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Sapota fruit tree biomass cannot be estimated using general forest tree allometric equations involving measurement of diameter at breast height (DBH) as grafted sapota tree branches below this height. Therefore grafted sapota trees of commercial orchards require an independent allometric equation for nondestructive estimation of tree biomass. Sapota allometric equations were hence developed with a destructive sampling of grafted sapota trees using parameters other than DBH and compared with other equations developed for grafted mangoes. The selected allometric parameters were significantly related to the age of the trees. The root-to-shoot ratio also differed from those reported for forest trees. The biomass expansion factor by and large attained stability beyond 16 years of tree age. The equations so developed generally fitted the data well, and in most cases, more than 50% of the observed variation in biomass was explained by primary branch girth x number of primary branches. There was a good agreement between the observed and the predicted biomass using both multiple linear regression and power model equations. Further, our purpose was to see if the grafted mango tree equation can predict sapota tree biomass. The results of this study confirmed that the mango equation equally predicts sapota tree biomass and hence the mango tree allometric equation can also be used for estimating sapota tree biomass efficiently. [ABSTRACT FROM AUTHOR]

Introduction: Extreme weather becomes increasingly frequent and severe under climate change, causing unexpected damage to trees. Among them, sleet damage is particularly harmful to evergreen trees in subtropical area. Camphor trees (Cinnamomum camphora), as dominant street trees in central China, are prone to sleet damage, resulting in loss of valuable ecosystem functions. Methods: By measuring tree size characteristics of 118 camphor trees before and after a record-breaking sleet event in Wuhan, a mega-city in central China, we built allometric equations between size and volume of broken branches and used the random forest regression to model sleet damage to camphor trees. Results: We identified that larger trees with intermediate bole height suffered more than smaller trees with tall bole height from the sleet event. We estimated the volume of broken branches of a camphor tree with DBH at 35.0 cm as 106.4 dm3, equivalent to 55.3 kg biomass loss, from the sleet event. Discussion: We suggest that pruning the branches instead of topping the main stems of small camphor trees would reduce the sleet hazard. To mitigate the negative impacts of climate change, regular pruning should be practiced on street camphor trees to protect them from future heavy sleet events. [ABSTRACT FROM AUTHOR]

Bamboo is an ideal plant for commercial production due to its rapid growth rate, high biomass production, low cost of production and environmental friendliness. Although Ethiopia has the highest bamboo cover in Africa, allometric equations for estimating its biomass are scarce. Most allometric models developed to date have been largely concerned with trees and shrubs. The objective of this study was to generate species- and site-specific allometric models that could be used to estimate the total aboveground dry biomass and culm dry biomass of lowland bamboo in northwest Ethiopia. Three power form-based allometric models were created using diameter at breast height (DBH) and culm height (H) as independent variables. One hundred and eight Oxytenathera abyssinica culms were used to predict the total aboveground biomass and culm biomass. Model one (M1) was the best model to predict the culm and total aboveground biomass of the species, regardless of forest type. The allometric models may provide useful information about aboveground biomass and culm biomass estimation methods to forestry professionals, bamboo producers and other stakeholders, and could help in the calculation of the country’s contribution to global carbon sequestration and trade.

The preparation of allometric equations generally uses multiple linear regression equations. The use of this regression equation is usually not carried out through various tests but goes directly to the t test and F test stages. Therefore, this paper aims to provide an example of determining the regression equation through the classical assumption test stages. Makila (Litsea angulata BI) was chosen as an example because it provides environmental services, especially in dealing with climate change and has not been widely studied. The research method used is a quantitative method. Destructive sampling was taken and the material used consisted of 40 young Makila plants planted in polybags next to the greenhouse at the Faculty of Agriculture, Pattimura University, Ambon. The diameter of each sample was measured from the ground surface to a height of 5 cm and numbered 1 to 40. After being coded, the plant samples were cut down and all young trees were divided into stem segments, branches, twigs, leaves and roots. The root segments were collected carefully to facilitate their separation from the soil in polybags. The parts of the roots that still contain soil are cleaned with a machete and brush until they are free of soil and other dirt. Next, each segment was weighed for its wet weight (in g) and dry weight, thus enabling the determination of the biomass content (in g) for each segment. Data analysis was carried out using the Multiple Linear Regression Equation method. The regression model was examined for normality and suitability in predicting independent variables, ensuring there were no issues with multicollinearity, heteroscedasticity, and autocorrelation. The results of the normality test showed that the significance value for the remaining data was 0.813 > 0.05, indicating normal distribution. TThe results of the multicollinearity test show that there is no multicollinearity problem, this is indicated by the VIF value of the two independent variables (tree diameter and tree height each 1.049) < 10 and the Tolerance value (tree diameter and tree height 0.953 each) > 0.100. The results of the heteroscedasticity test show that the two independent variables have a significance value of more than 0.05. The results of the autocorrelation test show the Durbin Watson (DW) value = 1.956 with a range of 1.65 – 2.35 which means there is no correlation problem. The results yielded a multiple linear regression: Y = -1131.146 + 684.799X1 + 4.276X2, where Y is biomass, X1 is the diameter, and X2 is the tree height. Based on the results of the t-test: variable X1 partially affected Y while variable X2 partially had no effect on Y. The F-test indicated that variables X1 and X2 jointly affected Y with R Square: 0.919 or 91.9% and the rest was influenced by other unexplored factors. To simplify biomass prediction and field measurement, a regression equation that used only 1 independent variable, namely tree diameter, was used for the experiment. Allometric equation only used 1 variable, Y = - 1,084,626 + 675,090 X1, where X1 = tree diameter, Y = Total biomass with R = 0.957, and R square = 0.915. The regression equation for young Makila plant provides assurance that the regression equation obtained is accurate in estimation, unbiased and consistent. This is because in data processing classical assumptions have been tested. This equation can save time, costs and energy, as well as make measurements easier in the field so that future researchers can develop allometric equations in other plants for efficiency.

The aboveground contributions of mangroves to global carbon sequestration reinforce the need to estimate biomass in these systems. The objective was to determine the aboveground biomass storage and quantify the carbon and CO2e content in Rhizophora mangle, Avicennia germinans, and Laguncularia racemosa present in southeastern Mexico. Based on the Forest Protocol for Mexico Version 2.0 methodology, published by Climate Action Reserve, 130 circular plots were randomly selected and established in an area of 930 ha of mangrove vegetation, and the aboveground biomass and stored carbon were determined. The mangrove had a density of 3515 ± 428.5 individuals per hectare. The aboveground biomass of the three species was 120.5 Mg ha−1. The biomass of L. racemosa was 99.5 Mg ha−1, which represents 82.6% of the total biomass. The biomass of R. mangle was 20.33 Mg ha−1, and that of A. germinans was 0.32 Mg ha−1. The total carbon retained in the trees was 60.25 Mg C ha−1 and 221.1 Mg CO2e ha−1. Laguncularia racemosa generated the highest contributions of CO2e. The area of mangroves accumulated 112,065 Mg of aboveground biomass. The carbon contained in this biomass corresponds to 205,623 Mg CO2e. This mangrove contributes to mitigating the effects of climate change globally through the reduction in greenhouse gases. [ABSTRACT FROM AUTHOR]

The fight against climate change is one of the major concerns of the international community and has led to a search to identify cost-effective ways to manage ecosystems in a way that removes atmospheric carbon-dioxide while providing essential societal benefits. As bamboo ecosystems in Cameroon are poorly known, this study sought to evaluate the distribution of bamboo, develop allometric equations for it and estimate carbon stocks associated with bamboo in the Western Highlands of Cameroon. Landsat 8 OLI imagery was used to increase data on the distribution and carbon stocks of Phyllostachys aurea Carrière ex Rivière & C. Rivière in the study area. P. aurea is reportedly the most exploited species for ecological, socioeconomic and cultural activities in the region, posing a challenge for the sustainability of the species. Twelve circular plots were established in P. aurea stands in the Western Highlands. Five per cent of the bamboo stems were harvested. Every stem was sorted into leaves, branches, and culms. These were weighed, and sub-samples taken to the laboratory. Sub-samples were oven-dried for biomass estimation. Regression analysis was used to develop the allometric equations. The best equation was used to estimate the carbon stocks. The Western Highlands region of Cameroon is estimated to have 241,296 ha of bamboos. The dominant bamboo species identified in the study area include Oldeania alpina (K.Schum.) Stapleton (syn. Yushania alpina K.Schum.), Bambusa vulgaris Schrad. ex J.C.Wendl. and P. aurea. Focusing on P. aurea the best fit equation with a bias of − 6.98%, Adj.R² = 0.711, AIC = 17 and RSE = 0.262, was selected. The mean density of P. aurea was 38,017 ± 4510 culms.ha−1. The mean culm aboveground biomass was 3.15 ± 0.94 kg. The AGB of P. aurea was estimated at 119.05 ± 3.63 t.ha−1, mean AGC was 55.95 ± 5.81 t C.ha−1 and mean aboveground carbon dioxide equivalence (AGCO2eq) was 205.35 ± 58.01.22 t CO2.ha−1. P. aurea constitutes a significant carbon sink in the Western Highlands of Cameroon. Policymakers and development planners should therefore recommend this species for carbon markets, international initiatives such as AFR100, and the Nationally Determined Contribution addressing the United Nations Sustainable Development Goal 13 (SDG13) to combat climate change.

Allometric equations are the most common way of assessing Aboveground biomass (AGB) but few exist for savanna ecosystems. The need for the accurate estimation of AGB has triggered an increase in the amount of research towards the 3D quantification of tree architecture through Terrestrial Laser Scanning (TLS). Quantitative Structure Models (QSMs) of trees have been described as the most accurate way. However, the accuracy of using QSMs has yet to be established for the savanna. We implemented a non-destructive method based on TLS and QSMs. Leaf-off multi scan TLS point clouds were acquired in 2015 in Kruger National Park, South Africa using a Riegl VZ1000. The 3D data covered 80.8 ha with an average point density of 315.3 points/m2. Individual tree segmentation was applied using the comparative shortest-path algorithm, resulting in 1000 trees. As 31 trees failed to be reconstructed, we reconstructed optimized QSMs for 969 trees and the computed tree volume was converted to AGB using a wood density of 0.9. The TLS-derived AGB was compared with AGB from three allometric equations. The best modelling results had an RMSE of 348.75 kg (mean = 416.4 kg) and a Concordance Correlation Coefficient (CCC) of 0.91. Optimized QSMs and model repetition gave robust estimates as given by the low coefficient of variation (CoV = 19.9% to 27.5%). The limitations of allometric equations can be addressed by the application of QSMs on high-density TLS data. Our study shows that the AGB of savanna vegetation can be modelled using QSMs and TLS point clouds. The results of this study are key in understanding savanna ecology, given its complex and dynamic nature. [ABSTRACT FROM AUTHOR]

Background and Objectives: Reforested stands are recognized as crucial contributors to forest carbon storage and play a significant role in mitigating the destruction of natural areas. Aboveground biomass (AGB) serves as a vital component in sustainable forest management, contributing to the reduction of global warming, and provides valuable information. Allometric equations represent a valuable tool for quantifying AGB in forests. In recent years, non-parametric remote sensing techniques, such as the Random Forest algorithm, have gained widespread popularity for estimating forest tree biomass. This study aimed to evaluate the capability of Sentinel 2 data, coupled with the Random Forest algorithm, to estimate above-ground biomass in Arabdagh reforested stands located in Golestān province. Materials and Methods: In this study, a total of 180 circular sample plots, each with an area of 400 m 2, were measured using the cluster sampling method. Measurements of the diameter at breast height (DBH) and tree height (H) were recorded. Additionally, the precise coordinates of the sample plot centers were captured using DGPS. Subsequently, the aboveground biomass of trees was calculated using the developed allometric equations. For this study, pre-processed radiometric and geometrical data from Sentinel 2 were utilized, enabling the creation of various vegetation indices. The random forest algorithm was employed to explore the relationship between biomass characteristics (dependent variable) and the spectral values of vegetation indices (independent variables). The modeling process involved using 75% of the sample plots (135 sample plots) for training the random forest algorithm, while the remaining 25% (45 sample plots) were reserved for validating the estimates. Results: The findings showed that the NDVI and GNDVI indices exhibited the strongest correlation in estimating above-ground biomass. By employing the random forest algorithm with 310 trees and 5 predictors, the percentage root mean square error was determined to be 35.83%, while the coefficient of determination was 0.51, indicating the algorithm's capability to estimate the above-ground biomass of Arabdagh reforested stands. Furthermore, the results indicated that the random forest algorithm, utilizing Sentinel 2 data, provided higher estimates for the above-ground biomass of trees compared to the actual values. However, at a 95% probability level, there was no significant difference between the estimated and real above-ground biomass values (p-value > 0.05). Conclusion: The findings demonstrated that Sentinel 2 data successfully estimated the above-ground biomass of Arabdagh reforested stands with satisfactory accuracy. Based on the results presented, it can be concluded that the information derived from the main bands and spectral indices played a crucial role in the estimation of above-ground biomass. [ABSTRACT FROM AUTHOR]

Breadfruit (Artocarpus altilis) is an underutilized Pacific tree crop that has been highlighted as having substantial potential to contribute to global food security and climate-smart agriculture, including adaptation to and mitigation of climate change. To explore the carbon sequestration potential of breadfruit production, we characterize tree volume, wood density, carbon density, foliar biomass, and growth rates of breadfruit in Hawai'i. Strong relationships to trunk or branch diameter were displayed for wood density (r2 0.81), carbon density (r2 0.87), and foliar biomass (r2 0.91), which were combined to generate an allometric prediction of tree volume (r2 0.98) based on tree diameter at breast height. Growth rates, as measured by diameter at breast height, were well predicted over time when trees were classified by habitat suitability. We extrapolate potential breadfruit growth and carbon sequestration in above-ground biomass to the landscape scale over time. This study shows that breadfruit is on the low end of broadleaf tropical trees in moist and wet environments, but in an orchard can be expected to sequester ~69.1 tons of carbon per hectare in its above-ground biomass over a 20-year period. [ABSTRACT FROM AUTHOR]

In recent times, airborne and terrestrial laser scanning have been utilized to collect point cloud data for forest resource surveys, aiding in the estimation of tree and stand attributes over hectare-scale plots. In this study, an automated approach was devised to estimate the diameter at breast height (DBH) and tree height across the entire sample area, utilizing information acquired from terrestrial laser scanning (TLS) and airborne laser scanning (ULS). Centered around a meticulously managed artificial forest in Northern China, where Mongolian oak and Chinese Scots pine are the predominant species, both TLS and ULS operations were conducted concurrently on each plot. Subsequent to data collection, a detailed processing of the point cloud data was carried out, introducing an innovative algorithm to facilitate the matching of individual tree point clouds from ULS and TLS sources. To enhance the accuracy of DBH estimation, a weighted regression correction equation based on TLS data was introduced. The estimations obtained for the Chinese Scots pine plots showed a correlation of R2 = 0.789 and a root mean square error (RMSE) of 3.2 cm, while for the Mongolian oak plots, an improved correlation of R2 = 0.761 and a RMSE of 3.1 cm was observed between predicted and measured values. This research significantly augments the potential for non-destructive estimations of tree structural parameters on a hectare scale by integrating TLS and ULS technologies. The advancements hold paramount importance in the domain of large-scale forest surveys, particularly in the calibration and validation of aboveground biomass (AGB) estimations. [ABSTRACT FROM AUTHOR]

Over the last two decades, bamboo has received increasing attention owing to its socio-economic and environmental importance. Environmentally, bamboo plays an important role in carbon sequestration, thus enhancing climate change mitigation. In Cameroon, knowledge about the importance of Bambusa vulgaris Schrad. ex J.C.Wendl. to climate change mitigation is deficient, despite the fact that it is the most abundant bamboo species in Cameroon's Bimodal rainforest agroecological zone (Agroecological zone 5 - AEZ5). This study was initiated to develop allometric equations and estimate carbon stocks of B. vulgaris in Cameroon's AEZ5. The destructive, clump-based method was used for bamboo biomass data collection on 40 clumps and 86 culms. Regression analyses were performed to obtain allometric models for B. vulgaris biomass prediction which were used for B. vulgaris carbon stocks estimation in AEZ5. The best allometric model for culms was obtained when all predictive variables including age, diameter and height were considered into the model. For clump, the best model was obtained when the number of culms per clump, girth and average diameter were considered in the model. Model quality adjustment was better for clump aboveground biomass (AGB) compared to those of culm AGB. The model of B. vulgaris of the evergreen rainfall forest was validated with a bias of 45.5 %. Bamboo aboveground biomass proportions were 77 %, 15 % and 8 %, respectively for culms, branches and leaves. The mean density and carbon stocks of B. vulgaris were estimated at 2,0679 culms.ha−1, 257 clumps.ha−1, and 61.65 tC ha−1. B. vulgaris has a veritable carbon sequestration capacity which policymakers should consider in climate change mitigation strategies like those linked to payments for ecosystem services, voluntary carbon stocks market, Bonn Challenge, AFR100 initiative, and the Paris agreement ratified by the government of Cameroon.

Mangroves, which have extremely high primary productivity, are efficient coastal blue carbon ecosystems. Aboveground biomass (AGB) is an important component of vegetation carbon pools. Thus, accurate estimation of mangrove AGB is critical for studying carbon cycle and climate change. While the practical significance and application of information obtained on mangrove AGB in China is apparent, studies of this nature in China at a national scale have rarely been reported. Remote sensing technology is convenient, efficient, has a wide observational range, and can be used for large-scale ecosystem monitoring. Canopy height is a structural parameter that is positively correlated with the AGB of vegetation. The Global Ecosystem Dynamics Investigation (GEDI) spaceborne Light Detection and Ranging (LiDAR) satellite, launched in recent years, is able to obtain vegetation canopy height. This study employed forest canopy height derived from GEDI satellite-based LiDAR and an allometric equation based on the allometric theory to estimate mangrove AGB in China in 2019, and the quantitative and spatial distribution of mangrove biomass and their main influencing factors were analyzed. The results showed that the total and mean AGB of mangroves in China in 2019 were about 1,974,827 t and 73.0 t/hm2, respectively. Guangdong-Hong Kong-Macao area showed the largest total mangrove AGB, reaching 843,836 t. The mean values of AGB in each province (region) with mangrove ecosystems nationwide ranged from 53.3 to 92.1 t/hm2, of which the largest was found in Hainan Province, reaching 92.1 t/hm2. In Hainan, Taiwan, and Fujian provinces, mean mangrove AGB was higher than the national mean. Considering nature reserves, the mean AGBs of mangroves in Neilingdingdao-Futian and Mai Po mangrove nature reserves in Shenzhen Bay in the Guangdong-Hong Kong-Macao area and Dongzhaigang mangrove nature reserve in Hainan province were relatively high, with values greater than 110 t/hm2. The accumulation and distribution of mangrove AGB in China are affected by latitude and anthropogenic factors. This study provides a comprehensive analysis of mangrove AGB in China based on remote sensing and an allometric equation and can provide a database and technical reference for estimating carbon storage in mangrove ecosystems. It will also contribute to the implementation of ecological restoration and protection measures for coastal mangroves, as well as carbon emission control in China.

We propose a tree-level biomass estimation model approximating allometric equations by LiDAR data. Since tree crown diameter estimation is challenging from spaceborne LiDAR measurements, we develop a model to correlate tree height with biomass on the individual-tree levels employing a Gaussian process regressor. In order to validate the proposed model, a set of 8342 samples on tree height, trunk diameter, and biomass has been assembled. It covers seven biomes globally present. We reference our model to four other models based on both, the Jucker data and our own dataset. Although our approach deviates from standard biomass–height–diameter models, we demonstrate the Gaussian process regression model as a viable alternative. In addition, we decompose the uncertainty of tree biomass estimates into the model- and fitting-based contributions. We verify the Gaussian process regressor has the capacity to reduce the fitting uncertainty down to below 5%. Exploiting airborne LiDAR measurements and a field inventory survey on the ground, a stand-level (or plot-level) study confirms a low relative error of below 1% for our model. The data used in this study are available at https://github.com/zhu-xlab/BiomassUQ.

Populus deltoides W. Bartram ex Marshall is one of the most important economically (wood production), environmentally (biomass production and carbon sequestration), and fast-growing species in plantations. Therefore, this study aimed to investigate biomass, volume, and carbon sequestration models according to age by using the stem analysis method in poplar plantations in five regions of Guilan province. Measurement features included DBH and the total height of all trees in two plots with one hectare area in each region. 30 trees were randomly selected in different diameter classes and cut down and the discs were obtained in order to stem analysis. The annual rings of discs were counted, ages at different tree heights were obtained, and the diameter of the annual rings was measured to determine the annual diameter and volume increments. Carbon measurement, volume measurement of wood, and volume conversion to weight ratio were calculated. Finally, regression analysis was performed by the relationship between volume and biomass growth by age based on the highest coefficient of determination and minimum standard error. The results showed that the nonlinear models were able to show the highest coefficient of determination (R2 = 0.93-0.99) and the least standard error of the relationship between these characteristics. These models can be applied for the annual growth assessment of poplar plantations in carbon sequestration and optimal management for achieving sustainable development of the various plantations. [ABSTRACT FROM AUTHOR]

Bamboo has great potential in restoring degraded lands while providing multiple environmental benefits and harvestable products at regular intervals due to its unique characteristics of rapid growth. However, a comprehensive and species-specific knowledge of biomass productivity, nutrient dynamics, and potential harvest-induced nutrient losses is lacking. This study compared the biomass and nutrient dynamics of four bamboo species Bambusa oldhamii Munro, Dendrocalamus latiflorus Munro, Bambusa tulda Roxburgh, and Dendrocalamus brandisii (Munro) Kurz in Northern Thailand. Field measurements, laboratory analyses, and statistical modeling were used to estimate their aboveground biomass (AGB), nutrient use efficiency (NUE), and nutrient stocks. Culm diameter at breast height (DBH) and age were identified as the most reliable predictors of AGB. The study revealed that D. brandisii had superior productivity and NUE compared to the other species, particularly the introduced non-native bamboo species. These findings emphasize the need for species-specific strategies that consider both biomass productivity and nutrient dynamics. Furthermore, D. brandisii can be suggested as a native candidate for bamboo plantations in Northern Thailand and similar environments, given its high productivity and efficient nutrient use, underpinning its potential contribution to environmental rehabilitation and rural livelihoods. However, more research is required to minimize nutrient losses and maintain a productive age structure. [ABSTRACT FROM AUTHOR]

Bottomland Hardwood Forests (BHFs) are commonly acknowledged worldwide for their vast carbon sequestration potential and carbon storage capacity. However, the paucity of forest carbon stock data from BHFs along the Lower Mississippi Alluvial Valley (LMAV) in Northeast Louisiana is an existing knowledge gap in understanding the carbon sequestration and storage dynamics across the region. This study was carried out in the Russell Sage Wildlife Management Area (RSWMA) in Northeast Louisiana using a protocol modified from the Terrestrial Carbon Observations Protocol for Vegetation Sampling. Comprehensive analyses of carbon stocks in trees, woody shrubs and seedlings, herbaceous vegetation, downed woody debris, leaf litter, and soil were carried out to quantify the carbon stored in each ecosystem component. Trees accounted for a carbon stock of 132.4 Mg C ha−1, approximately 99% of the total stock for the area. Woody shrubs and seedlings and leaf litter stored 0.4% (0.62 Mg C ha−1) and 0.3% (0.4 Mg C ha−1), respectively. Considering the sparse understory in a BHF, the carbon stored per hectare is comparable to other temperate forests in the conterminous United States. These findings highlight the importance of the BHF ecosystem in carbon storage and their overall role in regional and global ecosystem management in light of climate change. [ABSTRACT FROM AUTHOR]

The aboveground contributions of mangroves to global carbon sequestration reinforce the need to estimate biomass in these systems. The objective was to determine the aboveground biomass storage and quantify the carbon and CO2e content in Rhizophora mangle, Avicennia germinans, and Laguncularia racemosa present in southeastern Mexico. Based on the Forest Protocol for Mexico Version 2.0 methodology, published by Climate Action Reserve, 130 circular plots were randomly selected and established in an area of 930 ha of mangrove vegetation, and the aboveground biomass and stored carbon were determined. The mangrove had a density of 3515 ± 428.5 individuals per hectare. The aboveground biomass of the three species was 120.5 Mg ha−1. The biomass of L. racemosa was 99.5 Mg ha−1, which represents 82.6% of the total biomass. The biomass of R. mangle was 20.33 Mg ha−1, and that of A. germinans was 0.32 Mg ha−1. The total carbon retained in the trees was 60.25 Mg C ha−1 and 221.1 Mg CO2e ha−1. Laguncularia racemosa generated the highest contributions of CO2e. The area of mangroves accumulated 112,065 Mg of aboveground biomass. The carbon contained in this biomass corresponds to 205,623 Mg CO2e. This mangrove contributes to mitigating the effects of climate change globally through the reduction in greenhouse gases.

Biomass and morphological characteristics of plant species provide essential insight into how well a species adapts to its environment. The study aims to evaluate how environmental variables (viz., altitude, slope, aspect degree, and soil properties) influence the morphological traits and biomass variability of Calotropis procera (Aiton) W.T. Aiton in a semi-arid environment. C. procera sample locations were divided into 39 permanent sites (5×5 = 25 m2). Slope, aspect degree, slope aspect, altitude, and soil variables (soil moisture, organic matter, nitrogen (N %), and phosphorus (P) gradients were used to quantify morphological parameters (height, diameters, canopy area, volume, and leave/ branch biomass) and aboveground biomass. Environmental variables, i.e., altitude and aspect degree, were the most important factor influencing the biomass variation and affecting soil moisture content; however, they did not directly affect the total biomass of the species. The results also reveal significant plasticity in morphological traits exists concerning elevation and aspect degree at (p< 0.05). Plant volume was a better indicator of species’ total biomass revealed from the regression model showing significant at p< 0.05. The study also reveals that soil properties such as soil moisture and Phosphorus have an important role in enhancing the productivity of the studied plant species. The results concluded that plants functional traits and biomass shows significant variation across the altitude and these parameters could be consider in the conservation of this native species. [ABSTRACT FROM AUTHOR]

This study was conducted to estimate the above and below ground carbon storage in the Woodlands of Dirmaga Watershed, North Western Ethiopia. The field data were collected through systematic random sampling techniques of 40 sample plots. The above-ground biomass and below-ground biomass of the study area was collected from 20 m by 20 m area of the main plot. The biomass and carbon stock of the woodland was estimated using site-specific allometric models and Landsat 8 NDVI and analyzed by ArcGIS. The result showed that the mean carbon stock of above-ground carbon and below-ground carbon were accounted for about 291.47 t/ha and 24.81 t/ha, respectively. The relationship between AGC and NDVI was strong with correlation coefficient of 0.86 and R2 value of 0.745. Tree species of Anogeissus leiocarrpa, Adansonia digitata and Diospyros mespiliformis sequestered the largest portion of the carbon stockwhile, Ficus sycomorus L., Rhus glutinosa and Securinega virosa were the least contributor of carbon stock. The woodland has a great potential for carbon sequestration and biodiversity conservation and the concerned body should conserve and manage the resource properly.

Estimating forest biomass is imperative for comprehensively understanding the function of forest in regulating climate, providing theoretical support for vegetation management. Constructing allometric equations rationally is essential for accurate tree-level biomass estimation without destructive sampling, and optimizing the sample size for fitting allometric equations ensures a desirable balance between accuracy and cost. In this study, the optimal sample size and the best allometric equation for biomass estimation were discussed using unmanned aerial vehicles (UAVs) imagery and field measurements of tree height (H), diameter at breast height (DBH) and crown radius (R c) in an artificial Pinus tabuliformis forest. Results demonstrated that the optimal sample size for accurately estimating tree-level biomass with minimal manpower and time costs was 130. Besides, the estimating precision of allometric equations can be enhanced by increasing the number of suitable variables, altering the variables combination, and modifying functional forms. The proposed allometric equation based on H and R c in this study outperformed common equations in estimating Pinus tabuliformis forest biomass. This equation achieved a coefficient of determination (R2) of 0.72 and a root-mean-square error (RMSE) of 8.56 kg for biomass estimation, owing to its utilization of multivariate analysis and exclusive application of logarithmic transformation to the dependent variable. Moreover, the study revealed that the total biomass of 1490 planted Pinus tabuliformis trees in this study area was 67.3 t. This research offers valuable insights into accurately estimating tree-level forest biomass, which is essential for addressing challenging ecological issues and formulating rational forest management policies. • A new allometric equation based on H and R c was proposed to evaluate biomass. • 130 was the optimal sample size for biomass estimation by allometric equation. • Tree-level biomass of a Pinus tabuliformis forest was estimated using UAV data. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the effective of partial harvest method used for tree biomass estimation. Such method was analyzed for the correlation in compare with invented allometric equations. The advantages and disadvantages of those methods were discussed. The significantly difference between estimation methods were analyzed. As a result, tree biomass calculated by the Brown’s allometric equation produced highest results, followed by the partial harvest method, Tsutsumi’s, Chave’s and Pothong’s equations respectively. The selected allometric equations showed the positive correlation with partial harvest method. The highest positive correlation was found between the partial harvest method and both equation of Chave and Pothong (R = 0.99, p ≤ 0.05 and R = 0.99, p ≤ 0.05, respectively), followed by Tsutsumi’s and Brown’s equations (R = 0.96, p ≤ 0.05 and 0.88, p ≤ 0.05 respectively). Wood density significantly varied among tree species and that affected the effective of biomass estimation. The allometric equations of Pothong and Chave can be applied for tree biomass estimation in secondary forest or ‘mixed-species’ plantation. The use of site-specific allometric equation should be validated with the results from a partial harvest method to avoid the possible errors of model limitation. Moreover, the partial harvest method can be used for forest monitoring in the national park or protected area to obtain the accurate and reliable results.

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 lack of long-term high-resolution data makes it difficult to determine historical and future trends in net primary productivity (NPP). This study used tree rings as a proxy to investigate the dynamics of NPP in Tianshan forests where coniferous forests are the major species and the other species are deficient. All trees and some tree cores from five sample plots in different geographic locations in the western Tianshan Mountains were selected to reconstruct forest NPP data from 1950 to 2020. Multiple historical events that resulted in large-scale terrestrial carbon fluxes were identified and the existence of 28a and 17a time-scale cycles of historical forest NPP was observed. We discovered that the reconstructed forest NPP in the western Tianshan Mountains did not significantly correlate with satellite-based products (e.g., MODIS NPP, solar-induced chlorophyll fluorescence data). This result was attributed to the lag of forest growth for climate, the accuracy of the satellite-based products and statistical errors due to the short overlap time. We analysed the uncertainties in reconstructing historical forest NPP using tree ring widths and proposed corresponding solutions. We concluded that the reconstructed data remain the ideal proxy for regions lacking long-term empirical data and exhibit a high degree of confidence for expressing trends in forest productivity change over long time series.

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The development of allometric biomass models is important process in biomass estimation because the reliability of forest biomass and carbon estimations largely depends on the accuracy and precision of such models. National Forest Inventories (NFI) are detailed assessments of forest resources at national and regional levels that provide valuable data for forest biomass estimation. However, the lack of biomass allometric equations for each tree species in the NFI currently hampers the estimation of national-scale forest biomass. The main objective of this study was to develop allometric biomass regression equations for each tree species in the NFI of China based on limited biomass observations. These equations optimally grouped NFI and biomass observation species according to their phylogenetic relationships. Significant phylogenetic signals demonstrated phylogenetic conservation of the crown-to-stem biomass ratio. Based on phylogenetic relationships, we grouped and matched NFI and biomass observation species into 22 categories. Allometric biomass regression models were developed for each of these 22 species categories, and the models performed successfully (R² = 0.97, root mean square error (RMSE) = 12.9 t.ha-1, relative RMSE = 11.5%). Furthermore, we found that phylogeny-based models performed more effectively than wood density-based models. The results suggest that grouping species based on their phylogenetic relationships is a reliable approach for the development and selection of accurate allometric equations. [ABSTRACT FROM AUTHOR]

Simple Summary: Gonatus fabricii is the most abundant cephalopod in the Arctic and northern part of the North Atlantic, which are areas of rapid environmental change. It is very important as a predator of many species of fish and invertebrates and as a prey for larger fish, seabirds, and marine mammals. The life cycle of G. fabricii, in particular ontogenetic changes in diet and habitat, is little known. Ecological modelling is an important method to forecast ecosystems' dynamics in relation to a changing climate, and it is crucial to assess their present structure and functioning for a viable forecast. Here, the ontogenetic changes in the diet and habitat of large G. fabricii from West Greenland are studied using stable isotope analysis of carbon (δ13C) and nitrogen (δ15N) along trajectories in chitin beaks, which function as archival structures. Using this approach, four stages with clearly distinct ecologies were revealed in the species' life cycle. This novel ecological periodization is a crucial baseline tool in Arctic marine ecosystem studies and cephalopod biology, and should be used in models to correctly reflect the ecological roles of G. fabricii in marine ecosystems. Cephalopods are important in Arctic marine ecosystems as predators and prey, but knowledge of their life cycles is poor. Consequently, they are under-represented in the Arctic ecosystems assessment models. One important parameter is the change in ecological role (habitat and diet) associated with individual ontogenies. Here, the life history of Gonatus fabricii, the most abundant Arctic cephalopod, is reconstructed by the analysis of individual ontogenetic trajectories of stable isotopes (δ13C and δ15N) in archival hard body structures. This approach allows the prediction of the exact mantle length (ML) and mass when the species changes its ecological role. Our results show that the life history of G. fabricii is divided into four stages, each having a distinct ecology: (1) epipelagic squid (ML < 20 mm), preying mostly on copepods; (2) epi- and occasionally mesopelagic squid (ML 20–50 mm), preying on larger crustaceans, fish, and cephalopods; (3) meso- and bathypelagic squid (ML > 50 mm), preying mainly on fish and cephalopods; and (4) non-feeding bathypelagic gelatinous females (ML > 200 mm). Existing Arctic ecosystem models do not reflect the different ecological roles of G. fabricii correctly, and the novel data provided here are a necessary baseline for Arctic ecosystem modelling and forecasting. [ABSTRACT FROM AUTHOR]

Traditional forest inventories are based on field surveys of established sample plots, which involve field measurements of individual trees within a sample plot and the selection of proper allometric equations for tree volume calculation. Thus, accurate field measurements and properly selected allometric equations are two crucial factors for providing high-quality tree volumes. One key problem is the difficulty in accurately acquiring tree height data, resulting in high uncertainty in tree volume calculation when the diameter at breast height (DBH) alone is used. This study examined the uncertainty of tree height measurements using different means and the impact of allometric models on tree volume estimation accuracy. Masson pine and eucalyptus plantations in Fujian Province, China, were selected as examples; their tree heights were measured three ways: using an 18-m telescopic pole, UAV Lidar (unmanned aerial vehicle, light detection and ranging) data, and direct measurement of felled trees, with the latest one as a reference. The DBH-based and DBH–height-based allometric equations corresponding to specific tree species were used for the calculations of tree volumes. The results show that (1) tree volumes calculated from the DBH-based models were lower than those from the DBH–height-based models. On average, tree volumes were underestimated by 0.018 m3 and 0.117 m3 for Masson pine and eucalyptus, respectively, while the relative root-mean-squared errors (RMSEr) were 24.04% and 33.90%, respectively, when using the DBH-based model; (2) the tree height extracted from UAV Lidar data was more accurate than that measured using a telescopic pole, because the pole measurement method generally underestimated the tree height, especially when the trees were taller than the length of the pole (18 m in our study); (3) the tree heights measured using different methods greatly impacted the accuracies of tree volumes calculated using the DBH–height model. The telescopic-pole-measured tree heights resulted in a relative error of 9.1–11.8% in tree volume calculations. This research implies that incorporation of UAV Lidar data with DBH field measurements can effectively improve tree volume estimation and could be a new direction for sample plot data collection in the future. [ABSTRACT FROM AUTHOR]

The assessment of the surface area of all leaves from a tree crown is regarded as one of the key parameters in scaling ecophysiological processes, such as growth, carbon budget, and water management. The purpose of this study was to investigate the variation of the mass and surface area of Scots pine needles, obtained from trees growing in the same habitat conditions but at different stock densities, therefore occupying different biosocial positions. The mass of needles and the leaf area index (LAI) were determined for an even-aged 33-year-old Scots pine stand located at a fresh mixed coniferous forest site in southwest Poland (50°32′ N; 17°42′ E). The needles, collected from all the sample trees, were subjected to a biometric analysis to determine the total mass of needles from each tree, the mass of 1000 needle pairs, the number of needles per crown, and the needle length distribution. Based on the actual measurements, we derived allometric equations for finding the fresh mass (FMN) and the surface area of the needles (SN), using the diameter at breast height (DBH) as an independent variable. The relationships between the mass of the needles and the DBH were significant (p < 0.0001), and so were those between the surface area of the needles and the DBH (p < 0.001). The fresh mass of needles for the tree stands varied from 6458 kg·ha−1 to 11,102 kg·ha−1. The LAI was in the range of 3.2 to 5.4 m2·m−2. The mean value of the LAI for the Scots pine stand was 4 m2·m−2. Further studies are required and more algorithms need to be developed for the quantitative assessment of the LAI in Scots pine trees, using a larger number of sample trees with more varied biometric features. [ABSTRACT FROM AUTHOR]

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

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

The influence of spacing on the estimation of the leaf area index (LAI) in Eucalyptus tereticornis and Eucalyptus saligna plantations was evaluated. Three spacings per species were analyzed: 1.0 x 2.0 m, 1.0 x 1.0 m, and 1.0 x 0.5 m. Dasometric variables and the LAI were measured with two indirect methods (LICOR 2000 and digital hemispheric photography, DHP) and a direct method. The results showed no differences between species. On the other hand, spacing significantly influenced the diameter (less spacing, less diameter) and the LAI (less spacing, higher LAI), with variations from 2.11 to 3.96 m².m-2. An evaluation of the indirect methods showed that, by reducing the spacing, the bias in the estimation increased. FDH showed greater accuracy and tended to underestimate the LAI by 7%. In contrast, LICOR 2000 showed a high bias, with a tendency to overestimate the LAI by up to 30%. [ABSTRACT FROM AUTHOR]

Reliable data on CO2 quantification is increasingly important to quantify the climate benefits of forest landscape restoration and international commitments, such as the Warsaw REDD+ Framework and Nationally Determined Contributions under the Paris Agreement. Calophyllum inophyllum L. (nyamplung as a local name or tamanu tree for the commercial name) is an increasingly popular tree species in forest landscape restoration and bioenergy production for a variety of reasons. In this paper, we present allometric equations for aboveground biomass (AGB), belowground biomass (BGB), and total above- and belowground biomass (TABGB) predictions of C. inophyllum L. Data collection was carried out twice (2017 and 2021) from 40 trees in Java, Indonesia. Allometric equations using the natural logarithm of diameter at breast height (lnDBH) and ln height (lnH) for biomass prediction qualified the model's fit with statistical significance at 95% of the confidence interval for AGB, BGB, and TABGB predictions. The results showed that the linear models using both lnDBH and lnH were well fit and accurate. However, the model with lnDBH is more precise than the model using lnH. Using lnDBH as a predictor, the R2 values were 0.923, 0.945, and 0.932, and MAPE were 24.7, 37.0, and 25.8 for AGB, BGB, and TABGB, respectively. Using lnH as a predictor, the R2 values were 0.887, 0.918, and 0.898 and MAPE were 37.4, 49.0, and 39.8 for AGB, BGB, and TABGB, respectively. Consequently, the driven allometric equations can help accurate biomass quantification for carbon-trading schemes of C. inophyllum L. [ABSTRACT FROM AUTHOR]

Abstract Background To maximize photosynthetic efficiency, plants have evolved a capacity by which leaf area scales allometrically with leaf mass through interactions with the environment. However, our understanding of genetic control of this allometric relationship remains limited. Results We integrated allometric scaling laws expressed at static and ontogenetic levels into genetic mapping to identify the quantitative trait loci (QTLs) that mediate how leaf area scales with leaf mass and how such leaf allometry, under the control of these QTLs, varies as a response to environment change. A major QTL detected by the static model constantly affects the allometric growth of leaf area vs. leaf mass for the common bean (Phaseolus vulgaris) in two different environments. The ontogenetic model identified this QTL plus a few other QTLs that determine developmental trajectories of leaf allometry, whose expression is contingent heavily upon the environment. Conclusions Our results gain new insight into the genetic mechanisms of how plants program their leaf morphogenesis to adapt to environmental perturbations.

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

Copyright of Investigación Agraria is the property of Investigacion Agraria 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.)

Acacia saligna , originating from Australia, is a naturalized multipurpose tree species widely grown to restore degraded lands of Africa. The contribution of A. saligna in biomass restoration can be quantified using a precise estimation of tree biomass carbon. This study developed species-specific allometric models and evaluated the spatial variation of tree biomass across restored areas in exclosures and open grazing landscapes. These models could play a considerable role in the monitoring of carbon dynamics across A. saligna planation dominated areas. We harvested, excavated, and weighed twenty-one sample trees representing different size classes to develop allometric models for the estimation of aboveground (AGB), belowground (BGB) and total tree (TB) biomass. The average dry-to-fresh mass ratio and the root-to-shoot ratio was 0.47 (±0.13) and 0.28 (±0.14), respectively. Tree biomass significantly correlated with diameter at breast height ( r = 0.93; P < 0.001), diameter at stump height ( r = 0.88, P < 0.001) and tree height ( r = 0.56, P < 0.05). Ou r best biomass estimation models explained 86%, 82% and 87% of variations in AGB, BGB, and TB, respectively. Models using DSH and DSH & H explained 70%–78% of the variation in AGB, BGB, and TB. Estimated C-stock showed a significant relationship with stem density ( R ^2 = 0.9 1 , P < 0.01). Estimated TB varied between 1.5–18 Mg ha ^−1 on grazed land and exclosures. Estimated C-stocks in the exclosure exceeded the estimated C-stock in the open grazing land by ∼60%. This implies that with proper management practices and enrichment planting A. saligna significantly contributes to increasing carbon accumulation on degraded landscapes, playing a key role in climate change mitigation efforts while improving land productivity.

The oxygen consumption rate in a wild Mediterranean non-migratory population of a teleostean fish, the three-spined stickleback (Gasterosteus aculeatus), was measured to investigate if the infection by the cestode worm Schistocephalus solidus plerocercoids increases the oxygen consumption (Vo2) of the host fish. Previously, an increment in Vo2 has been reported but never found to be statistically significant. A new approach was carried out in the present study, using a small, but statistically representative, sample of uninfected and infected fish. We used the data on the parasite oxygen consumption available in the current literature to estimate the possible contribution of plerocercoids to the oxygen consumption of our experimental animals. The maximal plerocercoid putative contribution to the measured Vo2 of the infected host (i.e., host plus parasite) was assessed to be around 2.8%. Interestingly, a key role in the respiration rate of infected sticklebacks turned out to be played by the infection burden. In other words, the host specific Vo2 (sVo2) increment was significantly correlated with the number of plerocercoids harboured by the host. The observation implies that a reliable comparison of the physiological parameters between infected and uninfected stickleback populations can be only carried out if the number of parasites among the single individuals is homogeneous. Unfortunately, the random occurrence of the parasites makes this effort unpractical. [ABSTRACT FROM AUTHOR]

This study aimed to develop a generalized allometric model to predict the aboveground biomass (AGB) of various bamboo species. Four crucial bamboo species, namely, makino bamboo (Phyllostachys makinoi), moso bamboo (Phyllostachys pubescens), thorny bamboo (Bambusa stenostachya), and ma bamboo (Dendrocalamus latiflorus), were used. This study collected data from previous studies with sample sizes of 90, Diameter at breast height (DBH), culm height (H), and age (A) were used as independent variables to develop three models for predicting AGB, where Model I used only DBH, Model II used DBH and H, and Model III used DBH, H, and A as independent variables. The results showed that Models II and III were superior to Model I based on the root mean square error (RMSE). Moreover, Model III only slightly improved the AGB prediction compared with Model II. This study also evaluated the predictive ability of the models for bamboo species based on the bias and RMSE values. The bias value helps to assess whether the AGB of a single bamboo species is under- or overestimated when that value appears positive or negative in the models. The RMSE values were higher for makino and ma bamboo in Model III when comparing the RMSE values for each bamboo species between Models II and III. However, it was higher in moso and thorny bamboo in Model II. Therefore, it was difficult to infer which model was better for all species because the ability to predict AGB was inconsistent among species for these two models. • Bamboo resources with multiple uses and high productivity widely benefit people. • Taiwan is rich in bamboo resources with various species and high productivity. • Bamboos accumulate their biomass quickly with a high potential for carbon storage. • Independent variables increase predictability of aboveground biomass (AGB). • We recommend Models II and III for AGB prediction across various bamboo species. [ABSTRACT FROM AUTHOR]

Abstract Background In recent decades the future of global forests has been a matter of increasing concern, particularly in relation to the threat of forest ecosystem responses under potential climate change. To the future predictions of these responses, the current forest biomass carbon storage (FCS) should first be clarified as much as possible, especially at national scales. However, few studies have introduced how to verify an FCS estimate by delimiting the reasonable ranges. This paper addresses an estimation of national FCS and its verification using two-step process to narrow the uncertainty. Our study focuses on a methodology for reducing the uncertainty resulted by converting from growing stock volume to above- and below-ground biomass (AB biomass), so as to eliminate the significant bias in national scale estimations. Methods We recommend splitting the estimation into two parts, one part for stem and the other part for AB biomass to preclude possible significant bias. Our method estimates the stem biomass from volume and wood density (WD), and converts the AB biomass from stem biomass by using allometric relationships. Results Based on the presented two-step process, the estimation of China’s FCS is performed as an example to explicate how to infer the ranges of national FCS. The experimental results demonstrate a national FCS estimation within the reasonable ranges (relative errors: + 4.46% and − 4.44%), e.g., 5.6–6.1 PgC for China’s forest ecosystem at the beginning of the 2010s. These ranges are less than 0.52 PgC for confirming each FCS estimate of different periods during the last 40 years. In addition, our results suggest the upper-limits by specifying a highly impractical value of WD (0.7 t∙m− 3) on the national scale. As a control reference, this value decides what estimate is impossible to achieve for the FCS estimates. Conclusions Presented methodological analysis highlights the possibility to determine a range that the true value could be located in. The two-step process will help to verify national FCS and also to reduce uncertainty in related studies. While the true value of national FCS is immeasurable, our work should motivate future studies that explore new estimations to approach the true value by narrowing the uncertainty in FCS estimations on national and global scales.

The assessment of the surface area of all leaves from a tree crown is regarded as one of the key parameters in scaling ecophysiological processes, such as growth, carbon budget, and water management. The purpose of this study was to investigate the variation of the mass and surface area of Scots pine needles, obtained from trees growing in the same habitat conditions but at different stock densities, therefore occupying different biosocial positions. The mass of needles and the leaf area index (LAI) were determined for an even-aged 33-year-old Scots pine stand located at a fresh mixed coniferous forest site in southwest Poland (50°32′ N; 17°42′ E). The needles, collected from all the sample trees, were subjected to a biometric analysis to determine the total mass of needles from each tree, the mass of 1000 needle pairs, the number of needles per crown, and the needle length distribution. Based on the actual measurements, we derived allometric equations for finding the fresh mass (FMN) and the surface area of the needles (SN), using the diameter at breast height (DBH) as an independent variable. The relationships between the mass of the needles and the DBH were significant (p < 0.0001), and so were those between the surface area of the needles and the DBH (p < 0.001). The fresh mass of needles for the tree stands varied from 6458 kg·ha−1 to 11,102 kg·ha−1. The LAI was in the range of 3.2 to 5.4 m2·m−2. The mean value of the LAI for the Scots pine stand was 4 m2·m−2. Further studies are required and more algorithms need to be developed for the quantitative assessment of the LAI in Scots pine trees, using a larger number of sample trees with more varied biometric features.

Tree growth is an important means of capturing and storing atmospheric carbon and is thus a key to mitigating the effects of climate change. Over recent decades, mango (Mangifera indica L.) parklands have expanded in Burkina Faso, due to the species’ fast growth and its compatibility with local agricultural crops. However, the contribution of mango parklands to reducing atmospheric CO2 remains unknown, due to the lack of local tools for aboveground biomass (AGB) estimation. The aim of this study was to develop accurate allometric equations for estimating the AGB and carbon (C) stocks of mango parklands. A field survey was carried out to select mango parklands according to their age. These parklands were then classified into six groups age (0≦y

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

Introduction: Quantifying forest biomass requires the application of allometric equations which is a fundamental step. Generalized allometric equations have been applied to quantify aboveground biomass (AGB) of forests. But, adopting generalized allometric equations to quantify AGB of different forests creates uncertainty. Therefore, developing species- and site-specific allometric equations is essential to accurately quantify the biomass. The study was aimed to develop species-specific allometric equations for Diospyros abyssinica (Hiern) F. White in Yayu Coffee Forest Biosphere Reserve using the Semi-destructive method. The vegetation types of Yayu Coffee Forest Biosphere Reserve is categorized to Moist Evergreen Montane Rainforest of Ethiopia. Results and discussion: Evaluating statistical relationships of AGB against predictor variables, eight allometric equations were formulated. AGB was regressed against trunk diameter (D), total height (H), and wood density (ρ) individually and in combination. Selection of allometric equations was employed using model performance statistics. Equations with a higher coefficient of determination (adjusted R2), lower residual standard error, and Akaike information criterion (AIC) values were found best-fitted. Relationships of AGB and independent variables were found statistically significant (p

The increasing interest in carbon budget estimation and the growing use of woody biomass in bioenergy production raises the necessity for precise estimates of belowground biomass and soil carbon pools in forest ecosystems, particularly in terms of changes in the age structure of forests. The aim of this study was to estimate the belowground biomass of young (< 40 years) stands of Scots pine (Pinus sylvestris L.) in Latvia. The biomass of small roots (diameter 2-20 mm), coarse roots (diameter > 20 mm), and stumps of 39 trees from eight stands growing on dry, nutrient-poor mineral soils was measured and compared to the aboveground variables of sampled trees. The results revealed that stumps, small roots, and coarse roots comprised 43%, 35% and 22%, respectively, of the belowground biomass of young Scots pines. The proportion of belowground biomass over the total tree biomass was age-dependent, ranging from 33% to 17% for 8-year and 40-year old trees, respectively. Aboveground tree variables were significantly correlated with the belowground biomass, being stemwood volume and basal area the best predictors (R2 = 0.86-0.98, relative errors = 26-43%) of the belowground biomass components. Accordingly, the developed models produced more accurate estimates compared to previous models for the region, thus reducing the uncertainty in determining the carbon budget for belowground biomass. Still, an analysis of a more comprehensive dataset is needed to account for the effect of the social status of trees, as well as the within- and between-stand variation.

Bogs are nutrient poor, acidic ecosystems that receive their water and nutrients entirely from precipitation (= ombrogenous) and as a result are sensitive to nutrient loading from atmospheric sources. Bogs occur frequently on the northern Alberta landscape, estimated to cover 6% of the Athabasca Oil Sands Area. As a result of oil sand extraction and processing, emissions of nitrogen (N) and sulfur (S) to the atmosphere have led to increasing N and S deposition that have the potential to alter the structure and function of these traditionally nutrient-poor ecosystems. At present, no detailed protocol is available for monitoring potential change of these sensitive ecosystems. We propose a user-friendly protocol that will monitor potential plant and lichen responses to future environmental inputs of nutrients and provide a structured means for collecting annual data. The protocol centers on measurement of five key plant/lichen attributes, including changes in (1) plant abundances, (2) dominant shrub annual growth and primary production, (3) lichen health estimated through chlorophyll/phaeophytin concentrations, (4) Sphagnum annual growth and production, and (5) annual growth of the dominant tree species (Picea mariana). We placed five permanent plots in each of six bogs located at different distances from the center of oil sand extraction and sampled these for 2 years (2018 and 2019). We compared line intercept with point intercept plant assessments using NMDS ordination, concluding that both methods provide comparable data. These data indicated that each of our six bog sites differ in key species abundances. Structural differences were apparent for the six sites between years. These differences were mostly driven by changes in Vaccinium oxycoccos, not the dominant shrubs. We developed allometric growth equations for the dominant two shrubs (Rhododendron groenlandicum and Chamaedaphne calyculata). Equations developed for each of the six sites produced growth values that were not different from one another nor from one developed using data from all sites. Annual growth of R. groenlandicum differed between sites, but not years, whereas growth of C. calyculata differed between the 2 years with more growth in 2018 compared with 2019. In comparison, Sphagnum plant density and stem bulk density both had strong site differences, with stem mass density higher in 2019. When combined, annual production of S. fuscum was greater in 2019 at three sites and not different at three of the sites. Chlorophyll and phaeophytin concentrations from the epiphytic lichen Evernia mesomorpha also differed between sites and years. This protocol for field assessments of five key plant/lichen response variables indicated that both site and year are factors that must be accounted for in future assessments. A portion of the site variation was related to patterns of N and S deposition. [ABSTRACT FROM AUTHOR]

The objective of this study is to calculate the capacity of CO2 capture from the forest relict of the University of Quindio "Jardín Botánico Cedro Rosado" through the use of techniques that integrate in situ measurements with remote sensing. In the first phase, multispectral images. Normalized Differential Vegetation Index (NDVI), Improved Vegetation Index (EVI), Soil Adjusted Vegetation Index (SAVI), and object-based classification will be obtained. In the second phase, tree variables will be measured, and Leaf Area Index (LAI) and the Fraction of Absorbed Photosynthetically Active Radiation (Fapar) biophysical variables will be estimated with the Tracing Radiation and Architecture of Canopies (TRAC) optical instrument, in order to correlate them with the vegetation indexes. This will define the constants of the exponential regression model defining the local allometric equation, which will interpolate the biomass in the entire image. [ABSTRACT FROM AUTHOR]

Background: To maximize photosynthetic efficiency, plants have evolved a capacity by which leaf area scales allometrically with leaf mass through interactions with the environment. However, our understanding of genetic control of this allometric relationship remains limited. Results: We integrated allometric scaling laws expressed at static and ontogenetic levels into genetic mapping to identify the quantitative trait loci (QTLs) that mediate how leaf area scales with leaf mass and how such leaf allometry, under the control of these QTLs, varies as a response to environment change. A major QTL detected by the static model constantly affects the allometric growth of leaf area vs. leaf mass for the common bean (Phaseolus vulgaris) in two different environments. The ontogenetic model identified this QTL plus a few other QTLs that determine developmental trajectories of leaf allometry, whose expression is contingent heavily upon the environment. Conclusions: Our results gain new insight into the genetic mechanisms of how plants program their leaf morphogenesis to adapt to environmental perturbations. [ABSTRACT FROM AUTHOR]