Carbon Storage Dynamics and its Economic Values in Tropical Moist Afromontane Forests, South-West Ethiopia

Carbon Storage Dynamics and its Economic Values in Tropical Moist Afromontane Forests, South-West Ethiopia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Carbon Storage Dynamics and its Economic Values in Tropical Moist Afromontane Forests, South-West Ethiopia Semegnew Tadese, Teshome Soromessa, Abreham Berta Aneseyee, Getaneh Gebeyehu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2564786/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Dec, 2023 Read the published version in Carbon Balance and Management → Version 1 posted 7 You are reading this latest preprint version Abstract Forest plays an important role in the global carbon cycle by sequestering carbon dioxide and thereby mitigating climate change. In this study, an attempt has been made to investigate the effects of land use/land cover (LULC) change (1989–2017) on carbon stock and its economic values in tropical moist Afromontane forests of the Majang Forest Biosphere Reserve (MFBR), south –west Ethiopia. Systematic sampling was conducted to collect biomass and soil data from 140 plots in MFBR. The soil data were collected from grassland and farmland. InVEST modelling was employed to investigate the spatial and temporal distribution of carbon stocks. Global Voluntary Market Price (GVMP) and Tropical Economics of Ecosystems and Biodiversity (TEEB) analysis was performed to estimate economic values (EV) of carbon stock dynamics. Correlation analysis was also employed to identify the relationship between environmental and anthropogenic impacts on carbon stocks. The results indicated that the above-ground biomass and soil organic carbon stocks were higher than the other remaining carbon pools in MFBR. The total carbon stock (32.59 Mt ha –1 ) in 2017 was lower than 1989 (34.76 Mt ha –1 ). The EV of carbon stock in 2017 was lower than in 1989. Elevation, slope, and harvesting index are important environmental and disturbance factors resulting in major differences in carbon stock among study sites in MFBR. The correlation analysis for elevation showed a positive relationship with soil carbon stocks (r = 0.39) and aboveground biomass (r = 0.08), while a negative relationship was found for slope (r = –0.04) and harvesting index (r = –0.21). This calls for urgent attention to implement successful conservation and sustainable use of forest resources in biosphere reserves. Land use/cover carbon stock environment and disturbance factors InVEST model Africa Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Forests play an important role in the global carbon (C) cycle, sequestering carbon dioxide and thereby mitigating climate change (Hunter et al. 2013 , Sheikh et al. 2014 ). They control climate change by sinking over 200 billion metric tons of carbon a year and converting atmospheric carbon into biomass through photosynthesis (Lal 2010 , Marvin et al. 2014 ). They are significant carbon sinks, accounting for half of the above-ground biomass in vegetation (Hunter et al. 2013 ). Moreover, the current carbon stock in global forests is estimated at861 Gt of carbon, of which 363 and 383 Gt of carbon are stored in the living biomass and soil (up to 1 Mt), respectively (Pan et al. 2011 , Goodman and Herold 2014 , Dar et al. 2020 ). The global carbon cycle has sparked the most interest in recent years as it became clear that rising levels of CO 2 in the atmosphere cause rapid changes in global climate (Change 2007 , Zachos et al. 2008 ). In the international dialogue, issues such as biodiversity loss, ozone layer depletion, and desertification have taken a central stage (Speth and Haas 2007 ). Humans exert significant pressure on the carbon cycle through the use of large amounts of oil, gasoline, and coal, as well as deforestation and land degradation (Sabine et al. 2004 , Clark and York 2005 ). Deforestation and land degradation are also the major sources of anthropogenic greenhouse gas emissions in most tropical countries (Houghton 2005 , Pearson et al. 2017 ).Changes in land use/land cover (LULC) are reducing globally significant carbon storage that is currently sequestering CO 2 from the atmosphere, which makes them critical to long-term climate stability (McGuire et al. 2001 , Stephens et al. 2007 ). Every year, tropical deforestation accounts for 15–25% of global greenhouse gas emissions (Houghton 2005 ). Liu et al. ( 2015 ) indicated that between 1993 and 2012, the global Above-Ground Carbon (AGC) declined at a rate of − 0.07 PgC/yr due to the loss of tropical forest area. Pan et al. ( 2011 ) reported that the global soil organic carbon (SOC) decreased by 7.7% (12.7 PgC) between 1990 and 2007, owing primarily to tropical deforestation. Specifically, timber extraction and logging are accountable for over half of forest degradation (52%), followed by fuel wood extraction and charcoal production (31%), induced fire (9%), and overgrazing (7%) in the tropics (Hosonuma et al. 2012 ). This showed that forest degradation and deforestation are the main sources of greenhouse gas (GHG) emissions in most tropical countries. The InVEST models typically quantify and investigate trade-offs associated with alternative management options as well as indicate areas where natural capital projects can improve land conservation and development (Seppelt et al. 2013 , Sharps et al. 2017 , Zheng et al. 2019 ). InVEST models are spatially explicit (they use maps as input and output) and produce results in either biophysical (e.g., tons of carbon sequestered) or economic terms (e.g., the net present value of that sequestered carbon) (Nelson et al. 2009 , Imran 2021 ). Such a model effectively estimates carbon stock in the landscape ecosystem using carbon pools and LULC classes as input data(Fu et al. 2019 ). Therefore, it provides carbon stock estimates over a large area for trend analysis (Sharps et al. 2017 , Nyamari and Cabral 2021 ). Carbon valuation is a monetary estimation of carbon related to small changes in emissions of carbon dioxide (CO 2 ) (Smith and Braathen 2015 , Isacs et al. 2016 ). Carbon valuation is essential for evaluating the relative positive effects of climate mitigation and adaptation policy over time (Pearce 2003 , Tol 2005 , Brandão et al. 2013 , Locatelli et al. 2015 ). Future carbon benefits are strongly connected to risk management concerns because future values are affected by the chance that benefits may not emerge as expected (Ackerman et al. 2009 , Bowen and Wittneben 2011 ). Carbon valuation is complicated, and multiple methodologies and sources are used depending on whether a societal or market perspective is used (Nelson et al. 2009 , Tallis and Polasky 2009 ). Although there is no a single accepted technique for calculating carbon's social worth(Pearce 2003 , Valatin 2011 ), Global Voluntary Market Price (GVMP) and Tropical Economics of Ecosystems and Biodiversity (TEEB) databases are used to estimate carbon stock and its economic values(Van der Ploeg et al. 2010 , De Groot et al. 2012 ). Moist Afromontane forests provide a variety of ecosystem services, such as watershed protection, groundwater regulation, food control, prevention of soil erosion, provision of non-timber forest products, and climate change mitigation (Banana et al. 2014 , Negasi et al. 2018 , Negasi et al. 2019 ). More specifically, the Majang Forest Biosphere Reserve (MFBR) is one of the recently registered forest biosphere reserves in southern Ethiopia, which is part of the remnants of moist Afromontane forests that continue to provide essential services for people's livelihood (Choudhary et al. 2021 ). Anthropogenic activities have gradually degraded these moist Afromontane forests over time because they have not been managed sustainably (Demel et al. 2010 , Kefelegn et al. 2013 , Meron et al. 2018 ). Moreover, estimating changes in carbon stock and its economic value due to changes in forest cover has not been investigated yet. Understanding this encourages decision-makers to create a carbon credit negotiation and sustainable development and conservation of MFBR. Therefore, the aims of this study were to (i) examine the change in carbon stocks due to forest cover change over the last 30 years, (ii) map the carbon stock dynamics and its economic value, and (iii) analyse the impacts of environmental and disturbance factors on carbon stocks. 2. Material And Methods 2.1. The Study Area The study was conducted in the Majang Forest Biosphere Reserve (MFBR), situated in the Majang Zone, Gambella People National Regional State of Ethiopia. It has unique biogeography and shares a boundary with Sale Nono Woreda of the Oromia Regional State; Anderacha, Yeki, Sheka, and Gurafereda Woreda of the Southern Nations, Nationalities, and Peoples' Region (SNNPR). It covers a total area of 233,254 ha of forest and agricultural land and rural settlements and towns (Fig. 1 ). MFBR is located between the latitudes of 07°08'00" N and 07°50'00" N, and the longitudes of 34°50'00" E and 35°25'00" E, with elevations ranging from 562 m to 2444 m. It is distinctive biogeography and shares a boundary with Illubabor Zone of Oromia Regional State; Sheka and Bench-Maji Zones of the Southern Nations, Nationalities, and People Region (SNNPR). The climate in the area is generally hot and humid, which is marked on most rainfall maps of Ethiopia as the wettest part of the country. The annual average rainfall and temperature is 1774 mm and 22.1°C, the means annual minimum and maximum monthly temperature ranges between 13.9 and 31.8°C in Tinishu Meti metrological station respectively. The annual average rainfall and temperature are 2053 mm and 20.5°C, the means annual minimum and maximum monthly temperature ranges between 11.8 and 29.7°C in Ermichi Metrological station respectively. The vegetation in the area is divided into several categories based on its life forms, including high natural forests, woodlands, bush lands, and grasslands. Euphorbiaceae, Rubiaceae , and Moraceae were the most prevalent families in MFBR, with 13 species (8%), nine genera (7.8%), twelve species (7.4%) and eight genera (7%), and ten species (6.1%), and five genera (4.3%), respectively(Tadese et al. 2021b ). 2.2. Sampling Design A systematic sampling design was used to arrange quadrats and transects as well as to collect vegetation data (Kent 2012 ). The study area was stratified into four sites using Digital Elevation Model (DEM) in the Arc GIS software. These were site I ( 1800 m.a.s.l) (Table 1 ).The number of transect lines varied among study sites. A total of 140 quadrats were established for vegetation and forest soil data collection (Table 1 ). Farmland (40) and grassland (40) soil samples were acquired from adjoining forestland in each study sites of the MFBR. Table 1 Topographic and soil characteristics of the study sites Study site Ele (m) Slo pH TN (%) P (ppm) Area (ha) SP Site I 1,042 ± 42 5.3 ± 0.4 6.6 ± 0.4 0.32 ± 0.1 16.2 ± 1.1 22,826.1 40 Site II 1,365 ± 24 5.4 ± 0.4 6.3 ± 0.4 0.24 ± 0.8 17.48 ± 1.4 25,220.5 45 Site III 1,635 ± 24 7.2 ± 0.5 6.0 ± 0.5 0.14 ± 0.1 19.02 ± 1.8 14,053 30 Site IV 2,011 ± 42 11.1 ± 1.2 5.8 ± 0.4 0.11 ± 0.3 20.23 ± 2.4 11,783.5 25 Note: TN = total nitrogen, P = phosphorus, pH = soil pH, Ele = elevation, Slo = slope, SP = sample plots, and ppm = part per million The study sites polygon was digitized using Google Earth by elevation classes. The quadrats' X-Y coordinates were generated using GIS tools and loaded to a global positioning system (GPS) receiver for tracking quadrats. Later, a measuring tape was used to layout 20×20 m 2 (400 m 2 ) quadrats in each site in the biosphere. The sampling intervals between the transect line and the quadrats were 2 km apart. Biomass data for tree census in the tree sites were collected on 5.6 ha (4 sites = 140 quadrats). Above-ground biomass was estimated using a non-destructive sampling method by measuring the diameter at breast height (DBH), tree height, and wood density (Chave et al. 2005 ). 2.3. Data Collection Methods 2.3.1. Biomass and soil data During the field data collection, the main carbon measurement activities concerned above-ground tree biomass, below-ground biomass, leaf litter, deadwood, and soil organic carbon. Individual trees with a DBH of > 5 cm (Pearson 2007 ) were measured in each plot with a calliper and measuring tape (at 1.3 m). Each tree was individually recorded, along with its species name and ID. Clinometers and a meter tape were used to measure the heights of all individual trees in the sampling quadrats. Overhanging species were excluded, but trees with trunks inside the sampling plot and branches outside were included (MacDicken 1997 ). Five rectangular subplots of 1×1 m were established at the four corners and centre of each main plot for litter, herbs, and soil data collection. Where the samples were large, the fresh weight of the total sample was recorded in the field, and a manageable-sized (200 g) evenly mixed subsample was brought to the laboratory to determine dry biomass and percentage carbon (Pearson et al. 2005 ).The biomass in the pool of leaf Litter, Grass, and Herbs (LGH) was estimated using destructive sampling. Herbaceous samples were collected by clipping and weighing all vegetation before placing it in a sample weighing bag and transporting it to the laboratory to determine the oven-dry weight of the biomass. Forest floor litter materials (dead leaves, twigs, fruit, and flowers) were collected from a 1 m 2 area. The living components, primarily grass and herbs, were harvested and weighed as well. Dry weight was determined in laboratory samples of the materials. Within the 400 m 2 plot, standing dead trees, fallen stems, and fallen branches with a DBH ≥ 5 cm were measured (Pearson et al. 2005 ). Soil samples were taken with a soil auger from the topsoil at a depth of 0–30 cm, which is recommended as the default sampling depth for soil (Dick et al. 1997 ). Soil samples were taken from five different locations in each plot, four from the quadrat's corners and one from the quadrat's centre. A total of 220 soil samples, 140 from forestland, 40 from farmland, and 40 from grassland were collected, composited separately, labelled, and transported to the laboratory. To determine soil bulk density, the soils were collected on the centre of the quadrats using a stainless core sampler, then placed in plastic bags, and transported to the laboratory for dry weight determination. Fresh wet soil weights were measured in the field with a kitchen balance with 0.1 g precision. A composite sample of 200 g was taken from each quadrat to analyse its chemical composition (Pearson et al. 2005 ). 2.3.2. Environmental and disturbance factors Environmental factors such as aspect, slope, and elevation were measured and recorded for each of the 140 quadrats using a Garmin GPS receiver and clinometers. Elevation was arranged into four elevation (m.a.s.l) ranges (sites I–IV), namely: 1 = 1200, 2 = 1200–1500, 3 = 1500–1800 and 4 = > 1800. The slope range was classified into three major slope classes following (Gebeyehu et al. 2019 ). As a result, the classes were: 1) flat 20. The human disturbance (which includes harvesting trees for fuel, wood, charcoal, timber, and house construction) was computed as the harvesting index. The harvesting index was measured by counting individual stumps, which reflected illegally logged trees, within the quadrat and calculated from the relative density of individual tree stumps. The relative density of stumps was computed as the sum of stump density divided by the total density (the sum of the logged stump and living individual trees). Stumps are a small portion of the trunk that remains after a tree with about the 5 cm is chopped down (Sagar et al. 2003 ). 2.4. Spatial Data Analysis Methods 2.4.1. Land use/land cover data The LULC types and Tag Image File Format(TIFF) data were obtained from a previously published article by Tadese et al. ( 2021a ). They included area statistics for five different land cover types for the years 1987, 2002, and 2017 (Table 2 ). Table 2 Area of LULC classes from 1987 to 2017 in MFBR adopted from Tadese et al ( 2021a ) LULC classes 1987 2002 2017 Area (ha) Area (%) Area (ha) Area (%) Area (ha) Area (%) Forestland 196,761.6 84.4 188,413.7 80.8 181,504.9 77.8 Farmland 3,0781.8 13.2 36,906.4 15.8 40,554.8 17.4 Grassland 3,509.2 1.5 3,079.6 1.3 3,192.2 1.4 Settlement 2,050.7 0.9 4,744.3 2.0 7,866.2 3.4 Water body 141.0 0.06 141.0 0.06 141.0 0.06 Total 233,254 100% 233,254 100% 23,3254 100% The spatial distribution of carbon stock pools in different LULC types for each study year (forest land, farmland, and grassland) were analysed using the InVEST model (Fig. 2 ). 2.4.2. Carbon stock estimation using the InVEST model The InVEST modelling framework is a set of open-source models for mapping and valuing the goods and ecosystems that produce the flow of services required to sustain life on Earth (Guerry et al. 2012 , Bagstad et al. 2013 , Sharps et al. 2017 ). We customised the InVEST carbon stock mapping and sequestration model to assess the amount of total carbon stored in the five carbon pools (above-ground biomass, below-ground biomass, deadwood, litter, and soil organic matter) in different LULC classes of the study area. Carbon stock values were assigned to each LULC class for the selected years (i.e., 1987, 2002, and 2017) using field inventory data for forest land, farmland, and grassland. To meet the model's requirements, the LULC and carbon pool data sets were prepared and used as the primary input data to estimate carbon storage in each grid cell. Land use codes, the name of the LULC class, the amount of Above-Ground Biomass (AGB), Below-Ground Biomass (BGB), deadwood (DW), Litter, Grass, and Herbs (LGH), and Soil Organic Matter (SOC) are all included in the carbon stock data set in an MS Excel database. The LULC class is encoded with land-use codes in each row. Except for settlements and water bodies, which have zero carbon stock in all carbon pools, each column contains different attributes of the LULC type. Carbon in each pool was then combined across land-use types to estimate the total carbon storage. 2.5. Data Analysis 2.5.1. Soil laboratory analysis The soil samples were analysed in the Water Works Design and Supervision Enterprise laboratory (WWDSE) in Addis Ababa, Ethiopia. The Bouyoucos Hydrometer Method was used to determine the soil textures (expressed as a percentage of weight)(Bouyoucos 1962 ). Soil pH was determined using a pH meter and a 1:2.5 soil to water suspension potentiometric method (Sparks et al. 2020). The Micro-Kjeldahl (Bremner 1996 ) and Walkley and Black ( 1934 ) methods were used to determine total nitrogen (N) and soil organic carbon, respectively. The Bray-I method was used to determine available phosphorus, and the absorbance of the Bray-I extract was measured in a spectrophotometer at 882 nm (Bray and Kurtz 1945 ). Based on C and N concentrations, the Carbon to Nitrogen ratio (C/N) was calculated. The mass of each soil sample (MS) was determined using oven-drying set to 105°C for 24 h to achieve a constant weight (Pearson 2007 ). The volume of the Core Sampler (VC) was determined as VC = π r 2 h, where r is the radius and h is the height of the core sampler (VC = 3.14 × (2.5 cm) 2 × 5 cm = 98.125 cm 3 ). 2.5.2. Carbon stock and value analysis Data analysis of various carbon pools measured in the forests was performed in R version 4.0.1. The AGB of trees was calculated using a previously published allometric equation in which the independent variables were trunk diameter (D, cm), height (H, m), and wood density (p, g cm − 3 ) (predictors) (Chave et al. 2014 ).The Global Wood Density database was used to determine the wood density of different species (Zanne et al. 2009 ). The following formula (Chave et al. 2014 ) was employed to calculate the above-ground biomass with the BIOMASS package in R (Réjou-Méchain et al. 2017 ): \(\mathbf{A}\mathbf{G}\mathbf{B} \left(\mathbf{k}\mathbf{g}\right)=0.0673\mathbf{*} {({p}{D}}^{2}{{H})}^{0.976}\) 1 Where: AGB is the above-ground biomass of trees (kg), p is the specific wood density (g cm − 3 ), D is the trunk diameter at breast height (cm), and H is the total height of trees (m). The total AGB carbon for each quadrat was calculated as aggregate AGB carbon for all trees. Carbon stocks were determined for each quadrat and then extrapolated to tonnes per hectare. The carbon content in AGB is calculated by multiplying the default carbon fraction by 50% (Hiraishi et al. 2014 ). Below-ground biomass was estimated with the equation developed by (MacDicken 1997 ) \(\mathbf{B}\mathbf{G}\mathbf{B}=\mathbf{A}\mathbf{G}\mathbf{B}\mathbf{*}0.2\) 2 Where: BGB is below-ground biomass, AGB is above-ground biomass, 0.2 is the conversion factor (or 20% of AGB). For standing deadwood (SDW) which has branches, the biomass was estimated using the allometric equation for the estimation of above-ground biomass (Pearson et al. 2005 ). For the remaining standing deadwood, the biomass was estimated using wood density and volume calculated from the truncated cone (Pearson et al. 2005 ). \(\mathbf{V}\mathbf{o}\mathbf{l}\mathbf{u}\mathbf{m}\mathbf{e} {\left(\mathbf{m}\right)}^{3}\) = \(\frac{1}{3}{\pi }\mathbf{h} {\mathbf{r}}_{1}^{2}+{\mathbf{r}}_{2}^{2}\) + \({\mathbf{r}}_{1 }\) * \({\mathbf{r}}_{2}\) 3 Where: h is the height in meters, r 1 is the radius at the base of the tree, and r 2 is the radius at the top of the tree. \(\mathbf{B}\mathbf{i}\mathbf{o}\mathbf{m}\mathbf{a}\mathbf{s}\mathbf{s} = \mathbf{V}\mathbf{o}\mathbf{l}\mathbf{u}\mathbf{m}\mathbf{e} \mathbf{x} \mathbf{W}\mathbf{o}\mathbf{o}\mathbf{d} \mathbf{d}\mathbf{e}\mathbf{n}\mathbf{s}\mathbf{i}\mathbf{t}\mathbf{y} \left(\mathbf{f}\mathbf{r}\mathbf{o}\mathbf{m} \mathbf{s}\mathbf{a}\mathbf{m}\mathbf{p}\mathbf{l}\mathbf{e}\mathbf{s}\right)\) 4 The biomass of lying deadwood was estimated by the equation given below (Pearson et al. 2005 ). \(\mathbf{L}\mathbf{D}\mathbf{W} =\sum _{{i}=1}^{{n}}\mathbf{V}\mathbf{*}\mathbf{S}\) 5 Where: LDW is lying dead wood, V is volume, and s is the specific density of each density class. The lying deadwood volume per unit area is estimated with: \(\mathbf{V}= {{\pi }}^{2} \left(\frac{{\mathbf{D}}^{2}}{8\mathbf{L}}\right)\) 6 Where: V is the volume in m 3 /ha; L is the length of the line transect, and D is the diameter of the deadwood tree. The carbon content in AGB is calculated by multiplying the default carbon fraction by 50% (Hiraishi et al. 2014 ). The biomass in the pool of leaf litter, grass, and herbs was estimated using destructive sampling. Forest floor litter material (dead leaves, twigs, fruit, and flowers) was collected from a 1 m 2 area. The living components, primarily grass and herbs, were harvested and weighed as well. Dry weight was determined in laboratory samples of the material. To estimate the biomass carbon stock of the litter, 100 g of fresh litter subsample was taken for laboratory use, and each sample was then dried in an oven at 105 o C for 24 hours to obtain the dry weight (Pearson et al. 2005 ). The leaf litter, grass, and herbs (LGH) biomass per hectare was computed using the following formula: \(\mathbf{L}\mathbf{H}\mathbf{G} =\frac{\mathbf{W}{f}{i}{e}{l}{d}}{{A}}\) x \(\frac{\mathbf{W}{s}{u}{b}{s}{a}{m}{p}{l}{e},{d}{r}{y}}{\mathbf{W}{s}{u}{b}{s}{a}{m}{p}{l}{e}, {w}{e}{t}}\) x \(\frac{1}{10000}\) 7 Where: LHG is the leaf litter, herbs, and grass biomass (tonne ha –1 ), W field is the weight of fresh leaf litter, herbs, and grass sampled destructively within area A (g), A is the size of the area where leaf litter, herbs, and grass were collected (ha), W subsample , dry is the weight of oven-dried sub-sample of leaf litter, herbs, and grass taken to the laboratory for moisture content determination (g), W subsample , wet is the weight of fresh sub-sample of leaf litter, herbs, and grass taken to the laboratory for moisture content determination (g). Carbon stocks in litter biomass were calculated using the following formula: \(\mathbf{C}\mathbf{L} = \mathbf{L}\mathbf{H}\mathbf{G} \mathbf{*} \mathbf{\%}\mathbf{C}\) 8 Where: CL is the total carbon stocks in litter in tonne ha –1, and % C is the carbon fraction determined in the laboratory (Pearson et al. 2005 ). The soil carbon stock was assessed in this study using the fine soil fraction to a depth of 30 cm. The following equation was used to calculate the bulk density (BD): \(\mathbf{B}\mathbf{D} =\frac{\mathbf{M}\mathbf{S}}{\mathbf{V}\mathbf{C}}\) 9 Where: BD is the bulk density (g cm –3 ), MS is the mass of the oven-dry soil (g) The amount of carbon stored per hectare was calculated using the following formula, taking into account soil depth (cm), bulk density (g cm –3 ), the percentage of soil organic carbon content (SOC), and the Total Nitrogen (TN) in the recommended method (Pearson et al. 2005 ). \(\mathbf{S}\mathbf{O}\mathbf{C} = \mathbf{B}\mathbf{D} \mathbf{x} \mathbf{d} \mathbf{x} \mathbf{\%}\mathbf{C}\) 10 where SOC stock is the soil organic carbon stock per unit area (tonne ha –1 ), TN stock is the total nitrogen stock (tonne ha –1 ), BD is the bulk density (g cm –3 ), d is the total depth of the sample (cm), percent SOC is the soil organic carbon concentration, and VC is the volume of the core sampler (cm 3 ). The carbon stock density of each stratum was calculated by aggregating the carbon stock densities of each stratum's carbon pools using the formula in the following equation. \(\mathbf{C} \left(\mathbf{L}\mathbf{U}\right) = \mathbf{C} \left(\mathbf{A}\mathbf{G}\mathbf{B}\right) +\mathbf{C}\left(\mathbf{B}\mathbf{B}\right)+\mathbf{C} \left(\mathbf{D}\mathbf{W}\mathbf{B}\right) + \mathbf{C}\left(\mathbf{L}\mathbf{H}\mathbf{G}\right)+\mathbf{S}\mathbf{O}\mathbf{C}\) 11 Where: C (LU) is the carbon stock density for a land-use category (C t ha –1 ) C (AGB) is the carbon in above-ground tree components (C t ha –1 ) C (BB) is the carbon in below-ground components (C t ha –1 ) C (DWB) is the carbon in deadwood tree components (C t ha –1 ) C (LHG) is the carbon in the litter, herbs, and grass (C t ha –1 ) SOC is the soil organic carbon (C t ha –1 ) Carbon was summed, and the total was then multiplied by 44/12 (3.67) to convert it into the carbon dioxide equivalent. A chronological carbon storage change investigation was conducted at MFBR for the reference years 1987, 2002, and 2017 according to the method proposed by (Niquisse et al. 2017 ). After calculating the carbon stock and value based on the previous, baseline year in the MFBR, change was analysed using the below equation. ∆C = \(\frac{{C}{F}{i}{n}{a}{l}{y}{e}{a}{r}-{C}{i}{n}{i}{t}{i}{a}{l}{y}{e}{a}{r}}{{C}{i}{n}{i}{t}{i}{a}{l}{y}{e}{a}{r}}{*}100\) 12 Where: ∆C is the percentage change in carbon, C final year is the carbon stock in the final (recent) year, and C initial year is the carbon stock in the initial years. 2.6. Carbon Market Value Estimation 2.6.1. Global voluntary market price The global voluntary market price of carbon sequestration was compared using two data sources: the Global Voluntary Market Price (GVMP) and Tropical Ecosystems and Biodiversity (TEEB) database valuation. The carbon storage rate for the landscape is necessary to determine carbon sequestration (CO 2 e) in the GVMP set by different actors, such as the World Bank. The carbon storage rate (t ha –1 ) multiplied by 3.67 (44/12 = 3.67) is used to estimate CO 2 e (Pearson 2007 ). Hence, the sequestered carbon (CO 2 e) is multiplied by the market price of carbon storage (4.40 USD/tCO 2 e) which was the carbon credit used in the Clean Development Mechanism (CDM) project under Humbo forest rehabilitation in Ethiopia (Brown et al. 2011 , Kemerink-Seyoum et al. 2018 ). To analyse the monetary value, the annual rate of change in the carbon price of 3% and the market discount rate of 7% was required to estimate carbon storage value. The total value of carbon stock has been estimated by the sum of each land-use type area multiplied by the monetary value of its carbon stock. 2.6.2. TEEB carbon valuation data The Tropical Ecosystems and Biodiversity (TEEB) database ( http://www.teebweb.org ) contains the monetary value of carbon sequestration for various land-use types (McVittie and Hussain 2013 ).The TEEB data were collected from different parts of the biome and analysed using different methods such as direct market pricing, avoided cost, and benefit transfer (Van der Ploeg et al. 2010 , De Groot et al. 2012 ). These valuation data were adapted to East Africa to compare the carbon sequestration values for MFBR (Table 3 ). The total carbon value was calculated by multiplying the area (ha) of each LUC type by its corresponding value of CO 2 e for that particular LUC type (Temesgen et al. 2018 , Negasi et al. 2019 ). Table 3 Carbon sequestration value for each LULC type in the TEEB database No. LUC Carbon sequestration prices (USD/ha/yr) 1 Forest land 1,229.79 2 Grazing land 297 3 Farmland 96 In other words, the value of Co 2 e obtained from TEEB multiplied by the LUC area yields the total market value of carbon. The carbon stock value data obtained from the TEEB database has been rearranged (sorted, summed, filtered by region, etc.) for supplementary analysis. The carbon stock value was estimated based on two approaches. In the first approach, the carbon stock value was estimated using GVMP (4.40 USD), which is considered a discount rate (7%) and the annual rate of change in the carbon price (3%). It was calculated using the following equation: \(\mathbf{T}\mathbf{C}\mathbf{V} = \mathbf{C}\mathbf{S} \left(5 \mathbf{p}\mathbf{o}\mathbf{o}\mathbf{l}\mathbf{s}\right) \mathbf{t}/\mathbf{h}\mathbf{a} \mathbf{*} \mathbf{A}\mathbf{r}\mathbf{e}\mathbf{a} \left(\mathbf{h}\mathbf{a}\right) \mathbf{*} \mathbf{C}\mathbf{P} (\mathbf{\$}/\mathbf{t}\mathbf{o}\mathbf{n}\mathbf{n}\mathbf{e}) - \mathbf{D}\mathbf{R}+\mathbf{A}\mathbf{R}\mathbf{C}\) 13 Where: TCV is the total carbon value, CS is the carbon stock in five pools, CP is the carbon price per tonne, DR is the discount rate, and ARC is the annual rate of carbon price change. In the second approach, the carbon stock value was estimated using the TEEB database, which contains carbon sequestration values for each LUC type (Table 3 ). It was calculated using the equation below: \(\mathbf{T}\mathbf{C}\mathbf{V} = \mathbf{C}\mathbf{S} \left(5 \mathbf{p}\mathbf{o}\mathbf{o}\mathbf{l}\mathbf{s}\right) \mathbf{t}/\mathbf{h}\mathbf{a} \mathbf{*} \mathbf{A}\mathbf{r}\mathbf{e}\mathbf{a} \left(\mathbf{h}\mathbf{a}\right) \mathbf{*} \mathbf{C}\mathbf{P} \mathbf{o}\mathbf{f} \mathbf{L}\mathbf{U}\mathbf{C} \mathbf{t}\mathbf{y}\mathbf{p}\mathbf{e}(\mathbf{\$}/\mathbf{t}\mathbf{o}\mathbf{n}\mathbf{n}\mathbf{e})\) 14 Where: TCV is the total carbon value, CS is the carbon stock in five pools, CP is the carbon price for each LUC type per tonne. 2.7. Statistical analysis One-way ANOVA was used to determine whether there were significant differences between environmental and disturbance factors regarding carbon stocks in R software version 3.5. The statistical significance level was set at 5%. Pearson correlation analysis was used to examine the relationship between environmental-disturbance factors regarding carbon stocks. When the value of r approaches negative 1, the carbon stock and the independent variable (factors) are inversely proportional (carbon stock increases as the factors decrease). If r approaches positive 1, the carbon stock increases while the factors increase. 3. Results 3.1. Carbon Stock in Carbon Pools In the MFBR, the mean above-ground and below-ground carbon stocks were 272.57 and 54.97 t ha –1 , respectively. The minimum and maximum of the AGB carbon stock were 144.21 and 779.05, while BGB carbon stocks were 28.84 and 155.81 t ha –1 in MFBR, respectively (Table 4 ). The distribution patterns of the BGB carbon stock showed similar trends to those of the AGB carbon stock. The mean dead wood and litter, herbs, and grass (LHG) carbon stocks were 3.04 t ha –1 and 0.05 t ha –1 , respectively. The minimum and maximum deadwood carbon stocks were 0.13 and 6.11 t ha –1 , while litter, herbs, and grass (LHG) carbon stocks were 0.016 and 0.32 t ha –1 , respectively. The mean soil organic carbon (SOC) stock was 176.26 t ha –1, and the minimum and maximum soil organic carbon stocks were 116.96 and 280.31 t ha –1 , respectively (Table 4 ). Table 4 Total carbon stocks and CO 2 sequestration (t ha –1 ) in four study sites Carbon Pool Study Sites Site I Site II Site III Site IV Mean TCS (MFBR) AGC 269.7 ± 7.3 260.8 ± 8.5 277.9 ± 20.0 282.0 ± 15.1 272.57 BGC 53.9 ± 1.5 50.6 ± 1.6 55.6 ± 4.1 59.7 ± 4.1 54.97 DWC 2.4 ± 0.2 2.7 ± 0.1 3.3 ± 0.3 3.8 ± 0.27 3.04 LHGC 0.03 ± 0.01 0.04 ± 0.02 0.05 ± 0.01 0.08 ± 0.01 0.05 FoSOC 161.0 ± 2.1 166.2 ± 2.7 178.6 ± 5.1 199.3 ± 7.1 176.26 FaLSOC 128.6 ± 5.8 129.1 ± 4.1 131.1 ± 6.3 135.8 ± 2.7 131.16 GLSOC 145.7 ± 5.4 154.0 ± 6.7 148.0 ± 5.6 148.8 ± 4.1 149.09 TCS 761.3 ± 9.5 763.3 ± 10.5 794.5 ± 24.6 829.4 ± 26.2 787.14 CO 2 Seq. 2,794.0 ± 35.1 2,801.5 ± 38.5 2,915.7 ± 90.5 3,043.9 ± 25.6 2,888.79 Note: site I = 1800 m.a.s.l., AGC = above ground carbon, BGC = below ground carbon, DWC = dead wood carbon, LHGC = litter, herbs and grass carbon, FoSOC = forest soil organic carbon, FaLSOC = farmland soil organic carbon, GLSOC = grassland soil organic carbon, TCS = total carbon stock The mean carbon stock in the carbon pool increased from site one to site four. The mean above-ground carbon stock varied among the four study sites of the MFBR, ranging from 260.8 ± 8.5 to 282.0 ± 15.1 t ha –1 . The soil carbon pool has a significant contribution to the total carbon stock of MFBR. The soil organic carbon of MFBR fluctuated among the study sites; site four contributed the highest soil organic carbon (199.3 ± 7.1 t ha –1 ), followed by site one (178.6 ± 5.1 t ha –1 ). The smallest amount of soil carbon was obtained for site one (161.0 ± 2.1 t ha –1 ) (Table 4 ). The mean forest soil organic carbon stock in MFBR increased with elevation from study site one to four, ranging from 161.0 ± 2.1 to 199.3 ± 7.1 t ha –1 , respectively, (Table 4 ). Similarly, the mean soil organic carbon for farmland and grassland increases with elevation and varies from 128.6 ± 5.8 to 135.8 ± 2.7 and from 145.7 ± 5.4 to 148.8 ± 4.1 t ha –1 , respectively. In comparison, the total carbon stock stored in forest biomass was higher than on grassland and farmland in the MFBR. The overall mean total carbon stocks and sequestration for all LULC types were 787.14 t ha –1 and 2,888.79 t CO 2 e ha –1 , ranging from 761.3 ± 9.5 to 829.4 ± 26.2 t ha –1 for carbon stocks and from 2,794.0 ± 35.1 to 3043.9 ± 25.6 t CO 2 e ha –1 for carbon sequestration, respectively, along the elevation gradient in MFBR (Fig. 3 ). The total AGC stocks of five dominant species in four study sites are shown in (Table 5 ). In study site I, the total above-ground carbon stock of the first five species 119.4 t ha –1 (44.8%). The highest above-ground carbon stock was contributed by Cordia Africana (41.2 t ha –1 ) followed by Combretum molle (28.3 t ha –1 ) and Lecaniodiscus fraxinifolius (22.3 t ha –1 ). The first five species of the total AGC stock was 138.9 t ha –1 (55.1%), in study site II. The highest AGC stock was found for Fagaropsis angolensis (45.9 t ha –1 ), followed by Albizia grandibracteata (30.1 t ha –1 ), and Cordia africana (25.4 t ha –1 ). The total AGC stock of the first five species was 120 t ha –1 (46%) in site III. The highest mean AGC stock was contributed by Cordia Africana (38.0 t ha –1 ), followed by Ficus mucuso (28.7 t ha –1 ) and Croton sylvaticus (21.5 t ha –1 ). The total AGC stock of the first five species contributed 90.3 t ha –1 (35.1%), in study site IV. The highest AGC stock was contributed by Allophylus abyssinicus (29.2 t ha –1 ), followed by Prunus africana (16.9 t ha –1 ), and Ficus sur (12.3 t ha –1 ) (Table 5 ) Table 5 Mean above-ground carbon stocks in five dominant species in four study sites Species WD DBH H Ind AG-C % AG-C Site I Cordia africana 0.54 35.2 26.8 22 41.2 15.3 Combretum molle 0.73 29.1 30.8 14 28.3 10.7 Lecaniodiscus fraxinifolius 0.73 25.8 21.4 26 22.3 8.4 Morus mesozygia 0.72 23.7 19.3 8 15.4 5.8 Manilkara butugi 0.95 24.1 20.4 9 12.2 4.6 Site II Fagaropsis angolensis 0.57 44.8 37.2 12 45.9 18.2 Albizia grandibracteata 0.46 45.7 32.0 6 30.1 11.9 Cordia africana 0.54 33.2 24.5 43 25.4 10.1 Mimus opslanceolata 0.86 41.2 31.2 10 20.7 8.2 Grewia mollis 0.82 24.4 24.6 27 16.8 6.7 Site III Cordia africana 0.54 42.1 34.0 40 38.0 14.7 Ficus mucuso 0.44 28.2 21.9 14 28.7 11.1 Croton sylvaticus 0.64 26.2 27.8 20 21.5 8.3 Apodytes dimidiata 0.61 19.7 17.3 28 17.8 6.9 Blighia unijugata 0.56 23.6 21.8 42 13.0 5.0 Site IV Allophylus abyssinicus 0.61 28.3 25.0 34 29.2 11.3 Croton macrostachyus 0.52 26.8 25.8 8 21.1 8.2 Prunus africana 0.69 27.1 25.0 26 16.9 6.6 Ficus sur 0.41 32.1 25.3 10 12.3 4.8 Trilepisium madagascariense 0.50 28.7 24.4 49 10.8 4.2 Note: WD = wood density (g cm –3 ), DBH = diameter at breast height (cm), H = height (m), Ind = individual number, and AG-C = above-ground carbon (t ha –1 ) In this study, DBH classes are directly related to the above-ground carbon stock while inversely related to trunk density per hectare. The trunk density of smaller-sized classes is higher than that of larger-sized classes, although they contribute a smaller amount of above-ground carbon stock per hectare. Moreover, the larger trunk diameter classes (DBH ≥ 40) showed higher above-ground carbon stock in site I (60.9%), site II (63.1%), site III (61.4%), and site IV (63.1%) as compared to smaller trunk diameter classes (DBH ≤ 40) (Fig. 4 ). Therefore, the amount of above-ground carbon stock increased with DBH, which indicated that harvesting larger-sized trees leads to carbon stock reduction. The density per hectare decreases with an increase in DBH classes. Above-ground biomass carbon stock showed a strong positive correlation with DBH classes( r = 0.85 and P = 0.05), while density per hectare showed a strong negative correlation with DBH classes ( r = − 0.89 and P = 0.05). 3.2. Carbon Stock in Land Use/Land Cover Above-ground carbon (272.57 t ha –1 ) had the highest carbon pool in the forest land carbon stock pool, followed by soil organic carbon (176.26 t ha –1 ), while LHG biomass (0.05 t ha –1 ) had the lowest carbon pool (Table 6 ). The shares of carbon pools in forest land were the following: the above-ground carbon (53.77%), below-ground carbon (10.84%), deadwood (0.59%), LHG carbon (0.009%), and soil organic carbon (34.77%).In general, the highest contribution came from above-ground carbon, followed by soil organic carbon, below-ground carbon, and deadwood carbon. In comparison, soil organic carbon stock in forest land (176.26 t ha –1 ) was higher than for grassland (149.09 t ha –1 ) and farmland (131.16 t ha –1 ) (Table 6 ). Thus, the contributions of soil organic carbon were (22.39%), (18.94%), and (16.66%) in forest land, grazing land, and farmland, respectively. The above-ground, below-ground, and deadwood carbon pools were not estimated on grassland and farmland due to the absence of trees exceeding5 cm DBH in the study plots (Table 6 ). Table 6 Carbon pools by land use/land cover (t ha –1 ) LUC AGC BGC DWC LHGC SOC Total FoL 272.57 54.97 3.04 0.05 176.26 506.88 FaL 0 0 0 0 131.16 131.16 GL 0 0 0 0.001 149.09 149.09 Set 0 0 0 0 0 0 WB 0 0 0 0 0 0 Ave 0.055 152.17 152.225 Note: AGC = above ground carbon; BGC = below ground carbon; DWC = dead wood carbon biomass; LHGC = litter, herbs, and grass carbon; SOC = soil organic carbon, FoL = forestland, FaL = farmland, GL = grassland, Set = settlement, WB = water body and Ave = Average Above-ground, below-ground and deadwood carbon amounted to about 34.62%, 6.98%, and 0.38% of forest land storage, respectively. The share of LHG biomass carbon was 0.006% in the forest land and grassland. Likewise, there was no significant contribution from the water body and settlement (Table 6 ). 3.3. Effects of land cover change on carbon stock The LULC affected the carbon stock during the 1987 to 2017 period in the study area (Table 2 and Table 7 ). The maximum carbon stock was found in forest land (99.73 million t ha –1 ) followed by farmland (4.03 million t ha –1 ), whereas the lowest was identified in grassland (0.52 million t ha –1 ) in 1987 (Table 6 ). Similarly, the maximum carbon stock was shown in forest land (92.01 million t ha –1 ) followed by farmland (5.32 million t ha –1 ) whereas the lowest was identified in grassland (0.47 million t ha –1 ) in 2017. Based on the InVEST carbon model results, the conversion of forest land and grassland into farmland led to a reduction of carbon stock in MFBR (Fig. 5 and Fig. 6 ). The chronological investigation indicated that the carbon stock declined by 7.73 million t ha –1 on forest land from 1989 to 2017, while the average carbon stock was reduced by 2.16 million t ha –1 with an annual loss of 0.07 million t ha –1 . The drop in the carbon stock is due to the reduction of forest land and grassland from 1987 to 2017. Table 7 Carbon storage and its changes in the reference years (million t ha –1 ) LUC 1987 2002 2017 Change (1987–2017) CS t ha –1 CStCO 2 e ha –1 CS t ha –1 CStCO 2 e ha –1 CS t ha –1 CStCO 2 e ha –1 CS t ha –1 CStCO 2 e ha –1 FoL 99.73 366.02 95.50 350.49 92.01 337.64 –7.73 –28.28 FaL 4.03 14.82 4.84 17.76 5.32 19.52 1.28 4.70 GL 0.52 1.92 0.45 1.68 0.47 1.75 –0.04 –0.17 Ave 34.76 127.58 33.60 123.31 32.59 119.63 –2.16 –7.9 Note: CS t ha –1 = carbon stock tonne per ha and CStCO 2 e ha –1 = carbon stock tonne carbon dioxide equivalent per ha, FoL = forestland, FaL = farmland, GL = grassland and Ave = Average In forest land, the total carbon stock shrunk from 366.02 million t CO 2 e ha –1 in 1987 to 337.64 million tCO 2 e ha –1 in 2017. Forest land and grassland cover declined by 6.6% and 0.1%, respectively, which led to a reduction of 28.38 million and 0.17 million tCO 2 e ha –1 in the previous 30 years, respectively (Table 7 ). The average carbon stock was diminished by 7.9 million tCO 2 ha –1 with an annual loss of 0.26 million tCO 2 ha –1 , which is due to the reduction of forest land and grassland from 1987 to 2017 (Table 7 ). In general, changing LULC classes reduce vegetation cover, which directly contributes to increased or reduced carbon sequestration and carbon market value. 3.4. Carbon storage valuation The global voluntary market price analysis showed that the average carbon sequestration was reduced from $ 5.55 billion in 1987 to $ 5.21 billion in 2017 in MFBR. In other words, the mean carbon value shrunk by $ 0.011 billion t/ha/year over the previous 30 years. Forest land was the most important carbon-sequestering land-use class. However, the value of carbon sequestration decreased by $ 0.071 billion t/ha/year from $ 16.84 billion in 1987 to $ 14.70 billion in 2017(Table 8 ). Table 8 The estimated carbon storage valuation using GVMP and TEEB in each LU/LC in MFBR (billion USD) LUC 1987 2002 2017 Change (1987–2017) TEEB GVMP TEEB GVMP TEEB GVMP TEEB GVMP FoL 450.1 16.84 431 15.26 415.2 14.7 -34.9 -2.14 FaL 4.4 0.06 5.36 0.07 5.87 0.08 1.47 0.02 GL 0.18 0.0087 0.16 0.007 0.17 0.008 -0.01 -0.0007 Ave 151.56 5.55 145.48 5.37 140.39 5.21 -11.17 -0.34 Note: GVMP = global voluntary market price and TEEB = Tropical Economics of Ecosystems and Biodiversity, FoL = forestland, FaL = farmland, GL = grassland and Ave = Average According to the carbon sequestration monetary value analysis of TEEB, the mean value of carbon sequestration went down from $ 1,515.62 billion in 1987 to $ 1,403.89 billion in 2017. The TEEB carbon sequestration value estimation ( $ 1,403.89 billion) is greater than that of GVMP ( $ 5.21 billion) in 2017 (Table 8 ). This significant carbon value variation between GVMP and TEEB indicated a gap in carbon value estimation methods. Furthermore, the use of different methods of carbon pricing led to uncertainty in the estimation of carbon sequestration value. Based on the estimation of TEEB and GVMP, carbon sequestration for forest and grassland values have been drastically reduced as a result of human disturbances like vegetation. The TEEB and GVMP analyses estimated the carbon value of forest land to decline by $ 34.9 billion (7.75%) and $ 2.14 billion (12.70%), respectively, while grassland declined by $ 0.01 billion (5.55%) and $ 0.0007 billion (8.04%), respectively. Moreover, the average TEEB and GVMP valuation of carbon sequestration in MFBR declined by 11.17% and 0.34%, respectively (Table 8 ). 3.5. Effects of Environmental and Disturbance Factors on Carbon Stocks in Forests Based on the one-way ANOVA analysis, the harvesting index, elevation, slope, soil pH, total nitrogen, and phosphorus had a significant influence on carbon sequestration stock (p < 0.05) (Table 9 ). Table 9 One-way ANOVA analysis of impact factors associated with carbon storage Impact factors df Mean sq F value P-value H-index 1 17018.2 3350 4.25E + 06*** Elevation 1 887619.7 8391 5.99E + 12*** Slope 1 16976.7 3371 2.38E + 09*** Soil pH 1 17020.6 3348 3.79E + 05*** TN 1 15424.1 3434 6.90E + 07*** P 1 14216.2 3257 2.37E + 04*** Note: P-value: *** – 0.001 indicates significant impact on carbon storage; df = degree of freedom Pearson correlation (r) tests exhibited both positive and negative relationships between environmental and disturbance factors with carbon stock in MFBR (Table 10 ). The above-ground carbon stock showed a positive relationship with soil organic carbon (r = 0.10), elevation (r = 0.08), TN (r = 0.31), and P (r = 0.11), while a significant negative relationship with the harvesting index (r = − 0.21) and pH (r = − 0.09). Table 10 Pearson's correlation coefficient matrix for environmental and disturbance factors (N = 8) in MFBR Variables AGC SOC HI Ele Slo pH TN P AGC SOC 0.10 ns HI –0.21 * –0.13 ns Ele 0.08 ns 0.39 *** 0.06 ns Slo –0.04 ns –0.03 * –0.09 ns 0.27 *** pH –0.09 ** –0.22 ** –0.02 ns –0.69 *** –0.09 ** TN 0.31 *** 0.27 *** –0.31 ** 0.10 ns –0.12 ns –0.21 *** P 0.11 ns 0.14 ns –0.05 ns 0.06 ns –0.31 * –0.16 ** 0.19 *** Note: The magnitude indicates the degree of correlation and Positive signs indicate positive correlation and negative signs indicate inverse relation. *p < 0.05, **p < 0.01, ***p <0.001, ns = no significance, N = number of variables, MFBR = Majang Forest Biosphere Reserves, AGC = above ground carbon, SOC = soil organic carbon, HI = Harvesting index, Ele = elevation, Slo = slope, TN = total nitrogen, and P = available phosphorus. The variation in elevation gradient positively affects the above-ground carbon stock and soil organic carbon in MFBR. The lower elevation has lower AG carbon stocks, while the higher elevation has higher carbon stocks in above-ground carbon. Soil organic carbon showed a significant positive correlation with elevation (r = 0.39), and TN (r = 0.27), but a significant negative relationship with slope (r =–0.03), and pH (r =–0.22). Similarly, the harvesting index showed a negative relationship with slope (r = − 0.09), pH (r = − 0.02), TN (r = -0.31), and P (r = -0.05) and a positive correlation with elevation (r = 0.06). Soil pH showed a significant negative relationship with TN (r = − 0.21), and P (r = − 0.16) (Table 10 ). 4. Discussion 4.1. Carbon Stock in Carbon Pools and Land Use/Land Cover The results of this study on carbon stocks show the importance of biosphere reserves for climate change mitigation. The study has confirmed a diverse variation in LULC and carbon stock pools along the elevation gradient in MFBR. For instance, the mean carbon stock in the LULC carbon pool increases along the elevation gradient (from site I to site IV) (Table 4 ). This finding is similar to the earlier finding that reported a positive relationship between elevation and carbon stock (Sharma et al. 2010 ). Similarly, the amount of CO 2 e in all LULC types increases with the elevation gradient, which is linked to higher disturbances observed at lower elevations (Getaneh et al. 2019 ). On the other hand, high rainfall in higher elevations leads to increased moisture availability and microbial activity (Getaneh et al. 2019 ). In contrast, other findings indicate that carbon stock declines with increasing elevation (Moser et al. 2011 ). In comparison, the total carbon stock stored in forest land was higher than on grassland and farmland in MFBR (Table 4 ). Higher carbon stocks in forest land may be due to more vegetation cover and plant material decomposition (Wójcik-Leń and Leń 2021 ). Chuai et al. ( 2013 ) demonstrated that forest land also releases and absorbs huge amounts of carbon into and out of the atmosphere. Moreover, LULC conversion is the most important factor that causes the reduction and transformation of carbon sequestration in terrestrial ecosystems (Zhang et al. 2015 ). The above-ground carbon stock varied among study sites in MFBR (Table 4 ). The highest above-ground carbon stock was identified in study site IV (282 billion t ha –1 ), while the lowest (269.7 t ha –1 ) was in study site I of MFBR (Table 4 ). These results are consistent with carbon sequestration in the tropical Afromontane forest of Ethiopia (107–285) (Hamere et al. 2015 , Yelemfrhat and Teshome 2015 , Abyot et al. 2019 , Getaneh et al. 2019 , Abreham et al. 2022 ) and in other tropical forests (170–271) (Spracklen and Righelato 2014 , Willcock et al. 2014 , Tanner et al. 2016 ). This carbon stock difference may be related to DBH, height, and basal area of a tree. The variations in carbon sequestration at local, regional, and national levels could be the effect of human disturbances and environmental factors in the study site of MFBR. Similarly, the mean soil organic carbon stock (0–30 cm soil depth) was 176.26 t ha –1 in MFBR. The highest soil organic carbon stock was found in study site IV (199.3 t ha –1 ), while the lowest was in study site I (161 t ha –1 ) of MFBR (Table 4 ). The mean soil organic carbon in MFBR was higher than earlier estimated carbon stock in other tropical forests (121–123 t ha –1 ) (Lal 2004 ), a forest in Colombia (96 t ha –1 ) (Sierra et al. 2007 ), Singapore (110 t ha –1 ) (Ngo et al. 2013 ), the Humbo forest of Ethiopia (168 t ha –1 ) (Alefu et al. 2015 ), and the Awi Zone of Ethiopia (149.2 t ha –1 ) (Getaneh et al. 2019 ). Nevertheless, the mean soil organic carbon stock of MFBR was found to be lower than the soil organic carbon stock of tropical Afro-montane forests (194 to 288 t ha –1 ) (Selmants et al. 2014 ). The total carbon stocks varied from 487.04 to 544.87 t ha –1 in the study sites (Table 4 ), which is almost similar to the results quantified in the Adaba-Dodola community forest (507 t ha –1 ) (Nega et al. 2015 ), Gerba-Dima moist Afromontane forest (508.9 t ha –1 ) (Abyot et al. 2019 ) in Ethiopia, and IPCC (130–510 t ha –1 ) (Penman et al. 2003). Similarly, the mean total carbon stock of MFBR is higher than other findings in the Sheka Forest (461 t ha –1 ) (Ayehu et al. 2021 ), Humbo Forest (213.43 t ha –1 ) (Alefu et al. 2015 ), and Singapore (337 t ha –1 ) (Ngo et al. 2013 ). This difference could be due to the existence of diverse tree species, elevation, human disturbance, climate, and microbial activities. Moreover, the comparison of five carbon stock pools with other tropical forests studies were showed in the Table 11 . Table 11 Comparison of carbon stock with other tropical forests studies Study Area Carbon Stock in different pools (t ha − 1 ) Source AGBC BGBC DWC LHGC SOC TCS Anshirava forest 180.18 77.51 1.36 2.69 111.43 338.18 (Fikirte et al. 2022) Awi forests 191.7 38.24 - - 149.3 380.8 (Gebeyehu et al. 2019 ) Bangladesh forest 96.5 14.6 - 4.2 168.1 283.4 (Ullah and Al-Amin 2012) Central Africa 168.6 39.5 - - - 208.1 (Ekoungoulou et al. 2015) Egdu forest 278.08 55.62 - 3.47 277.56 614.73 (Adugna et al. 2013) Gedo forest 281 56.1 2.37 0.41 183.7 523.6 (Hamere et al. 2015 a) Gerba Dima forest 243.8 45.9 4.64 0.03 292.1 586.7 (Abyot et al. 2019 ) Gesha-Sayilem forest 164.5 32.9 1.27 137.67 362.4 (Admassu et al. 2019) Majang Forest 272.57 54.97 3.04 0.05 176.26 506.88 Present study Sheka Forest 176.3 44 14.7 - 233 461 (Ayehu et al. 2021 ) Singamba forests 142.3 38.45 - - - 175.82 (Mattia and Sesay 2020) Tara Gedam forest 306.4 61.5 - 0.9 274.3 643.1 (Mohammed et al. 2014) Tulu Lafto 218.4 43.5 6.2 2.4 128.9 399.4 (Fekadu et al. 2021) Upper Omo-Gibe 185 37 - 32 178 432 (Abreham et al. 2022 ) Usambra Forest 427 85.4 418 - - 930.4 (Munishi and Shear 2004) Wujig-Waren forest 65.8 11.4 - 2.25 102.3 181.78 (Negasi et al. 2017 ) Note: AGBC = carbon storage in above ground biomass, BGBC = carbon storage in below ground biomass; DWC= carbon storage in dead wood biomass, LHGC = carbon storage in litter, herbs, and grass biomass, and SOC = soil organic carbon. In the study sites, dominant species with higher basal areas demonstrated the highest carbon stock (Table 5 ). Individual plant species with higher DBH values contribute significantly to carbon sequestration in MFBR, while their extinction has a significant impact on biomass, carbon sequestration, and carbon trading. Deforestation and forest degradation also have an impact on the amount of carbon sequestered in larger trees with larger diameters (Gibbs et al. 2007 ). The above- and below-ground carbon in the study sites of MFBR was higher than the carbon value that was quantified by IPCC (Penman et al. 2003, Hiraishi et al. 2014 ). This difference in above-ground carbon might be associated with the greater tree height, DBH, and basal area in MFBR. 4.2. Effects of Land Use/Land Cover Change on Carbon Stock This study shows how the carbon stock pool (above-ground, below-ground, deadwood, litter, and soil) is affected by LULC change in the study period (1987–2017) (Table 6 and Fig. 6 ). Forest land showed higher carbon stock as compared to grassland and farmland in MFBR (Table 7 ). Similarly, other findings indicated higher carbon stock in forest land as compared to other LULC categories (Negasi et al. 2017 , Rajput et al. 2017 ). This significant difference in carbon stock across land cover categories could be due to the difference in tree size and trunk density per hectare. Furthermore, lower carbon stock was found in farmland that has been altered by intensive subsistence cultivation, deforestation, or anthropogenic disturbance that affected the tree, shrub, and herb growth (Tessema and Kibebew 2019 ). The InVEST carbon model results showed that conversion of forest land and grassland into farmland leads to a reduction in carbon stock in the study area (Fig. 5 ). The chronological investigation indicated that the highest carbon stock was reduced in forest land among all LULC types. Carbon stock reduction was identified in all carbon pools as a result of forest land and grassland being converted into farmland in the study period (Fig. 6 ). This reduction is similar to phenomena in other reports (Singh et al. 2018 ). This could be due to the expansion of settlements (urban and rural), agriculture expansion, and population pressure, which lead to deforestation and forest degradation (Solomon 2016 ). The increased urbanisation or settlement enlargement occurs at the expense of other LULC categories like farmland, forest land, and grassland (Bai et al. 2012 , Price et al. 2015 ). 4.3. Carbon Storage Valuation Effective carbon stock valuation is highly relevant to the successful management of climate change impacts. It is also important for evaluating the relative advantages of climate adaptation activities and mitigation measures over time. The global voluntary market price analysis showed that the mean carbon sequestration value declined from 1987 to 2017 (Table 8 ). This carbon stock value reduction is linked to the change from forest cover to other LULC type, which is consistent with other findings (Abreham et al. 2022 ). Similarly, according to the carbon sequestration monetary value analysis of TEEB, the mean value of carbon sequestration dropped from 1987 to 2017. The TEEB carbon sequestration value estimation is greater than that of GVMP in all study periods for MFBR (Table 8 ). This significant carbon value variation between GVMP and TEEB indicated a gap in carbon value estimation methods. Furthermore, using different methods of carbon pricing led to uncertainty regarding the carbon sequestration value (Canu et al. 2015 ). In addition, according to the estimation of TEEB and GVMP, forest and grassland carbon sequestration values have drastically shrunk as a result of such human disturbances as deforestation. Moreover, the average TEEB and GVMP valuation of carbon sequestration in MFBR declined (Table 8 ). 4.4. Effects of Environmental and Disturbance Factors on Carbon Stocks in Forests The relationship between carbon and environmental and disturbance factors has become more and more important in understanding the carbon sequestration cycle. In this study, environmental and anthropogenic factors highly influence forest cover and carbon sequestration in the pools. Accordingly, the variation in carbon stock was closely related to environmental and human disturbance. Elevation, slope, and harvesting index are important environmental and disturbance factors resulting in major differences in carbon stock among study sites in MFBR (Table 10 ). Comparatively, elevation was the most significant environmental factor influencing carbon sequestration in the pools: carbon stock pools increase with elevation. The correlation of carbon stock with elevation could be due to variations in disturbance and precipitation along the elevation gradient. This is consistent with the findings from other studies (McGroddy and Silver 2000 , Eisenlohr et al. 2013 ). The harvesting index and slope were also among the most important environmental factors that affected the variability of carbon in the pools. Carbon stock pools increase with decreasing slope, which may be related to the moisture and soil properties of the study sites. Furthermore, tree harvesting were the primary factors responsible for the decrease in biomass and carbon stocks. This shows that clear cutting contributes to higher carbon emissions into the atmosphere (Huang and Asner 2010 ). As a result, forest conservation and sustainable management help reduce carbon emissions and keep biomass and carbon in carbon pools (Sasaki et al. 2016 ). The correlation between elevation and above-ground and soil carbon stocks was positive. This finding is similar to an earlier study that stated a positive relationship between elevation and carbon stock (Khadanga and Jayakumar 2020 ). The positive correlation between soil carbon and elevation may be due to lower temperature and increasing moisture content with increasing elevation (Hoffmann et al. 2014 , Selmants et al. 2014 ). The rate of organic matter decomposition is sluggish in low temperatures, which leads to reduced microbial activities, thus assisting the increments of soil organic matter and thicker litter layer development (Walz et al. 2017 ). High organic matter content in soils at higher elevations has also been reported in other Afromontane forests of Ethiopia (Tamrat 1994 , Schindlbacher et al. 2010 ). This situation leads to a reduction in CO 2 release from the soil, which in turn increases soil organic carbon stocks. The correlation between slope and above-ground biomass and soil carbon stocks was negative. Greater slope with decreasing soil moisture resulted in decreased vegetation cover, hence a decline in above-ground carbon and soil carbon stocks. Similarly, the correlation between the harvesting index with above-ground biomass and soil carbon stocks was negative. This indicated that the harvesting index (selective exploitation) has a significant impact on above-ground carbon and soil carbon stocks. Thus, illegal harvesting focused on big trees for timber production leads to a reduction of carbon stocks, which is consistent with other findings (Sasaki et al. 2012 , Lindsell and Klop 2013 ). 5. Conclusion In this study, the results showed high carbon stocks in MFBR, which is higher than other findings in moist Afromontane forests in Ethiopia. As regards carbon pools, the mean above-ground carbon and soil organic carbon stocks were shown to be higher than other pools in MFBR. The total carbon stock and economic value for the 2017 LULC data are lower than for the 1987 LULC data. The conversion of forest land and grassland into farmland reduces the carbon stock and its economic value in MFBR. Forest cover and carbon sequestration in the pools are highly influenced by environmental and anthropogenic factors. Accordingly, the variation in carbon stock was closely related to environmental and human disturbance. Elevation, slope, and harvesting index are important environmental and disturbance factors resulting in major differences in carbon stock among the study sites in MFBR. The correlation between elevation and above-ground biomass and soil carbon stocks was positive, while a negative relationship was found for slope and harvesting index. This calls for urgent attention to implement successful conservation and sustainable use of forest resources in the biosphere reserve. Declarations Acknowledgements We would like to thank Addis Ababa University for financial and logistic support and Mr Demese Jemaneh for his contribution during data collection. The authors also wish to thank the University of Agriculture in Krakow, Poland for their professional linguistic support from a professional language services agency. This paper was produced under scientific efforts and in cooperation carried between Dr Tomasz Noszczyk and three Ethiopian universities. Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Availability of data and materials The available data can be upon request the Corresponding Author. Competing interests The authors declare that they have no competing interests Funding This research did not receive any specific grant from funding agencies in the public, commercial or non-profit sectors. Authors' contributions Semegnew Tadese conceptualized, collected, analyzed data and wrote a draft manuscript. Teshome Soromessa curated data and supervised work. Abreham Berta validated data using software and reviewed the review manuscript. Tomasz Noszczyk reviewed and edited the draft manuscript. Getaneh Gebeyehu reviewed and supervised the work. Mengistie Kindu reviewed and edited manuscript. Authors' information 1 Addis Ababa University, College of Natural and Computational Sciences, Centre for Environmental Science, Ethiopia. 2 Wolkite University, College of Agriculture and Natural Resource, Department of Natural Resource Management, Ethiopia. 3 Injibara University, College of Natural and Computational Sciences, Department of Biology, Injibara, Ethiopia. 4 University of Agriculture in Krakow, Faculty of Environmental Engineering and Land Surveying, Department of Land Management and Landscape Architecture, 21 Mickiewicza Street, 31-120 Krakow, Poland. 5 Institute of Forest Management, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Hans-Carl-von-Carlowitz-Platz 2, D-85354 Freising, Germany ORCID SemegnewTadese Feyisa https://orcid.org/0000-0001-6049-6309 Teshome Soromessa https://orcid.org/0000-0002-3680-0240 Abreham Berta https://orcid.org/0000-0002-3076-3768 Getaneh Gebeyehu https://orcid.org/0000-0003-3777-2533 Tomasz Noszczyk https://orcid.org/0000-0001-6420-633X Mengistie Kindu https://orcid.org/0000-0003-4026-8207 References Abreham, B. 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Proposed algorithm for the identification of rural areas with regard to variability of soil quality. Computers and Electronics in Agriculture 188 :106318. Yelemfrhat, S. T., and S. Teshome. 2015. Carbon stock variations along altitudinal and slope gradient in the forest belt of Simen Mountains National Park, Ethiopia. Am J Environ Prot 4 :199-201. Zachos, J. C., G. R. Dickens, and R. E. Zeebe. 2008. An early Cenozoic perspective on greenhouse warming and carbon-cycle dynamics. nature 451 :279-283. Zanne, A. E., G. Lopez-Gonzalez, D. A. Coomes, J. Ilic, S. Jansen, S. L. Lewis, R. B. Miller, N. G. Swenson, M. C. Wiemann, and J. Chave. 2009. Global wood density database. Zhang, M., X. Huang, X. Chuai, H. Yang, L. Lai, and J. Tan. 2015. Impact of land use type conversion on carbon storage in terrestrial ecosystems of China: A spatial-temporal perspective. Scientific reports 5 :1-13. Zheng, H., L. Wang, W. Peng, C. Zhang, C. Li, B. E. Robinson, X. Wu, L. Kong, R. Li, and Y. Xiao. 2019. Realizing the values of natural capital for inclusive, sustainable development: Informing China’s new ecological development strategy. Proceedings of the National Academy of Sciences 116 :8623-8628. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 07 Dec, 2023 Read the published version in Carbon Balance and Management → Version 1 posted Editorial decision: Major revision 11 May, 2023 Reviews received at journal 26 Mar, 2023 Reviewers agreed at journal 12 Mar, 2023 Reviewers invited by journal 13 Feb, 2023 Editor assigned by journal 13 Feb, 2023 Submission checks completed at journal 13 Feb, 2023 First submitted to journal 08 Feb, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2564786","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":175765095,"identity":"acb4307f-f22f-4113-b726-45226e102b86","order_by":0,"name":"Semegnew Tadese","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0ElEQVRIiWNgGAWjYPACZgZ+EJVQQKyGA0Atkg0gLQakaDE4AGIRo0W+vTvx8Ycaaznj86sTPzwwYJDnFzuAX4vBmbObDQ4cSzc2u/F2swTQYYYzZycQ0CKRu03iANvhxG03zm4AaUkwuE1Ai/z8t9t/HPh3uH7zjLObfxClheEG7zaGg22HEwz4e7cRZ4vBmdzNEmf70g1nAPVaJBhIEPaLfPvZjR8qvlnL8/ef3XzzR4WNPL80IYfBgQRYpQSxykGA/wApqkfBKBgFo2AkAQDC0UnIcP4lQgAAAABJRU5ErkJggg==","orcid":"","institution":"Addis Ababa University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Semegnew","middleName":"","lastName":"Tadese","suffix":""},{"id":175765096,"identity":"ec04111e-d7e1-499f-9c92-054fb297eb8d","order_by":1,"name":"Teshome Soromessa","email":"","orcid":"","institution":"Addis Ababa University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Teshome","middleName":"","lastName":"Soromessa","suffix":""},{"id":175765100,"identity":"d01eef35-41e5-4b32-88b5-77d0f3012988","order_by":2,"name":"Abreham Berta Aneseyee","email":"","orcid":"","institution":"Wolkite University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Abreham","middleName":"Berta","lastName":"Aneseyee","suffix":""},{"id":175765101,"identity":"4b21dbf4-c842-4876-bdd6-99f82afe3cdd","order_by":3,"name":"Getaneh Gebeyehu","email":"","orcid":"","institution":"Injibara University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Getaneh","middleName":"","lastName":"Gebeyehu","suffix":""},{"id":175765102,"identity":"94e2ee00-e007-46e5-bd52-dd327040a759","order_by":4,"name":"Tomasz Noszczyk","email":"","orcid":"","institution":"University of Agriculture in Krakow","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Tomasz","middleName":"","lastName":"Noszczyk","suffix":""},{"id":175765103,"identity":"b9e8564b-56ef-4cd5-ad24-ed9463a8a884","order_by":5,"name":"Mengistie Kindu","email":"","orcid":"","institution":"Technical University of Munich","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mengistie","middleName":"","lastName":"Kindu","suffix":""}],"badges":[],"createdAt":"2023-02-08 13:29:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2564786/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2564786/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13021-023-00243-z","type":"published","date":"2023-12-07T15:02:04+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":32936509,"identity":"7ec7eea6-15c0-4bac-8e31-871a17d13f91","added_by":"auto","created_at":"2023-02-14 19:13:39","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":334599,"visible":true,"origin":"","legend":"\u003cp\u003eLocation of the study sites (site I-IV)(\u003ca href=\"https://earthexplorer.usgs.gov/\"\u003ehttps://earthexplorer.usgs.gov\u003c/a\u003e)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-2564786/v1/b769efe68c4c22fb0adf2d09.png"},{"id":32936208,"identity":"dc279c36-2c5c-45c2-b7f2-4b9066243423","added_by":"auto","created_at":"2023-02-14 19:05:39","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":40822,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of the methodology\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-2564786/v1/cd180e8cf782e075618ef3f5.png"},{"id":32936213,"identity":"e47e18cc-2fc1-40d7-8252-7a9870c59bcc","added_by":"auto","created_at":"2023-02-14 19:05:39","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":21855,"visible":true,"origin":"","legend":"\u003cp\u003eTotal carbon stock t\u0026nbsp;ha\u003csup\u003e–1\u003c/sup\u003e and tCO\u003csub\u003e2\u003c/sub\u003ee\u0026nbsp;t\u0026nbsp;ha\u003csup\u003e–1\u003c/sup\u003e for each plot\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-2564786/v1/2d45864eb92f523476bedf82.png"},{"id":32936207,"identity":"a924d395-7120-4367-a5ba-593aba30116c","added_by":"auto","created_at":"2023-02-14 19:05:39","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":95759,"visible":true,"origin":"","legend":"\u003cp\u003eDensity and AG carbon stock along DBH classes in MFBR\u003c/p\u003e\n\u003cp\u003eNote: A = \u0026lt; 10, B = 10.1–20, C = 20.1–30, D = 30.1–40, E = 40.1–50, F = 50.1–60, and G = \u0026gt; 60 cm\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-2564786/v1/2c7613d8fb6ab21d7b25909c.png"},{"id":32936212,"identity":"84094a1c-2260-40c8-bb07-b49479390d4a","added_by":"auto","created_at":"2023-02-14 19:05:39","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":230789,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial and temporal description of carbon stocks for the reference years\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-2564786/v1/80987838e59d3d6495aa33b7.png"},{"id":32936510,"identity":"01349275-aedc-4e96-b181-d9c779919edc","added_by":"auto","created_at":"2023-02-14 19:13:39","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":216152,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of carbon pools per pixel\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-2564786/v1/3fc15bafc7f5ed58762c01ca.png"},{"id":32936722,"identity":"dfaa23b6-004c-4aec-984e-d30c967e89ca","added_by":"auto","created_at":"2023-02-14 19:21:39","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":30790,"visible":true,"origin":"","legend":"\u003cp\u003eCarbon storage in MFBR over the last 30 years (1987–2017)\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-2564786/v1/4f3182819311240525c69638.png"},{"id":47989506,"identity":"8cf9520d-cbbc-42b7-b17f-e98c1a2688f4","added_by":"auto","created_at":"2023-12-11 15:09:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1795526,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2564786/v1/9e825218-7ee7-43a4-98b6-8b4bb9af5c0f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Carbon Storage Dynamics and its Economic Values in Tropical Moist Afromontane Forests, South-West Ethiopia","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eForests play an important role in the global carbon (C) cycle, sequestering carbon dioxide and thereby mitigating climate change (Hunter et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, Sheikh et al. \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). They control climate change by sinking over 200\u0026nbsp;billion metric tons of carbon a year and converting atmospheric carbon into biomass through photosynthesis (Lal \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2010\u003c/span\u003e, Marvin et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). They are significant carbon sinks, accounting for half of the above-ground biomass in vegetation (Hunter et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Moreover, the current carbon stock in global forests is estimated at861 Gt of carbon, of which 363 and 383 Gt of carbon are stored in the living biomass and soil (up to 1 Mt), respectively (Pan et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, Goodman and Herold \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, Dar et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe global carbon cycle has sparked the most interest in recent years as it became clear that rising levels of CO\u003csub\u003e2\u003c/sub\u003e in the atmosphere cause rapid changes in global climate (Change \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2007\u003c/span\u003e, Zachos et al. \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). In the international dialogue, issues such as biodiversity loss, ozone layer depletion, and desertification have taken a central stage (Speth and Haas \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Humans exert significant pressure on the carbon cycle through the use of large amounts of oil, gasoline, and coal, as well as deforestation and land degradation (Sabine et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2004\u003c/span\u003e, Clark and York \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Deforestation and land degradation are also the major sources of anthropogenic greenhouse gas emissions in most tropical countries (Houghton \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2005\u003c/span\u003e, Pearson et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).Changes in land use/land cover (LULC) are reducing globally significant carbon storage that is currently sequestering CO\u003csub\u003e2\u003c/sub\u003e from the atmosphere, which makes them critical to long-term climate stability (McGuire et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2001\u003c/span\u003e, Stephens et al. \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Every year, tropical deforestation accounts for 15\u0026ndash;25% of global greenhouse gas emissions (Houghton \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Liu et al. (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) indicated that between 1993 and 2012, the global Above-Ground Carbon (AGC) declined at a rate of \u0026minus;\u0026thinsp;0.07 PgC/yr due to the loss of tropical forest area. Pan et al. (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) reported that the global soil organic carbon (SOC) decreased by 7.7% (12.7 PgC) between 1990 and 2007, owing primarily to tropical deforestation. Specifically, timber extraction and logging are accountable for over half of forest degradation (52%), followed by fuel wood extraction and charcoal production (31%), induced fire (9%), and overgrazing (7%) in the tropics (Hosonuma et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). This showed that forest degradation and deforestation are the main sources of greenhouse gas (GHG) emissions in most tropical countries.\u003c/p\u003e \u003cp\u003eThe InVEST models typically quantify and investigate trade-offs associated with alternative management options as well as indicate areas where natural capital projects can improve land conservation and development (Seppelt et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, Sharps et al. \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Zheng et al. \u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). InVEST models are spatially explicit (they use maps as input and output) and produce results in either biophysical (e.g., tons of carbon sequestered) or economic terms (e.g., the net present value of that sequestered carbon) (Nelson et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2009\u003c/span\u003e, Imran \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Such a model effectively estimates carbon stock in the landscape ecosystem using carbon pools and LULC classes as input data(Fu et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Therefore, it provides carbon stock estimates over a large area for trend analysis (Sharps et al. \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Nyamari and Cabral \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCarbon valuation is a monetary estimation of carbon related to small changes in emissions of carbon dioxide (CO\u003csub\u003e2\u003c/sub\u003e) (Smith and Braathen \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, Isacs et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Carbon valuation is essential for evaluating the relative positive effects of climate mitigation and adaptation policy over time (Pearce \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2003\u003c/span\u003e, Tol \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2005\u003c/span\u003e, Brand\u0026atilde;o et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, Locatelli et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Future carbon benefits are strongly connected to risk management concerns because future values are affected by the chance that benefits may not emerge as expected (Ackerman et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2009\u003c/span\u003e, Bowen and Wittneben \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Carbon valuation is complicated, and multiple methodologies and sources are used depending on whether a societal or market perspective is used (Nelson et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2009\u003c/span\u003e, Tallis and Polasky \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Although there is no a single accepted technique for calculating carbon's social worth(Pearce \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2003\u003c/span\u003e, Valatin \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), Global Voluntary Market Price (GVMP) and Tropical Economics of Ecosystems and Biodiversity (TEEB) databases are used to estimate carbon stock and its economic values(Van der Ploeg et al. \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2010\u003c/span\u003e, De Groot et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMoist Afromontane forests provide a variety of ecosystem services, such as watershed protection, groundwater regulation, food control, prevention of soil erosion, provision of non-timber forest products, and climate change mitigation (Banana et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, Negasi et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Negasi et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). More specifically, the Majang Forest Biosphere Reserve (MFBR) is one of the recently registered forest biosphere reserves in southern Ethiopia, which is part of the remnants of moist Afromontane forests that continue to provide essential services for people's livelihood (Choudhary et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Anthropogenic activities have gradually degraded these moist Afromontane forests over time because they have not been managed sustainably (Demel et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2010\u003c/span\u003e, Kefelegn et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, Meron et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Moreover, estimating changes in carbon stock and its economic value due to changes in forest cover has not been investigated yet. Understanding this encourages decision-makers to create a carbon credit negotiation and sustainable development and conservation of MFBR. Therefore, the aims of this study were to (i) examine the change in carbon stocks due to forest cover change over the last 30 years, (ii) map the carbon stock dynamics and its economic value, and (iii) analyse the impacts of environmental and disturbance factors on carbon stocks.\u003c/p\u003e"},{"header":"2. Material And Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. The Study Area\u003c/h2\u003e \u003cp\u003eThe study was conducted in the Majang Forest Biosphere Reserve (MFBR), situated in the Majang Zone, \u003cem\u003eGambella People National Regional State\u003c/em\u003e of Ethiopia. It has unique biogeography and shares a boundary with Sale Nono Woreda of the Oromia Regional State; Anderacha, Yeki, Sheka, and Gurafereda Woreda of the Southern Nations, Nationalities, and Peoples' Region (SNNPR). It covers a total area of 233,254 ha of forest and agricultural land and rural settlements and towns (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). MFBR is located between the latitudes of 07\u0026deg;08'00\" N and 07\u0026deg;50'00\" N, and the longitudes of 34\u0026deg;50'00\" E and 35\u0026deg;25'00\" E, with elevations ranging from 562 m to 2444 m.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIt is distinctive biogeography and shares a boundary with Illubabor Zone of Oromia Regional State; Sheka and Bench-Maji Zones of the Southern Nations, Nationalities, and People Region (SNNPR).\u003c/p\u003e \u003cp\u003eThe climate in the area is generally hot and humid, which is marked on most rainfall maps of Ethiopia as the wettest part of the country. The annual average rainfall and temperature is 1774 mm and 22.1\u0026deg;C, the means annual minimum and maximum monthly temperature ranges between 13.9 and 31.8\u0026deg;C in Tinishu Meti metrological station respectively. The annual average rainfall and temperature are 2053 mm and 20.5\u0026deg;C, the means annual minimum and maximum monthly temperature ranges between 11.8 and 29.7\u0026deg;C in Ermichi Metrological station respectively.\u003c/p\u003e \u003cp\u003eThe vegetation in the area is divided into several categories based on its life forms, including high natural forests, woodlands, bush lands, and grasslands. \u003cem\u003eEuphorbiaceae, Rubiaceae\u003c/em\u003e, and \u003cem\u003eMoraceae\u003c/em\u003e were the most prevalent families in MFBR, with 13 species (8%), nine genera (7.8%), twelve species (7.4%) and eight genera (7%), and ten species (6.1%), and five genera (4.3%), respectively(Tadese et al. \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Sampling Design\u003c/h2\u003e \u003cp\u003eA systematic sampling design was used to arrange quadrats and transects as well as to collect vegetation data (Kent \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The study area was stratified into four sites using Digital Elevation Model (DEM) in the Arc GIS software. These were site I (\u0026lt;\u0026thinsp;1200 m.a.s.l), site II (1200\u0026ndash;1500 m.a.s.l), site III (1500\u0026ndash;1800 m.a.s.l) and site IV (\u0026gt;\u0026thinsp;1800 m.a.s.l) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).The number of transect lines varied among study sites. A total of 140 quadrats were established for vegetation and forest soil data collection (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Farmland (40) and grassland (40) soil samples were acquired from adjoining forestland in each study sites of the MFBR.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTopographic and soil characteristics of the study sites\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy site\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEle (m)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSlo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTN (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP (ppm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eArea (ha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSite I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1,042\u0026thinsp;\u0026plusmn;\u0026thinsp;42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e5.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e6.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e16.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e22,826.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSite II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1,365\u0026thinsp;\u0026plusmn;\u0026thinsp;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e5.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e6.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e17.48\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e25,220.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSite III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1,635\u0026thinsp;\u0026plusmn;\u0026thinsp;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e7.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e6.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e19.02\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14,053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSite IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2,011\u0026thinsp;\u0026plusmn;\u0026thinsp;42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e11.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e5.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e20.23\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11,783.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eNote: TN\u0026thinsp;=\u0026thinsp;total nitrogen, P\u0026thinsp;=\u0026thinsp;phosphorus, pH\u0026thinsp;=\u0026thinsp;soil pH, Ele\u0026thinsp;=\u0026thinsp;elevation, Slo\u0026thinsp;=\u0026thinsp;slope, SP\u0026thinsp;=\u0026thinsp;sample plots, and ppm\u0026thinsp;=\u0026thinsp;part per million\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe study sites polygon was digitized using Google Earth by elevation classes. The quadrats' X-Y coordinates were generated using GIS tools and loaded to a global positioning system (GPS) receiver for tracking quadrats. Later, a measuring tape was used to layout 20\u0026times;20 m\u003csup\u003e2\u003c/sup\u003e (400 m\u003csup\u003e2\u003c/sup\u003e) quadrats in each site in the biosphere. The sampling intervals between the transect line and the quadrats were 2 km apart. Biomass data for tree census in the tree sites were collected on 5.6 ha (4 sites\u0026thinsp;=\u0026thinsp;140 quadrats). Above-ground biomass was estimated using a non-destructive sampling method by measuring the diameter at breast height (DBH), tree height, and wood density (Chave et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Data Collection Methods\u003c/h2\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1. Biomass and soil data\u003c/h2\u003e \u003cp\u003eDuring the field data collection, the main carbon measurement activities concerned above-ground tree biomass, below-ground biomass, leaf litter, deadwood, and soil organic carbon. Individual trees with a DBH of \u0026gt;\u0026thinsp;5 cm (Pearson \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) were measured in each plot with a calliper and measuring tape (at 1.3 m). Each tree was individually recorded, along with its species name and ID. Clinometers and a meter tape were used to measure the heights of all individual trees in the sampling quadrats. Overhanging species were excluded, but trees with trunks inside the sampling plot and branches outside were included (MacDicken \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e1997\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFive rectangular subplots of 1\u0026times;1 m were established at the four corners and centre of each main plot for litter, herbs, and soil data collection. Where the samples were large, the fresh weight of the total sample was recorded in the field, and a manageable-sized (200 g) evenly mixed subsample was brought to the laboratory to determine dry biomass and percentage carbon (Pearson et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).The biomass in the pool of leaf Litter, Grass, and Herbs (LGH) was estimated using destructive sampling. Herbaceous samples were collected by clipping and weighing all vegetation before placing it in a sample weighing bag and transporting it to the laboratory to determine the oven-dry weight of the biomass. Forest floor litter materials (dead leaves, twigs, fruit, and flowers) were collected from a 1 m\u003csup\u003e2\u003c/sup\u003e area. The living components, primarily grass and herbs, were harvested and weighed as well. Dry weight was determined in laboratory samples of the materials. Within the 400 m\u003csup\u003e2\u003c/sup\u003e plot, standing dead trees, fallen stems, and fallen branches with a DBH\u0026thinsp;\u0026ge;\u0026thinsp;5 cm were measured (Pearson et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSoil samples were taken with a soil auger from the topsoil at a depth of 0\u0026ndash;30 cm, which is recommended as the default sampling depth for soil (Dick et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). Soil samples were taken from five different locations in each plot, four from the quadrat's corners and one from the quadrat's centre. A total of 220 soil samples, 140 from forestland, 40 from farmland, and 40 from grassland were collected, composited separately, labelled, and transported to the laboratory. To determine soil bulk density, the soils were collected on the centre of the quadrats using a stainless core sampler, then placed in plastic bags, and transported to the laboratory for dry weight determination. Fresh wet soil weights were measured in the field with a kitchen balance with 0.1 g precision. A composite sample of 200 g was taken from each quadrat to analyse its chemical composition (Pearson et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2. Environmental and disturbance factors\u003c/h2\u003e \u003cp\u003eEnvironmental factors such as aspect, slope, and elevation were measured and recorded for each of the 140 quadrats using a Garmin GPS receiver and clinometers. Elevation was arranged into four elevation (m.a.s.l) ranges (sites I\u0026ndash;IV), namely: 1\u0026thinsp;=\u0026thinsp;1200, 2\u0026thinsp;=\u0026thinsp;1200\u0026ndash;1500, 3\u0026thinsp;=\u0026thinsp;1500\u0026ndash;1800 and 4\u0026thinsp;=\u0026thinsp;\u0026gt;\u0026thinsp;1800. The slope range was classified into three major slope classes following (Gebeyehu et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). As a result, the classes were: 1) flat\u0026thinsp;\u0026lt;\u0026thinsp;10, 2) intermediate 10\u0026ndash;20, and 3) steep\u0026thinsp;\u0026gt;\u0026thinsp;20.\u003c/p\u003e \u003cp\u003eThe human disturbance (which includes harvesting trees for fuel, wood, charcoal, timber, and house construction) was computed as the harvesting index. The harvesting index was measured by counting individual stumps, which reflected illegally logged trees, within the quadrat and calculated from the relative density of individual tree stumps. The relative density of stumps was computed as the sum of stump density divided by the total density (the sum of the logged stump and living individual trees). Stumps are a small portion of the trunk that remains after a tree with about the 5 cm is chopped down (Sagar et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Spatial Data Analysis Methods\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.4.1. Land use/land cover data\u003c/h2\u003e \u003cp\u003eThe LULC types and Tag Image File Format(TIFF) data were obtained from a previously published article by Tadese et al. (\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e). They included area statistics for five different land cover types for the years 1987, 2002, and 2017 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eArea of LULC classes from 1987 to 2017 in MFBR adopted from Tadese et al (\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLULC classes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1987\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2002\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArea (ha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArea (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eArea (ha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eArea (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eArea (ha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eArea (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eForestland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e196,761.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e188,413.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e80.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e181,504.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e77.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarmland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,0781.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36,906.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e40,554.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e17.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrassland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,509.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,079.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3,192.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSettlement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,050.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4,744.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7,866.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWater body\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e141.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e141.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e141.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e233,254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e233,254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23,3254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe spatial distribution of carbon stock pools in different LULC types for each study year (forest land, farmland, and grassland) were analysed using the InVEST model (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.4.2. Carbon stock estimation using the InVEST model\u003c/h2\u003e \u003cp\u003eThe InVEST modelling framework is a set of open-source models for mapping and valuing the goods and ecosystems that produce the flow of services required to sustain life on Earth (Guerry et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2012\u003c/span\u003e, Bagstad et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, Sharps et al. \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). We customised the InVEST carbon stock mapping and sequestration model to assess the amount of total carbon stored in the five carbon pools (above-ground biomass, below-ground biomass, deadwood, litter, and soil organic matter) in different LULC classes of the study area. Carbon stock values were assigned to each LULC class for the selected years (i.e., 1987, 2002, and 2017) using field inventory data for forest land, farmland, and grassland.\u003c/p\u003e \u003cp\u003eTo meet the model's requirements, the LULC and carbon pool data sets were prepared and used as the primary input data to estimate carbon storage in each grid cell. Land use codes, the name of the LULC class, the amount of Above-Ground Biomass (AGB), Below-Ground Biomass (BGB), deadwood (DW), Litter, Grass, and Herbs (LGH), and Soil Organic Matter (SOC) are all included in the carbon stock data set in an MS Excel database. The LULC class is encoded with land-use codes in each row. Except for settlements and water bodies, which have zero carbon stock in all carbon pools, each column contains different attributes of the LULC type. Carbon in each pool was then combined across land-use types to estimate the total carbon storage.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Data Analysis\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.5.1. Soil laboratory analysis\u003c/h2\u003e \u003cp\u003eThe soil samples were analysed in the Water Works Design and Supervision Enterprise laboratory (WWDSE) in Addis Ababa, Ethiopia. The Bouyoucos Hydrometer Method was used to determine the soil textures (expressed as a percentage of weight)(Bouyoucos \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1962\u003c/span\u003e). Soil pH was determined using a pH meter and a 1:2.5 soil to water suspension potentiometric method (Sparks et al. 2020). The Micro-Kjeldahl (Bremner \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1996\u003c/span\u003e) and Walkley and Black (\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e1934\u003c/span\u003e) methods were used to determine total nitrogen (N) and soil organic carbon, respectively. The Bray-I method was used to determine available phosphorus, and the absorbance of the Bray-I extract was measured in a spectrophotometer at 882 nm (Bray and Kurtz \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1945\u003c/span\u003e). Based on C and N concentrations, the Carbon to Nitrogen ratio (C/N) was calculated. The mass of each soil sample (MS) was determined using oven-drying set to 105\u0026deg;C for 24 h to achieve a constant weight (Pearson \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The volume of the Core Sampler (VC) was determined as VC\u0026thinsp;=\u0026thinsp;π r\u003csup\u003e2\u003c/sup\u003eh, where r is the radius and h is the height of the core sampler (VC\u0026thinsp;=\u0026thinsp;3.14 \u0026times; (2.5 cm) \u003csup\u003e2\u003c/sup\u003e \u0026times; 5 cm\u0026thinsp;=\u0026thinsp;98.125 cm\u003csup\u003e3\u003c/sup\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e2.5.2. Carbon stock and value analysis\u003c/h2\u003e \u003cp\u003eData analysis of various carbon pools measured in the forests was performed in R version 4.0.1. The AGB of trees was calculated using a previously published allometric equation in which the independent variables were trunk diameter (D, cm), height (H, m), and wood density (p, g cm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e) (predictors) (Chave et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).The Global Wood Density database was used to determine the wood density of different species (Zanne et al. \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The following formula (Chave et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) was employed to calculate the \u003cb\u003eabove-ground biomass\u003c/b\u003e with the BIOMASS package in R (R\u0026eacute;jou-M\u0026eacute;chain et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2017\u003c/span\u003e):\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\mathbf{A}\\mathbf{G}\\mathbf{B} \\left(\\mathbf{k}\\mathbf{g}\\right)=0.0673\\mathbf{*} {({p}{D}}^{2}{{H})}^{0.976}\\)\u003c/span\u003e \u003c/span\u003e \u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWhere: \u003cem\u003eAGB\u003c/em\u003e is the above-ground biomass of trees (kg), \u003cem\u003ep\u003c/em\u003e is the specific wood density (g cm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e), \u003cem\u003eD\u003c/em\u003e is the trunk diameter at breast height (cm), and \u003cem\u003eH\u003c/em\u003e is the total height of trees (m). The total AGB carbon for each quadrat was calculated as aggregate AGB carbon for all trees. Carbon stocks were determined for each quadrat and then extrapolated to tonnes per hectare. The carbon content in AGB is calculated by multiplying the default carbon fraction by 50% (Hiraishi et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eBelow-ground biomass\u003c/b\u003e was estimated with the equation developed by (MacDicken \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e1997\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\mathbf{B}\\mathbf{G}\\mathbf{B}=\\mathbf{A}\\mathbf{G}\\mathbf{B}\\mathbf{*}0.2\\)\u003c/span\u003e \u003c/span\u003e \u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWhere: \u003cem\u003eBGB\u003c/em\u003e is below-ground biomass, \u003cem\u003eAGB\u003c/em\u003e is above-ground biomass, 0.2 is the conversion factor (or 20% of AGB).\u003c/p\u003e \u003cp\u003eFor \u003cb\u003estanding deadwood\u003c/b\u003e (SDW) which has branches, the biomass was estimated using the allometric equation for the estimation of above-ground biomass (Pearson et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor the remaining standing deadwood, the biomass was estimated using wood density and volume calculated from the truncated cone (Pearson et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\mathbf{V}\\mathbf{o}\\mathbf{l}\\mathbf{u}\\mathbf{m}\\mathbf{e} {\\left(\\mathbf{m}\\right)}^{3}\\)\u003c/span\u003e \u003c/span\u003e \u003cb\u003e=\u003c/b\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{1}{3}{\\pi }\\mathbf{h} {\\mathbf{r}}_{1}^{2}+{\\mathbf{r}}_{2}^{2}\\)\u003c/span\u003e\u003c/span\u003e \u003cb\u003e+\u003c/b\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\mathbf{r}}_{1 }\\)\u003c/span\u003e\u003c/span\u003e\u003cb\u003e*\u003c/b\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\mathbf{r}}_{2}\\)\u003c/span\u003e\u003c/span\u003e \u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWhere: \u003cem\u003eh\u003c/em\u003e is the height in meters, \u003cem\u003er\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e is the radius at the base of the tree, and\u003cem\u003er\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e is the radius at the top of the tree.\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\mathbf{B}\\mathbf{i}\\mathbf{o}\\mathbf{m}\\mathbf{a}\\mathbf{s}\\mathbf{s} = \\mathbf{V}\\mathbf{o}\\mathbf{l}\\mathbf{u}\\mathbf{m}\\mathbf{e} \\mathbf{x} \\mathbf{W}\\mathbf{o}\\mathbf{o}\\mathbf{d} \\mathbf{d}\\mathbf{e}\\mathbf{n}\\mathbf{s}\\mathbf{i}\\mathbf{t}\\mathbf{y} \\left(\\mathbf{f}\\mathbf{r}\\mathbf{o}\\mathbf{m} \\mathbf{s}\\mathbf{a}\\mathbf{m}\\mathbf{p}\\mathbf{l}\\mathbf{e}\\mathbf{s}\\right)\\)\u003c/span\u003e \u003c/span\u003e \u003cb\u003e4\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe \u003cb\u003ebiomass of lying deadwood\u003c/b\u003e was estimated by the equation given below (Pearson et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\mathbf{L}\\mathbf{D}\\mathbf{W} =\\sum _{{i}=1}^{{n}}\\mathbf{V}\\mathbf{*}\\mathbf{S}\\)\u003c/span\u003e \u003c/span\u003e \u003cb\u003e5\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWhere: \u003cem\u003eLDW\u003c/em\u003e is lying dead wood, \u003cem\u003eV\u003c/em\u003e is volume, and \u003cem\u003es\u003c/em\u003e is the specific density of each density class.\u003c/p\u003e \u003cp\u003eThe \u003cb\u003elying deadwood volume per unit area\u003c/b\u003e is estimated with:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\mathbf{V}= {{\\pi }}^{2} \\left(\\frac{{\\mathbf{D}}^{2}}{8\\mathbf{L}}\\right)\\)\u003c/span\u003e \u003c/span\u003e \u003cb\u003e6\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWhere: \u003cem\u003eV\u003c/em\u003e is the volume in m\u003csup\u003e3\u003c/sup\u003e/ha; \u003cem\u003eL\u003c/em\u003e is the length of the line transect, and \u003cem\u003eD\u003c/em\u003e is the diameter of the deadwood tree. The carbon content in AGB is calculated by multiplying the default carbon fraction by 50% (Hiraishi et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe biomass in the pool of leaf litter, grass, and herbs was estimated using destructive sampling. Forest floor litter material (dead leaves, twigs, fruit, and flowers) was collected from a 1 m\u003csup\u003e2\u003c/sup\u003e area. The living components, primarily grass and herbs, were harvested and weighed as well. Dry weight was determined in laboratory samples of the material. To estimate the biomass carbon stock of the litter, 100 g of fresh litter subsample was taken for laboratory use, and each sample was then dried in an oven at 105\u003csup\u003eo\u003c/sup\u003eC for 24 hours to obtain the dry weight (Pearson et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe \u003cb\u003eleaf litter, grass, and herbs (LGH) biomass per hectare was\u003c/b\u003e computed using the following formula:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\mathbf{L}\\mathbf{H}\\mathbf{G} =\\frac{\\mathbf{W}{f}{i}{e}{l}{d}}{{A}}\\)\u003c/span\u003e \u003c/span\u003e \u003cb\u003ex\u003c/b\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{\\mathbf{W}{s}{u}{b}{s}{a}{m}{p}{l}{e},{d}{r}{y}}{\\mathbf{W}{s}{u}{b}{s}{a}{m}{p}{l}{e}, {w}{e}{t}}\\)\u003c/span\u003e\u003c/span\u003e\u003cb\u003ex\u003c/b\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{1}{10000}\\)\u003c/span\u003e\u003c/span\u003e \u003cb\u003e7\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWhere: \u003cem\u003eLHG\u003c/em\u003e is the leaf litter, herbs, and grass biomass (tonne ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e), \u003cem\u003eW\u003c/em\u003e\u003csub\u003e\u003cem\u003efield\u003c/em\u003e\u003c/sub\u003e is the weight of fresh leaf litter, herbs, and grass sampled destructively within area A (g), \u003cem\u003eA\u003c/em\u003e is the size of the area where leaf litter, herbs, and grass were collected (ha), \u003cem\u003eW\u003c/em\u003e\u003csub\u003e\u003cem\u003esubsample\u003c/em\u003e\u003c/sub\u003e, \u003csub\u003e\u003cem\u003edry\u003c/em\u003e\u003c/sub\u003e is the weight of oven-dried sub-sample of leaf litter, herbs, and grass taken to the laboratory for moisture content determination (g), \u003cem\u003eW\u003c/em\u003e\u003csub\u003e\u003cem\u003esubsample\u003c/em\u003e\u003c/sub\u003e, \u003csub\u003e\u003cem\u003ewet\u003c/em\u003e\u003c/sub\u003e is the weight of fresh sub-sample of leaf litter, herbs, and grass taken to the laboratory for moisture content determination (g).\u003c/p\u003e \u003cp\u003eCarbon stocks in litter biomass were calculated using the following formula:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\mathbf{C}\\mathbf{L} = \\mathbf{L}\\mathbf{H}\\mathbf{G} \\mathbf{*} \\mathbf{\\%}\\mathbf{C}\\)\u003c/span\u003e \u003c/span\u003e \u003cb\u003e8\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWhere: \u003cem\u003eCL\u003c/em\u003e is the total carbon stocks in litter in tonne ha\u003csup\u003e\u0026ndash;1,\u003c/sup\u003e and \u003cem\u003e% C\u003c/em\u003e is the carbon fraction determined in the laboratory (Pearson et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe soil carbon stock was assessed in this study using the fine soil fraction to a depth of 30 cm. The following equation was used to calculate the bulk density (BD):\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\mathbf{B}\\mathbf{D} =\\frac{\\mathbf{M}\\mathbf{S}}{\\mathbf{V}\\mathbf{C}}\\)\u003c/span\u003e \u003c/span\u003e \u003cb\u003e9\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWhere: \u003cem\u003eBD\u003c/em\u003e is the bulk density (g cm\u003csup\u003e\u0026ndash;3\u003c/sup\u003e), \u003cem\u003eMS\u003c/em\u003e is the mass of the oven-dry soil (g)\u003c/p\u003e \u003cp\u003eThe amount of carbon stored per hectare was calculated using the following formula, taking into account soil depth (cm), bulk density (g cm\u003csup\u003e\u0026ndash;3\u003c/sup\u003e), the percentage of soil organic carbon content (SOC), and the Total Nitrogen (TN) in the recommended method (Pearson et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\mathbf{S}\\mathbf{O}\\mathbf{C} = \\mathbf{B}\\mathbf{D} \\mathbf{x} \\mathbf{d} \\mathbf{x} \\mathbf{\\%}\\mathbf{C}\\)\u003c/span\u003e \u003c/span\u003e \u003cb\u003e10\u003c/b\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003eSOC stock\u003c/em\u003e is the soil organic carbon stock per unit area (tonne ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e), \u003cem\u003eTN stock\u003c/em\u003e is the total nitrogen stock (tonne ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e), \u003cem\u003eBD\u003c/em\u003e is the bulk density (g cm\u003csup\u003e\u0026ndash;3\u003c/sup\u003e), \u003cem\u003ed\u003c/em\u003e is the total depth of the sample (cm), \u003cem\u003epercent SOC\u003c/em\u003e is the soil organic carbon concentration, and \u003cem\u003eVC\u003c/em\u003e is the volume of the core sampler (cm\u003csup\u003e3\u003c/sup\u003e).\u003c/p\u003e \u003cp\u003eThe carbon stock density of each stratum was calculated by aggregating the carbon stock densities of each stratum's carbon pools using the formula in the following equation.\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\mathbf{C} \\left(\\mathbf{L}\\mathbf{U}\\right) = \\mathbf{C} \\left(\\mathbf{A}\\mathbf{G}\\mathbf{B}\\right) +\\mathbf{C}\\left(\\mathbf{B}\\mathbf{B}\\right)+\\mathbf{C} \\left(\\mathbf{D}\\mathbf{W}\\mathbf{B}\\right) + \\mathbf{C}\\left(\\mathbf{L}\\mathbf{H}\\mathbf{G}\\right)+\\mathbf{S}\\mathbf{O}\\mathbf{C}\\)\u003c/span\u003e \u003c/span\u003e \u003cb\u003e11\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWhere:\u003c/p\u003e \u003cp\u003e \u003cem\u003eC (LU)\u003c/em\u003e is the carbon stock density for a land-use category (C t ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e)\u003c/p\u003e \u003cp\u003e \u003cem\u003eC (AGB)\u003c/em\u003e is the carbon in above-ground tree components (C t ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e)\u003c/p\u003e \u003cp\u003e \u003cem\u003eC (BB)\u003c/em\u003e is the carbon in below-ground components (C t ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e)\u003c/p\u003e \u003cp\u003e \u003cem\u003eC (DWB)\u003c/em\u003eis the carbon in deadwood tree components (C t ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e)\u003c/p\u003e \u003cp\u003e \u003cem\u003eC (LHG)\u003c/em\u003e is the carbon in the litter, herbs, and grass (C t ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e)\u003c/p\u003e \u003cp\u003e \u003cem\u003eSOC\u003c/em\u003e is the soil organic carbon (C t ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e)\u003c/p\u003e \u003cp\u003eCarbon was summed, and the total was then multiplied by 44/12 (3.67) to convert it into the carbon dioxide equivalent.\u003c/p\u003e \u003cp\u003eA chronological carbon storage change investigation was conducted at MFBR for the reference years 1987, 2002, and 2017 according to the method proposed by (Niquisse et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). After calculating the carbon stock and value based on the previous, baseline year in the MFBR, change was analysed using the below equation.\u003c/p\u003e \u003cp\u003e \u003cb\u003e∆C =\u003c/b\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{{C}{F}{i}{n}{a}{l}{y}{e}{a}{r}-{C}{i}{n}{i}{t}{i}{a}{l}{y}{e}{a}{r}}{{C}{i}{n}{i}{t}{i}{a}{l}{y}{e}{a}{r}}{*}100\\)\u003c/span\u003e\u003c/span\u003e \u003cb\u003e12\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWhere: \u003cem\u003e∆C\u003c/em\u003e is the percentage change in carbon, \u003cem\u003eC\u003c/em\u003e \u003csub\u003e\u003cem\u003efinal year\u003c/em\u003e\u003c/sub\u003e is the carbon stock in the final (recent) year, and \u003cem\u003eC\u003c/em\u003e \u003csub\u003e\u003cem\u003einitial year\u003c/em\u003e\u003c/sub\u003e is the carbon stock in the initial years.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Carbon Market Value Estimation\u003c/h2\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e2.6.1. Global voluntary market price\u003c/h2\u003e \u003cp\u003eThe global voluntary market price of carbon sequestration was compared using two data sources: the Global Voluntary Market Price (GVMP) and Tropical Ecosystems and Biodiversity (TEEB) database valuation. The carbon storage rate for the landscape is necessary to determine carbon sequestration (CO\u003csub\u003e2\u003c/sub\u003ee) in the GVMP set by different actors, such as the World Bank. The carbon storage rate (t ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e) multiplied by 3.67 (44/12\u0026thinsp;=\u0026thinsp;3.67) is used to estimate CO\u003csub\u003e2\u003c/sub\u003ee (Pearson \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Hence, the sequestered carbon (CO\u003csub\u003e2\u003c/sub\u003ee) is multiplied by the market price of carbon storage (4.40 USD/tCO\u003csub\u003e2\u003c/sub\u003ee) which was the carbon credit used in the Clean Development Mechanism (CDM) project under Humbo forest rehabilitation in Ethiopia (Brown et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, Kemerink-Seyoum et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). To analyse the monetary value, the annual rate of change in the carbon price of 3% and the market discount rate of 7% was required to estimate carbon storage value. The total value of carbon stock has been estimated by the sum of each land-use type area multiplied by the monetary value of its carbon stock.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e2.6.2. TEEB carbon valuation data\u003c/h2\u003e \u003cp\u003eThe Tropical Ecosystems and Biodiversity (TEEB) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.teebweb.org\u003c/span\u003e\u003cspan address=\"http://www.teebweb.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) contains the monetary value of carbon sequestration for various land-use types (McVittie and Hussain \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).The TEEB data were collected from different parts of the biome and analysed using different methods such as direct market pricing, avoided cost, and benefit transfer (Van der Ploeg et al. \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2010\u003c/span\u003e, De Groot et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). These valuation data were adapted to East Africa to compare the carbon sequestration values for MFBR (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The total carbon value was calculated by multiplying the area (ha) of each LUC type by its corresponding value of CO\u003csub\u003e2\u003c/sub\u003ee for that particular LUC type (Temesgen et al. \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Negasi et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCarbon sequestration value for each LULC type in the TEEB database\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCarbon sequestration prices (USD/ha/yr)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForest land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,229.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrazing land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e297\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFarmland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn other words, the value of Co\u003csub\u003e2\u003c/sub\u003ee obtained from TEEB multiplied by the LUC area yields the total market value of carbon. The carbon stock value data obtained from the TEEB database has been rearranged (sorted, summed, filtered by region, etc.) for supplementary analysis.\u003c/p\u003e \u003cp\u003eThe carbon stock value was estimated based on two approaches. In the first approach, the carbon stock value was estimated using GVMP (4.40 USD), which is considered a discount rate (7%) and the annual rate of change in the carbon price (3%).\u003c/p\u003e \u003cp\u003eIt was calculated using the following equation:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\mathbf{T}\\mathbf{C}\\mathbf{V} = \\mathbf{C}\\mathbf{S} \\left(5 \\mathbf{p}\\mathbf{o}\\mathbf{o}\\mathbf{l}\\mathbf{s}\\right) \\mathbf{t}/\\mathbf{h}\\mathbf{a} \\mathbf{*} \\mathbf{A}\\mathbf{r}\\mathbf{e}\\mathbf{a} \\left(\\mathbf{h}\\mathbf{a}\\right) \\mathbf{*} \\mathbf{C}\\mathbf{P} (\\mathbf{\\$}/\\mathbf{t}\\mathbf{o}\\mathbf{n}\\mathbf{n}\\mathbf{e}) - \\mathbf{D}\\mathbf{R}+\\mathbf{A}\\mathbf{R}\\mathbf{C}\\)\u003c/span\u003e \u003c/span\u003e \u003cb\u003e13\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWhere: \u003cem\u003eTCV\u003c/em\u003e is the total carbon value, \u003cem\u003eCS\u003c/em\u003e is the carbon stock in five pools, \u003cem\u003eCP\u003c/em\u003e is the carbon price per tonne, \u003cem\u003eDR\u003c/em\u003e is the discount rate, and \u003cem\u003eARC\u003c/em\u003e is the annual rate of carbon price change.\u003c/p\u003e \u003cp\u003eIn the second approach, the carbon stock value was estimated using the TEEB database, which contains carbon sequestration values for each LUC type (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIt was calculated using the equation below:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\mathbf{T}\\mathbf{C}\\mathbf{V} = \\mathbf{C}\\mathbf{S} \\left(5 \\mathbf{p}\\mathbf{o}\\mathbf{o}\\mathbf{l}\\mathbf{s}\\right) \\mathbf{t}/\\mathbf{h}\\mathbf{a} \\mathbf{*} \\mathbf{A}\\mathbf{r}\\mathbf{e}\\mathbf{a} \\left(\\mathbf{h}\\mathbf{a}\\right) \\mathbf{*} \\mathbf{C}\\mathbf{P} \\mathbf{o}\\mathbf{f} \\mathbf{L}\\mathbf{U}\\mathbf{C} \\mathbf{t}\\mathbf{y}\\mathbf{p}\\mathbf{e}(\\mathbf{\\$}/\\mathbf{t}\\mathbf{o}\\mathbf{n}\\mathbf{n}\\mathbf{e})\\)\u003c/span\u003e \u003c/span\u003e \u003cb\u003e14\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWhere: \u003cem\u003eTCV\u003c/em\u003e is the total carbon value, \u003cem\u003eCS\u003c/em\u003e is the carbon stock in five pools, \u003cem\u003eCP\u003c/em\u003e is the carbon price for each LUC type per tonne.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e2.7. Statistical analysis\u003c/h2\u003e \u003cp\u003eOne-way ANOVA was used to determine whether there were significant differences between environmental and disturbance factors regarding carbon stocks in R software version 3.5. The statistical significance level was set at 5%. Pearson correlation analysis was used to examine the relationship between environmental-disturbance factors regarding carbon stocks. When the value of \u003cem\u003er\u003c/em\u003e approaches negative 1, the carbon stock and the independent variable (factors) are inversely proportional (carbon stock increases as the factors decrease). If \u003cem\u003er\u003c/em\u003e approaches positive 1, the carbon stock increases while the factors increase.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Carbon Stock in Carbon Pools\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIn the MFBR, the mean above-ground and below-ground carbon stocks were 272.57 and 54.97 t ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e, respectively. The minimum and maximum of the AGB carbon stock were 144.21 and 779.05, while BGB carbon stocks were 28.84 and 155.81 t ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e in MFBR, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The distribution patterns of the BGB carbon stock showed similar trends to those of the AGB carbon stock. The mean dead wood and litter, herbs, and grass (LHG) carbon stocks were 3.04 t ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e and 0.05 t ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e, respectively. The minimum and maximum deadwood carbon stocks were 0.13 and 6.11 t ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e, while litter, herbs, and grass (LHG) carbon stocks were 0.016 and 0.32 t ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e, respectively. The mean soil organic carbon (SOC) stock was 176.26 t ha\u003csup\u003e\u0026ndash;1,\u003c/sup\u003e and the minimum and maximum soil organic carbon stocks were 116.96 and 280.31 t ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTotal carbon stocks and CO\u003csub\u003e2\u003c/sub\u003e sequestration (t ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e) in four study sites\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCarbon Pool\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eStudy Sites\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSite I\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSite II\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSite III\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSite IV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMean TCS (MFBR)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAGC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e269.7\u0026thinsp;\u0026plusmn;\u0026thinsp;7.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e260.8\u0026thinsp;\u0026plusmn;\u0026thinsp;8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e277.9\u0026thinsp;\u0026plusmn;\u0026thinsp;20.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e282.0\u0026thinsp;\u0026plusmn;\u0026thinsp;15.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e272.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBGC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e53.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e50.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e55.6\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e59.7\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e54.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDWC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e2.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e3.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e3.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLHGC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFoSOC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e161.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e166.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e178.6\u0026thinsp;\u0026plusmn;\u0026thinsp;5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e199.3\u0026thinsp;\u0026plusmn;\u0026thinsp;7.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e176.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFaLSOC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e128.6\u0026thinsp;\u0026plusmn;\u0026thinsp;5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e129.1\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e131.1\u0026thinsp;\u0026plusmn;\u0026thinsp;6.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e135.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e131.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGLSOC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e145.7\u0026thinsp;\u0026plusmn;\u0026thinsp;5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e154.0\u0026thinsp;\u0026plusmn;\u0026thinsp;6.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e148.0\u0026thinsp;\u0026plusmn;\u0026thinsp;5.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e148.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e149.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTCS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e761.3\u0026thinsp;\u0026plusmn;\u0026thinsp;9.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e763.3\u0026thinsp;\u0026plusmn;\u0026thinsp;10.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e794.5\u0026thinsp;\u0026plusmn;\u0026thinsp;24.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e829.4\u0026thinsp;\u0026plusmn;\u0026thinsp;26.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e787.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e Seq.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2,794.0\u0026thinsp;\u0026plusmn;\u0026thinsp;35.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e2,801.5\u0026thinsp;\u0026plusmn;\u0026thinsp;38.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e2,915.7\u0026thinsp;\u0026plusmn;\u0026thinsp;90.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e3,043.9\u0026thinsp;\u0026plusmn;\u0026thinsp;25.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2,888.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: site I\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;1200, site II\u0026thinsp;=\u0026thinsp;1200\u0026ndash;1500, site III\u0026thinsp;=\u0026thinsp;1500\u0026ndash;1800, and site IV\u0026thinsp;=\u0026thinsp;\u0026gt;\u0026thinsp;1800 m.a.s.l., AGC\u0026thinsp;=\u0026thinsp;above ground carbon, BGC\u0026thinsp;=\u0026thinsp;below ground carbon, DWC\u0026thinsp;=\u0026thinsp;dead wood carbon, LHGC\u0026thinsp;=\u0026thinsp;litter, herbs and grass carbon, FoSOC\u0026thinsp;=\u0026thinsp;forest soil organic carbon, FaLSOC\u0026thinsp;=\u0026thinsp;farmland soil organic carbon, GLSOC\u0026thinsp;=\u0026thinsp;grassland soil organic carbon, TCS\u0026thinsp;=\u0026thinsp;total carbon stock\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe mean carbon stock in the carbon pool increased from site one to site four. The mean above-ground carbon stock varied among the four study sites of the MFBR, ranging from 260.8\u0026thinsp;\u0026plusmn;\u0026thinsp;8.5 to 282.0\u0026thinsp;\u0026plusmn;\u0026thinsp;15.1 t ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e. The soil carbon pool has a significant contribution to the total carbon stock of MFBR. The soil organic carbon of MFBR fluctuated among the study sites; site four contributed the highest soil organic carbon (199.3\u0026thinsp;\u0026plusmn;\u0026thinsp;7.1 t ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e), followed by site one (178.6\u0026thinsp;\u0026plusmn;\u0026thinsp;5.1 t ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e). The smallest amount of soil carbon was obtained for site one (161.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1 t ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The mean forest soil organic carbon stock in MFBR increased with elevation from study site one to four, ranging from 161.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1 to 199.3\u0026thinsp;\u0026plusmn;\u0026thinsp;7.1 t ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e, respectively, (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Similarly, the mean soil organic carbon for farmland and grassland increases with elevation and varies from 128.6\u0026thinsp;\u0026plusmn;\u0026thinsp;5.8 to 135.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7 and from 145.7\u0026thinsp;\u0026plusmn;\u0026thinsp;5.4 to 148.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1 t ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e, respectively. In comparison, the total carbon stock stored in forest biomass was higher than on grassland and farmland in the MFBR. The overall mean total carbon stocks and sequestration for all LULC types were 787.14 t ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003eand 2,888.79 t CO\u003csub\u003e2\u003c/sub\u003ee ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e, ranging from 761.3\u0026thinsp;\u0026plusmn;\u0026thinsp;9.5 to 829.4\u0026thinsp;\u0026plusmn;\u0026thinsp;26.2 t ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003efor carbon stocks and from 2,794.0\u0026thinsp;\u0026plusmn;\u0026thinsp;35.1 to 3043.9\u0026thinsp;\u0026plusmn;\u0026thinsp;25.6 t CO\u003csub\u003e2\u003c/sub\u003ee ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e for carbon sequestration, respectively, along the elevation gradient in MFBR (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe total AGC stocks of five dominant species in four study sites are shown in (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). In study site I, the total above-ground carbon stock of the first five species 119.4 t ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e (44.8%). The highest above-ground carbon stock was contributed by \u003cem\u003eCordia Africana\u003c/em\u003e (41.2 t ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e) followed by \u003cem\u003eCombretum molle\u003c/em\u003e (28.3 t ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e) \u003cem\u003eand Lecaniodiscus fraxinifolius\u003c/em\u003e (22.3 t ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e). The first five species of the total AGC stock was 138.9 t ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e (55.1%), in study site II. The highest AGC stock was found for \u003cem\u003eFagaropsis angolensis\u003c/em\u003e (45.9 t ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e), followed by \u003cem\u003eAlbizia grandibracteata\u003c/em\u003e (30.1 t ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e), and \u003cem\u003eCordia africana\u003c/em\u003e (25.4 t ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e). The total AGC stock of the first five species was 120 t ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e (46%) in site III. The highest mean AGC stock was contributed by \u003cem\u003eCordia Africana\u003c/em\u003e (38.0 t ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e), followed by \u003cem\u003eFicus mucuso\u003c/em\u003e (28.7 t ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e) and \u003cem\u003eCroton sylvaticus\u003c/em\u003e (21.5 t ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e). The total AGC stock of the first five species contributed 90.3 t ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e (35.1%), in study site IV. The highest AGC stock was contributed by \u003cem\u003eAllophylus abyssinicus\u003c/em\u003e (29.2 t ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e), followed by \u003cem\u003ePrunus africana\u003c/em\u003e (16.9 t ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e), and \u003cem\u003eFicus sur\u003c/em\u003e (12.3 t ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e) (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMean above-ground carbon stocks in five dominant species in four study sites\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDBH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eInd\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAG-C\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e% AG-C\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSite I\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCordia africana\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e41.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCombretum molle\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLecaniodiscus fraxinifolius\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMorus mesozygia\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eManilkara butugi\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSite II\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFagaropsis angolensis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e45.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e18.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAlbizia grandibracteata\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCordia africana\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMimus opslanceolata\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGrewia mollis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSite III\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCordia africana\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFicus mucuso\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCroton sylvaticus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eApodytes dimidiata\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBlighia unijugata\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSite IV\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAllophylus abyssinicus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCroton macrostachyus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePrunus africana\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFicus sur\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTrilepisium madagascariense\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \n\u003cp\u003e\u003cstrong\u003eNote:\u0026nbsp;\u003c/strong\u003eWD = wood density (g\u0026nbsp;cm\u003csup\u003e\u0026ndash;3\u003c/sup\u003e), DBH = diameter at breast height (cm), H = height (m), Ind = individual number, and AG-C = above-ground carbon (t\u0026nbsp;ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn this study, DBH classes are directly related to the above-ground carbon stock while inversely related to trunk density per hectare. The trunk density of smaller-sized classes is higher than that of larger-sized classes, although they contribute a smaller amount of above-ground carbon stock per hectare. Moreover, the larger trunk diameter classes (DBH\u0026thinsp;\u0026ge;\u0026thinsp;40) showed higher above-ground carbon stock in site I (60.9%), site II (63.1%), site III (61.4%), and site IV (63.1%) as compared to smaller trunk diameter classes (DBH\u0026thinsp;\u0026le;\u0026thinsp;40) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Therefore, the amount of above-ground carbon stock increased with DBH, which indicated that harvesting larger-sized trees leads to carbon stock reduction. The density per hectare decreases with an increase in DBH classes.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAbove-ground biomass carbon stock showed a strong positive correlation with DBH classes(\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.85 and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.05), while density per hectare showed a strong negative correlation with DBH classes (\u003cem\u003er\u003c/em\u003e = \u0026minus;\u0026thinsp;0.89 and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Carbon Stock in Land Use/Land Cover\u003c/h2\u003e \u003cp\u003eAbove-ground carbon (272.57 t ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e) had the highest carbon pool in the forest land carbon stock pool, followed by soil organic carbon (176.26 t ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e), while LHG biomass (0.05 t ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e) had the lowest carbon pool (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The shares of carbon pools in forest land were the following: the above-ground carbon (53.77%), below-ground carbon (10.84%), deadwood (0.59%), LHG carbon (0.009%), and soil organic carbon (34.77%).In general, the highest contribution came from above-ground carbon, followed by soil organic carbon, below-ground carbon, and deadwood carbon. In comparison, soil organic carbon stock in forest land (176.26 t ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e) was higher than for grassland (149.09 t ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e) and farmland (131.16 t ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e) (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Thus, the contributions of soil organic carbon were (22.39%), (18.94%), and (16.66%) in forest land, grazing land, and farmland, respectively. The above-ground, below-ground, and deadwood carbon pools were not estimated on grassland and farmland due to the absence of trees exceeding5 cm DBH in the study plots (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCarbon pools by land use/land cover (t ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAGC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBGC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDWC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLHGC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSOC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFoL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e272.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e176.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e506.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFaL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e131.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e131.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e149.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e149.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAve\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e152.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e152.225\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \n\u003cp\u003e\u003cstrong\u003eNote:\u0026nbsp;\u003c/strong\u003eAGC = above ground carbon; BGC = below ground carbon; DWC = dead wood carbon biomass; LHGC = litter, herbs, and grass carbon; SOC = soil organic carbon, FoL = forestland, FaL = farmland, GL = grassland, Set = settlement, WB = water body and Ave = Average \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAbove-ground, below-ground and deadwood carbon amounted to about 34.62%, 6.98%, and 0.38% of forest land storage, respectively. The share of LHG biomass carbon was 0.006% in the forest land and grassland. Likewise, there was no significant contribution from the water body and settlement (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Effects of land cover change on carbon stock\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe LULC affected the carbon stock during the 1987 to 2017 period in the study area (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The maximum carbon stock was found in forest land (99.73\u0026nbsp;million t ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e) followed by farmland (4.03\u0026nbsp;million t ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e), whereas the lowest was identified in grassland (0.52\u0026nbsp;million t ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e) in 1987 (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Similarly, the maximum carbon stock was shown in forest land (92.01\u0026nbsp;million t ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e) followed by farmland (5.32\u0026nbsp;million t ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e) whereas the lowest was identified in grassland (0.47\u0026nbsp;million t ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e) in 2017. Based on the InVEST carbon model results, the conversion of forest land and grassland into farmland led to a reduction of carbon stock in MFBR (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The chronological investigation indicated that the carbon stock declined by 7.73\u0026nbsp;million t ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003eon forest land from 1989 to 2017, while the average carbon stock was reduced by 2.16\u0026nbsp;million t ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e with an annual loss of 0.07\u0026nbsp;million t ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e. The drop in the carbon stock is due to the reduction of forest land and grassland from 1987 to 2017.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCarbon storage and its changes in the reference years (million t ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1987\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2002\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eChange (1987\u0026ndash;2017)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCS\u0026nbsp;t\u0026nbsp;ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCStCO\u003csub\u003e2\u003c/sub\u003ee ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCS\u0026nbsp;t\u0026nbsp;ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCStCO\u003csub\u003e2\u003c/sub\u003ee ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCS\u0026nbsp;t\u0026nbsp;ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCStCO\u003csub\u003e2\u003c/sub\u003ee ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCS\u0026nbsp;t\u0026nbsp;ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCStCO\u003csub\u003e2\u003c/sub\u003ee ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFoL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e99.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e366.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e95.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e350.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e92.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e337.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026ndash;7.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026ndash;28.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFaL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e19.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026ndash;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026ndash;0.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAve\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e127.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e123.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e32.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e119.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026ndash;2.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026ndash;7.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eNote: CS t ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e = carbon stock tonne per ha and CStCO\u003csub\u003e2\u003c/sub\u003ee ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e = carbon stock tonne carbon dioxide equivalent per ha, FoL\u0026thinsp;=\u0026thinsp;forestland, FaL\u0026thinsp;=\u0026thinsp;farmland, GL\u0026thinsp;=\u0026thinsp;grassland and Ave\u0026thinsp;=\u0026thinsp;Average\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn forest land, the total carbon stock shrunk from 366.02\u0026nbsp;million t CO\u003csub\u003e2\u003c/sub\u003ee ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e in 1987 to 337.64\u0026nbsp;million tCO\u003csub\u003e2\u003c/sub\u003ee ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e in 2017. Forest land and grassland cover declined by 6.6% and 0.1%, respectively, which led to a reduction of 28.38\u0026nbsp;million and 0.17\u0026nbsp;million tCO\u003csub\u003e2\u003c/sub\u003ee ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e in the previous 30 years, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The average carbon stock was diminished by 7.9\u0026nbsp;million tCO\u003csub\u003e2\u003c/sub\u003e ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e with an annual loss of 0.26\u0026nbsp;million tCO\u003csub\u003e2\u003c/sub\u003e ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e, which is due to the reduction of forest land and grassland from 1987 to 2017 (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). In general, changing LULC classes reduce vegetation cover, which directly contributes to increased or reduced carbon sequestration and carbon market value.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Carbon storage valuation\u003c/h2\u003e \u003cp\u003eThe global voluntary market price analysis showed that the average carbon sequestration was reduced from \u003cspan\u003e$\u003c/span\u003e5.55\u0026nbsp;billion in 1987 to \u003cspan\u003e$\u003c/span\u003e5.21\u0026nbsp;billion in 2017 in MFBR. In other words, the mean carbon value shrunk by \u003cspan\u003e$\u003c/span\u003e0.011\u0026nbsp;billion t/ha/year over the previous 30 years. Forest land was the most important carbon-sequestering land-use class. However, the value of carbon sequestration decreased by \u003cspan\u003e$\u003c/span\u003e0.071\u0026nbsp;billion t/ha/year from \u003cspan\u003e$\u003c/span\u003e16.84\u0026nbsp;billion in 1987 to \u003cspan\u003e$\u003c/span\u003e14.70\u0026nbsp;billion in 2017(Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe estimated carbon storage valuation using GVMP and TEEB in each LU/LC in MFBR (billion USD)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1987\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2002\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eChange (1987\u0026ndash;2017)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTEEB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGVMP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTEEB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGVMP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTEEB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGVMP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTEEB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eGVMP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFoL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e450.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e415.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e14.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-34.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-2.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFaL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.0007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAve\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e151.56\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e5.55\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e145.48\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e5.37\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e140.39\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e5.21\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-11.17\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e-0.34\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eNote: GVMP\u0026thinsp;=\u0026thinsp;global voluntary market price and TEEB\u0026thinsp;=\u0026thinsp;Tropical Economics of Ecosystems and Biodiversity, FoL\u0026thinsp;=\u0026thinsp;forestland, FaL\u0026thinsp;=\u0026thinsp;farmland, GL\u0026thinsp;=\u0026thinsp;grassland and Ave\u0026thinsp;=\u0026thinsp;Average\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAccording to the carbon sequestration monetary value analysis of TEEB, the mean value of carbon sequestration went down from \u003cspan\u003e$\u003c/span\u003e1,515.62\u0026nbsp;billion in 1987 to \u003cspan\u003e$\u003c/span\u003e1,403.89\u0026nbsp;billion in 2017. The TEEB carbon sequestration value estimation (\u003cspan\u003e$\u003c/span\u003e1,403.89\u0026nbsp;billion) is greater than that of GVMP (\u003cspan\u003e$\u003c/span\u003e5.21\u0026nbsp;billion) in 2017 (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). This significant carbon value variation between GVMP and TEEB indicated a gap in carbon value estimation methods. Furthermore, the use of different methods of carbon pricing led to uncertainty in the estimation of carbon sequestration value.\u003c/p\u003e \u003cp\u003eBased on the estimation of TEEB and GVMP, carbon sequestration for forest and grassland values have been drastically reduced as a result of human disturbances like vegetation. The TEEB and GVMP analyses estimated the carbon value of forest land to decline by \u003cspan\u003e$\u003c/span\u003e34.9\u0026nbsp;billion (7.75%) and \u003cspan\u003e$\u003c/span\u003e2.14\u0026nbsp;billion (12.70%), respectively, while grassland declined by \u003cspan\u003e$\u003c/span\u003e0.01\u0026nbsp;billion (5.55%) and \u003cspan\u003e$\u003c/span\u003e0.0007\u0026nbsp;billion (8.04%), respectively. Moreover, the average TEEB and GVMP valuation of carbon sequestration in MFBR declined by 11.17% and 0.34%, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Effects of Environmental and Disturbance Factors on Carbon Stocks in Forests\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eBased on the one-way ANOVA analysis, the harvesting index, elevation, slope, soil pH, total nitrogen, and phosphorus had a significant influence on carbon sequestration stock (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOne-way ANOVA analysis of impact factors associated with carbon storage\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImpact factors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean sq\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH-index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17018.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.25E\u0026thinsp;+\u0026thinsp;06***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElevation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e887619.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.99E\u0026thinsp;+\u0026thinsp;12***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSlope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16976.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.38E\u0026thinsp;+\u0026thinsp;09***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoil pH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17020.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.79E\u0026thinsp;+\u0026thinsp;05***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15424.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.90E\u0026thinsp;+\u0026thinsp;07***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14216.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.37E\u0026thinsp;+\u0026thinsp;04***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: P-value: *** \u0026ndash; 0.001 indicates significant impact on carbon storage; df\u0026thinsp;=\u0026thinsp;degree of freedom\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ePearson correlation (r) tests exhibited both positive and negative relationships between environmental and disturbance factors with carbon stock in MFBR (Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). The above-ground carbon stock showed a positive relationship with soil organic carbon (r\u0026thinsp;=\u0026thinsp;0.10), elevation (r\u0026thinsp;=\u0026thinsp;0.08), TN (r\u0026thinsp;=\u0026thinsp;0.31), and P (r\u0026thinsp;=\u0026thinsp;0.11), while a significant negative relationship with the harvesting index (r = \u0026minus;\u0026thinsp;0.21) and pH (r = \u0026minus;\u0026thinsp;0.09).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePearson's correlation coefficient matrix for environmental and disturbance factors (N\u0026thinsp;=\u0026thinsp;8) in MFBR\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAGC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSOC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEle\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSlo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAGC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSOC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.10\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;0.21\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;0.13\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEle\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.08\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.39\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSlo\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;0.04\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;0.03\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;0.09\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.27\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003epH\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;0.09\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;0.22\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;0.02\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;0.69\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;0.09\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.31\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.27\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;0.31\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.10\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;0.12\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;0.21\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.11 \u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.14\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;0.05\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.06\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;0.31\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;0.16 \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.19\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e The magnitude indicates the degree of correlation and Positive signs indicate positive correlation and negative signs indicate inverse relation. \u0026nbsp; *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt;0.001, ns = no significance, N = number of variables, MFBR = Majang Forest Biosphere Reserves, AGC = above ground carbon, SOC = soil organic carbon, HI = Harvesting index, Ele = elevation, Slo = slope, TN = total nitrogen, and P = available phosphorus. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\u003cp\u003eThe variation in elevation gradient positively affects the above-ground carbon stock and soil organic carbon in MFBR. The lower elevation has lower AG carbon stocks, while the higher elevation has higher carbon stocks in above-ground carbon. Soil organic carbon showed a significant positive correlation with elevation (r\u0026thinsp;=\u0026thinsp;0.39), and TN (r\u0026thinsp;=\u0026thinsp;0.27), but a significant negative relationship with slope (r =\u0026ndash;0.03), and pH (r =\u0026ndash;0.22). Similarly, the harvesting index showed a negative relationship with slope (r = \u0026minus;\u0026thinsp;0.09), pH (r = \u0026minus;\u0026thinsp;0.02), TN (r = -0.31), and P (r = -0.05) and a positive correlation with elevation (r\u0026thinsp;=\u0026thinsp;0.06). Soil pH showed a significant negative relationship with TN (r = \u0026minus;\u0026thinsp;0.21), and P (r = \u0026minus;\u0026thinsp;0.16) (Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Carbon Stock in Carbon Pools and Land Use/Land Cover\u003c/h2\u003e \u003cp\u003eThe results of this study on carbon stocks show the importance of biosphere reserves for climate change mitigation. The study has confirmed a diverse variation in LULC and carbon stock pools along the elevation gradient in MFBR. For instance, the mean carbon stock in the LULC carbon pool increases along the elevation gradient (from site I to site IV) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). This finding is similar to the earlier finding that reported a positive relationship between elevation and carbon stock (Sharma et al. \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSimilarly, the amount of CO\u003csub\u003e2\u003c/sub\u003ee in all LULC types increases with the elevation gradient, which is linked to higher disturbances observed at lower elevations (Getaneh et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). On the other hand, high rainfall in higher elevations leads to increased moisture availability and microbial activity (Getaneh et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In contrast, other findings indicate that carbon stock declines with increasing elevation (Moser et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn comparison, the total carbon stock stored in forest land was higher than on grassland and farmland in MFBR (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Higher carbon stocks in forest land may be due to more vegetation cover and plant material decomposition (W\u0026oacute;jcik-Leń and Leń \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Chuai et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) demonstrated that forest land also releases and absorbs huge amounts of carbon into and out of the atmosphere. Moreover, LULC conversion is the most important factor that causes the reduction and transformation of carbon sequestration in terrestrial ecosystems (Zhang et al. \u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe above-ground carbon stock varied among study sites in MFBR (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The highest above-ground carbon stock was identified in study site IV (282\u0026nbsp;billion t ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e), while the lowest (269.7 t ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e) was in study site I of MFBR (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). These results are consistent with carbon sequestration in the tropical Afromontane forest of Ethiopia (107\u0026ndash;285) (Hamere et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, Yelemfrhat and Teshome \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, Abyot et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, Getaneh et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, Abreham et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and in other tropical forests (170\u0026ndash;271) (Spracklen and Righelato \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, Willcock et al. \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, Tanner et al. \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). This carbon stock difference may be related to DBH, height, and basal area of a tree. The variations in carbon sequestration at local, regional, and national levels could be the effect of human disturbances and environmental factors in the study site of MFBR.\u003c/p\u003e \u003cp\u003eSimilarly, the mean soil organic carbon stock (0\u0026ndash;30 cm soil depth) was 176.26 t ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e in MFBR. The highest soil organic carbon stock was found in study site IV (199.3 t ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e), while the lowest was in study site I (161 t ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e) of MFBR (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The mean soil organic carbon in MFBR was higher than earlier estimated carbon stock in other tropical forests (121\u0026ndash;123 t ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e) (Lal \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), a forest in Colombia (96 t ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e) (Sierra et al. \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), Singapore (110 t ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e) (Ngo et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), the Humbo forest of Ethiopia (168 t ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e) (Alefu et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), and the Awi Zone of Ethiopia (149.2 t ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e) (Getaneh et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Nevertheless, the mean soil organic carbon stock of MFBR was found to be lower than the soil organic carbon stock of tropical Afro-montane forests (194 to 288 t ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e) (Selmants et al. \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe total carbon stocks varied from 487.04 to 544.87 t ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e in the study sites (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), which is almost similar to the results quantified in the Adaba-Dodola community forest (507 t ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e) (Nega et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), Gerba-Dima moist Afromontane forest (508.9 t ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e) (Abyot et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) in Ethiopia, and IPCC (130\u0026ndash;510 t ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e) (Penman et al. 2003). Similarly, the mean total carbon stock of MFBR is higher than other findings in the Sheka Forest (461 t ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e) (Ayehu et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), Humbo Forest (213.43 t ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e) (Alefu et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), and Singapore (337 t ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e) (Ngo et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). This difference could be due to the existence of diverse tree species, elevation, human disturbance, climate, and microbial activities. Moreover, the comparison of five carbon stock pools with other tropical forests studies were showed in the Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab11\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 11\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of carbon stock with other tropical forests studies\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStudy Area\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eCarbon Stock in different pools (t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAGBC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBGBC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDWC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLHGC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSOC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTCS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnshirava forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e180.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e111.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e338.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(Fikirte et al. 2022)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAwi forests\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e191.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e149.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e380.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(Gebeyehu et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBangladesh forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e96.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e168.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e283.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(Ullah and Al-Amin 2012)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e168.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e208.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(Ekoungoulou et al. 2015)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEgdu forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e278.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e277.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e614.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(Adugna et al. 2013)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGedo forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e183.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e523.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(Hamere et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2015\u003c/span\u003ea)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGerba Dima forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e243.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e292.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e586.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(Abyot et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGesha-Sayilem forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e164.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e137.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e362.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(Admassu et al. 2019)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMajang Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e272.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e176.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e506.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003ePresent study\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSheka Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e176.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e461\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(Ayehu et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingamba forests\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e142.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e175.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(Mattia and Sesay 2020)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTara Gedam forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e306.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e274.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e643.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(Mohammed et al. 2014)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTulu Lafto\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e218.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e128.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e399.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(Fekadu et al. 2021)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUpper Omo-Gibe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e432\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(Abreham et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUsambra Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e930.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(Munishi and Shear 2004)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWujig-Waren forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e102.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e181.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(Negasi et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u0026nbsp;\u003c/strong\u003eAGBC = carbon storage in above ground biomass, BGBC = carbon storage in below ground biomass; DWC= carbon storage in dead wood biomass, LHGC = carbon storage in litter, herbs, and grass biomass, and SOC = soil organic carbon.\u003c/p\u003e\n\u003cp\u003eIn the study sites, dominant species with higher basal areas demonstrated the highest carbon stock (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Individual plant species with higher DBH values contribute significantly to carbon sequestration in MFBR, while their extinction has a significant impact on biomass, carbon sequestration, and carbon trading. Deforestation and forest degradation also have an impact on the amount of carbon sequestered in larger trees with larger diameters (Gibbs et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The above- and below-ground carbon in the study sites of MFBR was higher than the carbon value that was quantified by IPCC (Penman et al. 2003, Hiraishi et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). This difference in above-ground carbon might be associated with the greater tree height, DBH, and basal area in MFBR.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Effects of Land Use/Land Cover Change on Carbon Stock\u003c/h2\u003e \u003cp\u003eThis study shows how the carbon stock pool (above-ground, below-ground, deadwood, litter, and soil) is affected by LULC change in the study period (1987\u0026ndash;2017) (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Forest land showed higher carbon stock as compared to grassland and farmland in MFBR (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Similarly, other findings indicated higher carbon stock in forest land as compared to other LULC categories (Negasi et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Rajput et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This significant difference in carbon stock across land cover categories could be due to the difference in tree size and trunk density per hectare. Furthermore, lower carbon stock was found in farmland that has been altered by intensive subsistence cultivation, deforestation, or anthropogenic disturbance that affected the tree, shrub, and herb growth (Tessema and Kibebew \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe InVEST carbon model results showed that conversion of forest land and grassland into farmland leads to a reduction in carbon stock in the study area (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The chronological investigation indicated that the highest carbon stock was reduced in forest land among all LULC types. Carbon stock reduction was identified in all carbon pools as a result of forest land and grassland being converted into farmland in the study period (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). This reduction is similar to phenomena in other reports (Singh et al. \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This could be due to the expansion of settlements (urban and rural), agriculture expansion, and population pressure, which lead to deforestation and forest degradation (Solomon \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The increased urbanisation or settlement enlargement occurs at the expense of other LULC categories like farmland, forest land, and grassland (Bai et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2012\u003c/span\u003e, Price et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Carbon Storage Valuation\u003c/h2\u003e \u003cp\u003eEffective carbon stock valuation is highly relevant to the successful management of climate change impacts. It is also important for evaluating the relative advantages of climate adaptation activities and mitigation measures over time. The global voluntary market price analysis showed that the mean carbon sequestration value declined from 1987 to 2017 (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). This carbon stock value reduction is linked to the change from forest cover to other LULC type, which is consistent with other findings (Abreham et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSimilarly, according to the carbon sequestration monetary value analysis of TEEB, the mean value of carbon sequestration dropped from 1987 to 2017. The TEEB carbon sequestration value estimation is greater than that of GVMP in all study periods for MFBR (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). This significant carbon value variation between GVMP and TEEB indicated a gap in carbon value estimation methods. Furthermore, using different methods of carbon pricing led to uncertainty regarding the carbon sequestration value (Canu et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In addition, according to the estimation of TEEB and GVMP, forest and grassland carbon sequestration values have drastically shrunk as a result of such human disturbances as deforestation. Moreover, the average TEEB and GVMP valuation of carbon sequestration in MFBR declined (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e4.4. Effects of Environmental and Disturbance Factors on Carbon Stocks in Forests\u003c/h2\u003e \u003cp\u003eThe relationship between carbon and environmental and disturbance factors has become more and more important in understanding the carbon sequestration cycle. In this study, environmental and anthropogenic factors highly influence forest cover and carbon sequestration in the pools. Accordingly, the variation in carbon stock was closely related to environmental and human disturbance. Elevation, slope, and harvesting index are important environmental and disturbance factors resulting in major differences in carbon stock among study sites in MFBR (Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). Comparatively, elevation was the most significant environmental factor influencing carbon sequestration in the pools: carbon stock pools increase with elevation. The correlation of carbon stock with elevation could be due to variations in disturbance and precipitation along the elevation gradient. This is consistent with the findings from other studies (McGroddy and Silver \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2000\u003c/span\u003e, Eisenlohr et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe harvesting index and slope were also among the most important environmental factors that affected the variability of carbon in the pools. Carbon stock pools increase with decreasing slope, which may be related to the moisture and soil properties of the study sites. Furthermore, tree harvesting were the primary factors responsible for the decrease in biomass and carbon stocks. This shows that clear cutting contributes to higher carbon emissions into the atmosphere (Huang and Asner \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). As a result, forest conservation and sustainable management help reduce carbon emissions and keep biomass and carbon in carbon pools (Sasaki et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe correlation between elevation and above-ground and soil carbon stocks was positive. This finding is similar to an earlier study that stated a positive relationship between elevation and carbon stock (Khadanga and Jayakumar \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The positive correlation between soil carbon and elevation may be due to lower temperature and increasing moisture content with increasing elevation (Hoffmann et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, Selmants et al. \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The rate of organic matter decomposition is sluggish in low temperatures, which leads to reduced microbial activities, thus assisting the increments of soil organic matter and thicker litter layer development (Walz et al. \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). High organic matter content in soils at higher elevations has also been reported in other Afromontane forests of Ethiopia (Tamrat \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e1994\u003c/span\u003e, Schindlbacher et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). This situation leads to a reduction in CO\u003csub\u003e2\u003c/sub\u003e release from the soil, which in turn increases soil organic carbon stocks.\u003c/p\u003e \u003cp\u003eThe correlation between slope and above-ground biomass and soil carbon stocks was negative. Greater slope with decreasing soil moisture resulted in decreased vegetation cover, hence a decline in above-ground carbon and soil carbon stocks. Similarly, the correlation between the harvesting index with above-ground biomass and soil carbon stocks was negative. This indicated that the harvesting index (selective exploitation) has a significant impact on above-ground carbon and soil carbon stocks. Thus, illegal harvesting focused on big trees for timber production leads to a reduction of carbon stocks, which is consistent with other findings (Sasaki et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2012\u003c/span\u003e, Lindsell and Klop \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn this study, the results showed high carbon stocks in MFBR, which is higher than other findings in moist Afromontane forests in Ethiopia. As regards carbon pools, the mean above-ground carbon and soil organic carbon stocks were shown to be higher than other pools in MFBR. The total carbon stock and economic value for the 2017 LULC data are lower than for the 1987 LULC data. The conversion of forest land and grassland into farmland reduces the carbon stock and its economic value in MFBR. Forest cover and carbon sequestration in the pools are highly influenced by environmental and anthropogenic factors. Accordingly, the variation in carbon stock was closely related to environmental and human disturbance. Elevation, slope, and harvesting index are important environmental and disturbance factors resulting in major differences in carbon stock among the study sites in MFBR. The correlation between elevation and above-ground biomass and soil carbon stocks was positive, while a negative relationship was found for slope and harvesting index. This calls for urgent attention to implement successful conservation and sustainable use of forest resources in the biosphere reserve.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch1\u003eAcknowledgements \u003c/h1\u003e\n\u003cp\u003eWe would like to thank Addis Ababa University for financial and logistic support and Mr Demese Jemaneh for his contribution during data collection. The authors also wish to thank the University of Agriculture in Krakow, Poland for their professional linguistic support from a professional language services agency. This paper was produced under scientific efforts and in cooperation carried between Dr Tomasz Noszczyk and three Ethiopian universities.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe available data can be upon request the Corresponding Author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial or non-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSemegnew Tadese conceptualized, collected, analyzed data and wrote a draft manuscript. Teshome Soromessa curated data and supervised work. Abreham Berta validated data using software and reviewed the review manuscript. Tomasz Noszczyk reviewed and edited the draft manuscript. Getaneh Gebeyehu reviewed and supervised the work. Mengistie Kindu reviewed and edited manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; information \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eAddis Ababa University, College of Natural and Computational Sciences, Centre for Environmental Science, Ethiopia. \u003csup\u003e2\u003c/sup\u003eWolkite University, College of Agriculture and Natural Resource, Department of Natural Resource Management, Ethiopia. \u003csup\u003e3\u003c/sup\u003eInjibara University, College of Natural and Computational Sciences, Department of Biology, Injibara, Ethiopia. \u003csup\u003e4\u003c/sup\u003eUniversity of Agriculture in Krakow, Faculty of Environmental Engineering and Land Surveying, Department of Land Management and Landscape Architecture, 21 Mickiewicza Street, 31-120 Krakow, Poland.\u003csup\u003e5\u003c/sup\u003eInstitute of Forest Management, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Hans-Carl-von-Carlowitz-Platz 2, D-85354 Freising, Germany\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eORCID \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSemegnewTadese Feyisa https://orcid.org/0000-0001-6049-6309\u003c/p\u003e\n\u003cp\u003eTeshome Soromessa https://orcid.org/0000-0002-3680-0240\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAbreham Berta\u003c/em\u003e https://orcid.org/0000-0002-3076-3768\u003c/p\u003e\n\u003cp\u003eGetaneh Gebeyehu https://orcid.org/0000-0003-3777-2533\u003c/p\u003e\n\u003cp\u003eTomasz Noszczyk https://orcid.org/0000-0001-6420-633X\u003c/p\u003e\n\u003cp\u003eMengistie Kindu https://orcid.org/0000-0003-4026-8207\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbreham, B. 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Proceedings of the National Academy of Sciences \u003cstrong\u003e116\u003c/strong\u003e:8623-8628.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"carbon-balance-and-management","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cbam","sideBox":"Learn more about [Carbon Balance and Management](https://cbmjournal.biomedcentral.com/)","snPcode":"13021","submissionUrl":"https://submission.nature.com/new-submission/13021/3","title":"Carbon Balance and Management","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Land use/cover, carbon stock, environment and disturbance factors, InVEST model, Africa","lastPublishedDoi":"10.21203/rs.3.rs-2564786/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2564786/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eForest plays an important role in the global carbon cycle by sequestering carbon dioxide and thereby mitigating climate change. In this study, an attempt has been made to investigate the effects of land use/land cover (LULC) change (1989–2017) on carbon stock and its economic values in tropical moist Afromontane forests of the Majang Forest Biosphere Reserve (MFBR), south –west Ethiopia. Systematic sampling was conducted to collect biomass and soil data from 140 plots in MFBR. The soil data were collected from grassland and farmland. InVEST modelling was employed to investigate the spatial and temporal distribution of carbon stocks. Global Voluntary Market Price (GVMP) and Tropical Economics of Ecosystems and Biodiversity (TEEB) analysis was performed to estimate economic values (EV) of carbon stock dynamics. Correlation analysis was also employed to identify the relationship between environmental and anthropogenic impacts on carbon stocks. The results indicated that the above-ground biomass and soil organic carbon stocks were higher than the other remaining carbon pools in MFBR. The total carbon stock (32.59 Mt\u0026nbsp;ha\u003csup\u003e–1\u003c/sup\u003e) in 2017 was lower than 1989 (34.76 Mt\u0026nbsp;ha\u003csup\u003e–1\u003c/sup\u003e). The EV of carbon stock in 2017 was lower than in 1989. Elevation, slope, and harvesting index are important environmental and disturbance factors resulting in major differences in carbon stock among study sites in MFBR. The correlation analysis for elevation showed a positive relationship with soil carbon stocks (r = 0.39) and aboveground biomass (r = 0.08), while a negative relationship was found for slope (r = –0.04) and harvesting index (r = –0.21). This calls for urgent attention to implement successful conservation and sustainable use of forest resources in biosphere reserves.\u003c/p\u003e","manuscriptTitle":"Carbon Storage Dynamics and its Economic Values in Tropical Moist Afromontane Forests, South-West Ethiopia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-02-14 19:05:34","doi":"10.21203/rs.3.rs-2564786/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2023-05-11T09:22:05+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2023-03-26T06:42:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"0dcb7748-f1e5-48ff-b668-aab8e22a0753","date":"2023-03-12T10:59:09+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-02-13T17:32:22+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-02-13T12:57:16+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-02-13T12:57:13+00:00","index":"","fulltext":""},{"type":"submitted","content":"Carbon Balance and Management","date":"2023-02-08T13:26:38+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"carbon-balance-and-management","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cbam","sideBox":"Learn more about [Carbon Balance and Management](https://cbmjournal.biomedcentral.com/)","snPcode":"13021","submissionUrl":"https://submission.nature.com/new-submission/13021/3","title":"Carbon Balance and Management","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"13220ce7-70ba-417d-93a1-06cbc6eb7587","owner":[],"postedDate":"February 14th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2023-12-11T15:08:34+00:00","versionOfRecord":{"articleIdentity":"rs-2564786","link":"https://doi.org/10.1186/s13021-023-00243-z","journal":{"identity":"carbon-balance-and-management","isVorOnly":false,"title":"Carbon Balance and Management"},"publishedOn":"2023-12-07 15:02:04","publishedOnDateReadable":"December 7th, 2023"},"versionCreatedAt":"2023-02-14 19:05:34","video":"","vorDoi":"10.1186/s13021-023-00243-z","vorDoiUrl":"https://doi.org/10.1186/s13021-023-00243-z","workflowStages":[]},"version":"v1","identity":"rs-2564786","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2564786","identity":"rs-2564786","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}