Performance of Agriculture in River Basins of Tamil Nadu in the Last Three Decades – a Total Factor Productivity Approach

Performance of Agriculture in River Basins of Tamil Nadu In the last three Decades – A Total Factor Productivity Approach

A Project Sponsored by Planning Commission, Government of India

Research Team K.Palanisami C.R.Ranganathan A.Vidhyavathi Rajkumar.M N.Ajjan

Final Report March 2011

Centre for Agricultural and Rural Development Studies Tamil Nadu Agricultural University Coimbatore – 641 0013 1

Acknowledgement

The authors express their sincere thanks to Planning Commission, Government of India for providing necessary financial support to carry out this study. The authors express their sincere thanks to Tamil Nadu Agricultural University for providing necessary facility to carry out the research work.

CONTENTS

Page S.No CHAPTER Topics No. 1 I 1. Executive Summary 1 2 II 2. Introduction 7 3 3. Objectives III 10 3.1. Review of Past Studies: TFP measures 4. Data Envelopment Analysis (DEA) IV 17 4 4.1. Input and output orientations 5. Profile of the Study Area: Tamil Nadu 5 5.1. Principal crops and production 5.2. Irrigation V 5.3. Problems facing Agriculture in the State 27 5.3.1. Land degradation and soil quality 5.3.2. Wastelands 5.3.3. Pollution 6 VI 6. Profile of River Basins of Tamil Nadu 35 7. Methodology 7 7.1. Estimation of basin areas and proportion of basin areas in each district of Tamil Nadu VII 7.2. Conversion of district-wise data to basin-wise 38 7.3. Estimation of Malmquist Index of Total Factor Productivity Growth in Agriculture 7.4. The Malmquist TFP Index 8 8. Basin coverage VIII 44 8.1. Time period 9 9. Output Series 9.1. Total inputs IX 9.1.1. Labor Input 45 9.1.2. Land Input

Page S.No CHAPTER Topics No. 9.1.3. Chemical Fertilizer input 9.1.4. Irrigation Input

9.1.5. Livestock inputs 9.1.6. Units of variables 10. Results and Discussions 10 10.1. Summary Statistics 10.1.1. Crop output 10.1.2. Livestock output X 47 10.1.3. Net Sown Area and net irrigated area 10.1.4. Fertilizer Usage 10.1.5. Labour input 10.1.6. Cattle and poultry input 11 11. Liberalization policies and their effects on XI 56 agriculture in the river basins 12 12. Comparison of crop out per unit of sown area XII 67 and per unit of water potential 13. Results of TFP analysis 13 13.1. Overall TFP growth XIII 71 13.2. Individual basin TFP 13.3. Growth rates of TFPs 14 XIV 14. Cumulative TFP indices 82 15. Results of DEA analysis 15 15.1. DEA with VRS technology and Output XV Orientation. 86 15.2. DEA with VRS technology and Input Orientation. 16 XVI 16. Summary and Conclusion 94 17 XVII 17. Policy recommendations 98 18 XVIII 18. References 100

LIST OF TABLES

Table Page List of Tables No No

1 Total Factor Productivity trends for crops in selected states 13

2 Land Use Pattern in Tamil Nadu (Lakh ha) 28

3 Land Holding Pattern in Tamil Nadu 29

4 Status of Principle Crops in Tamil Nadu 30

5 Reduction in Per Capita Availability of Water in Tamil Nadu

6 Season wise Rainfall in Tamil Nadu (mm) 31

7 Irrigation Status in Tamil Nadu ( Area in lakh ha)

8 Change in Availability of Groundwater in Tamil Nadu 32

9 Major River Basins of Tamil Nadu 35

10 Area and Rainfall of the River Basins 36

11 Surface and Groundwater Potential of the River Basins 37

12 Summary Statistics Crop output (Rs.Crores) 47

13 Summary Statistics - Livestock output (Rs.Crores) 51

14 Summary Statistics - Net-Area-Sown-Input (Area in ha) 52

15 Summary Statistics - Net Irrigated Area Input (Area in ha) 53 16 Summary Statistics - NPK-Value-Input (in lakh tonnes)

17 Summary Statistics - Labour input (in Numbers) 54

18 Summary Statistics - Cattle-Input (in Numbers) 55 19 Summary Statistics - Poultry-Input (in Numbers)

20 Crop output (Rs. In crores) in the pre and post liberalization periods 57

21 Livestock output (Rs. In Crores) in the pre and post liberalization periods 59

Table Page List of Tables No No

22 Net area sown (Area in ha) in the pre and post liberalization periods 60

23 Net area irrigated input (Area in ha) in the pre and post liberalization periods 61

24 N, P, K input (in lakh tonnes) in the pre and post liberalization periods 62

25 Labour input (number) in the pre and post liberalization periods 63

26 Cattle input (number) in the pre and post liberalization periods 64

27 Poultry input (number) in the pre and post liberalization periods 65

28 Value of crop output per ha. of sown area 67

29 Value of crop output per MCM of water potential 69

Mean Technical Efficiency Change, Technical Change and TFP Change, during 30 75 three decades in the seventeen river basins of Tamil Nadu

31 Table Mean TFPs in three periods 77

32 Growth rates of TFPs 80

33 Output Oriented VRS DEA model scores for the River basins of Tamil Nadu 87

34 Output Oriented VRS DEA model –benchmarks and projected values 89

35 Input Oriented VRS DEA model scores for the River basins of Tamil Nadu 91

LIST OF FIGURES

Figure List of Figures Page No No

1 Map of Tamil Nadu State 27

2 River Basins of Tamil Nadu 35

3 Crop output in Small Basins during 1975-76 to 2005 - 06 48

4 Crop output in Medium Basins during 1975-76 to 2005 – 06 49

5 Crop output in Large Basins during 1975-76 to 2005 - 06 50

6 Crop output/ ha of net sown area 68

7 Crop output/per unit of water 70

Trend in Total Factor Productivity Index in Small basins during 1975- 8 72 76 to 2005 - 06

Trend in Total Factor Productivity Index in Medium basins during 9 73 1975-76 to 2005 - 06

Trend in Total Factor Productivity Index in Large basins during 1975- 10 74 76 to 2005 - 06

11 Cumulative TFP Indices in Small basins during 1975-76 to 2005 – 06 83

12 Cumulative TFP Indices in Medium basins during 1975-76 to 2005 – 06 84

13 Cumulative TFP Indices in Large basins during 1975-76 to 2005 - 06 85

CHAPTER I Executive Summary 1. Introduction/Objectives Tamil Nadu has 17 major river basins and most of them are water stressed. Agricultural sector consumes about 75% of the water resources. Agriculture sector faces major constraints due to water scarcity. There is growing demands for water from industry and domestic users and also interstate competition for surface water resources also intensifies. Given the state water policy, priority is given for domestic use followed by irrigation and industry etc. indicating that agricultural sector has to manage the scarcity in the future. Further the canal systems have poor water control and management. Also, out of the 1.8 million wells, about 0.16 million wells are defunct in the state as the water table is fast declining. Again, out of the 385 blocks in the state, 90 are dark (extraction exceeding 100% of the recharge, 89 are grey (extraction exceeding 65%) and the rest are white where the extraction is less than 65%.

Given all these constraints and scarcities for the existing water supply scenarios, what is needed is the clear understanding of the value of water in alternate uses as well as the incentive to allocate the water among competing crops and uses in different river basins. However, currently the available information is related to the administrative boundaries such as districts, which as such are difficult to relate with the river basin boundaries. Hence, it is important to reorient the district level data to basin level for making basin level interventions. This will also help to work out the performance of both irrigation and agriculture sectors at basin level. Accordingly the main objectives of the study are as follows: i) To analyze the agricultural growth in all the 17 river basins of Tamil Nadu using the total factor productivity approach, ii) To study the income inequality in all the river basins of Tamil Nadu, and iii) To suggest policy options to improve the productivity of agriculture in the basins. iv) To assess the performance of agriculture, apart from growth rates, total factor productivity (TFP) was mainly used employing Data Envelopment Analysis (DEA). These objectives are set with a view to provide guidance in policy planning in river basins. Since the main objective of the study is to study agricultural growth in major river basins, historical data on agricultural production for the past three decades were used. District-wise data on agricultural production available from various government publications are the primary data for the present study.

1.1. Methodology All the 17 river basins of Tamil Nadu constituted our study area. They were Chennai basin, Palar basin, Varahanadhi basin, Ponnaiyaar basin, Vellar basin, Paravanar basin, Cauvery basin, Agniyar basin, Pambar and Kottakaraiyar basin, Vaigai basin, Gundar basin, Vaippar basin, Kallar basin, Thambaraparani basin, Nambiar basin, Kodaiyar basin and Parambikulam Azhiyar Project (PAP) basin. The study covers the period of 1975 -76 and 2005 -2006, which concerned with important changes in agriculture due to liberalization of trade and reforms in investment, initiation of privatization, tax reforms and inflation controlling measures. The study used two output variables, viz., crops and livestock output variables. The output series for these two variables were derived by aggregating detailed output quantity data of all agricultural commodities. Area under each crop was multiplied by the constant prices of respective crop to arrive at agricultural output. Total inputs use in agriculture included of labor, land, chemical fertilizers, and irrigation area were used.

The district-wise data was first converted into basin-wise data based on the area of each basin falling under each district. Total factor productivity (TFP) for each basin for each year was computed using Malmquist index methods. This approach employs data envelopment analysis (DEA) which a non-parametric method. The Malmquits index is computed by using the formula

1 / 2  d s y , x  d t y , x  m y , x , y , x  o t t x o t t , o  s s t t   s t  do ys , xs  do ys , xs 

s Where the notation do (xt , yt ) represents the distance from the period t observation to the period s technology. A value of mo greater than one will indicate positive TFP growth from period s to period t while a value less than one indicates a TFP decline. These distance functions are obtained by solving linear programming models derived from DEA methodology. 1.2. Findings/Conclusions There was wide range of crop and livestock outputs in all the river basins. Though net irrigated area increased over the decades, there was not much increase in net sown area. This was supported by the minimum of coefficient of variation. In addition, there was considerable increase in intake of NPK fertilizers in all river basins. As the decades under consideration were after green revolution, the intake of inorganic fertilizers had increased due to increase in area under high yielding varieties and area under

irrigation. There was tremendous increase in poultry population in Tamil Nadu especially in Cauvery basin and P.A.P basin. Only after 1990s, there was wide fluctuation in crop output in all the river basins. Before 1990s, the trend was smooth. The same trend was also noted in livestock output. Though net irrigated area has shown positive trend in pre liberalization period and negative trend in post liberalization period, the net sown area has sown negative trend invariably in both the periods in all basins. As expected net irrigated area was increasing at declining rate over the decades. After post liberalization period, the trend was vigorous. This was mainly due to proliferation of wells particularly bore wells. NPK consumption in agriculture was increasing at decreasing rate. Increase in net irrigated area has led to increased consumption of fertilizers. After liberalization period, change in labour use in agriculture was negative in few basins and was less in other basins compared to pre liberalization period. In pre liberalization period there was positive percentage change in all river basins. Comparing cattle input in base year and current year period, Tamil Nadu as a whole showed negative change. In general, poultry population was increasing over the decades. The total factor productivity indices of 17 river basins fluctuate during the whole period of study. Technical efficiency change was further decomposed into pure efficiency change and scale efficiency change. The TFP analysis showed that in Chennai basin agricultural production is technically efficient as the TFP was more than 1. In Palar basin the range of efficiency change was from 0.772 to 1.506. There was not much difference in TFP and other efficiency change in pre liberalization period and post liberalization period. It was more than one indicating that Palar basin was technically efficient in using inputs. In Varahanadhi basin TFP was more than one in pre and post liberalization periods indicating that the basin was technically sound. Though in Ponnaiyaar river basin average TFP was more than one, in post liberalization period it was less than one i.e. 0.957. In pre liberalization period, it was 1.229. In Paravanar basin, the average TFP was 1.034 and there was slight difference in TFP in pre (0.989) and post liberalization period (1.079). The efficiency change was one in both periods and the change in TFP was due to technical efficiency change. In Vellar basin the average TFP was more than one (1.070) in the last three decades. There was no difference noted in pre and post liberalization periods. Nevertheless, the efficiency change was less than one and the technical change was more than one. The average TFP was nearing one in post libralisation period and it was above one in pre liberalization period (1.115). 10

Though technical change was more than one in both periods, the efficiency change was less than one or nearing one. There is a possibility for improving efficiency of inputs in Agniyar basin as there was slight reduction in efficiency change from 1.013 (pre liberalization period) to 0.986 (post liberalization period). Though average TFP was more than one in both periods in Pambar & Kottakaraiyar river basin, there was slight reduction in TFP and technical change in post liberalization period. The same trend was noted in Vaigai basin as in case of Pambar & Kottakaraiyar basin. Gundar river basin also followed the same trend as that of Pambar and Vaigai basin. The average TFP for the last three decades was 0.99. In Kallar basin the changes in total factor productivity was mainly due to technical change. As efficiency change was 1 and there was no change in efficiency of inputs in last three decades, any development activity should focus on technical improvement. In Nambiar basin changes in total factor productivity was fully contributed by technical changes and not due to the efficiency of inputs in agriculture and allied sector. There was no change in TFP in two periods indicating that there was not much change in technology adopted by the farmers. Efficiency of inputs also needs attention, as it remained same in both the periods. In Kodaiyar basin also changes in total factor productivity was fully contributed by technical changes and not due to the efficiency of inputs in agriculture and allied sector. P.A.P was the only basin in which the total factor productivity was less than one in pre and post liberalization period. The average total factor productivity was 0.976 for the last three decades. All river basins had shown negative growth rate in pre liberalization period except P.A.P basin. In post liberalization period basins, namely Chennai, Palar, Varahanadhi, Ponnaiyaar, Paravanar, Vaippar, Thambaraparani and Nambiar river basins have shown positive growth rate. All other river basins showed negative growth rate in post liberalization period. The positive growth rate was mainly due to efficiency of inputs used for agriculture and livestock. Efficiency change has contributed much to the total factor productivity. But overall growth rate ie growth rate of total factor productivity for last three decades was negative for all river basins except Nambiar and P.A.P river basins. However, most of the river basins have shown total factor productivity more than one but there was no growth in the total factor productivity in last three decades except in one or two basins.

1.3. Recommendations 1. Since crop and livestock are the integral components of agricultural production, it is important to make developmental programs to be converging at basin level. All the ongoing and proposed programs should have common linkages and aim to deliver the target output. Livestock is the major supplementary income for farming community. As the number of animals maintained by a farm firm is merely for meeting domestic needs and meeting daily expenses. Dairying is not done as commercial activities by all farms. Farmers should be encouraged to practice dairying as commercial venture by providing technical guidance and credit facilities. Development of poultry industry in agricultural farms could lead to more area under maize and other cereals and development of feed units. Training and technical expertise in dairying and poultry will sustain marginal and small farming communities in Tamil Nadu.

2. The results of the DEA and TFP analyses help to identify the basins for efficient use of the resources. Increasing the cropping and irrigation intensity will help some of the basins to perform comparatively well. Hence using the results of the study the basins that have more potential to improve the performance through efficient use of the resources such as water, labour, fertilizer should be identified and interventions should be made to improve the performance. 3. Technology package should be updated and made available for each basin and the cost of transfer and adoption should be linked with the ongoing programs. Needed capacity building programs should be in built using the existing KVKs and regional agricultural research stations.

4. Conservation programs such as watershed management and improved water management techniques such as drip and sprinklers are still lacking behind due to poor adoption. Future water related investment programs should therefore aim to develop strategies and action plans to address the issue of efficient water allocation and management with the goal of maximizing the productivity per unit of water. Given the existing water supply scenarios, the demand management strategies will be considered more relevant for the efficient management of the available supplies. Therefore, what is needed is the clear understanding of the value of water in alternate uses as well as the incentive to allocate the water among competing crops and uses in different river basins.

5. Creation of strong database at basin level is important incorporating the supply and demand details of water crop, and livestock. Investment made, returns to investment in various activities in the basin should be documented and analyzed periodically for making future projects of the basin current and future potential.

6. Climate change will affect the water supplies and it is important to identify and implement the various adaptation measures at both micro (farm) level and macro (basin) level. This will help to improve the overall basin performance.

CHAPTER II Introduction 2. Introduction

Tamil Nadu's geographic area consists of 17 river basins, a majority of which is water- stressed. There are 61 major reservoirs; about 40,000 tanks and about 3 million wells that heavily utilize the available surface water (17.5 BCM) and groundwater (15.3 BCM). Agriculture is the single largest consumer of water in the State, using 75% of the State's water. Agriculture sector faces major constraints due to dilapidated irrigation infrastructure coupled with water scarcity due largely to growing demands from industry and domestic users and intensifying interstate competition for surface water resources. In some parts of the state, the rate of extraction of groundwater has exceeded recharge rates, resulting in falling water tables. Water quality is also a growing concern. Effluents discharged from tanneries and textile industries and heavy use of pesticides and fertilizers have had a major impact on surface water quality, soils, and groundwater. The State Government has taken a number of progressive actions on water resources and irrigation management, particularly through the World Bank-assisted Tamil Nadu Water Resources Consolidation Project (WRCP). Tamil Nadu was one of the first states to pass a groundwater bill, Procurement/Right to transparency act and a farmer‟s management of irrigation systems acts. The State has prepared a planning framework for water resources management, and a State Water Policy.

Given the geographical area of about 13 m.ha and the average annual rainfall of about 950 mm with bi-modal distribution, the surface water potential is estimated at 25000 MCM (893 TMC) and the ground water potential is about 22400 MCM (800 TMC). The demand for non- agricultural purposes in year 2025 will be about 16500 MCM (589 TMC) and the demand for agriculture purposes will be about 45000 MCM (1607 TMC) thus leaving a supply-demand gap of about 14100 MCM (504 TMC) (29.7 %). Given the state water policy, priority is given for domestic use followed by irrigation and industry etc. indicating that agricultural sector has to manage the scarcity in the future.

The major issues with the canal systems are poor water control and management, intersectoral water demand and the crop pattern with high water intensive crops such as rice,

sugarcane, banana, and turmeric. The irrigation efficiency is ranging from 40 to 50% only. Compared to the annual operation & maintenance expenditure of about Rs 400 million, The cost recovery is only about Rs.100 millions indicating poor maintenance of the systems. In the case of tanks, the major issues are tank siltation, encroachment, poor system management, and heavy dependence on rice cultivation. Out of 39200 tanks in the state, about 2 % are defunct in the tank intensive regions and about 67% in the tank-non intensive regions. This is because, in a 10-year period, the tanks fill fully only in 2 years, partially fill in 5 years and fail in 3 years. Mostly marginal and small farmers are distributed in the tank commands. Water market is getting importance in the recent years mainly to supplement the inadequate tank water particularly at the end of the rice crop period. Farmers normally spent about 20% of their rice crop income for buying water from wells owners. Since only about 15% of the farmers own wells in the tank command, there is great demand for well water. However, there is scope to diversify the crop pattern due to growing tank water scarcity. In the case of wells, the wells in the canal and tank commands perform well compared to non-command areas, due to declining water table. Out of the 1.8 million wells, about 0.16 million wells are defunct in the state as the water table is fast declining. Out of the 385 blocks in the state, 90 are dark (extraction exceeding 100% of the recharge, 89 are grey (extraction exceeding 65%) and the rest are white where the extraction is less than 65%. The average area irrigated per well has decreased from 1.4 ha during 1980s to 0.4 during 1990s indicating the water scarcity due to high well density and the associated well failure. The imputed cost of providing irrigation through wells is about Rs 0.3 million per ha. Further the flat rate of electricity from 1984 onwards and the free electricity introduced in the state from 1989 onwards also to some extent contributed for the over-exploitation of the ground water. The efficiency of the irrigation systems are also reflected in the productivity of crops per unit of water. Mostly crops under well irrigation systems are giving higher productivity per unit of water. The inter-sectoral water allocation is increasing in the recent years, as the wells, which are the main sources of domestic water sources are failing due to declining water table and poor water quality. The industrial demand for water is also increasing where the water charges paid by the industries form a sizeable portion of the O&M expenditure, thus indicating the scope for revenue generation through efficient water allocation.

Government is making serious efforts in improving the performance of the irrigation systems, through several interventions such as modernization of canal and tank irrigation systems. In the case of regions with groundwater irrigation, watershed programs are introduced in a big way. Still, the performance of these systems is comparatively poor due to less incentive to conserve water due to poor water control and management. The water users association formed in the canal and tank commands under the WRCP have started functioning. Conservation programs such as watershed management and improved water management techniques such as drip and sprinklers are still lacking behind due to poor adoption. Future water related investment programs should therefore aim to develop strategies and action plans to address the issue of efficient water allocation and management with the goal of maximizing the productivity per unit of water. Given the existing water supply scenarios, the demand management strategies will be considered more relevant for the efficient management of the available supplies. Therefore, what is needed is the clear understanding of the value of water in alternate uses as well as the incentive to allocate the water among competing crops and uses in different river basins. However, currently the available information is related to the administrative boundaries such as districts, which as such are difficult to relate with the river basin boundaries. Hence, it is important to reorient the district level data to basin level for making basin level interventions. This will also help to work out the performance of both irrigation and agriculture sectors at basin level. Accordingly, the following objectives are set forth:

CHAPTER III Objectives and Review of Literature 3. Objectives: i) To analyze the agricultural growth in all the 17 river basins of Tamil Nadu using the total factor productivity approach, ii) To study the income inequality in all the river basins of Tamil Nadu, and iii) To suggest policy options to improve the productivity of agriculture in the basins. iv) To assess the performance of agriculture, apart from growth rates, total factor productivity (TFP) was mainly used employing Data Envelopment Analysis (DEA).

3.1. Review of Past Studies: TFP measures

TFP growth shows the relationship between growth of output and growth of input, calculated as a ratio of output to input. In other words, productivity is raised when growth in output outpaces growth in input. Productivity growth without an increase in inputs is the best kind of growth to aim for rather than attaining a certain level of output by increasing inputs, since these inputs are subject to diminishing marginal returns. However, how to measure the total input and total output is both conceptually and empirically difficult. Methods to estimate TFP can be classified in four major groups:

1. least-squares econometric production models; 2. growth accounting TFP indices; 3. data envelopment analysis (DEA); and 4. Stochastic frontiers (Coelli et al., 2001).

The first two methods are normally used with times series data and assume that all production units are technically efficient. Methods (3) and (4) can be applied to a cross-section of firms, farms, regions, or countries to compare their relative productivity. In this study, we use both a Törnqvist-Theil index (growth accounting framework) and a non-parametric Malmquist index (DEA approach) to measure agricultural TFP growth in China and India.

The Malmquist index and based on distance functions, has become extensively used in the measure and analysis of productivity after Färe et al. (1994) showed that the index can be 17

estimated using a non-parametric approach. The non-parametric Malmquist index has been especially popular since it does not entail assumptions about economic behavior (profit maximization or cost minimization) and therefore does not require prices for its estimation, which in many cases are not available for international comparisons. Most important for this study is its ability to decompose productivity growth into two mutually exclusive and exhaustive components: changes in technical efficiency over time (catching-up) and shifts in technology over time (technical change).

To define the output-based Malmquist index assume, as in Färe et al. (1998), that for each time period t=1, 2…T the production technology describes the possibilities for the transformation of inputs x t into outputs y t .

This is the set of output vectors that can be produced with input vector x. For the

t m t n technology in period t and with y ∈ R outputs and x ∈ R inputs: The frontier of the output possibilities for a given input vector is defined as the output vector that cannot be increased by a uniform factor without leaving the set. In our analysis, we will refer to these production units as basins. The output distance function is defined at t as the reciprocal of the maximum proportional expansion of output vector y t given input x t . The distance measure equals 1 when the production point in period t is on the frontier for period t.

The Malmquist index measures the TFP change between two data points (e.g. those of a country in two different times) by calculating the ratio of the distance of each data point relative to a common technological frontier. Following Färe et al. (1994), the Malmquist output-oriented index between period t and t+1 is given by: as which is a geometric mean of two Malmquist indices: one using the technology frontier in t as the reference, and a second index that uses frontier in t+1 as the reference. Färe et al. (1994) showed that the Malmquist index could be decomposed into an efficiency change component and a technical change component, and that these results applied to the different period-based Malmquist indices. The ratio outside the square brackets measures the change in technical efficiency between period t and t+1. The expression inside brackets measures technical change as the geometric mean of the shift in the technological frontier between t and t+1 evaluated using frontier at t and at t+1, respectively, as the reference.

The efficiency change component of the Malmquist indices measures the change in how far observed production is from maximum potential production between period t and at t+1, and 18

the technical change component captures the shift of technology between the two periods. A value of the efficiency change component of the Malmquist index greater than one means that the production unit is closer to the frontier in period t+1 than it was in period t: the production unit is catching-up to the frontier. A value less than one indicate efficiency regress. The same range of values is valid for the technical change component of total productivity growth, meaning technical progress when the value is greater than one and technical regress when the index is less than one.

Research study done by Indian Institute of Agricultural Research, New Delhi indicated that public investment in irrigation, infrastructure development (road, electricity), research and extension and efficient use of water and plant nutrients were the dominant sources of TFP growth. The sharp deceleration in total investment and more so in public sector investment in agriculture is the main cause for the deceleration. This has resulted in the slow-down in the growth of irrigated area and a sharp deceleration in the rate of growth of fertiliser consumption. The most serious effect of deceleration in total investment has been on agricultural research and extension. This trend must be reversed as the projected increase in food and non-food production must accrue essentially through increasing yield per hectare. Recognising that there are serious yield gaps and there are already proven paths for increasing productivity. It is very important for India to maintain a steady growth rate in total factor productivity. As the TFP increases, the cost of production decreases and the prices also decrease and stabilise. Both producer and consumer share the benefits.

The fall in food prices will benefit the urban and rural poor more than the upper income groups, because the former spend a much larger proportion of their income on cereals than the latter. All the efforts need to be concentrated on accelerating growth in TFP, whilst conserving natural resources and promoting ecological integrity of agricultural system. More than half of the required growth in yield to meet the target of demand must be met from research efforts by developing location specific and low input use technologies with the emphasis on the regions where the current yields are below the required national average yield.

Many observers have expressed concern that technological gains have not occurred in a number of crops, notably coarse cereals, pulses and in rainfed areas. Recent analysis on TFP growth based on cost of cultivation data does not prove this perception. Tamil Nadu has shown increasing trend only in case of paddy. In all the 18 major crops considered in the analysis, 19

several states have recorded positive TFP growth. This is spread over major cereals, coarse grains, pulses, oilseeds, fibres, vegetables, etc. In most cases, in the major producing states, rainfed crops also, showed productivity gains. There is thus strong evidence that technological change has generally pervaded the entire crop sector. There are, of course, crops and states where technological stagnation or decline is apparent and these are the priorities for present and future agricultural research. Table 1. Total Factor Productivity trends for crops in selected states

TFP trend Crop Increasing No change Declining Bihar, Andhra Pradesh, Orissa, Punjab, Karnataka, Paddy Tamil Nadu, Uttar Pradesh, Assam, Haryana Madhya West Bengal Pradesh Haryana, Punjab, Rajasthan, Wheat Madhya Pradesh Uttar Pradesh Andhra Pradesh, Maharashtra, Madhya Pradesh, Sorghum Karnataka Rajasthan Pear millets Gujarat, Haryana, Rajasthan Maize Madhya Pradesh Rajasthan, Uttar Pradesh Barley Uttar Pradesh Rajasthan Chickpea Haryana Rajasthan, Uttar Pradesh Madhya Pradesh Andhra Pradesh, Madhya Black gram Maharashtra Orissa Pradesh, Uttar Pradesh Andhra Pradesh , Moong Madhya Pradesh Orissa Rajasthan Pigeon pea Madhya Pradesh Gujarat, Uttar Pradesh Andhra Pradesh , Karnataka, Groundnut Gujarat, Tamil Nadu Maharashtra, Orissa Rapeseed & Rajasthan, Uttar Pradesh Assam, Haryana Punjab Mustard Soybean Madhya Pradesh Andhra Pradesh , Haryana, Sugarcane Bihar Karnataka, Maharashtra, Uttar Pradesh Andhra Pradesh, Karnataka, Madhya Cotton Gujarat, Haryana, Tamil Nadu Pradesh, Maharashtra, Punjab Jute Assam, Bihar, West Bengal Bihar Onion Maharashtra Himachal Pradesh Potato Uttar Pradesh Himachal Pradesh Source: IARI-FAO/RAP study (2001) based on cost of cultivation data, DES, GOI.

Talluri (2000) provides an introduction to DEA and some important methodological extensions that have improved its effectiveness as a productivity analysis tool. They proposed a combination of models that allowed for effective ranking of DMUs in the presence of both quantitative as well as qualitative factors. Other ranking methods that do not specifically include cross-efficiencies were proposed by Rousseau and Semple (1995), and Andersen and Petersen (1993). Rousseau and Semple (1995) approached the same problem as a two-person ratio efficiency game. Their formulation provides a unique set of weights in a single phase as opposed to the two-phase approaches presented above. Andersen and Petersen (1993) proposed a ranking model, which is a revised version of problem. In this model, the test DMU is removed from the constraint set allowing the DMU to achieve an efficiency score of greater than 1, which provides a method for ranking efficient and inefficient units. He also discussed weight restrictions in DEA. The study on total factor productivity of agricultural commodities in economic community of West African states by Department of Agricultural Economics and Extension, Ladoke Akintola University of Technology, Nigeria (2005) provided a view on extent of productivity growth in crops relevant to food security and which have high potential for intra- ECOWAS trade. This paper done so by obtaining measures of Total Factor Productivity (TFP) for rice, cotton and millet over a 45-year period from 1961-2005 using a panel of major ECOWAS countries producing the crops. Calculations were based on data collected from FAOSTAT database, IRRI world rice statistics, international cotton advisory committee database, and individual country statistical database and studies. The data included output of each crop (rice, cotton and millet) and six input variables comprising land area, labour and seed fertilizer and irrigation and country dummies. The TFP measures were calculated using stochastic frontier approach. The TFP index was obtained by simply multiplying the technical change and the technological change. This is equivalent to the decomposition of the Malmquist index suggested by Fare et al (1994).The 45 year period is divided into two sub periods; 1961-1978 and 1979-2005 in order to study the effects of ECOWAS reforms on productivity growth of the selected crops. The results show evidence of phenomenal growth in the TFP of all the selected crops. Cotton however has the most impressive results followed by rice. A closer look at the TFP in ECOWAS and pre-ECOWAS sub-period shows larger TFP in ECOWAS period (1979-2005) for rice, and millet but larger TFP in pre-ECOWAS period for cotton. In both periods, productivity

growth in rice and cotton was sustained through technological progress while it was sustained through more efficient use of inputs in millet. Olajide (2003) examined changes in agricultural productivity in Sub-Sahara Africa countries in the context of diverse institutional arrangements using Data Envelopment Analysis (DEA). From a time, series, which consists of information on agricultural production and means of production, were obtained from FAO AGROSTAT and rainfall data from Steve O‟Connell database. The information was for a 43-year period (1961-2003); DEA method was used to measure Malmquist index of total factor productivity. A decomposition of TFP measures revealed whether the performance of factors productivity is due to technological change or technical efficiency change over the reference period. The study further examined the effect of land quality, malaria, education and selected governance indicators such as, control of corruption and government effectiveness on productivity growth. All the variables included in the model are significant with the exception of government effectiveness. They equally performed well in terms of expected relationship with TFP except education and land quality index, which unexpectedly had an inverse relationship with TFP.

There are different methods for estimating the total factor productivity (TFP) growth e.g. Malmquist and Tornquist indexes. The former had gained popularity in recent years since Fare et al., (1994) apply the linear programming approach to calculate the distance functions that make up the Malmquist index. According to Shih et al, (2003), since Data Envelopment Analysis (DEA) type of analysis can be directly applied to calculate the index, the Malmquist index has the advantage of computational ease, does not require information on cost or revenue shares to aggregate inputs or outputs, consequently, less data demanding and it allows decomposition into changes in efficiency and technology. This method does not attract any of the stochastic assumptions restriction, however, it is susceptible to the effects of data noise, and can suffer from the problem of „unusual‟ shadow prices, when degrees of freedom are limited (Coelli and Rao, 2003).

The issue of shadow prices is important and is one that is not well understood among authors who apply these Malmquist DEA methods; also, DEA methods in measuring productivity growth which made it distinct from pure index approach such as Fisher and Tornkvist indexes is that it does not require any price data, more so that agricultural input price data are seldom available and could at times be distorted by the government policies. 22

In the late 1970s, a mathematical programming approach known as Data Envelopment Analysis (DEA) was developed to measure technical efficiency by comparing the individual firm‟s production to the best practice frontier (Charnes, Cooper and Rhodes, 1978). The contribution of Farrell was path breaking as noted by Forsund and Sarafoglou (2000) in their article “On the origin of Data Envelopment Analysis”.

Efficiency measures were based on radial uniform contractions or expansions from inefficiency observations to the frontier. Thomson and Thrall (1995) observed Farrell seminal paper was followed by a relatively large number of refinement and extensions, which may be broadly classified into three schools of thought and identified as Afriat School, Charnes School and Shepherd School. Afriat School covers econometricians‟ parametric estimation approach, while the last two may more accurately be termed axiomatic production theory school.

CHAPTER IV Data Envelopment Analysis (DEA) 4. Data Envelopment Analysis (DEA) DEA is linear-programming methodology, which uses data on input and output quantities of a Decision Making Units (DMU) such as individual firms of a specific sectors to construct a piece-wise linear surface over data points. In this study, the countries were used as the DMU. The DEA method is closely related to Farrell‟s original approach (1957) and it is widely being regarded in the literature as an extension of that approach. This approach was initiated by Charnes et al.; (1978) and related work by Fare, Grosskopf and Lovell 1985) the frontier surface is constructed by the solution of a sequence of linear programming problems. The degree of technical inefficiency of each country, which represents the distance between the observed data point and the frontier, is produced as a by-product of the frontier construction method.

Either DEA can be input or output oriented depending on the objectives. The input- oriented method, defines the frontier by seeking the maximum possible proportional reduction in input usage while the output is held constant for each country. The output-oriented method seeks the maximum proportional increase in output production with input level held fixed. These two methods, that is, input-output oriented methods provide the same technical efficiency score when a constant return to scale (CRS) technology applies but are unequal when variable returns to scale (VRS) is assumed (Coelli and Rao, 2001). In this study, the output-oriented method will be used by assuming that in agriculture, it is common to assume output maximization from a given sets of inputs. The interpretation of CRS assumption has attracted a lot of critical discussion e.g. Ray and Desli, 1997, Lovell, 2001, but also monotonicity and convexity are debatable e.g. Cherchye, et al., 2000.

Fare et al., (1994) used Data Envelopment Analysis (DEA) methods to estimate and decompose the Malmquist productivity index. The DEA method is a non-parametric approach in which the envelopment of decision-making units (DMU) can be estimated through linear programming methods to identify the “best practice” for each DMU. The efficient units are located on the frontier and the inefficient ones are enveloped by it.

A key advantage of DEA over other approaches previously examined is that it more easily accommodates both multiple inputs and multiple outputs. As a result, it is particularly 24

useful for analysis of multispecies fisheries, because prior aggregation of the outputs is not necessary. Further, as will be outlined below, a specific functional form for the production process does not need to be imposed on the model (as is required in the use of the SPF approach). The envelopment surface will differ depending on the scale assumptions that underpin the model. Two scale assumptions are generally employed: constant returns to scale (CRS), and variable returns to scale (VRS). The latter encompasses both increasing and decreasing returns to scale.

CRS reflects the fact that output will change by the same proportion as inputs are changed (e.g. a doubling of all inputs will double output); VRS reflects the fact that production technology may exhibit increasing, constant and decreasing returns to scale. As demonstrated in Section 2.6, input- and output-based capacity measures are only equivalent under the assumption of constant returns to scale. However, there are generally a priori reasons to assume that fishing would be subject to variable returns and, in particular, decreasing returns to scale. Cooper, Seiford and Tone (2000) provide a discussion of methods for determining returns to scale. In essence, the researcher examines the technical efficiency given different returns to scale, and determines whether the observed levels are along the frontier corresponding to a particular returns to scale.

4.1. Input and output orientations

A range of DEA models have been developed that measure efficiency and capacity in different ways. These largely fall into the categories of being either input-oriented or output- oriented models.

With input-oriented DEA, the linear programming model is configured to determine how much the input use of a firm could contract if used efficiently in order to achieve the same output level. For the measurement of capacity, the only variables used in the analysis are the fixed factors of production. As these cannot be reduced, the input-oriented DEA approach is less relevant in the estimation of capacity utilization. Modifications to the traditional input-oriented DEA model, however, could be done such that it would be possible to determine the reduction in the levels of the variable inputs conditional on fixed outputs and a desired output level.

In contrast, with output-oriented DEA, the linear programme is configured to determine a firm‟s potential output given its inputs if it operated efficiently as firms along the best practice frontier. This is more analogous to the SPF approach, which estimated the potential output for a 25

given set of inputs and measured capacity utilization as the ratio of the actual to potential output, and is consistent with the illustration of the method.

Coelli and Rao (2003) paper examined levels and trends in agricultural output and productivity in 93 developed and developing countries that account for a major portion of the world population and agricultural output. We make use of data drawn from the Food and Agriculture Organization of the United Nations and our study covers the period 1980-2000. Due to the non-availability of reliable input price data, the study uses data envelopment analysis (DEA) to derive Malmquist productivity indexes. The study examines trends in agricultural productivity over the period. Issues of catch-up and convergence, or in some cases possible divergence, in productivity in agriculture are examined within a global framework. The paper also derives the shadow prices and value shares that are implicit in the DEA-based Malmquist productivity indices, and examines the plausibility of their levels and trends over the study period. *This issue of shadow prices is important, and is one that is not well understood among authors who apply these Malmquist DEA methods.

A major advantage cited in support of the use of DEA in measuring productivity growth, is that these methods do not require any price data. This is a distinct advantage, because in general, agricultural input price data are seldom available and such prices could be distorted due to government intervention in most developing countries. However, an important point needs to be added here. Even though the DEA-based productivity measures may not explicitly use market price information, they do implicitly use shadow price information, derived from the shape of the estimated production surface. This issue is described in some detail in Coelli and Prasada Rao (2001), who show that one can use these shadow prices to calculate shadow shares information, to help shed light on the factors influencing these productivity growth measures. Hence, a main aim of this paper is to demonstrate the feasibility of explicitly identifying the implicit shadow shares and to study regional variation and trends in these shares over time. They used shadow share information to provide valuable insights into why various authors have obtained widely differing TFP growth measures for some countries, when applying these Malmquist DEA methods. This has been particularly evident when the applications have involved panel data sets containing small groups of countries, and the countries included in each data set differ from study to study.

Some important findings of the paper were on levels and trends in global agricultural productivity over the past two decades. The results presented here examine the growth in agricultural productivity in 93 countries over the period 1980 to 2000. The results show an annual growth in total factor productivity growth of 2.1 percent, with efficiency change (or catch- up) contributing 0.9 percent per year and technical change (or frontier-shift) providing the other 1.2 percent. This is most likely a consequence of the use of a different sample period and an expanded group of countries.

In terms of individual country performance, the most spectacular performance is posted by China with an average annual growth of 6.0 percent in TFP over the study period. Other countries with strong performance are, among others, Cambodia, Nigeria and Algeria. The United States has a TFP growth rate of 2.6 percent, whereas India has posted a TFP growth rate of only 1.4 percent. Turning to performance of various regions, Asia is the major performer with an annual TFP growth of 2.9 percent. Africa seems to be the weakest performer with only 0.6 percent growth in TFP.

Examining the question of catch-up and convergence, we find that those countries that were well below the frontier in 1980 (with technical efficiency coefficients of 0.6 or below) have a TFP growth rate of 3.6 percent. This was in contrast to a low 1.2 percent growth for the countries that were on the frontier in 1980. These results indicate a degree of catch-up in productivity levels between high-performing and low-performing countries. Those results were quite interesting since they indicated an encouraging reversal during 1980-2000 period) in the phenomenon of negative productivity trends and technological regression reported in some of the earlier studies for the period 1961-1985.

Cheng Yuk-shing (1998) studied performance of Chinese agriculture and he used the Malmquist index to examine the sources of productivity growth in Chinese agriculture. Since the late 1980s, Chinese officials and economists had shown serious concern over the growth potential of Chinese agriculture. Relative returns to agricultural activities have been conceived to be too low and investment in agriculture insufficient. However, the fact was that China‟s agriculture experienced a period of rapid growth in the 1990s, after a slow down in the second half of the 1980s. In this study, Malmquist productivity indexes were computed for counties of 27

Jiangsu Province. They indicated that the total factor productivity growth in agriculture was as high as 7.8% per annum during 1991-95.

The decomposition result showed that there was rapid technical progress, along with a substantial decline in technical efficiency. This paper investigated the sources of productivity growth in Chinese agriculture over the period of 1988-95, using county-level data of Jiangsu Province. It had been shown that the growth of total factor productivity in 1991-95 was very rapid, averaging 7.8% annually. Yet contribution of inputs to agricultural growth was negative and technical efficiency declined substantially in this period. The productivity increase arose from entirely technical progress.

The impressive technical progress may indicate that the efforts of the Chinese government in boosting agricultural growth since the early 1990s might have been successful. Policies conducive to agricultural growth include an increase in investment in agricultural and irrigation facilities and an improvement in agriculture extensions. Output can be increased even if the original factors of production are used. Still, another possibility is that farmers have shifted their production more to cash crops that are high value-added products. In any case, further study is needed in order to understand more about the remarkable technical progress in Chinese agriculture. However, the major challenge to Chinese agriculture is the decline in technical efficiency. Previous studies suggest that there was substantial improvement in technical efficiency after the introduction of household responsibility system in the early 1980s. The empirical result of this study suggests that the efficiency level has not been maintained. The decline in efficiency in fact has eroded part of the positive impact of the technical progress. For agricultural growth to sustain in the future, the Chinese government might need to look more carefully into the factors that have caused such a serious decline in efficiency. Andre et al. showed a connection between Data Envelopment Analysis (DEA) and the methodology proposed by Sumpsi et al. (1997) to estimate the weights of objectives for decision makers in a multiple attribute approach in their working paper. This connection gave rise to a modified DEA model that allows estimating not only efficiency measures but also preference weights by radially projecting each unit into a linear combination of the elements of the payoff matrix (which is obtained by standard multicriteria methods). For users of Multiple Attribute Decision Analysis the basic contribution of this paper was a new interpretation of the 28

methodology by Sumpsi et al. (1997) in terms of efficiency. They also proposed a modified procedure to calculate an efficient payoff matrix and a procedure to estimate weights through a radial projection rather than a distance minimization. For DEA users, we provide a modified DEA procedure to calculate preference weights and efficiency measures, which does not depend on any observations in the dataset. This methodology has been applied to an agricultural case study in Spain.

This connection could be exploited in order to suggest a modified version of DEA in order to measure preference weights. The main idea is to use DEA including the elements of the payoff matrix as the only units in the reference set and interpret the λ parameters as the weights of each criterion or throughput. The purpose of this technique is to account for the effect of technological (feasibility) constraints in the decision making process. This way a single technique is capable of providing estimates of preference parameters and an alternative efficiency measure with the property of being independent of the DMUs in the sample. They had proposed a modified procedure to calculate the payoff matrix to guarantee that all its elements are efficient.

Moreover, they provided an approximate measure of efficiency that depends only on the information related to each DMU, being independent of the rest of the units in the sample. The main drawback of the modified DEA model for DEA users is the calculation of the payoff matrix, which usually requires full information about the decision problem that is faced by the DMU‟s. In a further research, we are working on a way to avoid this difficulty.

Fan Shenggen et al (2009) measured and compared agricultural total factor productivity (TFP) growth in China and India and relates TFP growth in each country to policy milestones and investment in agricultural research.

TFP was measured using a non-parametric Malmquist index, which allows the decomposition of TFP growth into its components: efficiency and technical change. The results showed that comparing TFP growth in China and India it was found that efficiency improvement played a dominant role in promoting TFP growth in China, while technical change had also contributed positively. In India, the major source of productivity improvement came from technical change, as efficiency barely changed over the last three decades, which explains lower TFP growth than in China. Agricultural research had significantly contributed to improve 29

agricultural productivity in both China and India. Even today, returns to agricultural R&D investments are very high, with benefit/cost ratios ranging from 20.7 to 9.6 in China and from 29.6 to 14.8 in India.

Rosegrant and Evenson (1995) assessed total factor productivity (TFP) growth in India, examines the sources of productivity growth, including public and private investment, and estimates the rates of return to public investments in agriculture. The results showed that significant TFP growth in the Indian crops sector was produced by investments -- primarily in research – but also in extension, markets, and irrigation. The high rates of return, particularly to public agricultural research and extension, indicated that the Government of India was not over investing in agricultural research and investment, but rather that current levels of public investment could be profitably expanded. Analysis of total factor productivity measured the increase in total output, which was not accounted for, by increases in total inputs. The total factor productivity index was computed as the ratio of an index of aggregate output to an index of aggregate inputs. Growth in TFP was therefore the growth rate in total output less the growth rate in total inputs. In this analysis, Tornqvist-Theil TFP indices were computed for 271 districts covering 13 states in India, 1956- 87.

Renuka Mahadevan (2003) assessed the productivity growth in Indian agriculture and to study the impact of globalisation. The study revealed that, there could easily be benefits that have not yet surfaced, or were yet to be identified and perhaps too difficult or intangible to measure. Whatever the case, it was highly likely that it is too soon to assess the full impact of globalization and economic reforms. Furthermore, the process of liberalization had been gradual and remained incomplete.

For example, the complete removal of quantitative restrictions after March 2001 would have provided an opportunity for Indian farmers to tap world markets and, if they were successful, results should start to become evident soon. Export promotion via the development of export and trading houses as well as effective liberalizing export promotion zone schemes for agriculture were fairly recent measures and only time will tell as to how effective these measures were. Other possibilities such as agro-industry parks for promoting exports were also in the pipeline. In conclusion, India had successfully set sail on the waters of globalization and

economic reforms and even in the wake of economic and political instability, she had to carefully steer her course in order to reap the benefits of increased productivity growth in the agricultural sector.

Canan et al. (2008) analyzed productivity growth in Turkey, EU-15 and CEE (Central and East European) Countries over the period 1995-2006. Malmquist productivity index had been used to measure the productivity. A nonparametric programming method is used to compute Malmquist productivity indexes, which were decomposed into two component measures, namely technical change and efficiency change. It was found that Hungarian productivity growth was higher than the other countries including EU-15 over the period 1995-2006, all with due to efficiency change. Productivity growth in Turkey within the period analyzed decreased especially in 2001, which was a crisis year.

Ramesh Chand (2005) measured the performance of agriculture sector in the country in the recent years. The result turned out to be quite dissatisfactory because of sharp deceleration in growth rate of agricultural output. Agricultural production over time was affected by interacting influences of technological, infrastructural, and policy factors. During the decade of 1990s, declining trend in public sector investment that set in year 1979-80 continued for most part of the decade. However, terms of trade were kept favourable to agriculture sector during 1990s by hiking level of cereal prices through government support, trade liberalization, exchange rate devaluation, and disprotection to industry. Several researchers felt that as economic reforms focused mainly on price factor and ignored infrastructure and institutional changes the overall impact on growth of agricultural sector has not been favourable. Highest response to fertilizer was obtained in the case of Tamil Nadu where one percent increase in fertilizer brought 0.7 percent increase in output. Elasticity of crop output with respect to irrigation was one. Tamil Nadu has scope to raise output by 0.65 and 0.82% per irrigation through irrigation. Shift in one percent area from food grain to non-food grain offers scope to raise crop output by 1.73 percent in Uttar Pradesh 1.6 percent in Karnataka and Assam, 2.4 percent in Bihar 1.5 percent in Maharashtra, 1.4 Percent in West Bengal, 1.2 percent in Orissa and 1.1 percent in Tamil Nadu. It seems likely that Andhra Pradesh, Bihar, Gujarat, Himachal Pradesh, Jammu and Kashmir, Karnataka, Maharashtra, Orissa, Punjab, Tamil Nadu, U.P, and West Bengal are in a 31

position to increase fertilizer use by same rate as witnessed during 1990s. Expansion of area under irrigation, improvement in total factor productivity, resource shift towards high value enterprises and increase in application of fertilizer were the four sources of growth in agriculture. Crop intensity is another source for output growth but in our exercise, its impact on output is captured by impact of irrigation on output.

Ashok and Balasubramanian (2006) explore the role of infrastructure in productivity and diversification of agriculture and discussed issues related to the project and advantage in development of Tamil Nadu state economy. Tamil Nadu‟s performance with respect to the Human Development Index (HDI) was also impressive; it ranked third among 29 states. This is especially true for human development indicators like female life expectancy, female mortality rate, and access to safe drinking water etc. Notwithstanding these achievements, Tamil Nadu was still a low-income state and had a relatively high incidence of poverty (20 per cent) and unemployment (14 per cent) in the country. There were intra-state disparities in key poverty and social indicators. About 12 million people live in poverty, and inequality in Tamil Nadu was higher than the all-India average, and was in fact, the highest among the fifteen major states. This uneven improvement in the quality of life had left a large section of the population, which has consistently failed to benefit from the economic and social development that the state has achieved.

Rural poverty is concentrated among those with marginal landholdings and dependent on rain-fed agriculture. Recurring droughts and price crashes due to seasonal gluts increase the vulnerability of these sections due to income variations. Investment in infrastructure like irrigation, road, education, markets, etc., would in the long run reduce this vulnerability and enable the small and marginal farmers to participate in the new development process ushered in by the liberalization and globalization of the economy.

Cereal based small farm agriculture in the State of Tamil Nadu in India was facing the challenge of accelerating crop productivity and diversification of crops in the context of declining public investment and in the globalizing economy.

The results of the study clearly established that the investments in rural infrastructure like irrigation, rural markets, and roads increase the total factor productivity in Tamil Nadu agriculture. Nevertheless, public investment in agriculture had been declining in real terms in the 90s. It was imperative that stepping up investment in rural infrastructure is not only essential to accelerate agricultural productivity but also to secure livelihoods for two-third of the population in the State in the emerging global economic order. The results showed that the effect of infrastructure on diversification is mixed. While irrigation intensity, the markets, and commercial vehicles had positive significant influence on crop diversification, road density had significant negative influence on diversification.

CHAPTER V Profile of the Study Area: Tamil Nadu

5. Profile of the Study Area: Tamil Nadu

Tamil Nadu is one of the progressive & largest states in India. The Gross State Domestic Product (GSDP) at factor cost at constant (1999-2000) prices in the State increased from Rs.183843 crore in 2005-06 to Rs.201042 crore in 2006-07 and registered a growth of 9.36 per cent which is more or less equal to that of the preceding year (9.39%). For the corresponding period, the GSDP measured at current prices increased from Rs.229543 crore to Rs.262692 crore that recorded a double-digit growth of 14.44 per cent. The State witnessed positive and comfortable growth rates in all the three-sub sectors viz. primary, secondary and services sectors during the last three years. All the three sub sectors in the recent past yielded desirable results. In real terms, the primary sector achieved a growth of 13.07 per cent, the secondary sector 7.49 per cent and the services sector recorded 9.45 per cent during 2006-07, which helped the State economy to achieve the overall growth of 9.36 per cent.

Figure 1. Map of Tamil Nadu State

In Tamil Nadu Large chunk of population is engaged in agriculture activities. Agriculture continues to be the prime mover of the State economy supporting 56 percent of the population (Tamil Nadu Agriculture Policy Note 2010-11, Government of Tamil Nadu) and contributes 12.3 percent of the State income of 2007-08 (Tamil Nadu - An Economic Appraisal 2006-07&2007- 08, Government of Tamil Nadu). Having geographical area of 130 lakh ha, its net sown area has come down to 50.62 lakh ha in 2007-08 from 61.35 lakh ha in seventies. Table 2.Land Use Pattern in Tamil Nadu (Lakh ha)

Classification 1970-80 1980-90 1990-00 2007-08

Forests 20.05 20.76 21.44 21.06 Barren and unculturable land 5.4 4.2 3.8 4.9 Permanent pastures and other grazing 1.98 1.45 1.25 1.10 lands Cultivable waste 4.15 3.08 3.25 3.47 Land put to non-agricultural uses 16.00 17.95 19.07 21.61 Land under miscellaneous tree crops and 2.15 1.82 2.25 2.68 groves not included in the net area sown Current fallows 12.02 16.18 10.57 9.81 Other fallows lands 5.31 7.03 10.93 14.99 Net area sown 61.35 56.22 56.32 50.62 Total Geographical area 130.06 130.06 130.16 130.27 Source: Department of Economics and Statistics, Chennai -6.

Land use pattern of the State has undergone rapid structural changes over the period. The decline in the net area sown was mainly attributed to increasing conversion of agricultural land into non-agricultural purposes including housing sites. The full impact of the above observations is that rising population, consequent urbanisation, rural-to-urban induced migration, falling net area sown, creation of substantial rural employment, indiscriminate housing activities, etc. are major areas of concern. Land put to non-agricultural purposes has increased from 16 lakh ha in 1970s to 21.61 lakh ha in 2007-08 (Table 2). Area under permanent pastures and grazing lands are shrinking; it is a sign of a decline in village common land due to encroachment and neglect. However, total area under these categories is very small. The area under miscellaneous tree crops and groves has increased which is a sign of growing interest in agro-forestry and horticultural trees.

Land holdings- Constantly rising demography pressure on land is a serious cause for concern. The marginal and small farm holdings accounts for 89% of the total holdings and the

area operated by them 52% of the total area. The per capita availability of land has been continuously declining and the availability of cultivable land is even worse. Land is not only an important factor of production, but also the basic means of subsistence for majority of the people in the State of Tamil Nadu.

Table 3.Land Holding Pattern in Tamil Nadu

Category Number of holdings (lakhs) Average of Size of Holdings (ha) 1970-71 1995-96 1970-71 1995-96 Marginal (< 1ha) 31.25 59.51 0.42 0.37 Small (1 – 2 ha) 11.09 12.34 1.42 1.39 Semi Medium (2-4ha) 6.96 6.01 2.75 2.70 Medium (4-10ha) 3.25 2.00 5.83 5.68 Large (> 10 ha) 0.59 0.26 17.00 23.62 Total 53.14 80.12 1.45 0.91

Together with the shrinking area under cultivation, the pattern of land ownership is also unfavourable for agricultural development. The average size of holdings has declined from 1.45 ha in 1970-71 to 0.91 ha in 1995-96 (Table 3.). The all India figure for average area owned per household is 1.59 ha. This reflects the pressure of population on land. The share of total land operated by small and marginal farmers has increased from 42 percent to 52 percent during the same period. The growth in number and extent of small and marginal farmers is a major hurdle in promoting capital investment in agricultural sector and modernizing agriculture sector. Fragmentation of land results in uneconomic land holdings.

5.1. Principal crops and production

Rice is the dominant crop in Tamil Nadu. Groundnut, Sugarcane and cotton are important commercial crops. Jowar, bajra and pulses are some important foodgrain crops. These seven crops account for about 73% of gross cropped area, while 42 other crops are each cultivated in small areas. They include minor millets, other oil seeds, turmeric, vegetables, fruits, coconut and other minor crops.

Area under paddy decreased to 17.89 lakh ha during 2007-08 compared to 19.31 lakh ha. In the preceding year (Table 4). Area under pulses also registered increase. The same trend follows in groundnut also. In respect of cotton, area remains almost same. To encourage cotton

growers in Tamil Nadu, contract farming is popularized with buy back arrangements. Under contract farming, the farmer is provided support in diverse areas such as marketing, input, credit, insurance coverage etc.

Table 4.Status of Principle Crops in Tamil Nadu

1989-1990 1999-2000 2005-06 2006-07 2007-08 Crops Area Yield Area Yield Area Yield Area Yield Area Yield Paddy 19.63 3088 21.64 3481 20.50 2541 19.31 3423 17.89 2817 Pulses 8.21 407 6.92 420 5.25 337 5.36 541 6.09 303 Sugarcane 2.22 104* 3.16 109* 3.35 105* 3.91 115* 3.54 108* Cotton 2.81 308# 1.78 324# 1.10 260# 1.00 374# 1.00 343# Groundnut 10.15 1195 7.59 1736 6.19 1775 5.08 1981 5.35 1957 Area in lakhs ha and Yield in Kg/ha; *in terms of cane‟ # in terms of lint Source: Compiled from various issues of Season and Crop Reports, Government of Tamil Nadu Productivity trend in paddy, sugarcane, and cotton was almost stagnant. Groundnut productivity has shown marginal increase. Wide variation has noticed in pulse productivity as major pulse area is under rainfed condition. 5.2.Irrigation The irrigation potential of the State has already been realized. Per capita availability of water is lowest in Tamil Nadu. Well irrigation is dominant in Tamil Nadu. Of the 1.8 million wells, approximately 10 per cent are defunct. The depth of bore wells in hard rock is between 600 and 1000 ft. This situation tends to the water management as the key to the priority area for both the farmers and implementing authority. It further focused on area of efficient water management and crop diversification imperative in the place of highly water intensive crops like paddy and sugarcane in the State Irrigation: The major irrigation sources in the State are canals, tanks, and wells. The per capita availability of water in the state stood at 900 cubic meters as against the All-India level of 1980 cubic meters as on 2001.

Table 5.Reduction in Per Capita Availability of Water in Tamil Nadu

Total water resources available per Surface water

Population annum availability Year Millions Cubic Km Per capita cubic meter Per capita cubic meter 1951 30.1 44.923 1492 803 1961 33.7 44.923 1333 717 1971 41.2 44.923 1090 586 1981 48.4 44.923 928 499 1991 55.9 44.923 804 432 2001 62.1 44.923 723 389 The per capita availability of surface water in Tamil Nadu has come down from 803 cubic meter in 1951 to 389 cubic meter in 2001(Table 5.). This is mainly due to population explosion and increase in usage of water in industrial sector. Table 6.Seasonwise Rainfall in Tamil Nadu (mm) Year Southwest Northeast Winter Summer Total Rainfall 1979-80 196.4 337.0 10.5 125.4 669.3 1989-90 348.8 341.0 90.2 136.7 916.7 1999-2000 199.9 499.5 119.5 77.9 896.8 2007-08 341.6 515.4 46.2 261.2 1164.4

The state‟s annual normal rainfall is 958.51mm. Nearly more than 30% of the crops grown in the state are under rainfed condition. From the table it is evidenced that variation in rainfall received was higher and more than 40% of the rainfall was received from Northeast monsoon period. With the total rainfall of 1164.4 mm received during 2007-08, it was rated as 'excess' and emerged to be more beneficial to cropping. Table 7. Irrigation Status in Tamil Nadu (Area in lakh ha) Particulars 1989-90 1999-2000 2006-07 Gross Irrigated Area 30.4 35.9 33.1 Net Irrigated Area 24.9 29.7 28.9 Canals 7.9 8.7 7.8 Tanks 5.2 6.3 5.3 Wells 11.7 14.5 15.7 Others 0.1 0.1 0.1 Area Irrigated More than Once 5.5 6.2 4.2

The age old structures, inadequate maintenance, encroachment in the catchments and foreshore areas, large scale siltation, the live practice of fragmentation of holdings, lack of institutional arrangements for the supply of water, widespread deviations from the intended cropping pattern, seepage, percolation, evaporation, diversion of ayacut for nonagricultural purposes, excessive drawal in the upper reaches, unauthorized drawal etc. have caused a wide gap between the potential created and its utilization in the case of surface flow sources of irrigation in the State. The net area irrigated by surface flow source has become stagnant as 13.1 lakh hectares in 1989-90 and in 2006-07(Table 7.). Due to the proliferation of wells, the extent of area irrigated increased from 11.7 in 1989-90 to 15.7 lakh hectares in 2006-07, increasing its relative share in the total net area irrigated in the State from 24 to 54 percent. Proliferation of wells and indiscriminate drawal of water has its own adverse effect on the water table. Due to this area irrigated more than once has come down from 5.5 lakh ha in 1989-90 to 4.2 lakh ha in 2006-07. Viewed against these serious limitations, the overall irrigation scenario in the State is uninspiring. At this juncture, even to maintain the existing irrigated area, the State has to focus its attention on popularization and adoption of water saving techniques which saves 40-70 per cent of water as compared to field irrigation, bringing in atleast 10 per cent of the total irrigated area under these techniques, popularization of rainwater harvesting and conservation techniques, evolving an integrated approach to use surface and groundwater conjunctively, equipping and involving the farmers in the maintenance of source and water distribution, regularizing the drawal of groundwater with the safe limits and minimization of water losses.

Table 8.Change in Availability of Groundwater in Tamil Nadu Total Categorization of Blocks Year of No. of S.No No. of White Assessment Districts Dark (85 – 100%) Grey (65 – 85%) Blocks (65%) 1 1987 19 378 41 86 251 2 1992 22 384 89 86 209 Over Critical Semi Safe Saline Exploited (90 – Critical (<70%) (>100%) 100%) (70- 90%) 3 1998 28 385 135 35 70 137 8 4 2003 28 385 135 37 105 97 8 Source: Water Resource Organisation, Govt. of Tamil Nadu.

As per the latest estimates of January 2003, the State has tapped 86 percent of groundwater potential. Across the State, the untapped groundwater potential is distributed in 97 safe blocks (tapping of potential <70%), 105 semi-critical blocks (>70% to<90%) and183 critical blocks (>90% to<100%). In about 138 blocks (36% of the total blocks in the State), the potential has been over exploited, exceeding the recharge capacity (Table 8.). As a result, the number of dark blocks is increasing.

5.3. Problems facing Agriculture in the State

5.3.1. Land degradation and soil quality Crop yields are dependent on certain soil characteristics- soil nutrient content, water- holding capacity, organic matter content, acidity, top soil depth and soil biomass and so on. Soil erosion is by wind or water. Erosion causes depletion of fertility through the removal of the valuable and fertile surface soil. In Tamil Nadu, erosion is observed in and around 13 lakh ha. The organic matter content in the soil has gone down from 1.20% in 1971 to 0.68% in 2002 in Tamil Nadu, because of less use of organic inputs. 5.3.2. Wastelands The adverse effect of salinity in soil is that it hinders crop growth and results in reduction in crop yield. The estimated extent of soils affected by salinity and alkalinity is estimated at 2.48 L.ha. Besides 1.23 L.ha. Suffering from acidic soils. Excess water hinders plant growth by reducing aeration, which in turn decreases the water absorption and nutrient uptake by roots. The coastal regions of Tamil Nadu face heavy damages due to water logging. The command areas in major irrigation projects experience water logging problem. In Tamil Nadu 44,820 ha is estimated as marshy lands. About 14 percent of the area in Tamil Nadu is under very poorly drained soils. Another 16 percent is under moderately well drained to well drain soils and 15 percent is somewhat excessively drained soil. The gullies are the first stage of excessive land dissection followed by their networking which lead to the development of ravine land. The ravines are extensive system of gullies developed along nullas, streams, and river coarse. It has been estimated that Tamil Nadu has 22,550 ha. Under gullied / ravine lands. Wastelands are degraded lands that can be brought under vegetative cover.

5.3.3. Pollution The study carried out by the Loss of Ecology Authority, Government of India, revealed that the tannery industries have adversely affected 15,164 ha of agricultural land in Vellore district and 2,005 ha in Dindigul district. Tirupur district is fast growing hosiery 'Industrial City' in Tamil Nadu. It is located on the bank of the Noyyal River. The effluent discharged by the textile industries released into the Noyyal River pollutes the surface and ground water and damages the agricultural land. In general, the agricultural performance in the state has been affected by marginalization of land holding, high variability in rainfall distribution, inadequate capital formation by the public sector, declining public investment on agriculture, declining net area sown, over - exploitation of ground water and inadequate storage and post harvest facilities... The state supports seven percent of the country's population but it has only four per cent of the land area and three percent water resources of the country. Of the total gross cropped area, only 50 percent of the area is irrigated in Tamil Nadu. Similarly, of the total area under food grains, only 60 percent of the area is irrigated. Nearly, 52 percent of area is under dry farming conditions in Tamil Nadu apart from stable cropping intensity, which is hovering around 120 percent over the period. In spite of the above constraints, the State has made tremendous performance in the production of crops, which is attributed mainly to the productivity increase and government intervention.

CHAPTER VI Profile of River Basins of Tamil Nadu

6. Profile of River Basins of Tamil Nadu

The river basins in Tamil Nadu are grouped into 17 major river basins as furnished below.

Figure 2. River Basins of Tamil Nadu

Table 9.Major River Basins of Tamil Nadu

Name of the Major River Basin Group River Basins in the Group 1. Chennai Basin Group 1. Araniyar 2. Kusaithalaiyar 3. Cooum 4. Adayar 2. Palar 5. Palar 3. Varahanadhi 6. Ongur 7. Varahanadhi 4. Ponnaiyaar 8. Malattar 9. Ponnaiyaar 10. Gadilam 5. Vellar 11. Vellar

6. Paravanar 7. Cauvery 12. Cauvery 8. Agniyar 13. Agniyar 14. Ambuliyar 15. Vellar 9. Pambar and 16. Koluvanar Kottakaraiyar 17. Pambar 18. Manimukthar 19. Kottakaraiyar 10. Vaigai 20. Vaigai 11. Gundar 21. Uthirakosamangaiyar 22. Gundar 23. Vembar 12. Vaippar 24. Vaippar 13. Kallar . 25.Kallar 2 26. Korampallam Aru 14. Thambaraparani 27. Thambaraparani 15. Nambiyar 28. Karmaniar 29. Nambiyar 30. Hanumanadhi 16. Kodaiyar 31. Palayar 32. Valliyar 33. Kodaiyar 17. PAP 34.West flowing river Table 10.Area and Rainfall of the River Basins

Normal Area of Normal Rain Non Name of the Major Annual System S.No the basin Volume system River Basin Group Rainfall tanks (sq.km) (Km3) tanks (mm) 1. Chennai Basin Group 5542 1130 6.26 1304 215 2. Palar 10911 940 10.03 661 3. Varahanadhi 4214 1250 4.55 131 1290 4. Ponnaiyar 11257 920 11.17 1133 5. Vellar 7659 980 386 71 6. Paravanar 760 8.39 2 9 7. Cauvery 43867 930 45.32 8. Agniyar 4566 910 4.06 346 3629 9. Pambar and 5847 880 3.07 160 1161 Kottakaraiyar 10. Vaigai 7031 900 6.97 521 976 11. Gundar 5647 770 3.73 526 123 12. Vaippar 5423 800 5.00 151 711 13. Kallar 1879 600 1.04 15 184 14. Thambaraparani 5969 1110 6.09 1300 15. Nambiyar 2084 950 1.48 559 38 16. Kodaiyar 1533 1720 2.64 2 1460 17. PAP 3462 610 1.33 43

Table 11.Surface and Groundwater Potential (MCM) of the River Basins

Name of the Major Surface water Groundwater Other Total water S.No River Basin Group potential potential sources potential 1. Chennai Basin Group 906.00 1120.22 2026.22 2. Palar 1758.00 2610.32 4368.32 3. Varahanadhi 412.09 1482.07 4.00 1898.16 4. Ponnaiyaar 1310.43 1560.00 2870.43 5. Vellar 1065.00 1344.00 6.00 2415.00 6. Paravanar 104.30 225.50 39.70 370.00 7. Cauvery 5962.00 2869.00 8831.00 8. Agniyar 585.00 920.00 499.00 2004.00 9. Pambar and 653.00 976.00 1629.00 Kottakaraiyar 10. Vaigai 1579.00 993.00 2572.00 11. Gundar 567.52 766.00 1334.00 12. Vaippar 611.00 1167.00 4.82 1782.82 13. Kallar 124.56 69.58 17.37 211.51 14. Thambaraparani 1375.00 744.00 2119.00 15. Nambiyar 203.87 274.74 478.61 16. Kodaiyar 925.00 342.10 1267.10 17. PAP 416.00 751.001 1167.00 Detailed particulars of the each river basin such as basin area, districts in which they fall, sub- basins etc are provided in Appendix-I

CHAPTER VII Methodology

7. Methodology The proposed methodology to study the total factor productivity (TFP) of agriculture in river basins of Tamil Nadu consists of the following three steps: 7.1. Estimation of basin areas and proportion of basin areas in each district of Tamil Nadu:

Estimates of 17 river basin areas are available from published records. Also rough estimates of area of each basin in each district are available and these figures must be checked for their accuracy. This will be done by using GIS techniques and the figures will be revised. Using these figures, the proportion of area occupied by each basin in each district will be estimated.

7.2. Conversion of district-wise data to basin-wise:

Data on various input and output variables are available district wise from published records. Further, these districts, which were 24 in number during 1970s have been subdivided over years and now there are 31 districts and recent data are available only for the new districts while figures for past years are available only for the original districts.

So first, these data will be aggregated either to the original districts or for the latest districts. Apportion these revised time series figures will be then to various basins based on the estimates obtained in Step 1 as follows: Let pij i 1,2,...B; j 1,2,...D be the proportion of area occupied by basin i in district j and B and D be respectively the total number of basins and districts. Also let xd be the value of a input or output variable for the district d in a certain year and yb be the estimated value of that variable for basin b during the same year. Also let

y1   x 1   p 11 p 12 . . p 1D  y   x   p p . . p  2   2   21 22 2D  YXP......    and          ......            yB   x D   p B12 p B . . p BD 

It can be easily checked that

Y PX

The above formula provides an elegant method of estimation of figures for each basin. 45

7.3. Estimation of Malmquist Index of Total Factor Productivity Growth in Agriculture

It is proposed to measure total factor productivity (TFP) using the Malmquist index methods. This approach uses data envelopment analysis (DEA) method to construct a piece-wise linear production frontier for each year in the sample. We firstly provide description of DEA methods before we go on to describe the Malmquist TFP calculations.

As already discussed, DEA is a linear-programming methodology, which uses data on the input and output quantities of a group of basins to construct a piece-wise linear surface over the data points. This frontier surface is constructed by the solution of a sequence of linear programming problems – one for each basin in the sample. The degree of technical inefficiency of each basin (the distance between the observed data point and the frontier) is produced as a by-product of the frontier construction method. DEA can be either input-orientated or output-orientated. In the input-orientated case, the DEA method defines the frontier by seeking the maximum possible proportional reduction in input usage, with output levels held constant, for each basin. While, in the output-orientated case, the DEA method seeks the maximum proportional increase in output production, with input levels held fixed. The two measures provide the same technical efficiency scores when a constant return to scale (CRS) technology applies, but are unequal when variable returns to scale (VRS) is assumed. For our proposed study, we assume a CRS technology. Hence, the choice of orientation is not a big issue on our case. However, we have selected an output orientation because we believe it would be fair to assume that, in agriculture, one usually attempts to maximise output from a given set of inputs, rather than the converse.

If one has data for N, basins in a particular time period, the linear programming (LP) problem that is solved for the i-th basin in an output-orientated DEA model is as follows:

max, ,

st   yi Y 0,

xi  X 0,  0, (1)

Where 46

yi is a Mx1 vector of output quantities for the i-th basin;

xi is a Kx1 vector of input quantities for the i-th basin; Y is a NxM matrix of output quantities for all N basins; X is a NxK matrix of input quantities for all N basins;

 is a Nx1 vector of weights; and

 is as scalar.

It must be noted that the parameter  will take a value greater than or equal to one, and that -1 is the proportional increase in outputs that could be achieved by the i-th basin, with input quantities held constant. Note also that 1/ defines a technical efficiency (TE) score with varies between zero and one. The above LP is solved B times – once for each basin in the sample. Each LP produces a

and a  vector. The -parameter – provides information on the technical efficiency score for the i-th basin the  -vector provides information on the peers of the (inefficient) i-th basin. The peers of the i-th basin are those efficient that define the facet of the frontier against which the (inefficient) i-th basin is projected.

7.4. The Malmquist TFP Index The Malmquist index is defined using distance functions. Distance functions allow one to describe a multi-input, multi-output production technology without the need to specify a behavioural objective (such as cost minimization or profit maximization). One may define input distance functions and output distance functions. An input distance function characterizes the production technology by looking at a minimal proportional contraction of the input vector, given an output vector. An output distance function considers a maximal proportional expansion of the output vector, given an input vector. We only consider an output distance function in detail in this paper. However, input distance functions can be defined and used in a similar manner.

A production technology may be defined using the output set, P(x), which represents the set of all output vectors, y, which can be produced using the input vector, x. That is,

P(x) y :x can produce y.

The output distance function is defined on the output set, P (x), as:

do x, ymin :y / P(x).

The distance function, do x, y, will take a value which is less than or equal to one if the output vector, y, is an element of the feasible production set, P(x). Furthermore, the distance function will take a value of unity if y is located on the outer boundary of the feasible production set, and will take a value greater than one if y is located outside the feasible production set. In our proposed study, we use DEA-like methods to calculate our distance measures.

The Malmquist TFP index measures the TFP change between two data points (e.g., those of a particular basin in two adjacent time periods) by calculating the ratio of the distances of each data point relative to a common technology. The Malmquist (output-orientated) TFP change index between period s and the base period t is given by

1 / 2  d s y , x  d t y , x  m y , x , y , x  o t t x o t t , o  s s t t   s t  do ys , xs  do ys , xs 

We can easily see that in the above equation, the right hand side is in fact, the geometric mean of two TFP indices. The first is evaluated with respect to period s technology and the second with respect to period t technology.

An equivalent way of writing this productivity index is

1 / 2 d t y , x  d s y , x  d s y , x  m y , x , y , x  o t t o t t x o s s , o  s s t t  s  t t  do ys , xs   d o yt , xt  do ys , xs 

Where the ratio outside the square brackets measures the change in the output-oriented measure of Farrell technical efficiency between periods s and t. That is, the efficiency change is equivalent to the ratio of the technical efficiency in period t to the technical efficiency in period s. The remaining part of the index in the above equation is a measure of technical change. It is the geometric mean of the shift in technology between the two periods, evaluated at xt and at xs. Given that suitable panel data are available, we can calculate the required distance measures for the Malmquist TFP index using DEA-like linear programs. For the ith basin, we must calculate

four distance functions to measure the TFP change between two periods, s, and t. This requires the solving of four linear programming (LP) problems. Assuming constant returns to scale (CRS) technology, the required LPs are:

1 t do y t ,x t  max ,  ,

st yit  Y t 0 , (1) xit X t 0 ,  0

1 s do y s ,x s  max ,  ,

st yis  Y s 0 , (2) xis X s 0 ,  0

1 t do y st ,x s  max ,  ,

st yis  Y t 0 , (3) xis X t 0 ,  0 and

1 s do y t ,x t  max ,  ,

st yit  Y s 0 , (4) xit X s 0 ,  0

It can be noted that in LP‟s 3 and 4, where production points are compared to technologies from different time periods, the  parameter need not be greater than or equal to one, as it must be when calculating standard output-orientated technical efficiencies. The data point could lie above the production frontier. This will most likely occur in LP 4 where a production point from period t is compared to technology in an earlier period, s. If technical

progress has occurred, then a value of  <1 is possible. It could also possibly occur in LP 3 if technical regress has occurred, but this is less likely. In the input-orientated case, the DEA defines the frontier by seeking the maximum possible proportional reduction in input usage, with output levels held constant, for each River Basin. In the output-orientated case, DEA seeks the maximum proportional increase in output production, with input levels held fixed. The two measures provide the same technical efficiency scores when a constant return to scale (CRS) technology applies. In this study, we select an output orientation with assumption of CRS. Because, in agriculture, one usually attempts to maximize output from a given set of inputs, rather than minimizing the inputs for a given level of output. Therefore, we are assuming constant returns to scale technology for this analysis. The Malmquist total factor productivity change indices are decomposed into technical change and technical efficiency change components. The above approach is further extended by decomposing technical efficiency change into scale efficiency and pure technical efficiency components. As the proposed study is of empirical in nature and the study is intended to utilize both primary and secondary data for past 30 years from published and unpublished records. Secondary sources for data collection were Seasons and Crop Report, Economic Appraisal of Tamil Nadu, Statistics at a Glance, Publications of Central Water Commission, Published, and unpublished records of Public Works Department, Census of India, Livestock Census, District Statistical Office, and Department of Agriculture etc.

CHAPTER VIII Basin coverage and Time Period

The data for the present study consisted of the following:

8. Basin coverage: All the river basins of Tamil Nadu were included in the present study. They were Chennai basin, Palar basin, Varahanadhi basin, Ponnaiyaar basin, Vellar basin, Paravanar basin, Cauvery basin, Agniyar basin, Pambar and Kottakaraiyar basin, Vaigai basin, Gundar basin, Vaippar basin, Kallar basin, Thambaraparani basin, Nambiar basin, Kodaiyar basin and Parambikulam Azhiyar Project (PAP) basin

8.1. Time period: the study covers the period of 1975 -76 and 2005 -2006, which concerned with important changes in agriculture due to liberalization of trade and reforms in investment, initiation of privatization, tax reforms and inflation controlling measures.

CHAPTER IX Output and Input Series 9. Output Series: The study used two output variables, viz., crops and livestock output variables. The output series for these two variables were derived by aggregating detailed output quantity data of all agricultural commodities. Area under each crop was multiplied by the constant prices of respective crop to arrive at agricultural output.

9.1. Total inputs: Use in agriculture included of labor, land, chemical fertilizers, and irrigation area were used.

9.1.1. Labor Input: This variable referred to economically active population in agriculture. Economically active population is defined as all persons engaged or seeking employment in an economic activity, whether as employers, own-account workers, salaried employees, or unpaid workers assisting in the operation of a family farm or business.

9.1.2. Land Input: Land input is measured by area sown rather than arable land because the arable land data is extremely inaccurate. Sown area is land on which crops are planted and from which a harvest is expected. Because land is frequently sown two or even more times a year depending on climate and soil quality, sown area is substantially larger than arable land. Therefore, sown area also indicates land quality more accurately.

9.1.3. Chemical Fertilizer input: Chemical fertilizer included weights of nitrogen, super- phosphate, and potassium sulfate.

9.1.4. Irrigation Input: This data referred to the area of land, which is equipped to provide water to crops. These included areas equipped for full and partial control irrigation, spate irrigation areas, and equipped wetland or inland valley bottoms.

9.1.5. Livestock inputs: Livestock inputs included cattle population comprising of cow, bullock, buffalo, sheep, goat, and poultry. 52

9.1.6. Units of variables: The table below provides the units of various variables used in the present study.

Variable Unit Agricultural output Rupees in Crores Net sown area hectare Rupees in Crores Crop output Net Irrigated area hectare

Rupees in Crores Livestock output NPK consumption lakh tones

Numbers Labour input Cattle and poultry numbers

CHAPTER X Results and Discussions

10. Results and Discussions

10.1. Summary Statistics 10.1.1. Crop output The summary statistics of output and input variables namely crop output, livestock output, net sown area, net irrigated area, NPK intake, labour input, cattle input and poultry input for all the basins are presented and discussed below. Table 12.Summary Statistics Crop output (Rs.Crores)

Area of the Sl.No Name of the basin Max Min Average SD CV (%) basin 1 Chennai Basin 5542 2002 113 822 669 81 2 Palar River Basin 10911 5537 320 2041 1697 83 3 Varahanadhi River Basin 4214 3850 134 1392 1289 93 4 Ponnaiyaar River Basin 11257 11553 374 2928 2614 89 5 Paravanar River Basin 7659 830 25 294 282 96 6 Vellar Basin 760 8737 280 2329 2091 90 7 Cauvery River Basin 43867 24550 1934 7435 5750 77 8 Agniyar River Basin 4566 2494 74 547 564 103 Pambar & Kottakaraiyar 5847 9 River Basin 1204 103 434 315 73 10 Vaigai River Basin 7031 3169 221 1001 766 77 11 Gundar River Basin 5647 1600 133 537 380 71 12 Vaippar Basin 5423 1045 96 445 280 63 13 Kallar River Basin 1879 137 31 85 33 39 14 Thambaraparani River Basin 5969 883 77 374 241 64 15 Nambiyar River Basin 2084 281 28 125 75 60 16 Kodaiyar River Basin 1533 757 10 106 130 123 17 P.A.P. Basin 3462 1589 246 596 321 54 From the above table it could be noted that there was wide range of crop output in all the river basins. The coefficient of variation was more than fifty percent in general and it was more than hundred in Agniyar and Kodaiyar river basin. The minimum value was less than hundred crores in river basins namely Paravanar, Agniyar, Vaippar, Kallar, Thambaraparani, Nambiar, and Kodaiyar. The crop output depended on the value of the crop and its area. For comparing the crop output, the basins were classified as small, medium, and large depending on net sown area. The following figures provide the performance of the basins in the three categories over the period 1975-76 to 2005-06.

Crop Outputs in Small Basins during 1975-76 to 2005-06

4000 Legend Chennai Varaha Paravan Agniyar Kallar Tambara Nambiyar Kodaiyar PAP 3000

2000

1000 Crop OutputCrop in Rs.Crores

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 0 0 0 0 0 0 7 7 7 7 7 8 8 8 8 8 8 8 8 8 8 9 9 9 9 9 9 9 9 9 9 0 0 0 0 0 0 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 Year

Figure 3. Crop output in Small Basins during 1975-76 to 2005 - 06

Crop Outputs in Medium Basins during 1975-76 to 2005-06

9000 Legend Vellar Pambar Vaigai 8000 Gundar Vaippar

7000

6000

5000

4000

3000

2000 Crop OutputCrop in Rs.Crores 1000

Figure 4. Crop output in Medium Basins during 1975-76 to 2005 – 06

Crop Outputs in Large Basins during 1975-76 to 2005-06

30000 Legend Palar Ponnaiya Cauvery

20000

10000 Crop OutputCrop in Rs.Crores

Figure 5. Crop output in Large Basins during 1975-76 to 2005 - 06

From the graphs, it could be seen that among the small basins, Varahanadhi Basin ranks first in terms of crop output during the last five years, 2001-02 to 2005-06. Among the medium basins, Vellar basin ranks first and Cauvery basin ranks first consistently among the large basins

10.1.2. Livestock output The table below provides a summary of livestock output in all the basins. Livestock is one of the major allied activities of agriculture. Highest value of livestock output was recorded in Cauvery basin followed by Ponnaiyaar and Vellar basins. The coefficient of variation was hundred and less than hundred. Comparing base year i.e. 1976 there was increase in livestock population in all the basins. This was mainly due to sustained income from livestock and in most of the farms, livestock was maintained by family labour they.

Table 13.Summary Statistics - Livestock output (Rs.Crores)

S.No Name of the basin Max Min Average SD CV (%) 1 Chennai Basin 402 11 136 132 97 2 Palar River Basin 570 27 251 197 79 3 Varahanadhi River Basin 196 8 79 67 85 4 Ponnaiyaar River Basin 663 17 210 183 87 5 Paravanar River Basin 31 1 11 11 100 6 Vellar Basin 593 17 190 169 89 7 Cauvery River Basin 2569 102 934 740 79 8 Agniyar River Basin 223 7 78 72 92 9 Pambar & Kottakaraiyar River Basin 164 7 68 55 80 10 Vaigai River Basin 324 10 127 103 81 11 Gundar River Basin 200 7 79 66 83 12 Vaippar Basin 223 5 62 64 103 13 Kallar River Basin 105 4 28 26 94 14 Tambarabarani River Basin 281 7 70 73 104 15 Nambiyar River Basin 104 3 27 27 99 16 Kodaiyar River Basin 115 6 42 36 86 17 P.A.P. Basin 171 6 67 51 76

10.1.3. Net Sown Area and net irrigated area The next two tables provide a summary of net sown area in all the 17 basins. Though net irrigated area increased over the decades, there was not much increase in net sown area. This was supported by the minimum of coefficient of variation as given in the table. It was evidenced from Tamil Nadu state data on net sown area. Average net sown area in the decade 1980-90 was 56.22 lakh ha and it was reduced to 50.62 lakh ha in the year 2007-08. Land put to non-agricultural purposes has increased from 16 lakh ha in 1970s to 21.61 lakh ha in 2007-08. Other fallow lands have increased from 5.31 lakh ha in 1970s to 14.99 lakh ha in 2007- 08. There was considerable increase in net irrigated area in all river basins over three decades. The coefficient of variation was in the range of 27 to 38 percentages. This was mainly due to development of groundwater irrigation. As per the latest estimates of January 2003, the State has tapped 86 percent of groundwater potential. This statement was well supplement by the statistics of increase in area under well-irrigated area from 11.7 lakh ha (1989-90) to 15.7 lakh.ha in 2006- 07 in Tamil Nadu state. Table 14.Summary Statistics - Net-Area-Sown-Input (Area in ha)

S.No Name of the basin Max Min Average SD CV 1 Chennai Basin 249108 150179 206499 25086 12 2 Palar River Basin 533525 306677 451031 51629 11 3 Varahanadhi River Basin 188903 143563 174477 11447 7 4 Ponnaiyaar River Basin 721218 539594 659571 49727 8 5 Paravanar River Basin 33585 25429 31030 1787 6 6 Vellar Basin 412352 314088 381649 25502 7 7 Cauvery River Basin 2046556 1644539 1907825 97386 5 8 Agniyar River Basin 249301 143965 205154 25491 12 Pambar & Kottakaraiyar 9 River Basin 253484 188930 220498 16560 8 10 Vaigai River Basin 344108 200851 277800 43811 16 11 Gundar River Basin 287527 207989 247578 23012 9 12 Vaippar Basin 280109 159031 217028 38600 18 13 Kallar River Basin 132971 63890 95403 21046 22 14 Tambarabarani River Basin 169148 118493 145162 13710 9 15 Nambiyar River Basin 75692 51010 62284 6719 11 16 Kodaiyar River Basin 81431 73000 77040 2333 3 17 P.A.P. Basin 172086 133868 153051 10095 7

Table 15.Summary Statistics - Net Irrigated Area Input (Area in ha)

S.No Name of the basin Max Min Average SD CV (%) 1 Chennai Basin 426456 133033 305920 97650 32 2 Palar River Basin 764884 202507 500127 171061 34 3 Varahanadhi River Basin 328864 80755 221957 70637 32 4 Ponnaiyaar River Basin 702206 161364 483752 176411 36 5 Paravanar River Basin 69643 12770 37270 12864 35 6 Vellar Basin 497413 130095 359684 124561 35 7 Cauvery River Basin 2499734 742220 1799856 595109 33 8 Agniyar River Basin 314983 120042 234890 69022 29 9 Pambar & Kottakaraiyar 266309 110143 204078 54700 27 10 Vaigai River Basin 350523 117251 258421 81892 32 11 Gundar River Basin 228040 92306 175828 47825 27 12 Vaippar Basin 168546 77505 128190 29441 23 13 Kallar River Basin 47870 16319 32647 9041 28 14 Thambaraparani River Basin 240706 74035 172689 56148 33 15 Nambiyar River Basin 79369 25108 57108 18276 32 16 Kodaiyar River Basin 77438 24681 55850 21005 38 17 P.A.P. Basin 172437 48208 124819 39585 32 10.1.4. Fertilizer Usage: Fertilizer was a major input for agriculture in Tamil Nadu. The relevant summary statistics are presented in the table below: Table 16.Summary Statistics - NPK-Value-Input (in lakh tonnes) S.No Name of the basin Max Min Average SD CV (%) 1 Chennai Basin 0.65 0.15 0.41 0.11 26.68 2 Palar River Basin 1.25 0.29 0.72 0.21 29.41 3 Varahanadhi River Basin 0.57 0.18 0.40 0.10 25.26 4 Ponnaiyar River Basin 0.91 0.11 0.45 0.25 56.00 5 Paravanar River Basin 0.12 0.04 0.08 0.02 27.75 6 Vellar Basin 1.17 0.22 0.61 0.22 35.92 7 Cauvery River Basin 4.16 0.92 2.65 0.75 28.08 8 Agniyar River Basin 0.92 0.13 0.40 0.18 44.05 9 Pambar & Kottakaraiyar 1.01 0.12 0.25 0.17 68.42 10 Vaigai River Basin 0.85 0.17 0.43 0.14 32.57 11 Gundar River Basin 0.44 0.12 0.25 0.08 31.06 12 Vaippar Basin 0.28 0.09 0.17 0.05 29.10 13 Kallar River Basin 0.10 0.02 0.05 0.02 29.43 14 Tambarabarani River Basin 0.37 0.10 0.26 0.07 28.76 15 Nambiyar River Basin 0.12 0.04 0.09 0.02 25.37 16 Kodaiyar River Basin 0.20 0.04 0.11 0.04 36.15 17 P.A.P. Basin 0.34 0.07 0.23 0.06 26.38 60

It could be seen from the above table that there was considerable increase in intake of NPK fertilizers in all river basins. As the decades under consideration were after green revolution, the intake of inorganic fertilizers had increased due to increase in area under high yielding varieties and area under irrigation. 10.1.5. Labour input Labour was a major input. Table below summarizes the usage of labour in all the river basins. Even though the quantum of usage varied widely across the basins, the coefficient of variation for this input ranged between 6 and 33 percentages between the basins.

Table 17.Summary Statistics - Labour input (in Numbers) S.No Name of the basin Max Min Average SD CV (%) 1 Chennai Basin 389181 261467 352125 46606 13 2 Palar River Basin 826761 506993 700631 104779 15 3 Varahanadhi River Basin 370331 185093 278078 58660 21 4 Ponnaiyaar River Basin 1077715 473443 765766 191128 25 5 Paravanar River Basin 68385 30956 48521 11769 24 6 Vellar Basin 784325 339241 537435 139010 26 7 Cauvery River Basin 3399740 1523559 2325241 567675 24 8 Agniyar River Basin 381088 131609 233214 77546 33 9 Pambar & Kottakaraiyar 276366 119130 197850 51409 26 10 Vaigai River Basin 537372 345827 475144 73805 16 11 Gundar River Basin 305872 202382 270506 37524 14 12 Vaippar Basin 280242 207580 242889 21031 9 13 Kallar River Basin 80968 58671 70333 6504 9 14 Thambaraparani River Basin 293731 218184 271100 26981 10 15 Nambiyar River Basin 105739 79115 95293 7960 8 16 Kodaiyar River Basin 166546 33227 121133 38121 31 17 P.A.P. Basin 207306 164241 192357 10585 6

10.1.6. Cattle and poultry input There was tremendous increase in poultry population in Tamil Nadu especially in Cauvery basin and P.A.P basin. Poultry is the perfect substitute for meat. Low price and adequate supply were main reason for development of poultry as commercial venture in this area. Weather and technical expertise were reasons for concentration of poultry units in these two basins.

Table 18.Summary Statistics - Cattle-Input (in Numbers)

S.No Name of the basin Max Min Average SD CV (%) 1 Chennai Basin 752277 554065 663668 52274 8 2 Palar River Basin 1625339 980850 1420464 141572 10 3 Varahanadhi River Basin 503334 309250 444831 42225 9 4 Ponnaiyaar River Basin 1774638 1328360 1607195 128155 8 5 Paravanar River Basin 77210 46171 66736 6612 10 6 Vellar Basin 1101706 895373 1025353 51730 5 7 Cauvery River Basin 4970544 3635009 4395856 445174 10 8 Agniyar River Basin 640813 450420 565003 39826 7 9 Pambar & Kottakaraiyar 488704 365397 410082 41472 10 10 Vaigai River Basin 565223 352605 505232 48955 10 11 Gundar River Basin 346104 264315 321232 20531 6 12 Vaippar Basin 333666 206782 247542 25064 10 13 Kallar River Basin 170760 76566 127885 34574 27 14 Thambaraparani River Basin 416170 167941 279588 60160 22 15 Nambiyar River Basin 135229 71726 107838 14649 14 16 Kodaiyar River Basin 140305 91156 117727 14472 12 17 P.A.P. Basin 330969 195723 271084 40670 15

Table 19.Summary Statistics - Poultry-Input (in Numbers)

S.No Name of the basin Max Min Average SD CV (%) 1 Chennai Basin 1502902 801189 1006880 153467 15 2 Palar River Basin 1701745 1010497 1216719 189486 16 3 Varahanadhi River Basin 544961 209341 380827 58500 15 4 Ponnaiyaar River Basin 3439596 1174639 1663580 660908 40 5 Paravanar River Basin 79448 37290 57086 7538 13 6 Vellar Basin 7479026 980567 2505125 1658225 66 7 Cauvery River Basin 58795422 4194115 11997350 12753889 106 8 Agniyar River Basin 1105081 531553 791015 106590 13 9 Pambar & Kottakaraiyar 990297 543712 667963 128887 19 10 Vaigai River Basin 1599609 665914 827478 223615 27 11 Gundar River Basin 852688 407185 518947 119991 23 12 Vaippar Basin 983364 289585 449979 179839 40 13 Kallar River Basin 251842 177155 219034 17249 8 14 Tambarabarani River Basin 1093991 485630 566359 137960 24 15 Nambiyar River Basin 358865 199298 225881 36222 16 16 Kodaiyar River Basin 611099 391501 463655 50853 11 17 P.A.P. Basin 20069070 242969 1939694 4621349 238 62

CHAPTER XI Liberalization policies and their effects on agriculture in the river basins 11. Liberalization policies and their effects on agriculture in the river basins

The liberalization policies and other related activities were implemented in India from 1990-91 onwards. In order to assess the impact of liberalization on agriculture particularly on the productivity of agriculture and livestock the last three decadal time period from 1975-76 to 2005- 06 was partitioned as period I pre liberalization period from 1975-76 to 1990-91 and period II post liberalization period from 1991-92 to 2005-06. The crop and livestock input and output trends were assessed in pre liberalization period (1975-76 to 1990-91) and post liberalization period (1991-92 to 2005-06) and presented in the following tables.

Triennium ending average was worked out for starting year and ending year of each period. For the period I (pre liberalisation period) for starting year triennium ending average was estimated by taking average of 1975-76, 1976-77 & 1977-78 year data and for ending year triennium ending average was estimated by taking average of 1988-89, 1989-90 & 1990-91. For the period II (post liberalisation period) for starting year triennium ending average was estimated by taking average of 1991-92, 1992-93 & 1993-94 year data and for ending year triennium ending average was estimated by taking average of 2003-04 & 2005-06.

Table 20.Crop output (Rs. In crores) in the pre and post liberalization periods

Period I Period II S. Name of the basins Triennium ending average % change Triennium ending average % change No 1975-76 to 1988-89 to 1991-92 to 2003-04 to 1977-78 1990-91 1993-94 2005-06 1 Chennai Basin 132.39 497.81 276.02 815.63 1346.57 65.10 2 Palar River Basin 423.74 1081.09 155.13 1855.76 3722.11 100.57 3 Varahanadhi River Basin 151.67 697.33 359.76 1040.99 3435.28 230.00 4 Ponnaiyaar River Basin 442.81 1745.85 294.27 2693.65 6941.13 157.68 5 Paravanar River Basin 28.57 143.55 402.51 207.93 755.01 263.10 6 Vellar Basin 293.21 1340.42 357.15 1916.42 5959.64 210.98 7 Cauvery River Basin 2043.49 4570.53 123.66 6557.22 15026.05 129.15 8 Agniyar River Basin 81.15 279.13 243.97 394.45 1791.81 354.26 Pambar & Kottakaraiyar 9 River Basin 110.78 304.56 174.93 424.09 859.77 102.73 10 Vaigai River Basin 278.11 674.74 142.62 1027.28 1540.40 49.95 11 Gundar River Basin 150.57 423.82 181.47 605.20 818.19 35.19 12 Vaippar Basin 105.25 497.96 373.12 642.87 627.57 -2.38 13 Kallar River Basin 46.63 112.68 141.65 120.19 92.34 -23.18 Tambarabarani River 14 Basin 99.25 386.92 289.86 510.48 608.71 19.24 15 Nambiyar River Basin 37.48 132.68 254.02 167.15 205.68 23.05 16 Kodaiyar River Basin 26.06 87.69 236.50 73.77 327.25 343.58 17 P.A.P. Basin 420.09 395.90 -5.76 550.72 918.76 66.83 Tamil Nadu 4871.24 13372.66 174.52 19603.81 44976.27 129.43

It is interesting to note that percentage change in output trend after liberalization period was less compared to pre liberalization period. It could be seen from the tables that only after 1990s there was wide fluctuation in crop output in all the river basins. Before 1990s, the trend was smooth curve. Before 1990s, country‟s economy was somewhat closed one. However, after liberalization, it is open economy and some decontrol measures were taken in export and import of agricultural and allied products. This is reflected in the growth of agricultural output in the post liberalization era... The same trend was also noted in livestock output as evidenced from the table. Except in Nambiar and Kodaiyar river basins, the percentage change in post liberation period was less compared to pre liberalization period in all other river basins. Maintenance of livestock for domestic purpose and unproductive or less productive milch animals were the prime reasons for less impact. Due to religious reasons and beliefs, people are maintaining unproductive milch animals in the farm. The livestock output of all river basins were presented in line graphs in the figures 3 and 4. Comparing crop output there was not much fluctuation in growth of livestock output over the three decades. Only after 1990s, some fluctuation was noticed almost in all basins.

Table 21.Livestock output (Rs. In Crores) in the pre and post liberalization periods

Period I Period II Triennium ending average Triennium ending average % S.No Name of the basins % change 1975-76 to 1988-89 to 1991-92 to 2003-04 to change 1977-78 1990-91 1993-94 2005-06 1 Chennai Basin 11.54 71.50 519.83 100.12 377.31 276.84 2 Palar River Basin 27.79 172.13 519.46 253.69 506.07 99.49 3 Varahanadhi River Basin 7.94 46.19 481.84 71.67 170.38 137.72 4 Ponnaiyaar River Basin 20.38 121.12 494.22 180.98 590.06 226.03 5 Paravanar River Basin 0.79 4.45 463.47 8.47 28.95 241.77 6 Vellar Basin 18.59 112.32 504.19 184.97 544.39 194.31 7 Cauvery River Basin 109.89 634.55 477.44 887.25 2385.61 168.88 8 Agniyar River Basin 7.74 37.55 384.93 58.06 195.15 236.12 Pambar & Kottakaraiyar 9 River Basin 7.43 39.04 425.29 58.83 154.90 163.31 10 Vaigai River Basin 11.42 70.30 515.37 113.62 258.81 127.79 11 Gundar River Basin 7.95 43.10 442.24 68.38 182.98 167.61 12 Vaippar Basin 5.54 25.03 351.66 43.94 203.71 363.61 13 Kallar River Basin 4.18 16.07 284.16 25.29 92.54 265.87 Tambarabarani River 14 Basin 7.06 27.09 283.40 56.81 232.09 308.55 15 Nambiyar River Basin 3.33 11.86 255.98 22.53 86.84 285.39 16 Kodaiyar River Basin 9.62 22.79 136.92 35.40 94.60 167.27 17 P.A.P. Basin 7.03 49.95 610.31 64.52 160.31 148.47 Tamil Nadu 268.23 1505.03 461.09 2234.53 6264.71 180.36

Though net irrigated area has shown positive trend in pre liberalization period and negative trend in post liberalization period, the net sown area has sown negative trend invariably in both the periods in all basins. The exceptional case was Vaigai basin, which had shown positive change in pre liberalization period but negative change in post liberalization period.

Table 22.Net area sown (Area in ha) in the pre and post liberalization periods Period I Period II Triennium ending Triennium ending average % average % S.No Name of the basins 1975-76 1988-89 change 1991-92 2003-04 change to 1977- to 1990- to 1993- to 2005- 78 91 94 06 1 Chennai Basin 243886 199476 -18.21 219957 162749 -26.01 2 Palar River Basin 526425 422501 -19.74 477556 394085 -17.48 Varahanadhi River 3 Basin 180705 168446 -6.78 188647 161867 -14.20 4 Ponnaiyaar River Basin 690396 662372 -4.06 705021 588745 -16.49 5 Paravanar River Basin 30823 30096 -2.36 33275 29638 -10.93 6 Vellar Basin 388446 380572 -2.03 408936 349809 -14.46 7 Cauvery River Basin 1935939 1951631 0.81 2023300 1782922 -11.88 8 Agniyar River Basin 238855 197875 -17.16 192452 186042 -3.33 Pambar & Kottakaraiyar 9 River Basin 234908 219481 -6.57 233688 207608 -11.16 10 Vaigai River Basin 224491 320862 42.93 318504 274973 -13.67 11 Gundar River Basin 267565 265305 -0.84 259050 217466 -16.05 12 Vaippar Basin 266955 231317 -13.35 215518 166703 -22.65 13 Kallar River Basin 120077 104419 -13.04 87867 71673 -18.43 Thambaraparani River 14 Basin 152746 150469 -1.49 154314 142044 -7.95 15 Nambiar River Basin 68893 65437 -5.02 63701 56931 -10.63 16 Kodaiyar River Basin 77809 77934 0.16 81070 74065 -8.64 17 P.A.P. Basin 164755 147649 -10.38 153854 142684 -7.26 Tamil Nadu 5813675 5595842 -3.75 5816710 5010002 -13.87

It could be clearly noted that the net sown area for past three decades had shown slight reduction or almost stable. This was mainly due to increase in fallow lands and land put into non- agricultural purposes. Intensive agriculture followed by policy measures to sustain current area under agriculture is the need of the hour.

Table 23.Net area irrigated input (Area in ha) in the pre and post liberalization periods Period I Period II Triennium Triennium Triennium Triennium ending ending % ending ending % S.No Name of the basins average average change average average change 1975-76 to 1988-89 to 1991-92 to 2003-04 to 1977-78 1990-91 1993-94 2005-06 1 Chennai Basin 185839 325743 75.28 393697 290026 -26.33 2 Palar River Basin 311827 465412 49.25 607258 540136 -11.05 3 Varahanadhi River Basin 120186 219636 82.75 261816 255613 -2.37 4 Ponnaiyaar River Basin 224792 488038 117.11 614772 548106 -10.84 5 Paravanar River Basin 19543 36667 87.62 42382 44357 4.66 6 Vellar Basin 173974 356974 105.19 427869 417792 -2.36 7 Cauvery River Basin 882820 1928592 118.46 2218516 2058522 -7.21 8 Agniyar River Basin 138032 255562 85.15 263124 284532 8.14 Pambar & Kottakaraiyar 9 River Basin 122415 223326 82.43 249454 238904 -4.23 10 Vaigai River Basin 130045 322734 148.17 320771 274977 -14.28 11 Gundar River Basin 100104 214972 114.75 213868 190791 -10.79 12 Vaippar Basin 83151 156023 87.64 152083 131678 -13.42 13 Kallar River Basin 18784 37578 100.06 41788 34006 -18.62 14 Thambaraparani River Basin 85219 206825 142.70 234047 201083 -14.08 15 Nambiar River Basin 28614 68698 140.09 76696 65495 -14.61 16 Kodaiyar River Basin 26761 75739 183.02 71751 60787 -15.28 17 P.A.P. Basin 60550 131946 117.91 141533 154481 9.15 Tamil Nadu 2712654 5514467 103.29 6331426 5791286 -8.53

As expected net irrigated area was increasing at declining rate over the decades. After post liberalization period, the trend was vigorous. All basins were showing negative percentage change after post liberalization period as shown from table except in basins like Agniyar and P.A.P basin. However, in case of pre liberalization period there was increase in percentage change in all basins indicating that net irrigated area was in increasing trend. Unlike net sown area, there was steady increase in net irrigated area. This was mainly due to proliferation of wells particularly bore wells. Exploitation of groundwater was on the high. However, area irrigated more than once was declining over the year in Tamil Nadu. As area irrigated per well was less than one hectare.

Table 24.N, P, K input (in lakh tonnes) in the pre and post liberalization periods Period Period I II Triennium ending Triennium ending % % S.No Name of the basins average average change change 1975-76 1988-89 1991-92 2003-04 to 1977- to 1990- to 1993- to 2005- 78 91 94 06 1 Chennai Basin 0.20 0.44 120.40 0.45 0.56 24.72 2 Palar River Basin 0.39 0.73 87.58 0.80 1.05 31.63 Varahanadhi River 3 Basin 0.25 0.51 108.48 0.47 0.45 -4.53 4 Ponnaiyaar River Basin 0.33 0.26 -20.49 0.45 0.80 77.42 5 Paravanar River Basin 0.05 0.10 105.09 0.09 0.08 -17.65 6 Vellar Basin 0.30 0.61 102.24 0.65 1.02 55.80 7 Cauvery River Basin 1.31 2.97 127.29 2.91 3.67 25.99 8 Agniyar River Basin 0.19 0.39 105.87 0.40 0.80 101.96 Pambar & Kottakaraiyar 9 River Basin 0.19 0.21 7.69 0.21 0.65 207.41 10 Vaigai River Basin 0.23 0.50 112.10 0.46 0.68 46.70 11 Gundar River Basin 0.16 0.29 84.80 0.27 0.37 36.24 12 Vaippar Basin 0.13 0.25 98.26 0.20 0.22 7.39 13 Kallar River Basin 0.04 0.07 68.78 0.05 0.06 32.35 Thambaraparani River 14 Basin 0.17 0.33 100.27 0.31 0.10 -66.26 15 Nambiar River Basin 0.06 0.11 99.57 0.10 0.05 -50.16 16 Kodaiyar River Basin 0.06 0.14 154.46 0.13 0.17 25.58 17 P.A.P. Basin 0.10 0.27 161.81 0.24 0.27 13.48 Tamil Nadu 4.15 8.20 97.70 8.21 11.00 34.04

It could be inferred from the table that decline in net sown and net irrigated area resulted in less usage of NPK. The percentage change was less in post liberalization period compared to pre liberalization period. Even negative change was noticed in some basins namely Varahanadhi, Paravanar, Thambaraparani and Nambiar basin during post liberalization period. The basins like Chennai, Varahanadhi, Ponnaiyaar, Paravanar, Vellar, Cauvery, Agniyar, Vaigai, Thambaraparani, Nambiar, Kodaiyar, and P.A.P basins have doubled their usage of NPK in pre liberalization period. NPK consumption in agriculture was increasing at decreasing rate as evidenced from the above table.

Not much fluctuation was noticed in usage except one or two basins like Cauvery basin. Increase in net irrigated area has led to increased consumption of fertilizers. Table 25.Labour input (number) in the pre and post liberalization periods Period I Period II Triennium ending Triennium ending average % average % S.No Name of the basins 1975-76 1988-89 change 1991-92 2003-04 change to 1977- to 1990- to 1993- to 2005- 78 91 94 06 1 Chennai Basin 267652 378884 41.56 389087 388521 -0.15 2 Palar River Basin 520572 729151 40.07 758329 821497 8.33 Varahanadhi River 3 Basin 190216 276183 45.19 295534 364578 23.36 4 Ponnaiyaar River Basin 488505 749170 53.36 813726 1057408 29.95 5 Paravanar River Basin 31865 46871 47.09 50922 67041 31.66 6 Vellar Basin 349523 505665 44.67 554886 766676 38.17 7 Cauvery River Basin 1581150 2122578 34.24 2328366 3317327 42.47 8 Agniyar River Basin 136972 204479 49.29 232979 369695 58.68 Pambar & Kottakaraiyar 9 River Basin 122716 198438 61.71 215515 271685 26.06 10 Vaigai River Basin 358001 514992 43.85 535612 537236 0.30 11 Gundar River Basin 208467 295261 41.63 304487 296177 -2.73 12 Vaippar Basin 212024 274146 29.30 271745 220760 -18.76 13 Kallar River Basin 59611 79114 32.72 78874 66311 -15.93 Thambaraparani River 14 Basin 222006 285500 28.60 291607 293568 0.67 15 Nambiar River Basin 80448 103610 28.79 104290 95596 -8.34 16 Kodaiyar River Basin 130573 163457 25.18 148770 42115 -71.69 17 P.A.P. Basin 171063 205728 20.26 203480 180528 -11.28 Tamil Nadu 5131365 7133227 39.01 7578207 9156718 20.83

The statements like rural population is moving out of agriculture and agriculture suffers from non-availability of laborers have been proved by the data given in the above table. After liberalization, percentage change in labour use in agriculture was negative in few basins and was less in other basins compared to pre liberalization period. In pre liberalization period there was positive percentage change in all river basins. From the tables it can be seen that labour usage showed a declining trend after 1990s due to introduction of mechanization.

Table 26.Cattle input (number) in the pre and post liberalization periods

Period I Period II Triennium ending Triennium ending average % average % S.No Name of the basins 1975-76 1988-89 change 1991-92 2003-04 change to 1977- to 1990- to 1993- to 2005- 78 91 94 06 1 Chennai Basin 744437 644478 -13.43 670935 573156 -14.57 2 Palar River Basin 1607469 1356681 -15.60 1382576 1107548 -19.89 Varahanadhi River 3 Basin 497988 424643 -14.73 434911 355763 -18.20 Ponnaiyaar River 4 Basin 1768466 1608348 -9.05 1507023 1396512 -7.33 5 Paravanar River Basin 74539 63009 -15.47 63883 54448 -14.77 6 Vellar Basin 1097054 1018839 -7.13 1006174 928847 -7.69 7 Cauvery River Basin 4724953 4696622 -0.60 4285740 3711614 -13.40 8 Agniyar River Basin 581628 577639 -0.69 625020 479306 -23.31 Pambar & 9 Kottakaraiyar 373945 410804 9.86 469229 382388 -18.51 10 Vaigai River Basin 523930 545132 4.05 514994 393209 -23.65 11 Gundar River Basin 321104 340546 6.05 344715 273709 -20.60 12 Vaippar Basin 248051 254536 2.61 244721 299830 22.52 13 Kallar River Basin 157484 147478 -6.35 112556 77643 -31.02 Thambaraparani River 14 Basin 259428 243512 -6.14 186833 397172 112.58 15 Nambiar River Basin 109035 101332 -7.06 79127 129975 64.26 16 Kodaiyar River Basin 138948 115838 -16.63 111702 100352 -10.16 17 P.A.P. Basin 297462 299064 0.54 251205 202322 -19.46 Tamil Nadu 13525920 12848500 -5.01 12291346 10863794 -11.61

Liberalisation policies on agriculture did not show any positive impact on livestock population. All basins except Nambiar basin had shown negative percentage change in post liberalization period. Even in pre liberalization period, also most of the basins showed negative percentage change except Pambar & Kottakaraiyar, Vaigai, and Gundar and Vaippar river basins.

In river basins like Pambar & Kottaikaraiyar, Vaippar, Tambarabarani and Nambiar showed increase in cattle input usage from the base year (1975-76 to 2005-06 and all other river basins had shown decline in cattel input usage for the above said period. Comparing cattle input in base year and current year period, Tamil Nadu as a whole showed negative change. The table also shows decrease in cattle inputs in all basins except Thambaraparani. Comparing base year (1975-76) cattle input used in agriculture and present data (2005-06) number itself reduced. There is wide scope for bringing livestock rearing and dairying as a commercial venture in Tamil Nadu.

Table 27.Poultry input (number) in the pre and post liberalization periods Period I Period II Triennium ending Triennium ending average % average % S.No Name of the basins 1975-76 1988-89 change 1991-92 2003-04 change to 1977- to 1990- to 1993- to 2005- 78 91 94 06 1 Chennai Basin 984989 863424 -12.34 936027 988312 5.59 2 Palar River Basin 1066035 1093367 2.56 1217672 1358716 11.58 Varahanadhi River 3 Basin 387383 343528 -11.32 361679 298840 -17.37 4 Ponnaiyar River Basin 1262555 1399800 10.87 1385879 3301544 138.23 5 Paravanar River Basin 58261 51665 -11.32 53361 48532 -9.05 6 Vellar Basin 1300313 1996879 53.57 2242363 6751094 201.07 7 Cauvery River Basin 5368687 7939348 47.88 8858910 47897893 440.67 8 Agniyar River Basin 735319 769482 4.65 798651 684494 -14.29 Pambar & 9 Kottakaraiyar 553990 604161 9.06 694833 943135 35.74 10 Vaigai River Basin 696536 761271 9.29 781816 1445188 84.85 11 Gundar River Basin 410809 470487 14.53 550945 781329 41.82 12 Vaippar Basin 294755 375588 27.42 504592 852462 68.94 13 Kallar River Basin 208734 220442 5.61 243992 193003 -20.90 Tambarabarani River 14 Basin 495098 520492 5.13 572784 947815 65.48 15 Nambiyar River Basin 205708 210806 2.48 228070 325309 42.64 16 Kodaiyar River Basin 483809 422250 -12.72 400744 450061 12.31 17 P.A.P. Basin 305400 482835 58.10 592908 15077533 2442.98 Tamil Nadu 14818381 18525822 25.02 20425226 82345259 303.15

Varahanadhi, Paravanar, Agniyar, and Kallar basins had showed negative percentage change in post liberalization period where the poultry population itself was in decline trend comparing base year and current year data.

All other river basins showed positive percentage change in poultry population. Basins like Cauvery and P.A.P basins had tremendous growth of poultry. Most of the poultry farms were commercial units in these basins.

Poultry has become commercial venture during current decade and it could be referred from the figures 15 and 16 where except one or two basins all other basins showed increasing trend in poultry population after 2000. Development of poultry industry in agricultural farms led to more area under maize and other cereals and development of feed units.

CHAPTER XII Comparison of crop out per unit of sown area and per unit of water potential

12. Comparison of crop out per unit of sown area and per unit of water potential

It is observed that the value of crop output per ha of area has varied among the basins. The increase in crop output was maximum in Paravanar basin followed by Vellar and Varahanadhi basin. Lowest crop output was noticed in Kallar, Vaippar and Nambiar basins. The reason for the vast difference was mainly due to the types of crops grown and the extend of crop area. In the case of value of crop output per MCM, the value was higher in the case of Ponnaiyaar basin followed by Vellar and Cauvery basins. Lower crop output was observed in Thambaraparani basin followed by Vaippar and Nambiar basins. There is some correlation among the crop outputs between the crop area and water storage. The reason for the higher output from the basins is that the water potential is comparatively lower in these basins and the crop is mainly diversified towards high value crops (Table 28).

Table 28. Value of Crop Output per ha of Sown Area

Basin 1975-76 80-81 85-86 90-91 95-96 2000-01 2005-2006 Chennai 4853 8184 15003 25884 73782 93215 93114 Palar River 7533 13057 18427 26878 81279 100068 126827 Varahanadhi River 7627 13602 27075 40014 108774 188492 231602 Ponnaiyaar River 5468 10351 18738 25135 64906 83280 190158 Paravanar River 8455 15078 30839 46574 123385 222839 274798 Vellar 7728 13559 23782 31509 83351 114906 235237 Cauvery River 10071 13447 17504 22229 58071 82383 131161 Agniyar River 3171 4598 9812 15685 40418 54732 125860 Pambar & Kottakaraiyar River 4483 5290 8996 15527 24018 43298 56927 Vaigai River 14718 8505 14516 23559 48513 70338 74606 Gundar River 5973 5444 9716 19560 26880 48019 49027 Vaippar 3961 4427 8453 28430 26083 44459 46595 Kallar River 4639 4363 8470 13009 5199 14499 15985 Thambaraparani River 7762 7300 14172 27510 41374 55503 50885 Nambiar River 6582 6182 12711 22555 29516 42615 47355 Kodaiyar River 5357 4650 18430 12849 9968 17201 101637 P.A.P. 18992 19262 27528 23483 64519 68474 111508

280,000 Crop output / ha of net sown area 255,000

230,000

205,000

180,000

155,000

Rs / ha / Rs 130,000

105,000

80,000 c

55,000

30,000

5,000 1975-76 80-81 85-86 90-91 95-96 2000-01 2005-2006

Chennai Palar River Varahanadhi River Ponnaiyar River Paravanar River Vellar Cauvery River Agniyar River Pambar & Kottakaraiyar River Vaigai River Gundar River Vaippar Kallar River Tambarabarani River Nambiyar River Kodaiyar River P.A.P.

Figure 6. Crop output/ha of net sown area

Table 29. Value of crop output per MCM of water potential

S.No Basin 1975-76 80-81 85-86 90-91 95-96 2000-01 2005-2006 1 Chennai 579119 798875 1651047 2715940 7703252 8714829 8364944 2 Palar River 898152 1247314 2057214 2640763 8630699 9497440 12304077 3 Varahanadhi River 705428 1137381 2624008 3552727 10323286 17312708 20283824 4 Ponnaiyaar River 1304490 2004029 4495255 5787412 15359238 19161418 40249549 5 Paravanar River 678510 1139579 2726138 3681854 10668863 19139062 22420026 6 Vellar 1217789 1897940 3751369 4872577 13709440 18638038 36177428 7 Cauvery River 2199458 2760756 3779911 4871066 12487879 17787266 27799468 8 Agniyar River 371719 435306 1128755 1468825 2903593 5407976 12446166 9 Pambar & Kottakaraiyar River 634079 694266 1258176 2172724 2785534 5486064 7392297 10 Vaigai River 1272589 1093899 1659186 2948961 5151004 7682754 8388698 11 Gundar River 1169910 1173428 1835690 3915834 4385686 7805190 8166278 12 Vaippar 563275 635856 1070477 3552093 2484029 4000726 4458871 13 Kallar River 2297539 2478872 4493891 5961352 1844608 4589545 5452148 14 Thambaraparani River 488102 526625 954707 2062737 2598607 3446250 3617932 15 Nambiar River 837667 887970 1717572 3127851 3426203 4746596 5878630 16 Kodaiyar River 339575 274830 1122387 786576 640593 1029929 5973029 17 P.A.P. 2661556 2588438 3773529 2863020 7749420 8447839 13616397

45,000,000 Crop output / per unit of water 40,000,000

35,000,000

30,000,000

25,000,000

/ ha / MCM

20,000,000

15,000,000

10,000,000

5,000,000

0 1975-76 80-81 85-86 90-91 95-96 2000-01 2005-2006

Figure 7. Crop output/per unit of water

CHAPTER XIII Results of TFP analysis

13. Results of TFP analysis

Using DEA methodology described already, total factor productivity was computed for all river basins for three decades starting from 1975-76 to and 2005-06. Technical efficiency change was decomposed into pure efficiency change and scale efficiency change. The details of technical efficiency change, technical change and TFP change for each basin and for each year are provided in Appendix-II. The geometric mean values are summarized in Table.30 the graphs of trends in TFP for small, medium, and large basins are presented in Figs.

Trend in Total Factor Productivity Index in Small Basins during 1975-76 to 2005-06

2.1 2.0 Legend Chennai Varaha Paravan Agniyar Kallar Tambara 1.9 Nambiyar Kodaiyar PAP 1.8 1.7 1.6 1.5 1.4 1.3 1.2 1.1 1.0

0.9 Total Factor Productivity Index Productivity Factor Total 0.8 0.7 0.6

1970 1980 1990 2000 2010

Year

Figure 8. Trend in Total Factor Productivity Index in Small basins during 1975-76 to 2005 - 06

Trend in Total Factor Productivity Index in Medium Basins during 1975-76 to 2005-06

1.5 Legend Vellar Pambar Vaigai Gundar Vaippar 1.4

1.3

1.2

1.1

1.0 Total Factor Productivity Index Productivity Factor Total 0.9

0.8

1970 1980 1990 2000 2010

Year

Figure 9. Trend in Total Factor Productivity Index in Medium basins during 1975-76 to 2005 - 06

Trend in Total Factor Productivity Index in Large Basins during 1975-76 to 2005-06

1.6 Legend palar Ponnaiya Cauvery 1.5

1.4

1.3

1.2

1.1

1.0

0.9 Total Factor Productivity Index Productivity Factor Total 0.8

0.7

1970 1980 1990 2000 2010

Year

Figure10. Trend in Total Factor Productivity Index in Large basins during 1975-76 to 2005 - 06

Table 30. Mean Technical Efficiency Change, Technical Change and TFP Change, during three decades in the seventeen river basins of Tamil Nadu Basin Efficiency Technical Scale efficiency Total factor change change change productivity change Chennai 1975-76 to 1990-91 0.9976 1.0877 0.9976 1.0849 1991-92 to 2005-06 1.0103 1.0024 1.0103 1.0126 1975-76 to 2005-06 1.0039 1.0442 1.0039 1.0481 Palar 1975-76 to 1990-91 0.9998 1.1434 0.9998 1.1431 1991-92 to 2005-06 0.9961 1.0102 0.9961 1.0063 1975-76 to 2005-06 0.9980 1.0747 0.9980 1.0725

Varahanadhi 1975-76 to 1990-91 0.9995 1.1104 0.9995 1.1098 1991-92 to 2005-06 1.0015 1.0063 1.0015 1.0080 1975-76 to 2005-06 1.0005 1.0571 1.0005 1.0577 Ponnaiyar 1975-76 to 1990-91 1.0229 1.1431 1.0229 1.1693 1991-92 to 2005-06 0.9437 1.0060 0.9437 0.9494 1975-76 to 2005-06 0.9825 1.0723 0.9825 1.0536 Paravanar 1975-76 to 1990-91 1.0000 1.0481 1.0000 1.0481 1991-92 to 2005-06 1.0000 0.9864 1.0000 0.9864 1975-76 to 2005-06 1.0000 1.0168 1.0000 1.0168 Vellar 1975-76 to 1990-91 0.9909 1.1348 0.9909 1.1245 1991-92 to 2005-06 0.9757 1.0212 0.9757 0.9965 1975-76 to 2005-06 0.9832 1.0765 0.9832 1.0586 Cauvery 1975-76 to 1990-91 0.9895 1.1199 0.9895 1.1080 1991-92 to 2005-06 0.9856 1.0073 0.9856 0.9929 1975-76 to 2005-06 0.9876 1.0621 0.9876 1.0489 Agniyar 1975-76 to 1990-91 1.0037 1.0587 1.0037 1.0628 1991-92 to 2005-06 0.9637 1.0140 0.9637 0.9770 1975-76 to 2005-06 0.9835 1.0361 0.9835 1.0190 Pambar-Kotta 1975-76 to 1990-91 0.9974 1.0758 0.9974 1.0731 1991-92 to 2005-06 1.0065 0.9898 1.0065 0.9963 1975-76 to 2005-06 1.0019 1.0319 1.0019 1.0340 Vaigai 1975-76 to 1990-91 0.9870 1.0720 0.9870 1.0582 1991-92 to 2005-06 1.0236 1.0048 1.0236 1.0284 82

1975-76 to 2005-06 1.0051 1.0379 1.0051 1.0432 Gundar 1975-76 to 1990-91 0.9876 1.0717 0.9876 1.0581 1991-92 to 2005-06 1.0260 0.9723 1.0260 0.9978 1975-76 to 2005-06 1.0066 1.0208 1.0066 1.0275 Vaippar 1975-76 to 1990-91 0.9872 1.0272 0.9872 1.0140 1991-92 to 2005-06 1.0045 0.9436 1.0045 0.9480 1975-76 to 2005-06 0.9958 0.9845 0.9958 0.9804 Kallar 1975-76 to 1990-91 1.0000 1.0296 1.0000 1.0296 1991-92 to 2005-06 1.0000 1.0021 1.0000 1.0021 1975-76 to 2005-06 1.0000 1.0158 1.0000 1.0158 Tambaraparani 1975-76 to 1990-91 0.9981 1.0086 0.9981 1.0067 1991-92 to 2005-06 0.9942 0.9833 0.9942 0.9775 1975-76 to 2005-06 0.9961 0.9959 0.9961 0.9920 Nambiyar 1975-76 to 1990-91 1.0000 0.9907 1.0000 0.9907 1991-92 to 2005-06 1.0000 0.9912 1.0000 0.9912 1975-76 to 2005-06 1.0000 0.9910 1.0000 0.9910 Kodaiyar 1975-76 to 1990-91 1.0004 0.9742 1.0004 0.9746 1991-92 to 2005-06 1.0000 1.0056 1.0000 1.0056 1975-76 to 2005-06 1.0002 0.9898 1.0002 0.9899 PAP 1975-76 to 1990-91 0.9998 0.9691 0.9998 0.9690 1991-92 to 2005-06 1.0033 0.9773 1.0033 0.9804 1975-76 to 2005-06 1.0015 0.9732 1.0015 0.9747

13.1. Overall TFP growth From the above table it could be seen that the mean TFP between basins ranged between 0.9747 to 1.0725. Palar basin had the highest TFP of 1.0725 and PAP had the least TFP of 0.9747. Except Vaippar, Thambaraparani, Nambiar and PAP, in all other 13 basins the TFP for the three decades is greater than 1 indicating positive TFP growth in all these basins. In the 4 basins though the TFP is less than 1, it ranges from 0.9747 to 0.992 which are very close to 1. Thus, we can conclude in general that there was total factor productivity growth in Tamil Nadu over the past three decades.

Further, in all basins except Vaippar, Tambarani, Nambiar, Kodaiyar and PAP, the technical change was more than one indicating technological advancements in agriculture in these basins. Nevertheless, the overall efficiency change was very close to 1 in all the basins. This means that the total factor growth is contributed mainly by technology and there is not much change in efficiency. Similarly, there is not much change in overall scale efficiencies. Further, during the pre-liberalization period, 14 river basins have registered positive TFP growth. Three basins, viz., Nambiar, Kodaiyar, and PAP have shown TFPs which are close to 1. During the post-liberalization period, 11 basins have TFPs less than but very close to 1 and 6 basins have TFPs greater than 1 (Table31 ). A simple t-test was carried out to test the significance of the difference between the TFPs of the two periods. The test rejected the null hypothesis (p- value=0.00041) that the mean TFPs are equal in the two periods. The averages of the TFPs in the pre and post liberalization periods are 1.0603 and 0.9975. These results imply that over all liberalization was not beneficial to agricultural growth in the river basins of Tamil Nadu. Table 31.Table Mean TFPs in three periods Period1(1975-76 Period2(Period1(1991- Overall (1975-76 Basin to 1990-91) 92 to 2005-06) to 2005-06) Chennai 1.0849 1.0126 1.0481 Palar 1.1431 1.0063 1.0725 Varaha 1.1098 1.1098 1.0577 Ponnaiyaar 1.1693 0.9494 1.0536 Paravanar 1.0481 0.9864 1.0168 Vellar 1.1245 0.9965 1.0586 Cauvery 1.1080 0.9929 1.0489 Agniyar 1.0628 0.9770 1.0190 Pambar 1.0731 0.9963 1.0340 Vaigai 1.0582 1.0284 1.0432 Gundar 1.0581 0.9978 1.0275 Vaippar 1.0140 0.9480 0.9804 Kallar 1.0296 1.0021 1.0158 Tambara 1.0067 0.9775 0.9920 Nambiyar 0.9907 0.9912 0.9910 Kodaiyar 0.9746 1.0056 0.9899 PAP 0.9690 0.9804 0.9747

13.2. Individual basin TFP The TFP growth rates of individual basins have been presented in Appendix. Detailed discussions are presented below. The average total factor productivity change for Chennai basin was more than one indicating that agricultural production is technically efficient. Both pre liberalization period TFP and post liberalization period TFP were more than one. In Palar basin the range of efficiency change was from 0.772 to 1.506. There was not much difference in TFP and other efficiency change between the two periods. It was more than one indicating that Palar basin was technically efficient in using inputs. In Varahanadhi basin TFP was more than one in pre and post liberalization period indicating that the basin was technically sound. TFP range was from 0.705 to 1.515. Though in Ponnaiyaar river basin average TFP was more than one, in post liberalization period it was less than one i.e. 0.949. In pre liberalization period, it was 1.169. Similarly, efficiency change was less than one in post liberalization period whereas technical efficiency change was more than one in both the period and it was 1.184 in pre liberalization period and 1.016 in post liberalization period. In Paravanar basin, the average TFP was 1.034 and there was slight difference in TFP in pre (0.989) and post liberalization period (1.079). The efficiency change was one in both periods and the change in TFP was due to technical efficiency change. In vellar basin the average TFP was more than one (1.070) in the last three decades. There was no difference noted in pre and post liberalization periods. Nevertheless, the efficiency change was less than one and the technical change was more than one. The average TFP was nearing one in post libralisation period and it was above one in pre liberalization period (1.115). Though technical change was more than one in both periods, the efficiency change was less than one or nearing one indicating there is wide scope to improve the efficiency of inputs used for agricultural production. There is a possibility for improving efficiency of inputs in Agniyar basin as there was slight reduction in efficiency change from 1.013 (pre liberalization period) to 0.986 (post liberalization period). Further improvement in TFP should come only from technology development.

Though average TFP was more than one in both periods in Pambar & Kottakaraiyar river basin, there was slight reduction in TFP and technical change in post liberalization period indicating that technology improvement is the need of the hour. Further improvement in TFP will come only from technology change and not from efficiency of inputs. The same trend was noted in Vaigai basin as in case of Pambar & Kottakaraiyar basin. Though efficiency of inputs have improved after liberalization period there was not much of improvement in technology. It was evidenced from the table that technical change was reduced from 1.078 in pre liberalization period to 1.008 in post liberalization period. Therefore, TFP also showed slight reduction in post period. Gundar river basin also followed the same trend as that of Pambar and Vaigai basin. Period II i.e. post liberalization period faced reduced TFP and technical change coefficients. There was slight improvement in efficiency change coefficients. The total factor productivity was less than one in period II (0.952) compared to pre liberalization period (1.028) in Vaippar basin. The average TFP for the last three decades was 0.99. The average technical change was nearing one but it was less than period I. Without improving technology liberalization policies alone would not bring prosperity in agriculture and livestock. In Kallar basin it was inferred from the table that changes in total factor productivity was mainly due to technical change. As efficiency change was one and there was no change in efficiency of inputs in last three decades, any development activity should focus on technical improvement. This was further stressed by the fact that reducing trend in total factor productivity after liberalization period. There was reduction in TFP in Thambaraparani basin as shown in the table. TFP has reduced from 1.019 in pre liberalization period to 0.984 in post liberalization period. Technical change also showed the same trend and it was less than one in post liberalization period. There was no change in efficiency coefficient in these two period and it was nearing one i.e. 0.998. In Nambiar basin changes in total factor productivity was fully contributed by technical changes and not due to the efficiency of inputs in agriculture and allied sector. There was no change in TFP in two periods indicating that there was not much change in technology adopted by the farmers. Efficiency of inputs also needs attention, as it remained same in both the periods. In Kodaiyar basin also changes in total factor productivity was fully contributed by technical changes and not due to the efficiency of inputs in agriculture and allied sector.

There was slight change in TFP in two periods indicating that there was not much change in technology adopted by the farmers. Efficiency of inputs remained constant in both the periods. P.A.P was the only basin in which the total factor productivity was less than one in pre and post liberalization period. The average total factor productivity was 0.976 for the last three decades. The efficiency coefficients of inputs used for agriculture and livestock was more than one in both the period indicating that technical expertise alone will help the farmers in getting higher productivity. 13.3. Growth rates of TFPs Using the total factor productivity indices simple growth rates were estimated for last three decades and for post and pre liberalization periods. The results are presented in Table 32.

Table 32.Growth rates of TFPs Period I Period Growth Basin Growth II rate % rate % Chennai Basin -2.94 0.69 -0.88 Palar River Basin -3.09 0.26 -1.2 Varahanadhi River Basin -2.55 0.08 -1.04 Ponnaiyar River Basin -5.08 0.36 -2.07 Paravanar River Basin -2.14 0.42 -0.7 Vellar Basin -2.59 -0.51 -1.28 Cauvery River Basin -1.83 -0.31 -1.08 Agniyar River Basin -1.94 -0.11 -0.81 Pambar & Kottakaraiyar River Basin -1.53 -0.34 -0.73 Vaigai River Basin -1.49 -0.16 -0.41 Gundar River Basin -2.03 -0.22 -0.71 Vaippar Basin -1.63 0.3 -0.55 Kallar River Basin -0.45 -1.15 -0.4 Tambarabarani River Basin -0.4 1.01 -0.12 Nambiyar River Basin -0.64 1.1 0.08 Kodaiyar River Basin -3.71 -0.67 -0.54 P.A.P. Basin 0.36 -0.11 0.09

It could be seen from the tables that all river basins had shown negative growth rate in pre liberalization period except P.A.P basin. In post liberalization period Chennai, Palar, Varahanadhi, Ponnaiyaar, Paravanar, Vaippar, Thambaraparani and Nambiar river basins have shown growth rates. 87

All other river basins showed negative growth rate in post liberalization period. The growth rate was mainly due to efficiency of inputs used for agriculture and livestock. Efficiency change has contributed much to the total factor productivity. But overall growth rate ie growth rate of total factor productivity for last three decades was negative for all river basins except Nambiar and P.A.P river basins. From above results, it could be concluded that though most of the river basins have shown total factor productivity more than one, there was no growth in the total factor productivity in last three decades except in one or two basins.

CHAPTER XIV Cumulative TFP indices

14. Cumulative TFP indices

Another approach to analyze the change of productivity is using cumulative TFP index. The values of these indices for all basins are provided in the Appendix-IV. In the figure, we display the cumulative TFP index of small, medium, and large basins. In the small basins, this index fluctuates drastically from 0.296 to 1.475. The highest value corresponding to Kallar basin for three year 1986-87 and the lowest value belongs to Kodaiyar for the year 1987-88. In fact, the cumulative TFP for all years are less than 1. This means that compared with the year 1975-76 in all the years, the TFP for this basin is very much lower. The same type of result can be drawn with respect to other small basins also except in few cases for some years. In medium basins, the TFP indices varied from 0.605 to 1.598 and the corresponding values for large basins are respectively 0.543 and 1.907.

Cumulative TFP Indices in Small Basins during 1975-76 to 2005-06

1.5 Legend Chennai Varaha Paravan 1.4 Agniyar Kallar Tambara

1.3 Nambiyar Kodaiyar PAP 1.2 1.1 1.0 0.9 0.8 0.7

Cumulative TFP Index TFP Cumulative 0.6 0.5 0.4 0.3

1970 1980 1990 2000 2010

Year

Figure 11. Cumulative TFP Indices in Small basins during 1975-76 to 2005 – 06

Cumulative TFP Index in Medium Basins during 1975-76 to 2005-06

1.2 Legend Vellar Pambar Vaigai Gundar Vaippar 1.1

1.0

0.9

0.8 Cumulative TFP Index TFP Cumulative

0.7

0.6

1970 1980 1990 2000 2010

Year

Figure 12. Cumulative TFP Indices in Medium basins during 1975-76 to 2005 – 06

Cumulative TFP Indices in Large Basins during 1975-76 to 2005-06

1.6 Legend palar Ponnaiya Cauvery 1.5

1.4

1.3

1.2

1.1

1.0

Cumulative TFP Index TFP Cumulative 0.9

0.8

0.7

1970 1980 1990 2000 2010

Year

Figure 13. Cumulative TFP Indices in Large basins during 1975-76 to 2005 - 06

CHAPTER XV Results of DEA analysis

15. Results of DEA analysis

The TFP growth analysis provides trend in agricultural growth for the past three decades. However, it does not provide options for further improvement in production and inputs usage. With this in view DEA was performed. Given that we have 31 annual observations for 17 river basins, we can perform DEA for each year by solving 17 Linear Programs for each year resulting in a total of 527 LP problems. This will add to complexity in presenting the results. Further to formulate policy options for future years, it will be more appropriate to perform DEA for the latest period, i.e., 2005-06. Hence, DEA was performed for the period 2005-06 and the results are discussed with a orientation for recommending policies for efficient utilization of resources.

The models with CRS technology assume that an increase in inputs will result in a proportional increase in outputs. However, it is difficult to find such a linear relationship between inputs and production in agriculture. For example, in agriculture, when the water volume applied to crops is increased, we do not necessarily obtain a linearly proportional increase in agricultural production. In order to account for this effect, the DEA model for variable-returns-to-scale (BCC) was developed [Banker et.al, 1984] and the same model has been used in the present study.

The other essential characteristic of DEA models is orientation. The output-oriented model refers to the capacity of a DMU to achieve the maximum volume of production (output) with the available inputs, while the ability to maintain the same capacity of production using a minimum of inputs is known as the input-oriented model. Input-oriented efficiency scores range between 0 and 1.0, and whereas output-oriented efficiency scores range between 1.0 to infinity; in both cases, 1.0 is efficient. In agriculture, it is very important not only to produce maximum production but also to use inputs efficiently. Hence, both orientations (with VRS technology) are used in the present study and the results are discussed below.

15.1. DEA with VRS technology and Output Orientation.

Table.33 provides a summary of Output Oriented VRS DEA, CRS DEA efficiency scores and scale efficiencies for each river basin. The table shows that out of 17 basins, 13 basins are 100% VRS efficient and out of these efficient basins 9 basins are scale efficient also. Palar,

Cauvery, Vaippar and PAP basins are though VRS efficient, they are scale inefficient as their CRS efficiencies are less than 1. Agniyar, Pambar & Kottakaraiyar, Vaigai and Gundar are inefficient as their efficiency scores are less than 1. It is interesting to note that these basins are CRS inefficient also. Their efficiency scores range between 0.642 (Pambar & Kottakaraiyar) and 0.994(Vaigai). The average score of all the basins is 0.969 and all these basins are located adjacently.

Table 33. Output Oriented VRS DEA model scores for the River basins of Tamil Nadu Basin Efficiency- Efficiency- Efficiency- Basin Name No. VRS CRS Scale 1 Chennai 1 1 1 2 Palar 1 0.798 0.798 3 Varahanadhi 1 1 1 4 Ponnaiyar 1 1 1 5 Paravanar 1 1 1 6 Vellar 1 1 1 7 Cauvery 1 0.898 0.898 8 Agniyar 0.864 0.798 0.924 9 Pambar & Kottakaraiyar 0.642 0.493 0.768 10 Vaigai 0.994 0.692 0.696 11 Gundar 0.967 0.658 0.680 12 Vaippar 1 0.839 0.839 13 Kallar 1 1 1 14 Tambarabarani 1 1 1 15 Nambiyar 1 1 1 16 Kodaiyar 1 1 1 17 P.A.P. 1 0.897 0.897 Mean 0.969 0.887 0.912

Data envelopment analysis identifies for each inefficient unit a set of excellent units, called a peer group that can be utilized as benchmarks (reference basins) for improvement, and also allows computing the projected values of inputs and outputs to make them efficient. The projected values are computed as a linear combination of the values of the benchmarks using suitable weights derived from DEA. Table.34 provides a summary of the benchmark basins for each inefficient basin and the projected values of inputs and outputs for all the basins. Pambar & Kottakaraiyar basin is the least inefficient basin with an efficiency score of 0.642. For this basin, water, land, labour and resources do not fully contribute to the agricultural production, and the usage patterns should be improved for all inputs according to the

corresponding efficient basins, viz., Vellar (0.046), Ponnaiyaar (0.034), Chennai (0.407), and Kodaiyar (0.512). In other words Pambar & Kottakaraiyar basin can follow the cropping pattern and input use as done in its benchmark basins and there is scope for further improvement in crop and livestock output. A simple calculation with actual and projected outputs shows that this basin can reduce the labor by 10%, net irrigated area by 18%, NPK usage by 55%, net sown area by 29% but still achieve and increase in each of the two outputs by 56%. Thus, for this basin there is scope for increase in production with reduction in inputs. In order to achieve this it can follow the combination of cropping patterns and inputs usage of Kodaiyar and Chennai basins, which are its two major benchmark basins. Agniyar is the next inefficient basin with a efficiency score of 0.864. Its benchmark basins are Ponnaiyar(0.013), Kodaiyar (0.021), Chennai (0.464) and Varahanadhi (0.501). The projected outputs for this basin are Rs.2887.3 (crores) for crop and Rs. 258.2(crores) for livestock. Calculations with actual outputs show that this basin can increase each one of these two outputs by 16% and at the same time reduce net-irrigated area by 6%, NPK usage by 37%, Net area sown by 10% and cattle by 4%. This improvement can be reached if this basin follows the cropping and input usage patterns of its major benchmark basins viz., Varahanadhi and Chennai. Gundar is the third inefficient basin and its output oriented VRS efficiency score is 0.967. Its benchmark basins are Kodaiyar (0.572), Chennai (0.314), Cauvery (0.006), and Kallar (0.108). There is potential for increasing its two outputs by 3% with a reduction in labour by 43%, net irrigated area by 23%, NPK usage by 21% and net sown area by 46%. Since Kodaiyar and Chennai are its benchmark basins with maximum weights, Gundar basin can achieve the above said targets by following the cropping pattern and input usage of these two basins. Vaigai basin is the last inefficient basin with an efficiency score of 0.994. Its benchmark basins are Vellar (0.136), Kodaiyar (0.565), Chennai (0.297), and Cauvery (0.002). Comparison of its actual and projected outputs shows that there is a possibility of a marginal increase of 1% in its crop and livestock production. It can be seen from the above analysis that Kodaiyar and Chennai basins are major benchmark basins for all the 4 inefficient basins. Hence, in general, it can be concluded that their efficiencies can be improved by adopting the farming practices followed in these two basins for maximising agricultural outputs.

Table 34. Output Oriented VRS DEA model –benchmarks and projected values

Projected values Basin Net- Basin Name Benchmarks NPK Net-Area No. Crop Livestock Labour Area Cattle Poultry used Sown Irrigated 1 Chennai 1694.9 364.6 388473.5 325097.7 0.653 182027.4 554065.2 801188.6 2 Palar 5374.8 492.5 826761.2 642806 1.252 423792.4 980849.8 1233978.4 3 Varahanadhi 3850.2 154.8 370331.5 270116.4 0.528 166241.6 309249.8 209341.4 4 Ponnaiyaar 11553.4 662.7 1077714.6 676100.4 0.915 607565.3 1328359.7 3439595.6 5 Paravanar 829.5 27.1 68384.7 46100.4 0.086 30187.3 46170.9 37289.6 6 Vellar 8736.8 546.9 784325.2 491844.8 1.166 371406.9 895372.8 7479026.5 7 Cauvery 24549.7 2568.9 3399740.0 2339278 4.157 1871729.3 3739470.6 58795422.1 Ponnaiyar(0.013) Kodaiyar (0.021) 8 Agniyar Chennai (0.464) 2887.3 258.2 381088.0 296680.3 0.584 177559.0 432255.4 531553.1 Varahanadhi (0.501)

Vellar (0.046) Ponnaiyar 9 Pambar & Kottakaraiyar (0.034) 1875.5 255.1 248188.1 210202.8 0.452 150143.5 365396.6 990297.5 Chennai (0.407) Kodaiyar (0.512)

Vellar (0.136) Kodaiyar (0.565) 10 Vaigai 2170.2 252.7 247830.4 203599.8 0.472 150533.1 352604.7 1599609.0 Chennai (0.297) Cauvery(0.002)

Kodaiyar (0.572) Chennai (0.314) 11 Gundar 1126.8 207.0 168746.8 155987.4 0.35 118934.8 264314.7 852688.3 Cauvery (0.006) Kallar (0.108)

12 Vaippar 794.9 223.3 216511.8 138460 0.244 170604.9 333666.2 983363.7 13 Kallar 115.3 104.6 65263.8 36111.63 0.067 72141.4 76566.3 177155.3 14 Tambarabarani 766.6 281.4 293731.3 224818.3 0.165 150659.9 416169.8 1093990.6 15 Nambiyar 281.4 103.9 94871.5 72526.13 0.02 59414.1 135229.2 358865.2 16 Kodaiyar 756.8 114.6 33227.0 62543.21 0.197 74465.5 103696.5 391501.3 17 P.A.P. 1589.0 171.2 178614.8 165222 0.303 142503.7 195723.5 20069070.2

15.2. DEA with VRS technology and Input Orientation.

Efficient usage of valuable inputs is important in agricultural production. This efficiency can be measured by knowing the extent to which the inputs can be reduced but at the same time, current level of production is maintained. In addition, agricultural outputs do not proportionately increase with increase in inputs. Hence, an inputs oriented VRS technology DEA is performed and the results are discussed below. Table.35 provides a summary of the efficiency scores.

Table 35. Input Oriented VRS DEA model scores for the River basins of Tamil Nadu

Basin Efficiency- Efficiency- Efficiency- Basin Name No. VRS CRS Scale 1 Chennai 1.000 1.000 1.000 2 Palar 1.000 0.798 0.798 3 Varahanadhi 1.000 1.000 1.000 4 Ponnaiyaar 1.000 1.000 1.000 5 Paravanar 1.000 1.000 1.000 6 Vellar 1.000 1.000 1.000 7 Cauvery 1.000 0.898 0.898 8 Agniyar 0.837 0.798 0.954 9 Pambar & Kottakaraiyar 0.545 0.493 0.905 10 Vaigai 0.993 0.692 0.697 11 Gundar 0.954 0.658 0.690 12 Vaippar 1.000 0.839 0.839 13 Kallar 1.000 1.000 1.000 14 Thambaraparani 1.000 1.000 1.000 15 Nambiar 1.000 1.000 1.000 16 Kodaiyar 1.000 1.000 1.000 17 P.A.P. 1.000 0.897 0.897 Mean 0.961 0.887 0.922

In the above table, the VRS efficiency scores of the basins are provided in the third column. The CRS scores are provided in the fourth column for comparison only. The average VRS score during 2005-06 is 0.961. This means that on the average, the current production from crop, livestock can be obtained with 96.1% of the current usage of all inputs only, and excess usage is 3.9%. Further the table shows that out of the 17 basins, 13 basins are 100% efficient in utilizing the resources, viz., labour, net area irrigated, NPK, net area sown, cattle, and poultry. The inefficient basins are Agniyar, Pambar & Kottakaraiyar, Vaigai, and Gundar. The efficiency scores of these basins range from 0.545 (Pambar & Kottakaraiyar) and 0.993 (Vaigai basin). 98

Agniyar is a small basin, the other three are medium basins, and it is interesting to note all these four basins are neighbors. It can be readily seen from the table that these four basins are inefficient under CRS technology also and their scale efficiencies are all less than . Table.36 gives the benchmark basins, projected values of inputs and outputs and the weights are given in brackets. The results for inefficient basins can be further analysed using the above table. Consider the most inefficient basin, Pambar & Kottakaraiyar. For this basin, water, land, labour and resources do not fully contribute to the agricultural production, and the usage patterns should be improved for all inputs according to the corresponding efficient basins, viz., Kodaiyar, Ponnaiyar, Chennai, and Vellar. In other words, Pambar & Kottakaraiyar basin can follow the cropping pattern and input use as done in its benchmark basins. Alternatively, since among its benchmark basins Kodaiyar basin has maximum weight of 0.839, Pambar & Kottakaraiyar basin can follow the cropping pattern of Kodaiyar basin in order to achieve improvement in efficiency of agricultural inputs usage. Thus, there should be a shift in agricultural operations in this basin to become more efficient. A simple comparison of its current usage of inputs and their corresponding projected values shows that it can attain the current level of output by reducing labour by 60%, net area irrigated by 55%, NPK usage by 72%, net sown area by 51%, cattle and poultry each by 45%. These extra resources can be efficiently used to increase the production of agricultural outputs. The next inefficient basin is Agniyar and it has an efficiency score of 0.837. Its benchmark basins are Chennai (0.361), Kodaiyar (0.128), Varahanadhi (0.462), and Kallar (0.049). In order to become efficient in using input resources, this basin can follow a combination of cropping patterns followed by Varahanadhi, Chennai, and Kodaiyar. In addition, it can reduce labour by 16%, net area irrigated by 20%, NPK usage by 45%, net sown area by 21%, cattle by 20% and poultry by 16% without reduction in the current outputs of crop and livestock. Thus, these over usage resources can be used to increase agricultural production from its current level. The third inefficient basin is Gundar and it has an efficiency score of 0.954. Its benchmark basins are Kodaiyar (0.587), Chennai (0.292), Cauvery (0.006), and Kallar (0.116). Since the weight for Cauvery basin is small, Gundar basin can follow a combination cropping patterns in Kodaiyar, Chennai, and Kallar basins. Its current level of crop and livestock outputs can be attained even by reducing reduce labour by 46%, net area irrigated by 27%, NPK usage by 24%, net sown area by 48%, cattle and poultry each by 5%. 99

Vaigai basin is the next inefficient basin with an efficiency score of 0.993. Its benchmark basins are Vellar (0.135), Kodaiyar (0.570), Chennai (0.293), and Cauvery (0.002). The maximum weight is for Kodaiyar basin followed by Chennai and Vellar. Hence, Vaigai basin can improve its efficiency by adopting a combination of cropping patterns followed in these three basins. Its current outputs can be realized by reducing labour by 54%, net area irrigated by 36%, NPK usage by 45%, net sown area by 48%, cattle and poultry each by 1%.

It can be seen that Kodaiyar and Chennai basins are major benchmark basins for all the inefficient basins. Hence, agricultural production in the inefficient basins can be improved by adopting the farming systems followed in these two basins.

It can be concluded that Pambar & Kottakaraiyar, Agniyar, Gundar, and Vaigai basins are inefficient under both input oriented and output oriented technologies and hence agricultural production in Tamil Nadu can be improved by paying more attention to farming activities in these 4 basins.

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CHAPTER XVI Summary and Conclusions

16. Summary and Conclusions

There was wide range of crop and livestock outputs in all the river basins. Livestock is one of the major allied activities of agriculture. Comparing base year i.e. 1976 there was increase in livestock population in all the basins. This was mainly due to sustained income from livestock and in most of the farms; family members only maintained livestock. Though net irrigated area increased over the decades, there was not much increase in net sown area. This was supported by the minimum of coefficient of variation. In addition, there was considerable increase in intake of NPK fertilizers in all river basins. As the decades under consideration were after green revolution, the intake of inorganic fertilizers had increased due to increase in area under high yielding varieties and area under irrigation. There was tremendous increase in poultry population in Tamil Nadu especially in Cauvery basin and P.A.P basin.

The liberalization policies and other related activities were introduced In India in the year 1990-91 onwards. In order to assess the impact of liberalization on agriculture particularly on the productivity of agriculture and livestock the last three decadal time period from 1975-76 to 2005- 06 was parted as period I pre liberalization period from 1975-76 to 1990-91 and period II post liberalization period from 1991-92 to 2005-06. The crop and livestock input and output trends were assessed in pre liberalization period (1975-76 to 1990-91) and post liberalization period (1991-92 to 2005-06). Triennium ending average was worked out for starting year and ending year of each period. For the period I (pre liberalisation period) for starting year triennium ending average was estimated by taking average of 1975-76, 1976-77 & 1977-78 year data and for ending year triennium ending average was estimated by taking average of 1988-89, 1989-90 & 1990-91. For the period II (post liberalisation period) for starting year triennium ending average was estimated by taking average of 1991-92, 1992-93 & 1993-94 year data and for ending year triennium ending average was estimated by taking average of 2003-04 & 2005-06.

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It was interesting to note that percentage change in output trend after liberalization period was less compared to pre liberalization period. Even negative changes were noted in Vaippar and Kallar river basins. As all 17 river basins in Tamil Nadu was taken into account for the present study, to have clear view on trends and for convenience graphs were presented as small, medium, and large basins. Only after 1990s, there was wide fluctuation in crop output in all the river basins. Before 1990s, the trend was smooth. The same trend was also noted in livestock output. Though net irrigated area has shown positive trend in pre liberalization period and negative trend in post liberalization period, the net sown area has sown negative trend invariably in both the periods in all basins. As expected net irrigated area was increasing at declining rate over the decades. After post liberalization period, the trend was vigorous. This was mainly due to proliferation of wells particularly bore wells. NPK consumption in agriculture was increasing at decreasing rate. Increase in net irrigated area has led to increased consumption of fertilizers. After liberalization period, change in labour use in agriculture was negative in few basins and was less in other basins compared to pre liberalization period. In pre liberalization period there was positive percentage change in all river basins. Comparing cattle input in base year and current year period, Tamil Nadu as a whole showed negative change. In general, poultry population was increasing over the decades.

Using DEA analysis total factor productivity was measured for all river basins for three decades starting from 1975-76 to and 2005-06. The TFP indices of 17 river basins fluctuate during the whole period of study. Technical efficiency change was further decomposed into pure efficiency change and scale efficiency change.

The average of all efficiency change via efficiency change, technical efficiency change, scale efficiency change, pure efficiency change and total factor productivity change for Chennai basin was one and more than one indicating that agricultural production is technically efficient. In Palar basin the range of efficiency change was from 0.772 to 1.506. There was not much difference in TFP and other efficiency change in pre liberalization period and post liberalization period. It was more than one indicating that Palar basin was technically efficient in using inputs. In Varahanadhi basin TFP was more than one in pre and post liberalization periods indicating 102

that the basin was technically sound. Though in Ponnaiyaar river basin average TFP was more than one, in post liberalization period it was less than one i.e. 0.957. In pre liberalization period, it was 1.229. In Paravanar basin, the average TFP was 1.034 and there was slight difference in TFP in pre (0.989) and post liberalization period (1.079). The efficiency change was one in both periods and the change in TFP was due to technical efficiency change. In Vellar basin the average TFP was more than one (1.070) in the last three decades. There was no difference noted in pre and post liberalization periods. Nevertheless, the efficiency change was less than one and the technical change was more than one. The average TFP was nearing one in post libralisation period and it was above one in pre liberalization period (1.115). Though technical change was more than one in both periods, the efficiency change was less than one or nearing one. There is a possibility for improving efficiency of inputs in Agniyar basin as there was slight reduction in efficiency change from 1.013 (pre liberalization period) to 0.986 (post liberalization period). Though average TFP was more than one in both periods in Pambar & Kottakaraiyar river basin, there was slight reduction in TFP and technical change in post liberalization period. The same trend was noted in Vaigai basin as in case of Pambar & Kottakaraiyar basin. Though efficiency of inputs have improved after liberalization period there was not much of improvement in technology. It was evidenced from the table that technical change was reduced from 1.078 in pre liberalization period to 1.008 in post liberalization period. Therefore, TFP also showed slight reduction in post period. Gundar river basin also followed the same trend as that of Pambar and Vaigai basin. Period II ie post liberalization period faced reduced TFP and technical change coefficients. There was slight improvement in efficiency change coefficients. The total factor productivity was less than one in period II (0.952) compared to pre liberalization period (1.028) in Vaippar basin. The average TFP for the last three decades was 0.99. The average technical change was nearing one but it was less than period I. In Kallar basin the changes in total factor productivity was mainly due to technical change. As efficiency change was one and there was no change in efficiency of inputs in last three decades, any development activity should focus on technical improvement. This was further stressed by the fact that reducing trend in total factor productivity after liberalization period. There was reduction in TFP in Tambarabarani basin. TFP has reduced 103

from 1.019 in pre liberalization period to 0.984 in post liberalization period. Technical change also showed the same trend and it was less than one in post liberalization period. There was no change in efficiency coefficient in these two period and it was nearing one i.e. 0.998. In Nambiar basin changes in total factor, productivity was fully contributed by technical changes and not due to the efficiency of inputs in agriculture and allied sector. There was no change in TFP in two periods indicating that there was not much change in technology adopted by the farmers. Efficiency of inputs also needs attention, as it remained same in both the periods. In Kodaiyar basin also changes in total factor productivity was fully contributed by technical changes and not due to the efficiency of inputs in agriculture and allied sector. P.A.P was the only basin in which the total factor productivity was less than one in pre and post liberalization period. The average total factor productivity was 0.976 for the last three decades. All river basins had shown negative growth rate in pre liberalization period except P.A.P basin. In post liberalization period basins, namely Chennai, Palar, Varahanadhi, Ponnaiyaar, Paravanar, Vaippar, Thambaraparani and Nambiar river basins have shown positive growth rate. All other river basins showed negative growth rate in post liberalization period. The positive growth rate was mainly due to efficiency of inputs used for agriculture and livestock. Efficiency change has contributed much to the total factor productivity. But overall growth rate ie growth rate of total factor productivity for last three decades was negative for all river basins except Nambiar and P.A.P river basins. However, most of the river basins have shown total factor productivity more than one but there was no growth in the total factor productivity in last three decades except in one or two basins. The cumulative TFP indices were more than one for majority of river basins except in case of basins like Thambaraparani, Nambiar, Kodaiyar and P.A.P. The cumulative indices also coincided with the results of TFP indices i.e. basins showed lower than one average TFP had showed the same result in case of cumulative indices.

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CHAPTER XVII Policy recommendations

17. Policy recommendations River basins are the major source of agricultural production to feed the increasing population. Several basins are facing the problems of reduced surface and groundwater supplies due to changes in rainfall intensity, poor catchment management and poor water distribution practices and increasing intersectoral water demand. In order to meet the future water demand, the available supplies should be efficiently used and one way to achieve this will be increasing the efficiency of the river basins. The following are suggested for up scaling at different levels: 1. Since crop and livestock are the integral components of agricultural production, it is important to make developmental programs to be converging at basin level. All the ongoing and proposed programs should have common linkages and aim to deliver the target output. Livestock is the major supplementary income for farming community. As the number of animals maintained by a farm firm is merely for meeting domestic needs and meeting daily expenses. Dairying is not done as commercial activities by all farms. Farmers should be encouraged to practice dairying as commercial venture by providing technical guidance and credit facilities. Development of poultry industry in agricultural farms could lead to more area under maize and other cereals and development of feed units. Training and technical expertise in dairying and poultry will sustain marginal and small farming communities in Tamil Nadu.

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3. Technology package should be updated and made available for each basin and the cost of transfer and adoption should be linked with the ongoing programs. Needed capacity building programs should be in built using the existing KVKs and regional agricultural research stations. 4. Conservation programs such as watershed management and improved water management techniques such as drip and sprinklers are still lacking behind due to poor adoption. Future water related investment programs should therefore aim to develop strategies and action plans to address the issue of efficient water allocation and management with the goal of maximizing the productivity per unit of water. Given the existing water supply scenarios, the demand management strategies will be considered more relevant for the efficient management of the available supplies. Therefore, what is needed is the clear understanding of the value of water in alternate uses as well as the incentive to allocate the water among competing crops and uses in different river basins.

5. Creation of strong database at basin level is important incorporating the supply and demand details of water crop, and livestock. Investment made, returns to investment in various activities in the basin should be documented and analysed periodically for making future projects of the basin current and future potential.

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CHAPTER XVIII References

18. References

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Appendix-I

River Basins of Tamil Nadu 1. Chennai Basin

1. Varahanadhi Basin

Description District Chengalpat Thiruvannamalai South arcot Total area of the 7857 6197 10895 district(Sq Km) Basin area in the 770 306 3138 district in Sq Km Percentage area of the 9.8 4.94 28.8 district Percentage of area of 18.27 7.26 74.47 basin in each district

Sub basins 1. Varahanadhi 2. Ongur.

Small sub basin 1. Nallamur 2. Kondamur.

Tributaries 1. Annamangalam 2. Nariyur 3. Tondiyur 4. Pambaiyur 5. Pambai Channel and 6. Chengai Odai.

Surface water Capacity Annual Ayacat (ha) (MCM) storage(MCM) Vidur dam 17.13 17.13 1295.02 Tamil Nadu – 890.33 ha Pondicherry - 404.69 ha

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Irrigation efficiency System tanks 131 Well 69 % of GIA 75% Non system tanks 1290 Tanks 30.30% of GIA 40% Storage capacity of 275.50 mm both System and Non system tanks Total storage capacity as created now – 292 MCM

Drinking 1994 1999 2004 2019 2044 MCM Urban 15.75(0.93%) 18.01(1.06%) 20.27(1.18%) 27.05(1.8%) 38.35(2.73) Rural 23.32(1.38%) 25.09(1.47%) 26.86(1.57%) 32.17(2.14%) 41.03(2.92%) Total 39.07 43.10 47.13 59.22 79.38 Agriculture 1604(94.18%) 1604(94.18%) 1604(93.57%) 1364(90.8%) 1204 Livestock 28.68 28.68 28.68 28.68 28.68 Industries (8% rise per year) SSI 2.5 3.14 3.78 5.18 8.9 Medium and 17.6 24.13 30.64 44.99 82.8 Large Total 20.10 27.26 34.42 50.17 91.7 Total 1691.85 1703.04 1714.23 1502.07 1403.76 Total 1898 1898 1898 1898 1898 potential Balance 206.15 194.96 183.77 395.93 494.24 % w.r to 10.86 10.27 9.68 20.86 26.04 potential

Total surface water potential – 412.09 MCM Ground water potential – 1482.07 MCM Diversion from Ponniyar basin for municipal water supply – 4 MCM Total – 1898 MCM

1991 1994 1999 2004 2019 2044 Population in thousands Urban 438.02 479.3 548.11 616.91 823.33 1167.36 Rural 1524.12 1596.92 1718.27 1839.61 2203.64 2810.37 Total 1962.14 2076.22 2266.38 2456.52 3026.97 3977.73

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2. Vaippar basin

Kamarajar Madurai Nellai V.O. kattabomman Chidambaranar Percentage 68 7 5 22 Area 3660.53 352.50 244.03 1165.94

Total area of the basin – 5423 Sq Km. Tributaries 1. Nichabanadhi 2. Uppodai 3. Kalingalur 4. Arjuna Nadhi 5. Nagariar 6. Kousiga Nadhi 7. Deviar 8. Senkottaiar 9. Kayalkudiar 10. Vallampatti Odai 11. Sevalaperiyar 12. Uppathur 13. Sindapalli. Irrigation sources

Particulars Ha Canal 476 (0.16%) Tank 33077 (11.2%) Well 64010 (21.68%)

S.No Name of the dam Capacity in Annual storage Ayacat in ha /reservoir MCM 1 Periyar dam 5.45 10.9 3652 2 Kovilar dam 3.77 7.54 - 3 Vembakottai reservoir 11.29 22.58 3280 4 Kullur sandai reservoir 3.59 7.18 1170 5 Anaikuttam reservoir 3.56 7.12 1214 6 Golwarpatty reservoir 5.20 10.40 1821 Sub total 32.86 65.72 11137 7 Irukkankudi reservoir 14.14 28.28 3787 8 Ullar reservoir 7.73 12.14 4694 Sub total 21.87 40.42 8481 Total 54.73 106.14 19618

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Irrigation efficiency System tanks 151 Well 53.5 % of GIA Non system 711 Tanks 48.2 % of GIA tanks Storage capacity 559.40 mm of both System and Non system tanks Total storage capacity as created now – 625.12 MCM

Annual surface water potential – 611 MCM Annual ground water potential – 1167 MCM Total water potential of the basin– 1778 MCM Sub surface water augmented from vaigai basin – 1.864 MCM Surface water diverted from tambaraparani – 2.952 Total potential of the basin - 1783 Population in millions 1991 1994 1999 2004 2019 2044 Urban 0.793 0.934 1.047 1.160 1.566 2.299 Rural 1.063 1.110 1.365 1.620 2.608 3.191 Total 1.856 2.044 2.412 2.780 4.174 5.490

Cross irrigated area - 104099 ha Un irrigated area – 187356 ha Out of the irrigated area paddy is – 46.2 % Gross irrigation requirement – 1302.65 MCM Water demand in MCM

S.No Sector 1994 1999 2004 2019 2044 Drinking 1 Urban 28.15 36.12 44.10 68.04 107.94 2 Rural 15.85 18.89 21.92 29.96 44.07 Total 33.99 55.01 66.02 98.00 152.01 3 Agriculture 1302.65 1302.65 1302.65 1386.15 1386.15 4 Livestock 13.76 13.76 13.76 13.76 13.76 Industries 5 SSI 19.62 27.47 35.31 43.16 98.1 6 Major and 1.0 1.40 1.80 2.20 5.00 medium Total 20.62 28.87 37.11 45.36 103.10 Total demand 1381.03 1400.29 1419.54 1543.27 1654.92 Total potential 1783 1783 1783 1783 1783

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Balance 402 383 363 240 128 % balance W.R.T 22.5 21.5 20.4 13.5 7.2 availability

3. Agniyar river basin

District: Pasumpon Muthuramalinga Thevar, Tiruchi, Thanjavur, Pudukottai.

Name of the district Area of the Area covered % of area % of area district (sq. in the basin covered in the covered Km) (sq. Km) district in the basin Pasumpon muthu 4086 69 1.70 1.5 ramalinga thevar Thiruchi 11096 415 3.7 9.0 Thanjavur 8280 922 11.10 20.0 Pudukottai 4651 3160 67.90 69.5 Total 28113 4566 84.40 100.0

The figures given for thiruchi district is before its trifurcation Sub basins 1. Agniar sub basin 2. Amballur sub basin 3. South velar sub basin

Total command area S.NO Name of the sub basin Number of tanks Ayacat ha acre I Agniar sub basin a Agniyar 959 17304 42756 b Maharaja samudram 321 6769 16725 II Vellar Basin a Uppar vellar 2118 22231 54932 b Lower vellar 416 24699 61032 III Ambuliyar sub basin a Uppar ambuliyar 136 2676 6612 b Lower ambuliyar 25 2671 6601 Total 3975 76350 188688 IV G.A Canal 44789 110671 Ground total 3975 121139 299359

Total command area fed by farmer is 16350 ha .this includes the command area of5997 ha of the system tanks under 16 anicuts supplemented by G. A. Canal.

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Agniyar river basins consists of 3 sub basins i.e ) agniyar, ambuliyur and south velar. There are seven tributaries in the basin. The river agniyar have three tributaries viz., nariar I, nariar II and maharaja samudram. The river ambuliyur have two tributaries viz., punakuttiyar and Maruthangudiyarthe River south vellar have two tributaries viz., nerunjikudiar and gundar. There are three gauging station in agniyar river basin managed by public work department. 1. Poovanam anicut- agniyar 2. Adaiklladevan anicut –ambuliyar 3. Manamelkudi anicut –south vellaran important point to be noted in this basin is that there are no reservoirs across any of the rivers of the basin. The main reason being none of the rivers have copious flow.the terrain of the country is also ------and it is difficult to construct any reservoir. There is no dual ayacat fed by the rivers of the basin. There are about 3975 tanks in the basin by which 76350 ha are being irrigated out of the above 346 are system tanks and 3629 are non-system tanks. The approximate storage capacity of these tanks is 560 MCM.

Surface Water Potential The total SWP for 15% probability for agniyar river basin is as follows 1. South west monsoon surface Water Potential – 222 MCM 2. North east monsoon surface Water Potential – 239 MCM 3. Annaur surface Water Potential – 585 MCM

Total surface water potential 1. Surface water potential generated within the basin I – 585 MCM 2. Surface water quantity diverted from Cauvery river - 499 MCM

Total - 1084 MCM The total water potential of the agniyar Surface water potential – 585 MCM Diversion from Cauvery basin through G.A canal – 499 MCM Total surface water potential – 1084 MCM Ground water potential – 920 MCM

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Total water potential – 1084 +920 -2004 MCM Population Year 1994 1999 2004 2019 2044 Urban 0.282 0.307 0.331 0.404 0.526 Rural 1.255 1.333 1.411 1.645 2.035 Total 1.537 1.640 1.742 2.049 2.561

Present and future water demand (in MCM) Sector 1991 1994 1999 2004 2019 2044 Agriculture - 2344.00 2344.00 2344.00 2344.00 2344.00 Domestic Urban 8.80 9.28 10.08 10.87 13.27 17.27 Rural 17.65 18.33 19.47 20.61 24.02 29.71 Sub total 26.45 27.61 29.55 31.48 37.29 46.98 Livestock - 14.80 14.80 14.80 14.80 14.80 Industries Small - 1.47 2.06 2.65 4.41 7.35 Major and - 15.62 21.87 28.12 46.86 78.10 medium Sub total - 17.09 23.93 30.77 51.27 85.45 Total 52.90 2403.50 2412.28 2421.05 2447.36 2491.23

Water balance Year Present Short term Long term 1994 1999 2004 2019 2044 Total demand 2404 2412 2421 2447 2491 Total potential 2004 2004 2004 2004 2004 Deficit 400 408 417 443 487 % of deficit 16.6 16.9 17.2 18.1 19.6 w.r to demand

Short term: In the short term period ending 2004 the water deficit is in the order of 16.9 % to 17.2 % Long term: In the Long term period ending 2044 water deficit is in the order of 18.1 % to 19.6 %

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4. Pambar and kottakaraiyar basin

S.No Name of the district Area of the Area covered by % area covered district in Sq. Km this basin in Sq. by this basin Km 1 Dindugal –mannar 6058 478 7.89 thirumalai 2 Tiruchy – 11096 44 0.40 perumpidugu mutharaiyar 3 Pudukottai 4651 809 17.39 4 Pasumpon - 4086 2989 73.15 muthuramalingam 5 Madurai 6565 279 4.25 6 Ramanathapuram 4232 1248 29.49

Three streams 1. Koluvanaru 2. Pambar 3. Kottakaraiyar. Cropping pattern and cropping calendar

S.No First crop Season 1 Paddy Aug –Jan to Oct -Feb 2 Cholam Mar – May 3 Ragi Mar – June 4 Chillies Dec –Apr 5 Gingelly Dec- Mar 6 Cotton Feb –July 7 Sunflower Jan -May Groundnut Jan – Apr

Total ayacat irrigated – 13204 ha Tanks – 1161 Surface water potential -653 MCM Ground water potential – 976 MCM Total water potential – 1629 MCM

Population in millions Sector 1994 1999 2004 2019 2044 Urban 0.439 0.472 0.505 0.604 0.769 Rural 1.281 1.350 1.420 1.627 1.972 Total 1.720 1.822 1.925 2.231 2.741

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Present and future demand Sectors 1994 1999 2004 2019 2044 Domestic Urban 14.41 15.49 16.58 19.85 25.28 Rural 18.71 19.72 20.73 23.75 28.79 Total 33.12 35.21 37.31 43.60 54.07 Agriculture 1960.73 1960.73 1960.73 1960.73 1960.73 Livestock 24.98 24.98 24.98 24.98 24.98 Industries SSI 4.04 5.66 7.27 12.12 20.20 Large and 30.55 41.87 53.19 87.13 143.71 medium Total 34.59 47.53 60.46 99.25 163.91 Total demand 2053.42 2068.45 2083.48 2128.56 2203.69 Water 1629 1629 1629 1629 1629 potential Deficit 424 439 454 500 575 Deficit % 26.03 26.95 27.87 30.69 35.30

5. Nambiyar basin

District Total area in Sq Km Basin area in Sq km % of th district area V.O. Chidambaranar 4621 520 11.26 Tirunelveli 6810 1464 21.49 kattabomman

Kanyakumari 1685 100 5.94

Sub basin: 1. Karumaniyar 2. Hanumanadhi 3. Pachaiyar 4. manimuthar canal 5. Nambiyar River

Tributaries 1. Thamarayar 2. Parattayar

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Name of the Capacity in Annual storage Ayacat in ha reservoir MCM in MCM

Nambiyar 2.33 2.59 705.65 Kodumudiyar 3.58 7.56 2340.00

System tanks- 559 Non System tanks – 38 Approximate storage capacity of these tanks – 94.54 MCM Total storage capacity as created now -100.45 MCM Surface water potential - 203.87 MCM Annual ground water potential – 274.74 MCM Total water potential – 478.61 MCM Basin GIA – 3365

Present and future water demand Sector 1994 1999 2004 2019 2044 Domestic use Urban 4.40(0.81) 4.80(0.82) 5.26(0.90) 6.54(0.10) 8.67(1.44) Rural 6.24(1.15) 6.94(1.18) 7.65(1.30) 9.77(1.65) 13.31(2.21) 10.64 11.74 12.91 16.31 21.98 Agriculture 523.59(96.7) 566(96.63) 566(96.32) 566(95.41) 566(93.93) Livestock 5.42(1.00) 5.42(0.93) 5.42(0.92) 5.42(0.91) 5.42(0.90) Industries 1.83(0.34) 2.56(0.44) 3.29(0.56) 5.49(0.93) 9.15(1.52) Total 541.48 585.72 587.62 593.22 602.55 Potential 478.61 478.61 478.61 478.61 478.61 Surflus / -62.87 -107.11 -109.01 -114.61 --123.94 deficit Percentage of -11.61 -18.29 -18.55 -19.32 -20.57 total dd

Population (in millions) Sector 1994 1999 2004 2019 2044 Urban 0.134 0.146 0.160 0.199 0.264 Rural 0.427 0.475 0.524 0.669 0.991 Total 0.561 0.621 0.684 0.868 1.155

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6. Palar river basin

Name of the district Area falling in the basin Vellore (north arcot- ambedkar) 4710.58 Thiruvannamalai (Thiruvannamalai- 4012.19 sambuvarayar) Kancheepuram (chengai MGR) 2187.90 Total 10910.67

Tributaries 1. Poiney 2. Kaudinganadhi 3. Malattar 4. Cheyyar 5. Agaramar 6. Killiyur 7. Vegavathiar

Name of the basin 1. Uppar palar 2. Kamandala naganadhi 3. Upper cheyyar 4. Kilya palar 5. Lower palar

Surface water Dams and reservoir The river palar is having 5 tributaries namely poiney, Kaudinganadhi, Malattar, Cheyyar Killiyur. Flow measurement is being taken in 7 locations namely 1. Palar anicut 2. Poiney 3. Aliabad 4. Kamandalanaganadhi 5.cheyyar 6. Tandalai and 7. Uthiramerur. Apart from the 4 gauge are maintained by central water commission.hey are avarakuppam, magaral, arcot and chengalpattu. There is no major reservoir in the basin. However, there is two reservoir under construction in the basin namely marthana andrajathoppu canal. There are about 661 system tanks by which 60972 ha are being irrigated. The storage capacity of these tanks is approximately355 MCM and total storage capacity as created now is 355 MCM (approximately) only.

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Population (in millions)

Area 1994 1999 2004 2019 2044 Urban 1.611 1.834 2.057 2.725 3.839 Rural 3.630 3.878 4.126 4.870 6.110 Total 5.241 5.712 6.183 7.595 9.949

Present and future water demand

Sector 1994 1999 2004 2019 2044 Domestic use Urban 52.95 60.26 67.58 89.53 126.12 Rural 53 56.62 60.24 71.11 89.21 105.95 116.88 127.82 160.67 215.33 Livestock 60.09 60.09 60.09 60.09 60.09 Industries SSI 4.47 5.32 6.16 8.69 12.91 Large and 8.63 49.66 60.70 93.81 148.99 medium Total 43.10 54.98 66.86 102.5 161.9 Atomic 5.00 10.00 10.00 10.00 10.00 power Total 2746.14 273.95 2796.77 2865.23 2979.36

Demand prediction

The future requirement has been assessed based on the information received from SG & SWRDE (ground water department) of WRO and is 100 MCM per year. Thus the future water demand computed as fallows. Particulars Water requirements in MCM 1994 1999 2004 2019 2044 Atomic power 5.00 10.00 10.00 10.00 10.00 plant at kalpakkam

Water balance The total surface water potential of the palar basin works out to 1758.00 MCM of the ground waater potential of the palar basin works out to 2160.32 MCM. Surface water potential of the basin -1758.00 MCM Ground water potential of the basin – 2610.32 Total water potential of the basin – 4368.32 121

Year 1994 1999 2004 219 2044 Total water 4368.32 4368.32 4368.32 4368.32 4368.32 potential of the basin(MCM) Total water 2746.14 2773.95 2796.77 2865.23 2979.36 demand(MCM) Balance water 1622.18 1594.37 1571.55 1403.09 1388.96 (MCM) % of surplus 37 36 36 32 32

Short term In general in the ST period ending 2004, the water balance of the basin varies from 1622.18 to 1571.55 MCM.

Long term In long term period ending 2044, the water balance of the basin decreases from 1403.09 to 1388.96 MCM. 7. Ponnaiyar river basin

District: Dharmapuri, North –Arcot, Thiruvannamalai, South Arcot, Villupuram S.No Name of the Total Area of Area of the % Area of the % Area of District the District basin falling dt falling in basin falling in the dt the basin in the dt 1. Dharmapuri 9622 6744.03 70.10 59.91 2. N.Arcot- 12268 1315.31 10.72 11.68 ambedkhar & thiruvannamalai - sambuvarayar 3. S.Arcot-vallalar 10894.00 3197.66 29.35 28.41 and Villupuram – ramasamy padiyachiyar Ponnaiyar River is having 10 tributaries namely, 1. Chinnar I 2. Chinnar II 3. Markandandhi 4. Pullam pattinadhi 5. Pambar 6. Vaniar 7. Kallar 8. Pambanar 9. Musukundanadhi 10. Thurinjalar

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Name of the Reservoir: 1. Krishnagiri 2. Sathanur 3. Pambar 4. Shoolagirichinnur 5. Vaniar 6. Thumbalahalli 7. Kelavarapalli

Irrigated Area – 650 Sq.km Unirrigated Area – 2975 Sq.km Dry Farm – 287 Sq.km

No Storage Capacity System Tanks 1133 119 Non-System Tanks 0 121 Total 240

Total Capacity of Reservoir – 311.00 Area cut in Ha – 32172 Total Storage Capacity as created now – 311 + 240 = 551 mcm System Tank – 304, Ayacut – 26133 Non-System Tank – 829, Ayacut – 18673 Direct area cut of this basin – 46010 Total ayacut of basin – 90806 Surface Water Potencial: 1310.43 mcm Ground water - 1560 Total Water Potencial: 2870 mcm

s.no crop Season Net crop water requirement 1 Paddy Aug –jan 655.00 Feb –june 1005.00 Oct -mar 92.00 2 Groundnut July –oct 453.00 Nov-mar 320.00 3 Sugarcane feb -jan 1300.00 4 Sorghum -bajra Jan -mar 318.00 5 Ragi & other millets - 405.50

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Irrigation requirement

Crop Area in ha Net crop water requirement mm System area Paddy 23973 206.80 Groundnut 13981 63.45 Ragi 12433 50.45 Sugarcane 5820 75.66 Total 62227 448.29 Non system area Paddy (tanks) 28579 246.54 Paddy (others) 41166 355.12 Sugarcane 26436 343.68 Total 412106 945.34 Present & Future Demand in mcm S.No Sector 1994 1999 2004 2019 2044 1. Domestic a) Urban 19.34 21.06 22.77 27.90 36.48 b) Rural 49.46 53.03 57.79 72.69 95.87 Subtotal 68.81 74.69 80.56 99.99 132.35 2. Agriculture 2668.8 2668.8 2668.8 2321.39 2089.78 3. Livestock 53.84 53.84 53.84 53.84 53.84 4. Industries a) Small scale 6.52 9.13 11.74 19.56 32.60 b) Large scale 63.07 86.43 109.79 179.87 296.67 Subtotal 69.59 95.56 121.53 199.43 329.27 Total 2861.04 2892.89 2924.73 2674.65 2605.24 For demand computed based on the strategy of 1351 pcd per person in urban area and 70 Lpcd per person in rural area, the demand will be shown in the following table,

S.No Sector 1994 1999 2004 2019 2044 1. Domestic a) Urban 27.90 30.38 32.85 40.29 52.68 b) Rural 86.55 92.80 99.05 117.80 149.05 Subtotal 114.45 123.18 131.09 158.09 201.73 2. Agri 2668.80 2668.80 2668.80 2321.39 2089.78 3. Livestock 53.84 53.84 53.84 53.84 53.84 4. Industrial a) Small scale 6.52 9.13 11.74 19.56 32.60 b) Large sclae 63.07 86.43 109.79 179.87 296.67 Subtotal 69.59 95.56 121.53 199.43 329.27 Total 2986.68 2941.38 2976.07 2732.75 2674.62

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Water Balance:

S.No Year 1994 1999 2004 2019 2044 01. Water Potential 2870 2870 2870 2870 2870 Low Projection 02. Water Demand 2861.04 2892.89 2924.73 2674.65 2605.24 03. Balance 8.96 - - 197.16 269.55 04. % Balance 0.31 - - 6.9 9.4 W.R.T Availability High Projection 05. Water Demand 2906.68 2941.38 2976.07 2732.75 2674.62 06. Balance - - - 129.21 185.08 07. % Balance - - - 4.5 6.4 W.R.T Availability

Gadilam Basin: Ponniyar Basin (Population in millions)

S.No Population 1994 1999 2004 2019 2044 1. Urban 0.589 0.641 0.693 0.850 1.110 2. Rural 3.386 3.673 3.958 4.813 6.238 Total 3.977 4.314 4.651 5.663 7.348

S.No Name of the Reservoir Capacity in mcm Ayacut in Ha 1. Krishnagiri 66.10 3642 2. Sathanur 207.00 1822 3. Pambar 7 1620 4. Shoolagiri Chinnar 2.30 352 5. Vaniar 11.80 4212 6. Thumbalahalli 3.70 884 Kelavarapalli (Under 7. 13.10 3240 Construction) Total 311 32172

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8. Vellar river basin

Area covered % area of the % area of the Area of the district S.No District by vellar dist covered basin covered in Sq.km basin in the basin by the dist sq.km 1. Dharmapuri 9622 69 0.72 0.90 2. Salem 8649 2439 28.20 31.90 3. Tiruchi 11096 1658 14.94 21.60 Villupuram 4. Ramasamy 6276 1855 29.56 24.30 Padayachiyar 5. South Arcot 4619 1638 35.46 21.30 Total 7659 100

Dams and Reservoir: The river vellar is having four main tributaries homely swethanadhi, manimukthnadhi chinnal and anavarai odai. At 10 places, river flows are measured. They are:

i) Anaimaduvu reservior ii) Kariyakoil reservior iii) Gomuki reservior iv) Manimuthanadhi reservior v) Willington reservior vi) Memattur anicuts reservior vii) Virudhachalam reservior viii) Tholudur reservior ix) Relandurai reservior x) Sethiyathope reservior

S.No Name of the Gross Capacity in Ayacut in Ha Reservoir mcm . Anaimadavu 7.56 2118 2. Kariyakoil 8.38 1457 3. Gomuki 15.86 2023 4. Mnaimuktha 20.87 1720 5. Millingdon 65.18 11068 Total 114.85 18386 126

The vellar basin system anicuts in the main river as well as in us tributaries. There are about 386 system tanks and 71 Non-system tanks. The total crop area of anicuts and tanks are given below:

S,No Anicuts and Tanks Area in Ha 1. Ayacuts under regulators 5 nos 24580 2. Ayacuts of minor anicuts (215 nos) 21516 including system tanks (386 nos) 3. Ayacuts of non-system tanks (71 nos) 6972 4. Ayacuts of Reservoir (5 nos) 18386 Total 71455

The storage capacity of these tanks and reservoir are 70.00 mcm & 115.00 mcm respectively. The total storage capacity of these basins as created now is (115.00 & 70.00 = 185 mcm)

Population in millions:

S.No Area 1994 1999 2004 2019 2044 1. Urban 0.767 0.824 0.881 1.051 2.335 2. Rural 2.679 2.867 3.056 3.622 3.565 Total 3.446 3.691 3.937 4.673 5.900

Total SW Potential: 1065 Total GW Potential: 1344 Water diverted received surplus water from veeranm tank of adjoining Cauvery basin: 78 mcm Water diverted from this basin adjoining paravanar basin: 72 mcm Total water potential of this basin: 2409 + 78 – 72 = 2415 mcm

Present & Future water demand in mcm:

S.No Sector 1994 1999 2004 2019 2044 1. Domestic a) Urban 25.20 27.06 28.93 34.53 43.87 b) Rural 39.11 41.86 44.62 52.88 66.65 Sub total 64.31 68.92 73.55 87.41 110.52 2. Agriculture 2229.26 2229.26 2229.26 1946.25 1759.47 3. Livestock 51.17 51.17 51.17 51.17 51.17 127

4. Industries a) Small scale 8.42 11.79 15.16 25.96 42.10 b) Large scale 24.63 33.76 42.88 70.25 115.87 Subtotal 33.05 45.55 58.04 96.21 157.92 Total 2377.79 2394.9 2415.02 2181.04 2079.13

For the demand computed based on the strategy of 135 lpcd per person in urban area & 70 lpcd per person in rural area, the demand will be as shown in the following table.

S.No Sector 1994 1999 2004 2019 2044 1. Domestic a) Urban 37.80 40.59 43.40 51.80 65.81 b) Rural 68.44 73.26 78.09 92.54 116.64 Sub total 106.24 113.85 121.49 144.34 182.45 2. Agriculture 2229.26 2229.26 2229.26 1946.25 1759.47 3. livestock 51.17 51.17 51.17 51.17 51.17 4. industries 5. Small sector 8.42 11.79 15.16 25.96 42.10 6. Large sector 24.63 33.76 42.88 70.25 115.87 Sub total 33.05 45.55 58.04 96.21 157.97 Total 2419.72 2439.83 2459.96 2237.97 2151.06

Water balance: the total water potential of vellar basin mt 2415 mcm

S.No Year 1994 1999 2004 2019 2044 1. Water potential 2415.00 2415.00 2415.00 2415.00 2415.00 Low Projection 2. Water Demand 2377.79 2394.90 2412.02 2181.04 2079.13 3. Balance 37.21 20.10 2.98 233.96 335.87 % Balance WRT 4. 1.5 0.8 0.1 9.7 13.9 Availability High Projection 5. Water Demand 2419.72 2439.83 2459.96 2237.97 2151.06 6. Balance - 4.72 -24.83 -44.96 177.03 263.94 % Balance WRT 7. - - - 7.3 10.9 Availability

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Surface water potential: The annual surface water potential for 95%, 75%, 50% probability has been assessed for vellar river basin and they are given below: 1. Annual surface water potential for 95% probability – 789326 mcm 2. Annual surface water potential for 75% probability – 962.74 mcm 3. Annual surface water potential for 50% probability – 1064.98 mcm 4. Annual ground water potential – 1344 mcm 5. Total water potential – 1065 + 1344 = 2409.26 mcm

Total water potential: The annual total water resource potential of this basin is (SW at 75% dependability = 1065 mcm + GW = 1344 mcm = 2409.26 mcm) this basin also receives surplus water of veeranam tank of adjoining Cauvery basin at sethiathope anicuts. It has been roughly estimated as 78 mcm/annum. Water is also diverting from thus basin to the adjoining paravanar basin to wallajab tank through vellar. Rajan channel is about 72.0 mcm per annum. Thus the total water potential of thus basin is 2409 + 78 – 72 = 2415 mcm.

Existing management system: These are 386 tanks both system and Non-system tanks. They irrigate about 11999 Ha and the 5 reservoir in the vellar basin irrigate about 18386 ha.

Competing water demand In vellar river basin crop area of 74106 ha are irrigated by reservoirs, system and non system tanks. For this area, an efficiency of 40% is adopted. An extent of 68658 ha is irrigated by wells. The efficiency for well irrigation is considered as 75% crop water requirement is computed using the crop. 9. Kodaiyar river basin

Districts: western Ghats of kanyakumari Basic area -1553 SSq Km Name of the dame /reservoir 1. Pechiparai dam 2. Perunchani dam 3. Chittar dam I 4. Kodaiyar dam I 5. Kodaiyar dam II

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6. Kuttiyar dam 7. Chittar dam II 8. Chinna kuttiyar dam 9. Poigaiyar dam Surface water potential South west monsoon potential -353 MCM North east monsoon potential - 379 MCM Annual potential – 925 MCM Annual groundwater potential The annual groundwater potential of the basin for the preparation of state frame work plan (SFWP) may be taken as the of the 2 annual recharge values (342.10 MCM). Total water potential Surface water potential at 75% dependability -925 MCM Groundwater potential -342.10 MCM Population in millions 1991 1994 1999 204 2019 2044 Urban 0.241 0.249 0.265 0.284 .313 0.512 Rural 1.283 1.327 1.411 1.507 1.664 2.771 Total 1.524 1.576 1.676 1.791 1.977 3.283

Present and future water demand Sector 1994 1999 2004 2019 2044 Urban 8.18 8.71 9.33 10.28 16.82 Rural 19.37 20.60 22.00 24.29 40.46 Total 27.55 29.31 31.33 34.57 57.28 Agriculture 728.33 728.33 728.33 728.33 728.33 Livestock 3.40 3.40 3.40 3.40 3.40 Industries 1.53 1.92 2.31 3.48 5.45 Aquaculture In significant Total 761 763 765 770 794

1991 1994 2004 2019 2044 Water potential 1267 1267 1267 1267 1267 available in MCM Total demand in 761 763 765 770 794 MCM Balance 506 504 502 497 473 Balance potential 39.94 39.77 39.62 39.23 37.33 in %

Short term plan: In the ST plan period received the balance available is of the order of the total water resources. 130

Long term plan: In the LT plan period the balance potential available ranges from 37.33 to 39.23 Reservoirs S.No Name of the dam Capacity MCM Annual storage Ayacut area in ha MCM 1 Pechiparai dam 152.36 152.36 2 Perunchani dam 81.84 81.84 3 Chittar dam I 17.28 17.28 Combined 4 Chittar dam II 28.25 28.25 ayacutof 5 Kodaiyar dam I 118.50 118.50 kodaiyar is 6 Kodaiyar dam II 0.883 0.883 36836 7 Kuttiyar dam 0.222 0.222 8 Chinna kuttiyar dam 2.776 2.776 9 Poigaiyar dam 2.700 2.700 Total 405.116

Out of the above 9 reservoirs the first and last reservoir are under the control of PWD & the other are under the control of TN electricity board.there are 2922 tanks by which 46024 ha are being irrigated out of the above 1462 are system tanks and 1460 are non system tanks . The approximate storage of these tanks is 268 MCM .the total storage capacity of the basin as created now is 673 MCM. .10. Kallar river basin

Total basin area – 1878.80 Sq Km. 40.66% of the district out of 4621 Sq Km is covered by the basin. Sub basin: 1. Kallar 2. Korampallamaru.

Kallar is having 3 tributaries (joining with uppar odai) viz. left arm of uppar odai, Chekarakudi River and Perurani River. There are no tributaries for Korampallamaru River. Tributaries – uppar odai 1. Left arm of uppar odai 2. Chekarakudi river 3. Perurani river

Name of the capacity Annual storage Ayacut area in ha dam/reservoir Eppothumventran 3.57 4.91 421

Cropping pattern and crop calendar

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Crop 1st season 2nd season 3rd season Paddy Pishanam Paddy Navarai (feb –jan ) Paddy (sornavari) Pishanam Paddy Sornavari (apr -july) Un irrigated crop Cotton/pulses (sep- oct,feb-mar) - - Cotton/vegetables Pulses sep- oct,nov-dec) - - Cholam sep- oct,dec- jan) - - Sunflower Perennial - -

Surface water potential South west monsoon – 12.96 MCM Narth east monsoon – 66.79 MCM Annual – 124.56 MCM Diversion from thabarabarani basin for irrigation -6.59 MCM The utilizable ground water recharge, draft and balance of potential of kallar basin have been estimated as 69.58 MCM, 26.94 MCM & 42.64 MCM / yr respectively. Population in millions 1994 1999 204 2019 2044 Urban 0.253 0.267 0.282 0.326 0.400 Rural 0.353 0.362 0.370 0.396 0.440 Total 0.606 0.629 0.652 0.722 0.840

Present and future water demand Sector 1994 1999 2004 2019 2044 Urban 8.30 8.78 9.27 10.72 13.15 4.01 4.12 4.14 4.42 4.82 Rural 5.16 5.28 5.41 5.79 6.42 2.49 2.48 2.41 2.39 2.35 Total 13.46 14.06 14.68 16.51 19.57 Agriculture 167.00 167.00 167.00 167.00 167.00 80.67 78.37 74.49 68.88 61.19 Livestock 0.90 0.90 0.90 0.90 0.90 0.43 0.42 0.40 0.37 0.33 Industries 14.88 20.35 25.84 42.28 69.68 7.19 9.55 11.53 17.44 25.53 SSI 0.28 0.35 0.42 0.64 1.06 Large & 14.60 20.00 25.42 41.64 68.68 medium Power 10.78 10.78 15.76 15.76 15.76 5.20 5.06 7.03 6.50 5.78 Total 207.02 213.09 224.18 242.45 272.92 132

Water balance The Total water potential of the basin is given below Surface water potential at 75% dependability -124.56 MCM Ground water potential -69.58 MCM Total -194.14 MCM In the basin transfer from tambarabarani river for irrigation and power is 17.37 MCM Total – 211.51 MCM When power generation made phase II is commenced additional 4.98 MCM will be made available from adjacent tambarabarani basin and hence total water potential in 2004 is taken as 216.49 MCM 1991 1994 2004 2019 2044 Water potential 211.51 211.51 216.49 216.49 216.49 MCM

Total demand in 207.02 213.09 224.18 242.45 272.91 MCM

Balance +4.49 -1.58 -7.69 -25.96 -56.42 Balance +2.12 -0.75 -3.55 -11.99 -26.06 potential in %

Short term plan: In the ST plan period the deficit of water potential available is of the order of 3.55% the total water resources. Long term plan: In the LT plan period the deficit of water potential ranges from 11.99 % to26.06 % Surface water Dam & reservoir There are about 199 tanks in the reservoir including the isolated tanks by which 4146 ha are being irrigated. Out of the above, 15 are system tanks and 184 are non system tanks. The total storage capacity of these tanks is 43.41 MCM. The total storage capacity as created now is 496.98 MCM. In Korampallamaru sub basin the Korampallamaru is the last tank having Ayacut of 578.51 ha. In additional to the drainage from its own catchment, it receives water from the adjacent basin from the perennial river thambarabarani through north main channel of srivaikundam anicut. The 50% of the requirement of water for the Ayacut can be assumed as met through this diversion which worked out to 6.59 MCM at 44% irrigation efficiency.

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1. Chennai basin

The details of the district and its area that come under this basin are S.No District District area District area Percentage of Percentage of in SqKm falling in the area in the district area basin (Sq Km) basin in the basin 1 Chennai 174 174 100 3.1 2 Chengai - MGR 7857 4275 54.4 77.1 3 North arcot 6077 1093 17.98 19.8 The Chengai – MGR District referred is undivided district Details of area of each sub basin S.No Name of the sub basin Area of the sub basin 1 Araniyar 763 2 Kusaimalaiyar 3240 3 Cooum 682 4 Adayar 857 Total 5542 The direct ayacut - 115479 ha 1304 tanks – 85208 215 – 21000 -indirect Ayacut The storage capacity of the exisisting reservoir – 320.0 MCM Well irrigation -46. 5% Tank irrigation – 42.2% Storage capacity of tanks – 619 MCM Total storage capacity as created now -939.0 MCM Total surface water potential -906.00 MCM Year 1991 1994 1999 2004 2019 2044 Urban 169.18 181.43 201.85 222.27 283.52 380.61 Rural 25.81 27.02 29.02 31.05 37.1 47.18 Total 194.99 208.85 230.88 253.32 320.62 432.79

Present and future water demand Sector 1994 1999 2004 2019 2044 Domestic 208.45 230.88 253.32 320.62 432.79 Agriculture 2864.7 2864.7 2864.7 2508.0 2393.0 Livestock 38 38 38 38 38 Industries Small, large 86.23 120.72 155.25 258.69 431.15 & medium Recreation 28.00 28.00 28.00 28.00 28.00 Power 3.32 22.40 23.00 25.00 30.00 Total 3228.7 3304.7 3362.27 3252.31 3352.94

134

Water balance Surface water potential for the year 1994 Total Surface water potential in Chennai basin -784.00 MCM Diversion from palar basin – 122.00 MCM Total water potential in 1994 is Surface water 906.00+ ground water 112.22 – 2026.22 MCM Total ground water potential as per ground water estimation committee norms-1119.39 MCM Ground water drawn from palar through filtration wells – 0.83 MCM Total – 1120.22 MCM Year 1994 1999 2004 2019 2044 Total water 2026.22 2026.8 2431.22 2431.22 2431.22 potential Total water 3228.70 3304.70 3362.27 3252.31 3352.94 demand in MCM Water deficit -1202.48 1277.90 -931.05 -821.09 -921.02 in MCM Water deficit 37.24 63.05 38.29 33.77 37.91 %

Surface water potential expected Diversion expected from Krishna water – 340.00 MCM Diversion expected from veeranam tank – 65 MCM Name of the dam / Capacity in MCM( raised by 0.61 m) Ayacut in ha reservoir Before raising F.R.L After raising F.R.L Pondi 77.96 97.98 - (sathyamoorthy) Red hills 80.71 93.46 - Cholavaram 25.63 25.30 - Chembarabakkam 88.36 103.23 5452

11. Paravanar basin

Name of the Name of block Area In Sq.km taluk Panrutti Panrutti 60 Cuddalore Cuddalore 15 Kurinjipadi 345 Vridhachalam Kammapuram 120 Chidambaram parengipettai 75 Mel buvanagin 145 Total basin area 760

135

Total area of the basin 760 sq. km Total ayacut: 8009 ha System ayacut: 7244 ha Non system ayacut: 765 ha Total no. of tanks: 10 No. of system tanks: 2 No. of non system tanks: 8+1 Total capacity of all tanks: 20 mcm. No reservoir, no anicuts, but two major tanks act as reservoirs.

Walajah tank Perumal tank Catchment area 191.58 Capacity mcm 2.57 Command area 4612 2632

Dams and reservoir: The Palar basin is a minor basin. In this basin, apart from the water form its own catchment the pumped water from neyveli mines is also received in this basin. There is no reservoir as well as no ancient in this basin, but there are 2 major tanks which act as reservoirs. Apart from the above 2 tanks there are 8 rainfed tanks which are located in the upper paravanar basin area.

S.no Crop Area (ha) 1 Rice 15844 2 Millets 290 3 Pulses 1492 4 Sugarcane 3560 5 Cotton 1141 6 Groundnut 4323 7 banana 364

Present and future population: 1991 1994 1999 2004 2019 2044 Urban 53.60 56.25 60.67 65.09 78.36 100.47 Rural 286.59 301.24 323.27 346.20 414.99 529.61 Total 340.19 367.49 383.94 411.29 493.35 630.08

136

Present and future water demands: (in MCM) Sector 1994 1999 2004 2019 2044

1. Urban 1.848 1.993 2.138 2.574 3.301 2. Rural 4.385 4.719 5.055 6.059 7.732 3. agriculture 311.00 311.00 311.00 311.00 311.00 4. livestock 5.12 5.12 5.12 5.12 5.12 5. Small scale 0.45 0.68 0.90 1.58 2.70 industries 6. Large and 1.83 2.75 3.66 6.40 10.98 medium industries 7. Thermal 15.27 17.11 17.71 19.51 22.51 power Total 339.91 343.37 345.59 351.94 363.94

Water balance The annual surface water potential and ground water potential are already worked out and furnished in 2.3.2 and para 2.4.6 and are reproduced below. The total water potential of the basin is given below: 1 Surface water potential at 75% dependability 104.3 MCM 2 Ground water potential 225.5 MCM 3 Sub total 329.8 MCM 4 Inter basin transfer from velar basin 39.7 MCM total 369.5 MCM or 370 MCM

Based on total water demand as worked out above, the water balance for the yrs, 1994, 1999 & 2004 A.D have been worked out as shown below: 1994 1999 2004 2019 2044 Total water 370 370 370 370 370 potential Total water 340 343 346 352 363 demand Balance 30 27 24 18 7 available Balance In % 8.11 7.3 6.49 4.86 1.89

At present the balance is 30 mcm and for the ST period it ranges from 27 to 24 mcm. For plan period 25 yrs the balance is 18 mcm & 7 mcm respectively. 137

Areas of potential deficiencies: Since the basin receives assures supply from velar basin through rajan channel also pumped water from the neyveli mine area there is no deficit of water potential at present. Further the average annual rainfall of the basin is also more than 1100 mm. Total surface water potential: Surface water potential= 104.3 mcm Quantity of water supplemented by velar rajan channel= 39.7 mcm Annual ground water potential= 144.0 mcm = 225.5 mcm Total water potential = 370 mcm

13. Vaigai river basin

Area of the % area of the Area covered by % area in the District district in dist covered in the basin sq.km basin Sq.km the basin Madurai 6565 3913 59.60 55.65 Dindugal 6058 1587 26.2 22.57 Ramanathapuram 4232 770 18.2 10.95 Sivagangai 4086 761 18.6 10.82 Total area 7031 The river vaigai originates in the varushanad area. Tributaries: 1. Urlier 2. Theniar 3. Varattar 4. Nagalar 5. Varahanadhi 6. Manjalar 7. Marudhanadhi 8. Sirumaliyar 9. Sathiar and 10. Uppar.

There are five reservoirs in the basin s.no Name of the dam Capacity (MCM) Ayacut (ha) 1 Periyar dam 443.23 84836 2 Vaigai dam 185.00 - 3 Manjalar dam 13.80 2214 4 Marudhanadhi dam 5.31 2633 5 Sathiar dam 1.59 607 Total 648.93 90290 138

The availability of surface water on annual basis of zone I and II for 50% ,75% and 90 % dependable year are given below.

Surface water availability Zone no Dependability 50% 75% 90 % I 993.75 814.89 729.41 II 266.37 192.30 170.50 1260.12 1007.19 899.91 III 279.86 224.22 184.24 IV 112.34 86.56 79.38 V 373.50 261.04 209.19 Total 765.70 571.82 472.81 Total Surface water potential Zone no Dependability 50% 50% 50% Periyar command 1206.12 1007.19 899.91 zone I& II Vaigai command 765.70 571.82 472.81 zone III,IV,V Total 2025.82 1579.01 1372.72

The annual surface water potential has been arrived at using water balance method for the 75% dependable year 1974-75 & found to be 2404 MCM. Total water potential The annual surface water potential of the basin at 75% dependability worked out to 1579 MCM. The annual ground water potential of the basin worked out to 993 MCM. The total water potential of the basin is 2572 MCM. Population in millions 1994 1999 204 2019 2044 Urban 1.175 1.272 1.369 1.658 2.140 Rural 1.881 2.004 2.125 2.489 3.097 Total 3.056 3.276 3.494 4.147 5.237

System tanks: the abstract of no. of system tanks with this command area is given below No. of tanks System tanks 521 Non system tanks 976 Total 1597

139

Zone Command area (ha) Requirement water (MCM) Old Modernized Old Modernized Zone I 7478 14070 177.90 192.32 Zone II 12788 - 286.71 - Zone III 33713 94435 182.11 1212.49 Zone IV 11107 - 259.56 - Zone V 42985 - 1004.42 - Total 108071 108505 1910.70 140.81

Grand total Cultivable command area -216576 ha Water requirement – 3315.51 or 3316 MCM Exisisting management system There are about 1497 tanks both system tanks and non system tanks, out of which the system tanks are 521. The command area fed by the system tanks are 55726 ha. The numbers of non system tanks are 1427 and command area fed by them is 14619 ha. In addition to the above there are 65975 wells in this basin. Present and future water demand Sector 1994 1999 2004 2019 2044 Urban 77.200 83.57 89.94 108.93 140.60 Rural 54.925 58.52 62.05 72.68 90.43 Total 132.125 142.09 151.99 181.61 231.03 Agriculture 3840.00 3840.00 3840.00 3840.00 3840.00 Livestock 28.08 28.08 28.08 28.08 28.08 Industries SSI 3.98 5.15 6.32 9.83 15.68 Large & 27.23 39.60 51.98 89.10 150.98 medium Sub total 31.21 44.75 58.30 98.93 166.66 Total 4031.42 4054.92 4078.37 4148.59 4265.77

Water balance 1991 1994 2004 2019 2044 Water potential 2572.00 2572.00 2572.00 2572.00 2572.00 MCM Total demand in 4031.42 4054.92 4018.37 4148.89 4265.77 MCM Balance –deficit -1459.42 -1482.42 -1506.37 -1576.59 -1693.77 in MCM Short term plan: In the ST plan period ending 2004 is 1506.37 MCM. Long term plan: In the LT plan period ending 2044, the deficit IS 1693.77 mcm 140

14. Thambarabarani river basin

% area of Area of the Area covered the dist % area in District district in by the basin District covered in the basin Sq.km sq.km the basin 1 Nellai 6780 5317 89.08 78.42 kattabomman 2 V.O.C 4649 652 10.92 14.02

The tributaries in the ghats are peyar, vellar, karayar, Pambar and servalar.the main tributaries are servalar, manimuthar, gadananadhi, pachayar and chittar. Out of these, chittar is the major tributary having large drainage area. Thambarabarani basin having 8 anicuts (with 11 channels) and they are: 1. Kodaimelalagian 2. Nadhiyunni 3. Cannadian 4. Ariyanayagiapuram 5. Palaver 6. Suthamalli 7. Marudhur 8. Srivaikundam

7 reservoirs in Thambarabarani and its tributaries 1. Papanasam (1941) 2. Servalar (1985) 3. Manimuthar (1958) 4. Gadana(1974) 5. Ramanadhi (1974) a tributary of Gadananadhi 6. Karuppanadhi (1977) ) a tributary of chittar 7. Gundar (1983) a tributary of chittar

The total surface water potential of Thambarabarani basin is as fallows 1. Available quantity at reservoir sites – 711.00 MCM 2. Available quantity from plain areas – 664.00 MCM 3. Total – 1375.00 MCM 4. The ground water potential – 744 MCM

The total water potential of the Thambarabarani basin is as fallows 1. Surface water potential – 1375 MCM 2. Ground water potential – 744 MCM 3. Total water potential – 2119 MCM 141

4. Population in millions

1994 1999 204 2019 2044 Urban 730815 775679 820543 955135 1179454 Rural 1506279 1591966 1677654 1934717 2363156 Total 2237093 2367645 2498197 2889852 3542610

Present and future water demand Sector 1994 1999 2004 2019 2044 Urban 24.01 25.48 26.95 31.38 38.75 Rural 21.99 23.24 24.49 28.25 34.50 Total 46.00 48.72 51.44 59.63 73.25 Agriculture 2645.00 2645.00 2645.00 2645.00 2645.00 Livestock 21.32 21.32 21.32 21.32 21.32 Industries SSI 4.52 6.33 8.14 13.56 22.60 Large & 8.72 25.65 32.58 53.38 88.04 medium Sub total 23.24 31.98 40.72 66.94 110.64 Total 2735.56 2747.02 2758.48 2792.89 2850.21

Water balance 1991 1994 2004 2019 2044 Water potential 2119 2119 2119 2119 2119 MCM Total demand in (- 50) (- 50) (- 55) (- 55) (- 55) MCM Balance –in 2069 2069 2064 2064 2064 MCM Total demand 2736 2747 2758 2793 2850 Deficit 667 678 694 729 786 % of deficit W.R 24.38 24.68 25.16 26.10 27.58 to demand

Short term plan: In the ST plan period ending 2004, the deficit of water potential available is of the order of 24.68 % to 25.16 %. Long term plan: In the LT plan period ending 2044 the deficit of water potential ranges from 26.01 % to 27.58 % Water balance of the year 1994 1. Surface water potential – 1375 MCM

142

2. Ground water potential – 744 MCM 3. Total water potential – 2119 MCM 4. Water diverted to kallar basin -50 MCM 5. Hence ,balance quantity -2069 MCM 6. Water demand for the year 1994 – 2736 MCM 7. Hence ,deficit -667 MCM

Exisisting management system There are 7 reservoirs, 105 anicuts & 1300 tanks. s.no Name of the reservoir Capacity Catchment Ayacut (ha) (MCM) (sq.km) 1 Papanasam 156.0 150.0 34848 2 Manimuthar 156.0 162.0 9879 3 Gadana 10.0 46.5 3685 4 Ramanadhi 4.3 16.6 2000 5 Karuppanadhi 5.2 29.3 3851 6 Gundar 0.7 9.9 454 7 Servalar 35.0 106.0 - Total 367.2 520.3 54717 Total tanks -1300 Storage capacity of these tanks -196 MCM Total Storage capacity of the basin as created now -563 MCM

15. PAP basin

District: Coimbatore, Periyar % area of Area of the Area covered the dist % area in District district in by the basin covered in the basin Sq.km sq.km the basin Coimbatore 7469 2829 37.88 81.722 Periyar 8209 633 7.71 18.28

The six rivers on anamalai hills are 1. Anamalaiyar 2. Nirar 3. Shalayar 4. Parambikulam 5. Thunacadavu 6. Peruvaripallam

143

The two rivers on the plains are 1. Aliyar 2. Palar

Note: The non system Ayacut is 25330 ha Details of reservoir s.no Description Catchment Capacity at F.R.L (ft) Maximum area sq km F.R.L (TMC) height (ft) 1 Upper nirar weir 75.11 0.04 3800 85 2 Lower nirar dam 96.35 0.27 3350 141 3 Shalayar dam 121.73 5.39 3290 345 4 Parambikulam dam 230.54 17.82 1825 240 5 Thunacadavu dam 43.36 0.66 1770 85 6 Peruvaripallam dam 15.80 0.62 1770 91 7 Aliyar dam 196.84 3.86 1050 145 8 Thitumurthy dam 80.29 1.94 1337 128 9 Upper aliar dam 16.52 0.94 2525 265 10 Anamalayar diversion The total capacity of all reservoir put together is 31.54 or 892.58 MCM. Total Ayacut area - 2575 ha New command area – 18098 ha Total ayacut Old New 377151 203299 173852 ac

Present and future water demand Sector 1994 1999 2004 2019 2044 Urban 29.73 32.47 35.21 43.42 57.10 Rural 11.59 12.13 12.68 14.32 17.06 Total 41.32 44.60 47.89 57.74 74.16 Agriculture 1558.00 1558.00 1558.00 1558.00 1558.00 Livestock 11.81 11.81 11.81 11.81 11.81 Industries SSI 9.40 13.16 16.92 28.20 47.00 Large & 12.74 17.46 22.18 36.34 59.94 medium Sub total 22.14 30.62 39.10 64.54 106.94 Total 1633.27 1645.03 1656.81 1692.09 1750.91 Total 1167 1167 1167 1167 1167 potential Deficit 466 478 490 525 584 % Deficit 40 41 42 45 50 w.r water potential

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Total net irrigation requirement – 1557.62 MCM Cropping Pattern s.no Crop Season Duration in days 1 Paddy Samba (aug – 135 sep to dec-jan)

Navarai (jan – 105 mar) Sornavari (apr 105 -july) 2 Groundnut Dec –apr 105 3 Sugarcane Jan - nov 300 4 Cholam - - 5 Cumbu Mar – june 90 6 Ragi - - 7 Vegetables Feb -july 135 8 Pulses Feb - apr 65 9 Gingelly Jan -feb 85 10 Chillies Feb -july 165 Total

The irrigation system of this basin mainly depends on tanks and wells. Canal irrigation is considerably small. Irrigation sectoral demand is worked out taking into account the conveyance efficiency, field application efficiency etc. in the present scenario considering all the factor ,the gross requirement is calculated at 75% overall efficiency for well irrigation and at 40% for canals and tank irrigation. Net irrigation requirement for various crops and the GIR as worked out are given below. Irrigation through canals and tanks at 40% efficiency covers 34.26% of gross cropped area =1415.79 MCM. Irrigation through wells at 75% efficiency covers 65.74 % of gross cropped area =1448.91 MCM The overall efficiency of irrigation through canals and tanks have to be stepped out in stages from 40% to 50% by 2019 AD and from 50% to 60% by 2044 AD corresponding to the above efficiencies, the irrigation demand were out to2582 MCM at 50% efficiency and 2393 MCM at 60% efficiency.

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16. Cauvery basin at grand anicuts

State wise drainage area of Cauvery basin: Karnataka, Kerala, Tamilnadu, Pondicherry. State Drainage area(Sq Km) % of the total area of the basin Tamilnadu 43867 54.1 s.no Sub basin State Drainage area % of the total (Sq Km) area 1 Chinnar Tamilnadu 3961 5.79 2 Palar Tamilnadu 1344 4.58 3 Bhavani Tamilnadu 5352 8.78 4 Noyil Tamilnadu 2999 4.28 5 Tirumanimuttar Tamilnadu 8429 12.02 6 Amaravathi Tamilnadu 7896 11.08 7 Ponnanai Tamilnadu 2050 2.92 Total

Ground water Tamilnadu Estimated potential 5962 Exisisting draft 2869

S.No Name of the sub basin State /category 1 Chinnar sub basin a. Thoppaiyar reservoir b. Chinnar reservoir c. Kasavigulihall reservoir d. Nagavathi reservoir

2 Palar sub basin

3 Bhavani sub basin a. Kodiveri anicuts b. Lower Bhavani c. Mettur channel d. Gunderipallam e. Varattapallam

4 Noyil sub basin a. Noyil river channels b. P.A.P system c. Lower Bhavani d. Kalingarayar anicut 5 Tirumanimuttar sub basin a. Mettur canals 146

b. Lower bhavani c. Salem Tiruchi channels d. Katalai canal scheme e. Kalingarayar anicut

6 Amaravathi sub basin a. Old Amaravathi channel b. Amaravathi reservoir e. P.A.P system f. Palar- porandalar scheme g. Varadamanadhi h. Upper reservoir i. Parappalar scheme j. Vattamalai karai odai scheme

7 Ponnanai sub basin a. Salem – tiruchi channels b. Katalai canal scheme c. New Katalai HLC d. Ponnanai Ar reservoir

s.no Name of the basin Estimated Exisisting draft Catchment area potential 1 Upper Cauvery 0 0 10619 2 Kabini 7 0 7040 3 Suvarnavathi 95 55 1787 4 Middle Cauvery 0 0 2676 5 Shimsha 0 0 8469 6 Arkavathi 27 10 4351 7 Chinnar 659 254 4061 8 Palar 230 133 3214 9 Bhavani 617 330 6154 10 Noyil 475 290 2999 11 Thirumanimuthar 1823 926 8429 12 Amaravathi 1489 696 8280 13 Ponnanai Ar 540 175 2050 Total 5962 2869 70129

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17. Gundar river basin

District: Ramnad, V.O.C, Kamarajar, Pasumpon & Madurai Dams and reservoir The river gundar is having 5 tributaries namely giridhamal river, terku river, kanal odai, utharakosamangai and vembar. Flow measurements are being taken in only one location from where water is diverted into raghunatha –cauvery channel. There is no major reservoir in the basin there are about 18 anicuts in the upper half of the basin. In the gundar basin there are about 18 anicuts in the upper half of the basin. Tanks irrigating a total of 56730 ha.out of this, the system tanks are 526 irrigating an extent of 2263 ha and non system tanks (maintained by panchayat union) 123 numbers, irrigating an extent of 2263 ha. The storage capacity of this tank is approximately 330.59 MCM. Crop water regulation is adopted by gundar basin NIR (mm) Paddy (105 days) aug / sep -dec-/jan 704.7 Paddy (133 days) Sep / oct – jan /feb 789.3 Cholam Feb -may 402.72 Ragi Feb -may 427.15 Gingelly Dec -mar 340.70 Cotton Feb -july 698.75 Sunflower Jan -may 380.60 Chillies Sep -feb 499.51 Groundnut Jan -apr 365.57

Assuming that there is no increase in command area due to conversion of wet lands for other usesand due to modernization of tank schemes,the efficiency is increased from 40 % to 50% in 2019 & 60 % in 2044 AD , the demand during 2019 & 2044 are below Demand during 2019 AD – 1556 MCM Demand during 2044AD – 1421 MCM

148

Appendix-II

Total Factor Productivities of River Basins in three decades

Chennai Basin Pure Total factor Efficiency Technical Scale efficiency Year efficiency productivity change change change change change 1976 - 77 0.976 1.468 1 0.976 1.432 1977 - 78 1.051 1.384 1 1.051 1.455 1978 - 79 1.089 1.209 1 1.089 1.317 1979 - 80 0.938 0.978 1 0.938 0.917 1980 - 81 0.96 1.414 1 0.96 1.357 1981 - 82 1.129 1.038 1 1.129 1.171 1982 - 83 0.889 1.324 1 0.889 1.178 1983 - 84 0.893 0.838 1 0.893 0.748 1984 - 85 1.099 0.822 1 1.099 0.904 1985 - 86 1.071 0.942 1 1.071 1.009 1986 - 87 0.891 1.205 1 0.891 1.073 1987 - 88 1.305 0.868 1 1.305 1.132 1988 - 89 0.872 1.017 1 0.872 0.886 1989 - 90 0.993 0.951 1 0.993 0.944 1990 - 91 0.903 1.151 1 0.903 1.04 1991 - 92 0.942 1.018 1 0.942 0.959 1992 - 93 1.019 1.088 1 1.019 1.109 1993 - 94 0.93 0.823 1 0.93 0.765 1994 - 95 1.007 0.893 1 1.007 0.899 1995 - 96 1.013 1.138 1 1.013 1.152 1996 - 97 0.841 1.26 1 0.841 1.059 1997 - 98 1.03 0.857 1 1.03 0.882 1998 - 99 1.122 0.928 1 1.122 1.042 1999 - 00 1.055 1.001 1 1.055 1.057 2000 - 01 1.08 1.061 1 1.08 1.146 2001 - 02 0.995 1.068 1 0.995 1.062 2002 - 03 1.184 1.055 1 1.184 1.25 2003 - 04 1.029 1.07 1 1.029 1.101 2004 - 05 1.127 0.887 1 1.127 0.999 2005 - 06 0.846 0.983 1 0.846 0.832 Period I 1.004 1.107 1.000 1.004 1.104 Period II 1.015 1.009 1.000 1.015 1.021 Average 1.009 1.058 1.000 1.009 1.063

149

Palar River Basin Pure Efficiency Technical Scale efficiency Total factor Year efficiency change change change productivity change change 1976 - 77 1.029 1.374 1 1.029 1.413 1977 - 78 1.063 1.404 1 1.063 1.492 1978 - 79 1.048 1.182 1 1.048 1.238 1979 - 80 1.011 1.177 1 1.011 1.19 1980 - 81 1.023 1.357 1 1.023 1.389 1981 - 82 0.772 1.674 1 0.772 1.292 1982 - 83 1.216 0.954 1 1.216 1.16 1983 - 84 0.866 1.009 1 0.866 0.874 1984 - 85 1.033 0.858 1 1.033 0.886 1985 - 86 1.093 1.069 1 1.093 1.169 1986 - 87 1.029 1.057 1 1.029 1.087 1987 - 88 0.967 1.106 1 0.967 1.069 1988 - 89 1.132 0.952 1 1.132 1.078 1989 - 90 0.984 1.05 1 0.984 1.034 1990 - 91 0.827 1.186 1 0.827 0.981 1991 - 92 1.014 1.027 1 1.014 1.041 1992 - 93 0.893 1.164 1 0.893 1.039 1993 - 94 0.89 0.897 1 0.89 0.799 1994 - 95 0.989 0.989 1 0.989 0.978 1995 - 96 1.093 1.017 1 1.093 1.112 1996 - 97 0.781 1.245 1 0.781 0.972 1997 - 98 1.042 0.886 1 1.042 0.924 1998 - 99 1.009 0.94 1 1.009 0.949 1999 - 00 1.339 0.988 1 1.339 1.323 2000 - 01 0.956 1.06 1 0.956 1.014 2001 - 02 0.953 1.055 1 0.953 1.005 2002 - 03 1.506 1.059 1 1.506 1.595 2003 - 04 0.811 1.053 1 0.811 0.853 2004 - 05 1.014 0.866 1 1.014 0.878 2005 - 06 0.873 0.976 1 0.873 0.852 Period I 1.006 1.161 1.000 1.006 1.157 Period II 1.011 1.015 1.000 1.011 1.022 Average 1.009 1.088 1.000 1.009 1.090

150

Varahanadhi River Basin Efficien Pure efficiency Technical Scale efficiency Total factor Year cy change change change productivity change change 1976 - 77 1.008 1.503 1 1.008 1.515 1977 - 78 1.004 1.396 1 1.004 1.401 1978 - 79 1.014 1.288 1 1.014 1.306 1979 - 80 0.969 0.931 1 0.969 0.902 1980 - 81 0.987 1.707 1 0.987 1.685 1981 - 82 1.014 1.361 1 1.014 1.381 1982 - 83 0.98 1.044 1 0.98 1.023 1983 - 84 0.98 0.865 1 0.98 0.848 1984 - 85 1.018 0.692 1 1.018 0.705 1985 - 86 1.017 0.992 1 1.017 1.009 1986 - 87 0.984 1.064 1 0.984 1.047 1987 - 88 1.041 1.034 1 1.041 1.076 1988 - 89 0.979 1.138 1 0.979 1.113 1989 - 90 1.022 0.864 1 1.022 0.883 1990 - 91 0.978 1.227 1 0.978 1.2 1991 - 92 0.983 0.986 1 0.983 0.969 1992 - 93 1.001 1.109 1 1.001 1.111 1993 - 94 0.972 0.876 1 0.972 0.852 1994 - 95 1 1.052 1 1 1.052 1995 - 96 1.025 1.009 1 1.025 1.034 1996 - 97 0.959 1.185 1 0.959 1.137 1997 - 98 1.001 0.914 1 1.001 0.915 1998 - 99 1.009 0.945 1 1.009 0.953 1999 - 00 1.047 0.946 1 1.047 0.991 2000 - 01 0.993 1.018 1 0.993 1.011 2001 - 02 0.991 1.039 1 0.991 1.03 2002 - 03 1.061 1.085 1 1.061 1.151 2003 - 04 0.968 1.033 1 0.968 1.001 2004 - 05 1.032 0.958 1 1.032 0.988 2005 - 06 0.986 0.983 1 0.986 0.97 Period I 1.000 1.140 1.000 1.000 1.140 Period II 1.002 1.009 1.000 1.002 1.011 Average 1.001 1.075 1.000 1.001 1.075

151

Ponnaiyar River Basin Efficien Pure efficiency Technical Scale efficiency Total factor Year cy change change change productivity change change 1976 - 77 0.99 1.353 1 0.99 1.34 1977 - 78 1.059 1.426 1 1.059 1.509 1978 - 79 1.017 1.176 1 1.017 1.197 1979 - 80 1.829 1.397 1 1.829 2.556 1980 - 81 1.088 1.015 1 1.088 1.105 1981 - 82 1.185 1.467 1 1.185 1.739 1982 - 83 0.885 0.825 1 0.885 0.731 1983 - 84 0.914 1.079 1 0.914 0.986 1984 - 85 0.966 1.016 1 0.966 0.981 1985 - 86 0.969 1.132 1 0.969 1.097 1986 - 87 0.817 0.978 1 0.817 0.799 1987 - 88 0.834 1.439 1 0.834 1.2 1988 - 89 2.193 0.537 1 2.193 1.177 1989 - 90 0.623 1.772 1 0.623 1.104 1990 - 91 0.792 1.149 1 0.792 0.909 1991 - 92 0.775 1.228 1 0.775 0.952 1992 - 93 0.882 0.939 1 0.882 0.828 1993 - 94 0.783 1.109 1 0.783 0.868 1994 - 95 0.914 1.213 1 0.914 1.108 1995 - 96 1.287 0.762 1 1.287 0.982 1996 - 97 0.606 1.327 1 0.606 0.804 1997 - 98 1.024 0.864 1 1.024 0.885 1998 - 99 1.007 0.968 1 1.007 0.975 1999 - 00 1.169 0.939 1 1.169 1.097 2000 - 01 0.985 0.992 1 0.985 0.977 2001 - 02 0.961 1.064 1 0.961 1.022 2002 - 03 1.195 1.011 1 1.195 1.208 2003 - 04 1.088 0.932 1 1.088 1.014 2004 - 05 0.811 0.896 1 0.811 0.727 2005 - 06 0.914 0.996 1 0.914 0.911 Period I 1.077 1.184 1.000 1.077 1.229 Period II 0.960 1.016 1.000 0.960 0.957 Average 1.019 1.100 1.000 1.019 1.093

152

Paravanar River Basin Efficien Pure efficiency Technical Scale efficiency Total factor Year cy change change change productivity change change 1976 - 77 1 1.458 1 1 1.458 1977 - 78 1 1.243 1 1 1.243 1978 - 79 1 1.111 1 1 1.111 1979 - 80 1 0.787 1 1 0.787 1980 - 81 1 1.574 1 1 1.574 1981 - 82 1 1.461 1 1 1.461 1982 - 83 1 0.973 1 1 0.973 1983 - 84 1 0.884 1 1 0.884 1984 - 85 1 0.645 1 1 0.645 1985 - 86 1 0.932 1 1 0.932 1986 - 87 1 1.005 1 1 1.005 1987 - 88 1 1.144 1 1 1.144 1988 - 89 1 1.021 1 1 1.021 1989 - 90 1 0.791 1 1 0.791 1990 - 91 1 1.157 1 1 1.157 1991 - 92 1 0.899 1 1 0.899 1992 - 93 1 1.05 1 1 1.05 1993 - 94 1 0.86 1 1 0.86 1994 - 95 1 1.03 1 1 1.03 1995 - 96 1 0.954 1 1 0.954 1996 - 97 1 1.11 1 1 1.11 1997 - 98 1 0.899 1 1 0.899 1998 - 99 1 1.034 1 1 1.034 1999 - 00 1 0.914 1 1 0.914 2000 - 01 1 1.051 1 1 1.051 2001 - 02 1 1.031 1 1 1.031 2002 - 03 1 0.991 1 1 0.991 2003 - 04 1 0.965 1 1 0.965 2004 - 05 1 1.03 1 1 1.03 2005 - 06 1 1.014 1 1 1.014 Period I 1.000 1.079 1.000 1.000 1.079 Period II 1.000 0.989 1.000 1.000 0.989 Average 1 1.034 1 1 1.034

153

Vellar Basin Efficien Pure efficiency Technical Scale efficiency Total factor Year cy change change change productivity change change 1976 - 77 1.019 1.34 1 1.019 1.366 1977 - 78 0.999 1.389 1 0.999 1.388 1978 - 79 1.006 1.186 1 1.006 1.193 1979 - 80 1.003 1.165 1 1.003 1.169 1980 - 81 1.065 1.338 1 1.065 1.425 1981 - 82 0.865 1.626 1 0.865 1.407 1982 - 83 1.066 0.889 1 1.066 0.948 1983 - 84 0.996 1.01 1 0.996 1.007 1984 - 85 1.114 0.794 1 1.114 0.884 1985 - 86 0.94 1.136 1 0.94 1.068 1986 - 87 0.945 1.063 1 0.945 1.004 1987 - 88 0.933 1.135 1 0.933 1.059 1988 - 89 1.17 0.926 1 1.17 1.083 1989 - 90 0.895 1.14 1 0.895 1.021 1990 - 91 0.897 1.155 1 0.897 1.035 1991 - 92 1.033 1.027 1 1.033 1.061 1992 - 93 0.844 1.231 1 0.844 1.039 1993 - 94 0.88 0.981 1 0.88 0.863 1994 - 95 0.93 1.108 1 0.93 1.031 1995 - 96 1.28 0.886 1 1.28 1.134 1996 - 97 0.762 1.227 1 0.762 0.936 1997 - 98 1.069 0.904 1 1.069 0.966 1998 - 99 1.04 0.931 1 1.04 0.968 1999 - 00 1.107 0.968 1 1.107 1.071 2000 - 01 0.96 1.048 1 0.96 1.006 2001 - 02 0.969 1.01 1 0.969 0.979 2002 - 03 1.217 1.06 1 1.217 1.29 2003 - 04 0.951 1.082 1 0.951 1.03 2004 - 05 0.899 0.937 1 0.899 0.842 2005 - 06 0.834 0.99 1 0.834 0.826 Period I 0.994 1.153 1.000 0.994 1.137 Period II 0.985 1.026 1.000 0.985 1.003 Average 0.990 1.089 1.000 0.990 1.070

154

Cauvery River Basin Efficien Pure efficiency Technical Scale efficiency Total factor Year cy change change change productivity change change 1976 - 77 1.001 1.312 1 1.001 1.313 1977 - 78 1 1.368 1 1 1.367 1978 - 79 1.021 1.187 1 1.021 1.213 1979 - 80 1.099 1.126 1 1.099 1.237 1980 - 81 0.895 1.373 1 0.895 1.229 1981 - 82 0.694 1.511 1 0.694 1.048 1982 - 83 1.314 0.849 1 1.314 1.116 1983 - 84 0.924 1.011 1 0.924 0.935 1984 - 85 1.197 0.858 1 1.197 1.027 1985 - 86 0.875 1.141 1 0.875 0.999 1986 - 87 1.002 1.065 1 1.002 1.066 1987 - 88 1.004 0.984 1 1.004 0.988 1988 - 89 1.042 0.993 1 1.042 1.034 1989 - 90 0.915 1.093 1 0.915 0.999 1990 - 91 1.003 1.149 1 1.003 1.153 1991 - 92 0.971 1.042 1 0.971 1.011 1992 - 93 0.919 1.089 1 0.919 1.001 1993 - 94 0.947 0.93 1 0.947 0.881 1994 - 95 0.93 1.023 1 0.93 0.951 1995 - 96 1.191 0.985 1 1.191 1.173 1996 - 97 0.811 1.179 1 0.811 0.957 1997 - 98 1.087 0.903 1 1.087 0.981 1998 - 99 1.079 0.958 1 1.079 1.034 1999 - 00 1.019 0.992 1 1.019 1.011 2000 - 01 0.963 1.031 1 0.963 0.993 2001 - 02 1.077 1.008 1 1.077 1.086 2002 - 03 1.122 1.039 1 1.122 1.165 2003 - 04 1.023 1.026 1 1.023 1.05 2004 - 05 0.921 0.931 1 0.921 0.858 2005 - 06 0.809 1.005 1 0.809 0.813 Period I 0.999 1.135 1.000 0.999 1.115 Period II 0.991 1.009 1.000 0.991 0.998 Average 0.995 1.072 1.000 0.995 1.056

155

Agniyar River Basin Efficien Pure efficiency Technical Scale efficiency Total factor Year cy change change change productivity change change 1976 - 77 0.864 1.52 1 0.864 1.314 1977 - 78 1.105 1.178 1 1.105 1.302 1978 - 79 0.948 1.128 1 0.948 1.07 1979 - 80 1.118 0.951 1 1.118 1.063 1980 - 81 1.13 1.33 1 1.13 1.502 1981 - 82 0.848 0.993 1 0.848 0.842 1982 - 83 0.91 1.377 1 0.91 1.254 1983 - 84 1.09 0.824 1 1.09 0.898 1984 - 85 1.054 0.842 1 1.054 0.888 1985 - 86 0.989 0.881 1 0.989 0.871 1986 - 87 0.791 1.353 1 0.791 1.07 1987 - 88 1.388 0.738 1 1.388 1.023 1988 - 89 0.943 1.12 1 0.943 1.056 1989 - 90 1.004 0.947 1 1.004 0.952 1990 - 91 1.015 1.041 1 1.015 1.057 1991 - 92 0.934 1.083 1 0.934 1.011 1992 - 93 0.92 1.001 1 0.92 0.921 1993 - 94 0.936 0.922 1 0.936 0.863 1994 - 95 1.042 0.938 1 1.042 0.977 1995 - 96 1.016 1.275 1 1.016 1.295 1996 - 97 0.745 1.176 1 0.745 0.876 1997 - 98 1.119 0.857 1 1.119 0.959 1998 - 99 0.991 0.974 1 0.991 0.965 1999 - 00 1.014 1.036 1 1.014 1.05 2000 - 01 0.926 1.089 1 0.926 1.008 2001 - 02 1.007 1.046 1 1.007 1.053 2002 - 03 1.708 1.042 1 1.708 1.779 2003 - 04 0.622 1.04 1 0.622 0.647 2004 - 05 0.993 0.853 1 0.993 0.847 2005 - 06 0.819 0.962 1 0.819 0.788 Period I 1.013 1.082 1.000 1.013 1.077 Period II 0.986 1.020 1.000 0.986 1.003 Average 1.000 1.051 1.000 1.000 1.040

156

Pambar & Kottakaraiyar River Basin Efficien Pure efficiency Technical Scale efficiency Total factor Year cy change change change productivity change change 1976 - 77 0.991 1.203 1 0.991 1.192 1977 - 78 1.001 1.088 1 1.001 1.09 1978 - 79 0.99 1.023 1 0.99 1.013 1979 - 80 1.146 1.235 1 1.146 1.416 1980 - 81 0.942 1.169 1 0.942 1.101 1981 - 82 0.99 1.153 1 0.99 1.142 1982 - 83 0.927 1.163 1 0.927 1.078 1983 - 84 1.084 0.956 1 1.084 1.036 1984 - 85 1.131 0.972 1 1.131 1.099 1985 - 86 0.997 0.989 1 0.997 0.987 1986 - 87 0.842 1.176 1 0.842 0.991 1987 - 88 1.226 0.867 1 1.226 1.063 1988 - 89 1.01 0.946 1 1.01 0.956 1989 - 90 0.85 1.222 1 0.85 1.038 1990 - 91 0.912 1.065 1 0.912 0.971 1991 - 92 1.106 1.037 1 1.106 1.147 1992 - 93 0.923 1.04 1 0.923 0.961 1993 - 94 0.835 1.07 1 0.835 0.893 1994 - 95 0.995 1.034 1 0.995 1.029 1995 - 96 1.057 0.96 1 1.057 1.015 1996 - 97 0.869 1.076 1 0.869 0.935 1997 - 98 1.052 0.889 1 1.052 0.935 1998 - 99 0.998 0.998 1 0.998 0.995 1999 - 00 1.046 1.034 1 1.046 1.081 2000 - 01 0.98 1.004 1 0.98 0.984 2001 - 02 1.02 1.067 1 1.02 1.088 2002 - 03 1.151 1.034 1 1.151 1.19 2003 - 04 1.082 0.897 1 1.082 0.97 2004 - 05 1.202 0.789 1 1.202 0.949 2005 - 06 0.861 0.968 1 0.861 0.834 Period I 1.003 1.082 1.000 1.003 1.078 Period II 1.012 0.993 1.000 1.012 1.000 Average 1.007 1.037 1.000 1.007 1.039

157

Vaigai River Basin Efficien Pure efficiency Technical Scale efficiency Total factor Year cy change change change productivity change change 1976 - 77 1.031 1.09 1 1.031 1.123 1977 - 78 0.995 1.147 1 0.995 1.141 1978 - 79 1.013 1.093 1 1.013 1.107 1979 - 80 1.195 1.082 1 1.195 1.293 1980 - 81 0.733 1.382 1 0.733 1.014 1981 - 82 1.047 1.197 1 1.047 1.254 1982 - 83 1.052 1.03 1 1.052 1.084 1983 - 84 1.004 0.994 1 1.004 0.999 1984 - 85 1.197 0.847 1 1.197 1.014 1985 - 86 0.915 1.133 1 0.915 1.037 1986 - 87 0.908 1.101 1 0.908 1 1987 - 88 0.963 0.911 1 0.963 0.877 1988 - 89 1.011 1.033 1 1.011 1.044 1989 - 90 0.886 1.03 1 0.886 0.913 1990 - 91 0.952 1.107 1 0.952 1.053 1991 - 92 1.064 1.049 1 1.064 1.116 1992 - 93 0.924 1.108 1 0.924 1.024 1993 - 94 0.956 0.962 1 0.956 0.919 1994 - 95 1.029 0.963 1 1.029 0.991 1995 - 96 1.013 1.147 1 1.013 1.162 1996 - 97 0.929 1.017 1 0.929 0.945 1997 - 98 0.998 0.992 1 0.998 0.99 1998 - 99 1.113 1.002 1 1.113 1.115 1999 - 00 0.932 1.008 1 0.932 0.94 2000 - 01 1.038 1.062 1 1.038 1.103 2001 - 02 1.041 1.058 1 1.041 1.101 2002 - 03 1.085 1.04 1 1.085 1.128 2003 - 04 1.072 0.986 1 1.072 1.057 2004 - 05 1.232 0.788 1 1.232 0.971 2005 - 06 0.972 0.94 1 0.972 0.913 Period I 0.993 1.078 1.000 0.993 1.064 Period II 1.027 1.008 1.000 1.027 1.032 Average 1.010 1.043 1.000 1.010 1.048

158

Gundar River Basin Efficien Pure efficiency Technical Scale efficiency Total factor Year cy change change change productivity change change 1976 – 77 1.008 1.04 1 1.008 1.048 1977 – 78 1.033 1.188 1 1.033 1.227 1978 – 79 0.936 1.097 1 0.936 1.027 1979 – 80 1.213 1.254 1 1.213 1.521 1980 – 81 0.86 1.107 1 0.86 0.951 1981 – 82 1.135 1.08 1 1.135 1.226 1982 - 83 0.928 1.102 1 0.928 1.022 1983 - 84 1.036 1.045 1 1.036 1.082 1984 - 85 1.128 0.983 1 1.128 1.108 1985 - 86 0.988 1.096 1 0.988 1.083 1986 - 87 0.847 1.179 1 0.847 0.998 1987 - 88 1.04 0.831 1 1.04 0.864 1988 - 89 1.137 0.955 1 1.137 1.086 1989 - 90 0.757 1.119 1 0.757 0.847 1990 - 91 0.885 1.07 1 0.885 0.946 1991 - 92 1.17 0.948 1 1.17 1.11 1992 - 93 0.939 0.967 1 0.939 0.908 1993 - 94 0.903 1.064 1 0.903 0.961 1994 - 95 1.036 0.921 1 1.036 0.954 1995 - 96 1.006 1.056 1 1.006 1.063 1996 - 97 0.937 1.034 1 0.937 0.969 1997 - 98 0.996 0.95 1 0.996 0.947 1998 - 99 1.01 1.002 1 1.01 1.012 1999 - 00 1.027 1.021 1 1.027 1.049 2000 - 01 0.985 1.062 1 0.985 1.046 2001 - 02 1.013 1.125 1 1.013 1.139 2002 - 03 1.06 1.018 1 1.06 1.079 2003 - 04 1.245 0.788 1 1.245 0.981 2004 - 05 1.208 0.77 1 1.208 0.93 2005 - 06 0.923 0.933 1 0.923 0.862 Period I 0.995 1.076 1.000 0.995 1.069 Period II 1.031 0.977 1.000 1.031 1.001 Average 1.013 1.027 1.000 1.013 1.035

159

Vaippar Basin Pure efficiency Total factor Efficiency Technical Scale efficiency Year change productivity change change change change 1976 - 77 1.011 0.986 1 1.011 0.997 1977 - 78 1.043 1.095 1 1.043 1.142 1978 - 79 0.892 1.013 1 0.892 0.904 1979 - 80 1.352 1.178 1 1.352 1.593 1980 - 81 0.855 1.028 1 0.855 0.879 1981 - 82 1.142 0.991 1 1.142 1.132 1982 - 83 0.872 1.096 1 0.872 0.956 1983 - 84 0.957 0.999 1 0.957 0.955 1984 - 85 1.2 0.962 1 1.2 1.154 1985 - 86 0.968 1.15 1 0.968 1.113 1986 - 87 1.004 1.005 1 1.004 1.009 1987 - 88 0.847 0.915 1 0.847 0.775 1988 - 89 1.113 0.89 1 1.113 0.99 1989 - 90 0.783 1.141 1 0.783 0.894 1990 - 91 0.924 1.007 1 0.924 0.93 1991 - 92 1.287 0.761 1 1.287 0.98 1992 - 93 0.913 0.943 1 0.913 0.862 1993 - 94 0.848 1.052 1 0.848 0.892 1994 - 95 1.122 0.844 1 1.122 0.947 1995 - 96 0.994 0.957 1 0.994 0.952 1996 - 97 0.95 1.042 1 0.95 0.99 1997 - 98 1.006 0.829 1 1.006 0.834 1998 - 99 0.855 1.037 1 0.855 0.886 1999 - 00 1.085 1.016 1 1.085 1.102 2000 - 01 0.959 1.018 1 0.959 0.977 2001 - 02 1.009 1.163 1 1.009 1.173 2002 - 03 0.989 1.049 1 0.989 1.038 2003 - 04 1.101 0.768 1 1.101 0.845 2004 - 05 1.009 0.843 1 1.009 0.851 2005 - 06 1.021 0.938 1 1.021 0.958 Period I 0.998 1.030 1.000 0.998 1.028 Period II 1.010 0.951 1.000 1.010 0.952 Average 1.004 0.991 1.000 1.004 0.990

160

Kallar River Basin Efficien Pure efficiency Technical Scale efficiency Total factor Year cy change change change productivity change change 1976 - 77 1 1.09 1 1 1.09 1977 - 78 1 1.057 1 1 1.057 1978 - 79 1 1.045 1 1 1.045 1979 - 80 1 1.258 1 1 1.258 1980 - 81 1 1.052 1 1 1.052 1981 - 82 1 0.998 1 1 0.998 1982 - 83 1 1.14 1 1 1.14 1983 - 84 1 0.961 1 1 0.961 1984 - 85 1 0.677 1 1 0.677 1985 - 86 1 1.027 1 1 1.027 1986 - 87 1 1.608 1 1 1.608 1987 - 88 1 0.811 1 1 0.811 1988 - 89 1 0.776 1 1 0.776 1989 - 90 1 1.257 1 1 1.257 1990 - 91 1 1.006 1 1 1.006 1991 - 92 1 1.011 1 1 1.011 1992 - 93 1 0.961 1 1 0.961 1993 - 94 1 1.136 1 1 1.136 1994 - 95 1 0.991 1 1 0.991 1995 - 96 1 1.296 1 1 1.296 1996 - 97 1 0.967 1 1 0.967 1997 - 98 1 0.817 1 1 0.817 1998 - 99 1 1.065 1 1 1.065 1999 - 00 1 1.059 1 1 1.059 2000 - 01 1 1.304 1 1 1.304 2001 - 02 1 1.183 1 1 1.183 2002 - 03 1 0.839 1 1 0.839 2003 - 04 1 0.774 1 1 0.774 2004 - 05 1 0.891 1 1 0.891 2005 - 06 1 0.915 1 1 0.915 Period I 1.000 1.051 1.000 1.000 1.051 Period II 1.000 1.014 1.000 1.000 1.014 Average 1 1.032 1 1 1.032

161

Tambarabarani River Basin Efficien Pure efficiency Technical Scale efficiency Total factor Year cy change change change productivity change change 1976 - 77 0.994 1.187 1 0.994 1.179 1977 - 78 0.977 0.949 1 0.977 0.927 1978 - 79 0.998 0.986 1 0.998 0.984 1979 - 80 0.998 1.074 1 0.998 1.072 1980 - 81 1.004 1.011 1 1.004 1.014 1981 - 82 1.015 1.019 1 1.015 1.035 1982 - 83 0.994 1.175 1 0.994 1.168 1983 - 84 0.997 0.879 1 0.997 0.876 1984 - 85 1.008 0.824 1 1.008 0.831 1985 - 86 1.003 0.994 1 1.003 0.997 1986 - 87 1.022 1.392 1 1.022 1.423 1987 - 88 0.923 0.776 1 0.923 0.717 1988 - 89 1.04 0.989 1 1.04 1.029 1989 - 90 1.018 1.07 1 1.018 1.089 1990 - 91 0.986 0.957 1 0.986 0.943 1991 - 92 0.956 0.831 1 0.956 0.794 1992 - 93 0.992 0.879 1 0.992 0.872 1993 - 94 0.963 0.918 1 0.963 0.884 1994 - 95 0.998 1.008 1 0.998 1.006 1995 - 96 1.033 1.083 1 1.033 1.119 1996 - 97 1.047 1.154 1 1.047 1.209 1997 - 98 0.944 0.887 1 0.944 0.837 1998 - 99 0.972 0.959 1 0.972 0.932 1999 - 00 1.011 0.959 1 1.011 0.97 2000 - 01 1.001 1.009 1 1.001 1.01 2001 - 02 1.015 1.081 1 1.015 1.097 2002 - 03 1.007 1.033 1 1.007 1.04 2003 - 04 1.013 0.955 1 1.013 0.967 2004 - 05 1.217 0.943 1 1.217 1.148 2005 - 06 0.794 1.111 1 0.794 0.882 Period I 0.998 1.019 1.000 0.998 1.019 Period II 0.998 0.987 1.000 0.998 0.984 Average 0.998 1.003 1.000 0.998 1.002

162

Nambiyar River Basin Efficien Pure efficiency Technical Scale efficiency Total factor Year cy change change change productivity change change 1976 - 77 1 1.152 1 1 1.152 1977 - 78 1 0.963 1 1 0.963 1978 - 79 1 0.963 1 1 0.963 1979 - 80 1 1.145 1 1 1.145 1980 - 81 1 1.032 1 1 1.032 1981 - 82 1 0.949 1 1 0.949 1982 - 83 1 1.154 1 1 1.154 1983 - 84 1 0.896 1 1 0.896 1984 - 85 1 0.739 1 1 0.739 1985 - 86 1 0.942 1 1 0.942 1986 - 87 1 1.416 1 1 1.416 1987 - 88 1 0.725 1 1 0.725 1988 - 89 1 0.975 1 1 0.975 1989 - 90 1 1.046 1 1 1.046 1990 - 91 1 0.963 1 1 0.963 1991 - 92 1 0.839 1 1 0.839 1992 - 93 1 0.921 1 1 0.921 1993 - 94 1 0.935 1 1 0.935 1994 - 95 1 0.956 1 1 0.956 1995 - 96 1 1.084 1 1 1.084 1996 - 97 1 1.119 1 1 1.119 1997 - 98 1 0.853 1 1 0.853 1998 - 99 1 0.937 1 1 0.937 1999 - 00 1 0.948 1 1 0.948 2000 - 01 1 0.984 1 1 0.984 2001 - 02 1 1.081 1 1 1.081 2002 - 03 1 1.048 1 1 1.048 2003 - 04 1 0.889 1 1 0.889 2004 - 05 1 0.927 1 1 0.927 2005 - 06 1 1.502 1 1 1.502 Period I 1.000 1.004 1.000 1.000 1.004 Period II 1.000 1.002 1.000 1.000 1.002 Average 1 1.003 1 1 1.003

163

Kodaiyar River Basin Efficien Pure efficiency Technical Scale efficiency Total factor Year cy change change change productivity change change 1976 - 77 1.006 2.082 1 1.006 2.094 1977 - 78 1 0.71 1 1 0.71 1978 - 79 1 1.162 1 1 1.162 1979 - 80 1 0.993 1 1 0.993 1980 - 81 1 1.042 1 1 1.042 1981 - 82 1 0.818 1 1 0.818 1982 - 83 1 1.076 1 1 1.076 1983 - 84 1 0.986 1 1 0.986 1984 - 85 1 0.677 1 1 0.677 1985 - 86 1 0.86 1 1 0.86 1986 - 87 1 1.411 1 1 1.411 1987 - 88 1 0.62 1 1 0.62 1988 - 89 1 0.808 1 1 0.808 1989 - 90 1 1.474 1 1 1.474 1990 - 91 1 0.722 1 1 0.722 1991 - 92 1 1.072 1 1 1.072 1992 - 93 1 1.253 1 1 1.253 1993 - 94 1 0.855 1 1 0.855 1994 - 95 1 0.73 1 1 0.73 1995 - 96 1 1.435 1 1 1.435 1996 - 97 1 0.944 1 1 0.944 1997 - 98 1 0.907 1 1 0.907 1998 - 99 1 0.962 1 1 0.962 1999 - 00 1 1.006 1 1 1.006 2000 - 01 1 1.062 1 1 1.062 2001 - 02 1 1.109 1 1 1.109 2002 - 03 1 1.088 1 1 1.088 2003 - 04 1 1.072 1 1 1.072 2004 - 05 1 0.926 1 1 0.926 2005 - 06 1 0.857 1 1 0.857 Period I 1.000 1.029 1.000 1.000 1.030 Period II 1.000 1.019 1.000 1.000 1.019 Average 1.000 1.024 1.000 1.000 1.024

164

P.A.P. Basin Efficie Pure efficiency ncy Technical change Scale efficiency Total factor Year chang change change productivity change e 1976 - 77 1.081 0.9 1 1.081 0.973 1977 - 78 0.899 0.996 1 0.899 0.896 1978 - 79 0.991 0.975 1 0.991 0.967 1979 - 80 0.994 0.991 1 0.994 0.985 1980 - 81 0.992 0.997 1 0.992 0.989 1981 - 82 0.986 0.997 1 0.986 0.983 1982 - 83 1 0.993 1 1 0.993 1983 - 84 0.998 0.984 1 0.998 0.982 1984 - 85 0.994 0.797 1 0.994 0.792 1985 - 86 0.998 0.993 1 0.998 0.992 1986 - 87 0.977 1.011 1 0.977 0.987 1987 - 88 1.011 0.981 1 1.011 0.992 1988 - 89 1.012 0.983 1 1.012 0.995 1989 - 90 0.993 0.995 1 0.993 0.988 1990 - 91 1.083 0.967 1 1.083 1.048 1991 - 92 0.903 1.026 1 0.903 0.926 1992 - 93 0.981 0.995 1 0.981 0.976 1993 - 94 1.002 0.997 1 1.002 0.999 1994 - 95 1.007 1.001 1 1.007 1.008 1995 - 96 0.983 0.992 1 0.983 0.975 1996 - 97 0.992 1.01 1 0.992 1.003 1997 - 98 1.011 0.984 1 1.011 0.994 1998 - 99 1.003 0.997 1 1.003 1 1999 - 00 0.992 0.988 1 0.992 0.98 2000 - 01 0.961 1.033 1 0.961 0.993 2001 - 02 0.992 1.083 1 0.992 1.074 2002 - 03 1.019 1 1 1.019 1.018 2003 - 04 1.027 0.891 1 1.027 0.915 2004 - 05 1.14 0.772 1 1.14 0.88 2005 - 06 1.054 0.931 1 1.054 0.981 Period I 1.001 0.971 1.000 1.001 0.971 Period II 1.004 0.980 1.000 1.004 0.981 Average 1.003 0.975 1.000 1.003 0.976

165

Appendix-III Summary of TFP Indices

a) Small basins

Year Chennai Varaha Paravan Agniyar Kallar Tambara Nambiyar Kodaiyar PAP 1976 1.432 1.515 1.458 1.314 1.09 1.179 1.152 2.094 0.973 1977 1.455 1.401 1.243 1.302 1.057 0.927 0.963 0.71 0.896 1978 1.317 1.306 1.111 1.07 1.045 0.984 0.963 1.162 0.967 1979 0.917 0.902 0.787 1.063 1.258 1.072 1.145 0.993 0.985 1980 1.357 1.685 1.574 1.502 1.052 1.014 1.032 1.042 0.989 1981 1.171 1.381 1.461 0.842 0.998 1.035 0.949 0.818 0.983 1982 1.178 1.023 0.973 1.254 1.14 1.168 1.154 1.076 0.993 1983 0.748 0.848 0.884 0.898 0.961 0.876 0.896 0.986 0.982 1984 0.904 0.705 0.645 0.888 0.677 0.831 0.739 0.677 0.792 1985 1.009 1.009 0.932 0.871 1.027 0.997 0.942 0.86 0.992 1986 1.073 1.047 1.005 1.07 1.608 1.423 1.416 1.411 0.987 1987 1.132 1.076 1.144 1.023 0.811 0.717 0.725 0.62 0.992 1988 0.886 1.113 1.021 1.056 0.776 1.029 0.975 0.808 0.995 1989 0.944 0.883 0.791 0.952 1.257 1.089 1.046 1.474 0.988 1990 1.04 1.2 1.157 1.057 1.006 0.943 0.963 0.722 1.048 1991 0.959 0.969 0.899 1.011 1.011 0.794 0.839 1.072 0.926 1992 1.109 1.111 1.05 0.921 0.961 0.872 0.921 1.253 0.976 1993 0.765 0.852 0.86 0.863 1.136 0.884 0.935 0.855 0.999 1994 0.899 1.052 1.03 0.977 0.991 1.006 0.956 0.73 1.008 1995 1.152 1.034 0.954 1.295 1.296 1.119 1.084 1.435 0.975 1996 1.059 1.137 1.11 0.876 0.967 1.209 1.119 0.944 1.003 1997 0.882 0.915 0.899 0.959 0.817 0.837 0.853 0.907 0.994 1998 1.042 0.953 1.034 0.965 1.065 0.932 0.937 0.962 1 1999 1.057 0.991 0.914 1.05 1.059 0.97 0.948 1.006 0.98 2000 1.146 1.011 1.051 1.008 1.304 1.01 0.984 1.062 0.993 2001 1.062 1.03 1.031 1.053 1.183 1.097 1.081 1.109 1.074 2002 1.25 1.151 0.991 1.779 0.839 1.04 1.048 1.088 1.018 2003 1.101 1.001 0.965 0.647 0.774 0.967 0.889 1.072 0.915 2004 0.999 0.988 1.03 0.847 0.891 1.148 0.927 0.926 0.88 2005 0.832 0.97 1.014 0.788 0.915 0.882 1.502 0.857 0.981

166

b) Medium Basins

Year Vellar Pambar Vaigai Gundar Vaippar 1976 1.366 1.192 1.123 1.048 0.997 1977 1.388 1.09 1.141 1.227 1.142 1978 1.193 1.013 1.107 1.027 0.904 1979 1.169 1.416 1.293 1.521 1.593 1980 1.425 1.101 1.014 0.951 0.879 1981 1.407 1.142 1.254 1.226 1.132 1982 0.948 1.078 1.084 1.022 0.956 1983 1.007 1.036 0.999 1.082 0.955 1984 0.884 1.099 1.014 1.108 1.154 1985 1.068 0.987 1.037 1.083 1.113 1986 1.004 0.991 1 0.998 1.009 1987 1.059 1.063 0.877 0.864 0.775 1988 1.083 0.956 1.044 1.086 0.99 1989 1.021 1.038 0.913 0.847 0.894 1990 1.035 0.971 1.053 0.946 0.93 1991 1.061 1.147 1.116 1.11 0.98 1992 1.039 0.961 1.024 0.908 0.862 1993 0.863 0.893 0.919 0.961 0.892 1994 1.031 1.029 0.991 0.954 0.947 1995 1.134 1.015 1.162 1.063 0.952 1996 0.936 0.935 0.945 0.969 0.99 1997 0.966 0.935 0.99 0.947 0.834 1998 0.968 0.995 1.115 1.012 0.886 1999 1.071 1.081 0.94 1.049 1.102 2000 1.006 0.984 1.103 1.046 0.977 2001 0.979 1.088 1.101 1.139 1.173 2002 1.29 1.19 1.128 1.079 1.038 2003 1.03 0.97 1.057 0.981 0.845 2004 0.842 0.949 0.971 0.93 0.851 2005 0.826 0.834 0.913 0.862 0.958

167

c) Large Basins

Year Palar Ponnaiya Cauvery 1976 1.413 1.34 1.313 1977 1.492 1.509 1.367 1978 1.238 1.197 1.213 1979 1.19 2.556 1.237 1980 1.389 1.105 1.229 1981 1.292 1.739 1.048 1982 1.16 0.731 1.116 1983 0.874 0.986 0.935 1984 0.886 0.981 1.027 1985 1.169 1.097 0.999 1986 1.087 0.799 1.066 1987 1.069 1.2 0.988 1988 1.078 1.177 1.034 1989 1.034 1.104 0.999 1990 0.981 0.909 1.153 1991 1.041 0.952 1.011 1992 1.039 0.828 1.001 1993 0.799 0.868 0.881 1994 0.978 1.108 0.951 1995 1.112 0.982 1.173 1996 0.972 0.804 0.957 1997 0.924 0.885 0.981 1998 0.949 0.975 1.034 1999 1.323 1.097 1.011 2000 1.014 0.977 0.993 2001 1.005 1.022 1.086 2002 1.595 1.208 1.165 2003 0.853 1.014 1.05 2004 0.878 0.727 0.858 2005 0.852 0.911 0.813

168

Appendix-IV

Cumulative TFP Indices

a) Small Basins

Year Chennai Varaha Paravan Agniyar Kallar Tambara Nambiyar Kodaiyar PAP 1976 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1977 1.016 0.925 0.853 0.991 0.970 0.786 0.836 0.339 0.921 1978 0.920 0.862 0.762 0.814 0.959 0.835 0.836 0.555 0.994 1979 0.640 0.595 0.540 0.809 1.154 0.909 0.994 0.474 1.012 1980 0.948 1.112 1.080 1.143 0.965 0.860 0.896 0.498 1.016 1981 0.818 0.912 1.002 0.641 0.916 0.878 0.824 0.391 1.010 1982 0.823 0.675 0.667 0.954 1.046 0.991 1.002 0.514 1.021 1983 0.522 0.560 0.606 0.683 0.882 0.743 0.778 0.471 1.009 1984 0.631 0.465 0.442 0.676 0.621 0.705 0.641 0.323 0.814 1985 0.705 0.666 0.639 0.663 0.942 0.846 0.818 0.411 1.020 1986 0.749 0.691 0.689 0.814 1.475 1.207 1.229 0.674 1.014 1987 0.791 0.710 0.785 0.779 0.744 0.608 0.629 0.296 1.020 1988 0.619 0.735 0.700 0.804 0.712 0.873 0.846 0.386 1.023 1989 0.659 0.583 0.543 0.725 1.153 0.924 0.908 0.704 1.015 1990 0.726 0.792 0.794 0.804 0.923 0.800 0.836 0.345 1.077 1991 0.670 0.640 0.617 0.769 0.928 0.673 0.728 0.512 0.952 1992 0.774 0.733 0.720 0.701 0.882 0.740 0.799 0.598 1.003 1993 0.534 0.562 0.590 0.657 1.042 0.750 0.812 0.408 1.027 1994 0.628 0.694 0.706 0.744 0.909 0.853 0.830 0.349 1.036 1995 0.804 0.683 0.654 0.986 1.189 0.949 0.941 0.685 1.002 1996 0.740 0.750 0.761 0.667 0.887 1.025 0.971 0.451 1.031 1997 0.616 0.604 0.617 0.730 0.750 0.710 0.740 0.433 1.022 1998 0.728 0.629 0.709 0.734 0.977 0.791 0.813 0.459 1.028 1999 0.738 0.654 0.627 0.799 0.972 0.823 0.823 0.480 1.007 2000 0.800 0.667 0.721 0.767 1.196 0.857 0.854 0.507 1.021 2001 0.742 0.680 0.707 0.801 1.085 0.930 0.938 0.530 1.104 2002 0.873 0.760 0.680 1.354 0.770 0.882 0.910 0.520 1.046 2003 0.769 0.661 0.662 0.492 0.710 0.820 0.772 0.512 0.940 2004 0.698 0.652 0.706 0.645 0.817 0.974 0.805 0.442 0.904 2005 0.581 0.640 0.695 0.600 0.839 0.748 1.304 0.409 1.008

169

b) Medium Basins

Year Vellar Pambar Vaigai Gundar Vaippar 1976 1.000 1.000 1.000 1.000 1.000 1977 1.016 0.914 1.016 1.171 1.145 1978 0.873 0.850 0.986 0.980 0.907 1979 0.856 1.188 1.151 1.451 1.598 1980 1.043 0.924 0.903 0.907 0.882 1981 1.030 0.958 1.117 1.170 1.135 1982 0.694 0.904 0.965 0.975 0.959 1983 0.737 0.869 0.890 1.032 0.958 1984 0.647 0.922 0.903 1.057 1.157 1985 0.782 0.828 0.923 1.033 1.116 1986 0.735 0.831 0.890 0.952 1.012 1987 0.775 0.892 0.781 0.824 0.777 1988 0.793 0.802 0.930 1.036 0.993 1989 0.747 0.871 0.813 0.808 0.897 1990 0.758 0.815 0.938 0.903 0.933 1991 0.777 0.962 0.994 1.059 0.983 1992 0.761 0.806 0.912 0.866 0.865 1993 0.632 0.749 0.818 0.917 0.895 1994 0.755 0.863 0.882 0.910 0.950 1995 0.830 0.852 1.035 1.014 0.955 1996 0.685 0.784 0.841 0.925 0.993 1997 0.707 0.784 0.882 0.904 0.837 1998 0.709 0.835 0.993 0.966 0.889 1999 0.784 0.907 0.837 1.001 1.105 2000 0.736 0.826 0.982 0.998 0.980 2001 0.717 0.913 0.980 1.087 1.177 2002 0.944 0.998 1.004 1.030 1.041 2003 0.754 0.814 0.941 0.936 0.848 2004 0.616 0.796 0.865 0.887 0.854 2005 0.605 0.700 0.813 0.823 0.961

170

c) Large Basins

Year Palar Ponnaiya Cauvery 1976 1.000 1.000 1.000 1977 1.056 1.126 1.041 1978 0.876 0.893 0.924 1979 0.842 1.907 0.942 1980 0.983 0.825 0.936 1981 0.914 1.298 0.798 1982 0.821 0.546 0.850 1983 0.619 0.736 0.712 1984 0.627 0.732 0.782 1985 0.827 0.819 0.761 1986 0.769 0.596 0.812 1987 0.757 0.896 0.752 1988 0.763 0.878 0.788 1989 0.732 0.824 0.761 1990 0.694 0.678 0.878 1991 0.737 0.710 0.770 1992 0.735 0.618 0.762 1993 0.565 0.648 0.671 1994 0.692 0.827 0.724 1995 0.787 0.733 0.893 1996 0.688 0.600 0.729 1997 0.654 0.660 0.747 1998 0.672 0.728 0.788 1999 0.936 0.819 0.770 2000 0.718 0.729 0.756 2001 0.711 0.763 0.827 2002 1.129 0.901 0.887 2003 0.604 0.757 0.800 2004 0.621 0.543 0.653 2005 0.603 0.680 0.619

171