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• Monitored microbial shifts during anaerobic degradation of two types of food waste. • Sampling of both leachate and solids revealed microbial composition differences. • Substrates led to different population dynamics and methanogenic pathways. A key question in anaerobic microbial ecology is how microbial communities develop over different stages of waste decomposition and whether these changes are specific to waste types. We destructively sampled over time 26 replicate bioreactors cultivated on fruit/vegetable waste (FVW) and meat waste (MW) based on pre-defined waste components and composition. To characterize community shifts, we examined 16S rRNA genes from both the leachate and solid fractions of the waste. Waste decomposition occurred faster in FVW than MW, as accumulation of ammonia in MW reactors led to inhibition of methanogenesis. We identified population succession during different stages of waste decomposition and linked specific populations to different waste types. Community analyses revealed underrepresentation of methanogens in the leachate fractions, emphasizing the importance of consistent and representative sampling when characterizing microbial communities in solid waste. [ABSTRACT FROM AUTHOR]

Using extensive legacy and new datasets, two equations of whole-tree green density, defined as the ratio of the green weight of stem wood with bark (GWob) divided either by the stem outside-bark volume (Vob) or by the stem inside-bark volume (Vib), were developed along with individual tree Vob , Vib , and GWob equations for loblolly and slash pines. The green density equations indicated that the GWob / Vob ratio increases while the GWob / Vib ratio decreases with an increase in tree size for both species. The transition from low-intensity management to intensive management has a notable impact on tree green weight characteristics. Generally, trees from older established plantations exhibited a higher GWob / Vob ratio compared with trees from more recently established plantations, spanning both loblolly and slash pines. Study Implications : Derived whole-tree green density equations, alongside updated stem green weight and volume equations, are valuable tools for estimating the volume and green weight of entire stem boles, facilitating volume-to-green-weight conversion for specific sections for loblolly and slash pines, the primary commercial timber species in the southern United States. [ABSTRACT FROM AUTHOR]

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.

The integration of trees into diverse land-use systems holds potential for India to meet nationally determined contribution (NDC) targets under the Paris Climate Agreement. With a target of sequestering 2.5–3 billion tons of CO2 equivalent by 2030, the study focused on the widespread and economically viable eucalyptus-based agroforestry, practiced widely in various planting geometries tailored to meet industrial end-use requirements. In this context, a detailed study was conducted to quantify the influence of five planting geometries [3 m × 3 m, 6 × 1.5 m, 17 × 1 × 1 m (paired row) and two boundary plantations (east–west and north–south directions) at 2 m away from tree to tree] of eucalyptus on intercrops [dhaincha (Sesbania aculeata)—barley (Hordeum vulgare L.) rotation] biomass, soil properties, and carbon stock of the system during 2009–2016. Results revealed that biomass accumulation of different tree components was 62.50%–74.09% in stem; 6.59%–9.14% in branch; 3.18%–5.73% in leaves; 12.20%–20.44% in stump roots; and 1.71%–3.48% in fine roots across the planting geometries. The mean carbon content of the stem, branch, leaves, and roots was 49.00, 47.00, 43.00, and 49.00%, respectively. Over the 8-year period, geometry of 3 × 3 m performed better in terms of total biomass production (344.60 Mg ha− 1 by tree biomass and 62.53 Mg ha−1 by intercrops). The independent parameter, DBH²H (DBH: diameter at breast height and H: tree height), was found to be a very good predictor of dry weight, followed by DBH alone. Among various functions (linear, allometric, logistic, Gompertz, Chapman, and exponential), the best-fit equation was allometric, i.e., B = 300.96 × DBH²H0.93 (adjusted R² = 0.96) for eucalyptus based on universal model adequacy and validation criteria. The carbon sequestration rate was maximum (20.79 Mg C ha−1 year−1) in 3 × 3 m followed by 17 × 1 × 1 m. The total carbon stock of eucalyptus-based system (tree + crop + soil) varied significantly under different planting geometries and sole crop rotation (dhaincha–barley). The higher carbon stock (237.27 Mg ha−1) was obtained from 3 × 3 m spacing and further partitioning carbon stock in trees—166.29 Mg ha−1, crops—25.01 Mg ha−1 and soil—45.97 Mg ha−1. The paired row spacing (17 × 1 × 1 m) yielded higher crop yield and net returns (Rs. 600,475 ha−1), underscoring wide spacing’s role in system productivity and sustainability. Tree-based systems were valuable components of agriculture, advocating for their widespread adoption to reduce CO2 emissions and generate income through carbon credits. These findings will provide crucial insights into sustainable land-use practices and advance India’s commitment toward adaptation of climate change mitigation strategies. [ABSTRACT FROM AUTHOR]

Spatial biomass estimation using remote sensing at a regional or national scale requires ground reference plots for developing calibration models and product validation. The reference plot above-ground biomass (AGB) estimates rely on allometric models, which estimate tree-level AGB through measurements of tree diameter, height, and species information. Recent developments in applying terrestrial laser scanning (TLS) to measure 3D canopy structure in great detail have enabled new opportunities for extracting tree volumes non-destructively. In this study, we aim to use 3D point clouds from TLS to model individual trees to extract tree volumes and thereby generate a framework for developing local allometric equations in tropical dry deciduous forests of Betul, Madhya Pradesh, India. We have used TLS scans of 127 individual trees across different species to generate site-level and species-specific allometric models. The generated TLS-based allometric models using diameter and height as predictor variables are validated with an independent destructive sampling dataset (n = 25 trees). Our TLS allometry models indicate superior predictions with lower error estimates (root mean square error (RMSE) of 10.9% (concordance correlation coefficient [CCC] = 0.98) compared to the equivalent traditionally used volume equations over the study site. We have also evaluated the uncertainty due to sample size and found that the prediction error stabilizes when the number of samples exceeds 100 trees. The results imply that TLS data can potentially increase the range and sampling size of allometric equations through non-destructive volume estimation to improve the traditional allometric models and reduce uncertainty in landscape-level biomass estimates. [ABSTRACT FROM AUTHOR]

A biodegradable hybrid polymer patch was invented at the University of Cincinnati to cover gaps on the skin over the spinal column of a growing fetus, characterized by the medical condition spina bifida. The inserted patch faces amniotic fluid (AF) on one side and cerebrospinal fluid on the other side. The goal is to provide a profile of the roughness of a patch over time at 0, 4, 8, 12, and 16 weeks with a 95% confidence band. The patch is soaked in a test tube filled with either amniotic fluid (AF) or phosphate-buffered saline (PBS) in the lab. If roughness is measured at any time point for a patch, the patch is destroyed. Thus, it is impossible to measure roughness at all weeks of interest for any patch. It is important to assess the roughness of a patch because the rougher the patch is, the faster the skin grows under the patch. We use a model-based approach with Monte Carlo simulations to estimate the profile over time with a 95% confidence band. The roughness profiles are similar with both liquids. The profile can be used as a template for future experiments on the composition of patches. [ABSTRACT FROM AUTHOR]

Leaf area index (LAI) is a canopy structure parameter used to characterize vegetation growth. It is increasingly important to obtain continuous and accurate ground LAI measurement data for the validation of remote-sensing products. In this study, direct destructive sampling method and indirect optical measurement including LAI-2200 plant canopy analyser (PCA) and Tracing Radiation and Architecture of Canopies (TRAC) were used for long-term continuous observation of paddy fields in southern China, to explore the variation of LAI during the growth period of paddy, and to quantitatively analyse the errors of indirect optical measurement. The results show that (1) LAI and Plant area index (PAI) increased first and then decreased in the growing period. The proportion of green leaf area gradually decreased at reproductive growth stage (less than 50%). There was a linear relationship between the proportion of green leaf area and PAI at this stage, with a good correlation (R2 = 0.73). This linear relationship can be used to convert PAI into LAI more conveniently. (2) Effective PAI (PAIe) calculated by the two methods (2000 method and Lang method) from PCA showed good consistency between each other in the growing season (the maximum value of R2 is 0.99). With the increase of zenith angle, the consistency between 2000 method and Lang method decreases (R2 = 0.83). Multiple scattering has an effect on PCA measurement, and this effect is more obvious at high zenith angle. Lang method is more sensitive to the change of zenith angle. (3) Compared to the destructive values, both 2000 method and Lang method are able to compute accurate LAI using four ring sensors (4 R), while TRAC underestimates the LAI and PAI from destructive sampling method. This study provided a new idea for reducing the impact of ground reference data errors on LAI product validation. [ABSTRACT FROM AUTHOR]

Agroforestry plays a significant role in climate change mitigation and thus buffers the pressure on forest resources. However, owing to the lack of accurate biomass models, the contribution of these systems towards carbon storage remains poorly understood, which makes it difficult to implement climate change mitigation initiatives. Besides, most of the biomass predictions for trees grown in agricultural lands rely on the models developed for natural forests. This study therefore aimed to develop species-specific and multi-species allometric equations for predicting the aboveground biomass (AGB) of native perennial plant species in the agricultural landscape of central Ethiopia. Ninety-five individuals representing six perennial plant species with diameter at breast height ranging from 3.5 to 65 cm were destructively harvested. Diameter at breast height (DBH), total height (ht), wood bulk density (wbd), and crown diameter (cd) were used as predictors of the AGB. The study found that DBH was the best single predictor of AGB for Oldeania alpina and Faidherbia albida, with options for other species. Multiple variable models combining DBH-ht exhibited the highest predictive capacity for AGB in Erythrina brucei, Albizia schimperiana, and Croton macrostachyus, whereas the combination of DBH–cd and DBH–ht–wbd–cd best predicted the AGB of Acacia abyssinica and mixed species, respectively. Species-specific and mixed-species models showed the best predictive capacity for AGB compared to other frequently used regional and pan-tropical models. The findings of the study suggest that mixed-species AGB models will be used when species-specific allometric models are not available at a given site. [ABSTRACT FROM AUTHOR]

Background: Most of the biomass equations were developed using sample trees collected mainly from pan-tropical and tropical regions that may over- or underestimate biomass. Site-specific models would improve the accuracy of the biomass estimates and enhance the country’s measurement, reporting, and verification activities. The aim of the study is to develop site-specific biomass estimation models and validate and evaluate the existing generic models developed for pan-tropical forest and newly developed allometric models. Total of 140 trees was harvested from each diameter class biomass model development. Data was analyzed using SAS procedures. All relevant statistical tests (normality, multicollinearity, and heteroscedasticity) were performed. Data was transformed to logarithmic functions and multiple linear regression techniques were used to develop model to estimate aboveground biomass (AGB). The root mean square error (RMSE) was used for measuring model bias, precision, and accuracy. The coefficient of determination (R2 and adjusted [adj]-R2), the Akaike Information Criterion (AIC) and the Schwarz Bayesian information Criterion was employed to select most appropriate models. Results: For the general total AGB models, adj-R2 ranged from 0.71 to 0.85, and model 9 with diameter at stump height at 10 cm (DSH10), ρ and crown width (CW) as predictor variables, performed best according to RMSE and AIC. For the merchantable stem models, adj-R2 varied from 0.73 to 0.82, and model 8) with combination of ρ, diameter at breast height and height (H), CW and DSH10 as predictor variables, was best in terms of RMSE and AIC. The results showed that a best-fit model for above-ground biomass of tree components was developed.AGBStem = exp {–1.8296 + 0.4814 natural logarithm (Ln) (ρD2H) + 0.1751 Ln (CW) + 0.4059 Ln (DSH30)}AGBBranch = exp {–131.6 + 15.0013 Ln (ρD2H) + 13.176 Ln (CW) + 21.8506 Ln (DSH30)}AGBFoliage = exp {–0.9496 + 0.5282 Ln (DSH30) + 2.3492 Ln (ρ) + 0.4286 Ln (CW)}AGBTotal = exp {–1.8245 + 1.4358 Ln (DSH30) + 1.9921 Ln (ρ) + 0.6154 Ln (CW)} Conclusions: The results demonstrated that the development of local models derived from an appropriate sample of representative species can greatly improve the estimation of total AGB.

The integration of trees into diverse land-use systems holds potential for India to meet nationally determined contribution (NDC) targets under the Paris Climate Agreement. With a target of sequestering 2.5–3 billion tons of CO2 equivalent by 2030, the study focused on the widespread and economically viable eucalyptus-based agroforestry, practiced widely in various planting geometries tailored to meet industrial end-use requirements. In this context, a detailed study was conducted to quantify the influence of five planting geometries [3 m × 3 m, 6 × 1.5 m, 17 × 1 × 1 m (paired row) and two boundary plantations (east–west and north–south directions) at 2 m away from tree to tree] of eucalyptus on intercrops [dhaincha (Sesbania aculeata)—barley (Hordeum vulgare L.) rotation] biomass, soil properties, and carbon stock of the system during 2009–2016. Results revealed that biomass accumulation of different tree components was 62.50%–74.09% in stem; 6.59%–9.14% in branch; 3.18%–5.73% in leaves; 12.20%–20.44% in stump roots; and 1.71%–3.48% in fine roots across the planting geometries. The mean carbon content of the stem, branch, leaves, and roots was 49.00, 47.00, 43.00, and 49.00%, respectively. Over the 8-year period, geometry of 3 × 3 m performed better in terms of total biomass production (344.60 Mg ha− 1 by tree biomass and 62.53 Mg ha−1 by intercrops). The independent parameter, DBH2H (DBH: diameter at breast height and H: tree height), was found to be a very good predictor of dry weight, followed by DBH alone. Among various functions (linear, allometric, logistic, Gompertz, Chapman, and exponential), the best-fit equation was allometric, i.e., B = 300.96 × DBH2H0.93 (adjusted R2 = 0.96) for eucalyptus based on universal model adequacy and validation criteria. The carbon sequestration rate was maximum (20.79 Mg C ha−1 year−1) in 3 × 3 m followed by 17 × 1 × 1 m. The total carbon stock of eucalyptus-based system (tree + crop + soil) varied significantly under different planting geometries and sole crop rotation (dhaincha–barley). The higher carbon stock (237.27 Mg ha−1) was obtained from 3 × 3 m spacing and further partitioning carbon stock in trees—166.29 Mg ha−1, crops—25.01 Mg ha−1 and soil—45.97 Mg ha−1. The paired row spacing (17 × 1 × 1 m) yielded higher crop yield and net returns (Rs. 600,475 ha−1), underscoring wide spacing’s role in system productivity and sustainability. Tree-based systems were valuable components of agriculture, advocating for their widespread adoption to reduce CO2 emissions and generate income through carbon credits. These findings will provide crucial insights into sustainable land-use practices and advance India’s commitment toward adaptation of climate change mitigation strategies.

Context: Global environmental modification is placing increased demand on taxonomic resources, especially herbarium collections, which are being utilised for information and as biological resource material in ways never previously envisaged. This increased use of herbarium collections presents current and on-going management challenges. Aims: To demonstrate, with two Australian studies, the essential role of herbarium collections in investigating long-term pollution; to emphasise the importance of on-going collecting for herbaria and make recommendations for future collection management. Methods: Australian case studies of a long-term pollution problem are used to show how verified lichen and fungal taxa in Australian herbaria were crucial in identifying sources of air pollution over time. Key results: Chemical and isotopic analyses of lead in samples from curated herbarium collections were used to reconstruct patterns of atmospheric lead deposition over 150 years in eastern Australia. This extended time series of samples facilitated documentation of the full cycle from natural background concentrations to the introduction and increasing usage of lead both in industry and motor vehicles, to the eventual discontinuation of its use in automotive fuel. In addition to demonstrating the value of regular, long-term sampling of selected taxa and potential applications to emerging environmental problems, the importance of strict protocols for destructive sampling and analysis of herbarium specimens, especially for older collections containing small quantities of material, has been highlighted. Conclusions: Both the Australian and previous studies have identified a number of problems and potential challenges to maintaining and maximising the utilisation of finite herbarium resources for the future. While acknowledging that Australasian herbaria have well documented policies and protocols for studies that sample their collections, six recommendations are made, for future implementation as appropriate. Implications: Current management strategies for finite reference collections are summarised and recommendations for the future include the following: repeated, ongoing collecting over broader areas; periodic re-collection of larger samples from select reference sites with detailed field data; preparation and storage of specimens under controlled environmental conditions, with minimal chemical pest control; utilisation of common widespread taxa for research; careful supervision of destructive sampling techniques to assess compounds present; and adequate funding to facilitate the digitisation of herbarium specimens to increase access and optimise use of curated collections. A photograph of Cladonia lichen growing on soil. Chemical and isotopic analyses of lead in lichen and fungal herbarium collections were used to reconstruct patterns of deposition over a period of 150 years in eastern Australia. The central role of herbaria in two case studies of temporal changes in atmospheric lead pollution is discussed and used to address future collection management challenges. Photograph by Dr Xinyu Wang. [ABSTRACT FROM AUTHOR]

Site class is a quantitative indicator used to evaluate site quality. It reflects site conditions, mainly climate, the suitability of soil for tree species and soil fertility. Despite the economic and ecological importance of tree competition and site class in sustainable forest management, there has been little research on its impact on the stump and coarse root biomass allocation within plantations. The stump and coarse roots were divided into five components ((stump disc (SD), stump knot (SK), coarse roots (>10 cm in diameter) (CR1), medium coarse roots (5–10 cm) (CR2) and fine coarse roots (2–5 cm) (CR3)), and the biomass of each component was obtained via the weighing method. It was found that the biomass of SD, CR1, CR2 and CR3 was mainly affected by competition (p ≤ 0.01). In the three site classes, the biomass of CR3 increased significantly with the increase in the competition index (CI) (p < 0.01); the biomass of CR1 decreased gradually. In site V, the biomass of SK, sapwood and heartwood increased significantly with the increase in CI. The results show that competition affects the allocation of stump and coarse root biomass mainly by changing the coarse root biomass. The development of stump knots is greatly influenced by site class. This study provides a reference for solving the competition mechanism underlying larch wood forest development, which will in turn promote more effective utilization of larch wood forests. This study also provides a scientific basis for accurately estimating the belowground biomass and carbon storage of artificial plantation forests. [ABSTRACT FROM AUTHOR]

A biodegradable hybrid polymer patch was invented at the University of Cincinnati to cover gaps on the skin over the spinal column of a growing fetus, characterized by the medical condition spina bifida. The inserted patch faces amniotic fluid (AF) on one side and cerebrospinal fluid on the other side. The goal is to provide a profile of the roughness of a patch over time at 0, 4, 8, 12, and 16 weeks with a 95% confidence band. The patch is soaked in a test tube filled with either amniotic fluid (AF) or phosphate-buffered saline (PBS) in the lab. If roughness is measured at any time point for a patch, the patch is destroyed. Thus, it is impossible to measure roughness at all weeks of interest for any patch. It is important to assess the roughness of a patch because the rougher the patch is, the faster the skin grows under the patch. We use a model-based approach with Monte Carlo simulations to estimate the profile over time with a 95% confidence band. The roughness profiles are similar with both liquids. The profile can be used as a template for future experiments on the composition of patches.

All ground-based estimations of leaf area index (LAI) of Moso bamboo canopies are currently conducted based on indirect remote sensing methods. However, the relatively small values of LAI estimated by previous studies conflict with the expected values of such extremely dense canopies of Moso bamboo. This is the first attempt to accurately estimate the LAI of Moso bamboo canopies using an allometric model based on destructive measurements. The results indicate that (1) LAI of Moso bamboo canopies range was 6.7–30.6 m2·m−2, which is clearly higher than the range 2.2–6.5 m2·m−2 estimated by previous studies; (2) there is a strong linear relationship between LAI and crown density (R2 = 0.947, RMSE = 1.343); (3) LAI is largely underestimated using the digital hemispherical photography (DHP) because of the overestimation of clumping index; and (4) there is a strong exponential relationship between LAI and effective leaf area (Le) estimated using DHP (R2 = 0.734, RMSE = 3.011). Based on the results, three methods are recommended for LAI estimations of Moso bamboo canopies using the allometric relationship, the empirical relationship with crown density, and the empirical relationship with Le. [ABSTRACT FROM AUTHOR]

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.

The stump and coarse root biomass remaining after tree harvesting are often overlooked by researchers, which may lead to underestimation of their role in carbon cycling, so we constructed two sets of additive models for larch (Larix olgensis Henry) plantations in Northeast China. Due to the absence of tree diameter at breast height data after harvesting, only the sole predictor variable stump disc diameter could be used to predict stump and coarse root biomass, and the results showed that stump disc diameter predicted stump biomass with higher accuracy than coarse root biomass predictions. In addition, to investigate the effect of the site class of complex stands on the predictive capability of the model, the generic model in this study was employed with all site class data and a specific model was developed and employed with all the site class data. We found that the generic model had different degrees of error compared to the predicted results for each site class, overestimating the total biomass by 15% and underestimating it by 10%, especially for site class IV. In conclusion, to obtain a biomass prediction model with reliable results, the impact of more complex site class effects should be considered. [ABSTRACT FROM AUTHOR]

Accurate equations are critical for estimating biomass and carbon accumulation for forest carbon projects, bioenergy, and other inventories. Allometric equations can provide a reliable and accurate method for estimating and predicting biomass and carbon sequestration. Cross-validatory assessments are also essential to evaluate the prediction ability of the selected model with satisfactory accuracy. We destructively sampled and weighed 52 sample trees, ranging from 11.8 to 42.0 cm in diameter at breast height from three plantations in Queensland to determine biomass. Weighted nonlinear models were used to explore the influence of different variables using two datasets: the first dataset (52 trees) included diameter at breast height (D), height (H) and wood density (ρ); and the second dataset (40 trees) also included crown diameter (CD) and crown volume (CV). Cross validation of independent data showed that using D alone proved to be the best performing model, with the lowest values of AIC = 434.4, bias = −2.2% and MAPE = 7.2%. Adding H and ρ improved the adjusted. R2 (Δ adj. R2 from 0.099 to 0.135) but did not improve AIC, bias and MAPE. Using the single variable of CV to estimate aboveground biomass (AGB) was better than CD, with smaller AIC and MAPE less than 2.3%. We demonstrated that the allometric equations developed and validated during this study provide reasonable estimates of Corymbia citriodora subspecies variegata (spotted gum) biomass. This equation could be used to estimate AGB and carbon in similar spotted gum plantations. In the context of global forest AGB estimations and monitoring, the CV variable could allow prediction of aboveground biomass using remote sensing datasets. [ABSTRACT FROM AUTHOR]

The mathematical models used applying the Nonlinear Seemingly Unrelated Regressions (NSUR) or Weighted Nonlinear Seemingly Unrelated Regressions (WNSUR) methodologies can contribute to generate acceptable and reliable estimates of total aboveground biomass and its constituent parts, which are needed to implement forest management strategies to maintain desirable and sustainable carbon stocks. The aim of this study was: 1) to fit the sample data with independent nonlinear regression models and present the results obtained from the respective statistical estimates for total biomass aboveground and the constituent parts of trees in native forest trees. 2) To fit the sample data with regression models simultaneously, that is, whose models are composed of appropriate combinations of their coefficients, in order to obtain additivity of the estimates and present better results for the total aboveground biomass and the constituent parts of the trees. 3) To apply weighting procedures to the variances of the fitted models. 4) To evaluate the error due to the regression function on forest biomass estimation. The data came from eight sites located in the states of Parana and Rio Grande do Sul, Brazil, and information was collected on diameter at 1.30 m aboveground (DBH), total height, biomasses of the trunk components (branches and leaves) and total aboveground biomass. Non-linear functions were independently and simultaneously fitted, using DBH and total height as independent variables in the regression models. Independent fitting of equations was performed using generalized nonlinear least squares (ENGLS) and simultaneous fitting of equations was obtained by means of NSUR. Weighting, by applying a variance structure in the two procedures, was done to solve the issue of heteroscedasticity. Numerically, the equations fitted simultaneously performed better and were more efficient than the independently fitted models, which resulted in biological inconsistency, that is, non-additivity of the biomass of constituent parts of the trees and the total biomass. Simultaneous fitting generated superior statistical and biological properties to obtain tree estimates of the of constituent parts of the trees and total aboveground biomass in native forests of southern Brazil. The smaller error due to the regression function used in the forest biomass inventory was obtained by simultaneous fitting. With these results, the procedure using simultaneous and weighted fitting of equations (WNSUR) is recommended to fit biomass equations for native forests in southern Brazil.

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.

In Malaysia, the demand for high-value agarwood produced by Aquilaria spp. and efforts to reduce harvesting pressure on wild stands of these trees have resulted in an increase of Aquilaria plantations, which are harvested as a short-rotation (7 years) tree crop. Aquilaria spp. plantations potentially contribute to biomass accumulation as the trees are planted as reforestation and afforestation projects on marginal and idle lands but harvesting may affect their function as carbon sink. There are important environmental impact assessments of Aquilaria spp. plantations related to biomass and carbon sequestration. This study was conducted to develop an allometric model to estimate aboveground biomass and carbon stocks for plantation-grown Aquilaria spp. A census was conducted on 418 ten-year-old Aquilaria malaccencis trees on a 1-ha plot and measurements of height and stem diameter were used to categorise the trees into three diameter classes (small, medium, large). Ten trees were randomly selected for destructive sampling and developing allometric equations for estimating aboveground biomass. The recommended model, which used stem diameter as the predictor variable, estimated 23,615 kg total aboveground biomass for this 1-ha plot. Carbon analysis of stem, branches and foliage revealed an average carbon content of 40.35%. Average carbon density was 285 kg tree-1 and 9353 kg ha-1. The 10 destructively sampled trees stored 1046 kg CO2 equivalent. Our findings provided baseline information on the biomass and carbon stock measurements for plantation-grown A. malaccensis and supported plantation in accumulating biomass and carbon storage. [ABSTRACT FROM AUTHOR]

Biomass accumulation in cuttings of four Bursera species during four seasons of the year, in five substrates under greenhouse conditions was evaluated. Cuttings were established under a completely randomized experimental design with factorial arrangement to apply destructive sampling at the end of each season of the year and to calculate structural biomass. The species factor and growing season showed effects on biomass accumulation. Bursera fagaroides accumulated higher biomass in roots, branches and leaves (2.85, 1.67 and 3.76 g/plant, respectively), along with B. glabrifolia showed higher leaf area (386.34 and 205.65 cm²/plant, respectively), good aboveground biomass/ground biomass ratio and high survival percentages (78 and 58%, respectively). [ABSTRACT FROM AUTHOR]

Copyright of Revista Argentina de Antropología Biológica is the property of Revista Argentina de Antropologia Biologica and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Premise Large phylogenetic data sets have often been restricted to small numbers of loci from GenBank, and a vetted sampling‐to‐sequencing phylogenomic protocol scaling to thousands of species is not yet available. Here, we report a high‐throughput collections‐based approach that empowers researchers to explore more branches of the tree of life with numerous loci. Methods We developed an integrated Specimen‐to‐Laboratory Information Management System (SLIMS), connecting sampling and wet lab efforts with progress tracking at each stage. Using unique identifiers encoded in QR codes and a taxonomic database, a research team can sample herbarium specimens, efficiently record the sampling event, and capture specimen images. After sampling in herbaria, images are uploaded to a citizen science platform for metadata generation, and tissue samples are moved through a simple, high‐throughput, plate‐based herbarium DNA extraction and sequencing protocol. Results We applied this sampling‐to‐sequencing workflow to ~15,000 species, producing for the first time a data set with ~50% taxonomic representation of the “nitrogen‐fixing clade” of angiosperms. Discussion The approach we present is appropriate at any taxonomic scale and is extensible to other collection types. The widespread use of large‐scale sampling strategies repositions herbaria as accessible but largely untapped resources for broad taxonomic sampling with thousands of species.

Premise: Large phylogenetic data sets have often been restricted to small numbers of loci from GenBank, and a vetted sampling‐to‐sequencing phylogenomic protocol scaling to thousands of species is not yet available. Here, we report a high‐throughput collections‐based approach that empowers researchers to explore more branches of the tree of life with numerous loci. Methods: We developed an integrated Specimen‐to‐Laboratory Information Management System (SLIMS), connecting sampling and wet lab efforts with progress tracking at each stage. Using unique identifiers encoded in QR codes and a taxonomic database, a research team can sample herbarium specimens, efficiently record the sampling event, and capture specimen images. After sampling in herbaria, images are uploaded to a citizen science platform for metadata generation, and tissue samples are moved through a simple, high‐throughput, plate‐based herbarium DNA extraction and sequencing protocol. Results: We applied this sampling‐to‐sequencing workflow to ~15,000 species, producing for the first time a data set with ~50% taxonomic representation of the "nitrogen‐fixing clade" of angiosperms. Discussion: The approach we present is appropriate at any taxonomic scale and is extensible to other collection types. The widespread use of large‐scale sampling strategies repositions herbaria as accessible but largely untapped resources for broad taxonomic sampling with thousands of species. [ABSTRACT FROM AUTHOR]

The numerous feedback loops between roots, microorganisms, soil chemical and physical properties, and environmental variables result in spatial parameter patterns which are highly dynamic in time. In order to improve our understanding of the related rhizosphere processes and their relevance at the soil–plant system scale, experimental platforms are required. Those platforms should enable (1) to relate small scale observations (nm to dm) to system behaviour, (2) the integration of physical, chemical and biological sampling approaches within the same experiment, and (3) sampling at different time points during the life cycle of the system in question. Here we describe what requirements have to be met and to what extent this has been achieved in practice by the experimental platforms which were set up within the framework of DFG priority programme 2089 "Rhizosphere Spatiotemporal Organisation—a key to rhizosphere functions". It is discussed to what extent theoretical considerations could be accommodated, in particular for the comparison across scales, i.e., from laboratory to field scale. The latter scale is of utmost importance to overcome the trade‐off between fraction of life cycle covered and the avoidance of unrealistic root length densities. [ABSTRACT FROM AUTHOR]

The Brazilian Atlantic Forest is a biologically and structurally diverse biome which covers a large area; however, few tree-level biomass models exist for this region. The objective of this study was to evaluate alternative individual tree biomass models for the Atlantic Forest using contrasting prediction approaches, and evaluate their implications for stand-level biomass and carbon. The data comprised 106 destructively sampled trees (dbh; 5.4 – 68.5 cm) from the 16 most abundant species. Different approaches, namely species-specific, functional-trait based, generalized, and previously developed equations, as well as alternative model forms using differing variables including diameter at breast height (dbh), wood specific gravity (wsg), and height to crown base (hcb) were examined. Species-specific models were developed using both ordinary least squares and linear mixed effects with species as a random effect. The various approaches resulted in statistically different estimates of biomass and carbon at both the individual tree- and stand-levels. Compared to a generalized biomass equation for Pan-tropical forests, our results indicate differences of over 46% and 52% for biomass and carbon, respectively. Overall, if no species-specific model is available, we suggest the functional plant trait based on dbh and wsg.

This study was conducted to fit and evaluate ten existing nonlinear height diameter functions for Cupressus lusitanica in Gergeda forest Ethiopia. A total of 260 trees were measured for their diameter at breast height (D) and height using destructive sampling methods. This data were randomly split in to two datasets for model development (50%) and for model validation (50%). Considering hetroscadasticity of variance, all functions were fitted using weighted nonlinear least square regression. To evaluate the performance of each model, five fit statistics-such as Coefficient of determination (R2), root mean square error (RMSE), bias (Ē), absolute mean deviation (AMD), and coefficient of variation (CV%) were used. Among all the models tested, the Weibull type function of the form H = 1.3 + a (1-exp (-bDc)) + ɛ was observed to give the best fit based on the model’s goodness of fit and predictive ability. Therefore, this model with three parameters has been conformed to provide reliable estimate of total tree height for Cupressus lusitanica in Gergeda forest, Ethiopia.

Bark of Daphne bholua is an important non-timber forest product and makes a substantial contribution to the Nepalese economy. A precise estimate of the amount of D. bholua bark in mountain forests is possible using a biomass model. We developed an allometric bark biomass model for naturally grown D. bholua in Baglung District in the mid-hills of Nepal. The model was based on data from 101 destructively sampled D. bholua on 20 sample plots representing different growth stages (regeneration, established, and matured), site qualities, and stand densities, and we used diameter and height–diameter ratio as predictors. Among 9 functions evaluated, a simple power function showed the best fit to the data. This model described most of the variations in bark biomass with no substantial trends in the residuals. Leave-one-out cross-validation also confirmed the high precision of this model, because it described most of the variations in bark biomass with no substantial trends in the prediction errors. The model can be applied for a precise prediction of bark biomass for individuals of D. bholua with diameters and height–diameter ratios similar to those used in this study. It is site-specific, and its application should therefore be limited to sites with growth stage, site quality, stand density, and species distribution similar to those that formed the basis of this study. Further validation and verification of this model, with a larger dataset collected from sites with a wider range of these characteristics, is recommended.

Allometric equations are important for quantifying biomass and carbon storage in terrestrial forest ecosystems. However, equations for dry deciduous woodland ecosystems, an important carbon sink in the lowland areas of Ethiopia have not as yet been developed. This study attempts to develop and evaluate species-specific allometric equations for predicting aboveground biomass (AGB) of dominant woody species based on data from destructive sampling for Combretum collinum, Combretum molle, Combretum harotomannianum, Terminalia laxiflora and mixed-species. Diameter at breast height ranged from 5 to 30 cm. Two empirical equations were developed using DBH (Eq. 1) and height (Eq. 2). Equation 2 gave better AGB estimations than Eq. 1. The inclusion of both DBH and H were the best estimate biometric variables for AGB. Further, the equations were evaluated and compared with common generic allometric equations. The result showed that our allometric equations are appropriate for estimating AGB. The development and application of empirical species-specific allometric equations is crucial to improve biomass and carbon stock estimation for dry woodland ecosystems. [ABSTRACT FROM AUTHOR]

Vitellaria paradoxa is one of the most economically important trees in West Africa. Although being a key component of most sub-Sahara agroforestry systems, little information and argument exist regarding its biomass and carbon potential. Here, we developed biomass equations for V. paradoxa tree components in Sudanian savannas. A destructive sampling approach was applied, which was based on measuring stem, branch and foliage biomass of thirty individual trees selected from a wide spectrum of diameter at breast height (dbh) and tree height (h). Basal diameter (d20), dbh, h and crown diameter (cd) were measured and used as predictors in biomass equations. Carbon content was estimated using the ash method. Variance explained in biomass allometric equations ranged from 81 to 98%, and was lower for foliage than for branch and stem biomass models, suggesting that leaf allometries are less responsive to tree size than branch and stem allometries. Stem biomass was best predicted by d20, branch biomass by dbh, and leaf biomass by crown diameter. For aboveground biomass, adding height to dbh as compound variable (dbh2 × h) did not make any significant change, as compared with model based on dbh alone. However, adding crown diameter to dbh and height reduced the error by 15% and improved model fits. Carbon contents in V. paradoxa foliage, branch and stem were 55.29, 55.37 and 55.82%, respectively, and higher than reference value suggested by the IPCC. Established allometric equations can be used to accurately predict aboveground biomass of the species in the Sudanian savannas of West Africa. [ABSTRACT FROM AUTHOR]

Stem volume equations were fitted for Cupressus lusitanica in Gergeda Forest, Ethiopia using six different established equation forms. A total of 260 trees were measured for their diameter at breast height (D), total height (H )and stem volume using destructive sampling methods. The data set was randomly divided into equal size for equation development and equation validation. Five fit statistics comprising of the fit index, root mean square error, bias (Ē ), absolute mean deviation and coefficient of variation were used to evaluate the performance of each equation. Among the different equations, the Schumacher and Hall function of the form V = b1Db2Hb3 (model 5), which estimates volume (V ) using diameter at breast height and total height as predicting variables, performed best and was then fitted to the combined data set for prediction of volume over-bark of C. lusitanica in Gergeda Forest. Overall, volume equations with two independent variables (D and H ) performed better than those with only one variable (D). The equations developed in this study can provide forest managers with accurate estimations of stem volumes for C. lusitanica in Gergeda Forest. [ABSTRACT FROM AUTHOR]

• Developed tree volume models for two important species in Karnali province in Nepal. • Used DBH, total height, and crown dimension as predictors in the tree volume models. • Models described most variations in the tree volume of the species of interest. • Model test against independent data confirmed the model's high accuracy. Effective forest management is based on information, such as current growing stock and its future state, which is provided by various forestry models, including tree volume models. Tree volume models are essential tools for estimating growing stock, carbon accounting, timber valuation, identifying forest stands to harvest, growth and yield modeling, and forest ecosystem analysis. We developed tree volume models for two important tree species (Pinus roxberghii Sarg. and Cedrus deodara Roxb.) in Nepal using data from many individuals (mature trees and juveniles) representing wide variations of tree volume allometry. We used diameter at breast height, total height, and crown width of the individuals as predictors in tree volume models for Pinus roxberghii while only the former two predictors in the models for Cedrus deodara. All the mathematical functions described more than 91% and 97% tree volume variations for Cedrus deodara and Pinus roxberghii , respectively. Models for Pinus roxberghii revealed almost similar effect of crown width as tree height on tree volume allometry. Models for this species can be applicable for predicting tree volume with and without bark; however, models for Cedrus deodara can only predict the over-bark tree volume. Testing Pinus roxberghii model against external independent data confirmed a high accuracy of the model. The proposed tree volume models for the species of interest are biologically plausible and statistically robust, and therefore, can be applied to estimate the growing stock precisely, carbon accounting, timber valuation, forest growth and yield modeling. However, users need to apply the model with caution for the forest conditions not covered by modeling data for Cedrus deodara. The prediction accuracy of the models can be further improved through recalibration with additional data collected from wider distributions of the species of interest across the Karnali province and beyond, and application of more robust modeling methods, such as mixed-effects modeling and machine leaning. [ABSTRACT FROM AUTHOR]

Forest above-ground biomass (AGB) can be estimated based on light detection and ranging (LiDAR) point clouds. This paper introduces an accurate and detailed quantitative structure model (AdQSM), which can estimate the AGB of large tropical trees. AdQSM is based on the reconstruction of 3D tree models from terrestrial laser scanning (TLS) point clouds. It represents a tree as a set of closed and complete convex polyhedra. We use AdQSM to model 29 trees of various species (total 18 species) scanned by TLS from three study sites (the dense tropical forests of Peru, Indonesia, and Guyana). The destructively sampled tree geometry measurement data is used as reference values to evaluate the accuracy of diameter at breast height (DBH), tree height, tree volume, branch volume, and AGB estimated from AdQSM. After AdQSM reconstructs the structure and volume of each tree, AGB is derived by combining the wood density of the specific tree species from destructive sampling. The AGB estimation from AdQSM and the post-harvest reference measurement data show a satisfying agreement. The coefficient of variation of root mean square error (CV-RMSE) and the concordance correlation coefficient (CCC) are 20.37% and 0.97, respectively. AdQSM provides accurate tree volume estimation, regardless of the characteristics of the tree structure, without major systematic deviations. We compared the accuracy of AdQSM and TreeQSM in modeling the volume of 29 trees. The tree volume from AdQSM is compared with the reference value, and the determination coefficient (R2), relative bias (rBias), and CV-RMSE of tree volume are 0.96, 6.98%, and 22.62%, respectively. The tree volume from TreeQSM is compared with the reference value, and the R2, relative Bias (rBias), and CV-RMSE of tree volume are 0.94, −9.69%, and 23.20%, respectively. The CCCs between the volume estimates based on AdQSM, TreeQSM, and the reference values are 0.97 and 0.96. AdQSM also models the branches in detail. The volume of branches from AdQSM is compared with the destructive measurement reference data. The R2, rBias, and CV-RMSE of the branches volume are 0.97, 12.38%, and 36.86%, respectively. The DBH and height of the harvested trees were used as reference values to test the accuracy of AdQSM’s estimation of DBH and tree height. The R2, rBias, and CV-RMSE of DBH are 0.94, −5.01%, and 9.06%, respectively. The R2, rBias, and CV-RMSE of the tree height were 0.95, 1.88%, and 5.79%, respectively. This paper provides not only a new QSM method for estimating AGB based on TLS point clouds but also the potential for further development and testing of allometric equations.

Copyright of Journal of Agricultural Science & Technology (1008-0864) is the property of Journal of Agricultural Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Quantitative information of tree biomass is useful for management planning and monitoring of the changes in carbon stock in both forest and agroforestry systems. An estimate of carbon stored in these systems can be useful for developing climate change mitigation strategies. A precise estimate of forest biomass is also important for other issues ranging from industrial forestry practices to scientific purposes. The individual tree-based biomass models serve as fundamental tools for precise estimates of carbon stock of species of interest in forest and agroforestry systems. We developed individual tree aboveground biomass models for Castanopsis indica using thirty-six destructively sampled tree data covering a wide range of tree size, site quality, growth stage, stand density, and topographic characteristics. We used diameter at breast height (DBH) as a main predictor and height-to-DBH ratio (a measure of tree slenderness) and wood density (a measure of stiffness and cohesiveness of wood fibres) as covariate predictors in modelling. We, hereafter, termed the biomass models with former two predictors as first category models (density independent models) and the models with all three predictors as second category models (density dependent models). Among various functions evaluated, a simple power function of the form yi=b1xib2, in each category, showed the best fits to our data. This formulation, in each category, described most of the biomass variations (Radj2 > 0.98 and RMSE < 72.2) with no significant trend in the residuals. Since both density dependent and density independent models exhibit almost similar fit statistics and graphical features, one of them can be applied for desired accuracy, depending on the access of the input information required by the model. Our biomass models are site-specific, and their applications should therefore be limited to the growth stage, stand density, site quality, stand condition, and species distribution similar to those that formed the basis of this study. Further research is recommended to validate and verify our model using a larger dataset with a wider range of values for site quality, climatic and topographic characteristics, stand density, growth stage, and species distribution across Nepal. [ABSTRACT FROM AUTHOR]

The Brazilian Atlantic Forest is a biologically and structurally diverse biome which covers a large area; however, few tree-level biomass models exist for this region. The objective of this study was to evaluate alternative individual tree biomass models for the Atlantic Forest using contrasting prediction approaches, and evaluate their implications for stand-level biomass and carbon. The data comprised 106 destructively sampled trees (dbh; 5.4 - 68.5 cm) from the 16 most abundant species. Different approaches, namely species-specific, functional-trait based, generalized, and previously developed equations, as well as alternative model forms using differing variables including diameter at breast height (dbh), wood specific gravity (wsg), and height to crown base (hcb) were examined. Species-specific models were developed using both ordinary least squares and linear mixed effects with species as a random effect. The various approaches resulted in statistically different estimates of biomass and carbon at both the individual tree- and stand-levels. Compared to a generalized biomass equation for Pan-tropical forests, our results indicate differences of over 46% and 52% for biomass and carbon, respectively. Overall, if no species-specific model is available, we suggest the functional plant trait based on dbh and wsg. [ABSTRACT FROM AUTHOR]

Context Accurate estimates of tree component and total biomass are necessary for evaluating alternative forest management strategies for biomass feedstock, carbon sequestration, and products. Previous biomass equations developed for white birch trees in natural stands provided substantially biased predictions for white birch plantations. Aims A new system of additive tree biomass equations was developed for juvenile white birch plantations in the northeastern China. Methods With destructive biomass sampling data from 501 trees sampled from white birch provenance and family trails at ages 7, 9, 10, and 13 in three provinces, a system of nonlinear additive tree biomass equations based on DBH and tree height was developed using the nonlinear seemingly unrelated regressions (NSUR) approach. Results Compared with previously published equations developed for natural white birch forests, the new system provided more accurate predictions of white birch tree component and aboveground and total biomass, especially of branch, foliage, and root biomass. Conclusion The new system extended the applicability of biomass equations to white birch plantations in the northeastern China. [ABSTRACT FROM AUTHOR]

Acacia nilotica is an invasive species which is difficult to eradicate up to present. A. nilotica is a fast growing species of savanna tree, and therefore is supposed probably to have relatively high biomass and carbon content. The objective of this research was to develop allometric equation of biomass and estimating the carbon storage potential in A. nilotica stand in Baluran National Park. This research used destructive method on 9 sample trees to construct allometric equation. For measuring biomass potency, vegetation analysis was conducted using 3 plots, measuring 100 m x 100 m each on the basis of A nilotica stand density categorization (high, medium, and low). For obtaining data of carbon potency, biomass value was multiplied with 0,47. Research results show that the selected allometric equation for tree total is W=0,34 D1,97 with R2of 98,4. Average biomass potency was as large as 25 tons/ha. Average carbon potency was 11,92 ton/ha. Management of A. niloticain Baluran National Park, in moderate and low density stand can be in the form of clear cutting, whereas high density stand can be maintained for carbon sequestration and utilization by the people community for supplying seeds, livestock feeds, firewood or charcoal briquette.

The study and curation of human remains in the United Kingdom underwent a sea-change in response to the publication of the Department for Culture, Media and Sport (DCMS) guidance in 2005. Curatorial and research ethics and practice have improved, but these unique collections face new challenges – archives which are full and an increasing demand for samples for stable isotope and ancient DNA studies. This article reviews how holdings were and continue to be created and the ethical and practical issues that arise from these decisions. In contrast to much of the literature on this topic, it does not focus on the repatriation process and its consequences for UK museum practice. Instead, we address contemporary and future concerns regarding human remains excavated and curated in England, particularly issues of consent, permission and ancient DNA analysis. [ABSTRACT FROM AUTHOR]

In recent decades, bamboos have become globally important biomass resources in tropical regions. However, bamboo stands have uneven age structure, and often differently aged culms are found distributed throughout a stand. This makes estimation of their biomass and carbon stocks more complicated than forest trees. Therefore, the present study was undertaken to: (i) determine whether or not growth and biomass accumulation in thin-walled bamboos follow simple height-diameter (H-D) relationships and allometric scaling rules, and (ii) develop models for estimation of biomass and carbon stocks in different aged stands of the bamboos Schizostachyum dullooa, Pseudostachyum polymorphum and Melocanna baccifera in North East India. The H-D relationships and allometric scaling between above-ground biomass (AGB), culm height (H) and diameter at breast height (D) were examined using various models. The results demonstrate that H, D and AGB are allometrically related in the different aged bamboo stands. However, these relationships significantly deviated from theoretical predictions. The common functions used for modelling H-D relationships also gave significantly differing estimates of the asymptotic height (H max ) highlighting large uncertainty in the choice of H-D functions. Our findings indicate that use of volume provides better biomass prediction in the three species than either D or H alone. Aboveground biomass carbon densities of the bamboo forests were estimated at 23 Mg ha –1 for S. dullooa , 21 Mg ha –1 for P. polymorphum and 58 Mg ha –1 for M. baccifera forests. The estimated carbon density of M. baccifera was comparable with values reported for thick walled bamboos in North East India and elsewhere. Although S. dullooa and P. polymorphum had lower carbon densities, the ability of these species to grow fast after forest disturbance indicates their potential for abating land degradation and carbon management by preventing losses from forest degradation. These species also hold great potential for biomass production given the local tradition of planting them in home gardens. [ABSTRACT FROM AUTHOR]

Accurate estimates of biomass are required for relating ecosystem functioning to atmospheric carbon regulation. Biomass may be directly measured through field sampling, which can then be used to calibrate biomass predictions from remote sensing and/or modelling. Field sampling generally entails measuring the fresh mass of individual trees or shrubs and then estimating the moisture contents of a representative sub-samples, which are then used to calculate dry mass. Because any errors in the estimation of the moisture content (MC) correction are translated proportionally to the biomass prediction of an individual tree or shrub, care is required to ensure MC estimates are unbiased and as precise as possible. There are numerous different protocols currently applied to attain MC, with these differing in accuracy (bias and precision) and cost of implementation. A dataset of MC of above-ground biomass (AGB) of 1396 individuals (trees or shrubs) was used to assess which protocols for within- and among-individual sampling are likely to provide the most cost-effective estimates of MC within acceptable bounds of accuracy. Monte-Carlo analysis was used to explore key sources of error in within-individual MC estimation. Results suggest these MC estimates may be based on at least the bole and crown components of AGB, with bias resulting if MC is based on stem wood only, particularly in young (or small) individuals. Little gain in accuracy was attained with more intensive sub-sampling (e.g. into foliage, twig, branches, bark, and stem wood components). Moreover, further efficiencies may be gained by applying existing empirical models to estimate the proportion of AGB that is crown based on easily measured variables such as stem diameter, thereby avoiding the resource-intensive process of partitioning to obtain fresh weights measurements of components. However to minimise bias, it is important to undertake MC sampling at each study site, and to stratify sampling among-individuals by both appropriate taxonomic grouping (e.g. plant functional type) and age-class. For a given plant functional type-by-size (or age) strata at a given site a precision of about 4% coefficient of variation of the average MC estimate can be achieved with intensive within- and among-individual sampling. However a precision of 8–10% is achievable using our recommended less intensive but more efficient protocol; derive an average MC for at least six individuals, and for each individual, intensively sub-sample bole and crown components for MC, which is then applied to the fresh weights of these components. This latter estimate may be obtained from partitioning of the AGB, or for the highest efficiency, from predictions obtained from the application of existing representative empirical relationships of partitioning based on the size of the individual. [ABSTRACT FROM AUTHOR]