Journal of
Ecology and The Natural Environment

  • Abbreviation: J. Ecol. Nat. Environ.
  • Language: English
  • ISSN: 2006-9847
  • DOI: 10.5897/JENE
  • Start Year: 2009
  • Published Articles: 408

Full Length Research Paper

Carbon stock in Adaba-Dodola community forest of Danaba District, West-Arsi zone of Oromia Region, Ethiopia: An implication for climate change mitigation

Muluken Nega Bazezew
  • Muluken Nega Bazezew
  • College of Agriculture and Natural Resources, Dilla University, P.O. Box 419, Ethiopia.
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Teshome Soromessa
  • Teshome Soromessa
  • Center for Environmental Science, College of Natural Science, Addis Ababa University, P.O. Box 1176, Ethiopia.
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Eyale Bayable
  • Eyale Bayable
  • Center for Environmental Science, College of Natural Science, Addis Ababa University, P.O. Box 1176, Ethiopia.
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  •  Received: 07 December 2014
  •  Accepted: 29 December 2014
  •  Published: 30 January 2015

 ABSTRACT

Forests can capture and retain enormous amount of carbon over long period of time. Their role in carbon emission balance is also well documented. However, especially in developing country, wide spread deforestation and forest degradation is continuing unknowingly and deliberately. This study was conducted to estimate carbon stock in dry Afromontane forest type of Danaba community forest (CF) of Oromia Regional State of Ethiopia. A systematic sampling method was used to identify each sampling point. Results revealed that the total mean carbon stock of the CF was 507.29 t·ha-1 whereas trees share 319.43 t·ha-1, undergrowth shrubs 0.40 t·ha-1, litter, herbs and grasses (LHGs) 1.06 t·ha-1 and soil organic carbon (SOC) 186.40 t·ha-1 (up to 30 cm depth). The ultimate result implies that Danaba CF is a reservoir of high carbon. To enhance sustainability of the forest potentiality, the carbon sequestration should be integrated with reduced emission from deforestation and degradation (REDD+) and clean development mechanism (CDM) carbon trading system of the Kyoto Protocol to get monetary benefit of CO2 mitigation.
 
Keywords: Carbon sequestration, climate change, community forest, mitigation.


 INTRODUCTION

Forests are known to play an important role in regulating the global climate. International agreements on climate change recognized forests playing an important role in mitigating climate change by naturally taking carbon out of the atmosphere, thereby reducing the impact of CO2 emissions (Perschel et al., 2007). The response of forests to the rising of atmospheric CO2 concentrations is crucial for the global carbon cycle as they have huge potential in sequestering and storing more carbon than any terrestrial ecosystem (Jandl et al., 2006; Sundquist et al., 2008). Even though the role of forests in climate change mitigation is widely recognized, the recent assessment shows carbon stocks in forest biomass decreased by an estimated 0.5 gigatonne annually during the period 2005–2010 because of a reduction in the global forest area (FAO, 2010). Loss of forest biomass through deforestation and forest degradation makes up 12 to 20% of annual greenhouse gas emission, which is more than all forms of transportation combined (Saatchi et al., 2011). Especially, in Africa, forest degradation is very high which accounts for nearly 70% of the continent’s total emissions (FAO, 2005). Hence, the endless rise of carbon emission is one of today’s major concerns as it is the main causal factor for climate change. 
 
Ethiopia is facing rapid deforestation and degradation of forest resources and experiencing the effects of climate change such as an increase in average tem-perature, and rainfall pattern variability, and is one of most vulnerable countries to climate change (World Bank, 2009). As Ethiopia is dependent on natural resources and agriculture, it is less able to cope with the shocks of climate change-induced droughts, floods, soil erosion and other natural disasters. People will find it hard to escape poverty if vulnerability to climate change persists. The government of the Federal Democratic Republic of Ethiopia has therefore implemented National REDD+ working document in 2008 and Climate Resilience Green Economy (CRGE) Framework in 2011 by means of protecting and re-establishing forests for their economic, ecosystem services and carbon storage.
 
Even if the strategic frameworks focus on carbon emission management, Ethiopia does not have carbon accumulation records and databank to monitor and enhance carbon sequestration potential of different forests. Working in CFs would highly support the CRGE of Ethiopia by achieving carbon sequestration and conservation of biodiversity on the one hand, and empowering communities to take part and improve their living condition on the other hand since state owned forests are unsuccessful in their sustainability in the past decades. Although many Ethiopian people are living close to forests, the relationship of these people to forests has not been emphasized as an opportunity for spreading CFs to improve carbon sequestration.
 
An integrated forest management approach has been initiated in 2000 and named Forest Dwellers Association in Danaba CF. Danaba CF is a heavily exploited remnant coniferous forests found in West-Arsi Zone of Oromia Regional State of Ethiopia. Ongoing threats of observed human activities such as agricultural expansion, livestock grazing, illegal charcoal production and harvesting for firewood and construction which will likely diminish all carbon pools unless effective measures are enforced. Since large numbers of people are living close to the forest, incorporating the existing forest management strategy through Forest Dwellers Association with climate change mitigation potential of CDM carbon trading system of the Kyoto Protocol is important to overcome the problem.
 
Therefore, the study was designed to estimate the reserved carbon in all carbon pools of trees, shrubs, litter, herbs and grasses (LHGs) and soil of Danaba CF which would have high important as an information basis that can create the environment to attract climate change mitigation finances and so to expand and conserve CFs in Ethiopia.


 MATERIALS AND METHODS

Study area
 
Danaba CF is a 5,437 ha forest that belongs to Adaba-Dodola CF priority areas under the administrative of Community Forest User Groups (CFUGs). The area is located in West Arsi Zone of Oromia National Regional State located 5-11 km South-East of Dodola town and 320 km South-East of Addis Ababa, Ethiopia (Figure 1). It lies between 06°54'20"N and 6°54'3"N latitude and between 39°8'19"E and 39°13'50"E longitude with an elevation ranging between 2490–3218 m a.s.l. According to Ethiopian National Meteorology agency weather data from 1995–2013, the mean minimum and maximum temperature of the study area is 3.6 and 24.3°C, respectively. The mean annual rainfall is 964 mm, of which 70-80% was received in main wet season of June to early September and 20-30% from remaining less pronounced wet periods. Vegetation of Danaba CF falls under dry-evergreen montane forest with strongly dominated by Juniperus procera and Podocarpus falcatus species. The parent soil material is made up of volcanic rocks of basalt and tuffs with rare rhyolites and the soils are brown or reddish brown of medium texture and freely draining. The soil is mostly of Luvisols type with Cherozem occurring in some place at lower altitudes (Digital soil and Terrain Data base of East Africa, 1997).
 
 
Sampling design and measurements
 
The field work for forest inventory was conducted from September 2013 to March 2014. A systematic sampling method was used for identification of sampling points distant 800 m from each other resulting in a total of 83 intersection points (Figure 1- Sample plots). In each intersection, 20 × 20 m (400 m2 equivalent to 0.04 ha) of plots were established for biomass inventory and identified using GPS and compass in the field.
 
In each biomass plot, all tree species were identified and had their diameter at breast height (DBH ≥ 2.5 cm) and height measured using diameter tape and Suunto Hypsometer, respectively. Following Bhishma et al. (2011) recommendations guideline for measuring carbon stocks in community managed forests, trees on the border were only included if more than 50% of their basal area falls within the plot. Trees overhanging into the plot were excluded, but trees with their trunks inside the sampling plot and branches outside were included.
 
Above-ground biomass calculation for trees used a two-way method: For tress ≥ 5 cm DBH, Chave et al. (2005) was used while trees having between ≥ 2.5 and < 5cm DBH, an allometric model of biomass and volume tables with species description for community forest management developed by Tamrakar (2000) was applied to calculate biomass.
 
Chave et al. (2005) model:
 
Y= Exp. {-2.187 + 0.916 ln (D2 × H × S)}                             
 
Where, Y: Above-ground biomass (kg), H: Height of tree (m), D: Diameter (cm) at breast height (1.3 m), and S: Wood density (t.m-3) for specific species (Morales, 1987; Reyes et al., 1992; IPCC, 2003). 
 
Tamrakar (2000):
 
Ln (AGSB) = a + b ln (D)
 
Where, AGSB: Above-ground sapling biomass (kg), a and b: species specific constants (Sharma and Pukkala, 1990; Tamrakar, 2000), and: Diameter (cm) at breast height (1.3 m).
 
Below-ground biomass of tree species was calculated considering 15% of the aboveground biomass (Macdicken, 1997). The biomass of stock density was converted to carbon stock density by multiplying 0.47 fraction of the IPCC (2006) default value.
 
Additionally, at the center of each main plot a 5 × 5 m sub-plots were used for shrub species sampling. Numbers of individuals of each shrub species were counted and samples were uprooted. The species were divided into above- and below-ground by identifying the collar region and fresh weights recorded, and brought to the laboratory to determine dry biomass and percentage of carbon. A procedure adapted by Ullah and Al-Amin (2012) of the loss on ignition (LOI) method was used to estimate percentage of carbon in shrub species. In this method, initially taken fresh weight of samples was dried at 65°C in the oven for 48 h to take dry weight. Oven dried grind samples were taken (3.00 g) in pre-weighted crucibles, and then put in the furnace at 550°C for one hour to ignite. The crucibles were cooled slowly inside the furnace. After cooling, the crucibles with ash were weighed and percentage of organic carbon was calculated according to Allen et al. (1986).
 
Ash = (W3 – W1) / (W2 – W1) × 100
 
C (%) = (100 – % Ash) × 0.58 (considering 58% carbon in ash-free litter material).
Where, C: Biomass carbon stock, W1: Weight of crucible, W2: Weight of the oven-dried grind sample and crucible and W3: Weight of ash and crucible.
 
For sampling of LHGs (litter, herbs and grasses), a 1 m × 1 m sub-plots at all corner and middle positions of each main plot were used. LHGs within five 1 m2 quadrats of each main plot were collected and weighed on the field, and 100 g of evenly mixed sub-samples were brought to the laboratory to determine dry biomass and percentage of carbon. To estimate the biomass carbon stock, the sub-samples taken in the field were used to determine an oven-dry-to-wet mass ratio that was used to convert the total wet mass to oven dry mass according to Pearson et al. (2005). The amount of biomass per unit area was calculated as:
 
 
Where: LHGs: Biomass of leaf litter, herbs and grasses (t.ha-1), Wfield: Weight of the fresh field sample of leaf litter, herbs, and grasses- destructively sampled within an area of size A (g), A: Size of the area in which leaf litter, herbs, and grasses were collected (ha), Wsub-sample, dry: Weight of the oven-dry sub-sample of leaf litter, herbs, and grasses taken to the laboratory to determine moisture content (g), and Wsub-sample, wet: weight of the fresh sub-sample of leaf litter, herbs, and grasses taken to the laboratory to determine moisture content (g).
 
To determine percent of carbon in LHGs, the loss on ignition (LOI) method of Allen et al. (1986) was applied. The carbon density of LHGs was then calculated by multiplying biomass of LHGs per unit area with the percentage of carbon determined for each sample.
 
For SOC determination, soil samples were collected within five 1 m2 quadrats in which LHGs samples were taken. Soil samples were collected up to 30 cm in depth (between 0–10, 10–20 and 20–30 cm depths) using a calibrated soil auger (IPCC, 2006). A composite sample was obtained by mixing soil from three layers taken from five sub-plots of each main plot in order to determine bulk density and organic carbon concentration. About 150 g of composite samples were collected from each main plot. To determine SOC, field’s moist soil were dried in an oven at 105°C for 12 h in laboratory, and re-weighted to determine moisture content and dry bulk density. To estimate the percentage of organic carbon, samples were analysed by the wet oxidation method (Huq and Alam, 2005). The carbon stock density of soil organic carbon was calculated as recommended by Pearson et al. (2005) from the percentage of carbon and bulk density of soil at predetermined depth of the samples were taken.
 
SOC = % C× ρ × d                                                                                                       
 
Where. SOC:  Soil organic carbon stock per unit area (t.ha-1), %C: carbon concentration (%), d: soil depth (cm), and ρ: bulk density (g.cm-3).
 
The carbon stock is then converted to tons of CO2 equivalent by multiplying it by 44/12 or 3.67 of molecular weight ratio of CO2 to O2 (Pearson et al., 2007) in order to understand climate change mitigation potential of the study area.
 
Data analysis
 
Data for carbon density in trees, shrubs, litter, herbs and grasses and organic soil were processed using MS Excel spreadsheet and analysed using SPSS statistical software package.

 


 RESULTS

Carbon store in tree species of Danaba CF
 
Out of the sixteen major tree species recorded in the study area, Juniperus procera and Podocarpus falcatus stored enormous density of carbon with 179.17 (56.1%) and 105.73 (33.1%)t·ha-1, respectively; that amount accounts for approximately 90% of the Danaba CF carbon stock. J. procera had the highest total above- and below-ground biomass carbon with 155.79 and 23.36 t·ha-1, respectively. The lowest carbon was recorded for Myrsine africana with 0.03 and 0.004 t·ha-1 of above- and below-ground carbon stock, respectively (Table 1). 
 
 
Carbon stock share within DBH and height classes of tree species
 
Within eight category of DBH classes, 5–20 cm DBH class had the highest density of trees with 401 trees ha-1 (41.8%) while trees with DBH greater than 120 cm were the least dominant in the study area and consisting of 4 trees ha-1 (0.5%). Irrespective of the highest density of DBH class, the highest corresponding carbon reserves were found in DBH class of >80–100 (25.3%), >60–80 (20.1%) and >100–120 (15.8%) cm with 80.74, 64.20 and50.60t·ha-1 of corresponding carbon density, respectively. DBH class of 2.5–<5 cm was the reservoir of least carbon stock in the CF with 3.65 (0.5%) t·ha- of the total stock density (Figure 2).
 
 
The height of tree species were categorized into eight classes, of which height class of >10–15 m had the highest density of 355 trees ha-1 (34.5%) while least density of trees were found within the uppermost canopy of trees with >35 m of height class by accounting 3 trees ha-1 (0.3%). From the mean total mean carbon stock of the study area stored in above- and below-ground biomass of tree species of the study area, the highest carbon reserves were found in height class of >25–30 (25.4%), >20–25 (23.1%) and >15–20 (18.8%) m with corresponding stock density of 81.27, 73.79 and 59.97 t·ha-1, respectively(Figure 3).
 
 
Carbon store in shrub species of Danaba CF
 
Among six frequently occurring shrub species of the study area, mean carbon density of 0.40 ± 0.16 t.ha-1 (1.47 CO2 equivalents) was recorded. Conyza hypoleuca and Carissa spinarum were the highest and least store of carbon with 0.19 (46.3%) and 0.03 (7.3%) t·ha-1, respectively (Table 2).
 
 
Carbon store in LHGs and organic soil
 
In current inventory of Danaba CF, mean value of 1.06± 0.31 t·ha-1 carbon density with highest store seems to be in grasses. Hence, 3.89 t·ha-1 of CO2 equivalents were stored in LHGs biomass.
 
The average bulk density of soil in the CF was estimated to be 0.937± 0.0535 g.cm-3. The percentages of carbon content of the soil in the study area ranges from 2.27–15.85% with mean value of 6.38±2.6764%. Thus, the current average soil organic carbon investigated in the study area was found to be 186.40±76.5465 t·ha-1. Accordingly, the study area could possibly store 684.088 t·ha-1 of CO2 equivalents within organic soil. The SOC share was varied at different soil depths. Table 3 and Figure 4 show variation of SOC among different soil profile. The average bulk density of the study area increased with depth increment. The mean values of bulk density from top, middle and deep soil profile were 0.82, 0.96 and 0.99 g.cm-3, respectively; however, SOC decreased with depth increment (Table 3).
 
 
 
Thus, this study showed that the carbon density of trees, shrubs, LHGs and organic soil were found to be 319.43, 0.40, 1.06 and 186.40 t·ha-1, respectively. Hence, in the current study, the total carbon stock in Danaba CF was 507.29 t·ha-1 (Table 4). Accordingly, the maximum quantity of carbon stock was found in tree species with reservoir of 63% of the total carbon. The forest soil organic carbon ranked the second reservoir of carbon which has accumulated 36.7% of the total carbon in the study area. Shrubs and LHGs’ biomass contributes small amount of carbon; stored only 0.1 and 0.2% of the total carbon, respectively (Figure 5).
 
 
 

 

 


 DISCUSSION

The assessment of Brown (1997) and Achard et al. (2004) on biome-average tropical forest biomass carbon stock estimates and implications for global carbon cycle, the average carbon stock of Sub-Saharan Africa, Tropical Asia and Brazilian Amazon forests are 143, 151, 186 t.ha-1, respectively. On the other hand, the mean biomass carbon stocks of trees in the Natural Forest of Bangladesh is 110.94 t·ha-1 (Ullah and Al-Amin, 2012), and Community Forest of Mid Hill Region of Nepal is 71.36 t·ha-1 (Anup et al., 2013). Hence, the present study was exceedingly higher than those continental and countries study as we found 507.29 t·ha-1. Above- and below-ground trees carbon stock was comparable to the previous Ethiopian studies of tree biomass carbon of Egdu Forest (Adugna et al., 2013) and Tara Gedam Forest (Mohammed et al., 2014) while greater than Selected Church Forests (Tulu et al., 2013) and Woody Plants of Mount Zequalla Monastery (Abel et al., 2014). The variation might come from variation of age of the trees, existing species, and management of the forests. The use of an allometric model for biomass estimation might also help in explaining the difference in estimated value as explained that reliance on allometric equations could be one of the limitations resulting in large variations in such estimates (Lasco et al., 2000).  
 
The mean carbon stock of shrub species of the CF was comparable to the carbon density found in Natural Forest of Bangladesh (Ullah and Al-Amin, 2012) while smaller than in Community Forests of Mid Hill Region of Nepal (Anup et al., 2013). Shrub species of Danaba CF contributed small biomass carbon by accounting only 0.1% of the total stock density. Huge canopies and observed seasonal plantation of tree species is unsuitable for shrub species regeneration.
 
LHGs biomass also shared small amount of carbon in the CF. The assessment on mean litter carbon of tropical forests varies between 2.6–3.8 t·ha-1 as reported by Brown and Lugo (1982) and 2–16 t.ha-1 by Brown (1997).The result was lower than those ranges. The mean stock density was also lower than most previous studies of Ethiopian forest. The reason for the small carbon stock of LHGs is due to huge closed canopies of J. procera and P. falcatus up to the near ground making the growth of herbs and grasses unsuitable. The dominance of evergreen tree species of Danaba CF has also contributed to the existence of small litter falls. As the study area had mountainous manifestation, litter run off occurred and might cause for small carbon account in this pool. As the field data measurement was conducted in partial dry season, seasonal variation might also had significant contribution.
 
SOC of the study area was higher than the above mentioned Ethiopian forests of Menagasha Suba State Forest, Selected Church Forest, Woody Plants of Mount Zequalla Monastery and Woody Plants of Arba Minch Ground Water Forest. SOC estimates of Afromontane Rain Forests varies between 252 and 581 t·ha-1 (Munishi and Shear, 2004). The result of the present study was lower than this range. Besides, the value was also lower than that of Tara Gedam and Egdu Forests of Ethiopia. Rainfall and temperature variation of the studies might have contribution for this variation. Besides, mountainous manifestation of the study area might cause early run off litter, herbs and grasses which contributed to soil organic matter in decomposition. In the present study, SOC was found to be the highest inthe soil top layer, and this may be due to the accumulation and rapid decomposition of forest litter in the top soil (Figure 4). The pattern indicates that soil carbon decreased significantly with soil depth which revealed major trends in carbon accumulation which shows that it is found in the upper soil layers. Mendoza-Vega et al. (2003), Chowdhury et al. (2007) and Ullah and Al-Amin, (2012) found that more SOC was stocked at the soil depth of 0–14 cm. So, the result was in high conformity with those findings.


 CONCLUSION

We observed that tree species stored the highest carbon stock of all carbon pools and J. procera reserved the highest biomass carbon stock. More than 50% of the trees were found in <20 cm DBH class. Hence, the study showed the forest is dominated by young trees after the implementation of community forest management through plantation and natural regenerations. The ultimate inference indicates that, there is high potential of increasing bio-mass carbon stock in the future if appropriate manage-ment of the forest is implemented. Existing timber harvesting should be done in a sustainable manner without disturbing the young trees to grow and increase their biomass. Communities should focus only on old and dead trees to fulfill the demand of firewood. Forest soil was also found to have a good reservoir of carbon stock in this forest. Different undergrowth shrubs and LHGs were also important pools that contributed to carbon sink in the CF though the carbon density were small as compared to many tropical forests. The CF was the reservoir of potentially high amount of carbon as compared to similar areas in the tropics particularly in tropical Africa, Asia and Latin America. Currently, the CF had the capacity to store 507.29 t·ha-1 carbon; helping in mitigating climate change by sequestering 1861.75 t·ha-1 of CO2 equivalents which implies that remarkable carbon finance benefit has to be demanded. However, ongoing threats of observed human activities such as agricultural expansion, livestock grazing, harvesting for firewood and construction and illegal charcoal production will likely diminish all carbon pools unless effective measures have to be enforced. The carbon sequestration should be integrated with REDD+ and CDM carbon trading system of the Kyoto Protocol to get monetary benefit of carbon dioxide mitigation which can be helpful for the sustainability of the forest. 


 CONFLICT OF INTERESTS

The author(s) have not declared any conflict of interest.


 ACKNOWLEDGEMENTS

The authors acknowledge Addis Ababa University, Oromia Forest and Wildlife Enterprise, Dilla University, Wondo Genet College of Forestry and Natural Resources and Ethiopian National Meteorology Agency, for the assistance during the research period. Fund was obtained from the Thematic Research of Addis Ababa University; “Floral and Fungal Diversity, Ethnobotany and Carbons Sequestration Potential of Western and Southwestern Forests of Ethiopia”.



 REFERENCES

Abel G, Teshome S, Tesfaye B (2014). Forest carbon stocks in woody plants of Mount Zequalla Monastery and it's variation along altitudinal gradient: Implication of managing forests for climate change mitigation. Sci. Technol. Arts Res. J. 3(2):132-140.
Crossref

 

Achard F, Eva HD, Mayaux P, Stibig HJ, Belward A (2004). Improved estimates of net carbon emissions from land cover change in the tropics for the 1990s Glob. Biogeochem. Cycles 18:1029-42.
Crossref

  

 

Adugna F, Teshome S, Mekuria A (2013). Forest carbon stocks and variations along altitudinal gradients in Egdu Forest: Implications of managing forests for climate change mitigation. Sci. Technol. Arts Res. J. 2(4):40-46.

  

 

Allen SE, Grimshaw HM, Rowland AP (1986). Chemical analysis. In: Methods in Plant Ecology (Moore PD, Chapman SB(eds.). Blackwell Scientific Publications, Boston, USA. pp.285-300.

  

 

Anup KC, Bhandari G, Joshi GR, Aryal S (2013). Climate change mitigation potential from carbon sequestration of community forest in mid hill region of Nepal. Int. J. Environ. Prot. 3(7):33-40.

  

 

Bhishma PS, Shiva SP, Ajay P, Eak BR, Sanjeeb B, Tibendra RB, Shambhu C, Rijan T (2011). Forest Carbon Stock Measurement: Guidelines for Measuring Carbon Stocks in Community Managed Forests.2nd ed. International Centre for Integrated Mountain Development, Asia Network for Sustainable Agriculture and Bioresearches, Federation of Community Forest Users,Kathmandu, Nepal. pp.15-36.

  

 

Brown S (1997). Estimating Biomass and Biomass Change of Tropical Forests: A primer.Vol. 134, Rome. Italy. pp. 20-55.

  

 

Brown S, Lugo AE (1982). The storage and production of organic matter in tropical forests and their role in the global carbon cycle.Biotropica 14:161-187.
Crossref

  

 

Chave J, Andalo C, Brown S, Cairns M, Chambers J, Eamus D, Folster H, Fromard F, Higuchi N, Kira T (2005). Tree allometry and improved estimation of carbon stocks and balance in tropical forests. Oecologia 145:87-99.
Crossref

  

 

Chowdhury MSH, Biswas S, Halim MA, Haque SMS, Muhammed N, Koike M (2007). Comparative analysis of some selected macronutrients of soil in orange orchard and degraded forests in Chittagong hill tracts, Bangladesh. J. For. Sci. 1:27-30.

  

 

Digital soil and terrain data base of East Africa (1997). Notes on the ARCINFO files. Food and Agricultural Organization of the United Nation, version 1.0.

  

 

FAO (2005).The State of the World's Forests.FAO, Rome, Italy.

  

 

FAO (2010).Global Forest Resource Assessment.Main report.FAO Forest paper 103.

  

 

Huq SMI, Alam MD (2005). A Handbook on Analyses of Soil, Plant and Water.BACER-DU, University of Dhaka, Bangladesh. p. 246.

  

 

Intergovernmental Panel on Climate Change (IPCC) (2003).Good Practice Guidance for Land Use, Land Use Change and Forestry. In: (Penman J, GytarskyM, Hiraishi T, Krug T, KrugerD, PipattiR, Buendia L, Miwa K, Ngara T, Tanabe K, Wagner F (eds.). Institute for Global Environmental Strategies, IPCC National Greenhouse Gas Inventories Programme, Hayama, Kanagawa, Japan.

  

 

Intergovernmental Panel on Climate Change (IPCC) (2006). IPCC Guidelines for National Greenhouse Gas Inventories, Vol 4.National Greenhouse Gas Inventories Program, Institute for Global Environmental Strategies Publishing, Hayama, Japan.

  

 

Jandl R, Rasmussen K, Tome M, Johnson DW (2006). The Role of Forests in Carbon Cycles, Sequestration and Storage.Forest management and carbon sequestration, Federal Research and Training Centre for Forests, Natural Hazard and Landscape, Vienna, Austria.

  

 

Lasco RD, Pulhin FB, Visco RG, Racelis DA, Guillermo IQ, Sales RF (2000). Carbon stocks assessment of Philippine forest ecosystems. Paper presented at the Science-Policy workshop on terrestrial carbon assessment for possible carbon trading, Bogor. pp. 28-29.

  

 

MacDicken KG (1997). A Guide to Monitoring Carbon Storage in Forestry and Agroforestry Projects. Forest Carbon Monitoring Program, Winrock International Institute for Agricultural Development, Arlington, Virginia. 84 p.

  

 

Mendoza-Vega J, Karltun E, Olsson M (2003). Estimations of amounts of soil organic carbon and fine root carbon in land use and land cover classes, and soil types of Chiapas highlands, Mexico. For. Ecol. Manage. 177: 191-206.
Crossref

  

 

Mohammed G, Teshome S, Satishkumar B (2014). Forest carbon stocks in woody plants of Tara Gedam forest: Implication for climate change mitigation. Sci. Technol. Arts Res. J. 3(1): 101-107.
Crossref

  

 

Morales JB (1987).Wood specific gravity from two tropical forests in Mexico. IAWA Bull. New Ser. 8(2):143-148.

  

 

Munishi PK, Shear T (2004). Carbon storage of two afromontane rain forests in the eastern arc mountains of Tanzania. J. Trop. For. Sci. 16(1):78-93.

  

 

National Meteorology Agency (2013). Weather Data of Dodola District from 1995-2013. National Meteorology Agency, Bole Road, Addis Ababa, Ethiopia.

  

 

Pearson T, Brown S, Birdsey R (2007). Measurement Guidelines for the Sequestration of Forest Carbon. USDA Forest Service Publication, Northern Research Station, Department of Agriculture, United States Department of Agriculture, Winrock International, Washington. pp.12-27.

  

 

Pearson T, Walker S, Brown S (2005). Sourcebook for Land-use, Land-use Change and Forestry Projects.Winrock International and the Bio-carbon Fund of the World Bank, Arlington, USA. pp.18-35.

  

 

Perschel RT, Alexander ME, Marcia JS (2007). Climate Change, Carbon, and the Forests of the Northeast. Forest Guild, Merck and Surdna Foundation, Santa Fe, Davis Conservation, France. pp.1-10.

  

 

Reyes G, Brown S, Chapman J, Lugo AE (1992).Wood Density of Tropical Tree Species. USDA Forest Service Publication, Southern Research Station, Department of Agriculture, United States Department of Agriculture, Winrock International, New Orleans, Louisiana. pp.10-30.

  

 

Saatchi SM, Harris S, Brown M, Lefsky E, Mitchard W, Salas B, Zutta W, Buermann S, Lewis S, Hagen S, Petrova L, White M, Silman A (2011). Benchmark map of carbon forest carbon stocks in tropical regions across three continents. Proc. Natl. Acad. Sci. 108(24): 9899-9904.
Crossref

  

 

Sharma ER, Pukkala J (1990). Volume Tables for Forest Trees of Nepal.Ministry of Forest and Soil Conservation, Forest Survey and Statistics Division, Kathmandu, Nepal. 84 p.

  

 

Sundquist E, Robert B, Stephen F, Robert G, Jennifer H, Yousif K, Larry T, Mark W (2008). Carbon Sequestration to Mitigate Climate Change.U.S. Geological Survey, Science for a changing world, New York, USA. pp.1-4.

  

 

Tamrakar PR (2000). Biomass and Volume Tables with Species Description for Community Forest Management.MFSC, NARMSAP-TISC, Kathmandu, Nepal.

  

 

Tulu T, Mekuria A, Zewdu E (2013). Estimation of carbon stock in church forests: Implications for managing church forest to help with carbon emission reduction. Climate-smart technologies, Climate change management, Springer-Verlag, Berlin Heidelberg. pp. 403-414.

  

 

Ullah MR, Al-Amin M (2012). Above- and below-ground carbon stock estimation in a natural forest of Bangladesh. J. For. Sci. 58(8):372-379.

  

 

World Bank (2009). Convenient solutions to an inconvenient truth: Ecosystem-based approaches to climate change. Environment Department, The World Bank, Washington DC. 19 p.

  

 




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