and dryland forests. Several combinations of weighted log-linear regression analyses of tree diameter (D) and tree height (H) versus tree biomass were applied to identify the best model for estimating AGB. The allometric models developed in this study used a power function with diameter (D) and diameter and height (D2H) as predictors and ecosystem as an additional predictor in the D model. Seemingly unrelated regression (SUR) was used to generate equations of biomass components incorporating additivity. For the additive biomass equation components, the allocation of biomass for these small trees was stem > leaves > branches. AGB estimates based on allometric equations developed for larger trees, sourced from the literature, overestimated small tree biomass by up to 25% when compared with AGB estimates from equations developed in this study. The allometric models of small trees and the root-to-shoot ratio values obtained in this study will improve biomass estimates for young regrowth forests of Indonesia and the tropical region in general.