15 With the adverse impacts of climate change felt across the globe, the agenda of climate change has become more important than ever before (UNFCCC, 2017). The world's climate is changing at an unprecedented rate, threatening the survival of humanity (UNFCCC, 2018). Carbon dioxide (CO2) in the atmosphere has increased to levels that are higher than they have been for 800,000 years, and it is rising (UNFCCC, 2018). Forests play a key role in the global carbon cycle by taking up CO2 from the atmosphere and storing it in biomass (Jenkins & Schaap, 2018). Therefore, quantifying the substantial roles of forests as storehouses of carbon has become one of the most important aspects to understand and modify global climate change. Banko Janakari, Vol 32 No. 1, 2022 Pp 15‒24https://doi.org/10.3126/banko.v32i1.45442 Aboveground carbon stocks and sequestration rates of forests under different management regimes in Churia region of Nepal The impact of forest management activities on the ability of forest ecosystems to sequester and store atmospheric carbon is of increasing scientific and social concern. This research estimated the aboveground carbon stocks and carbon sequestration rates of forests under various management regimes in the Churia region of Nepal. We used tree data from 469 permanent sample plots distributed across the region from the data archive of Forest Research and Training Centre for the study. The data from 2012 and 2017 were used. The volumes of individual trees were calculated using species–specific allometric equations, which were then converted to biomasses using their respective wood densities. The carbon content was calculated by multiplying the biomass by 0.47 and was converted to the amount of sequestrated CO2 by multiplying by 3.67. We found that the average estimated aboveground carbon stock increased from 78.43 t ha–1 in 2012 to 89.20 t ha–1 in 2017, resulting in an average annual carbon sequestration rate of 5.34 t ha–1 yr–1 (i.e. 7.90 t CO2 ha–1 yr–1). The results showed significant differences in aboveground carbon stocks and annual carbon sequestration rates among different forest management regimes in the region. Generally, aboveground carbon stock was found to be the highest in protected areas in both years whereas, the annual carbon sequestration rate was found to be the highest in government–managed forests. It can be concluded that the Churia region has great potential in terms of carbon sequestration. The evidence of the strong association of carbon stock and sequestration rate with management regimes provides valuable information for policymakers to maintain and further enhance carbon storage in a geographically vulnerable region like Churia. Keywords: Carbon assessment, Churia forests, climate change, diverse management regimes, sequestration rates, tree density B. Subedi 1*, P. Lamichhane 2, L. K. Magar 3 and T. Subedi 2 Received: 5, March 2022 Revised: 6, April 2022 Accepted: 20, May 2022 Published: 31, May 2022 1 Agriculture and Forestry University Hetauda, Makwanpur, Nepal, *E–mail: bipanasubedi632@gmail.com 2 Forest Research and Training Center, Ministry of Forests and Environment, Kathmandu, Nepal, 3 Agriculture and Forestry University, Hetauda, Makwanpur, Nepal, https://orcid.org/0000-0002-8336-7988 https://orcid.org/0000-0002-0355-0222 https://orcid.org/0000-0001-9314-0632 https://orcid.org/0000-0002-3772-2897 Banko Janakari, Vol 32 No. 1 16 Subedi et al. Until 10,000 years ago, forests used to cover 6 billion ha of Earth's land area, but they today only cover 4 billion ha, with an average yearly loss of around 5.2 million ha during the last ten years (FAO, 2012). Deforestation and forest degradation can account for up to 20% of the global CO2 emissions each year, which is more than that of the entire transportation industry (Acharya et al., 2009). Intending to reduce emissions from forest– based Green House Gases (GHGs), the United Nations Framework Convention on Climate Change (UNFCCC) has announced the carbon offset program of Reducing Emissions from Deforestation and Forest Degradation (REDD+) at COP 19 in 2013 (Poudel et al., 2019). Nepal ranks 11th in the world for the emission of GHGs from deforestation and other land– use changes (World Resources Institute, 2008). Forest and grassland conversion alone contributes about 80% of Nepal's GHG emissions (MoPE/UNEP, 2004). The REDD+ initiative assigns a monetary value to carbon stored in forests, incentivizing developing countries to cut emissions from forested areas and invest in low–carbon, sustainable development (Sharma & Kakchapati, 2018). Nepal is a poor country economically, and forests are one of the main sources of national income and a vital source of livelihood for the local population (Sharma & Kakchapati, 2018). Despite the opportunities, there are numerous challenges to the effective implementation of REDD+ in Nepal. One of the key barriers to the proper implementation of REDD+ in Nepal is the lack of research on the estimation of carbon stocks using established scientific methodologies for frequent assessment of emission reduction (Acharya et al., 2009). More studies on the quantification of forest carbon stocks and factors affecting the forest carbon stocks and sequestration rates are needed. Such studies would be useful for the sustainable management of forest resources and increasing contributions of the forests to the national economy (Sharma & Kakchapati, 2018). Carbon is stored in various pools in forest ecosystems. IPCC (2006) has identified five carbon pools in terrestrial ecosystems: aboveground biomass (AGB), belowground biomass, litter, woody debris, and soil organic matter. The world's forests are estimated to store 662 Gt of carbon (including all carbon pools), of which 295 Gt of carbon is stored in living biomass (FAO, 2020). The carbon amount sequestered in different pools is variable and is affected by various factors such as land use, species composition, management regimes, and soil profile (Poudel et al., 2019; Shrestha & Singh, 2007). This means that the carbon removals and carbon sequestrations must be tracked regularly, which is limited in developing countries like Nepal (Poudel et al., 2019). Carbon stored in AGB of trees is often the greatest pool, which is also most immediately affected by deforestation and forest degradation (Yang, 2013). The Churia region of Nepal is the physiographic region with the highest occurrence of forest disturbances. Churia forests are disappearing at an annual rate of 0.18% (DFRS, 2015). Reducing carbon emissions from forest degradation and deforestation in this region is one of the priorities of the program and it requires information on forest carbon stock and sequestration rates. The results could be beneficial to the government for identifying actions that maintain or enhance carbon storage in Churia that supports both natural resource conservation and United Nations– REDD+ program to mitigate carbon emission issues. Moreover, with the advent of REDD+, considerable attention has been directed towards community forests, but other locally managed forest regimes, which account for three–quarters of total forest area, have been overlooked (Poudel et al., 2019). Quantifying carbon stocks in other management regimes is necessary for management planning for effective climate change mitigation (Ghimire, 2019). Quantification of carbon stocks and sequestration rates of the Churia forests is not only necessary for the conservation of degraded natural resources but also for supporting the implementation of REDD+ in Nepal (Sharma & Kakchapati, 2018). Hence, the aim of this study is: (a) to estimate the above–ground carbon stocks of the forests in Churia physiographic region of Nepal, (b) to assess the average annual rates of carbon sequestration of the forests in the region, and (c) to compare the carbon stocks and sequestration rates of forests under various management regimes. Banko Janakari, Vol 32 No. 1 17 Subedi et al. Materials and method Study area The study was conducted in Churia physiographic region of Nepal. The Churia region covers 12.84% of the total area of the country (Survey Department, 2001). It extends between longitudes 800 9' 25" E and 880 11' 16" E and latitudes 260 37' 47" to 290 10' 27” (LRMP, 1986, Figure 1). Its elevation ranges from 93–1,955 masl and stretches from 10 to 50 km in width (LRMP, 1986). The region's climate varies from sub–tropical to warm temperate, with hot and humid summers, heavy monsoon rain, and frigid winter. Low elevation regions fall in the sub– tropical climatic zone whereas high hills fall in the warm temperate climatic zones (DFRS, 2015). The Churia region consists of 23.04% of the total forest area of Nepal (DFRS, 2015). Forests in the region are being managed under six management regimes as i. Community forests ii. Government–managed forests iii. Protected areas iv. Private forests v. Buffer zone forests managed by the government and vi. Buffer zone community forests (MoFSC, 2015). Sampling and data collection We used data from 469 permanent sample plots distributed across the Churia region (Figure 1) from the data archive of Forest Research and Training Center (FRTC) for this study. FRTC used two–phase cluster sampling method for the inventory. In the first phase, the entire country was divided into 4 km grids, and a cluster of plots was established at each grid point. The clusters were sub–sampled for field measurement in the second phase. Concentric circular sample plots (CCSPs) of radii 4, 8, 15, and 20 cm were used for the measurement of trees of DBH range 5–9.9, 10– 19.9, 20–29.9, and 30 cm and more, respectively (Figure 2). Trees were identified to species level and their diameter at breast height (DBH) and heights was measured. Diameter Tape was used to measure the trees' DBH (1.3 m above ground level), while Vertex IV and Transponder T3 were used to measure their heights. Figure 1: Map showing physiographic regions of Nepal. The Churia region, also known as Chure is marked in green color. Red dots represent the locations of 469 permanent sample plots established by Forest Research and Training Center Banko Janakari, Vol 32 No. 1 18 Subedi et al. r3=15 m r2=8m r1= 4m r4= 20m N Figure 2: Layout of Concentric Circular Sample Plots (CCSP) for the estimation of stem volume, biomass and carbon content using the following methods: Data analysis DBH and height of the tallied trees were used Stem volume estimation For the estimation of stem volume, the volume equations (Eqn. 1) developed by Sharma &Pukkala (1990) was used: ln (v) = a + b ln(d) + c ln(h)………….(Eqn. 1) Where 'ln' is the natural logarithm to the base 2.71828, 'v' is the volume per hectare(m3ha–1), 'd' is the diameter of the trees measured at breast height (cm), 'h' is the height of the trees (m) and 'a', 'b' and 'c' are coefficients depending on species (Table 1). The volume estimates were then divided by 1000 to convert them into cubic meters. Table 1: Species–specific coefficients used for the estimation of stem volume of individual tree S.N. Species Local Name a b c R2 1 Acacia catechu Khair –2.3256 1.6476 1.0552 99.2 2 Adina cordifolia Haldu/Karma –2.5626 1.8598 0.8783 98.1 3 Albizia spp. Siris –2.4284 1.7609 0.9662 98.8 Other trees in Terai –2.3993 1.7836 0.9546 98.3 Other trees in Hills –2.3204 1.8507 0.8223 97.7 Source: Sharma & Pukkala, 1990 Stem biomass estimation Eqn. 2 was used for the estimation of stem biomass. Stem biomass (ton kg–1) = Volume × Density……………….(Eqn. 2) Where, Volume = Stem volume in m3, Density = Air–dried wood density in kg m–3 Species–specific wood–density values were obtained from Sharma & Pukkala (19900 (Table 2). Table 2: Air–dried wood densities for different tree species S.N. Species Local name Air–dried wood density (kg m–3) 1 Acacia catechu Khair 960 2 Adina cordifolia Haldu/karma 670 3 Albizia spp. Siris 673 Other tree species in Terai 674 Other tree species in Hills 674 Source: Sharma & Pukala 1990. Banko Janakari, Vol 32 No. 1 19 Subedi et al. Above–ground biomass estimation Tree branch biomass and foliage biomass were estimated by using species– and size–specific branch– to–stem and foliage–to–stem biomass ratios recommended by MoFSC (1988) (Table 3). Table 3: Branch–to–stem and foliage–to–stem biomass ratios for different trees (MOFSC, 1998) S.N. Species Local name Biomass ratio Branch–to stem Foliage–to–stem Small Medium Big Small Medium Big 1 Alnus nepalensis Utis 0.803 1.226 1.510 0.169 0.089 0.060 2 Dalbergia sissoo Sissoo 0.684 0.684 0.684 0.010 0.010 0.010 3 Pinus roxburghii KhoteSalla 0.189 0.256 0.300 0.101 0.046 0.033 4 Schima wallichii Chilaune 0.520 0.186 0.168 0.064 0.035 0.033 5 Shorea robusta Sal 0.055 0.341 0.357 0.062 0.067 0.067 6 Other species – 0.400 0.400 0.400 0.070 0.057 0.040 Source: MoFSC, 1998 Then, total AGB was obtained by adding the stem biomass, branch biomass, and foliage biomass, i.e. Total above–ground biomass (AGB) = Stem biomass + Branch biomass + Foliage biomass……… (Eqn. 3) Carbon stock / Carbon content estimation The above–ground carbon content (t C ha–1) was calculated by multiplying total aboveground biomass (kg ha–1) by 0.47 and by dividing it by 1000 (Eqn. 4) (IPCC, 2006). Aboveground carbon content (t C ha–1) = (Aboveground biomass (kg ha–1)* 0.47)/ 1000… (Eqn. 4) CO2 sequestration estimation The amount of sequestrated CO2 was calculated by multiplying carbon stock by 3.67 (Eqn. 5) (Toochi, 2018). Amount of CO2 sequestered in aboveground biomass (t CO2 ha–1) = Aboveground carbon content (t C ha–1) * 3.67…………(Eqn.5). Statistical analysis The difference between the carbon stocks and sequestration rates of forests under various management regimes was analyzed using the Kruskal–Wallis test with a post hoc Dunn test. For this, forests were grouped into four management regimes, i.e. Community forests, Government managed forests, Forest protection areas, and Others that included Buffer zone forests, Collaborative forests, and Leasehold forests. All calculations and analyses were done with Google sheet and Statistical Package software R version 3.6.1. (R core team, 2020). Results Major tree species A total of 232 species of trees were recorded from the 469 permanent sample plots in Churia region in 2017. In terms of frequency, Shorea robusta (38.67%), Terminalia alata (10.35%), Anogeisus latifolia (4.96%), Lagerstroemia parviflora (3.86%), Buchanania latifolia (2.96%), Syzygium cuminii (2.11%), and Pinus roxburghii (2.09%) were the major tree species in the region (Figure 3). The remaining 35% was contributed by Banko Janakari, Vol 32 No. 1 20 Subedi et al. other tree species (Mallotus philippensis, Adina cordifolia, Aegle marmelos, etc.). 38.67 10.35 4.96 3.86 2.96 2.11 2.09 35 0 5 10 15 20 25 30 35 40 45 N um be r of tr ee s ( % ) Major tree species Figure 3: Frequency distribution of major tree species in the Churia region Tree stem volume and aboveground tree biomass of forests under different management regimes Tree stem volume increased from 158.19 m3 ha–1 in 2012 to 180.86 m3 ha–1in 2017 and aboveground biomass increased from 166.88 t ha–1 in 2012 to 189.79 t ha–1 in 2017 (Table 4). Protected areas had the highest stem volume of aboveground biomass in both years (Table 4). Table 4: Tree stem volume and aboveground tree biomass of forests under different management regimes in Churia region, Nepal S. N. Management regimes No. of plots Tree volume (m3ha–1) Aboveground tree biomass (t ha–1) 2012 2017 2012 2017 1 Community forests 269 155.63 179.57 163.42 187.11 2 Govt. managed forests 94 140.47 147.72 150.85 177.93 3 Protection areas 76 191.82 193.94 200.71 221.53 4 Others 30 151.47 165.04 162.41 170.58 Churia region 469 158.19 180.86 166.88 189.79 Carbon stock and CO2 sequestration of forests under different management regimes The total estimated carbon stock of forests in the Churia region was 78.43 t ha–1 in 2012 and it increased to 89.20 t Cha–1 in 2017 (Table 5). The total sequestrated CO2 of the forests of the Churia region in the year 2012 was 287.85 t CO2 ha–1 and it increased to 327.37 t CO2 ha–1 in 2017 (Table 5). Table 5: Carbon stock and CO2 sequestration of forests under different management regimes in Churia region, Nepal Management Regime Community forests Govt. managed forests Protected areas Others Churia region C ar bo n st oc k (t C ha –1 ) No. of plots 269 94 76 30 469 2012 Mean 77.32 68.22 95.18 76.84 78.43 S.D. 42.89 45.08 47.40 57.78 45.59 Median 73.14 62.45 87.31 70.49 74.00 2017 Mean 88.51 80.94 104.96 80.74 89.20 S.D. 44.54 47.70 42.09 48.54 45.46 Median 83.37 74.68 98.16 79.38 83.46 C O 2 se qu es tr at io n (t C o 2e ha –1 ) 2012 Mean 283.76 250.37 349.31 282.00 287.85 S.D. 157.40 165.43 173.94 212.05 167.31 Median 268.42 229.21 320.42 258.69 271.59 2017 Mean 324.84 297.04 385.22 296.32 327.37 S.D. 163.46 175.07 154.48 178.14 166.84 Median 305.96 274.08 360.27 291.34 306.31 (S.D. = Standard Deviation) Banko Janakari, Vol 32 No. 1 21 Subedi et al. The total increment in CO2 sequestration during these two assessment periods was 39.52 t CO2 ha–1. Similarly, the annual rate of CO2 sequestration in the region over the two assessment periods i.e. 2012 and 2017 was 7.90 t CO2 ha–1 yr–1) (Table 6). Table 6: Rate of CO2 sequestration of forests under different management regimes in Churia region Management Regime Community forests Govt. managed forests Protected areas Others Total Rate of CO2 sequestration (t CO2 ha–1 yr–1) Annual N 269 94 76 30 469 Mean 8.21 9.33 7.18 2.86 7.90 S.D. 11.81 7.99 10.54 16.18 11.35 Median 7.61 8.50 7.48 5.99 7.61 Rate of CO2 sequestration (t CO2 ha–1) Over a Period Mean 41.07 46.67 35.91 14.32 39.28 S.D. 59.07 39.95 52.70 80.90 56.71 Median 38.03 42.50 37.38 29.94 38.03 (S.D. = Standard Deviation) Kruskal Wallis test showed that there is a significant difference between annual CO2 sequestration rates among the forests under different management regimes. Post–hoc Dunn test showed that the annual CO2 sequestration rate of government–managed forests is significantly higher than that of forests under other management regimes (Figure 4). Figure 4: Annual CO2 sequestration rates of forests under different management regimes (CF– Community Forests, GMF– Government Managed Forests, PF– Protected Areas) in the Churia region, Nepal Discussion The carbon store of Nepal's forests, according to the Ministry of Forest and Soil Conservation, is estimated to be as 176.95 t C ha–1(MoFSC, 2015). According to the Department of Forest Research and Survey (DFRS), Churia forests have an average carbon stock of 116.94 t C ha–1 (DFRS, 2015). However, in this study, we estimated the carbon stock of the region to be 89.20 t C ha–1, which seems to be relatively lower than that reported by DFRS (2015). This might be because this study only considered the above–ground carbon stock and excluded deadwood carbon stock, which was included in DFRS (2015). Furthermore, carbon stock per unit area might vary based on a variety of factors such as geographic location, tree density, species diversity, tree stem volume, DBH, canopy, and other forest management or legal considerations (Brown, 2002; Sharma et al., 2011; Sharma & Kakchapati, 2018). The estimated carbon stock in the region increased from 78.43 t C ha–1 in 2012 to 89.20 t C ha–1 in 2017 (Table 4). This increase in carbon stock in the region is probably due to an increase in stem volumes and biomass due to the increase in diameter and height of individual trees from 2012 to 2017. The volume of the stem matters when assessing the carbon stocks of forests since Banko Janakari, Vol 32 No. 1 22 Subedi et al. the stem is the main section of the tree where the majority of carbon is stored (Sharma & Kakchapati, 2018). Increased tree size results in an increment in biomass, which ultimately leads to positive changes in carbon stock. In addition, the Government of Nepal has prioritized the Churia region as an environmental conservation area, which might have reduced deforestation and consequently increased carbon storage in the region. The average annual rate of CO2 sequestration in the region was estimated to be 5.4 t ha–1 yr–1 (i.e. 7.90 t CO2 ha–1 yr–1 i.e. 5.4 t C ha–1 yr–1) (Table 6). This value is comparable to that reported for forests in the central Himalayan region, which ranges from 2.4 to 5.6 t C ha–1yr–1 (Rana et al., 1989). The increment in CO2 sequestration during the assessment period (2012–2017) was 39.52 t CO2 ha–1 yr–1 (Table 6), which was around 54.3 million tonnes of carbon for the whole region, which is equivalent to 197 tonnes of CO2. This means that about 197 tonnes of CO2 were removed from the atmosphere by these forests over 5 years, which is lower than the actual value as the extraction of timber and fuelwood during these years was excluded in the study. Therefore, the Churia region is expected to have more potential to sequester carbon than illustrated in the results. Our study showed that carbon stocks and sequestration rates vary significantly among the forests under different management regimes. This result is in line with the results of the study by Gurung et al. (2015) from Terai Arc Landscape (TAL). The highest level of carbon stock was found in protected area forests, followed by community forests, government–managed forests, and other forests. Indeed, Mbaabu et al. (2013) showed that the forest management practices affect the carbon stock of the forest. The variation among forests under different management regimes could be the reflection of the degree of usage limits of forest products, primarily wood harvesting. Timber harvesting is strictly forbidden in protected areas because conservation is their major goal (Gurung et al., 2015), whereas the production of timber is one of the primary goals of community forests and government–managed forests. The annual carbon sequestration rate was found to be the highest for government–managed forests, followed by community forests (Table 6). However, it was comparatively lower for protected areas, which could be an indication of the need for proper silvicultural treatments and management activities. In addition to the management activities, other biotic and abiotic factors also affect carbon sequestration rates (Newell & Stavins, 2000). For instance, young stands and fast–growing species tend to have a high sequestration capacity (Nowak et al., 2013). However, the effects of such biotic and abiotic factors were not examined in the study. Having said that, the impact of other management activities on carbon stock, such as forest fire control, silvicultural activities, and other biotic factors, should not be disregarded, and more research on their effects on carbon stock is required. It is perhaps not surprising that community forests are not the highest carbon–sequestering management regime because community forest operational plans do not include carbon value. The main aim of community forests is not the sequestration of carbon but the better growth of trees so that they can, later on, be harvested to fulfill the needs of the local users. Similarly, the difference between carbon sequestration rates of CF and GMF is not too high indicating that both management regimes have great potential for sequestering carbon. Conclusion We found a significant difference among diverse management regimes in terms of their carbon sequestration potentials. Carbon stocks were found the highest in protected areas where tight restrictions on the exploitation of forest products are imposed, compared to government–managed forests, community forests, and other forests where timber harvesting occurs. The forests of the Churia region have a huge carbon reservoir. Because of the region's high rates of deforestation, most of the carbon previously held in the reservoir may have been released into the atmosphere. There is a great opportunity to cut future emissions by avoiding deforestation in the Churia region. Banko Janakari, Vol 32 No. 1 23 Subedi et al. Government–managed forests exhibited higher rates of carbon sequestration than community forests. Where carbon sequestration studies in Nepal are primarily focused on community forests, our study showed that management regimes other than community forests, too, have great potential for carbon sequestration. References Acharya, K. P., Dangi, R. B., Tripathi, D. M., Bushley, B. R., Bhandary, R. 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