Effect of Soil Fertility and Planting Density on the Partitioning of the Above-Ground Biomass of Eucalyptus in a Plantation (Pointe-Noire, Republic of Congo)

Afforestation and reforestation are useful approaches to improve carbon sequestration. With the advent of forest plantations, growing environment conditions have become increasingly restrictive for light, soil nutrients, and interactions between trees to acquire available resources. Tree biomass data are essential for understanding the forest carbon cycle and plant adaptations to the environment. The distribution of tree biomass depends on the sum of multiple stand conditions. The data are from a dedicated experiment with two very contrasting areas of fertility, and two planting densities, including a high density at planting in order to achieve thinning. The plant material consists of the high-performance clones of Eucalyptus urophylla × E. grandis and the reference clone E. PF1. We hypothesize that the distribution of biomass changes as the intensity of competition changes and that this is accelerated by the fertility of the sites in time. The results indicate that fertilization, planting density and clones have an impact on biomass partitioning.

Biomass and dominance of conservative species drive above-ground biomass productivity in a mediterranean-type forest of Chile

Background: Forest productivity has a pivotal role in human well-being. Vegetation quantity, niche complementarity, mass-ratio, and soil resources are alternative/complementary ecological mechanisms driving productivity. One challenge in current forest management depends on identifying and manipulating these mechanisms to enhance productivity. This study assessed the extent to which these mechanisms control aboveground biomass productivity(AGBP) of a Chilean mediterranean-type matorral. AGBP measured as tree aboveground biomass changes over a 7-years period, was estimated for twelve 25 m × 25 m plots across a wide range of matorral compositions and structures. Variables related to canopy structure, species and functional diversity, species and functional dominance, soil texture, soil water and soil nitrogen content were measured as surrogates of the four mechanisms proposed. Linear regression models were used to test the hypotheses. A multimodel inference based on the Akaike’s information criterion was used to select the best models explaining AGBP and for identifying the relative importance of each mechanism.Results: Vegetation quantity(tree density) and mass-ratio(relative biomass of Cryptocarya alba, a conservative species) were the strongest drivers increasing AGBP, while niche complementarity(richness species) and soil resources(sand, %) had a smaller effect either decreasing or increasing AGBP, respectively. This study provides the first assessment of alternative mechanisms driving AGBP in mediterranean forests of Chile. There is strong evidence suggesting that the vegetation quantity and mass-ratio mechanisms are key drivers of AGBP, such as in other tropical and temperate forests. However, in contrast with other studies from mediterranean-type forests, our results show a negative effect of species diversity and a small effect of soil resources on AGBP.Conclusion: AGBP in the Chilean matorral depends mainly on the vegetation quantity and mass-ratio mechanisms.The findings of this study have implications for matorral restoration and management for the production of timber and non-timber products and carbon sequestration.

Estimating above-ground biomass by fusion of LiDAR and multispectral data in subtropical woody plant communities in topographically complex terrain in North-eastern Australia

We investigated a strategy to improve predicting capacity of plot-scale above-ground biomass （AGB） by fusion of LiDAR and Land- sat5 TM derived biophysical variables for subtropical rainforest and eucalypts dominated forest in topographically complex landscapes in North-eastern Australia. Investigation was carried out in two study areas separately and in combination. From each plot of both study areas, LiDAR derived structural parameters of vegetation and reflectance of all Landsat bands, vegetation indices were employed. The regression analysis was carded out separately for LiDAR and Landsat derived variables indi- vidually and in combination. Strong relationships were found with LiDAR alone for eucalypts dominated forest and combined sites compared to the accuracy of AGB estimates by Landsat data. Fusing LiDAR with Landsat5 TM derived variables increased overall performance for the eucalypt forest and combined sites data by describing extra variation （3% for eucalypt forest and 2% combined sites） of field estimated plot-scale above-ground biomass. In contrast, separate LiDAR and imagery data, andfusion of LiDAR and Landsat data performed poorly across structurally complex closed canopy subtropical minforest. These findings reinforced that obtaining accurate estimates of above ground biomass using remotely sensed data is a function of the complexity of horizontal and vertical structural diversity of vegetation.

Estimation of the Above-ground Biomass of Tamarix Species along a Transect at the Tarim River's Middle Reaches

[Objective] The purpose was to research the distribution characteristics of Tamarix species above-ground biomass of Tarim River＇s middle reaches and to find out best-fit linear-regression model of Tamarix species in this area.[Method] By dint of the most common sampling method PCQ,five samples in the middle reaches of Tarim River were collected.The best-fit linear-regression model of Tamarix species of this area was set up,based on the fieldwork and the model of Evangelista and obtained the distribution rules of Tamarix species of Tarim River＇s middle reaches.[Result] The result indicated that this model fitted for the estimation of aboveground biomass of the study area.According to the distribution rules of aboveground biomass,it was clear that underground water was the major element which decided the distribution of aboveground biomass.[Conclusion] The study provided theoretical basis for the calculation of biomass of Tamarix.

Plant cover as an estimator of above-ground biomass in semi-arid woody vegetation in Northeast Patagonia,Argentina

The quantification of carbon storage in vegetation biomass is a crucial factor in the estimation and mitigation of CO2 emissions.Globally,arid and semi-arid regions are considered an important carbon sink.However,they have received limited attention and,therefore,it should be a priority to develop tools to quantify biomass at the local and regional scales.Individual plant variables,such as stem diameter and crown area,were reported to be good predictors of individual plant weight.Stand-level variables,such as plant cover and mean height,are also easy-to-measure estimators of above-ground biomass(AGB)in dry regions.In this study,we estimated the AGB in semi-arid woody vegetation in Northeast Patagonia,Argentina.We evaluated whether the AGB at the stand level can be estimated based on plant cover and to what extent the estimation accuracy can be improved by the inclusion of other field-measured structure variables.We also evaluated whether remote sensing technologies can be used to reliably estimate and map the regional mean biomass.For this purpose,we analyzed the relationships between field-measured woody vegetation structure variables and AGB as well as LANDSAT TM-derived variables.We obtained a model-based ratio estimate of regional mean AGB and its standard error.Total plant cover allowed us to obtain a reliable estimation of local AGB,and no better fit was attained by the inclusion of other structure variables.The stand-level plant cover ranged between 18.7%and 95.2%and AGB between about 2.0 and 70.8 Mg/hm^(2).AGB based on total plant cover was well estimated from LANDSAT TM bands 2 and 3,which facilitated a model-based ratio estimate of the regional mean AGB(approximately 12.0 Mg/hm^(2))and its sampling error(about 30.0%).The mean AGB of woody vegetation can greatly contribute to carbon storage in semi-arid lands.Thus,plant cover estimation by remote sensing images could be used to obtain regional estimates and map biomass,as well as to assess and monitor the impact of land-use change on the carbon balance,for arid and semi-arid regions.

Model-based estimation of above-ground biomass in the miombo ecoregion of Zambia

Background：Information on above-ground biomass（AGB） is important for managing forest resource use at local levels,land management planning at regional levels,and carbon emissions reporting at national and international levels.In many tropical developing countries,this information may be unreliable or at a scale too coarse for use at local levels.There is a vital need to provide estimates of AGB with quantifiable uncertainty that can facilitate land use management and policy development improvements.Model-based methods provide an efficient framework to estimate AGB.Methods：Using National Forest Inventory（NFI） data for a^1,000,000 ha study area in the miombo ecoregion,Zambia,we estimated AGB using predicted canopy cover,environmental data,disturbance data,and Landsat 8 OLI satellite imagery.We assessed different combinations of these datasets using three models,a semiparametric generalized additive model（GAM） and two nonlinear models（sigmoidal and exponential）,employing a genetic algorithm for variable selection that minimized root mean square prediction error（RMSPE）,calculated through cross-validation.We compared model fit statistics to a null model as a baseline estimation method.Using bootstrap resampling methods,we calculated 95% confidence intervals for each model and compared results to a simple estimate of mean AGB from the NFI ground plot data.Results：Canopy cover,soil moisture,and vegetation indices were consistently selected as predictor variables.The sigmoidal model and the GAM performed similarly;for both models the RMSPE was -36.8 tonnes per hectare（i.e.,57% of the mean）.However,the sigmoidal model was approximately 30% more efficient than the GAM,assessed using bootstrapped variance estimates relative to a null model.After selecting the sigmoidal model,we estimated total AGB for the study area at 64,526,209 tonnes（＋/- 477,730）,with a confidence interval 20 times more precise than a simple designbased estimate.Conclusions：Our findings demonstrate that NFI data may be combined with freely available satellite imagery and soils data to estimate total AGB with quantifiable uncertainty,while also providing spatially explicit AGB maps useful for management,planning,and reporting purposes.

Land use and above-ground biomass changes in a mountain ecosystem,northern Thailand

The conversion of forests into agricultural lands is a major cause of deforestation,particularly in the mountain ecosystems of northern Thailand.It results in a rapid loss of biological diversity of both flora and fauna.In addition,the above-ground biomass(AGB),which can be a major source of carbon storage,is also decreased.This study aimed to predict the AGB in Doi Suthep-Pui National Park,Chiang Mai province,based on land-use/land cover(LULC)changes from 2000 to 2030.Landsat-5 TM(2000)and Landsat-8 TM(2015)satellite images were analyzed to predict LULC changes to 2030.Temporary plots(30 m 930 m)were established in each LULC type for AGB analysis;trees with diameters at breast height≥4.5 cm were identified and measured.AGB of all LULC types were analyzed based on specific allometric equations of each type.The results show that area of forest and nonforested areas fluctuated during the study period.Through the first 15 years(2000–2015),5%(2.9 km^2)of forest changed to either agriculture or urban lands,especially mixed deciduous forest and lower montane forest.There was a similar trend in the 2030 prediction,showing the effect of forest fragmentation and the resultant high number of patches.Total AGB tended to decrease over the 30-year period from 12.5 to 10.6 t ha^-1 in the first and second periods,respectively.Deforestation was the main factor influencing the loss of AGB(30.6 t ha^-1)related to LULC changes.Furthermore,habitat loss would be expected to result in decreased biological diversity.Consequently,a management plan should be developed to avoid unsustainable land use changes,which may adversely affect human well-being.

Allometric prediction of above-ground biomass of eleven woody tree species in the Sudanian savanna-woodland of West Africa

Allometric models are necessary for estimating biomass in terrestrial ecosystems. Generalized allometric relationship exists for many tropical trees, but species- and region-specific models are often lacking. We developed species-specific allometric models to predict aboveground biomass for 11 native tree species of the Sudanian savanna- woodlands. Diameters at the base and at breast height, with species means ranging respectively from 11 to 28 cm and 9 to 19 cm, and the height of the trees were used as predictor variables. Sampled trees spanned a wide range of sizes including the largest sizes these species can reach. As a response variable, the biomass of the trees was obtained through destructive sampling of 4 754 trees during wood harvesting. We used a stepwise multiple regression analysis with backward elimination procedure to develop models separately predicting, total biomass of the trees, stem biomass, and biomass of branches and twigs. All species- specific regression models relating biomass with measured tree dimen- sions were highly significant （p 〈 0.001）. The biomass of branches and twigs was less predictable compared to stem biomass and total biomass, although their models required fewer predictors and predictor interac- tions. The best-fit equations for total above-ground biomass and stem biomass bad R2 〉 0.70, except for the Acacia species; for branches including twig biomass, R2-values varied from 0.749 for Anogeissus leiocarpa to 0.183 for Acacia macrostachya. The use of these equations in estimating available biomass will avoid destructive sampling, and aid in planning for sustainable use of these species.

Quantification of Above-Ground Biomass and Carbon Sequestration Potential of Roadside Trees in the Plateau Department of Benin Republic

Roadside trees are effective natural solutions for mitigating climate change. Despite the usefulness of trees to carbon sequestration, there is a dearth of information on the estimation of biomass and carbon stock for roadside trees in the study area. This study aimed to estimate the carbon stock and carbon dioxide equivalent of roadside trees. A complete enumeration of trees was carried out in Kétou, Pobè and Sakété within the communes of the Plateau Department, Bénin Republic. Total height and diameter at breast height were measured from trees along the roads while individual wood density value was obtained from wood density database. The allometric method of biomass estimation was adopted for the research. The results showed that the total estimations for above-ground biomass, carbon stock and carbon equivalent from all the enumerated roadside trees were 154.53 mt, 72.63 mt and 266.55 mt, respectively. The results imply that the roadside trees contain a substantial amount of carbon stock that can contribute to climate change mitigation through carbon sequestration.

Tree Allometry in Tropical Forest of Congo for Carbon Stocks Estimation in Above-Ground Biomass

The research was aimed to estimate the carbon stocks of above-ground biomass (AGB) in Lesiolouna forest in Republic of Congo. The methodology of Allometric equations was used to measure the carbon stock of Lesio-louna tropical rainforest. The research was done with six circular plots each 40 m of diameter, with a distance of 100 m between each plot, depending on the topography of the site of the installation of these plots. The six studied plots are divided in two sites, which are: Iboubikro and Ngambali. Thus, in the six plots, there are three plots in Iboubikro site and three plots in Ngambali site. The results of this study show that the average carbon stock of aboveground biomass (AGB) in six plots was 170.673 t C ha-1. So, the average of carbon stock of aboveground biomass (ABG) in Iboubikro site was 204.693 t C ha-1 and in the Ngambali site was 136.652 t C ha-1. In this forest ecosystem, the high stock of carbon was obtained in Plot 3, which was in Iboubikro site. Plot 3 contains 20 trees and an average DBH of 24.56 cm. However, the lowest carbon stock was obtained in Plot 4, which was in Ngambali site. Also, Plot 4 contains 11 trees and an average DBH of 31.86 cm. The results of this research indicate that, the forests in the study area are an important carbon reservoir, and they can also play a key role in mitigation of climate change.

Equations for estimating the above-ground biomass of Larix sibirica in the forest-steppe of Mongolia

Biomass functions were established to estimate above-ground biomass of Siberian larch （Larix sibirica） in the Altai Mountains of Mon- golia. The functions are based on biomass sampling of trees from 18 different sites, which represent the driest locations within the natural range ofL. sibirica. The best performing regression model was found for the equations y = （D2 H）/（a＋bD） for stem biomass, y = aDb for branch biomass, and y=aDb Hc for needle biomass, where D is the stem diameter at breast height and H is the tree height. The robustness of the biomass functions is assessed by comparison with equations which had been previously published from a plantation in Iceland. There, y=aDb Hc was found to be the most significant model for stem and total above-ground biomasses. Applying the equations from Iceland for estimating the above-ground biomass of trees from Mongolia resulted in the underesti- mation of the biomass in large-diameter trees and the overestimation of the biomass in thin trees. The underestimation of thick-stemmed trees is probably attributable to the higher wood density, which has to be ex- pected under the ultracontinental climate of Mongolia compared to the euoceanic climate of Iceland. The overestimation of the biomass in trees with low stem diameter is probably due to the high density of young growth in the not systematically managed forests of the Mongolian Altai Mountains, which inhibits branching, whereas the plantations in Iceland are likely to have been planted in lower densities.

Estimation of above-ground biomass and carbon stock of an invasive woody shrub in the subtropical deciduous forests of Doon Valley,western Himalaya,India

This study describes the different parameters used to derive the allometric equation for calculating the biomass of an invasive woody shrub Lantana camara L.from the subtropical conditions of western Himalaya.It identifies the most accurate and convenient method for biomass calculation by comparing destructive with nondestructive methodology.Different parameters were measured on a wide range of Lantana from different community levels for the non-destructive calculation of total aboveground biomass.Different explanatory variables were identified and measured such as basal diameter either as a single independent variable or in combination with plant height.The other suitable combinations of available independent variables include crown length,crown width,crown area,crown volume and coverage of the plant.Amongst the wide range of allometric equations used with different variables,the equation with D2 H as a variable was found to be the most suitable estimator of biomass calculation for Lantana.Sahastradhara,being the most disturbed area due to its high tourist activity round the year,showed maximum coverage（58.57 % ha-1）,highest biomass（13,559.60 kg ha-1） and carbon density（6,373.01 kg ha-1）of Lantana.The degree of Lantana’s invasiveness in subtropical conditions was also calculated on the basis of importance value index（IVI）.The maximum IVI（22.77）and mean coverage（26.8 % ha-1） was obtained from the areas near Jolly Grant airport,indicating that physically disturbed areas are more suitable for the growth of Lantana,which may significantly increase shrub biomass.The importance of incorporating allometric equations in calculation of shrub biomass,and its role in atmospheric carbon assimilation has thus been highlighted through the findings of this study.

Canopy cover or remotely sensed vegetation index,explanatory variables of above-ground biomass in an arid rangeland,Iran

Estimation of above-ground biomass is vital for understanding ecological processes. Since direct measurement of above-ground biomass is destructive, time consuming and labor intensive, canopy cover can be considered as a predictor if a significant correlation between the two variables exists. In this study, relationship between canopy cover and above-ground biomass was investigated by a general linear regression model. To do so, canopy cover and above-ground biomass were measured at 5 sub-life forms（defined as life forms grouped in the same height classes） using 380 quadrats, which is systematic-randomly laid out along a 10-km transect, during four sampling periods（May, June, August, and September） in an arid rangeland of Marjan, Iran. To reveal whether obtained canopy cover and above-ground biomass of different sampling periods can be lumped together or not, we applied a general linear model（GLM）. In this model, above-ground biomass was considered as a dependent or response variable, canopy cover as an independent covariate or predictor factor and sub-life forms as well as sampling periods as fixed factors. Moreover, we compared the estimated above-ground biomass derived from remotely sensed images of Landsat-8 using NDVI（normalized difference vegetation index）, after finding the best regression line between predictor（measured canopy cover in the field） and response variable（above-ground biomass） to test the robustness of the induced model. Results show that above-ground biomass（response variable） of all vegetative forms and periods can be accurately predicted by canopy cover（predictor）, although sub-life forms and sampling periods significantly affect the results. The best regression fit was found for short forbs in September and shrubs in May, June and August with R^2 values of 0.96, 0.93 and 0.91, respectively, whilst the least significant was found for short grasses in June, tall grasses in August and tall forbs in June with R^2 values of 0.71, 0.73 and 0.75, respectively. Even though the estimated above-ground biomass by NDVI is also convincing（R^2=0.57）, the canopy cover is a more reliable predictor of above-ground biomass due to the higher R^2 values（from 0.75 to 0.96）. We conclude that canopy cover can be regarded as a reliable predictor of above-ground biomass if sub-life forms and sampling periods（during growing season） are taken into account. Since,（1） plant canopy cover is not distinguishable by remotely sensed images at the sub-life form level, especially in sparse vegetation of arid and semi-arid regions, and（2） remotely sensed-based prediction of above-ground biomass shows a less significant relationship（R^2=0.57） than that of canopy cover（R^2 ranging from 0.75 to 0.96）, which suggests estimating of plant biomass by canopy cover instead of cut and weighting method is highly recommended. Furthermore, this fast, nondestructive and robust method that does not endanger rare species, gives a trustworthy prediction of above-ground biomass in arid rangelands.

Allometric relationship for estimating above-ground biomass of Aegialitis rotundifolia Roxb. of Sundarbans mangrove forest, in Bangladesh

Tree biomass plays a key role in sustainable management by providing different aspects of ecosystem. Estimation of above ground biomass by non-destructive means requires the dex;elopment of allometric equations. Most researchers used DBH （diameter at breast height） and TH （total height） to develop allometric equation for a tree. Very few spe- cies-specific allometric equations are currently available for shrubs to estimate of biomass from measured plant attributes. Therefore, we used some of readily measurable variables to develop allometric equations such as girth at collar-height （GcH） and height of girth measuring point （GMH） with total height （TH） for A. rotundifolia, a mangrove species of Sundarbans of Bangladesh, as it is too dwarf to take DBH and too ir- regular in base to take Girth at a fixed height. Linear, non-linear and logarithmic regression techniques were tried to determine the best re- gression model to estimate the above-ground biomass of stem, branch and leaf. A total of 186 regression equations were generated from the combination of independent variables. Best fit regression equations were determined by examining co-efficient of determination （R：）, co-efficient of variation （Cv）, mean-square of the error （Ms^r）, residual mean error （Rmax）, and F-value. Multiple linear regression models showed more efficient over other types of regression equation. The performance of regression equations was increased by inclusion of GMn as an independ- ent variable along with total height and GCH.

Effect of thinning on above-ground biomass accumulation in a Douglas-fir plantation in southern Italy

We investigated the effects of a long-term thinning experiment on the distribution of above-ground biomass of Douglas-fir （Pseudotsuga menziesii [Mirb.] Franco var. menziesii） in a plantation in southern Italy. Allometric equations were used to estimate biomass and partitioning to stem and crown compartments. Variation in biomass stock estimated with allometric equations were evaluated according to seven thinning treatments： geo- metric-systematic （1 row every 3）, selective （light-moderate-heavy）, mixed systematic-selective （1 row every 4, 1 row every 5）, unthinned （control）. Over the experimental period of 13 years, current annual increments of carbon were lower （3.4 Mg ha^-1 year^-1） in control plots than in treated plots. At age 30, plots subjected to light selective thinning showed higher values of above-ground biomass （249.7 Mg ha^-1）. The biomass harvested with this treatment was 29.3 Mg ha^-1, and the mean annual increment of carbon over 13 years was 4.8 Mg ha^-1. Our results showed that light thinning stimulated increase in carbon stock, with a minimal loss of carbon during the treatment and a current annual increment of carbon higher than in control sub-plots and sub-plots thinned using systematic methods. This treatment yielded least carbon emissions and we affirm it has discrete global warming mitigation potential.

Estimation of the carbon pool in soil and above-ground biomass within mangrove forests in Southeast Mexico using allometric equations

We report the results of carbon stored in soil and aboveground biomass from the most important area of mangroves in Mexico, with dominant vegetation of Red mangrove （Rhizophora mangle L.）, Black mangrove （Avicennia germinans L.）, white mangrove （Laguncularia racemosa Gaertn.） and button mangrove （Conocarpus erectus L.）. We sampled soils with high fertility during the dry season in 2009 and 2010 at three sites on Atasta Peninsula, Campeche. We used allometric equations to estimate above ground biomass （AGB） of trees. AGB was higher in C. erectus （253.18±32.17 t？ha-1）, lower in A. germinans （161.93±12.63 t？ha-1）, and intermediate in R. mangle （181.70±16.58 t？ha-1） and L. racemosa （206.07±19.12 t？ha-1）. Of the three studied sites, the highest absolute value for AGB was 279.72 t？ha-1 in button mangrove forest at any single site. Carbon stored in soil at the three sites ranged from 36.80±10.27 to 235.77±66.11 t？ha-1. The Tukey test （p 〈0.05） made for AGB was higher for black mangrove showed significant differences in soil carbon content between black mangrove and button mangrove. C. erectus had higher AGB compared with the other species. A. germinans trees had lower AGB because they grew in hypersaline environments, which reduced their development. C. erectus grew on higher ground where soils were richer in nutrients. AGB tended to be low in areas near the sea and increased with distance from the coast. A. germinans usually grew on recently deposited sediments. We assumed that all sites have the same potential to store carbon in soil, and then we found that there were no significant differences in carbon content between the three samples sites： all sites had potential to store carbon for long periods. Carbon storage at the three sampling sites in the state of Campeche, Mexico, was higher than that reported for other locations.

Assessment of tree diversity and above-ground biomass in coffee agroforest dominated tropical landscape of India's Central Western Ghats

The purpose of this study was to quantify the changes in tree diversity and above-ground biomass associated with six land-use types in Kodagu district of India's Western Ghats. We collected data on species richness,composition and above-ground biomass(AGB) of trees,shrubs and herbs from 96 sample plots of 0.1 ha. Totals of83 species from 26 families were recorded across the landuses. Tree species richness, diversity and composition were significantly higher in evergreen forest(EGF) than in other land-uses. Similarly, stem density and basal area were greater in EGF compared to other land-uses. Detrended correspondence analysis(DCA) yielded three distinct groups along the land-use intensities and rainfall gradient on the first and second axes, respectively. The first DCA axis accounted for 45% and second axis for 35% of the total variation in species composition. Together the first two axes accounted for over 2/3 of the variation in species composition across land-use types. Across the land-uses,AGB ranged from 58.6 Mg ha-1 in rubber plantation to327.3 Mg ha-1 in evergreen forest. Our results showed that species diversity and AGB were negatively impacted bythe land-use changes. We found that coffee agroforests resembled natural forest and mixed species plantation in terms of tree diversity and biomass production, suggesting that traditional coffee farms can help to protect tree species, sustain smallholder production and offer opportunities for conservation of biodiversity and climate change mitigation.

Stem and Total Above-Ground Biomass Models for the Tree Species of Freshwater Wetlands Forest, Coastal Areas and Dry Areas of Bangladesh: Using a Non-Destructive Approach

Biomass and carbon stock in a forested areas are now prime important indicators of forest management and climate change mitigation measures. But the accurate estimation of biomass and carbon in trees of forests is now a challenging issue. In most cases, pantropical and regional biomass models are used frequently to estimate biomass and carbon stock in trees, but these estimations have some uncertainty compared to the species-specific allometric biomass model. Acacia nilotica, Casuarina equisetifolia and Melia azedarach have been planted in different areas of Bangladesh considering the species-specific site requirements. While Barringtonia acutangula and Pongamia pinnata are the dominant tree species of the freshwater swamp forest of Bangladesh. This study was aimed to develop species-specific allometric biomass models for estimating stem and above ground biomass (TAGB) of these species using the non-destructive method and to compare the efficiency of the derived biomass models with the frequently used regional and pantropical biomass models. Four Ln-based models with diameter at breast height (DBH) and total height (H) were tested to derive the best fit allometric model. Among the tested models, Ln (biomass) = a + b Ln (D) + c Ln (H) was the best-fit model for A. nilotica, M. azedarach, B. acutangula and P. pinnata and Ln (biomass) = a + b Ln (D2H) was best-fit for C. equisetifolia. Finally, the derived best-fit species-specific TAGB models have shown superiority over the other frequently used pantropical and regional biomass models in relation to model efficiency and model prediction error.

Above-Ground Biomass, Nutrients and Carbon in Aegiceras corniculatum of the Sundarbans

Aegiceras corniculatum grows as single-stemmed evergreen shrub or small tree in the Sundarbans mangrove forest of Bangladesh. The objectives of this study were to derive the allometric models for estimating above-ground biomass, nutrients (N, P and K) and carbon stock in A. corniclatum. A total of 8 linear models (y = aX + b, , y = aLogX + b, Logy = aX + b, Logy = aLogX + b, y = alnX + b, Lny = aX + b and Lny = alnX + b) with 64 regression equations were tested to derive the allometric model for biomass of each plant part;and nutrients and carbon stock in total aboveground biomass. The best fit allometric models were selected by considering the values of R2, CV, Rmse, MSerror, Sa, Sb, F value, AICc and Furnival Index. The selected allometric models were Logbiomass = 0.76LogDBH2 - 1.39;Biomass = 0.07DBH2 - 0.49;Logbiomass = 1.04LogDBH2 - 1.80;Logbiomass = 1.04LogDBH2 - 0.99;= 0.48DBH - 0.13 for leaves, branches, bark, stem without bark and total above-ground biomass respectively. The selected allometric models for Nitrogen, Phosphorous, Potassium and Carbon stock in total above-ground biomass were = 0.67DBH + 0.11;= 0.94DBH + 0.08;= 1.06DBH - 0.18;= 0.33DBH - 0.09 respectively.

Above-ground Biomass Allocation in a Planted Forest in a Semi-arid Region of Northern Mongolia

Investigation of the above-ground biomass allocation patterns on Scots pine plantations is critical for quantifying the productivity and carbon cycle of forest ecosystems. We estimated above-ground biomass and net primary production of a 25-year-old Pinus sylvestris L. （Scots pine） plantation, in a semi-arid region of Mongolia. The above-ground biomass of sample trees was divided into stem wood, stem bark, live branches, dead branches and needles. Total biomass for the stand was only 18.03 Mg ha1, of which 47.6% was found in stem wood, 25.8% in live branches and 14.8% in needles. The growth rate of the Scots pine plantation in the study region was relatively low compared with other regions. In the study area, it was observed that the rate of biomass accumulation in the plantation was very slow; this can be explained by very limited growing conditions and intensive crown closure. The results from this study indicate that it may be necessary to carry out thinning to increase biomass production by reducing competition between trees in the Scotch pine plantation.