# Biomass and Carbon Stock Variation along slopes in Tropical Forest of Nepal: A case of Depard Community Forest, Makwanpur, Nepal

Birendra Bohara, Mahamad Sayab Miya, Sachin Timilsina, Deepak Gautam, Siddhartha Regmi

<figure><img src="image_1.png" /></figure>

## To cite this version:

Birendra Bohara, Mahamad Sayab Miya, Sachin Timilsina, Deepak Gautam, Siddhartha Regmi. Biomass and Carbon Stock Variation along slopes in Tropical Forest of Nepal: A case of Depard Community Forest, Makwanpur, Nepal. Journal of Multidisciplinary Applied Natural Science, 2021, 1 (2), pp.89 - 99. 10.47352/jmans.v1i2.85. hal-04178465

HAL Id: hal-04178465

https://hal.science/hal-04178465

Submitted on 8 Aug 2023

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_2.png" /></figure>
<figure><img src="image_3.png" /></figure>

# Biomass and Carbon Stock Variation along slopes in Tropical Forest of Nepal: A case of Depard Community Forest, Makwanpur, Nepal

Birendra Bohara, Mahamad Sayab Miya, Sachin Timilsina*, Deepak Gautam, and Siddhartha Regmi

Received : April 2, 2021

Revised : May 22, 2021

Accepted : May 27, 2021

Online : June 2, 2021

## Abstract

This study was conducted to assess biomass and carbon stock along slopes in Depard community forest, Manahari-6, Makwanpur district of Nepal. In Nepal, carbon stock estimation has been less practiced in community forest. A random sampling method was applied in this study to collect biophysical data i.e. DBH and height by non-destructive method to estimate the quantity of tree biomass and carbon stock. 21 sample plots with 1% sampling intensity were established within the study area. The circular area of 250 m² was predetermined with the radius of 8.92 m for this study. Secondary data were collected through published and unpublished literature. Data were pooled and analyzed with SPSS software. The total biomass and carbon stock were calculated to be 1381.30 t/ha and 649.21 t/ha, respectively. The biomass and carbon stock were highest (563.12 t/ha and 242.42 t/ha) in 0-5% slope, and lowest in >20% of slope (334.75 t/ha and 143.60 t/ha). The difference of biomass and carbon in slopes may be due to the accumulation of more organic matter and other minerals in the less sloped areas through rainfall, landslide.

**Keywords:** biomass, carbon stock, climate change, community forestry

## 1. INTRODUCTION

Globally, forest vegetation shares approximately 80% of terrestrial above-ground, and 40% of terrestrial below-ground biomass carbon storage [1]. Forest plays a significant role in the global carbon cycle as they acts as both sources and sinks of carbon, depending on specific management interventions and regimes [2]. Carbon is stored in carbon pools like standing forests, understory plants, leaf litter, soils, rocks, and sediments makes the forest function as both carbon source and carbon sinks [3]–[5]. About 43-50% of the dry biomass of trees is refered as carbon [6][7]. Growing forests have potential to sequester and stock carbon as biomass and mitigate global climate change [8][9]. Atmospheric carbon is acquired and stored in plant different parts in organic compounds form [10]. Soil sequester carbon by increasing soil organic

carbon when a plant dies or the plant material decomposes in the soil then this carbon content can be released in the form of CO₂ through decomposition of plant biomass and the respiration of plant roots and soil microbes [11]. Forests sequestrate the highest carbon among the terrestrial ecosystem [12].

Biomass and carbon stock of trees vary among natural and plantation forests [13][14]; between climatic zones and management regimes [15]; and according to age classes and species density [16]. The protection of forests, regeneration, and plantation in degraded areas enhance the productivity and carbon stock [17]. Atmospheric carbon can be sequestered through increased volume of plantation forest lands which help to mitigate atmospheric CO₂ [18][19]. CO₂ is considered as one of the major Green House Gases (GHGs) [20]. More than 1 trillion tons of carbon are currently store by the world's forests and forest soils which are twice the amount of floating free in the atmosphere. Therefore, several forestry projects aids to lower the GHGs emissions in different ways either by preventing the carbon stored in standing forests from being released into the atmosphere or actively increase carbon stocks through tree planting, improved soil management or enhancement in natural regeneration of degraded forest lands [21]. Plantations act as a reservoir of biomass carbon [22]. Improved silvicultural

**Copyright Holder:**

© Bohara, B., Miya, M. S., Timilsina, S., Gautam, D., and Regmi, S. (2021)

**First Publication Right:**

Journal of Multidisciplinary Applied Natural Science

**Publisher's Note:**

Pandawa Institute stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**This Article is Licensed Under:**

<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /></figure>
practices may enhance atmospheric carbon sequestration [12][23].

The scientists, policymakers, and the government are growingly concerned about climate change due to the continuous increase of GHGs concentration. In this sense, the interest in mounting carbon stocks in trees and substitution of fossil fuel by the use of tree biomass are also rising [24]. Globally, several studies have been performed on the role of tropical forests in climate change mitigation and possible effects on climate due to deforestation [25][26]. The magnitude of carbon change due to tropical forest deforestation is difficult to predict [8] as the tropical forest contain more species than any other ecosystems [27] and are large carbon sinks [28]. About 89% stored carbon get loss due to the loss of living biomass in the ecosystems [29]. It is essential to know the stocks of carbon as biomass per unit area for different forest types to assess the impact of deforestation and re-growth rates on the global carbon cycle. Therefore, both the Above Ground Biomass (AGB) and Below Ground Biomass (BGB) need to be measured for the better calculation of total forest carbon [30]. The forests and soil are necessarily conserved to maintain considerable amounts of carbon on the earth [31].

Aboveground biomass represents 60% of total tree biomass [23], hence, measured more importantly while calculating plant carbon pool [32]. Also, belowground biomass, deadwood biomass, and litter biomass are required to measure

to determine total carbon stock by plants over a specific time [11]. It also helps to determine the effects of land-use change and deforestation on net carbon fluxes.

In Nepal, various studies have been performed on agroforestry focusing on tangible benefits; however, studies focusing on intangible benefits like carbon sequestration are very less in number [33]. Pradhan et al. [34] have estimated that the forests of Nepal stored 897 million metric tons of carbon (including Carbon in above-ground biomass, carbon in below-ground biomass, Sub-total: carbon in living biomass, carbon in dead wood, carbon in litter, Sub-total: carbon in dead wood and litter, and soil carbon to a depth of 100m) in the year 2005. Similarly, the carbon in aboveground biomass in the forests of Nepal for the year 1986 by physiographic regions was found to be 36 million tons in Terai, 76 million tons in Siwaliks, 67 million tons in the Middle mountains, 123.5 million tons in High mountain, and 11.5 million tons in High Himalaya. However, a few studies has been carried out in estimating carbon stocks both in the biomass and in the entire soil profile under different land use categories in Nepalese mountain watershed. Also, very little studies has been carried out in estimating carbon pools in vegetation (Above and below ground biomass) and in soil profiles under different forest types in Nepal [35][36]. The updated data on national forest inventory and technical capacity is poor, and the changes in forest cover biomass stock, carbon removal, and carbon emission are limited in

<figure><img src="image_6.png" /><figcaption>Figure 1. Map of the study area.</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>**Figure 2.** Distribution of sample plots.</figcaption></figure>

the developing countries like Nepal [36]. Community forestry has been given major priority of Nepal's forestry sector and during the last 30 years of community forestry implementation, more than 25% of the national forest is handed to more than 14,200 Community Forest Users Groups (CFUGs) [37]. Despite protecting community forest by CFUGs for about last 30 years, forest and soil inventory has been paid little attention regarding carbon sequestration. Hence, amount of soil and biomass carbon sequestration is unknown [38]. Therefore, this study has endeavored to estimate the biomass as well as carbon stock and to compare biomass variation at the different slopes in Depard Community Forest of Makwanpur district, Nepal.

## 2. MATERIALS AND METHODS

### 2.1. Study area

The study was performed in the Depard Community Forest of Manahari-6, Makwanpur district (84°41' to 84°35'E longitude and 27°21' to 27°40'N latitude). The community forest occupies an area of 163.56 ha. The forest is mixed deciduous forest dominated by the species like *Schima wallichii* (Chilaune) and *Shorea robusta* (Sal). The district consists of several districts level roads which are reachable by Mahendra highway (47 km only) and Tribhuvan highway (110 km only). Mahabharat hills lie in the North and the Churia hills lie in the South of this district. Tropical and subtropical climate is found in the Churia range which lies in the southern part of the district while temperate climate is found in the Mahabharat range in the northern part [39]. Seasonal characteristics include cold, hot, and rainy seasons (each of four months) with an average relative humidity of 73.5 % in the district. Rapti and Bagmati are the major

river system in this district. Most of the people in the district depend on subsistence farming for economy rather than industrial sector. About 80.7 % of the population depends on livestock and agriculture while 17.3% of them rely on small scale business sectors.

### 2.2. Methods

#### 2.2.1. Data collection

Data was collected primarily through a direct field survey of biophysical measurement. The biophysical measurement i.e. Diameter at Breast Height (DBH) and height of trees was measured using Diameter-tape and Sunto-clinometer and Abney's label respectively. Forest inventory was conducted to estimate the present status of the forest. A random sampling method was applied to collect data for the estimation of tree biomass and carbon stock in the forest. A total of Twenty-one (21) concentric circular sample plots were laid out as per the forest carbon stock measurement guidelines with the radii of 8.92 m (for measuring trees and poles), 5.64 m (for measuring saplings), 1 m (for measuring seedlings) and 0.56 m (for taking the samples of the leaf litter, herbs, grass and soil) [40] along with 1% sampling intensity were randomly established within the study area referring to national inventory guideline developed by Department of Forest, Community and Private Forest Division [41] (Figure 2 and 3). The circular area of 250 m² was predetermined with a radius of 8.92 m for this study. All trees with DBH ≥ 5 cm were taken for estimation of biomass and carbon stock in the forest. Several research findings, publications, other relevant literatures related with carbon and biomass estimation were reviewed to perceive the better understanding, interpretation and analysis of the research.

#### 2.2.2. Data Analysis

Data were pooled and analyzed with SPSS software. Arc GIS 10.2 was used to fit a map. T-test

<figure><img src="image_8.png" /><figcaption>**Figure 3.** Sample Plot Layout.</figcaption></figure>
<table><caption>**Table 1.** Diameter and height distribution of trees per ha.</caption><thead><tr><th rowspan="2">No.</th><th rowspan="2">Slopes (%)</th><th rowspan="2">No./ha.</th><th colspan="3">Diameter (cm)</th><th colspan="3">Height(m)</th></tr><tr><th>Min</th><th>Max</th><th>Average</th><th>Min</th><th>Max</th><th>Average</th></tr></thead><tbody><tr><td>1</td><td>0-5</td><td>280</td><td>6.5</td><td>93.8</td><td>16.92</td><td>7</td><td>23</td><td>9.56</td></tr><tr><td>2</td><td>6-20</td><td>200</td><td>10</td><td>95.6</td><td>21.95</td><td>7</td><td>19</td><td>9.38</td></tr><tr><td>3</td><td>>20</td><td>168</td><td>13.6</td><td>90.4</td><td>31.68</td><td>8</td><td>17</td><td>11.40</td></tr></tbody></table>

was applied to compare the average biomass in different slopes of the forest because T-test is performed to determine if there is significant difference between the mean of two groups from randomly sampled data. In this study, to determine significant difference between average biomass in different slopes of forest, T-test is used.

### 2.2.3. Biomass Estimation and Net Carbon Content

The biomass of each tree includes stems, branches, leaves, and roots. It can be divided as aboveground biomass which includes stem, branch, and leaves and underground biomass which include the root. The important characteristics such as volume and biomass were predicted by biophysical measurement i.e. Non-destructive methods and mathematical models by measuring Diameter at Breast Height (DBH) directly.

**Above-ground biomass:** A simplified standard regression model was used to calculate the biomass of the trees; it is based on DBH, height, and wood density [42]–[44]. AGB is calculated by the formula given by Chave et al [45].

$$ (AGB) = 0.0509 * \rho D^2 H \quad (1) $$

Where, ρ= specific gravity of wood (gcm⁻³), D= tree DBH (cm) and H= Height of tree (m). The obtained AGB value for each individual weight (kg) of a sampling plot were summed up and divided by sampling plot area (250m²). The

biomass stock density value thus obtained is in kgm⁻² which was then converted to t/ha by multiplying it by 10. The wood-specific gravity used for *Shorea robusta* is 0.88 as its specific gravity value ranges from 0.83-0.93g/cm³ [46] and *Schima walichi* is 0.689 g/cm³ as its specific gravity value ranges from 0.45-0.92 g/cm³ [47].

**Below Ground Biomass (BGB):** It includes biomass of live roots (<2mm diameter). It was calculated by multiplying with AGB (0.26) [48].

$$ \text{Below Ground Biomass (BGB)} = 0.26 \times \text{AGB (ton)} \quad (2) $$

**Total Biomass:** Total biomass is the sum of the above and below ground biomass [49][50]. It is calculated as:

$$ \text{Total Biomass (TB)} = \text{AGB} + \text{BGB} \quad (3) $$

**Net carbon content:** The stock method was used to calculate biomass carbon; where carbon content is assumed to be approximately 50% of dry biomass [51]. The formulas used to calculate above ground carbon (AGC) and below-ground carbon (BGC) are:

$$ \text{Total AGC} = (\text{Total AGB of the tree}) \times 47\% \quad (4) $$

$$ \text{Total BGC} = (\text{Total AGOC}) \times 15\% \quad (5) $$

$$ \text{Total carbon content} = \text{Total AGC} + \text{Total BGC} \quad (6) $$

<figure><img src="image_9.png" /><figcaption>**Figure 4.** Estimated Biomass of the forest (ton/ha).</figcaption></figure>

## 3. RESULTS AND DISCUSSIONS

### 3.1. *Diameter and Height distribution*

The total number of trees was found to be 280, 200, and 168 per hectare in 0-5 %, 6-20 %, and >20 % of the slope respectively in the forest. The average diameter was 16.92 cm, 21.95 cm, and 31.68 cm and average height was 9.56 m, 9.38 m, and 11.40 m for the trees in given slopes
<figure><img src="image_10.png" /><figcaption>**Figure 5.** Carbon stock in the forest (t/ha).</figcaption></figure>

respectively (Table 1). The better frequency of the forest tree species was found in lower and medium slope area due to the presence of stable environmental conditions [52].

This study shows relatively low stem density (216 trees/ha) on average, however densities reported by Timilsina et al. [53] (220 trees/ha) in Bardia National Park a somewhat exceeded our overall mean. Meanwhile, Rautiainen measured similar densities (152–264 trees/ha) in Sal forest in the Bhabar–Terai zone of Nepal [54].

## 3.2. Biomass Estimation

The total biomass was found to be 565.13 t/ha, with AGB 448.51 t/ha and below-ground biomass to be 116.61 t/ha in 0-5 % of slope in the forest. In 6-20% of the slope of the forest, the total biomass was found to be 481.42 t/ha with above-ground tree biomass 382.08 t/ha and below-ground root biomass to be 99.34 t/ha. Similarly, the total biomass was found to be 334.75 t/ha with above-ground tree biomass 265.67 t/ha and below-ground root biomass to be 69.07 t/ha in >20 % of a slope of the forest (Figure 4).

Another study done by Maren and Sharma in Himalayas Mountain forests [55] showed the average above ground live biomass as 164 tons/ha which is slightly less than this study. Aboveground biomass varied from site to site because of varying plant community structures, variation in plant species and the succession stage of the forest.

The total biomass and total carbon stock are highest (563.12 t/ha and 242.42 t/ha) in 0-5% slope of the forest, followed by 6-20% of slope (481.42 t/ha and 205.38 t/ha) and lowest in >20% of slope (334.75 t/ha and 143.60 t/ha) in the study area (Figure 4 and Figure 5). The highest biomass and carbon stock in the 0-5% slope may be due to a higher density of trees (280 trees/ha) compared to 6-20% and >20% slope. More the tree density higher is the biomass [56]–[58]. The present study suggests that total biomass and carbon stock varies from site to site i.e. varies with slope in the forest. Altitudinal variation along with slope gradient has an impact on above ground carbon and below ground carbon because of its influence on soil water regime [59].

## 3.3. Carbon Estimation

The total carbon stock was found to be 242.42 t/ha with an average above-ground tree carbon stock 210.80 t/ha and average below ground root carbon stock to be 31.62 t/ha in 0-5 % of a slope. In 6-20% of slope, the total carbon stock was found to 205.38 t/ha with an average above-ground tree carbon stock 178.59 t/ha and average below ground root carbon stock to be 26.79 t/ha. Similarly, the total carbon stock was calculated to be 143.60 t/ha with an average above-ground tree carbon stock 124.87 t/ha and an average below-ground root carbon stock to be 18.73 t/ha in > 20 % of the slope of the forest (Figure 5). Around 53% of carbon stock was found in the forest area with slope 0-5%, while 41% carbon stock was present in the forest with slope gradient of 6-20%. Similarly, in the forest area with more than 20% of the slope, 7% carbon stock was present (Figure 6). Not only in the sal dominated forest, similar result was found by Feyissa et al. [60] while studying on Egdu forest. Moreover, Maggi et al. [61] also concluded very steep slope areas contain little vegetation cover compared to low slope areas. On the contrary, Zaki et al. [62] studied the forest carbon stock on tropical lowland dipterocarp forest and revealed that above ground carbon stock and below ground carbon stock tends to increase with slope. The distribution of biomass and carbon stocks in the forest is known to vary due to the presence of various tree species,

<table><caption>**Table 2.** Paired t-test.</caption><thead><tr><th>S1</th><th>S2</th><th>S3</th><th>M1</th><th>M2</th><th>M3</th><th>N1</th><th>N2</th><th>N3</th></tr></thead><tbody><tr><td>109.77</td><td>96.92</td><td>124.10</td><td>565.13</td><td>425.58</td><td>185.97</td><td>9</td><td>8</td><td>5</td></tr></tbody></table>
<figure><img src="image_11.png" /><figcaption>**Figure 6.** Carbon composition of forest (ton).</figcaption></figure>

nutrient availability in soil, climate, and other disturbance regime too [59]. In the national scenario, the Terai consists of a large amount of total organic carbon (479.29 t/ha) as compared to the average carbon stock of tropical forests of the world (285.0 t/ha) [63]. But it is lower than the average carbon stock of the community forests of Nepal [64]. The sparse and dense area has 89.2 t/ha and 129.0 t/ha carbon in the Kayerkhola watershed dominated by *Shorea robusta* forest in the Chitwan district [65].

The AGB and BGB are highest (448.51 t/ha and 116.61 t/ha) in 0-5 % and lowest (334.75 t/ha and 69.07 t/ha) in >20 % of a slope the forest. Similarly, above-ground carbon stock and below-ground carbon stock were highest (210.80 t/ha and 31.62 t/ha) in 0-5 % of slope and lowest (124.87 t/ha and 18.73 t/ha) in > 20 % of slope of the forest. Carbon composition is highest (53%) in 0-5% of slope and lowest (7%) in >20% of slope in the forest. Tree biomass and carbon stock has inverse relation with slope [66]–[68] and our result in this study supports the growing indications that forest ecosystems growing at lower slope store higher amounts of carbon than forest ecosystems at higher slopes. Both the above-ground and below-ground measurements should be carefully performed for precise estimation of biomass and carbon stock [58]. Our study shows quite greater biomass and carbon stock than other studies which can be supported by the study conducted by Yohannes which concludes that the highest amount of carbon stock was found in middle altitude area dominated

by *Shorea robusta* and *Termanalia tomentosa* [52].

### 3.4. Comparison of average biomass in different slopes of the forest

The estimated amount of average biomass in the different slopes of the forest was compared by using a T-test. The result is mentioned in (Table 2 and Table 3).

Since T calculated value is more than T tabulated value. Hence, it is concluded that biomass at different slopes in the forest is significantly different.

## 4. CONCLUSIONS

In the study of the biomass and carbon stock in the Depard Community Forest, the measurements was found to be highest (563.12 t/ha and 242.42 t/ha) in 0-5% slope of the forest, followed by 6-20% of slope (481.42 t/ha and 205.38 t/ha) and lowest in >20% of slope (334.75 t/ha and 143.60 t/ha) in the study area. This is related with the distribution of productive stem density within the forest as different areas with different slope varied significantly in the number, diameter and height of the tress as well. Moreover, the ability of carbon sequestration varies according to the site, presence of invasive alien species, tree density, fodder collection, species richness, gazing, canopy cover/strata, slope, and aspect, etc in different sites. Significant difference in the biomass and carbon stock along the slope gradient was also proven by T-test. This research provides the baseline data on the slope and biomass significance in study area and further study through LIDAR technology is recommended as well.

## AUTHOR INFORMATION

### Corresponding Author

Sachin Timilsina — Institute of Forestry, Tribhuvan University, Pokhara-33700

<table><caption>**Table 3.** Unequal variances.</caption><thead><tr><th>Test between</th><th>0 to 5 and 5 to 20</th><th>5 to 20 and >20</th><th>0 to 5 and >20</th></tr></thead><tbody><tr><th scope="row">Df</th><td>15</td><td>11</td><td>12</td></tr><tr><th scope="row">T-calc</th><td>0.0091</td><td>0.0103</td><td>0.0081</td></tr><tr><th scope="row">T-tab</th><td>0.0002</td><td>0.0082</td><td>0.0034</td></tr></tbody></table>
(Nepal);

ID orcid.org/0000-0002-4749-9289

Email: sachintimilsina66@gmail.com

## Authors

**Birendra Bohara** — Institute of Forestry,
Tribhuvan University, Hetauda-44107 (Nepal);

ID orcid.org/0000-0002-2280-0741

**Mahamad Sayab Miya** — Institute of Forestry,
Tribhuvan University, Pokhara-33700 (Nepal);

ID orcid.org/0000-0002-1675-593X

**Deepak Gautam** — Institute of Forestry,
Tribhuvan University, Pokhara-33700 (Nepal);

ID orcid.org/0000-0001-5239-365X

**Siddhartha Regmi** — Institute of Forestry,
Tribhuvan University, Hetauda-44107 (Nepal);

ID orcid.org/0000-0003-2731-7916

## ACKNOWLEDGMENT

The authors would like to thank all the helping hands.

## CONFLICT OF INTEREST

The authors declare that there is no conflict of interest.

## REFERENCES

[1] K. Panagiotopoulos, J. Holtvoeth, K. Kouli, E. Marinova, and A. Francke. (2020). “Insights into the evolution of the young Lake Ohrid ecosystem and vegetation succession from a southern European refugium during the Early Pleistocene”. *Quaternary Science Reviews*. **227**: 106044. [10.1016/j.quascirev.2019.106044](https://doi.org/10.1016/j.quascirev.2019.106044).

[2] L. Huang, M. Zhou, J. Lv., and K. Chen. (2020). “Trends in global research in forest carbon sequestration: A bibliometric analysis”. *Journal of Cleaner Production*. **252**: 119908. [10.1016/j.jclepro.2019.119908](https://doi.org/10.1016/j.jclepro.2019.119908).

[3] I. E. Olorunfemi, A. A. Komolafe, J. T. Fasinmirin, and A. A. Olufayo. (2019). “Biomass carbon stocks of different land use management in the forest vegetative zone of Nigeria”. *Acta Oecologica*. **95**: 45–56. [10.1016/j.actao.2019.01.004](https://doi.org/10.1016/j.actao.2019.01.004).

[4] S. Liu, H. Shen, X. Zhao, L. Zhou, H. Li, L. Xu, A. Xing, and J. Fang. (2019). “Estimation of plot-level soil carbon stocks in China’s forests using intensive soil sampling”. *Geoderma*. **348**: 107–114. [10.1016/j.geoderma.2019.04.029](https://doi.org/10.1016/j.geoderma.2019.04.029).

[5] A. Poudel, N. Sasaki, and I. Abe. (2020). “Assessment of carbon stocks in oak forests along the altitudinal gradient: A case study in the Panchase Conservation Area in Nepal”. *Global Ecology and Conservation*. **23**: e01171. [10.1016/j.gecco.2020.e01171](https://doi.org/10.1016/j.gecco.2020.e01171).

[6] Y. K. Bredin, C. A. Peres, and T. Haugaasen. (2020). “Forest type affects the capacity of Amazonian tree species to store carbon as woody biomass”. *Forest Ecology and Management*. **473**: 118297. [10.1016/j.foreco.2020.118297](https://doi.org/10.1016/j.foreco.2020.118297).

[7] G. N. Madapuri, H. N. Azwar, and M. A. Hasyim. (2021). “Estimation of CO2 Absorption, Biomass, and Carbon Deposit the Trees on the Street City of Malang”. *Journal of Multidisciplinary Applied Natural Science*. **1** (1): 18–24. [10.47352/jmans.v1i1.5](https://doi.org/10.47352/jmans.v1i1.5).

[8] S. Brown and A. E. Lugo. (1984). “Biomass of Tropical Forests: A New Estimate Based on Forest Volumes”. *Science*. **223** (4642): 1290–1293. [10.1126/science.223.4642.1290](https://doi.org/10.1126/science.223.4642.1290).

[9] M. A. Sheikh, M. Kumar, N. P. Todaria, and R. Pandey. (2020). “Biomass and soil carbon along altitudinal gradients in temperate Cedrus deodara forests in Central Himalaya, India: Implications for climate change mitigation”. *Ecological Indicators*. **111**: 106025. [10.1016/j.ecolind.2019.106025](https://doi.org/10.1016/j.ecolind.2019.106025).

[10] G. A. Alexandrov. (2007). “Carbon stock growth in a forest stand: the power of age”. *Carbon Balance and Management*. **2** (1): 4. [10.1186/1750-0680-2-4](https://doi.org/10.1186/1750-0680-2-4).

[11] B. Banik, D. Deb, S. Deb, and B. K. Datta. (2018). “Assessment of Biomass and Carbon Stock in Sal (Shorea robusta Gaertn.) Forests under Two Management Regimes in Tripura, Northeast India”. *Journal of Forest and Environmental Science*. **34** (3): 209–223. [10.7747/JFES.2018.34.3.209](https://doi.org/10.7747/JFES.2018.34.3.209).

[12] A. Dey, M. Islam, and K. M. Masum. (2014). “Above Ground Carbon Stock Through Palm Tree in the Homegarden of
Sylhet City in Bangladesh”. *Journal of Forest and Environmental Science*. **30** (3): 293–300. [10.7747/JFS.2014.30.3.293].

[13] M. Barrette, N. Thiffault, and I. Auger. (2021). “Resilience of natural forests can jeopardize or enhance plantation productivity”. *Forest Ecology and Management*. **482**: 118872. [10.1016/j.foreco.2020.118872].

[14] X. Su, S. Li, X. Wan, Z. Huang, B. Liu, S. Fu, P. Kumar, and Y. H. Chen. (2021). “Understory vegetation dynamics of Chinese fir plantations and natural secondary forests in subtropical China”. *Forest Ecology and Management*. **483**: 118750. [10.1016/j.foreco.2020.118750].

[15] J. Czerepko, R. Gawryś, R. Szymczyk, W. Pisarek, M. Janek, A. Haidt, A. Kowalewska, A. Piegdoń, A. Stebel, M. Kukwa, and C. Cacciatori. (2021). “How sensitive are epiphytic and epixylic cryptogams as indicators of forest naturalness? Testing bryophyte and lichen predictive power in stands under different management regimes in the Białowieża forest”. *Ecological Indicators*. **125**: 107532. [10.1016/j.ecolind.2021.107532].

[16] R. Baishya and S. K. Barik. (2011). “Estimation of tree biomass, carbon pool and net primary production of an old-growth Pinus kesiya Royle ex. Gordon forest in north-eastern India”. *Annals of Forest Science*. **68** (4): 727–736. [10.1007/s13595-011-0089-8].

[17] K. Liu, M. Bandara, C. Hamel, J. D. Knight, and Y. Gan. (2020). “Intensifying crop rotations with pulse crops enhances system productivity and soil organic carbon in semi-arid environments”. *Field Crops Research*. **248**: 107657. [10.1016/j.fcr.2019.107657].

[18] M. Peichl and M. A. Arain. (2006). “Above- and belowground ecosystem biomass and carbon pools in an age-sequence of temperate pine plantation forests”. *Agricultural and Forest Meteorology*. **140** (4): 51–63. [10.1016/j.agrformet.2006.08.004].

[19] A. R. Taylor, J. R. Wang, and H. Y. H. Chen. (2007). “Carbon storage in a

chronosequence of red spruce ( Picea rubens ) forests in central Nova Scotia, Canada”. *Canadian Journal of Forest Research*. **37** (11): 2260–2269. [10.1139/X07-080].

[20] M. Shahid and S. P. Joshi. (2018). “Carbon Stock Variation in Different Forest Types of Western Himalaya, Uttarakhand”. *Journal of Forest and Environmental Science*. **34** (2): 145–152. [10.7747/JFES.2018.34.2.145].

[21] C. Gong, Q. Tan, G. Liu, and M. Xu. (2021). “Forest thinning increases soil carbon stocks in China”. *Forest Ecology and Management*. **482**: 118812. [10.1016/j.foreco.2020.118812].

[22] M. Justine, W. Yang, F. Wu, B. Tan, M. Khan, and Y. Zhao. (2015). “Biomass Stock and Carbon Sequestration in a Chronosequence of Pinus massoniana Plantations in the Upper Reaches of the Yangtze River”. *Forests*. **6** (12): 3665–3682. [10.3390/f6103665].

[23] F. Turchetto, M. M. Araujo, L. A. Tabaldi, A. M. Griebeler, D. G. Rorato, A. L. P. Berghetti, F. M. Barbosa, M. S. de Lima, C. Costella, and V. M. Sasso. (2020). “Intensive silvicultural practices drive the forest restoration in southern Brazil”. *Forest Ecology and Management*. **473**: 118325. [10.1016/j.foreco.2020.118325].

[24] A. Baral. (2004). “Trees for carbon sequestration or fossil fuel substitution: the issue of cost vs. carbon benefit”. *Biomass and Bioenergy*. **27** (1): 41–55. [10.1016/j.biombioe.2003.11.004].

[25] B. H. J. De Jong, S. Ochoa-Gaona, M. A. Castillo-Santiago, N. Ramírez-Marcial, and M. A. Cairns. (2000). “Carbon Flux and Patterns of Land-Use/ Land-Cover Change in the Selva Lacandona, Mexico”. *AMBO: A Journal of the Human Environment*. **29** (8): 504–511. [10.1579/0044-7447-29.8.504].

[26] P. R. Grace, W. M. Post, and K. Hennessy. (2006). “The potential impact of climate change on Australia’s soil organic carbon resources”. *Carbon Balance and Management*. **1** (1): 14. [10.1186/1750-0680-1-14].

[27] L. Gibson, T. M. Lee, L. P. Koh, B. W.
Brook, T. A. Gardner, J. Barlow, C. A. Peres, C. J. A. Bradshaw, W. F. Laurance, T. E. Lovejoy, and N. S. Sodhi. (2011). "Primary forests are irreplaceable for sustaining tropical biodiversity". *Nature*. **478** (7369): 378–381. [10.1038/nature10425].

[28] P. E. Kauppi, R. A. Birdsey, Y. Pan, A. Ihalainen, P. Nöjd, and A. Lehtonen. (2015). "Effects of land management on large trees and carbon stocks". *Biogeosciences*. **12** (3): 855–862. [10.5194/bg-12-855-2015].

[29] H. Keith, D. B. Lindenmayer, B. G. Mackey, D. Blair, L. Carter, L. McBurney, S. Okada, and T. Konishi-Nagano. (2014). "Accounting for Biomass Carbon Stock Change Due to Wildfire in Temperate Forest Landscapes in Australia". *PLoS One*. **9** (9): e107126. [10.1371/journal.pone.0107126].

[30] S. P. Hamburg. (2000). "Simple rules for measuring changes in ecosystem carbon in forestry-offset projects". *Mitigation and Adaptation Strategies for Global Change*. **5** (1): 25–37. [10.1023/A:1009692114618].

[31] C. M. Sharma, S. Gairola, N. P. Baduni, S. K. Ghildiyal, and S. Suyal. (2011). "Variation in carbon stocks on different slope aspects in seven major forest types of temperate region of Garhwal Himalaya, India". *Journal of Biosciences*. **36** (4): 701–708. [10.1007/s12038-011-9103-4].

[32] Q. M. Ketterings, R. Coe, M. van Noordwijk, Y. Ambagau', and C. A. Palm. (2001). "Reducing uncertainty in the use of allometric biomass equations for predicting above-ground tree biomass in mixed secondary forests". *Forest Ecology and Management*. **146** (3): 199–209. [10.1016/S0378-1127(00)00460-6].

[33] R. A. Bluffstone, E. Somanathan, P. Jha, H. Luintel, R. Bista, M. Toman, N. Paudel, and B. Adhikari. (2018). "Does Collective Action Sequester Carbon? Evidence from the Nepal Community Forestry Program". *World Development*. **101** : 133–141. [10.1016/j.worlddev.2017.07.030].

[34] B. B. Pradhan, R. M. Shrestha, N. T. Hoa, and Y. Matsuoka. (2017). "Carbon prices and greenhouse gases abatement from agriculture, forestry and land use in Nepal".

*Global Environmental Change*. **43**: 26–36. [10.1016/j.gloenvcha.2017.01.005].

[35] B. M. Shrestha and B. R. Singh. (2008). "Soil and vegetation carbon pools in a mountainous watershed of Nepal". *Nutrient Cycling in Agroecosystems*. **81** (2): 179–191. [10.1007/s10705-007-9148-9].

[36] K. Hurni, J. Van Den Hoek, and J. Fox. (2019). "Assessing the spatial, spectral, and temporal consistency of topographically corrected Landsat time series composites across the mountainous forests of Nepal". *Remote Sensing of Environment*. **231**: 111225. [10.1016/j.rse.2019.111225].

[37] T. N. Maraseni, P. R. Neupane, F. Lopez-Casero, and T. Cadman. (2014). "An assessment of the impacts of the REDD+ pilot project on community forests user groups (CFUGs) and their community forests in Nepal". *Journal of Environmental Management*. **136**: 37–46. [10.1016/j.jenvman.2014.01.011].

[38] B. P. Shrestha. (2009). "Carbon Sequestration in Broad Leaved Forests of Mid-Hills of Nepal: A Case Study from Palpa District". *The Initiation*. **3**: 20–29. [10.3126/init.v3i0.2424].

[39] K. Bhattarai, D. Conway, and M. Yousef. (2009). "Determinants of deforestation in Nepal's Central Development Region". *Journal of Environmental Management*. **91** (2): 471–488. [10.1016/j.jenvman.2009.09.016].

[40] C. S. R. Neigh, R. F. Nelson, K. J. Ranson, H. A. Margolis, P. M. Montesano, G. Sun, V. Kharuk, E. Næsset, M. A. Wulder, and H. E. Andersen. (2013). "Taking stock of circumboreal forest carbon with ground measurements, airborne and spaceborne LiDAR". *Remote Sensing of Environment*. **137**: 274–287. [10.1016/j.rse.2013.06.019].

[41] E. B. Rana, H. L. Shrestha, and R. Silwal. (2008). "Participatory Carbon Estimation in Community Forest: Methodologies and Learnings". *The Initiation*. **2** (1): 91–98. [10.3126/init.v2i1.2528].

[42] G. Vachnadze, Z. Tiginashvili, G. Tsereteli, B. Aptsiauri, and L. Basilidze. (2018). "Carbon Stock Sequestered in the
phytocenosis of oak forests in Georgia". *Annals of Agrarian Science.* **16** (4): 476–480. 10.1016/j.aasci.2018.05.002.

[43] I. E. Olorunfemi, J. T. Fasinmirin, A. A. Olufayo, and A. A. Komolafe. (2020). "Total carbon and nitrogen stocks under different land use/land cover types in the Southwestern region of Nigeria". *Geoderma Regional.* **22** : e00320. 10.1016/j.geodrs.2020.e00320.

[44] E. T. Komolafe, K. S. Chukwuka, M. C. Obiakara, and O. Osonubi. (2020). "Carbon stock and sequestration potential of Ibodi monkey forest in Atakumosa, Osun state, Nigeria". *Trees, Forests and People.* **2** : 100031. 10.1016/j.tfp.2020.100031.

[45] J. Chave, C. Andalo, S. Brown, M. A. Cairns, J. Q. Chambers, D. Eamus, H. Fölster, F. Fromard, N. Higuchi, T. Kira, J. P. Lescure, B. W. Nelson, H. Ogawa, H. Puig, B. Riéra, and T. Yamakura. (2005). "Tree allometry and improved estimation of carbon stocks and balance in tropical forests". *Oecologia.* **145** (1): 87–99. 10.1007/s00442-005-0100-x.

[46] M. D. Behera, P. Tripathi, B. Mishra, S. Kumar, V. S. Chitale, and S. K. Behera. (2016). "Above-ground biomass and carbon estimates of Shorea robusta and Tectona grandis forests using QuadPOL ALOS PALSAR data". *Advances in Space Research.* **57** (2): 552–561. 10.1016/j.asr.2015.11.010.

[47] Y. K. Karna, Y. A. Hussin, H. Gilani, M. C. Bronsveld, M. S. R. Murthy, F. M. Qamer, B. S. Karky, T. Bhattarai, X. Aigong, and C. B. Baniya. (2015). "Integration of WorldView-2 and airborne LiDAR data for tree species level carbon stock mapping in Kayar Khola watershed, Nepal". *International Journal of Applied Earth Observation and Geoinformation.* **38** : 280–291. 10.1016/j.jag.2015.01.011.

[48] K. Kralicek, B. Huy, K. P. Poudel, H. Temesgen, and C. Salas. (2017). "Simultaneous estimation of above- and below-ground biomass in tropical forests of Viet Nam". *Forest Ecology and Management.* **390** : 147–156. 10.1016/

j.foreco.2017.01.030.

[49] A. N. Djomo and C. D. Chimi. (2017). "Tree allometric equations for estimation of above, below and total biomass in a tropical moist forest: Case study with application to remote sensing". *Forest Ecology and Management.* **391** : 184–193. 10.1016/j.foreco.2017.02.022.

[50] M. A. Sheikh, M. Kumar, R. W. Bussman, and N. Todaria. (2011). "Forest carbon stocks and fluxes in physiographic zones of India". *Carbon Balance and Management.* **6** (1): 15. 10.1186/1750-0680-6-15.

[51] A. R. Martin and S. C. Thomas. (2011). "A Reassessment of Carbon Content in Tropical Trees". *PLoS One.* **6** (8): e23533. 10.1371/journal.pone.0023533.

[52] H. Yohannes, T. Soromessa, and M. Argaw. (2015). "Carbon Stock Analysis Along Altitudinal Gradient in Gedo Forest: Implications for Forest Management and Climate Change Mitigation". *American Journal of Environmental Protection.* **4** (5): 237. 10.11648/j.ajep.20150405.14.

[53] N. Timilsina, M. S. Ross, and J. T. Heinen. (2007). "A community analysis of sal (Shorea robusta) forests in the western Terai of Nepal". *Forest Ecology and Management.* **241** (3): 223–234. 10.1016/j.foreco.2007.01.012.

[54] O. Rautiainen. (1999). "Spatial yield model for Shorea robusta in Nepal". *Forest Ecology and Management.* **119** (3): 151–162. 10.1016/S0378-1127(98)00519-2.

[55] I. E. Måren and L. N. Sharma. (2021). "Seeing the wood for the trees: Carbon storage and conservation in temperate forests of the Himalayas". *Forest Ecology and Management.* **487** : 119010. 10.1016/j.foreco.2021.119010.

[56] S. Brown and A. E. Lugo. (1982). "The Storage and Production of Organic Matter in Tropical Forests and Their Role in the Global Carbon Cycle". *Biotropica.* **14** (3): 161. 10.2307/2388024.

[57] B. Mwakisunga and A. E. Majule. (2012). "The influence of altitude and management on carbon stock quantities in rungwe forest, southern highland of Tanzania". *Open
*Journal of Ecology.* **2** (4): 214–221.
[10.4236/oje.2012.24025](https://doi.org/10.4236/oje.2012.24025).

[58] T. P. Gautam and T. N. Mandal. (2016). “Effect of disturbance on biomass, production and carbon dynamics in moist tropical forest of eastern Nepal”. *Forest Ecosystems*. **3** (1): 11. [10.1186/s40663-016-0070-y](https://doi.org/10.1186/s40663-016-0070-y).

[59] R. A. Houghton. (2005). "Aboveground Forest Biomass and the Global Carbon Balance". *Global Change Biology*. **11** (6): 945–958.
[10.1111/j.1365-2486.2005.00955.x](https://doi.org/10.1111/j.1365-2486.2005.00955.x).

[60] A. Feyissa, T. Soromessa, and M. Argaw. (2014). “Forest Carbon Stocks and Variations along Altitudinal Gradients in Egdu Forest: Implications of Managing Forests for Climate Change Mitigation”. *Science, Technology and Arts Research Journal*. **2** (4): 40. [10.4314/star.v2i4.8](https://doi.org/10.4314/star.v2i4.8).

[61] O. Maggi, A. M. Persiani, M. A. Casado, and F. D. Pineda. (2005). “Effects of elevation, slope position and livestock exclusion on microfungi isolated from soils of Mediterranean grasslands”. *Mycologia*. **97** (5): 984–995.
[10.1080/15572536.2006.11832748](https://doi.org/10.1080/15572536.2006.11832748).

[62] N. A. Mohd Zaki, Z. A. Latif, and M. N. Suratman. (2018). “Modelling above-ground live trees biomass and carbon stock estimation of tropical lowland Dipterocarp forest: integration of field-based and remotely sensed estimates”. *International Journal of Remote Sensing*. **39** (8): 2312–2340. [10.1080/01431161.2017.1421793](https://doi.org/10.1080/01431161.2017.1421793).

[63] B. S. Jina, P. Sah, M. D. Bhatt, and Y. S. Rawat. (2009). “Estimating Carbon Sequestration Rates and Total Carbon Stockpile in Degraded and Non-Degraded Sites of Oak and Pine Forest of Kumaun Central Himalaya”. *Ecoprint: An International Journal of Ecology*. **15**: 75–81. [10.3126/eco.v15i0.1946](https://doi.org/10.3126/eco.v15i0.1946).

[64] S. Charmakar, B. N. Oli, N. R. Joshi, T. N. Maraseni, and K. Atreya. (2021). “Forest Carbon Storage and Species Richness in FSC Certified and Non-certified Community Forests in Nepal”. *Small-scale Forestry*. [10.1007/s11842-020-09464-3](https://doi.org/10.1007/s11842-020-09464-3).

[65] I. Y. Pandya, H. Salvi, O. Chahar, and N. Vaghela. (2013). “Quantitative Analysis on Carbon Storage of 25 Valuable Tree Species of Gujarat, Incredible India”. *Indian Journal of Scientific Research*. **4** (1): 137–141.

[66] C. Leuschner, G. Moser, C. Bertsch, M. Röderstein, and D. Hertel. (2007). “Large altitudinal increase in tree root/shoot ratio in tropical mountain forests of Ecuador”. *Basic and Applied Ecology*. **8** (3): 219–230. [10.1016/j.baae.2006.02.004](https://doi.org/10.1016/j.baae.2006.02.004).

[67] G. Moser, D. Hertel, and C. Leuschner. (2007). “Altitudinal Change in LAI and Stand Leaf Biomass in Tropical Montane Forests: a Transect Study in Ecuador and a Pan-Tropical Meta-Analysis”. *Ecosystems*. **10** (6): 924–935. [10.1007/s10021-007-9063-6](https://doi.org/10.1007/s10021-007-9063-6).

[68] C. R. Sanquetta, J. Wojciechowski, A. P. D. Corte, A. L. Rodrigues, and G. C. B. Maas. (2013). “On the use of data mining for estimating carbon storage in the trees”. *Carbon Balance and Management*. **8** (1): 6. [10.1186/1750-0680-8-6](https://doi.org/10.1186/1750-0680-8-6).