impact assessment of watershed development projects in ...in rajasthan the state level watershed...
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LIVELIHOODS AND NATURAL RESOURCE MANAGEMENT INSTITUTE
2010
Impact Assessment of Watershed Development
Projects in Rajasthan LNRMI Hyderabad
1 2 - 2 - 4 1 7 / 1 8 , S A R A D A N A G A R , H Y D E R A B A D 5 0 0 0 6 7
Draft for Comments. Not to be quoted
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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
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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.
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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.
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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
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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
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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
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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
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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 Childrenโs 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
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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 Childrenโs 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)
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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, peopleโs participation through appropriate institutional arrangements is a prerequisite
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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 peopleโs participation is observed when compared to the watersheds
where peopleโs 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
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).
. 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).
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Table 1.1: Area Treated (m ha) and Investment (Rs. crores) in Watershed Programmes in India
Programmes Up to end of 8th Plan During 9th Plan During 10th Plan till
March 2005 Total (till March
2005) Area Investment Area Investment Area Investment Area Investment
I Ministry of Agriculture a) National Watershed
Development Project for Rain fed Areas (NWDPRA)
4.22 967.93 2.77 911.01 0.96 519.82 7.95 2398.76
b) River Valley Project (RVP) and Flood Prone Regions
(FPR) 3.89 819.95 1.60 696.26 0.60 377.91 6.09 1894.12
c) Watershed Development Project in Shifting Cultivation
Areas (WSDSCA) 0.07 93.73 0.15 82.01 0.06 60.61 0.28 236.35
d) Alkali Soils 0.48 62.29 0.08 20.25 0.56 82.54 e) Externally Aided Project
(EAP) 1.00 646.00 0.50 1425.01 0.86 2685.25 2.36 4756.26
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 Programme(DPAP) 6.86 1109.95 4.49 668.26 3.78 845.19 15.13 2623.40
b) Desert Development Programme (DDP) 0.85 722.79 2.48 519.80 2.38 615.19 5.71 1857.78
c) Integrated waste land development Programme
(IWDP) 0.28 216.16 3.58 943.88 2.46 1001.77 6.32 2161.81
d) Externally Aided Project (EAP) 0.14 18.39 0.22 194.28 0.36 212.67
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
Scheme(IAEPS) 0.30 203.12 0.12 141.54 0.40 469.07 0.82 813.73
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.
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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 8ยฐ to 28ยฐ C (46ยฐ to 82ยฐ F)
and summer temperatures range from 25ยฐ to 46ยฐ C (77ยฐ to 115ยฐ 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,
Barmer 100-400 mm Khariff: rainfed crops like bajra, kharif
pulses, guar etc Rabi: wheat, rape-seed and mustard
Transitional Plain of Luni Basin
Jalor and Sheoganj tehsils of Sirohi
300 to 500 mm kharif :bajra, maize, guar, sesamum and pulses Rabi:wheat, barley and mustard
Semi-arid Eastern Plain
Jaipur, Dausa, Tonk , Ajmer
500 to 600 mm kharif :bajra, sorghum and pulses; Rabi:wheat, barley, gram, mustard
Flood Prone Eastern Plains
Dhaulpur and the northern part of
Sawai Madhopur
750 mm Khariff: bajra, sorghum, maize, sugarcane, sesamum and a variety of pulses; Rabi:Wheat, barley, gram and
mustard Sub-humid Southern
Plains & The Aravalli Hills
Part of Udaipur and part of Sirohi
district
500 to 950 mm Kharif maize,paddy;Rabi:wheat, gram and oil seeds ;cotton and opium
Humid Southern Plains
Part of Udaipur > 700 mm Cash crops:Cotton,sugarcane; Khariff/food crops :Maize, sorghum and paddy ;Groundnut, mustard, sesamum
and rapeseed Humid South-Eastern Plains
Baran, Bundi, and part of Sawai
Madhopur
600 to 850 mm Kharif /food crops :Paddy and sorghum ; Rabi:Wheat, barley, grain and mustard
15
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
16
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 north-
east. 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.
17
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
18
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
Indiaโs 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 Indiaโs 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
19
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 Geo.Area
(in million ha)
% AF % UCL % CL % TF % NAS % GAS % GIA
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) ,
20
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 stateโs 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
21
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 peopleโs 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 peopleโs 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 (peopleโs
participation). All these result in overall up-liftment of the poor and disadvantaged sections of
the community.
22
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
23
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
24
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
โThe Impact Assessment Study of Watershed Development Projects in Rajasthanโ 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)
25
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).
26
Table 1.4: Distributions of Sample Watersheds across Schemes and Districts by Size Classes
Scheme District No of Blocks
Covered
No of Watersheds
Covered
Year of Sanction
Sample HHs taken
SMF(%) LMF(%) Total (Nos)
IWDP
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
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
DPAP
Sawai Madhopur 1 5 2000 60 40 203 Tonk 1 5 2000 69 31 205
Udaipur 1 5 1999 99 1 198 Total 3 15 76 24 606
DDP
Barmer 2 10 1999 30 70 400 Bikaner 2 5 2000 40 60 200
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.
27
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
governmentโs 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
28
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.
29
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 communitiesโ 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.
30
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 Rajasthanโs 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
31
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 (M. Ha)
ARF (mm)
FRST (%)
CW (%)
Fallow (%)
NAS (%)
NIA (%)
SLH (Ha.)
CI (%)
LSD (Sq.km)
% Agrl. Pop
WPR (%)
PCI (Rs.)
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)
32
Demographically the sample districts are sparsely populated when compared to rest of the
state. Sample districts account for 43 percent of the stateโs 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
population (%)
Female Population (%)
Total population (Millions)
SC Population (%)
ST Population (%)
Literacy Rate (%)
Life Expectancy
(years)
Access to safe drinking water
(%HH)
HDI (Rank)
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).
33
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: Communitiesโ 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
Change in Land use pattern
No Change 45 Investing more in good class lands 52
Moving to proper Land Use 3
Increase in Groundwater
Reduced 4 Nil 1 <1 56
1 to 2 39
Increase in Stream flow Nil 4 < 5 44
5 to 10 53
34
Runoff Reduction
Nil 4 <40 43
40 to 80 52 >80 2
Reduction in Soil erosion
increased 3 Nil 7 <20 45
25 to 50 43 >50 3
Increase in Surface water
Nil 5 < 20 49
20 to 40 46
Decrease in Wastelands
Increased 3 Nil 10
5 to 10 56 10 to 20 29
>20 2
Improvement in Vegetation
Nil 22 <10 15
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 โmeans t testโ. The tests
indicate that the difference in performance of IWDP and DPAP watersheds are not
35
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
Name of districts
Change in land
use pattern
Increase stream / spring flow
period
Increase Groundwat
er
Runoff reduction
Soil erosion
reduction
Increase Surface water
Decrease
wastelands
Improvement In vegetative
cover
Over all
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-DPAP
IWDP-DDP
DPAP-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
36
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.
Communitiesโ 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.
37
Table 2.6: Impact on Economic Indicators in the Sample Watersheds
Indicator Level of Impact % of Watersheds
Additional Employment
Nil 9
< 20 72
20-40 19 Additional expenditures (Rs./per capita/year)
< 50 6
50-75 10
75-100 58
100 25
Increase in cropping intensity (%)
< 10 40
10 50
10-20 6
> 20 4
Increase in Yield of Cereals (%)
Nil 5
< 50 93
50-100 2
Increase in Yield of Pulses (%) Nil 39
< 25 61
Increase in Yield of Oilseeds (%) Nil 43
< 25 57
Increase in Yield of Fruits (%)
Nil 87
< 25 9
25-50 4
Increase in Yield of Vegetables (%) Nil 94
< 50 6
Increase in Yield of Cash Crops (%) Nil 78
< 25 22
Increase in Milk Yield (%)
Nil 13
< 50 30
50-100 40
> 100 17
Reduction in debt (%)
Nil 41
0-50 22
50-100 37
Reduction in workload (hrs/day) Nil 70
1 30
Note: Zero responses are not presented
38
Table 2.7: Performance of Economic Indicators in the Sample Watersheds across Districts
Name of districts
Increase in
employment
Additional
expenditures
Increase in
cropping
intensity (%)
Increase in Yield- Cereals
Increase in
Yield-Others
Improvement
in Livesto
ck
Reduction in Debt
Reduction in work
load
Over all
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-
DPAP IWDP-DDP
DPAP-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
39
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. Communitiesโ 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
40
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
Contribution to WDF
Taken out of labour wages 17 Cash partly taken from labour wages 26
Cash partly paid by beneficiary 25 Beneficiary contributed as per norms 32
Quality of WHS
Poor 6 Satisfactory 4
Good 60 Very Good 30
Functioning of CBOs (%) <50 8
50-100 55 100 37
Social audit No 61 Yes 39
Benefit Sharing Mechanism No 61 Yes 39
Maintenance of CPRs No 58 Yes 42
Status of WHS
Fully Damaged 6 Partially Damaged 4
Silted up 59 Intact 31
Bank linkages Never Existed 92
Ended with completion of WDP 8
Line department linkage Ended with completion of WDP 92
Continuing 8
41
Table 2.10: Performance of Institutional Indicators in the Sample Watershed across Districts
District Contribution to WDF
Functional
CBOs
Social audit
Benefit sharing mechan
ism
Maintenance
of CPR
Quality of
WHS
Status of
WHSs
Linkages with
line departments
Linkages with
Banks
Over all
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
42
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 sample Watersheds above average
Main Districts No. of sample watersheds below average
Main Districts Average Score
Range CV
Bio-physical 57 Baran, Dausa, Jaipur, SMPur, Dholpur, Bundi, Tonk and Ajmer
53 Rajasamand, Jaisalmer, Jalore, Barmer, Bikaner, Sirohi and Udaipur
40 -17-83 50
Economic 54 Baran, Dausa, Jaipur, SMPur, Dholpur, Bundi, Tonk, Ajmer
56 Rajsamad, Jaisalmer, Jalore, Barmer, Bikaner, Sirohi and Udaipur
24 10-47 33
Institutional / Social
51 Baran, Dausa, Bundi, Tonk, Jaipur and Ajmer
59 Bikaner, Jalore, Jaisalmer, Barmer, Sirohi, Udaipur, Smpur, Rajsamad and Dholpur
57 19-73 37
Overall 55 Baran, Dausa, Jaipur, SMPur, Dholpur, Bundi, Tonk, Ajmer and Udaipur
55 Rajasmand, Bikaner, Jalore, Jaisalmer, Barmer and Sirohi
38 7-39 36
43
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 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 farmersโ 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 havenโt 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.
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.
44
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
communitiesโ 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
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 ยฝ 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 donโt 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.
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.
45
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 communitiesโ needs could result in adverse impacts. This is
evident in the case of Karkala village in Bhim block of Rajsamand district (Box 6).
V Conclusions
This chapter looks at the performance of WSD from the communitiesโ 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:
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 governmentโs 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.
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 donโt 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.
46
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.
47
APPENDIX
Table A2.1: Important Agrarian Features of the Sample Districts
District Soils Source of irrigation
Main Crops Livestock
Bikaner Fine sand to loamy fine sand
Canal
Kidney bean, Gram, Groundnuts, Wheat, Rapeseed & Mustard, Guarseed, Bajra, Rapeseed, Barley, etc.
Cattle, buffaloes, sheep and goats
Jaisalmer
Soils are pale brown, single grained, deep profile developed, texture and sandy type. These soils belong to aridisols order.
Canal and wells
Bajra, Guar seed, Jowar, wheat, rapeseed & mustard, groundnut and gram.
Sheep, goats and cattle
Barmer Sandy and Very poor quality
Groundwater, Over exploited in 62 % over exploited blocks. Few villages get canal water.
Bajra, Moth Pulses (kharif) and Cumi (rabi)
Traditionally dominated by small ruminants. Of late milch cattle are on the rise.
Jalore
Soils are classified in Aridisols order. At some places playas are observed belonging to salids great group of Aridisols order.
Wells
Oilseeds especially mustard is the predominant crop. Wheat, bajra, kharif pulses, barley, jowar and seasmum are other crops cultivated in this district.
Cattle, buffaloes, sheep and goats
Ajmer
Gray brown alluvial soil to non calcil brown soils and brown soils of recent origin. Soils are sandy loam to sandy clay loam in texture. Fertility status of these soil is low.
Wells
Kharif crops: bajra, jowar, pulses, maize and groundnut. Rabi crops: wheat, barley, gram and oilseeds.
Cattle, buffaloes, sheep and goats
Dholpur comparatively plain topography and a good soil base for agriculture
Groundwater Kharif: Bajra, oilseeds (til). Rabi:, cereals and oilseeds.
Buffaloes
Jaipur Loamy, Clay,. Sandy and Sandy-loam Soils
Groundwater
Wheat, Bajra, Rapeseed & Mustard, Barley, Groundnut, Gram, Jowar, Maize and Sugarcane.
Cattle, buffaloes, sheep and goats
Dausa
Sandy loam to clay loam having a clear upper boundary of argillite horizon. soils at places are affected by salinity- alkalinity.
Wells and Canal
Kharif: bajra, maize, groundnut and cotton. Rabi: wheat, barley, mustard and gram.
Cattle, buffaloes, sheep and goats
SMPur Alluvial in nature which is prone to water logging.
Wells and canal
paddy, jowar, bajra, maize, pulses, sesamum,
Cattle, buffaloes, sheep and goats
48
Soils are grayish brown to brown and yellowish brown with wide variations in texture having a clear upper boundary of argillic horizon.
groundnut, sugarcane, red chillies, wheat, and barley.
Bundi
Soil is rich and fertile. These soils can be classified into Inceptisol and Vertisol order
Canals and wells
Kharif : jowar, bajra, maize, pulses and ground nuts. Rabi: wheat, barley, gram, oil seeds and pulses.
Cattle, buffaloes, sheep and goats
Baran
Mainly Black-Kachari soil, which is highly fertile found in the Baran and Mangrol tehsils. Stony soil is found in the Southern & Eastern part of the district.
Tube wells, Wells and Canals
Kharif: Pulses and Soyabean along with Jowar, Bajra and maize Rabi: mustard, gram and coriander are grown with wheat as Rabi crop.
cattle, buffaloes and goats
Tonk
Sandy but fertile. The soils are grayish brown to brown and yellowish brown with wide variations in texture from sandy loam to loam. Some blocks have salinity and alkalinity problem.
Wells and Canals
paddy, jowar, bajra, maize, pulses, sesamum, groundnut, sugarcane, red chillies, wheat, and barley.
Cattle, buffaloes, sheep and goats
Rajsamand
Except some partially weathered rocks all types of soils in this district are moderately deep to deep. Sandy loam and clay loam soils also exist.
wells
wheat, maize, jowar, gram, pulses, sugarcane, barley, groundnut and rice
Cattle, buffaloes, sheep and goats
Udaipur soil type varies from red loamy to sandy, gravelly to medium black soils.
Wells, canals and ponds
Kharif: Maize, Paddy, Jowar, Urd, and Groundnut. Rabi: Wheat; Barley, Gram and Mustard
cattle, buffaloes, goats, and sheep
Sirohi soils are rich in nutrients having medium to high fertile status.
Wells Millets, pulses, sesame, and red chillies are the major crops
Goat, Sheep, Cattle and Buffaloes.
Table A2.2: Watershed Wise Performance
Dist Name Block Name Watershed Type of Scheme
Social Score
Bio-physical Score
Economic Score
Over 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
49
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
50
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
51
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
52
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
53
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
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
Baran
Dausa
Jaipur
Swaimadhpur
Dholpur
Bundi Tonk Rajsa
madAjmi
rBikaner Jalor Jaisl
merBarm
erSiroh
iUdaipur
Increased 1 0 0 8 0 1 0 4 2 6 1 7 6 0 3Nil 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 2425-50 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
010203040506070
% H
H
54
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
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.
Baran
Dausa
Jaipur
Swaimadhpur
Dholpur
Bundi Tonk
Rajsama
d
Ajmir
Bikaner Jalor Jaisl
merBarmer
Sirohi
Udaipur
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 3440-80 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
01020304050607080
% o
f HH
55
Drinking water Figure 3.3: Impact of WSD on Drinking Water across Sample Districts
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
Baran
Dausa
Jaipur
Swaimadhpur
Dholpur
Bundi
Tonk
Rajsama
d
Ajmir
Bikaner
Jalor
Jaislmer
Barmer
Sirohi
Udaipur
Less 1 0 1 0 4 0 3 8 0 17 35 41 55 2 1Adequate 56 43 78 78 73 65 76 72 63 73 62 58 39 88 86Adequate with Quality 43 56 21 22 23 35 22 20 37 10 3 1 6 10 14
0102030405060708090
100
% H
H
56
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
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
Baran
Dausa
Jaipur
Swaimadhpur
Dholpur
Bundi Tonk Rajsa
madAjmi
rBikaner Jalor Jaisl
merBarm
erSiroh
iUdaipur
decline 18 19 31 15 32 7 26 77 43 84 94 98 99 92 8110 to 20 33 27 18 19 24 33 26 10 19 13 6 1 1 4 1020-30 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
020406080
100120
% o
f HH
57
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
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
Baran
Dausa
Jaipur
Swaimadhpur
Dholpur
Bundi
Tonk
Rajsamad
Ajmir
Bikane
r
Jalor
Jaislme
r
Barmer
Sirohi
Udaipur
< 25 48 27 63 34 64 40 43 57 51 84 89 10 91 59 5525-50 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
0
20
40
60
80
100
120
% o
f HH
58
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
Figure 3.7: Impact of WSD on Adequacy of Feeds and Fodder across Sample Districts
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
Baran
Dausa
Jaipur
Swaimadhpur
Dholpur
Bundi
Tonk
Rajsama
d
Ajmir
Bikaner Jalor Jaisl
merBarmer
Sirohi
Udaipur
Less 14 10 28 7 30 20 14 9 7 10 0 36 16 6 1Adequate 76 69 69 72 67 63 75 82 83 89 89 60 76 79 84Excess 10 21 4 21 3 17 11 10 11 1 11 3 8 15 15
0102030405060708090
100
% o
f HH
Baran
Dausa
Jaipur
Swaimadhpur
Dholpur
Bundi Tonk Rajs
amadAjmi
rBikaner Jalor Jaisl
merBarmer
Sirohi
Udaipur
< 25 45 24 47 8 58 25 25 44 25 37 42 53 35 34 3725-50 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
010203040506070
% o
f HH
59
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
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
Baran
Dausa
Jaipur
Swaimadhpur
Dholpur
Bundi
Tonk
Rajsamad
Ajmir
Bikaner
Jalor
Jaislme
r
Barmer
Sirohi
Udaipur
Less 2 6 7 8 34 13 19 14 8 8 3 17 7 13 7Just Enough 88 77 83 74 64 70 73 81 83 92 96 83 93 80 90Adequate 10 18 9 18 3 17 9 5 9 0 1 0 0 7 3
0
20
40
60
80
100
120
% o
f HH
60
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
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 โmoreโ 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 โ0โ and โ100โ. 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
Baran
Dausa
Jaipur
Swaimadhpur
Dholpur
Bundi
Tonk
Rajsamad
Ajmir
Bikane
r
Jalor
Jaislme
r
Barmer
Sirohi
Udaipur
Less 62 49 48 73 73 43 81 67 33 72 49 71 59 31 36Adequate 24 42 48 27 21 44 17 32 67 28 51 29 41 66 63More 14 9 3 0 6 14 2 1 0 0 0 0 0 3 0
0102030405060708090
% o
f HH
61
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
Erosion Runoff Drinking
Water Irriga-tion
Vegetation Fodd
er
Fuel Manure Fish Adequacy of
Feeds & Fodder
Overall
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
(32) 60
(19) 53
(28) 27
(86) 20
(60) 49
(16) 48
(11) 23
(39) 5
(117) 39
(29) 43
(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
62
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
Increased Nil <25 25-50 >50SMF 3 21 22 39 15LMF 3 18 27 36 15Total 3 20 24 38 15
05
1015202530354045
% o
f HH
63
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
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
Nil <40 40-80 >80SMF 13 31 44 12LMF 12 39 36 12Total 13 34 41 12
05
101520253035404550
% o
f HH
Less Adequate Adequate with QualitySMF 9 71 20LMF 19 62 18Total 13 68 19
01020304050607080
% o
f HH
decline 10 to 20 20-30 >30SMF 62 14 15 9LMF 63 13 12 12Total 62 14 14 10
0
10
20
30
40
50
60
70
% o
f HH
64
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
Figure 3.15: Impact of WSD on Fuel by Farm Size Classes
Less Just Enough AdequateSMF 12 81 6LMF 9 85 6Total 11 83 6
0102030405060708090
% o
f HH
< 25 25-50 > 50SMF 57 42 2LMF 69 30 1Total 61 38 2
01020304050607080
% o
f HH
65
Figure 3.16: Impact of WSD on Manure by Farm Size Classes
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 โmeans tโ 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.
Less Just Adequate MoreSMF 58 38 4LMF 52 46 2Total 56 41 3
010203040506070
% o
f HH
66
Table 3.2: Performance of WSD between Size Class of Farmers (SMF-LMF)
District Soil Erosion
Runoff Drinking
Water
Irriga-tion Fodder
Fuel Manure Vegetation
Adequacy of Feeds
& Fodder
Overall
SMF-LMF
SMF-LMF
SMF-LMF
SMF-LMF
SMF-LMF
SMF-LMF
SMF-LMF
SMF-LMF
SMF-LMF
SMF-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
67
(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
Figure 3.18: Impact of WSD on Run off Reduction across Schemes
IWDP DPAP DDPIncreased 2 5 5Nil 11 22 36<25 20 25 3125-50 45 34 25>50 22 14 3
05
101520253035404550
% o
f HH
IWDP DPAP DDPNil 7 16 22<40 28 32 4640-80 49 40 27>80 16 12 5
0102030405060
% o
f HH
68
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
IWDP DPAP DDPLess 3 0 35Adequate 72 81 57Adequate with Quality 25 18 8
0102030405060708090
% o
f HH
IWDP DPAP DDPdecline 46 57 9210 to 20 19 13 520-30 21 14 2>30 14 16 1
0102030405060708090
100
% o
f HH
IWDP DPAP DDP< 25 50 51 8725-50 48 49 13> 50 2 1 0
0102030405060708090
100
% o
f HH
69
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
IWDP DPAP DDPLess 12 4 19Adequate 76 77 75Excess 12 19 6
0102030405060708090
% o
f HH
IWDP DPAP DDP< 25 36 24 4325-50 46 54 47> 50 18 23 10
0
10
20
30
40
50
60
% o
f HH
IWDP DPAP DDPLess 12 9 10Just Enough 80 79 89Adequate 8 12 1
020406080
100
% o
f HH
70
Figure 3.25: Impact of WSD on Manure across Schemes
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 โmeans tโ 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.
IWDP DPAP DDPLess 53 57 64Just Adequate 43 42 35More 4 1 1
010203040506070
% o
f HH
71
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 โ0โ and โ100โ. 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.
72
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
73
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
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.
Baran
Dausa
Jaipur
Swaimadhpur
Dholpur
Bundi
Tonk
Rajsamad
Ajmir
Bikane
r
Jalor
Jaislme
r
Barme
r
Sirohi
Udaipur
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
020406080
100120
% o
f HH
74
Figure 4.2: Impact of WSD on Yield Rate of Cereals across Sample Districts
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.
Baran
Dausa
Jaipur
Swaimadhpur
Dholpur
Bundi
Tonk
Rajsama
d
Ajmir
Bikaner Jalor Jaisl
merBarmer
Sirohi
Udaipur
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 3120 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
010203040506070
% o
f HH
75
Figure 4.3: Impact of WSD on Yield Rate of Pulses across Sample Districts
Baran
Dausa
Jaipur
Swaimadhpur
Dholpur
Bundi Tonk
Rajsama
d
Ajmir
Bikaner Jalor Jaisl
merBarmer
Sirohi
Udaipur
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 3810 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
01020304050607080
% o
f HH
76
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
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
Baran
Dausa
Jaipur
Swaimadhpur
Dholpur
Bundi
Tonk
Rajsama
d
Ajmir
Bikaner Jalor Jaisl
merBarmer
Sirohi
Udaipur
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 18 405 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
01020304050607080
% o
f HH
Baran
Dausa
Jaipur
Swaimadhpur
Dholpur
Bundi
Tonk
Rajsama
d
Ajmir
Bikaner Jalor Jaisl
merBarmer
Sirohi
Udaipur
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 1610 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
020406080
100120
% o
f HH
77
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
Baran
Dausa
Jaipur
Swaimadhpur
Dholpur
Bundi
Tonk
Rajsamad
Ajmir
Bikaner
Jalor
Jaislme
r
Barmer
Sirohi
Udaipur
<10 12 17 35 20 30 18 17 47 41 71 70 88 71 62 5410 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
0102030405060708090
100
% o
f HH
78
Figure 4.7: Impact of WSD on Employment (Agriculture: Female) across Sample Districts
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
Dausa
Jaipur
Swaimadhpur
Dholpur
Bundi
Tonk
Rajsama
d
Ajmir
Bikaner
Jalor
Jaislmer
Barmer
Sirohi
Udaipur
<20 35 62 25 41 24 16 53 47 72 89 94 91 78 6920 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
0102030405060708090
100
% o
f HH
Baran
Dausa
Jaipur
Swaimadhpur
Dholpur
Bundi
Tonk
Rajsamad
Ajmir
Bikaner
Jalor
Jaislme
r
Barmer
Sirohi
Udaipur
<10 29 19 28 21 35 17 18 27 8 27 26 23 36 38 3310 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
010203040506070
% o
f HH
79
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
Baran
Dausa
Jaipur
Swaimadhpur
Dholpur
Bundi
Tonk
Rajsama
d
Ajmir
Bikaner
Jalor
Jaislmer
Barmer
Sirohi
Udaipur
<5 30 20 20 25 39 20 21 41 17 34 39 40 44 48 405 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
010203040506070
% o
f HH
Baran
Dausa
Jaipur
Swaimadhpur
Dholpur
Bundi
Tonk
Rajsamad
Ajmir
Bikaner
Jalor
Jaislmer
Barmer
Sirohi
Udaipur
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
0102030405060708090
100
% o
f HH
80
Figure 4.11: Impact of WSD on Employment (Self: Female) across Sample Districts
Impact on Livestock
Livestock is an integral part of Rajasthanโs 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.
Baran
Dausa
Jaipur
Swaimadhpur
Dholpur
Bundi
Tonk
Rajsamad
Ajmir
Bikane
r
Jalor
Jaislme
r
Barmer
Sirohi
Udaipur
No Increase 93 93 90 90 97 89 93 95 98 98 96 99 99 94 9710 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
0
20
40
60
80
100
120
% o
f HH
81
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)
Baran
Dausa
Jaipur
Swaimadhpur
Dholpur
Bundi
Tonk
Rajsamad
Ajmir
Bikane
r
Jalor
Jaislme
r
Barmer
Sirohi
Udaipur
No use 4 2 10 7 1 5 8 26 3 5 3 3 2 18 22Primary tillage 51 45 54 65 55 50 58 51 71 68 54 62 65 55 57All operations 45 53 36 27 44 45 34 23 26 27 43 35 33 28 21
01020304050607080
% o
f HH
82
Figure 4.13: Impact of WSD on Livestock across Sample Districts (Shift from Draft to Milch cattle)
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.
Baran
Dausa
Jaipur
Swaimadhpur
Dholpur
Bundi
Tonk
Rajsamad
Ajmir
Bikaner
Jalor
Jaisl
mer
Barmer
Sirohi
Udaipur
No change 4 3 6 4 3 6 7 20 18 14 9 5 1 12 12Mixed 27 18 20 17 22 27 15 20 21 10 11 3 7 35 43Only Milch Animals 69 79 75 79 75 67 78 60 61 77 80 93 91 53 45
0102030405060708090
100
% o
f HH
83
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)
Baran
Dausa
Jaipur
Swaimadhpur
Dholpur
Bundi
Tonk
Rajsamad
Ajmir
Bikane
r
Jalor
Jaislme
r
Barmer
Sirohi
Udaipur
No Change 30 56 54 62 67 31 67 71 55 45 61 48 54 66 63Mixed 47 33 38 27 24 29 19 18 28 36 17 18 23 23 24All Sheep Replaced 23 10 8 11 10 41 14 10 16 19 22 34 23 11 13
01020304050607080
% o
f HH
Baran
Dausa
Jaipur
Swaimadhpur
Dholpur
Bundi
Tonk
Rajsamad
Ajmir
Bikane
r
Jalor
Jaislme
r
Barmer
Sirohi
Udaipur
Existing Breeds 36 54 53 81 39 68 79 86 85 98 98 100 100 98 95Part of Boath 61 33 37 19 51 28 16 12 11 2 1 0 0 2 4Improved Breeds 3 13 10 0 10 4 5 2 5 0 1 0 0 0 0
020406080
100120
% o
f HH
84
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
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
Baran
Dausa
Jaipur
Swaimadhpur
Dholpur
Bundi
Tonk
Rajsama
d
Ajmir
Bikaner
Jalor
Jaislmer
Barmer
Sirohi
Udaipur
< 25 43 57 46 51 32 54 42 29 25 26 11 12 14 20 2025-50 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
010203040506070
% o
f HH
Baran
Dausa
Jaipur
Swaimadhpur
Dholpur
Bundi
Tonk
Rajsamad
Ajmir
Bikane
r
Jalor
Jaislme
r
Barmer
Sirohi
Udaipur
Nil 0 1 2 2 1 0 7 5 1 3 0 7 1 2 2Mechanical 80 93 86 92 98 77 77 51 64 25 45 16 11 13 28Manual 20 6 13 6 1 23 16 45 35 72 55 77 88 85 70
020406080
100120
% o
f HH
85
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
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 householdโs 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.
Baran
Dausa
Jaipur
Swaimadhpur
Dholpur
Bundi
Tonk
Rajsamad
Ajmir
Bikaner
Jalor
Jaisl
mer
Barmer
Sirohi
Udaipur
Nil 3 4 7 10 10 2 7 26 20 23 25 35 48 38 30Slight Improvement 93 77 79 80 84 92 84 70 76 74 72 61 50 60 69Improved 4 19 14 10 6 6 9 4 3 3 3 4 2 3 1
0102030405060708090
100
% o
f HH
86
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
(70) 4
(98) 52
(28) 38
(33) 32
(35) 28
(26) 48
(27) 72
(22) 16
(20) 81
(07) 41
(21) 31
(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
87
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
Figure 4.19: Impact of WSD on Cereal Yields across Size Classes
Figure 4.20: Impact of WSD on Pulses Yields across Size Classes
Nil < 10 10 to 20 > 20SMF 54 19 23 5LMF 48 20 25 7Total 52 19 24 5
0102030405060
% o
f HH
Nil <20 20 to 40 >40SMF 10 31 50 9LMF 9 40 40 11Total 10 34 47 9
0102030405060
% o
f HH
Nil <10 10 to 20 >20SMF 37 35 24 4LMF 18 45 31 6Total 31 39 26 5
01020304050
% o
f HH
88
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
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).
Nil <5 5 to 10 >10SMF 30 26 33 11LMF 14 38 34 14Total 24 30 34 12
05
1015202530354045
% o
f HH
Nil <10 10 to 20 >20SMF 74 14 11 1LMF 55 27 16 2Total 68 18 12 1
01020304050607080
% o
f HH
89
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
<10 10 to 20 >20SMF 44 44 12LMF 51 36 13Total 47 41 12
0
10
20
30
40
50
60
% o
f HH
<20 20 to 30 >30SMF 53 42 5LMF 65 30 5Total 57 38 5
010203040506070
% o
f HH
<10 10 to 15 >15SMF 26 51 23LMF 27 49 24Total 26 50 23
0
10
20
30
40
50
60
% o
f HH
90
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
<5 5 to 10 >10SMF 34 46 20LMF 35 43 21Total 34 45 21
05
101520253035404550
% o
f HH
No Increase 10 to 20 >20SMF 70 15 15LMF 71 12 16Total 70 14 15
01020304050607080
% o
f HH
No Increase 10 to 20 >20SMF 95 4 1LMF 96 3 2Total 95 3 1
0
20
40
60
80
100
120
% o
f HH
91
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
Figure 4.30: Impact of WSD on Livestock (Shift from Draft to Milch Cattle) across Size Classes
No use only Critical (Primary tillage All operations
SMF 14 55 32LMF 4 62 34Total 11 57 32
010203040506070
% o
f HH
No change Mixed Only Milch Animals
SMF 10 23 66LMF 8 15 77Total 9 21 70
020406080
100
% o
f HH
92
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
No Change Mixed All Sheeps Replaced
SMF 58 24 17LMF 59 23 18Total 58 24 18
010203040506070
% o
f HH
Existing Breeds Part of Boath Improved BreedsSMF 84 14 2LMF 85 12 3Total 84 13 3
0102030405060708090
% o
f HH
Nil Mechanical ManualSMF 3 50 46LMF 1 53 46Total 3 51 46
0102030405060
% o
f HH
93
Figure 4.34: Impact of WSD on Standard of Living across Size Classes
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 โmeans tโ 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.
Nil Slight Improvement Improved
SMF 22 73 4LMF 22 71 7Total 22 72 5
01020304050607080
% o
f HH
94
Table 4.2: Average Economic Impact of WSD across Size Classes
Name of districts
CI AD CY OVY EMP LS PF PRF MI SL ALL SMF-LMF
SMF-LMF
SMF-LMF
SMF-LMF
SMF-LMF
SMF-LMF
SMF-LMF
SMF-LMF
SMF-LMF
SMF-LMF
SMF-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.
95
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
IWDP DPAP DDPNil 36 41 84< 10 23 23 1010 to 20 32 32 5> 20 8 4 0
0102030405060708090
% o
f HH
IWDP DPAP DDPNil 3 7 23<20 26 26 5120 to 40 57 55 24>40 13 12 1
010203040506070
% o
f HH
IWDP DPAP DDPNil 28 37 33<10 30 30 5510 to 20 35 29 11>20 7 4 1
0102030405060
% o
f HH
96
Figure 4.38: Impact of WSD on Oilseed Yields across Schemes
Figure 4.39: Impact of WSD on Cash crop Yields across Schemes
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.
IWDP DPAP DDPNil 18 33 40<5 26 17 475 to 10 40 40 11>10 16 10 1
05
101520253035404550
% o
f HH
IWDP DPAP DDPNil 68 63 72<10 15 21 2410 to 20 15 14 4>20 2 2 0
01020304050607080
% o
f HH
97
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
IWDP DPAP DDP<10 35 40 7110 to 20 48 44 27>20 17 16 2
01020304050607080
% o
f HH
IWDP DPAP DDP<20 45 51 8120 to 30 48 43 18>30 7 6 1
0102030405060708090
% o
f HH
IWDP DPAP DDP<10 26 26 2810 to 15 50 48 52>15 24 27 20
0102030405060
% o
f HH
98
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
IWDP DPAP DDP<5 32 30 395 to 10 44 44 47>10 24 26 14
01020304050
% o
f HH
IWDP DPAP DDPNo Increase 65 62 8410 to 20 17 16 9>20 18 23 7
0102030405060708090
% o
f HH
IWDP DPAP DDPNo Increase 94 97 9810 to 20 4 2 1>20 2 1 1
0
20
40
60
80
100
120
% o
f HH
99
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 โnon-usage of
tractorโ 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
IWDP DPAP DDPNo use 11 17 9Primary tillage 56 56 59All operations 33 27 32
010203040506070
% o
f HH
100
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
IWDP DPAP DDPNo change 10 10 9Mixed 24 35 10Only Milch Animals 67 55 81
0102030405060708090
% o
f HH
IWDP DPAP DDPNo Change 61 59 53Mixed 24 27 23All Sheep Replaced 14 15 24
010203040506070
% o
f HH
IWDP DPAP DDPExisting Breeds 75 89 98Part of Boath 20 10 2Improved Breeds 4 0 0
020406080
100120
% o
f HH
101
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
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
โmeans tโ 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
IWDP DPAP DDPNil 3 1 4Mechanical 64 57 24Manual 33 42 73
01020304050607080
% o
f HH
IWDP DPAP DDPNil 16 21 34Slight Improvement 77 73 63Improved 7 6 3
0102030405060708090
% o
f HH
102
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 โ 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
103
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.
104
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
poorโs 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
105
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
Baran
Dausa
Jaipur
SMPur
Dholpur
Bundi
Tonk
Rajsamad
Ajmir
Bikane
r
Jalore
Jaisalmer
Barmer
Sirohi
Udaipur
Overal
l
Broken 1 0 19 25 0 0 5 1 3 2 1 13 4 3 5 5Dysfunctional 1 3 3 4 11 1 1 9 17 18 27 44 33 8 10 13Partly functional 51 34 30 27 57 34 42 57 49 73 54 40 55 75 65 51Working with 48 64 47 45 32 65 53 33 31 7 18 2 8 14 19 30
01020304050607080
% o
f HH
106
Figure 5.2: Maintenance of Retention Wall across Sample Districts
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 โKhadinsโ, 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.
Baran
Dausa
Jaipur
SMPur
Dholpur
Bundi
Tonk
Rajsamad
Ajmer
Bikaner
Jalore
Jaisalmer
Barmer
Sirohi
Udaipur
Overall
Not Done 15 9 14 31 8 25 35 55 35 41 38 49 37 56 55 39Yes,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
01020304050607080
% o
f HH
107
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
Baran
Dausa
Jaipur
SMPur
Dholpur
Bundi
Tonk
Rajsamad
Ajmir
Bikaner
Jalore
Jaisalmer
Barmer
Sirohi
Udaipur
Overall
Not done 35 36 45 60 52 44 50 59 43 75 81 88 80 76 74 63Yes,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
0102030405060708090
100%
of H
H
Baran
Dausa
Jaipur
SMPur
Dholpur
Bundi
Tonk
Rajsamad
Ajmir
Bikane
r
Jalore
Jaisalmer
Barmer
Sirohi
Udaipur
Overal
l
Not Involved 11 35 43 44 60 23 36 45 37 66 70 72 71 63 46 49Partly Helping 88 65 52 51 39 74 59 55 61 34 29 26 27 37 51 49Solely Managing 1 0 5 4 1 3 5 1 2 0 1 2 2 1 3 2
0102030405060708090
100
% o
f HH
108
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).
109
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
Baran
Dausa
Jaipur
Smpur
Dholpur
Bundi
Tonk
Rajsamad
Ajmir
Bikaner
Jalor
Jaisalmer
Barmer
Sirohi
Udaipur
Overall
Not Possible 3 2 12 9 18 10 23 13 3 26 12 21 24 11 18 15Done Along with Watchman 7 6 8 6 11 3 15 15 10 24 6 6 12 72 9 13All Agreed no Watchman 90 91 80 85 71 87 62 73 88 50 82 73 64 17 73 72
0102030405060708090
100%
of H
H
Baran
Dausa
Jaipur
SMPur
Dholpur
Bundi
Tonk
Rajsamad
Ajmir
Bikaner
Jalore
Jaisalmer
Barmer
Sirohi
Udaipur
Overall
Not Possible 8 7 11 13 20 13 23 14 2 37 24 39 26 18 23 20Partly Achived 74 73 72 74 66 71 65 73 85 61 75 60 73 77 73 71Achieved 18 21 17 14 13 16 12 13 14 2 1 1 1 5 4 9
0102030405060708090
% o
f HH
Baran
Dausa
Jaipur
SMPur
Dholpur
Bundi
Tonk
Rajsama
d
Ajmir
Bikaner
Jalore
Jaisalmer
Barmer
Sirohi
Udaipur
Overall
No change 1 0 12 1 2 2 3 4 0 5 9 4 1 1 3 3Partly stall fed 99 92 76 96 71 90 85 89 99 88 88 95 97 97 94 91Fully stall fed 0 8 13 3 27 8 12 6 1 7 3 1 2 2 3 6
0
20
40
60
80
100
120
% o
f HH
110
Figure 5.8: Extent of Open Grazing across Sample Districts
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 householdโs preference for
childrenโs 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.
Baran
Dausa
Jaipur
SMPur
Dholpur
Bundi
Tonk
Rajsamad
Ajmir
Bikane
r
Jalore
Jaisalmer
Barmer
Sirohi
Udaipur
Overal
l
All livestock 38 23 25 37 35 28 35 29 24 41 26 33 31 35 29 31Not sent for grazing 4 6 15 2 25 6 12 10 7 4 2 0 3 0 7 7Limited to small ruminants 58 71 60 61 40 66 53 62 69 56 72 67 67 65 64 62
01020304050607080
% o
f HH
111
Figure 5.9: Preference for Childrenโs Education across Sample Districts
Figure 5.10: Level of education across Sample Districts
Baran
Dausa
Jaipur
SMPur
Dholpur
Bundi
Tonk
Rajsamad
Ajmir
Bikane
r
Jalore
Jaisalmer
Barmer
Sirohi
Udaipur
Overall
Only Male 39 28 29 36 19 30 56 80 22 19 18 25 39 72 43 42Both Male & Female 61 72 71 64 81 70 44 20 78 81 82 75 61 28 57 58
0102030405060708090
% o
f HH
Baran
Dausa
Jaipur
SMPur
Dholpur
Bundi
Tonk
Rajsama
d
Ajmir
Bikaner
Jalore
Jaisalmer
Barmer
Sirohi
Udaipur
Overall
Primary 73 53 58 70 46 54 53 53 72 71 72 78 65 46 62 60Secondary 23 28 33 26 43 33 13 1 26 24 24 19 13 0 12 17Collegiate 4 19 9 5 11 12 35 45 1 6 3 3 22 53 27 23
0102030405060708090
% o
f HH
112
Figure 5.11: Coverage of Health Care across Sample Districts
Figure 5.12: Coverage of Nutritional Care across Sample Districts
Householdโs 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
Baran
Dausa
Jaipur
SM
Pur
Dholpur
Bundi
Tonk
Rajsamad
Ajmir
Bikaner
Jalore
Jaisalmer
Barmer
Sirohi
Udaipur
Overall
No Extra Expenditure 0 0 22 0 0 17 0 8 0 60 58 30 10 20 0 13Limited to a Few 2 38 59 37 79 0 31 26 0 20 1 19 10 20 20 24Covering All Family
Members 98 62 19 63 21 83 69 66 100 20 41 50 79 60 80 63
020406080
100120
% o
f HH
Baran
Dausa
Jaipur
SMPur
Dholpur
Bundi
Tonk
Rajsamad
Ajmir
Bikaner
Jalore
Jaisalmer
Barmer
Sirohi
Udaipur
Overall
No extra Expenditure 0 1 0 0 20 0 10 21 0 41 0 0 0 20 0 8Improvement to some 2 39 19 59 41 20 29 20 0 21 40 0 20 0 1 18Improvement to all 98 60 81 41 39 79 61 59 100 39 60 100 80 80 99 74
0
20
40
60
80
100
120
% o
f HH
113
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
(31) 23
(50) 44
(37) 27
(31) 78
(14) 45
(18) 66
(09) 51
(07) 63
(04) 75
(27) 57
(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,
114
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
Figure 5.14: Periodic De-silting of Water Bodies across Size Classes
Broken Dysfunctional
Partly functional
Working with
SMF 4 12 51 33LMF 7 16 50 26Total 5 13 51 30
0102030405060
% o
f HH
Not done Yes,but by SMF and Landless
Yes,by all Stakeholders
SMF 62 28 9LMF 63 26 11Total 63 28 10
010203040506070
% o
f HH
115
Figure 5.15: Maintenance of Retention Walls across Size Classes
Figure 5.16: Women Participation in CPR maintenance across Size Classes
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).
Not Done Yes,UGs Doing using using WDF
Yes,UGs Doing by themselves
SMF 45 27 29LMF 29 47 24Total 39 34 27
01020304050
% o
f HH
Not Involved Partly Helping Solely ManagingSMF 47 51 2LMF 52 46 2Total 49 49 2
0102030405060
% o
f HH
116
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
Not Possible Done Along with Watchman
All Agreed no Watchman
SMF 16 15 69LMF 13 9 78Total 15 13 72
020406080
100
% o
f HH
SMF LMF TotalNo change 3 3 3Partly stall fed 91 90 91Fully stall fed 5 7 6
0102030405060708090
100
% o
f HH
Not Possible Partly Achived AcievedSMF 20 70 10LMF 19 73 8Total 20 71 9
01020304050607080
% o
f HH
117
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
SMF LMF TotalAll livestock 33 27 31Not sent for grazing 6 8 7Limited to small
ruminants 61 65 62
010203040506070
% o
f HH
SMF LMF TotalOnly Male 47 32 42Both Male & Female 53 68 58
01020304050607080
% o
f HH
SMF LMF TotalPrimary 59 63 60Secondary 13 24 17Collegiate 28 13 23
010203040506070
% o
f HH
118
Figure 5.23: Status of Health Coverage across Size Classes
Figure 5.24: Status of Nutritional Coverage across Size Classes
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
No Extra Expenditure Limited to a Few Covering All
Family MembersSMF 10 25 65LMF 18 21 60Total 13 24 63
010203040506070
% o
f HH
No extra Expenditure
Improvement to some Improvement to all
SMF 12 20 69LMF 0 16 84Total 8 18 74
020406080
100
% o
f HH
119
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
District WHS PDWB MRW PW Sfen SG SF GR EDU H&N Overall SMF-LMF
SMF-LMF
SMF-LMF
SMF-LMF
SMF-LMF
SMF-LMF
SMF-LMF
SMF-LMF
SMF-LMF
SMF-LMF
SMF-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*
Tonk 67-74* 30-28 46-51 33-38* 57-96* 39-56* 55-53 50-78* 63-67 74-100* 56-69*
Rajsamad 63-50 25-0 39-0 28-0 80-0 50-0 51-0 67-0 64-0 75-0 58-25*
Ajmir 73-53* 22-36* 41-55* 38-31 94-92 51-57* 51-50 73-73 59-62 100-100 65-66
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*
Udaipur 48-54 19-16 36-35 28-27 77-81 42-37 50-50 67-69 61-60 95-100* 58-59
Over all 57-49* 23-24 42-48* 27-25* 77-82* 45-44 51-52 64-69* 63-63 80-85* 57-58 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. 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
120
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
IWDP DPAP DDPBroken 4 15 5Dysfunctional 6 5 29Partly functional 51 47 53Working with 39 34 12
0102030405060
% o
f HH
121
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
IWDP DPAP DDPNot done 53 69 79Yes,but by SMF and
Landless 34 22 18
Yes,by all Stakeholders 13 9 2
0102030405060708090
% o
f HH
IWDP DPAP DDPNot Done 36 40 45Yes,UGs Doing using
using WDF 25 33 49
Yes,UGs Doing by themselves 38 27 7
0102030405060
% o
f HH
IWDP DPAP DDPNot Involved 41 47 66Partly Helping 57 49 33Solely Managing 2 4 1
010203040506070
% o
f HH
122
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
Figure 5.30: Practice of Staggered Grazing across Schemes
IWDP DPAP DDPNot Possible 12 17 19Done Along with
Watchman 15 5 12
All Agreed no Watchman 73 79 68
0102030405060708090
% o
f HH
IWDP DPAP DDPNot Possible 16 19 28Partly Achived 72 73 69Acieved 13 9 3
01020304050607080
% o
f HH
123
Figure 5.31: Practice of Stall Feeding across Schemes
Figure 5.32: Practice of Open Grazing across Schemes
In the case of householdโs preference for childrenโs 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).
IWDP DPAP DDPNo change 3 3 4Partly stall fed 89 93 94Fully stall fed 8 4 3
0102030405060708090
100
% o
f HH
IWDP DPAP DDPAll livestock 30 35 32Not sent for grazing 9 8 2Limited to small
ruminants 61 58 66
010203040506070
% o
f HH
124
Figure 5.33: Preference for Childrenโs Schooling across Schemes
Figure 5.34: Level of Education across Schemes
Figure 5.35: Status of Health across Schemes
IWDP DPAP DDPOnly Male 46 33 36Both Male & Female 54 67 64
01020304050607080
% o
f HH
IWDP DPAP DDPPrimary 55 72 68Secondary 17 24 15Collegiate 28 4 17
01020304050607080
% o
f HH
IWDP DPAP DDPNo Extra Expenditure 7 0 28Limited to a Few 27 38 14Covering All Family
Members 67 62 58
010203040506070
% o
f HH
125
Figure 5.36: Status of Nutrition across Schemes
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-DPAP
IWDP-DDP
DPAP-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*
IWDP DPAP DDPNo extra Expenditure 10 0 6Improvement to some 16 30 20Improvement to all 74 70 74
01020304050607080
% o
f HH
126
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.
127
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
128
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
Impacts
No. of sample
Watersheds above average
Main Districts
No. of sample
watersheds below
average
Main Districts Average Score Range CV
Environment 57
Baran, Dausa, Jaipur, SMPur, Dholpur,
Bundi, Tonk, Ajmer, Sirohi and Udaipur
53 Rajasamand,
Jaisalmer, Jalore, Barmer, Bikaner
43 11-73 27
Economic 39 Baran, Dausa, Jaipur,
SMPur, Dholpur, Bundi, Tonk, Ajmer
71
Rajasamand, Jaisalmer, Jalore, Barmer, Bikaner,
Sirohi and Udaipur
31 17-51 24
Institutional / Social 62
Baran, Dausa, Jaipur, SMPur, Rajsamand,
Dholpur, Bundi, Tonk, Ajmer and
Udaipur
48 Bikaner, Jalore,
Jaisalmer, Barmer, Sirohi
57 19-73 17
Overall 42 Baran, Dausa, Jaipur,
SMPur, Dholpur, Bundi, Tonk, Ajmer
68
Rajasmand, Bikaner, Jalore,
Jaisalmer, Barmer, Sirohi,
Udaipur
40 21-59 19
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
129
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
0
10
20
30
40
50
60
70
80
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
Scor
e %
Watersheds
Environmental Score (43) Economic Score (31) Social Score (57)
130
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
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
Linear Regression
20 40 60
Environmental Score
20
30
40
50Eco
nomic S
core
Economic Score = 11.69 + 0.44 * EnvironmentalScoreR-Square = 0.48
Linear Regression
20 30 40 50 60 70
Social Score
20
30
40
50
Econom
ic Scor
e
Economic Score = 6.23 + 0.43 * SocialScoreR-Square = 0.32
Linear Regression
20 30 40 50 60 70
Social Score
20
40
60
Envir
onme
ntal
Scor
e
Environmental Score = -2.56 + 0.80 * SocialScoreR-Square = 0.45
131
(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:
๐พ๐พ๐บ๐บ๐บ๐บ๐บ๐บ๐๐๐๐= ๐๐๏ฟฝ๐ป๐ป๐บ๐บ๐๐๐๐,๐น๐น๐น๐น๐๐๐๐,๐ฝ๐ฝ๐บ๐บ(๐ฏ๐ฏ๐ฏ๐ฏ)๐๐๐๐,๐ฝ๐ฝ๐บ๐บ(๐ฎ๐ฎ๐ฎ๐ฎ)๐๐๐๐, %๐ฎ๐ฎ๐จ๐จ๐๐๐๐,๐พ๐พ๐บ๐บ๐๐๐๐,๐ณ๐ณ๐บ๐บ๐บ๐บ๐๐๐๐,๐ฎ๐ฎ๐จ๐จ๐บ๐บ๐จ๐จ๐๐๐๐,๐ฎ๐ฎ๐บ๐บ๐ฏ๐ฏ๐บ๐บ๐จ๐จ๐๐๐๐,๐ฎ๐ฎ๐จ๐จ๐น๐น๐จ๐จ๐๐๐๐,๐ฎ๐ฎ๐บ๐บ๐พ๐พ๐บ๐บ๐๐๐๐,๐น๐น๐จ๐จ๐ญ๐ญ๐ญ๐ญ๐๐๐๐,๐ฎ๐ฎ๐น๐น๐จ๐จ๐๐๐๐,๐จ๐จ๐พ๐พ๐บ๐บ๐น๐น๐๐๐๐,๐บ๐บ๐จ๐จ๐ฎ๐ฎ๐ณ๐ณ๐๐๐๐, %๐จ๐จ๐บ๐บ๐น๐น๐๐๐๐,, %๐น๐น๐น๐น๐บ๐บ๐ป๐ป๐๐๐๐,๏ฟฝ
+ ๐จ๐จ๐๐๐๐ Where,
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 โiโ at time โtโ.
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
132
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
WD Number of functioning wells per unit of geographical area
+ve +ve +ve +ve
LSD Livestock population per unit of geographical area / and per human population
+/-ve +/-ve +/-ve +/-ve
AEDU School standard +ve +ve +ve +ve APHC Distance in range of KM -ve -ve -ve -ve
133
AMRK Distance in KM -ve -ve -ve -ve APWS Dummy (Yes=1 and No=0) +ve +ve +ve +ve
FCBO Dummy (1= Formed but not functional; 2= Partially functional; 3= fully functional)
+ve +ve +ve +ve
FM Dummy (0= No regular conduct of meeting; 1= Regular conduct of meetings
+ve +ve +ve +ve
CWDF Dummy (0= No; 1= Yes) +ve +ve +ve +ve
PIAL Dummy (1= linkage ended with the watershed; 2= Continuing)
+ve +ve +ve +ve
% 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 โVIFโ
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/ Independent
Economic Score Bio-physical Score Institutional Score Overall Score
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 --- ---
134
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 โtโ 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
2 Indicators like social audit and record keeping were also tried but dropped due to non-significance or multicolenearity.
. 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.
135
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 canโt be a perfect substitute
for these natural conditions. However, environmental impacts of watershed development
136
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 Olsanโs (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.
137
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.
138
Appendix Table A6.1: Watershed Wise Performance (Scores)
Name of the District
Name of Watershed (Village)
Overall Score (40)
Environmental Score (43)
Economic Score (31)
Social Score (57)
Baran
Bisali 46 45 37 66 Danitagaria 44 51 33 64 Bavergardh 54 63 43 71 Heerapur 53 55 43 71
Dausa
Chavonda 45 51 34 63 Bidholi 51 62 39 69 Ranoli 59 73 50 72
Geerotakalan 48 44 37 70 Arniya 59 62 50 73
Jaipur
Ajnora 46 59 37 58 Khedhahanumanji 41 45 27 68
Bicchi 35 34 30 48 Chadhamakala 46 39 39 67
Hatheli 43 49 33 60
Sawai Madhapur
Birpur 50 61 41 65 Pali 43 46 36 58
Govindpur 35 46 28 45 Talawada 48 56 37 67 Kschda 41 49 35 50
Dholpur
Madha Bugurg 44 40 37 60 Kailashpura 47 43 39 65
Dhodakapura 43 36 41 53 Vinathipur 41 55 35 46
Nadoli 52 50 43 70
Bundi
Bhawantipura VI 50 57 42 62 Pechkebavdi 55 58 46 70
Ransanda 51 57 45 58 Negad 51 56 41 70
Bhawaneegarh 51 58 40 69
Tonk
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
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
Rajsamad
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 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
139
Dhanin 36 36 27 57
Ajmer
Miya 43 53 31 67 Hingtoda 44 53 32 65 Ajayari 41 50 29 66 Ajgra 44 52 33 67
Bhatalov 46 53 37 65
Bikaner
Swaroop Deser 41 46 33 59 Udairamseer 35 38 28 51
Kakku 24 22 25 23 Hansasar 21 11 23 19 Rasisar 41 45 35 53
Jalor
Nosara 35 41 25 55 Neeltkanth 34 42 27 46
Ghana 37 38 29 55 Barawa 34 34 27 47 Narpura 37 40 30 50
Jaislmer
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 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
Barmer
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
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
Sirohi
Muri I 36 47 24 56 Muri II 36 45 28 50
Kheragegarwa 36 51 30 38 Kerlapadar 35 42 26 50
Viroli 39 50 27 57
Udaipur
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
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
140
Table 6.2A: Descriptive Statistics of selected variables
Variables Minimum Maximum Mean Std. Deviation
Skewness
Statistic Std. 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 village 0.00 12 7.43 2.10 -0.12 0.23
Distance to the PHC (1=more than 10 kms; 3=between 5 to 10 kms; 2=less than 5 kms; 4=within the village)
1 4 1.78 1.05 1.12 0.23
Distance to the nearest market (Km) 2 68 27.83 17.12 0.80 0.23
Access to Protected water supply (1=Yes and 0=No) 0 1 0.39 0.49 0.45 0.23
Functioning of CBO (1= not functional; 2= Partially functional; 3= fully functional)
1 3 2.29 0.61 -0.25 0.23
Frequency of meetings (0= No regular conduct of meeting; 1= Regular conduct of meetings)
0 1 0.67 0.47 -0.75 0.23
Contribution to WDF (0= No; 1= Yes) 0 1 0.41 0.49 0.37 0.23
Linkages with line departments (1= linkage ended with the watershed; 2= Continuing)
1 2 1.08 0.28 3.09 0.23
% of Forest area 0 92.94 6.44 15.36 3.24 0.23
141
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 โmeans t testโ and regression analysis are used to test
142
the robustness of the findings. The broad and brief conclusions of the analysis are presented
here.
The communitiesโ perspective on the performance of WSD gives an aggregated view of the
sample watersheds while the householdโs 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 communitiesโ 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
143
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.
144
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
145
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.
146
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 โ 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 farmersโ 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.
147
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 โde-linkingโ 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.
148
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.
149
References
Deshpande R. S and V. Ratna Reddy (1990), โSocial Dynamics and Farmerโs Society: A
Case Study of Pani-Panchayat,โ Indian Journal of Agricultural Economics, Vol. 45,
No. 3, July-September.
Deshpande R.S. and, V. Ratna Reddy (1991). Differential impact of watershed based
technology: Some analytical issues. Indian Journal of Agricultural Economics, 46(3):
261-269.
Fan S, and Hazell P. (2000). Should developing countries invest more in less favoured areas?
An empirical analysis of rural India. Economic and political weekly, 35(17): 1455-
1464.
GoI (2001) Approach Paper to the Tenth Five Year Plan (2002-07), Planning Commission,
New Delhi, September.
GoI (2003) Watershed Guidelines (Hariyali), Ministry of Rural Development, New Delhi
GoI (2006), From Haryali to Neeranchal: Report of the Technical committee on Watershed
Programmes in India, Department of Land Resources, Ministry of Rural
Development, Government of India, January.
Joshi, P.K and Bantilan, M.C.S (1997): Vertisol Watershed research in the semi-arid tropics:
Directions for impact Assessmentโ, Artha Vijnana, Vol.39, No.3.
Joshi,P.K.,Jha,A.K.,Wani,S.P.,Joshi,L., and Shiyani,R.L. (2005). Meta-Analysis to Assess
Impact of Watershed Programme and Peopleโs Participation, Comprehensive
Assessment Research Report No.8. International Crops Research Institute for the
Semi-Arid Tropics (ICRISAT), Patancheru 502 324,Andhra Pradesh, India.
Kolavalli, L. S. and J. Kerr (2002). "Mainstreaming Participatory Watershed Development".
Economic and Political Weekly. 37(3): 225-242.
MoRD (2006). From Hariyali to Neeranchal: Report of the Technical Committee on
watershed Programmes in India, Department of Land Resources, Ministry of Rural
Development, Government of India, New Delhi.
Nalatawadmath, S.K, M.S Rama Mohan Rao and M.Padmaiah (1997): โJoladarasi Model
Watershed Development Program in Bellary District of Karnataka-A Diagnostic
Evaluationโ, Journal of Rural Development, Vol 16,No.2 Nalatawadmath, S.K, M.S
Rama Mohan Rao and M.Padmaiah (1997): โJoladarasi Model Watershed
Development Program in Bellary District of Karnataka-A Diagnostic Evaluationโ,
Journal of Rural Development, Vol 16,No.2
150
Ninan, K. N. and S. Lakshmikantamma (1994). "Sustainable Development: The Case of
Watershed Development in India". International Journal of Sustainable Development
and World Ecology. 1(4): 229.
Olson Jr. Mancur (1965), The Logic of Collective Action: Public goods and the Theory of
Groups, Harvard University Press, Cambridge.
Pingali, P.L.and Rosegrant, M. (2001): Intensive food systems in Asia: Can the degradation
problems be resolved? In: Lee, D.R., and Barrett, C.B (eds) Tradeoffs or synergies?
Agricultural intensification, economic development and the environment. CABI
publishing.
Rao, C.H.H (2000): โWatershed Development in India โ Recent Experience and Emerging
Issues,โ Economic and Political Weekly, Vol 35, No 45, November 4. Pages 3943-47.
Reddy, V. Ratna (2000). Watershed Development for Sustainable Agricultural: Need for an
Institutional Approach, Economic and Political weekly, 35(38): 3435-3444.
Reddy, V. Ratna, et al. (2001). Watershed Development and Livelihood Security: An
Assessment of Linkages and Impact in Andhra Pradesh, Project Report, Centre for
Economic and Social Studies, Hyderabad.
Reddy. V. Ratna (2002). Water Security and Management: Lessons from South Africa,
Economic and Political Weekly. Vol.37 (28): 2827-81.
Reddy, V. Ratna, M. Gopinath Reddy and John Soussan (2010), Political Economy of
Watershed management: Policies, Institutions, Implementation and Livelihoods,
Rawat Publishers, Jaipur.
Reddy V. Ratna, M. Gopinath Reddy, S. Galab, John Soussan and Oliver Springate-Briganski
(2004), โParticipatory Watershed Development in India: Can it Sustain Rural
Livelihoods?โ, Development and Change, Vol. 35, No. 2, April 2004, pp 297-326.
Reddy, V Ratna; U. Hemantha Kumar; D. Mohan Rao (2005) โWatershed Management for
Sustainable Agriculture: Need for An Institutional Approachโ, Mimeo, CESS,
Hyderabad.
Shiferaw, B., V. Ratna Reddy, S. P. Wani., and G.D.N. Rao (2004).Watershed Management
and Farmer Conservation Investments in the Semi-Arid Tropics of India : Analysis
of Determinants of Resource Use Decisions and Land Productivity Benefits. Socio-
economics and Policy Working Paper Series No. 16. International Crops Research
Institute for the Semi-Arid Tropics (ICRISAT), Patancheru 502 324,Andhra Pradesh,
India.
151
Singh, Karam et al, (1993): โKandi Watershed Development Program โ A Critical
Evaluationโ, Economic and Political Weekly.
Singh, K., Nirmal Singh, et al. (1995). "Concurrent Evaluation of Watershed Development
Project: A Case Study of Punjab". Man and Development. 17(4): 74.