Environment & Ecosystem Science (EES)

There is now a lot of concern throughout the world about the rise in atmospheric carbon dioxide and how it could alter the climate. An efficient way to reduce elevated atmospheric carbon dioxide concentrations and help slow down global warming has been to expand the area covered by plantation forests. The aim of this study is to estimation of carbon stock variation and how they affect the biomass of selected tree species in the study area. A systematic sampling method was employed for this study. There were taken a total of 60 sample plots from this study area. To collect soil and litter samples, five 1 m2 subplots were laid out inside the main plots. From this study, the carbon pools in Cupressus lusitanica species, such as aboveground carbon, belowground carbon, litterfall carbon, and soil organic carbon were 159.46±70.91, 41.46±18.44, 0.07±0.01 and 111.37±8.52 t C/ha, respectively. From Eucalyptus globulus, such as aboveground carbon (AGC), belowground carbon (BGC), litterfall carbon (LBC), and soil organic carbon (SOC), were 170.63±75.21,44.37±19.55, 0.02±0.003, and 141.07±22.88 t C/ha, respectively. The total mean carbon storage of Cupressus lusitanica was 312.36±97.88 t C/ha, and that of Eucalyptus globulus was 356.09±117.64 t C/ha. This was equivalent to 1146.36±359.23 t C/ha CO2 (gas) of Cupressus lusitanica and 1306.85±431.75 t C/ha CO2 (gas) of Eucalyptus globulus. The over-all mean carbon storage of Eucalyptus globulus was higher than that of Cupressus lusitanica in all carbon pools, with the exception of litterfall biomass. The over-all carbon storage of the species from this research area can be characterized as intermediate when related to other research carried out elsewhere in the tropics.

KEYWORDS: Eucalyptus globulus, Cupressus lusitanica, carbon stock, plantation forest, Above and below ground biomass

1. INTRODUCTION

The destruction of forests around the world announcements carbon dioxide into the atmosphere, which underwrites to climate change (Nunes et al., 2020). Extended changes in the global average weather patterns have been referred to as climate change (Seneviratne et al., 2021).There is now a lot of concern throughout the world about the rise in atmospheric carbon dioxide (CO2) and how it could alter the climate (Dar et al., 2015). An efficient way to reduce elevated atmospheric carbon dioxide (CO2) concentrations and help slow down global warming has been to expand the area covered by plantation forests (Aba et al., 2017). According to a recent assessment, between 2005 to 2010, carbon reserves in forestry biomass declined by an assessed 0.5 gigatons per year (Köhl et al., 2015). Forfeiture of forest biomass over disforestation and forest dilapidation make up 12%–20% of yearly glasshouse gas emissions (Kumar et al., 2022).The buildup of carbon in the atmosphere is a global problem since it is the main driver of global warming and has a profound impact on ecosystems, aquatic characteristics, and ocean level rise. About four billion hectares of forest cover make about 31% of the world’s land area, and its biomass alone stores 289 gigatons of carbon(FAO, 2011).

African forests contribute 21% of the global forest biomass carbon pool and store up to 630 kg of carbon per hectare annually, making them a vital vibration absorber for climate change (Katerere et al., 2009). Due to extensive deforestation, Ethiopia’s forest cover decreased from 13.78% to 11.40% between 1990 to 2015 (FAO, 2015). Compared to other sub-Saharan African nations, where the average yearly deforestation rate was 0.8%, this one had a high rate of approximately 1% (FAO, 2010). The aboveground carbon pools of tropical forests contain more carbon per unit area than any other type of land cover when they are in their natural state (Tyukavina et al., 2015). The active biomass of trees and understory plants, along with the dead mass of litter, woody debris, and soil organic matter, are the primary carbon reservoirs in tropical forest ecosystems. However, natural forests store more carbon above ground than any other vegetation (Borma et al., 2022). To combat global warming and effectively balance increasing atmospheric carbon dioxide (CO2) concentrations, plantations forests were established (Bernal et al., 2018).

The global carbon cycle depends on how forests respond to growing atmospheric carbon dioxide (CO2) concentrations (Xiao et al., 2021). Forests store more carbon than any other terrestrial ecosystem, storing over 70% of all soil organic matter and over 80% of all terrestrial above-ground carbon (Alemu, 2014). The burning of fossil fuels and the conversion of forests to agricultural land are the primary causes of the 3.5 pentagrams (1015 gramme or billion tones) of carbon that has accumulated in the atmosphere annually over the past century (Paustian et al., 2000). Deforestation, soil erosion, changes in land cover, and land use all have an impact on the carbon stock density in the forest ecosystem. Slope, altitude, and land use types all have an impact on the carbon reservoirs in the forest ecosystem (Assefa et al., 2017). Impact of slope on above- and belowground biomass, soil organic carbon, and total carbon in forest ecosystems (Yohannes et al., 2015). By accelerating carbon emissions, land use in the tropics has an impact on the worldwide carbon cycle. The density of soil organic carbon pool decreased by 20–50% when forests were converted to other land uses or land-living shelter into farmed land use types (Ahirwal et al., 2022).

The properties of stand stage and tree species composition on carbonstorage in forest ecosystems are appropriately taken into justification(Kaushal et al., 2021). According to with increasing stand age, morebiomass is accumulated (Zhang et al., 2012). It has not been well studiedhow certain tree species affect the storage of carbon in Ethiopianplantation forests. There has been very little research in Ethiopia’s forestcarbon pool that takes into account the tree species and environmentalconditions that influence carbon stock. Due to their rapid growth, speciesof plantation forests like E. globulus and C. lusitanica were essential toefforts to allay and familiarize to climate change over carbonrequisitioning and storage. Additionally, by lessening the strain onruminant natural forests, these quickly growing tree varieties wereextremely important. This study was among the few that addressed theseresearch gaps. This study offered experimental evidence towardsdeliberate these fast-rising tree species as a basis of carbon pools forfounded climate alteration extenuation approaches in this research area,specifically in Goncha Siso Enesie District, and nationwide. For targetedspecies, the data can be applied as initial information for sustainable forestmanagement. Additionally, the findings will aid in the understanding ofcarbon routine difference of the four mainly carbon pools such as,aboveground, belowground, litterfall, and soil organic carbon in speciesspecificplantations in Ethiopia, however, has not received much attentionfrom the fast-growing tree species the targeted study area. Theassessment Enhancing estimates of forests’ potential to store carbon andproviding the basis for national forest management depend on this data.Prior research conducted in Ethiopia and other tropical regions has showna great deal of interest in assessing the quantity of carbon deposited in thesoil and standing biomass of forest ecosystems (Negash and Starr, 2015).For foreign and district government, politicians, and other preservationadministrations, this further illustrates the gap in the plantation forest’scapacity to store carbon and provides some helpful information in currentcondition, including E. globulus and C. lusitanica tree species. This studyaddresses the information gaps regarding the estimation of carbon stockvariation and how they affect the biomass of selected tree species in thisresearch area. Specifically, to determine and contrast the carbon stock ofselected plant species above and below ground, to calculate the carbonstorage of litterfall under specific plant species, and to calculate the carbonstock of soil organic matter under specific plant species.

2. MATERIALS AND METHODS

2.1 Description of Study Area

The study was conducted in the Goncha Siso Enesie District in Amhara,Ethiopia. It is far from 335 km from Addis Ababa and 155 km from BahirDar. The study area is situated at 10° 27′ 36” and 11° 53′ 52” northlatitudes and 37° 12′ 56”, 38° 43′ 45”, 3° 55′, and 38° 24′ east longitudeswith an average altitude of 2000 meters above sea level (m.a.s.l.) (Fig. 1)

2.1.1 Climate and Agro-ecological Condition

Agro-ecological conditions and climate are two of the most importantvariables in deciding if a given place is suitable for a given settlement.Highland or Dega (12%), Midland or Woyna Dega (48%), and Lowland orKola (40%), are the district’s three agro-ecological zones. Rainfallaverages between 800 to 1800 mm annually. However, the unimodalrainfall pattern, which normally lasts from June to September, is erraticand insufficient, especially for timely-planted long-season crops. Thetemperatures of Woyna Dega and Dega were 18-25oC, as were thevegetation type, socioeconomic activities, and human agro-ecologicconditions. The temperature gets very hot from March up to May, when it rises above 25⁰C. Generally, according to the Woreda Office of Agricultureand Rural Development, the rainfall pattern of major crops stops duringthe time when most crops are in vegetative growth or at flowering stages(WoARD, 2019).

2.1.2 Study Site Selection

The selection of the study site was thoughtful; selected species wereplanted in nearby locations with comparable climatic, topographic, andedaphic conditions at the time of plantation, which is why the study siteselection was deliberate. The Goncha Siso Enesie District plantationsforest was chosen for the study because it offered the optimum location inthis regard. The team leader of Natural Resources Managementparticipated in an initial conversation to gain more knowledge about inthis plantations forest. Finally, to estimation the biomass carbon storagevariation and soil carbon stores of the target tree species were employeda well-defined boundary.

2.2 Sampling Design and Forest Inventory

For this investigation, systematic sample strategy stayed employed todemeanor the forest record in these plantation forests. Because it is easyto use in the field and yields more precise findings. It was found that theplot and the transect line were 50 and 100 meters apart, respectively.According to Genene et al. (2013) suggest that compared to circular plots,square plots can be more cost-effective, contain greater within-plotvariance, and are more representative of the same area. As a result, squaresample quadrants with dimensions of 10 m x 10 m (100 m2) wereconsidered for plantation measurements. Five 1m x 1m (1m2) subsamplingunits were set up inside each 10m x 10m quadrate at the fourcorners and at the center of each square plot to collect soil and litterfallsamples. To collect data on tree stands based on DBH and overall height ofthe trees, a wood inventory stayed carried out. Since tree diameter was themain predictor of carbon and biomass in the allometric equations selectedfor this investigation, it was measured. It employed to develop standcharacteristics in conjunction with total tree height. In order to toleratethe demands of use in challenging circumstances, fieldwork equipmentneeds to be accurate and long-lasting.

Field measurements of four carbon pools, such as aboveground biomass,belowground biomass, litterfall biomass, and soil, were used to collect theprimary data, including tree height and diameter, litterfall, and soilsamples. The equipment needed was determined by the type ofmeasurements (Pearson et al., 2005). In order to collect the data for thisstudy, the following instruments were used: meter tape to measure sampleplot distances; a clinometer to measure tree height; a caliper to measuretree diameter at breast height (DBH); an auger to collect sample soil;plastic bags to collect sample soil; a core sampler to sample soil for bulkdensity; and a Global Positioning System (GPS) to collect study sitecoordinates. Samples of bulk density up to a depth of 60 cm were collectedusing a core sampler.

2.3 Sample Size Determination

It is necessary to measure a sufficient number of sampling units to achievethe required level of precision, neither more nor less, in order toevaluation the biomass and carbon storage capacity of planted forests in astatistically and practically efficient manner (Thomas et al., 2015). Usingthe resources at hand, a practical method was taken to regulate thenecessary amount of sample schemes (Woldemariam, 2015). According toa general rule, the smaller the sample size needed for a planting, the moreprecise the measurement and the more changeable the material (Thomaset al., 2015). A total of 60 sample schemes were taken from C. lusitanicaand E. globulus tree species. The analysis suggested that thirty (30) sampleplots for a single, similar estate site were required. Based on this, a total of60 sample plots were taken from the study area (Picard et al., 2012).

2.3.1 Litterfall Sampling

The litterfall models stayed calm from sub-plot of 1 m x1 m in apiecescheme. At the corners and in the center of the main plot, five equal subquadrantsof 1 m2 each were established. Whole litterfall models in thesub-quadrants were composed by physical from each sub-quadrant. All ofthe litterfall inside the surround was calm and put in a flexible basket anda new mass was measured there with a digital balance. After weighed andcoded, all samples were evenly mixed per sample plot to prepare a total of60 composite samples (30 for each stand). From each plot 100 g of thecomposite samples were taken to the lab to determine the oven dry mass(Pearson et al., 2007). Then after the new mass grit, the model was takenfor laboratory analysis, which was taken into the Bahir Dar ForestDevelopment Office to estimate the oven-dry mass of the litterfall. Thedead wood at the study sites did not exist; hence, dead wood was notestimated in the current study.

2.3.2 Soil Sampling

For the purpose of analyzing bulk density and soil organic carbon, soilmodels stayed engaged from each sample plot. Three types of variablesmust be known in order to acquire an accurate inventory of soil organiccarbon stocks in mineral or organic soil: (1) depth; (2) bulk density(determined by calculating the oven-dried weight of soil from a knownvolume of tested material); and (3) the concentrations of organic carbonwithin the sample (Pearson et al., 2005). For bulk density determination,soil sample was placid from sub-samples which were designed at thecenter of apiece model design (1mx1m). The models stayed engaged fromquadrants 1mx1m (1m2) assigned in the four angles of the model schemesand from the one center of within (10 m x 10 m). After clearing away anyfallen debris, soil samples were taken from a depth of 60 cm in two depthclasses (0–30 and 30–60 cm) in order to determine SOC and bulk density,respectively.

Soil models stood obtained using a circle auger and a core sampler. Fiveequal weights of each sample from each quadrant were taken and mixedtogether homogeneously, and a composite subsample of 100 g of wetweight from each plot was given to the lab for analysis. Subsequently,using the Walkley-Black Procedure, the carbon fraction of each samplewas calculated in the laboratory (Walkley and Black, 1934). Finally, thebulk density, organic carbon, and soil organic carbon were computedusing this information. Also, using a core sampler that was carefullyinserted into the soil to prevent compaction, identical quadrants ofundistributed soil samples for bulk density determination were takenfrom the surface soil at the same time, from the center (Roshetko et al.,2002).

2.4 Methods of Data Analysis

2.4.1 Aboveground Biomass

Estimation of AGB, the allometric equivalences established by Berhe et al.(2013) for C. lusitanica (Eq. 1) and Debela (2017) for E. globulus (Eq. 2)were employed.

AGB = 0.0319* DBH1.8903 * H0.9194 (1)
AGB =0.479* DBH 2.2578 *H-0.374 (2)
AGBC = AGB × CF (3)

2.4.2 Below Ground Biomass and Carbon Storage Estimation

Based on the AGB each stand, BGB of the stand were assessed. BGB wasestimated by multiplying the above-ground biomass by 0.26, whichrepresents the root-shoot ratio (Hangarge et al., 2012).

BGB = 0.26× ABG (4)

Where, BGB= below-ground biomass and AGB=above-ground biomass, theconversion factor for AGB is 0.26.

BGBC= BGB ×CF (5)

2.4.3 Litter Biomass Carbon Storage Estimation

The litter samples were:

The carbon content of litter biomass was calculated by 0.37 of the dryweight of litter biomass per unit area (IPCC, 2006) and was estimatedusing the following formula:

CL = LB × 37 % (7)

2.4.4 SOC Estimation

According to Pearson et al. (2007), to establish a precise inventory of theorganic carbon stocks in the soil, the following parameters must beexamined: organic carbon concentrations, soil bulk density (derived froman oven-dry weight of soil from a known volume of measured material),and soil depth to which carbon is estimated for (0-60 cm) are all factors.The same sampling sub-quadrats advised for litter sampling were used toobtain samples for soil carbon analysis. The soil samples were air dried,thoroughly mixed, and sieved through a 2mm mesh size sieve before beingused for soil carbon measurement at the Amhara Design and SupervisionWorks Enterprise (ADSWE) center soil analysis of laboratory.

A cylindrical core sampler with a 5 cm diameter and 5 cm height was usedto collect a soil sample and determine the volume of the core for soil bulkdensity analysis. Each plot had a soil sample taken from it in a pit that was0-60 cm deep. So, using a metal soil corer sampler to dig the soil, two layersof 0–30 cm and 30–60 cm were chosen to be the most representative. The

freshly sampled soil obtained by bulk density cores was stored in a plasticbag, which was then labeled and sealed. The samples were then broughtto the lab oven for oven drying, where the oven dry weight was measured(re-weighted) after drying for 48 hours at a constant temperature of 105°Cto ascertain moisture content and bulk density (Pearson et al., 2005).Samples were tested using the Walkley method to determine thepercentage of organic carbon (Walkley and Black, 1934). The studysuggested method was used to calculate the soil’s carbon stock based onthe bulk density and carbon content of the soil at a set depth (Pearson etal., 2007). It is important to know the volume of the soil collected beforecomputing SOC because doing so aids in computing bulk density.

V=h*Πr2 (8)

Moreover, the bulk density of a soil sample was calculated as follows(Pearson et al., 2007).

BD = WavdryV(9)

The soil organic carbon stock pool was calculated using the formula(Pearson et al., 2005):

𝑆𝑂𝐶=𝐵𝐷∗𝑑∗%𝐶 (10)

2.5 Total Carbon Storage

The total carbon storage has been estimated:

CT=AGC+BGC+CL+ SOC (11)

2.6 Data Analysis

The numerical information (DBH, height, fresh and dried litter weights,and soil weights) gathered from the field was entered into a computer andset up on an Excel data sheet for future study. A one-way analysis ofvariance (ANOVA) was carried out to check for significant differences incarbon storage between both targeted tree species of forest biomass andsoil organic carbon across the two depth classes. When the analysis ofvariance (ANOVA) showed a significant difference among the differentfactors taken into consideration at (P< 0.05), a mean separation for eachtreatment was made by the Tukey least significant different comparisonmethod. All statistical tests were performed using Statistical Package forSocial Science (SPSS) version 27.1. Descriptive statistics were used tosummarize the frequency and density of each tree species in the sampleplot of the study area, the DBH and height of trees, and the mean, standarddeviation, minimum, and maximum value of carbon storage in differentcarbon pools of the study area, while one-way ANOVA and an independentT-test were used to determine the statistical significance of carbon storagevariation along selected tree species.

3. RESULTS AND DISCUSSION

3.1 Stand Characteristics for the Studied Species

This study showed that the mean value results of the tree biomassdetermining parameters such as height (H) and diameter at breast height(DBH). The total mean height (m) and DBH (cm) of E. globulus and C.lusitanica significantly differed at (P <0.05) (Table 1).

These results showed that the total mean height and DBH of Cupressuslusitanica was lower than Eucalyptus globulus (Table 1). This variation inspecies features, although it was planted in the same year and in the sameagro-ecological zones, could be the cause of this difference. Eucalyptus treecan reach heights of 40–55 m, and sometimes 70 m, with a diameter of 2m in Ethiopia (Bekele et al., 2007). while C. lusitanica trees can reachheights of up to 35 m in environments that are favorable for them (Orwaet al., 2009). But in this study area, the height of E. globulus reaches 60–70m, and the height of C. lusitanica was 35–45 m.

3.2 Aboveground Biomass and Carbon Storage Estimation

This result showed that the presence of species variation affects thecarbon storage of different pools in the forest stand (Fig.2). Theaboveground carbon content of the two species at the same age showed asignificant difference (p=0.001).

The ecosystems have significant potential variation in both short-term and long-term carbon storage (Houghton, 2005). This study showed, the variation in carbon storage among various tree species varied, and those that have been found to have high carbon storage. The current study results include assessing carbon stock at the targeted species and are crucial aimed at determining the function of forest ecologies in managing local and worldwide carbon emissions. From this finding, E. globulus stored a larger amount of Aboveground biomass carbon storage than C. lusitanica. This difference might be due to the presence of larger trees in DBH in E. globulus and difference in aboveground tree biomass estimator (allometric equation). According to Moges, (2011) the amount of carbon assessed in a particular forest is impacted by the various types of representations used for biomass assessment. Based on this AGB of species depends on their DBH; this could be due to the DBH difference between the two species that affects carbon storage. The other scenario could be the type of species difference. The older trees with a large DBH value will have a large AGB. As the age of trees increases, biomass also increases (Mamo, 2007). These are considered sources of variation in carbon storage (Tefera and Soromessa, 2015).

The current total mean AGC storage of C. lusitanica was significantly higher as compared to other studies AGC storage of (158±66.6 Mg C/ha) in the baseline study in Kenya (98.4±44.4 Mg C/ha) in Kenya, (39.05 t C/ha) in Wondo Genet, Ethiopia (128.1 t C/ha) in Addis Ababa (89.7 t C/ha) in the Gamo zone in southern Ethiopia and lower than (184.80 t C/ha) in Chilimo dry Afromontane forest, central Ethiopia and (180.14 Mg/ha) in ChatoAfromontane forest, western Ethiopia (Iticha, 2017; Omoro et al., 2013; Patula and Oeba, 2016; Yirdaw, 2018; Yilma and Derero, 2020;Mada et al., 2022; Tesfaye et al., 2020). E. globulus was significantly lower than (221±143.2 Mg C/ha) in the baseline study in Kenya (205.07 Mg/ha) in Chato Afromontane forest, western Ethiopia and (287.8 t C/ha) in Addis Ababa and higher than (67.5 t C/ha) in other study areas of Ethiopia and (59.3 t C/ha) in the Gamo zone in southern Ethiopia (Mada et al., 2022; Omoro et al., 2013; Iticha, 2017; Yilma and Derero, 2020; Tefera and Soromessa, 2015).

These variations may be the result of differences in tree ages, species height, and DBH range of trees, management practices, the environment, and the different allometric equations used. Another differences in structural parameters such as tree diameter, ecological area, topography (Yilma and Derero, 2020). Generally, the difference may be that the majority of Ethiopia’s plantation forests’ carbon storage are assessed using general allometric equations without taking into consideration the value of species-specific equations as a whole or the differences in species. However, applying a species-specific allometric equation to estimate carbon stocks produces better and more dependable results than a generic equation, particularly in less diversified plantation forests.

3.3 Belowground Biomass and Carbon Storage Estimation

From this finding, there stood a significant difference in belowground carbon content between the two tree species (P=0.001) Fig.3).

From these results belowground carbon stock (BGC) of E. globulus was higher than C. lusitanica. These study, compared with other studies, the over-all mean BGC storage of C. lusitanica was higher from (39±17.8 t C/ha) in the baseline study in Kenya, (10.15 t C/ha) in Wondo Genet, Ethiopia, (17.9 t C/ha) in the Gamo zone in southern Ethiopia, and lower than (43.23 Mg/ha) in Chato Afromontane forest, western Ethiopia, and (54.5 t C/ha) in Addis Ababa (Yilma and Derero, 2020; Omoro et al., 2013; Mada et al., 2022; Yirdaw, 2018; Iticha, 2017). The results of E. globulus was lower than (72±46.0 t C/ha) in the baseline study in Kenya, (122.5 t C/ha) in Addis Ababa and (49.22 Mg/ha), in Chato Afromontane forest, western Ethiopia and higher than (11.9 t C/ha) in the Gamo zone in southern Ethiopia (Mada et al., 2022; Omoro et al., 2013; Iticha, 2017;Yilma and Derero, 2020). The variation was due to the fact that, compared to the smallest trees, changes caused by larger trees have a greater capacity to produce larger amounts of below-ground biomass.

3.4 Litterfall Biomass and Carbon Storage Estimation

According to these findings, the biomass of litterfall was calculated using the carbon storage and total mean biomass of C. lusitanica and E. globulus. There was a significant difference noticed in the two tree species at the 95% confidence interval (P< 0.001) (Fig.4).

Compared to E. globulus, C. lusitanica had a slightly greater mean litter biomass carbon storage. This difference might be due to E. globulus litterfalls for fuel wood being easier to collect by the local community and the consumption of fuel where it is common in that area compared with C. lusitanica. The current study, compared with other studies, the litter carbon storage value of E. globulus was higher than (0.0031 t C/ha) in the Gamo zone in southern Ethiopia and lower than (3.25 Mg/ha) in the Chato Afromontane forest, Western Ethiopia (Iticha, 2017; Mada et al., 2022). The result of C. lusitanica was higher than (0.007 t C/ha) in southern Ethiopia and lower than (2.10 Mg/ha) in the Chato Afromontane forest, Western Ethiopia (Iticha, 2017; Yirdaw, 2018). From these different tree species and, thus, different forest types, come various amounts of litterfall value (Nave et al., 2022).

3.5 Soil Organic Carbon Stock (SOC)

From these results, the soil organic carbon storage of the two species was not showed significant difference (P=0.093) (Table 2).

The results for bulk density were in opposition to the concentration (%) and stock of soil organic carbon. This outcome was corroborated by(Sheikh and Tiwari, 2013). This indicates that the SOC dropped as soildepth rose. The buildup of dead and rotting logs and decomposed forest litter over the floor was the primary cause of this variation, which was caused by the presence of greater organic matter at the top soil layer. These differences were comparable to those discovered in a research by (Sheikh and Tiwari, 2013). A number of factors, including soil properties, forest management techniques, litter inputs, decomposition rates, and root turnover, influence soil organic carbon (Jandl et al., 2007). In the global carbon cycle and the forest’s carbon stores, soil organic carbon is essential (Sundarapandian et al., 2015). According to reports, C. lusitanica (86.1 kg/ha) and E. globulus (87 kg/ha) in Ethiopia are found within the range of soil organic carbon stock (0–40 cm) depth (Abate, 2004). This discrepancy could result from variations in planted forests, soil microorganisms’ rate of mineralization, climate, and soil type (Lal, 2004).

The low carbon content beneath C. lusitanica in the 0–60 cm soil depth in comparison to E. globulus suggests a poor inflow of fresh litter. According to this data, the top layer (0–30 cm) of both plantation stands had a much greater SOC than the lower layer (30–60 cm). The land use history of the

of the constant supply of litter, lower rate of disturbance, minimal impact of erosion, and lower temperature beneath the closed forest canopy, which may slow down decomposition and favour an extension of the residence time of soil organic matter, the upper layers have a higher SOC than the lower depth (Erskine et al., 2002). These findings showed that the soil at the study site contained a significant level of organic matter. The greater mean SOC stock may be the result of high SOM and quick litter breakdown, which maximizes carbon storage (Wolde et al., 2014).

3.6 Total Carbon Stock from Plantation Forests

The finding of this research showed that AGC, BGC and SOC of E. globulus species were higher than those of C. lusitanica and lower than LBC (Table 3).

The capability of plants to absorb CO2 through photosynthesis may be the reason for the discrepancy in this discovery, where there is more carbon storage in the forest. In accordance with the report of (Omoro et al., 2013) in Kenya, which claimed that the C. lusitanica woodland ecosystem had the biggest reservoir of soil organic carbon. Nevertheless, this outcome runs counter to Abate’s findings.

(2004), who discovered that the biomass of standing trees had a significant carbon content, but the soil had a tiny amount of organic carbon. Variations in the soil depth at which the data was gathered (0–30 cm) may be the cause of this little value. The variations are caused by how the forest is managed, and they could also be explained by the use of various allometric models for biomass measurement. Therefore, one of the main constraints resulting in significant changes in such estimates could be the reliance on allometric equations.

According to this study, forests that are maintained for greater carbon storage have the greatest potential for sequestering carbon and offer important mitigation alternatives. Nonetheless, research demonstrated that there were variations in species type and species-specific biomass allometric equations among the exotic species, which could be caused by the carbon content of aboveground, belowground, litterfall, and soil carbon storage. Differences in the allometric equation, tree species kinds, and the existence of greater diameter trees at breast height in E. globulus could all be contributing factors to the variation.

4.CONCLUSION AND RECOMMENDATIONS

This study uses DBH and total tree height as independent variables to determine the biomass and carbon store of the two targeted tree species. The outcome reveals which of the targeted tree species is most secluded. Compared to the C. lusitanica stand, the E. globulus stand had a much higher total mean carbon storage. Nonetheless, C. lusitanica’s litterfall carbon storage was marginally greater than E. globulus’s. According to the current finding, the total mean carbon stock of C. lusitanica was 312.36±97.88 t C/ha and E. globulus was 356.09±117.64 t C/ha. These findings suggest that species-to-species heterogeneity in carbon storage variation occurs. This study result indicates that, C. lusitanica and E. globulus are important in storing carbon stock in their biomass and soil. Finally, it could be concluded that E. globulus has stored more carbon than C. lusitanica. Hence it has a considerable role in mitigating the climate change by sequestrating carbon dioxide and to earn income from the current carbon marketing system in addition to its direct economic benefit plantation forests. Large-scale plantations of E. globulus and C. lusitanica have been established throughout Ethiopia, so comparable studies on carbon resources would be advantageous to both the local community and the nation. The development and implementation of allometric equations that are country-specific should be the main subject of future study. Knowledge of how this component affects the biomass and soil carbon pools for scientific advancement, more topographic aspect-related research will be required. The findings of this study indicate that more research will be required in this area because they do not address the role that plantations of the C. lusitanica and E. globulus species play in mitigating climate change. In addition to above-ground, below-ground, and soil carbon storage, fine roots can also hold carbon in plantation forests. So, in this study carbon storage in fine root was not included. Therefore, further study should concentrate on estimating the amount of carbon in this carbon pool.

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