An Economic Analysis of Groundwater Development vis-à-vis Resource Use Efficiency in Tank Command Areas.


ABSTRACT: Out of total farmers 75 per cent were cultivating rice in tank command areas.
The rest of the farmers cultivated other crops and hence resource use efficiency has been estimated for rice crop alone. Among the rice growers 27 per cent of farmers have raised rice with tank water alone while the rest applied both tank water and well water. The analysis was done for two situations i) tank water alone and ii) tank cum well water application. The total cost of rice cultivation using only tank water was Rs 16016/- per hectare and tank cum well water situation, the total cost of rice cultivation was Rs 24628/- per hectare.

The Mean Technical Efficiency (MTE) was calculated to be 0.3996 for only tank water using farmers. It indicated that the technical efficiency of rice farmers were only 39.96 per cent and yield of rice could be increased by 60.04 per cent more by adopting a technically efficient plan without any increase in cost. The Mean Technical Efficiency (MTE) was calculated to be 0.6248 for tank cum well water users. It indicated that technical efficiency of rice farmers was only 62.48 per cent and yield of rice could be increased by 37.52 per cent more by adopting a technically efficient plan without any increase in cost.
Introduction

The important factor in agricultural development in India is going to be efficient use of available water resources for crop production. The increasing need for crop production due to growing population led to the rapid expansion of irrigation throughout the world.

Historically, groundwater is an important source of irrigation of India. Its contribution in enhancing agricultural production was better realized during the green revolution period. However, in the race for increasing agricultural production, its over–exploitation and mismanagement had resulted in several problems like fluctuations in water table and increase in depth of wells. Indian agriculture received the highest priority in irrigation development in successive Five-Year Plans. The irrigated area increased from 20 million ha in 1950-51 to 57.3 million ha in 1999-2000. With the advent of new agricultural technology in mid 1960s, several incentives (like electrification, bank credit, etc.) were given to the groundwater development. The area under groundwater irrigation, which was less than six million ha in 1959-60, went up to 18 million ha in 1980-81 to 33 million ha in 1999-2000. More than half of the total irrigation is done through groundwater. The share of groundwater in total irrigated area increased from 30 per cent in 1960-61 to 58.77 per cent in 1999-2000. Most of the groundwater development came through private investment. The area under groundwater is increasing progressively as this is the most reliable and cost-effective source of irrigation (Joshi, 2002).

This paper is based on the MSc (Agri.) of the first author. Thesis was submitted by the author (Venkatesh.G. 2003) to the Department of Agricultural Economics, Tamil Nadu Agricultural University (TNAU), Coimbatore.
The major sources of irrigation in India are tanks, canals and wells. The tanks have existed in India from time immemorial, and have been an important source of irrigation, particularly, in South India where it accounts for about one-third of the rice irrigated area. (Palanisami et al., 2001)

The recent estimate places the actual number of tanks in Tamil Nadu at 34,000, the remaining 5,000 plus has just disappeared over the past 15 years, so because of a variety of reasons during the 1980’s. Though there are several studies on tank irrigation and its problems, studies on groundwater development and resource use efficiency of rice in tank command areas are limited. However, in this paper we attempt to study the resource use efficiency in rice cultivation and returns to supplemental irrigation in tank command areas. In particular, we employ the stochastic frontier production techniques to measure technical efficiency of rice.

The study proceeds as follows Section I explains the methodology used in the study. Data, model and variables are discussed in section II. Section III provides the empirical results and the final section (IV) summarizes the findings and suggests policy implications.

Section I Methodology

The measurement of efficiency was the main motivation for the study of frontier. The technical efficiency literature begins with Farrell (1957), employed a deterministic approach in which he estimated a cost frontier by using linear programming (LP), requiring all observations to lie on or above the frontier. Aigner and Chu (1968) translated Farrell’s cost frontier into a production frontier, since outlier observations under a deterministic approach seriously affect the problem, by using a probabilistic frontier function. This approach deletes outlier observations, one at a time, to avoid spurious errors due to extreme observations, until the estimated coefficients stabilize. Then, Timmer’s (1971) approach yields a frontier, which is probabilistic rather than deterministic or stochastic. Later Aigner et al., (1977) developed a stochastic frontier model and key feature of the model was that the disturbance term is composed of two parts, one symmetric and the other ‘one-sided’.

A symmetric disturbance term is normally distributed component permits random variation of the frontier across firms and captures the effect of measurement error, other statistical noises and random shocks outside the firm’s control. A one-sided error component captures the effect of inefficiency relative to the stochastic frontier. Parameters of the stochastic frontier may be estimated by the Maximum Likelihood Estimate (MLE) or Corrected Ordinary Least Square (COLS), method if the probability function for symmetric and one-sided components of the error term is specified.

A number of comprehensive literature reviews are available, such as Battese (1992), Kalirajan & Shand (1994), Mythilli & Shanmugam (2000), Tim Coelli et al., (2002) and Shanmugam K.R (2003)

A (linear) stochastic frontier model is specified as

Y = f (X1,X2,……Xn) + (v ? u)

Where, v is the symmetric error component causing the deterministic part of the production frontier f (X1, X2…Xn) to vary across the firms. Technical efficiency relative to the stochastic production frontier is captured by the one-sided error component (? depending on whether one specifies a production or cost frontier), u?0. Given the density functions for u and v the frontier function defined above may be estimated by maximum likelihood techniques. While several distributions can be considered for the term u, the statistical estimation of the frontier model combining both u and v usually leads only to the estimation of average technical efficiency of the sample observations since their combined effects could not be separated under such general assumptions.

However, individual observation specific-technical efficiency measures are more useful from a policy viewpoint. The approach to identify firm specific technical efficiency requires some estimators that allow for separating the effects of the one-sided error term u from the combined effects of u and v using the estimated frontier functions. Therefore, the problem is to predict ui under the assumption that ui+vi is known. The best predictor of an unknown random variable (ui) under the value of the combined random variables ui+vi is the minimum mean squared error predictor given by the conditional expectation of ui. Assuming a half normal distribution for ui and normal distribution for vi, the frontier model becomes

Y=f (X1,X2,……Xn) + (v ? u)

where, u = ? u ? and u ?N ?0, ?2u ?

and v ? N ?0, ?2v ?

The components of the disturbance term are assumed to be independent and the frontier is assumed to be linear in above case. (In case of multiplicative models the ?(v-u) component is expressed as exp (v-u)). Now, the firm or observation specific ui can be estimated as

E? u i? ( ui + vi )?= - ?u ?v /? ? f(.) / (1-F(.) -?( ui + vi ) / ?? ?? / (1-? )? 1/2?

Where f(.) and F(.) are standard normal density and distribution functions evaluated at

?(ui+vi) / ? ? ? ? /1-? ? 1/2, ? = ?2u / ?2 and ?2= ?2u+?2v

Alternatively,

E (u? e) =??/ (1+?2)? ? f (E? / ?) / F (E? / ?) - E? / ??

Where ?=?2u / ?2v

One advantage of estimating the frontier production that is possible to find out whether the farmers deviation of yield from frontier is mainly because they did not use the best practical technique or due to external random factors. Thus, one can say whether the difference between the actual yield obtained and the frontier yield, if any, occurred accidentally or not.

Following Battese and Coelli (1988), when output is measured in logarithms, the farm-specific technical efficiency can be estimated as:

TEi = Exp (-ui)

i = 1,2,3…n, 0 ? TEi ?1

The variance ratio ?, explaining the total variation in output from the frontier level of output attributed to technical efficiencies, can be computed as:

?= ?2u /?2

Where ?2 = ?2u+?2v and 0 ? ? ? 1

? is an indicator of relative variability of ui and vi met differentiates the actual yield obtained from the frontier.

There are two interesting points about ?

1) When ?2v is tends to zero, which implied that vi is the predominant error, then the ?=1. This means that the farmer’s yield difference from the maximum feasible yield mainly because he did not use the best practice technique.

2) When ?2u are tends to zero, which implies that the symmetric error term vi the predominant error, ? is tending to zero. This means that the farmer’s yield difference from the frontier yield is mainly because of either technical error or external factors not under his control.

Direct estimates of the stochastic production function frontier model may be obtained by Maximum Likelihood Estimator (MLE) method. In this study MLE method is used to estimate (as was used by Olsen et al., (1980): and Banik Arindam (1994)). Measurement of technical efficiency has been attempted across crops such as Rice (e.g. Kalirajan & Shand 1994; Mythili & Shanmugam 2000); tea (e.g. Hazarika & Subramanian 1999); rice, groundnut and cotton (Shanmugam 2003); and coffee, orange, banana and pepper (e.g. Venkatesh et al., 2005).

Section II Data model and variables used in the study

The study area was Sivaganga District, Southern Region of Tamil Nadu, which has more number of tanks, has been purposefully selected as study area. Multi-stage Stratified Random sampling was used. In study area Sivaganga District, Sivaganga Taluk (Stage I) was selected and in that taluk four tanks were selected from PWD management and two were selected from PU maintenance based on command area of the tank (Stage II). So, six villages are benefited by the chosen tanks, namely Namanur, Kovanur, Panaiur, Mudikondon, Valuthani and Salur. Twenty farmers from each of the mentioned villages were randomly selected (Stage III). On the total 120 respondents were interviewed.

Rice was the major cereal crop in this district. Therefore, rice crop was chosen for further analysis. The survey was conducted during the year 2002-2003.

The empirical model consists of single stage. In that stage, the stochastic frontier production function was estimated. For that purpose, the Cobb-Douglas production function was employed and which is given by:

Cobb-Douglas production function was used to estimate the resource use efficiency.

Y = bo X1b1X2b2X3b3X4b4 X5b5U

Where,
Y = Rice yield in quintals per ha
X1 = Area under rice in ha.
X2 = Fertilizer applied (N+P+K kgs per ha)
X3 = Labour mandays per ha
X4 = Expenditure on bullock, machinery power, seeds and pesticides (Rs. per
ha)
X5 = Irrigation (ha cm)
bo = Intercept
bi = 1,2,3,4, and 5 are production elasticities.
U = Error term

Section III Empirical Results
Distribution of Land Holdings of the Sample Farmers in the Study Area
It could be seen from the Table 1 that out of 120 sample farmers 56.7 per cent were marginal farmers, while 32.5 per cent were small farmers and the remaining 10.8 per cent belonged to big farmers’ group.
Table 1. Distribution of Land Holdings of the Sample Farmers in the Study Area

Name of the Village Category of farmers
Marginal (< 1.25ha) Small (1.25 to 2.5ha) Big (>2.5ha)
Namanur 6 10 4
Kovanur 15 3 2
Mudikondum 10 7 3
Panaiyur 11 8 1
Valuthani 9 8 3
Salur 17 3 0
Total 68
(56.7) 39
(32.5) 13
(10.8)
Figures in parentheses indicate percentage to total
Distance of Sample Farms from Sluice of Tanks

Distance from sluice is very important to get water for field and also the distance decides the number of supplemental irrigation to be applied. The requirement of supplemental irrigation is less if the fields are nearer to head of the Tank and vice versa. The distances of farmers’ field from sluice of tanks are presented in Table 2. The distribution of farmers among the head, middle and tail end reach of the tank sluices were 38, 37 and 45 respectively. This clearly showed that majority of the farmers field were located at tail end of the tank sluice and the rest were equally distributed between head and middle reach from sluice.

Table 2. Distance of Sample Farms from Sluice of Tanks

Villages Head
(< 400 m) Middle
(401-800 m) Tail
(> 801 m)
Namanur 5 5 10
Kovanur 6 7 7
Mudikondum 8 7 5
Panaiyur 4 6 10
Valuthani 9 6 5
Salur 6 6 8
Total 38 37 45

Well Details of Sample Farmers

Details about wells owned by farmers are furnished in Table 3. Generally farmers owned open wells and open cum bore wells. Namanur village had more number of open wells numbering 10, while it was only one in Mudikondum village. The open cum bore wells were maximum in Salur village and they were least in Kovanur village. The average depth of wells was the highest in Salur village (18.8 m) and the least in Mudikondum village (12.7 m)

Average pumping hours of irrigation water from wells was the highest in Salur village (8 hours) during monsoon season and the least in Panaiyur village (5.10 hours per day). During non-monsoon season, the average pumping hours were the highest in Mudikondum village with 3.10 hours per day while it was the least in Panaiyur village (2.05 hours per day). The difference in pumping hours between monsoons was the highest in Salur village (5.50 hours per day) and the least in Namanur village (3.00 hours per day).
Table 3. Well Details of Sample Farmers

Name of the Villages No of
Open wells No of Open
cum
bore
wells Total no of wells Average depth of well (m) Average pumping
(in hours/day)
Monsoon Season (Sep-Dec) Non season (Jan-Aug) Difference between Monsoon and non-monsoon seasons
Namanur 10 6 16 13.0 5.15 2.15 3.00
Kovanur 5 1 6 13.6 6.00 2.00 4.00
Mudikondum 1 8 9 12.7 7.20 3.10 4.10
Panaiyur 7 4 11 13.6 5.10 2.05 3.05
Valuthani 7 2 9 13.6 7.15 2.33 4.82
Salur 0 13 13 18.8 8.00 2.50 5.50
Periodicity of Digging of Wells

It could be seen from the Table 4, that during 1980-90’s 38.1 per cent of wells were dug by the farmers. Twenty-four wells out of 63 wells were dug during this period. Next to this 31.7 per cent of wells were dug during 1970-80’s. During 1990-2000, 20.6 per cent of wells were dug and the rest were dug before 1970’s. There was no well digging activity after 2000. Thus, two third of wells were dug during 1970 to 1990 and thereafter there had been a slow down in well digging activity.
Table 4. Periodicity of Digging of Wells
(No. of wells)
Name of the Village Before 1970 1970-1980 1980-1990 1990-2000 After 2000 Total
Namanur 2 6 5 3 - 16
Kovanur - 3 1 2 - 6
Mudikondam - 3 5 1 - 9
Panaiyur 2 3 4 2 - 11
Valuthani 1 2 5 - - 8
Salur 1 3 4 5 - 13
Total 6 20 24 13 - 63
Per cent (9.5) (31.7) (38.1) (20.6) - (100.0)

Average Annual Decline of Water Table

It could be seen from the Table 5 that the average annual decline of water table was the highest in Salur village, with 0.396 m which indicated that more number of farmers resorted to groundwater use in that village. In Panaiyur and Kovanur villages the annual decline in groundwater table was 0.280 m. The least groundwater decline was recorded in Mudikondum with 0.163 m during the reference period.
Table 5. Average Depth and Decline of Water Table

Name of the Village Water Table (in mm) Average annual decline of water table (in meters)
1990 1995 2003
Namanur 9.7 11.2 13.0 0.256
Kovanur 10.0 10.9 13.6 0.280
Mudikondum 10.6 11.2 12.7 0.163
Panaiyur 10.0 11.5 13.6 0.280
Valuthani 10.2 11.2 13.6 0.221
Salur 13.6 15.8 18.8 0.396

Sample Households’ Participation in Groundwater Sales

It could be seen from Table 6 that in all the selected villages, own well water users were more in number because of demand for water for own-cultivation. Groundwater sellers sold it to neighbours because of the following reasons. 1) The lands belonging to small and marginal belonged to the poor farmers’ category do not have wells, in such a situation; well owners sold water to them. 2) The sellers reduced their own demand for water by reducing the number of irrigations, and the water thus saved was sold to other farmers.

Cost of well water varied among the villages. Also, it depended on whether the well water was pumped with electric motor or oil engine. It ranged from Rs. 20/- to Rs. 50/- for wells fitted with electric motor and oil motor. The price of well water per hour was high in Namanur village, (Rs.25/-) and low in Salur village (Rs.15/-). In wells fitted with oil motor, the farmers sold well water for Rs. 35/- to Rs. 50/- per hour which was the highest in Namanur village (Rs.50/-) and the least in Salur village (Rs. 35). There was neither selling nor buying of well water in Kovanur and Mudikondum villages because of high salt content in water.

Table 6. Sample Households’ Participation in Groundwater Sales

Name of
the Village Own users Sellers Buyers Water charge of irrigation water
with Electric motor (Rs/hr) with Oil motor
(Rs/hr)
Namanur 9 7 4 25 50
Kovanur* 6 - - - -
Mudikondam* 9 - - - -
Panaiyur 7 4 8 15-20 40-50
Valuthani 9 4 6 20 40
Salur 5 5 7 15 35
* No groundwater market emerged
Resource Use Efficiency

Out of 120 farmers, 100 were cultivating rice in tank command areas. The rest of the farmers cultivated other crops and hence resource use efficiency has been restricted for rice crop alone. Among the rice growers, 27 per cent of farmers have raised rice with tank water alone while the rest applied both tank and well water. The Cobb-Douglas production function was estimated as specified for rice growers with tank water alone as well as tank water plus well water and the results are presented in Table 7.

In case of tank water users alone, the co-efficient of multiple determinations was 0.897 which indicated that 89 per cent of variations in rice yield have been attributed by the independent variables included in the function and it was significant at one per cent probability level. Among the independent variables included in the function, area under rice and tank water application had significantly influenced rice yield at one per cent probability level. The partial regression coefficients revealed that elasticity of production for area under rice was 6.039 and 0.0393 for tank water application respectively.

The production function estimated for rice growers applying both tank and well water revealed that 77.80 per cent variation in rice yield was explained by independent variables included in the function and the function as a whole was significant at one per cent probability level. Among the explanatory variables included, the area under rice and well water application significantly influenced the rice yield at one per cent probability level while the other expenditures significantly influenced the rice yield at five per cent probability level. This showed that the availability of well water had encouraged farmers to spend more on seed, pesticides and machineries. The estimated partial regression coefficients showed the elasticity of production due to land; well water application and other expenditures were respectively 2.598, 0.276 and 0.0007.

The elasticities of production indicated that tank water, well water and other expenditures were less than one and were operating in the second zone of production. On the other hand, the elasticity of production for area under rice was more than one for both tank water users and tank and well water users. This showed that there is scope for increasing rice production through expansion of area in Sivaganga district provided the water is made available either in-sittu conditions or water application deliberately and crop management methods.

Table 7. Cobb-Douglas Production Function for Farms using Tank Water alone and Tank cum Well Water

Sl.No. Particulars Estimated partial regression
co-efficients
Tank water alone Tank and well water
1 Constant 5.4868
(10.2229) 5.6996
(6.2736)
2 Area under rice in ha 6.0359*
(1.3542) 2.5984*
(0.6899)
3 Fertilizer (N+P+K) in kg per ha 0.1090
(0.0398) 0.0934
(0.0189)
4 Labour man days per ha 0.2045
(0.1731) 0.1324
(0.173)
5 Expenditure on bullock, machine power, seeds and pesticides (Rs per ha) 0.0019
(0.0007) 0.0007**
(0.0003)
6 Tank Irrigation (ha cm) 0.0393*
(0.0454) 0.0289
(0.130)
7 Well irrigation (ha cm) NA 0.2762*
(0.0906)
N 27 73
R2 0.897* 0.778*
Figures in parentheses indicate standard errors
* Significant at 1 % level of probability
** Significant at 5% level of probability
Resource Use Efficiency of Rice Growers

Resource use efficiency of rice growers have been worked out for the resources which had significantly influenced the rice yield (Table 8). The ratio of VMP of resource to their price indicated that for farmers using only tank water, both of the area and tank water resource are over utilized. The ratio of VMP of resources to their price estimated for farmers using tank cum well also indicated the over utilization of land and other expenditures whereas underutilization of well water.
Table 8. Resource Use Efficiency of Rice Growers

A. Tank water alone
VMP Px VMP/Px
Land 4.57 1500* 0.003
Tank Irrigation 0.99 4 0.25
B. Tank cum well water
Land 2.5 1500* 0.002
Well Irrigation 15.16 15 1.01
Other expenditures. 0.58 912.0 0.0007

* Rental value of land was taken as the price of land
Marginal product=Elasticity* Geometric mean
VMP valued at output price of rice
Maximum Likelihood Estimator Method for Production Function for Farms using Tank Water alone and Tank cum Well Water

It could be seen from the Table 9 that the estimated discrepancy parameter (?) was 0.9703 and 0.9521 for tank water alone and tank cum well water application respectively. This implied that deviation in the output from the frontier yield was mainly due to technical inefficiency at the farmers’ level. The Mean Technical Efficiency was 0.3996 and 0.6248 respectively for tank water alone and tank cum well water applying farms. This implied that yield was 60 percent less than the maximum possible output for only tank water using farmers and 38 per cent less than the maximum possible output for tank cum well water using farmers. The low technical efficiency was due to inadequate water during crop period in the former category. Besides uncertainty in rainfall and poor filling of tanks had led to these problems.
Table 9. Maximum Likelihood Estimator Method for Production Function for Farms using Tank Water alone and Tank cum Well Water

Sl.No. Particulars Estimated partial regression coefficients
Tank water alone Tank and well water
1 Constant 6.5177
(7.1483) 6.2553
(4.7527)
2 Area under paddy in ha 4.9367*
(1.1734) 2.6494**
(1.1245)
3 Fertilizer (N+P+K) in kg per ha 0.1035
(0.0246) 0.0604*
(0.0156)
4 Labour man days per ha 0.1126**
(0.0578) 0.0232**
(0.0093)
5 Expenditure on bullock power, machine power, seeds and pesticides (Rs per ha) 0.0009
(0.0007) 0.0013*
(0.0005)
6 Tank Irrigation (ha cm) 0.0429*
(0.0129) 0.0304
(0.0689)
7 Well irrigation (ha cm) NA 0.6742*
(0.1603)
8 ?2u 1.7776 0.6788
9 ?2v 0.0544 0.0342
10 ?=?u / ?v
5.7153 4.4559
11 ?=?2u/ (?2u +?2v)
0.9703 0.9521
12 MTE=1- ?u?2/?
0.3996 0.6248

Figures in parentheses indicate standard errors
* 1% level of significant level
** 5% level of significant level
NA : Not Applied
Technical Efficiencies of Rice Growers

The farm specific technical efficiency is furnished in Table 10. It was found that a majority of farmers (55.6 per cent) using only tank water were operating at 40-50 per cent technical efficiency level. On the contrary, majority of the farmers (52.1 per cent) using tank cum well water, were operating at 70-80 per cent technical efficiency and 15.1 per cent of farmers were operating most efficient category (80-90 percent). This indicated that there is scope to improve the productivity of the rice farmers. Identification of farms, which lead to variation in the farm specific technical efficiency, is an important issue for formulating strategies to increase the productivity.
Farms Irrigated by Tank water alone

Figure 1 Technical Efficiency of Tank water alone irrigated Farmers

Farms Irrigated by Tank water and well water

Figure 2 Technical Efficiency of Tank and well water irrigated Farmers

Table 10. Technical Efficiencies of Rice Producing Farmers
(in numbers)
Sl.No. Technical efficiency of Rice growers Only tank water using farmers Tank and well water using farmers
1 <40 2
(7.4) -
2 40-50 15
(55.5) -
3 50-60 7
(25.9) 4
(5.5)
4 60-70 3
(11.1) 20
(27.4)
5 70-80 - 38
(52.1)
6 80-90 - 11
(15.1)
7 90-100 - -

Total 27
(100.0) 73
(100.0)

Figures in parentheses indicate percentage
Returns to Supplemental Irrigation

The supplemental irrigation became necessary when the tank water was not available for the entire rice growing period. When the rice growers could not supplement well water with tank water, they had to either harvest reduced yields or in several cases they had to abandon their entire standing crops. Thus, all the expenses incurred for the crop cultivation could not be recovered. The demand for supplemental irrigation depends upon the variety and duration of the crop and the yield depends on the number of supplemental irrigation provided.
In the tanks chosen for the present study, the rainfall received was less than normal rainfall in 2002-2003 and hence tank filling was less than full capacity. Therefore supplemental irrigation with well water played a vital role in obtaining increased rice yield.
Mean Irrigation Water Applied by Different Rice Growers

Table 11 shows the mean water applied for irrigation by rice farmers in tank command area. Farmers using tank water alone had applied 25.70 ha cm and tank cum well water users applied 23.24 ha cm of tank water and 50.95 ha cm of well water. Tank cum well users have applied 50.95 ha cm of well water over and above the tank water. Generally, farmers using tank water alone cultivated semi-dry rice varieties like PMK 1, PMK 2, MDU 5, IR 20, IR 36, ADT 36, ADT 39, ADT 43 and CO 43.
Table 11. Mean Irrigation Water Applied by Different Rice Growers
(ha cm)
Users Numbers Tank water Well water Total water applied
Only tank water 27 25.70 0 25.70
Tank cum well users 73 23.24 50.95 74.19
Mean Irrigation Water Application by Well Water Users

Table 12 shows the mean irrigated water applied by well water buyers and sellers. Farmers selling well water applied 19.67 ha cm of tank water and 53.53 ha cm of well water respectively to rice crop. On the other hand the well water buyer applied 16.94 ha cm of tank water and 43.78 ha cm of well water to rice crop and own users were applied 21.86 ha cm of tank water and 53.44 ha cm of well water to rice crop. The total water applied by well water buyers were 60.72 ha cm less than the well water sellers.
Table 12. Mean Irrigation Water used by Sellers and Buyers
( ha cm)
Groundwater users Number of farmers Tank water Well water Total
Sellers 16 19.67 53.53 73.20
Buyers 18 16.94 43.78 60.72
Own users 39 21.86 53.44 42.77
Comparison of Well Owners with Non-Well Owners

It could be seen from the Table 13 that the rice yield obtained with supplemental irrigation (5003 kg) was more than farmers using only tank water (3511 kg). Availability of well water for supplementing tank water enhanced the use of other inputs like fertilizers, labour man days and plant protection chemicals.
Table 13 Comparison of Well Owners with Non-Well Owners

Sl.No Variables Tank cum well water Tank water only
1 Total water applied (ha cm) 74.19 25.74
2 Lobour ( man days per ha) 197.3 170.7
3 (N+P+K) kg per ha 144.1 183.0
4
Other Expenditure on bullock, machine power, seed and plant protection chemicals (Rs / ha) 5742 4865
5 Rice yield (kg/ ha) 5003 3511
6 Rice yield (kg/ ha cm) 67.54 136.62

The water consumption for rice in tank cum well water situation was 74.19 ha cm whereas it was 25.74 ha cm for tank water situation. Rice yield per unit quantity of water was the highest at 136.62 kg per ha cm for farmers using tank water alone.
Average Returns to Supplemental Irrigation

It could be seen from the Table 14 that without supplemental irrigation yield of rice obtained as low as 2437 kg per hectare. As the number of supplemental irrigation increased the rice yield up to 10-12 supplemental irrigations and then declined. Likewise the average net return from rice cultivation has increased until 10-12 supplemental irrigations. The estimates of returns per supplemental irrigation applied were the highest for 10-12 supplemental irrigation.

Table 14 Average Returns to the Supplemental Irrigation

Sl.No Number of supplemental irrigations Well water
Applied
(ha cm) Rice yield
(kg/ha) Gross return
(Rs/ha) Net return (Rs/ha) Average return per irrigation
(Rs)
1 0 - 2437.5 12187.5 2737.0 -
2 2-4 8-16 3250.0 14200.0 3750.0 337.67
3 4-6 16-32 3737.0 16489.0 4929.0 438.40
4 6-8 32-48 4062.0 18500.0 6380.0 520.43
5 8-10 48-54 4875.0 21457 7806.0 563.22
6 10-12 54-60 5687.5 23789.0 9268.0 593.73
7 12-14 61-76 5118.75 24700.0 9911.0 551.85

Section IV Conclusions
1. Area under rice occupied 75 per cent of Gross Cropped Area in tank command areas. Only 27 farmers out of 100 got tank water for rice cultivation while the rest used well water as supplemental irrigation.
2. Inadequate tank water developed groundwater market where in there were 16 water sellers, 18 buyers and 39 own users of well water for crop cultivation.
3. In farms using tank water alone, the yield was significantly influenced by variables the area under rice and tank water application. In tank cum well water using farms, the yield was significantly influenced by variables the area under rice, well water and expenditure on bullock, machine power, seeds and plant protection chemicals.
4. In farms using only tank water, among the variables land and tank irrigation, significantly influenced the rice yield, both area under rice and irrigation were over utilized. The variables, other expenditures and area under rice were under utilized in farms using tank cum well water situation while, well irrigation was over utilized.
5. The technical efficiency, analysis showed that 60 per cent less than maximum possible output was achieved in tank water alone using farms and 38 per cent less than maximum possible output in tank cum well water using farms. Grouping of farmers based on efficiency showed 55.6 per cent of farmers operated at 40-50 per cent efficiency level in tank water alone situation. On the other hand, tank cum well water situation, 52 per cent of farmers’ efficiency operated at the between 70 and 80 per cent category.
6. An average return from supplemental well irrigation was increased up to 10-12 irrigation and thereafter it was decreased. Returns per supplemental irrigation increased up to six supplemental irrigations (Rs 337.67/- to Rs 593.73/-.) and decreased thereafter. (Rs 593.73/- to Rs 551.85/-)

Policy implications

? The elasticity of production was more than one for farms using tank water alone and tank cum well water. The resource use efficiency was over utilized for land tank water. Hence, there is scope to improve the rice yield by adopting improved land, water and crop management practices. Some of the proven technologies in the research stations are paddy drum seeding, seed broadcasting and aerobic seeding. For transplanted rice cultivation water depth maintenance in the field at five centimeters and irrigating at hair line crack of soils are advocated. These technologies can be demonstrated to farmers to enhance the technical efficiency in rice cultivation.

? Even though the returns to vegetable crops and sugarcane were more, the 75 per cent of farmers’ rice still preferred rice because of ready market for it; it’s by product use for livestock rearing, short duration of crop. Hence, crop diversification in tank command areas should address the fodder and feed for livestock, short duration of crop and marketability of produce throughout the year.

? The returns to supplemental irrigation were increasing up to 10 to 12 irrigations and decreased thereafter. The groundwater development has created a groundwater market but further extraction of groundwater will be haunted by salinity due to further deepening. The groundwater market sustainability is dependent on tank water availability for more number of months in a year. Hence, farmers using groundwater as supplemental irrigation should be educated on the tank-groundwater recharge relationship and motivated to take part in maintenance of tanks.

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