1985 NON-TIMBER FOREST PRODUCTS AND LIVELIHOOD SECURITY: AN ECONOMIC STUDY OF HIGH HILL TEMPERATE WET ZONE HOUSEHOLDS OF HIMACHAL PRADESH Thesis by KOMAL SHARMA Submitted in partial fulfilment of the requirements for the degree of MASTER OF SCIENCE (AGRICULTURE) AGRICULTURAL ECONOMICS COLLEGE OF FORESTRY Dr Yashwant Singh Parmar University of Horticulture and Forestry, Nauni Solan - 173 230 (HP), INDIA 2015
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1985
NON-TIMBER FOREST PRODUCTS AND LIVELIHOODSECURITY: AN ECONOMIC STUDY OF HIGH HILL
TEMPERATE WET ZONE HOUSEHOLDSOF HIMACHAL PRADESH
Thesis
by
KOMAL SHARMASubmitted in partial fulfilment of the requirements
for the degree of
MASTER OF SCIENCE(AGRICULTURE)
AGRICULTURAL ECONOMICS
COLLEGE OF FORESTRYDr Yashwant Singh Parmar University
of Horticulture and Forestry, NauniSolan - 173 230 (HP), INDIA
2015
Dr Ravinder Sharma Department of Social SciencesProfessor Dr Y S Parmar University ofTel: 01792-252333 (O) Horticulture and Forestry, Nauni-+919418148202 (M) 173 230, Solan (HP)Email: [email protected]
CERTIFICATE-I
This is to certify that the thesis entitled “Non-Timber Forest Products and
Livelihood Security: An Economic Study of High Hill Temperate Wet Zone
Households of Himachal Pradesh”, submitted in partial fulfillment of the
requirements for the award of degree of MASTER OF SCIENCE
(AGRICULTURE) AGRICULTURAL ECONOMICS to Dr Yashwant Singh
Parmar University of Horticulture and Forestry, Nauni, Solan (HP) is a bonafide
research work carried out by Ms KOMAL SHARMA (F-13-02-M) under my
guidance and supervision. No part of this thesis has been submitted for any other
degree or diploma.
The assistance and help received during the course of investigations have been
fully acknowledged.
Place: Nauni, Solan Dr Ravinder SharmaDated: 2015 Chairman
Advisory Committee
CERTIFICATE-II
This is to certify that thesis entitled “Non-Timber Forest Products and
Livelihood Security: An Economic Study of High Hill Temperate Wet Zone
Households of Himachal Pradesh”, submitted by Ms Komal Sharma (F-13-02-M)
to Dr Yashwant Singh Parmar University of Horticulture and Forestry, Nauni, Solan
(HP), in partial fulfillment of the requirements for the award of degree of MASTER
OF SCIENCE (AGRICULTURE) AGRICULTURAL ECONOMICS has been
approved by the Student’s Advisory Committee after an oral examination of the same
in collaboration with the external examiner.
Dr Ravinder Sharma (LR Sharma)Chairman External Examiner
Advisory Committee
Members of Advisory Committee
Dr Subhash Sharma Dr Bhupender DuttAssistant Professor Professor
(Deptt. of Social Sciences) Deptt. of Forest Product
This is to certify that all the mistakes and errors pointed out by the external
examiner have been incorporated in the thesis entitled “Non-Timber Forest
Products and Livelihood Security: An Economic Study of High Hill Temperate
Wet Zone Households of Himachal Pradesh”, submitted by Ms Komal Sharma
(F-13-02-M) to Dr Yashwant Singh Parmar University of Horticulture and Forestry,
Nauni, Solan (HP), in partial fulfillment of the requirements for the award of degree
of MASTER OF SCIENCE (AGRICULTURE) AGRICULTURAL
ECONOMICS.
Dr Ravinder SharmaProfessor
Chairman, Advisory Committee
Professor and HeadDepartment of Social Sciences
Dr Yashwant Singh Parmar University of Horticulture and ForestryNauni, Solan (HP), India
ACKNOWLEDGEMENTSEvery effort is motivated by an ambition an all ambitions are backed up by the strength given by God, a
constant source of positive energy. With deep sense of pleasure, I embellish my sojourn by expressing my gratitude tothe almighty for bestowing upon me his choices blessings, who blessed me with all favourable circumstances to gothrough this work.
With great reverence, I humbly express my gratitude to my guide Dr Ravinder Sharma, Department ofSocial Sciences. I have no words to express my heartfelt thanks to him for his illuminating guidance, unfailingencouragement, scholarly suggestions, constructive criticism and keen interest during the course of this investigationand preparation of this manuscript.
I emphatically owe my heartiest thanks to Dr Subhash Sharma, Dr Bhpender Dutt, and Dr Piyush Mehtaworthy members of my advisory committee for their valuable suggestions and full cooperation throughout the courseof investigation.
I also wish to express my sincere gratitude and appreciation to Dr A K Randev (Professor and Head) andall the teachers of Department of Social Sciences for their guidance and constant encouragement during the discourseof my academic pursuits.
A word of special appreciation goes to Dr L R Sharma, Dr Manoj Vaidya and Dr R S Prasher for theirvaluable suggestions during the investigation and manuscript preparations.
Every effort is motivated by an ambition and all ambitions have inspirations behind. I owe this pride place tomy ever loving papa Sh. Narayan Sharma and Mummy Smt. Sudarshana Sharma for their prudent persuasion,selfless sacrifices and heartfelt blessing.
I also want to Acknowledge my sweet Brothers (Sahil & Vasu), Sister (Sweety), Dada ji (Late Chuni LalSharma ), Dadi amma ji (Smt. Reshma Devi ), Nana ji (Late Sh.Ved Ram Sharma), Nani ji (Smt. Nathi Devi), Chachaji (Sh. Ved Sharma), Masi ji (Smt. Dropti Sharma), Masad ji (Sh. Nishu Acharya), Masi ji (Smt. Santosh Acharya),Mama ji (Sh. Roop Singh Negi), Mami ji (Smt. Santosh Negi), Bade Mama ji (Sh. Prithavi Raj Negi), Badi Mami ji(Smt. Usha Negi), Badi Bua ji (Smt. Meera Devi) and Choti Bua ji (Smt. Neeta Sharma) for their moral support, helpand encouragement.
It’s my fortune that I have been blessed with long lasting memorable company of Kailash, Divya, Yogi, Kavita,Shalu, Reena, Mami, Munchi, Shikha, Prakriti, Neha Puri, Komal Madhan, Preetika, Prianyka ,Shalley, Kinnu,Suman, Sujata, Ritu, Pushpa, Aruna, Astha, Anjali, Suman Deep, Archana, Shalini di, Preeti di, Gayatri, Vibhuti di,Kiran Knwar,Parneet, Nitika, Anu, Pammi, Sujata, Jugnu, Neha Dhiman, Moju, Rokuyio, Lalit, Yashpal, Nandu,Vikas, Ankush, Bhesu, Sachin, Mukesh, Tenzin Chhopel, Sanjeev Mohit, Ved Prakash, Tilak, Tript, Lokesh sir, GoravGarg, Sidharth, Neeraj, Rajat, Sushant and my seniors Usha di, Seema di, Vandna di Ankanksha di, Heena di, Alka di,Parika di, Jyoti di, Swati di, Monika di, Nisha di, Shilpa di, Kamini di, Amit sir, Kapil sir, Dev raj sir, Chandresh sirand Chaman sir for their kind co-operation and providing me with valuable information during my research work.
I also thank my juniors Piyanka Rajput, Neha Temta, Komal, Mamta, Sailja, Isha, Smriti, Shweta, Neha,Rupali, Shagun, Aarti, Priyanka, Girish, Gagan for boosting up my spirits and helping me in my studies and researchwork.
A special thanks to Mr. Mehar Chand uncle and Bintu mamu for their judicious help throughout my researchwork. This journey could not be possible without their help.
Above all, I wish to express my gratitude to my cousins, brother in laws, sister in law and lovely kids of myfamily Mridul, Mukul, Shilpi, Diwanshu, Himanshu, Pallavi, Anku bhai, Dimpu bhai, Dipi bhabhi, Vicky bhai, Pritibhabhi, Niju didi, Anjli, Anshu bhai, Aney bhai Snehal, Sanvi, Vihaan and Priyal for their constant blessings, love andaffection during the study.
Thanks are also due to staff workers of the Department of Social Sciences And library for their help andcooperation.
Needless to say, all omission and errors are mine.
Nauni- SolanDate: (Komal Sharma)
CONTENTS
Chapter Title Pages
1. INTRODUCTION 1-5
2. REVIEW OF LITERATURE 6-25
3. MATERIAL AND METHODS 26-34
4. RESULTS AND DISCUSSION 35-56
5. SUMMARY AND CONCLUSIONS 57-63
6. REFERENCES 64-71
ABSTRACT 72
APPENDICES I-IV
LIST OF TABLES
Table Title Page(s)
MATERIALS AND METHODS
3.1 Distribution of sampled households according to their landholdings
28
RESULTS AND DISCUSSION
4.1 Demographic profile of sampled household in the study area 36
4.2 Educational status of sampled households in the study area 37
4.3 Occupational distribution of the sampled households 38
4.4 Distribution of workers and dependents of the sampledhouseholds
39
4.5 Distribution of sampled households according to size of landholding
40
4.6 Land use pattern of sampled households 41
4.7 Cropping pattern of sampled households 42
4.8 Livestock inventory of sampled households 43
4.9 Sources of income of sampled households 45
4.10 Details of the NTFPs in the study area 46
4.11 Contribution of NTFPs in employment pattern 47
4.12 Variability in income from selected medicinal plants 48
4.13 Comparison of Gini concentration ratio of income with andwithout NTFPs
49
4.14 Cost of collection and net return from selected NTFPs 50
4.15 Growth and variability in nominal as well as real prices ofselected medicinal plants during 2004-05 to 2013-14
51
4.16 Nominal price elasticity of selected medicinal plants during2004-05 to 2013-14
53
4.17 Real price elasticity of selected medicinal plants during 2004-05 to 2013-14
54
4.18 Scarcity ratios of selected medicinal plants supplied during2004-05 to 2013-14
55
4.19 Regression of NTFPs income against socio-economic variables 56
LIST OF FIGURES
Figure Title Page(s)
4.1. Literacy rate of the sampled households 37
4.2 literacy index of the sampled households 38
4.3 Occupational status of the sampled households 39
4.4 Distribution according to size of land holding of the sampledhouseholds
40
4.5. Land use pattern of the sampled households 41
4.6. Land use pattern in overall 41
4.7. Income from different sources of sampled households 45
4.8. Contribution of different NTFPs in employment pattern 47
4.9 Trends in nominal and real prices of selected medicinal plants 52
ABBREVIATIONS
% : Per cent
& : And
@ : At the rate
CV : Coefficient of Variation
e.g : for example
et al. : et alii (Co- workers)
FAO : Food and Agriculture Organization
FAO : Food and Agriculture Organization
GCR : Gini Concentration Ratio
H.P. : Himachal Pradesh
Ha : Hectare
HH : Household
i.e. : that is
kg : Kilogram
LGR : Linear growth rate
MD : Man days
MP’s : Medicinal Plants
No. : Numbers
NTFPs : Non Timber Forest Products
NWFPs : Non-wood Forest Products
Qtl : Quintal
Qty : Quantity
Rs. : Rupees
SD : Standard Deviation
SE : Standard error
Spp. : Species
USD : United States Dollars
VFMPC : Village Forest Management and Protection Committee
viz. : That is to say
WHO : World Health Organization
Chapter-1
INTRODUCTION
There is an increasing recognition that Non Timber Forest Products
(NTFP) can fulfill important community needs and improve rural livelihood.
NTFPs are defined as products of biological origin other than timber that are
derived from forests, other wooded land, and trees outside forests (FAO, 1999).
Although timber is still considered a main forest product used for subsistence and
income, interest in NTFP management has increased over the last few decades
along with emerging global concern about rural poverty, deforestation, and most
recently by adopting the concept of sustainable development (Belcher et al.,
2005; Chamberlain and Predny, 2004).
World Health Organization (WHO 2003), estimates put the global market
for herbal products, including medicines, health supplements, and herbal beauty
and toiletry products at over USD 60 billion and is growing at a rate of seven per
cent per annum (Nagpal and Karki, 2004). Some species are used for medicine,
some for aromatic purposes, and many for both medicinal and aromatic purposes
(Malla et al, 1997). There is a growing demand for M&AP’s at the global level
(Schippmann et al., 2006).
The export of M&AP’s brings nominal money to the farmers at the local
level and often doesn't cover their labor cost. Fair benefit from the trade has not
been initiated yet. The challenging problem prospecting is not only to make a
comprehensive inventory of M&AP’s, but also to address the social, economic
and environmental issues in an integrated approach. A proper study of the market
and up-to-date market information can make the trade a highly profitable option
while increasing the livelihood options and diversifying the portfolio of products
(Bhattacharya et al., 2003). A study shows that annual trade of Cinnamom leaf is
about 2800 tons valued at close to a million dollar from only one district of
Meghalaya (Tiwari, 2002).
2
The range of livelihood strategies includes both off-farm and land-based
livelihood strategies, including the use of non-timber forest products (NTFPs)
both for household consumption and for sale. The contribution made by NTFPs
to household income has been found, in certain cases, to be considerable and
comparable to other income sources (Dovie, 2001). In recent years with an
increasing focus on poverty alleviation, NTFPs have been considered for their
role in minimising the impact of crises on rural households and as a possible
means to assist households to move out of poverty (Angelsen & Wunder, 2003;
Belcher et al., 2005). According to Wunder (2001) there is increasing evidence of
natural resources serving as “the poor man’s overcoat” providing rural
households with natural insurance through both consumption and income
smoothing.
Many households in rural and forested areas around the world depend
heavily on NTFPs for survival. World Bank (2001) estimates that one out of four
of the world‘s poor depend directly or indirectly on forests for their livelihood.
During the last decade, there has been a dramatic increase in interest and research
of NTFPs (Shillington, 2002). This is due to the increasing recognition of the fact
that NTFPs can provide important community needs for improved rural
livelihood, contribute to household food security and nutrition, help to generate
additional employment and income, offer opportunities for NTFPs based
enterprises, contribute to foreign exchange earnings, and support biodiversity and
other conservation objectives (FAO, 1995).
At global level, more than two billion people are dwelling in forest,
depending on NTFPs for subsistence, income and livelihood security
(Vantomme, 2003). NTFPs are considered to be important for sustaining rural
livelihoods, reducing rural poverty, biodiversity conservation, and facilitating
rural economic growth (Global NTFP partnership, 2005). An estimated 80 per
cent of the population of the developing world uses Non-Wood Forest Products
(NWFP) to meet some of their health and nutritional needs (FAO, 2008). It is an
important source of income for the poor in many developing countries. In
3
addition, several opportunities for improved rural development are linked to
NTFPs (Adepoju, 2007).
In India over 50 million people are dependent on NTFPs for their
subsistence and cash income (Hegde et al., 1996). This provides 50 per cent of
household income for 20 to 30 per cent of rural population particularly for tribals.
Potentially around 3000 species of forest products are found to be useful, but
only 126 have developed marketability (Maithani 1994). Around 50 per cent of
forest revenues and 70 per cent of forest based export income of the country
comes from NTFPs. Thus it can be depicted that NTFPs form one of the
mainstays of income and sustenance for many tribal communities (Gauraha,
1992; Chopra, 1993; Mallik, 2000).
Forests are associated with socio-economic and cultural life of rural
dwellers in India. These people inhabit wide ecological and geo-climatic
conditions in different concentrations throughout the country. The collection of
NTFPs by rural dwellers was primarily for meeting their subsistence needs. Over
time, these NTFPs acquired commercial value resulting from huge trade
transactions and income levels due to rising demand. Trade in NTFPs can act as
an incentive for forest conservation by providing a source of income from
resources that might otherwise appear to have little financial value (Cottray et al.,
2003).
Himachal Pradesh is privileged in having a bountiful of this nature’s gift,
which is housed in its varied forests spread across its four major agro-climatic
zones. The role the NTFPs play in the day to day life of the people of the state,
whether in the form of household use or as a source of cash income, is well
appreciated. However, the degradation of natural habitats of the NTFPs due to
biotic and developmental pressures has put this invaluable resource under threat.
The immediate constituency to suffer due to such degradation is the rural poor,
who have to spend increasingly more time for wild collections. Moreover, the
efforts at cultivation of NTFPs have also not received desired response.
4
The forests cover of Himachal Pradesh is 21,325 sq. km (38.3%) of total
geographical area and is rich in vascular flora, which forms the conspicuous
vegetation cover. Out of total 45,000 species of plants found in the country as
many as 3,295 species (7.32%) are reported in the State. Forest area of Kullu
district is 4,952sq.km and forest cover is 1,958 sq. km (35.58%) of total
geographical area and falls in high hill temperate wet zone of the state. This zone
extends from1801 to 2200 meters above mean sea level and covers about 35 per
cent of the total geographical and 21 per cent of the cultivated area of the state
(HP Forest Department, 2009). The commonly found NTFPs in this area are
conservation practices, impact of market forces and policies, of forest department
pertaining to medicinal plants at Chhakinal watershed in district Kullu, Himachal
Pradesh. He concluded that appropriate changes in the policies and programmes
would be needed for conservation and sustainable utilization of medicinal plants
for strengthening overall rural economy. Rural marketing cooperatives could
resist the exploitation in free market.
Shackleton et al. (2002) reported that majority of South Africans reside in
rural areas and use of non-timber-forest products (NTFPs) as part of their daily
livelihoods. The quantitative study was conducted in three villages situated in the
Savannas of the poorest province of South Africa. The results demonstrated
widespread use of a wide variety of NTFPs. There were no households that did
not make use of at least one NTFP from the surrounding woodlands. The five
contributing most to the total gross value per household were fuel wood, wild
herbs, wild fruits, bush meat and honey bees. The mean gross annual direct-use
value at the three villages ranged from $211 to $324 per household, averaged
across user and non-user households. The direct-use value to user households was
18
approximately doubles this. The net value differed between specific NTFPs
because of differential labour input. The net value represented between 39 per
cent and 86 per cent of the gross value, with a mean of 63 per cent. However, the
use of opportunity costs of labor in such areas requires examination.
Alibaba et al. (2000) showed that labour spent on gum and tamarind
collection was significant in generating income by tribals in forest areas. Their
study concluded that all the tribal households faced problems in searching minor
forest products and danger of wild animals. Furthermore there was a need for
controlled exploitation of minor forest products in order to give scope for
rejuvenation of forests.
A very comprehensive study of forest environmental income in
Zimbabwe conducted by Cavendish (2000) found that wild foods ( plants and
animals), medicinal plants various wood and grass uses, forage plants as well as
soil and termite uses even to account for 35 per cent of average rural income.
Tondon (1999) explained that H.P is among the major supplier of
medicinal herbs to the Indian market more than forestry species are exported
from the state. The existing system of trade in H.P as there are two agencies at
the village level i.e., village level agencies and local shopkeepers take raw herbs
from local collectors who have rights over forests and there exists further nodal
level traders who take produce from different valleys and sell in Amritsar/Delhi.
Another is village level traders collect herbs only from the same valley either by
employing even gurkhas also. Collectors usually pass on materials after drying.
Demand of MAPs outside the state resulted in illegal extraction of herbs.
Nag (1998) reported that medicinal plant trade in H.P is unorganized and
about 95 per cent of the plants are collected from wild in unscientific manner
especially the temperate species are highly demanded. He quoted that the main
reason behind their extinction is that their collection before flowering thereby the
total elimination of the species from that place. The herbs are generally sold by
the traders in Delhi/Amritsar/Saharanpur market so, their cultivation should be
undertaken in order to fulfill the requirements of pharmaceutical industries.
19
In South India studies have been conducted on the role of NTFPs which
indicated that forest dwellers in Western Ghats region depend for up to 50 per
cent for their income and employment on NTFPs (Girish, 1998; Ganapathy,
1998; Hegde et al., 1996; Suryaprakash, 1999). A study by Ganapathy (1998) on
role of NTFPs in the tribal economy of Kollegal taluk of Karnataka covered four
forest range of Kollegal taluk viz., Hanur, Kollegal, Malai Mahadeshwara Hills
(M. M. Hills) and Rampuram. He reported most employment (42.96%) was
generated by NTFPs for the tribals’ households followed by farm employment
(22.06%), allied employment (12.72%), wage employment (11.86%) and other
source of employment (10.40%). The analysis of the composition of the income
of tribal households revealed that NTFP was the main income generator. It
contributed for about 34.09 per cent of the total income of the household,
followed by farm income (28.26%), allied income (18.61%), wage income
(13.20%) and other sources of income (5.84%).
Shiva and Mathur (1996) reported that NTFPs not only support
livelihoods of rural people but also provides employment for unskilled and semi-
skilled rural poor. It is estimated that 1.6 million person-years of employment in
India are derived from NTFPs, while the forestry sector in total provides 2.3
million person-years of employment. In certain seasons, when there are no
regular work opportunities, the NTFP sector provides alternative sources of
livelihoods.
Tiwari and Campbell (1995) studied that the vast natural resources of
India’s forests, including NTFPs such as medicinal plants, leaves, fruits, seeds,
resins, gums, bamboos and canes offer employment that provides up to half the
income of about 25 per cent of the country’s rural labour force. Nearly half of the
country’s forest revenue and 70 per cent of export forest revenue comes from
NTFPs. The export potential of NTFPs is growing as the scope of globalization
increases and recognition of the health benefits of herbs becomes more
widespread.
20
The study by Namdeo and Pant (1994) highlighted that, Sal seeds had
potential to provide employment to 4.5 million persons for a period of 40 days
and regular employment of 300 days per year for 0.436 million persons in
processing of Sal seeds. The annual production of the gum Karaya10 was about
6000 tons and creation of 600000 man days of work at the rate of 10 kg per
person per day.
Negi (1993) reported that NTFPs provide subsistence and cash income to
millions of tribal and forest dwellers in India. Studies in Orissa, Madhya Pradesh,
Himachal Pradesh, and Bihar show that more than 80 per cent of forest dwellers
depend entirely on NTFP; 17 per cent of the landless depend on daily wage
labour, mainly consisting of the collection of NTFPs; and 39 per cent are engaged
in NTFP collection as a subsidiary occupation.
Prasad (1993) stated that production of NTFPs fluctuated also between
years. He observed that the rural communities living in and around such forests
depend only on selling forest produce. The situation could be altered only with
alternative sources of employment opportunities for cash income. The income
and labour relationships in collection of minor forest products examined by
Chauhan and Negi (1988) reported that quite a large number of medicinal
plants are regularly exported from H.P. every year. Many herbs are consumed
locally and proliferated for which there is no authentic information could be
available. The revenue involved in such items can thus, not be assessed. The
study revealed on an average, the export of Dhoop has been highest followed by
Valeriana and Dioscorea. The analysis of compound growth of the quantity
exported shows a decline in the annual export from the state. The highest decline
has been noticed in Dioscorea with a compound growth rate 21.06 percent.
Gupta and Guleria (1982) reported that compound growth rates in revenue
from NTFPs in India during the 1968/69 to 1976/77 period were 40 per cent
higher than those for timber. Export earnings from NTFPs on average account for
about 60 to 70 per cent of total export earnings from forest products. In the case
21
of non edible fibers and flowers, production is only 7 and 12 per cent,
respectively of the potential production.
2.2 SOCIO-ECONOMIC FACTORS AFFECTING THEDEPENDENCY OF RURAL HOUSEHOLD ON NTFPs
Mujawamariya and Karimov (2014) reported that collection of wild
Non-Timber Forest Products may improve the livelihoods of communities
through their direct consumption or marketing. Gum arabic is one of these
products by ‘Acacia senegal’ trees that are found in Kenya. It was a source of
additional income for rural households. The study explored the household
decisions to collect gum arabic in the forest using the two stage Heckman
selection model. The findings showed that livestock ownership, possession of
skills, insecurity and price obtained from the previous season impact on
decision making to collect gum arabic. Furthermore, household's age,
experience in collecting gum arabic and topography increase the quantities
collected while gender negatively impacts amounts of collection.
Azeez and Falade (2012) studied Egbeda Local Government Area (LGA),
Oyo state in Nigeria to examine the socioeconomic and institutional factors
influencing the sustainable production and conservation of forest products. Men
and women also play different roles in the collection and utilization of NTFPs.
They reported the collection and sales of NTFPs against some socioeconomic
background of identified collector. More than half (53.3%) of the respondents
involved in the NTFPs business in the study area were male. Majority of the
respondent (88.2%) engaged in NTFPs gathering as a secondary occupation.
Their access to credit facilities was low. The amount earned from sales without
credit facilities is reasonable. Inadequate funding, lack of credit facilities,
inadequate information about how to market their products and its neglect by
government were identified as a major constraints to sustainable collection and
use of NTFPs.
Kar and Jacobson (2012) examined the role of NTFPs in household
economy and how different socio-economic factors influence the contribution of
22
NTFPs in Chittagong Hill Tracts in Bangladesh. Results showed that the
contribution of different types of NTFPs to household economy varied in terms
of subsistence and cash income. However, overall NTFP income is much higher
than income from timber or firewood which indicates a larger dependence of the
households on NTFPs. The study revealed that there are many other socio-
economic factors at the household level such as number of members in household
and total value of household implements and furniture that are significantly
correlated with the NTFP income.
Rasual et al. (2012) studied the impact of medicinal and aromatic plants
(MAPs) project implemented in Nepal and parts of India from 2005 to 2009 by
the International Centre for Integrated Mountain Development to enhance the
livelihood options and reduce the poverty of poor rural households. The study
assessed the impact of the project on poverty and livelihood security. The project
improved the skills and knowledge of producers and collectors of MAPs in
production, management, processing, and marketing, as well as their negotiating
power with traders. This helped increase household income and alleviate poverty.
However, the project’s impact varied across project sites. While an impact was
quite visible in Nepal and Chhattisgarh, India, it was less visible in Himachal
Pradesh, India. Factors responsible for the different levels of impact were
analyzed and the findings suggested that locally available and commercially
valuable natural resources, including MAPs, had the potential to improve the
livelihoods of rural mountain people.
Raufu et al. (2012) studied the effects of Non-timber Forest Products
(NTFPs) on rural women income in Ife South Local Government area of Osun
State, Nigeria. The study discovered that there is a positive and significant
relationship between the year of education, total cost and income earned from
NTFPs activities, and a negative but significant negative association with
distance from forest to point of sale. The majority of the respondents (58.9%)
supported non availability of NTFPs as the significant effect of deforestation on
NTFPs activities. The major problem encountered in NTFPs gathering and
marketing were insufficient labour (38.9%), storage problem (23.2%), and
thieves (14.4%).
23
Damte and Koch (2011) examined the role of local level institutions and
property right regimes on the forest-poverty link, with respect to non-wood forest
products in Ethiopia. Households in the sample derive approximately 8.7 per cent
of their income from these products. The determinants of forest dependency were
examined separately for different types of forest property right regimes. The
findings suggested that forestry management devolution enhanced resource use
by the poor, while reducing dependency among the rich. Their estimation results,
which were consistent across the different measures of forest dependency, also
suggest that local level institutions were not significant factors in determining the
use of non-wood forest products, a result that differs from the analysis of timber
and other woody materials. From the study results, they concluded that
generalizations of the forest-poverty link were not possible, as the link depends
on the type of forest management and the specific characteristics prevail that in
the area.
Luni et al. (2011) analyzed the household socio-economic
characteristics that influence the collection and marketing of NTFPs by
Chepangs in Shaktikhor of Chitwan district using backward multiple regression
method. Empirical evidences showed that collection and marketing of NTFPs is
not an attractive source of income especially for those relatively better-off
Chepang households who possess higher landholdings, food self-sufficiency,
and income from other alternative sources. This is because the current price
offered for the NTFPs collected by the community is very nominal, that do not
even cover the labour costs involved. Praja Cooperative Limited (PCL), a
Chepang community based institution in Shaktikhor, is struggling to provide
better prices for Chepangs. However, it is facing challenges due to limited
institutional management capacity of Chepangs.
Vodouhe and Coulibaly (2008) assessed the effects of the marketing of
seven commonly-used medicinal plants on their sustainable use. Results showed
that collectors have the lowest margins while retailers have the highest.
Wholesalers have average margins from 1.37 to 20.69 times higher than
collectors’ per gram of species parts sold in urban markets. Collectors are
24
farmers who harvest plant parts and sell them to compensate for decreasing
agricultural income. Low margins and propensity to increase income, lead to
more harvesting pressure and consequent damage to harvested species.
Diversification of income sources and access to alternative cash resources
would reduce pressure on harvested species. Complementary studies are needed
on medicinal plants’ supply-chain to minimize pressure on resources for
enhanced biodiversity.
Dang and Tran (2006) analyzed that the commercial collection of
NTFPs could reduce both the number and population of species in the forests.
In order to keep the balance between biodiversity and commercial collection of
NTFPs, they evaluated the dependence of forest dwellers on NTFPs and
identified the relation between household characteristics and cash income
generated by NTFP collection. As a result, commercial collection of NTFPs is
negatively correlated to dependency ratio, poverty level and distance to the
provincial city, and positively correlated with female labors of households. The
households who have higher dependency ratio benefit less from NTFPs sale,
while those who lack rice for their own consumption or have a higher rate of
female labour depend more on NTFPs.
Kala et al. (2006) found that the medicinal properties of plants species
have made an outstanding contribution in the origin and evolution of many
traditional herbal therapies. These traditional knowledge systems have started to
disappear with the passage of time due to scarcity of written documents and
relatively low income in these traditions. Over the past few years, however, the
medicinal plants have regained a wide recognition due to an escalating faith in
herbal medicine in view of its lesser side effects compared to allopathic
medicine in addition the necessity of meeting the requirements of medicines for
an increasing human population. Through the realization of the continuous
erosion of traditional knowledge of plants used for medicine in the past and the
renewed interest at the present time, a need existed to review this valuable
knowledge of medicinal plants with the purpose of developing medicinal plants
sectors across the different states in India.
25
Ravi et al. (2006) studied the role of NTFPs in the life and economy of
the tribal community living in and around the protected forests of H.D. Kote
region. The income derived from NTFPs was the single largest sources but it
was not sufficient to meet even their subsistence requirement of food.
Therefore, in order to meet the caloric deficit they were forced to depend on
edible forest products to sustain themselves. The results of Logit analysis had
explained that wage employment, land ownership and agricultural income
significantly reduced the probability of tribal households involving in NTFPs
collection and showed that it was primarily out of sheer necessity that the
tribals venture for NTFPs and not for their commercial gains.
Das (2005) studied the dependency on NTFPs after declaration of Buxa as
protected area in West Bengal, dynamics of collection of NTFPs, people’s
perception about NTFPs collection etc. Study revealed that more than half of total
families are dependent on NTFPs to supplement their daily requirements.
Moreover, tribal populations are most dependent on NTFPs at Buxa Tiger
Reserve (BTR) among all the social categories. It was observed that number of
species as well as quantity of NTFPs collection for sale increased considerably
over the years due to the increasingly dependence on NTFPs as potential income
source. In BTR primary collectors are highly dependent on NTFPs than
secondary and tertiary collectors but still primary collectors were not getting
remunerative price for collection.
Mahapatra et al. (2005) found that Non timber forest products (NTFPs)
extracted from forests by rural people can make a significant contribution to
their well-being and to the local economy in the dry deciduous forests of Orissa
and Jharkhand, India. The study revealed that the contribution of NTFPs to cash
income varies across ecological settings, seasons, income level, and caste. Such
variation should inform where and when to apply NTFP forest access and
management policies.
Chapter-3
MATERIALS AND METHODS
The chapter on methodology consists of brief characteristics of the study
area, the methods adopted in selection of the sample, the nature and sources of
data and the various analytical tools and techniques employed, other information
to support the existing results like secondary sources of information required to
keep the reader inferred for reference and documentation. This chapter is
discussed under the following sub-heads.
3.1 Sampling procedure
3.2 Nature and sources of data
3.3 Analytical framework
3.4 Definition of terms and concept used
3.5 Limitation of the study
3.1 SAMPLING PROCEDURE
3.1.1 Selection of Study Area
Himachal is a hilly state which is situated between 300 22’ N to 330 13’ N
latitude and 750 23’ to 790 4’ East longitude. A north-west Himalayan state
having about 1.7 per cent of the India’s geographical area has vast potential of
medicinal plant wealth. The medicinal plant richness and diversity is spread over
its different agro-climatic zones. The trade in medicinal plants in the state
involves 165 species from wild and cultivated. An important aspect of the trade is
that 24 species out of 100 medicinal plant species traded in the country are found
in the state.
Present study was conducted in the High Hill Temperate Wet Zone of
Himachal Pradesh. Parvati forest division of Kullu circle was selected
purposively. This forest division has four ranges out of which Hurla and Kasol
ranges were selected. Two blocks each i.e., Garsa and Thela from Hurla range
and Pulga and Tosh from Kasol range were selected. Further from the selected
27
blocks one village each i.e., Garsa, Thela, Pulga and Barsheni were selected
respectively. Fifteen households were selected from the each village.
A Simple random sampling design was used for the selection of the
respondents.
3.2 NATURE AND SOURCES OF DATA
To meet the objectives of the present study, both primary as well as
secondary data were collected.
3.2.1 Primary data
The primary data were collected with the aid of structured and
comprehensive questionnaire exclusively prepared for the study. The data
collected included information on NTFPs collected and their quantities, together
with demographic information of the collectors (age, gender, literacy level, land
holding, livestock, total annual earnings, collection timings and availability). The
data were collected through a personal interview method from the selected
households and traders in the study area during the year 2014-15.
3.2.2 Secondary Data
Secondary data were collected from the records of the Forest Department
from Kullu Circle for a period of ten years (2004-05 to 2013-14). The secondary
data on quantity supplied, prices and export permit fee levied by the forest
department on medicinal plants were selected.
3.3 ANALYTICAL FRAMEWORK
To fulfill the specific objectives of the study and based on the nature and
extent of availability of data, the following analytical tools and techniques have
been employed for the analysis of the data.
3.3.1 Tabular analysis
Simple tabular analysis was used to examine socio-economic status of the
respondents, their resource structure, income pattern and opinions about the
collection and marketing problems of NTFPs.
28
For the analysis of data the total family heads were divided into two
classes according to the size of their land holdings, viz., marginal (<1 ha) and
small farmers (1-2 ha). The distribution of the sampled households according to
their holding size is presented in Table 3.1. It can be seen from the table that 63
per cent of the selected respondents belonged to marginal category and 37 per
cent belonged to small category. Further it can be observed that average size of
holding of the selected respondents varied between 0.38 ha to 1.13 ha with an
average of size of 0.66 ha.
Table 3.1: Distribution of sampled households according to their landholdings
Tabular presentation was adopted to compile the general characteristics of
the sampled farmers. Simple statistical tools like averages and percentages were
used to compare, contrast and interpret the results. The sex ratio, literacy rate and
index were calculated using the following formulas:
Sex Ratio = No. of femalesNo. of males × 1000Literacy rate = Total no. of literate personTotal population × 100
Literacy Index = ∑Wi Xi∑ XiWhere;
Wi = Weights (0, 1, 2, 3 and 4) for illiterate, primary, middle,
matriculation, and secondary & above respectively.
Xi = Number of persons in respective category.
Dependency ratiow. r. t. total workers = No. of dependents in a familyTotal workers
Category ofFarmers
Size of landholding (ha)
No. offarmers
Percentageof farmers
Average sizeof holding
(ha)Marginal <1 38 63.00 0.38
Small 1-2 22 37.00 1.13
Total 60 100.00 0.66
29
Dependency ratio w. r. t. average size of family = No. of dependents in a familyTotal workersCropping intensity = Gross cropped areaNet sown area × 100
3.3.2 Gini concentration ratio (CGR)
To estimate the income inequality both exclusive and inclusive NTFPs
Gini coefficient developed by Deaton (1997) was used. The value of CGR ranges
from 0 to 1. If the value is 0 it denotes perfectly equal distribution, while 1
denotes inequalities. In order to analyze if NTFPs has an equalizing effect on
total income distribution the following specification was used:
G = − ( ) × ∑Where;
G = Gini coefficient
µ = Population's mean income
Pi = Income rank P of person i with income X.
N = Rank of person with lowest income
3.3.3 Net return
Net return was calculated by deducting cost of collection from gross
return from NTFPs.
Net return = Gross return – cost of collection
3.3.4 Linear growth rate
For evaluating the trends in nominal and real prices of medicinal plants,
linear growth rates (LGR) were estimated. The following equation was used to
estimate growth rates.
Y = a + bt
30
Where;
Y = Prices of medicinal plants
t = Time variable in year (1, 2, ….. 10)a = Constantb = Rate of change.
The linear growth rate was calculated as:
Linear growth rate = × 100Where,
b = regression coefficientȲ Y = Mean value of the prices for the medicinal plants.
SE (Linear growth rate) = × SE (b)
Where,ȲY = Mean value of the prices for the medicinal plants.
SE (b) = Standard error of b
3.3.5 Nominal and Real Price
Nominal price means current money value in different years and real
prices means adjusted money value in different years. Thus the real prices of
medicinal plants were calculated as:
Real Price =
Where,
Pn = Nominal prices paid by individuals for each medicinal plant
CPI = Consumer price index 2014 with 2004 as base year
3.3.6 Coefficient of variation at nominal and real prices
CV is a standard measure of the dispersion. It is the standard deviation in price,
land holding and income by their average. It can be calculated as:
31
CV (%) =. .× 100
Where,
S.D. = Standard deviationY = Mean value
3.3.7 Price elasticity (Arc elasticity) of supply at real and nominal prices:
Price elasticity of supply =//
Q2 = Average quantity supplied at P2 price (Nominal/Real
prices) during 2011-14
Q1 = Average quantity supplied at P1 price (Nominal/Real
prices) during 2004-07
3.3.8 Scarcity ratio for medicinal plants
The increase in real price of the resource over time indicates the economic
scarcity of medicinal plant (Suneetha, 1998).
Scarcity ratio = [ ]× 100Where,
SPo = Average selling price of medicinal plant in 2004-07
SPt = Average selling price of medicinal plant during 2011-14
CPI = Consumer price index for 2014 with 2004 as base year
3.3.9 Linear regression
NTFPs dependency was measured as the share of income from NTFPs in
the total household income. In order to test which socio-economic variables
influence NTFPs dependency, linear regression analysis was used. An equation
with the following variables was estimated.
Y = a + b1x1 + b2x2 + b3x3 + b4x4 + b5x5 + µ
32
Where;
Y = Share of NTFPs in total household income
x1 = Farm size
x2 = Family size
x3 = Literacy index of the household
x4 = Farm income
x5 = Age of the household head
µ = Error term
The variables were selected with the following assumption:
Farm size determines the extent of crop production. If land size and thus crop
production is rising, NTFPs dependency is likely to decrease (Fisher, 2004;
Vedeld et al., 2007).
Family size determines the availability of labour. It was hypothesized that with
the higher family size NTFPs extraction will be higher and vice-versa.
Literacy index describes the level and quality of households’ education. It was
hypothesized that higher the educational status, lowers the NTFPs extraction.
Age of the household is linked with the knowledge about the NTFPs. Hence it
was hypothesized that older households are often engaged in the extraction of
NTFPs.
3.4 DEFINITIONS OF TERMS AND COST CONCEPTS USED IN THESTUDY
Fixed cost
The various items viz., depreciation, interest on equipment investment
and packaging material, which were used in the NTFPs collection.
Variable cost
Variable cost includes the expenditure on carrying and transportation and
material input cost and interest on working capital.
Inputs and costs
Following were the various inputs used in the collection of NTFPs.
33
Hired labour
Hired labour includes labour or carrying and transportation of NTFPs
from forest area.
Depreciation
The amount of depreciation for implements was calculated by the straight
line method i.e., by dividing the original cost less junk value of implement by its
expected life. This was apportioned to individual crop in proportion to the hectare
under the crop.
Interest on working capital
Interest on fixed and working capital is charged at the rate of 10 per cent
per annum for two months.
Interest on fixed capital
Interest on fixed and working capital has been charged at the rate of 10
per cent per annum on the value of farm implements and other fixed assets which
are exclusively used.
Family labour
Family labour cost was calculated on the basis of charges paid to hired
labour.
3.5 LIMITATIONS OF THE STUDY
Since, the data were collected by survey method; the inherent lacunae
associated with this type of enquiry have crept into the study, even though the
estimates were provided by the recall memory on account of the non-maintenance
of the proper records and reluctance of some of respondents in providing the
desired information. Sincere efforts have been made to elicit accurate and reliable
information as far as possible by cross questioning; the degree of discrepancy if
any would be negligible as the estimates presented are in averages.
34
The data collected from forest department was inconsistent so the
rigorous analysis of the data was not possible. Most of the trade from the state is
illegal so exact estimates of trade couldn’t be made.
It may however, be recognized that the findings of the study need not be
generalized beyond the boundaries of the area under investigation and applicable
to such other areas having similar agro-climatic and socio-economic conditions.
The study was based on the data collected for one year only (2014-2015)
which may not necessarily hold true for other periods as well.
Chapter-4
RESULTS AND DISCUSSION
The results obtained after a systematic analysis and interpretation of
data/information collected along with relevant discussion have been presented in
this chapter. The results so obtained are presented under the following heads.
4.1 Socio-economic characteristics of sampled farmers
4.2 Share of NTFPs in households’ income and employment
4.3 Cost of collection of NTFPs
4.4 Supply analysis of selected medicinal plants
4.5 Socio-economic factors affecting dependency on NTFPs
4.1 SOCIO-ECONOMIC CHARACTERISTICS OF SAMPLEDHOUSEHOLDS
To have a comprehensive profile of the farm households, a demographic
base becomes more relevant. The social characteristics such as family size, age,
work force and sex composition of farm households, dependency ratio and
literacy affect the economic conditions and in turn affect social conditions. The
significance of the social and demographic variables is discussed below. First, the
farmers are classified in to two categories (marginal and small) on the basis of
land holding.
4.1.1 Size and structure of family
The size and structure, work force and literacy status among the sampled
households are the important factors influencing the collection of NTFPs in this
area (Parvati forest division, Kullu), which happens to be family labour based
occupation at the village level. The size and structure of sampled households in
the studied area are presented in Table 4.1. The perusal of table shows that at
overall level the average family size was 6 members per household, 5 members
per household in case of marginal farmers and 6 members per household in small
farmers. Family size did not vary significantly across farm size and between
regions. Likewise, the average age of household heads was above 40 years old
36
and did not vary significantly across regions. Almost all the households in the
sample were male-headed. In the study area the percentage of males was 53 per
cent and females were 47 per cent. The number of females per thousand of males
ranged between 839 in case of marginal farm category to 939 in small farms with
an average of 889 at the overall level. Number of nuclear families was higher
(40) than the joint families (20). A positive relationship was found between the
farm size and the family in the study area.
Table 4.1 Demographic profile of sampled households in the study area
Particulars Farm size
Size of the Family Marginal Small Overall
Average size of Family (No.) 5 6 6
Number of Males (%) 54 52 53
Number of Females (%) 46 48 47
Sex Ratio (Females/1000 males) 839 939 889
Structure of Family
Joint Families (No.) 11
(29.94)
9
(40.90)
20
(33.33)
Nuclear Families (No.) 27
(71.05)
13
(59.09)
40
(66.66)
Figures in parentheses indicate percentage to total
4.1.2 Literacy status
There has been significant improvement in the literacy rate in the study
area as shown in Table 4.2 and Fig 4.1. The overall literacy rate varied from
82.35 per cent to 84.61 per cent in marginal and small farm categories
respectively. Male literacy rate was higher (85.91%) as compare to the female
literacy rate (80.77%). The table showed a marked improvement in the literacy
rate. However, literacy index varied from 1.80 to 1.88 among the different
categories of the farms with an overall index of 1.84. This highlighted the fact
that literacy rate was higher however, the quality of education was poor as
indicated by low literacy index.
37
Table 4.2 Educational status of sampled households in the study area
Particulars Farm SizeMarginal Small Overall
Illiterate 0.71(13.10)
0.63(10.84)
0.68(12.23)
Primary 1.34(24.72)
1.5(25.82)
1.4(25.17)
Middle 1.5(27.70)
1.54(26.51)
1.57(27.15)
Secondary 1.15(21.22)
1.4(24.10)
1.25(22.48)
High secondary 0.21(3.88)
0.22(3.80)
0.21(3.77)
Non-school going 0.5(9.23)
0.5(8.60)
0.5(8.99)
Total 5.42(100.00)
5.81(100.00)
5.56(100.00)
Literacy rate (%) 82.35 84.61 83.48
Male literacy rate (%) 85.15 86.67 85.91
Female literacy rate (%) 79.07 82.46 80.77
Literacy index 1.8 1.88 1.84Figures in parentheses indicate percentage to total
Fig 4.1 Literacy rate of sampled households
74
76
78
80
82
84
86
88
Marginal
Per
cent
37
Table 4.2 Educational status of sampled households in the study area
Particulars Farm SizeMarginal Small Overall
Illiterate 0.71(13.10)
0.63(10.84)
0.68(12.23)
Primary 1.34(24.72)
1.5(25.82)
1.4(25.17)
Middle 1.5(27.70)
1.54(26.51)
1.57(27.15)
Secondary 1.15(21.22)
1.4(24.10)
1.25(22.48)
High secondary 0.21(3.88)
0.22(3.80)
0.21(3.77)
Non-school going 0.5(9.23)
0.5(8.60)
0.5(8.99)
Total 5.42(100.00)
5.81(100.00)
5.56(100.00)
Literacy rate (%) 82.35 84.61 83.48
Male literacy rate (%) 85.15 86.67 85.91
Female literacy rate (%) 79.07 82.46 80.77
Literacy index 1.8 1.88 1.84Figures in parentheses indicate percentage to total
Fig 4.1 Literacy rate of sampled households
Marginal Small Overall
Male (%)
Female (%)
37
Table 4.2 Educational status of sampled households in the study area
Particulars Farm SizeMarginal Small Overall
Illiterate 0.71(13.10)
0.63(10.84)
0.68(12.23)
Primary 1.34(24.72)
1.5(25.82)
1.4(25.17)
Middle 1.5(27.70)
1.54(26.51)
1.57(27.15)
Secondary 1.15(21.22)
1.4(24.10)
1.25(22.48)
High secondary 0.21(3.88)
0.22(3.80)
0.21(3.77)
Non-school going 0.5(9.23)
0.5(8.60)
0.5(8.99)
Total 5.42(100.00)
5.81(100.00)
5.56(100.00)
Literacy rate (%) 82.35 84.61 83.48
Male literacy rate (%) 85.15 86.67 85.91
Female literacy rate (%) 79.07 82.46 80.77
Literacy index 1.8 1.88 1.84Figures in parentheses indicate percentage to total
Fig 4.1 Literacy rate of sampled households
Male (%)
Female (%)
38
Fig 4.2 Literacy index of the sampled households
4.1.3 Occupational distribution
Per household occupational structure of the selected households is given
in Table 4.3 and Fig 4.3. Occupational distribution showed that in selected
villages of Parvati forest division around 88.58 per cent of the population was
engaged in agriculture which was major constituents of livelihood occupation
whereas, 7.41 per cent households were engaged in business as secondary
occupation at overall level followed by services (4.01%) in private/public sectors.
Similar trends in occupational distribution were observed on small and marginal
farm categories. In case of marginal farms workers engaged in service were 3.54
per cent and in business were as 8.85 per cent. More members of the small farm
category were engaged in business (5.97%) than in services (4.48 %).
Table 4.3 Occupational distribution of the sampled households in the studyarea
(%)
ParticularsFarm Size
Marginal Small Overall
Service 3.54 4.48 4.01
Business 8.85 5.97 7.41
Agriculture 87.61 89.55 88.58
1.76
1.78
1.8
1.82
1.84
1.86
1.88
Marginal
38
Fig 4.2 Literacy index of the sampled households
4.1.3 Occupational distribution
Per household occupational structure of the selected households is given
in Table 4.3 and Fig 4.3. Occupational distribution showed that in selected
villages of Parvati forest division around 88.58 per cent of the population was
engaged in agriculture which was major constituents of livelihood occupation
whereas, 7.41 per cent households were engaged in business as secondary
occupation at overall level followed by services (4.01%) in private/public sectors.
Similar trends in occupational distribution were observed on small and marginal
farm categories. In case of marginal farms workers engaged in service were 3.54
per cent and in business were as 8.85 per cent. More members of the small farm
category were engaged in business (5.97%) than in services (4.48 %).
Table 4.3 Occupational distribution of the sampled households in the studyarea
(%)
ParticularsFarm Size
Marginal Small Overall
Service 3.54 4.48 4.01
Business 8.85 5.97 7.41
Agriculture 87.61 89.55 88.58
Marginal Small Overall
Literacy Index ofthe Family
38
Fig 4.2 Literacy index of the sampled households
4.1.3 Occupational distribution
Per household occupational structure of the selected households is given
in Table 4.3 and Fig 4.3. Occupational distribution showed that in selected
villages of Parvati forest division around 88.58 per cent of the population was
engaged in agriculture which was major constituents of livelihood occupation
whereas, 7.41 per cent households were engaged in business as secondary
occupation at overall level followed by services (4.01%) in private/public sectors.
Similar trends in occupational distribution were observed on small and marginal
farm categories. In case of marginal farms workers engaged in service were 3.54
per cent and in business were as 8.85 per cent. More members of the small farm
category were engaged in business (5.97%) than in services (4.48 %).
Table 4.3 Occupational distribution of the sampled households in the studyarea
(%)
ParticularsFarm Size
Marginal Small Overall
Service 3.54 4.48 4.01
Business 8.85 5.97 7.41
Agriculture 87.61 89.55 88.58
Literacy Index ofthe Family
39
Fig 4.3: Occupational status of the sampled households
4.1.4 Workforce
The proportion of active workers was worked out to be 59.70 per cent in
marginal farmers and 56.25 per cent in small farm categories. It was assumed that
persons in the age group of 15-60 year are actively engaged in useful economic
activities and were termed as working force. The dependents were found 43.64
per cent in case of small farmers and 40.29 per cent in the marginal farmers. The
overall dependency ratio with respect to total workers was found to be 1:1.40 and
among the different categories, it was observed 1:1.29 in small farms and 1:1.47
in marginal farms. Dependency ratio indicates that on an average one worker has
to support more than one member in the family in the sampled area. Dependency
ratio estimated with respect to family size was found 1:2.40 on an average.
Table 4.4 Farm category wise distribution of workers and dependents of thesampled Households
ParticularsFarm size
Marginal Small OverallAverage no. of workers 3.23
(59.70)3.27
(56.25)3.24
(58.27)
Average no. of dependents(<14 yrs & >65yrs)
2.18(40.29)
2.54(43.64)
2.31(41.54)
Average family size (No.) 5.42(100.00)
5.82(100.00)
5.56(100.00)
Dependency ratio w.r.t. total workers 1:1.47 1:1.29 1:1.40
Dependency ratio w.r.t. Family size 1:2.5 1:2.3 1:2.40
Figures in parentheses indicate percentage to total
4.1.5 Distribution of sampled households according to farm size
According to size of land holding the farmers were categorized in the two
categories; marginal and small farmers. Most of the farmers in the study area
0
20
40
60
80
100
Marginal
39
Fig 4.3: Occupational status of the sampled households
4.1.4 Workforce
The proportion of active workers was worked out to be 59.70 per cent in
marginal farmers and 56.25 per cent in small farm categories. It was assumed that
persons in the age group of 15-60 year are actively engaged in useful economic
activities and were termed as working force. The dependents were found 43.64
per cent in case of small farmers and 40.29 per cent in the marginal farmers. The
overall dependency ratio with respect to total workers was found to be 1:1.40 and
among the different categories, it was observed 1:1.29 in small farms and 1:1.47
in marginal farms. Dependency ratio indicates that on an average one worker has
to support more than one member in the family in the sampled area. Dependency
ratio estimated with respect to family size was found 1:2.40 on an average.
Table 4.4 Farm category wise distribution of workers and dependents of thesampled Households
ParticularsFarm size
Marginal Small OverallAverage no. of workers 3.23
(59.70)3.27
(56.25)3.24
(58.27)
Average no. of dependents(<14 yrs & >65yrs)
2.18(40.29)
2.54(43.64)
2.31(41.54)
Average family size (No.) 5.42(100.00)
5.82(100.00)
5.56(100.00)
Dependency ratio w.r.t. total workers 1:1.47 1:1.29 1:1.40
Dependency ratio w.r.t. Family size 1:2.5 1:2.3 1:2.40
Figures in parentheses indicate percentage to total
4.1.5 Distribution of sampled households according to farm size
According to size of land holding the farmers were categorized in the two
categories; marginal and small farmers. Most of the farmers in the study area
Marginal Small Overall
39
Fig 4.3: Occupational status of the sampled households
4.1.4 Workforce
The proportion of active workers was worked out to be 59.70 per cent in
marginal farmers and 56.25 per cent in small farm categories. It was assumed that
persons in the age group of 15-60 year are actively engaged in useful economic
activities and were termed as working force. The dependents were found 43.64
per cent in case of small farmers and 40.29 per cent in the marginal farmers. The
overall dependency ratio with respect to total workers was found to be 1:1.40 and
among the different categories, it was observed 1:1.29 in small farms and 1:1.47
in marginal farms. Dependency ratio indicates that on an average one worker has
to support more than one member in the family in the sampled area. Dependency
ratio estimated with respect to family size was found 1:2.40 on an average.
Table 4.4 Farm category wise distribution of workers and dependents of thesampled Households
ParticularsFarm size
Marginal Small OverallAverage no. of workers 3.23
(59.70)3.27
(56.25)3.24
(58.27)
Average no. of dependents(<14 yrs & >65yrs)
2.18(40.29)
2.54(43.64)
2.31(41.54)
Average family size (No.) 5.42(100.00)
5.82(100.00)
5.56(100.00)
Dependency ratio w.r.t. total workers 1:1.47 1:1.29 1:1.40
Dependency ratio w.r.t. Family size 1:2.5 1:2.3 1:2.40
Figures in parentheses indicate percentage to total
4.1.5 Distribution of sampled households according to farm size
According to size of land holding the farmers were categorized in the two
categories; marginal and small farmers. Most of the farmers in the study area
Service
Business
Agriculture
40
were having marginal and small land holdings. In case of marginal farmers the
minimum size of land holding was 0.16 hectares, whereas maximum was 0.8
hectares. In case of small farmers minimum land holding was 1.04 hectares and
maximum was 1.6 hectares. The data presented in Table 4.5, showed higher
variations in land holding in marginal farmers (46.79%) compared to small
farmers (16.70%) with overall variation of 63 per cent.
Table 4.5 Distribution of sampled households according to the size of landholding
Farm size (ha)Particulars Minimum Maximum Average CV(%)Marginal 0.16 0.8 0.38 46.79Small 1.04 1.6 1.13 16.7Overall 0.16 1.6 0.65 63
Fig: 4.4: Distribution according to size of land holding
4.1.6 Land use pattern of sampled households
Land use pattern determines the type of farming system in an area. Farm
categories wise land use pattern of sampled farmers is summarized in Table 4.6.
The average size of land holding on the overall category was found 0.66 hectares
of which 48.48 per cent was cultivated area and 22.72 per cent was under fruit
crops. The other uses of land were area under pastures/ghasnis (0.11 ha) and
barren land (0.07 ha). The cultivated area of marginal and small farms was 55.26
per cent and 46.01 per cent respectively. The average size of holding on marginal
and small farms was found to be 0.38 and 1.13 hectares, respectively. The results
have also been presented in Fig 4.5 and 4.6.
0
0.5
1
1.5
2
Marginal
ha
40
were having marginal and small land holdings. In case of marginal farmers the
minimum size of land holding was 0.16 hectares, whereas maximum was 0.8
hectares. In case of small farmers minimum land holding was 1.04 hectares and
maximum was 1.6 hectares. The data presented in Table 4.5, showed higher
variations in land holding in marginal farmers (46.79%) compared to small
farmers (16.70%) with overall variation of 63 per cent.
Table 4.5 Distribution of sampled households according to the size of landholding
Farm size (ha)Particulars Minimum Maximum Average CV(%)Marginal 0.16 0.8 0.38 46.79Small 1.04 1.6 1.13 16.7Overall 0.16 1.6 0.65 63
Fig: 4.4: Distribution according to size of land holding
4.1.6 Land use pattern of sampled households
Land use pattern determines the type of farming system in an area. Farm
categories wise land use pattern of sampled farmers is summarized in Table 4.6.
The average size of land holding on the overall category was found 0.66 hectares
of which 48.48 per cent was cultivated area and 22.72 per cent was under fruit
crops. The other uses of land were area under pastures/ghasnis (0.11 ha) and
barren land (0.07 ha). The cultivated area of marginal and small farms was 55.26
per cent and 46.01 per cent respectively. The average size of holding on marginal
and small farms was found to be 0.38 and 1.13 hectares, respectively. The results
have also been presented in Fig 4.5 and 4.6.
Marginal Small Overall
40
were having marginal and small land holdings. In case of marginal farmers the
minimum size of land holding was 0.16 hectares, whereas maximum was 0.8
hectares. In case of small farmers minimum land holding was 1.04 hectares and
maximum was 1.6 hectares. The data presented in Table 4.5, showed higher
variations in land holding in marginal farmers (46.79%) compared to small
farmers (16.70%) with overall variation of 63 per cent.
Table 4.5 Distribution of sampled households according to the size of landholding
Farm size (ha)Particulars Minimum Maximum Average CV(%)Marginal 0.16 0.8 0.38 46.79Small 1.04 1.6 1.13 16.7Overall 0.16 1.6 0.65 63
Fig: 4.4: Distribution according to size of land holding
4.1.6 Land use pattern of sampled households
Land use pattern determines the type of farming system in an area. Farm
categories wise land use pattern of sampled farmers is summarized in Table 4.6.
The average size of land holding on the overall category was found 0.66 hectares
of which 48.48 per cent was cultivated area and 22.72 per cent was under fruit
crops. The other uses of land were area under pastures/ghasnis (0.11 ha) and
barren land (0.07 ha). The cultivated area of marginal and small farms was 55.26
per cent and 46.01 per cent respectively. The average size of holding on marginal
and small farms was found to be 0.38 and 1.13 hectares, respectively. The results
have also been presented in Fig 4.5 and 4.6.
MinimumMaximumAverage
41
Table: 4.6 Land use pattern of sampled households(ha)
ParticularsFarm size
Marginal Small OverallCultivated Land 0.21
(55.26)0.52
(46.01)0.32
(48.48)Orchard 0.11
(28.94)0.23
(20.35)0.15
(22.72)Pasture 0.04
(10.53)0.22
(19.47)0.11
(16.18)Barren Land 0.02
(5.26)0.16
(14.16)0.07
(10.89)Total Land Holding 0.38
(100.00)1.13
(100.00)0.66
(100.00)Figures in parentheses indicate percentage to total
Fig: 4.5: Land use pattern of sampled households
Fig: 4.6: Land use pattern of sampled households in overall
0
10
20
30
40
50
60
Marginal
Per
cent
23%
16%
41
Table: 4.6 Land use pattern of sampled households(ha)
ParticularsFarm size
Marginal Small OverallCultivated Land 0.21
(55.26)0.52
(46.01)0.32
(48.48)Orchard 0.11
(28.94)0.23
(20.35)0.15
(22.72)Pasture 0.04
(10.53)0.22
(19.47)0.11
(16.18)Barren Land 0.02
(5.26)0.16
(14.16)0.07
(10.89)Total Land Holding 0.38
(100.00)1.13
(100.00)0.66
(100.00)Figures in parentheses indicate percentage to total
Fig: 4.5: Land use pattern of sampled households
Fig: 4.6: Land use pattern of sampled households in overall
Marginal Small Overall
Cultivated Land
Orchard
Pasture
Barren Land
Cultivated Land
Orchard
Pasture
Barren Land
23%
16%
11%
48%
41
Table: 4.6 Land use pattern of sampled households(ha)
ParticularsFarm size
Marginal Small OverallCultivated Land 0.21
(55.26)0.52
(46.01)0.32
(48.48)Orchard 0.11
(28.94)0.23
(20.35)0.15
(22.72)Pasture 0.04
(10.53)0.22
(19.47)0.11
(16.18)Barren Land 0.02
(5.26)0.16
(14.16)0.07
(10.89)Total Land Holding 0.38
(100.00)1.13
(100.00)0.66
(100.00)Figures in parentheses indicate percentage to total
Fig: 4.5: Land use pattern of sampled households
Fig: 4.6: Land use pattern of sampled households in overall
Cultivated Land
Orchard
Pasture
Barren Land
Cultivated Land
Orchard
Pasture
Barren Land
42
4.1.7 Cropping pattern of sampled households
Cropping pattern in any region depends mainly on soil, altitude, micro-
climate, availability of resources and management factors. The changes in the per
cent share of area under different crops in the gross cropped area reveals the
extent of agricultural diversification in sampled farms. This reflects the future
scope of each crop along with tentative requirement of the inputs for different
crops. A close scrutiny of the cropping pattern also suggests the status of
agriculture in the area. The proportional share of a particular crop in gross
cropped area on the farm suggests the importance that the farmer attaches to a
particular crop. This importance can be both of economic nature as well as social
considerations on the part of the farmer.
Table 4.7 Farm category wise cropping pattern of the sampled households(ha)
Particulars Farm sizeMarginal Small Overall
RabiWheat 0.07
(12.50)0.19
(15.07)0.11
(13.58)Barley 0.07
(12.50)0.10
(7.93)0.08
(6.35)Pea 0.03
(5.35)0.07
(5.56)0.04
(4.94)Potato 0.03
(5.35)0.07
(5.56)0.04
(4.94)KharifMaize 0.08
(14.28)0.13
(10.31)0.10
(15.35)Tomato 0.03
(5.35)0.06
(4.76)0.04
(4.94)Cabbage 0.02
(3.57)0.05
(3.97)0.03
(3.70)Cauliflower 0.02
(3.57)0.05
(3.97)0.03
(3.70)Urad 0.03
(5.35)0.12
(9.52)0.06
(7.40)Urad under fruit area 0.02
(3.57)0.04
(3.17)0.03
(3.70)Rajmah 0.03
(5.35)0.11
(8.73)0.06
(7.40)Rajmah under fruit area 0.02
(3.57)0.04
(3.17)0.03
(3.70)Fruit 0.11
(19.64)0.23
(18.25)0.15
(18.52)Gross Cropped Area 0.56
(100.00)1.26
(100.00)0.81
(100.00)Net sown area 0.32 0.75 0.48Cropping intensity (%) 175 168 172Figures in parentheses indicate percentage to total
43
The cropping pattern of sampled farms was analyzed and the results have
been presented in Table 4.7. It is evident from the table that the cropping
intensity was higher (175%) on marginal farm category as compare to small farm
category (168%). At overall level it was worked out to be 172 per cent, which
indicates that there is a scope for increase in farm efficiency. Wheat in rabi and
maize in kharif season were the predominant crops. Vegetable crops were also
grown in the study area however, area under kharif vegetable crops was found
higher in comparison to rabi vegetable crops. Cereal crops were grown in
cultivated land and small proportion was also grown under fruit crops. The area
under fruit crops was 19.64 per cent on marginal farms and 18.25 per cent was on
small farms. The analysis revealed that marginal farms were using the land more
intensively.
4.1.8 Livestock inventory
Livestock was raised traditionally in the study area for wide spectrum of
benefits such as cash income, food, manure, saving and insurance. Average
number of livestock is summarised in Table 4.8. Overall number of animals was
found to be 5.25 per household whereas, on marginal farms it was found 5.57 and
on small farmers 4.98 animals respectively. Out of total livestock population,
maximum proportion constituted sheep/goat (46.47%) followed by cows
(28.38%) and young stock (16.38%). Bullocks were found to be very few in
number 0.46 (8.76%). Similar trends were found on small and as well as on
marginal farms.
Table 4.8 Livestock inventory of sampled households(Number)
In order to test which socio-economic variable influences NTFPs
dependency linear regression model was used and the results of the analysis have
been presented in Table 4.19. Land holding of sampled households, size of
family, literacy index, farm income and age of household were found affecting
the NTFPs dependency. Out of these five variables only three variables, viz., land
holding, literacy index and age of household head significantly affected the
NTFPs collection dependence.
Land holding and literacy index were found affecting the NTFPs
dependency negatively i.e., with the increase in the land holding size and literacy
index, NTFPs dependency is going to decrease. The literacy index was very low
56
in the study area (1.84) indicating that quality of education was low. Thus lower
the formal education, more the dependency on NTFPs.
Age of household head was found positively related to the NTFPs
dependency, since old households were assumed to have more knowledge about
the NTFPs uses and their extraction. Moreover they were having lower formal
education. Both knowledge and skills were spread within the family.
Additionally, as elder people often were limited in their physical performance,
they were more likely to be engaged in NTFPs extraction. Thus, higher age of
household head positively affected NTFPs dependency.
Table: 4.19 Regression of NTFPs income against socio-economic variables(estimation of NTFP dependency model)
N= 39: R2 = 0.722: Adjusted R2 = 0.680* Significant at 5 per cent level of significance respectively
Other variables like size of family and farm income were also considered
which were found not significantly affecting the NTFPs income. It showed that
people from nuclear family and from joint family collect the species in the same
proportion. Similarly, farmers with high farm income were also collecting the
NTFPs for their additional income. The factors considered together were able to
explain the NTFPs dependency up to 68 per cent. There may be some other
factors affecting the dependency of NTFPs.
Terms Coefficient SE t-value
Intercept -17493.641 14891.213 -1.175
Land holding -33481.166 7613.612 -4.398*
Size of family 1189.579 1480.649 0.803
Literacy index -13358.672 3931.005 -3.398*
Farm income 0.15 0.076 1.979
Age of household head 1292.199 255.12 5.065*
Chapter-5
SUMMARY AND CONCLUSIONS
Non-timber Forest Products (NTFPs) are all resources that are extractable
from forest, have economic, cultural & social values and are utilizable in
households (FAO, 1990). They are usually harvested from the forests for uses
such as food, medicine, fuel, storage, fodder, etc. They may be of living or dead
plants and animals, so they constitute a wide range of products. NTFPs are used
by billions of people around the world for different purposes. They add to
peoples’ livelihood security, especially the rural dwellers that tend to share a sort
of cultural connection and significance with the forests and the NTFPs from
them. Although, there is a growing understanding about NTFPs but its
importance has not been fully introduced within government frameworks and
rural development policies and programmes.
With this background, the main thrust of the present study was to assess
the contribution of NTFPs to income and employment for ensuring food and
livelihood security of rural dwellers, cost and returns of NTFPs collection and
identifying the factors affecting rural peoples’ dependency on NTFPs and their
coping mechanisms in the Parvati forest division in Kullu circle (HP).
Keeping in view the significance of NTFPs in the economy of the
households, present study “Non-Timber Forest Products and Livelihood Security:
An Economic Study of High Hill Temperate Wet Zone of Himachal Pradesh”
was conducted to study the contribution of NTFPs in the total income and
employment pattern of the households. The specific objectives of the study were:
1. To study the socio-economic status of the sampled households
2. To estimate the contribution of NTFPs to household income and
employment.
3. To study the socio economic factors affecting the dependency of rural
household on NTFPs.
58
Present study was conducted in the High Hill Temperate Wet Zone of
Himachal Pradesh. Parvati forest division of Kullu circle was selected
purposively. This forest division has four ranges out of which Hurla and Kasol
ranges were selected. Two blocks each i.e., Blocks Garsa and Thela from Hurla
range and Pulga and Tosh from Kasol range were selected. Further from the
selected blocks one village each i.e., Garsa, Thela, Pulga and Barsheni were
selected respectively. Fifteen households were selected from the each village.
To meet the objectives of the present study, both primary as well as
secondary data were collected. The primary data were collected with the aid of
structured and comprehensive questionnaire exclusively prepared for the study.
The data collected included information on NTFPs collected and their quantities,
together with demographic information of the collectors (age, gender, literacy
level, land holding, livestock, total annual earnings, collection timings and
availability). The data were collected through a personal interview method from
the selected households and traders in the study area during the year 2014-15.
Secondary data were collected from the records of the Forest Department
from Kullu Circle for a period of ten years (2004-05 to 2013-14). The secondary
data on quantity supplied, prices and export permit fee levied by the forest
department on medicinal plants were selected.
To meet out the requirements of the study objectives, tabular analysis,
financial analysis and Gini concentration ratio, Coefficient of variation, linear
growth rate, arc elasticity, scarcity ratio and linear regression model have been
used.
The major findings of the study:
Socio-economic indicators revealed that majority of the sample
households have nuclear families. The proportion of nuclear families was
59.09 per cent on small farms and 71.05 per cent on marginal farms. On
an average, the family size ranged from 5 to 6 persons in categories.
59
At overall level, the average family size comprised of 6 persons, out of
which 53 per cent were males and rest were females. The number of
females per thousand of males worked out to be 839, 939 and 889 for
marginal, small and overall farms respectively.
Literacy situation revealed that nearly 83.48 per cent family members
were literates at overall level with a literacy index of 1.84, indicating poor
quality of education in the study area.
Occupational distribution revealed that 88.58 per cent of work force in the
sampled households practice farming, followed by business sector
(7.41%) and service sector (4.01%) at overall level.
On an average, 58.27 per cent were the workers in family in overall
farms. The proportion of workers found on marginal farms (59.70%)
higher compared to small farms (56.25%). The overall dependency ratio
w.r.t. total worker was worked out to be 1:1.40 and dependency ratio
w.r.t. family size was 1:2.40 indicating that on an average one worker has
to support more than two family members.
The average size of land holding of the sampled households was found
0.66 hectares of which 48.48 per cent was cultivated area. The other uses
of land were pastures/ghasnis (16.18%), orchards (22.72%) and barren
land (10.89%). The cultivated land varied from 55.26 per cent to 46.01per
cent in marginal to small categories of the farm.
The cultivation of cereal crops was more common among all the farm
categories followed by fruit crops. Cropping intensity was 172 per cent at
the overall level.
NTFPs contributed maximum in the total income in case of marginal
farms (28.86%) whereas, in small farms NTFPs contribution was 20.87
per cent. On an average, NTFPs contributed 24.99 per cent to the total
farm income in the study area.
60
Gini coefficient value without NTFPs income was 0.20, which reduced to
0.18 with the inclusion of NTFPs income. Thus reducing the income
inequality in the study area.
NTFPs collection generated on an average 72 days of employment to
sampled households. Maximum number (20 days) of employment was
provided by Dhoop (Jurinea macrocephala).
Among all NTFPs, maximum income (65.85%) was obtained from Kutki
(Picrorhiza kurroa) on the overall category whereas, minimum (1.14%)
share was from Sugandhbala (Valeriana jatamansi).
Maximum net returns (Rs. 8524.08/kg/annum) were found from the
collection of Guchhi (Morchella esculenta) because of its high prices (Rs.
15000/kg). Since this species is not found abundant in nature therefore its
cost of collection was estimated on per kg basis.
Nominal prices of all medicinal plants showed positive and significant
growth and maximum nominal price growth rate (17.15%/annum) was
recorded in Dhoop (Jurinea macrocephalla) and minimum
(0.57%/annum) in Patish (Aconitum heterophyllum).
Maximum variation in nominal prices over ten years was found in Dhoop
(Jurinea macrocephalla) (60.21%) whereas, minimum variation was
found in Patish (Aconitum heterophyllum) (12.61%).
The average nominal prices of the selected medicinal plants were found
higher as compared to the real prices.
Real prices of Dhoop (Jurinea macrocephalla) has shown significant
growth rate (6.57%). It indicated that in real sense prices of Dhoop
(Jurinea macrocephalla) were increasing over time.
Maximum variation in real prices was found in Dhoop (Jurinea
macrocephalla) (28.56%) and minimum variation in Kutki (Picrorhiza
kurroa) (9.10%).
61
The nominal price elasticity for Kutki (Picrorhiza kurroa) (2.10), Dhoop
(Jurinea macrocephalla) (1.07) and Akhnor (Aeculus indica) (1.09) was
found more than one which indicated that supply of these medicinal
plants is highly elastic or these medicinal plants are price responsive.
The nominal price elasticity for Guchhi (Morchella esculenta) (0.92),
Patish (Aconitum heterophyllum) (0.74) and Sugandhbala (Valeriana
jatamnsi) (0.16) was found less than one which indicated that supply of
these medicinal plants is highly inelastic.
Real price elasticity analysis showed that Picrorhiza kurroa (13.02),
Akhnor (Aeculus indica) (10.31) and Guchhi (Morchella esculenta) (1.30)
are highly price sensitive species.
Real price elasticity of Patish (Aconitum heterophyllum) and
Sugandhbala (Valeriana jatamnsi) and Dhoop(Jurinea macrocephala)
was found less than one which indicated that supply for these medicinal
plants is highly price insensitive.
Medicinal plants like Kutki (Picrorhiza kurroa), Guchhi (Morchella
esculenta) and Patish (Aconitum heterophyllum) showed positive scarcity
ratios, showing their scarcity in the nature. The scarcity ratio was found
highest in Guchhi (Morchella esculenta) (56.36) followed by Patish
(Aconitum heterophyllum) (8.20) and Kutki (Picrorhiza kurroa) (1.37)
whereas, scarcity ratio of Dhoop (Jurinea macrocephalla) (-0.45),
Sugandhbala (Valeriana jatamnsi) (-0.67) and Akhnor (Aeculus indica)
(0.95) was found negative, indicating their abundant available in nature.
Literacy index and land holding were found affecting NTFPs dependency
negatively i.e., with the increase in the land holding size and literacy
index, NTFPs dependency is going to decrease whereas, age of the
household head was found positively related to the NTFPs dependency,
since they had more knowledge about NTFPs uses and their method of
extraction.
62
Policy implications:
The findings of the study have resulted into a numbers of useful policy
implications for the development of NTFPs based livelihoods in the study area.
Some of the important policy implication emerged from the present study are
discussed below:
There is a need to train local people in cultivation of the medicinal and
aromatic plants. It is suggested that the low cost techniques to reduce the
initial planting cost be developed so that farmers may adopt their
cultivation.
Declining trends in majority of the medicinal plants emphasized the need
to formulate an effective policy for in-situ and ex-situ conservation of
medicinal plants especially for those, which have become scarce.
Maximum extraction of medicinal plants is taking place from temperate
region. There is a need to have a separate policy for the conservation of
temperate medicinal plants.
The government needs to implement a series of rural development
activities to generate employment for the rural poor in forested regions to
alleviate poverty. The effective implementation of these programmes
among the forest dependent communities will reduce their dependence on
the forests.
Information on the resource potential of different M&APs along with
their market potential such as demand, supply and price is lacking. Hence
there is need to develop a sound data base with market intelligence to
regulate the M&APs market.
Provision of education to the children and other skill development
trainings to youth enabling the forest dependent populations to diversify
their livelihood options and look beyond forest as their source of income.
Lack/poor/ unorganized marketing were found to be the major bottleneck
in the cultivation of M&APs. Hence there is need to include M&AP’s in
the regulated markets.
63
Identification of location specific medicinal and aromatic crops with the
post harvest technologies needs to be developed and disseminated to the
cultivators.
Provisions of subsidy which is available to the farmers for the
strengthening of production technologies needs to be extended for the
marketing since the demand for M&AP’s is derived demand.
Scientific studies have to be carried out to assess the short and long run
impact of NTFPs extractions on forests and ecosystems. Based on this,
collectors have to be educated on sustainable ways of harvesting NTFPs.
The concerned government authorities should ensure that the benefits of
the development policies and programs targeted exclusively at the forest
dwellers should effectively reach the needy people.
Chapter-6
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Dr YS Parmar University of Horticulture and ForestryNauni, Solan (HP) 173 230
Department of Social Sciences
Title of Thesis : Non-Timber Forest Products and Livelihood Security: AnEconomic Study of High Hill Temperate Wet Zone Householdsof Himachal Pradesh
Name of the Student : Komal SharmaAdmission Number : F- 2013-02-MMajor Advisor : Dr.Ravinder SharmaMajor Field : Agricultural EconomicsMinor Field : Agri-Business Management and Forest ProductDegree Awarded : M. Sc. (Agriculture) Agricultural EconomicsYear of Award of Degree : 2015No. of pages in Thesis : 72+IVNo. of words in Abstract : 455
ABSTRACT
Present study “Non-Timber Forest Products and Livelihood Security: An Economic Study of High Hill Temperate WetZone Households of Himachal Pradesh”, was conducted in Parvati Forest Division of Kullu circle. A sample of 60 householdswas selected for the present study. Results of the study revealed that 83.48 per cent of the households were literate however lowliteracy index (1.84) highlighted the fact that quality of education was poor. About 48.48 per cent of the total land holding wascultivated area, though the cropping intensity was 172 per cent indicating the scope to enhance the farm efficiency. NTFPscontributed about 24.99 per cent to the total farm income. NTFPs contribution to total income on marginal farms was higher(28.86%) compared to small farms (22.72%). Moreover inclusion of NTFPs income in the farm income resulted in reducing theincome inequalities as Gini coefficient with NTFPs income (0.20) reduced to 0.18. Among different NTFPs, the contribution ofPicrorhiza kurroa was found highest (65.85%) whereas, Aesculus indica contributed only 1.14 per cent. NTFPs also provided 72mandays/HH/annum of employment to rural dwellers and maximum days of employment were provided by Jureniamacrocephala. The nominal growth rate of all the medicinal plants showed positive and significant growth. Growth rate wasrecorded highest for Jurenia macrocepha (17.15%) and lowest for Aconitum heterophyllum (3.84%). Real price growth rateanalysis showed that all the medicinal plants were having negative value and decreasing over time except Jurenia macrocephalla(6.57%) which showed positive and significant growth rate over years. Nominal price elasticity for Picrorhiza kurroa, Jureniamacrocephalla and Aesculus indica was more than one i.e., highly elastic in nature with change in price whereas, nominal priceelasticity for Morchella esculanta, Aconitum heterophyllum and Valariana jatamansi was less than one indicated that thesespecies were inelastic in nature. Real price elasticity of Picrorhiza kurroa, Morchella esculanta and Aesculus indica was morethan one. It revealed that in real value term only these medicinal plants have shown positive and significant growth rate with timewhereas, Jurenia macrocephalla has shown negative elasticity indicating highly inelastic in nature. Scarcity ratio was foundpositive and highest for Morchella esculanta (56.36) followed by Aconitum heterophyllum (8.20) and Picrorhiza kurroa (1.37)and rest other medicinal plants were not found scarce in nature. Socio-economic factors like land holding, literacy index and ageof household head were found affecting the NTFPs dependency in the study area. It revealed that people with less land holdingwere more dependent on NTFPs collection and vice-versa. Similarly family with low literacy index was more dependent onNTFPs collection as compare to the family with high literacy index. Older household heads led to wide knowledge of profitableNTFPs, thus family with experienced elders had more dependency on NTFPs.
Signature of Major Advisor Signature of the student
Countersigned
Professor and HeadDepartment of Social Sciences
Dr YS Parmar University of Horticulture & ForestryNauni, Solan, (HP) -173 230