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Carbon stock in the Dirki woodland vegetation of Central Ethiopia A case study in Ilu Gelan District, West Shewa Zone, Oromia Regional State

The study was conducted in one of the woodland vegetation in central Ethiopia. The main objective of the study was to estimate the total amounts of carbon stock along the altitudinal gradient of Dirki vegetation. Seven transect lines were systematically established along the altitudinal gradient to collect above ground biomass data of woody species of ≥ 5cm diameter. A total of 54 sample plots (400 m = 20 m × 20 m area) were established on each transect line at 25 meter above sea level intervals. From the 54 sample plots, 86 woody species of 39 families were identified and used for the biomass estimation. The average quantity of carbon stored in the woody species was about 137.5 t ha; Above Ground Biomass (AGB) 87.77 C t ha and below ground biomass (BGB) 17.5 C ha. The average carbon stock of lower altitude was larger than the upper altitudinal 103.36 C t ha and 34.14 C t ha, respectively. This implies that, higher amount of carbon storage was recorded in above ground biomass, and lower at below ground biomass. Even if low woody species biomass and DBH were observed at higher altitude, higher density of woody species was observed at higher altitude than the lower altitude. To sum up, in the woodland ecosystem of the study area the amounts of carbon stock was decreased with increasing altitude. Therefore, to maintain the existing carbon stock in the AGB anthropogenic disturbances that cause loss of carbon from AGB has to be reduced using different management strategies.


INTRODUCTION
The estimated global forests cover is about 4.03 billion hectares, which is approximately 30% of Earth"s total land area and 75% of terrestrial gross primary production (GPP) (Pan et al. 2013). It was also stated that huge amount of carbon is stored in plant biomass and soils system than the atmosphere. The most important issues and key policy-related reasons for measuring carbon in forests: (1) commitments under The United Mega tone of carbon per year).
In recent time, forest management events are increasingly get due attention and concern for the role of forests as carbon sinks and information on factors that determine the forest carbon stock is given concern (McEwan et al. 2011). The carbon storage in forest can be affected by different environmental factors such as altitude, slope and aspect through affecting the patterns of tree species distribution and this further affects carbon stored in forest ecosystem (McEwan et al. 2011). Therefore, this study was intended to estimate and figure out the carbon stock in woodland vegetation along altitudinal gradient and to check out whether there was low carbon stock density in the higher altitudinal gradient than lower altitudinal gradient of the woodland ecosystems.

MATERIALS AND METHODS
The study was carried out in Ilu Gelan District, West Shewa Zone of Oromia Regional State, in Central Ethiopia (Fig. 1). The District is located to west of the Addis Ababa, the Capital of Ethiopia at about 200 km on Addis Ababa to Nekemte asphalts road. IJaji is the central town of the District and is located on geographical coordinates of 08 o 59′ 51″ N and 037o 19′ 49″ E with the average altitude of 1812 meter above sea level (masl).

Climate
Meteorological data obtained from National Meteorology Service Agency, indicates that Ilu Gelan area obtains high rainfall between May and September and low rainfall from December to February (Fig. 2). The mean annual rainfall of the study area was 1351 mm and recorded in July and the lowest 11.2 mm recorded in February. The mean maximum temperature over twenty years was 28.1℃ and the minimum was 13.8℃. The highest temperature, 31.7℃, was recorded in February whereas the lowest temperature, 11.2℃, was recorded in November.

Vegetation data collection and identification
The Vegetation data were collected by recording the vernacular and or/scientific names of the woody plant species in the sampling plot. The local name (Afan Oromo) was identified in the field by local field guides. The entire specimens were taken to the National Herbarium (ETH) Addis Ababa University. The vegetation sample and data collection was started in middle half October and November 2014 because this time duration is the flowering and fruiting season for most of the Dirki vegetation. The second final data collection period was during the months of April to May 2015. This time was preferred due to the fact that it is second high rainy season of the study area and it can help plant specimen identification purpose. Before collecting actual data, a reconnaissance survey was conducted on the aspect (North, South, West and East), and altitudinal range of the study area was visited and identified to establish using GPS (Global Positioning System,Garmin map,62). Based on the reconnaissance survey, study area was classified as altitude ranges of 1803-1902 m as low and 1903-2100 m asl as high altitudinal gradient. From these seven transects identified and 54 sample plots (each plot has 20 m × 20 m = 400 m 2 area) were established to collect vegetation data. Of the total sample plots, 32 plots situated at 1803-2078 m asl and 22 plots at 1905-2136 m asl.
Plant specimens were collected for every plant species of specified DBH, pressed and dried. Finally all of the plant specimens were identified and checked at the National Herbarium (ETH), Addis Ababa University. For each sample plot, altitude and aspect was measured using Garmin map 62 GPS with precision ±7 m and the areas of the woodland of each sample plots was determined by tape meter (steel long 50 m) measurement and the heights of trees were measured using Haga hypsometer and plastic bags were used to collect samples and leaf of litter and weighted in weighing machine.
The scientific names of the woody plant species were identified in National Herbarium (ETH) while the local by vernacular names were recorded on the field by local field guides. The specimens was collected for every species, pressed and dried then transported to the National Herbarium (ETH), Addis Ababa University for identification and confirmation. Kent & Coker (1992) stated that trees with multiple stems with diameter at breast height (DBH) of 1.3 m were treated as a single individual. The DBH of the largest stem was taken as the sample for the measurement. For trees near the plot that have >50% of their basal area falls within the plot was included and, trees overhanging into the plot are excluded, but trees with their trunks inside the sampling plot and branches outside were included (MacDicken 1997, Karky & Banskota 2007. For vegetation sampling, plot size of 20 m × 20 m (400 m 2 ) was recommended and widely used (Pearson et al. 2005). In each plot, trees with a DBH of ≥5 cm were measured for DBH and height because of the life zone (Brown et al. 1989).
The most appropriate method for the estimation of biomass is through cutting of trees and weighing of their parts. This destructive method is often used to validate established carbon stock models, less recommended and cost. Thus, carbon stock estimation using nondestructive, in-situ measurements and remote sensing techniques are more popular and widely used across the globe (Clark et al. 2001, Wang et al. 2003. Based on this fact, the carbon stock estimation technique applied in this research was the nondestructive one.

Statistical analysis
The data collected from field inventory were organized and recorded in Micro soft Excel 2007 spread sheet. The frequencies of each tree species in all 54 sample plots were analyzed. The data obtained from DBH, diameter, height of each species, field weight (Ww), fresh weight (FW) and dry weight (Wdry) of dead litter were Excel 2007 and analyzed using MINITAB software version 16. The relationship between each parameter was tested by descriptive statistics. One way ANOVA test was done to observe the effect of factors (altitude, disturbance and aspect) on woodland carbon stock.

Estimation of above ground biomass (AGB) and carbon
Different models are suggested to estimate above ground biomass and carbon stock of tropical forests (Appendix I). Among these models, the mode suggested by Brown et al. (1989) was used in this study because the conditions (rainfall and tree DBH) specified in the model match with the study area conditions. MacDicken (1997) stated that below ground biomass (BGB) is 20% of above ground biomass (AGB) i.e., root-to-shoot ratio value of 1:5. The litter carbon pool includes all non-living biomass with a size greater than 2mm diameter-the limit for soil organic matter (SOM), and smaller than 10 cm diameter -DOM wood. The samples of litter (leaves, twinges, fruits or flowers, and barks) were collected in five square meter subplots of 1 meter square in size inside the main sample plot 20 m × 20 m (400 m 2 ), which was established at the four corners and one at the center of each sample plot. The five samples of fine litter, including litter within the 1 m 2 subplot were collected and placed in a weighing bag. A composite sample of 100 g of evenly mixed sub-samples were brought to the laboratory for analysis placing in a plastic bag and oven dried at 70⁰C for 24 hours and weighed for analysis of total carbon concentrations to determine oven dry mass from which total dry mass and carbon fraction were calculated (Pearson et al. 2005). Dead wood was not measured in the woodland due to the nonexistence of dead wood within the sample plots because of uncontrolled production of charcoal (Fig. 3).
The model developed by Brown et al. (1989) to estimate above ground biomass has been used due to accuracy and life zone of the study is area fitted with the life zone recommended for the equation with a rain fall less than 1500 mm and the DBH ≥5 cm. It is also noted that this method is nondestructive method and the most appropriate method (Brown 1997, FAO 1997. The general equation that was used to calculate the aboveground biomass is given below: Where, Y is above ground biomass, DBH is diameter at breast height. The expansion factor was calculated as the area of a hectare in square meters divided by the area of the sample in square meters, that is: The carbon content in litter biomass was also calculated by multiplying herbaceous/litter biomass by 0.47% (IPCC 2006).

Estimation of biomass and carbon in different pool
MacDicken (1997), noted that, the appropriate method used for estimation of below ground biomass (BGB) can be obtained as 20% of above ground tree biomass i.e., root-to-shoot ratio value of 1:5 is used. Thus, the equation developed by MacDicken (1997) to estimate below ground biomass was used.

Estimation of carbon in the litter biomass
According to Pearson et al. (2005), estimation of the amount of biomass in the leaf litter can be calculated by: LB = Weight field × Wsub sample dry Wsub sample fresh × BEF ………….…. (Equation 7) Where, LB = Litter (biomass of litter t ha-1); Wfield = weight of wet field sample of litter sampled within an area of size 1 m 2 (g); A = size of the area in which litter was collected (ha); W sub-sample; dry = weight of the oven-dry sub-sample of litter taken to the laboratory to determine moisture content (g); W sub-sample, fresh = weight of the fresh sub-sample of litter taken to the laboratory to determine moisture content (g).

Carbon stocks in dead litter biomass
Where, CL is total carbon stocks in the dead litter in t ha -1 ; LB is litter biomass.

Total carbon stock density
The total carbon stock density was calculated by adding the carbon stock densities of the individual carbon pools using the formula (Pearson et al. 2005). Carbon stock density of the study area: C density = CAGB + CBGB + C Lit …………………. (Equation 9) Where, C density = Carbon stock density for all pools (t ha -1 ); C AGTB = Carbon in above ground tree biomass (t ha -1 ); CBGB = Carbon in below ground biomass (t ha -1 ); C Lit = Carbon in dead litter (t ha -1 ).
The total carbon stock was then converted to tons of CO 2 equivalent by multiplying it by 44/12, or 3.67 (Pearson et al. 2007).

RESULTS
As presented in Appendix II, total of 86 woody species of 39 families with DBH ≥5 cm were identified from the study area. Among these species Clausena anisata (Willd.) Benth. family Rutaceae was the dominant and Podocarpus falcatus (Thunb.) R.Br. ex Mirb. family Podocarpaceae was the least dominant (Appendix II).
The DBH classes of woody species were classified in to three: 5-10 cm, 11-15 cm, and ≥16 cm. The DBH ≥16 cm was added together because there was very few number of high DBH plants found with different DBH. The majority of woodland plants were distributed in the first class (5-10 cm) followed by the 3 rd class (≥16 cm) and the least woodland vegetation plants were recorded in the 2 nd (11-15 cm). This showed that the first classes were occupied by dense and short plants species of woodland vegetation (Fig. 3).

Woodland vegetation carbon stock
Carbon stock in the three pools: The largest carbon stock was found to be above ground biomass with average of 56.7% followed by below ground pool (5.33%), and the minimum in litter carbon pool (0.006%) ( Table 1).
Carbon stock and elevation: Based on the result from the mean above ground biomass the lower altitude was 183.23 ton per hectare and 60.49 ton per hectare higher altitude and the result for the mean carbon stock was 86.12 ton per hectare in the lower altitude while 28.43 ton per hectare in higher altitude which was indicated below in (Table 2).
The mean averages below ground biomass and below ground carbon stock were relatively similar with above ground biomass and above ground carbon stock. The shown in table 2, the mean below ground biomass for the lower altitude was 36.65 ton per hectare and the upper altitude about 12.10 ton per hectare. The respective mean carbon stock of the low and higher altitude class was 17.22 ton per hectare and 5.69 ton per hectare. Statistically, there was a significant (F= 6.63, P = 0.013) variation between above and below ground carbon stock within the altitude ( Table 2). The amount of litter biomass and carbon stock along the altitudinal gradient was different quite lower than the amount of biomass and carbon estimated above and below ground. As shown in table 3, average amount of the litter biomass at lower altitude was 0.04 ton per hectare corresponds with 0.02 ton per hectare carbon while at higher altitude 0.05 ton per hectare equivalent to 0.02 ton per hectare carbon. This indicates the amount of both litter biomass and carbon accumulated for at lower and higher altitude classes were similar. The total amount of carbon found at higher altitude (0.07 t ha -1 ) was numerical higher than the lower altitude (0.06) t ha -1 , however statistical the difference was non-significant (F= 1.11, P= 0.297) ( Table 3).
Total carbon density along altitudinal gradient: This study was aimed to check whether there is low carbon stock density in the higher altitudinal gradient than lower altitudinal gradient. As presented in table 4, the maximum amount of carbon stock was recorded at the lower altitude (103.36 t ha -1 ) and lower at the higher altitude (34.14 t ha -1 ). The total carbon stock density sums of each carbon pools in the altitude gradients of the study area. This implies the total amount of carbon stock were lower at higher altitudinal gradient than lower altitudinal gradient. Above ground carbon per altitude: As presented in figure 4, at lower altitude large amount of carbon was stored which contain high DBH plant species most. These high DBH plants have greater diameter in the third class and the lower carbon shows the result of first class with 5-10 cm DBH. The result from the higher altitude of (1903-2136) study area also shows unpattern variation between plots (Fig. 5).
Carbon stocks of different pools and aspect: As showed in table 7, the mean AGC stock was lowest in Gentle (without aspect) (5.13 ton ha -1 ) and highest in North (58.80 ton ha -1 ). But other values was observed for carbon stocks in below ground carbon pool with the highest value in Gentle direction (12.05 ton ha -1 ) and lower value South direction (3.56 ton ha -1 ). On the other hand, the highest carbon stocks in litter biomass was recorded in the North, South and West (0.02 ton ha -1 ) and the minimum carbon stock was recorded in East and Gentle (0.01 ton ha -1 ) direction (Table 5). Totally, for all pools the highest carbon stock was recorded in North aspect (70.62 ton ha -1 ) and the minimum carbon stock recorded was in the Gentle (17.21 ton ha -1 ) aspect. Finally, the carbon stock value of each aspect from highest to lowest was put as follow: North > West > East > South > Gentle (no aspect).  Disturbance effect on carbon stock with aspect: Table 6 indicated the presence of various kinds of disturbances that could be affected the carbon storage of the woodland vegetation. The human activities that disturb the ecosystem include charcoal production, cutting, grazing cutting, and agricultural expansion were the most important disturbances found throughout the study area. Gentle (without aspect) was one of the most speciously disturbed while the aspects (South (S), West (W) and North (N)) were relatively less disturbed. But, tree cutting and grazing practices were the serious in the N and S aspects, and low in East (E) and West (W). Table 7 indicated there was significance variation between the altitudinal gradients for above ground carbon, below ground carbon and litter carbon stock. The variation of a mean carbon stock in relation to altitude, aspect and disturbances where not statistically significant at (P < 0.005).

DISCUSSION
The mean total carbon stock of above and below ground biomass of the Dirqi woodland vegetation (study area) was comparable with the finding from Ethiopia (Arba Minch) forest (Tulu 2011, Wolde et al. 2014), Miombo woodland carbon in Zambia (Shirima et al. 2011) and tropical dry and wet forests (Murphy & Lugo 1986). The amount of carbon stored in above ground biomass was higher than belowground. This finding agrees with the finding of Tulu (2011) and Wolde et al. (2014). However, it is contradictory with Aticho (2013). This could be due to the fact that the carbon estimation made by Aticho (2013) was based on soil sampling and laboratory analysis whereas the current estimation of below ground carbon was based on models. The total amount of carbon stock of Dirqi vegetation was higher than other findings from Miombo woodland in Tanzania (Saw et al. 2014), Miombo woodland in Zambia (Shirima et al. 2011) and Miombo woodland in Mozambique (Ribeiro et al. 2013). The reason for higher carbon stock density in the study could be due to mixer of dry afromontane forest tree species (e.g. Olea europaea L. subsp. cuspidate (Wall. & G.Don) Cif., Carissa spinarum L. and some remnant trees like Ficus vasta Forssk., Podocarpus falcatus, Syzygium guineense (Willd.) DC., Prunus africana (Hook.f.) Kalkman) with high DBH range, higher elevation (1800-2100 m asl) than the other Africa woodlands (300-1700 m), variation in rainfall amount and models used to estimate the carbon stock. Brown & Lugo (1982) stated that, litter fall in dry tropical forests range between 2.52-3.69 t ha -1 year -1 . Compared to other studies, the mean carbon stock in litter biomass of the current studied is much more less than others, for instance, Miombo woodland in Mozambique (Ribeiro et al. 2013) and Menagasha Suba State Forest dry afromontane forest (Sahile 2011). The major reasons include climatic factors limiting carbon content, the total amount of litter fall. The carbon stock of the woodland can also be influenced by the woodland vegetation (species, age and density) and climate factor (Fisher & Binkly 2000). Since the study area is composed of shrubby plants, litter fall intensity is expected to decrease. In addition, in some season, (e.g. January up to February the part of the woodland vegetation area is dry and it may have lost its leaf due to high temperature. Also dominant plant species like, Bersama abyssinica Fresen., Clausena anisata, Maytenus arbutifolia (A.Rich.) Wilczek, Premna schimperi Engl., Acacia abyssinica Hochst. ex Benth, Calpurnia aurea (Ait.) Benth., Syzygium guineense, Rhus natalensis Krauss, Carissa spinarum, Albizia schimperiana Oliv., Grewia ferruginea Hochst. ex A. Rich, Rhus vulgaris Meikle, Osyris quadripartita Decne, Olinia rochetiana A.Juss., Rytigynia neglecta (Hiern) Robyns and Millettia ferruginea (Hochst.) Bak. control their moisture content which contributed less litter for the study area. The high decomposition rate of organic matter reduce the quality of carbon stoke (Tang et al. 2010). Because of disturbances from grazing, sparsely populated plant species and charcoal production the lower altitude litter carbon stock was lower than higher altitudinal gradient.

Comparison of DBH with carbon stock
Comparison of the ranges of tree diameters with respect to the above ground biomass accumulation revealed that tree species with lower range of diameter possess more density woody vegetation but accumulated less biomass. On the other hand, trees having bigger diameters were few in number but accumulated more biomass. Therefore, there is an inverse relationship between tree density and DBH, whereas a direct relationship was observed between the above ground biomass and DBH. In this regard, the findings from Terakunpisut et al. (2007);Juwarkar et al. (2011) noted similar results with the current study. Trees during their initial stages of growth when their DBH is smaller they sequester less carbon but gradually increases in DBH and accumulate more carbon. Moreover, it has been observed that the younger trees grow much faster as compared to older ones. Thomas (1996) suggested that fast growing tree species are expected to have higher growth rates, and may accumulate large amounts of carbon in the first stage of their life span while the high specific gravity of slower growing species allows them to accumulate more carbon in the long term.

Environmental factors affecting the carbon stocks of different pools
Environmental factors such as altitudinal gradient, disturbance and aspects were affected the carbon storage in different pools. With altitudinal changes many environmental factors such as temperature, precipitation, atmospheric pressure, solar and UV-B radiation, and wind velocity changing rapidly. Because of this fact, altitudinal gradients are among the most powerful "natural experiments" for testing ecological and evolutionary responses of biota to environmental changes (Fang et al. 2004, Cui et al. 2005, Korner 2007). The carbon stocks of the woodland vegetation were decreasing along the altitudinal gradient; highest at the lower altitudinal and lowest at higher. This may be due to the above mentioned environmental factors, absence of matured large trees at higher altitude and the presence of some big trees at the lower attitude contained rivers that make the condition favorable for tree growth. Because of this in the higher altitudinal gradient, shrubs and secondary vegetation were dominated, and in the lower altitude big mature trees has becomes common.
The highest carbon stock was found in the northern aspect and the least in the gentle part of the woodland vegetation which was perhaps due to the availability of moisture, and seeing protected by the river that surrounded it, matured vegetation (dense) and fertile soil in the northern part and less moisture, scattered tree, shrub dominance, rocky habitat and infertility of soil in gentle part. Different scholars reported altitude has been known to have a major impact on the diversity, biomass and carbon stock in the forest ecosystems (Luo et al. 2005, Alves et al. 2010. Further, studies reported above and below ground tree biomass and its carbon stock is decline with an increase in altitude (Luo et al. 2005, Zhu et al. 2011, this agree with the present findings.

Disturbance and carbon stock estimation
Disturbances are the main drivers altering forest structure, creating landscape mosaics, and setting the initial conditions for successional dynamics and structural development (Swanson et al. 2011). Human modified woodlands are increasingly prevalent in the tropics (Broadbent et al. 2008, Melo et al. 2013, hence understanding the effects of human disturbances on woodland carbon stocks is crucial for better practices of woodland management and conservation measures. In this study, there was human disturbances like cutting vegetation, charcoal production, grazing and agricultural practices that affected the above ground carbon stock of the woodland which was similar to previous studies. The spatial pattern of woody biomass described above is subject to frequent and widespread disturbances (Frost 1996) that reduce biomass due to primarily clearance for agriculture (Williams et al. 2008), charcoal production (Brouwer & Falcao 2004, Falcao 2008 and fire (Ryan & Williams 2010). In this study disturbance has direct relation with the result of carbon in different pools (Above ground carbon, below ground carbon and, litter carbon).
Various natural or human induced disturbances exert profound impacts on global woodland. According to a recent global assessment of forest more than 60% of the world"s 4 billion ha of forest are recovering from a past disturbance and 3% of the world"s forests are disturbed annually by logging, fire, pests, or weather (FAO 2006). Agricultural expansion has significant impact on forest and woodland and that could contribute for land degradation, biodiversity loss, and GHG emission (IPCC 2007, Walther 2010. These evidences justify the current finding, human disturbance of woodland vegetation for agricultural land expansion that could affect the health ecology and the society. Different authors reported that the amount of carbon stored in Ethiopian forest varies; It was found to be about 168 Mt C (Brown 1997, Achard et al. 2004; 867 Mt C (Gibbs & Brown (2007). As compared to forest lands, the amount of carbon stock in the woodlands is higher than other forest categories because of the higher area coverage 29.55 M ha woodlands of Ethiopia (Moges et al. 2010).

CONCLUSION
In the study area, total of 86 woody species of 39 families with stem number of 3175 plants were collected, of which Clausena anisata family Rutaceae was the dominant and Podocarpus falcatus family Podocarpaceae was the least dominant. The densities of tree species decreases as the DBH and height classes increased in the woodland vegetation. This implies that, the predominance of small sized tree species is in the higher classes than in the lower classes. The analysis of these two parameters in the study woodland indicated that higher percentage of number of tree species in the lower than in the upper frequency classes. The carbon stocks of the study area showed a variation among the plots due to the presence of high biomass plants in some plots and low biomass in other plots due to some remnant primary vegetation.
The lower altitude was high in above ground and below ground carbon stocks while the upper altitude had low carbon stock in carbon pools. This has observed due to the presence of high DBH and high biomass vegetation cover in the lower altitudinal range like Ficus vasta and Syzgiume guiensee. On the contrary, the litter carbon stock was similar in the upper and lower parts of gradient, due to the uniformity of vegetation coverage through the whole vegetation and the decomposition ability of litter due to high temperature.
The mean above ground carbon and below ground carbon stocks were lowest in gentle and southern part respectively but highest in northern and gentle parts respectively. In other cases, the highest carbon stocks in litter biomass were recorded in the northern, western and southern part equally and the minimum carbon stock was recorded in eastern and gentle aspects, due to the vegetation character and disturbances level at the aspect. Generally, the carbon stocks in the different pools were ranked in decreasing order as follow northern, western, eastern, southern and gentle.

RECOMMENDATION
Based on the findings of this study the following recommendations were made for Dirki woodland vegetation. Awareness creation at all levels of the society on how to use the woodland resource properly with respect to environmental, socio economic benefit, participatory forest management (PFM). Area closer is very important to save the woodland vegetation from degradation.  In Ethiopia, there are large woodlands conserved in different parts of the country so conducting similar research.  The carbon stock sequestered in some carbon pools in the present study site is significant so considering all carbon pools (soil carbon).  For highly threatened species the government should give due attention to plant and replace it by establishing in situ and ex situ for some endangered species.  Dirki woodland vegetation is facing a variety of threats, including destructive fuel wood collection, uncontrolled (charcoal burning, cutting and free grazing), and agricultural practices with settlement around and near the woodland vegetation must be controlled and managed with the supply of energy save materials.