1. "FOREST AND SOIL CARBON STOCKS AND SAP FLOW
MEASUREMENT OF GOSAIKUND COMMUNITY FOREST, KAVRE,
NEPAL"
A DISSERTATION
SUBMITTED FOR THE
PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR
THE MASTER OF SCIENCE DEGREE IN ENVIRONMENTAL SCIENCE
BY
Anustha Shrestha
DEPARTMENT OF ENVIRONMENTAL SCIENCE AND ENGINEERING
SCHOOL OF SCIENCE
KATHMANDU UNIVERSITY
DHULIKHEL, NEPAL
"September, 2011"
2. CERTIFICATION
This dissertation entitled "Forest and Soil Carbon Stocks and Sap Flow Measurement of
Gosaikund Community Forest, Kavre, Nepal", by Anustha Shrestha, under the supervision
of Dr. Roshan Man Bajracharya, Department of Environmental Science and Engineering,
Kathmandu University, Dhulikhel, Nepal, is hereby submitted for the partial fulfillment of the
Master of Science (M.Sc.) Degree in Environmental Science. This degree has not been
submitted in any other university or institution previously for the award of a degree.
Approved by:
_______________________ _________________________
Supervisor External Examiner
Date: Date:
____________________________
Head of Department
Date:
3. DECLARATION
I, Anustha Shrestha hereby declare that the work presented herein is genuine work done
originally by me and has not been published or submitted elsewhere for the requirement of a
degree programme. Any literature, data or works done by others and cited within this
dissertation has been given due acknowledgement and listed in the reference section.
Signature
____________________
Anustha Shrestha
Date: _________
4. i
ACKNOWLEDGEMENT
I would like to acknowledge the support, help and the encouragement of all those people who
are behind the successful completion of the project work.
I am extremely thankful to my supervisor Dr. Roshan Man Bajracharya for his noble
guidance, moral support and technical help. Very special thanks go to my juniors Samriddhi
Dhakal and Shradhdha Jnawali for constantly accompanying during field measurement and
laboratory analysis. I would also like to express my sincere gratitude to Mr. Chandra Ghimire
for helping me analyze the data of sap flow measurement and for providing me with valuable
literature and data.
I am really grateful towards Nirbachan Karmacharya, Sharad Mainali, Priyanka Timila and
Abhishek Manandhar for assisting me during sample collection. I would like to acknowledge
Prof. Dr. Subodh Sharma for his valuable and encouraging suggestions. I am especially
thankful towards the staffs of Aquatic Ecology Centre (AEC) and lab assistants of
Environment Laboratory for providing me with all the necessary equipments and chemicals
during lab analysis.
I would also like to thank Dr. Bed Mani Dahal and Ms. Smriti Gurung for helping me with
SPSS software. I really owe my thanks to Mr. Bidur Khadka for providing me with all the
necessary literature related to forest biomass and carbon. Furthermore, I would like to thank
staffs of District Forest Office, Kavre for providing site information of Gosaikund
Community Forest.
Lastly, I would like to thank my family members and friends for the moral boost,
encouragement and support that helped me stay optimist throughout the thesis work.
5. ii
ABSTRACT
Climate change has become one of the most important topics of debate in the international
arena. With increasing global greenhouse gas emissions, there have been focus on the ways to
offset the GHGs emission especially carbon dioxide which has increased drastically in the
recent decade. Forests have been considered as the important resource for the carbon
sequestration. Though forests play important role in sequestrating carbon dioxide, their
transpiration and sap flow process are also responsible for the use and loss of water obtained
from the ground. Therefore, this research focused on the estimation of forest carbon stock,
soil evaluation and water use and loss via sap flow and transpiration.
For the purpose of the comparative study, pine and mixed forest stand of Gosaikund
Community Forest of Kavre district, Nepal was chosen. The methodology include delineation
of forest boundary, plot set up, DBH and height measurement of trees and saplings, litter
collection, laboratory analysis of forest soil and use of Granier technique for sap flow
measurement.
The mean forest carbon stock and carbon dioxide equivalent for the pine stand based on land
use category were found to be 171.362 tons/ha and 628.898 tons/ha respectively. As for the
mixed stand, carbon stock and carbon dioxide equivalent values were 160.843 tons/ha and
590.293 tons/ha respectively.
Pinus roxburghii had the highest relative density of 77.94% along with the highest mean
biomass density (152.443 tons/ha) and carbon stock density (71.648 tons/ha). The mean
carbon stock density for Schima wallichhii and Alnus nepalensis were 9.392 tons/ha and
10.542 tons/ha respectively. Saplings of Schima wallichhii were found only in the mixed
stand with mean carbon stock density of 0.969 tons/ha. Litter, herb and grass (LHG) biomass
showed a strong correlation (R2
=0.838) with soil organic carbon and this showed the
influence of litter collection on soil organic carbon.
In the pine stand, the mean carbon stock density was found to be highest at the altitude of
1600-1650 meters and also in the northern aspect. In the mixed stand, the mean carbon stock
was highest at the altitude of 1760-1810 meters and also in the south eastern aspect.
Acidic soil was seen in both the pine and mixed stand with pH range of 4.96-5.75 in the pine
stand and 4.64-5.75 in the mixed stand. Silt loam soil dominates Gosaikund CF. High mean
C:N ratio of 17.539 and 17.860 was found in the pine and the mixed stand respectively.
6. iii
Sapwood area and sap flow showed strong correlation of R2
=0.890. Both sapflux density and
sap flow peaked during the time interval of 12-2 pm. Sap flow and transpiration both showed
strong correlation with above ground tree biomass (R2
=0.967, R2
=0.858 respectively).
With concern on increasing afforestation/ reforestation to increase forest carbon stock for
carbon credits, other related elements of the nature like water and soil have been ignored. So,
equal attention needs to be given to the different components of the nature and understand the
impacts that could result from the action of forest carbon financing.
7. iv
Acronyms
AGSB: Above Ground Sapling Biomass
AGTB: Above Ground Tree Biomass
ANSAB: Asian Network for Sustainable Agriculture and Bioresources
BB: Below Ground Biomass
DBH: Diameter at Breast Height
DWS: Dead Wood and Stumps
CF: Community Forest
cm: Centimeter
FECOFUN: Federation of Community Forest Users, Nepal
GHGs: Green House Gases
GIS: Geographical Information System
GPS: Global Positioning System
ha: Hectare
ICIMOD: International Centre for Integrated Mountain Development
IPCC: Intergovernmental Panel on Climate Change
Kg: Kilogram
LHG: Leaf litters, herb and grass
Mg: Mega gram (1000kg, or metric ton)
REDD: Reducing Emission from Deforestation and Forest Degradation
SOC: Soil Organic Carbon
8. v
Table of Contents
Acknowledgement…………………………………………………………………………….………..i
Abstract……………………………………………………………………..………………...……..ii-iii
Acronyms………………………………………………………………..…………………..…………iv
Table of Contents………………………………………………………..……………………..……v-vi
List of Tables…………………………………………………………………………..……………...vii
List of Figures……………………………………………………………………....…………….….viii
CHAPTER 1: INTRODUCTION………………………………………..………..…………….….1-5
1.1 Background………………………………………………………………………...…..……..…1-2
1.2 Rationale…………………………………………………………………………...…..……..….2-3
1.3 Objectives………………………………………………………………………...………..…….…4
1.3.1 General Objectives of the Study…………………………………...………………..…….….4
1.3.2 Specific Objectives of the Study ………………………………………………..……………4
1.4 Hypothesis………………………………………...……………………..…………..……..………4
1.5 Scope and Limitation………………………………………………………….……...…….……..5
CHAPTER 2: LITERATURE REVIEW………………………………..………………..………6-15
2.1 Carbon and Global Climate Change……………………………………………………..……….6
2.2 Role of Forest and its Soil in Carbon Sequestration……………………………..….…...……7-8
2.2.1 Carbon Cycling in Forest……………………………………………………………..…….8-9
2.3 Carbon Sequestration, REDD and Nepal………………………………………………….……..9
2.3.1 Status of Carbon in Forest and Shrub Land of Nepal…………...……………..…………10
2.3.2 Status of Carbon in Forest of Nepal by Legal Classification………………………….10-11
2.4 Transpiration………………………………………………………………...………………...…11
2.4.1 Transpiration and Ascent of Sap in Plants………………………………………….…..….12
2.4.2 Reforestation, Evaporation and Drying Water Sources…………………………….....12-14
2.5 Carbon Sequestration versus Transpiration…………...……………………………………14-15
CHAPTER 3: SITE DESCRIPTION………………………………………………………..…..16-20
3.1 Historical Context of Gosaikund CF……………………………………………………..….…..16
3.2 Geology and Topography…………………………………………………………..……….……18
3.3 Climate………………………………………………………………………………………....18-19
3.4 Major Tree Species……………………………………………………………………..………...19
3.5 Forest User Group Information……………………...………………………………..……..….20
CHAPTER 4: MATERIALS AND METHODS……………..………………………………….21-30
4.1 Forest Boundary Delineation………………………….……………………………………..…..21
4.2 Estimation and Layout of Sample Plots………………………….…………………………..….21
4.3 Field Measurements………………………………………………………………………..…21-24
4.3.1 Establishment of Sampling Plots………………………………..……………………….21-22
4.3.2 Above Ground Tree Biomass (AGTB)………………………………….…………....……..22
4.3.3 Above Ground Sapling Biomass (AGSB) and Regeneration……………………..……22-23
4.3.4 Leaf litter, Herb and Grass (LHG)………………………………………………....………23
4.3.5 Dead Wood and Stumps (DWS)………………..……………...…………………....………23
4.3.6 Sap Flux Measurements ……………………………………..…………………………..23-24
4.3.7 Estimation of Sapwood Area…………………………………………………….………….24
4.4 Soil Sampling and Laboratory Analysis………………………..…………….…………24-26
4.4.1 Soil Sampling……………………………………………………...……………….…………24
4.4.2 Bulk Density……………………………………………………...……………….………….24
4.4.3 Soil Organic Carbon (SOC)………………………………………………...….……………25
9. vi
4.4.4 Soil pH……………………………………………………………………………...…………25
4.4.5 Soil Texture……………………………………………………………………..……………25
4.4.6 Total Soil Nitrogen………………………………………………………….……………25-26
4.4.7 Soil Moisture……………………………………………………………..….……………….26
4.5 Data Analysis………………………………………………………………………………….26-30
4.5.1 Above Ground Tree Biomass (AGTB)…………………………………….………..………26
4.5.2 Above Ground Sapling Biomass (AGSB)…………………………………………………..27
4.5.3 Leaf litter, Herb and Grass (LHG) Biomass………………………..……………….….27-28
4.5.4 Below Ground Biomass (BB)…………………………………………….………………….28
4.5.5 Total Carbon Stock Density…………………………………………………………………28
4.5.6 Sap Flux Calculation…………………………………………………..…………………….29
4.5.7 Soil Bulk Density………………………………………………………………..……………29
4.5.8 Soil Organic Carbon……………………………………………………………...………….29
4.5.9 Soil Total Nitrogen………………………………………………………………...…………29
4.5.10 Soil Moisture……………………………………………………………………...………….30
4.5.11 Soil Texture……………………………………………………………………..……………30
CHAPTER 5: RESULTS AND DISCUSSION………………………………..………………...31-62
5.1 Tree Relative Density…………………………………………………………………………….31
5.2 Forest Biomass and Carbon……………………………………………………...…………..32-50
5.2.1 DBH Distribution and Biomass…………………………………………..……………..32-33
5.2.2 Above Ground Tree Biomass Density…………………………………………...……..33-36
5.2.3 Below Ground Biomass Density…………………….………………………………………36
5.2.4 Above Ground Sapling Biomass Density……………………………………………….36-37
5.2.5 Leaf Litter, Herb and Grass Biomass Density…………………………………..……..37-38
5.2.6 Soil Organic Carbon………………………………………………………………….….38-40
5.2.7 Forest Carbon stock in Pine and Mixed Stand……………………………………..……..40
5.2.8 Altitudinal Forest Carbon Stock Summary……………………………………………41-43
5.2.9 Aspect Wise Forest Carbon Stock Summary…………………………………..………44-47
5.2.10 Logged Trees……………………………………………………………………..………47-48
5.2.11 Carbon and Carbon Dioxide Equivalent in Pine and Mixed Stand……………………...49
5.2.12 Testing of Hypothesis for Carbon Stock in Pine and Mixed Stand…………….…..…….50
5.3 Soil Evaluation………………………………………………………………………..……….51-56
5.3.1 Soil pH……………………………………………………………………………..……...51-52
5.3.2 Soil Texture……………………………………………………………………...………..52-53
5.3.4 Soil Bulk Density…………………………………………………………..…….…………..54
5.3.5 Total Soil Nitrogen………………………………………………………………………54-55
5.3.6 Soil Moisture…………………………………………………………………………….55-56
5.3.7 Hypothesis Testing for Soil Parameters of Pine and Mixed Stand…..…………….……56
5.4 Sap flow and Transpiration…………………………………………………………………..57-62
5.4.1 Temporal Variation of Pine Sap Flux Measurement………………………….….………57
5.4.2 Sap Flow Correlation with Tree Parameters…………………………………………..57-59
5.4.3 Transpiration Relationship with AGTB, Temporal and Climatic Variability………59-62
CHAPTER 6: CONCLUSION AND RECOMMENDATIONS…………………………..……63-64
6.1 Conclusion………………………………………………………………………………..……63-64
6.2 Recommendations………………………………………………………………..……...………..64
References
Annexes
10. vii
List of Tables
Table 1: Status of Carbon in Forest and Shrubland of Nepal…………………………………….10
Table 2: Major Tree Species in Different Forest Block………………………….………..……….19
Table 3: Forest User Group Information…………………………..………………….……..……..20
Table 4: Summary Statistics of DBH of Pine and Mixed Stand…………………………..….……32
Table 5: Summary Statistics of AGTB and Tree Density of Pine and Mixed Stand……..………34
Table 6: Mean BB Density…………………………………………………………………………...36
Table 7: Mean DBH and Biomass Density of Schima wallichhii saplings……………...…………37
Table 8: Summary Statistics of LHG Biomass Density of Pine and Mixed Stand……………….37
Table 9: Summary Statistics of SOC of Pine and Mixed Stand…………………...……………..38
Table 10: SOC% in Pine and Mixed Stand at Different Depths…………………………………..39
Table 11: Mean Value for Different Carbon Pool in Pine Stand (altitude wise)…………………41
Table 12: Mean Value for Different Carbon Pool in Mixed Stand (altitude wise)……….………43
Table 13: Mean Value for Different Carbon Pool in Pine Stand (aspect wise)………...…………45
Table 14: Mean Value for Different Carbon Pool in Mixed Stand (aspect wise) …….………….46
Table 15: Number of Logged Trees and their Classes……………………………………………..47
Table 16: Carbon and Carbon Dioxide Equivalent in Pine and Mixed Stand……………………49
Table 17: Test of Homogeneity of Variance for Various Carbon Stock Categories of Pine and
Mixed Stand………………………………………………………………………………...…………50
Table 18: Robust Test of Equality of Means for Various Carbon Stock Categories of Pine and
Mixed Stand………………………………………………………………………………...…………50
Table 19: Soil pH of Pine and Mixed Stand……………………………………………………...…51
Table 20: Percentage Ranges of Clay, Sand and Silt at Different Depths………………………..52
Table 21: Mean Soil Bulk Density in Pine and Mixed Stand………………………………………54
Table 22: Total Soil Nitrogen and C:N Ratio of Pine and Mixed Stand……………..……………54
Table 23: Soil Moisture in Pine and Mixed Stand………………………………………………….55
Table 24: Test of Homogeneity of Variance for Soil Parameters in Pine and Mixed Stand…….56
Table 25: Robust Test of Equality of Means for Soil Parameters in Pine and Mixed Stand……56
Table 26: Correlation of Sap Flow with Various Tree Parameters………………………...……..58
Table 27: Correlation of Transpiration with Climatic Parameters……………………………….61
Table 28: Statistical Summary Data for Sap Flux, Sap Flow, Transpiration, Sapwood
Area and DBH ………………………………………………………………………………62
11. viii
List of Figures
Figure 1: Map of Kavre District……………………………………………………………………..17
Figure 2: Image of Gosaikund CF…………………………………………………..……………….18
Figure 3: Monthly Average of Atmospheric Temperature and Rainfall between June 2010-May
2011………………………………………………………………………………..…………………..19
Figure 4: Sampling Design of Circular Plot………………………………..……………………….21
Figure 5: DBH Measurements for Different Tree Types……………………..……………………22
Figure 6: Sensors Inserted in Sapwood…………………………………………..………………….25
Figure 7: USDA Soil Textural Triangle for the Classification of Soil Texture…..……………….30
Figure 8: Relative Density (%) of Tree Species………………………………………….…………31
Figure 9: DBH Distribution of Pine and Mixed Stand………………………………………..……32
Figure 10: DBH and Biomass Relations in Pine and Mixed Stand………………………………..33
Figure 11: Above Ground Tree Biomass Density (AGTB) of Pine and Mixed Stand……..…….34
Figure 12: AGTB and Carbon Density of Major Tree Species…………………………..……….36
Figure 13: Litter, Herb and Grass Biomass Density……………………………………...………..37
Figure 14: SOC in Pine and Mixed Stand…………………………………………………………..38
Figure 15: Relation between LHG Biomass and SOC……………………………..………………39
Figure 16: Different Carbon Stock in Pine and Mixed Stand……………………………………..40
Figure 17: Mean Carbon Stock of Pine Stand at Various Altitudes…………….………………..41
Figure 18: Mean Carbon Stock of Mixed Stand at Various Altitudes……………………………42
Figure 19: Mean Carbon Stock of Pine Stand at Different Aspects……………………………....44
Figure 20: Mean Carbon Stock of Mixed Stand at Different Aspects…………………………….46
Figure 21: Status of Different Soil Texture in Various Depths of Pine Stand……………...…….53
Figure 22: Status of Different Soil Texture in Various Depths of Mixed Stand…………..……..53
Figure 23: Mean Sap Flux Density versus Time…………………………………………...……….57
Figure 24: Transpiration versus AGTB…………………….………………………………………59
Figure 25: Temporal Variation of Transpiration……………………………………..……………59
Figure 26: Temporal Variation of Solar Radiation and Air Temperature during March-April
2011……………………………………………………………………………...…………………….60
Figure 27: Temporal Variation of Relative Humidity and Wind Speed during March-April
2011……………………………………………………………………………………………………60
12. 1
CHAPTER 1: INTRODUCTION
1.1 Background
The forest in Nepal is defined as all lands having trees with more than 10% crown cover
(DFRS, 1999). Covering 37% of the country‘s total area (JAFTA, 2000), the forest is Nepal‘s
largest natural resource. The forest has three important functions: production of goods
(firewood, fodder, timber, and herbs), protection of the natural environment, and regulation of
atmospheric conditions. Forest production enhances the economy of the community, while the
protection and regulation functions are concerned with ecological conservation (Dhital, 2009).
With regard to the climate regulatory function of forests, carbon sequestration in the woody
tissue of trees and water uptake and transpiration are the main mechanisms involved. Global
climate change is a widespread and growing concern that has led to extensive international
discussions and negotiations. Responses to this concern have focused on reducing emissions
of greenhouse gases, especially carbon dioxide, and on measuring carbon absorbed by and
stored in forests, soils, and oceans. One option for slowing the rise of greenhouse gas
concentrations in the atmosphere, and thus possible climate change, is to increase the amount
of carbon removed by and stored in forests (Gorte, 2007).
Worldwide, forests cover 4x106
ha (30% of land area) and, relative to non-woody vegetation,
have a large biomass per unit area of land (FAO, 2005). The main C pools in forests are plant
biomass (above- and below-ground), coarse woody debris, litter and soil (containing organic
and inorganic C; IPCC 2003; Richards and Evans, 2004).
The environmental services of the forest cross the border of forest and community itself. The
recognition of forest benefits are needed by the distant national and international
beneficiaries. Establishment of formal environmental payment mechanism in local and
international markets is practiced in recent days. Formalization of forest carbon finance
through the ratification of Kyoto Protocol in 1997 is the steps taken towards the consideration
of generating win-win situation between traditional forest managers and beneficiaries (Rana
et.al.,2008).
Another process that is common in the forest ecosystem is transpiration. Transpiration is the
evaporation of water from within plants. It occurs chiefly at the leaves while their stomata are
13. 2
open for the passage of CO2 and O2 during photosynthesis. Tree transpiration is the major
pathway for both water and energy leaving the forest ecosystem. Measurement of
transpiration provides access to the canopy conductance of the forest, a key parameter in
models of water- and carbon-exchange (Collins and Avissar, 1994), since the water and
carbon fluxes are strongly linked by their common passage through the stomata (Morén et al.,
2001).
Transpiration induces sap flow, where liquid water is pulled from the soil through the stem
and branches, up to the leaves where it evaporates in the air. The roots uptake water and
dissolved nutrients from the soil and the wooden structure serves as a pathway for water flow
from the roots to the shoots. Soil water availability, hydraulic conductance of the transport
pathway and evaporative demand of the ambient air influences the water status of the leaves,
which, in turn, can impose a limitation to gas exchange by controlling the stomata (Hari et al.
1999). Stomatal control has an effect on the carbon gain and thus on the growth of the tree.
Transpiration, photosynthesis, carbon allocation and growth, tree structure, and water flow are
all linked to each other (Peramaki, 2005).
The research mainly focused on forest carbon stock, nature of forest soil along with use and
loss of water involving sap flow and transpiration. This research seeks to quantify tree
biomass and its sap flow and further enhance our knowledge on photosynthesis, climate
change and tree transpiration.
1.2 Rationale
The greenhouse gas especially carbon dioxide have increased since the start of industrial
revolution from 280ppm to 379ppm (Rogner et.el.,2007). Greenhouse gas re emit the long
wave radiation back to earth surface and increase its temperature. This has induced global
warming which has further aggravated the problems related to the climate change. With
increasing power production using coal, gigantic industrial emissions, transportation and
domestic emissions; the concentration of carbon dioxide is continuously increasing in the
atmosphere. Nepal's contribution to global annual carbon dioxide emission is only 0.025%
(MoPE, 2004) but Nepal is facing several problems such as glacial retreat, water shortage,
agricultural decline, irregular and untimely rainfall which are all related to climate change.
14. 3
Forests are considered to be major natural sink for carbon dioxide where huge concentration
of carbon dioxide is used by plants for photosynthetic activity. Therefore, it is very important
to assess the forest carbon stock and its carbon dioxide equivalent. Such study helps to
understand how much of carbon dioxide is removed from the atmosphere and it may also help
to strengthen the REDD policy which talks about the payment that can be made for emission
reduction by reducing deforestation or forest degradation in developing countries. At the
same time this type of study also helps in documenting the carbon stock in different forest
stand and could be useful for comparative study. According to Dhital (2009), deforestation
contribute about 20% of total greenhouse gas emission so this research will contribute for the
better understanding of forest conservation, its benefit and may even encourage people to
practice reforestation with suitable plant species.
The water loss and water use due to transpiration and sap flow are the common phenomenon
in the forest. This may affect the forest soil and ground water aquifer. The recent studies in
Kalahari, Africa has shown that the root of certain tree species penetrate deep below and used
water from the aquifer affecting the ground water balance and huge amount of water was lost
through transpiration. Measuring transpiration and sap flow is also important in the forests of
Nepal because till date no transpiration study has been conducted in Nepal. It is likely that
trees of certain species or size may transpire more water than the others and may affect the
water status. So through this type of research, one can understand about the water use by
plants and also focus on the negative effect of the forest stand. Thus, it may help to resolve
the problem involving water loss and encourage similar other studies.
It is true that plants use both carbon dioxide and water for photosynthesis. On one hand, it
may help to offset carbon from the atmosphere where as in other case it may be responsible
for the excess water use and loss through sap flow and transpiration. In such a scenario, it is
very important to study both the process of forest carbon sequestration as well as transpiration
to ensure forest, soil and water conservation.
15. 4
1.3 Objectives
1.3.1 General Objective of the Study
The general objective of the study was to estimate the above ground and below ground forest
carbon stock in the homogenous stand (consisting only of Pinus roxburghii) and the mixed
stand (consisting of Pinus roxburghii, Schima wallichhii, Alnus nepalnesis and other less
dominant plant species) and also to estimate sap flow and tree transpiration of Pinus
roxburghii.
1.3.2 Specific Objectives of the Study
To determine biomass and carbon stock densities of trees, saplings, roots, litter and herb of
pine and mixed stand.
To measure the physical and chemical parameters of soil from pine and mixed stand.
To measure sap flux density and estimate sap flow and tree transpiration of pine trees.
1.4 Hypothesis
H0: There is no significant difference in the carbon stock density of tree, belowground,
sapling, soil, litter and herb in pine and mixed stand.
H1: There is significant difference in the carbon stock density of tree, belowground, sapling,
soil, litter and herb in pine and mixed stand.
H0: There is no significant difference in soil pH, bulk density, organic carbon percentage,
moisture content, total nitrogen percentage and C:N ratio in pine and mixed stand
H1: There is significant difference in soil pH, bulk density, organic carbon percentage,
moisture content, total nitrogen percentage and C:N ratio in pine and mixed stand.
H0: Sap flow and tree transpiration does not strongly correlate with above ground tree
biomass.
H1: Sap flow and tree transpiration is correlated with above ground tree biomass.
16. 5
1.5 Scope and Limitation of the Study
This research covers the forest biomass and carbon status in pine and mixed stand and makes
comparison. It also focuses on the carbon stock in different altitude and aspects of these two
types of stand. The research also includes the comparative analysis of different soil
parameters at various depths of pine and mixed stand. It further covers the tree water use by
Pinus roxburghii based on temporal and meteorological variation and establish relationship
with above ground tree biomass.
The research does not cover the biomass and carbon measurements from steeply sloping areas
and due to the compactness and generally shallow nature of the forest soil, the soil samples
were collected only up to a depth of 30 cm. The sap flow and transpiration were measured
only for pine species due to time constraint and to the sampling period covered only the pre
monsoon season.
17. 6
CHAPTER 2: LITERATURE REVIEW
2.1 Carbon and Global Climate Change
Greenhouse gases play an important role on Earth‘s climate. These include water vapor,
carbon dioxide, methane, nitrous oxide, and ozone. When sunlight reaches the surface of the
Earth, some are absorbed and warm the Earth. In turn, the Earth emits long wave radiation
towards the atmosphere, a fraction of which is absorbed by the greenhouse gases. The
Greenhouse gases then emits long wave radiation both towards space and back to the Earth.
The energy emitted downward further warms the surface of the Earth. The process of
absorbing long wave radiation by the greenhouse gases and emitting it back resulting to more
warming of the Earth‘s surface is called ―greenhouse effect‖ (Samalca, 2007)
When the concentration of greenhouse gasses in the atmosphere increased, temperature at the
Earth‘s surface is expected to rise. Climate models developed in the 90‘s have shown that
global surface air temperature may increase by 1.4 °C to 5.8 °C at the end of the century
(IPCC, 2001; Rahmstorf and Ganopolski, 1999). IPCC (2007) report predicted increase in
temperature with more precision at 1.8 °C to 4 °C at the end of the century. Petit et al. (1999)
linked increase in surface air temperature level to increase in the concentration of CO2 in the
atmosphere. Carbon dioxide (CO2) is one of the more abundant greenhouse gases and a
primary agent of global warming. It constitutes 72% of the total anthropogenic greenhouse
gases, causing between 9-26% of the greenhouse effect (Kiehl and Trenberth, 1997).
IPCC (2007) reported that the amount of carbon dioxide in the atmosphere has increased from
280 ppm in the pre-industrial era (1750) to 379 ppm in 2005, and is increasing by 1.5 ppm per
year. Dramatic rise of CO2 concentration is attributed largely to human activities. Over the
last 20 years, majority of the emission is attributed to burning of fossil fuel, while 10-30% is
attributed to land use change and deforestation (IPCC, 2001). Increase in CO2 concentration,
along with other greenhouse gases (GHG), raised concerns over global warming and climate
changes. IPCC (2001) report concluded that climate has changed over the past century.
Report from the recent conference of climate scientists in Paris concluded that human
activities are to be blamed for the observed climate change (IPCC, 2007). On this basis,
efforts to lower down the concentration of GHG‘s are focused, among others, on limiting
influx of carbon dioxide to the atmosphere (United Nations, 1992; United Nations, 1998).
18. 7
2.2 Role of Forest and Soil in Carbon Sequestration
Forest vegetation and soils share almost 60% of the world‘s terrestrial carbon (Winjum et al.,
1992). Vegetation and soils are viable sinks of atmospheric carbon (C) and may significantly
contribute to mitigation of global climate change (Bajracharya et al. 1998; Phillips et al. 1998;
Lal 2004; Smith 2004). Climate change in recent years has witnessed growing concern about
the accumulation of GHGs in the earth's atmosphere, which is significantly raising the global
temperature. It has been estimated that deforestation and forest degradation contribute up to
20 percent of global emissions of carbon dioxide annually—more than the entire
transportation sector—and that standing forests sequester about 20 percent of global carbon
dioxide emissions (Acharya et.al,.2009).
The carbon pool in a terrestrial ecosystem can be broadly categorized into biotic (vegetative
carbon) and pedologic (soil carbon) components. Vegetative carbon can be further
categorized into carbon in aboveground (shoot) biomass, belowground (root) biomass, and
necromass (Hamburg, 2000). FAO (2005) defined biomass as ―organic material both above-
ground and belowground, and both living and dead, e.g., trees, crops, grasses, tree litter,
roots etc.‖ Above-ground biomass consists of all living biomass above the soil including
stem, stump, branches, bark, seeds, and foliage. Below-ground biomass consists of all living
roots excluding fine roots (less than 2mm in diameter).
In forest biomass studies, two biomass units are used, fresh weight (Araujo, et al., 1999) and
dry weight (Aboal et al., 2005; Ketterings et al., 2001; Montagu et al, 2005; Saint-Andre et
al., 2005). For carbon sequestration application, the dry weight is more relevant because 50%
of it is carbon (Losi et al., 2003; Montagnini and Porras, 1998; Montagu et al., 2005). Many
biomass assessment studies conducted are focused on above-ground forest biomass (Aboal et
al., 2005) because it accounts for the majority of the total accumulated biomass in the forest
ecosystem.
These vegetative and soil carbon stocks are dynamic, depending upon various factors and
processes operating in the systems, the most significant being land use, land-use changes, soil
erosion, and deforestation (IPCC, 2000). Land-use change, especially the conversion of forest
to agricultural land, results in removal of trees, which displaces a large amount of sequestered
carbon and, consequently, reduces that held in the terrestrial biomass (Van Noordwijk et al.,
19. 8
1997; Glaser et al., 2000). The negative impact of deforestation on soil organic carbon (SOC)
is more pronounced in the upper soil layer (Sombroek et al., 1993; Batjes, 1996).
The carbon stock in forest vegetation varies according to geographical location, plant species
and age of the stand (Van Noordwijk et al., 1997). Soil carbon, on the other hand, depends on
the aboveground input received from leaf litter and on the decomposition of fine roots below
ground (Rasse et al., 2006). The recycling of carbon in the plant–soil system also depends
upon macro and micro-faunal activity (Hairiah et al., 2006) and on litter quality, usually
defined by its lignin content (Shrestha et.al.,2007).
2.2.1 Carbon Cycling in Forests
Forest ecosystem plays very important role in the global carbon cycle. It stores about 80% of
all above-ground and 40% of all below-ground terrestrial organic carbon (IPCC, 2001).
During productive season, CO2 from the atmosphere is taken up by vegetation and stored as
plant biomass (Losi et al., 2003; Phat et al., 2004).
Photosynthesis is the chemical process by which plants use sunlight to convert nutrients into
sugars and carbohydrates. Carbon dioxide (CO2) is one of the nutrients essential to building
the organic chemicals that comprise leaves, roots, and stems. All parts of a plant — the stem,
limbs and leaves, and roots — contain carbon, but the proportion in each part varies
enormously, depending on the plant species and the individual specimen‘s age and growth
pattern. Nonetheless, as more photosynthesis occurs, more CO2 is converted into biomass,
reducing carbon in the atmosphere and sequestering (storing) it in plant tissue (vegetation)
above and below ground (Gorte, 2007).
Plants also respire, using oxygen to maintain life and emitting CO2 in the process. At times
(e.g., at night and during winter seasons in non-tropical climates), living, growing forests are
net emitters of CO2, although they are generally net carbon sinks over the life of the forest.
When vegetation dies, carbon is released to the atmosphere. In addition to being sequestered
in vegetation, carbon is also sequestered in forest soils (Gorte, 2007).
Carbon is the organic content of the soil, generally in the partially decomposed vegetation
(humus) both on the surface and in the upper soil layers, in the organisms that decompose
vegetation (decomposers), and in the fine roots. The amount of carbon in soils varies widely,
20. 9
depending on the environment and the history of the site. Soil carbon accumulates as dead
vegetation is added to the surface and decomposers respond. Carbon is also ―injected‖ into the
soil as roots grow (root biomass increases). Soil carbon is also slowly released to the
atmosphere as the vegetation decomposes. Scientific understanding of the rates of soil carbon
accumulation and decomposition is currently not sufficient for predicting changes in the
amount of carbon sequestered in forest soils (Gorte, 2007).
2.3 Carbon Sequestration, REDD and Nepal
Nepal's contribution to the global annual GHG emission is 0.025% (MoPE, 2004). The total
GHG emission from Nepal is estimated at 39,265 Gega gram (Gg) and per capita emission is
1,977 kg (GoN, 2008). The world's forests and forest soils currently store more than 1 trillion
tonnes of carbon, twice the amount floating free in the atmosphere. Thus, increasing storage
and preventing stored carbon from being released back to the atmosphere are two of the most
important measures for combating global warming and conserving the environment (Oli and
Shrestha, 2009).
The forest area of Nepal is estimated to be 5.8 million hectare (40% of total geographical area
of the country), out of which 4.2 million ha (29%) is forest and 1.6 million ha (10.6%) is
shrubland (DFRS, 1999). The forest covering is necessary to stabilize country's farming
system and fragile geography. If people could be compensated for their effort to conserve the
forest area, it would provide twin benefits: conservation, which has enormous impact on
biodiversity and the local environment and rural economic development due to additional
cash flow in the rural economy (Dhital, 2009).
Reducing Emission from Deforestation and Forest Degradation (REDD) in developing
countries is the mechanism that allows industrialized countries to offset their emission by
purchasing carbon credits from developing countries, which reduce emission from
deforestation and forest degradation by avoiding such activities. The link between forest and
climate change was acknowledge at Bali Climate Conference 2007. The Bali Action Plan
(UNFCCC, 2007) acknowledges that forest cannot be ignored in any future strategy to combat
climate change and that REDD also has potential to deliver biodiversity conservation and
poverty alleviation outcomes (Dhital, 2009).
21. 10
2.3.1 Status of Carbon in Forest and Shrubland of Nepal
The table shows that the forests of Nepal store 897 million metric tonnes of carbon in the year
2005.
Table 1: Status of Carbon in Forest and Shrub Land of Nepal
Category Carbon(Million metric ton)
1990 2000 2005
Carbon in above ground biomass 278 385 359
Carbon in below ground biomass 97 135 126
Sub-total: carbon in living biomass 375 520 485
Carbon in dead wood 56 78 73
Carbon in litter 17 13 13
Sub-total Carbon in dead wood and
litter
73 91 86
Soil carbon to a depth of 100 cm 432 350 326
Total carbon 880 961 897
Source: FAO 2005
2.4 Transpiration
Transpiration involves the movement of water through plants from the soil through the root
system via the xylem to both the internal leaf parenchyma and the leaf atmosphere, and its
ultimate evaporation via the stomatal pores to the external atmosphere. This theory of water
movement is based on the apparent metastable state of water in the xylem elements. The
water, through common molecular cohesion and adhesion to xylem cell walls, exists as a
single unit of high tensile strength, which can be pulled upwards via a gradient in the internal
plant water potential and external atmospheric water potential. That is, the sap moves upwards
because of a decrease in water potential from the soil to the atmosphere (Mclaughlin, 1988).
Temperature has the greatest effect on transpiration. Higher the temperature, higher is the
transpiration and at the same time high temperature also lowers the relative humidity. Low
relative humidity accounts for greater transpiration. Light affects transpiration because
stomata usually opens in light and close in darkness so at night only small amount of water is
lost (Taylor et.al., 2005)
22. 11
During the daytime, roots uptake water for transpiration according to soil water availability
and evaporative demand. During the night, an increase in soil moisture in dry surface soil has
sometimes been observed (Nadezhdina, 2009).
2.4.1 Transpiration and Ascent of Sap in Plants
The upward movement of water from the base of the stem to the top is called the ascent of
sap. According to Dixon (1895), water forms a continuous column from the base of the plant
to its top and remains under cohesive tension due to transpiration pull and according to the
need water is being pulled up to the top of the tree. This important and widely accepted theory
has following essential features:
Water forms a continuous column from the base of the plant to its top.
Water is lost from mesophyll cells due to transpiration because of which a pulling
force develops. It keeps these cells under tension.
The tension may cause a break in water column but due to tension strength or cohesive
property of water molecules, the continuous column is not broken.
The tension of transpiration pull is transmitted to the root region to regulate
absorption.
2.4.2 Reforestation, Evapotranspiration and Drying Water Sources
Fast paced conversion and destruction of tropical forests has led to an unprecedented decline
in biodiversity and disruption of ecosystem services. At the same time, the need to supply
local communities and global markets with wood products and other forest commodities
remains. These factors have resulted in a strong demand for re- and afforestation in the
tropics, which may therefore become a key activity in future tropical forestry. Conventional
tree plantations with species from the genera Pinus, Eucalyptus and Acacia in single-species
stands address the need for wood products but have been criticised for contributing little to
ecosystem functioning and biodiversity (Lamb et al., 2005). In reaction to this, recent
reforestation approaches in the tropics highlight the use of native species in mixed stands
(Erskine et al. 2006; Petit and Montagnini, 2006), a strategy which can promote multi-
functional use of forests creating stands that help restoring biodiversity, produce diverse wood
products and sequester carbon (Dierick et.al,.2008).
A major objection to reforestation is the potentially high evapotranspiration rates of reforested
stands which could in turn lead to reductions in streamflow and groundwater recharge
23. 12
(Bruijnzeel, 2004). From a global synthesis Farley et al. (2005) concluded that annual runoff
was on average reduced by 44% and by 31% when reforesting grass- and shrubland
respectively. Long term catchment studies in South Africa revealed a clear pattern of
increased evapotranspiration rates in plantation forests, resulting in reductions in available
water resources and subsequent government regulation of the forestry sector (Dye and
Versfeld, 2007). Stream flow reductions of up to 50% were observed in the Ecuadorian Andes
(Buytaert et al., 2007) under Pinus plantations, while a water budget study in eucalypt
plantations in the Atlantic rainforest region of Brazil showed that 95% of the precipitation
(1147 mm yr_1) was used for evapotranspiration (Almeida et al., 2007).
A landmark study on the relationship between tree size and water use characteristics included
24 co-occurring species in old growth forest in Panama. Tree diameter was highly correlated
with sapwood area (R2
= 0.98) and with integrated daily sapflux in the outermost sapwood (R2
= 0.91) regardless of species (Meinzer et al., 2001). This suggests that tree size rather than
tree species determines tree water use characteristics. Subsequent analyses, including tropical
and temperate angiosperms as well as temperate conifers, supported the hypothesis of size
dependence of sap wood area and tree water use (Meinzer et al., 2005).
As a result of functional convergence, plants operating within given biophysical limitations
develop common patterns of sap flux and water use in relation to size characteristics across
taxa ( Meinzer, 2003). This would leave little room for species selection to serve as a tool to
influence stand water use when wood production and carbon fixation are the main
management objectives. However, other studies in old growth did provide an indication that
species differences in sap flux densities exist (Granier et al,. 1996).
Using a sap flux model, O‘Brien et al. (2004) looked into responses of normalized sap flux
density to environmental factors for ten co-occurring species with diverse traits in rainforest
in Costa Rica. Statistically significant differences in responses of normalized sap flux between
species were present, but the overall effect of species was judged to be small. Species
differences in absolute sap flux density – which differed more than twofold – and tree water
use were not assessed as sufficient data on absolute sap flux densities was lacking(Dierick
et.al.,2008) .
24. 13
The sap flow technique (Swanson, 1994) is very useful for obtaining the total water use of a
single tree. Sap flow is commonly scaled up to stand level and considered as representing
transpiration. A problem with this approach is that, because of the capacitance of the trunk
and branches, sap flow lags somewhat behind transpiration (Granier and Loustau, 1994;
Köstner et al. 1996). The lag is not constant over the course of the day, between days or
between trees (Lundblad and Lindroth, 2002; Phillips et al., 1997), even though in many
approaches it is assumed to be constant; modelling the lag is consequently not a simple task.
However, daily sap flow totals can often be assumed to equal the sum of transpiration,
although under some conditions, e.g. after a dry period followed by some rainy days, this may
not be true (Waring et al., 1979; Zweifel and Häsler, 2001).
Soil moisture is considered to be a critical parameter in many models of evaporation or
surface energy partitioning. Many models show large sensitivity to soil moisture, and general
circulation models, which are used to predict future climate change, are no exception (e.g.
Viterbo and Beljaars, 1995). Unfortunately, it is inherently difficult to establish firm
relationships between transpiration (or canopy conductance) and soil water content, mainly
because of the large spatial variation in soil properties and soil moisture, but also because of
transpiration‘s strong dependence on other weather parameters. There are, however, several
empirical studies of such relationships in which firm relationships have been established; e.g.
for Scots pine by Rutter (1967), Sturm et al. (1996) and Irvine et al.(1998), for Norway spruce
by Lu et al. (1995) and Luet al. (1996), and for mixed stands of these species by Cienciala et
al. (1998).
2.5 Carbon Sequestration Vs Transpiration
Trees and forests are being planted in the tropics for a broad range of (sometimes perceived)
benefits, including erosion control, sustained soil fertility, improved quality and quantity of
water supply, as well as socioeconomic benefits ranging from enhanced livelihoods and
poverty reduction to development and growth of national revenues. Lately potential benefits
of carbon sequestration have added value to forest establishment. Win–win scenarios for
environment, development and climate have been discussed (e.g., Lal et al., 1995; Wunder,
2007), and local examples are accumulating (Murdiyarso & Skutsch, 2006). Total areas of
forest plantations can be expected to increase rapidly in the near future with carbon markets
expanding and demands for bioenergy increasing (United Nations, 2008).
25. 14
Carbon sequestration strategies highlight tree plantations without considering their full
environmental consequences. Tree plantations feature prominently among tools for carbon
sequestration. Plantations typically combine higher productivity and biomass with greater
annual transpiration and rainfall interception, particularly for evergreen species such as pines
and eucalypts . In addition to influencing water budgets, plantations require additional base
cations and other nutrients to balance the stoichiometry of their extra biomass. In
consequence, trade-offs of sequestration with water yield and soil fertility, including nutrient
depletion and increased acidity, are likely (Jackson et.al. 2005).
Today water is increasingly precious in many tropical regions, and often the poor are paying
the highest price (Rockstrom et al., 2007). As competition for water is tightening, tree
planting has been under increased scrutiny because a number of studies have shown strongly
reduced stream flow after afforestation (Calder et al., 2004; Farley et al., 2005; Jackson et al.,
2005; Kaimowitz, 2005). In contrast, there is a widespread public perception that tropical
forests act like ‗sponges‘ providing dependable stream flow during the dry season. The
underlying scientific argument is that a well-developed forest cover promotes high
infiltrability and groundwater recharge during the wet season with a gradual release of water
during the dry season. Once the ‗sponge effect‘ is lost by mismanagement of the soil during
post-forest use, dry-season flows are often seen to decline (Bruijnzeel, 1989, 2004;
Sandstrom, 1998) despite the fact that the new vegetation cover (crops, grassland) typically
uses less water than the original forest (Zhang et al., 2004).
This ‗sponge theory‘ has long been one of the cornerstones of promotion of forest
conservation and reforestation of degraded lands, but while results with decreased dry-season
flows after afforestation accumulate there is no rigorous study to show improved dry-season
flows after planting trees on degraded tropical land (Malmer et.al.,2009). The global analysis
of 504 annual catchment observations shows that afforestation dramatically decreased stream
flow within a few years of planting. After slight initial increases in some cases, substantial
annual decreases of 155 mm and 42% were observed on average for years 6 to 10, and
average losses for 10- to 20-year-old plantations were even greater, 227 mm/year and 52% of
stream flow. Perhaps most important, 13% of streams dried up completely for at least 1 year,
with eucalypts more likely to dry up streams than pines (Jackson et.al., 2005) .
26. 15
Afforestation in drier regions [<1000 mm mean annual precipitation (MAP)] was more likely
to eliminate stream flow completely than in wetter regions. Mean annual renewable water
(percentage of annual precipitation lost as runoff) decreased around 20% with afforestation
(P< 0.0001). For many nations with total annual renewable freshwater <30% of precipitation
(Fig. 1B), afforestation is likely to have large impacts on water resources (Jackson et.al.
2005).
27. 16
CHAPTER 3: SITE DESCRIPTION
Kavrepalanchowk District of Nepal is a hilly district covering an area of 140,486 hectares. It
stretches between 85°24‘ - 85°49‘ E longitude and 27°20‘ - 27°85‘ N latitude. The district lies in
the middle mountainous region in the central part of Nepal as an eastern adjoining district of the
Kathmandu Valley. Dhulikhel, the district headquarter, is 31 km east from Kathmandu. The
district is rich in forest resources which occupies 39565 ha (Excluding shrub land) which is 28.2%
of the entire district. It is one of the pioneer districts for the implementation of the community
forestry program (Sharma, 2000).
Gosaikund Community Forest which lies in Banepa Municipality of Kavre district has been
selected for the purpose of the study. The total area of this place is 46.25 hectare and out of this,
30.25 hectare is managed as forest. The forest age is 25-30 years. Major portion of this area is
covered by Pinus roxburghii only but there are also some locations where one can find mix stand
of Schima wallichhii, Alnus nepalensis and Myrica esculenta along with Pinus roxburghii.
Gosaikund CF has been established with three major objectives:
Sustainable natural resource management
Commercial development planning
Improvement in livelihood with focus on racial and gender equality
3.1 Historical Context of Gosaikund CF
In order to understand the historical context of Gosaikund CF, one need to understand the events
before and after 1990 B.S. Prior to 1990 B.S. (1933 A.D.), the forest was quite dense with natural
pine. When the earthquake of 1990 B.S. destroyed many households, people started to harvest
wood from this forest. As a result of this, the forest was severely degraded.
Because of the mass forest destruction, several ecological problems such as flood, erosion and
desertification intensified. People also had to travel to faraway places in search of fuel and fodder.
People then felt that the forest establishment could be the solution to their problem. Therefore, in
2036 B.S. (1979 A.D.), District Forest Office, Kavre in collaboration with Nepal-Australia Forest
Development Planning Committee and local user group planted Pinus roxburghii in the area. In
2049 B.S. (1992 A.D.), the forest was officially handed to Local Forest User Groups.
29. 18
Figure 2: Image of Gosaikund CF (Source: Google Earth)
3.2 Geology and Topography
The bedrock of the area is mainly composed of metasandstone and siltstone with subordinate
amount of phyliite and slate of the Tistung formation (Stocklin and Bhattarai 1977). Current
soil development partly takes place on residual soil and colluvial materials (Dahal et al.
2005). The parent rock material is mainly composed of phyllites, slates, limestone and shale
where the erosion hazards and sediment production are high (Baral 2008). The area is mostly
confined to the Southern aspect of the hills with an altitudinal range of 1550 to 1800 meter
above mean sea level. The slope on average is 18 degrees.
3.3 Climate
The climate is warm temperate humid type. The summer is wet because most of the rainfall
occurs from June to September and the winter is dry between November-May although the
30. 19
little shower was all around the year. Based on the meteorological data of June 2010-May
2011, the average annual temperature was found to be 16.2°C and the average annual relative
humidity was 77.42%.
Figure 3: Monthly Average of Atmospheric Temperature and Rainfall between June
2010-May 2011 (Source: Ghimire, 2011)
3.4 Major Tree Species
The descriptions on major tree species of Gosaikund CF are in table 3:
Table: 2: Major Tree Species in Different Forest Block
Block number Area (ha) Forest Age Major species types
1 3.5 35 Pinus roxburghii
2 5 35 Pinus roxburghii, Alnus nepalensis,
Schima wallichhii
3 6.25 35 Pinus roxburghii,Alnus nepalensis,
Schima wallichhii
4 9.5 35 Pinus roxburghii, Alnus nepalensis,
Schima wallichhii
5 6 35 Pinus roxburghii, Alnus nepalensis
Source: District Forest Office, Kavre
Besides these three species, Myrica esculenta was found in block 3 and Mimosa sirissa in
block 2.
40
50
60
70
80
90
100
5
7
9
11
13
15
17
19
21
23
Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec
Relativehumidity(%)
Atmospherictemperature(degC)
Months of the year
Temperature
Relative Humidity
31. 20
3.5 Forest User Group Information
The forest users are from four different areas, i.e. Panchakumari, Gosaikund, Bhakteswor and
Buddhinarayan. The population and household information are given in table 4.
Table 3: Forest User Group Information
S.no Ethnic
Group
Household
numbers
Male Female Total
population
1 Chhetri 115 345 401 748
2 Brahamin 87 258 292 550
3 Janajati 33 99 101 200
4 Dalit 17 51 55 105
Total 252 753 849 1602
(Source: District Forest Office, Kavre)
The dominant population of the area belongs to ethnic group Chettri which is followed by
Brahamin, Janajati and Dalit group respectively. Most of these people are engaged in agriculture
and some are also engaged in business and office job.
32. 21
CHAPTER 4: MATERIALS AND METHODS
4.1 Forest Boundary Delineation
A sketch of forest boundary map was obtained from District Forest Office of Kavre in order
to recognize the different blocks of Gosaikund forest. Five different blocks were recognized
in map along with information on special features, vegetation and locations within Gosaikund
CF.
4.2 Estimation and Layout of Sample Plots
Approximately one percent of sampling plots was recognized to meet the requirements of the
survey. The totals of 18 plots were selected in which 9 plots were for the Pine stand and other
9 plots were for the mixed stand (including pine and other species). The plot locations were
systematically recorded with the help of GPS.
4.3 Field Measurements
The methodology for the forest carbon stock measurement is based on the guidelines
published by ANSAB, ICIMOD and FECOFUN (2010).
4.3.1 Establishment of Sampling Plot
Figure 4: Sampling Design of Circular Plot (Source: Subedi et.al., 2010)
Circular plots were used for the study purpose. Forest carbon measurement was carried out in
the circular plot of 8.92m radius. Within this circular plot, sub plot with radius of 5.64m was
established for sapling, 1 m for counting regeneration and 0.56m radius for herb, litter and
33. 22
grass. Long measuring tapes was used to measure distance for the plot and pegs and thread
were used to mark the plot area. Slope correction using clinometers was also done when
needed.
4.3.2 Above-Ground Tree Biomass (AGTB)
The DBH (at 1.3m) and height of individual trees greater than or equal to 5cm DBH was
measured in each permanent circular 250 m2
plot that is 8.92 m in radius using diameter tape,
clinometers and linear tape. Before measurements tree were marked with enamel paint to
prevent accidental double counting. Trees on the border was included if > 50% of their basal area
falls within the plot and excluded if <50% of their basal area falls outside the plot. Trees overhanging
into the plot were excluded, but trees with their trunk inside of the sampling plot and branches out
were included.
Figure 5: DBH Measurement for Different Tree Types (Source: Subedi et.al., 2010)
4.3.3 Above-Ground Sapling Biomass, and Regeneration (AGSB)
Nested sub plots having a 5.64 m radius inside larger plots was established for sapling
measurement. Smaller nested sub plots having a 1 m radius inside the larger nested plots was
established for assessing regeneration. Saplings with diameters of > 1 cm to < 5 cm was
34. 23
measured at 1.3 m above ground level, while saplings smaller than 1 cm in diameter at 1.3 m
above ground level will be counted as regeneration.
4.3.4 Leaf Litter, Herbs, and Grass (LHG)
All the litter (dead leaves, twigs, and so forth), herb and grass within the 1 m2
sub plots were
collected and were directly brought to laboratory of Kathmandu University and were weight.
Approximately 100 g of evenly mixed sub-samples were separated to determine moisture
content (by oven drying at 60-70 deg C) from which total dry mass was calculated. Herbs and
grass (all non woody plants) within the plots was collected by clipping all the vegetation
down to ground level with the help of sickle and clipper.
4.3.5 Dead Wood and Stumps (DWS)
Standing dead trees, fallen stems, and fallen branches with a diameter at breast height (DBH)
and/or diameter ≥ 5 cm was measured within the whole 250 m2
plot, branches with diameters
of 2-4 cm should be measured within the 100 m2
plot, and thinner branches should only be
measured within the 1m2
plot.
4.3.6 Sap Flux Measurement
Measuring sap flux is a key technique in understanding
and regulating plant water relations. Thermal
dissipation method developed by Granier was used to
measure sap flux density of trees. First 12 trees from
plot were identified and marked as thick, medium and
small based on their DBH and height. The sapwood of
trees was exposed by removing the outer bark.
Figure 6: Sensors Inserted in Sapwood (Source: Field Visit)
Then 2 cm was marked in the drill beat of 2.1 cm and two holes at the distance of 10 cm were
drilled on the sapwood. The two sensors with stainless steel needles (30mm length and 1.2mm
diameter) were inserted one above the other into the sapwood (after application of silica gel)
where holes were made. After that adhesion material terostat was applied to seal the needles.
35. 24
The upper needle contained a line heat source and a copper–constantan thermocouple junction
which was referenced to another junction in the lower needle, sensing sapwood temperature.
Both junctions were located 15mm from the tip of the needle. The upper needle was supplied
with a constant electric voltage (3Vfor TDP30) and the difference in temperature between the
two needles (_T) was monitored. This temperature difference is dependent on the sap flux
density: heat is dissipated more rapidly when sap flux density increases and _T decreases as a
result of the ―cooling‖ of the heat source. After installation the sensors were covered with
aluminum foil and thermocol for better insulation and protection.
4.3.7 Estimation of Sapwood Area
In this study, sapwood area was determined using several incremental core(s) taken with a
Pressler borer (Grissino‐Mayer, 2003) and immediately dyed with methyl orange for
estimating the actual depth of sapwood area. The core sample was taken approximately at
breast height.
4.4 Soil Sampling and Laboratory Analysis
4.4.1 Soil Sampling
Soil samples were collected from three different depths, i.e. 0-10 cm, 10-20 cm and 20-30 cm.
Thus, near the center of all plots and/or sub-plots a single pit of up to 30 cm in depth was dug.
Free composite samples and bulk density samples were properly labeled and brought to the
laboratory of Kathmandu University for analysis. Nitrogen analysis was done in Aquatic
Ecology Center of Kathmandu University whereas soil organic carbon, bulk density, pH,
texture and moisture were analyzed at the Environment laboratory of Kathmandu University.
4.4.2 Bulk Density
Bulk density soil cores of 4.7 cm in diameter and 6 cm long (volume= 101.06 cm
3
) were used
for determining the bulk density of the soil samples of each soil layer. The fresh soil samples
extracted by bulk density cores were kept in a plastic bag, sealed and labeled. Then the
samples were transported to laboratory. The initial weight was recorded and then the bulk
density samples were oven dried for 24 hours at 105
0
C and then oven dried weight were
noted (Blake and Hartge, 1986).
36. 25
4.4.3 Soil Organic Carbon (SOC)
Dry combustion method was used for determination of SOC (Nelson and Sommers, 1982).
The ground and sieved (through 0.5 mm mesh) soil sample of 20-25 gm was weight in
porcelain crucible of known weight and then the samples were oven dried at 105 deg C
overnight and weight in 3-digit balance after cooling. Then these samples were placed in
muffle furnace for 1 hour and were cooled in dessicator for half an hour. Again the samples
were measured in 3-digit balance.
4.4.4 Soil pH
To measure pH 10 gm of air dried soil samples and 10 ml of distilled water were taken and
stirred for 1-2 minutes. Then the mixture was allowed to sit for 30 minutes. After that pH
reading was taken with the help of pH meter (1:1 soil:water ratio using a glass-calomel
electrode, McLean, 1982).
4.4.5 Soil Texture
Hydrometer method was used for texture determination (Gee and Bauder, 1986). 51 gm of air
dried sampled were placed in 500 ml plastic bottles and were soaked overnight with 50 ml
Sodium hexametaphosphate (Na-HMP) and 100 ml tap water. Then the next day, the soaked
samples were shook in mechanical shakers for 2 hours and the shaken samples were poured in
1000ml cylinders. After adding 1000 ml water in cylinders, they were inverted 8-10 times by
closing the mouth of cylinders and were left undisturbed for 2 hours. Then soil hydrometer
was inserted and the readings were noted for the clay. The contents of the cylinder were then
poured into 0.003 mm mesh sieve and washed thoroughly with tap water. The sand obtained
was transferred from sieve to beaker and oven dried at 105 deg C and weights were noted.
4.4.6 Total Soil Nitrogen
Kjedahl‘s method employing wet oxidation procedure was used for the determination of total
nitrogen (Bremner and Mulvaney, 1982). 5 gm of sieved air dried samples were transferred to
digestion tube and to each sample 7 gm anhydrous potassium sulphate, 5 mg Selenium
powder, 7 ml concentrated sulphuric acid and 5ml hydrogen peroxide was added. Then the
samples were digested at 420 deg C for 30 min and the digestion tubes were allowed to cool
to 50-60 deg C. The digested tube was then placed in distillation unit in which programme
was set such that 50 ml distilled water and 50ml NaOH (10N) was added and samples were
collected in Erlenmeyer flask after the distillation was 100 ml. 10 drops of boric acid indicator
37. 26
solution were added to collected samples for titration with 0.1N HCl. The color change at
endpoint from green to pink was noted.
4.4.7 Soil Moisture
Soil moisture content was determined by the gravimetric method as described in Gardner
(1986). It involved weighing the air-dried sample, removing the water by oven drying at 105
deg Celsius for 24 hours and re-weighing the sample to determine the amount of water
respectively.
4.5 Data Analysis
4.5.1 Above-Ground Tree Biomass (AGTB)
An allometric equation is a statistical relationship between key characteristic dimension(s) of
trees that are fairly easy to measure, such as DBH or height, and other properties that are more
difficult to assess, such as above-ground biomass.
AGTB = 0.0509 * ρ D2
H ………….eq.1
AGTB = 0.112 * (ρ D2
H) 0.916
….…..eq. 2
AGTB = 0.0776 * (ρ D2
H) 0.940
……..eq. 3
Where,
AGTB = above-ground tree biomass [kg];
ρ = wood specific gravity [g cm-3
];
D= tree diameter at breast height [cm]; and
H= tree height [m].
After taking the sum of all the individual weights (in kg) of a sampling plot and dividing it by
the area of a sampling plot (250 m2
), the biomass stock density will be attained in kg m-2
. This
value can be converted to t ha-1
by multiplying it by 10. Eq. (1) is good for moist forest stand,
eq. (2) for dry forest stand, and eq. (3) for wet forest stand. For this study, equation 1 was
used. The biomass stock density of a sampling plot was converted to carbon stock densities
after multiplication with the IPCC (2006) default carbon fraction of 0.47.
38. 27
4.5.2 Above-Ground Sapling Biomass (AGSB)
To determine the above-ground sapling biomass (AGSB) (<5cm DBH), national allometric
biomass tables was used. These tables are developed by the Department of Forest Research
and Survey (DFRS) and the Department of Forest, Tree Improvement, and Silviculture
Component (TISC) (Tamrakar, 2000). Since the national allometric biomass table does not
contain all species present in Nepal, values for related or similar species may be used. The
biomass values of saplings include foliage, branch, and stem compartments. The following
regression model is used for an assortment of species to calculate biomass.
log(AGSB) = a + b log (D)
where,
log = natural log [dimensionless];
AGSB = above-ground sapling biomass [kg];
a = intercept of allometric relationship for saplings [dimensionless];
b = slope allometric relationship for saplings [dimensionless]; and
D = over bark diameter at breast height (measured at 1.3 m above ground) [cm].
Biomass stock densities were converted to carbon stock densities using the IPCC (2006)
default carbon fraction of 0.47.
4.5.3 Leaf Litter, Herb, and Grass (LHG) Biomass
For the forest floor (herbs, grass, and litter), the amount of biomass per unit area is given by:
where,
LHG = biomass of leaf litter, herbs, and grass [t ha-1
];
w field = weight of the fresh field sample of leaf litter, herbs, and grass, destructively sampled
within an area of size A [g];
A = size of the area in which leaf litter, herbs, and grass were collected [ha];
w subsample, dry = weight of the oven-dry sub-sample of leaf litter, herbs, and grass taken to
the laboratory to determine moisture content [g]; and
39. 28
w subsample, wet = weight of the fresh sub-sample of leaf litter, herbs, and grass taken to the
laboratory to determine moisture content [g].
The carbon content in LHG was calculated by multiplying LHG with the IPCC (2006) default
carbon fraction of 0.47.
4.5.4 Below-Ground Biomass (BB)
Belowground biomass estimation is much more difficult and time consuming than estimating
aboveground biomass. To simplify the process for estimating below-ground biomass, it is
recommended that MacDicken (1997) root-to-shoot ratio value of 1:5 is used; that is, to
estimate below-ground biomass as 20% of above-ground tree biomass.
4.5.5 Total Carbon Stock Density
The carbon stock density was calculated by summing the carbon stock densities of the
individual carbon pools of that stratum using the following formula. It should be noted that
any individual carbon pool of the given formula can be ignored if it does not contribute
significantly to the total carbon stock.
Carbon stock density of a stratum:
C (LU) = C (AGTB) + C(AGSB) + C (BB) + C (LHG) + C (DWS) + SOC
where,
C (LU) = carbon stock density for a land-use category [Mg C ha-1
],
C (AGTB) = carbon in above-ground tree biomass [Mg C ha-1
],
C (AGSB) = carbon in above-ground sapling biomass [Mg C ha-1
],
C (BB) = carbon in below-ground biomass [Mg C ha-1
],
C (LHG) = carbon in litter, herb & grass [Mg C ha-1
],
C (DWS) = carbon in dead wood and stumps [Mg C ha-1
], and
SOC = soil organic carbon [Mg C ha-1
]
The total carbon stock was then converted to tons of CO2 equivalent by multiplying it by
44/12, or 3.67 (Pearson et al. 2007).
40. 29
4.5.6 Sap Flux Calculation
Sap flux (v) measurements were carried out with the Granier‘s (Granier, 1987). According to
Granier (1987), v is typically expressed in cm3
/hr/cm2
, and can be estimated from the
continuously measured temperature difference ( T ) between the upper heated and lower
non-heated TDP sensors inserted in the tree xylem and referenced to maxT -which is the
maximum temperature difference between two probes, so that no sap flow occurs.
231.1
max
0119.0
T
TT
v
Tree sap flow (Qs), normally expressed in l/day, is a product of sap flux (v) and sap wood area
(Ax) or more precisely conductive (hydro-active) xylem area. Unfortunately, no method exists
yet that allows defining Qs directly, therefore Qs was defined in this study by separate
measurements of v and Ax as:
xs vAQ
Transpiration = Sapflow (Qs)/Crown Projection Area (CPA)
4.5.7 Soil Bulk Density
Dry bulk density = oven dry weight of soil / volume of core
Wet bulk density = wet weight of soil / volume of core
4.5.8 Soil Organic Carbon
SOC = ρ * d * %C
SOC = soil organic carbon stock per unit area (tons per ha)
ρ = soil bulk density (gm cm-3
)
d = total depth at which sample was taken (cm)
% C = carbon concentration (%)
4.5.9 Soil Total Nitrogen
1 ml 0.1 N HCl = 1.402 mg N-NH4
% TN in soil = ml HCl * 1.402 mg N * 100 / 5000 mg soil
42. 31
CHAPTER 5: RESULTS AND DISCUSSION
5.1 Tree Relative Density
The different tree species that were found in the research area were Pinus roxburghii
(Khotesalla), Schima wallichhii (Chilaune), Alnus nepalensis (Utis), Myrica esculenta (Kafal)
and Mimosa sirissa (Siris). The relative densities of different tree species are shown in Fig. 8.
Figure 8: Relative Density (%) of Tree Species
The relative density of Pinus roxburghii was found to be the highest (77.94%). This shows
that Pinus roxburghii is the most dominant species of Gosaikund Community Forest. This
dominancy is due to the plantation of only Pinus roxburghii during the year of 2036 B.S.
(1979 A.D.) under the collaborative effort of Kavre District Forest Office, Nepal-Australia
Forest Development Planning Committee and local users. During 1970s and 80s, chir pine
plantation was hugely practiced because of the high survival rate and ease of establishment of
the species. It was considered a suitable pioneer species for the rehabilitation of severely
degraded exposed sites of the hill (Tamrakar, 2003). That was why pine plantation was
practiced in the highly degraded areas of Gosaikund CF.
The relative densities of Schima wallichhi and Alnus nepalensis were found to be to 17.48%
and 3.72% respectively. Before the start of 2000 A.D., Gosaikund area did not have these
Schima and Alnus species. Only in the decade of 2000s, these species were planted near the
Block 4 (near Tower area) of Gosaikund CF which later spread to different locations of the
forest. The other species that are found in the area are Myrica esculenta (0.57%) and Mimosa
sirrisa (0.29%). Myrica was found only at the altitude of 1700 meters above mean sea level
and there was only one Mimosa tree at the altitude of 1600 meters.
77.94%
17.48%
3.72% 0.86%
Pinus roxburghii
Schima wallichhii
Alnus nepalensis
Others
43. 32
5.2 Forest Biomass and Carbon
5.2.1 DBH Distribution and Biomass
The variation of DBH was evident in both the pine and mixed stand. In pine stand, DBH
range was between 10.5cm to 36.6cm whereas in mixed stand it was between 5.1cm to
73.8cm.
Fig 9: DBH Distribution of Pine and Mixed Stand
Table 4: Summary Statistics of DBH of Pine and Mixed Stand (all values in centimeter)
Minimum Maximum Mean Median Standard
deviation
Pine Stand 10.5 36.6 22.70 22.5 5.47
Mixed Stand 5.1 73.8 19.4 21.1 9.74
DBH variation was found to be greater in the mixed stand than the pine stand. The variation
was due to the presence of different tree species with different age group in the mixed stand.
The minimum DBH (5.1 cm) in the mixed stand belonged to the species of Schima wallichhii
and the maximum DBH (73.8 cm) belonged to Mimosa sirrisa. Schima wallichhi on average
has DBH of 9.65cm and standard deviation of 5.37cm. This indicates the young age and the
growing stage of Schima wallichhii. Therefore, there is increasing potential of storage of
carbon stock in the mixed stand of Gosaikund CF in the future.
0
10
20
30
40
50
60
70
80
Pine Mixed
DBH(cm)
44. 33
As for pine stand, the DBH values are slightly positively skewed with lower standard
deviation of 5.47cm. The average DBH of pine stand (22.70 cm) showed that most of the trees
are in mature stage and are older than the trees in the mixed stand. The minimum DBH of
10.5 cm in the pine stand indicates that like in mixed stand, there are also few trees which are
still in growing phase.
Figure 10: DBH and Biomass Relation in Pine and Mixed stand
The DBH and biomass relation showed that there is strong positive correlation between DBH
and tree above ground biomass so with increase in DBH, biomass also becomes higher.
Overall, such relation influences biomass and carbon stock density of the area and thus helps
us to predict carbon sequestration potential of the tree species.
5.2.2 Above Ground Tree Biomass (AGTB) Density
The above ground tree biomass density of pine and mixed stand were compared and it was
found that the biomass values of pine stand were more spreading than the mixed stand. In
both types of stand the values were positively skewed with mean greater than median. The
skewness was found to be greater in the mixed stand
R² = 0.8808
0
200
400
600
800
0 10 20 30 40
Biomass(kgpertree)
DBH (cm)
DBH vs AGTB (Pine Stand)
R² = 0.9753
0
200
400
600
800
0 10 20 30 40
Biomass(kgpertree)
DBH (cm)
DBH vs AGTB (Mixed
Stand)
45. 34
Figure 11: Above Ground Tree Biomass Density of Pine and Mixed Stand
Table 5: Summary Statistics of AGTB and Tree Density of Pine and Mixed Stand
Minimum Maximum Mean Median Standard
deviation
Pine
stand
Biomass density
(tons ha-1
)
44.324 315.574 177.490 177.453 93.011
Tree density
(ha-1
)
560 1120 768.889 720 180.862
Mixed
stand
Biomass density
(tons ha-1
)
110.732 262.563 172.825 158.944 54.204
Tree density
(ha-1
)
480 1440 782.222 640 341.240
Above ground tree biomass is dependent not only on DBH but also the height and wood
specific gravity of species. Besides that the tree density also influences the biomass density to
certain extent. In the pine stand, the very low value of the minimum biomass density i.e.
44.324 tons ha-1
was due to the presence of trees with stunted height (average of 7.9 meter)
even though the DBH value was similar to other plots (average DBH= 19.13 cm). The lowest
tree density of only 560 trees/ha was also responsible for this very low minimum value.
0 50 100 150 200 250 300 350
Pine
Mixed
Tonnes per hectare
46. 35
In the mixed stand, the minimum biomass stock density was found to be 110.732 tons ha-1
.
However, the plot with this minimum value did not have minimum DBH, height or tree
density on average basis. Around 55.55% of the plots had height and tree density greater than
the plot with such minimum value.
As for maximum tree biomass stock density of pine stand (315.574 tons ha-1
), the high
biomass density was mainly due to high tree density (1000 trees/ha) and greater DBH
(average DBH=25.84). 77.77% of the plots had DBH and tree density less than the one with
maximum value. This also signifies a very good growth and development of pine stand which
could have positive impact on carbon storage potential.
On average the above ground tree biomass density of the pine stand (177.490 tons ha-1
) was
found to be greater than the mixed stand (172.825 tons ha-1
) even though the tree density was
higher in mixed stand. This was due to the increasing density of Schima wallichhii (average
density = 406.66 tree per ha) which are still in growing stage with lower DBH and height
(9.65 cm DBH and 7.53 m height on average). This is the indication of future growth of
biomass stock density in the mixed stand especially due to the growth of Schima wallichhii.
5.2.2.1 Species Wise AGTB and Carbon Stock Density
The species wise distribution of above ground tree biomass and carbon stock had shown that
Pinus roxburghii value had exceeded far more than any other species of Gosaikund CF as it
was the most dominating species of the area. Though the tree density of Schima wallichhii
(average density = 406.66 tree per ha) is greater than Alnus nepalensis (average density = 104
trees per ha), its biomass and carbon density is less than Alnus species. That is because the
Alnus nepalensis in the area are well grown and have already reached the mature stage with
average DBH and height of 21.9 cm and 17.3 m respectively.
Beside these three major species, a single Mimosa sirrisa (Siris) tree was also found in the
area with massive DBH and height of 73.8 cm and 30.62 meter respectively. As a result of
this massive structure, the biomass density for Siris was found to be 176.534 tons per ha
which was the highest biomass density recorded in the area. Myrica esculenta or Kafal had
the least biomass density of 0.269 tons per ha and carbon density of 0.127 tons per ha which
was due to the low relative density (0.57%) and small structure of Myrica (average DBH=5.3
cm and height = 4.3 meter).
47. 36
Figure 12: AGTB and Carbon Stock Density of Major Tree Species
5.2.3. Below Ground Biomass Density
Mean BGB is derived as 20% of AGTB. Thus the trend of BGB is same as AGTB, i.e. higher
in pine stand than the mixed stand. The summary of mean below ground biomass (root) stock
density is as shown in Table 7.
Table 6: Mean Belowground Biomass Density
Mean below ground biomass density (tons ha-1
)
Pine stand 35.498
Mixed stand 34.565
5.2.4. Above Ground Sapling Biomass Density
In Gosaikund Community Forest, saplings were found in the mixed stand only. There was no
sapling growth in the pine stand. One thing that needs to be noted is that the entire saplings in
the mixed stand belonged to Schima wallichhii only. Schima species which was first planted
near tower location now have pollinated to several other locations and they can co-exist with
Pinus roxburghii. This shows the better adaptation and increasing tendency of Schima
wallichhii in the area that was once covered by chir pine only.
Biomass
Density
Carbon-Stock
Density
Pinus roxburghii 152.443 71.648
Schima wallichhii 19.983 9.392
Alnus nepalensis 20.764 10.542
0
20
40
60
80
100
120
140
160
180
Tonsperha
48. 37
Table 7: Mean DBH and Biomass Density of Schima wallichhii Saplings
Mean value
DBH 2.8 cm
Biomass density 2.06 tons per ha
5.2.5 Leaf Litter, Herb and Grass (LHG) Biomass Density
For litter, herb and grass biomass density, LHG values were found to be more spreading in the
pine stand than on the mixed stand. In both the case negative skewness was found.
Figure 13: Litter, Herb and Grass Biomass Density
Table 8: Summary Statistics LHG Biomass Density of Pine and Mixed Stand
Minimum Maximum Mean Median Standard
deviation
Pine Stand 2.793 10.707 5.586 5.997 2.518
Mixed Stand 3.353 4.6 4.175 4.218 0.456
On average the LHG biomass stock density was found to be greater in pine stand than the
mixed stand. The lesser value of LHG in the mixed stand may indicate greater litter collection
and grazing practices in the mixed stand. Most of the locations of the mixed stand were nearer
0 2 4 6 8 10 12
Pine
Mixed
Tonnes per hectare
49. 38
to the human habitat and this might have triggered greater disturbance in the area and this in
turn might have influence LHG values. The pine litters were greatly used in making compost
by mixing the litters with the cow dung and dry pine litters were also used for making fire
while cooking.
5.2.6 Soil Organic Carbon
Soil organic carbon was also calculated in tons per hector for the pine and the mixed stand.
The data were found to be more spreading in pine stand than the mixed stand with greater
standard deviation. The skewness was negative in pine stand and positive in mixed stand.
Figure 14: SOC in Pine and Mixed Stand
Table 9: Summary Statistics of SOC of Pine and Mixed Stand
Minimum Maximum Mean Median Standard
deviation
Pine Stand
(tons per ha)
25.601 94.086 68.633 83.572 26.893
Mixed Stand
(tons per ha)
50.165 80.772 61.085 60.238 10.344
The mean SOC of pine stand was found to be greater than mixed stand. In most of the plots of
the pine stand SOC was greater than the mixed stand. One of the reasons could be more
disturbances in the mixed plot. Litter collection by local people was highly observed in the
0 20 40 60 80 100
Pine
Mixed
Tonnes per hectare
Soil Organic Carbon
50. 39
mixed stand so the litter contribution for SOC might be very less. Even the previously
calculated litter biomass density had shown greater value in pine stand than the mixed stand.
Attempt was made to establish the correlation between LHG biomass density and SOC and
the graph is shown as follows:
Figure 15: Relation between LHG Biomass and SOC
A quite strong correlation (R2
=0.8381, n=12) was established between the LHG biomass and
SOC. This might indicate certain contribution of litter for SOC status. Litter may not be the
only factor for the existing SOC. One has to keep in mind that the pine stand is older than the
mixed stand. So the contribution of plant debris and woody litter could be higher which leads
to greater decomposition and thus contributing in higher SOC (Kohler et.el.2008).
Table 10: SOC% in Pine and Mixed Stand at Different Depths
Soil depth (cm) Mean Soil Organic Carbon (%)
Pine Mixed
0-10 2.545 2.020
10-20 1.722 1.489
20-30 1.399 1.374
The SOC% at different depth of pine and mixed stand showed the decreasing tendency of
SOC% with the increase in depth. This signifies that above ground contribution on the soil is
greater than belowground. In the pine stand, carbon percentage was at the high level at the
depth of 0-10cm whereas in the mixed stand the surface carbon was at moderate level. Once
y = 13.514x - 5.3662
R² = 0.8381
0
20
40
60
80
100
0 1 2 3 4 5 6 7 8
SOC(tonnesperha)
LHG Biomass (tonnes per ha)
51. 40
again this may indicate greater litter contribution in the mixed stand. For the rest of the soil
depth in both in pine and mixed stand, soil organic carbon was found to be at moderate level.
5.2.7 Forest Carbon Stock in Pine and Mixed Stand
The forest carbon stock summary of pine and mixed stand are shown in figure 16:
Figure 16: Different Carbon Stock in Pine and Mixed Stand
In both the pine and the mixed stand, carbon stocks of the trees were found to be the highest.
This is the mean contribution of 769 trees ha-1
and 782 trees ha-1
from pine and mixed stand
respectively. Soil contribution was the second highest in both the stand due to presence of
high to moderate soil organic carbon. Sapling contribution was found only in mixed stand
whereas litter, herb and grass contribution was greater in pine stand with lesser disturbance.
Tree
47.996
%
Litter,
herb
and
grass
1.510%
Below
ground
11.006
%
Soil
39.488
%
Pine Stand
Tree
49.506
%
Litter,
herb
and
grass
1.196%
Below
ground
11.409
%
Soil
37.299
%
Sapling
0.590%
Mixed Stand
52. 41
5.2.8 Altitudinal Forest Carbon Stock Summary
5.2.8.1 Pine stand:
The altitudinal variation in per hectare mean forest carbon stock in each pool in pine stand is
shown in figure 17 and table 12:
Figure 17: Mean Carbon Stock of Pine Stand at Various Altitudes
Table 11: Mean Value for Different Carbon Pool in Pine Stand
Altitudinal
variation (m)
Carbon Stock (Tonnes per ha)
Tree Below ground Litter, herb
and grass
Soil
1550-1600 64.214 12.843 1.528 30.838
1600-1650 84.357 16.871 2.541 80.066
1650-1700 84.185 16.837 3.132 62.176
The altitudinal wise carbon stock result exhibit the highest carbon stock between the altitudes
of 1600-1650 meter above the mean sea level. This could be attributed to greater tree, below
ground and soil carbon stock in that particular altitudinal range. Among the three different
altitudes, tree density was found to be highest in the altitudinal variation of 1600-1650 meter
with 792 trees per ha. One can draw different assumption based on this result. More Pinus
roxburghii must have been planted in this particular range or survival and growth of the chir
0 50 100 150 200
1550-1600
1600-1650
1650-1700
Tonnes Carbon per ha
Altitudinalrange(meter)
Tree
Below ground
Litter, herb and grass
Soil
53. 42
pine must have been relatively good in this range. The stand in this range was found to be
well grown with average DBH and height of 22.694 cm and 15.34 meters respectively.
On the other hand, the carbon stock was at its lowest level at the altitude of 1550-1600 meter.
This could be the most disturbed altitudinal range with the least value of tree, belowground,
soil and litter, herb and grass carbon stock. Chir pine density was the lowest in this altitudinal
range with 680 trees per hectare. The logging of pine was also encountered within this
altitudinal range. Since this is the range at lower altitude, the accessibility is easier so this
could be the range where people frequently practice logging, grazing and litter collection.
The litter, herb and grass carbon was found to be the highest at the altitudinal range of 1650-
1700 meter. Since this altitude is at higher location, people may prefer to collect litter from
lower altitudinal range as it is easier and may save time and energy so the litter may have
remained undisturbed in higher altitudinal range.
5.2.8.2 Mixed stand
The altitudinal variation in per hectare mean forest carbon stock in each pool in pine stand is
shown in figure 18 and table 13:
Fig 18: Mean Carbon Stock of Mixed Stand at Various Altitudes
0 50 100 150 200 250
1560-1610
1610-1660
1660-1710
1710-1760
1760-1810
Tonnes Carbon per ha
Altitudinalrange(meter)
Tree
Sapling
Below ground
Litter, herb and
grass
Soil
54. 43
Table 12: Mean Value for Different Carbon Pool in Mixed Stand
Altitudinal
variation
(m)
Carbon Stock (Tonnes per ha)
Tree Below
ground
Litter, herb
and grass
Soil Saplings
1560-1610
82.38 16.476 1.984 56.397
1.226
1610-1660
59.935 11.987 2.037 70.505
1660-1710
107.817 21.563 2.149 50.547
1710-1760
66.818 13.364 1.576 69.962
0.454
1760-1810
107.023 21.405 1.925 62.658
The altitudinal wise mean carbon stock density was found to be highest between the
altitudinal ranges of 1760-1810 meter above the mean sea level. This could be attributed to
the greater carbon stock of trees, belowground and soil. The tree carbon stock density of
107.023 tons per ha also indicates the presence of well grown and mature stand in the area
along with less logging practice in higher altitude location.
The tree carbon stock was the highest between the altitude of 1660-1710 meters which was
mainly due to the highest tree density (1440 trees/ ha) in the area. There was not much
difference in LHG carbon stock in various altitudinal range but the values were found to be
lesser than the pine stand. Again this signifies continuous and higher litter and grass
collection as compare to the pine stand.
The saplings of Schima wallichhii species were found only at the altitudinal range of 1560-
1610 meters and 1710-1760 meters. Though the tree carbon stock densities were found to be
low in this two altitudinal range, the presences of saplings indicate the increasing growth of
tree carbon stock density in the future. The overall carbon stock density was found to be
lowest within the altitudinal range of 1610-1660 meter even though this range has the highest
soil carbon on average basis. This was mainly due to the lowest tree carbon stock value in the
area.
55. 44
5.2.9 Aspect Wise Forest Carbon Stock Summary
5.2.9.1 Pine stand
The aspect wise variation in per hectare mean forest carbon stock in each pool in the pine
stand is shown in Figure 19 and table 14.
Figure 19: Mean Carbon Stock of Pine Stand at Different Aspects
The mean carbon stock density was found to be the highest in the northern aspect. This was
mainly due to the highest mean carbon stock density of tree (148.32 tons per ha). It is also the
region with high tree density of 1000 trees per ha. The northern aspect receives less direct
sunlight and has shadier condition so there will be less evaporation of soil moisture and more
water will be available for plant growth for longer period of time (CWNP, 2010). This could
be one of the reasons for the better tree growth in the northern aspect.
0
50
100
150
200
250
300
E W N NE NW SE SW
TonnesCarbonperha
Aspects
Soil
Litter, herb
and grass
Below
ground
Tree
56. 45
Table 13: Mean Value for Different Carbon Pools in Pine Stand (aspect wise)
Aspects Carbon Stock (Tonnes per ha)
Tree Below ground Litter, herb
and grass
Soil
East 83.403 16.681 2.957 94.086
West 60.147 12.03 1.524 38.248
North 148.32 29.664 3.052 77.357
North East 34.19 6.838 2.027 84.818
North West 114.418 22.884 4.207 83.87
South East 20.832 4.166 1.313 25.601
South West 114.906 22.981 2.981 91.6
Soil carbon stock density was found to be highest in eastern aspect (94.086 tons per ha)
indicating better decomposition of organic matter in the area. South east aspect had the least
value of carbon stock density due to the lowest tree, below ground, LHG and soil carbon
stock density. This shows the degrading condition at south eastern aspect with lowest tree
density (560 trees/ha). Low tree density means less pine litter on the ground so there will be
low contribution of litter decomposition in soil organic carbon.
57. 46
5.2.9.2 Mixed Stand
The aspect wise variation in per hectare mean forest carbon stock in each pool in mixed stand
is shown in Figure 20 and table 15.
Figure 20: Mean Carbon Stock of Mixed Stand at Different Aspects
Table 14: Mean Value for Different Carbon Pool in Mixed Stand (aspect wise)
Aspects Carbon Stock (Tonnes per ha)
Tree Below
ground
Sapling Litter, herb
and grass
Soil
West 65.707 13.141 0.831 2.059 53.753
North 107.816 21.563 2.149 50.547
North East 66.818 13.364 0.454 1.576 69.962
North West 107.023 21.405 1.925 62.058
South East 123.407 24.681 1.621 2.161 50.165
South West 64.858 12.972 1.911 69.641
The overall mean carbon stock was found to be highest south eastern aspect. This was mainly
due to the highest mean value of tree carbon stock. Even though the tree density was found to
be the lowest (480 tree per ha) in the south east aspect, the high value of tree carbon stock is
0
50
100
150
200
250
W N NE NW SE SW
TonnesCarbonperha
Aspects
Soil
Litter,
herb and
grass
Below
ground
Sapling
Tree
58. 47
attributed to the presence of Mimosa sirrisa with the largest DBH (73.8 cm) and height (30.62
meter). The second largest tree carbon stock density was found in northern aspect with tree
density of 1440 trees per hectare. This high tree density value in northern aspect also shows
the presence of better environment for tree growth with availability of soil water for longer
duration as solar radiation is less direct in this region.
Saplings of Schima wallichhii were found in western, north eastern and south eastern aspects
and this reflects increase of the future carbon stock. It was mentioned earlier that the locals
collect pine litter for compost and fire purpose. The LHG carbon stock density was found to
be the highest in south eastern aspect. Again this could signify lower litter collection in this
particular aspect. One of the reasons for lower litter collection could be the low chir pine tree
density (160 trees per ha) in this area. So, in the area with low number of pine trees, there will
less pine litter so people do not come here for litter collection.
5.2.10 Logged Trees
The logged trees were also found in the plots of pine and mixed stand. The number of logged
trees along with their decay class is shown in table 16:
Table 15: Number of Logged Trees and their Classes
Stand types Number of logged trees in the plots Number of
logged trees
per ha
Class 1 Class 2 Class 3
Pine Stand 4 1 - 50
Mixed Stand 9 8 4 140
Total 13 9 4
Class 1: Sound wood; a machete cannot sink into the wood in a single strike
Class 2: Intermediate wood; a machete sinks partly into the piece in a single strike
Class 3: Rotten/crumbly wood; a machete cuts through the piece in a single strike
Most of the logged trees in both the pine and mixed stand fall into decay class 1. This shows
that the trees have been cut quite recently and the increased logging in Gosaikund community
forest. Another reason for the sound characteristics of class 1 wood could be their larger
diameter and stump height due to which decay process must have taken longer time. The
