P-ISSN: 2305-6622; E-ISSN: 2306-3599

International Journal of
Agriculture and Biosciences
www.ijagbio.com; editor@ijagbio.com
Research Article
Carbon Stock and Soil Properties Analysis along Altitudinal Gradient and Slope
in Gra Kahsu National Forest Priority Area: Southern Tigray, Ethiopia
Tesfay Atsbha*1, Anteneh Belayneh2 and Tessema Zewdu3
1
Tigray Agricultural Research Institute, Alamata Agriculture Research Center, Alamata, Ethiopia, 23Haramaya university,
Haramaya, Ethiopia
*Corresponding author: atsbhatesfay@gmail.com

Article History: Received: January 12, 2018 Revised: June 22, 2018 Accepted: August 23, 2018

AB STRACT
The study was conducted to assess the impacts of altitude and slope on carbon stock and soil properties on the slopes
of Gra-kahsu national forest priority area. Data were collected from 35 quadrats, each with 20 m X 20 m with trees of
diameter at breast or stump height >2.5 cm. Above and below ground carbon (allometric equation), organic carbon
(Walkely-Black), PH (1:25water), and total nitrogen (Kjedah) were the mothed used for analyzed. Analysis of one
way using R-software was used to analysis the mean of carbon stock pools and soil properties across the altitudinal
gradients and slopes. The upper altitudinal class of the study area had better carbon stock than the rest classes due to
the presence of high diameter at breast height. The distribution of carbon stocks with each sample quadrate in litter,
herb, above ground and below ground carbon pools was found positively correlated and had significant differences
with altitude. However, positively correlated and had non-significant differences in litter, dead woody carbon and soil
organic carbon pools with slope was found. Except organic carbon percentage, soil organic matter and total nitrogen,
all considered soil properties showed non-significant differences among the three-altitudinal class. The differences
may be attributed to leaching and differences in organic matter (carbon) contents within the soil profiles due to
altitude. The current study shows that carbon stock value, soil properties of study area was highly affected by
environmental factors such as altitude, and slope. Nevertheless, altitude was the only factor that showed significance
difference in carbon stocks of the study area.
Key words: Altitude, Carbon stock, Slope gradient; Soil organic carbon

INTRODUCTION influencing forest organic carbon in areas with the same

climate regime (Clark et al., 2000; Houghton, 2005;
Forests and trees absorb carbon dioxide from the Dianwei et al., 2006).
atmosphere and store it as carbon. Forests store about 20- Significant differences in soil chemical and physical
40 times carbon per unit area than most crops and most of properties in a small area on uniform geology are known
the carbon is released into the atmosphere through to be related to landscape position (Jenny, 1941; Ruhe,
deforestation (Emmanuel, 2013). Forest carbon more 1956). The relationships between soil physical properties
significantly, affects bioenergy emissions when biomass is and landscape attributes including slope and Altitude
source from standing trees compared to residues and when affect plant growth through indirect influences involving
less GHG-intensive fuels are displaced (Keith, 2014). soil physical properties (McIntosh et al., 2000; Seyed and
Forests have a large potential for temporary and long-term Robert, 2004). Altitude is often employed to study the
carbon storage (Houghton, 2005) and influence by effects of climatic variables on SOM dynamics, which
altitudinal variations (Alves et al., 2010). Forest carbon determines the level of decomposition of the organic
stock could be affects by different environmental factors matter (Lemenih and Itanna, 2004). The change in
such as topographical factors like altitude, slope and altitudinal gradients influences SOM by controlling soil
aspect gradients. Landscape attributes including slope, erosion, species and biomass production of the native
aspect, elevation, and land use are the dominant factors vegetation (Tan et al., 2004).

Cite This Article as: Atsbha T, A Belayneh and T Zewdu, 2018. Carbon stock and soil properties analysis along
altitudinal gradient and slope in Gra Kahsu national forest priority area: southern Tigray, Ethiopia. Inter J Agri Biosci,
7(3): 156-163. www.ijagbio.com (©2018 IJAB. All rights reserved)

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 Inter J Agri Biosci, 2018, 7(3): 156-163.

Carbon sequestration from atmosphere can be parallel line transects were laid at 500m interval that lie
advantageous from both environmental and with parallel to the slope of the stand was established. The
socioeconomic perspectives (Yohannes et al., 2015). quadrates were distributed along transects that ranges
According to Kumar (2012) although changes in species from 1 km to 1.5 km which were laid parallel to the slope.
composition and distribution, biodiversity and community Sample quadrate size 20mx20m was used to collect the
structure along topographic gradients have been well data (identity, DBH/DSH for both live and dead woody
documented in but altitudinal and slope patters of carbon plants) and 35 numbers of quadrats (1.4ha) were taken.
storage in forest ecosystems remain poorly studied. This is
true in Ethiopia particularly in Tigray, there has been very Data Collection Methods
limited forest carbon stock study by considering slopes Trees and other woody vegetation biomass
and altitudinal gradients that affect carbon stock and soil measurement
properties. All trees/shrubs (live and dead) within the quadrate
Gra Kahsu national forest priority area is one of were recorded and their diameter at breast height (1.30m
Tigray’s forests, which have tremendous role for stocking above the ground) for trees and diameter at stamp height
carbon within their biomass and soil. This contributes a (30cm above the ground) for shrubs was measured using
lot to mitigate climate change but different topographic caliper. Whereas, in cases where there are multi-stemmed
features could influence carbon-stocking process and soil small trees and shrubs (>1 stem on a sample shrub or
properties. There is limit scientific study that shows slopes small tree) prone to multi-stem below 1.3 m diameter the
and altitudinal gradients influence and variation on carbon measurement of the diameter was calculated by the
stock amount and soil physical and chemical properties in diameter equivalent (de) as follows:
Tigray, particularly in the study area. Therefore, this study
shows and proves scientifically the role of altitudinal de = √∑𝐧𝐢=𝟏 𝐝𝐢𝟐 (Snowdon, 2002)………..……... (Eq. 1)
gradients and slope on the amount of national forest
priority area carbon stock and soil properties in the study Where: di = diameter of the ith stem at 30 cm (d30)
area. height.
MATERIALS AND METHODS Herb and litter layer
A quadrate with a size of 1 m × 1 m was established
Description of the Study Area to sample litters and herb. In each sample quadrates, five
Alamata is located 600 km north of Addis Ababa and small quadrates were laid four at the corner and one in the
about 180 km south of the Tigray Regional capital state center to minimize heterogeneity. All the herbaceous
(Mekelle) (Figure 1). It is geographically located between vegetation emerging within the quadrate areas (1m x 1m)
12°19'21”N and 12°24'28.5” North and 39°14'52”E and were cut at the ground level, weighed, and a composite
39°45'47.8” East longitude, in Southern Tigray. Alamata sample was obtained from each sub-quadrate for oven-dry
Wereda is borders with Amhara region from the south and mass determination in the laboratory (Dossa et al., 2008;
west and Afar region from the East. The altitude of the Jina et al., 2008). Oven drying was set at 70 0C and
Wereda ranges from 1,178 to 2,300 meters above sea level observed for 24 hours or until the samples reached their
(masl).The annual mean precipitation ranges from 615- stable weight (Labata et al., 2012).
927 mm, with mean maximum and minimum
temperatures of 23 °C and 14 °C, respectively (Girmay et Sampling of soil
al., 2014). Soil sampling was done from five points per quadrate
Gra Kahsu national forest priority area is designated (400m2) using soil auger at depth of 30 cm and then the
to conserve unique natural features, historical interests soil samples from the five points were composited to
and other natural values with legal and administration represent a quadrat. Soil texture, PH, organic carbon,
supports on the upper part of Alamata town. It is endowed electrical conductivity, Cation exchange capacity, total
with different natural resources such as wildlife and other nitrogen, Available phosphorus and organic matter were
bio diversities, which contribute great potent source as analyzed for each sample at Mekelle soil laboratory
important pillars for future development. The total area is research center. In 20x20m2, an undisturbed soil was
3500ha. Monkey, Ethiopian Tiger (Panthera Tigris), taken through core sampling to determine bulk density
Menelik Bushbuck (Tragelapphus scriptus), Python (MacDicken, 1997). Soil sample was oven dried at 105°C
(Snake type), Fox and different species of birds were for 24 hours at Mekelle soil laboratory research center.
examples of wild life species that found at Gra Kahsu
national forest priority area (WAOARD, 2016). Estimation of aboveground trees and shrubs carbon
stock
Stratification and Sampling Techniques The AGB of trees ≥2.5 cm in DBH and ≥1.5 m in
A reconnaissance survey was conducted, to collect height estimated using the allometric model of Kuyah et
base line information, observe vegetation distribution and al, (2012). The equation is as follows:
determine number of transect lines to be laid.
Accordingly, stratified in to three units based on Altitude, AGB = 0.1428 ∗ DBH2.2471 ……………………... (eq. 2)
namely lower (1655-1869 masl), middle (1870-2084
masl) and higher (>2085 masl). Slope gradient was the AGB diameter measures at DSH of multi-stem trees and
second parameter to classify the area. Slope classified into shrubs were estimated from DSH using a regression
lower (0-25%), middle (26-45%) and higher (> 46%). A model WBISPP, (2000). The equation is as follows:

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Fig. 1: Location map of the study area.

AGB = (0.4861 ∗ DSH) + (0.1659 ∗ (DSH2.2 ))…. (eq.3) Total dry weight (kg m−2 ) =
𝐓𝐨𝐭𝐚𝐥 𝐟𝐫𝐞𝐬𝐡 𝐰𝐞𝐢𝐠𝐡𝐭 (𝐤𝐠)∗𝐬𝐮𝐛𝐬𝐚𝐦𝐩𝐥𝐞 𝐝𝐫𝐲 𝐰𝐞𝐢𝐠𝐡𝐭 (𝐠)
…………. (eq.7)
𝐒𝐮𝐛𝐬𝐚𝐦𝐩𝐥𝐞 𝐟𝐫𝐞𝐬𝐡 𝐰𝐞𝐢𝐠𝐡𝐭 (𝐠)∗ 𝐬𝐚𝐦𝐩𝐥𝐞 𝐚𝐫𝐞𝐚 (𝐦𝟐 )
To convert the above ground dry biomass to carbon,
50% of all trees and shrubs biomass were assumed to be
the carbon stock. So based on the aboveground trees and Carbon storage in herb and litter layer was computed
shrubs biomass carbon stock calculated as follows: using the formula (Lasco et al., 2006):

AG TSCS = AG TSDBM ∗ 0.5 (Brown, 2002)…… (eq.4) C stored (ton/ha) = Total dry weight ∗ C content (eq.8)

where; AGTSCS: Above ground trees and shrubs carbon The carbon stock (carbon content) for the dry biomass of
stocks AG TSDBM: Above ground trees and shrubs dry herbs and litters is 47% of the total dry biomass of the
biomass. quadrate (IPCC, 2007).

Below ground trees and shrubs dry biomass and Estimation of dry biomass and carbon stock in the
carbon stock dead wood
Below ground dry biomass for trees and shrubs were Dead wood biomass was computed using the formula
measured by taking 20% of above ground dry biomass of (Pearson et al., 2005):
trees and shrubs and accordingly 50% was adopted for its
carbon estimation. Below ground trees and shrubs dry BSDW = 0.139DBH2.32 -5.5% …………………….. (eq.9)
biomass was computed using the formula (MacDicken, Where, BSDW = Biomass of standing dead wood in
1997): ton/ha, DBH = Diameter at breast height of standing dead
wood (cm)
BG TSDBM = AG TSDBM ∗ 0.20 ………………… (eq.5) The total carbon stock in dead wood was computed
by multiplying the total biomass of the dead wood by 0.5
Where; BG TSDBM: Below ground trees and shrubs dry (Persson et al., 2005).
biomass AG TSDBM: Above ground trees and shrubs dry
biomass Estimation of soil organic carbon stock
Similarly, the carbon stock for below ground component Bulk density (ρb)
of trees and shrubs had measured as follows: Soil bulk density was determined after oven drying
the soil samples that are taken with core sampler as
BG TSCS = BG TSDBM ∗ 0.5 (Brown, 2002)…… (eq.6) follows formula as recommended by Pearson et al.
(2005).
Where; BG TSCS: Below ground trees and shrubs carbon
stocks BG TSDBM: Below ground trees and shrubs dry V = h ∗ πr 2 ………………………………………. (eq.10)
biomass.
Where: - V = volume of the soil in the core sampler in
Estimation of carbon stocks in the herb and litter layer cm3, h = the height of core sampler in cm, π =3.14 cm, r =
biomass the radius of core sampler in cm. Moreover, the bulk
Oven-dry weights of herb and litter subsamples were density (ρb) of a soil sample was calculated as follows:
determined to compute for the total dry weights using the
formula (Hairiah et al., 2001): ρb=
Wav,dry
v
…………………………… (eq.11)

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Where, ρb is bulk density of the soil sample per quadrate RESULTS

(g cm-3), Wav, dry is average air dry weight of soil sample
per the quadrate, V is volume of the soil sample in the Altitudinal variation of carbon stock pools
core sampler auger in cm3 (Pearson et al., 2005). Due to the altitudinal gradient, the values of litter and
herb carbon stock varied (Table 1). The upper altitudinal
Soil organic carbon (SOC) class had the highest herb carbon stock of 0.79 ton/ha,
Collected composite soil samples were examined for whereas the lower altitudinal class had the lowest herb
SOC estimation using the Walkely-Black methods (Gupta, carbon stock with the recorded value of 0.44 ton/ha. In
2000). SOC per quadrate and then per hectare in tons addition, the litter carbon stock was significantly lower in
calculated as follows: the lower altitudinal class compared to the other
altitudinal class (P<0.001) (Table 1).
g The AGC stock was significantly larger in the upper
SOC = ( ρb (cm3) ∗ D (cm) ∗ %C)……………… (eq.12)
altitudinal class compared to the others altitudinal class
(P<0.001) (Table 1).The AGC stock of the lower
Where, SOC = Soil organic carbon (t /ha), % OC = altitudinal class was between 11.59 and 25.76 ton/ha with
Organic carbon concentration of the quadrate (%) the average value of 16.44 ton/ha (Table 1). Similarly, the
expressed in decimal, ρb = Bulk density of the quadrate (g mean total SOC stock density was varied in classes of
cm-3), D = Depth of the soil sample (cm) lower, middle and higher altitude with carbon stock
density of 13.77±3.44, 18.49±4.60 , and 16.13±3.53
Soil textures were determined by hydrometer after ton/ha, respectively. Therefore, the mean total maximum
dispersion in a mixer with hex metaphosphate. soil carbon stock was stored in the middle altitudinal
Exchangeable base cations were extracted with 1N class, followed by higher and lower altitudinal classes
ammonium acetate at pH 7. Available phosphorus (Olson) with statistical significant differences along altitudinal
was analyzed according to the standard methods of gradient (p<0.05) (Table 1). Generally, the present study
analyses (Olsen et al., 1954). Soil pH was measured with revealed distinct pattern of variation of carbon stock in
combined electrodes in a 1:2.5 soil to water suspension. each pools although the variation has significant
Cation exchange capacity was estimated titrimetric ally by difference. The carbon stocks in AGC, BGC, herb, litter
distillation ammonium displaced by sodium (Chapman, carbon and SOC exhibited distinct patterns along
1965). Organic carbon was determined by the wet acid altitudinal gradients.
dichromate digestion method and SOM was calculated by
multi- plying percent OC by a factor of 1.724 (Walkley Altitudinal variation of soil physical and chemical
and Black. 1934) whereas total nitrogen was analyzed by properties
the semi-micro Kjeldahl digestion followed by SOM showed a significant variation across the
ammonium distillation and titrimetric determinations altitude (P<0.05) ranged from 2.85% for the lower (1655-
(Bremner M., 1965). 1869 m.a.s.l) to 3.24% for the higher (>2085 m.a.s.l).
Organic carbon percentage showed a decreasing trend
Statistical analyses across the altitudinal class which varied from 1.66% for
Analysis of one way ANOVA using R-software lower altitudinal class to 1.94% for middle class (Table 2).
Version 3.3.3 was used to analysis the mean of carbon Total nitrogen varied highly significantly (P<0.001) from
stock pools and soil restoration across the altitudinal as low as 0.22% for the lower altitudinal class to as high
gradients. The least significant difference was used to as 0.27% for the middle altitudinal class. According to the
separate the means. The correlation between carbon stock current study, non-significant differences (P>0.05) was
pools, soil properties with altitudinal gradients and slope noticed for available phosphorus, PH, soil bulk density,
was tested using the Pearson correlation matrix. CEC, EC, and soil texture across the three altitudinal class
Differences were considered significant at P<0·05. (Table 2).

Fig. 2: Linear regression model for carbon stock pools versus altitudinal gradient.

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Table 1: Altitudinal variations of carbon stock pools in Gra- Correlation of carbon stock pools and physical and
kahsu national forest priority area chemical soil properties with altitude
Altitude class Higher Middle Lower p-value AGC and BGC shows strong positive relation with
Values Herb carbon 0.79a 0.62ab 0.44b 0.02
altitude (r= 0.68 and 0.67; P<0.001) at 0.05 respectively.
(ton/ha) stock
Litter carbon 0.16a 0.12b 0.05c <0.001 In the presence study area the mean AGC and BGC, herb
stock carbon and litter carbons of all quadrates with
AGC 29.00a 20.02b 16.44b <0.001 corresponding altitude were more regressed linearly than
BCG 14.15a 9.96b 8.18b <0.001 SOC and dead wood carbon (Figure 2 a– f). On the other
WC 1.44a 0.16b 0c <0.001 hand, Litter carbon and herb carbon shows strong positive
SOC 16.13ab 18.49a 13.76b 0.03
relation with altitude (r= 0.71, P=0.004 and 0.47;
Different letters in the same row are significantly different
(P<0.05), AGC-above ground carbon, BGC-below ground P<0.001) at 0.05 respectively (Table 3). The distribution
carbon, WC-dead woody carbon, SOC-soil organic carbon. of carbon stocks with each sample quadrate in litter, herb,
AGC and BGC pools was found to be positively
Table 2: Altitudinal variations of physical and chemical soil correlated and had significant differences with altitude.
properties in Gra-kahsu national forest priority area The result showed that weak correlation (r= 0.24; P=0.19)
Altitude class Higher Middle Lower p-value (Table 3). EC, SBD and available of phosphorus shows
Soil PH (1:2.5) 6.79 6.78 6.79 0.96 strong negative relation with altitude (r= -0.9, P>0.05) at
physical EC (ds/m) 0.11 0.13 0.14 0.17
and OC (%) 1.90ab 1.94a 1.66b 0.03 0.05. In addition, organic carbon percentage, organic
chemical SBD (g cm-3) 0.99 1.02 1.03 0.31 matter, total nitrogen and CEC shows strong positive
properties OM (%) 3.24ab 3.31a 2.85b 0.04 relation with altitude (r= 0.8, 0.8, 0.6 and 0.9; P>0.05) at
TN (%) 0.25ab 0.27a 0.22b <0.001 0.05, respectively (Table 3).
Av.P (ppm) 0.99 1.16 1.24 0.15
CEC 43.86 43.12 41.33 0.14 Slope variation of carbon stock pools and soil
(meq/100gm)
Sand% 67.78 65.67 66.87 0.73
properties
Silt % 20.37 22.63 21.31 0.69 Litter biomass carbon, herb biomass carbon, AGC
Clay % 11.85 11.7 11.82 0.96 and BGC was no significant differences (p>0.05) along
Different letters in the same row are significantly different slope gradient. It was observed that the higher numerically
(P<0.05). AGC and BGC was estimated in the lower slope class
with mean total of 21.79± 9.03 ton/ha and 10.84 ± 4.52
Table 3: Pearson correlations of carbon stock with altitude
ton/ha, respectively. On the other hand, the higher carbon
Parameter Carbon Correlation P-value
pool coefficient value
in herb and litter biomass was estimated in the middle
AGC 0.68** <0.001 slope class with the mean total of 0.69 ± 0.33 and 0.12
BGC 0.67** <0.001 ±0.06 ton/ha and the lower litter biomass carbon was
Altitudinal WC 0.39* 0.022 computed in higher and lower slope class with no
gradients HC 0.47** 0.004 significant differences (p>0.05) along slope gradient. The
LC 0.71** <0.001 mean total soil carbon stock density was varied in classes
SOC 0.24 0.19
of lower, middle and higher slope with carbon stock
** Correlation is significant at the 0.01 level, * Correlation is
significant at the 0.05 level. density of 13.77±3.44, 18.49±4.60, and 16.13±3.53
ton/ha, respectively. Therefore, the mean total maximum
Table 4: Slope variations of carbon stock pools in Gra-kahsu soil carbon stock was stored in the higher slope class,
national forest priority area followed by middle and lower slope class with statistical
Slope class Higher Middle Lower p-value significant differences along slope gradient (Table 4). The
Values Herb carbon 0.43 0.69 0.57 0.18 effects of slope on the study area carbon stocks were very
(ton/ha) stock small; the relations were insignificant for all carbon pools
Litter carbon 0.09 0.12 0.09 0.41
stock except SOC.
AGC 19.88 20.97 21.79 0.86
BCG 9.89 10.24 10.85 0.84 Correlation of carbon stock pools with slope
WC 0.34 0.91 0.06 0.22 In the present study, the relationship between litter
SOC 16.13ab 18.49a 13.77b 0.04 biomass carbon and dead woody carbon related to slope
class was positive (r=0.06 and 0.04 respectively).
Table 5: Pearson correlations of carbon stock with slope
However, herb biomass carbon, AGC and BGC was
Parameter Carbon pool Correlation P-value
coefficient value negatively (r=-0.10, -0.09 and -0.10 correspondingly)
Slope gradient AGC -0.09 0.59 correlated with slope at the study area (Table 5). AGC,
BGC -0.10 0.56 BGC and herb carbon trend shows decrease as slope class
WC 0.06 0.72 increases. As the result revealed that the correlation
HC -0.10 0.56 between herb carbon and SOC distribution and slope
LC 0.04 0.83 gradient did not show clear pattern in linear regression.
SOC 0.24 0.19
Therefore, slope gradient did not influence the carbon
AGC-Above ground carbon, BGC-below ground carbon, WC-
dead woody carbon, HC-herb carbon, LC-litter carbon, SOC-soil pools considerably.
organic carbon

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DISCUSSION finding that, total AGC and BGC stock were significantly
and positively correlated with altitude (P<0.05).
Environmental factors disturbing different carbon Controversy, Luo et al. (2005) finding that, total AGC and
pools and soil properties BGC stock were significantly and negatively correlated
Altitude is a major effect on the biomass and carbon with altitude (P<0.05).Yohannes et al. (2015) also finding
stock in the forest ecosystems (Luo et al., 2005). The that altitude has significant difference and inverse
current study indicates that, the upper altitudinal class correlation with all carbon pools except litter biomass
showed an increasing herb and litter carbon stock and carbon. However, Muluken (2014); Alefu et al. (2015)
followed by the middle altitudinal class and decreased studies done in Ethiopia and Bayat (2011) in Apennine
when we go to bottom of the mountain. The reason greater Beech Italy forest, the distribution of carbon stocks in all
litter and herb biomass carbon stock in the upper carbon pools was found to be positively correlated and
altitudinal class could be due to the presence of higher had insignificant differences with altitude. According to
species density, less human and livestock interference Feyissa et al (2013) the AGC and BGC stock showed an
than in the others. This result was agree with many studies increasing trend with increasing altitude while the litter
it was reported that as altitude increase litter biomass carbon stock showed irregular patterns along altitude
carbon increases (Tsui and Hsieh, 2004; Zhang et al., though statistically there was no strong relationship
2008;Chang et al., 2010; Belay et al., 2014;Yohannes et between each of these carbon pools and altitudinal gradients.
al., 2015). Percentage of SOC was observed statically significant
The reason of high carbon stock in the upper altitude in the three altitudinal class (p<0.05). Dianwei et al.
might be its climatic conditions that allow many species to (2006) finding that, altitude is the dominant factors
coexist and due to the topographical nature where upper influencing forest organic carbon in areas with the same
altitude is almost steep slope made itself away from climate regime. In line with this, a positive correlation
human disturbance. On the other hand, lower altitude is between SOM and altitude has been reported by Abreha et
more prone to arable land due to gentle slope nature made al. (2012). The variability in SOM content among the
to store less carbon. Results of the study indicated that, three altitudinal classes was might be due to the difference
the highest SOC content (18.49ton/ha) was found in the in species composition, herb and litter biomass which
middle altitude whereas lowest SOC (13.76 ton/ha) was affects the SOM decomposition. The negative
observed in the lower altitude due to might be some relationships of pH with altitude according to Rezaei and
human interference, the presence of different tree species, Gilkes (2005) could be due to the fact that increasing
decomposition rate, disturbance regime and presence of altitude increases rainfall and thus causing increased
relatively high litter biomass. Yohannes et al. (2015) leaching and a reduction in soluble base cations leading to
conclude that, mountain forest mostly affects by higher H+ activity and registered as decreased pH levels.
environmental variables due to change in species structure Similarly, Slope gradient is also another
and composition. environmental factor that affects and limits the spreading
The carbon stocks in AGC, BGC, herb, litter carbon of carbon stock in the study site. Slope is one of the
and SOC exhibited distinct patterns along altitudinal environmental factors that influence the distribution of
gradients. This finding supports the reports of Feyissa et carbon density (Clark et al., 2000). In a higher human and
al. (2013) concluded that, the carbon storage in different animal interference site (lower slope) the above ground
carbon pools of the Egdu forest area varies with altitudinal and below ground biomass of the carbon pool reduced due
gradient. On the other hand, it has been reported by many to less vegetation coverage as a result of high human and
studies as an increase in carbon stocks with increasing livestock interference. on other hand, the above and below
altitude (Zhu et al., 2010; Gairola et al., 2011; ground biomass and carbon density showed higher values
Mwakisunga and Majule, 2012; Feyissa et al., 2013). The in higher slope because of having high vegetation
anthropogenic disturbances in the study area are higher at coverage due to the case of low human and livestock
the lower altitude (for example, logging and wood interference.
collection) and lower at the higher altitude. This is could In the present study, relatively, an overall increasing
be the main reason for the increasing trend of AGC and trend in mean SOC density with increasing slope was
BGC stock along altitudinal gradient. However, Moser et observed concurred with result found by Belay et al.
al (2011) showed that the litter biomass carbon and total (2014) and Alcántara, et al (2015). The mean SOC of the
AGC decreased by 50 to 70% between 1050 and 3060 m. study area was more or less comparable with studies of
Alefu et al (2015) also reported that, there was no slope variation effect on SOC (Feyissa et al., 2013) and
significant variation of carbon pools between altitudinal (Mohammed et al., 2014) .On the contrary, the effects of
ranges. On the contrary, in this study the carbon in herb, slope on the study area, carbon stocks were very small and
litter, AGC ,BGC and SOC increased by 18.92%, 33.33%, the relations were insignificant for all carbon pools except
19.18%, 18.49% and 10.93% respectively between 1655 SOC, which is similar with other studies of Bayat, 2011
and 2298 meter above sea level. and Muluken, 2014 with a slightly small variation among
In the present study, the distribution of carbon stocks slope classes. In general, the carbon stock of the current
with each sample quadrate in litter, herb, AGC and BGC study has been highly correlated with environmental
pools was found to be positively correlated and had factors, which were knowingly the altitude and slope.
significant differences with altitude. AGC and BGC
shows strong positive relation with altitude (r= 0.68 and Conclusion
0.67; P<0.001) at 0.05 respectively. Similarly, Moser and This study indicated that different environmental
Leuschner (2007); Mwakisunga and Majule (2012) variables has great role for the variation of carbon stock

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the national forest priority area. Further studies are Forest Ecology Manage, 137(3): 185-198.
required to fully understand the interactive relationships Dianwei L, Zongming Z Bai, S Kaishan, L Xiaoyan, L
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Acknowledgements Environment, 113: 73-81
We are greatly happy to thank Alamata agricultural Dossa E, E Fernandes, W Reid and K Ezui, 2008. Above
research center for financing the research work. Heartfelt and belowground biomass, nutrient and carbon stocks
appreciation also goes to Muez Mehari for his support contrasting an open-grown and a shaded coffee
towards analysis of the R-software. plantation. Agroforestry System, 72:103-115.
Emmanuel A, 2013. Environmental Impact Assessment
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