Livelihood Dependence, Traditional Knowledge Conservation ...
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Jayita Bit & Sarmila Banerjee/ Arthaniti 12 (1-2)/2013/1
Livelihood Dependence, Traditional Knowledge Conservation and Sustainable
Forest Management – A Study on Birbhum Forest Division, West Bengal1
Jayita Bit2
Department of Economics, University of Calcutta
Sarmila Banerjee Department of Economics,University of Calcutta
Submitted: 27.8.14; Accepted: 9.2.15
Abstract.
This paper attempts to verify whether the present forest management policies have ensured biodiversity
conservation and in turn livelihood protection for the forest dependent people. For this purpose a household survey
was conducted in the Birbhum district of West Bengal, India and various socio-economic determinants of livelihood
dependence on forests have been identified using simple OLS regression. LOGIT regression, both regular and
ordered, are applied to assess the marginal influences of different ‘cause factors’ on enhancing the probability of
conservation of traditional knowledge within the forest community. Results strongly suggested a cultural transition
among the local people who were once used to self-sustained forest dependent livelihood, but due to forest
degradation and non-availability of necessary resources/ services were gradually exposed to the outer world and
enhanced participation in general economic development related activities. The economic use of forest dominates
the newly afforested areas as against the concern for protecting their biodiversity leading to comprehensive
ecological balance.
Keywords: Joint Forest Management (JFM), Livelihood Dependence, Cultural Migration, Flow
of Traditional Knowledge, Ordered LOGIT regression, Birbhum
JEL Classification: Q23, Q57, C25
1The authors wish to thank Debabrata Biswas, the Chief Conservator of Forest (WB) for his guidance throughout the
field survey, the participants of the Research Scholars’ Workshop held in the University of Calcutta during July
2014 and Indrila Guha of Vidyasagar College for Women for her insightful suggestions on an earlier draft. The
comments of the anonymous journal referees add to both focus and clarity of presentation. Of course the usual
disclaimer applies. 2 Corresponding Author. 56A, B. T. Road, Kolkata – 700050; Tele-fax: 033-2546-5949.
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1. Introduction
Forests play a vital role in sustaining the life supporting systems of a country's environment and
the quality of life of its people. From time immemorial, the aboriginal people were the original
forest inhabitant whose livelihoods were based on hunting and gathering for the supply of food,
fodder, fuel‐wood and even cosmetics (wild gems) from the woods (Ghosal, 2011). Tagore
(1915) pointed out that “in India it was in the forests that our civilization had its birth”. Vedic
literature also indicates that forests were held in high esteem and ‘ashrams’ (hermitages) of the
sages existed in these forests. Thus forests always helped to create an integrated and harmonious
coexistence of man and nature through their various social and cultural uses. Even in our present
times, as noted in the State of Environment Report (2009), nearly 0.7 billion rural population of
India out of the total population of 1.21 billion directly depend on climate-sensitive sectors like
agriculture, forests, fisheries and other natural resources such as water, biodiversity, mangroves,
coastal zones, grasslands, etc. for their subsistence and livelihood. The share of forest dwellers
alone exceeds 100 million and 54 percent of them belong to the tribal communities (Sankaran et
al., 2002). Their living and livelihood are so intricately woven with the forest and its product
variety through customs and culture that they act as a niche for the tribal communities, who are
not only extracting forest resources but are also trying to maintain its ecological balance through
different rituals and practices at the same time (De, 2012). The destruction of forests through
land acquisition and conversion becomes a serious concern only when the influx of outsiders
raises the speed of deforestation beyond the carrying capacity of the system. A number of studies
have also observed greater extraction tendency of the forest resources from relatively well-off
people and the settlers compared to the poorer and native ones (Pandey, 2010). So poverty alone
cannot be blamed for forest degradation; it is a combination of ignorance, greed, power and
wealth that results in deforestation, especially in developing countries. The aborigines and
adivasis are essentially a part of the forest ecosystem and promotion of economic development at
the cost of forest degradation raises the vulnerability of their daily livelihood (Padel and Das,
2010). However, it cannot be denied that certain demographic, cultural and environmental
changes may alter the configuration of institutions and values that characterize traditional
resource-dependent communities and in turn decrease their willingness for forest conservation
(Robson and Nayak, 2010). So, sustainable forest management attempts to establish a balance
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between the global eco-system and the directly forest-dependent community at the local level;
this should ensure not only the protection of forest canopy cover but its richness in terms of
biodiversity should also be protected to guarantee essential livelihood support, which deserves
equal attention, if not more. So, now there is a need to understand the economic and social issues
related to forestry at the grass-root level faced by the forest dependent community at the micro
level by observing the impact of Joint Forest Management (JFM), the present forest management
approach on local livelihoods. Thus, in this paper we are interested in exploring issues related to
(a) the relationship of the nature of forest biodiversity with the pattern of forest dependent
livelihood, given control for different socio-economic characteristics of the users and (b) the
intergenerational flow of traditional knowledge about forest resource and its impact on the
observed pattern of forest dependence. Since all these issues deserve in-depth investigation, a
micro-level study is appropriate here as it creates the opportunity to look at the micro-level
interactions among individuals. And to collect fair and detail information from local people
(especially if they are indigenous) through such interactions, the interviewer must be an insider
in terms of ethnicity, community or locality. Also the possibility of interview at grass-root level
is easy if it is conducted in vernacular. So this paper is basically the interpretation of a primary
survey conducted with individual stakeholders in the Birbhum Forest Division of West Bengal
(WB), India. Given this background the rest of the paper will be organized as follows: section 2
will describe the features of our field area, section 3 will talk about the survey design and
questionnaire and section 4 will present the household level analysis. Finally, section 5 will
conclude the paper by extending an overall discussion.
2. Birbhum Forest Division
To document the pattern of livelihood dependence in afforested land, a field survey was
conducted during September- November, 2012 in the district of Birbhum, West Bengal. This part
of the state is bounded on the North by the Santhal Parganas of the state of Jharkhand and in the
West and South by the districts of Murshidabad and Burdwan of West Bengal. It is separated
from Burdwan by the Ajoy River (Figure 1) (Debnath & Mondal, 2014). This district is notable
for its undulating topography and its cultural heritage which is unique and is somewhat different
from that of the other districts of the state. In fact, in the native language of the region the
meaning of the term ‘bir’ is forest, so, Birbhum is basically a land of forest. Though at present
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the district is predominantly agricultural, 3.51% of total land area is reckoned as forest land
(Human Development Report, 2009). Forestry is one of the main industries in this district.
Among 425 Small and Micro Enterprises and Artisan units present over here, 145 depend on
forest products like timber and wood pulp. There are 13 wood and wood based furniture units
and 132 paper and paper product units existing in this district (Directorate of M & SSE, 2013).
From the administrative perspective of the Department of Forestry, Government of West Bengal,
this district is divided into 7 ranges (Bolpur, Md. Bazar, Rajnagar, Rampurhat, Sainthia,
Dubrajpur and Suri) containing 19 beat offices. Among them, 6 beats under 5 ranges (Map 1)
were randomly selected for the present study to cover 10 locales with the presence of active
Forest Protection Committees (FPCs), keeping in mind the variations in topographical properties,
cultural heritage as well as nature of forest biodiversity (Table 1) . Ranges with limited forest
cover were excluded from the sample.
Figure 1: Location of the Study Area
Source: Debnath & Mondal, 2014
3. Survey Design and Questionnaire
Historically the forests of Birbhum remain covered with Sal trees3. However, our survey found
dominant presence of Eucalyptus and Sonajhuri with Sal trees mainly present at jahir sthan4
3 The forest areas that remained dense at the time of introduction of JFM generally have not lost their density till
now. However, the less dense or scarce forest patches are afforested with the monoculture plantation of non-native
species.
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Map 1: Surveyed Ranges of Birbhum Forest Division
Source: Aranya Bhawan, Government of West Bengal, 2012
4 Jahir Sthan is a sacred place of worship in traditional tribal culture; the tribals worship trees like Sal.
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under most of the sampled FPCs. The study region was classified into three zones on the basis of
forest types (nature of forest biodiversity). This is because forest uses are governed by available
tree species and the more the variations among tree/animal species, the more the dependence
should be. From recent literatures like Rojas (2012) and McDermott (2012) it has also become
evident that dominance of traditional species increase forest biodiversity while monoculture
plantation of non-native eucalyptus or acacia species decreases the chance of existence of other
species. It is also found in our study that though the alien species are an efficient means for quick
afforestation and for rapid production of natural and renewable fiber for a growing industrialized
economy, they are associated with demolition of ecosystem health and biodiversity and loss of
the traditional forest dependent practices of local communities. Accordingly, all the FPCs are
divided into three zones: FPCs under natural forest type, those under mixed type of forest and the
rest under monoculture plantation forest type as shown in Table 1.
Table 1: Surveyed Forest Areas in Birbhum: Location and Characteristics
Source: DFO, Birbhum
In all, ten FPCs have been surveyed maintaining variations with respect to the percentage of
tribal members, proximity to township, quality of FPC performance, etc. From each of the
locations, ten households were randomly selected containing at least one family person as FPC
FPC Mouza Beat Range Nature of
Forest
Dominant
species Kheledanga
Adibasi Kheledanga Bolpur Bolpur
Monoculture
plantation
Eucalyptus,
Sonajhuri
Lohagodd Benuria Bolpur Bolpur Monoculture
plantation
Eucalyptus,
Sonajhuri
Jamboni Murgaboni Illambazar Bolpur Natural Sal
Bonvilla Ramnagar Illambazar Bolpur Natural Sal
Laxmipur Laxmipur Illambazar Bolpur Mixed
Sal,
Eucalyptus,
Sonajhuri
Bonsuli Usardihi Illambazar Bolpur Natural Sal
Pachiara
Chandrapur-
Srichandrapur-
Bodaguri
Hetampur Dubrajpur Monoculture
plantation
Eucalyptus,
Sonajhuri
Sultanpur Sultanpur L.N.Pur Rampurhat Mixed
Sal,
Eucalyptus,
Sonajhuri
Asna-
Sundarkhele
Asna,
Sundarkhele Aligarh Rajnagar Natural Sal
Jethia-Rampur Jethia Mallarpur Md. Bazar Monoculture
plantation
Eucalyptus,
Sonajhuri
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member and to isolate the impact of FPC participation on forest dependent livelihood,
conservation of traditional knowledge and sustainable forest management, similar information
was collected from ten households not containing any FPC member. Information pertaining to
personal details of household members, condition of the nearby forest-area, general awareness
regarding the role of forest, role of FPC and the flow of traditional knowledge was collected
from personal interviews made to a total of 204 households. To facilitate the ease of data
collection all the questionnaires have been developed in a bilingual mode. Finally data collected
was interpreted and analyzed by utilizing the direct, first-hand experience gathered from the
field.
4. Household Level Analysis
4.1 Salient Features of the Sample
Nearly 63 percent of the sampled households belong to the schedule tribe with around 49 percent
illiterate household head and 50 percent holding the Below Poverty Line (BPL) card. They are
mostly engaged in traditional vocations with 56 percent directly engaged in forestry and
agriculture based activities. Table 2 presents the member characteristics of the sampled FPCs in
terms of the tribal dominance in the membership pattern as is obtained from the official records
of the Forest Department and compares that with our sample properties to ensure
representativeness of the latter. From the table it appears that at least in four out of ten FPCs the
Schedule Tribe (ST) respondents are over represented. Though the overall presence of ST
members in the FPCs is around 40 percent in our selected sample, combining both the FPC and
non-FPC households the corresponding figure is around 63 percent. There was no official data
available on the education status of the households according to the forest ranges/ beats and
therefore no direct comparison of sample composition with the relevant population averages was
possible here. The following analysis is reported by keeping this limitation in mind.
4.2 Livelihood Dependence on Forest
The views of the local villagers (both FPC members and non-members) regarding forest
dependence and present management practices were widely divergent, depending upon their
culture, educational status, occupational pattern, etc. It is noticed that though nearly 90 percent of
the sampled households depend on forest for their daily needs (Table 3), figure 2 shows that out
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of this nearly 50 percent uses only fuel-wood, 11 percent uses only non-fuel wood related
products and only 29 percent uses both.
Table 2: Sample Coverage of ST Members of the Selected FPCs
FPC
Record of the Forest Department Sample Composition
No. of
member S.T. % ST
Sample
member
ST
member % ST
Kheledanga Adibasi 52 52 100.00 17 17 100.00
Lohagodd 26 0 0.00 10 0 0.00
Laxmipur 74 20 27.03 11 5 45.45
Bonsuli-Beloan 299 33 11.04 10 10 100.00
Jamboni 47 47 100.00 10 10 100.00
Bonvilla 51 30 58.82 10 8 80.00
Pachiara 65 0 0.00 10 0 0.00
Sultanpur 55 7 12.73 10 1 10.00
Asna-Sundarkhele 84 41 48.81 11 10 90.91
Jethia-Rampur 145 125 86.21 11 9 81.82
Total 898 355 39.53 110 70 63.64
Source: Department of Forestry, GoWB & Primary Survey, 2012
Table 3:Livelihood Dependence of Respondent's Family on Forest
Nature of Forest FPC Names Percentage of Positive Response
Natural Forest
Bonsuli-Beloan 95.00
Jamboni 100.00
Bonvilla 95.00
Asna-Sundarkhele 100.00
Total 97.50
Mixed Forest
Laxmipur 100.00
Sultanpur 75.00
Total 87.50
Monoculture Plantation
Kheledanga adibasi 100.00
Lohagodd 65.00
Pachiara 73.91
Jethia-Rampur 95.24
Total 83.54
Total 89.71
Source: Primary Survey, 2012
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Figure 2: Pattern of Usage of NTFP
Source: Primary Survey, 2012
Using the information collected on the nature of day-to-day dependence on forest bio-diversity,
an estimation of average dependence is also obtained in terms of the types of dependence (Table
4). Six broad types of dependence are identified as (a) using for fuel woods, (b) making of Sal
plates, (c) consumption of vegetables and other food items, (d) consumption of fruits, (e) using
for medicinal plants and (f) other usages including collection of materials useful for household
chores, using forest as grazing land, etc. From the survey responses it is found that nearly 78
percent households are relying on forest as a source of fuel wood, but less than 4 percent depends
on forest for collection of medicinal plants. The households depending on forest for at most one
factor is identified as low-dependent ones and those with dependence on at least 5 types or more
are marked as highly dependent. Those in-between are moderately dependent. It is apparent from
Table 5 that the majority of the surveyed are low dependent (65.69 percent) and the relative
share of the households under monoculture plantation is the highest in this category while that
under natural forest is the lowest. On an average only 7.35 percent have high dependence on
forest produces. Thus, there is a relation between nature of forest and livelihood dependence.
Venn Diagram
Total Sample size = 204
FW
NFW
(78 %)
(40 %)
ND 23 (11%)
FW: Collects fuel wood, NFW: Collects NTFPs other than fuel wood,
ND: Non-dependent on forest products for living.
60
29 %
22
11 %
99
49 %
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Table 4: Nature of Biodiversity-linked Livelihood Practices at Birbhum
Sl.
No.
Type of
Livelihood
practices
% of local
people
involved
Pattern of usage Depended Species
1 Using as fuel
wood 77.94 Personal consumption Dry leaves and twigs of any plants
2 Making of Sal
Plates 31.37
Stitching plates from
leaves of sal trees for
domestic use and also
for sale
Sal trees (Shorea robusta)
3 Consumption of
Vegetables 20.1
For personal
consumption and also
for sale
Varieties of mushroom (35), bon-alu (2), kapu
alu (1), mahua flower (3)
4 Consumption of
fruits 14.22
Personal
Consumption
Mahua (22), piyal (5), khejur (1), mango (3),
jamun (3), jackfruit (1)
5 Using as
medicines 3.92 Personal consumption
Joripat (4), horitoki (14), kalmegh (45), satmul
(19), got (7), kundri (1), boincha (2), mugar dal
(3), bohora (2), vela (10), wild garlic (1), rahim
chhal (5), arjun chhal (2), thankuni (3), ananta
mul (3), baranga (1), horek kolai (2), kanaklata
(2), chirchira (1), chorbori (1), etc.
6
Other (making
broom, talai,
chatai, etc.)
5.39 Personal consumption Sar pata (2), kuchi grass (3), khejur pata (8)
Source: Primary Survey, 2012
Table 5: Degree of Forest Dependence (in percentage)
Nature of Forest FPC Names Low Medium High
Natural Forest
Bonsuli-Beloan 65 20 15
Jamboni 20 80 0
Bonvilla 10 70 20
Asna-Sundarkhele 40 50 10
Total 33.75 55.00 11.25
Mixed Forest
Laxmipur 50 31.82 18.18
Sultanpur 70 20 10
Total 60.00 25.91 14.09
Monoculture Plantation
Kheledanga adibasi 100 0 0
Lohagodd 100 0 0
Pachiara 100 0 0
Jethia-Rampur 100 0 0
Total 100.00 0.00 0.00
Total 65.69 26.96 7.35
Source: Primary Survey, 2012
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Due to a long period of deforested state followed by monoculture plantation of alien species,
forest resources have turned out to be unsuitable and inadequate to guarantee sustainable
livelihood. So, the once forest-dependent villagers in these areas have been forced to reduce the
extent of forest dependence in their day to day living. Branches of Sal trees, which are auspicious
augury for tribal marriages and cremation ceremony, are nowadays available in markets to meet
such demand. Local people have even failed to recall the names of the extinct species as they
have been lost long. Thus the prolonged deforestation followed by monoculture plantation has
acted as push factor from inside the system which has increased the dependence of local people
on external economy. To isolate the marginal effect of these socio-economic factors on
livelihood dependence across different types of forestry, a livelihood dependency index has been
constructed and its relation with certain socio-economic factors is obtained by running simple
least square regression analyses. The construction of forest dependence index (dependent
variable) and its related independent variables is briefly described below.
The dependent variable Y, defined as the forest dependence index (FDI) is meant to capture the
extent of forest dependent livelihood in terms of the pattern of dependence. Since in all, six
different types of possible uses of forest has been identified, the maximum possible score of
dependence would be 6 and the minimum would be 0. Following the method of construction of
Human Development Index, the FDI can be proposed as
100x
reMaximumSco
eActualScor. This is a multi-
dimensional index5 and is constructed by combining responses to a set of questions related to
different aspects of forest dependence of household, viz., (i) both the type and variety of non-
timber forest produce (NTFP) collection, (ii) use of medicinal plants, (iii) use of grazing land and
(iv) direct financial dependence on forest. These variables are given equal weights for the
construction of the concerned index6.
The independent variables, Xs, are defined to represent certain socio-economic factors related to
family, society and the economy:
5 The minimum value of FDI is 0 and the maximum value is 60. The average value is 24.66 and the standard
deviation is 19.31. 6 FDI has also been constructed using Principal Component Analysis (PCA) where unequal weights are
endogenously determined for the same set of responses. The scatter plot suggested almost a near perfect correlation
between the Y-variables constructed by following these two alternative methods. Since PCA did not suggest any
significant improvement, we have taken the simple average based index of FDI as our dependent variable.
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Family Factor:
• SIZE: Size of the family, which is expected to have a positive association with FDI;
Social factors:
• TRIBE: a dummy variable is defined to assume value 1 if ST and 0 otherwise, which is
expected to have a positive influence with FDI;
• MEMBER: a dummy is defined to assume value 1 for the households containing at least
one FPC member and 0 otherwise and the variable is expected to have positive influence
on FDI;
Economic factors:
• BPL: a dummy is defined to assume value 1 if the household holds BPL card and 0
otherwise. The expected sign of influence is positive;
• ASSET: an index of households assets is constructed by collating information on various
types of assets like van, cycle, bike, radio, fan, television, etc. owned by it and the
expected influence of this variable on FDI is negative;
• LVSTCK: an index of household’s livestock holding is constructed by collating
information on different types of livestock like buffalo, cow, goat, pig, poultry animal,
etc. owned by it and the expected influence of this variable on FDI is negative;
• OCCUP: a dummy is defined to assume value 1 for agriculture and forestry related
occupations and 0 otherwise with an expected positive sign;
Since the original forest dwellers were not much interested in the employment opportunities
created outside the forest niche, the extent of forest dependence is likely to have a negative
correspondence with the extent of participation in the Mahatma Gandhi National Rural
Employment Guarantee Scheme (NREGA).
• NREGA: Here for each household the three year’s average annual job-day data have
been collated from the official website7.
The study variable FDI has been regressed on all these independent variables to isolate marginal
influence of all factors and to study the possible variations of these causal relations across
7 Out of 204 households surveyed the matching works for 190 cased and for the remaining 14 cases the village
average is taken as a proxy estimate.
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different types of forestlands, two forest-type dummies have been introduced additionally by
treating natural forest as the reference category.
• DMIXD: a dummy is defined to assume value 1 for mixed forest and 0 otherwise;
• DMONO: a dummy is defined to assume value 1 for monoculture forest and 0 otherwise;
The regression results are reported in Table 6. From Table 6 it is seen that initially, when no
distinction is made in terms of the forest-type, TRIBE and NREGA appear to be the only two
significant influences on FDI, the former with expected sign and the latter with an opposite sign.
So, in regression 2 the two forest-type dummies are incorporated and interestingly both of them
became statistically significant with predicted sign and lesser is the biodiversity component
weaker is the extent of forest dependence. However, besides these two dummy variables no other
explanatory factor was statistically significant, though the adjusted R2 increased substantially,
from 0.16 to 0.41, indicating the possible presence of multi-collinearity in the dataset. So, three
separate regressions were run on three different types of forestlands and reported as regression 3
(for natural forest), regression 4 (for mixed forest) and regression 5 (for monoculture plantation
forest).
Table 6: OLS Regression Results of Forest Dependence Index
X-Variables Y-variable: FDI
Reg_1 Reg_2 Reg_3
(Natural)
Reg_4
(Mixed)
Reg_5
(Mono)
SIZE 0.20 0.60 -- -- --
TRIBE 10.54*** 3.86 16.68*** -10.72 4.37**
MEMBER 0.28 0.93 -- -- 2.76*
BPL -2.48 1.90 6.57* -- --
ASSET -0.03 -0.07 -- -0.44** 0.11**
LVSTCK 0.04 0.06 -- 0.53** 0.05
OCCUP 2.71 1.62 5.22 -- --
NREGA 0.20*** 0.12 -- 0.44* 0.15**
DMIXD . -8.98*** . . .
DMONO . -23.72*** . . .
2R
0.16 0.41 0.12 0.17 0.33
DF 195 193 76 37 76
F 6.00*** 14.84*** 4.53** 3.04* 9.06*** Note: *** Significant at 1% level ** Significant at 5% level * Significant at 10% level
Source: Authors’ Calculations
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For natural forest TRIBE is a very important determinant of FDI followed by economic need
(BPL). For mixed forests three important factors are ASSET, LVSTCK and NREGA while the
former with a negative sign suggests same kind of connection between livelihood dependence on
forest and economic status, i.e., greater dependence for the economically weaker section, as was
suggested by BPL in case of natural forest. For LVSTCK also FDI increases with the increase in
the demand for grazing land. The most interesting relation is indicated by the statistically
significant positive influence of NREGA on FDI. We have included NREGA as a variable to
capture the impact of general development opportunities available from outside the forest based
system on the pattern of forest dependence. Though a negative relation is expected in this case,
contrary to our expectation, here the coefficient is positive as well as statistically significant.
Before making any attempt to explain this result it would be better to note the situation
prevailing in case of monoculture forests, i.e., regression 5. In this case the variables turned out
to be statistically significant as explanatory factors are MEMBER, TRIBE, ASSET and NREGA
with all imparting favorable influence. Moreover, this regression has strongest F among all
forest-type specific regressions and the adjusted R2 is also quite high (0.33). Barring the
exception of TRIBE, all other regressors collectively indicate a newly emerging politico-
economic scenario. Those who are relatively affluent (with access to better ASSET) are enjoying
more secured social position, becoming the members of the FPC, utilizing benefits of greater
man-days in NREGA and accessing forest resources more in their daily living. So, instead of
forming a support for the poorer section, under monoculture plantation in the newly afforested
areas, forest development has become a parallel agenda of general development and forest
resources are getting treated at par with any other economic resource. This tendency is of deep
concern to us as livelihood support is an in-situ experience whereas economic resource is an ex-
situ entity that can always be separated from the whole system, transformed into monetized units
and transacted in the market. If forest resources are perceived as economic resources and forest
development is treated as any other development agenda, then the marketable part of forest
resource would enjoy priority and protection of biodiversity would be of very little importance.
In fact, to be effective support of livelihood the conservation of biodiversity is necessary but not
sufficient. For it to be useful the traditional knowledge about the use of this diverse resource
needs to be transmitted across generations. An attempt has been made to construct an index of
retention of this traditional knowledge that is going to be reported in the following sub-section.
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4.3 Flow of Traditional Knowledge
Traditional Knowledge (TK) can be defined as “the knowledge base acquired by indigenous and
local people over hundreds of years through direct experience and contact with the
environment.” It is the product of keen observation, patience, experimentation, and long-term
relationships with plants and animals (http://npdc.usda.gov). Its importance for the protection of
biodiversity and the achievement of sustainable development is slowly being recognized
internationally and the Convention on Biological Diversity of 1992 has acknowledged these
contributions of traditional knowledge and therefore included laws pertaining to its access and
use (Mazzocchi, 2006).
Many studies like Folke (2004), Beamer (2009) and Johannes (1993) have shown several
innovative ways to incorporate traditional knowledge into modern practices of ecosystem
management. Some have solely focused on the integration of TK into forest management. Rist et
al. (2010) have discussed the merit of combining TK with scientific data to achieve improvement
in forest management in BRT Wildlife Sanctuary of South India where such an application
helped managing the mistletoe infection in Amla tree (with serious bio-diversity and livelihood
impacts) more efficiently. Butler (2010) also discussed how the tribes in Suriname, Brazil and
Colombia are combining their traditional knowledge of the rainforest with western technology to
conserve forests and maintain ties with their history and cultural traditions, which include
profound knowledge of the forest ecosystem and medicinal plants. Thakali and Lesko (1998)
have described some important contributions that several American Indian tribes have recently
made, applying their traditional knowledge to the management of forest resources in the United
States. Furthermore, Charnley et al. (2008) have paid attention to the ecological knowledge of
three local groups who inhabit in the Pacific Northwest region and have pointed out that
integration of traditional and local ecological knowledge into forest biodiversity conservation is
most likely to be successful if the knowledge holders are directly engaged with forest managers
and western scientists in on-the-ground projects in which interaction and knowledge sharing
would be facilitated.
Although all these works have become successful in stitching the local traditional knowledge
with the modern practices of managing ecosystems, there is lack of any methodological
intervention from accounting perspective to assess the quantitative significance of such
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knowledge conservation. Rarely documented, traditional knowledge is built on a history; gained
through many generations of human beings teaching their children practical techniques that
underscored this crucial human-environmental relationship upon which culture and life itself
depended (http://npdc.usda.gov). So to check whether traditional knowledge has been conserved
or not, one needs to look into its flow from one generation to the next. Construction of index for
traditional knowledge is apt for such purpose and would be a unique addition to existing
methodological approaches. Here the analysis has been carried out at two stages: (i) first we have
considered the case of complete knowledge transfer and defined the traditional knowledge flow
index TKNWI as a dummy variable and (ii) next we have defined another variable TTK
representing the extent of knowledge transmission as an ordered categorical variable to
accommodate the possibility of complete as well as partial knowledge transmission.
The dependent variable defined as the traditional knowledge flow index (TKNWI) is constructed
with variables representing the transmission of traditional knowledge within the household
across generations. Three specific questions were asked to each interviewee:
(a) Whether they have acquired this knowledge from their ancestors,
(b) Whether they are passing it on to their next generation and
(c) Whether the posterity is taking any interest in this information.
If all the answers are in the affirmative then the traditional knowledge is considered to be
conserved and conservation is thought to be inadequate when at least one answer is negative,
indicating break in the chain of intergenerational knowledge transmission.
TKNWI: a dummy variable is defined to assume a value 1 for knowledge conservation and 0
otherwise.
This is our dependent variable and, since it is binary by construction we may identify different
independent variables which are likely to influence the probability of TKNWI conservation, i.e.,
[ ]1== TKNWIPPi and a LOGIT regression can be run to find out the corresponding odd-ratios
and marginal effects8.
8 The odd-ratio with respect to the explanatory variable jX can be defined as ( )ji
jij
P
POR
−=
1and the corresponding
marginal effect is ( )[ ]jijijji
jiPP
X
P−= 1β
δ
δ. In this case unlike OLS regression the marginal effect is contingent on
both i-th observation and j-th regressor, indicating presence of a built-in non-linearity.
Jayita Bit & Sarmila Banerjee/ Arthaniti 12 (1-2)/2013/17
The probable causal influences are defined to capture the following dimensions: (i) forest-based
livelihood dependence, (ii) general awareness related to alternative land use pattern of forestland,
(iii) improvement in forest status after implementation of JFM and (iv) perception of the
household regarding different community forest based rituals. Consequently variables are
defined as:
PFDI: This is the predicted value of FDI obtained from the previous exercise. Since the
dependence on forest for livelihood (FDI) and the conservation of traditional knowledge
(TKNWI) are jointly endogenous, to avoid the problem of endogeneity we have replaced FDI
with its predicted value (PFDI), which served as a suitable instrumental variable in this context.
AWRI: An index of awareness regarding the felt importance of forest conservation has been
constructed on the basis of binary response on different dimensions of the land use related
problems faced by forestland and collated into a single measure by following the methodology of
human development index.
JFM: It is an index based on the pattern of change in forest conditions that has been experienced
since the formation of FPC. The constituent indicators of the index are: (a) area under forest, (b)
number of tree species, (c) tree on private land, (d) number of ponds within the forest and in the
fringe area, (e) availability of NTFP, (f) time to be spent for the collection of fuel, fodder and
leaf litter and (g) protection against flooding/landslides. In each case the improvement has been
assigned a score 1 and 0 otherwise and the individual scores have been aggregated and
normalized to produce the required index.
RITUL: This index is based upon the changes occurring in the importance of traditional forest-
based rituals like (a) collective hunting of animals, (b) collective gathering of fruits, fuel, fodder,
medicinal plants and other NTFP, (c) rituals related to the grazing of domestic animals. For
decrease in the importance of each of the rituals we have assigned value 0 while value 1 is
assigned otherwise. Then using the same methodology the forest ritual index is constructed.
Jayita Bit & Sarmila Banerjee/ Arthaniti 12 (1-2)/2013/18
Finally, a LOGIT regression is run and the marginal effects (evaluated at mean) are reported in
Table 7. To give control to the nature of biodiversity, the two forest-type dummies defined
earlier, viz., DMIXD and DMONO have been incorporated.
;6543210 iiiiii uDMONODMIXDRITULJFMAWRIPFDITKNWI +++++++= βββββββ
where ui follows a logistic distribution: i
i
x
x
ie
euf
βα
βα
+
+
+=
1)( ; this gives the probability equation
−=
i
ii
P
PTKNWI
1ln ;
Table 7: LOGIT Regression Results
X- Variables
LOGIT (Marginal Effect)
Y: TKNW
All Natural Mixed Monoculture
PFDI 0.005 -0.008 0.011 0.007
AWRI 0.004** 0.003 -0.002 0.017**
JFM 0.004** -0.001 0.005 0.009**
RITUL 0.002 0.006** 0.004 0.000
DMIXD 0.136 -- -- --
DMONO 0.109 -- -- --
Pseudo R2 0.05 0.07 0.11 0.18
No. Of Obs. 204 80 42 82
LR Chi2 14.05** 7.52 6.53 20.52***
Note: *** Significant at 1% level ** Significant at 5% level * Significant at 10% level
Source: Authors’ Calculations
When all responses are considered together and the forest-type dummies are incorporated, the
marginal effect of only two factors, AWRI and JFM, appear to be statistically significant. This
indicates that the success of the joint forest management program generates interest among the
local people to conserve the traditional knowledge about uses and abuses of biodiversity. For
deeper investigation we have run the same regression separately on three different types of forest
areas. The results turn out to be quite interesting.
Jayita Bit & Sarmila Banerjee/ Arthaniti 12 (1-2)/2013/19
Nowhere PFDI, i.e., the livelihood dependence on forest plays any statistically significant role in
conserving traditional knowledge. Effectively, TKNWI and PFDI behave like two disjoint
factors. In case of natural forest the only important variable having significant marginal effect is
RITUL. For mixed forestry no definite relationship has been suggested by our field observations
and the strongest results are obtained for monoculture plantation where the success of JFM and
general awareness about forest land use pattern appeared to be the most important. This shows
that the overall pattern is also dominated by the monoculture forests and what adds to this
discomfort is the fact that all respondents from this area admitted that they spent the proceeds of
timber sale obtained as FPC-members in personal consumption and not to promote any
community purpose. So, the incentive scheme adopted by the JFM is merely an economic
incentive that reduces forest resource into an ordinary economic resource, which places a high
weight on use the value of a product or service and generally fails to assess the existence value in
a non-market frame.
To explore this issue further, we have made a distinction between complete transmission vis-à-
vis partial transmission of traditional knowledge and defined another study variable TTK
(transmission of traditional knowledge), which is an ordinal (ordered) variable assuming values
1, 2, 3 & 4, according to the extent of knowledge transmission. If answer to all the relevant
questions are negative or only one is affirmative, there is no evidence of knowledge
transmission. So, here the assigned value of TTK is 1 (poor). If answer to each question is YES,
then there is perfect knowledge transmission, and the value of TTK is 4 (excellent). In between
there are two possibilities: (a) answer to the first two queries is affirmative and that of the last
one is negative and (b) answer to the first one is negative but that of the following two are
positive. In the former case the knowledge is flowing from the past to the present generation and
in the latter case it is passing from the present to the future generation. In terms of traditional
knowledge conservation the first case is more potent as here the future generation may gain
motivation at a future date in acquiring traditional knowledge and may make the transmission
process complete whereas the second case does not have any chance to lead to complete
knowledge transmission even at any future date. So, a score of 3 is assigned to the first case
Jayita Bit & Sarmila Banerjee/ Arthaniti 12 (1-2)/2013/20
(good) and that of 2 (moderate) is assigned to the other one where the traditional knowledge is
acquired from any formal exogenous source and not necessarily from within the community.
Thus, TTK is an ordered categorical variable on which an ordered LOGIT can be applied to
understand the prospect and probability of TK conservation across different types of forestlands.
In ordered LOGIT, an underlying score is estimated as a linear function of the independent
variables and a set of cut-points (threshold parameters). The probability of observing outcome i
corresponds to the probability that the estimated linear function, plus random error, is within the
range of the cut-points estimated for the outcome and is specified as:
),.....Pr()Pr( 111 ijkjkjiijj uxxpioutcome αββα ≤+++<=== − where uj has a logistic
distribution and there are k number of possible outcomes in all.
)exp(1
1
)exp(1
1
1 βαβα jijiij
xxp
+−+−
+−+=
−
.
To be consistent with the latent variable interpretation of this discrete ordering, α0 is taken as -∞
and αk is taken as +∞. Since we have 4 possible outcomes, there will be 3 cut-points9.
Summation of Pi’s would be unity for all j’s. Our prediction of y is simply the outcome with the
highest probability (Woolridge 2002).
Table 8: Ordered LOGIT Regression Results
X- Variables
OLOGIT (Odd-Ratio)
Y: TTK
All Natural Mixed Monoculture
PFDI 0.980 0.985 0.957 1.011
AWRI 1.019*** 1.020* 1.015 1.032**
JFM 1.012** 0.995 0.997 1.042***
RITUL 1.000 1.018** 0.998 1.004
DMIXD 1.092 -- -- --
DMONO 0.485 -- -- --
CUT_1 0.348 0.590 -1.445 3.786
9 In fact, the standard binary LOGIT model has a single cut-point.
Jayita Bit & Sarmila Banerjee/ Arthaniti 12 (1-2)/2013/21
CUT_2 0.929 1.231 -0.679 4.318
CUT_3 1.481 1.834 0.051 4.808
Pseudo R2 0.031 0.039 0.034 0.115
No. Of Obs. 204 80 42 82
LR Chi2 15.31** 7.6* 3.57 22.05***
Note: *** Significant at 1% level ** Significant at 5% level * Significant at 10% level
Source: Authors’ Calculations
Table 8 reports the ordered LOGIT results for overall Birbhum Forest Division along with the
areas with Natural forest, mixed forest and Monoculture plantation and the respective cut-points
for different levels of TTK. Similar to the case of TKNWI, in case of TTK also the overall
pattern of knowledge transmission probability is mostly matching with that of the Monoculture
plantation or the newly afforested areas. The statistically significant predictors are also not much
different, with a few notable variations: (a) in case of Natural forests the AWRI variable has
gained statistical importance in influencing the probability of TKK favorably, (b) the LR(χ2) of
this regression becomes statistically significant at less than 10% level and (c) all the regressions
improved statistically, indicating a distinct improvement in the predicted outcome.
The threshold levels reported in Table-8 deserves some special attention. No distinct pattern is
observed for the mixed forest area; though there is striking difference between the cut-points of
natural vis-à-vis monoculture forests. For all categories the cut-off values are much higher in
case of monoculture areas indicating a generally weaker possibility of traditional knowledge
transmission there. It is important to note here that while the direction of the effect of xk on the
probabilities P(y=1|x) and P(y=4|x) is unambiguously determined by the sign of βk, the sign of
βk does not always determine the direction of the effect for the intermediate outcomes (y = 2 & 3
here). Table 9 (a) & (b) report the observed frequency and the predicted probability of most
likely outcome of TTK for different types of forests. Though it is found that across all forest
groups there are a significant number of observations (almost 25 percent of the total) that fall
under category ‘moderate’ and ‘good’ when the frequency table is derived on the basis of sample
responses [Table 9(a)]. However, none of these observations with intermediate values have
retained their status in the model based predicted values [reported in Table 9(b)]. There a
Jayita Bit & Sarmila Banerjee/ Arthaniti 12 (1-2)/2013/22
tendency towards polarization is noted. Compared to the sample based estimation the model
based prediction shows much better prospect for knowledge transmission (0.49 and 0.89
respectively) and though the sample observations did not suggest much difference between
natural and monoculture forests in this regard, the model based predicted difference is 0.31.
Thus, it is expected that the newly afforested areas would be 31 percent weaker than natural
forest in conserving traditional knowledge.
Table 9(a): Sample frequency and predicted frequency of the level of traditional knowledge
conservation across forest types
Values Tabulate OLOGIT
Trad_Know All Natural Mixed Monoculture All Natural Mixed Monoculture
Poor 54 19 8 27 22 10 4 35
Moderate 24 10 6 8 0 0 0 0
Good 26 11 7 8 0 0 0 0
Excellent 100 40 21 39 182 70 38 47
Total 204 80 42 82 204 80 42 82
Source: Primary Survey, 2012 (Authors’ Calculations)
Table 9(b): Sample proportion and model based prediction of the probability of conservation of
traditional knowledge across forest types
Trad_Know Tabulate OLOGIT
All Natural Mixed Monoculture All Natural Mixed Monoculture
Poor 0.26 0.24 0.19 0.33 0.11 0.13 0.10 0.43
Moderate 0.12 0.13 0.14 0.10 0.00 0.00 0.00 0.00
Good 0.13 0.14 0.17 0.10 0.00 0.00 0.00 0.00
Excellent 0.49 0.50 0.50 0.48 0.89 0.88 0.90 0.57
Total 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
Source: Primary Survey, 2012 (Authors’ Calculations)
Jayita Bit & Sarmila Banerjee/ Arthaniti 12 (1-2)/2013/23
5. Discussion
An important implication of this study is the presence of cultural migration, which is pulling the
forest dwelling tribal people out of their natural habitat and community living and forcing them
to be a part of the outside monetized economy governed by the market incentives. The tribals
now take money as the only compensation to do any work instead of once practiced barter
system. Getting formal education from mainstream institutions, they have started socializing
outside their own communities and mould themselves for a better career in the outside world.
Money and jewelries have also cribbed as dowry into their marriage system in place of country
liquor and domestic animals and all legal issues are settled in courts or at police station as an
alternative for solution through the intervention of morol10
of a community. This cultural
transition gains momentum through the targeted socio-economic programs on education, health,
poverty alleviation to further intensify the pull effect. Even if someone has the information of
kabiraji11
flora/ fauna, there is no more reliance on such traditional line of herbal treatment due
to enhanced health awareness among local people and availability of modern treatment facilities.
It is noticed that though 89 percent of the sampled households depend on forest for their daily
needs, only 4 percent of them collect medicinal plants from it. So the indicator for livelihood
dependence on forest is not found to be significant anywhere when regressed for
intergenerational transmission of traditional knowledge. We have incorporated participation in
general development programs (like 100-days work in MGNREGA, etc.) in our regression
analysis and statistically significant results have been obtained to indicate the eradication of
difference between livelihood development policies in in-situ environment vis-à-vis economic
development policies in ex-situ environment. This transformation dampens the local households’
involvement and commitment towards forest protection irrespective of their level of awareness
regarding the long-term consequences.
In fact, the pull factor is closely connected with the push factor which, in its turn is the
consequence of rampant deforestation and rapid afforestation through monoculture plantation of
exotic species. There is a definite change in the nature of demand for forest products from
10
Morol is the head of a village/community, who has a final say on any judgment decisions. 11
Kabiraji is a traditional practice of Ayurveda in India.
Jayita Bit & Sarmila Banerjee/ Arthaniti 12 (1-2)/2013/24
outside world and this change is connected with the technological development. Nowadays
strong wooden poles are not that much demanded by the railways, mining and quarrying
industries, construction sector and others; instead the demand for paper has gone up manifold.
This has resulted in stagnant demand for many traditional timber products like sal (Shorea
robusta) which supply wood with discontinued fibers and there is vibrant claim for short and
uniform fiber trees like eucalyptus, sonajhuri, etc (Nanko, et al., 2005). This indicates a serious
compromise with biodiversity and creating a push effect for the traditionally forest dependent
people to culturally migrate to the outside world to satisfy their daily needs.
The local level practices are needed to be stitched with the national policies incorporating the
effects of modern development agenda. Pandey (retrieved from
http://www.infinityfoundation.com/mandala/t_es/t_es_pande_conserve.htm) has also indicated
the necessity of embedding local and traditional knowledge into the doctrines of formal science
to understand their implications for sustainable forest management. In the absence of such
theorization, it is impossible to preserve the traditional wisdom in a generalized framework and
enhance its applicability.
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