Biomass Of Tree Components Research Articles

Response of Individual-Tree Aboveground Biomass to Spatial Effects in Pinus kesiya var. langbianensis Forests by Stand Origin and Tree Size

To enhance forest carbon sequestration capacity, it is important to optimize forest structure by revealing the spatial effects of the aboveground biomass of individual trees, with particular emphasis on stand origin and tree size. Here, 0.3 ha clear-cut plots of Pinus kesiya var. langbianensis forest were selected in a typical plantation and natural stand. Then, the ordinary least squares model and spatial regression models were used to analyze the different responses between spatial position and individual tree biomass based on the stand origin and diameter at breast height (DBH) of the tree. Our study shows the following: (1) The spatial effect produced a stronger response in the natural stand than in the plantation. The amount of change in the adjusted R-squared (ΔRadj2) of tree component totaled 0.34 and 0.57 for Pinus kesiya var. langbianensis and other trees in the natural stand, compared to only 0.2 and 0.42 in the plantation; (2) Spatial effects had a stronger impact on the accuracy of the fit for the crown (ΔRadj2 = 0.52) compared to the wood and bark (ΔRadj2 = 0.03) in the plantation, and there were no significant differences in the natural stand (ΔRadj2 = 0.42, ΔRadj2 = 0.43); (3) When DBH reached a certain size, the impact of spatial effect for the crown showed a significant change from positive to negative. The sizes of DBH were 19.5 cm, 14 cm and 34.6 cm, 19 cm for branches of Pinus kesiya var. langbianensis and other tree species in the plantation and natural stand, and were 20.3 cm and 31.4 cm for the foliage of Pinus kesiya var. langbianensis. Differences in stand structure led to varied responses in the biomass of tree components to spatial effects.

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.

Tree Biomass Modeling Based on the Exploration of Regression and Artificial Neural Networks Approaches

Understanding the dynamics of tree biomass is a significant factor in forest ecosystems, and accurate quantitative knowledge of its development provides support for the optimization of forest management. This work aimed to employ innovative practices in tree biomass modeling, artificial neural network approaches along with the least-squares regression methodology, in order to construct reliable and accurate estimation and prediction models that contribute to solving the emerging problems in the field of sustainable forest management. Based on this aim, different modeling strategies were developed and explored. The nonlinear seemingly unrelated regression (NSUR) methodology, the generalized regression (GRNN), the resilient propagation (RPNN) and the Bayesian regularization (BRNN) artificial neural network algorithms were utilized for the construction of reliable biomass models to attain the most accurate and reliable tree biomass components and total tree biomass estimations. The work showed that GRNN models provided a significantly better performance compared with the other modeling methodologies tested. Considering the non-parametric nature of the GRNN neural network algorithm, the fact that it was designed for nonlinear regression-type problems capable of dealing with small datasets, this modeling approach warrants consideration as an effective alternative to nonlinear regression or to other neural network approaches to the field of tree biomass modeling.

Modelling tree biomass using direct and additive methods with point cloud deep learning in a temperate mixed forest

Airborne laser scanning (ALS) data has been widely used for total aboveground tree biomass (AGB) modelling, however, there is less research focusing on estimating specific tree biomass components (wood, branches, bark, and foliage). Knowledge about these biomass components is essential for carbon accounting, understanding forest nutrient cycling, and other applications. In this study, we compare additive AGB estimation (sum of estimated components) with direct AGB estimation using deep neural network (DNN) and random forest (RF) models. We utilise two point cloud DNNs: point-based Dynamic Graph Convolutional Neural Network (DGCNN) and Octree-based Convolutional Neural Network (OCNN). DNN and RF models were trained using a dataset comprised of 2336 sample plots from a mixed temperate forest in New Brunswick, Canada. Results indicate that additive AGB models perform similarly to direct models in terms of coefficient of determination (R2) and root-mean square error (RMSE), and reduced the mean absolute percentage error (MAPE) by 22% on average. Compared to RF, the DNNs provided a small improvement in performance, with OCNN explaining 5% more variation in the data (R2 = 0.76) and reducing MAPE by 20% on average. Overall, this study showcases the effectiveness of additive tree AGB models and highlights the potential of DNNs for enhanced AGB estimation. To further improve DNN performance, we recommend using larger training datasets, implementing hyperparameter optimization, and incorporating additional data such as multispectral imagery.

Effect of forest chronosequence on ecological stoichiometry and nitrogen and phosphorus stocks in Alnus nepalensis forest stands, central Himalaya

AbstractIn the mid and high elevations of the central Himalaya, Nepalese alder (Alnus nepalensis D. Don) occurs in areas affected by landslides/landslips and is a nitrogen‐fixing species; it quickly improves soil physical and chemical properties and facilitates the restoration of degraded forests. In the present study, we evaluated the effect of A. nepalensis forest chronosequence on the carbon (C), nitrogen (N), and phosphorus (P) concentrations, N and P stocks, and stoichiometry in the soil, including microbial biomass C (MBC), microbial biomass N (MBN) and microbial biomass P (MBP), and in the plant components. Six naturally occurring forest stands were identified in a chronosequence of A. nepalensis (age 3–270 years old) forest stands, namely alder‐early regenerating (AER), alder‐late regenerating (ALR), alder young‐mixed (AYM), alder mature‐oak (Quercus leucotrichophora) mixed (AMOM), alder mature‐rhododendron (Rhododendron arboreum) mixed (AMR), and alder old‐oak (Q. leucotrichophora) mixed (AOOM) forests. The biomass of tree components was estimated using species‐specific allometric equations developed by previous workers for the region. Soil total N and total P stocks of each species were determined by the N and P concentrations and bulk density. Structural equation modeling (SEM) was performed to quantify the contribution of N and P pools to ecosystem nitrogen and phosphorus stocks. The results of this study revealed that the stoichiometry (C:N, N:P, and C:P ratios) of tree components (i.e., leaves, leaf litter, twig), soil and microbial biomass varied widely, and the presence of nitrogen‐fixing A. nepalensis in different succession stages significantly improved the soil and microbial biomass stoichiometry. Total vegetation (tree, herbs, shrubs, and litter) biomass N stock ranged from 346.77 to 4662.06 kg N ha−1, and soil N stock varied from 816.48 to 7334.24 kg N ha−1. Total ecosystem N and P stocks ranged from 1163.26 to 11,996.31 kg N ha−1 and 76.10 to 799.28 kg P ha−1, respectively, and positively increased with A. nepalensis total biomass. The soil P stock accounted for 63.49% to 74.80% of the total P stocks of the forest ecosystems. Overall, our findings suggest that A. nepalensis forest chronosequence enhanced the N and P stocks, and introducing this species in degraded forests appears to be an option for enhancing forest conservation and rehabilitation actions in the central Himalaya.

Tree Biomass and Leaf Area Allometric Relations for Betula pendula Roth Based on Samplings in the Western Carpathians.

Biomass allometric relations are necessary for precise estimations of biomass forest stocks, as well as for the quantification of carbon sequestered by forest cover. Therefore, we attempted to create allometric models of total biomass in young silver birch (Betula pendula Roth) trees and their main components, i.e., leaves, branches, stem under bark, bark, and roots. The models were based on data from 180 sample trees with ages up to 15 years originating from natural regeneration at eight sites in the Western Carpathians (Slovakia). Sample trees represented individuals with stem base diameters (diameter D0) from about 4.0 to 113.0 mm and tree heights between 0.4 to 10.7 m. Each tree component was dried to constant mass and weighed. Moreover, subsamples of leaves (15 pieces of each tree) were scanned, dried, and weighed. Thus, we also obtained data for deriving a model expressing total leaf area at the tree level. The allometric models were in the form of regression relations using diameter D0 or tree height as predictors. The models, for instance, showed that while the total tree biomass of birches with a D0 of 50 mm (and a tree height of 4.06 m) was about 1653 g, the total tree biomass of those with a D0 of 100 mm (tree height 6.79 m) reached as much as 8501 g. Modeled total leaf areas for the trees with the above-mentioned dimensions were 2.37 m2 and 8.54 m2, respectively. The results prove that diameter D0 was a better predictor than tree height for both models of tree component biomass and total leaf area. Furthermore, we found that the contribution of individual tree components to total biomass changed with tree size. Specifically, while shares of leaves and roots decreased, those of all other components, especially stems with bark, increased. The derived allometric relations may be implemented for the calculation of biomass stock in birch-dominant or birch-admixed stands in the Western Carpathians or in other European regions, especially where no species- and region-specific models are available.

Effect of seedling production method on the growth of Pinus sylvestris L. on reclaimed post-industrial sites in Poland

Selection of tree species and proper technology of afforestation of post-industrial sites play a crucial role in new ecosystem stability and success of the restoration. There is still current discussion about the effectiveness of different methods in the production of seedlings for extreme site conditions. This study aimed to analyze the effect of the seedling production method on the growth of 6-year-old Scots pine (Pinus sylvestris L.) trees planted on reclaimed post-industrial sites in southern Poland. We compared four methods of seedling production: bare root system, containerized without and with inoculation of ectomycorrhizal fungi Laccaria bicolor (Maire) P.D. Orton, and Hebeloma crustuliniforme (Bull.) Quél. The experiment was carried out independently on two reclaimed sites: a post-sand-mining site in Bukowno, and a heavy metal polluted site in Miasteczko Śląskie. The effect of the seedling production method on tree growth expressed by height, root collar diameter, above- and belowground biomass of different tree components was analyzed on 240 sampled trees using analysis of variance and general linear model. In addition, the effect of the seedling production method on the root-to-shoot ratio was investigated. Scots pine tree growth was significantly affected by the seedling production method on heavy metal polluted site. The lowest tree growth parameters were observed in trees planted with bare roots, whereas the largest values were in the case of trees planted from containerized seedlings inoculated with H. crustuliniforme. In contrast, on the post-sand-mining site, the treatment effect turned out to be insignificant. The effect on biomass allocation was observed in the case of both sites. Trees prepared with the bare root method differed in greater biomass allocation to the belowground from other treatment groups. The use of containerized seedlings or additionally inoculated with ectomycorrhizal fungi in heavy metal contaminated sites improves the growth of trees and, although it is more costly and labor-intensive compared to the production of seedlings with a bare root system, should be preferred. In the post-sand-mining area, no effect of the seedling production method on tree growth was observed, and the use of bare root seedlings will be equally effective and additionally less time and cost-consuming.

Applying High-Resolution UAV-LiDAR and Quantitative Structure Modelling for Estimating Tree Attributes in a Crop-Livestock-Forest System

Individual tree attributes, such as stem volume and biomass, are usually predicted by using traditional field-derived allometric models. However, these models are derived from data collected from small areas and lack a level of detail of tree components (e.g., stem, branches, and leaves). Remote sensing techniques such as the Quantitative Structure Modelling (QSM) applied on high-density LiDAR data emerge as a promising solution for obtaining extensive and detailed tree attribute estimates. We used a high-density LiDAR data on board of a Unmanned Aerial Vehicle (UAV) to evaluate the performance of the QSM approach in estimating field-derived individual tree attributes such as the diameter at breast height (dbh), tree height (ht), and volume (v), as well as the stem (SAGB), branch (BAGB), and total (TAGB) aboveground biomass of eucalyptus trees. QSM was used in two different approaches: (i) using dbh and h derived from QSM and then applied into the field-based equations for estimation of volume and (ii) deriving tree volume directly from QSM. In general, all fitted models using the QSM approach were satisfactory, but with a slight tendency of over-estimation of dbh (9.33%), ht (12.40%), v-QSM1 (26.35%), v-QSM2 (26.66%), TAGB (27.08%), SAGB (25.57%), and BAGB (20.08%). Non-significant differences were noticed when estimating the dbh, tree volume, stem, and aboveground biomass. Despite the overestimation, this study indicates that using the QSM approach to estimate individual tree attributes from UAV-LiDAR is a promising alternative to support the decision-making process regarding forest management activities, especially when considering tree architecture and biomass components.

Comparison of artificial neural network with compatible biomass model for predicting aboveground biomass of individual tree

Forest biomass is an important index in forest development planning and forest resource monitoring. In order to provide a more efficient and low-biased method for estimating individual tree biomass, we introduced artificial neural network here. We used the data of aboveground biomass of 101 Larix olgensis trees harvested from the Dongzhelenghe Forest Farm in Heilongjiang Province to develop four aggregation model systems (AMS), based on different combination of the variables (diameter at breast height, tree height, crown width). The weighted functions were used to eliminate heteroscedasticity. Then, we trained artificial neural network (ANN) biomass model based on the optimal combination. The models were tested by the leave-one-out cross-validation method to compare the accuracy of the two biomass estimation methods. The results showed that biomass model based on only one variable, diameter at breast height, could accurately estimate the biomass of L. olgensis. Adding two indices, tree height and crown width, could improve the fitting performance of models, with AMS4 performing the best among the four addictive model systems. The biomass models developed by the two methods both could estimate biomass at tree level accurately, with the coefficient of determination (R2) of each component was higher than 0.87. Compared with the AMS4, R2 of leaf biomass model was about 0.05 higher, and that of other organs were also about 0.01 higher in artificial neural network model system. In addition, the root mean square error (RMSE) and other indicators were also significantly smaller. For example, the RMSE of tree stem and aboveground biomass were smaller by 2.135 kg and 3.908 kg, respectively. The model's validation statistics mean relative error (MRE) performed better. In general, ANN was a flexible and reliable biomass estimation method, which was worthy consideration when predicting tree component biomass or aboveground biomass.

General above-stump volume and biomass functions for Pinus radiata, Eucalyptus globulus and Eucalyptus nitens

Accurate estimations of stem volume and tree component biomass are of paramount importance for the study and management of forests. In Chile, the most important commercial tree species are Pinus radiata, Eucalyptus globulus and Eucalyptus nitens, which are successfully planted in other parts of the world. Available individual-tree allometric models are typically local models which do not include age or stand scale covariates and rely solely on stem diameter outside bark at breast height (DBH) and, in some cases, total tree height (H). We used data from 1571 trees across the distribution of the three species in Chile to develop general individual-tree equations for stem volume inside and outside bark, and biomass components (dead branch, live branch, foliage, bark and stem wood inside and outside bark). Our models include DBH or DBH and H as predicting variables, as well as stand-level variables like age, basal area and stand density. General allometric models that include stand-level variables have not been previously reported for P. radiata, E. globulus and E. nitens. Given the broad area of inference in our data, the models are applicable over a large geographical area and a variety of stand ages and characteristics and can be used to support management decisions or integrated in stand-level productivity models to enhance the accuracy and precision of model outputs. The collective effort behind this study is a remarkable example of cooperation across researchers and practitioners in order to address complex questions in a transdisciplinary world.

Biomass Production and Nutrient Accumulation by Natural Rubber (Hevea brasiliensis Wild. Ex A. Juss.) Müell. Arg. Clones in a Humid Tropical Area in South India

Natural rubber (Hevea brasiliensis Wild. Ex A. Juss.) Müell. Arg. is an important commodity crop grown in world over for industrial raw material rubber latex for various products, mainly tyre manufacturing. Hevea propagation is through clones evolved by breeding as cultivars with desired characters. This article presented the biomass and nutrient accumulation of four important Hevea clones viz. RRII 105, RRII 118, RRII 203 and GT1 at 30 years age. Biomass and nutrient concentration of tree components viz. trunk, branches, leaf and root were assessed by uprooting the trees in the field and standing trees using allometric equation. Among the different clones, RRII 118 and GT1 recorded higher biomass compared to RRII 105 and RRII 203. Above-ground biomass (88-93 per cent) varied more than below-ground biomass (7-11 per cent). The high yielding clones had higher leaf and root biomass. Drought tolerant and timber clones viz. RRII 118 and RRII 203 recorded higher K and high yielding clone RRII 105 had higher Ca accumulation. Biomass removal of these clones may lead to deficiency of K and Ca in soil and hence needs the external supplements. The relation of high Ca content and leaf disease of fungal origin is promising for further studies. The higher accumulation of iron and manganese indicated the tolerance of Hevea to these elements and possibility of phytoremediation. The per cent contribution of nutrients to total biomass varied less between clones and was below 3 percent at the age of 30 years and this is evidence of adjustments in proportions of nutrients in Hevea irrespective of clonal variations.

Analysis of tree biomass, net sequestered carbon and bioenergy potency in dry forest ecosystem, Indonesia

This study examines the potential to tree component biomass, biomass for bioenergy product and sequester carbon of forest area. CO2FIX program was used to determine about these value. In order to know about these values, we used CO2FIX program. Thinning harvesting scenarios were analysed, involving the establishment of short rotation harvesting (each 10 years) and long rotation plantations (200 year). As a result, an overall tree biomass components (stem, foliage, branch and root) were respectively recorded as follow: 2.49 ton/ha±0.67, 0.14 ton/ha±0.03, 0.35 ton/ha±0.09 and 0.65 ton/ha±0.18. The potential of biomass for bioenergy product and sequester carbon was increase until the end of project simulation. The increase average biomass of bioenergy was 25.96 Mg/ha±13.46 and the average of net sequestered carbon increase about 16.6±35.9 MgCO2equiv/ha. Our analysis on this study for all research variables is highest at each 40 years period because at this age, the rate of increment in the biomass of the tree is maximized.

Conventional and additive models for estimating the biomass, carbon and nutrient stock in individual Shorea robusta Gaertn. f. tree of the Sal forests of Bangladesh

Accurate tree biomass estimation is critical and crucial for calculating carbon stocking as well as for studying climate change, forest health, productivity, nutrient cycling and budget etc. A total of 50 individuals of Shorea robusta Gaertn. f. were harvested to assess the biomass of tree components (leaf, branch, bark and stem). Carbon and nutrients (N, P and K) content in the tree components were also measured. This study adopted component biomass models with cross-validation technique. Additive biomass models were developed following the modified Gaussian maximum likelihood aggregated approach using open source software R (version 4.0.1). Component and additive biomass model used D (Diameter at Breast Height) as a sole predictor performed satisfactorily, the inclusion of total tree height (H) in Da*Hb form showed its supremacy over all the models. The best fitted additive model (AGB = 0.002056*D2.923998*H−0.69278 + 0.00848*D2.3896*H0.29648 + 0.04224*D2.06986*H0.65549 + 0.00552*D2.06723*H0.70536) and conventional model (Ln (AGB) = -2.7977 + 2.1829*ln(D) + 0.5073*ln(H)) took the lowest AIC, MPE and MAE and the highest model efficiency values. The derived species-specific additive and non-additive model showed its superiority over the frequently used pan-tropical models and suggested using this model for estimating aboveground biomass of S. robusta in Bangladesh.

Allometric models for improving aboveground biomass estimates in West African savanna ecosystems

West African Sudanian savanna ecosystems greatly contribute to local peoples’ livelihoods and climate change mitigation. Yet, the contribution of these ecosystems to carbon storage remains poorly documented due to the lack of accurate biomass predictive tools. Therefore, biomass allometric models developed at both the species-level and site-level may greatly improve carbon stock estimation. In this study, we developed allometric models for estimating aboveground biomass (AGB) at the tree component-, species- and site-levels in Burkina Faso. Five woody species with high socio-economic significance (Anogeissus leiocarpa, Combretum nigricans, Isoberlinia doka, Mitragyna inermis and Pterocarpus erinaceus) were selected at two forest sites based on their dominance in the savanna ecosystem. A total of 150 trees (30 trees per dominant species) spanning a wide range of diameter at breast height (DBH) were destructively sampled. Models to predict tree component biomass at the species-level were independently built with the Ordinary Least Squares (OLS) technique. Allometric models to predict tree total aboveground biomass (TAGB) for each species and all species were developed using both the OLS technique and the Seemingly Unrelated Regression (SUR) method. Biomass estimates were regressed with DBH as a single predictor, DBH and height (H) as interacted variables, and DBH, H and wood density (ρ) as three independent-input variables. The performance of the validated allometric models were compared with the generalized pantropical equation model for African tropical forests (Chave et al., 2014). The findings revealed that biomass predictors varied between the five species. The goodness-of-fit statistics revealed that both the OLS and SUR methods provide accurate biomass allometric equations, with the SUR method being the most accurate. The developed allometric models provide more accurate estimation of AGB than the pantropical model. Therefore, we recommend the use of these local models to improve the quantification of AGB and carbon stocks in Sudanian savanna ecosystems. Furthermore, the established species-specific and mixed-species allometric equations may constitute a useful tool for the monitoring, reporting and verification of carbon stocks within the REDD+ framework in Burkina Faso.

Effects of stand age on tree biomass partitioning and allometric equations in Chinese fir (Cunninghamia lanceolata) plantations

Although stand age affects biomass partitioning and allometric equations, the size of these effects and whether it is worth incorporating stand age into allometric equations, requires further attention. We sampled a total of 90 trees for 10 Chinese fir (Cunninghamia lanceolata) plantations at seven stand age classes to obtain the data of tree component biomass using destructive harvesting. A multilevel modeling approach was applied to examine how stand age effects differ among tree components and predictor variables (diameter at breast height, DBH and tree height, H). Age class-specific allometric equations and the best fitting generalized equation that included stand age as a complementary variable were developed for each tree component. Large differences in both the intercept and slope for different stand age classes indicated that stand age affected allometric models. Branch and leaves were more sensitive to the environment and were the tree components most affected by stand age. Age class-specific allometric equations fitted well (R2 > 0.65, p < 0.001) using DBH and the combined form DBH2H as predictor variables. Including stand age as a complementary variable improved the fit of generalized allometric equations. Stem, aboveground and total tree biomass predicted by the multilevel model and generalized equation were comparable to the observed data. However, the multilevel model and generalized equations had a relatively low predictive capacity for branch, leaf and root biomass. These results could improve our capacity to evaluate carbon sequestration and other ecosystem functions in plantations.

Biomass and carbon storage in an age-sequence of Acacia mangium plantation forests in Southeastern region, Vietnam

Aim of the study: The major objective of this study was to estimate the biomass increment and carbon (C) storage of the main ecosystem components in an age-sequence of three Acacia mangium plantation stands.Area of study: Chang Riec Historical - Cultural Forest, Southeastern region, Vietnam.Material and methods: In order to assess the biomass of different tree components, 36 trees with diameter at breast height ranging from 13.38 to 22.87 cm were harvested from the different aged stands. Biomasses of understory (shrubs and herbs), and litter were also determined. Carbon storage in the trees and understory biomass, litter, and mineral soil (0-50 cm) were determined by analyzing the C content of each compartment.Main results: The biomass in trees, understory vegetation, litter, and ecosystem increased with stand age. Soil C represented 61.99% of the total, aboveground tree biomass C made up 26.73%, belowground tree biomass C accounted for 7.01%, and litter comprised 2.96%, whereas only a small amount (1.30%) was associated with understory vegetation. The average C content of total tree (47.97%) was higher than those of understory and litter. Soil organic C stock in the top 50 cm depth in 4-, 7- and 11-year-old stands of A. mangium were 86.86, 126.88 and 140.94 Mg. C ha-1 respectively. Soil C concentration decreased continually with increasing soil depth. Total C storage of three planted forests ranged from 131.36 to 255.86 Mg. C ha-1, of which 56.09 - 67.61% of C storage was in the soil and 26.88 - 40.40% in the trees.Research highlights: These results suggest that A. mangium is a promising afforestation tree species with fast growing, high biomass accumulation and high C sequestration potential.Keywords: Acacia mangium plantations; Biomass; Ecosystem carbon storage; Age-sequence; Vietnam.

Consequences of different sample drying temperatures for accuracy of biomass inventories in forest ecosystems

Biomass estimation is one of the crucial tasks of forest ecology. Drying tree material is a crucial stage of preparing biomass estimation tools. However, at this step researchers use different drying temperatures, but we do not know how this influences accuracy of models. We aimed to assess differences in dry biomass between two drying temperatures (75 °C and 105 °C) in tree biomass components and to provide coefficients allowing for recalculation between the given temperatures. We used a set of 1440 samples from bark, branches, foliage and wood of eight European tree species: Abies alba Mill., Alnus glutinosa (L.) Gaertn., Betula pendula Roth., Fagus sylvatica L., Larix decidua Mill., Picea abies (L.) H. Karst., Pinus sylvestris L. and Quercus robur L. The differences between drying temperatures were 1.67%, 1.76%, 2.20% and 0.96% of sample dry masses of bark, branches, foliage and stem wood, respectively. Tree species influenced these differences. Our study provided coefficients allowing for recalculation of masses between the two temperatures, to unify results from different studies. However, the difference in dry mass between the two temperatures studied is lower than the range of uncertainty of biomass models, thus its influence on results of large-scale biomass assessments is low.

Allometric equations to estimate above-ground biomass of small-diameter mixed tree species in secondary tropical forests

Abstract: Accounting for small-size tree biomass is critical to improve total stand biomass estimates of secondary tropical forests, and is essential to quantify their vital role in mitigating climate change. However, owing to the scarcity of equations available for small-size trees, their contribution to total biomass is unknown. The objective of this study was to generate allometric equations to estimate total biomass of 22 tree species ≤ 10 cm in diameter at breast height (DBH), in the Yucatan peninsula, Mexico, by using two methods. First, the additive approach involved the development of biomass equations by tree component (stem, branch and foliage) with simultaneous fit. In the tree-level approach, total tree biomass equations were fit for multi-species and wood density groups. Further, we compared the performance of total tree biomass equations that we generated with multi-species equations of previous studies. Data of total and by tree component biomass were fitted from eight non-linear models as a function of DBH, total height (H) and wood density (ρ). Results showed that two models, identified as model I and II, best fitted our data. Model I has the form AGB = β0 (ρ·DBH2·H)β1 + e and model II: AGB = exp(-β0)(DBH2·H)β1 + e, where AGB is biomass (kg). Both models explained between 53% and 95% of the total observed variance in biomass, by tree-structural component and total tree biomass. The variance of total tree biomass explained by fit models related to wood density group was 96%-97%. Compared foreign equations showed between 30% and 45% mean error in total biomass estimation compared to 0.05%-0.36% error showed by equations developed in this study. At the local level, the biomass contribution of small trees based on foreign models was between 24.38 and 29.51 Mg ha-1, and model I was 35.97 Mg ha-1. Thus, from 6.5 up to 11.59 Mg ha-1 could be excluded when using foreign equations, which account for about 21.8% of the total stand biomass. Local equations provided more accurate biomass estimates with the inclusion of ρ and H as predictors variables and proved to be better than foreign equations. Therefore, our equations are suitable to improve the accuracy estimates of carbon forest stocks in the secondary forests of the Yucatan peninsula.

Species-specific biomass allometric models and expansion factors for indigenous and planted forests of the Mozambique highlands

Secondary Miombo woodlands and forest plantations occupy increasing areas in Mozambique, the former due to anthropogenic activities. Plantations, mainly species of Eucalyptus and Pinus, are being established on sites previously covered by secondary Miombo woodlands. This affects the evolution, cycle and spatiotemporal patterns of carbon (C) storage and stocks in forest ecosystems. The estimation of C storage, which is indispensable for formulating climate change policies on sequestrating CO2, requires tools such as biomass models and biomass conversion and expansion factors (BCEF). In Mozambique, these tools are needed for both indigenous forests and plantations. The objective of this study is to fit species-specific allometric biomass models and BCEF for exotic and indigenous tree species. To incorporate efficient inter-species variability, biomass equations were fitted using nonlinear mixed-effects models. All tree component biomass models had good predictability; however, better predictive accuracy and ability was observed for the 2-predictors biomass model with tree height as a second predictor. The majority of the variability in BCEF was explained by the variation in tree species. Miombo species had larger crown biomass per unit of stem diameter and stored larger amounts of biomass per stem volume. However, due to relatively rapid growth, larger stem diameters, heights, and stand density, the plantations stored more biomass per tree and per unit area.

Biomass, carbon and nitrogen in single tree components of grey poplar (Populus × canescens) in an uncultivated habitat in Van, Turkey.

The biomass, carbon and nitrogen storage in the single tree components (foliage, branch, crown, bark, stem and total aboveground) of the grey poplar (Populus × canescens) in its distribution in Eastern Anatolia (Van, Turkey) were determined and modelled. The biomass, carbon and nitrogen storages were not estimated at a stand level but were based on single trees. Regression models based on the tree diameter at breast height (DBH) and total tree height (H) were developed to estimate the biomass, carbon (C) storage and nitrogen (N) storage of the different tree components of a total of 28 grey poplar trees. The two main regression models in the power function were developed based only on the DBH (Model 1) and the combination of the DBH and height (D2H) (Model 2). All regression models, except for those of the foliage components, developed to estimate the biomass and C and N storages of the tree components were found to be statistically significant (p< 0.001). The partitioning of the total aboveground biomass in the bark, foliage, branches and stems was 0.7, 9, 17 and 73%, respectively. The average C concentrations of the tree components ranged from 48 (foliage) to 50% (bark, branch and stem), while the N concentrations ranged from 0.35 (stem) to 1.32% (foliage). Higher biomass and lower nitrogen concentrations of foliage compared with cultivated poplars were likely related to the natural site conditions, low soil nitrogen and/or characteristics of single tree growth.