An Economic Analysis of Intersectoral Water Allocation In

AN ECONOMIC ANALYSIS OF INTERSECTORAL WATER ALLOCATION IN

SOUTHEASTERN SRI LANKA

SARATH PARAKRAMA WELIGAMAGE

A dissertation submitted in partial fulfillment of the requirements for the degree of

DOCTOR OF PHILOSOPHY

WASHINGTON STATE UNIVERSITY School of Earth and Environmental Sciences

AUGUST 2011

To the Faculty of Washington State University:

The members of the Committee appointed to examine the dissertation of SARATH PARAKRAMA WELIGAMAGE find it satisfactory and recommend that it be accepted.

______Keith A. Blatner, Ph.D., Chair

______C. Richard Shumway, Ph.D.

______Jill J. McCluskey, Ph.D.

ACKNOWLEDGMENT

Earning a PhD from WSU fulfills a long held aspiration in my life to earn a doctorate from a US university. I thank all those who have contributed to my achieving this goal at Washington

State University.

Frank Rijsberman, Director General (2001-2007) of the International Water Management

Institute (IWWI), was the key person behind meeting my aspiration to earn a PhD. His vision and passion for capacity building of scientific manpower in the South led to the initiation of the

IWMI’s program for capacity building that supported my dissertation research. Frank also authorized the initial support for my PhD program. I thank Frank’s successor Colin Chartres, and David Molden, Interim Director General for continued support to me.

At WSU, my major professor Keith Blatner was the key person behind fulfilling my goals.

In addition to his unmatched knowledge spanning across many disciplines, Keith was a constant source of support and I also appreciate his compassion and empathy. I thank Richard Shumway for helping me fulfill my academic aspirations at a very high level. Walter Butcher and Jill

McCluskey also provided invaluable support as committee members. It was a pleasure to work with Jill at a later stage in my project. Steve Burkett, Associate Dean of the Graduate School, deserves a special thank you for his in-depth understanding of the issues faced by a person from half a world away trying to complete an advanced degree.

Randy Barker, Piya Abeygunawardena, and Nihal Atapattu patiently went through previous drafts of my dissertation chapters and provided suggestions for improvement of academic content. They helped me shape my ideas to help make them more policy relevant.

Farmers at Kirindi Oya deserve to receive my unreserved gratitude for sharing their information with me and for providing me with access to their homes. This was during a time of disturbance in their communities. However, they supported me and devoted their time to help me complete my work. I also want to thank the participants of my contingent valuation survey for

iii

agreeing to respond to a questionnaire and for suggesting valuable insights when responding to

the open ended questions. I thank students of my agricultural marketing class for volunteering to

help me facilitate the survey.

I should thank all of “IWMI Family” for their untiring support and well-wishes towards the

success of my program. Special thanks are due to Mark Giordano and David Van Eyck for

unreserved roles played as my immediate manager and IWMI’s capacity building officer

respectively. I also thank my colleagues at the Department of Agricultural Economics and

Extension, University of Peradeniya, for their encouragement and support.

I was fortunate to have friends supporting me and encouraging me in this journey. All of

them cannot be mentioned by name here due to space constraints. The following are just a few

of these individuals. Pullman-WA had changed a lot from Sumathy’s “little humorless city 1” by the beginning of my program. Even before reaching there, Dinali and Ranil helped me to make an unknown territory a second home. Other friends at Pullman: Dharshani and Thilina, Gamini,

Mahesha and Vidhura, Natniel, Renuka, Shahla and Vugar and Susanne showed me much needed care beyond the limits of a simple friendship at different times. In distant parts in North

America, Jeevika and Sunil, Mala and Prax, and Malika and Gamini extended friendship when needed and at critical points when I would have stopped progressing. At Peradeniya, Anusha and Rohitha, and Pushpika and Chamika kept their homes open for me whenever I wanted to stay. Elkaduwa family and Prasanthi were true friends in need throughout the period.

I also wish to thank my sisters; Sriya and Susima; brother-in-law Premasiri; cousins:

Lakshmi, Lal, Kanthi and Sirima, and their families and nephew Pathum who looked after my mother while I was away.

1 Sivamohan, Sumathy. 1999. Postcolonial Dis/Content: South Asian Women And Feminist Theory. P.hD diss. Washington State University. p v.

AN ECONOMIC ANALYSIS OF INTERSECTORAL WATER ALLOCATION IN

SOUTHEASTERN SRI LANKA

Abstract

by Sarath Parakrama Weligamage, Ph.D. Washington State University August 2011

Chair: Keith A. Blatner

This dissertation analyzes current patterns and expected benefits of allocation of water in Kirindi-Menik-Kumbukkan composite river basin in southeastern Sri Lanka. The Veheragala

Diversion Project changed the historical flow regime of Menik Ganga River by diverting water to the Kirindi Oya Basin for irrigation. This diversion reduced water flow to the Yala Protected Area

Complex, a unique, nationally and globally important wildlife refuge situated further downstream, but dry season flows were enhanced. This study develops and applies empirical methods to estimate economic benefits related to two major uses: irrigation and environment.

A procedure to quantify water applied on rice farms, based on farmer recall, was developed and empirically used in the Kirindi Oya Irrigation and Settlement Project (KOISP), where differential access to water between its two subareas exists. A production function for rice with water quantity as an input was estimated. Plans to allocate newly diverted water to maximize system-wide annual net benefits by equating marginal value products of water were generated. The value of water for the environment was estimated through a contingent valuation study that asked respondents about their willingness to pay for water releases through the YPC. Benefits were expected as emanating from non-use values of water.

Findings showed a diverse pattern of distribution and abundance of irrigation systems across three river basins when the sizes and the types of systems were considered. Average water quantity applied by farmers in the Old Irrigated Area of the KOISP was 17 percent higher than that of farmers in the New Irrigated Area, while annual net rice revenues were 36 percent higher in the Old irrigated Area. The optimum water allocation plan for the KOISP would generate annual incremental net benefits of SLR 157 million and is 28 percent higher than the

“Business as Usual” Plan. Mean willingness to pay for water releases estimated using random willingness to pay method was SLR 627 per household per annum. This can be aggregated to a national benefit stream with net present worth of SLR 17.4 billion. This value can be considered as the value of water allocated for environmental uses.

TABLE OF CONTENTS

ACKNOWLEDGMENT ...... iii

Abstract...... v

TABLE OF CONTENTS ...... vii

LIST OF TABLES ...... xi

LIST OF FIGURES ...... xiv

DEDICATION ...... xv

CHAPTER ONE

INTRODUCTION ...... 1

1.1: INTRODUCTION ...... 1

1.2: SRI LANKA: BACKGROUND INFORMATION ...... 2

1.3: STUDY AREA AND STUDY PROBLEM ...... 3

1.4: ORGANIZATION OF THE DISSERTATION ...... 4

1. 5: REFERENCES ...... 6

CHAPTER TWO

WATER RESOURCES IN SRI LANKA WITH SPECIAL REFERENCE TO SOUTHEASTERN

DRY ZONE ...... 7

2.1: INTRODUCTION ...... 7

2.2: SRI LANKA: ADMINISTRATIVE REGIONS AND CLIMATIC ZONES ...... 7

2.3: WATER RESOURCES OF SRI LANKA AND ITS UTILIZATION ...... 8

2.4: WATER RESOURCES IN THE SOUTHEASTERN DRY ZONE OF SRI LANKA 11

2.4.1: Irrigation in KMK Basins ...... 12

2.5: SPATIAL PATTERNS OF DISTRIBUTION OF IRRIGATION SYSTEMS ...... 13

vii

2.6: DESCRIPTION OF THE STUDY AREAS ...... 14

2.6.1: Kirindi Oya Irrigation and Settlement Project (KOISP) ...... 14

2.6.2: Veheragala Diversion Project ...... 16

2.6.3: Yala Protected Area Complex (YPC) ...... 16

2.7: REFERENCES ...... 19

CHAPTER THREE

QUANTIFYING THE IMPACT OF DIFFERENTIAL WATER ACCESS ON FARM

PRODUCTIVITY AND FARMERS’ WELFARE: THE CASE OF KIRINDI OYA IN SRI LANKA

...... 21

3.1: ABSTRACT ...... 21

3.2: INTRODUCTION ...... 22

3.3: METHOD OF ANALYSIS ...... 24

Welfare Indicators ...... 28

3.4: DATA ...... 29

3.5: RESULTS ...... 32

3.5.1: Input Use Patterns and Production Relationships ...... 32

3.5.2: Marginal Value Productivities of Inputs ...... 34

3.5.3: Welfare Implications of Differential Access to Water ...... 36

3.5.4: Water Allocation Plans for Augmented Water ...... 38

3.6: CONCLUSIONS ...... 43

3.7: REFERENCES ...... 45

CHAPTER 4

VALUATION OF ENHANCED DRY SEASON FLOW THROUGH YALA PROTECTED AREA

COMPLEX: ESTIMATING NON-USE VALUES OF WATER ...... 69

4.1: ABSTRACT ...... 69

viii

4.2: INTRODUCTION ...... 70

4.3: THEORETICAL FRAMEWORK ...... 72

4.3.1: Categories of Economic Values ...... 72

4.3.2: Estimation of Economic Values ...... 74

4.4: DESCRIPTION OF STUDY AREA AND THE PROBLEM ...... 75

4.5: METHODS AND DATA ...... 78

4.5.1: Econometric Model ...... 78

4.5.2: Survey ...... 82

4.6: RESULTS AND DISCUSSION ...... 85

4.6.1: Determinants of Willingness to Pay ...... 85

4.6.2: Calculation and Aggregation of WTP ...... 87

4.7: CONCLUSIONS AND POLICY IMPLICATIONS ...... 89

4.8. REFERENCES ...... 91

APPENDICES ...... 103

APPENDIX 01

TABLES AND MAPS FOR CHAPTER 2 ...... 104

APPENDIX 02 ...... 126

WTP QUESTION ...... 126

APPENDIX 3

METHODS OF NON PARAMETRIC ESTIMATION OF WILLINGNESS TO PAY ...... 127

A.3.1: INTRODUCTION ...... 127

A.3.2: THEORETICAL FRAMEWORK ...... 127

A.3.3: CALCULATION AND AGGREGATION OF WTP ...... 129

A.3.4: REFERENCES ...... 131

APPENDIX 04

SCHEDULE FOR FARM HOUSEHOLD SURVEY ...... 135

APPENDIX 05

QUESTIONNAIRE FOR CONTINGENT VALUATION SURVEY ...... 147

LIST OF TABLES

Table 3. 1. Construction of water application indicator ...... 47

Table 3. 2. Variables for estimating Cobb-Douglas production function ...... 48

Table 3. 3. Calculation of total household income ...... 49

Table 3. 4. Indicators used to construct household asset Index ...... 50

Table 3. 5. Means of rice yield reported by sample households, by season and subarea (in kg/ha) ...... 51

Table 3. 6. Means of water related variables by season and by subarea...... 52

Table 3. 7. Mean input intensities per ha and factor prices in Sri Lankan Rupees (SLR) a, by season and by subarea ...... 54

Table 3. 8. Results of independent sample t-tests for comparing means of input quantities applied per ha ...... 56

Table 3. 9. Parameter estimates and model details of the production function ...... 57

Table 3. 10. Marginal value productivities of inputs by season and by subarea ...... 58

Table 3. 11. Rice budgets per hectare by sub area and season (SLR/ha) ...... 59

Table 3. 12. Means of rice area and income share and distribution of income shares by sub area ...... 60

Table 3. 13. Means of welfare measures by sub area (and results of independent sample t- tests) ...... 61

Table 3. 14. Effect of water level change on net revenue from rice per ha ...... 62

Table 3. 15. Description of water allocation plans ...... 63

Table 3. 16. Marginal value productivities computed at data means for each plan ...... 64

Table 3. 17. Water volume allocations for each plan ...... 65

Table 3. 18. Incremental benefits of water allocation by plan …………………………….66

Table 3. 19. Aggregate incremental benefits of water allocation by subarea, by season and for

KOISP ...... 67

Table 3. 20. Increase in annual household income from Plan A and Plan C by income pentile

(SLR) ...... 68

Table 4. 1. Uses of water flow to an ecosystem and perceived benefits within a TEV framework

...... 95

Table 4. 2. Descriptions of variables used in estimating WTP ...... 96

Table 4. 3. Distribution of response for each level of bid for releasing water to YPC ..... 97

Table 4. 4. Parameter estimates of the binary logistic regression (n=584) ...... 98

Table 4. 5. Parameter estimates for WTP function (n=584) ...... 99

Table 4. 6. Properties of estimated WTP (n=584) ...... 100

Table 4. 7. Aggregation of WTP for Sri Lanka: data and results ...... 101

Table A.1. 1. Mid-Year populations in districts in 2009 by climatic zone ...... 104

Table A.1. 2 . Details of river basins of Sri Lanka by climatic zone ...... 105

Table A.1. 3. Share of wet zone and dry zone districts in cumulative irrigable area ..... 106

Table A.1. 4. Description of rivers in the southeastern Dry Zone of Sri Lanka ...... 107

Table A.1. 5. Administrative divisions in river basins in southeastern Sri Lanka ...... 108

Table A.1. 6. Locations and stream orders of tributaries of KMK rivers ...... 109

Table A.1. 7. Distribution of minor irrigation systems in southeastern river basins ...... 112

Table A.1. 8. Distribution of major irrigation systems in southeastern river basins ...... 113

Table A.1. 9. Summary of irrigable areas in KMK Basins (2000) ...... 114

Table A.1. 10. Shares of irrigable areas of irrigation systems by type and size category115

Table A.1. 11. Details of sub-systems of the KOISP ...... 116

Table A.1. 12. Salient features of Veheragala Diversion Project ...... 117

xii

Table A.1. 13. Protected areas in Sri Lanka in 2007 ...... 118

Table A.1. 14. Extent and status of Yala National Park and adjoining protected areas 119

Table A.3. 1. Point estimates of non-parametric survivor function (n = 584) ...... 132

Table A.3. 2. Comparison of aggregation of WTP by different methods ...... 133

xiii

LIST OF FIGURES

Figure 4. 1. Distance decay characteristic of visits to YPC ...... 102

Figure A.1. 1. Map of Sri Lanka showing wet zone and dry zone districts ...... 120

Figure A.1. 2. River Basin Map of Sri Lanka ...... 121

Figure A.1. 3. Map the study area showing divisional secretary areas ...... 122

Figure A.1. 4. System map of Kirindi Oya Irrigation and Settlement Project (KOISP) .. 123

Figure A.1. 5. Map showing the location of Veheragala Reservoir Project ...... 124

Figure A.1. 6. Generalized vegetation map of Yala National Park: Block 01 ...... 125

Figure A.3. 1. Non parametric survivor function ...... 134

xiv

DEDICATION

This dissertation is dedicated to Ven. Mihiripanne Sobitha, M.A., for his contribution toward

illuminating an otherwise backward community.

CHAPTER ONE

INTRODUCTION 1.1: INTRODUCTION

Water is a multi-purpose natural resource and has been the foundation of life and human civilizations. Water contributes to the diversity of the biological and physical environment of the world as well as to the diversity of the human culture. Access to adequate water for all is recognized as a universal basic human need. The natural availability of water in an area is governed by hydrological factors. However, people throughout history have continued to alter the natural patterns of spatial and temporal availability of water for their benefit.

Valuation of water, due to its diverse and multiplicative uses, is a complex task. Water is valued differently across cultures and is a source of spiritual and religious meaning for many religions and cultures. Economists advocate valuing water using standard techniques of natural resource valuation. Water allocation rules increasingly use economic values, it is important to identify and quantify the values generated by all potential uses of water. Failure to include some uses leads to under-allocations that deprive some users of water and may result in irreversible consequences. While the many anthropocentric uses of water including domestic uses, food production, industry, hydropower generation, recreation, and navigation, are well recognized, the need to recognize the value of flowing water for environmental benefits is increasingly emphasized.

This dissertation focuses on water allocation in the southeastern Dry Zone of Sri Lanka where a development project reallocated available water among several sectors. Menik Ganga

River flows through an environmentally sensitive wildlife refuge of national and global importance, the Yala Protected Area Complex (YPC). The natural flow pattern of this river was altered by Veheragala Diversion Project by diverting a portion of its flow to the adjacent Kirindi

Oya river basin for agricultural use, while the downstream ecosystems are expected to receive

1 enhanced regulated flows through storage and release. We analyze the change in benefits of two major water use sectors: agriculture and the environment. We first quantify the potential benefits accruing to farmers through improved water supply, using a production function approach. Then we apply non-market valuation techniques to estimate benefits accrued by the general population of Sri Lanka due to enhanced dry season flow to the YPC as a proxy to the value of water used for environmental purposes.

This introductory chapter has four sections. A brief description of the land and economy of Sri Lanka will follow this introductory section. The next section describes the study area and the study problem in particular. The final section describes organization of this dissertation.

1.2: SRI LANKA: BACKGROUND INFORMATION

Sri Lanka is an island located in South Asia. Geographical coordinates of the country are

7.00 N and 81 E. The country has a total land area of 65,610 km 2. Its land form is generally low, flat and undulating terrain. A mountain mass known as central-highlands is located in the south-central interior. The climate is tropical monsoon with a bi-modal rainfall pattern. A large share of annual rainfall is received through two monsoons known as the northeast and the southwest. The southwest monsoon occurs from May to September and causes rain in the southwest parts of the island. The northeast monsoon that occurs during November and

January covers all parts of the island but leads to weaker rains than the southwest monsoon.

Inter-monsoon rains occur during March-April and October-November, and atmospheric depressions during October-November also cause heavy rains (Panabokke 1996).

The country is divided into two major climatic zones − the Wet Zone and the Dry Zone − based on the magnitude of annual rainfall and its distribution across months of the year. Mean annual rainfall, which is the major determinant of country's agro-climatic variability, varies from

2,500 mm to 5,000 mm in southwest parts of the island, but is less than 1,250 mm in other parts

2 of the country. Areas receiving over 1,500 mm of mean annual rainfall are considered Wet

Zone areas. Rainfall in the Wet Zone is evenly distributed throughout the year, while the Dry

Zone receives the largest share of annual rainfall from the northeast monsoons.

While about 60 percent of all rainfall in the Dry Zone areas is received during Maha

Season, this difference is not clear in the Wet Zone. There are two major cultivating seasons for rice and other field crops. Maha Season, the major cultivation season, usually starts in October before the onset of the northeast monsoon and continues until March, while the Yala Season

(minor) usually starts at the end of April and ends in August/September.

The estimated population of the country in 2010 was 20.6 million (Department of Census and Statistics 2011). The country’s gross domestic product (GDP) in 2010 was reported as

US$49.5 billion. Sectoral contributions in GDP in 2010 were 11.9 for agriculture, 28.5 for industry, and 59.6 for services. However, shares of the total labor force (about seven million people) indicate that the share of agriculture sector of the total labor force was much greater than its comparative contribution to GDP (Central Bank of Sri Lanka 2011). When compared with overall economic welfare indices, the Human Development Index (HDI) and the Human

Poverty Index (HPI), the country is in a favorable position with respect to other south Asian nations (UNDP 2010). The value of GDP is more than doubled when adjusted to purchasing power parity (PPP) (US-CIA, 2011).

1.3: STUDY AREA AND STUDY PROBLEM

The geographical area of this study is the river basins located in the southeastern Dry

Zone of Sri Lanka. Selected basins cover 6.8 percent of the land area and account for 2.6 percent of cumulative estimated annual run-off of the country. Kirindi Oya, Menik Ganga and

Kumbukkan Oya, referred to as KMK Basins, cover 84 percent of the watershed area and account for 90 percent of the cumulative estimated annual run-off of all southeastern river

3 basins. This composite basin accounts for almost 100 percent of irrigated area in the region. A more detailed description of these river basins is provided in Chapter Two of this dissertation.

A new irrigation reservoir at Veheragala in the Menik Ganga Basin diverts water to

Kirindi Oya Basin. The potential for another trans-basin diversion of water from Kumbukka Oya

Basin towards Menik-Kirndi Basins is also identified. Veheragala Reservoir will regulate water flow to Yala Protected Area complex (YPC) This case provides an interesting and important question to address the concerns of national policy with respect to water resources development. The broader objective of the study is to investigate the economic benefits and distributional effects of such a water diversion.

This study was carried out with three specific objectives: a) Document existing and prospective water use patterns in the Kirindi-Menik-Kumbukkan Oya (KMK) composite river basin; b) Develop a framework to estimate the value of water as a production input in surface irrigated peasant farming scheme, and assess the welfare implications to households with differential access to water; and c) Determine the value of a quantity of water release to enhance conditions of the forest ecosystem.

1.4: ORGANIZATION OF THE DISSERTATION

This dissertation is organized into four chapters. Chapter Two presents a description and analysis of water resources in Sri Lanka in general and in the study river basins in particular. A description of two detailed study areas, the Kirinidi Oya Irrigation and Settlement Project

(KOISP) and Yala Protected Area Complex (YPC) will also be included. The third chapter is entitled, “Quantifying the impact of differential water access on farm productivity and farmers’ welfare: The case of Kirindi Oya in Sri Lanka.” Chapter Four presents the results of a contingent valuation study of the proposed dry season flow enhancement to YPC. This chapter is entitled,

“Valuation of enhanced dry season flow through Yala Protected Area Complex: estimating non- use values of water”.

1. 5: REFERENCES

Central Bank of Sri Lanka. 2011. Annual Report-2010. Colombo: Central Bank of Sri Lanka.

Panabokke, C.R. 1996. Soils and Agro-ecological Environments of Sri Lanka . Colombo: Natural Resources Energy and Science Authority of Sri Lanka.

Sri Lanka Department of Census and Statistics. 2011. Statistical Abstract of the Democratic Socialist Republic of Sri Lanka - 2010 . Colombo: Sri Lanka Department of Census and Statistics.

United Nations Development Program. 2010. Human Development Reports. http://hdr.undp.org/en/reports/global/hdr2010/chapters/en/ (Accessed June 20 2011)

United States Central Intelligence Agency. 2011 . CIA- The World FactBook – Sri Lanka: https://www.cia.gov/library/publications/the-world-factbook/geos/ce.html. (Accessed June 20 2011).

CHAPTER TWO

WATER RESOURCES IN SRI LANKA WITH SPECIAL REFERENCE TO SOUTHEASTERN DRY ZONE

2.1: INTRODUCTION

The purpose of this chapter is to present details of water resources in Sri Lanka and the factors contributing to its diversity. We first present material linking the administrative areas and classification of water resources into two climatic zones. Irrigation as the principal user of water is discussed in the context of climatic zones. A detailed presentation of water resources in the southeastern Dry Zone, with special focus on three major rivers, will be presented. A description of Veheragala Diversion Project and geographical areas for two detailed studies will conclude this chapter.

2.2: SRI LANKA: ADMINISTRATIVE REGIONS AND CLIMATIC ZONES

The administrative divisions of Sri Lanka are arranged into four hierarchical layers. The highest of them is the province, followed by district, divisional secretary (DS) areas and, finally, the lowest unit, the Grama Niladari Division (GND). The traditional neighborhood unit referred to as the village includes one or more GNDs in highly populated areas, or in sparsely populated areas, one GND may include several villages. There are nine provinces, 25 districts, 328 DS areas and 14,016 GNDs in the country. GND level statistics on population are reported based on decennial National Census of Population and Housing. However, updates of mid-year population details for inter-census are available at the district level. Sri Lanka has used a definition of residential sectors as urban, rural, and estate, based on the local government regime of the geographical location of households for a number of years. Urban sectors include households located within municipal and town councils. Estate sectors include households located in commercial plantations of 20 hectares (ha) or more with 10 or more residential

7 workers. Rural sectors encompass all areas that are not included into any of the categories described as urban or estate. Sri Lanka is predominantly non-urban with 85 percent of its population living in areas considered as rural or estate.

The boundaries of the climatic zones mentioned in Chapter One do not coincide with sub-national administrative regions of the country. Areas for eight administrative districts fall within more than one climatic zone. However, researchers in irrigation development are in consensus in classifying districts into two groups -- the Wet Zone and the Dry Zone. This aggregation of districts is aimed at facilitating the use of information collected by various agencies for analytical purposes rather than following geographical considerations, per se. In this classification districts are considered as dry if the larger share of irrigated lands falls within dry or intermediate zones. Figure A.1.1 shows the classification of districts into wet and dry zones, while estimated mid-year populations by district are given in Table A.1. 1.

2.3: WATER RESOURCES OF SRI LANKA AND ITS UTILIZATION

Rainfall received in the country flows to the Indian Ocean through 103 rivers, showing a

radial drainage pattern. Water within a river basin is available either as surface water or

groundwater. Except in the northeastern and northern coastal areas where myocene limestone

aquifers exist, groundwater is replenished by rain. River basins are identified using a unique

national river basin number. Rivers are numbered consecutively from one through 103,

beginning from the Kelani River near Colombo and proceeding counter-clockwise. Rivers are

categorized into two broad groups as wet zone and dry zone rivers, excluding the Mahaweli, the

largest and the longest river. The river basin map of Sri Lanka indicating rivers by climatic

zones and in the southestern Dry Zone is presented in Figure A.1.2. Table A.1.2 reports, based

on their location, details of watershed areas and estimated annual runoff for rivers. Eighty-six

rivers in the Dry Zone cover an aggregate watershed area of 37,542 km2 and receive 40

8 percent of the total estimated annual runoff, while 16 wet zone rivers with less than half of the cumulative watershed area of all dry zone rivers account for 38 percent (only 2 percent less than the total for all dry zone rivers), indicating the relatively higher abundance of water in the

Wet Zone than in the Dry Zone. Exclusion of the Mahaweli River from two broad groups is due to its large size and the spread of the watershed over both the Dry and Wet Zones. This river accounts for about 22 percent of the 50 billion cubic meters of cumulative estimated annual runoff for the country.

As the water availability patterns change, the water utilization patterns also change.

Without irrigation, agriculture in the Dry Zone predominantly depended on cultivation of seasonal crops under rain-fed conditions. Irrigation has changed this pattern. Irrigation based on rivers was a key contributor to growth and sustainability of the civilization in ancient Sri

Lanka. Irrigation systems gradually emerged from community based village systems to state sponsored large systems that sometimes spanned across many river basins. After reaching its peak in the 13th Century AD, the center of civilization was shifted to the Wet Zone, leading to abandonment of the irrigation works in the Dry Zone (Gunawardana 1971). Irrigation developments in modern Sri Lanka aimed at the two objectives of increasing rice self-sufficiency and easing land pressure in the Wet Zone areas began in the 1930s (Farmer 1958[1976]).

Areas irrigated grew from 0.182 million hectare (ha) in 1950 (Kikuchi et. al. 2003a) to 0.521 million hectare in 2009 (Department of Census and Statistics). A recent development in the irrigation sector in Sri Lanka is the emergence of well-irrigation. Large diameter dug-wells together with pumps to irrigate areas of 0.2 to 0.8 hectare, especially in the Yala Season, began to be popular among farmers in the irrigation systems of the Dry Zone of the country since the mid-1980s. Kikuchi et. al. (2003b) estimated the number of agricultural wells as 50,000.

A large majority of irrigation systems in Sri Lanka can be classified as surface gravity systems. Based on the type of infrastructure for supply of water to farmers’ fields, irrigation

9 systems are further categorized into two types: river diversion systems (also called run of the river systems) and reservoir systems. The main function of an irrigation reservoir (referred to as a tank in the south Asian context) is to store water for delivery to end-users or to another reservoir. Water storage is essential in the Dry Zone due to the mismatch of times of demand and availability of water. In contrast, river diversion systems do not have storage infrastructure and divert water from a structure located across a flowing river to a channel from which farmers receive water. Farmers in river diversion schemes, therefore, receive water only when the river is flowing. River diversions also augment reservoirs in the Dry Zone during the high flow times of rivers.

In an alternative classification, irrigation systems are categorized by size of the area irrigated by the system (also known as irrigable area or command area). These size categories vary by country and by management agency considered. For the purpose of this study we use the system of classification by Sri Lanka Department of Census and Statistics widely used in Sri

Lanka. This classification considers all irrigation systems with irrigable areas below 80 hectare as minor systems, while systems with irrigable areas of 80 hectares or above are major systems. Classification by size is also recognized as the basis to classifying systems for management purposes. Minor systems are also known as village irrigation works or farmer managed systems. State intervention in the management of these systems is minimal and confined to facilitative functions by Department of Agrarian Development. Major systems are managed by the Department of Irrigation, the state agency responsible for construction and management of irrigation systems.

Data for cumulative irrigable areas of irrigation systems by type of infrastructure ( i.e. river diversions or reservoirs, and system size) and by climatic zone are presented in Table

A.1.3. Most recent data classifying irrigation systems into river diversions and reservoirs are available for the year 2000. We used this data and then compared it with updates for all minor

10 systems and major systems for 2007. As revealed by the upper part of Table A.1.3, 99 percent of total areas of major systems and 96 percent of minor reservoir systems, and 86 percent of all irrigation systems are found in the Dry Zone. This demonstrates that storage of water for irrigation is more important in the Dry Zone than in the Wet Zone.

According to Kikuchi et. al. (2003a) irrigation investments were a main factor leading to the country’s achievement of rice self-sufficiency, a major policy objective of all governments after the independence. Irrigation systems played a decisive role in the creation of employment and making the otherwise marginal agricultural areas habitable. Despite these advances, the irrigation sector in Sri Lanka is at a crossroads today. Further expansion of existing irrigation is limited by lack of suitable areas, the escalating cost of new construction, and the environmental consequences of land conversion. Construction costs (in 1995 constant values) for one hectare of new irrigation have increased from an average of Sri Lanka Rupees (SLR) 0.1 million in the

1950s to SLR 0.7 million in the 1990s (Kikuchi et. al 2003a). The cost of construction for one hectare for Deduru Oya Reservoir Project, a recent new irrigation construction project in northwestern Sri Lanka, was reported as SLR 0.675 million (Government of Sri Lanka, 2005).

2.4: WATER RESOURCES IN THE SOUTHEASTERN DRY ZONE OF SRI LANKA

Rivers in the southeastern Dry Zone of Sri Lanka (shown in Figure A.1.2) are related to

each other due to the overlapping of the current water use, or based on proposed trans-basin

water diversions. Selected basins cover 6.8 percent of the land area, but account for less than

three percent of cumulative estimated annual run-off of the country. Hydrological details of the

southeastern Dry Zone Rivers are presented in Table A.1.4. These river basins span four

provinces and 18 DS areas while the entire land areas for five DS areas falls within these river

basins. Administrative areas covering their watershed areas are listed in Table A.1.5.

As shown in Table A.1.4, three 5th order rivers, Kirindi Oya, Menik Ganga, and

Kumbukkan Oya (referred hereafter as KMK Rivers) cover 84 percent of the total watershed area and account for 90 percent of cumulative estimated annual runoff of all southeastern rivers.

This composite basin accounts for almost 100 percent of irrigated area in the region. DS areas within KMK river basins are shown in Figure A.1.3. A description of tributaries of KMK Rivers including the stream order based on Horton-Strahler laws of river morphology is presented in

Table A.1.6. The Kirindi Oya, the river with the smallest watershed area of the three KMK rivers; originates from the southern ranges of the Central Highlands within the Bandarawela DS Area.

Menik Ganga River originates from the Namunukula mountain range situated in the eastern part of central -highlands. The main river flows through Kataragama and Tissamharama DS Areas before it reaches the sea at Yala. Kumbukkan Oya River, with a total watershed area of 1,233 km2 and a length of 128 km, is the longest of the three rivers in the KMK Basins. The lower reaches of this river flows through the Yala Protected Areas and into the sea at Kumbukkana.

2.4.1: Irrigation in KMK Basins

The KMK Basins account for three percent of all irrigated areas and four percent of area irrigated by river diversion systems and three percent of area irrigated by reservoirs in the country. Area irrigated by river diversions, reservoirs and total irrigation in KMK Basins as a percentage of the Dry Zone are, three, four and nine, percent respectively. Table A.1.7 and

A.1.8 present the number of systems and total area irrigated in southeastern river basins by type, river basin, and DS area. Table A.1.9 presents total area under irrigation, number of minor systems, and the percentage shares for three river basins of the total irrigable area and number of minor systems in the KMK basin. The total irrigable area under all systems in the KMK Basin is approximately 18,108 ha. Sixty percent of this is within the Kirndi Oya Basin. Irrigable areas for minor systems are distributed among three river basins more or less equitably. River diversion systems are higher in number than the reservoir systems in all three river basins.

Table A.1.10 presents the shares for different type and size categories of irrigation command areas for three individual river basins and for the KMK Basin. However, the share of irrigated area under minor river diversion systems in the KMK Basin (57 percent) is less than its share in total number of minor irrigation systems (72 percent, based on Table A.1.9.).

The pattern is interestingly different when major irrigation systems are considered. Major systems in total irrigated areas in KMK Basins account for 69 percent of the total area (based on

Table A.1.9.). In the Kirindi Oya Basin, major irrigation systems account for 82 percent of the total, while major systems in the other two basins are approximately 50 percent. Kirindi Oya

Basin is predominantly irrigated by reservoirs. Seventy five percent of the total area irrigated by major irrigation systems within the KMK Basin is located in the Kirindi Oya Basin. Kumbukkan

Oya Basin has no major reservoir systems. In contrast, that basin accounts for 63 percent of all major river diversion systems within the KMK Basin. Reservoir systems account for 75 percent of major irrigation areas in the KMK Basin, while its share of individual basins shows notable differences. In Kirindi Oya, reservoirs account for 98 percent of area under major irrigation, while reservoirs in the Menik Ganga Basin have a 39 percent share.

While the spatial distribution of all minor irrigation systems and the shares of different types do not show striking differences, the major systems show a bias toward tank systems, and a large majority of all major irrigation is found within one basin.

2.5: SPATIAL PATTERNS OF DISTRIBUTION OF IRRIGATION SYSTEMS

Patterns revealed by Tables A.1.9 and A.1.10 provide insights useful in understanding the historical development of the different types of irrigation systems. The need to efficiently harness water resources in the localities, after considering the natural patterns, was the key motivating factor in deciding which type of system to construct. Minor systems were historically constructed by communities; and, even today, they have a strong voice in management of these

13 systems. Hence, system development was based on the local people’s initiative in harnessing the natural resources in their respective regions. Factors leading to the construction of river diversions or reservoirs included the pattern of stream flow, the topography of the area, available construction technology, and financial considerations.

Hilly terrain does not facilitate development of large systems. Small scale river diversion systems are the most appropriate for hilly areas, as the water availability is perennial and, therefore, no storage is needed. Numerous lower order streams in upper reaches are used to harness water by diverting them to rice fields. Irrigation return flows enter back into the same stream. As indicated in Table A.1.6, a large number of river diversion systems are located in the

DS areas, situated near river origins. The irrigable areas of diversion systems increase as the river flows towards the ocean, but the number of systems decreases.

As the river flows further downstream the terrain becomes flatter and the rainfall becomes seasonal. As indicated by the rainfall pattern, more rain is received during the wet season; hence it is essential to capture water during the rainy season and store it in reservoirs.

Most of the small reservoirs are part of a series that permits reuse of water by additional reservoirs located further downstream. Diversions from major rivers are also used to augment the water available in large reservoirs for irrigation.

2.6: DESCRIPTION OF THE STUDY AREAS

2.6.1: Kirindi Oya Irrigation and Settlement Project (KOISP)

Kirindi Oya Irrigation and Settlement Project (KOISP) is a major surface gravity irrigation system situated in southeastern Sri Lanka. It has two subareas known as the Old Irrigation

Area (OIA) and the New Irrigation Area (NIA). OIA comprises five ancient reservoirs that provided the base for an agrarian civilization believed to have its origins in the Third Century

BC. It is believed that these reservoirs were augmented with water diversions from the main

Kirindi Oya (Brohier 1934). A new diversion structure constructed by the British in 1876 provided the first step for irrigation development in modern Sri Lanka. In 1979, the new larger reservoir (Lunugamvehera), with active storage capacity of 200 MCM (Million Cubic Meters), was constructed upstream of the diversion point to the OIA. Settlers for NIA were comprised of two categories: a) alternate settlers, belonging to displaced farm families due to reservoir construction, and b) new settlers selected from other areas of the country.

Water delivery to the NIA occurs directly from the main reservoir through two main irrigation canals located on both sides of the main dam. Each main canal has several distributory canals (DCs) that have several field canals (FCs) on which the farm water inlet structures are located. Water for the OIA is released through a special delivery structure in the

Left Bank Main Canal and is stored in reservoirs before releasing it through one or more regulatory sluices that lead to main irrigation canals. Depending on their location, farmers have access to water through inlets located at DCs or FCs. System Map of the KOISP, showing locations of the main reservoir, OIA reservoirs and irrigated areas, and main canals is presented in Figure A.1.4.

Farmers are organized into legislative bodies called farmer organizations (FOs) based on distributory canals through which their fields receive irrigation. In the NIA, several DCs are organized to form a field tract. Sizes of FOs vary as the service areas of distributory canals vary.

Table A.1.11 presents details of irrigation infrastructure and FOs. Water management in KOISP has been a complex task since the commissioning of the new reservoir in 1986. The inflow to the reservoir has declined and the OIA farmers claimed senior water rights, leading to deprive

NIA farmers of irrigation water for many seasons. Farmers were constantly demanding irrigation authorities to augment water to the main reservoir. At the height of the protests, farmers began to dig a canal from Menik Ganga to channel additional water to the Lunugamvehera Reservoir.

The Government declared the action as illegal, but started construction of the Veheragala

Diversion Project in 2005.

2.6.2: Veheragala Diversion Project

The Veheragala Diversion Project developed by the Sri Lanka Department of Irrigation was included for many years in potential water development plans for the region. The project was designed to augment the Lunugamvehera Reservoir through trans-basin diversion. Other project objectives included: regulating the river flow within the Menik Ganga Basin through flood mitigation, release of water downstream for rice farmers, and improved water availability for domestic users of the religious festival of the Katragama Sacred City. Planned releases were deemed beneficial to Yala Protected Area Complex (YPC) and special water releases to flood wetlands within the YPC was also expected. Quality enhancement of Lunugamvehera National

Park was also considered an important project objective.

The project design included a dam across the the Menik Ganga River approximately 60 km upriver from the ocean, and a trans-basin diversion canal of 23 km long to release water at Sittarama Ara, one of the small tributaries of Kirindi Oya that flows directly into the Lunugamvehera Reservoir. The project work commenced in 2005 and water releases began in 2009. Salient features of the project are presented in Table A.1.12, while Figure A.1.5 shows the project layout including main structures.

2.6.3: Yala Protected Area Complex (YPC)

Protected areas in Sri Lanka are broadly classified into two categories: wildlife protected areas and protected forests. By 2007, 2.3 million hectare of protected areas, accounting for 3.6 percent of the total landmass of the country have been declared. The Department of Forest

Conservation (DFC) manages natural forests covering 60 percent of all protected lands, while the Department of Wildlife Conservation (DWC) administers the wildlife protected areas.

There are four categories of forest reserves: a) conservation forests (CF), b) national heritage wilderness areas (NHWA), national biosphere reserves (NBR), and international biosphere reserves (IBR). Wildlife protected areas are included in four broad categories based on the nature of access to humans: a) strict nature reserves (SNR), b) nature reserves (NR), c) national parks (NP), and d) sanctuaries. No human activities are allowed in SNRs, and activities are restricted to historical uses (if any) in NRs. NPs are for public wildlife viewing, while sanctuaries limit human activities in order to protect wildlife. There are three SNRs, four NRs,

14 NPs and 56 sanctuaries. (Table A.1.13).

The Yala Protected Area Complex (YPC) is an aggregation of wildlife protected areas extending to the northeast from the southeastern shoreline of Sri Lanka. The complex is located in the southeastern Dry Zone of Sri Lanka, 280 km from Colombo. This complex of reserves spans three administrative districts: Hambantota, Monaragala, and Ampara. The shoreline bordering YPC extends to 85 km. It is the southernmost undisturbed coastal area of the Indian subcontinent. The complex has a total area of 144,934 hectare. The YPC and its adjoining protected areas form the largest contagious protected area in the country, expanding to cover 175,700 ha. Physical and administrative description of YPC and adjoining protected areas are presented in Table A.1.14. Only Block 1 of Yala National Park, accounting for about

10 percent of total area of YPC, is open for wildlife viewing at present.

The area is a flat coastal plain with a few isolated rocky outcrops. The main cover vegetation is woody, mostly scrubs. Forests are found as islands of various sizes mingling within the scrub and as continuous cover spreading from the coast towards inland. Near the coast, the scrub is frequently interrupted by dry grasslands of various sizes (Mueller-Dombois 1972). A generalized vegetation map of Block 1 of the park shows a riverine forest stretching over the flow area of Menik Ganga River (Figure A.1.6).

A large number of abandoned small reservoirs and the traces of ancient canals indicate the existence of historic irrigation developments in the area. However, the area is believed to have been abandoned since the Thirteenth Century AD. More details related to water resources and the consequences of the Veheragala Diversion Project on the YPC will be presented in Chapter Four of this dissertation.

2.7: REFERENCES

Amarasinghe, Upali and L.P. Mutuwatte 2000. Water Scarcity Variation within a Country. Research Report 31. Colombo: International Water management Institute.

Arumugam, S. 1968. Water Resources of Ceylon and Its Utilisation . Colombo: Water Resources Board.

Brohier, Richard Leslie. 1934. Ancient Irrigation Works in Ceylon . Colombo: Ceylon Government Press.

Central Engineering Consultancy Bureau. 2004. Quality Enhancement Of Lunugamvehera National Park in the Menik Ganga and Kirindi Oya Basins by Harnessing The Development of Water Resources of Menik Ganga . EIA Studies-Main Report. Unpublished.

Da Costa, F. P., M. Grinfeld, and J. A. D. Wattis. 2002. “A Hierarchical Cluster System Based on Horton–Strahler Rules for River Networks.” Studies in Applied Mathematics 109(3):163-204.

Farmer, B.H. 1958 [1976]. Pioneer Peasant Colonization in Ceylon: A Study in Asian Agrarian Problems . Greenwood Reprint. CT: Greenwood.

Government of Sri Lanka. 2005. Proposal for Millennium Challenge Account . Department of Development Finance, Ministry of Finance and Planning.

Gunawardana, R.A.L.H. 1971. “Irrigation and Hydraulic Society in Early Medieval Ceylon.” Past and Present 53: 3-27.

Kikuchi, M., R. Barker, P. Weligamage, and M. Samad. 2003a . Irrigation Sector in Sri Lanka: Recent Investment Trends and the Development Path Ahead. Research Report 63. Colombo: International Water Management Institute.

Kikuchi, M., R. Barker, P. Weligamage, and M. Samad. 2003b . Agro-well and Pump Diffusion in the Dry Zone of Sri Lanka: Past Trends, Present Status and Future Prospects. Research Report 66. Colombo: International Water Management Institute.

Madduma Bandara, C.M., and P. Manchanayake 1999. Water Resources in Sri Lanka . Natural Resources Series 2. Colombo. Natural Resources, Science and Energy Authority of Sri Lanka.

Mueller-Dombois, D. 1972. “Crown Distortion and Elephant Distribution in the Woody Vegetations of Ruhuna National Park, Ceylon.” Ecology 53(2): 208-226.

Peiris, G.H. 2006. Sri Lanka: Challenges of the Millennium . Kandy, Sri Lanka: Kandy Books.

Sri Lanka Department of Agrarian Development. 2000. Village Irrigation Data Book. 25 vols. Colombo: Sri Lanka Department of Agrarian Development.

Sri Lanka Department of Census and Statistics. Statistical abstract of the Democratic Socialist Republic of Sri Lanka . (Various Years).

Sri Lanka Department of Irrigation. 1975. Register of Irrigation Projects in Sri Lanka . Colombo: Sri Lanka Department of Irrigation.

CHAPTER THREE

QUANTIFYING THE IMPACT OF DIFFERENTIAL WATER ACCESS ON FARM PRODUCTIVITY AND FARMERS’ WELFARE: THE CASE OF KIRINDI OYA IN SRI LANKA

3.1: ABSTRACT

Irrigation is a key production practice that facilitates agricultural development. Welfare differences resulting from differential access to water in irrigation systems are common.

Econometric studies of farm-level water use have been limited to using number of irrigations as a proxy for water use. This study develops and empirically uses a measure for the quantity of water applied based on farmer recall.

The study was conducted in the Kirindi Oya Irrigation and Settlement Project (KOISP), which is a surface-gravity system in the southeastern Dry Zone of Sri Lanka. Water availability differs between two subareas in this project. Findings indicate statistically significant differences in water used at the farm level across seasons and across subareas. Estimated water quantity was used as an input in the production function estimation of rice. Estimated production functions exhibited positive and diminishing marginal productivities of water, fertilizer, and labor, and the data indicated factor substitutability between capital and labor. Calculated marginal value products for water and fertilizer were higher than the respective opportunity cost of inputs.

This challenges the popular claim of low value of water used in rice production. Farm household net revenues from rice were calculated using farm budgeting, and these budgets were used as the indicators of welfare.

Alternative plans for enriching net revenues from rice through increased application of water are examined. The recommended water allocation plan predicts an increase in net revenues from rice at increased level of water input. They are higher than the revenues that would be generated if additional water was allocated, based on the current criteria, and also

21 have a higher impact on the incomes of lower income households. Concurrent application of fertilizer at recommended levels would increase net revenues further.

3.2: INTRODUCTION

Irrigation contributes to agricultural development through improving land productivity.

Improved irrigation facilities and introduction of new seed-fertilizer technology are considered the two main sources for improving land productivity (Kikuchi and Hayami 1978). Irrigation developments coupled with introduction of high yielding wheat and rice varieties during the

1960s led to a five-fold increase in world grain production between 1960 and 1990 (IWMI 2002).

For countries in Monsoon Asia, which have been historically dependent on wet rice cultivation, improved irrigation systems have been influential to development because systematic irrigation also encourages farmer application of fertilizer. The combination of timely irrigation and fertilizer application significantly reduces the risk of crop failure.

However, water is a multi-purpose resource. Agriculture is only one of its many uses.

Irrigation management institutions all over the world are facing a multitude of challenges arising both from increased water demand and from changing social and economic environments, including concerns about water pollution and water uses for environmental purposes. As availability of water for agriculture decreases, institutions have evolved to address the problem.

Water rotation, diversification with low water-use crops, and implementation of water-conserving techniques are examples of methods used to address water shortages in irrigation systems.

Irrigation developments have contributed to increased food production and livelihood security of farmers. The quantity of water applied and its timing largely determine crop output of a farm. Therefore differences in water availability inevitably lead to differences in income.

Although water is one of the most important inputs in irrigated agriculture, water input is seldom carefully quantified at the farm level in developing countries. Water used at the farm

22 level can be best expressed using real-time measurements of water inflow to a given field.

However, these monitoring procedures are cumbersome and require a high level of commitment in measuring and reporting. Alternatives to real-time measures that have been used for economic analysis include the number of irrigations (Hussain and Young 1985; Young 2005) and an index of water availability (Wijayaratne 1986). As competition for water from other uses increases, understanding the quantitative relationships between farm-level water use and agricultural output level becomes more important. Water has to be used more efficiently, and available water at the system level should be allocated among farmers in the way that is most welfare enhancing. It is therefore important to quantify and record the irrigation water input at the farm level more precisely. In this chapter, farmer recall is proposed as a means of more accurately determining water quantities applied by individual farms. These measures of water input are then used in production function estimation.

This chapter conceptualizes and empirically estimates the relationship between water use and irrigated rice production and household welfare at the farm level within an irrigation system in Sri Lanka. Specific objectives of the study are to: a) use farmer recall methods to obtain water use and other input data at the farm level, b) estimate the production function for rice, c) determine the effect of differential access to water on household welfare, and d) predict changes in farmer welfare under alternative water allocation policy scenarios.

The geographic focus of the study was the Kirindi Oya Irrigation and Settlement Project

(KOISP), a major surface gravity irrigation system situated in southeastern Sri Lanka. The problem of differential access to water is well known in the KOISP because one of its two subareas has received ready access to water, while the other has had limited water access. The socioeconomic status of farmers in the two subareas is also markedly different. No previous economic research has addressed the possible connection between water access differentials and socioeconomic welfare of farmers in these two subareas. We collected water quantity and

23 other input data through farmer recall and estimated the rice production function to document the relationship between rice yield and water application. The study uses farm budgeting techniques to calculate net farm incomes and conduct simulations of alternative policy scenarios. Because this study uses a farm-level water quantity indicator that is relatively easy to obtain and useful for both production function and welfare analysis, it may serve as a useful prototype for additional economic research in developing country irrigation systems where smallholder peasant farmers are predominant.

This chapter is organized as follows. It begins with the conceptual framework of the study, and the data collection and analysis process is described next. In the subsequent section, results of production function estimation, summaries of farm budgets, and predicted rice yields and net revenues from water applications under alternative policy scenarios are presented. The final section concludes.

3.3: METHOD OF ANALYSIS

The conceptual foundation of the analysis is production by a semi-subsistence household firm (Strauss 1984). We assume that farmers operate in a perfectly competitive market and are price takers in both output and input markets. Profit-maximizing behavior of farmers in Sri Lanka has been examined by Herath et. al. (1983) and Jegasothy et. al. (1990).

Rice production in the country is dominated by smallholders, and well developed factor and product markets are observed in the country. As reservation prices are equal to the market prices, the household decisions in consumption and production can be considered as separable. We treat the household model as separable between production and consumption decisions. Production decisions in this study are, therefore, modeled subject to the behavioral objective of maximizing profit without regard to possible interactions with utility maximizing consumption decisions.

A farm household produces rice in the context of a larger economic system. The rice farm is a subsystem of the family farm, and the family farm operates within the rural farming system, which is a part of the broader economic system of the country. Rice is produced using cash inputs, water, and labor and management input by the household. Resultant net income from rice is a part of the family farm income (farm income), and households have access to off- farm income arising from work or investment in the rural farm sector or non-farm income other sectors of the economy.

Differential input levels (controlled exogenously) lead to different production levels under the same technology and subsequently to different levels of net farm income and household welfare. Farm households’ welfare is related to access to water as an input in agricultural production and also due to its life-supporting domestic uses. Water also generates utility through indirect means such as recreation and scenic value. This study is confined to welfare effects generated by production of rice.

We hypothesize that higher quantities of applied water lead to increased rice production, which in turn increases net income from rice. Increased net rice income leads to higher net farm income and potentially to higher household incomes. The farmers’ recall-based indicator of water quantity was constructed using data gathered through structured survey schedules. Using this indicator we first established the quantitative differences of water used by farmers in two subareas of the system. Details of water related information collected from farmers and constructed relationships are presented in Table 3.1. The final water quantity was computed as the product of flow rate and total flow duration.

Farmers in both subareas followed standard water use practices during land preparation and early vegetative phase of the rice crop. Almost all farmers in our sample used chemical herbicides, and water was not used as a means of weed control. Irrigation authorities provided continuous water for all farms, irrespective of their location, until 30 days after the agreed date

25 for crop establishment. Once the crop was established, minimum wetting of the field three- times during the early vegetative phase (first ten days after crop establishment) was practiced throughout the study area. Based on these observations, we assume that farmers use constant volumes of water during land preparation and the early vegetative phase. Therefore, access- induced differential use would exist only during the latter two phases of the rice crop, i.e. late vegetative and maturity. Estimation of a single-output production function was considered appropriate here, since most households selected for the study grew rice as the major crop in their farms, and rice was the primary user of irrigation water.

The theoretical framework for the producer’s production decision, following Silberberg and Suen (2000), is described below.

The production function for rice can be expressed as

Y = F(X, Θ), (1) where Y is the output of rice per hectare, X is the vector of input allocations, (i = 1,2,…, j) i =

1,…,m are purchased inputs, i = m+1 ,…,j are inputs with constrained supply, and Θ is a vector of environmental and policy variables.

o Given output price p, and, xi the levels of constrained inputs, the producer’s decision

problem is given by

m j o MaxL= pF(,) X Θ− rii x +λ i ( x ii − x ) (2) X,λ ∑ ∑ i=1 i = m + 1 where, r is the vector of purchased input prices, and Lambda is the Lagrange multiplier. This leads to first order conditions:

∂L ' =pF(.) − r i = 0 (3) ∂x i

∂L ' =pF (.) −λ i = 0 (4) ∂x i

26 and

∂L o =xi − x i = 0 (5) ∂ λ i .

Deciding on the functional form to be used in production analysis is an important

consideration. Previous researchers have used a wide variety of functional forms. For example,

agricultural production functions related to rice farming in other irrigation systems in the Sri

Lankan Dry Zone were previously estimated by Abeygunawardena (1986) using a linear

functional form, and by Wijayaratne (1986) and Jegasothy et. al. (1990) using a quadratic

functional form. The Cobb-Douglas is the most widely used functional form in agricultural

production and has been used in a variety of applications ranging from farm level (Bakhshoodeh

and Thomson 2001) to international comparisons (Barker et. al. 1985) and, despite its

limitations, continues to be the choice of recent researchers (Di Falco et. al. 2008; Benjamin et.

al. 2007; Ferreira et. al. 2007; Wei 2007). This functional form assumes that a constant

percentage change in inputs leads to a constant percentage change in outputs at all levels of

inputs. Less restrictive alternatives to the Cobb-Douglas, such as the translog and quadratic,

are expected to provide a more accurate description of production technology. The Cobb-

Douglas functional form has several advantages over these more “flexible” functional forms,

including parsimony of parameters, convenience for inference and, perhaps more importantly, it

tends to be globally well behaved (Antle and Pingali 1995). This last property is particularly

important in simulating production at different levels of inputs, an important part of our study. We

therefore used the Cobb-Douglas functional form to model rice yield in this study.

Illustrated for many explanatory variables, rice output for the i th producer is expressed

using the stochastic Cobb-Douglas production function as

k m n (6) LogYi=+ Logαβ∑ j Logx ij ++ ∑ β jij D ∑ β jij DLogx iji + u j=1 jk =+ 1 jm =+ 1

where log is the natural logarithm, Y i is output of rice per ha, x ij and D ij are respectively,

th th th values of j explanatory variable and j dummy variable for the i producer, α and βjs are parameters to be estimated and ui is the stochastic disturbance term. Variables used to estimate

production function in our study included six input variables: water, rice seed, fertilizer, cost of

machinery, cost of chemicals, and total labor. Two intercept dummy variables for season and

subarea, and slope dummy variables for interactions of season and subarea for all the inputs

were also used. Other variables included were: education level (expressed as number of years

of schooling), years of farming experience, a dummy variable for attainment of ten years of

schooling, and distance from distributor canal to the field inlet of irrigation as a proxy to the

quality of irrigation supply (Table 3.2.). The Cobb-Douglas production function was estimated

using ordinary least square (OLS) techniques.

Welfare Indicators

Following Just et. al. (2004), we considered two widely used welfare indices for

producers – profit and producer surplus. In the long run, producer surplus for the industry is

equal to the sum of producer profits. Consequently, profit, defined as gross revenue minus total

costs, is an obvious candidate as a measure of producer welfare for the firm seeking to

maximize profit. However, profit serves as an appropriate welfare measure only in certain

cases. An alternative to profit as a welfare measure is producer’s net revenue, defined as the

excess of total revenue over variable costs. This measure, also known as quasi-rent (R), is

generally considered to be more appropriate in the context of smallholders who have difficulty

valuing fixed costs, especially land.

We compute net revenue from rice farming for each cultivation season through farm

budgeting. We define farm income as net revenue generated from activities within the family

farm. To obtain household income, we also include off-farm income and non-farm income. Off-

28 farm income is the income received from the agricultural sector. This includes net revenue received by the household as factor rentals plus wages earned from working in other farmers’ fields. Non-farm income is income received from sources outside the agricultural sector. Total annual household income of a semi-subsistence household is calculated using the definitions and steps presented in Table 3.3.

3.4: DATA

A bi-modal rainfall pattern is a key factor considered in demarcating Sri Lanka into two major geographical regions, the Dry Zone and the Wet Zone. About 60 percent of total annual rainfall in Dry Zone areas (e.g., Kirindi Oya) is received during the Maha Season, the major cultivation season that runs from October through March. The Yala Season (minor), which usually starts toward the end of April and ends in August or September, receives less rain. This seasonal difference is much less pronounced in the Wet Zone. Agriculture in the Dry Zone consists predominantly of cultivation of seasonal crops (mainly rice), and cultivation in the Yala

Season is dependent on irrigation. Irrigation infrastructure, largely in the form of storage reservoirs, plays a vital role in facilitating continuous water availability in order to grow two rice crops a year. Although there is more need for irrigation water in the Yala Season than in the

Maha Season, current practice in the survey area is to provide irrigation water in both seasons.

This sometimes exhausts the water stored in reservoirs earlier than expected and leads to water shortages toward the end of the Yala Season.

The Kirindi Oya Irrigation and Settlement Project (KOISP) is a major surface gravity irrigation system situated in southeastern Dry Zone of Sri Lanka. The KOISP has two subareas known as Old Irrigation Area (OIA) and New Irrigation Area (NIA). OIA has five ancient reservoirs that are believed to have provided the base for an agrarian civilization with origins in the Third Century BC. In 1979, a new reservoir (Lunugamvehera) with an active storage

29 capacity of 200 MCM (million cubic meters) was constructed to augment irrigation to the OIA and to irrigate new lands (NIA) developed and distributed among smallholder farmers. Since the inception of the larger system, OIA farmers have been entitled to senior water rights.

Water inflow to the main reservoir has declined since the commissioning of the KOISP in

1986, while demand by other sectors for water from the reservoir has increased. Managers have addressed the problem by reducing water allocations to the NIA (Marikar 1999). Declining water availability and subsequent lack of farm income prompted NIA farmers to demand augmentation of water in the main reservoir through trans-basin diversions. The government responded by planning and constructing the Veheragala Reservoir Project mainly to divert water to the KOISP. New water diversions had not begun by the study period.

Irrigation scheduling policy by authorities continued to cause marked differences in water distribution between the two subareas. Farmers in OIA enjoyed a continuous flow of water in their respective field channels and had the freedom of diverting water to rice fields at their will.

In contrast, field channels in NIA had continuous water flows only until the end of the early vegetative phase. Water flows during the late vegetative phase and maturity phase in both seasons were restricted to a few days each week.

Our initial plan was to sample two percent of farmers in both subareas. That would have resulted in sampling 97 farms from NIA and 67 farmers from OIA. However, the sample size for

OIA was increased to 90 to better represent the heterogeneity of the population. Farm households for the study were selected from the KOISP area through probability proportionate to size (PPS) sampling. In the first stage, ten farmer organizations (FOs) from NIA and nine from OIA were randomly selected. Subsequently, ten farmers were randomly selected from each FO using membership lists. PPS is a multi-stage cluster sampling method that produces a scheme giving equal probabilities of selection to all elements in the sample frame (Babbie

2005). This method is widely used when cluster sizes are different. The process of searching

30 and conducting interviews is more cost-effective when PPS is used, compared to simple random sampling. PPS used fewer resources in selecting farmers and implementing a survey, since our sample households were concentrated in clusters rather than scattered throughout the study area.

Data were collected during July through December 2007 using a pre-structured survey schedule. All selected enumerators were members of young farmers’ societies, had a farming background, were high school graduates, had prior experience in conducting social science surveys, and were trained by the Principal Investigator (Weligamage) prior to the survey. The

Principal Investigator visited each household to introduce the study and provide a letter soliciting participation in the survey. This step was included to assure respondents of the importance the study, to build investigators’ rapport with farmers, leading to increased reliability of data. Data for two consecutive cultivation seasons, Maha 2006/07 and Yala 2007, were collected through two separate interviews to avoid recollection errors and also to avoid fatigue due to long interview times. Enumerators visited households during principal operators’ leisure time to collect data. Consistency of data was verified through cross-checking by the Principal

Investigator and review with field-level officers from the Sri Lanka Department of Agriculture.

Data were collected on type of rice grown; quantities and prices of inputs; sources, number of hours and rental rates of farm machinery; labor use by operation and type of labor

(i.e. family labor and hired labor); irrigation practices; production levels; quantities sold and retained for consumption; and farm-gate price of rice. Information was also collected on FO membership, which included duration of membership, current and past office holdings, and participation in training activities related to agriculture. Type of land ownership and rental rates, socioeconomic information about the household head and other members assisting in farming, and shares of different income sources were also collected. A description of primary variables is presented in Tables 3.1-3.3. Information on 13 housing quality and household asset indicators

31 were also obtained through observations and by questioning, recorded using a 5-point ordinal scale. These variables are reported in Table 3.4.

A total of 190 interviews were conducted in the first round. However, it was not possible to contact a few of the farmers at their original locations during the second round. The second round of the survey faced considerable logistical difficulties due to the unsettled security situation in the research area. Three of the survey villages had fatalities due to terrorism, and day-to-day activities in the area were severely disrupted. We were ultimately able to use a total of 360 questionnaires in the analysis. Econometric analysis was conducted using Gnu

Regression Econometrics and Time Series Library (GRETL) version 1.8.1.

3.5: RESULTS

3.5.1: Input Use Patterns and Production Relationships

Data were analyzed to determine relationships between input intensities and rice production on a unit area basis. Rice production per unit area (kg per hectare), reported by sample households by subarea and by cultivation season, together with results of t-tests for comparison of their means are presented in Table 3.5. T-tests indicate statistically significant inter-seasonal and inter-subarea differences in means of rice yields.

Descriptive statistics of water related data generated through the interviews are presented in Table 3.6. We conducted t-tests for all primary water related variables with the hypothesis that there are no differences between the two subareas during each season and between seasons for each subarea. The findings lead us to reject this hypothesis for all variables except number of irrigations and total quantity of water per farm between cultivation seasons in OIA.

Mean input intensities reported by farmers for all production inputs by season and by subarea are presented in Table 3.7. The amount of fertilizer applied is the arithmetic sum of

32 quantities of all chemical fertilizers. During both study seasons, farmers received a maximum of

185 kg of chemical fertilizer per acre at the subsidized price of SLR 7.00/kg. Issuance of rice fertilizer was highly regulated on the basis of expected cultivation area, and fertilizer was unavailable on the open market. It was also verified that farmers in general do not trade fertilizer with others, so the maximum quantity of fertilizer available to a farmer was a function of land area. However, the decision to apply fertilizer was at each farmer’s discretion.

In Table 3.8, we compare the means of input quantities using independent sample t- tests to test for significance of differences in input levels by season and by subarea. Statistically significant differences in input quantities exist for all inputs except fertilizer. Mean water quantities applied per hectare show statistically significant differences between subareas in both cultivation seasons. Significant differences in water quantities applied per hectare also exist between seasons in both subareas. Mean quantity of water applied per hectare is higher in the

Maha Season than in the Yala Season and is higher in the OIA subarea than in the NIA subarea. Farmers in both subareas use higher levels of chemicals during the Maha Season.

This is due to preventive measures taken due to the higher incidence of pests and diseases during the wetter Maha Season. OIA farmers use significantly less rice seed and hired labor and more machinery than NIA farmers during both seasons, indicating substitution of machinery for both hired labor and seed in both seasons. Farmers in both subareas demonstrate substitutability of family labor for machinery during the Maha Season in both subareas.

Correlation analysis indicates a strong negative relationship (-0.531 for Maha Season, -0.526 for

Yala Season) between quantities of total labor and machinery.

Estimated parameters and model details of the production function to describe the input- output relationship of rice are presented in Table 3.9. The model had a reasonable fit with an adjusted R2 value of 0.70, all estimated parameters for input quantities had expected signs so each input had a positive impact on yield, and two-thirds of the parameters were statistically

33 significant at the 0.10 level of significance. The area dummy was statistically significant, but the season dummy was only significant as interaction terms with area and total labor. Estimated coefficients on all individual production inputs were statistically significant. Inclusion of the interaction terms area and fertilizer, season and seeds, and season and fertilizer led to high collinearity and consequent insignificant coefficients for fertilizer and seeds as production inputs.

They were subsequently removed from the regression. Only the indirect variables of education level and distance to the distributor canal had no significant impact on yields. Area had a significant interaction effect with total labor, chemicals, and machinery. In each of these cases, the OIA had a lower partial elasticity of production than did the NIA. The significantly positive coefficients on farming experience and the completed schooling dummy indicate that yields increase with farming experience and with attainment of 10 years of education.

3.5.2: Marginal Value Productivities of Inputs

Marginal value products (MVPs), obtained by multiplying MPs by average product prices, are presented in Table 3.10 together with their respective opportunity costs. Marginal productivity for each input was calculated for each subarea by cultivation season using geometric means of inputs, predicted yields, and estimated parameters of the production function. All calculated MPs were positive. Each was also declining with increasing level of input use. Thus, our estimated production function was consistent with the theory.

Farmers in gravity irrigation systems in Sri Lanka do not pay for water. Although farmers are expected to contribute labor for operations and maintenance work programs (O&M) of the canal system, FO officeholders indicated that few participate. Therefore, it is difficult to calculate farmers’ opportunity cost of water applied to fields based on contributions by cash or labor. For opportunity cost of water, we used the current value of net benefits realized by inland capture fishery, when water available in the reservoirs is not released for rice farming. Calculations were

34 based on technical details for inland capture fishery in the area, as reported by Renwick (2001), and the current wholesale price of fish at landing sites. Wage rates for hired labor and the subsidized price of fertilizer were used as opportunity costs of labor and fertilizer respectively.

The ratio of MVPs and opportunity costs (OC) are much larger than unity for water and fertilizer, suggesting that an additional unit of water or fertilizer in rice production will generate additional revenues, higher than the opportunity cost of the respective resource. In contrast, the

MVP/OC ratio for labor is less than unity in all four cases, indicating that the true opportunity cost for additional labor is much less than the wage rate for hired labor.

This information provides important policy insight for allocation of inputs. Since there is no established market price for water, the calculated MVP for water can be used as an estimate of the true value of water at the prevailing market price for rice. The MVP/OC ratio for fertilizer is around 10 to 12. Farmers pay a subsidized price for the allocated fertilizer, but there is no market for them to purchase additional fertilizer at market price. Sri Lanka depends largely on imported chemical fertilizer. Border price of fertilizer (cost-insurance and freight (cif) at Port of

Colombo) during 2007 was 37.65 SLR/kg (Central Bank of Sri Lanka 2008), indicating that the economic cost of fertilizer would also be only about half the MVP. It can be inferred that farmers are likely to apply more fertilizer (until MVP equals OC) even if they are not subsidized. It should be cautioned, however, that agronomic constraints may limit use of fertilizer inputs in this area.

The agronomically maximum realizable farm level yield is considered to be about 10.0 t/ha. The mean yield in sample farms is around 75 percent of this expected yield plateau. As goodness of fit and F-tests are satisfactory, the estimated production function can be used to predict output due to changes in input combinations within the range of our data. We use the estimated model to simulate the effects of applying different quantities of water on rice yields.

3.5.3: Welfare Implications of Differential Access to Water

Farm budgeting techniques were used to compute gross revenue, net revenue, and profit from rice for representative farms. Their averages and standard deviations by subarea and by season are reported in Table 3.11. In preparing rice budgets, household-owned inputs were valued at ongoing market rental/sales values for the respective season and area, and the opportunity cost of capital per season was considered to be 4.5 percent of all cash costs, to reflect the ongoing short-term market borrowing rate of about 18 percent per annum.

Imputed costs include family labor, returns to management, and land rent. Family labor was imputed at the wage rate incurred by the household during the respective season. Based on the convention used in standard farm budgeting, five percent of the net revenue was imputed as returns to management. Land rental practices varied greatly within and across subareas.

Precise land rental rates were difficult to assess. Based on information from farmer leaders, land rental values per hectare per season were imputed at SLR 20,000 for OIA and 15,000 for

NIA.

Farmers reported higher farm-gate prices during Yala 2007 season than the preceding

Maha 2006/07 season. This increase in price was unexpected, as it was contrary to the normal pattern of rice price behavior in the country. Constant-Maha 2006/07 prices were used to avoid inflating expected profits due to the latter season’s unusually high relative price.

Seasonal net revenues from one hectare of rice, presented in Table 3.11, were summed across seasons and were multiplied by farm size (Table 3.12, uppermost row) to obtain annual household net revenues from rice, which were used in the calculation of annual household incomes. The mean size of OIA farms was slightly larger than the mean of NIA farms, but the difference was not statistically significant. The middle part of Table 3.12 reports average income shares, while the lower part of the table reports the distribution of rice income shares in total household income. Shares of rice income in total annual household income varied from 10

36 percent to 100 percent and averaged 50 percent in OIA and 44 percent in NIA. This indicates that rice is somewhat more important as an income source in the more established farming system. The distribution of OIA households is slightly skewed to higher percentages of household income from rice production, and the distribution of NIA households is strongly skewed toward lower percentages.

Calculated means and standard deviations of annual net revenue from rice, net farm income, and household income are reported in Table 3.13. The mean value and standard deviation of the assets index, calculated by Principal Component Analysis using ordinal scale data on availability of household assets and level of housing quality, is also reported in this table. Each of these indicators of welfare is higher in OIA than in NIA, and statistical tests indicate that they are significantly higher. As expected, the household assets index showed a significant positive correlation with total annual household income (0.349 for OIA and 0.441 for

NIA, both significant at 0.01 level).

As evident, water availability and rice income was associated. This finding is complemented by our production function estimates that reveal large positive impacts for quantity of water applied. The effect of water use on farm household welfare was simulated by changing water quantities applied per hectare of rice, while holding all other input quantities at current levels. We first simulated net revenues from rice for all farms, holding applied water quantities at the current NIA mean values. Water quantities were then increased to the current

OIA mean values, with all other input levels kept at current levels. A paired sample t-test (Table

3.14) indicated that net rice revenues are higher when water levels are set at the OIA mean, and the differences are significant. It is anticipated that producer surplus (net revenue from rice), consequently annual household income, will be increased if the quantity of water applied per hectare is increased. In the next section, we examine the effects of a specific intervention to allocate a given volume of water to increase welfare of farm households.

3.5.4: Water Allocation Plans for Augmented Water

The Veheragala Reservoir Project commissioned by the Government of Sri Lanka plans to augment water at Lunugamvehera Reservoir through a trans-basin diversion from the adjacent Menik Ganga Basin by 50 MCM annually (CECB 2004). The project does not envisage increasing the irrigated area in the KOISP. Therefore, additional water would be available for distribution among existing farms. Based on the current conveyance efficiency of

0.7, we expect 35 MCM of additional water will be available to the farms. Interventions would target farms that currently use quantities of water less than a minimum level defined based on allocation criteria. Water will not be taken away from users currently using water quantities higher than the defined minimum. Therefore, water quantities applied and thus yields for farms that currently use higher quantities of water will remain unchanged.

Minimum water quantities per one hectare of rice in different subareas were based on allocation criteria under the respective plan. When water was allocated based on economic criteria in one plan, minimum water quantity was defined as the level of water at which mean

MVPs for subareas receiving additional water are equated when all available water is exhausted. The agronomic criteria used in the other plans ensured that all farms receive sufficient water to meet minimum agronomic requirements.

The estimated production function was used to predict new yields at new water levels for each farm assuming quantities of other inputs remained at current levels. Water was allocated to each farm until all farms in the respective subarea received the minimum water quantities as defined above.

We consider five alternative water allocation plans. They are described in Table 3.15.

All plans are constrained by the availability of 50 MCM of additional water at the reservoir (or 35

MCM at farm level). According to economic principles, equalizing MVP of water in all subareas

38 receiving additional water will generate maximum welfare. Plan A is based on allocating all additional available water based solely on this economic criterion.

Plan B deviates from Plan A as the latter assures that all farms are ensured that they receive sufficient water to meet minimum agronomic requirements for crop evapo-transpiration

(ET) and percolation losses. Based on: Dimantha and de Alwis (1985); Guerra et. al. (1998); and Punyawardana (2008), we define the minimum water levels as 10,250 m 3/ha and 10,750 m3/ha, respectively, for the Maha and Yala seasons. Once the minimum water requirement is

met for all farms, any additional water quantity available will be allocated based on the

economic principle defined above.

Average net revenue from rice is higher in the Maha Season than in the Yala Season

(see Table 3.11). The allocation rule in Plan B would lead to a large increase in the Yala

Season’s share in annual income. Plan C adds a second constraint to reallocate a portion of

water from the season with higher benefits to the one with lesser benefits to promote inter-

seasonal equity in household income. This would help to even out the inter-seasonal

distribution of annual net rice revenue. Plan D is an extension of Plan C and illustrates welfare

improvements if farmers also apply fertilizer at rates recommended by the Rice Fertilizer

Subsidy Program of the Government of Sri Lanka. It supports the government’s objective of

increasing crop productivity by promoting fertilizer application. Finally, we include Plan E, the

“Business as Usual” Plan to compare net benefits if KOISP managers continue to allocate

additional water in the same proportions by subarea and season as the current allocation.

At the first stage, we compute mean incremental net revenue for one hectare of rice

arising from changes in water quantities applied in each subarea by season. Increases in mean

rice yield due to increased allocation of water to each subarea are valued at current producer

price of rice to obtain net incremental rice revenues per hectare. These findings are then used

to upscale benefits to the system level. We first compute the benefits at the subarea level by

39 season and then aggregate by season and by subarea. Finally benefits are aggregated to the system level. Several important assumptions are made for this exercise. First, we assume that intensities and factor prices for non-water inputs and the market price of rice will remain constant. Second, we assume that our estimated production function holds valid over the range of interventions. Third, we expect that there are no positive or negative production externalities as we change water levels that will affect predicted yields. Based on the above assumptions, any increase in rice yield as a result of higher quantities of water applied will generate net rice revenues equal to the increase in production multiplied by product price.

Estimated mean MVPs for water, fertilizer and labor calculated before and after the intervention are reported for each allocation plan in Table 3.16. MVPs of water are equated in

Plans A and B for all three subarea-season strata other than for OIA-Maha. OIA does not receive any additional water during Maha season under Plan A because the available water quantity is exhausted before reaching the current low MVP of that stratum. In Plan B, OIA receives additional water during the Maha season only to meet the minimum agronomic requirements on each farm.

Consistent with production theory, the estimated production function exhibits decreasing marginal productivity in each input when all other input levels are held constant. With constant output price, decreasing marginal productivity implies that MVP also decreases as the quantity of an input increases. When the mean water quantity increases under a plan, the MVPs of water decrease. This is clearly shown when moving from the current level to the Plan B allocation.

Similar effects are observed for the MVPs of fertilizer as we move from Plan C to Plan D. As fertilizer level increases, the MVP of fertilizer decreases. Because all pairs of inputs are presumed to be technical substitutes when the Cobb-Douglas functional form is used to estimate the production function, the estimated MVPs of fertilizer and labor increase when moving from the current level to the Plan B allocation.

Table 3.17 presents incremental water quantities for each plan compared to current water quantities used. Under current water allocation, per hectare water use shows notable differences between subareas and seasons. OIA-Maha, the highest water user, reports 24.4 percent higher per hectare use of water than the lowest user, NIA-Yala. However, annual total water used by both subareas is almost equal. When the economic criterion is used to allocate additional water (Plan A), OIA would receive only about 1/10 th of the additional water available.

This quantity would increase to roughly 1/7 th if the agronomic criterion is implemented (Plan B).

This introduces a new challenge to KOISP managers compared to their current allocation plan.

It would require a higher level of managerial attention to store more water in the reservoir during

the Maha season and greatly change the allocation when releasing it to subareas during the

Yala season. A little more than half the total incremental water is allocated to NIA during the

Yala Season under Plans C and D, and less than 1/4 th is allocated to this stratum under Plan E.

Table 3.18 presents increases in yield, net revenue per ha, and total net revenue from

alternative allocation plans by season and by subarea. Highest incremental mean yields in the

Yala Season from increased water allocation for both subareas occur with Plan A for which the

economic criterion is used. In all other plans a part of the water is reallocated based on other

reasons so incremental yields for the Yala Season are less. Incremental mean yields for both

subareas in OIA are the same in Plans B and C. Incremental yield for NIA-Maha is highest in

Plan C due to reallocating water from the Yala Season to attain greater inter-seasonal

distributional equity. In Plan D, mean yield increases due to fertilizer are higher in the NIA for

both seasons because yields before additional fertilizer applications are lower and incremental

fertilizer quantities are higher. Increases in net revenue follow similar patterns.

Table 3.19 summarizes system-wide incremental net benefits of alternative plans. As the

last row of Table 19 indicates, total annual incremental net benefits (without changes in

fertilizer) are highest under Plan A, when water is allocated based solely on the economic

41 criterion. It is only slightly higher than under the constrained Plans B and C, but the distribution of annual incremental net benefits among the subareas is highly dependent on which plan is selected. Due to different water allocations, the increase in net benefits for NIA-Yala is lower in

Plan C than in Plan B, while the benefits to NIA-Maha are greater. The seasonal distribution is more equitable in Plan C for NIA. For the OIA, the benefits vary from a low of 10 percent under

Plan A to 14 percent under Plan C, but they are larger under Plan D and reach a high of 44 percent under Plan E. Plan E generates considerably lower annual incremental benefits than other alternatives, demonstrating that, the current water allocation criterion is decidedly sub- optimal at the margin. Annual net benefits are somewhat more equally distributed among subareas and seasons in Plan E and, if implemented, will contribute to a continuation of current income disparities in the study area. Net benefits are expected to increase substantially by adding fertilizer as well as water. Comparing Plans C and D, we find that nearly 1/3 rd of total net

benefits in OIA accrue because of increased fertilizer use. The corresponding portion of total

net benefits in NIA is much lower at less than 1/10th .

Annual net farm incomes and annual household incomes were calculated using simulated net rice incomes and assuming no changes to non-rice incomes. Mean values of the welfare measures for both subareas increased after the intervention. Simulated net benefits

(incremental net revenue from rice) for Plans A and C generate nearly the same total incremental benefits. These two plans were used to investigate the distributional effects of the intervention on household income reported in Table 3.20. Both plans revealed similar distributions of increases across income pentiles. Both revealed a strong declining pattern of incremental income as a percentage of current income with increasing household income. Thus, the interventions have considerably higher relative impact on households with lower incomes and thus would have a positive effect in moving household incomes toward a more equitable income distribution.

3.6: CONCLUSIONS

Quantitative measures to describe the differences in water use can be considered an important addition to the study of relationships between water and household welfare.

Differential access to water in the Kirindi Oya Irrigation and Settlement Project in Sri Lanka is a well known problem since the commissioning of the new main reservoir in the late 1980s.

Unequal access to water between subareas of the study area was documented.

Estimation of an econometric model with water and other explanatory variables supported the hypothesis that, at the margin, rice yields increase with quantity of water applied.

As a consequence, it is concluded that there exists a relationship between water quantities applied to rice and net income from rice, as well as total household income.

Alternative water allocation plans to distribute among current users an estimated 50

MCM of additional water were examined. While the plan that maximized total welfare by equalizing MVPs of water across seasons and production areas had the highest annual net benefits, an alternative plan that assured minimum quantities of water to all farms and enhanced inter-seasonal income distribution produced only slightly lower net benefits. It did, however, substantially alter the distribution of net benefits across production areas.

An estimated annual net incremental benefit from rice (producer surplus) of SLR 137-

138 million per annum (in current values) is expected to be realized if the anticipated water quantity is delivered to about 8,000 hectare on farms in the study irrigation area. This is 12 percent higher when compared with the benefits if the current water allocation criterion were used to distribute the new water. If fertilizer is also applied at recommended levels, an additional

14 percent in annual net benefits could potentially occur with the application of the additional water. This finding of increased benefits of fertilizer application with additional water should be applicable to other irrigation systems in Sri Lanka as well. In our study area, the greatest incremental benefits from additional water are gained by applying the water in the new irrigation

43 area (NIA) and in the Yala Season. KOISP system managers can assure these benefits by storing water for the Yala season and taking appropriate measures to allocate a large share of the additional water to NIA farms. Two of the proposed plans show that incremental revenues as a percentage of current household income decrease with higher current household income and, therefore, have a relatively larger impact on households with lower incomes. Thus, the income distributional effects of following either of these two plans would reduce household income inequality.

3.7: REFERENCES

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Table 3. 1. Construction of water application indicator

Variable name Symbol Units Description Primary variables (1) Water duration t hour Time duration between opening and closing of the water inlet in an average irrigation (2) Number of irrigations n count Number of irrigations per season after early vegetative phase (3) Height of irrigation water h cm a Average height of irrigation water in an average irrigation after early vegetative phase (4) Farm Size a Ha Area sown of rice Calculated variables (5) Water quantity per V m3 a x h X 10 2 irrigation (6) Total hours of irrigation T hour t x n (7) Flow rate R m3/ hour V / t (8) Total quantity of water TQ m3/ farm R x T applied per farm (9) Water quantity per uwtrq m3/ ha TQ/a hectare

Notes:

a Water height was recorded to the nearest inch during the interviews and was converted to cm for calculation purposes.

Table 3. 2. Variables for estimating Cobb-Douglas production function

Variable name a Symbol Description Dependent variable updtn Per hectare production of rice by i th farm Production of rice (kg/ha) in j th area during t th season (yield) Explanatory variables: Input quantities

Water(m 3/ha) uwtrq Total quantity of water applied to the farm during vegetative phase divided by sown area Rice seed(kg/ha) uqseed Total quantity of rice seed divided by sown area Fertilizer (kg/ha) ufert Total quantity of fertilizer divided by sown area Cost of machinery (SLR/ha) umech Total cost of machinery divided by sown area Cost of chemicals (SLR/ha) uchem Total cost of crop protection chemicals divided by sown area

Total labor (days) ualllbr Sum of hired labor days and family labor days divided by sown area Area and season dummy variables Area dummy areeadum 1=OIA, 0 = NIA, Season dummy sesndum 1=Maha Season, 0 = Yala Season, Area X Season dummy areasesn 1 = OIA and Maha, 0 = otherwise

Socioeconomic variables Duration of full-time education of Level of education (Years) educ household head in years Farming experience (Years) Number of years of experience in farming fmgexp after leaving school) of household head Completed schooling dummy b 1 if household head had 10 or more years hhgce of schooling, 0 otherwise Irrigation quality variable Distance to distributor canal Distance from farm water inlet to the dstfdc relevant distributor canal (m)

Notes:

a Variables listed here were combined to create slope dummy variables listed in Table 3.9. b Completion of 10 years of schooling is a significant educational attainment in Sri Lanka as employers generally set this as the minimum level of competency for recruitment.

Table 3. 3. Calculation of total household income

Variable name Symbol Description Source Primary variables (1) Ratio of rice Share of annual net income Direct questions in the income to farm income from rice in annual net survey farm income (2) Ratio of farm µ Share of annual net farm Direct questions in the income to total income in annual survey household income household income (3) Information for Direct questions in the computing farm survey budgets

Computed variables stage I

(4) Seasonal net rice UNRI (j) Net rice income from one Computed based on income per ha hectare of for the i th farm production and input in j th area for the t th season quantities and prices

Computed variables stage II (5) Seasonal net rice SFRI UNRI multiplied by income to household rice area

(6) Annual net rice AFRI Sum of seasonal net rice SFRI (1) + SFRI (2) income to household incomes across seasons (7) Total annual farm AFFI AFRI/ income to household (8) Total annual AHHI AFFI / µ household income

(9) Ratio of annual net ω AFRI/AHHI or µ income from rice and total household income

Table 3. 4. Indicators used to construct household asset Index

Variable Name Variable Name Group A: Quality of Dwelling Group B: Availability of Household Assets (1) Type of walls (9) Type of telephone (2) Type of floor (10) Availability of furniture (3) Quality of windows (11) Availability of transport equipment (4) Type of roof (12) Availability of consumer durables (5) Type of lavatories (13) Availability of agricultural implements (6) Quality of water supply (7) Number of bedrooms (8) Type of roof ceiling

Note:

Data were reported on a five-point ordinal scale. For details of data collected, please refer appendix 4.

Table 3. 5. Means of rice yield reported by sample households, by season and subarea (in kg/ha)

Season \Sub area Old Area New Mean t-Statistic Area Difference a

Maha Season 7,157 6,275 882 8.37*** (578) (815) (105)

Yala Season 6,722 5,894 828 7.97*** (606) (776) (104)

Mean Difference b 435 381 (88) (119) t-Statistic 4.92*** 3.21***

Notes:

Numbers in parentheses are standard deviations for means and standard errors for mean differences. a Column difference indicating difference between sub areas by season. b Row difference indicating difference between seasons by sub area.

*** p< .01

Table 3. 6. Means of water related variables by season and by subarea

Variable Name OIA NIA Mean Differences a, c Maha Season Water duration h 7.0 9.5 -2.49 *** (3.2) (2.5) (0.43) Number of irrigations count 13.2 12.5 0.73 *** (1.6) (1.7) (0.25) Water height cm 8.2 7.6 0.5694 *** (1.24) (1.13) (0.1768) Total hours of irrigation h 93.7 118.4 -24.7 *** (47.0) (35.9) (6.23) Flow rate m3/ h 118.9 75.2 43.642 *** (53.3) (32.0) (6.555) Total quantity of water m3/ farm 10,350.3 8,197.4 2,152.83 *** (5,854.9) (2,303.2) (663.2) Yala Season Water duration h 8.6 14.6 -6.01 *** (4.4) (10.5) (1.2) Number of irrigations count 13.4 13.9 -0.57 * (1.8) (2.6) (0.33) Water height cm 7.8 6.4 1.3611 *** (0.84) (1.41) (0.1729) Total hours of irrigation h 115.7 201.1 -85.41 *** (60.4) (145.3) (16.58) Flow rate m3/ h 99.6 50.0 49.611 *** (55.5) (30.6) (6.68) Total quantity of water m3/ farm 9,825.3 7,508.9 2315 *** (5,194.7) (2,117.2) (591.32) Mean Differences b, c Water duration -1.59*** -5.11*** (0.57) (1.14) Number of irrigations -0.14 ns -1.44*** (0.25) (0.33) Water height 0.4444*** 1.2361*** (0.1584) (0.19) Total hours of irrigation -21.98*** -82.69*** (8.07) (15.77) Flow rate 19.248*** 25.217*** (8.112) (4.666) Total quantity of water 525 ns 687.17** (825.06) (329.8)

Notes:

Numbers in parentheses are standard deviations for means and standard errors for mean differences a Column difference indicating difference between sub areas by season. b Row difference indicating difference between seasons by sub area.

* p<. 10 ** p<.05 *** p< .01

Table 3. 7. Mean input intensities per ha and factor prices in Sri Lankan Rupees (SLR) a, by season and by subarea

Input Unit Old Area New Area Quantity Prices b Quantity Prices b Maha (2006/07)

Water m3/ha 10,783 na 9,425 na (1,832) (1,410) Seeds kg/ha 204 25.93 187 25.14 (31) (2.77) (40) (2.92) Fertilizer kg/ha 421 7.00 415 7.00 (36) (0.00) (37) (0.00) Chemicals SLR/ha 3,959 na 3,908 na (1,356) (1,230) Machinery SLR/ha 26,511 na 24,286 na (4,624) (5,104) Total labor c Person days/ha 36 na 49 na (11) (17) Hired labor Person days/ha 22 583.89 35 562.22 (9) (26.85) (15) (36.19) Family labor Person days/ha 14 na 15 na (10) (9) Yala (2007) Water m3/ha 10,303 na 8,667 na (1,388) (1,448) Seeds kg/ha 201 23.13 193 22.97 (23) (3.23) (30) (3.61) Fertilizer kg/ha 419 7.00 413 7.00 (26) (0.00) (41) (0.00) Chemicals SLR/ha 3,550 na 3,321 na (1,058) (1,098) Machinery SLR/ha 28,362 na 25,823 na (3,499) (4,968) Total labor c Person days/ha 34 na 46 na (14) (15) Hired labor Person days/ha 24 561.11 35 535.00 (11) (50.71) (13) (68.47) Family labor Person days/ha 10 na 11 na (6) (7)

Notes:

Standard deviations are in parentheses.

a Official exchange rate during the survey period was SLR 108.89 per US$ 1.

b Prices reported are means of current prices for respective seasons. Water did not have a market price. Rental prices for machinery widely varied across farms and farmers used an assortment of crop protection chemicals. Therefore unit input cost for these two variables were calculated by dividing total costs for the respective input by farm size. c Labor input used in production function estimation was the sum of quantities of hired labor and family labor.

Table 3. 8. Results of independent sample t-tests for comparing means of input quantities applied per ha

Comparison of Means Mean t-Ratio Difference (1) (2) (1) (2) (1) – (2) Water OIA-Maha OIA Yala 10,783 10,303 480 1.98 ** NIA-Maha NIA Yala 9,425 8,667 760 3.56 *** OIA-Maha NIA-Maha 10,783 9,425 1,358 5.57 *** OIA - Yala NIA - Yala 10,303 8,667 1,638 7.74 *** Rice Seeds OIA-Maha OIA Yala 204 200 4 0.91 NIA-Maha NIA Yala 187 193 -6 -1.15 OIA-Maha NIA-Maha 204 187 17 3.16 *** OIA -Yala NIA - Yala 200 193 7 1.79 * Fertilizer OIA-Maha OIA Yala 421 419 1.5 0.32 NIA-Maha NIA Yala 415 413 2.7 0.45 OIA-Maha NIA-Maha 421 415 5.5 1.01 OIA -Yala NIA - Yala 419 413 6.7 1.29 Chemicals OIA-Maha OIA Yala 3,959 3,550 409 2.26 *** NIA-Maha NIA Yala 3,908 3,321 587 3.38 *** OIA-Maha NIA-Maha 3,959 3,908 51 0.27 OIA -Yala NIA - Yala 3,550 3,321 229 1.42 Machinery OIA-Maha OIA Yala 26,511 28,362 -1,851 -3.03 *** NIA-Maha NIA Yala 24,286 25,823 -1,537 -2.05 ** OIA-Maha NIA-Maha 26,511 24,286 2,225 3.06 *** OIA -Yala NIA - Yala 28,362 25,823 2,539 3.96 *** Total Labor OIA-Maha OIA Yala 36 34 2 0.91 NIA-Maha NIA Yala 49 46 3 1.12 OIA-Maha NIA-Maha 36 49 -13 -5.97 *** OIA -Yala NIA - Yala 34 46 -12 -5.60 *** Hired Labor OIA-Maha OIA Yala 22 24 -2 -1.55 NIA-Maha NIA Yala 35 35 0 -0.07 OIA-Maha NIA-Maha 22 35 -12 -6.62 *** OIA -Yala NIA - Yala 24 35 -10 -5.64 *** Family Labor OIA-Maha OIA Yala 14 10 4 3.37 *** NIA-Maha NIA Yala 15 11 3 2.58 *** OIA-Maha NIA-Maha 14 15 0 -0.36 OIA -Yala NIA - Yala 10 11 -2 -1.51

* p<. 10 ** p<.05 *** p< .01

Table 3. 9. Parameter estimates and model details of the production function

Parameter Standard Variable Symbol t-Ratio Estimates Errors a Intercept constant 2.0770 0.6346 3.27 *** Area dummy (OIA = 1) areadum 2.2606 0.6803 3.32 *** Season dummy (Maha =1) sesndum 0.3557 0.6957 0.51 Area X season areasesn 0.0435 0.0194 2.24 ** Log of water l_uwtrq 0.4088 0.0425 9.62 *** Log of fertilizer l_ufert 0.1546 0.0455 3.40 *** Log of labor days l_ualllbr 0.0923 0.0238 3.88 *** Log of machinery l_ucmech 0.0833 0.0410 2.03 ** Log of chemicals l_ucchem 0.0306 0.0184 1.68 * Log of seeds l_uqseed 0.0745 0.0406 1.84 * Log of education l_educ 0.0097 0.0196 0.49 Log of farming experience l_fmgexp 0.0334 0.0099 3.39 *** Log of distance to D canal l_dstfdc -0.0003 0.0032 -0.09 Completed schooling dummy hhhgce 0.0673 0.0119 5.64 *** Area X log of water l_areawater -0.0658 0.0518 -1.27 Area X log of total labor l_arealbr -0.0753 0.0263 -2.86 *** Area X log of chemicals l_areachem -0.0420 0.0205 -2.05 ** Area X log of machinery l_areamech -0.0976 0.0458 -2.13 ** Season X log of water l_sesnwater -0.0598 0.0509 -1.17 Season X log of total labor l_sesnlbr 0.0427 0.0256 1.67 * Season X log of chemicals l_sesnchem -0.0221 0.0204 -1.08 Season X log of machinery l_sesnmech 0.0488 0.0454 1.07 Season X log of seeds l_sesnseed -0.0496 0.0495 -1.00 Adjusted R-squared = 0.70 F-statistic (22, 337) = 39.07 (p-value < 0.00001) Log-likelihood = 444.39

White's test for heteroskedasticity - Null hypothesis: heteroskedasticity not present Test statistic: LM = 187.23 with p-value = P(Chi-Square(185) > 187.23) = 0.44

Ramsey-RESET test for specification - Null hypothesis: specification is adequate Test statistic: F(1, 336) = 0.56 with p-value = P(F(1, 336) > 0.56) = 0.45

Notes:

a Standard errors are heteroskedasticity consistent robust standard errors.

* p<. 10 ** p<.05 *** p< .01

Table 3. 10. Marginal value productivities of inputs by season and by subarea

Input , Season and MVP (SLR/unit) Opportunity Cost Ratio of MVP to OC Sub Area (OC ) (SLR/unit) Water OIA-Maha 3.16 1.07 3.0 OIA -Yala 3.74 1.07 3.5 NIA-Maha 3.91 1.07 3.7 NIA -Yala 4.71 1.07 4.4

Fertilizer OIA-Maha 85.17 7.00 12.2 OIA -Yala 80.05 7.00 11.4 NIA-Maha 76.15 7.00 10.9 NIA -Yala 71.96 7.00 10.3

Labor OIA-Maha 161.95 583.89 0.28 OIA -Yala 59.44 561.11 0.11 NIA-Maha 277.65 562.22 0.49 NIA -Yala 208.06 535.00 0.39

Notes:

Product prices used in the calculations are 16.59 SLR/kg for OIA and 16.74 SLR/kg for NIA for both seasons.

a Opportunity cost of water is the value of freshwater artisan fishery if water is not released for irrigation. Values updated using current prices and technical information from Renwick (2001), for fertilizer, the current subsidized price under GOSL’s Rice Fertilizer Subsidy Scheme, and for labor the current market wage rate.

Table 3. 11. Rice budgets per hectare by sub area and season (SLR/ha)

Old Area New Area Cost or Revenue Maha Yala Maha Yala (1) Seeds 4,707 5,199 4278 4,849 (947) (748) (1,022) (907) (2) Fertilizer 2,946 2,935 2,907 2,889 (251) (182) (261) (290) (3) Chemicals 3,959 3,550 3,908 3,321 (1,356) (1,058) (1,230) (1,098) (4) Machinery 26,511 28,362 24,286 25,823 (4,624) (3,499) (5,104) (4,968) (5) Hired labor 12,296 14,262 18,391 19,486 (5,126) (6,566) (8,292) (7,523) (6) Total paid-out costs 50,419 54,308 53,770 56,368 Sum of (1) through (5) (6,559) (6,939) (6,548) (6,532) (7) Operating interest 2,269 2,444 2,420 2,537 (295) (312) (295) (294) (8) Total cash costs (6) plus (7) 52,688 56,752 56,190 58,905 (6,854) (7,251) (6,842) (6,826) (9) Family labor 7,846 5,838 7,874 6,444 (5,223) (3,748) (5,085) (4,041) (10) Returns to management 3,303 2,741 2,446 1,992 (683) (709) (908) (891) (11) Land rent 20,000 20,000 15,000 15,000 (0) (0) (0) (0) (12) Total imputed costs (9) + (10) + (11) 31,149 28,579 25,320 23,436 (5,417) (3,901) (5,441) (4,464) (13) Gross revenue 118,738 111,563 105,103 98,737 (12,501) (12,879) (18,927) (179,640 (14) Net revenue [(13) minus (8)] 66,050 54,811 48,913 39,832 (13,663) (14,177) (18,167) (17,825) (15) Profit [(14) minus (12)] 34,901 26,233 23,593 16,397 (12,865) (13,520) (16,380) (15,810)

Notes:

Reported values are the means for subareas.

Numbers in parentheses are standard deviations for means and standard errors for mean differences. Prices used are 2006/07 Maha constant product prices (15.59 SLR/kg for OIA and 15.74 SLR/kg for NIA.). a Gross revenue = yield x product price. b Net revenue = gross revenue minus sum of cash costs. c Profit = net revenue minus sum of imputed costs.

Table 3. 12. Means of rice area and income share and distribution of income shares by sub area

Description Old Area New Area

(a) Mean rice area (ha) 0.94 0.88

(b) Income Share Share of net rice revenue in farm income ( ) 0.77 0.69 (0.21) (0.22) Share of farm income in household income (µ) 0.66 0.66 (0.20) (0.21) Share of net rice revenue in household income ( ω ) 0.50 0.44 (0.21) (0.19)

(c) Income Share of Rice in Household Income Percentage of Households Share Class (%) Old Area New Area Less than 20 percent 4 2 20 - 39.9 percent 24 44 40 - 59.9 percent 37 29 60 - 79.9 percent 22 18 80 - 100 percent 12 7

Note:

Standard deviations are in parentheses.

Table 3. 13. Means of welfare measures by sub area (and results of independent sample t- tests)

Welfare Measure Old Area New Mean t- Area Difference Statistic (1) Annual net revenue 114,271 73,899 40,372 5.47*** from rice (SLR/Household) (63,250) (30,007) (7,379)

(2) Annual net farm 159,453 120,397 39,056 3.00*** income (SLR/Household) (100,008) (71,876) (12,982)

(3) Annual household 261,903 194,043 67,860 2.88*** income (SLR/Household) (190,351) (116,973) (23,550)

(4) Assets index 8.08 7.47 0.61 2.75** (1.48) (1.49) (0.22)

Note:

Numbers in parentheses are standard deviations for means and standard errors for mean differences.

** p<.05 *** p< .01

Table 3. 14. Effect of water level change on net revenue from rice per ha

Mean Net Revenue in SLR/ha Sub Area and Water Level Water Level Difference of t-Ratio a Season = Current = Current Means OIA Mean NIA Mean

OIA-Maha 66,251 61,804 4,447 116.9 *** (11,302) (11,016) (361)

OIA-Yala 54,735 48,316 6,419 121.3 *** (10,785) (10,417) (502)

NIA-Maha 53,643 48,604 5,039 71.2 *** (13,838) (13,247) (671)

NIA-Yala 46,883 39,664 7,219 73.9 *** (13,202) (12,404) (926)

Note:

Standard deviations are in parentheses.

Simulated net revenue is: predicted yield x product price - total cash costs. Yields were predicted using parameters of the estimated production function (Table 3.9) and actual quantities of inputs.

a Based on paired sample t-test on mean differences at two water levels.

*** p< .01

Table 3. 15. Description of water allocation plans

Plan Name Plan Objective Minimum Inter- Change Water seasonal of Other Allocation Equity Inputs Plan A Maximize net benefits No No No

Plan B Maximize net benefits after Yes No No applying minimum water

Plan C Maximize net benefits after Yes Yes No applying minimum water and providing inter-seasonal equity

Plan D Maximize net benefits through a Yes Yes Yes combination of water and fertilizer, subject to minimum water and inter-seasonal equity

Plan E Continue the current water na na na allocation policy of KOISP managers

na Not applicable

Table 3. 16. Marginal value productivities computed at data means for each plan

Before After Plan A Plan B Plan C Plan D Plan E Water OIA-Maha 3.16 3.16 3.05 3.05 3.08 2.73 OIA -Yala 3.74 3.50 3.52 3.52 3.57 3.28 NIA-Maha 3.91 3.50 3.52 3.34 3.40 3.43 NIA -Yala 4.71 3.50 3.52 3.67 3.72 4.17

Fertilizer OIA-Maha 85.17 85.17 86.32 86.32 81.28 83.98 OIA -Yala 80.05 81.26 80.97 80.97 77.56 79.71 NIA-Maha 76.15 80.65 80.31 82.61 76.91 75.02 NIA -Yala 71.96 84.24 83.83 81.51 78.78 71.41

Labor OIA-Maha 161.95 161.95 164.15 164.15 165.91 171.42 OIA -Yala 59.44 61.46 61.24 61.24 61.93 63.64 NIA-Maha 277.65 294.08 292.84 301.23 305.12 297.63 NIA -Yala 208.06 253.93 252.71 245.72 249.06 225.75

Note:

Product prices used are 16.59 SLR/kg for OIA and 16.74 SLR/kg for NIA for all calculations.

Table 3. 17. Water volume allocations for each plan

Current Water After Applying Incremental Water Quantity Allocation Unit Allocation Plan A Plan B Plan C and D Plan E m3/ha MCM a m3/ha MCM m3/ha MCM m3/ha MCM m3/ha MCM

OIA-Maha Season 10,783 40.9 0 0.0 461 1.8 461 1.8 2,354 8.9 OIA -Yala Season 10,303 39.1 1,001 3.8 883 3.4 883 3.4 2,249 8.5 NIA-Maha Season 9,425 41.8 1,646 7.3 1,513 6.7 2,432 10.8 2,057 9.1 NIA -Yala Season 8,667 38.5 5,373 23.8 5,208 23.1 4,288 19.0 1,892 8.4

By season Maha Season 82.8 7.3 8.5 12.5 18.1 Yala Season 77.6 27.7 26.5 22.4 16.9 Annual quantities OIA 80.1 3.8 5.1 5.1 17.5 NIA 80.3 31.2 29.8 29.8 17.5 65 KOISP 35.0 160.4 35.0 34.9 34.9

Annual Water at 229.1 49.9 49.9 49.9 50.0 Reservoir

Note:

Water quantities are for vegetative and maturity phases of rice.

a MCM refers to million cubic meters. Water quantities reported are for the respective allocation unit.

Table 3. 18. Incremental benefits of water allocation by plan by subarea and by season

Old Area New Area Item Maha Yala Maha Yala Current mean yield (kg/ha) 7,157 6,261 6,696 5,892 Rice area (ha) 3,796 3,796 4,438 4,438

Increase with Plan A Mean yield (kg/ha) 0 228 370 1,296 Net revenue (SLR/ha) a 0 3,780 6,191 21,691 Incremental total net revenue b 0.00 14.35 27.48 96.26

Increase with Plan B Mean yield (kg/ha) 97 203 342 1,261 Net revenue (SLR/ha) a 1,610 3,361 5,723 21,112 Incremental total net revenue b 6.11 12.76 25.40 93.70

Increase with Plan C Mean yield (kg/ha) 97 203 531 1064 Net revenue (SLR/ha) a 1,610 3,361 8,886 17,808 Incremental total net revenue b 6.11 12.76 39.43 79.03

Increase with Plan D Compared to Plan C Mean fertilizer (kg/ha) 29 31 35 36 Mean yield (kg/ha) 78 78 88 94 Net revenue (SLR/ha) c 1,084 1,076 1,224 1,325 Incremental total net revenue over Plan C b 4.12 3.98 5.56 5.88 Compared to Current Scenario Incremental total net revenue 10.23 16.74 44.99 84.91

Increase with Plan E Mean yield (kg/ha) 418 474 450 500 Net revenue (SLR/ha) a 6,734 7,627 7,314 8,108 Incremental total net revenue b 25.56 28.95 32.46 35.98

Notes:

a Yield increase multiplied by rice price of SLR 16.74/kg and 16.59/kg for OIA and NIA respectively. b Incremental total net revenues are in SLR million = Net revenue per ha in SLR multiplied by total area X 10 -6. c Yield increase multiplied by rice price minus increase in fertilizer quantity multiplied by fertilizer price of SLR 7.00/kg

Table 3. 19. Aggregate incremental benefits of water allocation by subarea, by season and for KOISP

Incremental Net Benefits (SLR Million) Percentage Share Plan A Plan B Plan C Plan D Plan E Plan A Plan B Plan C Plan D Plan E From Total Fertilizer Area and Season OIA-Maha 0.00 6.11 6.11 4.12 10.23 25.56 0.0 4.4 4.5 6.5 20.8 OIA-Yala 14.35 12.76 12.76 3.98 16.74 28.95 10.4 9.2 9.3 10.7 23.5 NIA-Maha 27.48 25.40 39.43 5.56 44.99 32.46 19.9 18.4 28.7 28.7 26.4 NIA-Yala 96.26 93.70 79.03 5.88 84.91 35.98 69.7 67.9 57.5 54.1 29.3

Seasonal Benefits Maha a 27.48 31.51 45.55 9.67 55.22 58.02 19.9 22.8 33.2 35.2 47.2 Yala b 110.61 106.45 91.79 9.86 101.65 64.94 80.1 77.2 66.8 64.8 52.8

Annual Benefits 67 OIA c 14.35 18.87 18.87 8.09 26.96 54.51 10.4 13.7 13.7 17.2 44.3

NIA d 123.74 119.10 118.47 11.44 129.90 68.44 89.6 86.3 86.3 82.8 55.7 KOISP e 138.09 137.97 137.34 19.53 156.87 122.96 100.0 100.0 100.0 100.0 100.0

Source:

Values are based on Table 18. a (Row1) +( Row 3), b (Row 2) +( Row 4), c (Row 1) +( Row 2), d (Row 3) +( Row 4), and e Sum of Rows (1) through (4).

Table 3. 20. Increase in annual household income from Plan A and Plan C by income pentile (SLR)

Plan A Plan C Income Before After Increase Percentage After increase Percentage Pentile increase increase 1 90,989 105,744 14,756 16.2 105,538 14,550 16.0 2 133,018 148,361 15,343 11.5 147,947 14,929 11.2 3 178,175 193,675 15,501 8.7 193,869 15,694 8.8 4 248,331 260,194 11,863 4.8 259,922 11,591 4.7 5 489,352 503,052 13,700 2.8 503,681 14,329 2.9

Note:

Income pentiles are based on ranking of household income before the intervention. Each pentile includes 20% of households.

CHAPTER 4

VALUATION OF ENHANCED DRY SEASON FLOW THROUGH YALA PROTECTED AREA COMPLEX: ESTIMATING NON-USE VALUES OF WATER

4.1: ABSTRACT

Man-made dams and lakes alter historical river flow regimes, and downstream ecosystems are often threatened. Although maintenance of flow volumes to meet downstream requirements is recommended, adherence to such directives is challenged due to increasing scarcity and competition for water. An understanding of value of water in various sectors is, therefore, important to assist policy makers in decisions on water allocation. This paper uses non-market valuation methods to estimate the economic value of dry season flow through Yala Protected Area Complex (YPC), an important wildlife refuge in Sri Lanka. A survey was conducted in ten districts of Sri Lanka. A single bounded dichotomous choice approach was used as the elicitation format. Fifty- seven percent of respondents were willing to make a payment to assure maintenance of an adequate flow of water through the YPC. Estimated mean WTP using random willingness to pay methods was SLR 627 per household per annum. The present value of the aggregate WTP over the payment horizon of ten years was SLR 17.4 billion that can be considered as the value of dry season water flow to the YPC.

4.2: INTRODUCTION

Man-made dams have become key elements of economic infrastructure in many

countries of the world. More than 18,000 large dams (heights ≥ 20 m) were constructed worldwide during the four decades that followed the World War II (IWMI 2002). The major purposes of dams include irrigation, hydropower, and municipal water supplies.

Yet they provide secondary services such as inland fisheries, flood protection, and recreation benefits. Man-made dams interfere with historical river flows through four types of manipulations: blockage, storage, regulation, and withdrawal of water (Brismar

2002). Dams are obvious causes of forced migration (Heming and Rees 2000), and also affect the livelihoods of downstream users of rivers and associated ecosystems (Richter et. al. 2010).

The need to consider the influence of large man-made lakes and dams on the physical, biotic, and sociocultural systems in respective river basins began in early

1970s (Scudder 1973), and continued to raise worldwide concerns (Adams 1985;

Tockner and Stanford 2002). Lemly et. al. (2000 p. 485), reviewing the conflict between irrigated agriculture and wetlands, stated “…. irrigation robs wetlands of their source of water, and they simply dry up.”

Several alternative management strategies, including the maintenance of environmental flows, have been proposed to address the issue. As many authorities manage reservoirs for minimum downstream flow volumes, models and methods for instream flow management are often subject to criticism by ecologists (Richter et. al.

1997; Walker et. al. 1997). An improved understanding of the various dimensions of instream flow regimes and a recognition of the need for substantial stream flows for maintaining ecosystems is sorely needed (Whiting 2002). More recently, Hamstead

(2007) called for a clear differentiation between measurable objectives and strategies

needed to achieve sustainable environmental flows. Richter (2009) suggested the

“sustainability boundary approach” as a quantitative management tool for managing environmental flows.

Despite increasing recognition of the need for substantial environmental flows, demand for water from other sectors and uses continues to increase. There is also an increasing trend toward recognizing water as an economic good, (ICWE 1992; World

Commission on Water for the 21st Century 2000). Although the applicability of this concept, especially in developing countries, has been questioned (Davis 2005;

Theesfeld 2004), economic values are increasingly being used in guiding water allocation decisions. Failure to include other potential uses would result in under allocations to the respective sectors and thereby to irreversible consequences.

Therefore, it is important to incorporate appropriate values for all potential water uses within a total economic value framework. Wildlife and park reserves are an important component of land use in many countries. Despite increasing recognition for the need for information on value placed by different segments of society on water used to enrich and maintain ecosystems, information available to guide policy decisions remains limited.

Previous studies on economic values of increased water flows to ecosystems. Ward

(1987), Douglas and Taylor (1999), and Mathis et. al. (2006) estimated values from increased use benefits such as recreation and fish production. Thus, there is a need for a study to show how non-use values emanating from water flows through natural ecosystems can be estimated.

Veheragala Reservoir Project in southeastern Sri Lanka is a unique case that can provide useful information and insights for policy makers and the general public on valuing water flow to a wildlife reserve amidst the competition from agriculture for increased diversions. The natural flow regime of Menik Ganga, one of the two major

rivers flowing through Yala Protected Area Complex (YPC) was altered by construction of Veheragala Reservoir and diversion of water to the adjoining Kirindi Oya Basin. While there is a directive on mandatory downstream releases to enhance the dry season water availability at the YPC, demands from irrigators pose potential threat to this allocation.

The objectives of this study are to: a) to develop a conceptual framework for estimating non-use values of water, and b) empirically use the developed framework to estimate the economic value of water used for enhancement of the dry season flow through the YPC. The remainder of this paper is as follows. The next section presents theory and methods of non-market valuation with special reference to water. The following section describes the YPC and the proposed water allocation plan and presents the research problem. This section is followed by a description of the research procedures, the results, and discussion. The final section presents a discussion of the policy implications of the research within the Sri Lankan context and elsewhere.

4.3: THEORETICAL FRAMEWORK

4.3.1: Categories of Economic Values

Economic values arise as humans assign values to benefits emanating from a resource. All economic values are thus anthropocentric. The sum of economic values from a multiple benefit resource is referred to as the total economic value (TEV).

Economic values are broadly grouped into two categories: a) use values, and b) non-use values. Use values arise due to benefit flows in the current time period, and have three components: a) direct use values, b) indirect use values, and c) passive use or vicarious values. Non-use values arise from benefits to an individual without utilization of the resource during the current period (Weisbrod 1964; Krutilla 1967). There are three widely accepted components of non-use values: a) existence values, b) option values,

and c) bequest values. Existence values arise from an individual obtaining satisfaction knowing that a resource exists. However, the individual has no motive to use it during the current period or in the future. Options values arise as individuals profess a desire to use the resource at some future time period during their life-time, while bequest values arise as individuals wish to conserve the resource for the benefit of future generations.

Conversely, Tisdell (2005) argues that values stated by an individual as willingness to pay (WTP) for preserving a site or species are a mixture of the three components of non- use values. Non-use values occur from local to global levels and can account for a substantial share of the TEV. According to a review by Pyo Hee-Dong (2002), the share of non-use values in the TEV varies from 0.41 to 0.89, and higher shares of non-use values are reported in water quality studies.

Previous studies have indicated that the general public is willing to pay for preservation of wilderness (Walsh et. al. 1984). However, differences exist among the

WTP of various segments of society. The general public’s WTP for preserving overall wildlife populations is higher than WTP for preserving specific types of species

(Whitehead 1993). Loomis and Larson (1994) reported that WTP for increasing grey whale population by the general public (who were principally non-users) had positive

WTP values. However, WTP values for non-user households were less than the respective values by visitors. They defined current users as those who visited the resource during the season of the study. Silberman et al. (1992) found that potential future users are more willing to pay for enrichment of a resource than potential nonusers, while a significant negative relationship existed between WTP and the distance to respondent’s residence from the resource concerned.

In Sri Lanka, Ekanayake and Abeygunawardena (1994) and Gunawardena

(1997) reported no differences between non-use values placed on Sinharaja Biosphere

Reserve by urban and rural residents based on the percentage annual income.

However, urban incomes were higher in absolute values, indicating that non-use values for urban residents are higher in absolute terms than for rural residents. Bandara and

Tisdell (2005) reported that urban residents in Sri Lanka had a higher WTP for conserving Asian elephants than their rural counterparts.

4.3.2: Estimation of Economic Values

Non-market valuation methods are classified into two broad classes: a) revealed preference (RP) methods, also known as behavioral methods; and b) stated preference

(SP) methods. RP methods link the behavior of individuals in actual markets with values associated with environmental quality, while stated preference methods, in contrast, are used when observable actual markets are not available (Grafton et al 2004).

In SP methods, respondents act as participants of a hypothetical market that is created by researchers. SP methods therefore have their own niche. However, an accurate understanding of the hypothetical scenario by respondents is important. Since having been introduced by Davis (1963), SP methods have been widely used in broader valuation contexts.

The theoretical basis for non-market valuation is the welfare measures for a utility-maximizing consumer (Freeman 2003). Valuing pure public goods using SP methods is done through evaluating changes in the indirect utility function and the expenditure function of a representative consumer based on exogenous changes. This approach is analogous to the use of area under the demand curve in revealed preference (behavioral) methods (Haab and McConnell 2002).

Compensating variation and equivalent variation are monetary measures of consumer welfare that are less demanding alternatives to consumer surplus (Just et. al.

2004). These respective changes are alternatively stated as willingness to pay (WTP) or willingness to accept (WTA) from the agent’s perspective. WTP is the maximum amount of income a person will pay to remain in the new improved status of welfare or to avoid moving to a lower level of welfare from their current level. Alternatively, when the new level of welfare is anticipated as inferior to the current level, WTA is the minimum amount of additional income the consumer will accept in return to the reduced level of welfare.

Enhanced water flows through an ecosystem generate values in two ways: a) increased direct benefits to humans, and b) as input to ecosystem functions. Improving conditions for flora and fauna leads to increased productivity and subsequently higher levels of material, on-site non-material, and off-site service flows, as well as higher passive use values. All these can be categorized as indirect uses of water, since the individual does not use the water directly but enjoys benefits generated through enhanced water availability. It should be noted, however, that water alone will not generate passive use values. It is the use of a given quantity of water as an input to a system that generates passive use values. If individuals get satisfaction without using indirect benefits but they value improvements, then such values can be identified as non-use values. Different benefits emanating from water flowing through an ecosystem are illustrated in Table 4.1.

4.4: DESCRIPTION OF STUDY AREA AND THE PROBLEM

Yala Protected Area Complex (YPC) is an aggregation of protected areas in southeastern Sri Lanka. This complex and adjoining protected areas form the largest contiguous protected area in the country, covering 171,000 hectare. YPC is rich in wildlife, including Asian elephants, leopards, sloth bears (the only bear species found in

Sri Lanka), and endangered marsh and estuarine crocodiles. Only 10 percent of YPC’s total area is open to the public, providing large contiguous areas of undisturbed habitat needed by large animals. This makes the YPC an important undisturbed ecosystem of national and global interest, especially given the highly developed Sri Lankan landscape.

Rainfall in the area shows a bi-modal pattern. The rainy season spans from mid-

October to January, with the dry season spanning the remainder of the year. Two rivers,

Kumbukkan Oya and Menik Ganga, contribute about 80 percent of the annual water availability of about 866 MCM (million cubic meters) in YPC. The Veheragala Diversion

Project constructed a 75 MCM storage reservoir within the YPC to divert 60 MCM of water annually from Menik Ganga to the adjoining Kirindi Oya River Basin to augment the existing irrigation system, known as the Kirindi Oya Irrigation and Settlement Project

(KOISP). Once the reservoir was commissioned, 47 MCM of water annually was committed to maintain mandatory downstream flows through the YPC. Water at the

Veheragala Dam is allocated among two different major uses: a) agricultural uses, and b) environmental uses. Weligamage et. al. (2009) estimated the value of annual agricultural water allocation of 50 MCM at Sri Lankan Rupees (SLR) 137-157 million.

Research problems presented in this paper are: a) a method for valuing water allocated for environmental purposes by downstream releases, and b) the value of water allocated to enhance the environment of the YPC.

The historical pattern of monthly flow volumes at the Veheragala dam site and from downstream watersheds is highly seasonal. Most of the wet season flow escapes freely to the sea due to high riverbanks and lack of floodplains. In contrast, the river is almost dry during June to September, creating severe stress for the animals as they lack food and drinking water. De Silva and de Silva (2005) reported finding 30 buffalo carcasses along the Menik River course during a severe drought in 1992. Although

severity of droughts has varied over time, this situation prompted wildlife authorities to adopt mitigation measures. The practice of transporting water using tankers and maintaining water holes was reported to cost an additional US$490 monthly at 2006 prices over the usual monthly park maintenance expenditure of US$863 (Dissanayake and Smaktin 2007).

The recommended flow-rate according to proposed water allocation plan (CECB

2004) was greater than the current average flow-rate during the dry season. This change would have increased flow volumes in the YPC to about 165 percent of the current levels during dry months. According to Rushton (2000 p. 44), the optimum in stream flow is “usually defined as a flow that is adequate to meet specific needs or management objectives for the river .” In the context of Menik River flow, it can be argued that the recommendation for downstream releases is consistent with management needs of the river.

Water flowing through the YPC is unlikely to generate direct benefits to the general public, as the current management policy makes only about 10 percent of the

YPC’s total area available for game viewing from vehicles (under strict surveillance of accompanying wildlife officers). Harvesting and removal of forest products are strictly prohibited. It is expected that individuals will generate indirect benefits and non-use benefits through ecosystem enhancement. We estimate the non-use value of water through a contingent market and equate the estimated WTP of the general population as the value of water released downstream.

4.5: METHODS AND DATA

4.5.1: Econometric Model

The indirect utility function of a representative consumer shows changes

consequential to the changing status of the YPC. We use superscripts ‘o’ to denote

original status and ‘h’ to denote the status after water releases. The values of indirect

utility functions are the same in both situations, and are written as,

v(P, E h, m - WTP) = v(P, E o, m) (1)

where, Eh≥ E o and indicates an enhanced environment, m is monetary income, and P

is a vector of parametric prices. This enhancement is considered desirable if changes in

the value of the indirect utility function are positive with respect to increase of the jth

th public good, such that for the j public good, (∂V / ∂ e j > 0) .

Alternatively, the WTP can be defined using the expenditure function of the

individual.

WTP = mPE,,o u− mPE ,, h u (2) ( ) ( ) when, u= V( PEm, , ) , assuming that the original situation is described first, the WTP is

the amount of income an individual would give up to make him or her indifferent between

the states of the YPC without and with water releases. In this situation, income is initially

at level m and the condition of YPC at E o. After the change, status of the YPC would

increase to E h. If the individual gains utility from the improved status of the YPC, his or her WTP is the amount of income that compensates for the welfare gained.

Consequently, this new income level would be m-WTP, where m is the monetary income before the change.

The expected improvements are beneficial to individuals, and benefits gained are non-rival and non-excludable. As this situation satisfies both the conditions of public goods, the value of water can be estimated using contingent valuation methods and aggregated for the whole population.

As described by Carson (2000), important steps in designing and conducting contingent valuation studies include issue identification, the design and description of current situation in comparison to the proposed alternative, the structure of the payment method, and the format for elicitation of responses. It is also important to ascertain the validity of responses through follow-up questions.

Various elicitation formats, such as open ended questions, bidding games, payment ladders, or referendum methods, are used in contingent valuation studies. Of the several variants of referendum methods, single bounded dichotomous choice (SB-

DC) format is widely used over other methods. This method makes the cognitive task of the respondent simple and avoids outliers. It is compatible with incentives, and eliminates protest bids (Haab and McConnell 1998). The double bounded dichotomous choice format adds a follow-up question to the first WTP question to get more information. Although more efficient than SB-DC, this format is considered more biased.

Information elicited through SB-DC format is used to estimate the mean WTP for the sample. Practitioners widely use parametric methods due to the dual capability of calculating WTP as well as identifying the determinants of respondents’ WTP. Widely used parametric methods include the random utility model (Hanneman 1984) and the random willingness to pay model (Cameron and James 1987). The success of parametric methods depends largely on accurate specification of probability distribution of WTP. An alternative to parametric methods are non-parametric methods of estimating WTP. Although these latter methods, including Kristrom (1990), Turnbull

(1976) and Watanabe and Asano (2007), do not require distribution assumptions, they

are less frequently used by practitioners. The following section presents the procedure

for estimating mean WTP from SBDC data.

Suppose that there is a sample of H respondents (i = 1,2,…,H) who are assigned

randomly among h sub samples of equal size labeled as h = 1,2,…,h. There are m

monetary values (B m) presented as the expected payment for the good to be valued. All

respondents in h th sub sample are asked whether they would pay the m th monetary value

(m = h for all i) assigned and the responses are recorded in binary format. The

respondents’ WTP is implicit and the information that can be recovered from a SB-DC

survey is whether the WTP for ith respondent is equal or greater than the bid value

presented to him or her, or less than that. Thus SB-DC elicitation format generates two

discrete outcomes (D) that can be modeled as,

0 0 ≤WTPi < B i Di =  1 B≤ WTP (3)  i i .

th Willingness to pay (WTP i) of the i respondent can be modeled as,

' WTPi=+α ρ B i + λ z ii + u for i= 1,2,3,…..,H. (4)

where, WTP i is respondent i’s unobservable true willingness to pay, Bi is the bid value

for the ith respondent, z is an s-dimensional column vector of respondent’s known i characteristics, u is the stochastic error term. α , ρ , and λ are unknown parameters i s

to be estimated.

We assume linearity in z and u for all respondents. We also assume that

2 ui ~Φ (0,σ ) , where Φ is the cumulative normal distribution function. Under these

assumptions the choice probabilities of ith respondent can be expressed as,

~ ~  −1  Φ(α + ρ Bi + λ z) i  0 Pr ob(D= j) = for j = (5) i ~ ~    −1  1 1(−Φα + ρ Bi + λ z) i  .

The log likelihood function is:

n ~ ~ ~ ~ LI=ln[ Φ++ (αρλ BzI−1 )] + ln[1 −Φ++ ( αρλ Bz− 1 )] (6) ∑ Di =0 iiDi = 1 ii i =1

th where Ik is the indicator function for event K and Di = j denotes the occurrence of the j alternative, j ∈{0,1 } .

The empirical model is estimated using binary logistic regression. In this formulation we define Φ as the standard logistic distribution function with mean zero

and standard deviation

(σ ) = D/ 3 . Following Cameron and James (1987), we use the binary indicator variable as the dependent variable, and a vector of respondents’ characteristics augmented with bid values as predictors. The estimated parameters,α , ρ , and λ are

subsequently used to recover parameters to predict WTP for each respondent using,

Yi= WTP i =ω + β s z is for i= 1,2,3,…..,H. (7)

* where ω= − ( α / ρ ) , and βs= − ( λ s / ρ ) . The estimated β vector represents marginal

WTP for increase in one unit of each explanatory variable.

Variances of β , and ω are computed using the formulae suggested by Kmenta s

(1971) and used by Cameron and James (1987).

Var(βλρ )= [ /22 ] Var ()[1/] ρ +− ρ 2 Var ( λ )2[ + λρ / 2 ][1/]cov(, − ρ ρλ ) (8) s s s s s

Variance ω is computed by replacing λ in equation (8) by α .

As non-use values are public goods, following Samuelson (1954), the total value

of the society’s WTP is obtained by vertical aggregation. Accordingly, WTP for the

population of interest is calculated using estimated mean WTP of the sample and total population size (G) using,

Aggregate WTP = G •(meanWTP ) . (9)

4.5.2: Survey

The contingent valuation survey was conducted during 2008. Out of 25 administrative districts in Sri Lanka, seven located in the North and Eastern Provinces could not be surveyed because of logistical difficulties. The remaining 18 districts accounted for 90 percent of Sri Lanka’s total population in 2001 (Department of Census and Statistics 2004). Households were selected by multi-stage random sampling. In the first stage, ten districts were selected using probability proportionate to size (PPS) sampling method. Subsequently, two divisional secretary (DS) areas within each selected district, one village from each selected DS area were randomly selected. At the final stage, respondent households were randomly selected using household registers.

This sampling scheme was preferred, since a combined sampling frame was not available at levels other than the village. This method also reduced the need for extensive travel in search of respondents, facilitating survey logistics.

The standard procedure for drop-off and pick-up surveys was followed. Graduate students from the University of Peradeniya acted as facilitators of the survey. They visited households to describe the survey, hand delivered the letter soliciting participation and the survey questionnaire to be completed by the household head.

Completed questionnaires were collected after two weeks.

A one-page insert describing the YPC and proposed changes to the current water flow and the nature of expected improvements (using pictures) was included.

Since pictures were not available showing the exact status of the environment after

water releases, we used pictures from current wet season. Changes in the flow pattern were described, including the quantity of water to be released and the potential competition from irrigators. The need for monetary contributions from the general public to assure financing planned downstream water releases was presented. In order to reduce the potential influence on WTP due to option values, we emphasized that the area through which the river flows is not open to the public and will remain so in the future.

A non-obligatory specific voluntary contribution mechanism (VCM) was considered the appropriate payment vehicle, since charitable contributions of various sorts are widespread in Sri Lanka, but personal income tax collection is a relatively weak institution. This method was used previously by researchers in environmental valuation in Sri Lanka, including Ekanayake and Abeygunawardena (1994), and this was the method preferred by respondents in a hypothetical elephant conservation program

(Bandara and Tisdell 2005). Respondents were asked if they would contribute for membership in a hypothetical organization called Yala Environment Protection

Organization (YEPO). YEPO was prototyped as a not-for-profit organization with democratic member control and expected to work closely with YPC management.

A single bounded dichotomous choice format was used in eliciting WTP values.

Based on actual payments made by Sri Lankans for memberships in social and professional organizations, bid values selected were, SLR 100, 300, 500, 700, and 900.

Respondents were asked whether they would agree to contribute annually for a period of

10 years the presented bid value as the household’s membership to YEPO. Payments were expected during August, the month that financial commitments for social and family causes are relatively low in Sri Lanka. Refer to Appendix C for the WTP question.

Demographic and socioeconomic information and information on possible determinants to the WTP decisions were also collected (Table 4.2). Distances from respondents’ residence to YPC were obtained from 1:50,000 scale maps published by

Sri Lanka Department of Surveys. Data were used to calculate mean sample WTP for water releases to YPC. Results were used in aggregating WTP for the national level.

We present in the next section results of data analysis and discuss the findings.

Of the total 800 questionnaires distributed, 635 were picked up after completion, yielding a response rate of 79 percent. After removing incomplete responses, data for

584 households were used in estimating WTP. Descriptive statistics for sample characteristics are shown in Table 4.2. Ages of household heads ranged from 20 to 88 years with an average age of 46. Eighty-eight percent of the households were male- headed. These demographics on age and gender of household heads are somewhat different from national statistics. Household Income and Expenditure Survey (2006/07), the latest available of the series of national surveys, reports 26 percent of household heads as over 60 years of age and 23 percent of household heads as females. In contrast, our sample reports lower percentages. This can be attributed to the practice of assigning the eldest member of the household as the head (in reporting for official surveys), when the principal decision maker may not be the eldest.

The distribution of households in annual income categories closely followed national level data. Households in the lowest annual income category represented 24 percent of the sample population; that exactly matches national level figures.

Households with annual incomes of SLR 12,000-240,000 represented 38 percent in our sample and 35 percent in the national survey. Sixty-six percent of surveyed households indicated that they contribute regularly to a place of worship of their respective religious faith, and 28 percent of household heads were office-holders in a non-political or social

community level organization. Ninety-two percent of heads of household had completed more than 10 years of schooling, with average of 12 years of education, comparable to

Bandara and Tisdell (2005) data. Almost all respondents had previously heard of YPC, and 46 percent of them had previously visited there at least once.

4.6: RESULTS AND DISCUSSION

4.6.1: Determinants of Willingness to Pay

Based on prior empirical evidence, we expected user status (prior visits to YPC), educational status, and income to positively influence on WTP. It was also hypothesized that distance to YPC and rural living would negatively influence WTP, since a visit to a distant destination (such as YPC) is a once in a lifetime practice for many Sri Lankans.

We consider anyone who reported “visited YPC at least once” as users of the resource.

Apart from the expected relationships based on previous studies, we also hypothesized that concerns for the natural environment are influenced by an appreciation of traditional values and engagement in social activities. We considered regular non-obligatory contributions to a religious place of respondents’ faith as a proxy to the household’s attachment to value traditional customs of the society, and holding an office in a village organization to indicate engagement in social activities. Both these characteristics were expected to positively influence the decision to contribute for water releases to YPC.

As expected, the percentage of respondents who had previously visited YPC declined with the increasing distance, indicating a clear distance-decay relationship

(Figure 4.1). More than half (57 percent) of the respondents answered “yes” to the

WTP question, indicating that their WTP is equal or greater than the bid value, and indicate their willingness to pay for water releases (Table 4.3).

Table 4.4 presents parameter estimates, p values, marginal effects and their p- values for preliminary binary logistic regression. Magnitudes of parameter estimates in a binary logistic regression model cannot be interpreted per se. But, signs and marginal effects odds ratios of the estimated model are useful in interpreting determinants of WTP for water releases. As expected, the parameter estimate for the bid value is negative and significant at the level of 0.01. Marginal effect of household heads’ age, significant at the level of 0.05, ceteris paribus , indicates a decreasing probability of WTP with increasing age. Intercept dummy variable for household heads of over 60 years of age is positive and significant at the level of 0.05. The cumulative effect of the two predictor variables above can be interpreted as household heads having high probability of WTP when they are younger, and this probability decreases as they grow older. This can be due to increasing commitments as they get older. As household heads reach their late 50s, family commitments are possibly fulfilled and retirement benefits begin to accrue.

Households with heads over 60 years of age have less burdens and relatively less workplace related social commitments, leading to a sudden increase in probabilities of

WTP. However, as senior citizens grow older, there are possible increases of health related expenses and their WTP subsequently begin to decrease.

As expected, higher income groups have a higher WTP for water releases. This is indicated by 0.11 and 0.15 additional probabilities for households with annual incomes of SLR 240,000 – 360,000 and over 360,000 respectively, both significant at 0.10.

Furthermore, college graduates exhibit a 0.20 higher probability in WTP for water releases over the reference group of households that did not complete secondary schooling. However, household heads with less education than a college degree do not show a significant difference in WTP when compared to the reference group.

Households with involvements in social organizations have additional 0.10 probability in

WTP, while those households with regular non-obligatory religious commitments show

additional 0.12 probability in WTP over households that do not engage in such

practices.2 Marginal effects for all three predictors mentioned last are significant at

0.05 level. Based on the findings for the last two of three predictors, it can be concluded

that households contributing towards enhancement or continuation of socially-valued

traditions have a greater WTP for environmental enhancement.

Marginal effects for households in urban areas, interior districts, with previous

visits to YPC, and with different travel distances from YPC do not show significant

differences. The non-significance of marginal effects for these parameters indicates that

the decision to contribute for water releases is not influenced by these factors. It can be

concluded that the YPC is a unique environmental commodity and a national asset, so

respondents from all segments of society are willing to pay for water releases to the

YPC, with the expectation that such actions would be beneficial to its ecosystem.

Furthermore, respondents were aware that there will be no opportunities for getting on-

site use benefits from water releases and from resulting ecosystem improvements. It can

be concluded that enhancements to the YPC are appreciated by the general public

among survey respondents. The results can be up-scaled to the entire population of Sri

Lanka.

4.6.2: Calculation and Aggregation of WTP

Parameter estimates used in predicting WTP are reported in Table 4.5, together

with standard errors and asymptotic t-ratios. Also shown are the contributions by each

factor to the estimated WTP at respective data means. These parameters are directly

2 The nature of this contribution was different for households of different religious faiths. For Buddhists the practice was contribution to village temples with cooked meals at frequent intervals. For Hindus it was regular monetary contributions to upkeep and maintenance of places of religious worship.

interpreted as the marginal contribution towards WTP. For example, household heads involved in social activities contribute about SLR 162 more towards water releases than households with no social activities reported.

Table 4.6 details the distribution of estimated WTP and prediction success. Mean annual household WTP was estimated as SLR 627, which is within the bid vector.

Estimated 95 percent confidence intervals for mean WTP were [594.50, 659.01]. The model correctly predicted 67 percent of the cases. Predicted values ranged from SLR

124 to 1,325.

As the contingent valuation scenario in the study emphasized that there would be no user opportunities of the enhanced YPC, WTP estimated using this data can be considered as the non-use value of water. In the next section we calculate WTP for enhancement of YPC that is considered as a proxy for the value of water released downstream from Veheragala Dam Site.

Estimated WTP values were aggregated for the whole population of survey districts and subsequently for the entire country. This vertical aggregation procedure is appropriate here, since non-use values are considered public goods. Steps of the aggregation procedure are described in Table 4.7. The aggregate value of annual WTP for water releases for the entire country was estimated as SLR 3087.4 million, and the stream of benefits for ten years resulted in NPV of SLR 17,444.3 million at 12 percent discount rate. This value can be considered as the value of the quantity of water released downstream during the dry season and is attributed to the non-use values, since respondents valued the quantity of water without actually using water or expecting indirect use benefits through ecosystem enhancements. Based on the methods and assumptions of benefit estimation by Weligamage et.al (2009), the lost value of net benefits to irrigators due to non-availability of 50 MCM of downstream water allocation

would be SLR 112.7 million, amounting to an NPV of SLR 640 million over 10 years at a

12 percent discount rate. Therefore, estimated benefits to the nation emanating from non-use values are about 30 times that of lost value for irrigation water.

4.7: CONCLUSIONS AND POLICY IMPLICATIONS

Based on the study, it can be shown that non-use values of water in a developing country can be estimated using contingent valuation methods. We used WTP for enhancement of a nationally important ecosystem as a proxy. Findings indicate that the general population of Sri Lanka values the enhancement of Yala Protected Area

Complex (YPC), irrespective of households’ sector or residence, distance to YPC, and their previous experiences with it. The presented contingent market emphasized the need for the release of water through the YPC and its impact on the ecosystem. Values elicited arise due to non-use benefits to the general population and can be considered as the value of water released downstream during the dry season. While farmers in the adjoining Kirindi Oya Basin clearly benefit from water diversions through enhanced production, assurance of downstream releases will extend the benefits of Veheragala

Project to a broader population.

Although non-use values are generally considered as global, we did not have

WTP for outside Sri Lanka from this study, thus our findings underestimate the true value of the downstream water flow. Our study was conducted during a period of tense social conditions in Sri Lanka due to critical stages in the war with separatists. Also, many areas of the country were experiencing a drought, reducing agricultural household incomes. Therefore, it is likely that economic hardships have negatively influenced our estimates, relative to those generated during a normal period. While the findings of this study can be used by policy makers in Sri Lanka in allocating water at Veheragala Dam

site, the procedures may be useful in other countries. This study also provides broader insights in analyzing water conflicts when some uses have no established values.

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Table 4.1. Uses of water flow to an ecosystem and perceived benefits within a TEV framework

Water Use Benefits Type of Benefits in Benefit Characteristics Major Benefit Category the context of water Category Water available Consumptive Use Direct Use Benefits are incurred Use Values for human use through the use of [eg: Drinking Water] increased availability of resource or enhanced Non-Consumptive service flows use[On-stream recreation and bathing]

Water input to Material Benefits In-Direct Use ecosystem [increased wood and 95 functions non-wood production] On-site Recreation [increased visibility of wildlife and photography opportunities] Vicarious Benefits Off-site Benefits Existence Non-use Individuals gain Non-use Values Option satisfaction by knowing the Bequest improvement of the ecosystem but do not actually use enhanced service flows

Source: Authors’ construction

Table 4. 2. Descriptions of variables used in estimating WTP

Variable Name Description Measure Mean hhhage Age Years 46.1 (11.1) oldhhh Old Household Head 1, if over 60 years of age 0.07 gender Gender 1, if male 0.88 educat01 Some primary or secondary school (Reference Category) 0.08 educat02 High School 1, if completed 12 years of schoolig 0.34 educat03 High school + 1, if education is above (12 technical or vocational years ) but less than a education college degree 0.40 educat04 College graduate 1, if the household head had a college degree 0.18 inccat01 Annual Household Less than SLR 120,000 0.24 Income (SLR) (Reference Category) inccat02 SLR 120,000 and 240,000 0.38 inccat03 SLR 2400,000 and 360,000 0.26 inccat04 over SLR 360,000 0.11 scenvcon Office holder in local 1, if office holder of a local organizations apolitical organization or a 0.28 member of environmental organization religious A proxy variable to 1, if the contributes regular 0.66 measure close donations to religious association with institutions religion Distance to one-way road YNP distance from YNP to the village (km) discat01 120 -200 km 0.32 discat02 200 – 280 km 0.32 discat03 280- 359 km 0.36 distlotn District location 1, if resident of a coastal district 0.45 urban Sector of residence 1, if urban 0.18 visitypc Visitor status 1, if has visited YPC 0.46

Source: WTP Survey-2008

Table 4. 3. Distribution of response for each level of bid for releasing water to YPC

Bid in SLR

Response 100 300 500 700 900 Total

No to Bid

(j=0) 20 42 55 66 70 253 Yes to Bid

(j=1) 96 72 64 55 44 331 Total 116 114 119 121 114 584

Source: WTP Survey-2008

Table 4. 4. Parameter estimates of the binary logistic regression (n=584)

Parameter Variable Estimate P-value of Marginal Effects P-value of Estimate Marginal Effects

α intercept 1.6346 ** 0.0268 - - ρ bid -0.0025 *** 0.0000 -0.0006*** 0.0000

λ1 hhhage -0.0234 ** 0.0177 -0.0057** 0.0210

λ2 oldhhh 0.7715 * 0.0777 0.1728 0.0390

λ3 gender 0.0070 0.9813 0.0017 0.9800

λ4 inccat02 -0.0003 0.9989 -0.0001 0.9990

λ1 inccat03 0.4516 0.1020 0.1077* 0.0840

λ5 inccat04 0.6592* 0.0674 0.1511* 0.0530

λ6 educat02 0.3186 0.3748 0.0769 0.3700

λ7 educat03 0.0576 0.8720 0.0140 0.8720

λ8 educat04 0.8612 ** 0.0348 0.1956** 0.0200

λ9 religious 0.4712 ** 0.0154 0.1156** 0.0160

λ10 scenvcon 0.4023 * 0.0566 0.0963** 0.0490

λ11 urban 0.1002 0.6772 0.0243 0.6870

λ12 distlotn 0.0799 0.6786 0.0195 0.6790

λ13 distcat02 0.1149 0.6213 0.0279 0.6270

λ14 distcat03 0.0706 0.7600 0.0172 0.7600

λ15 visitypc -0.1735 0.3632 -0.0423 0.3600

Source: WTP Survey-2008

Notes:

Log-likelihood -353.5857

Likelihood ratio test statistic: Chi-square (17) = 91.9756 [0.0000]

*P<.1 **P<.05 ***P<.01

Table 4. 5. Parameter estimates for WTP function (n=584)

Parameter Variable Estimate Standard Asymptotic t- Contribution to WTP at Error of Ratios Data Means Estimate

ω intercept 656.83 282.28 2.33*** 656.83

β1 hhhage -9.39 4.02 -2.34*** -433.10

β 2 oldhhh 309.99 179.90 1.72 * 22.82

β 3 gendum 2.83 120.89 0.02 2.50

β 4 inccat02 -0.13 95.36 0.00 -0.05

β 1 inccat03 181.48 111.49 1.63 47.86

β 5 inccat04 264.87 148.20 1.79 * 29.48

β 6 educat02 128.03 145.15 0.88 43.19

β 7 educat03 23.13 143.78 0.16 9.15

β 8 educat04 346.03 165.35 2.09*** 63.99

β 9 religious 189.32 80.44 2.35*** 124.49

β 10 scenvcon 161.64 86.62 1.87 * 44.84

β 11 urban 40.25 97.27 0.41 7.17

β 12 distlotn 32.10 77.78 0.41 14.57

β 13 distcat02 46.17 93.74 0.49 14.78

β 14 distcat03 28.37 93.04 0.30 10.35

β 15 visitypc -69.71 77.24 -0.90 -32.11

Source: WTP Survey-2008.

*P<.1 **P<.05 ***P<.01

Table 4. 6. Properties of estimated WTP (n=584)

Description Value a Estimated Number Percentage (SLR) WTP of Category Respon- (SLR) dents

Mean 626.75 less than 250 19 3.3

95% CI b [594.50,659.01] 250-499 158 27.1

Minimum 123.79 500-749 260 44.5

Maximum 1,323.70 750-999 105 18.0

Standard 225.92 1,000 or above 42 7.2 Deviation

Success in Prediction c Correctly Predicted Not Predicted Correctly

Offer WTP i >= Bi WTP i < Bi WTP i >= Bi WTP i < Bi Value j =1 j=0 j =0 j=1 100 96 (83) 0 (0) 20 (17) 0 (0) 300 70 (61) 2 (2) 40 (35) 2 (2) 500 56 (47) 21 (18) 34 (29) 8 (7) 700 17 (14) 52 (43) 14 (12) 38 (31) 900 13 (11) 65 (57) 5 (4) 31 (27)

Total 252 (43) 140 (24) 113 (19) 79 (14)

Source: Calculated from data from WTP Survey 2008

Notes

a Values are in Sri Lankan Rupees (SLR), 1US$ = 115 SLR during the study period. b Values for 95 percent confidence interval for mean WTP was calculated based on critical values for logistic distribution reported by Antle et.al.(1970). c Figures in parenthesis are percentage shares of respective categories.

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Table 4. 7. Aggregation of WTP for Sri Lanka: data and results

Description Value Mean sample WTP (SLR) a 626.75 Sample district Mid-year population (“million”) b 18.1 Average family size (persons) 4.1 Number of households (“000”) c 4,423.0 Aggregate WTP (SLR million) d 2,772.1 Excluded districts Mid-year population (“million”) b 2.0 Average family size (persons) 4.1 Number of households (“000”) c 503 Aggregate WTP (SLR million) d 315.3 For Sri Lanka Aggregate WTP (SLR million) e 3,087.4 NPV (SLR million) f 17,444.3

Sources:

For mid-year population, Central Bank of Sri Lanka, Annual Report-2009. Colombo: Central Bank of Sri Lanka.

For family size, Sri Lanka Department of Census and Statistics. 2009. Household Income and Expenditure Survey -2006/07. Final Report. Colombo: Department of Census and Statistics.

Notes: a Mean of estimated WTP for individuals. b Estimated mid-year population for 2008. c Midi-year population divided by average family size. d Mean WTP multiplied by number of households eSum of aggregate WTP for sample districts and excluded districts f NPV for a 10 year benefit flow discounted at 12 percent per annum.

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Figure 4. 1. Distance decay characteristic of visits to YPC

Source: WTP Survey-2008

Note: Based on data for 584 questionnaires.

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APPENDICES

APPENDIX 01

TABLES AND MAPS FOR CHAPTER 2

Table A.1. 1. Mid-Year populations in districts in 2009 by climatic zone

Climatic Zone District Mid Year Population “000” Wet Zone Colombo 2,521 Gampaha 2,165 Kalutara 1,128 Galle 1,074 Matara 831 Kandy 1,415 Nuwara Eliya 755 Ratnapura 1,113 Kegalle 813

Dry Zone Matale 490 Hambantota 565 Jaffna 607 Mannar 103 Vauniya 169 Mullativ 154 Kilinochchi 154 Baticalao 537 Ampara 634 Trincomalee 368 Kurunegala 1,550 Puttalama 770 Anuradhapura 820 Polonnaruwa 405 Badulla 874 Monaragala 435

Total Sri Lanka 20,450

Source: Sri Lanka Department of Statistics. 2011. Statistical Abstract of the Democratic Socialist Republic of Sri Lanka. Colombo: Sri Lanka Department of Census and Statistics.

Note:

Population given is estimated mid-year population.

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Table A.1. 2. Details of river basins of Sri Lanka by climatic zone

Aggregate Watershed Estimated Climatic Zone/ Number of Area Annual Runoff % Share of Watershed River Basins (Km 2) (m 3 x 10 6) Total Runoff Wet Zone a 16 17,392 19,400 38 Dry Zone (All) b 86 37,542 20,162 40 Includes of SEDZ c 14 4,423 1,338 3 Other DZ 72 33,119 18,824 37

Mahaveli Basin d 1 10,327 11,016 22

All River Basins 103 65,261 e 50,578 100

Sources:

Adopted from G.H. Peries. 2006. Sri Lanka: Challenges for the New Millennium . Kandy, Sri Lanka: Kandy Books.

Data for South Eastern Dry Zone (SEDZ) Rivers are from C.M. Madduma Bandara and A. Manchanayake. 1999. Water Resources in Sri Lanka . Natural Resources Series, No 2. Colombo: National Science Foundation except for Menik River flow volumes for which the source is Central Engineering Consultancy Bureau. 2004. Quality Enhancement of Lunugamvehera National Park in the Menik Ganga and Kirindi Oya Basins by Harnessing the Development of Water Resources of Menik Ganga . EIA Studies-Main Report. Unpublished.

Notes: a Wet Zone Rivers are 16 rivers from Karabalan Oya to Nilwala Ganga (National River Basin Numbers 100 through 103 and 1 through 12, and inclusive of both). b Rivers 13 to 99 are Dry Zone Rivers. c South Eastern Dry Zone (SEDZ) river basins are a sub set of dry zone river basins. National river basin numbers included are 21 through 34. d Mahaveli Basin is national river basin number 60. e About 0.5 % of total land area of 65,210 Km 2 of the country has no surface drainage.

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Table A.1. 3. Share of wet zone and dry zone districts in cumulative irrigable area

Irrigable Area (ha) Zonal Share in Total Irrigable Area Dry Zone Wet Zone a Total Dry Zone Wet Zone a Minor Irrigation Reservoir Systems b 136,598 4,972 141,571 96 4 River Diversion Systems 38,012 62,261 100,273 38 62 All Minor Systems 174,610 67,234 241,844 72 28

Major Irrigation Reservoir Systems b 282,158 3,199 285,357 99 1 River Diversion Systems 26,643 11,317 37,960 70 30 All Major Systems 308,801 14,516 323,317 96 4

All Irrigation Systems (2000) 483,411 81,750 565,161 86 14

Irrigable Area (2007) c All Minor Systems 143,281 31,145 174,426 82 18 All Major Systems 319,368 13,291 332,659 96 4 All Irrigation Systems 462,649 44,436 507,085 91 9

Source: Data for minor irrigation are from Department of Agrarian Development, Village Irrigation Data Book. 2000.

Data for major irrigation were compiled by the author by updating information from, Register of Irrigation Systems in Sri Lanka . 1975. Colombo: Sri Lanka Department of Irrigation by published data sources and information from field level professionals.

Data for 2007 are based on Department of Census and Statistics.

Notes: a Districts included in the Wet Zone are: Colombo, Gampaha, Kalutara, Kandy, Nuwara Eliya, Galle, Matara, Ratnapura and Kegalle. b Irrigable areas of reservoirs augmented by river diversions are included under reservoir irrigation. c Discrepancy between the cumulative extents of minor irrigation systems between 2000 and 2007 can be attributable to abandoned systems for which areas were included in calculating total area. Department of Census and Statistics do not report irrigable areas under minor systems in Galle district, but the Department of Agrarian Development does so leading to lower total area under minor irrigation in the Wet Zone.

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Table A.1. 4. Description of rivers in the southeastern Dry Zone of Sri Lanka

River Name NRB Stream Watershed Annual Annual Runoff Dry d Order c Area Precipitation Runoff as a % of Season (Km 2) b (m 3 x 10 6) b (m 3 x Precipitation Flow as 10 6) b b a % of Annual Flow a Embilikala Oya 21 2 60 57 7 12 0.50 Kirindi Oya e 22 5 1,178 1,774 428 24 0.43 Bambawe Ara 23 3 80 89 13 15 0.50 Mahasilawa Oya 24 1 13 12 2 17 0.20 Butawa Oya 25 2 39 35 5 14 0.20 Menik Ganga e f, 26 5 1,287 2,098 346 16 0.55 Katupila Ara 27 2 86 98 15 15 0.20 Kurunda Ara 28 3 132 149 31 21 0.30 Namadagas 29 2 109 122 18 15 0.50 Ara Karambe Ara 30 1 47 52 8 15 0.20 Kumbukkan Oya e 31 5 1,233 1,938 428 22 0.22 Bagura Oya 32 2 93 105 16 15 0.25 Girikula Oya 33 1 15 17 3 18 0.20 Helawa Ara 34 2 51 81 18 32 0.25

Total for all Rivers ...... 4,423 6,627 1,338 18 0.32 Total for KMK ...... 3,698 5,810 1,202 21 0.40 % of KMK in SE ...... 84 88 90

Source: a For percentages of seasonal flows volumes, Upali Amarasinghe and Lal Mutuwatte 2000. Water Scarcity Variation within a Country . IWMI Research Report 31. Colombo: International Water Management Institute.

b C.M. Madduma Bandara and A. Manchanayake. 1999. Water Resources in Sri Lanka. Natural Resources Series-2. Colombo: National Science Foundation.

Notes: c Based on Horton-Strahler rules for stream order classification. d National river basin number.

e Indicates Kirindi Oya-Menik Ganga and Kumbukkan Oya Rivers. f For Menik river flow volume Central Engineering Consultancy Bureau. 2004. Quality Enhancement of Lunugamvehera National Park in the Menik Ganga and Kirindi Oya Basins by Harnessing the Development of Water Resources of Menik Ganga . EIA Studies-Main Report. Unpublished.

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Table A.1. 5. Administrative divisions in river basins in southeastern Sri Lanka

Province District DS Area c Rivers b River Zone a Uva Badulla Ella KRD Stage l Bandarawela KRD Stage l Haldummulla KRD Stage l Passara MNK Stage l Lunugala KBK Stage l Uva Monaragala Wellawaya c KRD Stage II Tanamalvila KRD and MNK Stage III Badalkumbura c MNK and KBK Stage II Buttala c KRD, MNK and KBK Stage II Kataragama c MNK and 27--29 Stage II Medagama KBK Stage l Madulla KBK Stage l Monaragala KBK Stage II Siyambalanduwa KBK Stage III Southern Hambantota Lunugamvehera KRD Stage III Hambantota 21 and KRD Stage III Tissamaharama c All Rivers Stage III Eastern Ampara Lahugala KBK and 32- 34 Stage III

Source: Village Irrigation Data Book-2000. Colombo: Sri Lanka Department of Agrarian Development.

Notes: a Three stages of a river, stage I, II, and III are upper reaches, middle reaches and lower reaches, respectively. b KRD, MNK and KBK refer respectively to Kirindi Oya, Menik Gnga and Kumbukkan Oya. c One or more southeastern river basins cover the entire land area of these DS areas. .

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Table A.1. 6. Locations and stream orders of tributaries of KMK rivers

Distance to Point of ID Tributary Name Bank Confluence Stream Order a from Sea (km) River: Kirindi Oya (22) 1 Yodawewa L 7.0 2 2 Weerawila Ara R 14.5 1 3 Kapukinissa Ara L 27.5 2 4 Galketiya Ara L 28.5 2 5 Unigal Ara L 33.5 2 6 Keual Ara R 44.0 3 7 Kuda Oya R 53.0 4 8 Herameti Ara L 60.0 3 9 Maha Ara L 62.5 2 10 Dambakola Ara R 71.0 3 11 Dingi Ara R 74.5 2 12 Botala Ara R 76.0 3 13 Godapola Ara R 78.0 3 14 Radapola Oya R 82.0 3 15 Kolabere Oya R 82.5 3 16 Alikote Oya R 86.0 3 17 Kunupada Ara L 91.0 3 18 Kurundugolla Oya R 100.0 3 19 Rawana Ella R 106.0 3 20 Head Reaches Table Contd. …..

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Table A.1.6. Locations and stream orders of tributaries of KMK rivers (Contd.)

Distance to Point of ID Tributary Name Bank Confluence Stream Order from Sea (km) River: Menik Ganga (26) 1 Darage Ara L 21.5 4 2 Kachchipatana Wewa Ara R 28.5 1 3 Dikgala Ara L 32.5 2 4 Hangunne Ara R 43.5 3 5 Weddange Wadiya L 46.0 2 6 Kalawel Ara/ Ganagan Ara L 56.0 2 7 Bulugaha Ara R 57.5 2 8 Ratpana Ara/ Dunugane Ara R 60.0 1 9 Kuda Oya (MNK) R 62.5 4 10 Pethiyan Ara/Galapitigala Ara L 66.0 2 11 Tittanel Ara L 70.0 2 12 Aluthwela Ara L 80.5 2 13 Weli Ara R 88.0 3 14 Muthukeliyawa Oya L 91.5 3 15 Parapawa Oya L 104.5 3 16 Gagalkulugolla Oya R 106.5 4 17 Head Reaches 114.0

Table Contd. …..

110

Table A.1.6. Locations and stream orders of tributaries of KMK rivers (Contd.)

Distance to Point of ID Tributary Name Bank Confluence Stream Order from Sea (km) River: Kumbukkan Oya (31) 1 Ala-kola Ara L 17.5 2 2 Agalle-wewa Ara L 26.0 1 3 Gana-mala Ara R 31.5 2 4 Midandu Ara L 34.0 2 5 Tributory # 5 R 35.0 2 6 Nambana Oya L 39.5 3 7 Malhuranevela Ara R 41.0 2 8 Polguluwela Ara R 43.0 2 9 Orukem Ara R 44.0 2 10 Hulanda Oya L 44.5 4 11 Yodamalakapu Ara R 45.0 1 12 Suandana Ara R 46.5 3 13 Bindunkada Ara R 53.5 3 14 Weli Ara R 61.0 3 15 Kuda Oya 1 R 72.5 3 16 Kuda Oya 2 R 85.0 4 17 Uluassa Ara L 89.0 3 18 Kuda Oya 3 R 91.5 3 19 Mille Oya R 100.5 3 20 Kiruwe Oya L 110.0 4 21 Head Reaches 116.0 4

Source: Based on 1:50,000 metric maps published by Sri Lanka Department of Surveys.

Note: Stream orders were determined following Horton-Strahler stream order rules. For a simple description of Horton-Strahler stream order rules, see Da Costa, F. P., M. Grinfeld, and J. A. D. Wattis. 2002. “A Hierarchical Cluster System Based on Horton–Strahler Rules for River Networks.” Studies in Applied Mathematics 109(3):163-204.

111

Table A.1. 7. Distribution of minor irrigation systems in southeastern river basins

River Diversion Systems Tank Systems Basin and DS Area Number of Total Area (ha) Number of Total Area (ha) Systems Systems Embilikala Oya Hambantota na - 4 83 Tissamaharama na - 3 46

Kirindi Oya Bandarawela 110 456 1 32 Haldummulla 63 292 na - Wellawaya 32 363 54 680 Tanamalvila 2 55 1 2 Buttala na - 20 104 Lunugamvehera na - na - Hambantota na - na - Tissamaharama na - na -

Bambawe Ara Tissamaharama na - 6 168

Menik Ganga Passara 87 191 na - Badalkumbura 78 550 3 76 Wellawaya na - 16 186 Buttala 10 117 44 175 Tanamalvila na - 4 71 Kataragama na - 12 204 Tissamaharama na - na -

Kumbukkan Oya Lunugala 91 367 2 11 Passara 33 95 na - Badalkumbura 16 106 na - Medagama 22 172 na - Madulla 15 84 18 227 Monaragala 22 142 27 304 Buttala 9 93 10 31 Siyambalanduwa 6 72 15 280 Tissamaharama na - na -

Source: Village Irrigation Data Book-2000. Colombo: Sri Lanka Department of Agrarian Development.

Note: Irrigation systems were reassigned to river basins based on verification using topographic maps and field observations.

112

Table A.1. 8. Distribution of major irrigation systems in southeastern river basins

River Basin DS Area System Name Irrigable Area (ha) Reservoir Systems

Kirindi Oya Wellawaya Handapanagala 328 Balaharuva 84 Tanamalvila Dambe Wewa 96 KOISP-NIA-LB-Tract 1 754 Lunugamvehera KOISP-NIA-RB 2,804 Tissamaharama KOISP-NIA-LB-Tract 2-3 880 KOISP-OIA 3,796

Menik Ganga Badalkumbura Kongaha Wewa 82 Buttala Yudaganawa a 103 Kataragama Karavila a 103 Kuda Gal Amuna Complex a 208 Detagamuwa a 150 River Diversion Systems

Kirindi Oya Wellawaya Sudupanawela 150 Menik Ganga Badalkumbura Muthukeliyawa 150 Katugaha Galge 200 Buttala Buttala 640 Kumbukkan Oya Monaragala Hulanda Oya 200 Kumbukkana 1,750

Source:

Compiled by the author by updating information from, Register of Irrigation Systems in Sri Lanka. 1975. Colombo: Sri Lanka Department of Irrigation, using published data sources and information from field level professionals.

Note ; a Reservoirs indicated are augmented through river diversions. Rice fields receive water through respective reservoir only.

113

Table A.1. 9. Summary of irrigable areas in KMK Basins (2000)

Number of Minor Systems, Irrigable % Shares of Description Area by River Basin Individual Basins KMK KRD MNK KBK KRD MNK KBK

Total Irrigable Area (ha) 18,018 10,875 3,208 3,936 60 18 22

Number of Systems Minor Irrigation River Diversion Systems 596 207 175 214 35 29 36 Reservoir Systems 227 76 79 72 33 35 32 All Minor Systems 823 283 254 286 34 31 35

Total Irrigable Area (ha) Minor Irrigation River Diversion Systems 3,156 1,166 859 1,132 37 27 36 Reservoir Systems 2,384 818 713 854 34 30 36 All Minor Systems 5,540 1,983 1,572 1,986 36 28 36 Major Irrigation River Diversion Systems 3,090 150 990 1,950 5 32 63 Reservoir Systems 9,388 8,742 646 0 93 7 0 All Major Systems 12,478 8,892 1,636 1,950 71 13 16 By Type River Diversion Systems 6,246 1,316 1,849 3,082 21 30 49 Reservoir Systems 11,772 9,560 1,359 854 81 12 7

Source: Table A.1.6 and Table A.1.7.

114

Table A.1. 10. Shares of irrigable areas of irrigation systems by type and size category

System Category River Basin KMK KRD MNK KBK Percentage Irrigable Area Minor Systems 31 18 49 50 Major Systems 69 82 51 50 River Diversion Systems Minor Systems 57 59 55 57 Major Systems 25 2 61 100 All Irrigation Systems 35 12 58 78 Reservoir Systems Minor Systems 43 41 45 43 Major Systems 75 98 39 0 Irrigation Systems 65 88 42 22

River Diversion Systems as a % of All Minor Systems 72 73 69 75

Source: Computed using data from Table A.1.6.

115

Table A.1. 11. Details of sub-systems of the KOISP

Name Sub- Irrigable Number of Number of Number of system Area (ha) a Branch/Distributory Farmers Farmer Canals Organizations

OIA-Left Bank Gemunupura b 117 8 145 1 Debarawewa 376 26 288 2 Tissawewa 1,017 63 779 6 Yodawewa 1,104 77 892 8 Total 2,615 174 2,104 17 OIA-Right-Bank Pannegamuwa 277 24 220 2 Weerawila 904 86 1,023 11 Total 1,181 110 1,243 13 NIA-Left Bank NIA-LB-T-01 754 62 692 4 NIA-LB-T-02 638 54 699 5 NIA-LB-T-03 242 19 278 2 Total 1,634 135 1,669 11 NIA-Right Bank NIA-RB-T-01 671 55 736 4 NIA-RB-T-02 847 83 931 4 NIA-RB-T-05 781 87 967 4 NIA-RB-T-06 405 47 458 2 NIA-RB-T-07 100 13 110 2 Total 2,805 285 3,202 16

NIA-Total 4,438 420 4,871 27 OIA-Total 3,796 284 3,347 30

KOISP-Total 8,235 704 8,218 57

Source: Based on Information from the KOISP Project Manager’s Office (August 2007)

Notes:

a Conversion factor: 1 ha = 2.47 acres. Sub-System totals may show discrepancies due to rounding off errors. b Water to the fields of this sub-scheme are supplied through the feeder canal to reservoirs in OIA-LB

116

Table A.1. 12. Salient features of Veheragala Diversion Project

Reservoir Capacity 75 MCM Area of inundation at FSL 1,416 ha Dam Length 2,031 m Dam Bed level 73.50 m amsl Dam Height Level 95.12 m amsl FSL Level 92.47 m amsl Dead Storage 6.5 MCM Length of Trans-basin Canal 22.35 km Annual Diversion to KO 75 MCM Estimated Investment (2004) 1,822 SLR million Net Present Value at 12% (2004) 505.71 SLR million Benefit -Cost Ratio 1.42 Internal Rate of Returns 17.1%

Source: Central Engineering Consultancy Bureau. 2004. Quality Enhancement of Lunugamvehera National Park in the Menik Ganga and Kirindi Oya Basins by Harnessing the Development of Water Resources of Menik Ganga . EIA Studies-Main Report. Unpublished.

117

Table A.1. 13. Protected areas in Sri Lanka in 2007

Description by Agency Area (ha) % Share in Total Protected Areas Department of Forest Conservation (DFC) Closed Canopy Forests Forest Reserves 289,824 12 Proposed Reserves 252,540 11 Other State Forests 503,927 21 Total 1,046,291 44 Sparse Forests and Mangroves Sparse Forests 366,848 16 Mangroves 8,815 0 Total 375,663 16 Total area under DFC 1,421,954 60

Department of Wildlife Conservation (DWC) National Parks 513,688 22 Strict Nature Reserves 31,574 1 Nature Reserves 51,736 2 Jungle Corridors 19,141 1 Sanctuaries 314,674 13 Total area under DWC 930,813 40

Total Protected Areas 2,352,767 100

Source: Statistical Abstract of Sri Lanka-2008.

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Table A.1. 14. Extent and status of Yala National Park and adjoining protected areas

Name Status Extent (ha) Sections of YNP 1 Yala Sec-I-V National Park 97,880.7 2 Yala SNR Strict Nature Reserve 28,904.7 3 Yala East National Park 18,148.5 4 Total YNP (Sum of 1-3) 144,933.9 Adjacent Protected Areas

5 Katagamuwa Sanctuary 1,003.6 6 Kataragama Sanctuary 837.7 7 Kudumbigala Sanctuary 4,403.0 8 Lunugamvehera National Park 23,498.8 9 Nimalawa Sanctuary 1,065.8 Total Contagious 175,742.8 Protected Areas (YPC)

Source: Sri Lanka Department of Wildlife Conservation

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Figure A.1. 1. Map of Sri Lanka showing wet zone and dry zone districts

Source: Survey Department of Sri Lanka.

Note: Shaded areas are Wet Zone districts.

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LEGEND: River Basins in

Southeastern Dry Zone

Wet Zone

Mahaweli River

1 31 22 3 26

Figure A.1. 2. River Basin Map of Sri Lanka

Map Source: Arumugam (1968)

Note: 22, 26 and 31 are national river basin numbers for Kirindi Oya, Menik Ganga and Kumbukkan Oya respectively.

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Figure A.1. 3. Map the study area showing KMK Basin and divisional secretary areas

(Map Credits): Ranjith Alankara (International Water Management Institute)

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Figure A.1. 4. System map of Kirindi Oya Irrigation and Settlement Project (KOISP)

Source: International Water Management Institute Staff Files

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Figure A.1. 5. Map showing the location of Veheragala Reservoir Project

Source: Government of Sri Lanka. Proposal to the millennium challenge account . Sri Lanka Department of Development Finance, Ministry of Finance and Planning.

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Figure A.1. 6. Generalized vegetation map of Yala National Park: Block 01

Source: Mueller-Dombois, D. 1972. “Crown distortion and elephant distribution in the woody vegetations of Ruhuna National Park, Ceylon.” Ecology 53(2): 208-226.

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APPENDIX 02

WTP QUESTION

19. Would you be willing to pay as annual membership contribution of your household to YEPO,

Rs ………….. [100, 300, 500, 700, 900] during the month of August in every year beginning

2009 and continue for ten years. Your contribution will be exclusively used to assure water releases to Yala National Park? Please be sure about your ability to meet this commitment without affecting your other financial commitments?

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APPENDIX 3

THOERY AND METHODS OF NON PARAMETRIC ESTIMATION OF WILLINGNESS TO PAY

A.3.1: INTRODUCTION

The purpose of this appendix is to present the procedure for non parametric estimation of willingness to pay (WTP) and compare results obtained using non parametric methods with those from parametric methods. Procedure and underlying theoretical framework in contingent valuation surveys are similar up to the point that information on WTP is elicited from respondents. In a contingent valuation survey that uses SB-DC format to elicit responses, available information can be used to estimate WTP either using parametric methods or non parametric methods. We used parametric estimation methods in Chapter 4 of this dissertation.

The following section presents the procedure for estimating mean WTP using SB-DC methods of elicitation and non parametric survivor function.

A.3.2: THEORETICAL FRAMEWORK

Non parametric methods for estimating WTP does not require distribution assumptions.

Well known non parametric methods include Turnbull (1976) and Kristrom (1990), while another method was proposed by more recently by Watanabe and Asano (2007). We use in this study the non parametric approach to estimate WTP used by Kristrom (1990). This method based on empirical survivor function is simple. Lower-bound of the mean willingness to pay for the population of interest is estimated using the following theoretical framework.

Let there is a sample of H respondents named as i = 1,2,3…,H who are assigned randomly among h sub samples of equal size labeled as h = 1,2,…,h. Then there are are m monetary values (B i)s that are presented to respondents. These values are arranged in

th ascending order leading to a sequence, (B 1, B 2, B 3,….,B m). All respondents in h sub sample

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are asked to pay the m th monetary value (h = m for all i) and the responses are recorded in binary format.

^ gm This process generates a sequence of proportions defined as, φ m = , Where h m is the h m

h th sub sample size for m bid value and H = ∑ hh , and g m is the number of respondents who said h=1 yes to the bid value. Also recall that, h=m for all i. There is one-to one mapping between the

^ ^ ^ ^ ^ series of bid values and the corresponding proportion = (φ 1, φ 2, φ 3, ……. , φ m). It is φm assumed that all respondents answer affirmatively if the bid value is zero, and consequently,

^ ^

φ 0 = 1, and φ m+ k = 0, where k is an arbitrary positive integer.

Based on Ayer et. al. (1955), if this sequence of proportions is monotone and non-

increasing, then it provides a distribution free maximum likelihood estimator of the probability of

acceptance of the bid value. This expectation is also consistent with the economic theory. If

this condition is not met, the pooled adjacent violator algorithm method (PAVA), suggested by

(Ayer et. al. 1955) is used to achieve consistency by calculating a modified sequence of

proportions, η m s, where,

^ ^ ^ ^ η m = η m+1 = (g m + g m+1 )/ (h m + h m+1 ) for all m s if φ m <φ m+1 (1)

^ ^ ^ ^ ^ ^ Replacing the φ sequence with η m sequence where η m = η m and η m = φ m for violators of monotonacity condition, and for all others respectively, generates a sequence that is monotone and strictly non-increasing sequence of proportions.

^ ^ ^ ^ ^ ^ ^ η m= ( η 1, η 2, η 3, ……., η n), where, ηm≥ η m +1 for all m, and represents the probability

of acceptance of bid value or Pr(WTP ≥Bm).

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Elements in this sequence are then mapped in X-Y space (with bid values as x variable),

^ and η m s are connected to form a continuous non parametric survivor function (NSF). This function shows the relationship between bid values and their respective probability of acceptance. To be conservative, we consider the lower probability value associated with the higher bid value of the two as the function value between successive point estimates of NSF.

The mean WTP for the sample is the area bounded by the NSF. This is calculated using,

− ∧ m+ k =()WTP =∑ η m [ B m − B m −1 ] (2) m+1

As non-use values are public goods, following Samuelson (1954) the total value of the society’s WTP is obtained by vertical summation. Accordingly, lower bound of WTP for the population is calculated using estimated mean WTP of the sample and total population size (G) using,

∧ Aggregate WTP = G •(WTP ) (3)

A.3.3: CALCULATION AND AGGREGATION OF WTP

We use the method described above to calculate mean WTP for the same sample of

respondents that we used to calculate parametric estimates reported in Chapter 4 of this

dissertation. Point estimates for probabilities of agreement for bid values are reported in Table

A.3.1 while the non parametric survivor function constructed based on point estimates is shown

in Figure A.3.1. By summing the areas under the curve, we estimated the mean household

annual WTP for water releases to YPC for the sample as SLR 485. Preliminary estimates of

WTP using non parametric methods using a sample of 531 respondents were reported in

Weligamage et.al. (2010), and Weligamage (2009), as SLR 435 per annum. In the current

sample more respondents were added. Estimated mean WTP using parametric methods (SLR

627) are 30 percent higher than the non parametric estimate while net present value for a ten

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year stream of benefits is 29 percent higher. This higher value of WTP for parametric estimation is consistent with findings of Hite et.al. (2002) who reported that parametric estimates are larger than the non parametric estimates generated using same data by Kaplan-Mayer method.

Results of the preliminary binary logistic regression for parametric estimation of WTP, reported in Table 4.4, can be used to describe determinants of WTP. Non parametric and parametric estimates and their respective aggregate values at the national level are compared in Table A.3.2.

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A.3.4: REFERENCES

Ayer, Miriam. H. D. Brunk, G. M. Ewing, W. T. Reid, and Edward Silverman. 1955. “ An Empirical Distribution Function for Sampling with Incomplete Information.” The Annals of Mathematical Statistics . 26(4): 641-647.

Central Bank of Sri Lanka. 2009. Annual Report -2008 . Colombo.

Hite, Diana, Darren Hudson, and Walaiporn Intarapapong. 2002. “Willingness to Pay for Water Quality Improvements: The Case of Precision Application Technology.” Journal of Agricultural and Resource Economics 27(2): 433-449.

Kristrom, Bengt.1990. “A Non-Parametric Approach to the Estimation of Welfare Measures in Discrete Response Valuation Studies.” Land Economics , 66(2): 135-139.

Sri Lanka Department of Census and Statistics. 2004. Census of Population and Housing- 2001: Final Report . Colombo: Department of Census and Statistics.

___. 2009. Household Income and Expenditure Survey, 2006/2007-Final Report . Colombo: Department of Census and Statistics.

Turnbull, Bruce W. 1976. “The Empirical Distribution Function with Arbitrarily Grouped, Censored and Truncated Data.” Journal of the Royal Statistical Society , Series B 38(3):290-95.

Wanatabe, Mahide and Kota Asano 2007. Distribution Free Consistent Estimation of Mean WTP in Dichotomous Choice Contingent Valuation . Discussion Paper 07-03. Multi-level Environmental Governance for Sustainable Development. http://khattori.net/seidopaper/watanabe.pdf.

Weligamage, P. 2009. “ Willingness to Pay for Enhancement of Natural Ecosystems: the Case of Yala Protected Area Complex” In. Exploring New Spheres for a Better Future: Abstracts of Contributed Papers for Annual Sysmposium-2009 . Edited by Wattuhewa, I.D. Ratmalana: Sir Jhon Kotalawala Defense University. 28.

Weligamage, P. W.R. Butcher K.A. Blatner, C. Richard Shumway, and M. Giordano. 2010. “Non-user Benefits Emanating from Enhanced Water Flow to the Yala Protected Area Complex.” In. Proceedings of the National Water Conference on Water, Climate Change and Food Security in Sri Lanka, Vol 2 . Edited by Evans, A. and K. Jinapala. Colombo: International Water Management Institute. 37-47.

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Table A.3. 1. Point estimates of non parametric survivor function (n = 584)

(1) (2) (3) (4) (5) (6) (7) a Offer Number of Number ɸm Offer Area Mean Value respondents of Value under WTP d (O i) (hm) responses Difference the (SLR) “Yes” b curve c (g m) Sum of Col (6) 0.00 na na 1.00 e 0 0.00 100.00 116 96 0.83 100 82.76 - 300.00 114 72 0.63 200 126.32 - 500.00 119 64 0.54 200 107.56 - 700.00 121 55 0.45 200 90.91 - 900.00 114 44 0.39 200 77.19 - Total 584 331 484.74

Source: WTP Survey-2008

Notes: a Point estimate of Non-Parametric Survivor Function b (B m) minus (B m-1) c Product of Col (5) and Col (4) d Sum of values across all rows of Column (6) e Assuming everyone answers “yes” if the bid value is zero

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Table A.3. 2. Comparison of aggregation of WTP by different methods

Description Value by Estimation Method Non Parametric parametric Mean sample WTP (SLR) a 484.74 626.75

Sample district Mid-year population (“million”) b 18.1 18.1 Average family size (persons) c 4.1 4.1 Number of households (“000”) d 4,423 4,423 Aggregate WTP (SLR million) e 2,144.0 2,772.1 Excluded districts Mid-year population (“million”) b 2.0 2.0 Average family size (persons) c 4.1 4.1 Number of households (“000”) d 503 503 Aggregate WTP (SLR million) e 243.8 315.3 For Sri Lanka Aggregate WTP (SLR million) f 2,387.8 3,087.4 NPV (SLR million) g 13,491.7 17,444.3

Sources: a Mean of calculated WTP for the sample

b Estimated mid year population for 2008. Source: Central Bank of Sri Lanka. 2009. Annual Report-2008. Colombo: Central Bank of Sri Lanka. c Sri Lanka Department of Census and Statistics. 2009. Household Income and Expenditure Survey -2006/07. Final Report. Colombo: Department of Census and Statistics.

Notes: d Midi-year population divided by average family size. e Mean WTP multiplied by number of households f Sum of aggregate WTP for sample districts and excluded districts g NPV for a 10 year benefit flow discounted at 12 percent per annum.

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Figure A.3. 1. Non parametric survivor function

Source: Willingness to Pay Survey - 2008

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APPENDIX 04

SCHEDULE FOR FARM HOUSEHOLD SURVEY

Department of Natural Resource Sciences/Washington State University:

An Economic Analysis on Inter-Sector Water Allocation in the Southeastern Dry Zone of Sri Lanka (PhD dissertation project for Parakrama Weligamage) KOISP and Buttala Area Schemes Farm Productivity Survey-2007 Farm Household Questionnaire

Confidential

Investigator affirms that Information collected through this form will not be disclosed to a third party without the prior consent of the interviewee. All information collected will be presented in aggregate form and no individual level information will be disclosed to any person, legal entity or their representatives. Information gathered is expected to help understand the historical development and current status of agriculture in the area. This understanding will be important in addressing possible impacts of changes in the external economic and social environment.

Section 01: Basic Information

0. Interviewer code: ------Date of interview ------1. Name of the Household Head Mr/Mrs/Miss ……………:…………………………………………. (Survey ID Number……………….)

2. Full Address :

3. District :Hambantota/Monaragala

4. DS Division: ……………………………

5.GN Division:………………………………… 6.GN Division Code:…………………

7. Village Name: …………………………………………….

Indicate the locations of the house and the main farm in a rough diagram. Give reference to an easily identifiable location in the village such as the road, temple, school, river etc. N

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(Survey ID Number……………….)

Section 02: Farming History and Family Information

8. When did you first start farming ______(Year)

9. Is farming the only occupation? : Yes (Please go to Q 11) NO

10. If No, What is your most important other occupation: ------

11. Please give following information on yourself and other members in the household who participate in farming

Family Relationship Age Sex Education % of time Member to you (Years) (M/F) Level allocated Name to farm activities 1 (You) ------2 3 4 5 6 7 8

12. State the income from rice as a percentage of total gross farm income ------

13. What percentage of your total gross household income comes from rice and other farming activities including livestock? ………………………………..

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Section 03: Cost of production and revenue from rice during the last season

Please answer section 03 based on the information related to the field that you paid most attention during, Maha 2006/07 or Yala 2007 Cropping Season. (Please provide information on ONE field only)

14. Which crop did you grow during the season immediately proceeding the above mentioned season?

□ Rice □ Other (Please specify ------

15. What was the area of rice grown in this farm during the above mentioned season? ……………….. ( ha)

Section 03.1: Land Preparation

16. Did you incorporate rice straw and or organic matter into the field? YES NO ((Please go to Q 18)) 17. Please describe details of organic matter used Type Source Cost/unit Number of units 1 2 3 18. Please give details of farm machinery used during Land Preparation? Operation Type of Source No of Unit Cost Total Cost Farm Power [own/hired] Days Plowing Tilling

Codes for farm power Code Response 1 4 wheel tractors 3 Buffalos 2 2 wheel tractors 4 Mammoties (Hand hoes) If you used your own equipment for these operations, please state the cost that you would have paid if equipment were hired.

Section 03.2: Seeds and Planting Material and Crop Establishment

19. What variety of rice did you use for the last season?……………………..

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20. What is the quantity of seeds you used? ……………………… bushels

21. What was the source of seed used during the last season? Own seeds Purchased seeds ( → Q 23)

22. What would have been the expenditure certified seed if you had purchased them for the last season? …………..…………………… Rs/bushel

23. What was the expenditure for seeds? …………………… Rs/bushel

24. Which method did you use to establish the crop? (1) Broadcast seeding (2) Transplanting

Section 03.3: Chemical Fertilizer Application

25. How many times did you apply chemical fertilizer?……… Times

26. Please give details of chemical fertilizer used during each of these applications? Operation Type of Fertilizer Quantity Unit Fertilizer Transport (Kg) Price Cost (Rs) Cost (Rs) Nursery Basal Second Third Fourth Fifth

Section 03.4: Weeds,: Insect and Disease control

27. How many times did you apply chemical herbicides? …,,,,,,,,,,,,,,,,,,…. Times

28. How many times did you apply chemicals for pest control? ……………..Times

29. Did you use your own equipment or hired equipment to apply these chemicals? OWN Equipment □ HIRED Equipment □

30. What was the cost of hiring per one application? …………………………….. Rs/ operation

31. If you were to hire the equipment to do the same job, how much would you have to pay for one application? …………………………….. Rs /operation

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32. Please give total expenditure for chemicals used during each of these applications? Operation Herbicides Pesticides First Second Third Fourth Fifth

Section 03.5: Harvesting

33. What was the mode of threshing?

Response Code Portable mechanical thresher 1 4-W Tractors 2 Buffalos 3 Other (Please specify) 4 34. What was the cost of hiring the equipment? …………….. Rs + ……………………. In kind payment

35. If you used your own equipment, how much would you have to pay if you were to hire equipment to do this job? ………………………………………………………………………………….. Rs

Section 03.6: Labor Costs 36. Can you tell us the total number of labor days used for the rice crop during the last season? If it is more convenient for you to give details on individual operations, please go to question 38.

Total Family Labor Total Hired Labor (days) (days)

37. What is the payment for one day of hired labor during the last season for these operations …………Rs/day

38. What are the details of labor use for Land Preparation operations? Operation Family Labor Hired Labor (days) (days) Organic matter

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incorporation Bund Preparation Plowing Tilling Land Leveling Broadcast seeding Transplanting

39. Please give details of labor used for fertilizer applications? Operation Family Labor Hired Labor (days) (days) Nursery Basal Second Third Fourth Fifth

40. Please give details of labor use for a typical chemical application. Family Labor Hired Labor Price of Hired (days) (days) Labor (Rs/day)

41. Did you do any hand weeding of your field during the last season? YES NO (Please go to Q 43) 42. Please give details of labor use for hand weeding

Operation Family Labor Hired Labor Price of Hired (days) (days) Labor (Rs/day) First Second Third 43.. How many days did it take for harvesting/threshing and transportation of paddy to the store? ……………………. days.

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44. Please give details of labor use for this operation Operation Family Labor Hired Labor Price of Hired (days) (days) Labor (Rs/day)

Section 03.7.: Irrigation and Water Management

45. What is the type of water inlet does your farm have? Response Code Regulated inlet pipe 1 Irrigation ditch 2 Flow through another field 3 Other (Please specify) 4

46. What is the relative location of your distributor canal? Head/Middle/Tail

47. How far is your farm inlet from the distribution canal? ……….. meters.

48. Who is responsible for determining water issues to your field?

49. What is the basis for this person’s decisions to issue water to your field? Response Code Farmer Organization decisions 1 Irrigation agency decisions 2 Demand from your crop 3 Other (Please specify) 4

50. How many times during the last season did you divert water from the main inlet? ……….

51. What was the average length of one irrigation? ………………… hrs

52. What is the average height of water in your field after crop establishment? ………………inches.

52a. What is the average height of water in your field during land preparation? ………………inches.

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53. Please give the following details in order to understand the nature of water availability to your farm Irrigation # Crop Stage Average Duration Did you get Did you get Water height (Hrs) enough water on (in) water? time? 1 2 3 4 5 6 7 8 9 10 54. In your opinion, did you get enough water to irrigate your crop during the last season? Please select one Code Response 1 Available water quantity was always satisfactory 2 Available water quantity was satisfactory in most of the irrigations 3 Available water quantity was satisfactory in only about half of the irrigations 4 Available water quantity was unsatisfactory in most of the irrigations 5 Available water quantity was always insufficient

55. In your opinion, did you get water when it was needed depending the stage of crop growth Code Response 1 Water was always available at proper times as required by the stage of the crop 2 Water availability was timely during most of the times as required by the stage of the crop 3 Water availability was timely during only a few times as required by the stage of the crop 4 Timing of water availability was never as required by the stage of the crop

56. Do you use supplementary water sources if main source is not available? No: YES (What are these sources) ……………………………………

57. What proportion of the total irrigation requirement of the last season did you fulfill from these sources? ……………………….. %

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Section 03.8: Outputs and Disposal

58. What was the quantity of your total harvest? ………………….. (Bushels of un-milled rice)

59. How much did you sell? ………………………… (Bushels of un-milled rice)

60. What was the price? ………………… per bushel of un-milled rice

61. Did you sell soon after the harvest? YES/NO

62. If No When did you sell? ………………………….. Months after harvest

63. If you were to sell soon after harvest, what would be the realized price? ………………… Rs per bushel of un-milled rice

Section 04: Farmer Organization Participation

64. Are you a member of the Farmer Organization? Yes NO (Please go to Q 83) 65. When did you first become a member of the Farmer Organization?

…………………………………. Year

66. Are you a current office holder? YES No (Please go to Q 80)

67. If yes, what is the position? ………………………………

68. How many years have you held this position? ……………….. Years

69. For how many years have you held a position in the Farmer Organization? …………………..Years

70. How many farming related training activities did you take part in during the last two seasons? ………………….

71. What are the reasons for not becoming a Farmer Organization member?

………………………………………………………………………………………..

…………………………………………………………………………………………

…………………………………………………………………………………………..

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Section 05: Land Tenure Information

72 What is nature of property rights to the farm Code Response 1 Owned (Please go to Q 810) 2 Government Permit 3 Long-term sharecropper (secured tenure) (Please go to Q 83) 4 Seasonal lease(Please go to Q 85)

73. If you rent it to someone else, what is the current realizable rent ……………………Rs/ season

If long-term sharecropper

74. How long is the relationship? …………………………………..

75. What is the arrangement for sharing? Code Response 1 Fixed Quantity? …………………………….. Bushels 2 Fixed share without inputs………………………………… % 3 Fixed share with input sharing (please describe below) 4 Fixed monetary payment of …………………………… Rs/season

If seasonal sharecropper

76. For how many years have you rented this farm? …………………… Years

77. What is the current rent? ……………………….Rs/Season

Section 06: Housing Quality of the present house 78. Number of Bed-Rooms ------79. Kitchen is in house/separate 80. House electrified [yes/No ] by Type [Battery/Main-grid/Solar]

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81. Housing Quality Indicator 1: Constructing materials Ran Walls floor Roof k 1 Palm leaf or Mud Mud Palm leaves 2 Brick Un-plastered Partly Cemented + Mud Tin sheets 3 Brick plastered only on internal Fully cemented rough Asbestos walls finish 4 Bricks plastered both sides Fully cemented well Local Tiles finished 5 Well finished and maintained Floor Tiles Tiles walls 82. Housing Quality Indicator 2: Windows and Ceiling Rank Windows Type of roof ceiling 1 Windows not available No roof ceiling 2 Windows not yet fixed Polythene 3 Window frames fixed Asbestos 4 Complete windows wood 83. Housing Quality Indicator 3: Water and Sanitation Rank Toilets Water supply 1 No Toilet No water source at home 2 Outside pit Own well 3 Away from home Water Seal Piped water outside the house 4 Close to home Water Seal Own in-house water supply 5 Inside Flush In-house through Overhead tank

84. Housing Quality Indicator 4: Telephone Facility Rank Telephone 1 No Telephone 2 Mobile Phone 3 Fixed Wireless through mobile operators 4 Fixed Wireless through fixed operators 5 SLT (Landline through wired )

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85. Availability of Household Assets Code Asset Title 1 Plastic chairs 2 Wooden chairs 3 Beds 4 Tables 5 Wardrobe 86. Availability of Transport Equipment Code Asset Title 1 By-cycle 2 Motorbike 3 Trishaw 4 Van/ Car 5 Lorry/Truck/ Bus

87. Availability of Farm Equipment 1 Hand-sprayer 2 Power sprayer 3 2-wheel Tractor 4 4-wheel Tractor 5 Power Thresher

88. Availability of Consumer Durables Code Asset Title 1 Radio 2 Sewing Machine 3 Television 4 Refrigerator 5 Washing Machine

Thank You for your valuable time and responses. I appreciate your patience and willingness to participate in this survey.

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APPENDIX 05

QUESTIONNAIRE FOR CONTINGENT VALUATION SURVEY

Department of Natural Resource Sciences/Washington State University:

An Economic Analysis on Inter-Sector Water Allocation in the Southeastern Dry Zone of Sri Lanka (PhD dissertation project for Parakrama Weligamage) Willingness to Pay Survey-2008 Questionnaire

Confidential

Section 01: Basic Information

0. Interviewer code: ------Date of interview ------

1. Name of the Household Head

Mr/Mrs/Miss ……………:………………………………………….

(Survey ID Number……………….)

2. Full Address:

3. District:

4. DS Area:

5. GN Area:

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Survey ID Number (……………….)

Section 02: Survey Participant’s Background Information

6. What is your educational status (state the number of years schooling)

------Years

7. Are you a member of any organization related to environmental conservation?

□ YES □ NO (Please go to Q 9)

8. For how many years were you a member of any organization related to environmental conservation? ------Years

9. In how many voluntary social/religious/ community organization of this village/area you have ever held an office?

------(Number of organizations)

10. How many environmental related training activities did you take part in during the last twelve months? ------(number)

Section 03: Environmental Awareness of the respondent

11. Have you heard of Yala National Park? □ YES □ NO (Please go to Section 04)

12. According to your opinion, please indicate an important city near the Yala National Park?

------

13. How many times during your life time have you visited the Yala National Park? □ I have not visited Yala (Go to Q 17)

I have visited ------Times (Q 12)

14. When did you last visit the Yala National Park? ------Year

15. How many hours did you spend within the Yala National Park during your last visit? ------Hours

16. Please mention to us the other places you visited during this last visit?

□ ------□ ------□ ------□ ------□ ------

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17. Which of the places mentioned above did you visit immediately before visiting Yala National Park? (Please tick one response)

18. What is the total number of travelers who visited the park with you during the above said visit? (Please mention an approximate number or select from the following)

□ Approximately ______persons □ You and one other person only □ five people (by a car) □ about ten people □ about twenty five people and one other person □ about fifty people □ about 100 people □ about 200 people

Blank Page

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Section 04: Eliciting WTP Response

Description of hypothetical Scenario

A large share of the world’s natural forests have been cleared due to anthropogenic reasons since the beginning of human civilization. The purpose of national parks, sanctuaries and nature reserves are to provide and enhance habitats for wildlife. They remain as remnants of undisturbed natural ecosystems or in some cases in areas inhabited by wildlife for long periods. National parks also provide visitors with opportunities for viewing wild animals within their natural habitats. Sri Lanka has about 500,000 ha of national parks. About 500,000 people visit these parks annually. 85% of them are locals. Yala National Park (YNP) is one of 11 national parks in Sri Lanka.

Introduction to Yala National Park Yala National Park complex is situated in South-Eastern Sri Lanka. The complex spreads over 151,778 ha in Southern, Uva and Eastern Provinces. Established first in 1889 as the Asia’s first wildlife sanctuary, YNP today is the most visited wild life reserve in the country. Yala is famous for its elephant herds. They mainly live near water bodies such as rivers and lakes. There are about 250 elephants in the entire national park area. About 200 of them live near Menik Ganga. Other mammals living in the park includes, leopards bears, deer, sumbor, white faced monkey (Macaca sinica), golden mongoose and wild buffalo. There are 280 species of plants in YNP of which around 72 percent are endemic to Sri Lanka. 282 species of birds have been reported within the YNP. Seven of these are endemic to Sri Lanka. These birds include the tallest and the largest birds living in Sri Lanka. Also there are 40 species of butterflies and 48 species of reptiles. Crocodile is an important member of the fauna of the YNP. Menik Ganga (the river flows through the Kataragama Sacred City) flows 57 kms through Yala National Park before it reaches the sea. Water received through this river is very important to maintain the current vegetation and animal populations within Yala. Almost two third of the annual flow volume of the river flows withi three months during the rainy season (October-December). The flow is minimal from July to September and the river goes almost dry. Weheragala Project will be implemented to dam the Menik River to control the volume and the timing of water flow. About half of the current water will also be diverted to adjacent Kirndi Oya Basin. The expected profit by cultivating rice using this amount of diverted water is about Rupees per year. According to project documents, Wet Season average monthly flow will

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be reduced from about one third of the current flow volume. The project proposes to enhance the dry season flow by releasing water and therefore the drying-up of the river for about 50 km will be avoided. This will make more water and food available to animals living in Yala. Anticipated differences are shown in the pictures shown. There will be no immediate economic gains of this water releases to Yala. However, water releases are needed during the period of highest demand for rice fields. A new organization called “Yala Environment Protection Organization “ will be established to ensure the enhancement of environment of YNP. YEPO will be an autonomous national organization established with a parliamentary act. Its operational funds will be generated through public. YEPO’s annual plans will be subjected to members’ approval at the Annul General Meeting. YEPO will closely collaborate with the Department of Wildlife Conservation. A part of YEPO’s role will be to assure this proposed water releases regularly. YEPO members will receive free-of-charge a bi-annual newsletter briefing the current situation of environment of YNP. We want to know how much you value improvement of the ecosystem of Yala National Park in maintaining it as a national asset for future. Please answer the following questions.

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Please answer the following questions.

WTP Question

19. Would you be willing to pay as annual membership contribution of your household to YEPO, Rs ………….. [100, 300, 500, 700, 900] during the month of August in every year beginning 2009 and continue for ten years. Your contribution will be exclusively used to assure water releases to Yala National Park? Please be sure about your ability to meet this commitment without affecting your other financial commitments?

□ YES (Go to Q-22) □ NO (Go to Q-20)

20. Do you think this plan to ensure river flow during dry months as good?

□ YES (Go to Q-21) □ NO (Go to Section 05)

21. If this plan is good, Are you willing to contribute any positive contribution annually for YEPO for ten years?

□ YES Rs. ……………………. □ NO (Go to Section 05)

Section 05: Demographic Information

22. What was your age at your last birthday? ------Years

23. What is your primary occupation? : ------

24 Please tell us your approximate annual income of your household based on the categories mentioned below?

Annual Income Categories □ Less than Rs 60,000 □ Rs 60,000 – 74,999 □ Rs 75,000 – 89,999 □ Rs 90,000 – 119,999 □ Rs 120,000 – 179,999 □ Rs 180,000 – 239,999 □ Rs 240,000 – 359,999 □ Over Rs 360,000 25. How many members from each of the following age group live in your household currently? (Please mention the numbers)

Age category Males Females 1 Less than 5 years 2 5-16 years 3 17-25 years

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4 25-40 years 5 40-60 years 6 Over 60 years 26. Does this household offer a periodic meals offer to village temple?

□ YES □ NO (Go to Q-29)

27. If yes, what is the nature of commitment?

□ monthly □ bi-monthly □ brekfast □ Lunch □ evening tea

For ------Heads of monks

Section 06: Housing Quality and Household Assets 28.Number of Bed-Rooms ------29.House electrified? [No] Yes] by Type [Battery/ Solar /Main-grid] 30. Housing Quality Indicator 1: Constructing materials Ran Walls floor Roof k 1 Palm leaf or Mud Mud Palm leaves 2 Brick Un-plastered Partly Cemented + Mud Tin sheets 3 Brick plastered only on internal Fully cemented rough Asbestos walls finish 4 Bricks plastered both sides Fully cemented well Local Tiles finished 5 Well finished and maintained Floor Tiles Tiles walls 31. Housing Quality Indicator 2: Windows and Ceiling Rank Windows Type of roof ceiling 1 Windows not available No roof ceiling 2 Windows not yet fixed Polythene 3 Window frames fixed Asbestos 4 Complete windows wood

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32. Housing Quality Indicator 3: Telephone Facility Rank Telephone 1 No Telephone 2 Mobile Phone 3 Fixed Wireless through mobile operators 4 Fixed Wireless through fixed operators 5 SLT (Landline through wired )

33. Availability of Household Assets Code Asset Title 1 Plastic chairs 2 Wooden chairs 3 Beds 4 Tables 5 Almairah / Wardrobe

34. Availability of Transport Equipment Code Asset Title 1 By-cycle 2 Motorbike 3 Trishaw 4 Van/ Car 5 Lorry/Truck/ Bus

35. Availability of Farm Equipment 1 Hand-sprayer 2 Power sprayer 3 2-wheel tractor 4 4-wheel tractor 5 Power thresher

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36. Availability of Consumer Durables Code Asset Title 1 Radio 2 Sewing Machine 3 Television 4 Refrigerator 5 Washing Machine

37. According to your opinion what are the steps to be taken to improve the current condition of Yala National Park?

------

38. What are the current and anticipated environmental problems in the area you are currently living? ------

Thank You for your valuable time and responses. I appreciate your patience and willingness to participate in this survey.

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