Chapter 4: Material and Method
Selection of sites: The study was conducted in twelve
forests located in Pithoragarh district of Uttarakhand state, India. Twelve
sites represents sacred forests: (1) Haat Kalika sacred forest, Gangolihaat,
(2) Haat Kalika non sacred forest, Gangolihaat (3) Chamunda Devi sacred forest,
Gangolihaat(4) Chamunda Devi non sacred forest, Gangolihaat (5) Betal devta
sacred forest, Kanalichina,(6) Betal devta non sacred forest, Kanalichina, (7)
Thal Kedar sacred forest, Badabe (8) Thal Kedar non sacred forest, Badabe (9)
Pasupatinath sacred forest, Chandak,(10) Pasupatinath non sacred forest,
Chandak (11) Golu devta sacred forest, Ratwali (12) Golu devta non sacred
forest, Ratwali. The approximate area of the twelve forests ranges from 120
-195 ha and are located at an elevation range of 1497-2602 m above sea level.
Survey, Sampling and Identification of the Species
Surveys were conducted in and around twelve forests.
The rapid sampling was done for the floristic inventory. The samples of the
vascular plants were collected and for each species, information on habit,
habitat/s, altitudinal range, etc. was gathered. The species were identified
with the help of flora (Polunin & Stainton, 1984; Chowdhery & Wadhwa,
1984; Aswal & Mehrotra, 1994; Dhaliwal & Sharma, 1999 and Singh &
Rawat, 2000; Pande & Pande; 2003; Khullar 1994, 2000). All the identified
species were identified, listed and
analyzed for floristic diversity.The herbarium was prepared following the method of Rao &Jain 1975 and
deposited to the herbarium of Botany
Department, D..S.B. Campus, Kumaun University, Nainital.
Analysis of Data
The field surveys were conducted during the year
2014-2016 within the selected sites habitats for the quantitative assessment of
vegetation. In each site, Trees were sampled by randomly laid 30, 10x10m
quadrate; shrubs by 30, 5x5m quadrate and herbs by 30, 1x1m quadrate. For the
collection of data from these quadrats standard ecological methods (Saxena
& Singh, 1982; Singh & Singh, 1992; Dhar et al., 1997; Joshi &
Samant, 2004; and Samant & Joshi, 2004) were followed. The circumference at
breast height (cbh at 1.37m from ground) for each tree individual was recorded.
Shrubs were considered as the woody species having several branches arising
from their base (Osmaston, 1927; Saxena & Singh, 1982). Samples of each
species were collected from each site and identified with the help of different
floras and research papers. For the ecological analysis of data Curtis &
McIntosh (1950); Grieg-Smith (1957); Kersaw (1973); Mueller-Dombois &
Ellenberge (1974); Singh and Singh (1992); Dhar et al. (1997); Samant et al.
(2002a&b); Samant & Joshi (2004) and Joshi & Samant (2004) have
been followed. Communities were identified based on the Importance Value Index
(IVI). The abundance data of different sites were pooled to get community
averages in terms of Density, Total Basal Area and IVI.
vegetational data would be analyzed quantitatively for frequency, density,
abundance (Curtis and McIntosch, 1950), relative frequency, relative density
and relative basal area represented as importance value index (IVI) for the
various species and for the forest sites (Curtis, 1959) by using following
expressions (Curtis and McIntosh, 1950).
This term refers to the degree of dispersion of
individual species in an area and usually expressed in terms of percentage
occurrence. It was studied by sampling the study area at several places at
random and recorded the name of the species that occurred in each sampling
units. It is calculated by the equation:
Density is an expression of the numerical strength
of a species where the total number of Individuals of each species in all the
quadrates are divided by the total number of quadrats studied. Density is
calculated by the equation:
It is the study of the number of individuals of
different species in the community per unit area. By quadrates method,
samplings are made at random at several places and the number of individuals of
each species was summed up for all the quadrates divided by the total number of
quadrates in which the species occurred. It is represented by the equation:
The distribution pattern of
different species would be studied using the ratio of abundance to frequency
(A/F) following (Cottom and Curtis, 1956). This ratio indicates regular (less
than 0.025), random (0.025 – 0.05) and contagious (more than 0.05) distribution
basal Area would be calculated by:
Circumference at Breast Height
Basal area = Mean basal area of a species × Density of that species
Value Index (IVI)
This index is used to determine the overall
importance of each species in the community structure. In calculating this
index, the percentage values of the relative frequency, relative density and
relative dominance are summed up together and this value is designated as the
Importance Value Index or IVI of the species (Curtis, 1959).
The degree of dispersion of individual species in an
area in relation to the number of all the species occurred.
Relative density is the study of numerical strength
of a species in relation to the total number of individuals of all the species
and can be calculated as:
of a species
is determined by
the value of
the basal cover.
Relative dominance is the coverage value of a species with respect to
the sum of coverage of the rest of the species in the area.
and herbs provenance value (PV) index would be calculated by summing up the
value of relative frequency and relative density.
Provenance Value (PV) = RF +
Species richness, diversity and dominance indices
number of species is divided by the square root of the number of individuals in
the sample. This particular measure of species richness is known as D, the
s / ?N
s equals the number of different
species represented in your sample, and N
equals the total number of individual organisms in your sample.
diversity and dominance
were evaluated by
using the following
Shannon’s diversity index and Simpson’s index of
dominance were calculated using important value index (IVI) of species.
The Shannon- Weiner index:
The Shannon- Weiner index (Shannon- Weiner, 1963)
would be calculated to analyze the diversity for each study site with-
H’=-? (pi log pi).
Where, H’ =
Shannon index of diversity pi = the
proportion of important value of the
ith species ( pi = ni / N, ni is
the important value index of i the
species and N is the important
value index of all the species).
Simpson’s index would be estimated according to
Where, D = Simpson index of dominance pi = the proportion
of important value of the ith species ( pi = ni / N, ni is
the important value index of i the species and N is the important value index
of all the species).
of the soil was carried out under the following two major categories:
4.3.1 Physical Examination:
Samples collected from different depths viz., (i) upper (0–10 cm),
(ii) middle (11–30 cm) and (iii) lower middle (31–60 cm) (iv) Lower (61-90 cm)
for assessing the physical and chemical properties of the soil in all the
selected forest. All the samples were brought separately to
the laboratory in polythene bags for the analysis of physical properties and
.1 Soil texture:
Soil texture is a qualitative
classification tool used in both the field and laboratory to determine classes
of soils based on their physical texture. Texture refers to the size of the
particles that make up the soil. The classes are distinguished in the lab by
the “sieving method” and clarified by separating the relative proportions of sand, silt and clay using grading
sieves( Piper, 1966):
distribution (PSD).Soil separates are specific ranges of
particle sizes. In the United States,
the smallest particles are clay particles and are classified by the USDA as having diameters of less than
0.002 mm. The next smallest particles are silt particles and have diameters
between 0.002 mm and 0.05 mm. The largest particles are sand
particles and are larger than 0.05 mm in diameter. Furthermore, large sand
particles can be described as coarse, intermediate as medium, and the smaller
as fine. Other countries have their own particle size classifications.
of soil separate
The first classification, the
International system, was first proposed by Albert Atterberg (1905), and was based on
his studies in southern Sweden. (Source; Wikipedia)
.2 Soil moisture:
difficult to define because it means different things in different disciplines.
For example, a farmer’s concept of soil moisture is different from that of a
water resource manager or a weather forecaster. Generally, however, soil
moisture is the water that is held in the spaces between soil particles.
Surface soil moisture is the water that is in the upper 10 cm of soil, whereas
root zone soil moisture is the water that is available to plants, which is
generally considered to be in the upper 200 cm of soil.
moisture was determined by the following for formula (Bouyoucos, 1921):
weight of soil- Dry weight of soil
content (%) = ——————————————————– x 100
Dry weight of the soil
Soil bulk density:
bulk density of soil depends greatly on the mineral make up of soil and the
degree of compaction. The
density of quartz is around 2.65 g/cm³ but the (dry)
bulk density of a mineral soil is normally about half that density, between 1.0
and 1.6 g/cm³. Soils high in organics and some friable clay may have a bulk
density well below 1 g/cm³.Bulk density of soil is usually determined from a core sample which is taken by driving a metal corer into the soil at
the desired depth and horizon. This
gives a soil sample of known total volume.
bulk density was calculated by following formula(Black, 1965):
Bulk density =
mass was the oven dried weight (at 700C) weight of soil and volume
is the soil volume in soil corer.
Porosity or pore space
is the amount of air space or void space between soil particles. Infiltration, groundwater movement, and
storage occur in these void spaces. The porosity of soil or
geologic materials is the ratio of the volume of pore space in a unit of
material to the total volume of material.
Soil porosity was
calculated by following formula(Gupta
and Dhakshinamoorthy, 1980):
Porosity (%) = 100% –
————————————— x 100
Particle density (2.65)
18.104.22.168 Soil water holding capacity: One of the main
functions of soil is to store moisture and supply it to plants between
irrigations. Evaporation from the soil surface, transpiration by plants and
deep percolation combine to reduce soil moisture status between water
applications. If the water content becomes too low, plants become stressed. The
plant available moisture storage capacity of a soil provides a buffer which determines
a plant’s capacity to withstand dry spells. Water is held in soil in various
ways and not all of it is available
It is determined by the following formula (Piper, 1950):
W2 – W3 – W4
WHC (%) = ————————— x 100
W3 – W1
= weight of sieve + filter paper
= weight of sieve + filter paper + wet soil
= weight of sieve + filter paper + oven dried soil
= water absorbed by filter paper
4.3.2 Chemical examination:
Soil pH- Soil
pH would be measured by digital pH meter ( Jackson, 1958)
Soil Organic Carbon (SOC) – Organic carbon would be estimated by the modified Walkely-Black method
(Walkely and Black, 1934). Organic matter in the soil would be oxidized with a
mixture of potassium dichromate (K2Cr2O7) and
concentration H2SO4 utilized the heat of Dilution of H2SO4.
Unused K2Cr2O7 would be back-titrated with
ferrous sulphate (FeSO4.7H2O) or ferrous ammonium
Nitrogen content- The
procedure involves distilling the soil with alkaline potassium permagnate
solution and determining the ammonia liberated. This serves as an index of the
availablr nitrogen status of a soil sample and therefore proposed as a soil
tesr for nitrogen by Subbiah and Asiji (1956).
Phosphorous – The Olsen’s
method (1954) was used for estimation of phosphorus in soil. Sodium
bicarbonate(NaHCO3) solution extracts some exchangeable or surface
adsorbed Al-P, Fe-P, calcium phosphate and other phosphates.
Potassium- The available
potassium in soil was determined by flame photometer using neutral normal
ammonium acetate method of Black (1965).
SOCIOECONOMIC AND ETHNO BOTANICAL SURVEY
In order to achieve authentic information, an
extensive dialogue with the inhabitants of 24 Villages around sacred and non
sacred forests conducted. The respondents comprised young and old, male and
female diversified in five age group up to 15, 16-30, 31-60, 60-80 and above 80
year from 19 different communities (Table1). The survey was standardised and
included sections that covered the following topics: Socieo-economic
characteristics; use of sacred forests; knowledge about the sacred forests;
attitude towards the sacred forests; and perceptions of changes (Format 1, 2
Age group upto15
wood /day in kg from non sacred forest
you like or dislike the sacred forest?
the sacred forest have benifits?
are the benefits of sacred forests?
the forest changed?
you worry about area?
Do you know the meaning of Sacred
Do you know the creation story?
Who makes the rule
Who enforce the rules?
Do you know the rules?
Statistical Analysis: The value for each sample was
calculated as the Mean, SE, SD. Analysis of variance and significant difference
among means were tested on mean values by one way ANOVA; Pearsons Correlation
coefficients (r), Cluster analysis by ward’s method using SPSS 22, and Statistical analysis of the data like K-mean
cluster, Agglomerative hierarchical clustering, PCA and Correspondence analysis
was done by XLSTA.
The study aims to provide an understanding of how
communities utilize and value the forests and to assess through what ways they
are likely to participate in and engage for future forest management
activities. The objectives are to generate baseline data on the socio-economic
and livelihoods status of communities living adjacent to the forest reserves
(incorporating data on social and economic values of the forest and its
products) and to evaluate their capacities and willingness to support improved
management regimes aimed at increased conservation of the target forests. The
study also offers evaluation of the options for improved management of the
forests in the light of collected data.
PHOTOPLATE 3.1: COLLECTION OF DATA