Allometric equations for estimating stem biomass of Artocarpus chaplasha Roxb. in Sylhet hill forest of Bangladesh

Estimation of biomass, volume and growth of subtropical forests in Shitai County, China

Accurate quantification of tree biomass is critical and essential for calculating carbon storage, as well as for studying climate change, forest health, forest productivity, nutrient cycling, etc. Tree biomass is typically estimated using statistical models. In this study, a total of 289 trees were harvested and measured for stem, root, branch, and foliage biomass from three coniferous plantation species in northeastern P.R. China. We developed two additive systems of biomass equations based on tree diameter (D) only and both tree diameter (D) and height (H). For each system, likelihood analysis was used to verify the error structures of power functions in order to determine if logarithmic transformation should be applied on both sides of biomass equations. The model coefficients were simultaneously estimated using seemingly unrelated regression (SUR). The results indicated that stem biomass had the largest relative contribution to total biomass, while foliage biomass had the smallest relative proportion for the three species. The root to shoot ratio averaged 0.27 for Korean pine, 0.25 for larch, and 0.23 for Mongolian pine. The two additive biomass systems obtained good model fitting and prediction performance, of which the model Ra2 > 0.80, and the percent mean absolute bias (MAB%), was <17%. The second additive system (D and H) had a relatively greater Ra2 and smaller root mean square error (RMSE). The model coefficient for the predictor H was statistically significant in eight of the twelve models, depending on tree species and biomass component. Adding tree height into the system of biomass equations can marginally improve model fitting and performance, especially for total, aboveground, and stem biomass. The two additive systems developed in this study can be applied to estimate individual tree biomass of three coniferous plantation species in the Chinese National Forest Inventory.

To determine the predictors of magnitude of change in response to a participation-focused leisure-time physical activity intervention in children with cerebral palsy (CP) using the ParticiPAte CP protocol. We included 33 children (16 males, 17 females) aged 8 to 12years (mean age=10y, SD=1y 6mo) with CP with pre/postintervention data from a wait-list randomized trial. The hypothesized linear predictors of change in primary outcomes (Canadian Occupational Performance Measure [COPM]-performance and COPM-satisfaction, Belief in Goal Self-Competence Scale (BiGSS), and minutes per day moderate-to-vigorous physical activity [MVPA]) were: age; Gross Motor Function Classification System level; comorbid autism spectrum disorder (ASD); Goal Attainment Scaling T score; Problems in Schools Questionnaire; Physical Activity Climate Questionnaire; Motives for Physical Activities Measure-Revised; and stage of behaviour change. Multivariable models were selected using the Bayesian information criterion. Overcoming barriers to participation, age, and comorbid ASD explained 49% of the variance in change in COPM-performance. Being motivated by interest and/or enjoyment and age explained 32% of the variance in change in COPM-satisfaction. Being motivated by physical activity competence or appearance (extrinsic motivation) explained 24% of the variance in change in BiGSS. Parental autonomy supportiveness, overcoming barriers to participation, appearance motivation, and baseline MVPA explained 59% of the variance in change in MVPA. These findings support a behaviour paradigm for conceptualizing physical activity in children with CP. Children who met their treatment goals showed a greater increase in physical activity participation. Children who were more intrinsically motivated by physical activity at baseline improved more. Being older and having a comorbid diagnosis of autism spectrum disorder were associated with an attenuated effect of the therapy.

We developed two additive systems of biomass equations based on diameter and tree height for nine hardwood species by SUR, and used a likelihood analysis to evaluate the model error structures. In this study, a total of 472 trees were harvested and measured for stem, root, branch, and foliage biomass from nine hardwood species in Northeast China. Two additive systems of biomass equations were developed, one based on tree diameter (D) only and one based on both tree diameter (D) and height (H). For each system, three constraints were set up to account for the cross-equation error correlations between four tree component biomass, two sub-total biomass, and total biomass. The model coefficients were simultaneously estimated using seemly unrelated regression (SUR). Likelihood analysis was used to verify the error structures of power functions in order to determine if logarithmic transformation should be applied on both sides of biomass equations. Jackknifing model residuals were used to validate the prediction performance of biomass equations. The results indicated that (1) stem biomass accounted for the largest proportion (62 %) of the total tree biomass; (2) the two additive systems of biomass equations obtained good model fitting and prediction, of which the model R 2 was >0.89, and the mean absolute percent bias (MAB %) was <35 %; (3) the system of biomass equations based on both D and H significantly improved model fitting and performance, especially for total, aboveground, and stem biomass; and (4) the anti-log correction was not necessary in this study. The established additive systems of biomass equations can provide reliable and accurate estimation for individual tree biomass of the nine hardwood species in Chinese National Forest Inventory.

Consequences of alternative tree-level biomass estimation procedures on U.S. forest carbon stock estimates

Habitat loss, fragmentation, and alteration of the landscape matrix are interdependent processes, collectively responsible for most recent species extinctions. Thus, determining the extent to which these landscape processes affect animals is critical for conservation. However, researchers have often assumed that interdependent effects are independently related to animals’ responses, underestimating the importance of one or several landscape processes in driving species declines. We demonstrate how to disentangle the interdependent effects of habitat amount, fragmentation, and edge context on population size by assessing abundance of a rapidly declining grassland songbird species (grasshopper sparrow Ammodramus savannarum) in eastern Kansas (USA). We conducted &gt;7,000 point count surveys at &gt;2,000 sites over two breeding seasons, then modelled the direct, interactive, and indirect effects of landscape factors on abundance within spatial scales (200‐, 400‐, 800‐, and 1,600‐m radii) relevant to our focal species’ dispersal behaviour. Sparrow abundance correlated most strongly with landscape structure within 400‐m radii, increasing nonlinearly with grassland area and decreasing with the proportion of grassland near cropland or woody edges. Sparrows’ negative response to cropland edges was mostly an added, indirect consequence of reduced grassland area, whereas sparrows’ stronger negative response to woody edges was not attributable to variation in grassland area. Fragmentation and edge context mattered most in landscapes comprising c. 50%–80% grassland. Synthesis and applications. In our research, abundance of a threatened grassland songbird was influenced more by core grassland area (a function of total grassland area, fragmentation, and edge context) than total grassland area per se. Moreover, a local extinction threshold of c. 50% grassland indicated that small amounts of habitat were unsuitable for our focal species regardless of habitat configuration or matrix type. Local extinction thresholds in response to habitat area provide clear baseline targets for land managers; above those thresholds, configuration and the matrix can be modified to increase abundance of edge‐sensitive animals. Conflicting evidence in the literature regarding the importance of fragmentation and matrix features could be partially explained by species‐level traits, or methodological issues such as defining landscapes at ecologically arbitrary spatial scales, assessing landscape quality using species richness, and ignoring interactive and indirect effects.

Exploring urban tree diversity and carbon stocks in Zaria Metropolis, North Western Nigeria

To develop a more complete understanding of the ecological factors that regulate crop productivity, we tested the relative predictive power of yield models driven by five predictor variables: wheat and wild oat density, nitrogen and herbicide rate, and growing-season precipitation. Existing data sets were collected and used in a meta-analysis of the ability of at least two predictor variables to explain variations in wheat yield. Yield responses were asymptotic with increasing crop and weed density; however, asymptotic trends were lacking as herbicide and fertilizer levels were increased. Based on the independent field data, the three best-fitting models (in order) from the candidate set of models were a multiple regression equation that included all five predictor variables (R2= 0.71), a double-hyperbolic equation including three input predictor variables (R2= 0.63), and a nonlinear model including all five predictor variables (R2= 0.56). The double-hyperbolic, three-predictor model, which did not include herbicide and fertilizer influence on yield, performed slightly better than the five-variable nonlinear model including these predictors, illustrating the large amount of variation in wheat yield and the lack of concrete knowledge upon which farmers base their fertilizer and herbicide management decisions, especially when weed infestation causes competition for limited nitrogen and water. It was difficult to elucidate the ecological first principles in the noisy field data and to build effective models based on disjointed data sets, where none of the studies measured all five variables. To address this disparity, we conducted a five-variable full-factorial greenhouse experiment. Based on our five-variable greenhouse experiment, the best-fitting model was a new nonlinear equation including all five predictor variables and was shown to fit the greenhouse data better than four previously developed agronomic models with anR2of 0.66. Development of this mathematical model, through model selection and parameterization with field and greenhouse data, represents the initial step in building a decision support system for site-specific and variable-rate management of herbicide, fertilizer, and crop seeding rate that considers varying levels of available water and weed infestation.

The loss of ecosystem services due to deforestation is of global concern. Financial mechanisms such as REDD+ (reducing emissions from deforestation and forest degradation) have been proposed as ways to support the conservation of tropical forests. Crucial steps in the implementation of REDD+ are to estimate national-level carbon emissions from deforestation and forest degradation and to collect data on local biomass and carbon stocks. In this research, above-ground biomass (AGB) values and associated carbon stocks in a lowland secondary forest are estimated and compared with those in an adjacent primary forest, both growing on limestone in Seram, the Moluccas, Indonesia.<br>Suitable allometric equations for secondary forests in this region and on limestone were not available, so destructive sampling was necessary to determine the AGB in the secondary forest. An allometric equation was developed that makes it possible to estimate the AGB when tree diameter, height and wood density data are available. This biomass estimate was compared with AGB values that were calculated using existing allometric equations for secondary forests. To calculate the biomass and carbon values for the primary forest, an allometric equation from the literature was used.<br>The AGB for trees =10 cm dbh in the secondary forest (140.7 Mg ha–1) was 2.5 times lower than that in the primary forest (349.9 Mg ha–1). Converting these biomass estimates into carbon stocks gave a value of 70.3 Mg ha–1 for the secondary forest and 175.0 Mg ha–1 for the primary forest. The AGB estimate for the secondary forest differs from published values for other areas within the region, because age, type of disturbance and original forest type are non-uniform. The AGB value for the primary forest is comparable to that found in a biomass study conducted in a Malaysian primary limestone forest, but lower than those found in primary forests in Borneo that are dominated by dipterocarps. Ecological limestone studies in the tropics are very rare and more studies of this forest type, and comparisons with adjacent forests on different soil types, are recommended.<br>When the biomass of understory vegetation and other life forms was included, the total AGB in the secondary forest was equal to 176.5 Mg ha–1. As much as 20% of the total AGB was found in life forms other than trees =10 cm dbh. Because secondary forests generally contain many small stems, it is recommended that understory vegetation be included in total AGB estimates for secondary forests.<br>The AGB estimate in the secondary forest varied greatly depending on which of the existing allometric equations was used. Therefore, this study confirms the importance of choosing suitable allometric equations for each forest type and the need to consider destructive sampling when suitable equations are not available. We stress that the allometric equation developed in this study should be used only for old secondary lowland limestone forests in the Moluccas.<br>The fieldwork for this research was carried out in Seram, the Moluccas, Indonesia, from April to June 2011. This research project received financial support from the CoLUPSIA project, Hendrik Muller Fonds and Het Miquel Fonds.

Forests play an important role in global carbon cycling, since they hold a large pool of carbon as well as potential carbon sinks and sources to the atmosphere. Accurate estimation of forest biomass is required for greenhouse gas inventories and terrestrial carbon accounting. The information on biomass is essential to assess the total and the annual capacity of forest vigor. Estimation of aboveground biomass is necessary for studying productivity, carbon cycles, nutrient allocation, and fuel accumulation in terrestrial ecosystem. The possibility that above ground forest biomass might be determined from space is a promising alternative to ground-based methods. Remote sensing has opened an effective way to estimate forest biomass and carbon. By the combination of data field measurement and allometric equation, the above ground trees biomass possible to be estimated over the large area. The objectives of this research are: (1) To estimate the above ground tree biomass and carbon stock of forest cover in Lore Lindu National Park by combination of field data observation, allometric equation and multispectral satellite image; (2) to find the equation model between parameter that determines the biomass estimation. The analysis showed that field data observation and satellite image classification influencing much on the accuracy of trees biomass and carbon stock estimation. The forest cover type A and B (natural forest with the minor timber extraction) has the higher biomass than C and D (natural forest with the major timber extraction and agro forestry), it is about 607 ton/ha and 603 ton/ha. Forest cover type C is 457 ton/ha. Forest cover type D has the lowest biomass is about 203 ton/ha. Natural forest has high biomass, because of the tropical vegetation trees heterogeneity. Forest cover D has the lowest trees biomass because its vegetation component as secondary forest with the homogeneity of cacao plantation. The forest biomass and carbon estimation for each cover type will be useful for the further equation analysis when using the remote sensing technology for estimating the total biomass and for the economic carbon analysis.Forests play an important role in global carbon cycling, since they hold a large pool of carbon as well as potential carbon sinks and sources to the atmosphere. Accurate estimation of forest biomass is required for greenhouse gas inventories and terrestrial carbon accounting. The information on biomass is essential to assess the total and the annual capacity of forest vigor. Estimation of aboveground biomass is necessary for studying productivity, carbon cycles, nutrient allocation, and fuel accumulation in terrestrial ecosystem. The possibility that above ground forest biomass might be determined from space is a promising alternative to ground-based methods. Remote sensing has opened an effective way to estimate forest biomass and carbon. By the combination of data field measurement and allometric equation, the above ground trees biomass possible to be estimated over the large area. The objectives of this research are: (1) To estimate the above ground tree biomass and carbon stock of forest cover in Lore Lindu National Park by combination of field data observation, allometric equation and multispectral satellite image; (2) to find the equation model between parameter that determines the biomass estimation. The analysis showed that field data observation and satellite image classification influencing much on the accuracy of trees biomass and carbon stock estimation. The forest cover type A and B (natural forest with the minor timber extraction) has the higher biomass than C and D (natural forest with the major timber extraction and agro forestry), it is about 607 ton/ha and 603 ton/ha. Forest cover type C is 457 ton/ha. Forest cover type D has the lowest biomass is about 203 ton/ha. Natural forest has high biomass, because of the tropical vegetation trees heterogeneity. Forest cover D has the lowest trees biomass because its vegetation component as secondary forest with the homogeneity of cacao plantation. The forest biomass and carbon estimation for each cover type will be useful for the further equation analysis when using the remote sensing technology for estimating the total biomass and for the economic carbon analysis.

: Problem statement: There was no information about the relationship between growth parameters, such as diameter and height and tree component biomass of Khaya ivorensis plantations with different soil types. The objectives of this study were, first, to determine and compare the growth of K. ivorensis in three different (Padang Besar, Durian and Rengam) soil series of Ultisols and, second, to develop an allometric equation that estimates the biomass accumulation of the K. ivorensis plantation in three different soil series five years after planting. Approach: This study was conducted at a K. ivorensis plantation in the Forest Research Institute Malaysia (FRIM) Research Station in Segamat, Johor, Malaysia. The tree height (H) and Diameter at Breast Height (DBH) were measured to evaluate the growth performance of the K. ivorensis plantation. Five sampled or trees stand of K. ivorensis in each soil series were destructively analyzed. Results: The highest growth rates in terms of MAI diameter and height, and basal area were found for the Padang Besar soil series, which was followed by the Durian and Rengam soil series. The best fit regression of site-specific equations developed from the independent variable D are recommended for estimating tree component biomass and stem volume in each site. A single allometric equation using D was applicable for the estimation of biomass and stem volume however, in Padang Besar, stem biomass and stem volume were estimated with an equation using D2H. The highest stem volume and biomass accumulation value were recorded at Padang Besar (77.99 m3 h-1 and 63.16 t ha-1, respectively), which was followed by the Durian (53.10 m3 h-1 and 46.33t ha-1, respectively) and Rengam soil series (43.13 m3 h-1 and 40.96 t ha-1, respectively). Conclusion: Differences in the growth and biomass accumulation data indicate that forest productivity of K. ivorensis was affected by different site conditions. The higher growth performance and productivity of K. ivorensis in terms of the stem volume and biomass accumulation in Padang Besar compared those in the Durian and Rengam soil series shows that the species was able to adapt to the soil characteristics of the Padang Besar soil series.

Background. Structural and floristic characteristics are a crucial aspect in proposing technical solutions for forest ecosystem restoration. The study was conducted in Kon Chu Rang Natural Reserve, Gia Lai, Vietnam. The differences in structure (such as density, tree size, abundance, diversity, species composition, etc.) between secondary and old-growth forests were shown in the several previous researches. However, within secondary forests, different histories of disturbance have resulted in very different stages of forest succession, despite the same length of time since human influence ceased and the same protection conditions. Secondary forests recovering after shifting cultivation, with directly light and fast-growing species, have higher density and abundance, while tree size indices and the number of dominant species are lower compared to secondary forests recovering after logging, which are mainly composed of shade-tolerant species. The research results provided a basis for group classification and the application of silvicultural measures to effectively promote forest recovery processes. Purpose. To study the potential for natural successional recovery as a basis for proposing the application of silvicultural measures to rehebilitate the evergreen closed tropical rain forest in the Kon Ha Nung Biosphere Reserve. Materials and methods. The subject of the study was the tropical evergreen broadleaf forest types in Kon Chu Rang Nature Reserve. In this study, satellite imagery (Landsat 8) was collected in the same season from 2013 to 2022 and Normalized Difference Vegetation Index was calculated to determine the forest successional stages of tropical evergreen broadleaf rainforest in Kon Chu Rang Nature Reserve. The results of classification combined with field survey based on the establishment of 09 permanent sample plots (50×50 m, 2,500 m²) to ensure the forest successional stages. These plots were established in each typical successional stage (secondary forest after logging, secondary forest after shifting cultivation, and old-growth forest). In each plot, all live woody stems with a diameter at breast height greater than 10 cm were measured, including tree diameter at breast height and tree species. All data collected in each plot were then used for data analysis using SPSS software. This research conducted an ANOVA (Analysis of Variance) with a Fisher's Least Significant Difference post hoc test to explore differences between multiple group means of tree density, number of trees distribution in each group of tree diameter, tree diameter, basal area and tree diversity. In addition, to investigate forest structure and diversity, the Impotance Value Index, Shannon-Wiener Index (He') and Simpson Index, and Jaccard's coefficient of similarity were calculated in this study. Results. Tree density ranged from 347 to 763 stems per hectare and total basal area from 15.5 to 42.8 m² per hectare. No significant difference was observed among the three forest types for tree diameter classes from 10 to 25 cm, while for tree diameter classes greater than 25 cm, old growth forest had the highest tree density, significantly different from the others. A decrease in tree density was observed in all forest types except old growth, which had the highest tree density and basal area for tree diameter classes greater than 25 cm. Diversity was found to be significantly higher in the old-growth forest compared to the secondary forest, which may be due to the duration of the restoration process and the initial stage of disturbance cessation. A total of 31 to 43 tree species were identified in 28-38 genera and 19-22 families, with the lowest species richness observed in the secondary forest after logging and only 3-7 tree species calculated in the tree composition. The dominant species in the post-logging secondary forest were heliophilous and fast-growing tree species such as Machilus parviflora, Macaranga tanarius, Litsea elongata, Clausena sp. and Prunus arborea, whereas in the post-shifting secondary forest they were shade-tolerant such as Rehderodendron truongsonense, Cinnamomum mairei, Castanopsis pseudoserrata, Litsea elongata, Syzygium wightianum. In particular, the associations of Clusiaceae and Myrtaceae species in old-growth forests were a novel finding. Conclusions. It can be concluded that the structure and diversity characteristics of these successional stages exhibited remarkable variation. The old-growth forest had greater tree density, basal area, tree diversity and evenness than those of in secondary forest, along with the differences in number tree distribution, tree composition and diversity. These differences may come from the regeneration time and site condition. These results suggest that long-term monitoring and research are essential to assess restoration success over time.

Allometric models defining the relationship between stem diameter and total tree height in the Amazon basin are important because they refine the estimates of tree carbon stocks and flow in the region. This study tests different allometric models to estimate the total tree height from the stem diameter in an ecotone zone between ombrophilous and seasonal forests in the Brazilian state of Roraima, in northern Amazonia. Stem diameter and total height were measured directly in 65 recently fallen trees (live or dead). Linear and nonlinear regressions were tested to represent the D:H relation in this specific ecotone zone. Criteria for model selection were the standard error of the estimate (Syx) and the adjusted coefficient of determination (R²adj), complemented by the Akaike Information Criterion (AIC). Analysis of residuals of the most parsimonious nonlinear models showed a tendency to overestimate the total tree height for trees in the 20-40 cm diameter range. Application of our best fitted model (Michaelis-Menten) indicated that previously published general equations for the tropics that use diameter as the independent variable can either overestimate tree height in the study area by 10-29% (Weibull models) or underestimate it by 8% (climate-based models). We concluded that our site-specific model can be used in the ecotone forests studied in Roraima because it realistically reflects the local biometric relationships between stem diameter and total tree height. Studies need to be expanded in peripheral areas of northern Amazonia in order to reduce uncertainties in biomass and carbon estimates that use the tree height as a variable in general models.

Chapter 9 - Tree biomass and carbon stocks of different landscape elements in the Western Ghats of Karnataka, India

The effects of soil on vegetation structural attributes (tree density, tree height, tree diameter, basal area and estimated aboveground biomass) of a secondary forest following the abandonment of a degraded rubber plantation in Orogun area of Delta state Nigeria was studied using the multiple linear regression model. Four secondary forests of different age categories (1-year, 5-year, 10-year old secondary forests and a mature secondary forest of about 80 years old) were studied. Vegetation parameters were measured by quadrat method. Forty quadrats (30m x 30m), ten in each secondary forest categories were delineated for soil sampling and for the measurement of vegetation structural properties. Twenty composite soil samples were collected in each secondary forest category and analyzed for organic matter, pH, available phosphorus, total porosity, ECEC (effective cation exchange capacity) and water holding capacity. Vegetation structural properties for each of the quadrats were measured using appropriate field measurement techniques. Multiple linear regression was used to investigate the relationship between the vegetation structural properties and soil properties. The result of multiple linear regression showed that soil organic matter, total porosity, water holding capacity, available phosphorus and ECEC were the most outstanding soil factors influencing the regenerative capacity of the vegetation structural properties in secondary forests. Relationship between Soil and vegetation Structural Properties of Secondary Forest regenerating... Key words : Tree height, Tree density, Basal area, multiple regression, ECEC, Water holding capacity.