Impact Assessment of Watershed Development Projects in Rajasthan LNRMI Hyderabad

Draft for Comments. Not to be quoted LIVELIHOODS AND NATURAL RESOURCE MANAGEMENT INSTITUTE

2010

12- 2 - 417/18, S ARADANAGAR, H YDERABAD 500067 CONTENTS Page No Preface 3 Glossary 5 List of Tables 6 List of Figures 7 List of Maps 10 I. Introduction 11 -Profile of Rajasthan 13 -Watershed Development in Rajasthan 20 -Objectives 23 -Methodology 24 -Structure of the Report 29 II. Performance of Watershed Development Programme: Perceptions of the Communities 30 -Introduction 30 - Profile of the sample districts 30 - Performance of the Sample Watersheds 33 -Case Studies 43 -Conclusions 45 III. Watershed Development Programme: Bio-physical Impact 52 -Introduction 52 -District-wise Analysis 53 -Size class-wise Analysis 61 -Scheme-wise Analysis 66 -Conclusions 71 IV. Watershed Development Programme: Economic Impact 72 -Introduction 72 -District-wise Analysis 72 -Size class-wise Analysis 86 -Scheme-wise Analysis 94 -Conclusions 102 V. Watershed Development Programme: Institutional Impact 104 -Introduction 104 -District-wise Analysis 104 -Size class-wise Analysis 113 -Scheme-wise Analysis 119 -Conclusions 126 VI. Factors Influencing the Impact of Watershed Development Programme 127 -Introduction 127 -Watershed Wise Analysis 127 -Determinants of Impacts/ Factors Influencing WSD Performance 131 -Conclusion 137 VII. Conclusions and Policy Implications 141 References 149

Preface

This study is part of a larger all India level study across states initiated by the Ministry of Rural Development, Government of India and Coordinated by the National Institute of Rural Development (NIRD), Hyderabad. While watershed development (WSD) is a flagship programme of Government of India, its implementation has reached a crucial stage with recent policy changes. WSD is being brought under the National Rainfed Area Authority (NRAA) with a set of new guidelines doubling the per hectare allocations, increase in the size of watersheds (5-10000 ha), extended implementation time frame, emphasis on livelihoods components, etc. Besides, new institutional structures have been brought in for better implementation. The new watersheds under these guidelines are being implemented from 2010 onwards.

In the above context, the set of large scale studies initiated by MORD, GOI are expected to identify various concerns for improved performance of the WSD programme. These concerns can be addressed in the implementation of the new schemes. The methodology and approach of the present study was pre-designed in order to ensure comparability and consistency across states. It follows a direct assessment approach rather than the standard deductive approach thus reducing the scope for subjective interpretations. Besides, the scale and coverage of the study is large enough to make generalisations at the state level for policy.

Livelihoods and Natural Resource Management Institute, Hyderabad has been entrusted with the study in Rajasthan. The study covered 110 watersheds spread over 15 districts. Number of people have contributed and facilitated the completion of the study. First of all we would like to thank the Ministry of Rural Development, Government of India, and the National Institute of Rural Development (NIRD), Hyderabad, for giving us the opportunity to take up the study and providing the financial support. In this regard, the study benefited from regular inputs in the form of suggestions and comments from Dr. S. S. P. Sharma and Dr. J. Venkateswarlu. We gratefully acknowledge and thank them for their inputs, support and encouragement throughout the study. Dr. M. S. Rammohan Rao has gone through the report and provided valuable inputs. Our grateful thanks are due to him. Dr. P. Prudhvikar Reddy Dr. M. Srinivas Reddy, CESS, provided lot of support in organising and collating secondary data at various levels.

In Rajasthan the state level Watershed Department, the nodal agency, provided all the support in conducting the study in all the fifteen districts. The district level and block level officials have provided full support in providing information despite our numerous demands at odd hours and even holidays. Our grateful thanks are due to the state level nodal agency, district and block level officials for providing all the information and facilitating the fieldwork in a smooth manner. The Watershed implementing agencies, various line departments, were kind enough to share their views and spend time with the team in discussing various issues pertaining to the implementation. The study would be incomplete without their cooperation despite the fact that their role as an implementing agency was over long back. Our grateful thanks are due to all the officials of the implementing agencies. Members of the watershed associations and committees have provided the much needed support in collecting information and details at the watershed level (Village and Rapid questionnaires). Sample households have been kind enough to spare their time in sitting through the household interviews and answering our complicated and sensitive questions with lots of patience. We are thankful to the support and cooperation received from all the village people.

Prof. Surjit Singh, Director, Institute of Development Studies, Jaipur, has helped in organising the field work in Rajasthan. But for his help and support the field work would have taken much longer time. Our profuse thanks are due to him for all his support. Dr. Jaisingh Rathore and Mr. Ratanlal Jogi, have helped us in putting together a good team of investigators, planned the field work and supervised the fieldwork at various stages. We gratefully acknowledge their help in conducting the field work. A team of thirteen field investigators (Mr. R. C. Sharma, Mr. R. S. Rathore, Mr. Mahesh Soni, Mr. S. S. Rathore,, MR. V. S. Kuhar, Mr. M. K. Sain, Mr. P. K. Sharma, Mr. J. Singh, Mr. D. S. Khangarot, Mr. R. Parekh, Mr. S. K. Naga, Mr. A. Gothwal, and Mr. A. Jain) participated in the data collection at the watershed level tirelessly with good quality. Their quality inputs are gratefully acknowledged. Our thanks are due to the team consisting of Mr. P. R. Narender Reddy, Ms. P. Bhushana, Ms. K. Panchakshri, Ms. Rama Devi and Mr. B. Sridhar for processing the data efficiently.

V. Ratna Reddy Sanjit Kumar Rout T. Chiranjeevi LNRMI.

Glossary

CBO: Community Based Organisation CSS: Centrally Sponsored Scheme CPR: Common Pool Resources CV: Coefficient of Variation DDP: Desert Development Programme DPAP: Drought Prone Area Development Programme GDP: Gross Domestic Product GoR: Government of Rajasthan HDI: Human Development Index IWDP: Integrated Wasteland Development Programme IWMP: Integrated Watershed Management Programme LMF: Large and Medium Farmers NIRD: National Institute of Rural Development NRAA: National Rainfed Area Authority NWDB: National Wasteland Development Board PIA: Project Implementing Agency RRS: Rapid Reconnaissance Survey SC: Scheduled Caste SDP: State Domestic Product SHG: Self Help Groups SMF: Small and Marginal Farmers ST: Scheduled Tribes ToR: Terms of Reference UG: User Group VIF: Variance Inflation Factor WA: Watershed Association WC: Watershed Committee WDF: Watershed Development Fund WDP: Watershed Development Programme WHS: Water harvesting Structures WSD: Watershed Development

List of Tables

Table 1.1: Area Treated (m ha) and Investment (Rs. crores) in Watershed Programmes in India Table 1.2: Salient Features of Agro climatic Zones in Rajasthan Table 1.3: Land Utilization across Agro climatic Zones of Rajasthan Table 1.4: Distributions of Sample Watersheds across Schemes and Districts Table 2.1: Bio-Physical and Economic Features of Sample Districts Table 2.2: Demographic Features of the Sample Districts Table 2.3: Impact on Bio-physical Indicators across Sample Watersheds Table 2.4: Performance of Bio-physical Indicators in the Sample Watershed across Districts Table 2.5: Performance of Bio-physical Indicators across Schemes in the Sample Watersheds Table 2.6: Impact on Economic Indicators in the Sample Watersheds Table 2.7: Performance of Economic Indicators in the Sample Watersheds across Districts Table 2.8: Performance of Economic Indicators across Schemes in the Sample Watersheds Table 2.9: Impact on Institutional Indicators in the Sample Watersheds Table 2.10: Performance of Institutional Indicators in the Sample Watershed across Districts Table 2.11: Performance of Institutional Indicators across Schemes in the Sample Watersheds Table 2.12: Distribution of Watersheds by their Performance Table 3.1: Average Performance of WSD across Districts (% score) Table 3.2: Performance of WSD between Size Class of Farmers (SMF-LMF) Table 3.3: Performance of WSD between Schemes Table 4.1: Average Economic Performance of WSD across Districts and Indicators. Table 4.2: Average Economic Impact of WSD across Size Classes Table 4.3: Performance of WSD between Schemes Table 5.1: Performance of WSD in Terms of Social Impacts Table 5.2: Performance of WSD across Size Classes Table 5.3: Performance of WSD in Terms of Social Impacts across Schemes Table 6.1: Performance of Watersheds in Rajasthan Table 6.3: Measurement and Expected Signs of the Selected Variables Table 6.4: Regression Estimates of Selected Specifications Appendix Table A6.1: Watershed Wise Performance (Scores) Appendix Table A6.2: Descriptive Statistics of Selected Variables

List of Figures Figure 3.1: Impact of WSD on Soil Erosion across Sample Districts Figure 3.2: Impact of WSD on Runoff Reduction across Sample Districts Figure 3.3: Impact of WSD on Drinking Water across Sample Districts Figure 3.4: Impact of WSD on Irrigation across Sample Districts Figure 3.5: Impact of WSD on Vegetation across Sample Districts Figure 3.6: Impact of WSD on Fodder across Sample Districts Figure 3.7: Impact of WSD on Adequacy of Feeds and Fodder across Sample Districts Figure 3.8: Impact of WSD on Fuel Wood across Sample Districts Figure 3.9: Impact of WSD on Manure across Sample Districts Figure 3.10: Impact of WSD on Soil Erosion by Farm Size Classes Figure 3.11: Impact of WSD on Runoff Reduction by Farm Size Classes Figure 3.12: Impact of WSD on Drinking water by Farm Size Classes Figure 3.13: Impact of WSD on Irrigation by Farm Size Classes Figure 3.14: Impact of WSD on Vegetation by Farm Size Classes Figure 3.15: Impact of WSD on Fuel by Farm Size Classes Figure 3.16: Impact of WSD on Manure by Farm Size Classes Figure 3.17: Impact of WSD on Soil Erosion across Schemes Figure 3.18: Impact of WSD on Run off Reduction across Schemes Figure 3.19: Impact of WSD on Drinking Water across Schemes Figure 3.20: Impact of WSD on Irrigation across Schemes Figure 3.21: Impact of WSD on Vegetation across Schemes Figure 3.22: Impact of WSD on Fodder across Schemes Figure 3.23: Impact of WSD on Adequacy of Feeds and Fodder across Schemes Figure 3.24: Impact of WSD on Fuel across Schemes Figure 3.25: Impact of WSD on Manure across Schemes Figure 4.1: Impact of WSD on Cropping Intensity across Sample Districts Figure 4.2: Impact of WSD on Yield Rate of Cereals across Sample Districts Figure 4.3: Impact of WSD on Yield Rate of Pulses across Sample Districts Figure 4.4: Impact of WSD on Yield Rate of Oilseeds across Sample Districts Figure 4.5: Impact of WSD on Yield Rate of Cash Crops across Sample Districts Figure 4.6: Impact of WSD on Employment (Agriculture: Male) across Sample Districts Figure 4.7: Impact of WSD on Employment (Agriculture: Female) across Sample Districts

Figure 4.8: Impact of WSD on Employment (Non-agriculture: Male) across Sample Districts Figure 4.9: Impact of WSD on Employment (Non-agriculture: Female) across Sample Districts Figure 4.10: Impact of WSD on Employment (Self: male) across Sample Districts Figure 4.11: Impact of WSD on Employment (Self: Female) across Sample Districts Figure 4.12: Impact of WSD on Livestock across Sample Districts (Shift from Cattle to Tractor) Figure 4.13: Impact of WSD on Livestock across Sample Districts (Shift from Draft to Milch cattle) Figure 4.14: Impact of WSD on Livestock across Sample Districts (Shift from sheep to Goat) Figure 4.15: Impact of WSD on Livestock across Sample Districts (Shift to Improved Breeds) Figure 4.16: Impact of WSD on Purchase of Fodder across Sample Districts Figure 4.17: Impact of WSD on Processing of Fodder across Sample Districts Figure 4.18: Impact of WSD on Standard of Living across Sample Districts Figure 4.18: Impact of WSD on Crop Intensity across Size Classes Figure 4.19: Impact of WSD on Cereal Yields across Size Classes Figure 4.20: Impact of WSD on Pulses Yields across Size Classes Figure 4.21: Impact of WSD on Yields of Oilseeds across Size Classes Figure 4.22: Impact of WSD on cash Crop Yields across Size Classes Figure 4.23: Impact of WSD on Employment (Agrl.: Male) across Size Classes Figure 4.24: Impact of WSD on Employment (Agrl: Female) across Size Classes Figure 4.25: Impact of WSD on Employment (Non-Agrl.: male) across Size Classes Figure 4.26: Impact of WSD on Employment (Non-agrl: Female) across Size Classes Figure 4.27: Impact of WSD on Employment (Self-Employment: Male) across Size Classes Figure 4.28: Impact of WSD on Employment (Self-Employment: Female) across Size Classes Figure 4.29: Impact of WSD on Livestock (Shift from Cattle to Tractor) across Size Classes Figure 4.30: Impact of WSD on Livestock (Shift from Draft to Milch Cattle) across Size Classes Figure 4.31: Impact of WSD on Livestock (Shift from Sheep to Goat) across Size Classes Figure 4.32: Impact of WSD on Livestock (Shift to Improved Breeds) across Size Classes Figure 4.33: Impact of WSD on Fodder Processing across Size Classes Figure 4.34: Impact of WSD on Standard of Living across Size Classes Figure 4.35: Impact of WSD on Crop Intensity across Schemes

Figure 4.36: Impact of WSD on Cereal Yields across Schemes Figure 4.37: Impact of WSD on Pulses Yields across Schemes Figure 4.38: Impact of WSD on Oilseed Yields across Schemes Figure 4.39: Impact of WSD on Cash crop Yields across Schemes Figure 4.40: Impact of WSD on Employment (Agrl.: Male) across Schemes Figure 4.41: Impact of WSD on Employment (Agrl: Female) across Schemes Figure 4.42: Impact of WSD on Employment (Non-agrl.: Male) across Schemes Figure 4.43: Impact of WSD on Employment (Non-agrl.: Female) across Schemes Figure 4.44: Impact of WSD on Employment (Self-employment: male) across Schemes Figure 4.45: Impact of WSD on Employment (Self-Employment: Female) across Schemes Figure 4.46: Impact of WSD on Livestock (Shift from draft cattle to Tractor) across Schemes Figure 4.47: Impact of WSD on Livestock (Draft to Milch cattle) across Schemes Figure 4.48: Impact of WSD on Livestock (Sheep to Goat) Schemes Figure 4.49: Impact of WSD on Livestock (Shift to Improved breeds) across Schemes Figure 4.50: Impact of WSD on Livestock (Processing of Fodder) across Schemes Figure 4.51: Impact of WSD on Standard of Living across Schemes Figure 5.1: Status of Water Harvesting Structures across Sample Districts Figure 5.2: Maintenance of Retention Wall across Sample Districts Figure 5.3: Periodic De-silting of Water Bodies across Sample Districts Figure 5.4: Participation of Women in the Maintenance of CPRs across Sample Districts Figure 5.5: Social Fencing of Community Lands across Sample Districts Figure 5.6: Practice of Staggered Grazing across Sample Districts Figure 5.7: Extent of Stall Feeding across Sample Districts Figure 5.8: Extent of Open Grazing across Sample Districts Figure 5.9: Preference for Children’s Education across Sample Districts Figure 5.10: Level of education across Sample Districts Figure 5.11: Coverage of Health Care across Sample Districts Figure 5.12: Coverage of Nutritional Care across Sample Districts Figure 5.13: Maintenance of Water Harvesting Structures across Size Classes Figure 5.14: Periodic De-silting of Water Bodies across Size Classes Figure 5.15: Maintenance of Retention Walls across Size Classes Figure 5.16: Women Participation in CPR maintenance across Size Classes Figure 5.17: Social Fencing of Community Lands across Size Classes

Figure 5.18: Practice of Staggered Grazing across Size Classes Figure 5.19: Extent of Stall Feeding across Size Classes Figure 5.20: Extent of Grazing Practice across Size Classes Figure 5.21: Preference for Children Schooling across Size Classes Figure 5.22: Level of Education across Size Classes Figure 5.23: Status of Health Coverage across Size Classes Figure 5.24: Status of Nutritional Coverage across Size Classes Figure 5.25: Status of Water Harvesting Structures across Schemes Figure 5.26: Periodical De-silting of Water Bodies across Schemes Figure 5.27: Maintenance of Retention Walls across Schemes Figure 5.28: Participation of Women in CPR Maintenance across Schemes Figure 5.29: Practice of Social Fencing across Schemes Figure 5.30: Practice of Staggered Grazing across Schemes Figure 5.31: Practice of Stall Feeding across Schemes Figure 5.32: Practice of Open Grazing across Schemes Figure 5.33: Preference for Children’s Schooling across Schemes Figure 5.34: Level of Education across Schemes Figure 5.35: Status of Health across Schemes Figure 5.36: Status of Nutrition across Schemes Figure 6.1: Variations and Trends in the Performance of Different Components across Watersheds Table 6.2a: Regression Plot of the Economic and Environmental Scores Table 6.2b: Regression Plot of the Economic and Social Scores Table 6.2c: Regression Plot of the Environment and Social Scores

List of Maps Map 1.1: Location of Watersheds Implemented under Different Schemes Map 1.2: Location of Sample Districts (Yellow colour)

CHAPTER I Introduction

I Background Rainfed regions account for about 60 percent of the net sown area land and support 40 percent of India's population. Watershed development is among the flagship programmes of rural development that assist in rural poverty alleviation, particularly in the more marginal semi-arid, rain fed areas. These areas house a large share of the poor, food insecure and vulnerable populations in the country. Moreover, as productivity growth in the more favoured green revolution areas is already showing signs of slowing down or stagnation (Pingali and Rosegrant, 2001), future growth in agricultural production and food security is likely to depend on improving the productivity in the semi-arid rain fed areas (Fan and Hazell, 2000).

Watershed development (WSD) as a technology and its management as a philosophy has gained the attention of both social and natural scientists. The research studies undertaken in the 1990s and early 2000s to examine the socio-economic impacts of the watershed technology have endorsed the program in terms of costs and benefits (Deshpande and Reddy 1990, Singh et al. 1993, Ninan and Lakshmikantamma 1994 Singh et al. 1995, Nalatawadmath et al. 1997, Joshi and Batlan 1997, Reddy 2000, Kolavalli and Kerr, 2002). These studies not only vindicated the economic viability of WSD but also underlined that it is among the most important options to the development of rainfed agriculture in India.

A Watershed is a topographically delineated area that is drained by a stream system. It is a hydrologic unit that has been described and used both as a bio-physical unit and as a socio- economic and socio-political unit for planning and implementing resource management activities. Watershed development is a land based technology that would help conserve and improve insi tu moisture, check soil erosion and improve water resources, especially groundwater in the rainfed regions. Watershed simply means improving the management of a watershed or a catchment area, for instance, by building contour bunds, water harvesting structures (check-dams), field bunds (raised edges), etc. It facilitates higher land productivity through improved moisture and water availability for agriculture.

Watersheds transcend households, communities and even villages, and so their sustainable development is critically linked with both inter household and inter village cooperation. Hence, people’s participation through appropriate institutional arrangements is a prerequisite

for successful implementation and sustainable management of watersheds in the medium and long run. A widely held and endorsed view is that impact of WSD is effective in the case of watershed where active people’s participation is observed when compared to the watersheds where people’s participation is either passive or absent. Recognising this aspect the 1995 watershed guidelines provided a definite design for a participatory approach. Therefore, any attempt to assess the impact of WSD in the post 1995 context, need to cover the three important aspects of natural or bio-physical, institutional or social and economic. Though natural and social impacts are necessary to ensure economic impacts, they are also equally important per se.

In recognition of the socioeconomic and bio-physical benefits, India has one of the largest micro-watershed development (WSD) programs in the world. More than US$4 billion were spent by the central government alone since the beginning of the 8th plan (1992). About Rs. 2300 crores (US $ 600 million) is being spent annually through various projects supported by the government, NGOs and bi-lateral funds (Table 1.1). The allocations are expected to be doubled (crossing Rs. 1000 crores or US$1 billion) during the 11th Plan period with enhanced per hectare investments1. Allocations towards WSD in the current annual budget are above Rs. 2000 crores. However, the cost effectiveness of these allocations and the sustainability of the programme are widely questioned (GoI, 2001).

1 The proposed watershed guidelines recommend a raise in the per hectare expenditure from the existing Rs. 6000 (US$150) to Rs. 12000 (US$300). 12

Table 1.1: Area Treated (m ha) and Investment (Rs. crores) in Watershed Programmes in India

Up to end of 8th During 10th Plan till Total (till March Programmes During 9th Plan Plan March 2005 2005) Area Investment Area Investment Area Investment Area Investment I Ministry of Agriculture a) National Watershed Development Project for Rain 4.22 967.93 2.77 911.01 0.96 519.82 7.95 2398.76 fed Areas (NWDPRA) b) River Valley Project (RVP) and Flood Prone Regions 3.89 819.95 1.60 696.26 0.60 377.91 6.09 1894.12 (FPR) c) Watershed Development Project in Shifting Cultivation 0.07 93.73 0.15 82.01 0.06 60.61 0.28 236.35 Areas (WSDSCA) d) Alkali Soils 0.48 62.29 0.08 20.25 0.56 82.54 e) Externally Aided Project 1.00 646.00 0.50 1425.01 0.86 2685.25 2.36 4756.26 (EAP) Sub Total 9.66 2589.90 5.02 3114.29 2.56 3663.84 17.24 9368.03 II Department of Land

Resources (MoRD) a) Drought prone areas 6.86 1109.95 4.49 668.26 3.78 845.19 15.13 2623.40 Programme(DPAP) b) Desert Development 0.85 722.79 2.48 519.80 2.38 615.19 5.71 1857.78 Programme (DDP) c) Integrated waste land development Programme 0.28 216.16 3.58 943.88 2.46 1001.77 6.32 2161.81 (IWDP) d) Externally Aided Project 0.14 18.39 0.22 194.28 0.36 212.67 (EAP) Sub Total 7.99 2048.90 10.69 2150.33 8.84 2656.43 27.52 6855.66 III Ministry of Environmental

& Forests (MoEF) a) Integrated Afforestation & Eco-Development Projects 0.30 203.12 0.12 141.54 0.40 469.07 0.82 813.73 Scheme(IAEPS) Grand Total 17.95 4841.92 15.83 5406.16 11.80 6789.34 45.58 17037.42

Source: MoRD (2006)

II. Profile of Rajasthan The state of Rajasthan is situated in the north western part of India between 23o 3' and 30o 12' North latitudes, and 69o30' to 78o17' East longitudes. Rajasthan occupies the western most part of India and shares International boundary with Pakistan in the west. The adjoining States are Punjab and Haryana in the North, Uttar Pradesh in the Northeast, Madhya Pradesh in the Southeast, and Gujarat in the Southwest. With its geographical area of 3,42,239 sq. km, accounting for 10.41 percent of all India, Rajasthan is the largest State in the country in terms of area and also the one with the highest proportion of land occupied by desert. For administrative purpose the State is divided into 7 Divisions, 33 districts, 188 sub-divisions, 241 tehsils and 237 development blocks (Panchayat Samitis). With, 9,188 Village Panchayats the state has 39753 inhabited and 1600 un- inhabited villages.

The climate of the State is the driest in the country, which varies from semi-arid to arid. The climate is characterized by low rainfall with erratic distribution, extremes of diurnal and annual temperatures, low humidity and high wind velocity. The arid climate has marked variations in diurnal and seasonal ranges of temperature, characteristic of warm-dry continental climates. On average winter temperatures range from 8° to 28° C (46° to 82° F) and summer temperatures range from 25° to 46° C (77° to 115° F). May and June are the hottest months, while January is the coldest month. The rainfall of the Sate is not only meager but also varies significantly from year to year, quite frequently leading to droughts. The distribution of annual rainfall is also uneven and decreases from southeast to northwest. The average rainfall ranges from 480 mm to 750 mm being as low as 150 mm in arid region and 1000 mm in the south-eastern plateau, most of which falls from July through September during the monsoon season.

The physiography of Rajasthan is the product of harsh climatic conditions resulting in erosion and denudation over time. On the basis of climatic conditions and agricultural produce, Rajasthan has been divided into nine agro-climatic zones, each one having special characteristics of its own (Table 1.2). Table 1.2: Salient Features of Agro climatic Zone in Rajasthan

Agroclimatic Zones Districts Annual Rain fall Crops grown Arid Western Plain Bikaner, Jaisalmer, 100-400 mm Khariff: rainfed crops like bajra, kharif Barmer pulses, guar etc Rabi: wheat, rape-seed and mustard Transitional Plain of Jalor and Sheoganj 300 to 500 mm kharif :bajra, maize, guar, sesamum and Luni Basin tehsils of Sirohi pulses Rabi:wheat, barley and mustard Semi-arid Eastern Jaipur, Dausa, Tonk 500 to 600 mm kharif :bajra, sorghum and pulses; Plain , Ajmer Rabi:wheat, barley, gram, mustard Flood Prone Eastern Dhaulpur and the 750 mm Khariff: bajra, sorghum, maize, Plains northern part of sugarcane, sesamum and a variety of Sawai Madhopur pulses; Rabi:Wheat, barley, gram and mustard Sub-humid Southern Part of Udaipur and 500 to 950 mm Kharif maize,paddy;Rabi:wheat, gram Plains & The part of Sirohi and oil seeds ;cotton and opium Aravalli Hills district Humid Southern Part of Udaipur > 700 mm Cash crops:Cotton,sugarcane; Plains Khariff/food crops :Maize, sorghum and paddy ;Groundnut, mustard, sesamum and rapeseed Humid South- Baran, Bundi, and 600 to 850 mm Kharif /food crops :Paddy and sorghum ; Eastern Plains part of Sawai Rabi:Wheat, barley, grain and mustard Madhopur

i) Arid Western Plain This region comprises of Bikaner, Jaisalmer and Barmer districts, along with parts of Jodhpur and Churu districts. This is the most arid part of the state where the annual rainfall varies from 100 to 400 mm, quite often erratic (entire rainfall of the year may fall on a single day). Summer temperatures are high and day temperatures may be as high as 49o C though the night temperatures may fall to less than 20o C. In winters, the day temperatures are higher but the night temperatures may be near freezing point. Owing to poor rainfall, surface water resources do not exist while groundwater resources are often deep and brackish. Natural vegetation is only seasonal. Mostly rainfed crops like bajra, kharif pulses, guar, etc. are grown during the kharif season. Rabi crops like wheat, rape-seed and mustard are grown only in areas where irrigation water is available. ii) Irrigated North-Western Plains The entire Ganganagar district, which is an alluvial and aeolian plain, formed by the river Ghaggar (the ancient river Saraswati) forms this agro-climatic zone. A part of this region is arid, which is the northern extension of the Indian Thar Desert covered with wind-blown sand. The average annual rainfall is about 400 mm. The area is rich in agricultural produce due to the well-developed canal irrigation. Today, a large network of Gang Canal, Bhakhra Canal and Indira Gandhi Canal has made the entire area green and productive. Amongst the kharif crops cotton, sugarcane and pulses are of importance. In the rabi season, wheat, mustard, gram, vegetables and fruits are produced. The total production as well as productivity levels of all crops is relatively higher in this zone compared to other parts of the state. iii) Transitional Plain of Inland Drainage This zone comprises of Nagaur, Sikar and Jhunjhunun districts and parts of Churu district. The area is covered with sand dunes and inter-dunal sandy plains. Climatically, this zone is slightly better as compared to the adjoining zone of the Arid Western Plain. Rainfall is slightly higher, temperatures in summer months do go very high and the winters are very cold. Irrigation is restricted to areas with good groundwater potential. Bajra, sesamum and kharif pulses are the main crops during rainy season. Wheat, barley, mustard and gram are grown as irrigated crops or on conserved soil moisture during rabi. iv) Transitional Plain of Luni Basin This zone lies between the Aravalli ranges and western arid region. The region encompasses of Jalor and Pali districts along with parts of Sirohi and Jodhpur districts. The region has semi-arid climate with an annual rainfall of 300 to 500 mm. It is drained by the river Luni

which is seasonal and flows only during rainy season. The western part of the region is dotted with sand dunes, interspersed in alluvial soil. Luni and its several tributaries, like Sukri, Mithri and Jawai have made this area productive. The climatic conditions are almost the same as in the western arid region except that the rainfall is slightly higher. The groundwater level is high in the river basins and has been usefully tapped for irrigation. Vegetation is sparse in the western part but in the east and on the slopes of the Aravalli ranges vegetation is in the form of woodlands, open forests and grasslands. The area produces bajra, maize, guar, sesamum and pulses in the kharif season and wheat, barley and mustard crops during rabi, especially in irrigated areas. v) Semi-arid Eastern Plain This region comprises of four districts namely, Jaipur, Dausa, Tonk and Ajmer. River Banas, with its several tributaries, forms a rich fertile plain in this zone. On the western side, the region is flanked by the low Aravalli hills which extend from the south-West to the northeast. The annual rainfall of the region varies from 500 to 600 mms. Summer and winter temperatures are not as extreme as in the arid west but the summer temperature may reach around 45o C and in the winter, minimum may be 8o C. The water table varies from 15 to 25 meters with high fluctuations, especially in the years when the south-west monsoon fails and the yearly replenishments are low. Natural vegetation is of type, but owing to heavy felling of trees the surface mantle has been robbed of its natural wealth. Bajra, sorghum and pulses are grown in the kharif and wheat, barley, gram, mustard in the rabi season. Productivity of all crops in this zone is relatively better when compared to the zones west of the Aravalli range. vi) Flood Prone Eastern Plains This region comprises of Alwar, Bharatpur and Dhaulpur and the northern part of Sawai Madhopur districts. Except for few low hills in Alwar and Sawai Madhopur districts, the entire region is a flood plain of the Banganga and the river Ghambhiri. The region has rich alluvial soils. Climatically, the area is similar to the plains of Banas, but the rainfall is relatively higher in the east with an annual average of about 750 mm. Natural vegetation exists on mountain slopes, wetland areas, and protected zones but the excessive plundering of forest wealth has degraded the natural cover. The region produces a variety of crops due to the availability of surface and groundwater irrigation sources. A network of canals drawn from the upper Yamuna Canal and the Panchana Dam irrigate this area. Groundwater aquifers vary from 5 to 15 meters supporting well irrigation. The region produces bajra, sorghum, maize, sugarcane, sesamum and a variety of pulses in the kharif season. Wheat, barley, gram and mustard are the dominant crops during rabi season.

16 vii) Sub-humid Southern Plains and The Aravalli Hills The districts of Bhilwara, Udaipur and most parts of Chittaurgarh and Sirohi districts form this agro-climatic zone. The region has a moderately warm climate in summers and with mild winters. The annual rainfall varies from 500 to 950 mms. The highest precipitation in the state is recorded in Abu hills (Sirohi district). There are number of surface water streams like Ghambiri, Sabarmati, Banas and its tributaries but they are all ephemeral. The area is rich in natural vegetation, which grows on the slopes of the Aravallis and in the wetland areas but excessive felling of trees has degraded these open forests. Tank irrigation is most common in this zone. The area produces maize as the chief food crop during Kharif season though paddy is also grown in the irrigated areas. In the Rabi season, wheat, gram and oil seeds are the main crops. Cotton and opium are cultivated in the black soil. viii) Humid Southern Plains The districts of Dungarpur and Banswara, parts of Udaipur and Chittorgarh are included in this region. This is mostly a tribal area where Bhils, Garasiyas and Damors live amidst forests and hills. The area has humid climate with an average annual rainfall of more than 700 mm. The temperature regimes do not fluctuate much in summer and winter and the area has mild winters and mild summers. There are a number of surface water streams. Mahi and its tributaries like Anas, Arau and Jhakham have made this area very fertile with profuse natural vegetation. The commissioning of Mahi Bajaj Sagar multipurpose project has provided this area with canal irrigation and hydel power. Cotton and sugarcane are the chief cash crops grown in the black soils of the zone. Maize, sorghum and paddy are the main food crops during Kharif season. Groundnut, mustard, sesamum and rapeseed are also grown. ix) Humid South-Eastern Plains Popularly known as the Hadauti plateau, this region includes the districts of Kota, Baran, Bundi and Jhalawar and a small part of Sawai Madhopur district. Chambal is the main river along with its main tributaries like Parvati, Kali sindh, Parwan and Banas. Development of canal irrigation system with a series of dams and barrages on the Chambal has made this area rich in agricultural production. Gandhi Sagar, Rana Pratap Sagar and Jawhar Sagar dams together with Kota Barrage have generated enough resources of electricity and canal water for irrigation. The region has warm summers and mild winters. Summer temperatures sometimes touch 45o C. The relative humidity is generally high and the annual rainfall varies from 600 to 850 mm. The zone has fertile black soils with natural vegetation in the form of woodlands, parklands and open forests though degraded. Paddy and sorghum are the chief

food crops grown in the Kharif season. Wheat, barley, grain and mustard are grown in rabi season. Population and Demography According to 2001 census, the population of Rajasthan is 56.51 million (5.5 percent of India’s population), of which 76.61 percent lives in the rural areas. The density of population is 165 per sq km as against the all India average of 325. Infant mortality rate is 78 (rural 81 and urban 55). Seventy seven percent of the population lives in rural areas compared to 72 percent at the all India level. Work participation rate is 42 percent with high male participation (50 percent). Another important characteristic of population in Rajasthan is its high percentage of Scheduled Caste (17 percent) and Scheduled Tribe population (13 percent) as against 16 and 8 percent respectively at the all India level. An important gender related feature of the population in Rajasthan is its low sex ratio of 922 women per 1000 male against 933 females in India. However, sex ratio has improved significantly from 910 in 1991 to 922 in 2001. Sex ratios are worse in western and northern regions compared to southern and south-eastern regions. Literacy Rajasthan ranks 29th in literacy among the states / UTs of India though made significant progress during the last decade. Total literacy has gone up to 61.03 percent in 2001 from 38.55 percent in 1991. The literacy rate among males in 2001 was 75.70 percent compared to 54.99 percent in 1991.Female literacy has also reached 44.34 per cent compared to 20.44 per cent in 1991. These numbers make Rajasthan among the best performers on this count during the decade. Consequently, the gap between literacy rates in the state compared to the national aggregate has reduced. Land Utilization Rajasthan accounts for 10.4 percent of geographic area of India with 30.9 percent of its geographical area under all 12 categories of wastelands. About 47 percent of the India’s total degraded pastures and grazing lands are distributed throughout Rajasthan. Extent of land utilization varies widely across agro climatic zones and districts (Table 1.3). During the year 2006-07, the area covered by forest was about 8 percent of the total geographical area. Uncultivable waste lands account for 12 percent of the geographical area, cultivable wastes account for 18 percent and fallow lands account for 11 percent. Gross cropped area in 2006- 07 was about 63 percent of the total geographical area. Net area under irrigation was about 34 percent. There are four major sources of irrigation viz, Canals, tanks, wells and tube-wells. The proportion of area irrigated by wells and tube-wells in 2006-07 was 71 percent and the

contribution of wells and tube-wells were 39 and 31 percent respectively. Contribution of canals was 26 percent, whereas contribution of tanks was only 2 percent.

1.3: Land Utilization across Agro climatic Zones of Rajasthan

District Zone Total % AF % UCL % CL % TF % NAS % GAS % GIA Geo.Area (in million ha) Bikaner I 30 3 10 25 10 53 56 17 Jaisalmer I 38 1 13 68 4 15 16 24 Barmer I 28 1 7 15 17 60 64 11 Jalore IV 11 2 12 7 17 63 77 29 Ajmer V 8 7 16 18 10 49 56 19 Dholpur VI 3 9 25 10 6 50 68 52 Jaipur V 11 7 12 10 11 60 86 41 Dausa V 3 7 11 10 8 64 100 50 S Madhopur VI & IX 5 16 14 8 7 56 75 57 Bundi IX 6 24 15 10 8 42 64 68 Baran IX 7 31 9 8 6 45 64 66 Tonk V 7 4 10 12 10 64 77 39 Rajsamand VII 5 5 28 39 7 20 22 7 Udaipur VII & VIII 15 28 34 15 6 17 20 22 Sirohi IV & VII 5 30 19 8 13 30 39 40 Rajasthan 343 8 12 18 11 51 63 34 Note: AF= Area under forests; UCL= Uncultivable waste lands; CL= Cultivable waste lands; TF= Total fallow lands; NAS= Net area sown; GAS= Gross area sown; GIA= Gross irrigated area.

Agricultural Scenario Rajasthan is predominantly an agricultural state with about 70 percent of the population depending on agriculture and allied activities. Agriculture contributes about 27 to 32 percent of the gross state domestic product (SDP). Water resources being scarce, agriculture is basically rain fed and continues to be vulnerable to the vagaries of monsoon. Due to high dependence on groundwater area under irrigation in the state also fluctuates. This is reflected in the fluctuations in gross cropped area depending on the condition of monsoon and groundwater situation. Rajasthan grows both kharif and rabi crops though the former is more important. Cultivation under kharif season is about 61 percent of the total cultivation, which depends on rainfall to a large extent. The principal crops cultivated in the state are wheat, rice, barely, jowar, millet, maize, gram, oilseeds, pulses, cotton and tobacco. Other crops are red chillies, mustard, cumin seeds, methi and hing. Some of the important crops grown in the State in order of production during the period 2006-07 were wheat (77,56,000 tonnes), rapeseed & mustard (37,67,000 tonnes), bajra (34,40,000 tonnes), maize (11,18,000 tonnes) ,

gram (8,73,000 tonnes), sugarcane (6,29,000 tonnes), barley (5,92,000 tonnes), groundnut (3,99,000 tonnes), jowar (3,68,000 tonnes), rice (1,70,000 tonnes), coriander (1,55,000 tonnes), cotton (lint) (1,27,000 tonnes) and caster seed (1,01,000 tonnes). Animal Husbandry: In Rajasthan, Animal Husbandry is not merely a subsidiary to Agriculture but it is a major economic activity especially in arid and semi-arid areas, thus providing the much needed insurance against the vagaries of weather. Livestock sector in Rajasthan is thus extremely livelihood intensive, closely interwoven into the socioeconomic fabric of the rural society. The livestock sector of Rajasthan provides almost 9 percent of the total milk production, 30 percent of the goat meat production and 39 percent of the total wool production and 35 percent of draught power in the country. The animal husbandry is contributing about 13 percent to the state’s economy (GDP).

Rajasthan has the second largest livestock population of the country accounting for11 percent of the total animal population of India, which is 49 million (Livestock Census, 2003). Cattle, buffaloes, sheep and goats constitute the main livestock population of the State. Donkeys and mules, horses and ponies, camels and pigs are also reared in the State in small numbers. Out of the total livestock population 22 percent are cattle and 21 percent are buffaloes, 34 percent are goats and 20 percent are sheep. As against twenty five well defined breeds of cattle and seven buffalo breeds in the country, the state is endowed with seven breeds of finest drought hardy milch breeds (Rathi, Gir and Tharparkar), dual purpose breeds (Kankrej and Haryana) and the famous draught breeds of Nagauri and Malvi. III. WSD in Rajasthan WSD is being implemented in Rajasthan under three different schemes, namely, Drought Prone Area Programme (DPAP), Integrated Wasteland Development Programme (IWDP) and Desert Development Programme (DDP). These schemes are mostly location specific (Map 1). In order to combat the frequent recurrence of droughts in the state DPAP was introduced during the year 1975, as a Centrally Sponsored Scheme (CSS) with a matching state share of 50:50 and adopted the watershed approach in 1987. While DPAP concentrates on non-arable lands, drainage lines for in-situ soil and moisture conservation, agro-forestry, pasture development, horticulture and alternate land use were its main components. The basic objective of the programme is to minimize the adverse effects of drought on the production of crops and livestock and productivity of land, water and human resources thereby ultimately leading to the drought proofing of the areas. The programme aims at promoting the overall

20 economic development and improving the socio-economic condition of the resource poor and disadvantaged sections inhabiting the programme areas through creation, widening and equitable distribution of resource base and increased employment opportunities. The objectives of the programme are being addressed in general by taking up development works through watershed approach for land development, water resource development and afforestation / pasture development.

IWDP was introduced during 1991 with 100 percent central assistance. IWDP included silvi- culture and soil and moisture conservation in lands under government or community or private control as its predominant activity, without any regard for the complete micro- watershed principle or with people’s participation. IWDP was transferred to the Department of Land Resources along with the NWDB in July 1992. From 1 April 1995, the scheme is being implemented on a watershed basis under the common Guidelines for Watershed Development. The Programme is expected to promote employment generation in the rural areas besides enhancing people’s participation at all stages in the development of wastelands- leading to sustainable development and equitable sharing of the benefits. The main objective of the IWDP are (1) to promote the overall economic development and improvement of the socio-economic conditions of rural poor of the programme areas through optimum utilization of resources, (2) generation of employment and (3) augmentation of other income generating activities. Further, it also aimed at encouraging restoration of ecological balance in the village through simple, easy and affordable technological and sustained community action (people’s participation). All these result in overall up-liftment of the poor and disadvantaged sections of the community.

Map 1.1: Location of Watersheds Implemented under Different Schemes

The major activities taken up under the Programme are: Soil and moisture conservation measures like terracing, bunding, trenching, vegetative barriers, etc. Planting and sowing of multipurpose trees, shrubs, grasses, legumes and pasture land development. Encouraging natural regeneration in the programme areas. Promotion of agro-forestry and horticulture. Wood substitution and fuel-wood conservation measures. Measures needed to disseminate technology such as training, extension and creation of greater degree of awareness among the participants are encouraged through people's participation, especially women.

The Desert Development Programme (DDP) was started in the hot desert areas of Rajasthan in 1977-78. In hot sandy desert areas, sanddune stabilization through shelterbelt plantations were given greater weightage. The programme was reviewed in 1994-95 by a Technical Committee headed by Prof. C.H. Hanumantha Rao and observed that the main reason for below satisfactory results was that area development was not taken up on watershed basis and the involvement of the local people was virtually non-existent, both in planning and execution of the programme. Besides inadequacy of funds, non-availability of trained personnel and taking up of too many activities, which were neither properly integrated nor

22 necessarily related to the objectives of the programme, were identified as contributory factors towards reducing the impact of the programme. Based on the recommendations of the Committee, new Blocks/Districts were included under the programme alongwith comprehensive Guidelines for Watershed Development were issued in 1994 and made applicable to the area development programme with effect from 1.4.1995. Subsequently, Rajasthan has distinct problems because of large tracts of Hot Arid (Sandy) areas. In view of the problem of sand dune stabilization in ten districts of this State, special projects are under implementation under DDP since 1999-2000 for combating desertification by way of shelterbelt plantation, sanddune fixation and afforestation. These ten districts are Barmer, Bikaner, Churu, Jaisalmer, Jalore, Jhunjhunu, Jodhpur, Nagaur, Pali and Sikar.

The programme has been conceived as a long term measure for restoration of ecological balance by conserving, developing and harnessing land, water, livestock and human resources. It seeks to promote the economic development of the village community and improve the economic conditions of the resource poor and disadvantaged sections of society in the rural areas. The major objectives of the programme include: i) to mitigate the adverse effects of desertification and adverse climatic conditions on crops, human and livestock population and combating desertification; ii) to restore ecological balance by harnessing, conserving and developing natural resources i.e. land, water, vegetative cover and raising land productivity; iii) to implement developmental works through the watershed approach, for land development, water resources development and afforestation / pasture development.

IV. Objectives Several past reviews have critically evaluated the key success factors required for effective watershed development but most of the studies were based on micro evidence from a few watersheds. There were no attempts to assess whether these investments are effective in achieving the stated objectives of the programme at a wider scale of a state as whole and across the states. On this back drop the Ministry of Rural Development (MoRD), Government of India has initiated a large scale impact assessment programme covering most of the states. The main objective of the programme is to assess the impact of the WSD programme after the introduction of the 1995 guidelines based on large sample of watersheds across states. Specific objectives include: a) Assess the bio-physical, economic and institutional impacts of WSD b) Examine the impacts on small and large farmers

c) Assess the differential impact of different programmes like WDP, DPAP and DDP, and d) Identify factors influencing the performance of watersheds. These broad objectives are assessed in the context of Rajasthan state, which is the focus of this report. V Methodology “The Impact Assessment Study of Watershed Development Projects in Rajasthan” was taken up at the instance of National Institute of Rural Development (NIRD), under the Ministry of Rural Development (MoRD), Government of India. As per the specifications, 110 watersheds were selected from 15 districts comprising watersheds implemented under three different schemes, i.e. 60 watersheds implemented under IWDP scheme, 15 under DPAP and 35 under DDP schemes (Map 2 and Table 1.4). The sampling design to select the districts, number of watersheds and year of sanction of the Watershed projects to be covered was determined by the Monitoring division, MoRD prior to the commencement of the fieldwork. Accordingly the study was undertaken in 15 districts of Rajasthan spread over 21 blocks to cover the 110 watersheds. Watershed Development Projects (WDPs) implemented under DPAP, DDP and IWDP schemes which are sanctioned between April 1, 1998 and March 31, 2002 by MoRD, GoI were considered for impact evaluation. Map 1.2: Location of Sample Districts (Yellow colour)

The methodological approach adopted in the field involves a survey-based data collection exercise comprising close-ended questionnaires. Three independent sets of questionnaires were used to collect the data, which were developed by at the ministry level. All the three questionnaires were prepared to capture the change due to the advent of WSD in order to understand the impact of the programme in the light of adaptation to 1995 guidelines. Following are the three questionnaires used in the data collection process.

1. Schedule I: Rapid Reconnaissance Survey (RRS) Schedule 2. Schedule 2: Village Profile (VP) schedule 3. Schedule 3: Field Survey (FS) Schedule

The Rapid Reconnaissance Survey was targeted at understanding the impacts of WSD at the aggregate level involving the implementing agencies, village communities, and other key stakeholders. Village profile schedule is used to capture the profile of the village with the help of village officers, local leaders, other key informants, etc. Field Survey Schedule is targeted at the household level. The main purpose is to assess the impacts of WSD at the household level pertaining to different indicators. The schedule is prepared in such a way that it captures the changes, positive and negative or neutral impacts.

About 40 sample households from each watershed were interviewed using the field survey schedule. These sample households are divided between Small and Marginal Farmers (SMF) and Large and medium Farmers (LMF) using probability proportionate sampling. Thus a total of 4448 households were covered across 110 watersheds in 15 sample districts. Of which 65 percent are SMF and 35 percent are LMF. The composition of the sample varied across districts and schemes due to the prevailing agrarian structure. The IWDP and DPAP districts have roughly 3:1 ratio of SMF: LMF while DDP districts have almost 1:1 ratio (Table 1.4).

Table 1.4: Distributions of Sample Watersheds across Schemes and Districts by Size Classes

No of No of Year of Sample HHs taken Scheme District Blocks Watersheds Sanction Covered Covered SMF(%) LMF(%) Total (Nos) Ajmer 1 5 1999 21 79 207 Baran 1 5 2001 82 18 187 Dausa 2 5 2000 60 40 192 Dholpur 1 5 2000 40 60 202 Jaipur 1 5 1999 39 61 203 IWDP Rajasamand 2 10 1999 100 0 412 Sirohi 1 5 2000 99 1 200 Tonk 1 5 1998 69 31 224 Udaipur 2 10 2000 80 20 393 Bundi 1 5 2000 99 1 219 Total 13 60 72 28 2439

Sawai Madhopur 1 5 2000 60 40 203 Tonk 1 5 2000 69 31 205 DPAP Udaipur 1 5 1999 99 1 198 Total 3 15 76 24 606

Barmer 2 10 1999 30 70 400 Bikaner 2 5 2000 40 60 200 DDP Jaisalmer 2 10 1999 59 41 400 Jalore 2 5 2000 20 80 201 Rajasamand 1 5 2000 100 0 202 Total 9 35 48 52 1403

Overall 21 110 65 35 4448

While the first two schedules were filled up based on the interviews with the PIA, Watershed committee and association members and focus group discussions and key stakeholder interviews at the village level, the third schedule was based on household interviews. Two teams of 6 and 7 field investigators each were engaged under the supervision of 3 senior researchers. All the research investigators employed for the purpose of data collection were natives of Rajasthan and had prior experience in such type of activity because of their association with some of the research Institutes like Institute of Development Studies, Jaipur, Rajasthan. The entire team was fluent in the local language and familiar with the study areas as most of them had already visited some of the study sites in the context of other projects. Field investigators were trained on canvassing the questionnaires and acquaint themselves about the objectives of the project and the importance / relevance of the questionnaires to the study. This ensured good understanding of the content of the schedule and facilitated better understanding of the team to collect relevant information.

Field Work Before start of the field work in each district, contacts were established with the concerned district administration and discussions were held regarding the implementation of watershed development programme in their district. These meetings were useful to establish contact with the higher-level officials and gave the research team an opportunity not only to know the government’s point of view, but also an opportunity to know about the overall picture of watershed development at the district level. List of all the watershed development projects sanctioned in the reference year and under the specified scheme were sought from the District administration. The requisite number of Watersheds (each having an area of approximately 500 ha.), as specified in the terms of reference (ToR), were selected from the list on mutual agreement of the research team and the concerned district administration.

Once the watershed villages were selected, the implementing agency and with the village level institutions / organisations were contacted for obtaining relevant information. At the village level meetings were held with all the available villagers to brief them about the purpose of the study and also to clarify doubts thus avoiding any expectations among the people. Help of the Sarpanch, ward members, or the members of village level institutions like watershed committee, youth club, village elders were taken to conduct such meetings.

In each watershed all the farming households were divided into two major landholding categories i. e 1) small and marginal farmers (SMF) and, 2) large and medium farmers (LMF). Then, sample farmers were drawn from each group using the probability proportionate (to farm size) sampling method (approximating). A sample of 40 farmers was selected from each watershed. On the whole, detailed information from 4448 farming households was collected spreading over 110 watersheds. The coverage of sample farmers shows that about 55 percent of the households are from IWDP Schemes, 14 percent from DPAP Schemes and the rest (31 percent) from the DDP Schemes. Approximately 65 percent of the sample households belong to the SMF category and 35 percent to the LMF category. Field visits for data collection were carried out during August through October 2009.

Data Analysis Household level data were collected mainly under three broad categories viz., bio-physical, economic and institutional. Some of the important indicators include soil conservation works, water harvesting structure works, maintenance of CPRs, etc., under the bio-physical factors; employment generation, diversification in agriculture, income, standard of living, etc; under

the economic category and education of children, healthcare, participation in user groups, etc under the institutional factors. Each assessment indicator has been assigned a pre-determined score as per its importance in its overall impact on the watershed development. All the scores, so distributed total up to 100. Scores are assigned in a descending order so that higher level of impacts gets higher score than lower level impacts.

For analytical purposes as well as to assess the impact of the watershed development programme, all the assessment indicators for which data were collected through rapid and household level surveys were categorized into 3 broad impact categories namely, bio- physical impact, economic impact and institutional impact. Separate analysis was carried out for assessing the impact at the district level, farm size wise and scheme wise considering each as one assessment unit.

To determine the performance level with respect to each assessment unit, maximum achievable scores and actual scores were calculated for each indicator. Maximum achievable score was calculated by multiplying the maximum score that could be assigned to each indicator with the total number of respondents associated. Actual score was calculated by multiplying the actual score given by the respondents to an indicator with the number of respondents giving the particular score.

After calculating the maximum achievable scores and actual scores for every indicator belonging to the three broad categories (i.e. bio-physical, Economic and Institutional), performance level of each assessment unit (i.e. District, Group and Scheme) was derived by taking the ratio of the sum of the actual scores of all the underlying indicators to the sum of the maximum achievable scores of those indicators and the resultant number is subsequently converted / standardized in percentage values in order to facilitate comparison across groups.

The performance level of all the assessment units with respect to each individual assessment indicator is also determined following the same method; by taking the ratio of actual scores of that assessment indicator to the maximum achievable scores for that indicator for a particular assessment unit like a district, group or a scheme, which is subsequently converted / standardized in percentage terms, so that comparison can be made. Apart from the scoring tables, which help assess the impact, frequency distribution tables/graphs were also generated for all the assessment units with respect to each individual assessment indicator so as to provide an in depth understanding of the impact.

VI Structure of the Report The impact assessment report is organised in six chapters. The present introductory chapter provides the project background, objectives, profile of Rajasthan, and methodology of the study. Chapter two presents the impact assessment from the communities’ perspective along with the profile of the sample districts. Case studies from some the districts were presented to highlight the deviations from the aggregate picture. Chapters 3, 4 and 5 respectively assess the bio-physical, economic and institutional impacts of the watershed programme in Rajasthan. These chapters are based on the household level information using the field survey schedule. These three chapters form the core of the impact assessment. The impacts are analysed across districts, farm sizes and schemes. Factors influencing the performance of WSD across watersheds were identified using a regression analysis. The last chapter pulls together the analysis and provides some policy implications.

CHAPTER II Performance of the Watershed Development Programme: Perceptions of the Communities I Introduction This chapter is based on the secondary information collected from the sample districts and the Rapid Reconnaissance Survey (RRS), Village Survey and the field notes at the watershed level. Focus group discussions, key stakeholder interviews and case study interviews form the basis of analysis. Some of the positive and negative aspects drawn from case studies are presented to highlight the issues and concerns of the programme. The information is collected at the watershed level and hence covers 110 watersheds. Scoring was given for various indicators falling under the broad categories of bio-physical, economic and social or institutional components. Though similar approach is adopted in collecting the data at the household level, these two are not strictly comparable. For, the indicators are different and the weighs given to different components are not the same. Hence this analysis could be treated as a reflection of aggregate or community level impressions of the performance of the WSD. This is a precursor and sets the background for the more detailed household impact assessment in the following chapters. II Profile of the Districts The sample districts cover 53 percent of Rajasthan’s geographical area. All the sample districts are in the low and medium rainfall category (Table 2.1). Eight of fifteen sample districts receive less than the state average rainfall of 575 mm. Average rainfall ranges between 164 mm in Jaisalmer and 858 mm in Udaipur. In fact, three of the desert districts receive less than 300 mm rainfall. Given the rain fall pattern WSD appears to be an appropriate intervention in the sample districts. At the same time the low rainfall and harsh climatic conditions limit the impacts. This needs to be kept at the background while assessing the performance of the WSD in these regions. On the other hand, the high variations in rainfall across districts give us an opportunity to assess the impacts of WSD in different rainfall conditions. Besides, the sample districts portray wide variations in other bio-physical attributes as well. These include area under irrigation, average holding size, livestock density, etc. It may be noted that the wide variations in bio-physical attributes in case of important indicators like rainfall and irrigation is not reflected in per capita incomes (Table 2.1). This could be due to the reason that some of the arid districts like Bikaner and Jaisalmer are world famous tourist places attracting income flows round the year. Besides, they also attract central and state assistance under the desert development programme. Some of the districts

also house army head quarters for the region. The high livestock density in the state indicates the importance of livestock in the economy. Livestock density is above state average in ten of the fifteen sample districts. Livestock density is the lowest in the low rainfall districts due to the scarcity of water and fodder. More than fifty percent of the population depend on agriculture in most of the districts indicating the predominance of agriculture in the state. Similarly, work participation rates are higher than state average in majority of the districts. At the aggregate level, dependence on agriculture and work participation rates in Rajasthan are higher than the all India average. This coupled with the fragile natural resource base and dependence on highly vulnerable resources like groundwater makes farming adventurous and risky livelihood activity. Most of the sample districts are characterised with poor soils of sandy and sandy loam (see Appendix Table A2.1). In some parts these soils are also affected by salinity and alkalinity. Irrigation is excessively dependent on groundwater. Given the precarious nature of rainfall dependence on groundwater makes agriculture vulnerable, especially in the absence of sufficient surface water bodies and replenishment mechanisms. Such vulnerabilities result in high dependence on livestock, especially small ruminants, as an alternative livelihood. Table 2.1: Bio-Physical and Economic Features of Sample Districts

District TGA ARF FRST CW Fallow NAS NIA SLH CI LSD % WPR PCI (M. (mm) (%) (%) (%) (%) (%) (Ha.) (%) (Sq.km) Agrl. (%) (Rs.) Ha) Pop Bikaner 30 243 3 23 10 53 12 10 111 89 61 40 18633 Jaisalmer 38 164 1 65 4 15 18 11 112 46 55 42 15386 Barmer 28 278 1 8 17 60 7 11 105 116 78 47 11995 Jalore 11 419 2 2 17 63 30 6 120 154 78 50 13050 Ajmer 8 527 7 8 10 49 19 2 121 190 48 39 18483 Dholpur 3 508 9 4 6 50 69 1 139 158 56 44 10895 Jaipur 11 556 7 3 11 60 49 3 159 221 41 36 21937 Dausa 3 552 7 2 8 64 74 2 156 243 73 41 11424 SMPur 5 837 16 2 7 56 68 2 124 160 72 42 15337 Bundi 6 764 24 5 8 42 83 2 151 155 72 47 18211 Baran 7 855 31 3 6 45 89 2 150 112 77 43 19560 Tonk 7 614 4 6 10 64 50 3 129 141 69 44 16043 Rajsamand 5 794 5 26 7 20 7 2 129 281 54 41 17355 Udaipur 15 858 28 9 6 17 29 2 146 221 64 42 17925 Sirohi 5 665 30 2 13 30 34 3 137 189 51 40 18340 Rajasthan 343 575 8 13 11 51 36 4 129 144 --- 42 --- Note: TGA= Total Geographical area in million hectares; ARF= Average Rainfall; FRST= % Area under forests; CW= % % area under culturable wastes; NAS= % Net area sown; NIA= % net area irrigated; SLH= Average size of land holding; CI= Cropping intensity; LSD= Livestock density per square kilometre; % Agrl. Pop= % of population depending on agriculture; WPR= Work participation rates; PCI= Per capita Income Source: GoR (2004)

Demographically the sample districts are sparsely populated when compared to rest of the state. Sample districts account for 43 percent of the state’s population as against 53 percent of the geographical area they occupy. For, some of the sample districts are located in the Thar Desert. But the sample districts house a larger proportion of rural population (Table 2.2). Twelve of the sample districts have higher proportion of rural population than the state average. Similarly, majority of the sample districts have larger proportion of SC population. On the other hand, sample districts have recorded below average (state) performance in the case of important indicators like literacy, life expectancy and access to drinking water. Majority of the sample districts rank low (high ranks) in terms of human development index (HDI). On the whole, the bio-physical and human development situation is the sample districts emphasises the criticality and importance of WSD, which is being promoted as an important option for enhancing the resource base and agricultural productivity as well as creating employment opportunities. Besides, the 1994 guidelines with emphasis on participatory development were expected to strengthen participatory institutions and human development. Table 2.2: Demographic Features of the Sample Districts

District Rural Female Total SC ST Literacy Life Access to safe HDI populat Populat population Populat Popul Rate (%) Expect drinking water (Rank) ion (%) ion (%) (Millions) ion (%) ation ancy (%HH) (%) (years) Bikaner 64 47 1.7 20 0 .6 57 75 71 3 Jaisalmer 85 45 0.5 15 5 51 70 59 11 Barmer 93 47 2.0 16 6 60 69 72 21 Jalore 92 49 1.4 18 9 46 63 84 29 Ajmer 60 48 2.2 18 2 65 59 99 10 Dholpur 82 45 1.0 20 5 60 53 99 30 Jaipur 51 47 5.3 15 8 70 62 98 4 Dausa 90 47 1.3 21 27 62 62 99 23 SMPur 81 47 1.1 20 22 57 55 99 26 Bundi 81 48 1.0 18 20 56 59 99 13 Baran 83 48 1.0 18 21 60 63 99 12 Tonk 79 48 1.2 19 12 52 53 99 24 Rajsamand 87 50 1.0 12 13 56 60 99 22 Udaipur 81 49 2.6 6 48 59 60 98 20 Sirohi 82 49 0.9 19 25 54 60 98 14 Rajasthan 76 48 56.5 17 13 60 61 ------

Note: HDI= Human Development Index. Source: Census (2001) and GoR (2004).

III Performance of the Sample Watersheds Here the impact of WSD is measured at the watershed and district levels. At the watershed level impact is assessed using the frequency distribution of sample watersheds where communities reported their perceptions about different indicators. At the district level the average scores were calculated in percentage terms to the maximum score provided by each watershed community. That is performance is directly linked to the score viz., higher the score higher the performance. The assessment is carried out for the three components and overall as well.

Bio-Physical Impacts: Communities’ perceptions of the impact of WSD on various bio- physical indicators indicate that majority of the sample watersheds have experienced positive impacts. Most of the impacts are moderate in nature (Table 2.3). In most of the indicators, except in the case of land use pattern, very few watershed communities have reported negative or no impact. Even in the case of land use pattern majority (52 percent) of the watershed communities reported that investment in good class lands has gone up after the advent of WSD. This indicates the complimentarity between public and private investments in land improvements (Shiefraw, et. al., 2004). That is WSD can check land degradation or improve land quality indirectly through encouraging private investments. This is also reflected in the decrease in wastelands. For, in 87 percent of the watersheds the decline in wastelands is to the extent of 5 to 20 percent and above. In some cases like groundwater the improvements are marginal, as the increase in groundwater is less than one percent in 56 percent of the watersheds. Higher proportion of watershed communities have reported higher level of impact in terms of vegetation cover, reduction in runoff and soil erosion, surface water and stream flows, which are inter linked positively.

Table 2.3: Impact on Bio-physical Indicators across Sample Watersheds

Indicator Level of Impact % of Watersheds No Change 45 Change in Land use pattern Investing more in good class lands 52 Moving to proper Land Use 3 Reduced 4 Nil 1 Increase in Groundwater <1 56 1 to 2 39 Nil 4 Increase in Stream flow < 5 44 5 to 10 53

Nil 4 <40 43 Runoff Reduction 40 to 80 52 >80 2 increased 3 Nil 7 Reduction in Soil erosion <20 45 25 to 50 43 >50 3 Nil 5 Increase in Surface water < 20 49 20 to 40 46 Increased 3 Nil 10 Decrease in Wastelands 5 to 10 56 10 to 20 29 >20 2 Nil 22 <10 15 Improvement in Vegetation 10 to 20 25 >20 38

When the performance of WSD is measured in terms of scoring, the overall score communities gave across the sample districts is 40 percent, which can be treated as satisfactory. Across the districts the scores range between 61 percent in Dausa and Bundi to 9 percent in Jaisalmer (Table 2.4). Of the fifteen sample districts ten have scored above forty, with high variations across the districts. Interestingly, increase in groundwater got good score (45 percent) despite marginal improvements. This could be due to the sever scarcity conditions in the districts, which might have caused positive response for any marginal improvements i.e., base is very low. In the case of groundwater increase the variations across the districts are also low. In most of the indicators medium rainfall and endowed districts are performing better in most of the indicators. When the performance is compared across the different schemes, communities rated the performance of IWDP watersheds far above the DDP watersheds, while the performance of DPAP watersheds is very close or to IWDP watersheds in most indicators (Table 2.5). In fact, in case of some indicators, DPAP watersheds perform better than IWDP watersheds. The differences in performance of watersheds across schemes are tested for significance using the ‘means t test’. The tests indicate that the difference in performance of IWDP and DPAP watersheds are not

significantly different. On the other hand, the performance of IWDP and DPAP watersheds are significantly higher than that of DDP performance. Table 2.4: Performance of Bio-physical Indicators in the Sample Watershed across Districts Increase Change Decreas stream / Increase Soil Increase Improvement Name of in land Runoff e Over spring Groundwat erosion Surface In vegetative districts use reduction wastela all flow er reduction water cover pattern nds period Baran 60 60 52 60 60 52 52 87 59 Dausa 67 52 56 60 60 52 60 93 61 Jaipur 40 44 48 44 52 36 36 60 44 SMPur 53 60 48 36 36 52 60 100 53 Dholpur 53 36 44 36 52 32 36 53 42 Bundi 67 60 52 60 68 60 44 93 61 Tonk 53 48 50 60 56 44 36 90 53 Rajsamad 18 33 45 39 23 33 19 64 34 Ajmer 67 52 56 60 60 52 52 80 58 Bikaner 27 32 28 24 28 32 20 27 24 Jalor 0 44 48 52 4 20 20 13 31 Jaislmer 0 14 16 14 4 14 0 0 9 Barmer 13 20 38 20 18 20 14 10 20 Sirohi 67 44 52 44 52 44 36 80 50 Udaipur 44 47 52 44 33 47 25 78 45 Over all 37 (58) 40 (32) 45 (24) 41 (36) 36 (52) 38 (35) 29 (52) 60 (55) 40 (38) Note: Figures in Brackets are coefficient of Variation. Table 2.5: Performance of Bio-physical Indicators across Schemes in the Sample Watersheds Indicators/Type of Scheme IWDP DPAP DDP Overall IWDP- IWDP- DPAP- DPAP DDP DDP Change in land use pattern 49 44 13 37 49-44 49-13* 44-13* Increase in stream / spring flow period 47 47 26 40 47-47 47-26* 47-26* Increase in Groundwater 51 49 33 45 51-49 51-33* 49-33* Runoff reduction 51 39 26 41 51-39* 51-26* 39-26* Soil erosion reduction 49 36 15 36 49-36* 49-15* 36-15* Increase in Surface water 45 44 22 38 45-44 45-22* 44-22* Decrease in wastelands 36 41 13 29 36-41 36-13* 41-13* Improvement in vegetative cover 78 84 18 60 78-84 78-18* 84-18* Over all 49 46 21 40 49-46 49-21* 46-21*

Note: * indicate significance at less than 10 percent level.

Economic Impact: For the sake of brevity some of the indicators like yield rates of non-cereal crops and various livestock activities are merged. Economic impacts as measured in various indicators reveal a moderate impact in majority of the watersheds. Increase in crop intensity is less than 10 percent in 90 percent of the watersheds (Table 2.6). Increases in crop yields are observed in the case of cereals, pulses and oil seeds. While cereals recorded less than 50

35 percent in most of the watersheds, in case of pulses and oil seeds the increase is less than 25 percent in majority of the watersheds. Besides, a substantial proportion of watersheds have recorded zero increase in pulses and oilseeds. On the other hand, none of the other crops recorded any increase in yields. Watershed development seems to have a greater impact on livestock economy, as reflected in the increased milk yields. Milk yields have gone up by more than 50 percent in 57 percent of the watersheds. Another important indicator of economic impact is additional employment generation. Additional employment has increased in 91 percent of the watersheds, but the increase is less than 20 percent in 72 percent of the watersheds. Though additional expenditure and debt reduction also reported, attributing the impact entirely to WSD could be difficult.

Communities’ perceptions about the economic impact of WSD are quite poor. The overall score the community accorded to economic performance is just 24 percent (Table 2.7). This is very low compared to the bio-physical performance. If 40 percent is considered as threshold level or satisfactory level, none of the sample districts crossed this threshold level. Among the indicators additional expenditure and increase in additional employment received as score or above. Though additional expenditure got a score of 76 percent, it may not be entirely due to WSD, as there could be due to other factors like inflation. In the case of all other indicators the scores are not only low but also show high inter district variations. For instance, increase in yield of cereals received a 40 percent score only in Dausa district. For most of the indicators scores are above 40 in the medium rainfall districts (above 500 mm). In the case of increase in employment, twelve of the 15 sample districts received more than 40 percent score. It may be concluded that WSD has a clear and prominent impact on employment across the districts. On the other hand, the scores given by the communities on various indicators do not fully commensurate with the improvements reported across watersheds.

Table 2.6: Impact on Economic Indicators in the Sample Watersheds

Indicator Level of Impact % of Watersheds Nil 9 Additional Employment < 20 72 20-40 19 Additional expenditures (Rs./per < 50 6 capita/year) 50-75 10 75-100 58

100 25

< 10 40

10 50 Increase in cropping intensity (%) 10-20 6 > 20 4 Nil 5 Increase in Yield of Cereals (%) < 50 93 50-100 2 Nil 39 Increase in Yield of Pulses (%) < 25 61 Nil 43 Increase in Yield of Oilseeds (%) < 25 57 Nil 87 Increase in Yield of Fruits (%) < 25 9 25-50 4 Nil 94 Increase in Yield of Vegetables (%) < 50 6 Nil 78 Increase in Yield of Cash Crops (%) < 25 22 Nil 13 < 50 30 Increase in Milk Yield (%) 50-100 40 > 100 17 Nil 41 Reduction in debt (%) 0-50 22 50-100 37 Nil 70 Reduction in workload (hrs/day) 1 30

Note: Zero responses are not presented

Table 2.7: Performance of Economic Indicators in the Sample Watersheds across Districts

Increas Improv Increase Additio e in Increas Increase ement Reductio Name of in nal croppin e in Reductio Over in Yield- in n in work districts employm expendi g Yield- n in Debt all Cereals Livesto load ent tures intensit Others ck y (%) Baran 56 90 30 33 14 44 50 27 35 Dausa 56 80 40 40 12 46 50 27 35 Jaipur 44 65 25 33 8 38 30 7 26 SMPur 56 80 30 33 13 28 20 13 29 Dholpur 44 75 30 33 13 48 20 13 29 Bundi 56 85 45 33 11 66 35 33 37 Tonk 46 90 48 37 9 32 23 23 29 Rajsamad 37 75 10 33 4 23 23 0 20 Ajmer 40 85 25 33 9 22 45 33 27 Bikaner 40 80 10 20 7 28 25 0 21 Jalor 40 75 10 33 4 32 20 0 21 Jaislmer 24 75 0 23 1 39 15 0 17 Barmer 24 60 0 33 6 26 3 0 16 Sirohi 40 70 10 33 1 38 15 13 21 Udaipur 37 68 15 33 4 23 23 2 20 Over all 40 (24) 76 (11) 19 (70) 32 (15) 7 (55) 33 (34) 24 (51) 10 (100) 24 (27)

Note: Figures in Brackets are coefficient of Variation.

Economic impacts across the schemes reveal that the performance of DPAP watersheds is as good as that of IWDP watersheds. IWDP watersheds perform significantly better only in the case of livestock. This is in line with the performance of bio-physical indicators. DDP watersheds have scored less than 40 percent in all the indicators, except in the case of expenditure. This sheds poor light on the DDP schemes. Though the reasons are not farfetched as DDP schemes are located in the harshest climatic conditions. Given the severe conditions, even the limited impact ought to be seen as a positive indication. However, this needs further probing of understanding the reasons behind the poor performance. Table 2.8: Performance of Economic Indicators across Schemes in the Sample Watersheds

Indicator IWDP DPAP DDP Overall IWDP- IWDP- DPAP- DPAP DDP DDP Increase in Crop Intensity 52 60 7 19 52-60 52-7* 60-7* Increase in Yield-Cereals 34 36 29 32 34-36 34-29* 36-29* Increase in Yield-Others 8 8 4 7 8-8 8-4* 8-4* Increase in Livestock 36 27 30 33 36-27* 36-30* 27-30 Increase in Employment 44 48 31 40 44-48 44-31* 48-31* Reduction in Work load 15 13 0 10 15-13 15-0 13-0 Increase in Expenditure 77 78 72 76 77-78 77-72 78-72 Reduction in Debt 30 20 16 24 30-20 30-16* 20-16 Overall 27 26 18 24 27-26 27-18* 26-18*

Note: * indicates significance at less than 10 percent level

Institutional Impacts: Institutional impacts of WSD assume significance from two angles for this study. One, number of studies have emphasised that the impacts are more in the watersheds with active peoples participation. Two, the new guidelines have emphasised participatory approach through institutional development at the watershed level. This study being one of the few state wide studies, it would be pertinent to examine the influence of the guidelines on the institutional aspects of the sample watersheds across the districts. Some of the indicators reflect the functioning of the institutional arrangements and others reveal the benefits from the institutional arrangements. One of the important indicators of active and sincere involvement of the communities in WSD is the contribution to watershed development funds. Earlier studies have revealed that the rule of beneficiary contribution is often flouted due to the low willingness of the communities to contribute (Reddy, et. al., 2005). But the obligation is met with back door mechanisms like taking from labour wage through paying under wages. It is heartening to note that contributions are made as per norms in 32 percent of the watersheds (Table 2.9). This is a significant achievement. Similarly, in 92 percent of the sample watersheds more than 50 percent of the CBOs are functional. On the other hand, social audit and benefit sharing mechanisms are absent in majority of the watersheds. But, the fact that these mechanisms exist in 39 percent of the watersheds is a positive sign. The impact of the active institutions is reflected in the quality and status of water harvesting structures. While the quality of the structures reported to be good and very good in 90 percent of the watersheds, the status of the structures is intact only in 31 percent of the watersheds. But, damages were reported only in 10 percent of the cases. Major problem seems to be silting up. The failure of local level watershed institutions to maintain their linkages with the line departments in the post completion period could weaken the sustainability of the institutions as well as benefit flows.

The better performance of WSD in terms of institutional indicators is further emphasised in the scoring exercise. Communities’ accord 57 percent score for the institutional performance, which is much above the bio-physical and economic performance (Table 2.10). None of the districts score less than 40 percent at the overall level. The scores vary between 83 percent in Dausa to 40 percent in Jaisalmer. Across the indicators, social auditing and benefit sharing mechanisms get marginally low scores (39 percent). Unlike in the case of frequency distribution, linkages with line departments get relatively high score of 69 percent. In the case of some indicators the scores are as high as 100 percent in some districts, which appears to be on the higher side (Table 2.10). In case of institutional performance in districts endowed with

better rainfall and access to irrigation performed better when compared to low rainfall districts. This is also reflected in the scheme wise performance. As in the case of other components, DDP districts perform poorly when compared IWDP and DDP districts (Table 2.11). But the performance of DDP watersheds is not significantly different from that of DPAP watersheds. This indicates, DDP schemes have narrowed the gap in terms of institutional performance. This is a good indicator of improving and sustaining the bio- physical and economic impacts in the medium to long run. The performance of DDP watersheds is particularly better in the case of quality of water harvesting structures and linkages with line departments. These two indicators again could sustain the impacts in the medium and long term.

Table 2.9: Impact on Institutional Indicators in the Sample Watersheds

Indicator Level of Impact % of Sample Watersheds Taken out of labour wages 17 Cash partly taken from labour wages 26 Contribution to WDF Cash partly paid by beneficiary 25 Beneficiary contributed as per norms 32 Poor 6 Satisfactory 4 Quality of WHS Good 60 Very Good 30 <50 8 Functioning of CBOs (%) 50-100 55 100 37 No 61 Social audit Yes 39 No 61 Benefit Sharing Mechanism Yes 39 No 58 Maintenance of CPRs Yes 42 Fully Damaged 6 Partially Damaged 4 Status of WHS Silted up 59 Intact 31 Never Existed 92 Bank linkages Ended with completion of WDP 8 Ended with completion of WDP 92 Line department linkage Continuing 8

Table 2.10: Performance of Institutional Indicators in the Sample Watershed across Districts

District Contrib Functio Social Benefit Mainte Quality Status Linkag Linkages Over ution to nal audit sharing nance of of es with with all WDF CBOs mechan of CPR WHS WHSs line Banks ism depart ments Baran 92 90 100 100 100 92 80 73 5 81 Dausa 100 90 100 100 100 92 80 73 5 83 Jaipur 80 50 40 80 80 84 75 67 0 67 SMPur 56 70 60 80 80 56 30 67 0 54 Dholpur 68 55 80 100 60 84 60 80 10 68 Bundi 72 100 100 100 80 100 100 80 10 82 Tonk 66 75 70 70 70 88 70 73 5 68 Rajsamad 52 73 13 7 0 84 60 67 0 53 Ajmir 84 80 100 100 100 88 70 67 0 75 Bikaner 24 40 40 40 40 64 40 67 0 42 Jalor 40 50 0 0 0 84 60 67 0 47 Jaislmer 38 48 0 0 0 62 37.5 67 0 40 Barmer 44 58 0 0 0 70 45 67 0 44 Sirohi 24 50 0 0 20 80 50 73 5 44 Udaipur 55 70 20 0 40 77 50 67 0 53 Over all 57 (39) 67 (27) 39 (85) 39 (88) 42 (77) 79 (15) 58 (31) 69 (7) 2 (139) 57 (26)

Note: Figures in Brackets are coefficient of Variation.

Table 2.11: Performance of Institutional Indicators across Schemes in the Sample Watersheds

Indicator IWDP DPAP DDP Overall IWDP-DPAP IWDP-DDP DPAP-DDP CWDF 67 55 41 57 67-55 67-41* 55-41 FCBO 73 70 54 67 73-70 73-54* 70-54* SA 57 40 9 39 57-40 57-9* 40-9* BSM 57 47 6 39 57-47 57-6* 47-6* MCPR 62 47 6 42 62-47 62-6* 47-6* QWHS 86 72 71 79 86-72* 86-71* 72-71 SWHS 69 43 46 58 69-43* 69-46* 43-46 PIAL 71 69 67 69 71-69 71-67* 69-67 BLINK 3 2 0 2 3-2 3-0* 2-0 Overall 66 55 44 57 66-55** 66-44* 55-44

Note: CWDF= Contribution to watershed development fund, FCBO= Functioning of community based organisations; SA= Social audit; BSM= Benefit sharing mechanisms; MCPR= maintenance of common pool resources; QWHS= Quality of water harvesting structures; SWHS= Status of water harvesting structures; PIAL= Linkages with the line department/PIA.; BLINK= Linkages with the banks.

Overall Performance: The overall performance is calculated using the communities scoring of different communities. The sample watersheds get an overall score of 38 percent, which ranges from 7 to 39. Distribution of districts by watersheds getting above and below average

scores is more or less equally distributed for all the components (Table 2.12). All the low rainfall and arid districts are in the below average category. The set of districts remain same across the components. As far as overall performance is concerned 48 percent of the watersheds perform above the threshold level score (40 percent) i.e., satisfactory performance. This appears very reasonable when compared to the meta analysis where only 35 percent of the watersheds have performed above average at the all India level (Joshi, et. al, 2004). Given the harsh climatic conditions in Rajasthan the performance is quite encouraging. If measured only in terms of economic benefits or performance only four percent of the sample watersheds could be ranked as better performing. In the case of bio- physical benefits 52 percent of the watersheds scored above the threshold level while 80 percent of the watersheds perform better in the case of institutional performance. The watershed wise performance varies widely, especially in the case of bio-physical indicators. Poor performance in some districts does not mean all the watersheds are performing low. For instance, despite very poor performance (even negative) in some watersheds in Jaisalmer district, some watersheds (2) reported above average economic performance (See appendix Table 2.2A). Such variations or deviations are highlighted in the case studies presented in the next section. Table 2.12: Distribution of Watersheds by their Performance

Impacts No. of Main Districts No. of Main Districts Average Range CV sample sample Score Watersheds watersheds above below average average Bio-physical 57 Baran, Dausa, 53 Rajasamand, 40 -17-83 50 Jaipur, SMPur, Jaisalmer, Jalore, Dholpur, Barmer, Bikaner, Bundi, Tonk Sirohi and Udaipur and Ajmer Economic 54 Baran, Dausa, 56 Rajsamad, Jaisalmer, 24 10-47 33 Jaipur, SMPur, Jalore, Barmer, Dholpur, Bikaner, Sirohi and Bundi, Tonk, Udaipur Ajmer Institutional / 51 Baran, Dausa, 59 Bikaner, Jalore, 57 19-73 37 Social Bundi, Tonk, Jaisalmer, Barmer, Jaipur and Sirohi, Udaipur, Ajmer Smpur, Rajsamad and Dholpur Overall 55 Baran, Dausa, 55 Rajasmand, Bikaner, 38 7-39 36 Jaipur, SMPur, Jalore, Jaisalmer, Dholpur, Barmer and Sirohi Bundi, Tonk, Ajmer and Udaipur

IV. Case Studies The purpose of the case studies is to highlight the issues, positive or negative, under varying agro-climatic situations. These case studies are based on the stakeholder interviews from some of the watersheds across the districts. In Rajasthan the performance of WSD seems to be predominantly dependent on the rainfall. WSD has been doing well where the rains have been good. In areas where the rainfall has been scanty the benefits are very limited. Perhaps due to this reason that there are not many success stories in the sample districts. The following case studies from the sample villages illustrate the various aspects of WSD performance in varying situations. The performance of WSD is closely linked to rainfall. But this does not mean that impact of watershed development is negative in the absence of good rainfall. The impact needs to be looked from the counter factual view. That is the situation could have been worse in the absence of the programme. While the benefits are manifold in the good rainfall regions due to increased availability of more water for irrigation (Box 1). Evidence also shows that impact of WSD can overcome or constrained by scarce rainfall conditions depending on the relative endowment situation in the specific locations (see Boxes 2 and 3). Box 1: Benefits from Check dams

Village: Dadia, District: Tonk

Dadia village has benefited a lot from the construction of a check dam. Due to good catchment area water gets collected in the check dam that is sufficient for irrigation. There is an increase in the livestock population. Agriculture employment has also gone up. Though contour bunding and check dams built on the common lands are not benefiting the individual farmers much but there is a definite growth in the crop output. There are also contour bunds constructed on private lands which have helped in reducing the runoff and soil erosion.

Box 2: No Rains but... Village: Dondlapura,District: Dholpur Dondlapura village has benefited significantly from the WSD. The major crops here are Bajra and Mustard. Farmers with less than 1 hectare of land are mainly growing mustard. They are growing 2 crops in a year. They also grow Bajra for the purpose of fodder for the livestock.

The village has a common land of 16 bighas (64 acres.) This land is on the banks of a river. Plantations were carried out on this land for the past many years but without much success. But with the construction of check dams on the farmers’ own lands and also due to the contour bunding of the farmlands the in situ moisture has improved and the yields have also improved.

As the rains in the past many years haven’t been good the water situation has not improved but the soil erosion has been completely arrested. The water runoff has also stopped completely. It is also expected that these check dams would help in significantly improving the water situation in the future. 43

Box 3: Constrained by Rains Village: Ajgara, District: Ajmer One check dam constructed in the village for the benefit of 10 to 15 wells surrounding the check dam. Prior to the WSD 100 bigha land was irrigated by 50 wells when there were good rains. Now only one well irrigates 4 bigha land as there was one good rain fall year during the past ten years. Benefits from the WSD were observed only when there is good rain. Fifty percent of the HHs have bunding in their farm lands and soil erosion has come down in these lands.

Watershed Committee meetings are held only when there is a need. One SHG was formed during the WSD intervention which is currently functional. The minutes of the meeting are said to be maintained but the chairman seemed to be unaware of the number of meetings held by the WSC.

Box 4: Can WSD Mitigate Drought? Village: Aajnota; District: Jaipur Construction of contour bunds, as part of WSD, was carried out in 30 percent of lands. And 2 to 3 check dams were also constructed. This helped in checking water flow in the farmlands resulting in enhanced crop yields. Though there is no increase in the irrigation facility due to WSD, increased moisture retention capacity of the soils resulting in double cropping.

Prior to WSD water in the wells levels were below 25 feet, which came up to 10 to 15 feet after the WSD. Farmers could extract water for only ½ hour earlier but after the WSD they are able to run their engines for up to 6 hours. This helped in growing vegetables like Chillies, Tinda, bottle gourd, etc. Prior to WSD there was less planting of maize but as water availability increased Maize cultivation has increased. The increased water levels have also reduced the drudgery of women. Earlier women used to draw water from very deep levels but now with the increase in the water levels in the wells women have to exert less for drawing water. Also since the water in the wells in the middle of the village was not good, they had to bring water from far off wells. Now they don’t have to.

As part of the program CPRs like the Grazing area which were lying idle earlier are now used for planting trees (about 10,000 plants) of Sheesham, Babool (6000 trees), Amla, Jamun and Ardu. There are also about 500 trees of RatnaJot (Jathropha). However about 2000 plants died due to drought.

Despite adverse climatic conditions and drought situation, there are instances where WSD proved to an affective drought coping mechanism in one of the sample villages. The communities’ active involvement seems to have helped in achieving overall improvement in the Ajanota village of Jaipur district (see Box 4). This was possible due to the active involvement of the watershed committee (WC) in the implementation of the watershed. The committee has 30 percent representation in the WC, which is very rare in Rajasthan. Perhaps due to this reason the community could construct a pond as part of WSD works and creating a facility for women to wash and bathe, though this pond was full only once (2008) during

44 the past ten years. The WC conducts one or two monthly meetings are held where discussions related to crops and farming are held and information about the status of contour bunding is also discussed. The absence of such active institutions or the absence of active community participation in Ranwasi village of Kishenganj Tehsil in Baran district is very much evident in the performance of the WSD (Box 5). On the other hand, institutional arrangements that are not designed to suit the local communities’ needs could result in adverse impacts. This is evident in the case of Karkala village in Bhim block of Rajsamand district (Box 6).

Box 5: Weak Institutions and Poor Performance

Village: Ranwasi, District: Baran The village Ranwasi is 9 to 10 kms away from Kelwad. With no road to the village it is one of the remotest villages in the study areas. Though the quality of water is good it is not sufficient for cultivation. Due to the water shortage agriculture in the village is declining in the village despite WSD. The villagers don’t see any benefits from the WS programme. As per the villagers the benefits are mostly to the downstream farmers only. Livestock is one of the major sources of livelihood for the villagers here.

Most of the villagers have no idea about the watershed Committe. Only 3 to 4 villagers are apparently involved in the WSD affairs. One check dam was constructed but there was no reduction in the soil erosion. The carrying capacity of the common lands being limited there is a shortage of fodder in the village.

Box 6: Institutions and Community Benefits Village: Karkala, District: Rajsamand Karkala village has about 150 HHs. Majority of them belong to OBCs. Under the WSD Water Harvesting Structures were constructed in the village. No major benefits are realized so far from the WSD intervention. Soil erosion has only reduced marginally. No maintenance activities are undertaken as the people think that it is the government’s responsibility. Although government is planting trees every year they are not surviving due to lack of rains. The village is facing shortage of fodder. There has been a decline in the number of livestock in the village over the years. Earlier HHs used to have 5 cows each but now the number is declining drastically (one cow per HH). People stopped selling milk. Some of the villagers opined that due to the restriction on the grazing activity (fines up to Rs. 1000) HHs are forced to sell their livestock.

V Conclusions This chapter looks at the performance of WSD from the communities’ perspective. This gives an aggregated view of the sample watersheds. Assessment is carried out at the indicator level and also across districts using the frequency distribution and scoring methods. The analysis brings out the following observations:

 The impacts of WSD on various indicators pertaining to bio-physical, economic and institutional are moderate in majority of the cases. Proportion of watersheds reporting negative or no impact is marginal in most cases.  Bio-physical and institutional impacts are more widespread across indicators, while economic impacts are limited to cereal crop yields, livestock and employment.  Performance as measured in scoring indicates that bio-physical and institutional impacts are more prominent when compared to economic impacts. This indicates that bio-physical and institutional impacts are not translated in to economic impacts.  There appears to be a clear linkage between resource endowments and WSD performance. That is performance levels are better in medium rainfall and irrigated districts when compared to arid districts. This vindicates the findings of meta analysis where the performance of watersheds are observed to be better in the 700-1100 mm rain fall regions. In the present case the performance of WSD is relatively better in the above 500 mm rainfall districts. And the average rainfall does not cross 900 mm is any of the districts of Rajasthan.  The case studies presented also high light the importance of rainfall and institutions in the performance of WSD in number of districts and watersheds. But the evidence also suggests that WSD can overcome the natural constrains with proper institutional arrangements.

APPENDIX Table A2.1: Important Agrarian Features of the Sample Districts

Source of District Soils Main Crops Livestock irrigation Kidney bean, Gram, Groundnuts, Wheat, Fine sand to loamy fine Bikaner Canal Rapeseed & Mustard, Cattle, buffaloes, sheep sand and goats Guarseed, Bajra, Rapeseed, Barley, etc. Soils are pale brown, Bajra, Guar seed, Jowar, single grained, deep profile Canal and wheat, rapeseed & Jaisalmer developed, texture and Sheep, goats and cattle wells mustard, groundnut and sandy type. These soils gram. belong to aridisols order. Groundwater, Over exploited in 62 % over Traditionally dominated Sandy and Very poor Bajra, Moth Pulses by small ruminants. Of Barmer exploited quality (kharif) and Cumi (rabi) late milch cattle are on blocks. Few the rise. villages get canal water. Oilseeds especially Soils are classified in mustard is the Aridisols order. At some predominant crop. Wheat, bajra, kharif Jalore places playas are observed Wells Cattle, buffaloes, sheep pulses, barley, jowar and and goats belonging to salids great seasmum are other crops group of Aridisols order. cultivated in this district.

Gray brown alluvial soil to non calcil brown soils and Kharif crops: bajra, brown soils of recent jowar, pulses, maize and Cattle, buffaloes, sheep Ajmer origin. Soils are sandy Wells groundnut. Rabi crops: and goats loam to sandy clay loam in wheat, barley, gram and texture. Fertility status of oilseeds. these soil is low. comparatively plain Kharif: Bajra, oilseeds Dholpur topography and a good soil Groundwater (til). Rabi:, cereals and Buffaloes base for agriculture oilseeds. Wheat, Bajra, Rapeseed Loamy, Clay,. Sandy and & Mustard, Barley, Cattle, buffaloes, sheep Jaipur Groundwater Sandy-loam Soils Groundnut, Gram, Jowar, and goats Maize and Sugarcane. Sandy loam to clay loam having a clear upper Kharif: bajra, maize, boundary of argillite Wells and groundnut and cotton. Cattle, buffaloes, sheep Dausa horizon. soils at places are affected by salinity- Canal Rabi: wheat, barley, and goats alkalinity. mustard and gram.

Alluvial in nature which is Wells and paddy, jowar, bajra, Cattle, buffaloes, sheep SMPur prone to water logging. canal maize, pulses, sesamum, and goats

Soils are grayish brown to groundnut, sugarcane, brown and yellowish red chillies, wheat, and brown with wide barley. variations in texture having a clear upper boundary of argillic horizon. Kharif : jowar, bajra, Soil is rich and fertile. maize, pulses and ground These soils can be Canals and Cattle, buffaloes, sheep Bundi nuts. Rabi: wheat, barley, classified into Inceptisol wells and goats gram, oil seeds and and Vertisol order pulses. Mainly Black-Kachari soil, Kharif: Pulses and which is highly fertile Soyabean along with found in the Baran and Tube wells, Jowar, Bajra and maize cattle, buffaloes and Baran Mangrol tehsils. Stony soil Wells and Rabi: mustard, gram and goats is found in the Southern & Canals coriander are grown with Eastern part of the district. wheat as Rabi crop.

Sandy but fertile. The soils are grayish brown to paddy, jowar, bajra, brown and yellowish maize, pulses, sesamum, brown with wide Wells and Cattle, buffaloes, sheep Tonk groundnut, sugarcane, variations in texture from Canals and goats sandy loam to loam. Some red chillies, wheat, and blocks have salinity and barley. alkalinity problem. Except some partially weathered rocks all types wheat, maize, jowar, of soils in this district are gram, pulses, sugarcane, Cattle, buffaloes, sheep Rajsamand wells moderately deep to deep. barley, groundnut and and goats Sandy loam and clay loam rice soils also exist. Kharif: Maize, Paddy, soil type varies from red Jowar, Urd, and Wells, canals cattle, buffaloes, goats, Udaipur loamy to sandy, gravelly to Groundnut. Rabi: Wheat; and ponds and sheep medium black soils. Barley, Gram and Mustard soils are rich in nutrients Millets, pulses, sesame, Goat, Sheep, Cattle and Sirohi having medium to high Wells and red chillies are the Buffaloes. fertile status. major crops

Table A2.2: Watershed Wise Performance

Type of Social Bio-physical Economic Over Dist Name Block Name Watershed Scheme Score Score Score all Baran Kishanganj Bislai 1 86 42 26 47 Baran Kishanganj Tagariya Dhani 1 73 61 35 54 Baran Kishanganj Bavergardh 1 93 72 40 65 Baran Kishanganj Hirapura 1 77 58 38 55 Baran Kishanganj Ranwasi 1 77 61 35 55 Dausa Dausa Chawand 1 73 58 29 51 Dausa Lalsot Beedoli 1 86 64 32 58 Dausa Lalsot Ranoli 1 84 83 44 69

Dausa Dausa Jirota kalan 1 86 31 32 45 Dausa Lalsot Aranya Kalan 1 86 67 37 60 Jaipur Phagi Ajnota 1 77 61 29 53 Jaipur Phagi Khera Hanumanji 1 59 53 22 43 Jaipur Phagi Beechi 1 34 22 22 25 Jaipur Phagi Chandama Kalan 1 82 25 28 40 Jaipur Phagi Hatheli 1 82 61 26 53 SMPur Khandar Beerpur 2 86 64 35 59 SMPur Khandar Pali 2 55 44 31 42 SMPur Khandar Baler 2 9 44 19 27 SMPur Khandar Talawara 2 86 58 32 55 SMPur Khandar Goth Bihari 2 32 56 26 39 Dholpur Dhaulpur Marha Bujurg 1 68 25 28 36 Dholpur Dhaulpur Kailashpura 1 70 42 26 43 Dholpur Rajakhera Dhodi ka Pura 1 55 28 24 33 Dholpur Dhaulpur Bintipura 1 52 58 28 46 Dholpur Rajakhera Nadauli 1 93 56 41 59 Bundi Hindoli Bhawanipura VI 1 77 61 41 58 Bundi Hindoli Pech ki Baori 1 93 64 40 62 Bundi Hindoli Umar V(Rosanda) 1 68 64 37 55 Bundi Hindoli Rigardi 1 93 56 34 57 Bundi Hindoli Ralayata 1 77 61 35 55 Tonk Tonk Mandawar 1 45 36 22 33 Tonk Deoli Chandsinghpura 2 77 58 32 53 Tonk Tonk Deoli 1 93 61 35 59 Tonk Tonk Dadiyan 1 86 39 25 45 Tonk Tonk Baroni 1 73 53 31 49 Tonk Deoli Kanwara III 2 84 64 47 63 Tonk Todaraisingh Borkhandi/Ojhapura 1 73 58 28 51 Tonk Todaraisingh Ralawata A 2 77 58 32 53 Tonk Todaraisingh Ralawata-C(Bassi) 2 36 39 21 32 Tonk Deoli Hanumanpura 2 36 61 21 40 Rajsamand Bhim Lasariya 3 55 31 19 32 Rajsamand Bhim Karkaro 3 55 25 22 31 Rajsamand Kumbhalgarh Jawariya 3 45 53 16 38 Rajsamand Bhim Thaneta 3 50 47 18 37 Rajsamand Nathdwara Parawal 1 73 42 25 43 Rajsamand Nathdwara Bara Bhanuja 1 55 44 25 40 Rajsamand Nathdwara Molela 1 45 28 18 28 Rajsamand Rajsamand Atma 1 36 47 10 31 Rajsamand Bhim Saroth 3 68 28 25 36 Rajsamand Rajsamand Keringji Ka Khera 1 50 28 21 30 Rajsamand Nathdwara Machind 1 45 25 18 27 Rajsamand Nathdwara Karai 1 36 22 18 24 Rajsamand Rajsamand Mandawada 1 82 33 25 42

Rajsamand Rajsamand Parasli 1 50 25 19 29 Rajsamand Rajsamand Dhani 1 50 25 19 29 Ajmir Sarwar Miyan 1 86 61 29 55 Ajmir Sarwar Heengtara 1 77 61 28 53 Ajmir Sarwar Ajgari 1 64 44 25 42 Ajmir Sarwar Ajgra 1 82 64 26 54 Ajmir Sarwar Bhatolao 1 68 61 28 51 Bikaner Bikaner Saroop Desar 3 59 44 26 41 Bikaner Bikaner Udai Ramsar 3 45 19 22 27 Bikaner Nokha Kakkoo 3 23 8 16 15 Bikaner Nokha Hansasar 3 23 -11 15 7 Bikaner Bikaner Raisar 3 59 58 25 46 Jalor Ahore Nosra 3 59 36 18 35 Jalor Ahore Neelkanth 3 36 39 19 31 Jalor Ahore Ghana 3 50 28 24 32 Jalor Ahore Barawan 3 45 19 18 25 Jalor Jalor Narpara 3 45 31 28 33 Jaisalmer Jaisalmer Kathodi 3 34 -6 13 11 Jaisalmer Jaisalmer Manglivawas 3 39 -6 25 16 Jaisalmer Jaisalmer Kuchhri 3 36 8 24 21 Jaisalmer Jaisalmer Kumhar kotha 3 45 19 16 24 Jaisalmer Jaisalmer Ramgarh 3 7 14 15 13 Jaisalmer Jaisalmer Kanoi 3 50 -17 16 11 Jaisalmer Jaisalmer Lanela 3 73 19 18 32 Jaisalmer Jaisalmer Loonon ki Basti 3 7 14 19 14 Jaisalmer Jaisalmer Dedha 3 50 19 12 24 Jaisalmer Jaisalmer Baramsar 3 55 19 13 26 Barmer Pachpadra Gharoi Nadi 3 45 19 15 24 Barmer Pachpadra Kharwa 3 73 31 18 36 Barmer Pachpadra Kalawa 3 34 14 13 18 Barmer Pachpadra Sinli Chauseera 3 7 19 16 15 Barmer Pachpadra Mewa Nagar 3 7 14 15 13 Barmer Siwana Indrana 3 36 22 21 25 Barmer Siwana Harmalpur 3 45 17 15 23 Barmer Siwana Khandap 3 73 19 16 31 Barmer Pachpadra Bhandiyawas 3 73 25 19 34 Barmer Siwana Ramniya 3 50 19 13 24 Sirohi Pindwara Muri I 1 45 58 21 41 Sirohi Pindwara Muri II 1 36 56 15 36 Sirohi Pindwara Khari Gegarwa 1 36 39 25 33 Sirohi Pindwara Kerlapadar 1 45 39 22 34 Sirohi Pindwara Viroli 1 57 58 25 46 Udaipur Kherwara Bhauwa 2 50 22 19 28 Udaipur Kherwara Maliphala 2 55 33 16 32 Udaipur Kherwara Bao 2 45 28 22 30

Udaipur Kherwara Kalkardurga 2 45 42 19 34 Udaipur Ballabhnagar Padmela 1 68 58 10 43 Udaipur Ballabhnagar Wilkawas 1 45 61 18 41 Udaipur Girwa Karmal 1 68 58 26 49 Udaipur Ballabhnagar Mansing Pura 1 18 42 19 28 Udaipur Ballabhnagar Bhopa Sagar 1 77 58 24 50 Udaipur Sarada Badawali 1 50 50 24 40 Udaipur Sarada Rathora 1 45 33 19 31 Udaipur Sarada Gudiya wara 1 45 58 22 42 Udaipur Sarada Intali 1 68 56 21 46 Udaipur Kherwara Dabaycha 2 45 22 15 25 Udaipur Sarada Bhorai 1 64 47 21 41 Over all 57 40 24 38

CHAPTER III Watershed Development Programme: Bio-physical Impact

I Introduction Poverty is multi-dimensional and hence poverty reduction efforts have to be multi-pronged and are expected to show impact on wide and diverse targets. Watershed development encompasses three distinct and inter linked components viz., bio-physical or environmental, economic and institutional. In the context of watershed development environmental and economic factors are intertwined due the organic linkages between natural resource base and the factors of production. Institutional or social component, on the other hand, work as a catalyst to stimulate and enhance bio-physical / environmental as well as economic impacts. In fact, institutional factors are seen as crucial for effective and sustainable impact of watershed development in the long run. For the present analysis, impact indicators are grouped under these three components. The present exercise is an attempt to assess the impact of watershed development in Rajasthan across districts and schemes by sections of farming community i.e., small farmers and large farmers. In this chapter we assess the impact of watershed development on bio-physical or environmental factors across the different assessment units.

Sustainable usage of natural resources is essential to realize the sustainable agricultural growth and development to meet the food needs of the growing population. In this context, the concept of watershed and bio-physical concerns become quite useful. The main watershed development components such as contour bunds, nala bunds, check dams, and vegetative barriers are targeted at reducing soil erosion, runoff reduction, increasing moisture content of the soil, etc. These in turn will have an impact on availability of water (for drinking as well as irrigation), facilitating vegetative growth resulting in improved availability of fodder, fuel wood, etc. However, the intensity of these impacts are linked to the soil conditions, rainfall, etc. These impacts are assessed using several indicators in each case. The important bio- physical or environmental impacts assessed here are in terms of changes in availability of water, drinking as well as irrigation, fodder, fuel wood, etc.

The pre prepared questionnaire was framed in such a way that households are enquired about the improvements in various indicators in terms of pre and post WSD. That is households are asked to assess the impacts due to the implementation of WSD i.e., after WSD situations in

comparison with before. Impacts are assessed at three levels namely across different districts, across the two size classes and across different schemes. Though one of the objectives was to assess the differential impacts of NGO and GO implemented watersheds, there were no NGO implemented watersheds in the sample districts and hence NGO-GO analysis is not attempted here. As explained in the methodology section (chapter I) the impact is assessed using frequency distribution of farmers and the scores given by the sample farmers for each indicator. II District-wise Analysis Soil Erosion Figure 3.1: Impact of WSD on Soil Erosion across Sample Districts

70 60 50 40 30 % HH 20 10 0 Swai Bara Daus Jaipu Dhol Bund Rajsa Ajmi Bika Jaisl Barm Siroh Udai madh Tonk Jalor n a r pur i mad r ner mer er i pur pur Increased 1 0 0 8 0 1 0 4 2 6 1 7 6 0 3 Nil 7 11 15 21 6 5 10 23 6 41 19 53 35 4 15 <25 23 23 31 16 20 21 11 25 21 24 28 27 37 26 24 25-50 39 29 29 30 38 35 44 40 56 30 45 12 22 62 54 >50 31 36 25 25 36 38 35 8 15 1 7 1 1 8 5

Checking soil erosion is one of the main objectives of the soil conservation techniques that are central to watershed development. Changes in soil erosion due to watershed development (WSD) is measured in terms of (i) increased erosion, (ii) no change and (iii) reduction in soil erosion to the extent of 25 percent; (iv) 25-50 percent and (v) above 50 percent. The best performing watersheds are those where soil erosion was reduced by more than 50 percent and the worst performing are the ones where there is an increase in soil erosion. Frequency distribution of farmers reporting increased soil erosion is marginal in all the districts, except in Jaisalmer. We may safely conclude that WSD has not caused any adverse impact on soil erosion (Fig. 3.1). On the other hand, more than 50 percent of the farmers in 14 of the fifteen districts have reported a reduction in soil erosion to the extent of more than 25 percent. In the case of Jaisalmer, Bikaner and Barmer districts in the arid zone, substantial number of farmers have reported that there is no reduction in soil erosion. Highest reduction is reported

from the humid south eastern plains districts of Bundi, Baran and semi-arid eastern plains districts of Tonk and Dausa along with Dholpur from the flood prone zone. On the whole, impact of WSD on soil erosion is prominent in the districts with rainfall ranging between 500 and 900 mm. Overall impact is positive in all the districts except Jaisalmer. Runoff Reduction

Figure 3.2: Impact of WSD on Runoff Reduction across Sample Districts

80 70 60 50 40

% of HH 30 20 10 0 Swai Rajs Bara Daus Jaipu Dhol Bun Ajmi Bika Jaisl Bar Siro Udai mad Tonk ama Jalor n a r pur di r ner mer mer hi pur hpur d Nil 6 4 17 24 4 3 9 11 3 19 4 42 19 3 6 <40 35 26 35 19 46 23 15 28 18 47 40 41 57 47 34 40-80 35 40 32 37 46 44 38 59 70 34 40 16 17 44 56 >80 24 30 16 20 4 30 38 3 9 0 15 1 7 7 4

Runoff reduction is another important objective of water conservation techniques of WSD. Here the impact is assessed in terms of (i) no reduction (NIL) and (ii) reduction to the extent of less than 40 percent, (iii) 40-80 percent and (iv) above 80 percent. Of the 15 districts only Jaisalmer recorded substantial proportion of farmers reporting no reduction in runoff indicating a positive impact in most of the sample districts (Fig. 3.2). More than 50 percent of the farmers reported more that 40 percent reduction in runoff in 11 of the 15 districts. As in the case of soil erosion here also the arid districts of Jaisalmer, Barmer and Bikaner where more than 65 percent of the sample farmers reported less than 40 percent (including zero) reduction in runoff. This indicates that arid climatic conditions are technically less responsive when compared to medium rainfall zones. How these technical impacts reflect in their impact on related indicators like water, fodder, fuel, etc., need to be assessed.

Drinking water

Figure 3.3: Impact of WSD on Drinking Water across Sample Districts

100 90 80 70 60 50

% HH 40 30 20 10 0 Swa Rajs Bara Dau Jaip ima Dho Bun Ton Ajm Bika Jalo Jaisl Bar Siro Uda ama n sa ur dhp lpur di k ir ner r mer mer hi ipur d ur Less 1 0 1 0 4 0 3 8 0 17 35 41 55 2 1 Adequate 56 43 78 78 73 65 76 72 63 73 62 58 39 88 86 Adequate with Quality 43 56 21 22 23 35 22 20 37 10 3 1 6 10 14

Access and availability of drinking water is the most strident in most of the rainfed regions. Provision of quality drinking water in adequate quantities is an important aspect of WSD. Availability of drinking water in the sample households is assessed at three levels viz., (i) less, (ii) adequate and (iii) adequate with quality. Majority of the households reported that drinking water is available in adequate quantities in all but Barmer district. Barmer falls in

the less than 500 mm rainfall region. At the same time in only one district (Dausa) more than 50 percent of the households reported availability of adequate quantity and quality of drinking water. This is despite the fact that Dausa falls in the semi-arid zone with a rainfall of 500-600 mm. Districts like Baran and Bundi falling in the rainfall zone of 600-850 mm (humid south-eastern plains) also have substantial proportion of households (35 and 43 percent respectively) reporting adequacy of drinking water in quantity as well as quality terms. Baran and Dausa seem to be ideal cases where the reporting on quantity matches that on the quality. In all other districts there is a clear mandate on the quantity but not on the quality. Jalor, Jaisalmer and Barmer need to be noted for the conflicting report on the quantity along with a very poor mandate on the quality. Irrigation

Figure 3.4: Impact of WSD on Irrigation across Sample Districts

120 100 80 60

% of HH 40 20 0 Swai Bara Daus Jaipu Dhol Bund Rajsa Ajmi Bika Jaisl Barm Siroh Udai madh Tonk Jalor n a r pur i mad r ner mer er i pur pur decline 18 19 31 15 32 7 26 77 43 84 94 98 99 92 81 10 to 20 33 27 18 19 24 33 26 10 19 13 6 1 1 4 10 20-30 22 28 29 30 20 38 31 7 29 3 0 0 0 4 8 >30 26 25 22 36 24 21 17 7 9 0 0 0 0 0 1

Improved irrigation facilities arguably hold the key for success of agriculture and WSD in the context of low rainfall and rain fed regions like Rajasthan. Despite the reasonably positive impact of WSD on soil erosion, runoff and drinking water, irrigation impact is marginal. In fact, in 7 out of 15 sample districts 77 - 92 percent of the farmers reported a decline in irrigation (Fig. 3.4). All the 7 districts fall (fully or partially) in the low rainfall zone. The worst are the districts like Barmer and Jaisalmer (arid zone), which have almost 100 percent households reporting decline in irrigation. Improved irrigation is reported mainly from the humid and endowed (surface irrigation) districts. In three districts more than 25 percent of the households reported more than 30 percent increase in irrigation. The evidence on irrigation is distributed evenly across the 3 levels of impact in 7 out of 15 districts. Thus it is safe to

conclude that majority of farmers in these districts felt that the WSD has a positive but limited impact on irrigation. In the remaining districts the evidence is conclusive to say that there has been a decline in irrigation despite the advent of WSD.

Poor performance of WSD in improving irrigation reflects the low rainfall pattern and high dependence on groundwater in Rajasthan. As observed in earlier studies (Despande and Reddy, 1991; Joshi, et. al, 2004; Reddy, Kumar and Rao, 2005), performance of WSD is often better in the medium rainfall (700-900 mm) regions. Besides, given the high dependence and exploitation of groundwater, irrigation growth is reaching limits in most parts of Rajasthan. In fact, groundwater exploitation rates crossed 100 percent in most parts (especially in arid parts) of Rajasthan (Reddy, 2010). Therefore, the limitation of WSD in improving irrigation needs to be understood in the context of low rainfall regions like Rajasthan. What needs to be examined in the districts reporting a decline in irrigation is whether there has been any improvement in assurance of irrigation or if there has been a decline in well failures if any before.

Vegetation Figure 3.5: Impact of WSD on Vegetation across Sample Districts

120 100 80 60

% of HH 40 20 0 Sw Raj Dh Bik Jais Ud Bar Da Jai aim Bu To sa Aj Jal Bar Sir olp ane lme aip an usa pur adh ndi nk ma mir or mer ohi ur r r ur pur d < 25 48 27 63 34 64 40 43 57 51 84 89 10 91 59 55 25-50 52 63 34 65 35 58 54 43 48 16 11 0 8 31 45 > 50 0 11 3 1 1 2 2 0 1 0 0 0 0 10 0

Common pool resources (CPRs) play an important role in the livelihoods of rain fed communities. Maintenance and management of CPRs also assumes an important role among these communities. CPRs mainly supplement the fodder and fuel wood needs of the communities. The health of the CPRs is often reflected in the vegetative cover. Majority of the households (more than 50 percent) reported that the impact of WSD on the vegetative

57 cover is less than 25 percent in 10 out of the 15 districts. This is more so in the case of arid districts of Jaisalmer, Barmer, Jalore and Bikaner. Let us examine how the impact of vegetative cover is translated in to the impact on fodder, fuel wood and manure.

Fodder

Figure 3.6: Impact of WSD on Fodder across Sample Districts

100 90 80 70 60 50 40 % of HH 30 20 10 0 Swai Rajs Bara Dau Jaip Dhol Bun Ton Ajm Bika Jaisl Bar Siro Udai mad ama Jalor n sa ur pur di k ir ner mer mer hi pur hpur d Less 14 10 28 7 30 20 14 9 7 10 0 36 16 6 1 Adequate 76 69 69 72 67 63 75 82 83 89 89 60 76 79 84 Excess 10 21 4 21 3 17 11 10 11 1 11 3 8 15 15

Figure 3.7: Impact of WSD on Adequacy of Feeds and Fodder across Sample Districts

70 60 50 40 30 % of HH 20 10 0 Swai Bara Daus Jaipu Dhol Bund Rajs Ajmi Bika Jaisl Bar Siroh Udai mad Tonk Jalor n a r pur i amad r ner mer mer i pur hpur < 25 45 24 47 8 58 25 25 44 25 37 42 53 35 34 37 25-50 31 42 36 49 30 42 44 46 58 57 43 38 57 55 58 > 50 24 35 17 43 12 33 30 10 17 6 15 9 8 12 5

There has been an increase in livestock in some of the districts where as some districts have experienced a change in the composition of livestock and in yet others there has been a decline in the livestock (see district profiles in the third chapter). The data in Fig 3.6 clearly

shows that the conditions have definitely been conducive for a growth in livestock across all the districts as more than 75 percent of the farmers across all the districts have reported adequate to excess availability of fodder (Fig. 3.6). A healthy percentage of farmers also reported an excess in fodder availability across all the districts. There is a slight conflict in evidence in few districts like Jaipur, Dholpur, Bundi and Jaisalmer where a substantial proportion of farmers reported less than adequate availability of fodder. It would be interesting to see if there is any difference in reporting between the LMF and SMF size classes. A major proportion of farmers in Ajmer, Bikaner, Barmer, Sirohi and Udaipur have reported an increase of 25 to 50 percent in fodder availability (Fig 3.7). Majority (more than 50 percent) of the households in Dholpur and Jaisalmer reported less than 25 percent in adequacy. A large percent of farmers (more than 1/3rd) in Dausa, Swaimadhapur, Bundi and Tonk felt that there was more than 50 percent increase in availability of fodder. On the whole it can be concluded that WSD did have a positive impact on the availability of fodder.

Fuel wood

Figure 3.8: Impact of WSD on Fuel Wood across Sample Districts

120 100 80 60

% of HH 40 20 0 Swa Raj Jais Bar Dau Jaip ima Dho Bun Ton Aj Bik Jalo Bar Siro Uda sam lme an sa ur dhp lpur di k mir aner r mer hi ipur ad r ur Less 2 6 7 8 34 13 19 14 8 8 3 17 7 13 7 Just Enough 88 77 83 74 64 70 73 81 83 92 96 83 93 80 90 Adequate 10 18 9 18 3 17 9 5 9 0 1 0 0 7 3

In case of fuel wood WSD seems to have a more even distribution of impact across the districts. More than 80 percent of the households in 11 out of 15 districts reported just enough fuel wood (Fig. 3.6). Given the precarious weather conditions coupled with poor tree cover in the state, this reflects a positive impact of WSD in most of the districts. The proportion of households reporting less is not substantial in most of the districts. It is heartening to note that fuel wood situation appears to be better in the arid districts of Jaisalmer, Barmer, Jalore and

Bikaner when compared to other districts. One reason could be that the high proportion of cultivable waste lands in districts of Jaisalmer and Bikaner (Chapter 1). Given that a large percentage of farmers depend on CPRs for their fuel wood requirements in most of the districts, especially arid, it is a clear sign of improvement in the conditions of CPRs.

Manure

Figure 3.9: Impact of WSD on Manure across Sample Districts

90 80 70 60 50 40

% of HH 30 20 10 0 Sw Raj Dh Bik Jais Ud Bar Da Jai aim Bu To sa Aj Jal Bar Sir olp ane lme aip an usa pur adh ndi nk ma mir or mer ohi ur r r ur pur d Less 62 49 48 73 73 43 81 67 33 72 49 71 59 31 36 Adequate 24 42 48 27 21 44 17 32 67 28 51 29 41 66 63 More 14 9 3 0 6 14 2 1 0 0 0 0 0 3 0

Majority of the households in fifty percent of the sample districts reported limited impact of WSD on manure (Fig. 3.9). Proportion of households reporting ‘more’ is marginal in all the districts. This indicates that the impact of WSD on manure is quite low in the state. One reason could be the declining livestock population coupled with the changing composition of livestock in the state. Livestock composition is changing towards small ruminants could adversely affect the availability of left over forage and stubs that make the manure.

Overall Performance of WSD For the purpose of assessing the absolute and relative performance of the WSD the scores received for each indicator of the bio-physical or natural factors are calculated in terms of percentages. The performance of WSD in terms of bio-physical or environmental impact is assessed by estimating the overall actual score as a percentage of maximum score. The score, hence, ranges between ‘0’ and ‘100’. In the case of bio-physical or environmental impacts ten indicators were used. The scores across the districts emphasise the observations made in terms of frequency distribution of farmers by their assessment of impact. The overall performance level of the 15 districts in Rajasthan is 43 percent (Table 3.1). The performance

varies widely across sample districts i.e., from 24 percent in Jaisalmer to 58 percent in Dausa. In 11 out of 15 sample districts the overall score is above 40. As observed in the earlier discussion performance levels are poor in the arid districts of Jaisalmer, Barmer, Bikaner and Jalore.

Table 3.1: Average Performance of WSD across Districts (% score)

District Soil Runoff Drinking Irriga- Vegeta Fuel Manure Fish Adequac Overa Erosion Water tion tion Fodd y of ll er Feeds & Fodder Baran 71 68 71 59 26 48 54 26 0 39 54 Dausa 70 73 78 60 42 56 56 30 0 56 58 Jaipur 62 58 60 52 20 38 51 27 11 35 45 Swaimadhapur 51 58 61 68 33 57 55 13 0 68 52 Dholpur 74 61 59 51 19 37 35 16 25 27 45 Bundi 75 75 68 67 31 49 52 35 0 54 57 Tonk 74 74 60 53 29 48 45 10 2 53 52 Rajsamad 49 61 56 17 22 51 45 17 3 33 40 Ajmir 67 71 69 41 25 52 51 33 7 46 52 Bikaner 32 49 46 8 8 45 46 14 0 35 34 Jalor 54 66 34 3 6 56 49 26 18 37 39 Jaislmer 19 33 30 1 0 33 42 15 0 28 24 Barmer 32 48 26 0 4 46 47 21 13 36 31 Sirohi 67 63 54 4 26 55 47 36 17 39 47 Udaipur 56 63 56 12 22 57 48 32 4 34 46 Over all 54 60 53 27 20 49 48 23 5 39 43 (32) (19) (28) (86) (60) (16) (11) (39) (117) (29) (22)

Note: Figures in brackets are coefficient of variation

Across the indicators only soil and water conservation methods along with drinking water get reasonably good scores (above 50). Runoff reduction scores are high followed by soil erosion and drinking water (Table 3.1). Among the important indicators vegetation cover and irrigation get very low scoring. While irrigation got better scoring in the endowed and medium rainfall districts, it scores poorly in the arid districts. On the other hand, vegetative cover scoring is poor across the districts. Variations across the districts are low except in the case of availability of fish, irrigation and vegetative cover.

III Size class-wise Analysis WSD is basically a land based intervention. It is often argued that WSD benefits the large and medium farmers more than that of small and marginal farmers. This is mainly due to the reason that large and medium farmers have better access to quality land resources and their 61 ability (financial) to invest in irrigation equipment. For WSD is expected to strengthen and enhance soil and water resources. At the aggregate level the composition of sample farmers in terms of SMF and LMF is 65: 35 respectively, though wide variations are observed across districts and schemes (Chapter 1).

In this section the differential impacts between small and marginal farmers (SMF) and large and medium farmers (LMF) are examined with respect to bio-physical or environmental indicators. Here we examine only those indicators in which there are differences between size classes. In the case of soil erosion and runoff reduction the impact seems to be mixed (Figs. 3.10 and 3.11). While there is no difference in the case of negative impact of WSD on soil erosion, small and marginal farmers have reported better impact of WSD at 25-50 range (Fig. 3.10). Similarly, SMF reported relatively higher impact in 40-80 range in the case of runoff reduction when compared to their counter parts (Fig. 3.11). In the case of drinking water SMF have benefited more when compared to LMF (Fig. 3.12). As observed in the case of district wise analysis, majority of the households (62 percent) indicated that irrigation declined after the WSD. Among the remaining households impact of WSD on irrigation has a marginally better impact in the lower range (10-30) among SMF while it has a better impact on LMF at the higher range (above 30) (Fig. 3.13). This indicates that large farmers are able to gain more from the irrigation impact of WSD, which could be attributed to their better investment capabilities in groundwater exploitation.

Figure 3.10: Impact of WSD on Soil Erosion by Farm Size Classes

45 40 35 30 25 20 % of HH 15 10 5 0 Increased Nil <25 25-50 >50 SMF 3 21 22 39 15 LMF 3 18 27 36 15 Total 3 20 24 38 15

Figure 3.11: Impact of WSD on Runoff Reduction by Farm Size Classes

50 45 40 35 30 25 20 % of HH 15 10 5 0 Nil <40 40-80 >80 SMF 13 31 44 12 LMF 12 39 36 12 Total 13 34 41 12

Figure 3.12: Impact of WSD on Drinking water by Farm Size Classes

80 70 60 50 40

% of HH 30 20 10 0 Less Adequate Adequate with Quality SMF 9 71 20 LMF 19 62 18 Total 13 68 19

Figure 3.13: Impact of WSD on Irrigation by Farm Size Classes

70 60 50 40 30 % of HH 20 10 0 decline 10 to 20 20-30 >30 SMF 62 14 15 9 LMF 63 13 12 12 Total 62 14 14 10

With regard to vegetation cover, majority of the households indicated that the impact of WSD is below 25 percent (Fig. 3.14). While more of LMF observed that the impact is in the <25

63 range when compared to SMF and vice versa in the 25-50 range. The benefits from vegetation in terms of availability of fodder and adequacy of feed and fodder are neutral. Whereas, the benefits accrued in terms of fuel and manure are more to LMF than SMF (Figs 3.15 and 3.16).

Figure 3.14: Impact of WSD on Vegetation by Farm Size Classes

80 70 60 50 40

% of HH 30 20 10 0 < 25 25-50 > 50 SMF 57 42 2 LMF 69 30 1 Total 61 38 2

Figure 3.15: Impact of WSD on Fuel by Farm Size Classes

90 80 70 60 50 40

% of HH 30 20 10 0 Less Just Enough Adequate SMF 12 81 6 LMF 9 85 6 Total 11 83 6

Figure 3.16: Impact of WSD on Manure by Farm Size Classes

70 60 50 40 30 % of HH 20 10 0 Less Just Adequate More SMF 58 38 4 LMF 52 46 2 Total 56 41 3

Differences in the WSD impact between size classes and indicators were further assessed using the scoring method. Since the absolute differences could be misleading, we have examined the statistical significance of the differences using the ‘means t’ test. Over all the differential impacts between SMF and LMF are significant only in a third of the cases (Table 3.2). There is no set pattern of the impact in terms of benefit flows. That is the impact of WSD is neither in favour nor against any particular group though variations can be observed across the districts. At the aggregate level SMF seem to have gained more in the case of runoff reduction, drinking water and vegetative cover, while LMF gained more in terms of fuel and manure. Across the districts, LMF have gained more in terms of most indicators in six (Baran, Tonk, Bikaner, Jalore, Jaisalmer and Udaypur) of the 15 sample districts, while SMF gained more in only one district (Dholpur). As far as the overall impact at the district level is concerned LMF have reported significantly better impacts in five districts while SMF reported significantly better impact in two of the districts. In the remaining eight districts the differences are not statistically significant. Majority of the districts where LMF benefited more are from arid and low rainfall regions. This points towards a disturbing fact that benefits from WSD in poor and backward regions not only low but are mostly cornered by large farmers resulting in aggravation of inter and intra regional inequalities.

Table 3.2: Performance of WSD between Size Class of Farmers (SMF-LMF)

District Soil Runoff Drinkin Irriga- Fuel Manure Vegetat Adequ Overall Fodder Erosion g tion ion acy of

Water Feeds

Fodder SMF- SMF- SMF- SMF- SMF- SMF- SMF- SMF- SMF- SMF- LMF LMF LMF LMF LMF LMF LMF LMF LMF LMF Baran 70-75 67-69 68-83* 57-66 45-60* 54-56 24-38 24-37* 35-60* 52-62* Dausa 69-71 72-74 81-73* 62-58 56-55 56-56 31-28 42-41 54-58 58-58 Jaipur 69-58* 67-51* 56-63* 59-48* 38-38 48-53* 29-27 17-21 39-32 47-44* Swaimadhapur 56-45 57-58 60-63 70-66 55-60 54-57 14-13 33-34 67-68 52-52 Dholpur 73-75 68-57* 64-56* 58-47 44-30* 41-28* 13-19 31-9* 44-17* 49-41* Bundi 75-100 75-83 68-50* 67-0 49-0 52-0 35-0 31-0 54-100 57-58 Tonk 73-75 72-78* 61-57 49-63* 42-61* 39-57* 1-28* 28-33* 50-59* 49-58* Rajsamad 49-0 61-0 56-0 17-25 51-0 45-25 17-0 22-0 33-0 40-20 Ajmir 72-65 70-71 74-67 25-44* 57-51 55-50 18-36* 21-26 39-48 52-52 Bikaner -4-54* 31-61* 31-57* 2-12* 10-49* 18-49* 0-16 9-7 24-42* 16-43* Jalor 45-57* 68-65 47-31* 4-2 50-57* 50-49 15-28* 4-6 28-39* 38-39 Jaislmer 16-25* 30-38* 28-33* 1-1 32-36* 42-42 12-18* 0-0 30-26 23-26* Barmer 30-33 47-48 24-26 0-1 43-47* 46-47 20-21 6-4 40-35 30-31 Sirohi 67-0 63-63 54-0 4-0 55-0 47-0 37-0 26-0 39-0 47-42 Udaipur 54-65* 62-70* 56-59 11-16 57-55 48-47 32-29 23-20 32-43* 45-48* Over all 54-54 61-59* 55-49* 27-28 49-48 47-49* 23-25* 23-16* 39-40 44-43

Note: SMF= Small and Marginal farmers; LMF= Large and Medium farmers * Indicates that the differences are significant at less than 10 percent level.

IV Scheme-wise Analysis WSD is being implemented in Rajasthan under three different schemes, namely, Drought Prone Area Programme (DPAP), Integrated Wasteland Development Programme (IWDP) and Desert Development Programme (DDP). Of the total 110 sample watersheds spread over 15 districts, 60 watersheds were implemented under IWDP; 15 watersheds under DPAP and the remaining 35 under the DDP schemes. DDP districts include Barmer, Bikaner, Churu, Jaisalmer, Jalore, Jhunjhunu, Jodhpur, Nagaur, Pali and Sikar.

Wide variations could be observed in terms of WSD impacts across schemes. The differences between schemes are consistent across indicators and also method of assessment i.e., frequency distribution and scoring. Over all the performance of IWDP watersheds are relatively better in the case of most indicators (Figs. 3.17 to 3.25). The performance of DDP watersheds is poor for all the indicators. In fact, 92 percent of the sample households reported a decline in irrigation (Fig. 3.20) and 35 percent reported less availability of drinking water

(Fig. 3.19) in the DDP watersheds. On the other hand, DPAP watersheds reported marginally better impact when compared to IWDP watersheds in a few indicators like fodder and fuel (Figs. 3. 22 to 3.24). However, this is not to say that DDP watersheds do not have any impact. Except in the case of irrigation, majority of the farmers (more than 50 percent) reported improvement in all the indicators including vegetation. And the decline in irrigation among the DDP watersheds could be mainly due to the poor groundwater governance (over exploitation).

Figure 3.17: Impact of WSD on Soil Erosion across Schemes

50 45 40 35 30 25 20 % of HH 15 10 5 0 IWDP DPAP DDP Increased 2 5 5 Nil 11 22 36 <25 20 25 31 25-50 45 34 25 >50 22 14 3

Figure 3.18: Impact of WSD on Run off Reduction across Schemes

60 50 40 30

% of HH 20 10 0 IWDP DPAP DDP Nil 7 16 22 <40 28 32 46 40-80 49 40 27 >80 16 12 5

Figure 3.19: Impact of WSD on Drinking Water across Schemes

90 80 70 60 50 40

% of HH 30 20 10 0 IWDP DPAP DDP Less 3 0 35 Adequate 72 81 57 Adequate with Quality 25 18 8

Figure 3.20: Impact of WSD on Irrigation across Schemes

100 90 80 70 60 50 40 % of HH 30 20 10 0 IWDP DPAP DDP decline 46 57 92 10 to 20 19 13 5 20-30 21 14 2 >30 14 16 1

Figure 3.21: Impact of WSD on Vegetation across Schemes

100 90 80 70 60 50 40 of% HH 30 20 10 0 IWDP DPAP DDP < 25 50 51 87 25-50 48 49 13 > 50 2 1 0

Figure 3.22: Impact of WSD on Fodder across Schemes

90 80 70 60 50 40 % of HH 30 20 10 0 IWDP DPAP DDP Less 12 4 19 Adequate 76 77 75 Excess 12 19 6

Figure 3.23: Impact of WSD on Adequacy of Feeds and Fodder across Schemes

60 50 40 30

% of HH 20 10 0 IWDP DPAP DDP < 25 36 24 43 25-50 46 54 47 > 50 18 23 10

Figure 3.24: Impact of WSD on Fuel across Schemes

100 80 60 40 % of HH 20 0 IWDP DPAP DDP Less 12 9 10 Just Enough 80 79 89 Adequate 8 12 1

Figure 3.25: Impact of WSD on Manure across Schemes

70 60 50 40 30 % of HH 20 10 0 IWDP DPAP DDP Less 53 57 64 Just Adequate 43 42 35 More 4 1 1

Performance across schemes as assessed in terms of scoring also reveals a clear bias against DDP watersheds. DDP watersheds scored 32 percent when compared to 49 percent in the case of IWDP and 47 percent in the case of DPAP watersheds (Table 3.3). As in the case of size class wise analysis the statistical significance of these differences between the schemes was tested using the ‘means t’ test. The differences tested significant in all the indicators, except fish, confirming the poor performance of DDP watersheds when compared to IWDP and DPAP districts. Where as in the case of IWDP and DPAP watersheds the differences are significant for most indicators. DDP districts being poorly endowed and backward, the poor performance of WSD in these watersheds when compared to other schemes in the better endowed regions may further aggravate regional imbalances in terms of natural endowments.

Table 3.3: Performance of WSD between Schemes (IWDP-DPAP / IWDP-DDP / DPAP-DDP)

Indicator Name IWDP DPAP DDP Overall IWDP-DPAP IWDP-DDP DPAP-DDP Soil Erosion Reduction 65 50 35 54 65-50* 65-35* 50-35* Runoff Reduction 67 58 48 60 67-58* 67-48* 58-48* Assured Drinking Water 61 59 36 53 61-59* 61-36* 59-36* Increase Irrigation 39 33 5 27 39-33* 39-5* 33-5* Fodder 50 58 44 49 50-58* 50-44* 58-44* Fuel 48 51 46 48 48-51* 48-46* 51-46* Manure 26 22 18 23 26-22* 26-18* 22-18** Fish 5 3 5 5 5-3 5-5 3-5 Vegetative Improvement 26 25 7 20 26-25 26-7* 25-7* Level of Adequacy 41 49 33 39 41-49* 41-33* 49-33* Over all 49 47 32 43 49-47* 47-32 47-32*

Note: IWDP= Integrated Wasteland Development Programme; DPAP= Drought Prone Area Programme; DDP= Desert Development Programme. *Indicates the statistical significance at less than 10 percent level.

V Conclusions The prime objective of WSD is to enhance land productivity through strengthening of the natural resource base viz., soil and water resources. In this chapter an attempt is made to assess the impact of WSD on various indicators pertaining to bio-physical factors across districts, size classes and schemes. The performance of WSD in terms of environmental impact is assessed by estimating the overall actual score as a percentage of maximum score. The score, hence, ranges between ‘0’ and ‘100’. Important indicators include reduction in soil erosion, reduction in runoff, availability of drinking water, irrigation, fodder, fuel, etc. The impact assessment brings out the following main points.  The overall performance level of the 15 districts in Rajasthan is 43 percent. The performance varies widely across sample districts i.e., from 24 percent in Jaisalmer to 58 percent in Dausa. Performance levels are poor in the arid districts of Jaisalmer, Barmer, Bikaner and Jalore.  Soil and water conservation methods along with drinking water get reasonably good scores (above 50). Runoff reduction scores high followed by soil erosion and drinking water. This is in line with the prime objective of WSD.  Among the important bio-physical indicators vegetation cover and irrigation get very low scoring. While irrigation got better scoring in the endowed and medium rainfall districts, it scores poorly in the arid districts. In fact, seven of the fifteen sample districts reported decline in area under irrigation, which is likely to have an adverse economic impact. On the other hand, vegetative cover scoring is poor across the districts.  There is no set pattern of the impact in terms of benefits flows to small and marginal farmers vis-a-vis large and medium farmers. However, the evidence on the overall performance level suggests bias in favour of large and medium farmers. That is the impact of WSD is in favour large farmers though variations can be observed across the districts.  At the indicator level SMF seem to have gained more in the case of runoff reduction, drinking water and vegetative cover, while LMF gained more in terms of fuel and manure.  WSD under the three different schemes have shown positive impact in most indicators as well as over all. Between the schemes, IWDP watersheds are performing better, while DDP watersheds revealed poor performance.  The analysis points towards a disturbing trend that benefits from WSD in poor and backward regions are not only low but are mostly corned by large farmers resulting in aggravation of inter and intra regional inequalities.

CHAPTER IV Watershed Development Programme: Economic Impact

I Introduction Economic impacts are critical for the success and sustainability of the WSD programme. Economic benefits accrue through enhancement of bio-physical or natural resource base in the context of WSD. But, unless bio-physical benefits are translated in to economic benefits farmers may not show much interest in adopting the WSD programme. Various studies have shown that WSD has positive economic impacts ranging between 20-40 percent improvements in yield rates, employment, migration, etc (Rao, 2000; Reddy, 2001, Joshi, et. al., 2004). In this chapter, an attempt is made to assess the economic impact of WSD across districts of Rajasthan.

The economic development cannot be aptly summarised by any single indicator. A combination of relevant indicators enables a comprehensive and realistic assessment of watershed development. For the present analysis, economic impact is assessed in terms of changes in agricultural development activities, land productivity, employment, livestock, standard of living, etc. Impact of WSD is captured with the help of frequency distribution of farmers reporting different levels of impact and scoring of the impact. Impact is measured across districts, between small and marginal farmers and large and medium farmers and across schemes. Here we present the important indicators, especially in the case of frequency distribution, where there are substantial impacts. The coverage of indicators will be more in the case scoring.

II District-wise Analysis Land use and land productivity are the main impact indicators that are directly translated from enhanced or improved bio-physical or environmental conditions at the WSD level. With the improved soil quality (reduced soil erosion), moisture content (reduced run off) and improved irrigation facilities, farmers shift towards water intensive and remunerative crops, more crops in a year and get higher yields from the same crops. Crop Intensity is often directly linked to availability of soil moisture and irrigation. Impact of WSD on crop intensity is observed mainly in the districts with relatively better rainfall and existing irrigation facilities (Fig. 4.1). Most of the households (more than 70 percent) from the arid districts

72 have reported no increase in cropping intensity. In fact, these districts have reported decline in area under irrigation. Of the 15 sample districts 8 districts have majority of the households (more than 50 percent) reporting increase in crop intensity up to 20 percent. The increase is more than 20 percent in Dausa as a substantial proportion (35 percent) of households reporting the increase. On the whole, the impact of WSD on crop intensity is up to 20 percent in the districts with medium rainfall. Given the sever climatic conditions of the state this is a significant impact. This results in increased area under crops and incomes of the farmers.

Figure 4.1: Impact of WSD on Cropping Intensity across Sample Districts

120 100 80 60 40

% of HH 20 0 Sw Raj Dh Bik Jais Bar Ud Bar Da Jaip aim Bu Ton sa Aj Jalo Siro olp ane lme me aip an usa ur adh ndi k ma mir r hi ur r r r ur pur d Nil 11 16 37 9 9 7 8 69 34 73 70 96 98 82 62 < 10 21 16 20 24 42 12 27 18 44 12 23 4 2 10 23 10 to 20 58 34 25 61 37 66 52 13 22 14 5 0 0 6 14 > 20 11 35 17 6 12 14 13 0 0 1 1 0 0 1 1

Yield Rates Impact on land productivity or yields is another critical factor that enhances the conditions of farmers. Most important crops grown in Rajasthan include cereal crops like wheat, bajra, jowar, maize, etc; pulses, oil seeds and some cash crops like cotton, chillies, etc. The yield impact is assessed for cereals, pulses, oilseeds and cash crops. Impact of WSD on cereal yields is positive in all the sample districts, without any exception, as majority of the farmers (more than 60 percent) have reported increase in yields up to 40 percent and more (Fig. 4.2). In 13 out of 15 districts very few households reported no increase in yield rates of cereals. Only in arid districts of Jaisalmer and Bikaner substantial number of households reported no increase (NIL) in cereal yields. In 8 of 15 sample districts majority of the households (more than 50 percent reported an increase of 20-40 percent rise in cereal yields. This is despite that fact that area under irrigation declined in seven of the fifteen sample districts. Improved soil moisture could have helped in overcoming the reduction in irrigation in some districts.

Figure 4.2: Impact of WSD on Yield Rate of Cereals across Sample Districts

70 60 50 40 30

% of HH 20 10 0 Swai Rajs Bara Dau Jaip Dhol Bun Ton Ajm Bika Jaisl Bar Siro Udai mad ama Jalor n sa ur pur di k ir ner mer mer hi pur hpur d Nil 1 2 1 3 0 0 4 6 1 39 8 40 15 3 7 <20 23 17 28 16 35 13 11 34 33 29 56 53 64 52 31 20 to 40 56 46 52 57 48 54 64 59 60 27 34 7 21 45 61 >40 20 34 18 23 17 33 21 1 5 5 1 0 0 0 1

Impact on productivity of pulses also revealed same pattern as cereals, as majority of the farmers (above 50 percent) in all the districts indicated a rise in pulses yields up to more than 20 percent (Fig. 4.3). However, unlike in the case of cereals, in ten of the 15 sample districts substantial proportion of households (above 25 percent) reported no increase in pulses yields. In the arid districts (four) the yield increases were mostly below 10 percent. Yield increase is between 10-20 percent in the districts of Ajmer, Jaipur and Sawaimadhapur. Only in Dausa substantial proportion of households reported yield increase of more than 20 percent. The impact of WSD on yields appears to have weakened in the case of oilseeds and cash crops (Figs. 4.4 and 4.5). While in 3 of the 15 sample districts majority of the households reported no increase in oilseed yield rates, in the case of cash crops 10 districts reported no increase. In the case of oilseeds the rise in yield is in the range of up to 10 percent in most cases. In the case of cash crops the increase is up to 20 percent in majority cases in the five districts where majority of the households reported increases in the productivity of cash crops.

Figure 4.3: Impact of WSD on Yield Rate of Pulses across Sample Districts

80 70 60 50 40

% of HH 30 20 10 0 Swai Rajs Bara Daus Jaipu Dhol Bun Ajmi Bika Jaisl Bar Siro Udai mad Tonk ama Jalor n a r pur di r ner mer mer hi pur hpur d Nil 42 3 9 22 35 25 34 30 7 45 18 39 33 41 49 <10 31 25 26 21 30 28 18 35 32 25 68 60 67 54 38 10 to 20 15 45 57 51 23 38 42 33 58 26 13 1 0 5 13 >20 12 27 8 6 12 9 6 1 4 5 1 0 0 0 0

Figure 4.4: Impact of WSD on Yield Rate of Oilseeds across Sample Districts

80 70 60 50 40 30 % of HH 20 10 0 Swa Rajs Bara Dau Jaip ima Dho Bun Ton Ajm Bika Jaisl Bar Siro Udai ama Jalor n sa ur dhp lpur di k ir ner mer mer hi pur d ur Nil 1 2 9 6 1 2 6 42 4 10 33 44 51 74 52 < 5 11 19 23 11 38 8 13 38 37 37 60 55 49 18 40 5 to 10 58 40 56 67 36 57 61 20 54 42 5 1 0 8 8 >10 31 39 13 17 25 32 20 0 5 10 2 0 0 0 0

Figure 4.5: Impact of WSD on Yield Rate of Cash Crops across Sample Districts

120 100 80 60

% of HH 40 20 0 Swai Rajs Bara Daus Jaip Dhol Bun Ton Ajm Bika Jaisl Bar Siro Udai mad ama Jalor n a ur pur di k ir ner mer mer hi pur hpur d Nil 33 58 91 46 28 51 71 86 41 13 66 72 71 99 82 <10 17 6 1 19 22 17 13 10 41 58 34 28 28 0 16 10 to20 49 17 7 30 43 31 14 3 18 29 0 0 1 1 2 >20 0 19 0 5 7 1 2 1 0 0 0 0 0 0 0

Employment Employment is another important indicator that helps in improving economic status of the households. WSD is expected to have direct as well as indirect employment impacts at the community and village level. The direct benefits accrue mainly during the implementation phase of the WSD, as the implementation process is labour intensive. Some studies even reported that employment gains vanish once the implementation is over. On the other hand, the indirect impacts accrue due to the improvements in crop intensity, yield rates and shift towards labour intensive crops. These employment gains are sustainable. Moreover, employment impact would be more widespread than crop impacts, as land less households

76 also benefit from increased employment or demand for labour. However, as our sample is limited to landed households, the employment gains observed are partial. More so in the case of female employment in states like Rajasthan where a woman from higher socio-economic classes going out for employment is not widely accepted socially.

Three indicators of employment impact are assessed here viz., agricultural, non-agricultural and self employment. Agricultural employment has gone up in all the districts for male as well as female workers (Figs. 4.6 and 4.7). The increase in employment is up to 20 percent in majority of the cases. Impact is more in the case of female employment, as majority of households reported above 20 percent increase in employment in eight districts while the increase is less than 20 percent in the case of male employment. In both the cases, employment impact is on the lower side in the arid and low rainfall districts.

Figure 4.6: Impact of WSD on Employment (Agriculture: Male) across Sample Districts

100 90 80 70 60 50 40 % of HH 30 20 10 0 Swa Raj Jais Bar Dau Jaip ima Dho Bun Ton Aj Bik Jalo Bar Siro Uda sam lme an sa ur dhp lpur di k mir aner r mer hi ipur ad r ur <10 12 17 35 20 30 18 17 47 41 71 70 88 71 62 54 10 to 20 41 46 42 52 47 53 55 50 50 28 28 12 28 37 45 >20 47 37 23 28 22 29 28 3 9 1 2 1 1 1 1

Figure 4.7: Impact of WSD on Employment (Agriculture: Female) across Sample Districts

100 90 80 70 60 50 40 % of HH 30 20 10 0 Swa Rajs Dau Jaip ima Dho Bun Ton Ajm Bik Jalo Jaisl Bar Siro Uda ama sa ur dhp lpur di k ir aner r mer mer hi ipur d ur <20 35 62 25 41 24 16 53 47 72 89 94 91 78 69 20 to 30 43 33 63 50 69 74 45 49 27 11 5 8 22 31 >30 22 4 12 9 7 10 2 4 1 1 0 1 0 1

Impact of WSD on non- agricultural employment is also positive in all the districts both for male and female workers (Figs. 4.8 and 4.9). Employment impact is relatively more in the case of male workers. And non-agricultural employment appears to be more evenly spread across the districts, as the arid districts are also reporting higher employment generation (above 10 percent). On the contrary, impact of WSD on self employment creation is marginal (Figs. 4.9 and 4.10). And whatever self employment is created that is mainly among male workers. This indicates that watershed impact is not big enough to generate enough incomes and demand for services that results in creation of self-employment.

Figure 4.8: Impact of WSD on Employment (Non-agriculture: Male) across Sample Districts

70 60 50 40 30

% of HH 20 10 0 Swa Raj Jais Bar Dau Jaip ima Dho Bun Ton Aj Bik Jalo Bar Siro Uda sam lme an sa ur dhp lpur di k mir aner r mer hi ipur ad r ur <10 29 19 28 21 35 17 18 27 8 27 26 23 36 38 33 10 to 15 42 53 46 50 39 47 52 56 61 64 49 49 46 43 49 >15 28 28 26 29 26 35 30 17 30 9 25 28 18 19 18

Figure 4.9: Impact of WSD on Employment (Non-agriculture: Female) across Sample Districts

70 60 50 40 30 % of HH 20 10 0 Swa Rajs Bar Dau Jaip ima Dho Bun Ton Ajm Bik Jalo Jaisl Bar Siro Uda ama an sa ur dhp lpur di k ir aner r mer mer hi ipur d ur <5 30 20 20 25 39 20 21 41 17 34 39 40 44 48 40 5 to 10 44 45 41 52 31 47 52 44 58 55 42 47 44 33 42 >10 26 35 39 23 30 33 27 16 25 12 19 13 11 19 18

Figure 4.10: Impact of WSD on Employment (Self: male) across Sample Districts

100 90 80 70 60 50 40 % of HH 30 20 10 0 Swa Rajs Bar Dau Jaip ima Dho Bun Ton Ajm Bika Jalo Jaisl Bar Siro Uda am an sa ur dhp lpur di k ir ner r mer mer hi ipur ad ur No Increase 66 65 78 21 71 39 58 63 63 73 95 90 90 85 78 10 to 20 12 23 19 25 12 11 13 22 21 14 3 2 5 8 15 >20 22 13 3 54 17 49 30 15 16 14 3 7 4 7 7

Figure 4.11: Impact of WSD on Employment (Self: Female) across Sample Districts

120 100 80 60

% of HH 40 20 0 Sw Dh Raj Bik Jais Ud Bar Da Jaip aim Bu Ton Aj Jalo Bar Sir olp sam ane lme aip an usa ur adh ndi k mir r mer ohi ur ad r r ur pur No Increase 93 93 90 90 97 89 93 95 98 98 96 99 99 94 97 10 to 20 5 7 10 8 2 6 3 4 2 2 0 1 1 6 2 >20 3 0 0 3 1 5 4 1 0 0 4 0 1 0 0

Impact on Livestock Livestock is an integral part of Rajasthan’s economy. Rajasthan is among the most livestock dense states in India. Of late, pressure on the livestock economy is increasing mainly due to the decline in trans-humans. This has led to shortage of fodder, especially during drought years. Farmers are increasingly finding it economical to substitute drought power with mechanical power. Similarly, shifting to milch cattle is also becoming more remunerative due to the expanding market for milk and milk products. Besides, WSD is expected to increase the availability of fodder, which is conducive for milch cattle. While the shift from drought animals to tractors or milch cattle could be seen as improved economic condition of the household, shift from sheep to goat is often understood as an indication of declining economic status. Moving to hybrid cattle, purchase of fodder and mechanical processing of fodder also reflect the economic status of the household.

The impact of WSD on livestock economy is very clear from the responses of sample households. In all the districts majority of households shifted to tractor, though most of them use them for the purpose of primary tillage (Fig. 4.12). Proportion of sample households not using tractor is negligible in most of the districts. Only in Dausa majority (above 50 percent) of the sample households reported usage of tractor for all operations. Usage of tractor in the place of drought power reduces the pressure on fodder. This coupled with improved vegetation and availability of fodder due to WSD paved the way for more remunerative milch animals. The shift towards milch animals is conspicuous across the districts. In all the sample districts, with an exception of Udaipur, over whelming majority of households reported a shift only to milch cattle (Fig. 4.13). Figure 4.12: Impact of WSD on Livestock across Sample Districts (Shift from Cattle to Tractor)

80 70 60 50 40 30 % of HH 20 10 0 Sw Dh Raj Bik Jais Ud Bar Da Jaip aim Bu Ton Aj Jalo Bar Sir olp sam ane lme aip an usa ur adh ndi k mir r mer ohi ur ad r r ur pur No use 4 2 10 7 1 5 8 26 3 5 3 3 2 18 22 Primary tillage 51 45 54 65 55 50 58 51 71 68 54 62 65 55 57 All operations 45 53 36 27 44 45 34 23 26 27 43 35 33 28 21

Figure 4.13: Impact of WSD on Livestock across Sample Districts (Shift from Draft to Milch cattle)

100 90 80 70 60 50 40 % of HH 30 20 10 0 Sw Raj Jai ai Dh Bi Ba Ud Ba Da Jai Bu To sa Aj Jal sl Sir ma olp ka rm aip ran usa pur ndi nk ma mir or me ohi dh ur ner er ur d r pur No change 4 3 6 4 3 6 7 20 18 14 9 5 1 12 12 Mixed 27 18 20 17 22 27 15 20 21 10 11 3 7 35 43 Only Milch Animals 69 79 75 79 75 67 78 60 61 77 80 93 91 53 45

The improved economic situation of the households is also reflected in the majority of the households reporting no change in the composition of small ruminants (Fig. 4.14). But the impact has remained limited to local breeds, as there is no substantial shift towards hybrid cattle (Fig. 4.15). This could be due to two reasons: i) the capital and maintenance costs are quite high in the case of hybrid cattle, and ii) maintaining hybrid cattle is a high fodder and water intensive activity. It may be deduced from this, that the positive impact of WSD on the fodder availability and general economic conditions at the household level is not enough to prompt a shift to hybrid cattle. As it is the demand for fodder seems to have increased due to the shift towards milch cattle (existing breeds) as revealed by the increase in the purchase of fodder by households across the sample districts (Fig. 4.16). Majority of the farmers reported an increase of 25-50 percent in the purchase of fodder. Mechanical processing of fodder is also reported in 9 out of the 15 sample districts (Fig. 4.17). Mechanical processing is mainly reported from the medium rainfall and better endowed districts, while manual processing is still the dominant practice in the low rainfall and arid districts.

Figure 4.14: Impact of WSD on Livestock across Sample Districts (Shift from sheep to Goat)

80 70 60 50 40

% of HH 30 20 10 0 Sw Raj Dh Bik Jais Bar Ud Bar Da Jai aim Bu To sa Aj Jal Sir olp ane lme me aip an usa pur adh ndi nk ma mir or ohi ur r r r ur pur d No Change 30 56 54 62 67 31 67 71 55 45 61 48 54 66 63 Mixed 47 33 38 27 24 29 19 18 28 36 17 18 23 23 24 All Sheep Replaced 23 10 8 11 10 41 14 10 16 19 22 34 23 11 13

Figure 4.15: Impact of WSD on Livestock across Sample Districts (Shift to Improved Breeds)

120 100 80 60

% of HH 40 20 0 Sw Raj Dh Bik Jais Bar Ud Bar Da Jai aim Bu To sa Aj Jal Sir olp ane lme me aip an usa pur adh ndi nk ma mir or ohi ur r r r ur pur d Existing Breeds 36 54 53 81 39 68 79 86 85 98 98 100 100 98 95 Part of Boath 61 33 37 19 51 28 16 12 11 2 1 0 0 2 4 Improved Breeds 3 13 10 0 10 4 5 2 5 0 1 0 0 0 0

Figure 4.16: Impact of WSD on Purchase of Fodder across Sample Districts

70 60 50 40 30 % of HH 20 10 0 Swa Rajs Bar Dau Jaip ima Dho Bun Ton Ajm Bik Jalo Jaisl Bar Siro Uda ama an sa ur dhp lpur di k ir aner r mer mer hi ipur d ur < 25 43 57 46 51 32 54 42 29 25 26 11 12 14 20 20 25-50 42 33 40 26 46 35 50 46 46 47 54 37 58 57 53 > 50 14 10 14 23 23 11 8 25 29 27 35 51 28 24 26

Figure 4.17: Impact of WSD on Processing of Fodder across Sample Districts

120 100 80 60

% of HH 40 20 0 Sw Raj Dh Bik Jais Bar Ud Bar Da Jai aim Bu To sa Aj Jal Sir olp ane lme me aip an usa pur adh ndi nk ma mir or ohi ur r r r ur pur d Nil 0 1 2 2 1 0 7 5 1 3 0 7 1 2 2 Mechanical 80 93 86 92 98 77 77 51 64 25 45 16 11 13 28 Manual 20 6 13 6 1 23 16 45 35 72 55 77 88 85 70

All the positive impacts of WSD discussed so far are expected to culminate in to improved standard of living at the household level. Standard of living is linked to disposable income at the household level i.e., gross income minus costs and social payments. The reported changes in the standard of living at the household level in the sample districts indicate that the positive impacts of WSD on various indicators have not fully translated in to disposable income or net gains to improve the standard of living. Majority of the households across all the sample districts have reported only slight improvement in the standard of living (Fig. 4.18). And this improvement is on a relatively lower scale in the low rainfall and arid districts. None of districts have any substantial proportion of households reporting improved standard of living. This clearly indicates that the extent of impacts in terms of yield improvements, employment

84 generation, livestock economy are not enough to bring any substantial changes in the living standards. Figure 4.18: Impact of WSD on Standard of Living across Sample Districts

100 90 80 70 60 50 40

% of HH 30 20 10 0 Sw Raj Jai ai Dh Bi Ba Ud Ba Da Jai Bu To sa Aj Jal sl Sir ma olp ka rm aip ran usa pur ndi nk ma mir or me ohi dh ur ner er ur d r pur Nil 3 4 7 10 10 2 7 26 20 23 25 35 48 38 30 Slight Improvement 93 77 79 80 84 92 84 70 76 74 72 61 50 60 69 Improved 4 19 14 10 6 6 9 4 3 3 3 4 2 3 1

Performance of WSD In terms scores accorded by sample households economic indicators scored less when compared bio-physical indicators. The overall score for all the sample districts is 31 percent as against 43 percent in the case of bio-physical or environmental indicators (Table 4.1). Across the districts the scores range between 23 percent in Barmer to 43 percent in Bundi. While household expenditure got highest score across all the districts with very low variation (07 percent). Given the inflation and the household’s tendency to overestimate the expenditure, the scoring on household expenditure is not very realistic. Processing of fodder also got high average scoring of 72 percent followed by cereal yields and purchase of fodder and standard of living all of which got above 40 percent score. Agricultural diversity and major investments got low scores. Average scores are high in the endowed and irrigated districts in the case of cropping intensity, yield rates, standard of living and employment. Impact on livestock is also subdued across the districts. The economic impact of WSD in the low rainfall arid districts is marginal in the case of important indicators like yield rates, employment, etc. This commensurate with bio-physical or environmental impact of WSD, though these impacts have not fully translated in to economic impacts.

Table 4.1: Average Economic Performance of WSD across Districts and Indicators.

District CI AD CY OVY EMP LS PF PRF MI EXP SL Overall Baran 64 8 65 54 50 39 36 60 18 95 51 39 Dausa 68 16 71 63 50 43 27 53 17 84 58 42 Jaipur 46 4 63 47 37 35 34 55 14 74 53 33 Swaimadhpur 64 6 67 52 50 26 36 52 17 84 50 36 Dholpur 61 9 60 51 38 47 45 50 19 80 48 39 Bundi 70 12 73 56 51 38 29 61 24 88 52 43 Tonk 65 4 68 49 48 29 33 55 21 89 51 37 Rajsamad 19 3 52 32 29 22 48 70 13 79 39 26 Ajmir 39 4 57 43 36 26 52 67 15 87 42 32 Bikaner 17 2 32 31 23 25 51 84 18 80 40 29 Jalor 17 1 43 32 22 27 62 77 15 80 39 28 Jaislmer 2 0 23 18 19 29 69 85 13 78 34 25 Barmer 1 1 35 24 19 27 57 93 12 70 27 23 Sirohi 11 2 47 27 22 22 52 92 16 72 33 27 Udaipur 23 2 52 29 25 21 53 84 13 78 36 26 Over all 33 4 52 38 32 28 48 72 16 81 41 31 (70) (98) (28) (33) (35) (26) (27) (22) (20) (07) (21) (20)

Note: CI= Cropping Intensity; AD= Agricultural Diversification; CY= cereal Yields; OVY= Overall yields; EMP= Employment; LS= Livestock; PF= Purchase of Fodder; PRF= Processing of fodder; MI= Major Investments; EXP= expenditure; SL= Standard of Living.

III Size class-wise Analysis Distribution of economic benefits across socio-economic groups holds the key for success of WSD. It is often argued that benefit flows from WSD are often cornered by the large land owners. This in turn weakens the collective action possibilities at the community level. Community participation and collective action are critical for proper implementation and sustenance of WSD. Here we examine the differential impact of WSD on small and marginal farmers vis-a-vis large and medium farmers.

Impact of WSD on economic indicators across size classes is mixed. As expected crop intensity is in favour of large farmers (Fig. 4.18) due their better access to irrigation. Majority of SMF reported no change in crop intensity. In the case of crop yields small farmers seem to be doing marginally better, as majority of them reporting yield increases in the range of 20-40 percent and above (Fig. 4.19). On the other hand, large and medium farmers have a clear advantage in the case of pulses, oilseeds and cash crops (Figs. 4.20-4.22). This could be due to focus of small and marginal farmers on subsistence cereal crops rather than on other crops. In the case of cash crops majority of the sample households in both the groups reported no

86 increase in yields. This also reflects the limited acreage under cash crops in the sample districts. Figure 4.18: Impact of WSD on Crop Intensity across Size Classes

60 50 40 30

% of HH 20 10 0 Nil < 10 10 to 20 > 20 SMF 54 19 23 5 LMF 48 20 25 7 Total 52 19 24 5

Figure 4.19: Impact of WSD on Cereal Yields across Size Classes

60 50 40 30

% of HH 20 10 0 Nil <20 20 to 40 >40 SMF 10 31 50 9 LMF 9 40 40 11 Total 10 34 47 9

Figure 4.20: Impact of WSD on Pulses Yields across Size Classes

50 40 30 20 % of HH 10 0 Nil <10 10 to 20 >20 SMF 37 35 24 4 LMF 18 45 31 6 Total 31 39 26 5

Figure 4.21: Impact of WSD on Yields of Oilseeds across Size Classes

45 40 35 30 25 20

% of HH 15 10 5 0 Nil <5 5 to 10 >10 SMF 30 26 33 11 LMF 14 38 34 14 Total 24 30 34 12

Figure 4.22: Impact of WSD on cash Crop Yields across Size Classes

80 70 60 50 40

% of HH 30 20 10 0 Nil <10 10 to 20 >20 SMF 74 14 11 1 LMF 55 27 16 2 Total 68 18 12 1

Impact of WSD on additional employment generation between size classes does not show any clear pattern, though agricultural employment is in favour of small and marginal farmers, male as well as female (Figs. 4.23 and 4.24). For, proportionally higher share of small and marginal farmers are reporting increased additional employment in the range of 10-20 percent in the case of males and in the range of 20-30 percent in the case of females. Impact of WSD on female employment is in favour of SMF when compared to male employment. On the other hand, impact of WSD on non-farm additional employment generation is more evenly spread between SMF and LMF, both male and female (Figs. 4.25 and 4.26). And, most the sample households (above 70 percent) reported no increase in self employment in both groups and genders (Figs. 4.27 and 4.28).

Figure 4.23: Impact of WSD on Employment (Agrl.: Male) across Size Classes

60 50 40 30

% of HH 20 10 0 <10 10 to 20 >20 SMF 44 44 12 LMF 51 36 13 Total 47 41 12

Figure 4.24: Impact of WSD on Employment (Agrl: Female) across Size Classes

70 60 50 40 30 % of HH 20 10 0 <20 20 to 30 >30 SMF 53 42 5 LMF 65 30 5 Total 57 38 5

Figure 4.25: Impact of WSD on Employment (Non-Agrl.: male) across Size Classes

60 50 40 30

% of HH 20 10 0 <10 10 to 15 >15 SMF 26 51 23 LMF 27 49 24 Total 26 50 23

Figure 4.26: Impact of WSD on Employment (Non-agrl: Female) across Size Classes

50 45 40 35 30 25 20 % of HH 15 10 5 0 <5 5 to 10 >10 SMF 34 46 20 LMF 35 43 21 Total 34 45 21

Figure 4.27: Impact of WSD on Employment (Self-Employment: Male) across Size Classes

80 70 60 50 40 30 % of HH 20 10 0 No Increase 10 to 20 >20 SMF 70 15 15 LMF 71 12 16 Total 70 14 15

Figure 4.28: Impact of WSD on Employment (Self-Employment: Female) across Size Classes

120 100 80 60

% of HH 40 20 0 No Increase 10 to 20 >20 SMF 95 4 1 LMF 96 3 2 Total 95 3 1

As far as the impact of WSD on livestock is concerned LMF have benefited more when compared to SMF in the case of two indicators namely shift from draft cattle to tractors and shift to milch cattle (Figs. 4.29 and 4.30). This could be due to the capital intensive nature of these two activities. But similar bias is not observed in the case of shift towards improved breeds, which may also be capital intensive. However, most of the sample households (above 80 percent) reported to be maintaining the existing breeds. The impact seems to be evenly spread between LMF and SMF in the case of other indicators like shift from sheep to goat and fodder processing. This fair distribution of WSD impacts on various indicators is reflected in the impact on standard of living as well (Fig. 4.31). However, this needs to be reassessed in terms of scoring and statistical significance of the differences between SMF and LMF. Figure 4.29: Impact of WSD on Livestock (Shift from Cattle to Tractor) across Size Classes

70 60 50 40 30

% of HH 20 10 0 only Critical No use All operations (Primary tillage SMF 14 55 32 LMF 4 62 34 Total 11 57 32

Figure 4.30: Impact of WSD on Livestock (Shift from Draft to Milch Cattle) across Size Classes

100 80 60 40

% of HH 20 0 Only Milch No change Mixed Animals SMF 10 23 66 LMF 8 15 77 Total 9 21 70

Figure 4.31: Impact of WSD on Livestock (Shift from Sheep to Goat) across Size Classes

70 60 50 40 30 % of HH 20 10 0 All Sheeps No Change Mixed Replaced SMF 58 24 17 LMF 59 23 18 Total 58 24 18

Figure 4.32: Impact of WSD on Livestock (Shift to Improved Breeds) across Size Classes

90 80 70 60 50 40

% of HH 30 20 10 0 Existing Breeds Part of Boath Improved Breeds SMF 84 14 2 LMF 85 12 3 Total 84 13 3

Figure 4.33: Impact of WSD on Fodder Processing across Size Classes

60 50 40 30

% of HH 20 10 0 Nil Mechanical Manual SMF 3 50 46 LMF 1 53 46 Total 3 51 46

Figure 4.34: Impact of WSD on Standard of Living across Size Classes

80 70 60 50 40 30 % of HH 20 10 0 Slight Nil Improved Improvement SMF 22 73 4 LMF 22 71 7 Total 22 72 5

The overall performance of WSD on various economic indicators between size classes is also assessed using the scoring and the differences are tested using the ‘means t’ test. As observed earlier the differences between SMF and LMF are marginal in majority of the cases. The differences are significant in about a third of cases across districts and indicators (Table 4.2). Of the significant cases in about 60 percent cases LMF are doing better when compared SMF. At the over performance also LMF are doing significantly better in four of the fifteen sample districts and in o district SMF are doing significantly better. This indicates a slight bias in favour of large and medium farmers in terms of WSD benefit flows. Across the districts, benefit flows from WSD are more in favour of LMF mostly in the endowed and medium rainfall districts like Baran, Dausa, and Tonk, though Bikaner, Jaisalmer and Udaipur also reported evidence in favour of LMF. In terms of indicators, LMF have significantly higher benefit flows in the case of crop intensity, agricultural diversification, purchase of fodder and major investments. All these indicators are capital intensive and hence large farmer bias is expected. On the other hand, benefit flows are significantly higher for SMF in the case of improvements in livestock and generation of additional employment. In the case of cereal yields there is no clear bias as the benefit flows are in favour of LMF in some districts and in favour of SMF in some districts. Similar is the case with processing of fodder.

Table 4.2: Average Economic Impact of WSD across Size Classes

Name of CI AD CY OVY EMP LS PF PRF MI SL ALL districts SMF- SMF- SMF- SMF- SMF- SMF- SMF- SMF- SMF- SMF- SMF- LMF LMF LMF LMF LMF LMF LMF LMF LMF LMF LMF Baran 63-73* 7-12* 62-74* 52-60 49-58* 39-37 37-28 60-58 18-17 50-53 39-42* Dausa 54-87* 10-25* 70-73 62-65 48-52 41-46* 27-27 50-56* 18-17 53-66* 40-44* Jaipur 43-49 4-4 67-60* 50-46 37-36 38-33* 39-31 55-55 7-19* 53-54 30-35 Swaimadhpur 62-66 5-6 67-67 50-53 49-52 24-28 33-43 54-49* 18-15* 49-52 35-37 Dholpur 65-58* 11-7* 65-57* 56-47 44-34* 42-51* 37-50* 49-50 17-20* 46-50 37-40 Bundi 70-0 12-18 73-56 56-67 51-75* 38-25 29-0 61-100* 24-20 52-0 43-43 Tonk 60-76* 4-7* 64-77* 44-62 46-52* 27-34* 30-39* 57-50* 17-30* 50-55* 34-44* Rajsamad 19-0 3-0 52-0 32-0 29-33 22-25 48-0 70-0 13-0 39-0 26-20 Ajmir 34-40 4-4 59-56 41-43 34-37 27-26 51-53 70-67 11-17* 42-42 32-32 Bikaner 1-31* 0-2 5-51* 3-46 27-21* 26-25 52-50 82-86 13-21* 32-45* 24-32* Jalor 14-18 2-1 40-44 28-33 21-23 26-27 43-67* 79-77 20-13* 47-37* 29-27 Jaislmer 2-2 0-0 21-25 17-21 19-19 29-28 69-70 83-87 11-15* 31-38* 24-27 Barmer 1-2 0-1 32-37* 19-25 21-18* 28-27 58-57 90-95* 11-12 30-26 22-24 Sirohi 11-0 2-0 47-0 27-0 22-0 22-0 52-0 92-0 16-0 33-0 27-0 Udaipur 20-42* 2-3 51-56* 29-31 25-25 22-19 53-52 83-89* 14-9* 36-37 26-24 Over all 31-36* 3-4 52-51 37-41 33-31* 27-30* 46-51* 71-72 15-17* 41-43* 30-32

Note: CI= Cropping Intensity; AD= Agricultural Diversification; CY= Cereal Yields; OVY= Overall yields; EMP= Employment; LS= Livestock; PF= Purchase of Fodder; PRF= Processing of fodder; MI= Major Investments; EXP= expenditure; SL= Standard of Living.

IV Scheme-wise Analysis The scheme wise assessment of WSD economic impacts also indicates a clear bias in favour of IWDP watersheds. But, unlike in the case of bio-physical or environmental indicators, DDP watersheds are performing equally, especially in comparison with the DPAP watersheds, in the case of some important indicators like yields of pulses and oilseeds (Figs. 4.37 and 4.38). This could be mainly due to the low coverage of cereal crops consequent to low rainfall in these districts. Most the sample farmers in the DDP districts reported no increase in crop intensity (Fig. 4.35) and substantial proportion of sample households (23 percent) reported no increase in cereal yields. In the case of cash crops the performance of all the three schemes is equally poor (Fig. 4.39). The performance of DPAP watersheds fall in between IWDP and DDP watersheds. In the case of some indicators like crop intensity, cereal yields and cash crop yields DPAP watersheds are performing on par with IWDP watersheds.

Figure 4.35: Impact of WSD on Crop Intensity across Schemes

90 80 70 60 50 40

% of HH 30 20 10 0 IWDP DPAP DDP Nil 36 41 84 < 10 23 23 10 10 to 20 32 32 5 > 20 8 4 0

Figure 4.36: Impact of WSD on Cereal Yields across Schemes

70 60 50 40 30 % of HH 20 10 0 IWDP DPAP DDP Nil 3 7 23 <20 26 26 51 20 to 40 57 55 24 >40 13 12 1

Figure 4.37: Impact of WSD on Pulses Yields across Schemes

50 40 30

% of HH 20 10 0 IWDP DPAP DDP Nil 28 37 33 <10 30 30 55 10 to 20 35 29 11 >20 7 4 1

Figure 4.38: Impact of WSD on Oilseed Yields across Schemes

50 45 40 35 30 25 20

% of HH 15 10 5 0 IWDP DPAP DDP Nil 18 33 40 <5 26 17 47 5 to 10 40 40 11 >10 16 10 1

Figure 4.39: Impact of WSD on Cash crop Yields across Schemes

80 70 60 50 40 30 % of HH 20 10 0 IWDP DPAP DDP Nil 68 63 72 <10 15 21 24 10 to 20 15 14 4 >20 2 2 0

In the case of additional employment generated from WSD, impact on agriculture employment, male as well as female, is more in the case of IWDP and DPAP watersheds (Fig. 4.40). In the case of DDP watersheds, more than 70 percent of the sample households reported less than 10 percent increase in the additional male employment created and more than 80 percent reported less than 20 percent increase in the case of female employment. On the other hand, non-agricultural employment impacts are not very different across schemes. This is true of male as well as female employment (Figs. 4.42 and 4.43). This indicates that employment impact of watershed development seems to have limited agricultural employment only. The non-agricultural employment generated could be due to reasons other than WSD. Self employment generated is very limited under all the schemes, though IWDP and DPAP watersheds have reported slightly better performance in the case of male employment (Fig. 4.44). In the case of self employment also the impact need not be entirely due to WSD, as it may also depend on general economic conditions of the region. Therefore, employment impacts of WSD are mainly limited to agriculture sector.

Figure 4.40: Impact of WSD on Employment (Agrl.: Male) across Schemes

80 70 60 50 40 30 % of HH 20 10 0 IWDP DPAP DDP <10 35 40 71 10 to 20 48 44 27 >20 17 16 2

Figure 4.41: Impact of WSD on Employment (Agrl: Female) across Schemes

90 80 70 60 50 40

% of HH 30 20 10 0 IWDP DPAP DDP <20 45 51 81 20 to 30 48 43 18 >30 7 6 1

Figure 4.42: Impact of WSD on Employment (Non-agrl.: Male) across Schemes

60 50 40 30

% of HH 20 10 0 IWDP DPAP DDP <10 26 26 28 10 to 15 50 48 52 >15 24 27 20

Figure 4.43: Impact of WSD on Employment (Non-agrl.: Female) across Schemes

50 40 30 20 % of HH 10 0 IWDP DPAP DDP <5 32 30 39 5 to 10 44 44 47 >10 24 26 14

Figure 4.44: Impact of WSD on Employment (Self-employment: male) across Schemes

90 80 70 60 50 40 % of HH 30 20 10 0 IWDP DPAP DDP No Increase 65 62 84 10 to 20 17 16 9 >20 18 23 7

Figure 4.45: Impact of WSD on Employment (Self-Employment: Female) across Schemes

120

100

% of HH 40

0 IWDP DPAP DDP No Increase 94 97 98 10 to 20 4 2 1 >20 2 1 1

DDP watersheds are performing better in the case some indicators pertaining to livestock improvements due to WSD. Usage of tractor in farming has gone up substantially in all the three schemes. While only 9 percent of the sample households are reporting “non-usage of tractor” in the case of DDP watersheds, non-usage is reported by 11 and 17 percent of sample farmers respectively in the case of IWDP and DPAP (Fig. 4.46). Similarly, shift from draft to milch cattle is more prominent in the case of DDP watersheds. More than 80 percent of the sample households reported complete shift to mich cattle, while it is 67 percent in the case of IWDP and 55 percent in the case of DPAP watersheds (Fig. 4.47). Shift from sheep to goat is also more in DDP watersheds when compared to other two schemes, which could be due to the degraded natural resource base in the DDP regions (Fig. 4.48). This is also reflected in the shift towards improved breeds, which are water and fodder intensive, and processing of fodder i.e., the shift is close to zero in the case of DDP watersheds (Fig. 4.49) and fodder processing is predominantly manual in DDP watersheds when compared to IWDP and DPAP watersheds (Fig. 4.50). On the whole, the economic impact, as reflected in the standard of living of households, of WSD is relatively better in the case of IWDP watersheds followed by DPAP and DDP districts (Fig. 4.51).

Figure 4.46: Impact of WSD on Livestock (Shift from draft cattle to Tractor) across Schemes

70 60 50 40 30 % of HH 20 10 0 IWDP DPAP DDP No use 11 17 9 Primary tillage 56 56 59 All operations 33 27 32

Figure 4.47: Impact of WSD on Livestock (Draft to Milch cattle) across Schemes

90 80 70 60 50 40

% of HH 30 20 10 0 IWDP DPAP DDP No change 10 10 9 Mixed 24 35 10 Only Milch Animals 67 55 81

Figure 4.48: Impact of WSD on Livestock (Sheep to Goat) Schemes

70 60 50 40 30 % of HH 20 10 0 IWDP DPAP DDP No Change 61 59 53 Mixed 24 27 23 All Sheep Replaced 14 15 24

Figure 4.49: Impact of WSD on Livestock (Shift to Improved breeds) across Schemes

120 100 80 60

% of HH 40 20 0 IWDP DPAP DDP Existing Breeds 75 89 98 Part of Boath 20 10 2 Improved Breeds 4 0 0

100

Figure 4.50: Impact of WSD on Livestock (Processing of Fodder) across Schemes

80 70 60 50 40 30 % of HH 20 10 0 IWDP DPAP DDP Nil 3 1 4 Mechanical 64 57 24 Manual 33 42 73

Figure 4.51: Impact of WSD on Standard of Living across Schemes

90 80 70 60 50 40

% of HH 30 20 10 0 IWDP DPAP DDP Nil 16 21 34 Slight Improvement 77 73 63 Improved 7 6 3

Variations between the schemes come out clearly reemphasising the earlier analysis when assessed in terms of scoring. Scores across indicators between schemes also reveals a clear bias against DDP watersheds. DDP watersheds score 26 percent when compared to 33 percent in the case of IWDP and 31 percent in the case of DPAP watersheds (Table 4.3). When compared to bio-physical or environmental indicators, the differences between schemes are much less. DDP watersheds are performing better than DPAP watersheds in the case of livestock and DDP watersheds score higher than IWDP watersheds in the case of purchase of fodder and processing of fodder. As in the case of size class wise analysis we have also tested the statistical significance of these differences between the schemes using the ‘means t’ test. Despite the reduced differences between schemes they have tested significant in most of the indicators, confirming the poor performance of DDP watersheds when compared to IWDP and DPAP districts. In the case of IWDP and DPAP watersheds and DPAP and DDP schemes also the differences are significant for most indicators. DDP districts being poorly endowed and backward, the poor performance of WSD in these districts

101

when compared to other schemes in the better endowed regions would result in aggravation of economic inequalities.

Table 4.3: Performance of WSD between Schemes (IWDP-DPAP / IWDP-DDP / DPAP-DDP)

IWDP DPAP DDP Overall IWDP-DPAP IWDP-DDP DPAP-DDP Crop Intensity 44 39 9 23 44-39* 44-9* 39-9* Cereal Yields 60 57 34 52 60-57* 60-34* 57-34* Overall Yield 44 39 26 38 44-39* 44-26* 39-26* Agrl. Diversification 5 4 1 4 5-4* 5-1* 4-1* Employment 37 37 22 32 37-37 37-22* 37-22* Livestock 29 23 26 28 29-23* 29-26* 23-26* Purchase of Fodder 43 45 59 48 43-45 43-59* 45-59* Processing of Fodder 65 70 85 72 65-70* 65-85* 70-85* Major Investments 17 15 13 16 17-15* 17-13* 15-13* Expenditure 82 85 78 81 82-85 82-78 85-78 Standard of Living 45 43 34 41 45-43 45-34 43-34 Overall 33 31 26 31 33-31* 33-26* 31-26*

Note: IWDP= Integrated Wasteland Development Programme; DPAP= Drought Prone Area Programme; DDP= Desert Development Programme. *Indicates the statistical significance at less than 10 percent level.

V Conclusions The ultimate success of any developmental programme is often determined by its economic impact in terms of improved production, income, living standards, etc. This is more so at the household level, where households or community accepts (adopts) or rejects (dis-adopts) a particular programme. In the context of WSD attaining economic impacts is rather slow due to its long gestation period (5 – 7 years). Besides, economic impacts are not dramatic, unlike in the case of irrigation, making it less attractive to farmers. Together they become the bottlenecks for the sustainability of the WSD. In the present case, the sample watersheds have been completed 4-5 years prior to the field work. Hence, the economic impacts, whatever observed, seem to be accruing even after five years of implementation. This indicates that whatever positive impacts the assessment captured are sustainable at least in the medium term in the sample districts. The preceding analysis of economic impact brings out the following issues.

 The overall score obtained for economic impacts for all the sample districts is 31 percent as against 43 percent in the case of bio-physical or environmental impacts. This

102

indicates that bio-physical or environmental impacts are not fully translated in to economic impacts.  Average scores are high in the endowed and irrigated districts in the case of cropping intensity, yield rates, standard of living and employment, while the impact in the low rainfall arid districts is marginal in the case of important indicators like yield rates, employment, etc. This commensurate with bio-physical or environmental impact of WSD.  Differential impact between size classes is marginal in majority of the cases. The size class wise analysis indicates a slight bias in favour of large and medium farmers in terms of WSD benefit flows.  Across the districts, benefit flows from WSD are more in favour of LMF mostly in the endowed and medium rainfall districts like Baran, Dausa, and Tonk, though Bikaner, Jaisalmer and Udaipur also reported evidence in favour of LMF.  LMF have shown significantly higher benefit flows in the case of capital intensive activities and hence large farmer bias is expected. On the other hand, benefit flows are significantly higher for SMF in the case of improvements in livestock and generation of additional employment.  The scheme wise analysis reemphasises the clear bias against DDP watersheds. DDP watersheds score of 26 percent when compared to 33 percent in the case of IWDP and 31 percent in the case of DPAP watersheds.  When compared to bio-physical or environmental indicators, the differences between schemes are much less but they have tested significant in majority of the indicators, confirming the poor performance of DDP watersheds when compared to IWDP and DPAP watersheds.  DDP districts being poorly endowed and backward, the poor performance of WSD in these districts when compared to other schemes in the better endowed regions would result in aggravation of economic inequalities.

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CHAPTER V Watershed Development Programme: Institutional Impact

I Background The development of social and human capital (through watershed approach) is increasingly recognized not only as an end in itself but also one of the most effective methods of combating poverty, since human as well as social capital enhance the productivity of the poor’s most abundant and often only asset-labour. There is a growing awareness of the links between different factors. Recent progress in development of social / human capital through watershed approach was measured by a number of possible indicators (Reddy, et. al., 2004). Moreover, social and human impacts are seen as long term and sustainable when compared to economic impacts, which are closely linked to bio-physical or environmental impacts. In fact, all the three impacts are interlinked. For, social impacts are critical proper implementation and maintenance of watershed structures.

The present analysis considers the impact on social and human capital through watershed approach and activities that are linked with community participation, rather than measuring community participation directly i.e., functioning of groups, group meetings, etc. And hence we term the impacts as institutional as the social impacts are measured in terms of watershed institutions and their functionality. Here we assess the community or participatory institutions activities like maintenance of water harvesting structures, retention walls, grazing practices, women participation, etc. Indicators pertaining to human capital impacts such as preference for sending children to school, level of education, health care in terms of coverage of members and nutritional care are also included. The impact is assessed at the household level by asking them to report the functioning of community activities and the changes due to WSD. Analysis of institutional or social impact is also carried out at the district level, size class wise and schemes wise using frequency distribution of households and scoring. II District-wise Analysis Water harvesting structures like check dams are among the major investments in WSD and also most popular interventions among the farmers. These structures help storing water for longer periods after the rainfall or even beyond rainy season. Depending on the size of the structure and geo-hydrology of the location, the water could be used for direct irrigation also. But, in most cases, especially in places like Rajasthan these structures recharge groundwater and support livestock during post rainy season for shorter periods. Usually these structures

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are built on common streams and community lands. Maintaining these structures is the responsibility with the watershed development fund or revolving fund. In the long run, however, communities need to contribute as the repair or maintenance costs could be high. Therefore, functioning of these structures not only reflects community participation and functioning of institutions but also ensure bio-physical (soil and water conservation) and economic impacts. Similar is the case of maintenance of retention wall and de-silting of water bodies. In all the sample districts, except Jaisalmer, majority of the households reported either partly functional or fully functional water harvesting structure (Fig. 5.1). In Jaisalmer majority of the households (more than 50 percent) reported broken or dysfunctional structures. On the other hand, medium rainfall and endowed districts like Bundi, Dausa and Tonk majority of the households reported that the structures are fully functional. This could be due to the greater benefits from the WHS in the medium and higher rainfall regions or districts. At the same time the criticality of these structures in the low rainfall arid districts need not be over emphasised. This is very well reflected in the partial functioning of the structures and also the maintenance of retention walls in majority of the cases. Majority of the households from the arid districts of Jaisalmer, Bikaner, Barmer and Jalore reported that user groups are maintaining the retention walls with the watershed development fund (WDF) (Fig. 5.2). Though user groups maintain these structures on their own without the WDF, these funds may be a necessity in the poor regions. For, very few households from the arid districts have reported the maintenance of the retention walls on their own. Figure 5.1: Status of Water Harvesting Structures across Sample Districts

80 70 60 50 40 30 % of HH 20 10 0 Raj Dh Bik Jais Bar Ud Ov Bar Da Jai SM Bu To sa Aj Jal Sir olp ane alm me aip eral an usa pur Pur ndi nk ma mir ore ohi ur r er r ur l d Broken 1 0 19 25 0 0 5 1 3 2 1 13 4 3 5 5 Dysfunctional 1 3 3 4 11 1 1 9 17 18 27 44 33 8 10 13 Partly functional 51 34 30 27 57 34 42 57 49 73 54 40 55 75 65 51 Working with 48 64 47 45 32 65 53 33 31 7 18 2 8 14 19 30

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Figure 5.2: Maintenance of Retention Wall across Sample Districts

80 70 60 50 40 30 % of HH 20 10 0 S Dh Ra Bi Jai Ba Da Jai Bu Aj Jal Ba Sir Ud Ov M ol To jsa ka sal ra us pu nd me or rm oh aip era Pu pu nk ma ne me n a r i r e er i ur ll r r d r r Not Done 15 9 14 31 8 25 35 55 35 41 38 49 37 56 55 39 Yes,UGs Doing using using WDF 22 27 40 37 22 39 35 12 27 57 58 50 59 33 17 34 Yes,UGs Doing by themselves 63 64 47 32 70 36 30 33 38 2 3 1 5 10 28 27

Maintenance of water bodies was not part of the WSD works initially and it was integrated in the later years. Surface water bodies are mostly found in the arid districts of Rajasthan. These bodies, locally known as ‘Khadins’, are usually small in size compared to the large tanks, found in other parts of the country. Though they are small in size due to poor rainfall conditions, they play a critical role in protecting crops and supporting livestock in these regions. But, as reported by majority of the households, de-silting of these water bodies not done in eleven of the fourteen sample districts and more so in the case of arid districts (Fig. 5.3). Whatever little de-silting is done it is done by small and landless households. This reflects the functioning of collective institutions in the state. The limited de-silting activity could be due to the low rainfall in these regions and hence carrying out de-silting activities is neither necessitated nor a regular phenomenon in Rajasthan. Perhaps due to this reason, the absence of periodic de-silting of water bodies need not be seen as a negative social impact. Despite the traditional taboo on women working, in nine of the fifteen sample districts majority of the households reported partial involvement of women in the maintenance of common pool resources (CPRs) (Fig. 5.4). This could be due to the successful women development programme in the state. In other words, involvement of women in the maintenance of CPRs is a positive sign though it may not be entirely due to WSD.

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Figure 5.3: Periodic De-silting of Water Bodies across Sample Districts

100 90 80 70 60 50 40 % of HH 30 20 10 0 S Ra Jai Da Jai Dh Aj Bi Ba Ud Ov Ba M Bu To jsa Jal sal Sir us pu olp mi ka rm aip era ran Pu ndi nk ma ore me ohi a r ur r ner er ur ll r d r Not done 35 36 45 60 52 44 50 59 43 75 81 88 80 76 74 63 Yes,but by SMF and Landless 43 37 28 34 21 55 41 32 49 25 17 12 16 21 15 28 Yes,by all Stakeholders 23 27 27 7 27 1 9 9 8 0 2 0 4 3 12 10

Figure 5.4: Participation of Women in the Maintenance of CPRs across Sample Districts

100 90 80 70 60 50 40 % of HH 30 20 10 0 Raj Dh Bik Jais Ud Ov Bar Da Jai SM Bu To sa Aj Jal Bar Sir olp ane alm aip eral an usa pur Pur ndi nk ma mir ore mer ohi ur r er ur l d Not Involved 11 35 43 44 60 23 36 45 37 66 70 72 71 63 46 49 Partly Helping 88 65 52 51 39 74 59 55 61 34 29 26 27 37 51 49 Solely Managing 1 0 5 4 1 3 5 1 2 0 1 2 2 1 3 2

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Impact of WSD on social institutions related to livestock is more conspicuous. Four different indicators viz., social fencing, staggered grazing, practice of open grazing and stall feeding are assessed. Impact of WSD on social fencing of community lands is widespread across the districts without any exception (Fig. 5.5). Social fencing of community lands help in checking degradation and rejuvenating them for enhanced productivity. Social fencing is practiced in all the districts with or without a watchman after the advent of WSD. And in 14 out of 15 sample districts majority of the sample households reported that social fencing is practiced without watchman. Similarly, institutional arrangement of staggered grazing is followed at least partially in all the districts (Fig. 5.6). In few, that too arid, districts households reported that the institution of staggered grazing is not possible, though they are not in majority (less than 50 percent). Staggered grazing practice is supported by the practices of stall feeding and reduced open grazing. In all the sample districts most of the households (more than 90 percent) shifted to either partial or complete stall feeding (Fig. 5.7). Similarly open grazing is restricted to small ruminants in most of the cases (Fig. 5.8).

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Figure 5.5: Social Fencing of Community Lands across Sample Districts

100 90 80 70 60 50 40 % of HH 30 20 10 0 Ra Jai Dh Aj Bi Ba Ud Ov Ba Da Jai Sm Bu To jsa Jal sal Sir olp mi ka rm aip era ran usa pur pur ndi nk ma or me ohi ur r ner er ur ll d r Not Possible 3 2 12 9 18 10 23 13 3 26 12 21 24 11 18 15 Done Along with Watchman 7 6 8 6 11 3 15 15 10 24 6 6 12 72 9 13 All Agreed no Watchman 90 91 80 85 71 87 62 73 88 50 82 73 64 17 73 72

Figure 5.6: Practice of Staggered Grazing across Sample Districts

90 80 70 60 50 40

% of HH 30 20 10 0 Raj Jais Bar Dau Jaip SM Dho Bun Ton Aj Bik Jalo Bar Siro Uda Ove sam alm an sa ur Pur lpur di k mir aner re mer hi ipur rall ad er Not Possible 8 7 11 13 20 13 23 14 2 37 24 39 26 18 23 20 Partly Achived 74 73 72 74 66 71 65 73 85 61 75 60 73 77 73 71 Achieved 18 21 17 14 13 16 12 13 14 2 1 1 1 5 4 9

Figure 5.7: Extent of Stall Feeding across Sample Districts

120 100 80 60

% of HH 40 20 0 Rajs Jais Bar Dau Jaip SM Dho Bun Ton Ajm Bik Jalo Bar Siro Uda Ove ama alm an sa ur Pur lpur di k ir aner re mer hi ipur rall d er No change 1 0 12 1 2 2 3 4 0 5 9 4 1 1 3 3 Partly stall fed 99 92 76 96 71 90 85 89 99 88 88 95 97 97 94 91 Fully stall fed 0 8 13 3 27 8 12 6 1 7 3 1 2 2 3 6

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Figure 5.8: Extent of Open Grazing across Sample Districts

80 70 60 50 40

% of HH 30 20 10 0 Raj Dh Bik Jais Ud Ov Bar Da Jai SM Bu To sa Aj Jal Bar Sir olp ane alm aip eral an usa pur Pur ndi nk ma mir ore mer ohi ur r er ur l d All livestock 38 23 25 37 35 28 35 29 24 41 26 33 31 35 29 31 Not sent for grazing 4 6 15 2 25 6 12 10 7 4 2 0 3 0 7 7 Limited to small ruminants 58 71 60 61 40 66 53 62 69 56 72 67 67 65 64 62

Impact of WSD on education and health can also be considered as part of economic impacts, as the households expenditure on education and health directly linked to the economic well- being of the household. On the other hand, increased expenditure on education and health reflects the households improved awareness of the importance of these two human capital indicators. In both the cases WSD has a clear impact. That is household’s preference for children’s education in all the districts (Fig. 5.9). In 13 out of 15 sample districts the preference has gone up in the case of both male and female child and in two districts it is limited to male child only. While the thrust on education in Rajasthan is rising in general at the policy level, the advent of WSD seems to have helped strengthening the demand for education at the household level. This is also reflected in the level of education, though it is mostly confined to primary level (Fig. 5.10). Similarly, majority of the households reported increase in expenditure on health care in all but two districts i.e., Bikaner and Jalore (Fig. 5.11). And coverage is for all member in 11 of the fifteen sample districts. Impact on nutrition is stronger, as almost all the households from all the districts reported increased expenditure on nutrition (Fig. 5.12). And in 13 of the 15 sample districts the coverage is for the entire family. This reflects not only the increased awareness but also the economic ability to spend on health and nutrition.

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Figure 5.9: Preference for Children’s Education across Sample Districts

90 80 70 60 50 40 % of HH 30 20 10 0 Dh Raj Bik Jais Bar Dau Jaip SM Bun Ton Aj Jalo Bar Siro Uda Ove olp sam ane alm an sa ur Pur di k mir re mer hi ipur rall ur ad r er Only Male 39 28 29 36 19 30 56 80 22 19 18 25 39 72 43 42 Both Male & Female 61 72 71 64 81 70 44 20 78 81 82 75 61 28 57 58

Figure 5.10: Level of education across Sample Districts

90 80 70 60 50 40 % of HH 30 20 10 0 Rajs Bara Dau Jaip SMP Dhol Bun Ton Ajm Bika Jalor Jaisa Bar Siro Udai Over ama n sa ur ur pur di k ir ner e lmer mer hi pur all d Primary 73 53 58 70 46 54 53 53 72 71 72 78 65 46 62 60 Secondary 23 28 33 26 43 33 13 1 26 24 24 19 13 0 12 17 Collegiate 4 19 9 5 11 12 35 45 1 6 3 3 22 53 27 23

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Figure 5.11: Coverage of Health Care across Sample Districts

120 100 80 60

% of HH 40 20 0 Raj Jai S Dh Bi Ba Ud Ov Ba Da Jai Bu To sa Aj Jal sal Sir M olp ka rm aip era ran usa pur ndi nk ma mir ore me ohi Pur ur ner er ur ll d r No Extra Expenditure 0 0 22 0 0 17 0 8 0 60 58 30 10 20 0 13 Limited to a Few 2 38 59 37 79 0 31 26 0 20 1 19 10 20 20 24 Covering All Family Members 98 62 19 63 21 83 69 66 100 20 41 50 79 60 80 63

Figure 5.12: Coverage of Nutritional Care across Sample Districts

120 100 80 60

% of HH 40 20 0 S Ra Jai Dh Aj Bi Ba Ud Ov Ba Da Jai M Bu To jsa Jal sal Sir olp mi ka rm aip era ran usa pur Pu ndi nk ma ore me ohi ur r ner er ur ll r d r No extra Expenditure 0 1 0 0 20 0 10 21 0 41 0 0 0 20 0 8 Improvement to some 2 39 19 59 41 20 29 20 0 21 40 0 20 0 1 18 Improvement to all 98 60 81 41 39 79 61 59 100 39 60 100 80 80 99 74

Household’s assessment of various institutions and human indicators through scoring revealed that the institutional impact of WSD is much higher when compared to bio-physical and economic impacts. Overall performance of WSD in terms of institutional impact is 57 percent. Across the districts the performance ranged from 69 percent in Dausa to 43 percent in Bikaner (Table 5.1). Even in the case of institutional impact the performance of arid and low rainfall districts is on the lower side. Though it is often argued that social institutions are more vibrant in the less endowed parts of Rajasthan, this does not reflect in the context of WSD. Livestock related institutional impacts like social fencing and grazing practices followed by the maintenance of WHS, staggered grazing, stall feeding, etc., get high scores

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among the social indicators. Social fencing gets high scoring universally in all the sample districts. Arid districts such as Jaisalmer, Bikaner, Barmer, Jalore and Sirohi perform poorly (below 40 percent) in the case of de-silting of water bodies, maintenance of retention walls and participation of women in CPR maintenance. Inter-district variations are also low in general, especially in the case of livestock related institutional impacts. All most all the districts perform well in the case of education and health impacts though Bikaner performed poorly (less than 40 percent) in the case of health and nutrition. Variations across districts are much less when compared to health and nutrition. The better performance of human capital indicators across the districts is in line with the overall performance of Rajasthan. Rajasthan performed extremely well in literacy and mortality rates between 1991-2001 census. WSD appears to have further strengthened the performance in the target districts. Table 5.1: Performance of WSD in Terms of Social Impacts

District WHS DSWB MRW WP SFen SG GR SF EDU H&N Overall Baran 73 44 74 45 94 55 60 49 60 99 68 Dausa 81 45 78 33 94 57 74 54 69 81 69 Jaipur 44 41 66 31 84 53 67 51 64 49 60 SMPur 35 23 51 30 88 51 62 51 62 82 57 Dholpur 63 37 81 21 77 46 52 62 69 60 59 Bundi 82 28 56 40 88 52 69 53 63 83 66 Tonk 69 30 48 34 69 44 59 55 64 85 60 Rajsamad 63 25 39 28 80 50 67 51 64 79 58 Ajmir 58 33 52 32 93 56 73 51 62 100 66 Bikaner 46 12 30 17 62 32 58 51 63 30 43 Jalor 51 11 33 16 85 38 73 47 63 42 51 Jaislmer 20 6 26 15 76 31 67 48 60 60 49 Barmer 39 12 34 15 70 38 68 51 60 85 53 Sirohi 51 13 27 19 53 43 65 50 60 70 50 Udaipur 49 19 36 28 77 41 68 50 61 90 58 Over all 54 23 44 27 78 45 66 51 63 75 57 (31) (50) (37) (31) (14) (18) (09) (07) (04) (27) (12)

Note: WHS= Water harvesting Systems; De-silting of water bodies; Maintenance of retention well; WP= women participation; SFen= Social fencing; SG= Staggered grazing; GR= Grazing; SF= Stall feeding; EDU= Education; H&N= Health and Nutrition. Figures in brackets are respective coefficient of variation.

III Size class-wise Analysis Institutional and human capital impact of WSD across size classes is not expected to be as prominent as in the case of bio-physical and economic impacts. For, social institutions come in to practice only when they get broader acceptance at the community level. But there could be differences between SMF and LMF in the absence of local institutions due to their own,

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self-interest driven, initiatives. This could have prompted the differentials in the proportion of households reporting on the performance between SMF and LMF for various indicators. In the case of maintenance of WHS, greater proportion of SMF reported better maintenance and functioning when compared LMF (Fig. 5.13). Such differentials are not reported in the case of de-silting of water bodies (Fig. 5.14). This could be due to the reason that de-silting is not a usual practice and a major expensive work. Such activities are beyond the capacity of SMF to take up on their own initiative. Where as in the case of maintaining retention walls substantial proportion of sample SMF (45 percent) reported that they are not maintained at all (Fig. 15). Substantial proportion (47 percent) of LMF reported that user groups maintain the retention walls with the funds from WDF. In the absence of WDF it is more of SMF reporting maintaining on their own. This could be due to their family labour situation when compared to LMF. Similarly, women participation in CPR maintenance is reported to be more among SMF when compared to LMF (Fig. 5.16). For, traditionally participation of women from well to do families in Rajasthan is a taboo. Figure 5.13: Maintenance of Water Harvesting Structures across Size Classes

60 50 40 30

% of HH 20 10 0 Dysfunctiona Partly Working Broken l functional with SMF 4 12 51 33 LMF 7 16 50 26 Total 5 13 51 30

Figure 5.14: Periodic De-silting of Water Bodies across Size Classes

70 60 50 40 30 % of HH 20 10 0 Yes,but by SMF Yes,by all Not done and Landless Stakeholders SMF 62 28 9 LMF 63 26 11 Total 63 28 10

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Figure 5.15: Maintenance of Retention Walls across Size Classes

50 40 30 20 % of HH 10 0 Yes,UGs Doing Yes,UGs Doing Not Done using using WDF by themselves SMF 45 27 29 LMF 29 47 24 Total 39 34 27

Figure 5.16: Women Participation in CPR maintenance across Size Classes

60 50 40 30

% of HH 20 10 0 Not Involved Partly Helping Solely Managing SMF 47 51 2 LMF 52 46 2 Total 49 49 2

As far as social institutions and practices pertaining to livestock management are concerned more LMF are reporting possibility of practicing social fencing when compared to SMF (Fig. 5.17) while the differences are marginal in the case of staggered grazing (Fig. 5.18) and stall feeding (Fig. 5.19). Whereas, greater proportion of SMF when compared to LMF are reporting the practice of open grazing of all animals though the differences are not substantial (Fig. 5.20). In the case of human capital indicators the impact of WSD across size classes is mixed. While the preference for children education, male and female, is reported to more among LMF, more of households from SMF reported higher education compared to LMF (Fig. 5.21 and 5.22). As far as health and nutritional status is concerned more of SMF reported better coverage, while more of LMF reported better coverage of nutrition (Fig. 5.23 and 5.24).

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Figure 5.17: Social Fencing of Community Lands across Size Classes

100 80 60 40

% of HH 20 0 Done Along with All Agreed no Not Possible Watchman Watchman SMF 16 15 69 LMF 13 9 78 Total 15 13 72

Figure 5.18: Practice of Staggered Grazing across Size Classes

80 70 60 50 40 30 % of HH 20 10 0 Not Possible Partly Achived Acieved SMF 20 70 10 LMF 19 73 8 Total 20 71 9

Figure 5.19: Extent of Stall Feeding across Size Classes

100 90 80 70 60 50

% of HH 40 30 20 10 0 SMF LMF Total No change 3 3 3 Partly stall fed 91 90 91 Fully stall fed 5 7 6

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Figure 5.20: Extent of Grazing Practice across Size Classes

70 60 50 40 30 % of HH 20 10 0 SMF LMF Total All livestock 33 27 31 Not sent for grazing 6 8 7 Limited to small ruminants 61 65 62

Figure 5.21: Preference for Children Schooling across Size Classes

80 70 60 50 40

% of HH 30 20 10 0 SMF LMF Total Only Male 47 32 42 Both Male & Female 53 68 58

Figure 5.22: Level of Education across Size Classes

70 60 50 40 30

% of HH 20 10 0 SMF LMF Total Primary 59 63 60 Secondary 13 24 17 Collegiate 28 13 23

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Figure 5.23: Status of Health Coverage across Size Classes

70 60 50 40 30

% of HH 20 10 0 No Extra Covering All Limited to a Few Expenditure Family Members SMF 10 25 65 LMF 18 21 60 Total 13 24 63

Figure 5.24: Status of Nutritional Coverage across Size Classes

100 80 60 40

% of HH 20 0 No extra Improvement to Improvement to all Expenditure some SMF 12 20 69 LMF 0 16 84 Total 8 18 74

When assessed in terms of scoring size class wise differences are more prominent when compared to frequency distribution of sample household responses. The differences are not only substantial but also turned out significant in majority of the cases. At the aggregate level, LMF have reported better performance in five of the nine districts where the differences turned out significant (Table 5.2). The districts where SMF reported better performance include Jaipur, Jalore, Rajasmand and Sirohi, whereas Baran SMpur, Bundi, Tonk and Bikaner have shown LMF bias. Though there is no pattern of bias in performance across districts, large farmer bias appears to be more prominent in the endowed districts. Similarly, in four of the six significant indicators LMF reported better performance. But overall across the districts and indicators the differences are not statistically significant. In terms of community institutions, maintenance of water harvesting structures received better scoring from SMF while maintenance of retention walls received from LMF. And women participation is more among SMF. Social fencing of community lands along with open grazing practices received more support from LMF. That is large and medium farmers are

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more in support of community based institutions that check degradation of community lands. Health and nutrition coverage at the household level received better scoring from LMF due to their better economic status. On the whole, it may be concluded that there is large and medium farmer bias as far as institutional and human impact of watershed development. However, institutional or social impacts only strengthen the bio-physical or environmental and economic benefits and does not provide any direct or tangible benefits to the farmers, the size class biases do not really matter in this case.

Table 5.2: Performance of WSD across Size Classes

WHS PDWB MRW PW Sfen SG SF GR EDU H&N Overall District SMF- SMF- SMF- SMF- SMF- SMF- SMF- SMF- SMF- SMF- SMF- LMF LMF LMF LMF LMF LMF LMF LMF LMF LMF LMF Baran 71-81* 45-40 75-71 44-50* 93-98* 55-56 49-50 62-48 59-67* 99-100 67-71* Dausa 85-75* 40-53* 75-82 28-40* 98-89* 57-58 54-55 68-83* 73-64* 80-91* 69-70 Jaipur 75-25* 45-39 66-67 17-40* 90-80* 54-52 49-52 72-65 65-64 80-73* 64-58* SMPur 15-66* 24-21 47-56* 28-33 90-85 50-51 51-50 59-66 62-61 74-90* 54-63* Dholpur 64-62 24-45* 70-86* 23-19 83-71* 53-41* 56-67* 68-40* 68-69 49-73* 56-61 Bundi 82-100* 28-0 56-0 40-50 88-100 51-100* 53-0 69-0 63-0 89-0 66-88* 74- Tonk 67-74* 30-28 46-51 33-38* 57-96* 39-56* 55-53 50-78* 63-67 56-69* 100* Rajsamad 63-50 25-0 39-0 28-0 80-0 50-0 51-0 67-0 64-0 75-0 58-25* 100- Ajmir 73-53* 22-36* 41-55* 38-31 94-92 51-57* 51-50 73-73 59-62 65-66 100 Bikaner 39-51* 2-19* 2-46* 2-20* 0-75 2-40* 47-53 33-70* 60-65* 0-73 21-54* Jalor 51-51 19-9* 40-31* 26-13* 90-84 35-39 50-47* 58-77* 59-64 80-66* 55-50* Jaislmer 29-7* 7-5 26-25 15-14 74-79 32-31 48-49 65-70 60-61 84-84 49-48 Barmer 38-40 9-13 27-37* 15-15 60-74* 39-37 50-51 62-71* 60-60 93-88* 52-54 Sirohi 51-50 13-0 27-0 19-0 53-0 43-0 50-0 65-0 60-0 76-0 50-17* 95- Udaipur 48-54 19-16 36-35 28-27 77-81 42-37 50-50 67-69 61-60 58-59 100* Over all 57-49* 23-24 42-48* 27-25* 77-82* 45-44 51-52 64-69* 63-63 80-85* 57-58

IV Scheme-wise Analysis The scheme wise variations in institutional impacts of WSD are important in understanding the differential economic and bio-physical impacts across the schemes. Though bio-physical or environmental impacts are critical for realising economic impacts, environmental aspects are often dictated by natural factors that are beyond human control. Social impacts, however, could minimise the adverse natural events to some extent through institutional or community action. Therefore, better social impacts in some schemes could explain their relatively better

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economic impacts. This is especially true in the context of DDP watersheds, which face natural disadvantages. Better performing institutional arrangements could improve the situation in the DDP watersheds. Our assessment of social impacts across schemes reveal that DDP watersheds perform poorly even in the case of institutional or social impacts.

Though majority of the sample households reported partly functional water harvesting structures in the DDP watersheds, a substantial portion (34 percent) indicated broken or dysfunctional structures (Fig. 5.25). Here also IWDP schemes are doing better when compared to DPAP and DDP watersheds. In the case of de-silting of water bodies most of the households (79 percent) reported that de-silting was not done at all in the DDP districts, while it is 53 percent in the case of IWDP and 63 percent in the case of DPAP watersheds (Fig. 5.26). Though de-silting is not a critical issue in Rajasthan, DDP schemes, given their harsh environments, could improve the situation with better maintenance and management of water bodies. The DDP schemes are doing better in the case of maintenance of retention walls when compared to de-silting of water bodies. For, 49 percent of the sample households reported that user groups maintain the retention walls with watershed development funds as against 25 percent and 33 percent in the case of IWDP and DPAP watersheds respectively (Fig. 5.27). This indicates that DDP watersheds, which are located in poor regions, need the funding more than the other schemes that are relatively better off for maintaining the structures. Strengthening the process for provision of WDF would go a long way in improving maintenance of the structures and sustaining the impacts of WSD. And participation of women in managing the CPRs is also the lowest in DDP schemes when compared to IWDP and DPAP (Fig. 5.28).

Figure 5.25: Status of Water Harvesting Structures across Schemes

60 50 40 30 20 % of HH 10 0 IWDP DPAP DDP Broken 4 15 5 Dysfunctional 6 5 29 Partly functional 51 47 53 Working with 39 34 12

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Figure 5.26: Periodical De-silting of Water Bodies across Schemes

90 80 70 60 50 40

% of HH 30 20 10 0 IWDP DPAP DDP Not done 53 69 79 Yes,but by SMF and Landless 34 22 18 Yes,by all Stakeholders 13 9 2

Figure 5.27: Maintenance of Retention Walls across Schemes

60 50 40 30

% of HH 20 10 0 IWDP DPAP DDP Not Done 36 40 45 Yes,UGs Doing using using WDF 25 33 49 Yes,UGs Doing by themselves 38 27 7

Figure 5.28: Participation of Women in CPR Maintenance across Schemes

70 60 50 40 30 % of HH 20 10 0 IWDP DPAP DDP Not Involved 41 47 66 Partly Helping 57 49 33 Solely Managing 2 4 1

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As far as social institutions for managing livestock and grazing lands are concerned, the performance of DDP districts is fairly good and in some cases better than DPAP schemes. For instance, social fencing is widely accepted and practiced even among DDP watersheds, as 68 percent of the sample households follow social fencing of community lands without a watchman (Fig. 5.29). This is not very different from other two schemes that have 73 and 79 percent of the sample households following the practice. Similar pattern can be observed in the case of other indicators like system of staggered grazing, practice of stall feeding and open grazing (Figs. 5.30 to 5.32).

Figure 5.29: Practice of Social Fencing across Schemes

90 80 70 60 50 40

% of HH 30 20 10 0 IWDP DPAP DDP Not Possible 12 17 19 Done Along with Watchman 15 5 12 All Agreed no Watchman 73 79 68

Figure 5.30: Practice of Staggered Grazing across Schemes

80 70 60 50 40

% of HH 30 20 10 0 IWDP DPAP DDP Not Possible 16 19 28 Partly Achived 72 73 69 Acieved 13 9 3

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Figure 5.31: Practice of Stall Feeding across Schemes

100 90 80 70 60 50 40 % of HH 30 20 10 0 IWDP DPAP DDP No change 3 3 4 Partly stall fed 89 93 94 Fully stall fed 8 4 3

Figure 5.32: Practice of Open Grazing across Schemes

70 60 50 40 30 % of HH 20 10 0 IWDP DPAP DDP All livestock 30 35 32 Not sent for grazing 9 8 2 Limited to small ruminants 61 58 66

In the case of household’s preference for children’s education DPAP and DDP watersheds are doing better. In the DPAP watersheds sixty seven percent of the sample households report that they prefer to send both male and female children to school as against 64 percent in the case of DDP watersheds and 54 percent in the case of IWDP watersheds (Fig. 5.33). As far as level of education is concerned, while 45 percent of the households in IWDP watersheds are reporting secondary and college education status as against 32 percent in DDP and 28 in DPAP watersheds (Fig. 5.34). In the case of health and nutrition impacts also the DDP watersheds are doing fairly well (figs. 5.35 and 5.36) though 28 percent of the households reported no increase on health expenditure (Fig. 5.35).

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Figure 5.33: Preference for Children’s Schooling across Schemes

80 70 60 50 40

% of HH 30 20 10 0 IWDP DPAP DDP Only Male 46 33 36 Both Male & Female 54 67 64

Figure 5.34: Level of Education across Schemes

80 70 60 50 40

% of HH 30 20 10 0 IWDP DPAP DDP Primary 55 72 68 Secondary 17 24 15 Collegiate 28 4 17

Figure 5.35: Status of Health across Schemes

70 60 50 40 30

% of HH 20 10 0 IWDP DPAP DDP No Extra Expenditure 7 0 28 Limited to a Few 27 38 14 Covering All Family Members 67 62 58

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Figure 5.36: Status of Nutrition across Schemes

80 70 60 50 40

% of HH 30 20 10 0 IWDP DPAP DDP No extra Expenditure 10 0 6 Improvement to some 16 30 20 Improvement to all 74 70 74

The scoring exercise revealed that the differences in performance across schemes are not as wide as they appeared in the frequency distribution analysis. But the differences are statistically significant in most of the cases (Table 5.3). The overall scores range between 61 for IWDP to 52 for DDP watersheds. High scores are observed in the case of health and nutrition, social fencing and grazing practices. For most of the indicators the performance of IWDP watersheds is significantly better than DPAP watersheds and the performance of DPAP watersheds is significantly better than DDP watersheds. On the whole, IWDP districts are performing better than other two schemes, DDP watersheds are doing fairly well when compared to bio-physical and economic impacts.

Table 5.3: Performance of WSD in Terms of Social Impacts across Schemes

Indicators/Type of Scheme IWDP DPAP DDP Overall IWDP- IWDP- DPAP- DPAP DDP DDP Status of Water Harvesting Structures 62 43 41 54 62-43* 62-41* 43-41 Periodical de-silting of Water bodies 30 20 11 23 30-20* 30-11* 20-11* Maintenance of Retention Wall 51 44 31 44 51-44* 51-31* 44-31* Participation of Women 31 29 18 27 31-29 31-18* 29-18* Social Fencing of Community lands 80 81 74 78 80-81 80-74* 81-74* Staggered Grazing 48 45 38 45 48-45* 48-38* 45-38* Stall Feeding 52 51 50 51 52-51* 52-50* 51-50 Grazing 66 62 67 66 66-62* 66-67 62-67* Preference of sending children to school 86 84 89 87 86-84 86-89* 84-89* Level of Education 50 44 46 48 50-44* 50-46* 44-46 Health 80 81 65 75 80-81 80-65* 81-65* Nutritional Care 85 90 88 86 85-90* 85-88* 90-88* Overall 61 57 52 57 61-57* 61-52* 57-52*

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V Conclusions The success of WSD critically depends on the community or collective institutions at the village level. These institutions play an important role in proper implementation and sustaining watersheds at the village level. For, implementation of WSD transcends individual households, communities and also villages. WSD also covers private as well as community resources like land and water. Cooperative or collective strategies are sine quo none for making WSD effective in addressing its objectives. In fact, as per WSD guidelines (GoI, 1994) attaining participation through institutional arrangements is the starting point for the implementation of WSD. Our assessment of institutional impacts of WSD brings out the following observations:  Institutional impacts of WSD are much higher when compared to bio-physical and economic impacts. This is a positive dimension in the context of watersheds implemented after the 1994 guidelines that emphasise participatory watershed development.  Institutional impacts in the arid and low rainfall districts are on the lower side. Though it is often argued that social institutions are more vibrant in the less endowed parts of Rajasthan, this does not reflect in the context of WSD. This could be due to the reason that financial support in the nature of watershed development fund is necessary for community based activities, especially in the poorly endowed districts.  Size class wise differences are more prominent in the case of institutional impacts. The differences are not only substantial but also turned out significant in majority of the cases.  There is large and medium farmer bias regarding institutional and human impacts of watershed development. That is large and medium farmers are more in support of community based institutions that check degradation of community lands.  Across the schemes for most of the indicators the performance of IWDP watersheds is significantly better than DPAP watersheds and the performance of DPAP watersheds is significantly better than DDP watersheds. On the whole, IWDP watersheds are performing better than the other two schemes. But, DDP watersheds are doing fairly well when compared to bio-physical and economic impacts.

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CHAPTER VI Factors Influencing the Impact of Watershed Development Programme

I Introduction The aggregated analysis, district level, size class and scheme wise, helped in assessing the impact of WSD on the three important components. However, the aggregate analysis rises further questions like whether the level of impacts is similar across all the sample watersheds in the district? If not, what are the factors that explain the level of impact across watersheds? While differential impacts are observed for the three components, the reasons are not clear why higher level of social and environmental impacts failed to translate in to economic impacts? What could be the possible inter-relationships between bio-pysical, institutional and economic impacts. Is the performance of the WSD really linked to the scheme under which it is implemented? In order to answer some of these queries an attempt is made in this chapter to analyse watershed level information. For this purpose, data are drawn from primary as well as secondary sources. Primary information has been drawn from rapid survey, village and household surveys. Secondary sources include census data and published documents at the district and block level.

II Watershed Wise Analysis The scores accorded at the household level for each watershed for the three components and the overall score are examined in order to assess the WSD performance at the watershed level. As expected, the performance of WSD in terms of economic impacts received lowest score with an average score of 31. Across the watersheds the score ranges between 17 and 51. Of the 110 watersheds 39 watersheds fall in the range of above average performance (Table 6.1). While eight districts got above average scores seven got below average scores. Most of the below average districts are from low rainfall arid regions. If we consider 40 as the threshold level score for a fair level of performance only 16 sample watersheds fall in this category. That is only 15 percent of the watersheds showed fair level of performance. And only 3 sample watersheds scored 50 or above. On other hand, in the case of bio-physical or environmental and social impacts 57 and 62 watersheds respectively have shown above average performance. Some of the watersheds have scored as high as 70 percent and above in both the cases. Above average performance is recorded in 10 of the 15 sample districts. Though the below average performing five district are not the same, they are mostly from the arid districts. Therefore, low performing watersheds are mostly from the low rainfall arid

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districts. But, there are watersheds that got above average score even from these districts (see appendix). This indicates that the reasons for better performance go beyond natural or climatic factors. The average over all score is 40, which is also considered as threshold level, and 35 percent of the sample watersheds are above this score. The set of districts housing the watersheds above and below the average score are more are less same as that of other components. Table 6.1: Performance of Watersheds in Rajasthan

No. of No. of sample sample Average Impacts Watershe Main Districts watersheds Main Districts Range CV Score ds above below average average Baran, Dausa, Jaipur, Rajasamand, SMPur, Dholpur, Environment 57 53 Jaisalmer, Jalore, 43 11-73 27 Bundi, Tonk, Ajmer, Barmer, Bikaner Sirohi and Udaipur Rajasamand, Baran, Dausa, Jaipur, Jaisalmer, Jalore, Economic 39 SMPur, Dholpur, 71 Barmer, Bikaner, 31 17-51 24 Bundi, Tonk, Ajmer Sirohi and Udaipur Baran, Dausa, Jaipur, SMPur, Rajsamand, Bikaner, Jalore, Institutional / 62 Dholpur, Bundi, 48 Jaisalmer, 57 19-73 17 Social Tonk, Ajmer and Barmer, Sirohi Udaipur Rajasmand, Baran, Dausa, Jaipur, Bikaner, Jalore, Overall 42 SMPur, Dholpur, 68 Jaisalmer, 40 21-59 19 Bundi, Tonk, Ajmer Barmer, Sirohi, Udaipur

Note: Main districts are those where a majority of the watersheds are in the category. CV= Coefficient of Variation.

How does the present assessment of WSD performance compares with the earlier assessments? The nature and method of the present assessment deviates from the earlier ones in two ways. First, beneficiaries were asked to assess the WSD performance in a close ended format by asking them to choose one of the answers, while the earlier assessments mostly used the deductive methods of collecting actual changes that have taken place due to the WSD programme through adopting before or after / with or without methods of assessment. Second, beneficiaries are asked to provide a score based on the performance of the particular indicator. The sum of score for all the indicators is 100. The overall score a household accords would be based on the households own experience. This is also different from the earlier assessments where evaluators take an object view of the WSD success based on the

128 performance of various indicators. Often these indicators are excessively biased in favour of economic impacts or indicators. In the present case all the important indicators are included.

The earlier assessments were often based on either cost benefit ratios or economic impacts. The literature on impact of WSD in general indicates only about 20 percent success rate, whatever be the measure of success. The recent meta analysis observes that 35 percent of the watersheds perform above average level (Joshi, et, al, 2004). The present assessment provides a clear picture of the impact. To assess the success rate we assume that a score of 40 and above at the household level indicate a fairly satisfactory performance of the WSD. This appears reasonable given the harsh climatic conditions of Rajasthan. At this level 43 percent of the sample watersheds have performed well as far as overall performance is concerned. In terms of economic impacts only 15 percent of the watersheds performed well as against 68 percent in the case of bio-physical and 96 percent in the case of social impacts. This brings out two important aspects: i) better performance of bio-physical or environmental and institutional impacts are not translated in to economic impacts. This could be due to the climatic conditions in most parts of the state. ii) given the emphasis on participatory aspects in the 1994 guidelines the performance of watersheds in terms institutional or social impacts appears commendable. It may be noted that the traditional institutional mechanisms existing in the state would have enhanced the impacts. The better performance of institutional and bio-physical impacts could ensure the sustainability of the limited economic impacts. Figure 6.1: Variations and Trends in the Performance of Different Components across Watersheds

Score % 30

0 1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91 94 97 100 103 106 109 Watersheds

Environmental Score (43) Economic Score (31) Social Score (57)

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Table 6.2a: Regression Plot of the Economic and Environmental Scores

 Linear Regression 50  



     Economic Score = 11.69 + 0.44 * EnvironmentalScore    40R-Square = 0.48                       30                     Economic Score Economic                          20  



20 40 60 Environmental Score

Table 6.2b: Regression Plot of the Economic and Social Scores

 Linear Regression 50 

 

      40             Economic Score = 6.23 + 0.43 * SocialScore R-Square = 0.32         30                               Economic Score Economic                  20   

20 30 40 50 60 70 Social Score

Table 6.2c: Regression Plot of the Environment and Social Scores

 Linear Regression

    60                 Environmental Score = -2.56 + 0.80 * SocialScore     R-Square = 0.45                         40                     

Environmental Score Environmental     20      

20 30 40 50 60 70 Social Score

Our analysis also brings out clearly that these three components move together in most of the cases (Fig. 6.1). They are also found to be interlinked as reflected in the regression plots

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(Table 6.2a to 6.2c). All the three components are highly correlated. The regression coefficient between economic and bio-physical as well as economic and institutional components are of same magnitude (0.44 and 0.43) and significant. Though the regression coefficient between institutional and bio-physical components is high at 0.80 and statistically significant. And the explanatory power of the specification (R2) ranges from 48 (economic and bio-physical) to 32 (economic and social) percent. Despite the significant linkages, economic impacts are quite subdued. There is need for converting the bio-physical and institutional impacts in to economic impacts. For this one has to understand the factors that determine the economic impacts as well as other impacts. For, the impacts are not the same across watersheds of different agro-climatic zones or districts. This aspect is taken up in the next section.

III Factors Influencing WSD Performance At this juncture it would be pertinent to examine the factors determining the variations in the performance of WSD across sample watersheds. For this purpose, a multiple regression analysis was adopted using number of indicators that influence the performance. The basic specification is as follows:

𝑾𝑾= 𝑺𝑺𝑺𝑺𝑺𝑺𝒊𝒊𝒊𝒊 , , ( ) , ( ) , % , , , , , , , , , , , % ,, % , + 𝒇𝒇�𝑻𝑻 𝑻𝑻𝒊𝒊𝒊𝒊 𝑹𝑹𝑹𝑹𝒊𝒊𝒊𝒊 𝑽𝑽𝑽𝑽 𝑯𝑯𝑯𝑯 𝒊𝒊𝒊𝒊 𝑽𝑽𝑽𝑽 𝑮𝑮𝑮𝑮 𝒊𝒊𝒊𝒊 𝑨𝑨𝑨𝑨𝒊𝒊𝒊𝒊 𝑾𝑾𝑾𝑾𝒊𝒊𝒊𝒊 𝑳𝑳𝑳𝑳𝑳𝑳𝒊𝒊𝒊𝒊 𝑨𝑨𝑨𝑨𝑨𝑨𝑨𝑨𝒊𝒊𝒊𝒊 𝑨𝑨𝑨𝑨𝑨𝑨𝑨𝑨𝑨𝑨𝒊𝒊𝒊𝒊 𝑨𝑨𝑨𝑨𝑨𝑨𝑨𝑨𝒊𝒊𝒊𝒊 𝑨𝑨𝑨𝑨𝑨𝑨𝑨𝑨𝒊𝒊𝒊𝒊 𝑭𝑭𝑭𝑭𝑭𝑭𝑭𝑭𝒊𝒊𝒊𝒊 𝑨𝑨𝑨𝑨𝑨𝑨𝒊𝒊𝒊𝒊 𝑪𝑪𝑪𝑪𝑪𝑪𝑪𝑪𝒊𝒊𝒊𝒊 𝑷𝑷𝑷𝑷𝑷𝑷𝑷𝑷𝒊𝒊𝒊𝒊 𝑪𝑪𝑪𝑪𝑪𝑪𝒊𝒊𝒊𝒊 𝑭𝑭𝑭𝑭𝑭𝑭𝑭𝑭𝒊𝒊𝒊𝒊 � Where,𝑼𝑼𝒊𝒊𝒊𝒊

WSDPdt = WSD performance i.e., scores as assigned by the sample households in the four components viz., bio-physical, economic, institutional and over all in watershed ‘i’ at time ‘t’.

TSit = Type of Scheme under which the watershed was implemented i.e., IWDP, DPAP and DDP.

RFit = Normal Rainfall in millimetres (at the district where the watershed is located).

VS(HH)it = Watershed Village size in terms of number of households.

VS (GA)it = Watershed Village size in terms of geographical area.

%AIit = % area irrigated of the Watershed village.

WDit = Well density (number of wells per unit of land) LSDit = Livestock Density (livestock in standardized units- TLU per unit of land or population)

AEDUit = Access to education (school standard in the village).

APHCit = Access to Primary Health centre in terms of distance from the village

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AMRKit = Access to market (distance in km between the WSD village and market place).

APWSit = Access to protected water supply.

FCBOit = Functioning of community based organisations.

FMit = Frequency of Meetings

CWDFit = Contribution to watershed development fund

PIALit = Linkages between the project implementing agency / the line department and the watershed institutions.

%CPRit = % of Area under common pool resources.

%FRSTit = % of area under forests.

Uit = Error term. The independent variables are selected based on the theoretical considerations and the availability of data at the watershed level. The variables are drawn mainly from different sources like Rapid Reconnaissance Survey, village survey, household survey, secondary sources like census, departmental records, etc. An exhaustive list of indicators that are likely to influence the performance was prepared. All these variables were tried in different combinations and permutations were tried. But, some of the variables, though important, did not find place in the specifications due to various reasons like multicollinearity, non- significance and also the absence of variation. For instance, maintenance of CPRs is highly correlated with contribution to WDF; nomination of leaders is the main practice in all the watersheds and no elections were observed for electing the leaders. And variables like size of watershed, social audit, sharing of benefits, maintenance of records, number of tanks, % of SC/ST households, etc., did not turn out significant and hence dropped from the analysis. Details of variable measurement and their theoretical/ expected impact on the components of WSD are presented in table 6.3.

Table 6.3: Measurement and Expected Signs of the Selected Variables

Variable Measurement Theoretical or Expected Impacts ECO BIOPHY INST OVAL TS Dummy (IWDP=1; DPAP=2 and DDP= 3) -ve -ve -ve -ve RF Normal rainfall in mm +ve +ve -ve +ve VS (HH) Number of Households -ve -ve -ve -ve VS (GA) Geographical area -ve +ve -ve -ve %AI Percentage of Area Irrigated +ve +ve -ve +ve Number of functioning wells per unit of geographical +ve +ve +ve +ve WD area Livestock population per unit of geographical area / and +/-ve +/-ve +/-ve +/-ve LSD per human population AEDU School standard +ve +ve +ve +ve APHC Distance in range of KM -ve -ve -ve -ve

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AMRK Distance in KM -ve -ve -ve -ve APWS Dummy (Yes=1 and No=0) +ve +ve +ve +ve Dummy (1= Formed but not functional; 2= Partially +ve +ve +ve +ve FCBO functional; 3= fully functional) Dummy (0= No regular conduct of meeting; 1= Regular +ve +ve +ve +ve FM conduct of meetings CWDF Dummy (0= No; 1= Yes) +ve +ve +ve +ve Dummy (1= linkage ended with the watershed; 2= +ve +ve +ve +ve PIAL Continuing) % CPR Percentage of Geographical area -ve -/+ve -/+ve -/+ve %FRST Percentage of geographical area +ve +ve -/+ve -/+ve

Note: ECO= Economic Score; BIOPHY= Bio-physical or Environmental Score; INST= Institutional or Social Score; OVAL= Overall Score

Some of the variables were measured in two ways viz., size of the village is measured in terms of area and population and livestock density was measured in relation to population and area. Linear regressions applying Ordinary Least Squares (OLS) were estimated to regress the dependent variables (WSDP) against the selected independent variables (SPSS package). Regressions were run on cross sectional data across 110 sample watersheds. Various permutations and combinations of independent variables were used to arrive at the best fits. Multi-colinearity between the independent variables was checked using the Variance Inflation Factor (VIF) statistic. Multi-colinearity is not a serious problem as long as ‘VIF’ value is below 2. The best fit specification was selected for the purpose of final analysis for each dependent variable. While the descriptive statistics of the selected indicators are presented in the appendix, the estimates of the selected specifications are presented in table 6.4. Table 6.4: Regression Estimates of Selected Specifications

Dependent/ Economic Score Bio-physical Score Institutional Score Overall Score Independent Variable Coefficient VIF Coefficient VIF Coefficient VIF Coefficient VIF Constant 2.66* (5.2) --- 14.58* (2.8) --- 33.31* (7.1) --- 19.67* (9.3) --- TS ------RF 0.02* (5.9) 1.6 0.023* (6.76) 1.6 0.016* (4.0) 1.7 0.003** (1.7) 1.5 VS (HH) ------0.002 (0.60) 1.8 -0.01** (2.1) 1.9 ------VS (GA) ------0.12* (2.49) 1.6 ------%AI 0.05 (1.04) 1.7 0.15*(3.29) 1.4 0.12* (2.37) 1.3 0.07* (3.26) 1.7 % CPR -0.09* (3.06) 1.2 ------0.02 (1.12) 1.2 % FRST ------0.08 (1.4) 1.2 ------WD 18.8* (2.9) 1.4 11.75 (1.63) 1.4 -4.34 (0.52) 1.4 5.20*** (1.63) 1.4 LSD ------0.48 (0.56) 1.2 -0.44 (0.45) 1.2 ------AEDU -0.76* (2.6) 1.2 -0.59 (1.52) 1.5 ------0.22 (1.55) 1.1 AMRK ------0.015 (0.39) 1.1 0.03 (0.57) 1.1 0.02 (1.03) 1.1 APWS 2.95** (2.12) 1.4 -2.24 (1.32) 1.6 2.38 (1.23) 1.6 ------

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APHC ------0.90 (1.18) 1.1 ------FCBO 2.38** (1.89) 1.8 3.06*** (1.9) 2.0 ------3.84* (6.1) 1.8 FM 2.88*** (1.7) 2.0 ------1.49*** (1.7) 2.0 CWDF 6.48* (3.95) 2.0 4.14** (2.12) 1.9 ------6.3*(7.3) 1.9 PIAL 5.46* (2.45) 1.1 7.21* (2.8) 1.2 11.5* (3.9) 1.1 5.87* (5.3) 1.1 R Square 0.76 0.68 0.40 0.86 R Bar Squ 0.73 0.64 0.34 0.85 N 110 110 110 110

Note: Figures in brackets indicate ‘t’ values. *;** and *** indicate level of significance at 1, 5 and 10 percent levels respectively.

The explanatory power of the selected specifications is quite good for three of the four components. The selected indicators explain about 70 percent of the variations in the dependent variables of economic and bio-physical scores (Table 6.4). In fact, the selected indicators explain 86 percent of the variations in the overall performance of the watersheds. In the case of institutional impacts the explanatory power is low at 40 percent. Most of the independent variables have the expected signs or relationships with the dependent variables. One unique feature of all the specifications is that the social or institutional indicators have revealed a positive and significant impact on all the other components of watershed performance including the overall performance. These indicators include functioning of CBOs (FCBO), frequency of meetings (FM), contribution to watershed development fund (CWDF) and the linkages of project implementing agency / line department with the watershed institutions (PIAL)2. This reemphasises the importance of the participatory institutions in the WSD, which was at the core of the 1994 watershed guidelines. This is despite the better performance of WSD in social / institutional component when compared to economic and bio-physical components. On the other hand, type of scheme (TS), which differentiates the IWDP, DPAP and DDP watersheds, did not turn out significant in any of the specifications. The variable had to be dropped in some specifications where it turned out significant due to multicollinearity problem. At the same time its inclusion reduced the explanatory power of the specification. Though the performance of various indicators among these three schemes differ significantly (see chapters 3, 4 and 5) at the aggregate level, the type of scheme did not explain variations when other factors are controlled. For, it could be either irrigation or rainfall that explains the variations rather than the scheme.

2 Indicators like social audit and record keeping were also tried but dropped due to non-significance or multicolenearity. 134

Economic impact: Not many factors turned out significant in explaining the economic performance of WSD. As mentioned earlier economic performance has received lowest scores when compared to other components. Proportion of area under irrigation turned out to be significant with a positive sign. This indicates that economic impacts are better in the watersheds where irrigation is available. In other words watersheds perform better in the better endowed regions i.e., medium rainfall regions when compared to arid regions with very low rainfall. For, WSD is not taken up in the high rainfall and high irrigation regions. Access to protected water supply (APWS) also turned out significant with a negative sign. Theoretically APWS is expected to have a positive sign, as protected water supply is more common in the better endowed regions. But in case protected water supply is competing with irrigation then it could have negative impact on the economic performance of WSD. In Rajasthan multi village drinking water schemes provide water from bore wells located at one point. This may adversely affect the availability of groundwater in the surrounding areas, given the extreme water scarce situation in the arid parts of the state in particular. The three institutional factors i) functioning of CBOs; ii) contribution to watershed development fund (CWDF) and iii) linkage of PIA / line departments with watershed institutions have revealed a positive and significant impact on the economic performance of the WSD. These three factors are critical for maintaining the watershed structures. For, the active or functioning of CBOs ensure fund generation. Funds can be effectively used for the maintenance of structures with the help or support of the department. More over linkages with the line departments can provide technical support in crop, livestock development, etc. Of these three factors CWDF and PIAL larger influence on the economic impacts. Therefore, economic impacts can be enhanced by strengthening the institutional indicators. On the other hand, the impact of irrigation is not only low but also difficult to enhance it in the given climatic conditions.

Bio-physical or Environmental Impact: Number of variables turned out significant in explaining the variations in the performance of bio-physical impact. Rainfall (RF) and area irrigated (%AI) have a significant and positive impact indicating bio-physical or environmental impacts are more pronounced in high rainfall and irrigated regions. These impacts are in the expected lines. This is obviously due to the conducive natural conditions, as the improvements in vegetation cover, availability of fodder or fuel would be difficult in harsh climatic conditions of arid regions. Watershed development can’t be a perfect substitute for these natural conditions. However, environmental impacts of watershed development

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could be enhanced with institutional arrangements like functioning CBOs (FCBO), regular meetings of watershed committees (FM), contribution to development fund (CWDF) and the linkage between the implementing agency and the line department (PIAL). Of these variables, WD, CWDF and PIAL have greater influence on environmental impacts.

Social Impacts: In the case of social impacts the goodness of fit is not as robust as other impacts. Not only the explanatory power of the specification is low but also only a few variables turned out significant. Size of the village in terms of population (VS-HH) has a negative influence on social impact. This follows Olsan’s (1965) classic theory of size and collective action, where he argues that collective action would be successful in small groups rather than in bigger groups. On the other hand, geographical area (VS-GA), rainfall (RF) and irrigation (%AI) revealed a positive and significant impact. This indicates that institutional or social impacts are stronger in better endowed regions or watersheds, though it is generally believed that social capital is stronger in backward regions. This could be due to the inter linkages between institutional, economic and bio-physical impacts. While size of the population is deterrent, size of the area appears to have positive impact on institutional score. As in the case of economic and environmental impacts, PIAL has a positive and significant impact on institutional impacts. In fact, PIAL has much stronger impact than any other variable on the institutional performance of WSD.

Overall Impacts: Selected indicators explain 86 percent of the overall performance of watershed development in the sample watersheds. Seven variables turned out significant in explaining the variations and all of them are positively correlated with the overall performance of WSD. The indicators include rainfall (RF), irrigation (%AI), well density (WD), functioning of CBOs (FCBO), frequency of meetings (FM), contribution to the watershed development fund (CWDF) and the linkages of the implementing agency / line department and the watershed institutions (PIAL). This brings out clearly that the overall performance depends on natural endowments and institutional strengths of the communities. Stronger collective and participatory approach seems to hold the key for the overall success of the WSD. Though it may be argued that some threshold level of natural endowments like medium rainfall along with protective irrigation facilities are necessary for effective WSD impacts, institutional aspects seem to have much stronger influence on the performance.

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IV Conclusions The preceding analysis brings out that the performance of watersheds varies as much as, if not more, across districts. The watershed wise analysis indicates that forty three percent of the sample watersheds show overall performance above forty percent score. While substantial proportion of watersheds perform above the threshold level (40 percent score) in terms of bio-physical (68 percent) and institutional (96 percent) impacts, these impacts are not translated in to economic impacts only in fifteen percent of the cases. This is despite the strong linkages between the three components. Regression analysis carried out to examine the factors that influence economic as well as other components brings out the following aspects:  Natural and participatory institutional aspects play an important role in determining the performance of WSD in Rajasthan  The analysis does not support the earlier conclusion that IWDP watersheds perform better than DPAP and DDP watersheds. Regression analysis suggests that impact of WSD is determined mainly by natural factors like rainfall and access to irrigation rather than the type of scheme. The scheme is a manifestation of natural factors.  Participatory institutions like functioning of community based organisations, regular meetings of watershed institutions, contributions to watershed development fund and continued linkage with the line department are critical the success of WSD.  Continuation of WSD institutions like watershed committee and watershed association even after the completion of the programme is essential for enhancing and sustaining the impacts.  Contributing to and management of watershed development is another import element in ensuring sustained benefit flows through proper maintenance of watershed structures.  And the continued support from the line department, which could be possible through maintaining the relationship between the watershed committees and the department, would help in continued technical support and sustained impacts of WSD.

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Appendix Table A6.1: Watershed Wise Performance (Scores)

Name of the Name of Overall Environmental Economic Social Score District Watershed Score (40) Score (43) Score (31) (57) (Village) Bisali 46 45 37 66 Danitagaria 44 51 33 64 Baran Bavergardh 54 63 43 71 Heerapur 53 55 43 71 Chavonda 45 51 34 63 Bidholi 51 62 39 69 Dausa Ranoli 59 73 50 72 Geerotakalan 48 44 37 70 Arniya 59 62 50 73 Ajnora 46 59 37 58 Khedhahanumanji 41 45 27 68 Jaipur Bicchi 35 34 30 48 Chadhamakala 46 39 39 67 Hatheli 43 49 33 60 Birpur 50 61 41 65 Pali 43 46 36 58 Sawai Govindpur 35 46 28 45 Madhapur Talawada 48 56 37 67 Kschda 41 49 35 50 Madha Bugurg 44 40 37 60 Kailashpura 47 43 39 65 Dholpur Dhodakapura 43 36 41 53 Vinathipur 41 55 35 46 Nadoli 52 50 43 70 Bhawantipura VI 50 57 42 62 Pechkebavdi 55 58 46 70 Bundi Ransanda 51 57 45 58 Negad 51 56 41 70 Bhawaneegarh 51 58 40 69 Mandawer I 39 46 30 56 Chandsingpura 48 52 39 65 Deoli 54 61 43 72 Dadiya 48 44 41 68 Maroni 43 53 36 54 Tonk Kanwara 58 61 51 70 Borkhandi 44 51 35 56 Ralawata 46 52 36 61 Ralawata C 38 47 30 50 Hanumanpura 36 51 28 45 Lasaraya 37 39 24 65 Kerkala 35 36 24 61 Jawariya 37 45 26 56 Thaneta 39 42 28 59 Parawal 38 42 27 63 Boabanuja 39 43 28 60 Molela 33 39 27 45 Rajsamad Atma 32 42 21 48 Sarodh 40 41 30 63 Kereegyikakheda 38 38 27 63 Machind 37 38 27 59 Krai 33 38 28 43 Mandawada 40 40 28 67 Posali 37 40 26 59

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Dhanin 36 36 27 57 Miya 43 53 31 67 Hingtoda 44 53 32 65 Ajmer Ajayari 41 50 29 66 Ajgra 44 52 33 67 Bhatalov 46 53 37 65 Swaroop Deser 41 46 33 59 Udairamseer 35 38 28 51 Bikaner Kakku 24 22 25 23 Hansasar 21 11 23 19 Rasisar 41 45 35 53 Nosara 35 41 25 55 Neeltkanth 34 42 27 46 Jalor Ghana 37 38 29 55 Barawa 34 34 27 47 Narpura 37 40 30 50 Kathodi 25 13 21 41 Mangliyawas 31 20 25 51 Kuchdi 31 18 26 47 Kumharkota 32 27 27 48 Ramgarh 28 24 26 35 Jaislmer Kanoi 30 15 26 51 Lanaila 39 36 30 62 Lunki Basti 28 20 24 44 Decha 31 27 24 51 Brahmsar 35 36 24 58 Gadainatee 34 31 26 53 Kharwa 38 40 27 60 Kalawa 29 26 22 47 Sinlichosira 25 25 17 44 Mewanagar 26 27 17 46 Barmer Indruna 33 30 26 49 Harmalpura 30 25 23 52 Khandap 37 34 26 61 Bhandiawas 37 38 26 61 Ramaniya 34 33 24 58 Muri I 36 47 24 56 Muri II 36 45 28 50 Sirohi Kheragegarwa 36 51 30 38 Kerlapadar 35 42 26 50 Viroli 39 50 27 57 Bhauwa 38 42 28 59 Maliphala 33 41 19 60 Waw 39 45 29 57 Kalkardurga 36 43 26 57 Padmela 35 47 19 64 Bilkabas 36 48 22 56 Karmal 40 54 27 60 Udaipur Masinghpura 36 42 28 49 Bhopasagar 40 47 27 63 Badawali 39 48 25 62 Rthauda 38 45 28 58 Gudiyawada 38 48 25 59 Intali 40 49 28 59 Dwayacha 35 39 27 53 Bhoraipal 40 45 31 59

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Table 6.2A: Descriptive Statistics of selected variables

Skewness Std. Variables Minimum Maximum Mean Std. Deviation Statistic Error Type of the Scheme 1 3 1.77 0.91 0.47 0.23 Rain fall 164 858 593.39 241.59 -0.49 0.23 Total Geographical area 161 68246 2343.60 6817.97 8.59 0.23 Total HHs 30 1333 268.30 246.36 2.20 0.23 % of CPR 0 94.55 23.23 22.14 1.38 0.23 % Area Irrigated 0 87.79 13.34 17.33 2.12 0.23 Well density 0 0.81 0.06 0.11 4.41 0.23 Ratio (Pop/Livestock) 0.03 4.82 0.98 0.84 1.89 0.23 Highest School standard in the 0.00 12 7.43 2.10 -0.12 0.23 village Distance to the PHC (1=more than 10 kms; 3=between 5 to 10 kms; 1 4 1.78 1.05 1.12 0.23 2=less than 5 kms; 4=within the village) Distance to the nearest market 2 68 27.83 17.12 0.80 0.23 (Km) Access to Protected water supply 0 1 0.39 0.49 0.45 0.23 (1=Yes and 0=No) Functioning of CBO (1= not functional; 2= Partially functional; 1 3 2.29 0.61 -0.25 0.23 3= fully functional) Frequency of meetings (0= No regular conduct of meeting; 1= 0 1 0.67 0.47 -0.75 0.23 Regular conduct of meetings) Contribution to WDF (0= No; 1= 0 1 0.41 0.49 0.37 0.23 Yes) Linkages with line departments (1= linkage ended with the 1 2 1.08 0.28 3.09 0.23 watershed; 2= Continuing) % of Forest area 0 92.94 6.44 15.36 3.24 0.23

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CHAPTER VII Conclusions and Policy Implications

I Introduction Watershed Development is among the policy thrust areas of rural development in India. It has transformed from resource conservation programme to a comprehensive livelihoods and rural development programme over the years. Establishment National Rainfed Area Authority in 2008 and bringing watershed development under its purview with doubling of allocations for watershed development under the common guidelines has confirmed the primacy of the programme at the policy and planning level. Besides, the common guidelines of 2009 expanded the watershed programme beyond 500 ha. along with extending the time frame with emphasis on livelihoods. The 2010-11 annual budget consolidated three schemes viz., IWDP, DPAP and DDP under the Integrated Watershed Management Programme (IWMP) and made a provision of Rs. 2021 crore for the programme.

Given the huge allocations improving the efficiency of the allocation is of utmost priority. In this regard improving the performance and distribution of benefits across income groups assumes importance at this juncture. The present study of Rajasthan focuses on assessing the performance and identifying the factors influencing the performance in 110 watersheds spread over 21 blocks and 15 districts. This study along with number of other studies across states initiated by MORD, GOI is expected to identify various concerns for improved performance of the WSD programme. These concerns can be addressed in the implementation of the new schemes. The methodology and approach of the present study was pre-designed in order to ensure comparability and consistency across states. It follows a direct assessment approach rather than the standard deductive approach thus reducing the scope for subjective interpretations. Besides, the scale and coverage of the study is large enough to make generalisations at the state level for policy. The assessment was carried out at two levels i.e., community level and individual household level. At both the levels performance of the programme was assessed for the three important components viz., bio- physical or environmental, economic and institutional or social components. The analysis was carried out at district, size class and scheme level using frequency distribution and scoring methods. Statistical tools like ‘means t test’ and regression analysis are used to test

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the robustness of the findings. The broad and brief conclusions of the analysis are presented here. The communities’ perspective on the performance of WSD gives an aggregated view of the sample watersheds while the household’s assessment provides concrete evidence on the impact and performance of the programme. Our analysis at the household level emphasises the observations at the community level though the assessments are not strictly comparable. The performance of WSD is more pronounced at the household level when compared to the community level. The performance levels are higher by 25 percent at the household level when compared to communities’ assessment. Experience and benefits received at the household level are more realistic as the assessment is more detailed intensive. Watershed level performance assessment was compared with average scores and threshold level scores. Threshold level score is assumed to be 40 percent that represent satisfactory performance.

II Summary of Findings The analysis brings out the following observations: i) Overall Performance: The present assessment of WSD in Rajasthan provides a fairly positive indication when compared to the earlier assessments. The earlier assessments while focusing on either cost benefit ratios or economic impacts, revealed that the success rate was only about 20 percent, irrespective of the measure of success. The recent meta analysis observed that 35 percent of the watersheds perform above average level (Joshi, et, al, 2004). Against this back ground the present assessment puts that 43 percent of the sample watersheds have performed well as far as overall performance is concerned. Despite the differences in methods of assessment this appears reasonable given the harsh climatic conditions of Rajasthan. But, the performance levels vary widely across components. ii) Economic vis-a-vis non-economic performance: In terms of economic impacts only 15 percent of the watersheds performed well as against 68 percent in the case of bio-physical and 96 percent in the case of social impacts. This brings out two important aspects: i) better performance of bio-physical or environmental and institutional impacts are not translated in to economic impacts. This could be due to the climatic conditions in most parts of the state. ii) Given the emphasis on participatory aspects in the 1994 guidelines the performance of watersheds in

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terms of institutional or social impacts appears commendable. It may be noted that the traditional institutional mechanisms existing in the state would have enhanced the impacts. The better performance of institutional and bio-physical impacts could ensure the sustainability of the limited economic impacts.

The prime objective of WSD is to enhance land productivity through strengthening of the natural resource base viz., soil and water resources. The overall score obtained for economic impacts for all the sample districts is 31 percent as against 43 percent in the case of bio-physical or environmental impacts and 57 percent in the case of institutional impacts. This indicates that bio-physical or environmental impacts are not fully translated in to economic impacts. The higher institutional performance of WSD is a positive dimension in the context of watersheds implemented after the 1994 guidelines that emphasise participatory watershed development. iii) Resource Endowments and Performance There appears to be a clear linkage between resource endowments and WSD performance. That is performance levels are better in the medium rainfall and irrigated districts when compared to arid districts. This vindicates that the findings of meta analysis where the performance of watersheds are observed to better in the 700-1100 mm rain fall regions. In the present case the performance of WSD is relatively better in the above 500 mm rainfall districts. And the average rainfall does not cross 900 mm is any of the sample districts of Rajasthan. Average scores are high in the endowed and irrigated districts in the case of cropping intensity, yield rates, standard of living and employment, while the impact in the low rainfall arid districts is marginal in the case of important indicators like yield rates, employment, etc. This commensurate with bio-physical or environmental impact of WSD.

Institutional impacts are also on the lower side in the arid and low rainfall districts. Though it is often argued that social institutions are more vibrant in the less endowed parts of Rajasthan, this does not reflect in the context of WSD. This could be due to the reason that financial support in the nature of watershed development fund is necessary for community based activities, especially in the poorly endowed districts.

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Benefit flows from WSD are more in favour of LMF mostly in the endowed and medium rainfall districts like Baran, Dausa, and Tonk, though Bikaner, Jaisalmer and Udaipur also reported evidence in favour of LMF. Similarly, DDP districts being poorly endowed and backward, the poor performance of WSD in these districts when compared to other schemes in the better endowed regions would result in aggravation of economic inequalities. This points towards a disturbing trend that benefits from WSD in poor and backward regions are not only low but are mostly corned by large farmers resulting in aggravation of inter and intra regional inequalities. iv) Large verses Small Farmers There is no set pattern of the impact in terms of benefits flows to small and marginal farmers vis-a-vis large and medium farmers. The differences between large and small farmers are statistically significant in a third of the cases in all the three components. However, the evidence on the overall performance level suggests a bias in favour of large and medium farmers. That is the impact of WSD is in favour large farmers though variations can be observed across the districts. At the indicator level differential impacts between size classes is marginal in majority of the cases.

Large farmers have shown significantly higher benefit flows in the case of capital intensive activities like groundwater development and hence large farmer bias is expected. On the other hand, benefit flows are significantly higher for small farmers in the case of improvements in livestock and generation of additional employment.

Size class wise differences are more prominent in the case of institutional impacts. The differences are not only substantial but also turned out significant in majority of the cases. That is large and medium farmers seem to be more in support of community based institutions that check degradation of community lands. v) Performance across Schemes WSD under the three different schemes have shown positive impact in most indicators as well as over all. Between the schemes, IWDP watersheds are performing better, while DDP watersheds revealed poor performance. The scheme wise analysis emphasises the clear bias against DDP watersheds. DDP watersheds

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score of 26 percent when compared to 33 percent in the case of IWDP and 31 percent in the case of DPAP watersheds.

When compared to bio-physical or environmental indicators, the differences in economic performance between schemes are much less but they have tested significant in majority of the indicators, confirming the poor performance of DDP watersheds when compared to IWDP and DPAP watersheds. In the case of institutional impacts also for most of the indicators the performance of IWDP watersheds is significantly better than DPAP watersheds and the performance of DPAP watersheds is significantly better than DDP watersheds. On the whole, IWDP watersheds are performing better than the other two schemes. But, DDP watersheds are doing fairly well in terms of institutional performance when compared to bio-physical and economic impacts. There is also evidence that DDP districts performing equally well in the case of some indicators. vi) Factors Influencing the performance The regression analysis for identifying the factors influencing WSD performance brings out clearly that bio-physical and participatory institutional aspects play an important role in determining the performance of WSD in Rajasthan. The analysis does not support the view that performance is linked to the type of scheme i.e., IWDP or DPAP or DDP. Performance of WSD is determined mainly by factors like rainfall and access to irrigation rather than the type of scheme. The scheme is a manifestation of natural factors. In this context the recent merger of IWDP, DPAP and DDP under IWMP is not a bad idea.

Despite the better institutional performance, they continue to play an important role in enhancing the performance of WSD. Participatory institutions like functioning of community based organisations, regular meetings of watershed institutions, contributions to watershed development fund and continued linkage with the line department are critical the success of WSD. Continuation of WSD institutions like watershed committee and watershed association even after the completion of the programme is essential for enhancing and sustaining the impacts.

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Contributing to and management of watershed development is an import element in ensuring sustained benefit flows through proper maintenance of watershed structures. And the continued support from the line department, which could be possible through maintaining the relationship between the watershed committees and the department, would help in continued technical support and sustained impacts of WSD.

III Implications for Policy The prime objective of WSD is to enhance land productivity through strengthening of the natural resource base viz., soil and water resources. Strengthening and sustaining the natural resource base is possible through better management practices at the community level with appropriate institutional arrangements. Our analysis suggests that the absence of appropriate or effective institutions could limit the economic benefits to the communities. The challenge is to covert the higher bio-physical and institutional performances in to economic performances. Based on the evidence from Rajasthan, an attempt is made here to draw some policy implications.  While overall performance of WSD is satisfactory, its sustainability is critically linked to its economic impacts at the household level. Given the climatic conditions attaining economic impacts is rather slow due to its long gestation period (5 – 7 years). Besides, economic impacts are not dramatic, unlike in the case of irrigation, making it less attractive to farmers. Together they become the bottlenecks for the sustainability of the WSD. In order to maintain the tempo of farmers’ interest, there is need for creating extra economic benefits in the form of supporting additional livelihood activities. While this aspect is incorporated in the new common guidelines, identifying and designing appropriate location specific livelihoods programmes is a challenge.  The limitations of WSD in the low rainfall regions should be understood and addressed rather than blaming the programme implementation. In these regions WSD is a necessary but not a sufficient condition for improving the livelihoods. More emphasis on livelihood activities in such locations would enhance the sustainability of the programme. In the context of Rajasthan strengthening the livestock economy appears to be a viable option. Such initiatives should be planned according to the existing bio- physical environment rather than importing from outside. Besides, innovative pro-poor interventions need to be explored.

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 Groundwater is the main source of irrigation in Rajasthan. Sustainability and equity in its distribution holds the key for better economic benefits. This calls for appropriate institutional arrangements for managing groundwater. Groundwater need to be treated as a common pool resource in the real sense of the term rather than leaving to private people. It should be brought under a management regime similar to the water user associations of surface water systems. One issue raised at a senior policy level in both Rajasthan and the national level is the need to ensure better access to the benefits of watershed development for landless and land poor people by ‘de-linking’ access to water from land ownership: in other words, to treat water as a public good to which all sections of the community have equal rights and entitlements (Reddy, 2002). To achieve this may require primary legislation. It certainly requires a clear institutional basis at the community level through which these entitlements are translated into access to water resources.  The integration of all the schemes as proposed in the recent budget is in line with our findings. This, however, should not reduce the additional financial allocations provided for the DDP schemes at present (Rs. 500 per ha.). For our analysis suggest that financial requirements are more in the low rain fall arid regions. As it is the funding appears to be insufficient to maintain the watershed development fund, though we acknowledge the institutional issues in this regard. In fact, we argue for higher allocations for these regions.  Strengthening of institutions is critical for enhancing economic benefit flows. The evidence from Rajasthan suggests that functioning of CBO is not satisfactory. Most of the watershed institutions cease to function after the termination of the programme. Post completion phase appears to be very important for maintaining the structures. Even the new guidelines do not seem to have proper approach in this regard. For this involvement of PR institutions in a formal manner is essential. PR bodies have the constitutional authority and mandate to oversee the activities of developmental programmes. While it is difficult to provide a blueprint on how to integrate the numerous parallel institutions with PR bodies, some suggestions can be made based on the experience elsewhere (Reddy, et. al, 2010). These include3: a) The village level PR bodies (gram panchayat) should be made the project-implementing agency (PIA) with little change in the existing institutional structure at the village level i.e.,

3 This sounds hackneyed in the context of the new common guidelines. But we strongly believe that not involving PRI bodies in the process in formal way is trying to escape the reality that might prove costly in the long run. 147

WC would continue and carryout the works. PRI will receive the funds directly from the district level PR body and spends through WC. It takes the responsibilities of WA such as monitoring the activities and determine on follow up actions after the completion of the watershed works, etc. Village PRI would be accountable to mandal level PRI and mandal level PRI (MPP) to the district level PRI (ZPP). b) The capacity of the PR bodies at all levels should be enhanced in a systematic fashion in order to make them effective PIAs. PR bodies and WCs at the village level need training in various aspects of watershed development. While PRIs need administrative and accounting skills, WC committees need technical skills. Once these skills are imparted they would remain at the village level for any future needs. c) ZPP should identify selected NGOs, with considerable experience in watershed development and these NGOs should be made nodal agencies at the district level (1 or 2 NGOs in a district) to identify and impart training to other local NGOs and WDTs. These nodal NGOs are accountable to the ZPP.  This would also facilitate the post implementation linkages with the line departments, which is found to be an important factor in explaining the performance.  The mandatory contribution rule is often flouted at the cost of wage labour. This anomaly needs to be corrected so that casual labour can get their fair wages. In this regard the role of PIA is very important. In the present case, we are not in a position to say that NGO PIAs are better, as there are no NGO PIAs in the sample watersheds.

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