master of science (agriculture) agricultural … · nauni, solan (h p), in partial fulfillment of...

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

______________________________________________________________

Dr. Piyush MehtaAsst. Professor

(Deptt. of Agri-Business Management)

(KR Sharma)Dean’s Nominee

Professor and HeadDepartment of Social Sciences

DeanCollege of Forestry

CERTIFICATE-III

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


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

Aconitum heterophyllum, Acorus calamus, Ajuga bracteose, Plantago ovata,

Morchella esculanta, Jurinea macrocephala, Juglans regia, Saussuria costus,

Arctium lapa, Rhododendron spp. Picrorhiza kurroa, Bergenia ciliata,Potentilla

nepalensis, Salvia moorcroftiana, Morus alba, populus spp., Trilium govanianum

etc.

NTFPs provide important products for local, national and international

markets. These markets are growing rapidly and steadily (Wilkinson & Elivitch,

2000). Non timber resources have great potential for enhancing sustainable rural

development and diversified economic growth, cultural endurance, and

environmental health. Few NTFPs have low cash values and hence are used for

consumption, rather than for sales whereas, rest NTFPs has highly commercial

value. NTFPs are significant especially for poor, because they are available at

low cost on common property lands. They are used by people because they have

less alternative access to food and income. In a country like India, which has

more than half of its population in rural areas and a large tribal population reliant

on forest produce for their sustenance, NTFPs play a major role (Sawhney &

Engel, 2003). At the same time, NTFPs collection should not hamper the

environmental objectives such as conservation of forest and biological diversity.

NTFPs are also of great cultural importance. Preservation of NTFPs is

fundamental to maintenance and continuation of traditional ways of life. The

field of herbal medicine and biomedical research are growing rapidly. Often

people who used them traditionally studied the plants, their uses and techniques

5

of harvesting and processing over generations. As these discoveries blossom into

lucrative industries an equitable share of benefits is due to the people,

communities and countries from which they originate (Prakash, 2003).

Despite of huge importance, non-timber forest products are being

depleted at an alarming rate and forest dependent communities continue to lose

them, either through overexploitation or habitat destruction. Since the objective

of NTFPs is to improve livelihoods and conservation of forest resources, these

resources can best be assured through a process of gradual domestication in

human-modified systems. Unless urgent measures are taken to reverse the current

trend of harvesting NTFPs from the wild, most of these products will disappear

before they are documented and the term NTFPs will eventually be a myth.

However, there is the need for sub-division of the NTFPs continuum into natural,

managed, and cultivated in order to enhance their research development, and

promotion.

Keeping in view the significance of NTFPs in the economy of

households, present study “Non-Timber Forest Products and Livelihood Security:

An Economic Study of High Hill Temperate Wet Zone Households 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:

i) To study the socio-economic status of the sampled households

ii) To estimate the contribution of NTFPs to household income and

employment.

iii) To study the socio economic factors affecting the dependency of rural

household on NTFPs.

Chapter-2

REVIEW OF LITERATURE

This chapter reviews the research work done in the country and abroad on

the various aspects related to present study. There are hardly few comprehensive

studies conducted on economics of collection and contribution of Non-Timber

Forest Products of in the study area. The available literature have been reviewed

and presented under the following sub heads:

2.1 Contribution of NTFPs to household income and employment.

2.2 Socio economic factors affecting the dependency on NTFPs.

2.1 CONTRIBUTION OF NTFPs TO HOUSEHOLD INCOME ANDEMPLOYMENT

Sharif et al. (2015) studied the contribution of NTFPs in sustaining forest-

based rural livelihood in and around a protected area of Bangladesh, and their

potential role in enhancing households’ resilience capacity. The study revealed

that, local communities gather a substantial amount of NTFPs from national park

despite the official restrictions. 27 per cent households (HHs) of the area received

at least some cash benefit from the collection, processing and selling of NTFPs,

and NTFPs contribute as HHs primary, supplementary and emergency source of

income. NTFPs also constituted an estimated 19 per cent of HHs net annual

income, and were the primary occupation for about 18 per cent of the HHs.

Kumar Vikas (2014) studied the contribution of NTFPs to income and

employment by ensuring food and livelihood security for the tribal economy in

the Peechi Vazhani Wildlife Sanctuary, Western Ghats, Kerala. This indicates

that most employment (54.04%) was generated by the wage sector followed by

NTFPs collection (33.77%). About 84 species of NTFPs were found to be

collected and used by the tribals for various purposes such as food (19),

medicines (31), and raw materials for making implements (6) and also as a source

of income (18). It suggested that alternate sources of income to the villagers to

improve their socioeconomic conditions as well as increasing the income level

7

and employment opportunities by effective collection and marketing of Non-

Timber Forest Products.

Schaafsma et al. (2014) estimated the transferable household production

functions of NTFP extraction in the Eastern Arc Mountains (EAM) in Tanzania.

The study showed that the total benefit flow of charcoal, firewood, poles and

thatch from the EAM to the local population has an estimated value of TSH 59

billion (USD 42 million) per year, and provides an important source of additional

income for local communities, especially the poorest. They, therefore, argue that

further restrictions on forest access to promote conservation will require

additional policies to prevent a consequent increase in poverty, and an enforced

trade‐off between conservation and energy supply to rural and urban households.

According to Shrestha and Bawa (2014) harvesting of Chinese caterpillar

fungus, one of the most expensive biological commodities in the world has

become an important livelihood strategy for mountain communities of Nepal.

Results showed that Chinese caterpillar fungus income is the second largest

contributor to the total household income after farm income with 21.1 per cent

contribution to the total household income and 53.3 per cent to the total cash

income. The contribution of Chinese caterpillar fungus income to total household

income decreases as the household income increases making its contribution

highest for the poorest households. There was significant correlation between

Chinese caterpillar fungus dependency and percentage of family members

involved in harvesting, number of food-sufficient months, and total income

without Chinese caterpillar fungus income. Income from Chinese caterpillar

fungus is helping the poorest to educate children, purchase food, and pay debts.

Ajaz-ul-Islam et al. (2013) analyzed the livelihood contribution of forest

resources to the tribal communities of Bundu block in Ranchi district of

Jharkhand state. The result revealed that the average size of forest based direct

paid employment among sample population was 19.82 man days/ household per

annum and the mean income earned from these activities was Rs 2199.70 per

household per annum in the area. The average income and unpaid employment

8

generated through NTFPs based self-employment for the area were estimated to

be 4791.16/household/annum and 88.22 mandays/annum. The percentage of

household involved in collection of various NTFPs varied from 1.83 to100 per

cent and that involved in NTFPs marketing was 1.83 to 80.49 per cent in the

sample villages.

Yadav and Basera (2013) in their study found that increasing trade in forest

products (both timber and non-timber) has supported economic growth and has

helped in reducing poverty in a number of emerging countries. The present study

based on secondary data related to availability, production, marketing and the

dependency of rural population on forest products. The study revealed that

forestry and logging contributes to 1.2 per cent of India's GDP (Economic

Survey, Ministry of Finance, 2011). The Indian forest products industry had total

revenue of $65,844.6 million in 2011, representing a compound annual growth

rate (CAGR) of 5.5 percent between 2007 and 2011. Industry consumption

volumes increased at a CAGR of 0.2 percent between 2007-2011, to reach a total

of 355.4 million cubic meters in 2011.The performance of the industry is

forecasted to accelerate, with an anticipated CAGR of 7.7 percent for the five-

year period 2011-2016, which is expected to drive the industry to a value of

$95,467 million by the end of 2016. Thus, the present study endeavored to assess

the trade status of forest products and their role in economy at global, national as

well as regional level.

Bhavannarayan et al. (2012) studied the dependency and usage pattern

of the NTFPs in Sudikonda range of East Godavari district, Andhra Pradesh,

India and found that 38 different plant species were mostly extracted as NTFPs

for food, fodder, fuel, medicine, household and commercial purposes. Of the

widely exploited 38 species, 5 (13.5 %) are food products, 4 (10.5 %) are used

as fodder, 13 (34.2 %) are commercial, about 8 (21.05 %) are used for

household purpose, and 2 (5.26 %) are plants having medicinal properties and

the rest are used as fuel. The study revealed that almost all of the forest dwellers

depend on the forest products other than timber at varying degrees. The study

9

indicated that NTFPs is a possible solution to release the dependency on timber

and can be sustainable source of income for the people living in the forest.

Reddy (2012) studied the dependency on NTFPs and life style of the

people of Jiribam sub-division of Manipur. 55 plant species belonging to 35

families and 49 genera has been recorded that yield NTFPs. Bamboo, broom,

betel leaf and rattan play a very important role in the socio- economic status of

the region. Wild edible fruits like Artocarpus lacucha, Baccaurea ramiflora,

Ficus racemosa and medicinal plants like Homalomena aromatica Oroxylum

indicum, Croton caudatus are collected from wild habitat. The high rate of

extraction from forests is one of the reasons for the rapid deterioration of the

forests in this region. The need of the hour is to conserve the fragmented

repositories of natural forests by implementing stringent conservation measures

such as scientific management and proper method of harvesting.

Tewari (2012) found that South African forests provide a variety of non-

timber forest products (NTFPs) ranging from building materials to food and

medicinal products. Economically, the NTFPs sustain the rural and peri-urban

poor in meeting their subsistence needs; about 85per cent of households in rural

South Africa use NTFPs in their daily lives. The shortage of NTFPs is bound to

intensify the impacts on rural poor. It is to be noted that continual under-reporting

of NTFPs from forests and woodlands has resulted into lesser importance being

attached to them in the national accounting; consequently their role in supporting

livelihoods and food security is also undermined at the policy formulation stage.

NTFPs can play a bigger role in South Africa by mitigating the effects of hunger

and malnutrition and engendering rural development.

Awe et al. (2011) assessed rural households’ objectives for gathering non-

timber forest products in Kogi State, Nigeria, with specific focus on identifying

some species of non-timber forest products present in the area, identifying

reasons why they are engaged in the gathering of the non-timber forest products

(NTFPs) as well as determining the relative importance of the identified reasons

to the households. The study identified some species of NTFPs that were found in

10

the study area which included locust bean (Parkia biglobosa), water leaf

(Talinium triangulare), bitter leaf (Vernonia amygdalina) and a host of others.

Also, reasons why the rural households in the study area collected NTFPs were

identified. Among the reasons given were food security, self employment,

income generation and continuity. The relative importance of the given reasons

was also determined and it was discovered that food security was the most

important reason the households engaged in NTFPs gathering while continuity

objective was ranked least.

Ghosal (2011) investigated that the Non-Timber Forest Products have an

important role in the household economy of forest fringe dwellers living in the

dry-deciduous forests of Purulia, Bankura and West Midnapur districts of West

Bengal, India. Due to the lack of agricultural land and industrial activities, forest

fringe people collect forest products on regular basis for their livelihoods. They

also make some value-added products to have some extra money. It is

comparatively easier to calculate the monetary value of NTFPs, which are

marketed at the local market or by intermediaries. However, the products which

are normally collected for domestic uses carry enough monetary value too, but it

never calculated in a proper way. It is very difficult to get a proper information

from forest villagers that which product how much each household collect

annually as the production of NTFPs vary from one year to another. A relative

value can be calculated based on the importance, availability and quantity of

these products.

Heubach et al. (2011) examined the NTFPs dependency on of different

socio-economic groups in Northern Benin and compared incomes of five

different ethnic groups being differentiated by their traditional sources of

livelihood and regional provenance. They investigated disparities between three

income groups. On an average, income from NTFPs accounted for 39 per cent of

total household income and more than 80 per cent households were engaged in

collection and sale of NTFPs. NTFPs had a strong equalizing effect on total

income. They observed that poor households are relatively more dependent on

NTFPs than wealthier households. However, the latter extract more NTFPs in

11

quantitave terms and have significantly higher cash returns than poorer ones. This

is mainly due to a significant higher land holding. Moreover, the study revealed

that net income from NTFPs reflects traditional sources of livelihoods of different

ethnic groups.

Maske et al. (2011) conducted the study in the tribal area of Gonida

district of Maharashtra and study suggested that the locals are dependent on

Non-Timber Forest Products for their daily need and income after agriculture.

The average agricultural income was estimated Rs10361.38 per household

compared to average income from NTFPs which was Rs.9371.89 per

Households. It showed that NTFPs collection and selling earned extra income to

the rural tribal economy in Gonida district.

Sarmah and Arunachalam (2011) studied that the Non-timber Forest

Products possess imperative part of the traditional life style in Arunachal Pradesh

and utilization of these products has been contributing much to the local

livelihood. The total contribution of NTFPs to annual household income was

maximum in the villages of Miao circle (23%) followed by Diyun circle (12%)

and Nampong circle (19%) and Vijaunagar (18%) of the total income,

respectively. The minimum income (11%) was found in the villages of Bordumsa

circle.

Manish and Umesh (2010) found that the demand for ayurvedic

medicines is growing at the compound rate of 10-12 per cent annum in Kerala.

The increasing awareness on effectiveness of traditional system has resulted in

higher demand for MAP’s, used by pharmaceutical industries while the area

under the medicinal plants is the same. On the other hand insufficiency in supply

resulted in destructive harvesting methods from the wild is tempering the forest

resource. The estimated demand elasticities were positively varying from 1.27-

2.85 per cent in Sia and Plumbago respectively. The margin of pharmaceuticals is

50 per cent of sale price. The share of cultivators in consumer’s final price was

more than collectors.

12

Shahidullah and Haque (2010) analyzed the value chain for medicinal

plants produced by village-based marginal farmers and homestead growers whose

livelihoods are significantly supported by the commercial scale production of

several plant species. A field-based investigation was carried out in Natore

district of northwest Bangladesh where farmers were engaged in the production

of medicinal plant species. The research gathered explanations for the resultant

improvements in livelihoods and the wider acceptance of such unconventional

agricultural practices in the locality. The findings revealed that the primary and

wholesale secondary markets were mostly dominated by middlemen who cause

inflated prices due to lack of competition in the medicinal plants value chain. A

closer linkage between the producers and processors through vertical integration

in the value chain could result in a multitude of benefits to both the producers and

processors of medicinal plants in terms of price, quality, lead time and overall

control of the supply chain.

Sundarbans are the world’s largest mangrove biomes having highest

mangrove biodiversity, which are used for subsistence and commercial purpose

by the local inhabitants. It is one of the essential components of the livelihood of

the forest dependant population of the area. Singh et al. (2010) tried to explore

the spectrum of NTFPs being collected from Sundarbans and its contribution in

the livelihood of the people. It also attempts to assess the livelihood assets of the

people and how much of it is attributed to NTFPs. The study showed that the

contribution of NTFPs is quite high as it contributes almost 79per cent (Rs.

80,000) on an average to the annual income of the collector’s family. The major

NTFPs that are being collected include firewood, prawn, fishes, crab, honey and

bee wax. The collection of NTFPs is a daunting task, which involves risk from

man-eater tigers. High livelihood vulnerability was also observed with little help

from government. If, these biomes are to be conserved it is necessary that

Sunderban Development Authority must consider the role of NTFPs in the

livelihood and develop the Sustainable Livelihood Framework accordingly so

that the biomes as well as people’s need could be sustained.

13

Wagh et al. (2010) studied the tribal district in Madhya Pradesh where

people live close to forest and are totally dependent on the forest for their daily

needs. The role of NTFPs became more significant for less agriculture dependent

communities with small land holdings residing in remote forests. Collecting and

selling of NTFPs is considered as an important way of using vegetation in

sustainable manner. Edible and medicinal plants are principle NTFPs. Types,

species and amount of NTFPs are different in different seasons and also

influenced by the location. 39 economically important species utilized by the

local people have been recorded from the Jhabua district. The importance of

NTFPs collection and trading in local communities has shown decreasing trend

due to the exotic culture invading in the study region.

Yadav et al. (2010) studied the status and potential of NTFPs in

community forests and the national forest of Bardiya district in western Nepal.

The results indicated that the area harbours a high diversity of potential NTFPs.

Altogether, 101 species and their traditional uses were recorded. For the different

categories, medicinal plants comprised the highest number of 56 species (55%);

followed by wild edible 26 (25%) (vegetables 13 and fruits 13); fermentation

(15); fish poison (9); fodder (7); agricultural implements (6); rope making (5);

plants having religious importance (3); and thatching (2). Despite a gradual but

systematic socio-cultural transformation of the local communities, they still

possess ample knowledge of plants and their uses. It was observed that the proper

management of the NTFPs could play a vital role in the improvement of people’s

livelihood on a sustainable basis.

Babulo et al. (2009) calculated that the provision of consumptive forest

environmental products (i.e. fuel wood, farm implements, construction materials,

wild food items, herbs, medicines) contributes 27 per cent of the income in

northern Ethiopia.

Sandemose (2009) found the effect in the cash income after an official

memorandum banning NTFPs collection for commercial use. This study was

conducted in Soligas tribes’, in the forests of Biligiri Rangaswamy Temple

14

Wildlife Sanctuary (BR Hills), NTFP dependence before the ban was

implemented in 2006, their cash income constraints after the ban, and their

present attitudes towards conservation. The Soligas lost more than half of their

cash income after the ban, in addition to losing a substantial source of products

for subsistence use. This has further led to a larger cash income inequality among

the Soligas. Cash income from NTFPs collection constituted 85.2 per cent of

their total cash income before the ban, and the Soligas have few other options to

sustain their livelihoods. They are highly dependent on NTFPs to sustain their

livelihood needs and to supplement their daily requirements. 81 per cent of the

Soligas own land, and coffee production is increasing among the Soligas. After

the ban cash income from off-farm activities increased from constituting 12.8 per

cent of the total cash income to 71.8 per cent. The study also found that the ban

and the conflict with the Forest Department have had an adverse effect on

Soligas’ attitudes towards conservation. The Soligas feel alienated from the forest

that holds their history of social and cultural heritage and their identity.

Rasul et al. (2008) found that NTFPs are an integral element of the

livelihoods of millions of rural poor in India. The growing local and global

markets for natural products have created great opportunities for improving the

lives of poor and disadvantaged groups who are largely bypassed by India’s rapid

urban based economic growth. Having recognized this potential, the government

of India has recently made a policy shift by according a higher priority to NTFPs

than to timber. In view of that, several initiatives have been taken by State

governments, particularly in Northeast India, in order to better utilize these

resources for improving the livelihoods of forest dependent people, as well as for

environmental conservation

Bista and Webb (2006) reported non-timber forest products (NTFP) use is

an integral part of the rural economy of Nepal. A marketing chain and financial

analysis of Valeriana jatamansi (Sugandhbala) revealed that various trading and

marketing channels existed from the district level NTFP outlets. Domestication is

an important strategy to ensure a continuous supply of NTFPs. Middleman

15

traders are seen as important stabilizers of prices as long as fair trading practices

are followed.

Gopalakrishnan et al. (2005) studied the behavior of peripheral

communities towards non-timber forest product (NTFP) harvesting by estimating

the demand functions for NTFP. They tested the competitive time allocation

hypothesis between NTFP extraction and tea plantations, the predominant

agricultural sector in the vicinity of Sinharaja rain forest in Sri Lanka. Results

provided the statistical evidence for the existence of competitive time allocation

between tea plantations and NTFP extraction. Own-price elasticities are

consistently inelastic, except for one product. NTFPs have an important role in

the rural economy and also provide benefits of considerable importance at the

national level. The knowledge, skills, social customs and traditions related to

NTFPs have been passed on from one generation to another, and form part of the

traditional culture. Around 40 per cent of the rural population in the dry zone

derives some benefits from NTFPs, and the value of NTFPs from the lowland

rain forest has been estimated to be $ 300/ ha/ year. Inadequate implementation

of policy guidelines, shrinking resource base and inadequate knowledge on

cultivation, management, harvesting, processing and storage are identified as the

major issues which hinder the development of the NTFP sector.

Mahapatra and Tewari (2005) studied that dry deciduous forests in India

is one such typical case where forest valuation is yet to integrate the NTFP stock,

widely used for subsistence and cash income. A detailed accounting of

commercially valuable forest products harvested from dry deciduous forests of

Eastern India was made to highlight the economic worth of forests and

contribution of NTFPs. The net present value of revenues from NTFP was

estimated to be US$ 1016/ha in the coastal area and US$ 1348/ha in the inland

area, which proved to be significantly higher than the returns from alternative

land uses. Dry deciduous forests were estimated to have more value than had

been assumed previously and compared favorably against potential timber

revenue (US$ 268/ha).

16

Paloti and Hiremath (2005) studied the time spent pattern in non-timber

forest products (NTFPs) activities and income earned from them by the rural

women. The study was conducted in Dharwad Taluk of Dharwad District in

Karnataka, India during 2002-03. The results showed that NTFPs play a

significant role in the livelihood of forest dwellers. A total of 9 NTFPs, namely

leaves for meal plates, edible gum, fruits, fuelwood, Pongamia seeds, materials

for basket and brooms were being collected, processed and marketed by the

rural women and help to supplement their family income during off-season.

Rural women account for more of the households’ time spent in collection and

processing of NTFPs.

Sundariyal (2005) highlighted, that if cultivation of M&APs is planned

properly, it could emerge as a potential sector to support large number of people

with high revenue generation. Young and unemployed rural youths and poor

farmers could be motivated to raise selected species and suitable support be made

available to them in terms of planting material, technology practice, capacity

building and infrastructure. The state departments should start funding potential

growers.

Bhattacharya and Hayat (2004) reported that in the dry deciduous forests

of central India, non-timber forest products (NTFPs) are the major source of

livelihood and income generation to local people. Due to the extremely dry

climate and the erratic nature of rainfall, only approximately 20 per cent of

people undertake rain fed agriculture with the result that forests are under

tremendous pressure from cattle grazing and fire. Unsustainable harvesting and

collection of NTFPs has reduced their availability in the natural forest, which was

threatening the livelihood of the tribal collectors. The study was conducted in

Sheopur district of Madhya Pradesh.

De ME et al. (2004) examined the relationship between poverty and the

use of wild food, namely bush meat, fish and wild plants, within a Congolese

agricultural community. The analysis indicated that wild foods play a small role

in household consumption but a major role in household income. 90 per cent of

17

both bush meat and fish production was sold at market. In addition, the value of

wild foods increases in the “lean season” when agricultural production was low.

Poorest households in this community were unable to capitalize on the most

valuable wild foods, bush meat and fish, as a source of food or cash income. The

study revealed that overall the small scale commercialization of wild foods

provides a vital source of income for rural households living in extreme poverty.

Shackleton and Shackleton (2004) examined the role and importance of

NTFPs in the daily lives of rural people in South Africa. The users extract large

volume of NTFPs annually, amounting to (per household on average)

approximately 5.3 tonnes of fuel wood, 58 kg of wild spinaches, 104 kg of edible

fruit and 185 large poles for fencing, karaals and housing. The NTFPs used by

more than 85 per cent households which serve as safety and emergency net in the

times of misfortune.

Singh (2004) studied indigenous knowledge, marketing channels,

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




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)


1.4(24.10)

1.25(22.48)


0.22(3.80)

0.21(3.77)


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





Marginal Small Overall

Male (%)

Female (%)

37




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)


1.4(24.10)

1.25(22.48)


0.22(3.80)

0.21(3.77)


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 (%)

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


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














(%)



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

38














(%)



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


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


4.1.4 Workforce














(59.70)3.27

(56.25)3.24

(58.27)


2.18(40.29)

2.54(43.64)

2.31(41.54)


5.82(100.00)

5.56(100.00)








39


4.1.4 Workforce














(59.70)3.27

(56.25)3.24

(58.27)


2.18(40.29)

2.54(43.64)

2.31(41.54)


5.82(100.00)

5.56(100.00)







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





















40




















MinimumMaximumAverage

41

Table: 4.6 Land use pattern of sampled households(ha)


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




(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


(5.26)0.16

(14.16)0.07


(100.00)1.13

(100.00)0.66





Cultivated Land

Orchard

Pasture

Barren Land

Cultivated Land

Orchard

Pasture

Barren Land

23%

16%

11%

48%

41




(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


(5.26)0.16

(14.16)0.07


(100.00)1.13

(100.00)0.66




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)

Particulars Marginal Small OverallCow 1.47

(26.39)1.54

(30.92)1.49

(28.38)Milk 1.15 1.27 1.2Dry 0.31 0.27 0.29Sheep/Goat 2.82

(45.24)1.81

(36.34)2.44

(46.47)Young stock 0.76

(13.64)1.04

(20.88)0.86

(16.38)Bullock 0.52

(9.33)0.36

(7.22)0.46

(8.76)Total 5.57

(100.00)4.98

(100.00)5.25


44

4.2 INCOME AND EMPLOYMENT STRUCTURE OF SAMPLEDHOUSEHOLDS

4.2.1 Income structure

In order to calculate income structure, share of NTFPs income in the total

income and employment pattern, only those households was selected who were

actively engaged in NTFPs collection. Total number of those households was 39

out of which 24 were marginal and 15 were small farmers.

The present study revealed that a as good number of villagers have

knowledge of exploring NTFPs as about 24.99 per cent of the gross income was

generated from the collection of NTFPs on the overall while fruit, cereal and

vegetable contributed 27.49per cent, 14.76 per cent and 14.25 per cent

respectively (Table 4.9). In case of marginal farms NTFPs contributed maximum

share (28.86%) followed by fruit (25.26%), whereas, on small farms maximum

share was from fruit (29.87%) followed by NTFPs (20.87%). Vegetables

contribution varied from 11.72 per cent to 16.94 per cent on marginal and small

farms. Thus the study revealed that the majority of households from marginal

farms preferred to collect the NTFPs from the forest area as NTFPs contributed

maximum proportion in their household income.

Minimum income obtained from the collection of NTFPs was Rs.

12400/household/annum and maximum was Rs. 93450/HH/annum in case of

marginal farms with on an average income of Rs. 38038/HH/annum. In case of

small farms it was Rs. 41383/HH/annum. Mostly farmers sell their produce to the

local traders and shopkeepers, since majority of farmers were poor and

unemployed who collected NTFPs in large quantities. The collectors mostly

recognize NTFPs by leaves, flowers or by smelling. According to them,

nowadays, the NTFPs are not easily available in the forests. The collectable

quantity of NTFPs is decreasing over the years.

Variation in income from different sources was also calculated and

maximum variation was found in income from vegetables (61.35%) due to the

variation in land holding. Overall variation in income from farm activities was

45

recorded as 35.32 per cent and from NTFPs 54.40 per cent respectively. The

variation in total income was found to be decreased to 30.99 per cent when

income from NTFPs was added to total farm income. The higher variation in

income from NTFPs was reported due to the variations in their prices by the

collectors.

Table: 4.9 Source of income of sampled households(Rs./HH/year)


Fig: 4.7: Income from different sources of sampled households

4.2.2 Scenario of NTFPs in the study area

The details of various NTFPs available i.e., the period of availability, the

harvesting parts, methods of collection and end use of the products is presented in

0

5

10

15

20

25

30

Marginal

Per

cen

t

ParticularsMarginal Small Overall

Mini. Max. Average CV(%)

Mini. Maxi. Average CV(%)

Average CV(%)

Vegetables 5000 3115015454(11.72)

41.95 18125 7387533602(16.94)

44.9322434(14.25)

61.35

Cereal crops7950 42100

16864(12.79)

55.95 15900 4990033437(16.86)

32.2823238(14.76)

55.02

Fruits 0 7050033292(25.26)

48.33 27500 10100059233(29.87)

42.3443269(27.49)

54.29

Livestock 0 4480028125(21.34)

26.09 28000 4480030613(15.44)

15.2529082(18.48)

22.31

Farm55450 146219

93735(71.13)

25.23 94800 222863156885(79.12)

21.8118023(75.00)

35.32

NTFP12400 93450

38038(28.86)

52.87 13150 7980041383

(20.87057.72

39324(24.99)

54.4

Farm+NTFP85794 227088

131773(100.00)

25.52 107950 251500198268(100.00)

20.68157348(100.00)

30.99

45





collectors.







Small Overall

vegetables

Cereal crops

Fruits

Livesock

NTFPs




Average CV(%)

Vegetables 5000 3115015454(11.72)

41.95 18125 7387533602(16.94)

44.9322434(14.25)

61.35


16864(12.79)

55.95 15900 4990033437(16.86)

32.2823238(14.76)

55.02

Fruits 0 7050033292(25.26)

48.33 27500 10100059233(29.87)

42.3443269(27.49)

54.29

Livestock 0 4480028125(21.34)

26.09 28000 4480030613(15.44)

15.2529082(18.48)

22.31

Farm55450 146219

93735(71.13)

25.23 94800 222863156885(79.12)

21.8118023(75.00)

35.32

NTFP12400 93450

38038(28.86)

52.87 13150 7980041383

(20.87057.72

39324(24.99)

54.4

Farm+NTFP85794 227088

131773(100.00)

25.52 107950 251500198268(100.00)

20.68157348(100.00)

30.99

45





collectors.







vegetables

Cereal crops

Fruits

Livesock

NTFPs




Average CV(%)

Vegetables 5000 3115015454(11.72)

41.95 18125 7387533602(16.94)

44.9322434(14.25)

61.35


16864(12.79)

55.95 15900 4990033437(16.86)

32.2823238(14.76)

55.02

Fruits 0 7050033292(25.26)

48.33 27500 10100059233(29.87)

42.3443269(27.49)

54.29

Livestock 0 4480028125(21.34)

26.09 28000 4480030613(15.44)

15.2529082(18.48)

22.31

Farm55450 146219

93735(71.13)

25.23 94800 222863156885(79.12)

21.8118023(75.00)

35.32

NTFP12400 93450

38038(28.86)

52.87 13150 7980041383

(20.87057.72

39324(24.99)

54.4

Farm+NTFP85794 227088

131773(100.00)

25.52 107950 251500198268(100.00)

20.68157348(100.00)

30.99

46

Table 4.10. NTFPs were collected all year round. However, all of them were

seasonal in nature. Kutki, Dhoop, Patish and Sugandhbala were collected in the

same season i.e., during July–September. The collection of Guchhi was done

from January to March while Akhnor was collected during November-December.

Each collector either used to make on an average of 2 to 3 visits to the forest in a

month or stayed for 15-20 or 30 days in a group of 5-10 persons. However, this

frequency varied according to season and type of NTFPs collected. In the study

area collection of NTFPs was carried out only by men, though women were

engaged in household and farm activities only.

Table 4.10 Details of the NTFPs collected in the study area

BotanicalName

Local name Period ofavailability

Part used Methodof

collection

End use

Picrorhizakurroa

Kutki July-September

Roots/rhizomes Uprooting Fever,hypertension,muscle spasms,parasitic worms,cancer, malaria etc

Jurineamacrocephala

Dhoop July-September

Roots/tubers Uprooting Fever, mentaldepression,digestive disorder,dyspepsia

Morchellaesculenta

Guchhi January-March

Entire plantUprooting

Cold and paste isapplied to cureboils, highlynutritious used asvegetable

Aconitumheterophyllum

Patish July-September

Roots Uprooting Stomachdisorders, fever

Valerianajatamansi

Sugandhbala July-September

Rhizomes andRoots

Uprooting Hysteria, epilepsy,shell shock andnervousProblems

Aesculusindica

Akhnor November-December

Fruits Pluckingby hand

Seed oil is usedfor skin diseases,headache, anti-inflammatory

4.2.3 Composition of NTFP employment pattern

The collection of NTFPs generated employment to rural households. On

an average 72 mandays were generated in the study area (Table 4.11). The

employment generated per households per year varied between about 70 to 73

mandays on small and marginal farms. Dhoop was the major employment source

contributing 28.10 per cent (21mandays/HH/annum) to the total NTFPs

47

employment. The collection of Dhoop was time consuming process due to the

deep root system of the plant. Kutki collection was the next important

employment generating activity providing employment of 14

mandays/HH/annum followed by Guchhi (13 mandays/HH/annum). The

collection of Sugandhbala, Patish and Akhnor were contributed 14.64%

(11mandays/HH/annum), 14.07% (10 mandays/HH/annum) and 6.67% (5

mandays/HH/annum) respectively to the total NTFPs employment. The collection

of Akhnor was generating less than 5 mandays of employment because of its easy

availability and ease in its collection.

Table 4.11 Contribution of NTFPs collection in employment generation(Mandays)


Fig: 4.8 Contribution of different NTFPs in employment pattern

0

5

10

15

20

25

Marginal

Day

s/H

H/a

nnum

Name of Species

Marginal Small OverallEmployment

generated(days/HH/year)

Employmentgenerated

(days/HH/year)

Employmentgenerated

(days/HH/year)

Picrorhiza kurroa13.28

(17.97)14.31

(20.16)13.66

(18.76)

Jurinea macrocephala21

(28.42)19.54

(27.53)20.46

(28.10)

Morchella esculenta12.63

(17.09)13.4

(18.88)12.91

(17.73)

Aconitum heterophyllum10.92

(14.78)9.09

(12.80)10.25

(14.07)

Valeriana jatamansi11.31

(15.31)9.54

(13.44)10.66

(14.64)

Aesculus indica4.73

(6.40)5.09

(7.17)4.86

(6.67)

Total73.87

(100.00)70.97

(100.00)72.81

(100.00)

47














Name of Species



Employmentgenerated

(days/HH/year)

Employmentgenerated

(days/HH/year)


(17.97)14.31

(20.16)13.66

(18.76)


(28.42)19.54

(27.53)20.46

(28.10)


(17.09)13.4

(18.88)12.91

(17.73)


(14.78)9.09

(12.80)10.25

(14.07)


(15.31)9.54

(13.44)10.66

(14.64)

Aesculus indica4.73

(6.40)5.09

(7.17)4.86

(6.67)

Total73.87

(100.00)70.97

(100.00)72.81

(100.00)

47













Kutki

Dhoop

Guchhi

Patish

Sugandhwala

Akhnor

Name of Species



Employmentgenerated

(days/HH/year)

Employmentgenerated

(days/HH/year)


(17.97)14.31

(20.16)13.66

(18.76)


(28.42)19.54

(27.53)20.46

(28.10)


(17.09)13.4

(18.88)12.91

(17.73)


(14.78)9.09

(12.80)10.25

(14.07)


(15.31)9.54

(13.44)10.66

(14.64)

Aesculus indica4.73

(6.40)5.09

(7.17)4.86

(6.67)

Total73.87

(100.00)70.97

(100.00)72.81

(100.00)

48

4.2.4 Variability in income from selected medicinal plants

Non-timber forest products were the next major alternative source of

income after agriculture in the study area because of the availability of large scale

forest area. It is clear from Table 4.9 that on an average income generated from

NTFPs was Rs. 38038, Rs. 41383 and Rs. 39234 contributing 28.89 per cent,

20.87 per cent and 24.99 per cent on marginal, small and on overall farmers,

respectively. NTFPs played a critical role in providing subsistence and cash

income to the households of the study area. Six types of species were being

collected by local households and share of these species in total NTFPs income

has been presented in Table 4.12. The share of Kutki was maximum (65.85%)

followed by Patish (12.93%) on the overall category. Similar results were found

in case of marginal and small farms. The minimum share (1.14%) was

contributed by Sugandhbala. The income from NTFPs varied between Rs. 38038

on marginal farms to Rs. 41383 on small farms in the study area. It was

concluded that from the foregoing analysis that Kutki and Patish were major

contributors of NTFPs income in the study area.

Table: 4.12 Variability in income from selected medicinal plants(Rs./HH/annum)


4.2.5 Gini concentration ratio

To study the role of NTFPs in household income inequality Gini

coefficients were estimated and results have been presented in Table 4.13. The

Name of species Marginal Small Overall

Picrorhiza Kurroa24167(63.53)

28667(69.27)

25898(65.85)

Jurineamacrocephala

4125(10.84)

4400(10.63)

4231(10.75)

Morchella esculenta2375(6.24)

2250(5.43)

2327(5.91)

Aconitumheterophyllum

5600(14.72)

4267(10.31)

5087(12.93)

Valeriana jatamansi437

(1.14)473

(1.14)450

(1.14)

Aesculus indica1334(3.50)

1326(3.20)

1330(3.38)

Total38038

(100.00)41383

(100.00)39324

(100.00)

49

inclusion of NTFPs income in total household income considerably reduced

inequality between households from 0.20 to 0.18. The value of Gini coefficient of

livestock income was very small (0.02) indicating that equal distribution of

income among the households in the study area. In case of marginal farms Gini

coefficient for farm income without NTFPs (0.14) reduced to 0.13 with the

inclusion of NTFPs. In small farm category it has decreased to 0.12 from 0.13.

Thus from this study it was proved that the NTFPs helped in diminishing income

disparities between rural people. Similar findings were reported by Heubach et al.

(2011).

Table: 4.13 Comparison of Gini coefficient of total household incomewithout and without NTFPs income

Particulars Marginal Small Overall

Vegetables 0.07 0.06 0.18

Annual crops 0.26 0.18 0.13

Fruits 0.15 0.17 0.12

Livestock 0.01 0.02 0.02

Farm 0.14 0.13 0.20

NTFP 0.28 0.33 0.17

Farm + NTFP 0.13 0.12 0.18

4.3 COST OF COLLECTION AND NET RETURN FROM NTFPsCOLLECTION

Afore mentioned species were not cultivated as such on the private land

but were collected from the forest area. Hence their cost of collection and net

return were estimated and the results are presented in Table 4.14. Cost of

collection of NTFPs was calculated on per qtl basis. On this basis gross return

and net return were calculated for all the selected species except Guchhi. In case

of Guchhi as its quantity collected was very less because of its less availability,

its cost and returns were estimated on per kg basis.

50

4.3.1 Net return from collection of NTFPs

Table 4.14 showed the gross return, cost of collection and net return from

different species. Gross returns from Kutki were Rs. 200000/qtl and cost of

collection was Rs. 7260.50/qtl. Therefore net returns obtained from Kutki were

Rs. 192739.50/qtl. Net returns from Dhoop, Patish, Sugandhbala and Akhnor

were Rs. 11306.17/qtl, Rs. 312506.17/qtl, Rs. 506.17/qtl, Rs. 342.50/qtl,

respectively. Gross returns from Guchhi were Rs. 15000/kg whereas cost of

collection was Rs. 4493.83/kg and net returns received by collection of Guchhi

were Rs. 8524.08/kg. The study revealed that the farmers were getting a good

amount of net returns from the NTFPs collection.

Table: 4.14 Cost and Returns from NTFPs of Collection

Name of species Units Gross return Cost of collection Net return

Kutki Rs/qtls 200000 7260.50 192739.50

Dhoop Rs/qtls 20000 8693.83 11306.17

Guchhi Rs/ Kg 15000 6475.92 8524.08

Patish Rs/qtls 320000 7493.83 312506.17

Sugandhwala Rs/qtls 5000 4493.83 506.17

Akhnor Rs/qtls 1000 657.50 342.50

4.4 SUPPLY ANALYSIS OF SELECTED MEDICINAL PLATS

Based on the data collected from the forest department supply of different

medicinal plants and the results of analysis have been described in the following

section.

4.4.1 Growth and variability in prices

The price analysis for the selected medicinal plants was carried out by

considering their nominal and real prices. Nominal prices means current market

price in different years and real values means value at constant prices. Therefore,

Current/nominal prices are deflated by the consumer price index to estimate the

real prices. The linear growth rates of nominal as well as real prices were

estimated and the results have been presented in Table 4.15.

51

Table: 4.15 Growth and variability in nominal as well as real prices ofselected medicinal plants (2004-05 to 2013-14)

MedicinalPlants

Nominal Prices Real PricesMean(Rs.)

Nominalprice

growthrate (%)

CV(%)

Mean(Rs.)

Percentagedecrease

over nominalprices

Realprice

growthrate (%)

CV(%)

Kutki 361.00 9.05*

(1.46)30.10 238.55 33.92 -1.35

(0.95)9.10

Dhoop 64.50 17.15*

(3.56)60.21 39.80 38.30 6.57*

(2.39)28.56

Guchhi 10000.00 5.09*

(0.63)16.33 6820.50 31.80 -4.67*

(0.83)15.84

Patish 1690.00 3.84*

(0.57)12.61 1164.10 31.12 -6.15*

(0.49)19.00

Sugandhbala 50.00 8.85*

(1.43)29.44 33.12 33.76 -1.33

(1.29)11.74

Akhanor 7.70 7.79*

(0.78)24.42 4.64 39.77 -2.03

(1.27)10.69

Figures in the parenthesis are the standard errors of the linear growth rates.* Significant at 5 per cent level of significance respectively.

The results showed that average current prices for the selected medicinal

plants were found higher as compare to average real prices. It can be seen from

the table that the real prices showed a decrease of 39.77 per cent to 31.12 per

cent.

Linear growth rate of nominal and real prices of selected medicinal plants

showed that nominal prices of all the medicinal plants showed positive and

significant growth where as in case of real prices only Dhoop showed significant

growth indicating that in real term prices of Dhoop were increasing. Whereas,

Guchhi and Patish were recorded negative and significant growth rate. In

nominal prices highest growth rate (17.15%) was observed in Dhoop followed by

Kutki (9.05%), Sugandhbala (8.85%) and Akhnor (7.79%). The lowest growth

rate was observed in Patish (3.84%) followed by Guchhi (5.09%). In real sense

only Dhoop price (6.57%) showed significant growth whereas, real prices of

other M&APs did not show significant growth.

The analysis of coefficient of variation of nominal prices showed highest

variation in prices of Dhoop (60.21%) followed by Kutki prices (30.10%) and

lowest in Patish prices (12.61%). In real prices also highest variation was shown

by Dhoop prices (28.56%) followed by Patish prices (19.00%) and lowest

variation by Kutki prices (9.10%). The results have also been presented in Fig.

4.9.

52

Fig: 4.9 Trends in nominal and real prices of selected medicinal plantsduring 2004-05 to 2013-14

0100200300400500600700

2004

-05

2005

-06

2006

-07

2007

-08

2008

-09

2009

-10

2010

-11

2011

-12

2012

-13

2013

-14

N

R 0

50

100

150

200

2004

-05

2005

-06

2006

-07

2007

-08

2008

-09

2009

-10

2010

-11

2011

-12

2012

-13

2013

-14

N

R

02000400060008000

100001200014000

2004

-05

2005

-06

2006

-07

2007

-08

2008

-09

2009

-10

2010

-11

2011

-12

2012

-13

2013

-14

N

R

0500

1000150020002500

2004

-05

2005

-06

2006

-07

2007

-08

2008

-09

2009

-10

2010

-11

2011

-12

2012

-13

2013

-14

N

R

020406080

100

2004

-05

2005

-06

2006

-07

2007

-08

2008

-09

2009

-10

2010

-11

2011

-12

2012

-13

2013

-14

N

R02468

1012

2004

-05

2005

-06

2006

-07

2007

-08

2008

-09

2009

-10

2010

-11

2011

-12

2012

-13

2013

-14

N

R

Kutki Dhoop

Guchhi Patish

Sugandhbala Akhanor

53

4.4.2 Nominal Price elasticity

The arc elasticity was used to estimate the elasticity of supply of selected

medicinal plants. The values were positive for all the medicinal plants showing

the positive price supply elasticity relationship.

Table: 4.16 Nominal price elasticity of supply of selected medicinal plantsduring 2004-05 to 2013-14

Name of thespecies

Averagequantity in

theterminalyears inqtls(Q2)

Averagequantity

in thebase yearsin qtls(Q1)

Averageprice in

theterminalyears (P2)

Averageprice inthe base

years (P1)

Elasticityof

Supply

Kutki 270.90 64.66 500 273.33 2.10

Dhoop 125.66 35.66 116.66 36.66 1.07

Guchhi 10.51 7.24 12000 8000 0.92

Patish 1.93 1.6 1933.33 1500 0.74

Sugandhbala 7.31 6.66 66.66 36.66 0.16

Akhanor 4.36 2.33 10 5.66 1.09

The results have been shown in the Table 4.16. The own price elasticity

for Guchhi, Patish and Sugandhbala were found less than one i.e., proportionate

increase in the price brings about a less than proportionate change in the quantity

supplied which indicated that supply of these medicinal plants were inelastic.

Whereas, own price elasticity for Kutki, Dhoop and Akhnor were elastic i.e.,

proportionate change in the price brings about significant change in the quantity

supplied. The highest price elasticity was recorded in Kutki (2.10) followed by

Akhanor (1.09) and Dhoop (1.07). The lowest price elasticity was recorded for

Sugandhbala (0.16) followed by Patish (0.74) and Guchhi (0.92) highlighting

their responsiveness to price change. As there is positive relationship between

price and quantity supplied it means more quantity will be supplied higher price

and vice-versa.

54

4.4.3 Real price elasticity

The own price elasticity was also estimated for real prices and results

have been shown in the Table 4.17. The price elasticity for all the medicinal

plants was positive except Dhoop (-2.50) which indicated that Dhoop was very

inelastic in nature, i.e., change in price does not affect the quantity supplied of the

species. The price elasticity for two species Sugandhbala (0.89) and Patish (0.43)

were found less than one i.e., these medicinal plants were inelastic in real sense,

whereas, other species were highly elastic in nature. The highest elasticity was

shown by Kutki (13.02) followed by Akhnor (10.31) and Guchhi (1.30). Negative

elasticity of supply figures result in an inelastic relationship between quantity

supplied and the price.

Table: 4.17 Real price elasticity of supply of selected medicinal plants during2004-05 to 2013-14

Name of thespecies

Averagequantity in

theterminalyears inqtls (Q2)

Averagequantity

in thebase years

in qtls(Q1)

Averageprice in

theterminalyears (P2)

Averageprice inthe base

years (P1)

Elasticityof

Supply

Karoo 270.903 64.66 236.55 260.00 13.02

Dhoop 125.667 35.66 54.95 34.90 -2.50

Guchhi 10.51 7.24 5735.87 7636.62 1.30

Patish 1.93 1.6 920.89 1431.87 0.43

Sugandhbala 7.31 6.66 31.38 34.84 0.89

Akhanor 4.36 2.33 4.78 5.07 10.31

4.4.4 Scarcity ratio of selected medicinal plants

The real increase in the price of the resources over a period of time

indicates the economic scarcity of the resources (Suneeta, 1998). Considering this

rule, the scarcity ratios for the selected medicinal plants were estimated by

comparing the change in the real prices during 2004-2014 and results have been

presented in Table 4.18. The medicinal plants which showed the positive ratio

were considered as scarce. Kutki, Guchhi and Patish had positive ratio implying

thereby that these species are highly scarce in nature. The scarcity ratio was

55

found highest in Guchhi (56.36) followed by Patish (8.20) and Kutki (1.37).

Hence, availability of these species is becoming scarce over the period of time.

Other selected medicinal plants showed negative ratios indicating their abundant

availability in nature.

Table: 4.18 Scarcity ratios of selected medicinal plants supplied during2004-05 to 2013-14

YearsSelected medicinal plants

Karoo Dhoop Guchhi Patish Sugandhbala Akhnor

Quantity(qtls)

2004-05 37 38 2.61 0.3 7 0.5

2005-06 74 46 15.21 1.6 9 3

2006-07 83 23 3.9 2.9 4 3.5

2007-08 46.5 18 1.44 4 9 5

2008-09 125.9 29 2.55 1.9 5 10

2009-10 103 23 2.9 3 4 7

2010-11 92.7 55 3.5 0.11 1 6.5

2011-12 231.71 48 5.2 0.41 4.2 4.3

2012-13 259 119 15.66 2.5 7.25 5

2013-14 322 210 10.69 2.9 10.5 3.8

Scarcity ratio 1.37 -0.45 56.36 8.20 -0.67 -0.95

4.5 SOCIO-ECONOMIC FACTORS AFFECTING DEPENDECY ONNTFPs

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


Dr YS Parmar University of Horticulture & ForestryNauni, Solan, (HP) -173 230

CURRICULUM VITAE

Name : Komal Sharma

Father’s Name : Sh. Narayan Sharma

Date of Birth : 2nd March, 1992

Sex : Female

Marital status : Unmarried

Nationality

Email

:

:

Indian

[email protected]

Academic Qualification:

Examination

Passed

Year of

Passing

University/Board Division

Matriculation 2007 HPSEB, Dharamshala FIRST

10+2 2009 HPSEB, Dharamshala FIRST

Graduation 2013Dr. Y S Parmar UHF, Nauni,

Solan (HP)FIRST

Whether sponsored by some state/Central

Govt./Univ./SAARC

: N.A.

Scholarship/Stipend/Fellowship/Any other financial

assistance received during study period

: University Scholarship

(Komal Sharma)

master of science (agriculture) agricultural … · nauni, solan (h p), in partial fulfillment of...

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