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The economics of groundwater irrigation in the Indus Basin,Pakistan: tube-well adoption, technical and irrigation waterefficiency and optimal allocationWatto, A. (2015). The economics of groundwater irrigation in the Indus Basin, Pakistan: tube-well adoption,technical and irrigation water efficiency and optimal allocation

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The Economics of Groundwater Irrigation in the Indus Basin, Pakistan: Tube-well Adoption, Technical and Irrigation Water Efficiency and Optimal Allocation

Muhammad Arif Watto

M.Sc. (Hons.) Agricultural Extension Education, University of

Agriculture, Faisalabad, Pakistan

Thesis presented for the degree of

Doctor of Philosophy

The University of Western Australia

School of Agricultural and Resource Economics

2015

“When the well is dry, we know the worth of water.” (Benjamin Franklin, 1746)

Declaration for thesis containing published work and/or work prepared for publication

This thesis contains published work and/or work prepared for publication, some of which

has been co-authored. The bibliographical details of the work and where it appears in the

thesis are outlined below. The student must attach to this declaration a statement for each

publication that clarifies the contribution of the student to the work. This may be in the

form of a description of the precise contributions of the student to the published work

and/or a statement of percent contribution by the student. This statement must be signed

by all authors. If signatures from all the authors cannot be obtained, the statement

detailing the student’s contribution to the published work must be signed by the

coordinating supervisor.

Journal Publications

1. Watto, M.A., and Mugera, A.W. (2014). Groundwater depletion in the Indus plains of Pakistan: Imperatives, repercussions and management issues. International Journal of River Basin Management (forthcoming).

M.A. Watto contributed 70%

2. Watto, M.A., Mugera, A.W. and Kingwell, R. (2014). Adoption of tube-well technology under the groundwater depletion risk: Evidences from Punjab, Pakistan.(Submitted for publication to Journal of Hydrology)


3. Watto, M.A., and Mugera, A.W. (2014). Wheat farming system performance and irrigation efficiency: A nonparametric metafrontier approach. International Transactions in Operation Research. (Accepted for Publication)


4. Watto, M.A., and Mugera, A.W. (2014). Econometric estimation of technical and irrigation efficiency of groundwater irrigated cotton cultivation in Pakistan. Journal of Hydrology: Regional Studies. doi:10.1016/j.ejrh.2014.11.001


5. Watto, M.A., and Mugera, A.W. (2014). Measuring production and irrigation efficiencies of rice farms: Evidence from the Punjab, Pakistan. Asian Economic Journal, Vol. 28 (3): 301–322.


http://dx.doi.org/10.1016/j.ejrh.2014.11.001

6. Watto, M.A., and Mugera, A.W. (2014). Irrigation water demand and implications for groundwater pricing in Pakistan.(submitted for publication to Water Policy)


Conference Papers

7. Watto, M.A. and Mugera, A.W. 2012. Measuring groundwater irrigation efficiency in Pakistan: A DEA approach using the sub-vector and slack-based models. Presented at 57th Annual AARES Conference at Sydney, Australia 4-9 February, 2013.


8. Watto, M.A. and Mugera, A.W. 2014. Does the risk of groundwater depletion drive tube-well technology adoption? A case of Pakistan. HIC-2014, International Conference on Hydroinformatics, New York, USA August 17-21.


Student signature…………………………………………………………..

Coordinating supervisor signature………………………………………...

I

Certification

I certify that this thesis has been substantially completed during the course of enrolment

in this degree at The University of Western Australia and has not previously been

submitted or accepted for a degree at this or any other institution. I certify that help

received in preparing this thesis and all sources used have been acknowledged.

Arif Watto

Perth, May, 2015

III

Acknowledgements

I would like to express my sincere gratitude and indebtedness to all who guided and

facilitated me during my journey of doctoral study. First, this study was made possible

by the generous support from the University of Agriculture, Faisalabad, Pakistan and

from the University of Western Australia. Many thanks to Professor Iqrar Ahmad Khan

(Vice Chancellor, UAF) and Professor Kadambot Siddique (Director Institute of

Agriculture, UWA) for organizing the UWA Ad Hoc UAF Pakistan programme.

Second, I’m highly indebted to my coordinating supervisor Dr Amin W. Mugera for his

guidance early from my writing the research proposal and to the completion of this

dissertation. The patience, kindness and friendship I received from my supervisor made

my PhD experience an enjoyable one. I never impaired by diminution but always

remained committed and motivated throughout my doctoral study under his supervision.

I would always remember his indefatigable assistance for the completion of this

dissertation.

I also benefitted immensely from Professor Ross Kingwell from his remarkable

knowledge. I really acknowledge his assistance and value his suggestions to improve

this dissertation. I owe many thanks to the former and present heads of school Ben

White and David Pannell for their timely support and guidance. Many thanks to

Atakelty Hailu, Jo Pluske, Ram Pandat and Chunbo Ma, members of the School of

Agricultural and Resource Economics (SARE) postgraduate progress review committee

for their keen interest in my studies, research endeavours and personal well-being. I also

want to thank all SARE staff for the encouragement, support and friendship I received

from all of them. I am thankful to Deborah Swindles, Emma Smith and Theresa Goh for

their all types of administrative support. I also want to acknowledge the friendship and

wonderful time I had with Tas Thamo, Manoj Thibbotuwawa, Khalid Bashir, Donkor

Adai, and Katrina Davis in the ‘Cabinet Room’. Let me mention my other SARE

colleagues especially Masood Azeem, Luke Abatania, Tran Doc Lap, Alison Wilson

and Veronique Florec for their friendship and moral support. I will forever cherish the

friendship that you accorded me.

I also owe a debt of gratitude to my teachers Dr Tanvir Ali, Dr Munir Ahmad, Dr Babar

Shahbaz, Dr Gazanfar Ali and Dr Amir Shah who were guiding lights for me. I also

want to acknowledge my friendship with Arbab, Mudasar, Umar, Asim, Ayaz and many

more who made my stay at Perth memorable and many thanks to Dr Shoukat Ali, Dr

IV

Sajid and Asif Iqbal from Pakistan who kept sending best wishes to me at every

occasion.

Finally, I want to express my sincerest gratitude to my mother, brothers Asif and Bilal

and sisters for their unwavering moral support and best wishes.

V

Abstract

This PhD study explored the economics of groundwater irrigation in the Indus basin of

Pakistan where groundwater exploitation is escalating due to high irrigation water

demands. Recent trends in groundwater withdrawals for irrigation and increases in

number of tube-wells have brought into greater prominence the challenge to control

groundwater over-exploitation. Besides this, hydrological assessments indicate that

groundwater extraction rates have exceeded the annual recharge rates the available

literature highlights the inefficient use of water resources in the irrigation sector.

This study had four main objectives: 1) to review the causes and consequences of

groundwater overdrafting in the region; 2) to investigate farmers’ adoption decisions

regarding tube-well technology; 3) to analyse irrigation water use efficiency for

different crop enterprises; and 4) to estimate the derived demand for irrigation. Data

used for analyses come from a survey of 200 rural households that predominately use

groundwater for irrigation in the arid to semi-arid plains of the Punjab province of

Pakistan.

The review found that groundwater expansion in the Indus basin was mainly as a result

of the rigidity of the surface water allocation system, increased crop intensities during

the Green Revolution and the division of the Indus river tributaries under the Indus

Water Treaty in the 1960s. Later, overexploitation of groundwater was as a result of

increase in population and lack of effective groundwater management policies.

A moment-based approach was used to analyse farmers’ decisions to adopt tube-well

technology when groundwater table is declining. The estimation procedure consisted of

two steps. First, the moments of profit distribution were computed using an expected

utility maximization framework. In the next step, the estimated moments were

incorporated into a probit model to estimate their impact on tube-well adoption

decisions. Analysis of tube-well adoption decision reveals that farmers are not risk-

neutral. The results indicate that the probability of tube-well adoption increases

significantly with increase in expected mean and variance of profit. The non-significant

third moment (skewness) indicates that downside profit risk does not have significant

impact on tube-well adoption. The highly significant fourth moment (kurtosis) indicates

that adoption of tube-well technology decreases significantly in the presence of extreme

events.

VI

Both parametric (stochastic frontier analysis) and non-parametric (data envelopment

analysis) approaches were used to estimate technical and irrigation water use efficiency

separately for tube-well owners (adopters) and water buyers (non-adopters) for different

crops viz. wheat, cotton and rice. Results indicate that the average technical efficiency

(TE) scores are fairly high but there are still inefficiencies in the production of all crops.

The TE efficiency distribution for both tube-well owners is right skewed suggesting that

tube-well owners are slightly more efficient compared to water buyers. The highest

level of TE is in rice production and lowest is in cotton farming. The estimated mean

irrigation water efficiency (IWE) of tube-well owners and water buyers are less than

their respective technical efficiencies. On average, tube-well owners are more irrigation

water-use efficient than water buyers. Again the lowest IWE is in cotton farming and

the highest is in rice farming. The IWE estimates of all the three crops (wheat, cotton

and rice) suggest that there is considerable potential to improve irrigation water use

efficiency for both tube-well owners and water buyers.

The derived demand for groundwater was estimated using the Positive Mathematical

Programming (PMP) approach. The estimates indicate that the actual crop water

requirement is lower than the amount of groundwater that is being extracted for

irrigation. Given the limited land available for different crops, additional irrigation

water supply would not increase farm profit. Therefore, producers would not respond to

any pricing policy unless their current groundwater extraction rate is constrained to

certain limits. It is suggested that by introducing groundwater pricing at Rs. 0.04/ m3 for

water sellers and Rs. 0.036/m3 for water buyers could induce them to reduce their

current groundwater extraction rates by 2%.

Results from this study have policy implications. First, farmers adopt the tube-well

technology to overcome crop production risk and variability in farm profits. However,

tube-well adoption does not necessarily improve irrigation water use efficiency nor

conserve the groundwater resources. Therefore, tube-well adoption must be

accompanied by complimentary policies that promote efficient use of groundwater for

irrigation, such as adoption of sprinkler or drip irrigation technologies, and limit

extraction in order to ensure the sustainability of groundwater resources. Second, there

is need for policies that educate farmers on actual crop water requirements as a way to

promote irrigation water use efficiency. This may involve extending extension advice

from crop management to groundwater management or creating a separate water

extension wing. Third, to ensure sustainable groundwater use, there is need for policies

VII

that will constrain groundwater extraction. This could involve metering of groundwater

extraction and pricing to induce farmers to reduce irrigation water demands. Finally,

additional policies are also required to improve equity of access for water buyers who

generally face more irrigation water uncertainties being located down the water supply

chain.

IX

Table of Contents

Certification ....................................................................................................................... I

Acknowledgements ......................................................................................................... III

Abstract ............................................................................................................................ V

Table of Contents ............................................................................................................ IX

List of Tables ............................................................................................................... XIII

List of Figures ............................................................................................................... XV

1. Introduction ............................................................................................................. 1

1.1 Overview ........................................................................................................... 1

1.2 Context and Problem Statement ........................................................................ 2

1.3 Conceptual Framework ..................................................................................... 5

1.4 Research Objectives .......................................................................................... 6

1.5 Data Description and Research Design ............................................................. 7

1.6 Contribution to Scholarship and Originality ..................................................... 8

1.7 Thesis Organisation ........................................................................................... 9

2. Imperatives and Repercussions of Groundwater Depletion ............................. 11

Abstract ....................................................................................................................... 11

2.1 Introduction ..................................................................................................... 12

2.2 Groundwater from Menace to Mainstay ......................................................... 13

2.3 Warabandi- A Rigid Irrigation Delivery System ............................................ 15

2.4 The Green Revolution ..................................................................................... 16

2.5 The Indus Water Treaty and Beyond the Indus Water Treaty ........................ 17

2.6 Myopic Groundwater Policies ........................................................................ 18

2.7 Population Growth and Water Demands ........................................................ 20

2.8 Lack of Surface Water Developments ............................................................ 21

2.9 Groundwater Overuse Externalities ................................................................ 22

2.10 Environmental Externalities ............................................................................ 23

2.10.1 Soil Salinization ...................................................................................... 23

2.10.2 Land Subsidence ..................................................................................... 24

2.10.3 Seawater Intrusion ................................................................................... 24

2.10.4 Drying of Wetlands and Vegetation ........................................................ 25

2.11 Economic Externalities ................................................................................... 25

2.12 Spatial Externalities ........................................................................................ 26

2.13 Groundwater Management Problems .............................................................. 28

2.13.1 Institutional Impediments ....................................................................... 28

X

2.13.2 Lack of Entitlements and Informal Groundwater Marketing .................. 29

2.13.3 Irrigation Efficiencies and Water Productivity ....................................... 30

2.14 Conclusions ..................................................................................................... 32

3. The Groundwater Depletion Risk and Tube-well Technology Adoption ........ 35

Abstract ....................................................................................................................... 35

3.1 Introduction ..................................................................................................... 35

3.2 Literature Review on Irrigation Technology Adoption .................................. 38

3.2.1 Adoption of Tube-well Technology in Pakistan ......................................... 40

3.3 Theoretical Framework ................................................................................... 42

3.3.1 Empirical Estimation Procedure ................................................................. 44

3.4 Data Descriptions ............................................................................................ 47

3.4.1 Salient Features of Study Districts .............................................................. 47

3.4.2 Data Descriptions ........................................................................................ 49

3.5 Results and Discussion ................................................................................... 52

3.6 Conclusions ..................................................................................................... 56

4. The Efficiency of Irrigation Water Use and its Determinants .......................... 59

4.1 Technical and Irrigation Efficiency of Wheat Farms in Pakistan: A

Nonparametric Meta-frontier Approach ..................................................................... 61

Abstract ................................................................................................................... 63

4.1.1 Introduction ................................................................................................. 63

4.1.2 Methodological Framework ........................................................................ 66

4.1.3 Methodological Framework ........................................................................ 69

4.1.4 Study Areas, Data and Variable Definitions ............................................... 73

4.1.5 Empirical Results and Discussion ............................................................... 75

4.1.6 Conclusion .................................................................................................. 82

4.2 Econometric Approach to Estimating Technical and Irrigation Efficiency in

Cotton Farming in Pakistan ......................................................................................... 85

Abstract ................................................................................................................... 87

4.2.1 Introduction ................................................................................................. 87

4.2.2 Conceptual and Methodological Framework .............................................. 91

4.2.3 Study Area and Data ................................................................................... 96

4.2.4 Estimation Results ...................................................................................... 99

4.2.5 Discussion and Conclusions ..................................................................... 104

XI

4.3 Measuring Production and Irrigation Efficiencies of Rice Farms: Evidence

from Punjab, Pakistan ............................................................................................... 109

Abstract ................................................................................................................. 111

4.3.1 Introduction ............................................................................................... 111

4.3.2 Review of Literature ................................................................................. 113

4.3.3 Methodological Framework ...................................................................... 115

4.3.4 Study Area and Data ................................................................................. 120

4.3.5 Results and Discussion .............................................................................. 123

4.3.6 Conclusion ................................................................................................ 129

5. Derived Demand for Irrigation Water .............................................................. 131

Abstract ..................................................................................................................... 131

5.1 Introduction ................................................................................................... 131

5.2 Irrigation Water Pricing and Demand ........................................................... 134

5.3 Theoretical Framework ................................................................................. 136

5.3.1 Approaches to Derive Demand for Irrigation Water ................................. 136

5.3.2 Method of Analysis-Positive Mathematical Programming (PMP) ........... 137

5.4 Study Areas and Data Description ................................................................ 139

5.4.1 Nature of Irrigation Water Demand in the Central and South Punjab ...... 139

5.5 Results and Discussion .................................................................................. 142

5.6 Conclusions ................................................................................................... 149

6. Conclusions .......................................................................................................... 151

6.1 Summary ....................................................................................................... 151

6.2 Methods ......................................................................................................... 152

6.3 Main Results ................................................................................................. 153

6.4 Synthesis of Main Findings ........................................................................... 155

6.5 Policy Recommendations .............................................................................. 157

6.6 Limitations and Future Research Needs ....................................................... 158

7. References ............................................................................................................ 159

8. Appendix .............................................................................................................. 177

XIII

List of Tables

Table 3.1: Summary statistics of the variables for cotton and wheat crops .................... 50

Table 3.2: Summary statistics of the variables used in the probability model ................ 51

Table 3.3: Estimation of the results for the probability of adopting a tube-well ............ 53

Table 3.4: Marginal effects of the explanatory variables ................................................ 55

Table 4.1.1: Descriptive statistics of the variables used in the DEA analysis ................ 75

Table 4.1.2: Metafrontier and groupfrontier technical efficiency frequency distribution

......................................................................................................................................... 76

Table 4.1.3: Average groupfrontier and metafrontier technical efficiency scores and the

technology gap ratio ........................................................................................................ 77

Table 4.1.4: Frequency distribution of irrigation water use efficiency under the

metafrontier and groupfrontiers ...................................................................................... 78

Table 4.1.5: Spearman’s rank correlation among technical efficiency and the sub-vector

irrigation water use efficiencies ...................................................................................... 79

Table 4.1.6: Bootstrap truncated estimates of the determinants of technical and

irrigation water use efficiency ......................................................................................... 81

Table 4.2.1: Summary statistics of the variables used in the empirical model ............... 98

Table 4.2.2: Restricted and unrestricted model parameter estimates ............................ 100

Table 4.2.3: Inefficiency model estimates .................................................................... 101

Table 4.2.4: Proportion of farms satisfying the monotonicity and quasi-concavity

conditions ...................................................................................................................... 101

Table 4.2.5: Partial production elasticities for the sample mean from the unrestricted

and restricted models .................................................................................................... 102

Table 4.2.6: Frequency distribution of technical and irrigation water use efficiency for

tube-well owners from the unrestricted and the restricted models ............................... 103

Table 4.2.7: Frequency distribution of technical and irrigation water use efficiency for

water buyers from the unrestricted and the restricted models....................................... 103

Table 4.3.1: Descriptive statistics of the variables used in the DEA analysis .............. 121

Table 4.3.2: Summary statistics of variables included in the truncated regression ...... 123

XIV

Table 4.3.3: Frequency distribution of technical, scale, cost and allocative efficiencies

....................................................................................................................................... 124

Table 4.3.4: Distribution of returns to scale for tube wells owners and water buyers .. 124

Table 4.3.5: Frequency distribution of sub-vector and slack-based water use efficiencies

....................................................................................................................................... 125

Table 4.3.6: Spearman’s rank correlation among technical efficiency and the sub-vector

and slack-based irrigation water use efficiencies .......................................................... 126

Table 4.3.7: Paired samples t-test demonstrating the difference between technical and

irrigation water use efficiencies .................................................................................... 126

Table 4.3.8: Bootstrap truncated estimates of the determinants of technical and

irrigation water use efficiency....................................................................................... 128

Table 5.1: Number of sample households that grew wheat and cotton in the Lodhran and

Jhang districts during 2010-2011 .................................................................................. 140

Table 5.2: Area allocation to different crops, yield, irrigation water requirements and

crop prices ..................................................................................................................... 141

Table 5.3: Input cost for different farm operations in Rs.ha-1 ....................................... 142

Table 5.4: PMP step 1, water sellers ............................................................................. 143

Table 5.5: PMP step 1, water buyers ............................................................................ 143

Table 5.6: PMP Step 2, dual multipliers, yield slope coefficient and the intercept

coefficient...................................................................................................................... 144

Table 5.7: PMP Step 3, water sellers ............................................................................ 145

Table 5.8: PMP step 3, water buyers ............................................................................ 146

Table 5.9: Percent change in water demand given and the percent change in shadow

price ............................................................................................................................... 148

XV

List of Figures

Figure 1.1: Conceptual framework for the study .............................................................. 5

Figure 1.2: Map showing study districts (Jhang and Lodhran) in red colour ................... 8

Figure 2.1: Historical development of tube-wells and share of groundwater in irrigated

agriculture in Pakistan ..................................................................................................... 14

Figure 2.2: The red depression highlights groundwater depletion rates with expanding

sphere towards Pakistan side ........................................................................................... 18

Figure 2.3: Groundwater use trends in Pakistan ............................................................. 20

Figure 2.4: Different water shortage versus population growth projections ................... 21

Figure 2.5: Storage loss in the capacity of different dams in Pakistan ........................... 22

Figure 2.6: Province wise soil salinity status for the period 2001-04 ............................. 24

Figure 2.7: Variability of the groundwater recharge from head to tail of the LBD canal

command ......................................................................................................................... 27

Figure 2.8: Water productivity as a ratio of total GDP to the total annual water

withdrawals ..................................................................................................................... 31

Figure 2.9: Yield (in kg/ha) in the selected countries ..................................................... 32

Figure 4.1.1: Graphical representation of the technical and sub-vector irrigation water

use efficiency .................................................................................................................. 71

Figure 4.1.2: Cumulative distribution of meta-frontier and group-frontier technical

efficiency ......................................................................................................................... 77

Figure 4.1.3: Cumulative distribution of metafrontier and groupfrontier irrigation water

use efficiency .................................................................................................................. 80

Figure 4.2.1: Historical trends in cotton production and consumption in Pakistan ........ 89

Figure 4.2.2: Graphical representation of irrigation water use efficiency ...................... 91

Figure 4.2.3: Technical efficiency estimates from the restricted and the unrestricted

models ........................................................................................................................... 104

Figure 4.2.4: Irrigation water use efficiency estimates from the restricted and the

unrestricted models ....................................................................................................... 104

XVI

Figure 4.3.1: Graphical representation of the sub-vector and the slack-based input

oriented efficiency models ............................................................................................ 117

Figure 4.3.2: Cumulative distribution for technical, sub-vector and slack-based

irrigation water use efficiencies .................................................................................... 126

Figure 5.1: Derived demand for groundwater for irrigation for water sellers .............. 147

Figure 5.2: Derived demand for groundwater for irrigation for water buyers .............. 147

1

CHAPTER 1 1. Introduction

1.1 Overview

Water is a critical resource for a variety of users including irrigators, residential

customers and industrial producers. It is expected that the future water demand and

supply relationship will be greatly influenced by human actions that would greatly

affect water availability and accessibility (Barnett et al., 2005, Laghari et al., 2012,

Sharma et al., 2010, Karagiannis et al., 2003, Hanjra and Gichuki, 2008, Tilman et al.,

2002, Vörösmarty et al., 2000, Archer et al., 2010). Consequently, one third of the

population living in developing countries will be facing water scarcity by 2025 (Molden

et al., 2001). Indeed, water scarcity is also projected to greatly increase inter-sectoral

water competition. Hence, addressing and managing water scarcity has become a

critical policy challenge on international and national agendas. Worldwide, growing

water scarcity raises two important concerns on how to: (i) use available water

resources more efficiently and sustainably and; (ii) find possible ways to address and

manage water scarcity to meet inter-sectoral multiple water demands equitably.

The agriculture sector is by far the largest user of global freshwater withdrawals.

Agricultural intensification has led to a rapid increase in irrigation water demands in

many parts of the world (Yang et al., 2003, Koundouri et al., 2006, Jara-Rojas et al.,

2013, Biswas, 1993). The sector withdraws about 70% of the global water resources and

this is expected to further increase by 28% in 2025 relative to 1995 under the business

as usual scenario1(Rosegrant et al., 2002). Nevertheless, increasing inter-sectoral water

demands and growing awareness of environmental and in-stream water values have

increased pressure to reduce water diversions for the agriculture sector (Seo et al., 2008,

Hussain and Hanjra, 2004, Malano et al., 2004, Wichelns, 2002). Because of rapid

population growth, water demands for industrial and domestic consumption, together

with livestock consumption, would dramatically increase by 62% in 2025 relative to

1995 (Rosegrant et al., 2002). Within these prospects, increasing water scarcity would

require a re-allocation of water resources from low to high valued users and agriculture

1Based on the assumption that current trends in agricultural water use can be extrapolated—that reservoirs will be constructed as in the past, Shiklomanov (1999) projected that the world’s irrigated area will expand by 30% and irrigation water withdrawal will increase by 28% from 1995 to 2025.

Introduction

2

would obviously be perceived as an inefficient water user (Donohew, 2009).

Consequently, irrigated agriculture would be under great pressure to improve on its

water efficiency and productivity (Wichelns, 2002, Malano et al., 2004). Many studies

from different parts of the world already suggest considerable scope for improving

efficiency and productivity of water in irrigated agriculture (Cai et al., 2003, Gleick,

2000, Molden et al., 2010).

1.2 Context and Problem Statement

As in many parts of the world, water scarcity is one of the key factors constraining

agricultural development in the Indus river basin of Pakistan. Pakistan’s current per

capita water availability of about 1,066 m3 has placed it in the “high water stress

category” (Govt. of Pakistan, 2009). Water resources in Pakistan are under extreme

pressure from massive population growth, rapid industrial and domestic demands and

climate change (Archer et al., 2010, Barnett et al., 2005, Laghari et al., 2012, Sharma et

al., 2010). Archer et al. (2010) appraised various per capita water availabilities for

different population projections in Pakistan. They estimated a per capita water

availability of 725 m3 for 2025 and 415 m3 for 2050 if population continues to increase

under the current population growth rate. Indeed, these estimates indicate severe water

scarcity by any standard2 (Gleick, 1992, Postel, 1999).

Due to the arid and semi-arid climate, agriculture in Pakistan heavily relies on irrigation

water from both canal water supplies and groundwater withdrawals. Nevertheless,

surface water resources are deficient and are unevenly distributed across the Indus basin

of Pakistan. Inadequate and uneven canal water supplies have led farmers to meet their

irrigation water requirements through massive groundwater extractions. Over the past

half century, groundwater-fed irrigation has become the mainstay of irrigated

agriculture in Pakistan. Currently, more than 50% of the country’s irrigation

requirements are being met through groundwater extractions (Archer et al., 2010,

Qureshi et al., 2009).

2Water scarcity refers to a situation where there is insufficient water to satisfy normal human water needs for food, feed, drinking and other uses, implying an excess of water demand over available supply. It is a relative concept, therefore, difficult to capture in single indices.

Introduction

3

The remarkable increase in groundwater use commenced after the 1960s with the

installation of large capacity (0.084 to0.14 m3 sec-1) tube-wells3. During the first half of

19th century, the introduction of gravity based large scale irrigation system without

proper drainage system resulted in rising groundwater tables and, consequently, turned

large tracts of irrigated lands into waterlogged soils. In 1960, the government of

Pakistan initiated numerous schemes to overcome waterlogging and salinity problems

including mega salinity control and reclamation programme (SCARP) to lower

groundwater tables through vertical drainage in the form of thousands of shallow tube-

wells. The SCAPR tube-wells not only helped reclaiming waterlogged and saline soils

but also augmented irrigation water supplies by many times. The successful

demonstration of the benefits of SCARP tube-wells set the scene for rapid development

of private tube-wells in coming years. The government encouraged the adoption of

private tube-wells on a large scale with the aim of controlling the rising groundwater

tables in the waterlogged areas and encouraging agricultural production in fresh

groundwater areas (Steenbergen and Oliemans, 2002). The adoption of tube-well

technology was largely aided by government support policies such as rural

electrification, subsidization of electricity, diesel and drilling services, provision of free

pump sets and soft long-term loans (Falcon and Gotsch, 1968, Papanek, 1968, van

Steenbergen and Oliemans, 2002, Johnson, 1989 ). Later, higher crop yields and greater

economic returns from groundwater use (Meinzen-Dick, 1996, Byrelle and Siddiq,

1994) encouraged farmers to adopt tube-well technology even without government’s

support (Muhammad, 1964, Muhammad, 1965, Falcon and Gotsch, 1968, Nulty, 1972).

Subsequently, with the continued increase in demand for irrigation water in the face of

dwindling surface water supplies, more and more irrigation water supplies were met

through groundwater abstractions (Shiva, 1991, Ahmad et al., 2004b, Rodel et al.,

2009).

Although the number of tube-wells has gone beyond one million, many small-scale

farmers do not own tube-wells. These smallholder farmers and tenants purchase

groundwater from their neighbouring tube-well owners under informally developed

3A tube-well is a type of water well, drilled to extract subsurface water through a pump. In Pakistan, tub-wells of 5-7 inch diameter are usually drilled to extract groundwater. These tube-wells are mounted with either 15-25 horsepower diesel engine or 15-30 horsepower electrical motor depending upon the depth of water table.

http://en.wikipedia.org/wiki/Water_well

Introduction

4

groundwater markets. Informal groundwater markets4 are reported throughout Pakistan

but are more common in Punjab and Balochistan provinces (Khair et al., 2012,

Meinzen-Dick, 1996). These markets generally function under social settings and are

greatly influenced by the social ties between tube-well owners and water buyers

(Meinzen-Dick, 1996, Rinaudo et al., 1997b, Khair et al., 2012). Markets for

groundwater involve the informal sale of groundwater from private tube-wells without

involving the exchange of permanent water rights. Tube-well owners receive payments

for groundwater that allows buyers to access the opportunity to increase agricultural

productivity (Manjunatha et al., 2011, Meinzen-Dick, 1996, Shiferaw et al., 2008).

Nevertheless, in the absence of groundwater entitlements and any formal regulatory

mechanism, sometimes tube-well owners prefer certain water buyers due to social ties

with them, thus discriminating on whom to sell water to (Shah, 1993, Jacoby et al.,

2004, Khanna, 2007). Although, such informal groundwater markets improve the equity

of access to groundwater resources and play an important role in addressing water

scarcity (Khair et al., 2012), these may not fully convey the scarcity value of water and

so do not prevent over-exploitation (Meinzen-Dick, 1996).

Over the past three decades, massive pumping of groundwater aquifers to meet

increasing irrigation water demands has started lowering groundwater tables rapidly in

different parts of the country (Kijne, 1999b, Shah et al., 2000, Khan et al., 2008a,

Qureshi et al., 2009). Besides lowering groundwater tables, the unimpeded tube-well

growth has led to many negative environmental externalities such as salt water intrusion

and secondary salinity which portend serious repercussions to the sustainability of

irrigated agriculture in the region (Kijne, 1999b, Shah et al., 2000, Khan et al., 2008a,

Qureshi et al., 2009). Khan et al. (2008b) projects that in the next 25 years there will be

a 10 to 20 metres decline in groundwater levels in the upper and lower regions of the

Rachna Doab5 in North-East Pakistan if it continues to be depleted at the current rate. In

many parts of the country, rapid depletion of the water table is not only pushing farmers

to re-install tube-wells at greater depths but also impacting their future decisions to

adopt tube-well technology. Despite a wide array of studies on hydrological assessment

4In South Asia (Pakistan, India, Bangladesh and Nepal) irrigation is highly dependent on groundwater supplies through tube-well pumping. 5The word “Doab" means land of two rivers. The Rachna Doab is one of the main agricultural regions of the Punjab. The Rachna Doab lies between 30° 35' and 32° 50' N. and 71° 50' and 75° 3'E., between the Chenab and Ravi rivers.

Introduction

5

of escalating groundwater exploitation, there remains a vast gap in the literature on

farmer’s tube-well adoption decisions, irrigation water use performance and irrigation

water demand patterns given the current state of rapid depletion of groundwater

resources.

1.3 Conceptual Framework

Figure 1.1 provides a conceptual framework that will be used to guide this study .The

conceptual framework links together different study objectives in the form of challenges

and opportunities for sustainable groundwater management. The conceptual framework

shows that ineffective groundwater management policies and inefficient utilization of

groundwater resources through unimpeded tube-well growth has led to groundwater

resource depletion. As a sequence of multiple steps, an ex post analysis of the factors

which have contributed to groundwater depletion is an important step to address the

groundwater depletion problem through policy instruments and appropriate practice

change.

Figure 1.1: Conceptual framework for the study

Introduction

6

As indicted by the conceptual framework, the rapid lowering of groundwater tables is as

a result of farmers’ decisions to adopt tube-well technology for groundwater extraction.

Assuming that farmers are risk-averse and want to maximize their farm profits, it

becomes important to analyse their decisions regarding the adoption of tube-wells when

groundwater resources are under rapid decline. It is unknown whether the decision to

adopt the tube well technology is influenced by farmers’ expectation about farm returns,

its variance, and downside risk. Because tube-well installation requires a large up-front

investment and is not a portable technology (i.e., a potentially stranded asset), lowering

groundwater tables may influence farmer’s investment decisions. Moreover, tube-well

installation entails a sunk cost if the tube-well goes unproductive. In such scenarios,

only financially secure farmers with enough capacity to bear sunk costs would decide to

invest in the tube-well technology. .

Because groundwater management requires multidimensional actions and policies,

assessing irrigation water efficiency and estimating irrigation water demand are

complimentary water management policy objectives. As the conceptual framework

shows, any value generated in terms of improving irrigation water use efficiency and

optimising irrigation water demands by both adopters and non-adopters is directly

linked to sustainable groundwater management. Whilst, irrigation efficiency analysis

helps identifying opportunities for sustainable groundwater management by

benchmarking inefficient water users against the efficient users, economic instruments

like water pricing foster efficient water allocation and induce allocation from lower to

higher values. The combined effect of improving water use efficiency and optimal water

allocations holds the key for sustainable groundwater management practice.

1.4 Research Objectives

The purpose of this doctoral study is to explore the economics of groundwater use for

irrigation in the Indus river basin of Pakistan. Specific objectives of the research are to:

1) identify causes and consequences of groundwater overdrafting and draws

attention about groundwater resource management issues;

2) analyse farmer’s decisions to adopt tube-well technology under the risk of

groundwater depletion and associated production uncertainties;

3) estimate efficiency of groundwater use in irrigation at for different crops and;

4) estimate the derived demand of groundwater use in irrigation by different

groundwater users i.e., water sellers and water buyers.

Introduction

7

1.5 Data Description and Research Design

The data used in this study come from a rural household survey conducted during the

2010 to 2011cropping year. Detailed farm level data was collected from 200

groundwater users randomly selected from two different cropping regions in the semi-

arid plains of the Punjab province of Pakistan: the cotton-wheat region and the mixed-

cropping region.

The study is conducted in the Jhang and the Lodhran districts of the Punjab province of

Pakistan. The study districts are show in Figure 1.2. In both study districts, agriculture

heavily relies on groundwater for irrigation purposes due to the arid and semi-arid

climate. The selected farms solely depend on groundwater for irrigation purposes in the

Jhang district while partly on canal water in the Lodhran district. Besides limited canal

water supplies, both districts receive very little rainfall. The average precipitation rate in

the Lodhran district is 71mm-1 while it is 180mm-1 in the Jhang district. Therefore,

majority of the irrigation water comes from groundwater which is extracted mainly

through deep tube-wells. The two study districts have large variation in the depths of

installed tube-wells. In the Lodhran district, the variation was observed to be between

60 to 99 meters compared to the Jhang district where it was between 33 to 57 meters.

As a result of deep groundwater tables and the high installation cost, tube-well

population is relatively less dense in the Northern part of the Jhang and Southern part of

the Lodhran district. Therefore, farmers generally engage in informal groundwater

trading. A multi-stage sampling technique was used in data collection. In the first stage,

one tehsil6 was selected purposively from the Lodhran and the Jhang district. In the next

stage, 10 villages were selected at random from each purposively selected tehsil. Then,

from each village 10 groundwater users (5 tube-well owners and 5 water buyers) were

selected randomly. A village is usually comprised of 60-70 farming households in the

study areas. Finally, we collected farm level data from 200 groundwater-fed agricultural

farms. The dataset used in this study is relatively small and is collected from one tehsil

in two districts. However, the sample farms reflect the typical situation of groundwater

irrigated farms in the study districts in particular and in the rural areas of the Punjab in

general.

6Tehsil is an administrative unit. A district usually comprise of 5-6 tehsils (sub-districts). Lodhran district is comprised of three tehsils i.e., Dunyapur, Kahror Pakka and Lodhran while Jhang district is comprised of four tehsils i.e. Athara Hazari, Shorkot, Ahmad Pur Sial and Jhang.

Introduction

8

Figure 1.2: Map showing study districts (Jhang and Lodhran) in red colour

1.6 Contribution to Scholarship and Originality

This study has carefully reviewed the existing literature on groundwater use for

irrigation in the Punjab, Pakistan. We find empirical studies that explore the economics

of groundwater use for irrigation to be rare. We did not find any study that focused on

analysing adoption of tube-well technology under the water scarcity risk, irrigation

water use efficiency and the groundwater derived demand for irrigated agriculture for

different water users under the traditional groundwater use rights and informal markets.

Although, some studies address challenges and prospectus of groundwater use and the

functioning of groundwater markets in Pakistan (Renfro and Sparling, 1986, Rinaudo et

al., 1997a, Meinzen-Dick, 1996, Khair et al., 2012), they have not addressed the above

mentioned research challenges.

Introduction

9

This study contributes to the existing literature on groundwater resource economics in

Pakistan by assessing irrigation water use efficiency for different crop enterprises and

optimal water allocation under water constraint. Overall, the results from this study

provide insights for policy makers and practitioners on how to ensure sustainable

irrigation using groundwater resources.

1.7 Thesis Organisation

In total this dissertation consists of five chapters with each empirical chapter as a stand-

alone research paper. A brief introduction of each chapter is discussed below.

Chapter 2 is a review paper on the current groundwater situation in Pakistan. It

provides the backdrop for this study. An overview of the current groundwater situation,

causes and consequences of groundwater overdrafting and groundwater policy

framework in Pakistan is discussed. The chapter highlights the challenges the

groundwater sector is facing in Pakistan.

Chapter 3 analyses farmers’ decisions to adopt tube-well technology under the

depleting groundwater resources and associated production uncertainties in the irrigated

semi-arid plains of the Punjab Province, Pakistan. Knowledge of the impact of adoption

of new technology is crucial to understanding how policy interventions can help to

overcome the effects of growing water scarcity and production uncertainties. In this

chapter, moments of the profit distribution are modelled as key determinants of farmer’s

decision regarding adoption of tube-well technology.

Chapter 4 estimates the technical and irrigation groundwater efficiency of irrigated

agriculture within the context of declining groundwater tables. Both non-parametric and

parametric input-specific technical efficiency approaches are used to evaluate the

irrigation water efficiency in wheat, cotton and rice cultivation. A meta-frontier data

envelopment analysis is used to investigate technology gap ratios while the sub-vector

DEA is used to estimate irrigation water use efficiency in wheat farming. A restricted

stochastic production frontier is used to estimate technical and irrigation water

efficiency in cotton cultivation. Finally, the sub-vector and slack-based DEA models are

used to estimate irrigation water efficiency along with production efficiencies in rice

farming.

Chapter 5 employs the Positive Mathematical Programming (PMP) approach to

estimate the derived demand for groundwater for irrigation among water sellers and

water buyers i.e., tube-well owners and water buyers. The shadow price of water is

Introduction

10

estimated to represent farmers’ willingness to pay when groundwater resources become

constrained at different levels.

Chapter 6 summarizes the main findings of this research. Innovative aspects of this

study that add to the existing knowledge of groundwater management, irrigation water

efficiency and the derived demand for groundwater and implications for groundwater

pricing are highlighted and discussed.

11

CHAPTER 2

2. Imperatives and Repercussions of Groundwater Depletion7

Abstract

The sustainability of agricultural growth has been greatly influenced by the massive use

of groundwater in Pakistan for the last few decades. However, the groundwater

economy of Pakistan is at a critical juncture. Concomitant with massive pumping of

groundwater aquifers through unrestricted expansion of tube-wells, groundwater

exploitation has led to many negative environmental, economic and spatial externalities

and serious threats to the sustainability of irrigated agriculture in the region. The

spectacular increase in the groundwater development during the last half-century has

manifested as a kind of “silent revolution” carried out by thousands of farmers in the

pursuit of reliable irrigation water supplies. The groundwater revolution in the Indus

basin has been a result of a succession of factors –each of which has exacerbated the

groundwater crises in the subsequent periods. Initially, groundwater extraction was

started to overcome waterlogging and salinity which was blown up by large scale

surface water developments in coming years.

Within this backdrop, this article attempts to identify the causes and consequences of

groundwater overdrafting in Pakistan and draws attention to groundwater resource

management issues. In this article, we discuss how the rigidity of the surface water

allocation system (Warabandi), the Green Revolution, the Indus Water Treaty, soaring

population and the ineffective groundwater management policies have led to the

“groundwater revolution”. Major environmental externalities identified include soil

salinization, salt water and sea water intrusions, land subsidence and drying up of lakes

and vegetation in different parts of the country. Various pecuniary externalities such as

increasing pumping costs and decreasing land values are also very prominent. Migration

and prospective social conflicts are the potential spatial externalities. We find that

decreasing surface water supplies, unimpeded extraction of groundwater through

7This chapter is accepted for publication as “Groundwater depletion in the Indus Plains of Pakistan: Imperatives, repercussions and management issues” in the Journal of International River Basin Management.

Imperatives and Repercussions of Groundwater Depletion

12

aquifers in the absence of groundwater entitlements and institutional impediments are

major bottlenecks to the sustainable management of groundwater in Pakistan.

2.1 Introduction

In many parts of the world, an increased demand for irrigation water, coupled with

uncertain surface water supplies, have led to an expanded reliance on groundwater as a

resource for irrigation systems (Morris et al., 2003 ). At global scale, out of 300 million

hectares of irrigated area, 113 million hectares (38%) relies on groundwater for

irrigation (Siebert et al., 2010a). Since the 1960s agriculture in many countries has

become increasingly dependent on groundwater. An approximate doubling of

groundwater use occurred between 1960 and 2000, bringing into question whether such

a high rate of withdrawal is suitable (Werner and Tom, 2012). Many studies have

estimated groundwater depletion rates (Döll et al., 2012, Wada et al., 2010, Schwartz

and Ibaraki, 2011, Konikow, 2011) and have indicated that depletion of groundwater

aquifers is a reality in many regions (Döll et al., 2012, Döll and Fiedler, 2008).

Agriculture in Pakistan highly dependent on irrigation water supplies both from canal

and groundwater sources due to its arid and semi-arid climate. Due to the changing

climate, surface water supplies have decreased by 15% over the past decade. The Indus

basin is now considered to be one of the most depleted river basins in the world

(Sharma et al., 2010). It is estimated that water demand in Pakistan is growing at an

annual rate of 10% whereas water resources are under rapid decline. Water resources

are under extreme pressure from domestic and industrial demands due to the rapid

population growth (Sharma et al., 2010, Archer et al., 2010, Laghari et al., 2012). As a

result, there has been a substantial increase in the use of groundwater to sustain irrigated

agriculture in the Indus basin of Pakistan.

Pakistan meets more than 50% of its overall irrigation requirements through

groundwater extraction (Qureshi et al., 2010). Over the past decade, groundwater

extraction rates have increased to 60 km3 , exceeding the annual recharge rate of 55 km3

(FAO, 2012a). Such prolonged overuse has raised concerns about the sustainability of

groundwater resources in the region. Using scenario analysis, Khan et al. (2008b) show

that if the dry conditions persist, there will be a 10 to 20 metre decline in groundwater

levels over the next 25 years in the upper and lower regions of Rachna Doab in North-

East Pakistan. Wada et al. (2010) identified several hot spots of groundwater depletion


13

in different regions of the world, with the highest depletion rates being observed in

North-East Pakistan and North-West India.

As a result of excessive extraction, groundwater tables are lowering rapidly. Numerous

tube-wells in the Punjab and Sindh regions have run dry and much of the Karez8 tunnel

system in Balochistan has left the tunnels dry and susceptible to collapse. The Karez

system has been replaced by dug-wells in many valleys of Baluchistan. The quest for

water has driven many farmers to invest in submersible electrical pumps where even the

dug well have also run dry. In some areas of Quetta, such as in the Kuchlak valley, ever

deeper tube-wells have now hit bedrock (Steenbergen, 2002). Declining water tables are

not only making irrigation water supplies expensive and unreliable (Banerji et al., 2006)

but are also creating many environmental concerns with serious repercussions to the

sustainability of irrigated agriculture in affected regions (Kijne, 1999b, Shah et al.,

2000, Kelleners and Chaudhry, 1998, Kahlown and Azam, 2002, Khan et al., 2008b,


The recent shortages of surface water, combined with declining water tables, have led to

policy debates about the sustainable management of groundwater resources. This paper

traces the key trends and factors that have led to the current situation of over-

exploitation of groundwater resources and highlights policy and government failure.

The paper shows that the sustainable use of groundwater resources for food production

is not a technical challenge but rather an economic and political challenge. Noting

recent experience, it is noted that Pakistan’s capacity to appropriately and quickly

implement desired organisational and policy reforms appear to be limited.

In the next section, we identify the factors which increased reliance on groundwater and

provide a synthesis of different environmental, economic and spatial externalities

related to groundwater overuse in the Indus basin. In the last section we discuss some of

the groundwater management problems and finally provide some conclusions.

2.2 Groundwater from Menace to Mainstay

The utilisation of groundwater resources has played a key role in Pakistan’s agricultural

sector. A spectacular increase in the number of tube-wells began in the 1960s.

8Karez is an underground tunnel that is constructed to collect subsoil water through the gravitational pull at the foot of hills. This water is then diverted towards fields or villages either for irrigation or domestic needs.


14

Introduction of gravity based large scale irrigation systems without proper drainage

resulted in rising groundwater tables and consequently created waterlogging and salinity

problems in many areas. In order to overcome these problems a massive groundwater

extraction Salinity Control and Reclamation Programme (SCARP) was commenced to

prevent waterlogging. Initially, some 16,700 large capacity (0.084–0.14 m3 sec-1) tube-

wells were installed. Most of these tube-wells were operated on low speed crude oil

engines. The rural electricity grid expansion in 1970s made it possible to transition to

electricity operated pumps in tube-wells. Installation of tube-wells not only helped to

lower water tables but also supplemented canal supplies. As a result further irrigation

was possible (Ahmad et al., 2004b, Kazmi et al., 2012) causing groundwater to become

an important source of water for irrigation as traditional canal water.

Figure 2.1: Historical development of tube-wells and share of groundwater in irrigated agriculture in Pakistan

The SCARP programme was the beginning of the groundwater boom in the Indus basin

of Pakistan. However, irrigation area expansion supported by tube-well expansion and

increased groundwater extraction eventually caused a widening gap in water supplies to

crop water requirements due to spatio-temporal inflexible surface water allocations, loss

0

200

400

600

800

1000

1200

1965 1970 1975 1680 1985 1990 1995 2000 2005 2010

No.

of t

ube-

wel

ls (0

00)

a) tube-well development

PunjabRest of countryPakistan

0

10

20

30

40

50

60

70

1965 1970 1975 1980 1985 1990 1995 2000 2005 2010

Bill

ion

cubi

c m

eter

s

b) increase in gorundwater share for irrigation

Groundwater Share (billion m3)% Contribution of Surface Water


15

of near eight million cusecs of water under the Indus Water Treaty and increased

cropping intensities during the Green Revolution. Extraction of groundwater aquifers

continued through rapid and unrestricted construction of thousands of small capacity

(0.028 m3 sec-1 or less) private tube-wells. Until the 1960s the number of tube-wells was

limited to less than thirty thousand, yet currently there are over one million.

Figure 2.1shows the historical development of tub-wells and increase in the share of

groundwater use in irrigation. After an exponential growth during the 1990 to 2005

period, there is however, a decreasing trend in the number of tube-wells. This indicates

that besides a decreasing trend in adoption of tube-well, the number of previously

installed tube-wells is also decreasing. Similarly, the share of groundwater in irrigation

decreased slightly over the same period (Figure 2.1b).

2.3 Warabandi- A Rigid Irrigation Delivery System

Warabandi 9emerged as a protective10 irrigation system in the Indus-Ganges basin

during the British rule over the Indian sub-continent in the 19th century. The protective

irrigation was based on “scarcity by design” as its objective was to maximize the

irrigated area through canal water supplies and to overcome crop failures (Bandaragoda

and Badruddin, 1992, Gilmartin, 1994, Jurriens and Mollinga, 1996). Warabandi aimed

to distribute available water supplies equitably as a fixed weekly rotation and in

proportion to farm size (Bandaragoda and Rehman, 1995, Jurriens and Mollinga, 1996,

Rinaudo et al., 1997b, Bandaragoda, 1998 ). It was inflexible, however, and could not

be adapted to the differing temporal and spatial water requirements of various crops

(Kahlown et al., 2007, Zardari and Cordery, 2009). The rigidity of this system has

caused an increased dependence on groundwater for irrigation in the Indus basin

(Zardari and Cordery, 2009).

Moreover, under the Warabandi system water allocations occurred when water was not

scarce and cropping intensity was low. The Warabandi system discharged a meagre

allowance of 0.085 l/s per acre for a cropping intensity of 75% at that time

(Bandaragoda, 1998 ). However, during the Green Revolution cropping intensity

9Warabandi system distributes the canal water supplies equitably proportional to the farm size as a fixed weekly rotation. 10The notion “protective irrigation” means to design and operate an irrigation system based on the principle that the available water should be spread equitably in order to cover as many farmers as possible without taking into consideration the full crop water requirements (Jurriens et al., 1996).


16

increased by almost twofold which widened the gap between crop water requirement

and water allocations. .

2.4 The Green Revolution

Pakistan was amongst the early adopters of the new agricultural technology that

characterized the Green Revolution (Byrelle and Siddiq, 1994). The most intriguing

aspect of the Green Revolution, perhaps, in Pakistan was the groundwater revolution.

Groundwater irrigation was protective irrigation until the Green Revolution. Adoption

of high yielding and water sensitive crop varieties during the Green Revolution changed

the demand for irrigation (Mukherji and Shah, 2005). Improved modern crop varieties

increased yields two to three times those of conventional varieties and crop water

requirements increased about three times (Shiva, 1991, Ahmad et al., 2004b, Rodel et

al., 2009).

The widespread adoption of semi-dwarf varieties of wheat and rice was stimulated and

was made possible by the rapid expansion in irrigation water supplies. In the presence

of a vast irrigation network and a favourable environment, Pakistan was considered an

ideal place to take advantage of the new technology. New crop varieties, improved

inputs aided by government policies and improved water supplies played a key role in

increasing agricultural productivity.

Irrigation water supplies expanded in the Punjab province during the 1967 to86 period.

Over this period, total water supplies increased more than twice. The completion of

Tarbela and Mangla dams almost doubled canal water supplies and the installation of

private tube-wells expanded the groundwater supplies up to 8%. The growth in water

supply turned previously rain-fed lands into irrigated lands on a vast scale. By 1986

canal water supplies were fairly constant yet the irrigated area continued to grow

through greater reliance on groundwater extractions.

By 1960 groundwater contributed only 8% to total irrigation water supplies at the farm

gate in the Punjab Province but 25 years later this share had increased to 40%. By 1986,

groundwater contributed 59% to the total Rabi11 water requirements, compared to 36%

only two decades earlier (Byrelle and Siddiq, 1994). Later on canal water supplies

11 There are two cropping seasons in Pakistan, Kharif and Rabi. Kharif starts from June, July and goes to October, November, while the Rabi season starts from September, October and continues to April, May. However, cropping time varies geographically across the country.


17

began to decrease, causing an increased dependence on groundwater resources. The

inflexibility of canal water allocations and increased cropping intensities during the

Green Revolution placed great pressure on groundwater resources. Exacerbating water

management in Pakistan led to political wrangles between India and Pakistan over the

river water resources that flowed through both countries. The final signing of the Indus

Water Treaty provided clarity over access rights to river water but also triggered further

exploitation of groundwater resources in Pakistan.

2.5 The Indus Water Treaty and Beyond the Indus Water Treaty

The partition of the Indian subcontinent divided the Indus river basin and its irrigation

system between India and Pakistan. It was proposed to declare the regional river basins

as boundaries for the new countries; however, this did not receive much support

(Schwartzberg, 1990). Later, new boundaries were declared using canal head-works

near the Ferozpur and Gurdaspur districts in India and the Lahore district in Pakistan.

By that time, much of the construction work was completed on the eastern rivers which

enter into Pakistan after passing through India. After division, most of the head works

and canals on these rivers were left on the Indian side of the border. India claimed

sovereign rights over the waters passing through its territory and on April 1, 1948 India

completely diverted water supplies irrigating Pakistan’s fields (Wescoat et al., 2000).

Although canals supplying Pakistan's plains were eventually reopened, India’s claim

over river flows became the basis for wider transboundary conflicts between the two

countries (Michel, 1967, Barrett, 1994, Alam, 2002). Pakistan proposed to settle the

dispute through arbitration but India refused Pakistan’s proposal. Soon after the

partition, the severity of the dispute led to a war threat between the two countries

(Barrett, 1994).

The World Bank realised the situation and offered mediation to resolve the conflict and

both countries agreed. In 1952 negotiations were started between the three parties i.e.

India, Pakistan and the World Bank. However, the sovereignty concerns by India made

the World Bank’s first proposal of joint use and development of the Indus basin

unacceptable. In 1954, the World Bank proposed the division of the tributaries of the

Indus River. In 1960 India and Pakistan mutually agreed to sign the Indus Water Treaty.

The Indus Water Treaty allocated the three eastern rivers (Sutlej, Bias and Ravi) to

India and the three western rivers (Jehlum, Chenab and Indus) to Pakistan. Although

compensations were made to construct link canals and dams on western rivers to ensure


18

adequate water supplies in eastern rivers, the loss of access to the three eastern rivers

deprived Pakistan of nearly 8 million cusecs of surface water (Amir, 2006). At that time

the effect of diminished supplies from the eastern rivers on groundwater recharge was

not discussed nor covered under the Indus Water Treaty. As a result of diminishing

water flows into the eastern rivers groundwater recharge has reduced significantly

because groundwater aquifers in Indus basin by more than 60% are recharged through

river and canal flows (Amin, 2004). It is expected that transboundary conflict may go

beyond the surface water resources as it is highlighted recently by the National

Aeronautics and Space Administration (NASA) that groundwater depletion in India is

likely to impact on the aquifer on Pakistan’s side of the border (Figure 2.2).

Figure 2.2: The red depression highlights groundwater depletion rates with expanding sphere towards Pakistan side

2.6 Myopic Groundwater Policies

Management of groundwater resources in Pakistan continues to face many policy

challenges. The groundwater policy is historically was centred on two key requirements.

The first was to install public tube-wells to control rising water tables in waterlogged

areas and the second was to encourage agricultural production in areas with good

quality groundwater reserves through private tube-well adoption (Steenbergen and

Oliemans, 2002). However, the success of these policies led to a dramatic increase in

groundwater extraction that has become a policy challenge. Declining groundwater

resource now threatens the sustainability of irrigated agriculture in Pakistan.


19

Exacerbating the overuse of groundwater was the massive transfer of water from the

western rivers to the eastern rivers that formed part of the Indus Water Treaty (Michel,

1967, Biswas, 1992). A network of link canals was constructed to supplement the ‘lost

water’ of eastern rivers identified as part of the Treaty negotiations. However, use of

this large scale irrigation infrastructure led to waterlogging and secondary salinity in

many areas. To combat these problems a comprehensive public groundwater

development programme was initiated with the installation of 16,700 tube-wells during

the 1960s under the first Salinity Control and Reclamation Project (Bhutta and

Smedema, 2007). Over the next two decades, the government’s policy focused on

aiding the adoption of tube-well technology (Falcon and Gotsch, 1968, Papanek, 1998,

Johnson, 1989) through subsidies on electricity, diesel and drilling services, free pump

sets and easy and long term loans (Johnson, 1989).

Later, higher yields and greater economic returns to groundwater users (Meinzen-Dick,

1996) encouraged farmers to adopt the tube-well technology even without government

aid (Muhammad, 1964, Muhammad, 1965, Falcon and Gotsch, 1968, Nulty, 1972). In

the 1980s, a 227% increase in the number of electric tube-wells made the government

reconsider its support policies such as subsidies on electricity (Qureshi et al., 2003).

In1980s, the government introduced a new flat rate tariff on electricity for electric tube-

wells that was increased by 126% between 1989 and 1993 to slow down the pace of

tube-well adoption. Removing subsidies and increasing the tariff rate on electricity use

only made electric tube-wells less attractive and so farmers started switching to diesel

operated engines. The low installation costs for diesel tube-wells and their lesser energy

cost incentivized farmers to install greater numbers of diesel tube-wells. Moreover,

introduction of locally made diesel engines provided additional impetus for the

construction of diesel tube-wells. Between1990 to 1995 a twofold increase was

observed in the number of diesel tube-wells (Qureshi et al., 2010).

The momentum for adoption of tube-well technology during the 1960s and 1970s was

difficult to arrest, even when subsidies were lifted and electricity prices were increased

significantly. The flat rate tariff on electricity use largely proved to be electricity

conserving policy rather than an effective groundwater conservation policy.

Neither customary laws nor government policies and actions so far have adequately

dealt with finding the balance between groundwater recharge and extraction.


20

Figure 2.3: Groundwater use trends in Pakistan

2.7 Population Growth and Water Demands

Pakistan is the sixth largest country by population. Pakistan’s population grew from 40

million in 1950 to 173 million in 2012 and is expected to be 237 million by 2025 (FAO,

2012a). As the population grows, demand for water for all aspects of life also increases.

It is estimated that the agricultural sector will have to grow more than 4% per year and

water supplies to grow by almost 10% to meet the country’s growing population’s food

requirements (FAO, 2012a). By 2025 irrigation water demands in Pakistan would reach

to 349.2 km3 under the business as usual scenario. This represents a 48.3% increase in

current water availability (David et al., 1998).

Although the demand for irrigation water is projected to increase, the supply of

irrigation water is unlikely to increase, particularly as there is a limited potential for

surface water developments. Current per capita water availability has already hugely

decreased from 5260 m3 in the early 1950s to 1040 m3 in 2010; a per capita decline of

400%. Archer et al. (2010) estimates per capita water availability to be 725 m3 by 2025

and only 415 m3 by 2050 if population continues to grow at the current rate

(Figure 2.4).


21

Figure 2.4: Different water shortage versus population growth projections

2.8 Lack of Surface Water Developments

Since the completion of Tarbela and Mangla dams in the early 1970s, Pakistan has not

considered developing further surface water resources. Rather, groundwater resources

have been exploited. Yet the capacity of Tarbela and Mangla has been declining due to

heavy loads of sedimentation that has caused a storage loss of 3.67 billion cubic metres

in the gross initial capacity of the Tarbela and 1.21billion cubic metres of the Mangla

reservoir in 2009-10 (Pakistan, 2009-10). Until new reservoirs are constructed there will

be gradual decline in the storage capacity of these existing dams which will decrease the

overall surface water storage and heighten demand for use of groundwater resources

(Archer et al., 2010). Surface water reservoirs not only provide a substitute for

groundwater but they also play role in aquifer recharge.

050

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Figure 2.5: Storage loss in the capacity of different dams in Pakistan

2.9 Groundwater Overuse Externalities

Ostrom (1990) considers groundwater a typical example of a common-pool resource. It

is non-excludable in the sense that exclusion of multiple users is not easy and it is

subtractible in the sense that pumping beyond optimum limits leads to aquifer depletion.

Yet it is a rival good insofar as users compete over the resource and their use of the

resource leads to its depletion.

Non-excludability makes it difficult to regulate and rivalry leads to depletion of the

aquifer which may cause problems in fragile environments (Steenbergen, 1995, Reddy,

2005). Besides aquifer depletion, overdrafting of groundwater can have many

environmental, economic and social impacts on surrounding ecosystems (Skurray and

Pannell, 2012, Danielopol et al., 2003, Zektser et al., 2005, Harou and Lund, 2008).

Pakistan is among those countries whose groundwater withdrawals are greater than their

rate of renewal. The rapid decline in groundwater tables has also raised many

environmental, economic and social concerns in Pakistan. The environmental impacts

of groundwater overdrafting include: soil salinization, salt water and sea water

intrusions, land subsidence, drying up of lakes and loss in vegetation in different parts

of the country. In addition to these environmental problems, declining groundwater

tables have led to uneconomic pumping conditions, increased irrigation costs, and have

imposed population migrations due to the temporal and spatial impacts surrounding the

cost and availability of irrigation water.

Any cost-benefit analysis of groundwater pumping would need to take explicit account

of the wider impacts of the environmental, economic and social changes that

accompany groundwater use. However, there is a dearth of research that would facilitate

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the estimation of these costs of groundwater depletion in the Indus basin of Pakistan. In

the following section, we identify and outline the nature and possible magnitude of

some of these costs.

2.10 Environmental Externalities

2.10.1 Soil Salinization

Soil salinization is one of the main negative impacts of groundwater overuse for

irrigation in the Indus basin of Pakistan (Kijne, 1999b, Shah et al., 2000, Kahlown and

Azam, 2002, Ahmad et al., 2002a, Aslam and Prathapar, 2006, Khan et al., 2008b,

Bhutta and Smedema, 2007, Qureshi et al., 2009). Salinization refers to the gradual

accumulation of salts on soil surface. Salinization can be caused by excessive irrigation

or by using poor quality water for irrigation or rising groundwater tables. During the

Indus basin development, introduction of large-scale irrigation network without

adequate drainage systems changed the hydrological balance in the basin. Continuous

seepage from the newly constructed canals caused 20-30 meters deep groundwater

tables to rise up to the level of soil surface, turning millions of acres of land into

waterlogged and saline soils. Installing tube wells as a vertical drainage was proposed

as a potential solution. Following their installation, waterlogged soils were reclaimed to

a great extent. By reducing water logging through groundwater pumping, it was hoped

that the problem would be solved. But, this did not prove to be the case. Reclaiming

waterlogged soils created another problem. Firstly, large quantities of salts were already

present in the soils and pumping brought those salts to the surface. Secondly, the

groundwater contained far more salts than canal water. Hence, large quantities of salts

were left behind in the root zone after the water evaporated (Kijne, 1999b).

Salinity remains a major threat to the sustainability of irrigated agriculture in Pakistan

(Kahlown and Azam, 2002, Bhutta and Smedema, 2007) as there are currently 6.3

million hectares of land affected by different types and levels of salinity. Of the 25% of

irrigated land affected by salinity, about 1.4 million hectares of agricultural land is not

cultivable. In addition to rendering millions of acres of land unproductive, the losses

attributable to salinity’s adverse impacts on existing crops amounts to Rs. 55 billion

(US$1.5 billion) per annum. The costs of salinity were estimated to equate to 0.6% of

Pakistan’s GDP in 2004(Corbishley and Pearce, 2007).

Figure 2.6 shows province wise soil salinity status during 2001-04. Sindh has more area

affected by salinity than any other province while Khyber Pakhtunkhwa (KPK) has the


24

least affected area by salinity. Punjab has 1.6% of its cultivated area strongly affected

by salinity while Sindh has 20% area affected by strong salinity.

Figure 2.6: Province wise soil salinity status for the period 2001-04

2.10.2 Land Subsidence

Land subsidence is a well-known consequence of intense groundwater pumping

(Domenico and Schwartz, 1997, Zektser et al., 2005). Land subsidence occurs when

large amounts of groundwater are withdrawn from an aquifer. Excessive pumping

reduces the hydraulic head in an aquifer that in turn reduces soil pore pressure. The

draining of pore space reduces the pore space which in turn causes land subsidence. The

reduction in pore space can lead to a permanent diminution in the storage capacity of an

aquifer and subsidence can damage vital infrastructure such as roads and buildings. In

many parts of Pakistan, excessive pumping of groundwater aquifers has raised severe

concerns about land subsidence. Khan et al. (2013) reported a subsidence rate of 10

cm/year during the mid-2006 to the early 2009 in the Quetta valley of Balochistan.

2.10.3 Seawater Intrusion

The Indus River basin has a large underground water resource formation. However,

almost the entire aquifer is underlain by saline water. In most of the Indus basin, fresh

water is a narrow strip over the saline water where excessive pumping can cause mixing

of the deep mineralized water resources with freshwater resources (Sufi et al., 1998,

Saeed and Bruen, 2004, Qureshi et al., 2010, Chandio et al., 2012). In Punjab province

23% of groundwater is saline whereas in the Sindh Province about 78% groundwater is

of poor quality (Haider, 2000). Similar to get contaminated by the underlying saline

water layers, groundwater resources are seriously confronted by seawater intrusion in

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coastal areas (Sufi et al., 1998). In the coastal areas of Sindh Province, groundwater

overdrafting has led to sea water intrusion inland up to 100 km north of the Arabian

Sea, seriously impacting10 million acre feet of land in the Indus delta (Shah et al., 2000,


2.10.4 Drying of Wetlands and Vegetation

Declining groundwater tables can severely affect wetlands and sparse vegetation cover

in arid and semi-arid plains. Phreatophytes are the plants most commonly affected by

declining groundwater tables. A classic example is date palm (Phoenix dactylifera)

which in many regions has died due to decreasing groundwater tables. Similarly, lower

groundwater tables compounded with prolonged droughts have affected wetlands and

their adjacent ecosystems. There are many examples of drying up or shrinking of

wetlands in Pakistan. In Balochistan, Spin Karez reservoir is one example. In 1975 it

covered an area of 1.04 Km2. By 1989 it covered an area of only 0.69 Km2 and by 2001

this lake had totally dried up (Khan et al., 2013).

2.11 Economic Externalities

Most groundwater pumping is for economic purposes. Groundwater is preferred for

irrigation due to its ready access, minimal infrastructure requirements and generally

more continuous water supply. However, the ubiquity of groundwater and the wide

applicability of pumping technology also encourage overdrafting. In an overdraft

regime, groundwater extraction continues until the marginal cost of groundwater

extraction begins to exceed the value of the pumped water for economic production. At

this point, farmers can either reduce crop production or implement more efficient

irrigation water use technologies or switch to higher value crops. Contrary to

hydrological concepts where aquifer exploitation is defined by its sub-optimality,

economic over-exploitation is not wise only if the net return on the least profitable crop

is less than the present value of all future pumping cost savings (Harou and Lund,

2008).

Declining water tables have greatly increased tube-well installation and groundwater

pumping costs in many parts of Pakistan. The cost of lowering a tube-well to a depth of

24 metres is seven times more than that of a tube-well drilled to a depth of 6 metres

(Qureshi et al., 2003). Similar to drilling costs, extraction costs and irrigation costs have

also greatly increased with lowering of groundwater tables. Watto and Mugera (2013)

found that irrigation costs per acre of cotton were 23.5% more in Lodhran district


26

compared to irrigation costs in Jhang district for tube-well owners. In addition, for non-

owners (who buy water from tube-well owners) irrigation costs were estimated at 30.1%

higher in Lodhran district compared to water buyers in Jhang district. The implications

of these spatial cost differences are that some small farmers or tenants are leaving

farming either selling their lands or leasing to other farmers that have better or cheaper

access to irrigation water. Farm size is already shrinking in Pakistan and the number of

landless farmers is increasing (Mustafa et al., 2013).

2.12 Spatial Externalities

Being a common-pool resource, groundwater is not usually regulated by well-defined

property rights, especially in a country like Pakistan where a large proportion of

population depends on it for domestic, industrial and agricultural purposes. However,

even under clearly defined property right scenarios, the fugitive nature of groundwater

can impose many spatial externalities. The ubiquitous nature of groundwater means that

a farmer’s water resource can be simultaneously accessed by other equally entitled

users. Due to this non-excludible nature of access to groundwater and the fact that few

limits on access apply, there is a little incentive for a farmer to forego his current use in

return for access at some future time. As a consequence, there is a rush to extract

causing a more rapid depletion of the groundwater resource (Reddy, 2005, Pfeiffer and

Lin, 2012). In many cases excessive extractions by one tube-well owner can cause

declining yields of adjacent tube-wells (Reddy, 2005). Such interference can be gradual

or sudden depending upon the hydrological conditions of the aquifer and rates of

extraction. Pfeiffer and Lin (2012) documented that 100 acre-feet of pumping can lower

the static level of groundwater table at one’s own well by 0.31 to 0.48 feet, and a

pumping of 1000 acre-feet by a neighbouring tube-well within about a two-mile radius

can reduce the static level at one’s well by 0.8 to 1.5 feet. In such situations individuals

with greater access to capital can strategically capture all the available groundwater and

deprive others (Reddy, 2005).

In Punjab and Sindh province many tube-wells have gone out of production due to rapid

falling of groundwater tables (Qureshi et al., 2003). Similarly, in Balochistan province

lowering groundwater tables have collapsed much of the traditional Karez based

irrigation system. Seawater intrusion miles up into the Indus delta have caused land

inundation and saline intrusion of aquifers. Consequently thousands of farmers and

fishermen have migrated to Karachi and other neighbouring cities. Sea-water intrusion


27

has forced 0.35 million people in the Badin and Thatta districts to migrate to some other

cities in search of livelihood (Gitanjali and Sahiba, 2011). Similar to sea-water

intrusion, drying up of tube-wells has also caused migrations from the tail ends of canal

systems.

As already reported, much of the groundwater recharge in the Indus basin occurs

through rivers and canals. The recharge to groundwater varies across the canal system,

being more at the head and far less at the tail ends of canals. Figure 2.7 shows the

recharge trend to groundwater along the Lower Bari Doab (LBD) canal in five different

districts. As the canal water supplies decrease towards the tail ends, recharge to

groundwater also decreases. In response to limited canal water supplies tube-well

densities are higher in canal command tail areas compared to the head areas.

Figure 2.7: Variability of the groundwater recharge from head to tail of the LBD canal command

In Balochistan many of the shareholders have lost their water shares because of the

drying of the Karezes. Karez is not only a source of livelihood but also the symbol of

social status in the Baloach community. Losing a water right simply means loss of

social status. Once they lose their water right, they have to either migrate to nearby

cities in search of livelihoods or work as farm labourers with tube-well owners. The

collapse of the Karez system has resulted in the breakdown of social cohesion in the

community. It is highly likely that social disintegration based on the Karez collapse may

create long-lasting social conflicts and enmities among the shareholders and non-

shareholders (Mustafa and Usman Qazi, 2008).


28

2.13 Groundwater Management Problems

The diminution of canal water supplies has increased the importance of groundwater in

irrigation. Yet groundwater use is poorly monitored and tube-well construction is not

regulated. Comprehensive information on groundwater withdrawals, water use and

groundwater quality are absent.

Water management policies have centred on canal water management whilst

groundwater management has been largely neglected. Some indirect groundwater

management strategies have been tried in Pakistan in recent years, but these strategies

have not proved very effective (Steenbergen and Oliemans, 2002, Qureshi et al., 2010).

The major reasons for the poor management of groundwater resources are as follows:

2.13.1 Institutional Impediments

Groundwater resource management faces a common-set of policy and institutional

challenges in Pakistan (Wescoat et al., 2000). The legislative foundation for water

management in Pakistan was the Irrigation and Drainage Act of 1873, which became

the basis for provincial irrigation and power departments. The Soil Reclamation Act of

1952 came much later. The Act empowered the Soil Reclamation Board to combat

waterlogging and salinity using tube-wells as means of vertical drainage. The Board

worked in designated land reclamation areas, but also issued permits to install private

tube-wells outside the reclamation areas. Later, the Board was merged with the

provincial irrigation departments. In 1958 the Water and Power Development Authority

(WAPDA) was formed. It served until 1970, after which the WAPDA became a large

federal agency having roles in resource allocation for irrigation, power development,

planning and executing of all major development interventions in the rural sector

(Bandaragoda and Badruddin, 1992). The Water and Power Development Authority Act

gave the WAPDA authority to control Pakistan’s groundwater resources and issue

official area-specific rules such as issuance of licenses for further installation of tube-

wells. However, the licensing rules framed in 1965 under the Reclamation Act and later

under the WAPDA Act have never been enforced. Hence, in spite of the formation of

agencies with regulatory powers those powers have not been exercised, indicating

widespread government failure. To illustrate this failure; in response to the rapid falling

of water tables, the government of Balochistan outlined a Groundwater Rights

Administration Ordinance in 1978. The Ordinance established an area-specific

procedure to issue licences for tube-well installation. Local District Water Committees


29

were supposed to be the sanctioning bodies with the possibility of appeal to the

Provincial Water Board. One of the special features of the Ordinance was that future

licensing would be based on area-specific guidelines. However, such area-specific

guidelines were ever formulated. Instead, the Ordinance was hardly ever implemented,

despite a dramatic fall in groundwater tables in the province. Similarly, the provincially

administered groundwater regulatory framework under the Provincial Irrigation and

Drainage Authority Act (PIDA) of 1990-2000, the Canal Act of 2006, and the National

Groundwater Management Rules are still awaited to be implemented, indicating the

consistent pervasiveness of government failure at a range of levels.

Hence, Pakistan’s water crises are mostly attributable to government failure whereby

institutions supposedly empowered to prevent unsustainable practices have been

muzzled or made ineffectual and thereby failed to prevent the exploitation of

groundwater resources. Government apathy towards empowering or reforming

ineffective institutions, policies and practices is very evident (Mustafa et al., 2013).

Although, 10 public sector institutions, 28 national organizations, and 19 academic and

research institutions cover the water sector their combined efforts amount to little

regarding effective regulation, penalty impositions, conservation guidelines and laws to

govern water distribution and use. Pakistan continues to have no single national

regulatory framework dealing with the use of groundwater (kamal, 2009).

2.13.2 Lack of Entitlements and Informal Groundwater Marketing

The open access nature of groundwater resource may lead to sub-optimal and

potentially wasteful uses. Anyone can extract groundwater and inflict social and

environmental costs on surrounding users and ecosystems in Pakistan.

Unlike the surface water, groundwater entitlements are not defined in Pakistan. Access

to groundwater for irrigation is open and is generally tied to land ownership. Similar to

access, the right to extract groundwater is not defined nor confined. There is neither any

restriction in terms of policy nor governance regarding groundwater use and allocation.

A tube-well owner has exclusive rights to use groundwater. He can extract and even sell

groundwater without any interference (Meinzen-Dick, 1996, Hussain, 2002, Qureshi et

al., 2010). Such informal groundwater transactions occur through locally governed

groundwater markets (Meinzen-Dick, 1996, Thobani, 1997). These informal markets

offer opportunities for tube-well owners to increase economic benefits and non-owners

to increase agricultural productivity (Shiferaw et al., 2008, Manjunatha et al., 2011).


30

However, the groundwater transactions under informal water markets do not consider

the shadow price of groundwater (Meinzen-Dick, 1996, Banerji et al., 2006).

Recently, increasing water shortages and energy crises have changed the nature and

functioning of informal groundwater markets which have become more complicated. A

water buyer has to pay water charges in advance and let the tube-well owner know

about his water demands well in advance in order to purchase water. In some areas

tube-well owners have developed a schedule for water buyers through a mutual

consensus based on their farm location relative to the tube-well owner’s farm. The

closer is the water buyer’s farm; the more immediate will be his access to the water.

Hence, after irrigating his fields the tube-well owner sells water to his immediate

neighbour and then to a more distant neighbour and so on. Sometimes a water buyer

seeking to defer payment is replaced by another buyer prepared to offer an advance

payment. In areas with a limited density of tube-wells but high dependency on

groundwater, water buyers have no or limited choice to choose among a number of

sellers, which lets tube-well owners exercise monopoly power in groundwater trading.

Unimpeded access has allowed tube-well owners in certain areas to enter into water

markets in such a way that they rent out their land and hold a piece of land where tube

well is installed. They offer their tube-well water as a discounted price conditional on

the water buyer using their own tractor or diesel engine to extract the water. This

practice leads to over-exploitation of groundwater and hampers efficiency of water use

in irrigation. The operation of these water markets also raises equity concerns. Social

ties among water sellers and water buyers can cause social discrimination (Shah, 1993,

Jacoby et al., 2004, Khanna, 2007).

2.13.3 Irrigation Efficiencies and Water Productivity

The cropping system in the Indus basin is dominated by wheat, rice, cotton, maize and

sugarcane. Most of these crops require water applications by flood or furrow irrigation

methods which are among the least efficient irrigation methods. By illustration, on-farm

water application efficiencies of these methods range between 23% and 70% depending

upon the crop type, farm characteristics and cropping region etc. (Kahlown et al., 1998).

Compounding the low application efficiencies of flood irrigation are actions of farmers

who apply more water than is actually required by their crops due to the farmers’ lack

of knowledge about crop water requirements and poor land levelling. Despite running

short of water, over-irrigation is one of the major limitations to crop production and


31

crop productivity in many parts of the Indus basin (Kahlown and Kemper, 2004). Rice

growers in particular over-irrigate their fields. Rice growers in Pakistan apply 13–18 cm

water per irrigation event, which is considerably higher than the consumptive use of

approximately 8 cm (Kahlown et al., 2001). Similarly, in Balochistan, where

groundwater is under rapid decline, irrigation to apple orchards sometimes exceeds

100% of requirements. Sarwar and Perry (2002) demonstrated that under water scarce

conditions, deficit irrigation practices can increase water productivity by almost 50% if

irrigation supplies are restricted to 80% of the total crop water requirements. Similarly,

Kahlown et al. (2007) demonstrated that using sprinkler irrigation allows rice yields to

increase by 18% and water savings of up to 35% are possible.

Pakistan is rapidly consuming its available water resources whilst generating, by

international comparison, very low productivities of water use. Water productivity for

wheat in Pakistan (0.76 kg/m3) is 24% less than the global averages of ~1.0 kg/m3 while

the water productivity of rice (0.45 kg/m3) is 55% lower than the average value of ~1.0

kg/m3 for rice in Asia (Water Watch, 2003). Another study, however, reports a higher

average of 0.69 kg of rice productivity per m3 of water in the Indus basin of Pakistan

(Cai et al., 2010). Similarly, water productivity for cereal crops in Pakistan is 0.13

kg/m3 which is very low compared to India’s 0.39 kg/m3 and China’s 0.82 kg/m3

(Kumar, 2003).

Figure 2.8: Water productivity as a ratio of total GDP to the total annual water withdrawals

Figure 2.8 gives another measure of overall water productivity for the 5 major wheat,

cotton, rice and sugarcane producing countries calculated as the ratio of total GDP

(2005 in US$) to the total water withdrawals. Using this measure, amongst the selected

countries, water productivity is the lowest in Pakistan. Although water productivity is

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very low, nonetheless Pakistan is able to produce large volumes of agricultural

products. Pakistan is ranked at number 8th in global wheat production, 4th in cotton, 6th

in sugarcane and 11th in rice. However, per hectare yields of all these crops in Pakistan

is much lower when compared to 5 major producing countries of these crops (see

Figure 2.9). Although, wheat yield has increased from 822 kg/ha in 1965 to 2713 kg/ha

in 2013, the average wheat yield is 17% and 65 % lower than its neighbouring countries

India and China, respectively. In case of cotton crop the average yield (485kg/ha) is,

however, 44% higher than India but still remains 64% lower than China. Average per

hectare sugarcane yield in Pakistan is also 40% and 42% lower than India and China

respectively. Pakistan and India respectively produce 2490 kg/ha and 2415 kg/ ha rice

on average. The average per hectare yield of Basmati rice which is more dominated

variety in Pakistan is, however, about 1500 kg/ha.

Figure 2.9: Yield (in kg/ha) in the selected countries

2.14 Conclusions

Over-extraction of groundwater for irrigation has raised concerns about the

sustainability of irrigated agriculture in the Indus basin of Pakistan. Besides threatening

the sustainability of irrigated agriculture, rapidly declining groundwater tables are

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imposing many negative environmental, economic and social costs on Pakistan. As the

dependence on groundwater is increasing, understanding groundwater availability,

allocation mechanisms, and future demand is increasingly critical if water use and

irrigation agriculture is to be sustained.

The purpose of this paper is to outline the nature, causes and impacts of groundwater

overuse in Pakistan. This paper has reviewed the causes and history of the unsustainable

exploitation of groundwater. Groundwater pumping which was started in the 1960s with

the objective of overcoming waterlogging has continued and expanded so rapidly that it

is now central to irrigation rather than being a supplementary source. Nowadays more

than 50% of the irrigation requirements are met through groundwater extractions. Over

the last decade the extraction rate has risen to 60 km3 which is 5 km3 more than the

renewability requirement of groundwater aquifers. This unsustainable extraction of

groundwater is rapidly lowering groundwater. Further, negative externalities associated

with over pumping of groundwater are arising, including salinization and land-surface

subsidence. Unchecked drilling of groundwater aquifers is also triggering the upward

flow of salts into freshwater aquifers.

Managing groundwater resources typically requires multidimensional actions,

management strategies and coordination activities across a range of institutions,

jurisdictions and stakeholders. Despite the need for such management and its associated

policy design and implementation activity, unfortunately it is not evident in Pakistan.

Unfortunately Pakistan’s groundwater management is characterised by government and

institutional failure. Moreover, there seems to be little political appetite to deal with this

pressing issue of how to sustainably manage Pakistan’s water resources to meet its

rising food demand. The front line challenge is to ensure sustainable groundwater

extraction. The future groundwater management strategies should not be based on the

conventional wisdom “where it exists----is used to meet growing water demands” but

rather on supply and demand management strategies under consideration of efficiency

objectives and resource sustainability concerns.

35

CHAPTER 3

3. The Groundwater Depletion Risk and Tube-well Technology Adoption12

Abstract

We employ a moment-based approach to empirically analyse farmer’s decisions to

adopt tube-well technology under the depleting groundwater resources and associated

production uncertainties. We use cross-sectional farm level data from 200 farming

households comprised of 100 adopters and 100 non-adopters. Risk is found to play an

important role in the adoption process. The results indicate that the higher the expected

profit the greater the probability of adoption. Similarly, with increasing variance of

profit the probability of adopting a tube-well increases significantly. The third moment

is statistically non-significant suggesting that farmers generally do not consider

downside profit risk when deciding to adopt tube-well technology whereas the highly

significant fourth moment suggests that extreme events decrease adoption significantly.

In addition, we show that land tenureship, extension services, access to different sources

of information and off-farm income play a significant role in the adoption process.

3.1 Introduction

It is an a priori expectation that irrigation increases agricultural production, decrease

variability of production, and hence the variability of farm income (Foudi and

Erdlenbruch, 2012). Irrigation consumes about 80 per cent of global freshwater

resources worldwide (Jury and Vaux, 2005). However, the limited potential for surface

water developments and declining groundwater aquifers is causing water shortages for

irrigated agriculture in many regions of the world (Tilman et al., 2002, Karagiannis et

al., 2003, Hanjra and Gichuki, 2008). Moreover, increasing inter-sectoral water demand

is limiting water allocations for irrigation purposes. In particular, the growing awareness

of environmental and in-stream water values has added new impetus on water

reallocations from low-value to high-value uses and the need to achieve greater

efficiencies in irrigation water applications (Seo et al., 2008, Hussain and Hanjra, 2004,

12This chapter was accepted and selected for presentation at the 11th Hydroinformatics Conference, New York, USA (August 17-21, 2014) as “groundwater depletion risk and the adoption of tube-well technology: some farm level evidences from Pakistan”.

The Groundwater Depletion Risk and Tube-well Technology Adoption

36

Malano et al., 2004, Wichelns, 2002). It is anticipated that that one third of the

population in developing countries will face severe water shortages by 2025 (Molden et

al., 2001).

Adoption of modern irrigation technology has been proposed as one of the major

solutions to overcome water scarcity in many agricultural countries (Caswell and

Zilberman, 1986, Dinar and Zilberman, 1991, Dinar et al., 1992, Shah et al., 1995,

Pereira et al., 2002, Karami, 2006, Bjornlund et al., 2009). Modern irrigation

technologies such as sprinkler and drip irrigation have been playing an increasingly

important role in agricultural production and water conservation (Caswell and

Zilberman, 1986). However, the adoption of modern irrigation technologies, such as

sprinkler and drip irrigation, is not yet common in many developing countries. This is

partly because of the high cost of installation and implementation of new irrigation

technology. Another reason is the perceived riskiness of the technology and the

uncertain outcome of adoption due to lack of knowledge and information about that

technology (Just and Zilberman, 1983, Abadi Ghadim and Pannell, 1999, Koundouri et

al., 2006). Many studies argue that risk is one of the major factors determining the rate

of adoption of new technology (Feder and Umali, 1993, Carey and Zilberman, 2002,

Marra et al., 2003, Lindner et al., 1982). However, the link between farmers’ adoption

decisions about irrigation technology and production risk under uncertain water

availabilities is rarely addressed in the empirical literature. A notable exception is the

work by Koundouri et al. (2006), who argue that farmers adopt irrigation technology as

a risk-reducing strategy under uncertain water availabilities.

Empirical evidences suggest that most decision makers are risk-averse and that

technology adoption contributes to reducing the exposure to risk, especially downside

risk (Chavas and Holt, 1996, Kim and Chavas, 2003). However, the perceived riskiness

associated with technological adoption and uncertainties related to future farm

production can also make low-income and risk-averse farmers reluctant to adopt new

technologies. Only financially secure farmers with enough capacity to cope with

downside risk would decide to make investment in new technology (Juma et al., 2009).

Intuitively, downside risk aversion means that farmers do not like to be exposed to

unexpectedly low returns. However, in the case of arid and semi-arid regions where

water availabilities are not secured, expected farm output and, therefore, farm profit is

random because it is dependent on exogenous climatic conditions. Therefore, risk-


37

averse farmers might consider adopting water saving irrigation technologies or securing

irrigation supplies (Koundouri et al., 2006).

As in many other parts of the world, the agriculture sector is facing severe water

shortage in Pakistan. The Asian Development Bank has warned in its Asian

Development Outlook 2013 report that Pakistan is close to being classified as a ‘water

scarce’ country (ADB, 2013). The risk of vulnerability to water resources, climate

change and human growth and development is likely to affect the sustainability of

irrigated agriculture. Evidences suggest that the Indus basin is one of the most depleted

river basins in the world. It is expected that recent climate change trends and continued

population growth would substantially increase pressure on water resources in near

future. Existing surface water resources are not only deficient but are also highly

skewed in time and space throughout the Indus basin. Due to spatiotemporal variations

in surface runoffs, agricultural intensification in the pursuit of reliable irrigation

supplies have led to the expansion of a large scale groundwater-fed irrigation system in

the Indus basin.Since, 1960 groundwater contribution to irrigation sector has been

increased by more than 40 per cent (Byrelle and Siddiq, 1994, Qureshi et al., 2009).

Consequently, groundwater extraction rates have increased to 60 km3 y-1 which exceed

the recharge rate of 55 km3 y-1 (Giordano, 2009). Although, the utilisation of

groundwater resources has played a key role in agricultural development, the ongoing

excessive use of groundwater aquifers for irrigation is causing groundwater tables to

lower at alarming rates (Kijne, 1999b, Shah et al., 2000, Khan et al., 2008a, Qureshi et

al., 2009). Khan et al. (2008b) using scenario analysis project that in the next 25 years

there will be a 10 to 20 metres decline in groundwater levels in the upper and lower

regions of the Rachna Doab in North-East Pakistan. Rapidly declining groundwater

tables are not only making irrigation water supplies economically unviable (Banerji et

al., 2006) but are also creating many environmental concerns with serious repercussions

to the sustainability of irrigated agriculture in the region (Kijne, 1999b, Shah et al.,

2000, Kelleners and Chaudhry, 1998, Kahlown and Azam, 2002, Khan et al., 2008b,

Qureshi et al., 2009). Despite the continued depletion of groundwater resources, the

number of tube-wells has kept on increasing until recently. As of 2010, there were more

than one million tube-wells in the country (Pakistan, 2009-10). However, over the last

decade there has been a decline in the number of tube-well adoption as shown in Figure

2.1of Chapter 2.


38

The purpose of this chapter is to investigate farmers’ decisions to adopt tube-well

technology under the risk of falling groundwater tables and associated production

uncertainty using the Punjab province of Pakistan as a case study. We follow the

approach by Koundouri et al. (2006) but analyse the adoption of tube-well technology

under depleting groundwater resources. Unlike sprinkler and drip irrigation

technologies, the adoption of tube-well technology does not necessarily increase the

efficiency of irrigation water use. However, tube-well ownership ensures more

promising irrigation water supplies and hence lessens production uncertainties during

irregular canal water supplies or uncertainties involved in purchasing groundwater13.

In this chapter, we use a rural household survey data of 200 farms from the irrigated

semi-arid plains of Punjab to analyse whether farmers facing high profit variability and,

hence a higher exposure to risk, adopt tube-well technology as a means to hedge against

production risks due to uncertain water availabilities. We also investigate whether

farmers consider downside risk and extreme events when they decide to adopt tube-well

technology (Antle, 1983, Antle, 1987, Antle and Goodger, 1984, Koundouri et al.,

2006).

The rest of the paper is organised as follows. Section 2 presents literature reviews on

irrigation technology adoption and considers the case of tube-well technology adoption

in Pakistan. Section 3 describes the theoretical framework used to analyse farmers’

decision to adopt tube-well technology under production uncertainties. Section 4

describes the data used in the estimation of the empirical model. The results are

presented in Section 5 and Section 6 provides conclusions and policy implications.

3.2 Literature Review on Irrigation Technology Adoption

Many researchers have empirically investigated adoption and diffusion of technological

innovations and proposed the use of irrigation efficient technology as a potential

solution to overcome water scarcity (Caswell and Zilberman, 1986, Dinar and

Zilberman, 1991, Dinar et al., 1992, Shah et al., 1995, Pereira et al., 2002, Karami,

13In South Asia (Pakistan, India and Bangladesh) irrigation is highly dependent on groundwater supplies through tube-well pumping. In response to diminishing canal water supplies, informal groundwater markets have spontaneously evolved over the time through trading surplus pumping capacities between the tube-well owners and non-owners (Meinzen-Dick, 1996) Such markets offer opportunities to non-owners to overcome crop failures through purchasing groundwater from their nearby tube-well owners (Manjunatha et al., 2011, Meinzen-Dick, 1996, Shiferaw et al., 2008). However, such markets do not guarantee access over spatial and temporal water requirements of different crops (Jacoby et al., 2004).


39

2006, Bjornlund et al., 2009, Schuck et al., 2005) . However, despite the various

economic and environmental benefits of the new technology, farm-specific irrigation

technological adoption is still lagging around the world (Isik, 2004). Shrestha and

Gopalakrishnan (1993) argue that technological adoption is a result of profit

maximizing behaviour of a heterogeneous population. Besides water conservation, some

incentives (at least some increase in yield and some decrease in production costs) are

necessary in order to make farmers to be willing to adopt a water conserving technology

(Arabiyat et al., 2001, Wichelns, 1991).

Much of the literature on irrigation technology adoption does not provide a link between

the dynamic nature of adoption, including the effect of uncertainty and irreversibility,

and the option to wait on a farm’s investment strategy (Caswell et al., 1990, Abadi

Ghadim and Pannell, 1999). Carey and Zilberman (2002) conclude that the adoption of

modern irrigation technology takes place when water becomes increasingly scarce and

the expected investment return exceeds the cost of investment. Many farmers adopt

irrigation technologies under conditions of uncertainty in order to hedge against

production risk (Koundouri et al., 2006). Zilberman et al. (1995) outline that adoption of

drip irrigation increased dramatically during the drought period of 1987 to 1991 in

California. Dridi and Khanna (2005) show that adverse selection induces less

technology adoption than full information but that even under adverse selection,

bilateral water trading among farmers can reduce the distortion created in the allocation

of water quotas relative to the situation with full information. Besides technological

solutions, it is believed that tradable rights in water and the development of markets in

these rights can lead to efficiency increases in irrigated production. Markets for water

are considered as the favoured solution to water allocation by economists. These

markets play an important role in supporting some farmers’ adoption of modern water

conserving technologies (Rosegrant and Binswanger, 1994, Carey and Zilberman,

2002). Water markets may induce some farms to adopt while others to delay the

adoption of irrigation technology (Carey and Zilberman, 2002).

Caswell and Zilberman (1986) report the effect of farm characteristics and irrigation

technology characteristics on a farmer’s decision to adopt irrigation technology. They

argued that to understand the adoption process of irrigation technology, one must

consider land quality and variation in well depth. Modern irrigation technologies are

more likely to be adopted in locations with relatively deep water tables (Dinar et al.,

1992). Realistic but economically viable water costs are necessary to substitute for


40

open-ditch furrow irrigation by surge-flow irrigation technology (Coupal and Wilson,

1990). Adoption of modern irrigation technology can help in reducing environmental

and agronomic impacts on profitability. Green et al. (1996) assess the effect of

economic variables, environmental characteristics, and institutional variables on

irrigation technology choices. They concluded that water pricing is not the most

important factor governing irrigation technology adoption. Rather, environmental

considerations are a major incentive for adoption of these technologies, such as drip and

sprinkler irrigation methods. In general, adoption is found to more likely occur among

growers having lower quality land, higher value crops, a high purchase price for water

or greater depth to groundwater, and more severe drainage problems.

A major focus in the recent literature has been the investigation of the decision-making

process in technological adoption process over time. Many researchers have empirically

investigated the impact of risk factors, credit and information availability and farm size

on adoption behaviour of farmers (Feder and Umali, 1993). However, except for

Koundouri et al. (2006) and Torkamani and Shajari (2008) empirical studies that

investigate irrigation technology adoption under agricultural production risk are rare.

The extant literature on tube-well technology adoption argues that neither the

indivisibility of the technology nor the risk behaviour of adopters has restricted the

adoption of the tube-well technology in Pakistan (Chaudhry, 1990). The very few

studies on the subject have merely documented the extent of adoption among farmers or

the economic returns to the tube-well owners and non-owners (Chaudhry, 1990,

Meinzen-Dick, 1996). Empirical literature that provide a link between the tube well

adoption decisions and variability in farm performance is still sparse.

3.2.1 Adoption of Tube-well Technology in Pakistan

Pakistan was amongst the early adopters of the new agricultural technology during the

Green Revolution (Byerlee and Siddiq, 1994). The most important aspect of the Green

Revolution in Pakistan was the groundwater revolution14. The role of groundwater

14In the early 1960s, massive groundwater extractions were initiated to combat the problem of water logging and salinity in some areas and to meet rising irrigation demands due to growing cropping intensities. Higher yields and economic returns to groundwater users, in subsequent periods, encouraged the farmers to adopt tube-well technology to a great extent.


41

irrigation was protective15 irrigation until the Green Revolution. However, the adoption

of high yielding and water sensitive crop varieties during the Green Revolution changed

crop management. Modern crop varieties increased yield two to three times more than

the traditional varieties but their crop water requirements increased about three times

(Shiva, 1991, Ahmad et al., 2004a). The adoption of new crop varieties led to a rapid

increase in irrigation water demands. Consequently, irrigation water supplies doubled

from 1967 to 1976. With the completion of two major reservoirs Mangla16and Tarbela,

canal water supplies nearly doubled. The installation of private tube-wells expanded the

groundwater supplies by about 8 % during the same period. The overall growth in water

supply helped to turn rain-fed lands into irrigated lands on a vast scale. In the next

decade, 1976 to 1986, canal water supplies remained unchanged while the irrigated area

continued to increase. As a result, increasing irrigation water demands were met

through groundwater extractions. By 1986, pumped water contributed to about 59% of

the total Rabi water requirements (Byerlee and Siddiq, 1994).

In early 1960s, the adoption of tube-well technology was facilitated by government

support policies such as rural electrification, subsidization of electricity, diesel and

drilling services, free pump sets and low interest long-term loans (Falcon and Gotsch,

1968, Papanek, 1968, van Steenbergen and Oliemans, 2002, Johnson, 1989 ). The

objective of these policies was to control waterlogging in high water table areas and to

encourage agricultural production in areas with limited canal water supplies

(Steenbergen and Oliemans, 2002). Later, higher yields and greater economic returns

(Meinzen-Dick, 1996) encouraged farmers to adopt tube-well technology and transition

into growing water intensive crops such as sugarcane and rice (Muhammad, 1964,

Muhammad, 1965, Falcon and Gotsch, 1968, Nulty, 1972). Consequently, the number

of tube-wells which was limited to less than 30 thousand in the early 1960s now has

exceeded over one million. Owing to the recent and rapid declining of groundwater

tables and the number of tube-wells, ensuring sustainable groundwater extraction has

become a policy imperative in Pakistan. Knowing farmer’s adoption decisions about

15The notion “protective irrigation” means to design and operate an irrigation system based on the principle that the available water should be spread equitably in order to cover as many farmers as possible without taking into consideration the full crop water requirements (Jurriens et al., 1996) 16The Mangla Reservoir is located on the Jehlum River. It was completed in 1967. The Tarbela Dam is situated on the Indus River and is the largest earth filled dam in the World. It was completed in 1976.

http://en.wikipedia.org/wiki/Indus_River


42

tube-well technology under the prevailing water shortage scenarios may help in

informing policy makers in designing appropriate groundwater management policies.

3.3 Theoretical Framework

It is known that farmers’ attitude to risk will affect their adoption of some technologies.

Empirical evidences suggest that most farmers are risk-averse (Antle, 1987, Saha et al.,

1994, Dercon, 2004). In particular, farming households in low-income countries are

generally more risk-averse and adopt different strategies to minimise risk impacts. Risk-

averse decision makers may experience welfare losses due to variability (as measured

by the variance) in consumption or production (Dercon, 2004, Kim and Chavas, 2003).

However, variance may not completely capture the degree of risk exposure nor identify

unexpected extreme events (Di Falco and Chavas, 2006). In this study, we go beyond

the mean-variance framework to test whether farmers also consider downside profit risk

and outlier activity such as extreme events when making decisions about the adoption of

new irrigation technology i.e., tube-well technology.

Since farming households are risk-averse and face water scarcity, we employ an

expected utility maximization framework based on Koundouri et al. (2006) to represent

adoption decisions under depleting groundwater resources. We conjecture that the farm

household j is risk averse and uses a vector of conventional inputs jx together with

applied irrigation water jwx to produce a single output q and profit j through a

technology described by a well-behaved (i.e., continuous and twice differentiable)

production function f . Let jp denote output price and jr the corresponding vector of

input prices for the household j . The farm is assumed to incur production risk as crop

yield might be affected by the climatic conditions. This risk is represented by a random

variable j , whose distribution G is exogenous to the farmer’s decisions. This is the

only source of risk we consider as jp and jr are assumed to be non-random (i.e., farmers

are assumed to be price takers in both output and input markets).

Unlike Koundouri et al. (2006), in this study we deal with the adoption of tube-well

technology which does not necessarily increase efficiency of irrigation water like

sprinkler and drip irrigation. By contrast, tube-well ownership ensures more promising

irrigation water supplies and hence lessens production uncertainties (Qureshi et al.,

2009). Allowing for risk aversion, the farmer’s problem is to maximize the expected

utility of profit such as:


43

j j jw jw jw j jj

j jw j jwx ,x x ,x3.1 Max U Max U p f ,x ,x r x r x dG

where U is the von Neumann-Morgenstern utility function. Assuming that jp and jwr

are non-random, the first order condition for groundwater irrigation water input can be

rewritten as follows:

j jw j

jw j

jw

f ,x ,x3.2 E r U E p U

x

j jw j jwj jw jjw

j

j jw

cov U ; f ,x , / xf ,x ,r3.3 E p Up x E[U ]

xx

where

U U . In the case of a risk-neutral farmer, the first term of Equation 3.3 i.e.,

the ratio of input price to output price jw jr p is equal to the expected marginal product

of irrigation water. However, for a risk-averse farmer the second term in the right hand

side of the relation Equation 3.3 is different from zero and measures deviations from the

risk-neutrality case. More precisely, this term is proportional and is opposite in sign to

the marginal risk premium with respect to the irrigation water input (Koundouri et al.,

2006).

Let us now incorporate into the above general model, the farmer’s decision whether or

not to adopt tube-well technology. This decision can be modelled using a binary choice

model, where a farmer can choose to adopt (A=1) or not (A=0). Suppose the farmer is

fully aware of the use and future costs and benefits of the tube-well technology at the

time of adoption, adopting the new technology implies a fixed cost 1 0I 0 and I 0 and

might change the marginal cost of water 1 0jw jwr r . Denote 1 0

j jx x the optimal input

use by the adopters and non-adopters. A farmer will decide to install a tube-well if the

expected utility with adoption 1E U is greater than the expected utility without

adoption 0E U such that:

1 03.4 E U E U 0

where

j j1 1 1 1j jw j jw

j j j0 0 0 0j jw j jw

1 1 1 1 1 1 1jw j jw jw j

,x ,x

0 0 0 0 0 0 0jw j jw jw j

,x ,x

3.5 Max U Max U p f x , r x I dG

and

3.6 Max U Max U p f ,x , r x I dG

x x

x x

x r x

x r x


44

Both Equation 3.5 and Equation 3.6 are the expected utility for an adopter and non-

adopter respectively. For the risk-averse farmer, the first order condition for water input

corresponding to the case of adoption and non-adoption is given by Equation 3.7 and

Equation 3.8 respectively:

jj

j

j

1 1 11 11jw j jwjw jjw

1jw

cov U ; f ,x , / xf ,x ,r3.7 E p Up x E[U ]

xx

jj

j

j

0 0 00 00jw j jwj jjw

0j

cov U ; f , x , / xf , x ,r3.8 E p Up x E[U ]

x x

As the tube-well technology ensures more reliable access to irrigation water and offers

farmers more promising supplies and better control over spatio-temporal irrigation

water requirements, the option to invest in tube-well technology is likely to be valuable.

Now, assume that future profit flows after adopting tube-well technology are known

with certainty as a result of more reliable access to irrigation water. Installing a tube-

well entails a sunk cost due to either uncertainty associated with falling groundwater

tables or deteriorating groundwater qualities. The magnitude of the sunk cost depends

on tube-well construction/installation costs, the rate of obscelence of the technology and

the rapidity with which groundwater tables fall to render the investment less effective.

The magnitude of these sunk costs is likely to affect adoption of the technology and is

an uncertain cost conditional on the farmer’s perceptions about groundwater quality,

declining rates of water tables and the perceived impacts of water shortage on future

cropping patterns, production and grain prices. Farmers may delay adoption to acquire

more knowledge about groundwater level or quality rather than rush to adopt. The

farmers will decide to adopt if:

1 03.9 E U E U VI

where VI 0 represents the value of information diffusion for the farmers who might be

willing to adopt the technology and this should depend on the fixed cost of investment,

level of uncertainty related to future outcomes of the technology and the farmer’s own

characteristics.

3.3.1 Empirical Estimation Procedure

At the time of the survey, some of the farmers were found to be adopters whilst others

were non-adopters, and hence we cannot estimate the structural Equation 3.9. Rather we


45

estimate a reduced form of this equation and focus on the impact of risk to explain the

adoption decisions. First, in order to avoid specifying a functional form for the

probability function of profit , the distribution of risk G , and farmer’s risk

preferences i.e., utility function U , we use a moment based approach which allows a

flexible representation of risk (Antle, 1983, Antle, 1987, Koundouri et al., 2006).

Production risk and thus profit uncertainty are accounted for in the adoption model by

using sample moments of the profit distribution as explanatory variables. As we

explained earlier, another source of risk associated with adoption could be due to falling

groundwater tables and increasing salinity levels in groundwater supplies. This cost of

uncertainty is represented by a premium (VI ) in Equation 3.9 which indicates the value

of seeking information either to confirm their perceptions or misperceptions about the

pros and cons before investing in the technology. In the empirical model, we use the

farmer’s education, access to different sources of information e.g., radio, television and

newspaper etc., and access to agricultural extension services as a measure of human

capital.

The econometric estimation procedure to analyse the impact of production risk on the

adoption process is a two stage procedure. Firstly, we compute the four sample

moments of profit distribution of each farm i.e., the coefficients of the mean, variance,

skewness and kurtosis. Many empirical studies have focused on estimating the first two

central moments i.e., mean and variance e.g., Just and Pope (1979) and Traxler et al.

(1995). However, we can go beyond the mean and variance and can get consistent

estimates of all relevant central moments econometrically i.e., skewness (Antle, 1983,

Kim and Chavas, 2003) and kurtosis (Koundouri et al., 2006). In this study we derive

the first four central moments of the profit distribution following (Kim and Chavas,

2003, Koundouri et al., 2006). In the second stage we incorporate the estimated central

moments of the profit distribution along with other explanatory variables and farm

characteristics into a probability model in order to analyse how production risk affects

the decision to adopt the tube-well technology.

Firstly, profit is regressed on the farm level input variables to estimate the “mean”

effect. The model takes the following general form:

j jw j j j3.10 f x , , ; u x z β

where j is the value of crop production i.e. profit of a household jwith j 1,...., N

denoting individual farms in the sample, jx is the vector of inputs (seed, labour,


46

chemical, fertilizer and farm machinery, etc.), jz is the vector of extra shifters including

farmer’s characteristics (farmer’s age, education and off-farm income and other farm-

specific characteristics), ju is the usual identically independently distributed error term

which captures unobserved variations in crop production and production shocks while

β is the vector of parameters to be estimated. The Ordinary Least Squares (OLS)

estimation of Equation 3.10 gives consistent estimates of the parameter vectorβ . Then

the thj central moment of profit j 2,...,m is defined as:

j 1

j3.11 E

where 1 represents the mean or first moment of profit. The estimated errors from the

mean effect regression j j jw j ju f x , , ; x z β are estimates of the first moment of

profit distribution. The estimated errors ju are then squared and regressed on the same

set of explanatory variables:

jw j j j2j3.12 u g x , , ; u x z δ

The Ordinary Least Square (OLS) estimates of Equation 3.12 provide consistent

estimates of the parameterδ . The predicted values 2ju from Equation 3.12 are consistent

estimates of the second central moment i.e., variance of the profit distribution (Antle,

1983). We estimate the third and fourth moment i.e., skewness and kurtosis of profit

distribution by raising the estimated errors from the mean regression model to the power

of three and four. The four estimated moments are then incorporated into a discrete

model of technology adoption along with farmer’s structural and demographic

characteristics.

Given the expected utility maximization assumption and the additional value of

information (VI ), a farmer will only choose to adopt tube-well technology if:

* 1 0j3.13 Y E U E U VI 0

*jY is an unobservable random index for each farmer that defines their propensity to

adopt tube-well technology.

We assume that a farmer’s decision to make investments in installing a tube-well is also

based on their perceptions about groundwater characteristics i.e., groundwater table

falling rates, salinity levels and the level of uncertainty related to the groundwater


47

characteristics etc. Moreover, farmers are likely to seek information according to their

perceptions. In order to capture the impact of the farmer’s perceptions on adoption of

tube-wells, we incorporate three dummy variables i.e., salinity perception (1=yes,

0=no), water table decline (1=yes, 0=no) and the farmer’s perceptions of the impact of

these changes on future cropping patterns (1=yes, 0=no) in the probability model.

The estimation of the indirect utility (per year) of farmer j if he is a non-adopter is

denoted as:

0 j 0 j 0 0 j 0 j 0 jm k0 0 ,3.14 Y z α m α k α

The indirect utility (per year) of farmer j if he is an adopter is given as:

1 j 1 j 1 1 j 1 j 1 jm k1 1 ,3.15 Y z α m α k α

where jz is a vector of regressors including all structural and demographic

characteristics , jm is the vector of four profit moments that introduce uncertainty into

the model, jk is the vector of farmer’s perception , is a vector of parameters to be

estimated and j is the error term.

From Equation 3.14 and Equation 3.15, the probability of farmer j adopting tube-well

technology is represented by the following model:

r j 0 j 1j3.16 P Y 1 = Y Y ,

jPr[ ] [ ]where

j j j jjm k m k

j z α m α k α , z α m α k α

j 0j 1j j 0j 1j j 0j 1j j 0j 1j 1 0m m m k k k

1 0 1 0ν =ν -ν ,z =z -z ,m =m -m ,k =k -k ,α=α -α ,α =α -α and

The binary choice model in Equation 3.16 is estimated using a probit model, i.e.,

assuming that j is 2N 0, and that is a cumulative normal distribution. Since we

incorporate estimated profit moments in the probit model, we use a bootstrapping

procedure to obtain consistent estimates of the corresponding standard errors (Politis

and Romano, 1994).

3.4 Data Descriptions

3.4.1 Salient Features of Study Districts

The study area is a part of the mixed-cropping zone and cotton-wheat zone of the

Punjab province of Pakistan. The mixed-cropping region is the alluvial plain between


48

the rivers Ravi and Chenab while the cotton-wheat region lies between the rivers Ravi

and Sutlej. Both regions have arid to semi-arid continental subtropical climate with long

hot summers and cool winters. The mean annual rainfall is also very low with 360 mm

in the mixed-cropping zone and 120 mm in the cotton wheat-zone. Due to the arid and

semi-arid climate, agriculture in these regions is highly dependent on irrigation water.

However, due to scant canal water supplies agriculture heavily relies on groundwater as

a major source of irrigation water in some districts within these regions. The study area

in the Jhang district is solely groundwater irrigated while in the Lodhran district some

irrigation via canal water is available. In Lodhran, canals are used to supply water

during the Kharif season only. For instance, the canal water contribution during the

Kharif season of 2010 was observed to be between 20-44 percent of the total irrigation

requirement across different farms. The shortfall of the irrigation water comes through

groundwater.

The study areas in both districts are characterised by deep groundwater tables which

require high installation costs for tube-wells. The installation costs for a 24 metre tube-

well are seven times those for a 6 metre tube-well (Qureshi et al., 2003). The variation

in the bore depth was observed to be between 60 metres to 99 metres in Lodhran and

between 33 metres to 57 metres in Jhang district. Due to limited tube-well ownership in

some parts of these districts, farmers who do not own a tube-well opt to buy water from

their surrounding tube-well owners to meet their crop water requirements. Such

groundwater transactions occur as a result of social contract under informal

groundwater markets.

Farm size usually plays an important role in informal trading of groundwater in the

Indo-Pak region. Large farms are often involved in selling groundwater (Meinzen-Dick,

1996, Shah et al., 2008). However, due to electricity shortage and growing cropping

intensities, large farms now have less surplus water. Currently, only medium sized

farms or large farms having more than one tube-well are mostly involved in selling

groundwater. Since water buyers (non-adopters) get water after the tube-well owners

have irrigated their own fields, they face more uncertainties in getting irrigation water

and are more prone to crop failures than the tube-well owners (adopters). Moreover,


49

variable higher prices for water paid by buyers (3-4 times more17) also add into

uncertainties for groundwater purchasers.

3.4.2 Data Descriptions

The data used in this study is collected from two districts i.e., Lodhran, a cotton-wheat

region18 and Jhang, a mixed-cropping region of the Punjab province, Pakistan. The data

were collected using a detailed survey during the Kharif season in 2010. Based on a

multi-stage sampling technique, a cross-sectional sample size of 200 farms was

randomly selected. In the first stage, one tehsil19 was selected purposively from each

district. In the next stage, 10 villages were selected at random from each selected tehsil.

A village usually comprises of between 70-80 farming households in the study districts.

Finally, from each village 10 groundwater users (5 adopters of tube-well technology and

5 non-adopters) were selected randomly, thus having 50% adopters and 50% non-

adopters in the study.

The survey provides detailed farm level information about production patterns, input

use, and output produced, gross revenues, structural characteristics and the number of

farms that either adopted tube-well technology or did not. Various inputs are measured

such as: (1) seed and fertilizer in kg/acre; (2) pesticide and farm operations as cost of

each application/acre; (3) labour in hours/acre; and (5) groundwater use in cubic

metres/acre. Output is measured in kg/acre as well. In order to calculate profit, total

crop revenue and different inputs and output costs were collected in Pakistani Rupees20.

The estimation was done at a farm level rather than at a household level.

Table 3.1compares selected variables used in the estimation. Overall, we see that the

average farm size for adopters is larger than that of non-adopters. On average, adopting

farms have 10.05 acres under cotton cultivation and for non-adopters it is 5.5 acres.

Similarly, adopting farms have 12.08 acres under wheat cultivation compared to 6.02

17Tube-well owners pay only extraction cost for groundwater while water buyers have to pay wear and tear charges (in other words some profit as well to the tube-well owner) along with the extraction costs. 18Due to climatic variations and the nature of cropping patterns, the Punjab province is classified into five cropping regions; barani region, mixed cropping region, rice-wheat region, cotton-wheat region and pulses-wheat region. 19Tehsil is an administrative unit. A district usually comprise of 5-6 tehsils (sub-districts) in Pakistan. 20Average exchange rate at the time of data collection (June-November 2010) was Rs.85.25/US$.


50

acres for non-adopters. There is no statistical significant difference in the use of

different inputs between adopters and non-adopters except for the chemical use in

cotton cultivation and fertilizer use in wheat cultivation. However, adopting farms

generate, on average, more profit on per acre basis for cotton and wheat compared to

non-adopters.

Table 3.1: Summary statistics of the variables for cotton and wheat crops

Economic Data Adopters Non-adopters Mean Std. Dev. Mean Std. Dev. Cotton Farm size (acres) 10.05*** 6.78 5.47 4.33 Farm production (kg/acre) 838.25 177.08 821.47 181.70 Seed quantity (in kg/acre) 8.31 1.29 8.31 1.35 Labour (hours/acre) 326.84 55.63 328.34 51.68 Fertilizer (kg/acre) 215.00 63.07 200.82 56.60 Chemical input (Rs./acre) 4480.89* 1157.37 4219.75 1361.01 Machinery cost (Rs./acre) 3962.37 757.59 4050.60 898.98 Irrigation water (m3/acre) 2277.67** 424.80 2130.28 362.30 Total cost (Rs. /acre) 34011.10 4494.16 36729.10*** 4989.10 Total revenue (Rs. /acre) 74250.97 17541.13 71239.74 16914.02 Profit (Rs. /acre) 40239.88** 15899.22 34510.65 16069.19 Wheat Farm size (acres) 12.08*** 5.50 6.02 3.74 Farm production (kg/acre) 1556.58** 205.94 1475.59 224.07 Seed quantity (in kg/acre) 57.38 5.77 58.75 6.83 Labour (hours/acre) 56.48 22.97 61.70 37.58 Fertilizer (kg/acre) 201.00** 41.73 181.85 41.88 Chemical input (Rs./acre) 1324.76 458.36 1324.27 467.46 Machinery cost (Rs./acre) 4024.37 738.06 4374.60** 994.32 Irrigation water (m3/acre) 783.35 318.00 775.62 338.67 Total cost (Rs. /acre) 18475.24 2467.10 19894.46*** 2963.09 Total revenue (Rs. /acre) 35427.74*** 5047.03 33337.13 5197.05 Profit (Rs. /acre) 16952.50*** 4987.89 13442.67 4897.39

Note: The null hypothesis is that the mean difference between two subsamples (i.e., adopters and non-adopters) is not statistically significant. Double asterisks indicate statistically significant difference at 5%, triple asterisks indicate significance at 1% in the respective variables between two subsamples.

On average, adopters generate Rs. 40,239 profit from one acre cotton cultivation, or

16% more profit than non-adopters who generate on average Rs. 34,510.

Both adopters and non-adopters, on average, use similar seed rate (approximately 8

kg/acre) for cotton cultivation. There is also not a significant difference in the number

of hours worked on cotton farms, with adopters working on average 326 hours per acre


51

compared to non-adopters with 328 hours. Adopters and non-adopters do not use

significantly different amounts of fertilizer in cotton cultivation. However, there are

statistically significant differences in chemical applications between adopters and non-

adopters with adopters being greater users. The average irrigation water use for adopters

is 2,277 m3 in contrast to 2,130 m3 for non-adopters. Finally, the average per acre cotton

yield is 838 kg for adopters versus 821kg for non-adopters.

Table 3.2: Summary statistics of the variables used in the probability model

Adopters Non-adopters Variable Mean Std.

Dev. Mean Std.

Dev. Farm Characteristics Farmer’s Age (years) 43 9 44 8 Land tenureship (1=owner, 0=tenant) 0.99 0.10 0.65 0.48 Off-farm income in Rs. 91,220 2,20,090 50,236 84,489 Farm debt in Rs. 32,000 68,854 41,333 60,207 Access to credit services (0=no, 1=yes) 0.25 0.435 0.47 0.502 Farmer’s education (years of schooling) 5.87 4.47 3.67 3.62 Access to extension services (1=yes, 0=no) 0.51 0.50 0.09 0.29 Access to information sources (0=no, 1=yes) 0.52 0.502 0.131 0.339 Salinity perception (1=yes, 0=no) 0.26 0.44 0.26 0.44 Water table decline perception (0=no, 1=yes) 0.54 0.50 0.58 0.50 Effect on future cropping pattern (1=yes, 0=no)

0.73 0.45 0.75 0.44

We find a similar difference between adopters and non-adopters for wheat cultivation in

the use of different farm inputs. Adopters on average work 56 hours versus 61 hours for

non-adopters. Both adopting and non-adopting farms slightly differ in the seed rate,

with an average of 57 kg/acre for adopters and 58 kg/acre for non-adopters. In contrast

to cotton cultivation, adopters use significantly higher amounts of fertilizer compared to

non-adopters. However, both adopters and non-adopters do not significantly differ in

chemical use in wheat cultivation. The average irrigation water use for adopters is

783m3 and 775 m3 for non-adopters. The average wheat yield is 1,556kg/acre for

adopters versus 1,475kg/acre for non-adopters. Adopters generate Rs. 16,952 profit

from one acre wheat cultivation, or 21% more profit than non-adopters who generate on

average Rs. 14,442.

Table 3.2 presents information on the socio-economic characteristics of the surveyed

farms. It is evident that age is not a decisive factor in adopting a tube-well technology

because both adopters and non-adopters on average are very similar in age, 43 years and


52

44 years respectively. The majority of adopters (99%) cultivate their own land,

indicating that land owners are more likely to have a tube-well compared to tenants. The

difference in off-farm income shows that adopters, on average, generate 45% more from

off-farm business compared to non-adopters suggesting that off-farm income may play

an important role in adopting a tube-well. Off-farm income provides adopters with

additional financial resources and, perhaps indicative of their greater financial strength,

they owe 23% less farm debt compared to non-adopters. Information on access to credit

services indicates that higher proportion of non-adopters generally apply for credits

from different crediting agencies. There are statistically significant differences

regarding access to extension advice and to other information sources e.g., radio,

television and newspapers, etc. The average education level of adopters is 6 years of

schooling whereas for non-adopters the average education is 4 years of schooling.

Statistically there is a significant difference in education attainment between adopters

and non-adopters education at 0.05% level of significance. Non-adopters spend 12.5%

more on irrigation related expenditures compared to the adopters. There is little

difference in perceptions of adopters and non-adopters about the salinity level, rate of

decline in groundwater tables and their impact on future cropping patterns. The lack of

difference in perceptions about the salinity contents in groundwater between adopters

and non-adopters could be due to the reason that they both use water from the same

tube-well.

3.5 Results and Discussion

Estimation results of the bootstrapped probit model are presented in Table 3.3.The

statistical significance of the impact of three (first, second and fourth) out of four

distributional moments suggests that decision makers are not risk-neutral. Even though

most distribution functions are well approximated by their first three moments, the

estimation of the fourth moment helps to understand decision makers’ responses under

extreme events (Koundouri et al., 2006, Antle, 1983). Since moments of profit

distributions are assumed to be exogenous to a farmer’s adoption decision, their signs in

the probit model indicate that farmers who are more risk averse are more likely to install

a tube-well. The majority of the farmer’s characteristics are highly significant regarding

the choice of adopting tube-well technology. The age and education of the farmer do not

play a significant role in the adoption process. However, the statistically significant

association between tube-well adoption and land tenureship suggests that land owners

are more likely to adopt a tube-well than tenants or non-land holders.


53

Table 3.3: Estimation of the results for the probability of adopting a tube-well

Variable Estimate Bootstrapped Std. Error

t-Ratio

Household and farm characteristics Age 0.008 0.011 (0.73) Land tenure status (0=tenants, 1=owners) 2.298*** 0.395 (5.83) Percentage of farm income spent on irrigation -0.681*** 0.213 (-3.20) Off-farm income Rs. 0.904*** 0.190 (4.76) Farm debt in Rs. -0.015 0.061 (-0.24) Access to credit services (0=no, 1=yes) 0.033 0.012 (0.84) Education (years of schooling) 0.007 0.028 (0.23) Access to extension services (0=no, 1=yes) 1.253*** 0.246 (5.10) Access to sources of information (0=no, 1=yes) 1.015*** 0.213 (4.76) Water scarcity perceptions Salinity perception (0=no, 1=yes) -0.288 0.235 (-1.23) Water table decline perception (0=no, 1=yes) -0.014 0.114 (-0.13) Change in cropping pattern perception (0=no, 1=yes)

-0.165 0.218 (-0.76)

Profit moments First moment 0.485* 0.278 (1.74) Second moment 7.858*** 1.746 (4.50) Third moment 0.634 1.017 (0.62) Fourth moment -2.683*** 0.846 (-3.17) Constant -3.617*** 0.676 (-5.35) Valid chi2 97.79 McFadden's R2 0.518

Note: *, **, *** indicate significance at 10%, 5% and 1% levels respectively. Number of

bootstraps=2000

In our study sample, 82% of respondents are land owners while the remaining 18% are

tenants or they have rented in land for farming. Because tube-well installation requires a

large up-front investment and is not a portable technology (i.e. a potentially stranded

asset), tenants put a much lower value on adopting a tube-well. Moreover, the presence

of water markets where tenants have the option to buy water does not make it necessary

to have their own tube-well. By illustration, Carey and Zilberman (2002) found that

water markets may induce farmers to delay the adoption of irrigation technology.

The estimated results indicate that off-farm income significantly increases tube-well

adoption. Since off-farm income helps to bear unexpected farming outcomes and

ensures a consistent income, this financial security may allow some farmers to invest in

installing tube-well. In other cases, the off-farm income may actually be one


54

ramification of the farmer investing in a tube-well, whereby greater profits from using

the tube-well fund off-farm investments.

Similar to farm debts, access to credit services do not have significant impact on tube-

well adoption. A possible explanation is that usually small holder farmers get small

amounts of credit that is not sufficient to install a tube-well. Access to extension

services and different sources of information both have statistically significant positive

impacts on tube-well adoption. These findings suggest a positive value on waiting for

better information before deciding to adopt. Also adopters may inherently seek contact

with agricultural extension staff and use different other sources of information not just

to facilitate decisions over tube-well adoption but for many other agricultural decisions.

All the three explanatory variables representing farmer’s perceptions about groundwater

resource i.e., perception about salinity, groundwater table decline and potential impact

on future cropping patterns, do not seem to significantly impact the tube-well adoption.

The role of risk in a farmer’s adoption decision is highlighted through the significance

of the sample moments of the profit distribution. The first and the second moments,

which approximate mean profit and profit variance, are highly significant while the

fourth moment (kurtosis) is marginally significant. The third moment, i.e. skewness is

not statistically significant. The results indicate that the higher the expected profit the

greater the probability that a farmer decides to adopt a tube-well technology. In other

words as the mean or expected profit increases, the affordability to install a tube-well

also increases. Similarly, in case of variance, we see that with increasing variance the

probability of adopting tube-well increases significantly.

More generally, the higher is the variance of profit (and greater the probability of facing

extreme profit values), the greater is the probability to adopt tube-well. Based on these

results we can infer that: 1) since tube-well installation requires a large up-front

investment, farmers prefer to reduce production risks in order to get consistent and

reliable profits; 2) under uncertain water supplies for irrigation, farmers generally tend

to install tube-well to improve the reliability of their water supplies, especially to hedge

against crop failures. However, non-significant third moment (skewness) indicates that

downside yield risk does not have significant impact on tube-well adoption. However,

highly significant fourth moment may indicate the propensity to adopt will decrease

significantly as a result of extreme events. We estimate the marginal effects of each

explanatory variable by calculating their derivatives at their means. These derivatives


55

are reported in Table 3.4 and represent the marginal effect of each regressor,

approximating the change in the probability of adoption at the regressor’s mean.

As shown in Table 3.4, the sample moments of profit distribution, in particular mean,

variance and kurtosis affect a farmer’s decision to adopt tube-well technology and

confirm that farmers are not risk-neutral but rather are likely to be risk-averse.

Table 3.4: Marginal effects of the explanatory variables

Variable Estimate Bootstrapped Std. Error

t-Ratio

Household and farm characteristics Age 0.003 0.004 (0.72) Land tenure status (0=tenants, 1=owners) 0.649*** 0.048 (13.56) Percentage of farm income spent on irrigation

-0.265*** 0.095 (-2.78)

Off-farm income in Rs. 0.361*** 0.076 (4.75) Farm debt in Rs. -0.005 0.024 (-0.23) Access to credit services (0=no, 1=yes) 0.013 0.012 (0.64) Education (years of schooling) 0.002 0.011 (0.21) Access to extension services (0=no, 1=yes) 0.4454*** 0.071 (6.29) Access to sources of information (0=no, 1=yes)

0.378*** 0.069 (5.41)

Water scarcity perceptions Salinity perception (0=no, 1=yes) -0.115 0.093 (-1.24) Water table decline perception (0=no, 1=yes) -0.014 0.114 (-0.13) Change in cropping pattern perception (0=no, 1=yes)

-0.066 0.086 (-0.77)

Profit moments First moment 0.192* 0.110 (1.74) Second moment 3.130*** 0.692 (4.52) Third moment 0.253 0.405 (0.63) Fourth moment -1.068*** 0.336 (-3.18)

Note: *, **, *** indicate significance at 10%, 5% and 1% levels respectively.

The highest marginal effect arises from the second moment (i.e. profit variance)

followed by land tenure status, access to extension services and access to different other

sources of information. A 1% increase in the value of these variables, ceteris paribus,

results in an increase in the probability of adoption by 3.13%, 0.65%, 0.45% and 0.38%,

respectively. The statistically significant relationships between access to agricultural

extension services, and access to different other sources of information and likelihood

of adoption suggests that quasi-option value (value of waiting to get more information)

may play an important role in adoption decisions.


56

3.6 Conclusions

In this article we employed a moment-based approach to analyse farmers’ decisions to

adopt tube-well technology to hedge against production risks associated with

diminishing irrigation water supplies due to declining groundwater tables. We find that

farmers are not risk-neutral and that uncertain water supplies add to future uncertainty

relating to crop production and consequently profits.

We estimated an adoption model using a randomly selected sample of 200 farming

households located in two different districts of the Punjab province in Pakistan. The

estimation procedure followed two stages. In the first stage, we estimated the first four

sample moments of the profit distribution, namely the mean, variance, skewness and

kurtosis coefficients. In the second stage, we incorporated the estimated moments along

with other explanatory variables into a probit model to analyse how production risk

affects the decision to adopt tube-well technology.

We find that the sample moments of the profit distribution affect the farmers’ adoption

decisions. The first, second and fourth sample moments of profit (mean, variance and

kurtosis) are significantly associated with the probability to adopt tube-well technology,

thus confirming that farmers are not risk-neutral. Estimates show that the higher the

expected profit the greater the probability that a farmer decides to adopt a tube-well

technology. We also find that the probability of adopting tube-well increases

significantly with increasing variance of profit. These results imply that the farmers

adopt tube-well technology in pursuit of greater expected profits and more reliable

profits, generated by more reliable access to water resources that provide a hedge

against production risks.

However, as a result of production risks due to crop failures farmers face profit

uncertainties and some farmers, due to low or inconsistent profits, may not have

sufficient means to invest in tube-well technology. Most of the farmer’s own

characteristics are also highly significant in the choice of adopting tube-well

technology. Farmers with higher off-farm income, better access to agricultural

extension services and different sources of information, and those who cultivate their

own lands are found to be more likely to be tube-well owners. Farmers’ perceptions

about the magnitude and reliability of groundwater supplies are not an important

indicator of tube-well adoption. The statistically non-significance of the variables

representing farmers’ perceptions about groundwater levels and quality indicate that

farmers neither consider lowering groundwater tables nor increasing salinity levels


57

when make decisions to adopt tube-wells. Moreover, farmers’ perceptions about the

potential impact of declining groundwater tables on future cropping patterns do not

significantly affect tube-well adoption.

The results have important policy implications. First, encouraging the adoption of tube-

well technology can be a pathway to increasing farm profitability and facilitating

production risk management. However, within the context of declining groundwater

tables sustainable extraction of groundwater aquifers should be encouraged to ensure

the longevity of groundwater resources. As tube-wells serve only to increase access to

irrigation water but do not improve irrigation water use efficiency nor conserve the

groundwater resource, policy interventions that encourage adoption of tube-wells needs

to be accompanied by other policies that require efficient use of the water resource (e.g.

joint adoption of sprinkler or drip irrigation technologies) and that limit extraction in

order to ensure sustainable use of groundwater resources. To establish these multi-

dimensional policies requires assessing the merits of policies that give incentives for

tube-well adoption (e.g., subsidies, long-term loans or provision of adoption related

information) within a larger cost-benefit framework that accounts for groundwater

resource management both in terms of short-term gains (i.e. farm profits) and long-term

future social benefits from water resource management. Balancing the income and food

needs of the current generation with the need for sustainable use of groundwater

resources required to serve the needs of future generations.

59

Chapter 4 4. The Efficiency of Irrigation Water Use and its Determinants21

Chapter 4 estimates the technical and irrigation water use efficiency of groundwater-fed

irrigated agriculture within the context of declining groundwater tables. We apply both

non-parametric and parametric approaches to estimate irrigation water use efficiency in

wheat, cotton and rice cultivation. The irrigation water requirements considerably vary

for these crops and even climatic variability can change irrigation water requirements

for the same crop cultivated indifferent geographic localities. Moreover, farmers may

grow different combinations of different crop such as wheat, rice, cotton, sugarcane,

maize etc., so farm-level estimates of irrigation water use efficiency may not be

rationally generalized at broader level due to heterogeneous crop enterprise choices.

Beyond these points, irrigation water use efficiency estimates derived based on

economic principles are directly comparable to technical efficiency which involves

measurement of managerial capability of the irrigators. Such irrigation water use

efficiency measure is defined as the ratio of minimum feasible to observed use of

irrigation water, conditional on observed levels of the desirable output and conventional

inputs. Hence, dealing with irrigation water use efficiency at a crop level is likely to be

much more useful in guiding irrigation decisions on farms. Hence, we estimate the

irrigation water use efficiency at crop level for two reasons: (i) to guide policy makers

and extension agents who may advise farmers about irrigation water use; and (ii) to

avoid potential aggregation bias of output that arises from inclusion of different annual

and biannual crops such as wheat, cotton, rice and sugarcane which also have different

production technologies, cropping seasons and irrigation water requirements.This

chapter comprises of three sub-chapters , each focusing on the analysis of irrigation

water efficiency of different crops i.e., wheat, cotton and rice.

21This Chapter is based on:

4.1Technical and irrigation efficiency of wheat farms in Pakistan: A nonparametric meta-frontier approach

4.2Econometric approach to estimating technical and irrigation efficiency in cotton farming in Pakistan

4.3Measuring production and irrigation efficiencies of rice farms: evidence from the Punjab, Pakistan

The Efficiency of Irrigation Water Use and its Determinants

61

4.1 Technical and Irrigation Efficiency of Wheat Farms in Pakistan: A

Nonparametric Meta-frontier Approach

(Accepted for publication in International Transactions in Operational Research)


63

Abstract

Given the importance of water in agriculture, this study examines the level of, and

factors affecting technical and irrigation water use efficiency of irrigated wheat farms in

the Punjab, a province of Pakistan. We employ a non-parametric meta-frontier approach

to investigate both technical and irrigation water use efficiency for a randomly selected

sample of 200 groundwater-fed wheat farms in two different cropping zones i.e., a

cotton-wheat region and a mixed-cropping region. The mean technical efficiency (TE)

of wheat farming differs slightly under the metafrontier and groupfrontier estimations.

On average, water sellers (tube-well owners) are found to be more efficient under the

metafrontier and groupfrontier estimates (91% and 94%) compared to water buyers with

the mean TE (90% and 93%). The mean irrigation water use efficiency suggests

substantial inefficiencies among water sellers and water buyers. The metafrontier results

indicate average irrigation water use efficiency (IWE) estimates of 66% and 65% while

the groupfrontiers indicate 71% and 67%. Amongst the most influential factors

affecting TE and IWE are the farmer’s education level, improved seed variety and the

farmers’ perceptions about groundwater resource quality and availability.

4.1.1 Introduction

Agriculture heavily relies on groundwater for irrigation in Pakistan. Over the last half

century, groundwater contribution to overall irrigation water supplies has increased by

almost 50 percent (Byrelle and Siddiq, 1994, Qureshi et al., 2009). The rapid increase in

groundwater use has evolved as a “silent revolution” carried out by thousands of

farmers in quest of reliable irrigation water supplies. During early 1960s, the adoption

of tube-well technology was encouraged by government support policies such as rural

electrification, subsidization of electricity, diesel and drilling services, free pump sets

and soft long-term loans (Falcon and Gotsch, 1968, Papanek, 1968, van Steenbergen

and Oliemans, 2002, Johnson, 1989 ). However, the higher yields and greater economic

returns from the cultivation of high yielding but water intensive crop varieties

(Meinzen-Dick, 1996, Byrelle and Siddiq, 1994) encouraged farmers to adopt tube-well

technology even without government support in subsequent years (Muhammad, 1964,

Muhammad, 1965, Falcon and Gotsch, 1968, Nulty, 1972). As a result of the continued

increase in demand for irrigation water, more and more water supplies were rendered

with groundwater extractions (Shiva, 1991, Ahmad et al., 2004b, Rodel et al., 2009).

Limited to less than 10% in 1960s, the groundwater contribution to total irrigation


64

supplies reached to 40% in the next 25 years (Byrelle and Siddiq, 1994). Having more

than one million tube-wells installed across the country, Pakistan irrigates 5.2 million

hectares of land area through groundwater extractions (Siebert et al., 2010b). As the

result of unrestricted expansion of tube-wells the excessive overdrafting of groundwater

aquifers has led to many negative externalities such as rapid declining groundwater

tables, salt water intrusions and secondary salinity etc. (Kijne, 1999a, Shah et al., 2000,

Khan et al., 2008a, Qureshi et al., 2009).

Given the rapid depletion of groundwater resources and various spatial and temporal

negative externalities, ensuring the sustainable use of groundwater resources has

become a policy imperative rather than a choice. Improving irrigation water use

efficiency is being considered as the best solution towards the sustainable use of

groundwater resources. The objective of this paper is to examine technical efficiency

and the extent of irrigation water use efficiency of groundwater irrigated agricultural

farms in Pakistan. To meet this objective, we use a randomly selected dataset of 200

groundwater-irrigated wheat farms from the Punjab province of Pakistan, including 100

water sellers (tube-well owners) and 100 water buyers (non-owners). We use the data

envelopment (DEA) metafrontier approach to estimate technical and irrigation water

use efficiencies of the selected wheat farms. It is well documented that tube-well

ownership ensures more promising irrigation water supplies and hence lessens

production uncertainties during irregular canal water supplies or uncertainties involved

in purchasing groundwater. The uncertain and delayed water application can have

serious impacts on crop growth and may decrease the marginal product of other inputs

such as fertilizer, labour and chemical inputs. Given that tube-well ownership ensures

reliable access to irrigation water over the spatio-temporal crop water requirements and

not having a tube-well adds into irrigation water uncertainties, we assume that tube-well

owners and water buyers operate under different states of technology. Hence, we

estimate a separate frontier for each group to reveal the difference between the

technology and efficiency levels.

The rest of the paper is organised as follows. The next section provides the background

about informal groundwater markets and the wheat farming system in Pakistan. Section

3 explains methods and Section 4 describes the data and principle features of the study

areas. The results are presented in Section 5. The final section draws conclusions and

provides some policy implications.


65

4.1.1.1 Nature of Groundwater Markets

Although tube-wells ownership has been on the increase, thousands of smallholder

farmers (usually subsistent farmers and tenants) still do not own tube-wells. Those who

do not own tube-well, irrigate their lands by buying surplus pumped water from their

nearby tube-well owners (Meinzen-Dick, 1996, Qureshi et al., 2009). Such informal

groundwater marketing offer economic benefits to tube-well owners and opportunities

to non-owners to increase agricultural productivity by increasing access to irrigation

water (Manjunatha et al., 2011, Meinzen-Dick, 1996, Shiferaw et al., 2008). Informal

groundwater markets are reported throughout Pakistan but are more common in the

Punjab and Balochistan provinces (Khair et al., 2012, Meinzen-Dick, 1996). Markets

for groundwater generally function under social settings and are greatly influenced by

the social ties between the tube-well owners and water buyers. These markets involve

informally selling groundwater from the private tube-wells without involving the

exchange of permanent water rights (Meinzen-Dick, 1996, Rinaudo et al., 1997b, Khair

et al., 2012).

Access to groundwater resources in Pakistan is open and generally tied to land-

ownership. In the absence of permanent groundwater entitlements, farmers who have

capacity to invest in tube-well technology have exclusive control over groundwater

resources. The right to extract groundwater is not defined and confined. A tube-well

owner can extract and sell groundwater without any interference either under the

customary law or the local social setting (Meinzen-Dick, 1996, van Steenbergen and

Oliemans, 2002). Since these markets are not formally regulated, sometimes tube-well

owners prefer certain water buyers due to social ties with them, thus discriminating to

whom to sell water (Shah, 1993, Jacoby et al., 2004, Khanna, 2007).

It is believed that informal groundwater markets improve the equity of access to

groundwater and are playing an important role in addressing water scarcity (Khair et al.,

2012). However, informal groundwater markets may not fully convey the scarcity value

of water, and can encourage over extraction of groundwater resources (Meinzen-Dick,

1996). Hence, within the context of rapidly declining groundwater tables it is important

to assess the extent of groundwater use efficiency in irrigation under such informal

groundwater market structure.


66

4.1.1.2 The Wheat Farming System in Pakistan

Wheat is the most important crop in Pakistan due to its importance in the national food

security and economic development. It is grown under different agro-climatic and

geographic environments but nearly all the wheat crop is cultivated on irrigated lands.

Wheat holds an important position in the agricultural and national economy, accounting

for 14.4 percent of the value added in agriculture and 3.1 percent of the country’s gross

domestic product (GOP, 2010-11). Being the staple food, wheat occupies the largest

portion of farmland, with 9.13 million hectares being devoted to wheat production each

year. In 2010/11 period, Pakistan produced 23.3 million tonnes of wheat, and was

ranked 7th among the world's wheat producing nations (FAO, 2012b, GOP, 2010-11).

Despite having such an important role in the national economy, wheat production has

been facing widespread stagnation in per hectare yields for more than a decade.

Inefficient management practices at the farm level and uncertain water supplies are

considered some of the major reasons for low wheat productivity (Ahmad et al., 2002b).

Pakistan was among one of the early adopters of the Green Revolution (1966-76)

technologies. The diffusion and adoption of semi-dwarf wheat varieties and associated

inputs accelerated wheat growth to 5.1% during the 1970s (Byrelle and Siddiq, 1994).

However, this growth in wheat yields could not be sustained. In the Post-Green

Revolution period the growth rate in wheat yields fell to 2.7% (Farooq and Iqbal, 2002).

Later, as a result of various policies and considerable expansion in the irrigated area,

Pakistan achieved self-sufficiency in wheat production at the beginning of the 2nd

millennia. However, due to the scarcity of new arable land and increasing water

shortage, area expansion is no longer a viable strategy to increase wheat production.

Given the water shortage backdrop, the widespread stagnation in wheat yields has

prompted research efforts to improve efficiency and productivity of wheat farming and

to make efficient use of dwindling water resources in Pakistan.

4.1.2 Methodological Framework

4.1.2.1 The Basic Analytical Metafrontier Framework

The analytical framework is used to assess the technical efficiency using the data

envelopment analysis method. This entails estimating separate frontiers for each group

and a metafrontier is then estimated by pooling all observations together for all groups

(O’Donnell et al., 2008).


67

Let y and x denote non-negative output and input vectors of dimensions N 1 and M 1

respectively. We consider the case of a group of K farms ( where k 1 ) where each

farm operates under a specific technology kT k 1,2,....., k .

The technology set contains the set of all feasible output and input vectors.

p q4.1.1 T x, y R | x can produce y

We can define the input and output sets associated with the production technology set T,

which provides an equivalent representation of the production technology. The input set

for a specific output vector y is the set of all input vectors x which can produce y:

4.1.2 X y x : x, y T

The output set for a specific vector of input x is the set of all output vectors y that can

be produced using x:

4.1.3 P x y : x, y T

In a production process, the boundary of the output set is the production possibility

frontier and it represents technically efficient farms. This boundary envelope the set of

all technically efficient farms and can be regarded as the output metafrontier. For

instance, a particular output y can be produced using input vector x in one of the groups,

then (x, y) are considered as part of the metatechnologyT , which is defined by

O’Donnell et al. (2008) as:

*

1 2 k

such that x can produce y in at

least one of the production technologie

x,

s

y : x 0 and y 0, 4.1.4

i.e.,T

,

T ,......,T T

By defining the metatechnology as the convex hull of the union of group-specific

technologies, metatechnology ensures the convexity property as:

* 1 2 k4.1.5 T Covex Hull T T ...... T

Let *iD x, y denote the input distance function, the input distance function in terms of

production technology set *T can be defined as follows:

* *i4.1.6 D x, y sup 0 : x / , y T

The input distance function for a groupk can equivalently be defined in term of the

input sets as follows:


68

k ki4.1.7 D x, y sup 0 : x / P x

It indicates the maximum degree to which a given input vector can be radially

contracted and yet producing the same output. As input distance function is defined

with respect to the input set, an input oriented measure of technical efficiency can be

defined in terms of input distance function as:

* *i i4.1.8 TE 1 D x, y

where *iD x,y is the input metadistance function. For groupk , an input oriented

technical efficiency in terms of the input distance function can be represented as:

k ki i4.1.9 TE 1 D x, y

where kiD x,y is the input distance function for groupk . In view of the fact that the

metafrontier envelops every groupfrontier production possibility set, the input distance

function *iD x, y should satisfy that for any given groupk :

k *i i4.1.10 D x, y D x, y , where k=1,2,....,k

Since k *i iD x, y D x, y , there exist technology gap22 between the groupfrontiers and

the metafrontier. An input oriented technology gap ratio (TGR) of each firm in group kis defined as follows:

k *

k i ii * k

i i

D (x, y) TE (x, y)4.1.11 TGR (x, y)D (x, y) TE (x, y)

where *iTE is the technical

efficiency with respect to the metafrontier, and kiTE is the technical efficiency with

respect to the groupk . The estimated TGR takes a value between zero and one and

measures the ratio of the output for the groupfrontier for each of the k group relative to

the metafrontier (Battese et al., 2004). The technical efficiency relative to the

metafrontier is always less than the technical efficiency relative to the groupfrontiers,

thus bounding the TGR value between 0 and 1. If the TGR value is close to 1, this

indicates that a group specific production frontier is close to the metafrontier, indicating

a more advanced technology level. In contrast, the closer the TGR is to 0, the further the

22Battese et al. (2004) refer to this measure as the “technology gap ratio”. However, an increase in the (technology gap) ratio suggests a decrease in the gap between the group-frontier and meta-frontier. To avoid this confusion, O’Donnell et al. (2008) used the term “metatechnology ratio”.


69

group frontier is from the metafrontier, indicating a less developed production

technology level (O’Donnell et al., 2008).


We can estimate technical efficiencies and technology gap ratio or metatechnology ratio

either by using the data envelopment analysis or a stochastic frontier approach. We opt

to employ DEA in this study because : 1) DEA does not assume any a priori functional

relationship between the inputs and outputs in the production function and the error

term distribution; thus, potential misspecifications that could occur when using

stochastic frontiers are avoided (Latruffe et al., 2012) ; 2) multiple inputs and outputs

can be handled using DEA without input and output aggregation bias and; 3) DEA is

more appropriate approach to estimate efficiency when sample size is small. Banker et

al. (1989) propose that the sample size should be three times greater than the sum of the

number of inputs and outputs.

4.1.3.1 Metafrontier DEA Efficiency Estimation

We can construct a convex groupfrontier for thek group by applying the DEA method

to all the observed inputs and outputs of farms or decision making units (DMUs) ink

group. Let the groupk consist of data on kL decision making units (farms), the VRS

input-orientated DEA model for jDMU can then be formulated as follows:

j j j4.1.12 Min

Subject to:

j

j

j

j

j

j

j

j

j 1

n

j 1

1

n

n

j

y 0,

0,

1,

0

x X

Y

I

where jy is the output quantity for the jDMU; jx is the vector of input quantities used by

the jDMU ; jY is kL 1 vector of all output quantities for all kL DMUs; jX is kN L matrix

of input quantities for all kL DMUs; I is kL 1 vector of ones; j is vector of weights; and


70

j is scalar. The equation njj 1

I 1 is a convexity constraint to compute technical

efficiency under a VRS specification.

Similarly, a convex metafrontier can be estimated by applying another DEA model to

the inputs and outputs of all kkL L DMUs. The structure of this linear programming

(LP) is identical to that of Equation 4.1.12 except that jX is of dimension N L , and jY

and j are L 1 . By solving this metafrontier LP separately for each DMU in the sample,

we get the efficiency estimates with respect to the metafrontier. The value of j that

solves the group k problem should not be greater than that the value of j that solves the

metafrontier problem. In other words, farms will not be more technically efficient when

they are evaluated under the metafrontier than the groupfrontiers, and the metafrontier

will never lie below any of the groupfrontiers (O’Donnell et al., 2008). Once, we have

estimated technical efficiencies with respect to the metafrontier and groupfrontiers, it is

straightforward to measure the technology gap ratio at observed input and output levels

using the expression in Equation 4.1.11.

4.1.3.2 The DEA Sub-vector Model

The DEA sub-vector approach is used to measure the input-specific technical

efficiency. The sub-vector efficiency considers the possible reduction of a sub-set of

inputs while keeping other inputs and output constant. In the literature, the sub-vector

efficiency concept has been widely used for measuring input-specific technical

efficiencies. We use the sub-vector concept following Speelman et al. (2008) to

estimate the possible reduction in irrigation water use. This “possible reduction” in the

case of irrigation water can be referred as the “irrigation water use efficiency”. We use

Figure 4.1.1 to illustrate the concept of technical efficiency and the sub-vector input-

specific technical efficiency.

Let us consider six farms using two inputs, irrigation water and fertiliser, to produce a

single output. Based on the efficiency concept, farms B, C, D, E and F are the best

performers because they are located on the frontier. A linear combination of their input

use defines a production frontier that envelops all of the other observed farms. Farm A

is inefficient because it is not located on the frontier. The radial contraction of inputs 1x

and 2x (irrigation water and fertiliser) produces a projected point A on the frontier,

which is a linear combination of all the observed data points. The technical efficiency of


71

farm A with respect to farms B, C, D, E and F can be measured by the ratio

A OA ATE / O .

Figure 4.1.1: Graphical representation of the technical and sub-vector irrigation water use efficiency

The technical efficiency concept involves radial contraction of all inputs. However, the

sub-vector approach involves non-radial contraction of a particular sub-set of inputs or

an individual input while keeping output and other inputs constant (Fare et al., 1994). In

terms of Figure 4.1.1, the sub-vector efficiency of farm A for input 1x (here irrigation

water) could be measured by reducing 1x to a point 'A while keeping 2x and the output

constant. Hence, the sub-vector efficiency of input 1x (irrigation water) for farm A can

be given by the ratio ' ' 'IWE OA / OA .We solve the following LP to estimate the sub-

vector efficiency (irrigation water efficiency w ) of a particular jDMUfollowing

Speelman et al. (2008):

w

j j

j m w ,n j

j w ,n

j

j

j j

(λ , )

j 1

n

j 1

nw

j 1

n

j 1

w

n

4.1.13 Min

y 0,

x X 0,

x X 0,

I

Subject

1,

to:

Y

0.


72

Similar to LP 1in Equation 4.1.12 jy is the output quantity for the jDMU

; jY is kL 1

vector of all output quantities for all kL DMUs ; I is kL 1 vector of ones; j is vector of

weights; and w is a scalar. However, in the second constraint, the input"w"column is

excluded, whereas the third constraint includes only the"w" input. Here, w is scalar and

has a score between 0 and 1, where a score of 1 for a given farm indicates that the farm

is using irrigation water efficiently. A value of less than 1 for a farm indicates that

irrigation water use inefficiency exists, meaning that there is some potential to reduce

irrigation water applications.

4.1.3.3 Truncated Regression

Tobit regression is the most commonly used approach to investigate the determinants of

DEA efficiency measures (Dhungana et al., 2004, Frija et al., 2009, Speelman et al.,

2008, Wadud and White, 2000). The use of tobit regression in a second stage has been

justified by the argument that because efficiency scores vary between zero and one, they

are censored values. However, McDonald (2009) argued that efficiency scores are not

censored but are actually fractional values. Alternatively, McDonald (2009) and Banker

and Natarajan (2008) proposed that Ordinary Least Squares (OLS) in a second stage

yields more consistent results than the tobit regression. However, the use of OLS is

consistent only under very peculiar and unusual assumptions of the data-generating

process (Simar and Wilson, 2011).

In an earlier paper, Simar and Wilson (2007) noted that conventional approaches to

inference in two-stage efficiency are invalid due to the complex and unknown serial

correlation among estimated efficiencies and the lack of description about the data-

generating process. They proved that in the second stage, single bootstrap truncated

regression yields more consistent results. We, thus, chose a single bootstrap truncated

regression to identify the determinants of technical and irrigation water use efficiency.

The estimated specification for the regression model takes the following general form:

n

j i i i ii 1

4.1.14 y z 0;

for i 1,....,N and 2i N(0, )

where jy

is either technical or irrigation water use efficiency, jZ is the set of explanatory

variables and i is the error term.


73

4.1.4 Study Areas, Data and Variable Definitions

The data used in this study is based on a detailed survey conducted during the Kharif23

season in two districts - Lodhran, from the cotton-wheat region, and Jhang, from the

mixed-cropping region - of the Punjab province, Pakistan.

Rural households heavily rely on groundwater as their major source of irrigation water

in both districts. The study areas in both districts have deep groundwater tables that

require high tube-well installation costs. The variation in the bore depth was observed to

range between 60 metres and 99 metres in Lodhran and from 33 metres to 57 metres in

Jhang. Due to low groundwater tables and the high installation costs, the tube-well

population is relatively less dense in Lodhran and in parts of the Jhang district.

Therefore, water trading is more common among tube-well owners and non-owners in

Lodhran and Jhang compared to other districts having shallow water tables.

A multi-stage sampling technique was used in data collection. At the first stage, one

tehsil was purposively selected from each district. In the next stage, 10 villages were

selected at random from each selected tehsil. In the study areas, a village usually

comprise of 70 to 80 household farms. The information about tube-well owners and

water buyers was collected with the help of extension field staff and key informants in

the selected villages. Finally, from each village, 10 groundwater users (5 tube-well

owners and 5 water buyers) were selected randomly to obtain the differential impact of

tube-well ownership and to reveal the difference in water applications and grain yield

for tube-well owners (water sellers) and non-owners (water buyers), thus making a total

sample size of 200 groundwater users, i.e., 100 tube-well owners and 100 water buyers.

The data was collected using an interview schedule. During the interview, we collected

farm-level information on various inputs and output quantities. The inputs measured

were: (1) seed and fertiliser in kg/acre; (2) pesticide and farm operations as number of

applications/acre; (3) total labour, consisting of hired (casual and permanent) and family

labour in hours/acre and (5) groundwater use in cubic metres/acre. Wheat yield (output)

was measured in kg/acre. Because, farmers generally follow recommended agronomic

practices for wheat crops, we do not observe much variation in per acre use of inputs.

Hence, we aggregated per acre inputs and output at a farm level before analysing the

23There are two cropping seasons in Pakistan, Kharif and Rabi. Wheat is a Rabi crop.


74

data. The descriptive statistics of the variables used in the DEA model are presented in

Table 4.1.1.

In this study, we collected information about the number of irrigations for wheat crop

and the duration of irrigation water application per irrigation. We used an approximate

estimation model, as used by Eyhorn et al. (2005) and Srivastavaa et al. (2009) to

measure groundwater extraction in litres and then converted into m3:

2 2 4t 129574.1 BHP

[d (255.4.1.15

5998 BHP

) / d D )]Q

where Q represents the volume of water in litres, t is the total irrigation time, d is the

depth of bore, D is the diameter of the suction pipe, and BHP is the power of the engine.

Table 4.1.1 compares the selected variables used in the DEA analysis on per acre basis.

The average farm size is 12.08 acres for tube-well owners and 6.02 for water buyers.

The seed rate and labour usage per acre do not differ considerably for tube-well owners

and water buyers. However, there is a significant difference in the amount of fertilizer

with an average rate of 201 kg/acre for tube-well owners and 181kg/acre for water

buyers. We also do not find a significant difference in the use of groundwater for

irrigation with tube-well owners, on average, using 783m3/acre and compared to water

buyers using 775m3/acre. The average wheat yield is slightly above 1,550 kg/acre for

tube-well owners and 1,469 kg/acre for water buyers.

The average farmer’s age is 45 years for tube-well owners and 42 years for water

buyers. The statistics on education clearly reflect lack of education. In terms of years of

schooling, tube-well owners on average have 5 years of schooling while water buyers

have less than 4 years of average schooling.

Amongst the tube-well owners, 4% farms are categorised as tenant farms whereas

amongst the water buyers 65% farms are tenants. A small proportion of the farms with

34% tube-well owners and 22% water buyers have adopted different types of

agricultural innovations such as improved seed varieties and seed treatments. Because

farming is a major livelihood activity among rural communities, only a small proportion

(20%) of the tube-well owners and water buyers (13%) has off-farm income sources.

The statistics show that 47% of the water buyers whereas 22% of the tube-well owners

received credit from private banks or public agencies. Nearly 30% of the tube-well

owners and water buyers participated in agriculture related training programmes or

received advice from the agricultural extension field staff.


75

Table 4.1.1: Descriptive statistics of the variables used in the DEA analysis

Tube-well owners

Water buyers

Variable Mean Std. Dev. Mean Std. Dev. Economic Data Wheat yield/acre (kg/acre) 1556 205 1475 224 Farm size (acres) 12 5 6 3 Seed rate (kg/acre) 57 5 58 6 Labour (hours/acre) 56 22 61 37 Fertilizer (kg/acre) 201 41 181 41 Chemical applications per acre 2.5 0.54 2.63 0.48 Farm operations per acre 6.8 1.65 5.7 1.29 Irrigation water (in m3/acre) 783 318 775 338 Farm Characteristics Farmer’s age (years) 45 8 42 8 Farmer’s education (years of schooling) 5 4 3 3 Proportion of farm characteristics 0 1 0 1 Off-farm income (0=no, 1=yes) 4 96 35 65 Land tenureship (0= tenants, 1=owners) 80 20 87 13 Seed (0=unimproved, 1=improved) 66 34 78 22 Access to credit services (0=no, 1=yes) 75 25 53 47 Access to extension advice (0=no, 1=yes) 69 31 68 32 Salinity perception (0=no, 1=yes) 83 17 74 26 Is the water table declining? (0=no, 1=yes) 59 41 49 51

More water buyers (26%) than tube-well owners (17%) perceived that salinity was

increasing in groundwater. Similarly, more water buyers (51%) than tube-well owners

(41%) think that groundwater tables are lowering in the study regions.

4.1.5 Empirical Results and Discussion

4.1.5.1 Technical Efficiency

Table 4.1.2 presents technical efficiency (TE) estimates for tube-well owners and water

buyers under both the metafrontier and groupfrontier specifications. The metafrontier

TE score for tube-well owners vary from a minimum of 65% to a maximum of 100%

with a mean score of 91% whereas for water buyers the TE scores vary from a

minimum of 64% to a maximum of 100% with a mean value of 90%. The groupfrontier

estimates of TE for tube-well owners vary from a minimum of 69% to a maximum of

100% with a mean score of 93% whereas water buyers’ TE scores vary from a

minimum of 67% to a maximum of 100% with a mean value of 94%.


76

On average, both the tube-well owners and water buyers operate at fairly high

efficiency levels. Based on the mean estimates, the gains from improving technical

efficiency are small, although across all farms, only 29% of the tube-well owners and

water buyers were fully technically efficient (TE=1) when assessed under the

metafrontier settings. Similarly, under the groupfrontier estimates only 37% of the tube-

well owners and 53% of water buyers were completely technically efficient (TE=1).

These estimates suggest that a significant majority of the wheat growers including tube-

well owners and water buyers are operating with technical inefficiencies. We find that

average technical efficiency estimates for tube-well owners and water buyers slightly

vary under the metafrontier and groupfrontier estimations. The overall frequency

distribution, however, shows that metafrontier and groupfrontier technical efficiency

estimates vary considerably24. Similarly, the cumulative frequency distribution

(Figure 4.1.2) of technical efficiency clearly indicates that when assessed against the

groupfrontier wheat farms are more technically efficient than when compared against

the metafrontier. The results for the technology gap ratio (i.e. ratio of the metafrontier

technical efficiency and the groupfrontier technical efficiency) are presented in

Table 4.1.3.

Table 4.1.2: Metafrontier and groupfrontier technical efficiency frequency distribution

Metafrontier Groupfrontier Frequency (%) Tube-well

owners Water buyers Tube-well

owners Water buyers

<40 0 0 0 0 40-50 0 0 0 0 50-60 0 0 0 0 60-70 4 1 1 1 70-80 10 13 7 9 80-90 26 34 18 24 90-99 31 22 37 13 100 29 29 37 53 Mean 0.91 0.90 0.94 0.93 Std. Deviation 0.09 0.09 0.08 0.09 Minimum 0.65 0.64 0.69 0.67 Maximum 1 1 1 1

24A paired sample t-test is used to test that whether the mean difference between meta-frontier and group-frontier technical efficiency estimates is significantly different from zero or not? The t-statistics of 8.55 with a p-value of 0.000 reject the null hypothesis that the difference between the technical estimates from both estimations is equal to zero.


77

The results show that tube-well owners are operating closer to the metafrontier than are

the water buyers. In other words, tube-well owners perform better in terms of exploiting

their productivity potential compared to the water buyers.

The productivity potential ratio (technology gap ratio) estimates for tube-well owners

range between a minimum of 0.81 and a maximum of 1.00 with a mean estimate of

0.97. With a slight difference, the productivity potential ratio estimates for water buyers

range between a minimum of 0.71 and a maximum of 1.00 with a mean estimate of

0.96. Not surprisingly, water buyers have lower productivity potential ratio compared to

tub-well owners.

Figure 4.1.2: Cumulative distribution of meta-frontier and group-frontier technical

efficiency

Table 4.1.3: Average groupfrontier and metafrontier technical efficiency scores and the technology gap ratio

Group TE Meta TE Technology gap ratio Tube-well owners Average 0.94 0.91 0.97 Minimum 0.64 0.65 0.81 Maximum 1 1 1.00 Water buyers Average 0.93 0.90 0.96 Minimum 0.67 0.69 0.71 Maximum 1 1 1.00

4.1.5.2 Irrigation Water Use Efficiency

The metafrontier and groupfrontier sub-vector estimates of irrigation water use

efficiency are presented in Table 4.1.4. The results show substantial inefficiencies in

irrigation water use among tube-well owners and water buyers. For tube-well owners

00.10.20.30.40.50.60.70.80.9

0 0.2 0.4 0.6 0.8 1

Perc

ent o

f far

ms

Technical efficiency distribution

Group-frontier TE estimatesMeta-frontier TE estimates


78

and water buyers their mean sub-vector irrigation water use efficiency score under the

metafrontier setting are 66% and 65%, respectively whereas the groupfrontier estimates

are 71% and 67%, respectively. In other words, metafrontier results indicate that on

average tube-well owners and water buyers can save 34% and 35% of current irrigation

water usage in wheat farming without decreasing their existing output level. The mean

groupfrontier estimates suggest a 29% and 37% reduction in irrigation water use for

tube-well owners and water buyers, respectively.

As we observe in the case of technical efficiency, statistically significant difference25

between the metafrontier and groupfrontier sub-vector estimates suggest that combining

tube-well owners and water buyers in one sample, would lead to biased efficiency

estimates. The cumulative frequency distribution (Figure 4.1.3) of irrigation water use

efficiency clearly indicates that when assessed against the groupfrontier wheat farms are

more technically efficient than when assessed against the metafrontier.

Table 4.1.4: Frequency distribution of irrigation water use efficiency under the metafrontier and groupfrontiers

Metafrontier Groupfrontier Frequency (%) Tube-well


owners Water buyers

<30 10 7 5 7 30-40 17 17 16 17 40-50 11 14 9 11 50-60 11 12 12 11 60-70 3 5 4 6 70-80 6 13 6 9 80-90 10 3 9 6 90-99 4 7 3 3 100 28 22 36 30 Mean 0.66 0.65 0.71 0.67 Std. Deviation 0.28 0.26 0.27 0.27 Minimum 0.23 0.24 0.26 0.24 Maximum 1 1 1 1

The sub-vector estimates under the groupfrontier specification imply a considerable

scope for reducing irrigation water use, with the observed values of other inputs and

maintaining the same output level. These results suggest that if the efficiency improves,

25A paired t-test statistic is 6.63 with P-value of 0.000. Hence the null hypothesis that mean difference between meta-frontier and group-frontier sub-vector estimates is equal to zero is rejected.


79

it would be possible to reallocate groundwater to other usage without compromising

wheat production.

As we can see from the Table 4.1.4, water buyers are slightly less efficient in irrigation

water use than tube-well owners. This can be attributed to several reasons: (1) the

functioning of informal groundwater markets where water buyers usually need to be in

the queue to buy water; (2) the energy crises which has increased uncertainty for water

buyers to get water on time; (3) social ties between the tube-well owners and water

buyers which results to preference for certain users and discrimination against others

(Jacoby et al., 2004); and (4) lack of surplus water to sell by tube-well owners because

of large farm sizes or competing demand from water buyers. Therefore, in such

circumstances, the water buyer is highly likely to face delays in getting water for

irrigation, resulting in serious impacts on crop growth and ultimately on productivity.

Based on the correlation26 between output produced using per m3 of groundwater and

the per m3 price27 of groundwater, we can infer that water buyers produce more output

per m3 of groundwater. This result may be because water buyers pay a higher price for

groundwater than tube-well owners, which induces them to use water more efficiently.

These results also imply that water pricing can trigger improved groundwater use

efficiency in irrigation as argued by some authors (Gómez-Limón and Riesgo, 2004,

Johansson et al., 2002)28.

Table 4.1.5: Spearman’s rank correlation among technical efficiency and the sub-vector irrigation water use efficiencies

TE SV-IWE TE 1.000 SV- IWE 0.763* 1.000

Note: * indicates a 5% significance level

Irrigation water use inefficiencies are not uncommon in other parts of the world. A large

degree of irrigation water use inefficiency was also reported by Karagiannis et al.

(2003) for out-of-season vegetable farming. Similarly inefficiency in use of irrigation

26The Spearman’s correlation coefficients between wheat yield and price per m3 of groundwater are 0.78 and 0.81for tube-well owners and water buyers, respectively. 27Water buyers paid Rs. 6.4 per m3 of groundwater, while tube-well owners paid Rs. 3.4. 28It has been argued that at least some pricing is necessary to make farmers aware of the water scarcity and to induce them to adopt water-saving technologies. Therefore, “getting prices right” is considered an important tool to improve water use efficiency and to encourage its conservation.


80

water has been reported by Lilienfeld and Asmild (2007) for irrigated agriculture in

Western Kansas, USA, Speelman et al. (2008) for small-scale irrigators in South Africa

and Frija et al. (2009) for small-scale greenhouse farmers in Tunisia.

Figure 4.1.3: Cumulative distribution of metafrontier and groupfrontier irrigation water use efficiency

As shown in Table 4.1.5, technical efficiency is highly correlated with the irrigation

water use efficiency. The correlation between technical efficiency and irrigation water

use efficiency suggest that if irrigation water use efficiency increases, it will also

improve overall technical efficiency of wheat farming.

Similar to technical efficiency, the cumulative frequency distribution (Figure 4.1.3) of

irrigation water use efficiency clearly indicates that when assessed against the

groupfrontier wheat farms are more technically efficient than when compared against

the metafrontier.

4.1.5.3 Explaining Efficiency Differentials

The results of the determinants of technical and water use efficiency are presented in

Table 4.1.6.

The farmer’s age is not found to be significantly associated either with the technical or

irrigation water use efficiency. The level of education has positive and significant

impact on technical and irrigation water use efficiency. In the literature, we find mixed

results for the efficiency and education relationship, e.g., Karagiannis et al. (2003)

found that the degree of technical and irrigation water use efficiency is positively

affected by the level of education. However, Speelman et al. (2008) found that

education does not significantly affect technical and irrigation water use efficiency. The

00.10.20.30.40.50.60.70.8

0 0.2 0.4 0.6 0.8 1

Perc

ent o

f far

ms

Irrigation water efficiency distribution

Group-frontier IWE estimatesMeta-frontier IWE estimates


81

mixed results of the impact of education in the literature suggest that when interpreting

the impact of education on efficiency levels researchers should consider the relevance of

a farmer’s education to his farming business. We find that land ownership is positively

and significantly associated with technical efficiency which is intuitive. Many other

studies have found that that land owners are more efficient than tenants (Frija et al.,

2009, Speelman et al., 2008). The results for seed quality show a statistically significant

and positive association between the seed quality and technical and irrigation water use

efficiency.

We find that off-farm income significantly affects farmer’s technical efficiency,

implying that with alternative income resources, farmers have a better edge to purchase

inputs and therefore use an optimal mix of inputs. Hence, these farmers tend to be more

technically efficient. Likewise, those farmers who opted to get credit are more

technically efficient than those who did not. The findings of Karagiannis et al. (2003)

and Haji (2007) also confirm the positive impact of off-farm income and credit in

improving farmer’s technical efficiency.

Table 4.1.6: Bootstrap truncated estimates of the determinants of technical and irrigation water use efficiency

Explanatory variables Technical efficiency

Irrigation water use efficiency

Coefficient

Std. Dev.

Coefficient Std. Dev.

Farmer’s age (years) 0.005 0.001 0.007 0.002 Education (years of schooling) 0.025** 0.009 0.118*** 0.023 Land tenureship (0= tenants, 1=owners) 0.027** 0.013 0.017 0.034 Seed (0=unimproved, 1=improved) 0.053*** 0.012 0.265*** 0.036 Off-farm income (0=no, 1=yes) 0.027* 0.014 0.023 0.038 Access to credit services (0=no, 1=yes) 0.033** 0.012 0.048 0.030 Access to extension advice (0=no, 1=yes)

0.032** 0.013 -0.019 0.033

Salinity perception (0=no, 1=yes) 0.025* 0.013 0.137*** 0.034 Is water table declining? (0=no, 1=yes) 0.011 0.010 0.070*** 0.026 Constant 0.804** 0.029 0.365*** 0.077 Log-likelihood 255.20 63.49

Note: *, **, *** indicate significance at 10%, 5% and 1%, respectively. Number of

bootstraps=4,000

The positive significant impact of extension advice on technical efficiency confirms the

belief that the farmers who tend to seek more extension advice are technically more

efficient than those who have less or no contact with the agricultural extension staff


82

(Parikh and Shah, 1994). In contrast, the impact of extension services is non-significant

on irrigation water use efficiency. The significant impact of extension advice in

improving technical efficiency suggests that extension advice could also have

significant impact on rationalising irrigation water use (Frija et al., 2009, Karagiannis et

al., 2003).

Amongst the explanatory variables representing farmers’ perceptions about

groundwater resource, perception about salinity is positively and significantly

associated with technical efficiency and irrigation water use efficiency while perception

about the decline in groundwater tables suggest that farmers seriously consider the

declining groundwater tables.

4.1.6 Conclusion

The objective of this study was to estimate technical efficiency (TE) and irrigation

water use efficiency (IWE) of groundwater-fed wheat farms. We employed the data

envelopment analysis method to compute TE and IWE using a cross-sectional dataset of

200 wheat growing farms from Punjab, Pakistan. We estimated TE efficiency using

DEA metafrontier framework and irrigation water use efficiency using the DEA sub-

vector model.

The mean TE estimates suggest that wheat farms are operating at fairly high technical

efficiencies. The mean technical efficiency scores for the metafrontier and groupfrontier

estimates only suggest little scope for improving technical efficiency among tube-well

owners and water buyers. However, there is a substantial scope for improving irrigation

water use efficiency in wheat farming. In the case of irrigation water use efficiency,

metafrontier estimates suggest a 34% and 35% potential saving of groundwater for

tube-well owners and water buyers, respectively. However, the groupfrontier estimates

suggest slightly lower reductions in irrigation water use with 29% reductions for tube-

well owners and 33% for water buyers. In terms of total groundwater volumes, based on

the sub-vector estimates, we calculated that the tube-well owners and water buyers in

our sample could save a total volume of 0.48 million m3 of groundwater, with 0.32

million m3 reductions for tube-well owners and 0.15 million m3 for water buyers. Put in

monetary terms, tube-well owners and water buyers could save up to Rs. 0.79 million

and Rs. 0.83 million from irrigation costs during the wheat cropping season.


83

Whilst this study has policy implications for improving technical efficiency in wheat

farming, the study also suggest possible reductions in the current irrigation water

application to wheat crop. The study results indicate that irrigation water use

inefficiencies are more pronounced than the technical inefficiencies, implying that that

access to technology is not the major factor constraining efficiency improvements, but

rather inefficient use of irrigation water. The bottlenecks to the inefficient use of

irrigation water arise perhaps due to the lack of information about the crop water

requirement and groundwater resource availability. We suggest that educating farmers

about the actual crop water requirement either by extending the extension advice from

crop management to groundwater management or creating a separate water extension

wing can be important for the sustainable use of groundwater resources. Greater

provision of advice may have spin-off benefits such as encouraging greater use of

improved seed and providing information to facilitate decision-making by elderly risk-

averse farmers.

We suggest that any policy intervention for sustainable groundwater management

should also consider regulating informal groundwater markets to improve the security

of water allocation and to improve equity of access to non-tube-well owners.


85

4.2 Econometric Approach to Estimating Technical and Irrigation

Efficiency in Cotton Farming in Pakistan

(Journal of Hydrology: Regional Studies. doi:10.1016/j.ejrh.2014.11.001)

http://dx.doi.org/10.1016/j.ejrh.2014.11.001


87

Abstract

Massive pumping of groundwater aquifers in pursuit of reliable irrigation water supplies

is lowering groundwater tables in Pakistan. Consequently, depletion of groundwater

resources has raised concerns to examine more closely the level of, and the factors

affecting, technical efficiency and irrigation water use efficiency. We employ a

stochastic production frontier method to estimate the technical and irrigation water use

efficiency of 173 randomly selected groundwater irrigated cotton farms in the Punjab

province of Pakistan. The mean technical efficiency results suggest considerable scope

for improvements in technical efficiency, with water buyers being more inefficient than

the tube-well owners. Irrigation water use inefficiency is even more pronounced than

the technical inefficiency. Results on the determinants of efficiency indicate that

improved seed, consultation with extension field staff and farmers’ perceptions about

the future state of groundwater resources are positively associated with efficiency.

4.2.1 Introduction

Groundwater irrigation contributes significantly to agricultural production in many parts

of South Asian countries (Shah, 2007). In Pakistan, dwindling surface water supplies

have increased reliance on groundwater resources more than in many other Asian

countries. Evidences suggest that existing surface water resources are not only deficient

in Pakistan but are also highly skewed in time and space. The spatio-temporal variations

in surface runoffs have led to the development of a large scale groundwater-fed

irrigation system in the Indus basin of Pakistan. The spectacular increase in

groundwater use over the last half-century has emerged as a “silent revolution” carried

out by thousands of farmers in the pursuit of reliable irrigation water supplies. Since

1960 groundwater contribution to the total irrigation water supply has increased by

more than 50% in Pakistan (Byrelle and Siddiq, 1994, Qureshi et al., 2009).

In contrast to the uncertain canal water supplies, on-demand availability and reliability

of groundwater resources has helped farmers to hedge against low and uncertain crop

production. Presently, more than one million farmers have invested in installing tube-

wells across the country. Earlier government policies such as rural electrification,

subsidization of electricity, diesel and drilling services, free pump sets and easy access

to long-term loans have encouraged the adoption of tube-well technology while higher

yields and greater economic returns from groundwater use have encouraged farmers to

adopt tube-wells in subsequent periods (Falcon and Gotsch, 1968, Papanek, 1968, van


88

Steenbergen and Oliemans, 2002, Johnson, 1989 ). Although, the number of tube-wells

has increased manyfold, thousands of smallholder farmers still do not own tube-wells.

Many of them irrigate their lands by informally buying surplus pumped water from their

neighbouring tube-well owners (Meinzen-Dick, 1996, Qureshi et al., 2009). Such

informal groundwater transactions offer economic benefits to tube-well owners and

offer non-owners opportunities to hedge against water scarcity risk and to increase farm

production (Manjunatha et al., 2011, Meinzen-Dick, 1996, Shiferaw et al., 2008).

Although groundwater resources have played a key role in agricultural production,

overdrafting of groundwater resources is at a critical juncture (Kijne, 1999b, Shah et al.,

2000, Khan et al., 2008a, Qureshi et al., 2009). Massive groundwater extractions of up

to 60 km3 y-1 have exceeded the recharge rate of 55 km3 y-1, resulting in substantial

depletion of groundwater aquifers (Giordano, 2009). Wada et al. (2010) mapped various

hot spots of groundwater depletion in different regions of the world and noted that the

highest depletion rates were in north-east Pakistan. Rapidly depleting groundwater

resources are not only making relative accessibility of groundwater resources

economically unviable, but are also creating many environmental concerns with serious

repercussions to the sustainability of the agrarian economy of Pakistan (Kijne, 1999b,

Shah et al., 2000, Kelleners and Chaudhry, 1998, Kahlown and Azam, 2002, Khan et

al., 2008b, Qureshi et al., 2009).

Whilst the agrarian economy of Pakistan is mainly dominated by wheat, cotton, rice and

sugarcane crops, cotton production remains the most important agricultural commodity

due to its export value. It holds an important position in the national economy;

accounting for 6.9% of the value added in agriculture and 1.4% of the country’s gross

domestic production (GDP). Pakistan remained the 4th largest cotton producer with

9.80% share in global cotton production during the period 2011/12. Over the same

period, Pakistan’s yarn and apparel exports were 26% and 14% of the global market

shares. At a national level, cotton exports account for 46% of the country’s entire

exports and the cotton sector employs 35% of the total industrial labour force (Pakistan,

2011-12, FAO, 2012b). In brief and by any measure, cotton production and processing

are the most important economic sectors of Pakistan’s economy. Therefore, national

economic growth is greatly influenced by the volume and value of cotton production

and its by-products. As a result, cotton production has always been under agricultural

policy limelight. Due to different policy supports, the area under cotton cultivation and

production has grown by 33% and 163% respectively since 1980, while domestic


89

consumption for cotton has increased by almost 400% over the same period (Pakistan,

2011-12, USDA, 2012). However, cotton production has been facing widespread

stagnation in per hectare yields. Based on per hectare yield estimates, Pakistan is ranked

at number 20th in world cotton production, and has enormous potential for improving

cotton productivity. Figure 4.2.1 shows historical trends in cotton production and

consumption in Pakistan.

Figure 4.2.1: Historical trends in cotton production and consumption in Pakistan

Pakistan Central Cotton Committee (PCCC) aims to increase cotton production by 40%

to 60% as a national strategy to achieve the 19.1 million bales target by 2015. Major

components of this strategy include: 1) to increase the area under cotton cultivation; 2 )

to encourage adoption of genetically modified cotton varieties; 3) to improve production

technology; 4) to subsidize fertilizers; and 5) to apply integrated pest management

(PCCC, 2008). However, despite widespread policy efforts and other encouraging

incentives, the planned outcomes may not be realized. On-going water stress may

undermine the potential of this policy. Water availability unfortunately has not been

taken into consideration under this policy. Besides its environmental and ecological

footprints that result from excessive chemical use, cotton production is also associated

with excessive water applications, in spite of water being the key limiting factor in

agricultural production. Evidences suggest that inefficient irrigation water application is

one of the major reasons for low water productivity. Owing to the poor irrigation water

0

2000

4000

6000

8000

10000

12000

1400019

6019

6219

6419

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6819

7019

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7419

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0020

0220

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12

000,

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lb. b

ales

ProductionDomestic Consumption


90

management, there is a considerable scope for improving current water productivity29 of

0.22 kg m-3 which is far below the productivity of other major cotton producing

countries (Shabbir et al., 2012). The severe water stress facing cotton producers and the

widespread stagnation in cotton yields has thus prompted research efforts to improve

efficiency and productivity of cotton farming and to make more efficient use of

dwindling water resources.

The objective of this paper is to estimate farm level technical efficiency and irrigation

water use efficiency for groundwater irrigated cotton farms in Pakistan. We apply a

stochastic production frontier to 173 randomly selected cotton farms from the Punjab

province of Pakistan to estimate the extent of farm level technical and irrigation water

use efficiencies.

From a theoretical and methodological perspective, micro-economic theory postulates

that production functions should increase monotonically in all inputs (Henningsen and

Henning, 2009). A production function which is not increasing monotonically inhibits

the reasonable interpretation of the efficiency estimates. Satisfying monotonicity

conditions becomes more important when considering the efficiency of a particular

input (e.g., fertilizer or water). Therefore, this study advances the frontier of existing

input-specific technical efficiency (in this study irrigation water use efficiency) by using

restricted translog model along with the traditional unrestricted translog model. This is

one of the few studies which have focused on irrigation water use efficiency in

agricultural production and is the only study to use parametric methods to estimate

irrigation water use efficiency in irrigated cotton farming in Pakistan.

The rest of the chapter is organised as follows. The next section describes the stochastic

production frontier used to estimate technical and input-specific (irrigation water use

efficiency) technical efficiency. Section 3 describes the data and principal features of

the study areas. The results are presented in Section 4. The final section draws

conclusions and provides some policy implications.

29Water productivity (kg/m3) is defined as crop yield (kg) per accumulated actual evapotranspiration for the growing season (m3):

a,seasonal

Crop yieldWPEt

Increasing water productivity means that producing more output per unit of water application while irrigation efficiency is a measure to suggest possible reductions in the current use of irrigation water.


91

4.2.2 Conceptual and Methodological Framework

4.2.2.1 Definition and Measurement of Technical and Irrigation Water Use

Efficiency

In production economics, technical efficiency is defined as the ability of a decision

making unit (DMU) to produce the maximum possible output within the available set of

inputs, under the given technology (Coelli et al., 2002). However, irrigation water use

efficiency is defined as the ratio of minimum feasible water use to observed use of

irrigation water, conditional on observed levels of the desirable output and conventional

inputs. More generally, irrigation water use efficiency is an input-oriented, single factor

measure of technical efficiency (Karagiannis et al., 2003). The standard technical

efficiency involves radial contraction of all inputs which does not allow estimation of

the efficiency of individual input use. However, input-specific technical efficiency,

which is a non-radial measure, can also be used to estimate the efficiency of individual

inputs. The idea of input-specific efficiency or irrigation water use efficiency is

illustrated in Figure 4.2.2.

Figure 4.2.2: Graphical representation of irrigation water use efficiency

Source: Karagiannis et al. (2003)


92

Let us consider three farms A, B and C using two different inputs ( 1x = irrigation water

and 2x = fertiliser) to produce a single output oY . Farm A is inefficient because it is not

located on the frontier. Let the inefficient farm A produce an output oY using 2Ax amount

of fertilizer and 1w units of irrigation water. The radial contraction 1x and 2x produces a

projected point oA on the frontier which is technically efficient. The technical efficiency

of farm A is given by the ratio A OA ATE / O and the irrigation water use efficiency is

given by the ratio 2 12C 2Ax / xI wW wE / . The proposed irrigation water use

efficiency measure determines both the minimum feasible water use 2w and the

maximum possible reduction in water use 1 2w w without compromising the existing

output level oY .

Figure 4.2.2 shows that in order to make thi farm technically efficient, the maximum

possible reduction required in water use 1 3w w is lower than the reduction 1 2w w

required making the thi farm efficient in irrigation water use. Hence, the maximum

possible reduction in water use , i.e., irrigation water use efficiency, is an upper bound

(Akridge, 1989).

4.2.2.2 The Estimation of Technical and Irrigation Water Use Efficiency

Let technology be described by the stochastic production function as follows:

i i i ii4.2.1 y f x ,w ; exp v u where iy denotes the amount of crop output

for farm i (i = 1,…,N); ix represents the vector of conventional inputs; iw is the volume

of groundwater for irrigation; is a vector of parameters to be estimated; i is a

composed error term and iv is a symmetric and normally distributed error term,

independently and identically distributed as 2vN 0, , intended to capture the exogenous

random forces which are beyond the control of the farmers; iu 0 is a non-negative

random error term independently and identically distributed as 2uN 0, , that captures

the shortfall of output from the production frontier.

The stochastic version of the output- oriented technical efficiency for the thi farm is

expressed as:

i i i i i4.2.2 TE y f x ,w ; exp v

i i4.2.3 TE exp u


93

Since iu 0 and i0 exp u 1 , technical inefficiency has to be separated from

statistical noise in the composed error term. Battese and Coelli (1992) proposed the

technical efficiency estimator as:

i i i4.2.4 TE E exp u |

The outlined measure of efficiency does not estimate the efficient use of individual

inputs which in our case is irrigation water. Conceptually, irrigation water use

efficiency measurement requires an estimate of the quantity 2w which is not observed as

illustrated by Figure 4.2.2. However, using i 2 1IWE w w , we can easily observe that

2 1 iw w IWE . By substituting this into Equation 4.2.1 output can be expressed as:

i iE

i i4.2.5 y f x ,w ; exp v where 2Eiw w (Reinhard et al., 1999). A

measure of iIWE can be obtained by equating Equation 4.2.1 with Equation 4.2.5 and

by using the estimated parameters .

4.2.2.3 Empirical Model

The unknown production frontier (Equation 4.2.1) is specified by the following translog

specification:

j ji jk jii i

w

0 ki

ji i

wj

j j

1 j 1 k 1

2ww i jw i i

j

j

j

1

1lnx lnx lnx lnw2

1 lnw lnx ln

4.2.6 lny

x v u2

where iy denotes the level of production; ix represents the vector of conventional inputs

as described in Equation 4.2.1; iw represents the amount of irrigation water; is a vector

of parameters to be estimated; iv is a random error term, independently and identically

distributed as 2vN 0, , and iu is a non-negative random error term independently and

identically distributed as 2uN 0, . To separate the stochastic and inefficiency effects in

the model we need to impose a distributional assumption. In this study, inefficiency is

modelled explicitly as a function of known characteristics and exogenous effects, such

that:

i ij

j

0 j ij 1

4.2.7 u z


94

where iz is a vector of variables which explain efficiency differentials among farmers;

is the vector of parameters, and i is a random variable defined by the truncation of

the normal distribution with mean zero and variance 2 where the point of truncation is

iz such that i iz (Battese and Coelli, 1995).

The translog production function is widely used in efficiency estimation models.

However, there are several concerns about its flexibility and theoretical consistency

when used in efficiency estimation (Sauer, 2006). Since micro-economic theory

requires that a production function should be monotonically increasing in all inputs

(Henningsen and Henning, 2009) and quasi-concave (Lau, 1978), it is necessary to test

the estimated production frontier for theoretical consistency and, if necessary, to impose

conditions for consistency. The monotonicity restrictions require holding

i iy x 0 i, x, for all observations (Coelli et al., 2005, Perelman and Santin, 2011).

However, imposing global convexity restrictions to ensure local quasi-concavity of the

production function greatly restricts the flexibility of the functional form (Lau, 1978,

Sauer, 2006). . Henningsen and Henning (2009) argue that when estimating a

production function under the assumption of output maximization, quasi-concavity is

not essential. Monotonicity can be imposed by using Bayesian techniques (O’Donnell

and Coelli, 2005, Pascoe et al., 2010) or a non-parametric approach (Grosskopf et al.,

1995) or a multistage process as proposed by Henningsen and Henning (2009).

In this study, we follow Henningsen and Henning (2009) three step procedure to adjust

the model. First, we estimate the translog frontier and extract the unrestricted

parameters and their covariance matrix. Second, we estimate the restricted parameters

through a minimum distance approach as follows:

10 0 0ˆ ˆ ˆ ˆ ˆ4.2.8 arg min

Subject to :

0f x,4.2.9 0 i, x

x

Then, Equation 4.2.8 is solved using quadratic programming to get the revised set of

coefficients that ensure the monotonicity assumption holds. These restricted parameters

0 are asymptotically equivalent to the restricted parameters of a one-stage maximum

likelihood (ML) estimation model (Koebel et al., 2003). Finally, the stochastic frontier

model (adjusted-restricted) is re-estimated as:


95

i 0 1 i i4.2.10 ln y ln y v

where i0ˆy f x, . That is, the only input is the estimated frontier output based on the

restricted parameters. The parameters 0 and 1 represent final adjustments to the

parameter estimates. The advantage of the three step approach is that the parameter

values estimated in the first stage provide appropriate starting values where the

variance-covariance matrix limits the degree to which these parameters are altered when

imposing monotonicity in the non-parametric component (Gedara et al., 2012b).

Since the above mentioned measure of technical efficiency is incapable of identifying

the efficient use of individual inputs such as fertilizer or irrigation water etc. In the next

step, we drive the efficiency of individual input i.e., irrigation water using the Equation

4.2.11 following Reinhard et al. (1999) who proposed this model to estimate

environmental efficiency. Later, this same approach was adopted by Karagiannis et al.

(2003) to estimate irrigation water use efficiency. We can write the equation for the

translog specification as follows:

i i ww2i ww i4.2.11 IWE exp 2 u /

where

ii w jw ji ww i

i

j

j 1

ln y ln x ln wln w

where iw represents the irrigation water variable input. Assuming weak monotonicity, a

technically efficient farm should also be efficient in its irrigation water use, although

this may not be necessarily true (Karagiannis et al., 2003).

As micro-economic theory requires that a production function is increasing

monotonically in all inputs, satisfying the monotonicity assumptions in estimating

single-input efficiency is also important. A technically efficient farm is supposed to use

all inputs efficiently; however, a technically inefficient farm could be efficient in the

use of at least one input whilst being inefficient in the use of the other inputs. Hence, if

we are concerned about any particular input, we must ensure that monotonicity

assumption holds for that input. So, we estimate input-specific technical efficiency in

two ways. We extract the estimated coefficients from the unrestricted and restricted

stochastic frontier models and use Equation 4.2.11 to get the irrigation water use

efficiency estimates.


96

4.2.3 Study Area and Data

4.2.3.1 Characteristics of Study Areas

This study is conducted in the Jhang and the Lodhran districts of the Punjab province of

Pakistan. In these districts, cotton farming heavily relies on groundwater for irrigation

purposes. However, farmers in the study area solely depend on groundwater for

irrigation purposes in the Jhang district and partly on canal water in the study area of the

Lodhran district. In the Lodhran district, canals supply water only during the Kharif30

season. The canal water’s contribution during the Kharif season of 2010/11 was

observed to range between 20 to 44 percent of the total irrigation requirements.

Therefore, the majority of the irrigation water comes from groundwater which is

pumped through mainly electricity operated tube-wells.

Tube-well installation costs are very high due to deep groundwater tables and are

further projected to increase due to the rapid falling of groundwater tables. The cost of

lowering a tube-well to a depth of 24 metres is seven times that for a tube-well of 6

metres (Qureshi et al., 2003). For this study, the variation in the bore depth was

observed to range between 60 metres and 99 metres in the Lodhran district and 33

metres and 57 metres in the Jhang district during the 2010/11 field survey.

We find that due to low groundwater tables and the high installation cost, tube-well

populations are relatively less dense in the northern parts of the Jhang and southern part

of the Lodhran district. As a result, farmers generally engage in informal groundwater

trading. Such informal groundwater transactions have increased access to irrigation

water for tenants and smallholder farmers who do not own tube-wells. However, water

buyers have equity concerns under such informal market settings (Jacoby et al., 2004).

Despite the fact that the cost of buying groundwater is 3 to 4 times more than that of

pumping groundwater, sometimes water buyers face delays in getting water for

irrigation (Shah, 1993, Jacoby et al., 2004, Khanna, 2007).

4.2.3.2 Data Collection and Variable Definition

A multi-stage sampling technique was used in data collection. In the first stage, one

tehsil was selected purposively from the Lodhran and the Jhang districts. In the next

stage, 10 villages were selected at random from each selected tehsil. Finally, from each

30Cotton is a Kharif crop.


97

village 10 groundwater users (5 tube-well owners and 5 water buyers) were selected

randomly to assess the difference in the amount of irrigation water applied and yield for

tube-well owners and water buyers. Data was collected from a total sample of 200

farming households. However, out of the selected 200 farming households, only 92

tube-well owners and 81 water buyers cultivated cotton crop during the cropping season

of 2010-11.

The data was collected using an interview schedule. During the interview, we collected

information on various inputs and output quantities. The inputs are measured as (1) seed

and fertiliser in kg/acre, (2) total labour, consisting of hired (casual and permanent) and

family labour in hours/acre, (3) farm operations as number of applications/acre, and (5)

groundwater use in cubic metres/acre. Cotton yield (output) is measured in kg/acre as

well. Various studies have used different approaches to compute the volume of

irrigation water. However, they do not give actual estimates of irrigation water used.

For example, Gedara et al. (2012a) measured the quantity of water used which was

related to the proportion of total land owned by the farmer and total quantity of water

released, assuming that this was distributed evenly across the irrigated area. Sharma et

al. (2001) measured irrigation water by the number of times water was released to the

farm from the main water source. In contrast to the surface water volumes, groundwater

use estimates are more realistic and reliable. In this study, we collected information

about the number of irrigations to cotton crop and the duration of water application per

irrigation event. We estimated groundwater extractions using an approximate estimation

model, as used by Eyhorn et al. (2005) and Srivastavaa et al. (2009) as follows:

2 2 4t 129574.1 BHP

[d4.2.12

(255.5998 BHP ) / d D ) Q

]

where Q represents the volume of water in litres, t is the total irrigation time, d is the

depth of the bore, D is the diameter of the suction pipe, and BHP is the power of the

engine. The descriptive statistics of the variables used in the estimation model are

presented in Table 4.2.1.

Table 4.2.1 compares selected variables for both the tube-well owners and water buyers

used in the analysis. It is evident from the descriptive statistics that on average there is

little variation in the use of farm inputs on per acre basis for seed, labour and fertilizer

application. Similarly, output produced by the tube-well owners and water buyers does

not vary considerably. In contrast, there is some variation in the number of farm

operations and irrigation water applied by the tube-well owners and water buyers. On


98

average, tube-well owners use 7% more groundwater for irrigation compared to the

water buyers. The average cotton yield is 838 kg/acre, with a maximum of 1400 kg/acre

for tube-well owners and 821 kg/acre, with a maximum of 1200 kg/acre for water

buyers.

Table 4.2.1: Summary statistics of the variables used in the empirical model

Tube-well owners Water buyers Variable Mean Std.

Dev. Mean Std. Dev.

Economic Data Farm production (kg/acre) 838 177 821 181 Seed quantity (kg/acre) 8.31 1.29 8.31 1.35 Labour (hours/acre) 326 55 328 51 Fertilizer (kg/acre) 215 63 200 56 Machinery cost (Rs. /acre) 3962 757 4050 898 Irrigation water (m3/acre) 2277 424 2130 362 Farmer’s age (years) 45 8 42 8 Land tenureship (1=owners, 0=tenants) 0.815 0.390 0.813 0.393 Off-farm income in Rs. 0.174 0.381 0.113 0.318 Seed (1=improved, 0=not-improved) 0.261 0.442 0.263 0.443 Farmer’s education (years of schooling) 5.674 4.401 3.750 3.733 Access to extension services (1=yes, 0=no) 0.337 0.475 0.263 0.443 Salinity perception (1=yes, 0=no) 0.261 0.442 0.213 0.412 Is water table declining? (1=yes, 0=no) 0.750 0.435 0.238 0.428 Effect on cropping pattern (1=yes, 0=no) 0.543 0.501 0.288 0.455

The average farmer’s age is 42 years, ranging from 27 to 60 years. The rural sociology

of the study districts is dominated by the joint family system. Among the sampled

farmers, approximately 68% are living as joint families. The statistics on education

clearly reflect lack of education. The average education level of tube-well owners is

slightly above 5 years of schooling whereas for water buyers the average education is

slightly less than 4 years of schooling. A significant proportion of the surveyed farmers

cultivate their own land. Only 12% of the farmers are tenants. Because farming is a

major livelihood activity among rural communities, only a small proportion (13%) of

the farmers has an off-farm income source. Similarly, one third (33%) of the farmers

participate in agricultural training programmes or obtained advice from the agricultural

extension field staff.


99

4.2.4 Estimation Results

The parameter estimates of the stochastic frontier model are presented in Table 4.2.2

and the estimates of the inefficiency model are presented in Table 4.2.3. The estimated

parameters of the unrestricted and the restricted models show clear differences;

however, these differences are less than standard errors of two.

The initial maximum likelihood estimates of the production frontier indicate that none

of the variables fully satisfy monotonicity conditions for all observations (Table 4.2.4).

Irrigation water which we are particularly concerned about satisfies monotonicity

conditions for only 78% of the total observations. Similarly, quasi-concavity is satisfied

for only 29% of the total observations in the initial model. The monotonicity condition

is fully satisfied for all observations and all variables in the adjusted model. Similarly,

quasi-concavity is also improved in the final adjusted model where 95% of the

observations satisfy the conditions.


100

Table 4.2.2: Restricted and unrestricted model parameter estimates

Parameters MLE Estimates Minimum Distance Estimates Final SFA Estimates Estimate SE Coefficient Difference Diff/SE Estimate SE Constant -13.523* 7.351 -13.236 0.287 0.039 -13.534 Ln Seed (kg) 1.108 1.946 0.962 -0.146 -0.075 0.944 Ln Labour (hours) -1.990* 1.145 0.059 2.049 1.790 0.023 Ln Fertilizer (kg) 0.480 1.615 0.524 0.044 0.027 0.497 Ln Machinery (no. of farm operation) 1.936 2.186 1.344 -0.592 -0.271 1.334 Ln Water (m3) 1.071 1.023 0.345 -0.727 -0.711 0.315 Ln Seed × Seed -0.549 0.427 -0.538 0.012 0.027 -0.585 Ln Seed × Labour -0.545** 0.195 -0.128 0.417 2.138 -0.167 Ln Seed × Fertilizer 0.423 0.286 0.342 -0.081 -0.283 0.312 Ln Seed × Machinery 0.004 0.295 0.014 0.010 0.035 -0.022 Ln Seed × Water 0.348 0.219 0.120 -0.228 -1.041 0.086 Ln Labour × Labour -0.218 0.220 -0.004 0.215 0.975 -0.040 Ln Labour × Fertilizer 0.370** 0.172 0.094 -0.276 -1.607 0.059 Ln Labour × Machinery 0.561** 0.272 0.030 -0.530 -1.948 -0.006 Ln Labour × Water -0.195 0.168 -0.016 0.179 1.063 -0.053 Ln Fertilizer × Fertilizer -0.604** 0.257 -0.444 0.160 0.623 -0.490 Ln Fertilizer × Machinery 0.011 0.265 0.037 0.027 0.101 0.001 Ln Fertilizer × Machinery -0.102 0.188 -0.020 0.082 0.436 -0.057 Ln Machinery × Machinery -0.332 0.361 -0.110 0.222 0.614 -0.149 Ln Machinery × Water -0.194 0.194 -0.050 0.144 0.742 -0.088 Ln Water × Water 0.088 0.252 -0.030 -0.118 -0.470 -0.068 Model Variance 2 2 2

u v 0.055*** (0.011) 0.058*** (0.012) Variance Ratio 2 2 2

u u v/ 0.833*** (0.107) 0.827*** (0.100) Intercept -0.036 (0.541) IcFitted 1.000*** (0.234)


101

Table 4.2.3: Inefficiency model estimates

Initial Estimates (MLE) Final Estimates (adjusted model) Parameter Coefficient Estimate Std. Error Coefficient Estimate Std. Error

AGE 0.007*** (0.002) 0.007*** (0.002) EDC -0.003 (0.007) -0.001 (0.007) OFIN -0.131 (0.102) -0.168 (0.112) LTS 0.173** (0.088) 0.149* (0.099) SDQ -0.221** (0.088) -0.176** (0.088) EXT -0.282*** (0.097) -0.294*** (0.102) WTD 0.001 (0.065) 0.003 (0.065) SPER -0.048 (0.074) -0.030 (0.073) GWSH -0.254*** (0.092) -0.262*** (0.100)

Note: **, *** indicate statistical significance at 10% and 5% levels respectively. AGE: is the farmer’s age in years, EDC: is a dummy variable indicating farmer’s education level, OFIN: is a dummy variable indicating farmer’s participation in off-farm business activities, LTS: is a dummy variable representing land tenure status, SDQ: is a dummy variable for seed quality, EXT: is a dummy variable representing access to extension services, WTD: is a dummy variable indicating farmer’s perception about decline in groundwater table, SPER: is a dummy variable indicating farmer’s perception about salinity perception.

Table 4.2.4: Proportion of farms satisfying the monotonicity and quasi-concavity conditions

Variables Maximum likelihood model Final adjusted model Monotonicity Seed 93.1% 100% Labour 67.7% 100% Fertilizer 94.2% 100% Farm machinery 97.7% 100% Irrigation water 78.0% 100% Quasi-concavity 28.9% 95.4%

We can interpret this scenario as, for the remaining 5% of observations that are not

quasi-concave, the individual inefficiency score may be either over or under estimated

(Sauer, 2006). Since the standard micro-economic theory requires satisfying quasi-

concavity under the profit-maximizing assumption, Henningsen and Henning (2009)

argue that the technical efficiency concept assumes that producers tend to maximize

their output given their input quantities rather than to maximize profit. Thus, in contrast

to the monotonicity condition, there is no technical rationale for satisfying the quasi-

concavity assumption. The intercept term in the final step is not significantly different

from zero, while the scaling coefficient is not significantly different from 1. From these


102

results we can infer that the three-step procedure has not introduced substantial bias in

the model (Gedara et al., 2012b).

The partial production elasticities with respect to all inputs are reported in Table 4.2.5.

These results indicate that production is inelastic with respect to each of the inputs

included in the model. The elasticities at the sample mean are almost identically ranked

under both estimations. The seed variable exhibits the largest partial production

elasticity while labour displays the smallest. The elasticities relating to seed and labour

are slightly lower in the initial estimate compared to the final estimate. Irrigation water,

with an elasticity of 0.079, is ranked 4th out of the five variables included in the model.

Similar results were reported by Karagiannis et al. (2003) in his study for out-of-season

vegetable farms in Greek. Regardless of the measurement units, cotton production is

highly responsive to type and quality of seed (0.41) while it is least responsive to labour

(0.039) and irrigation water (0.079), respectively.

The returns to scale (derived from the sum of input elasticities) was estimated to be

1.174, suggesting that cotton farms on average were operating under increasing return

to scale. The cross-product of the input elasticities are relatively small, suggesting that

there is limited opportunity for input substitution.

Table 4.2.5: Partial production elasticities for the sample mean from the unrestricted and restricted models

Variables Maximum likelihood model Final adjusted model Seed 0.409 0.455 Labour 0.039 0.079 Fertilizer 0.288 0.273 Farm machinery 0.359 0.323 Irrigation water 0.079 0.079

As far as estimates of the inefficiency model (Table 4.2.3) are concerned, the estimated

coefficients and standard errors of the unrestricted and restricted models differ slightly

in some cases. However, the difference is not statistically significant. We see that

farmer’s education and off-farm business activities do not significantly affect technical

efficiency. As expected, old farmers and tenants have slightly lower technical efficiency

levels than their counterparts. We find that improved seeds and extension services play

a significant role in improving technical efficiency. The results for farmers’ perceptions

indicate that farmers who perceive that over-extraction of groundwater resources may

deteriorate its quality and availability, are generally more efficient than the farmers who

think oppositely.


103

Table 4.2.6 and Table 4.2.7 present the technical efficiency and irrigation water use

efficiency estimates derived from both the unrestricted and the restricted models.

Table 4.2.6: Frequency distribution of technical and irrigation water use efficiency for tube-well owners from the unrestricted and the restricted models

Efficiency Range

TE (unrestricted)

TE (restricted)

IWE (unrestricted)

IWE (restricted)

<30 0 0 9 14 30-40 0 0 7 11 40-50 1 2 16 11 50-60 7 5 12 17 60-70 12 12 7 21 70-80 16 15 15 18 80-90 28 31 18 0 90-100 28 27 8 0 Mean 0.810 0.810 0.614 0.558 Std. Dev. 0.133 0.131 0.225 0.223 Minimum 0.405 0.412 0.079 0.124 Maximum 0.966 0.967 0.943 0.893

Table 4.2.7: Frequency distribution of technical and irrigation water use efficiency for water buyers from the unrestricted and the restricted models

Efficiency Range

TE (unrestricted)

TE (restricted)

IWE (unrestricted)

IWE (restricted)

<30 0 0 20 24 30-40 0 0 18 16 40-50 7 7 10 9 50-60 10 9 9 9 60-70 19 19 6 8 70-80 14 20 13 8 80-90 19 14 4 7 90-100 12 12 1 0 Mean 0.729 0.725 0.471 0.459 Std. Dev. 0.146 0.146 0.219 0.221 Minimum 0.405 0.413 0.041 0.111 Maximum 0.962 0.959 0.932 0.895

The unrestricted and the restricted TE estimates for tube-well owners have average

scores of 81% while the average IWE scores are 61% and 56% for the unrestricted and

the restricted models respectively. For water buyers, the average TE scores are 71%

while the average IWE scores are 47% and 46% for the unrestricted and the restricted

models. The equality of means test (t-test) for the unrestricted and the restricted TE

estimates cannot be rejected at the 1% significance level. However, we reject the null


104

hypothesis that mean IWE estimates derived from the unrestricted and the restricted

models are not significantly different from zero.

Figure 4.2.3: Technical efficiency estimates from the restricted and the unrestricted models

Figure 4.2.4: Irrigation water use efficiency estimates from the restricted and the unrestricted models

Figure 4.2.3 and Figure 4.2.4 also illustrate that estimates of TE based on the

unrestricted and the restricted models are highly correlated; the coefficient of

correlation for TE is 0.99 and that for IWE is 0.80.

4.2.5 Discussion and Conclusions

This study has estimated the level of, and factors affecting, technical efficiency and

irrigation water use efficiency among groundwater-fed cotton farms in Pakistan. The

.4.5

.6.7

.8.9

1

TE e

stim

ates

from

the

rest

ricte

d m

odel

.4 .5 .6 .7 .8 .9 1TE estimates from the unrestricted model

.1.3

.5.7

.9

IE e

stim

ates

from

the

rest

ricte

d m

odel

.1 .3 .5 .7 .9IE estimates from the unrestricted model


105

results obtained from a cross-sectional data of 173 cotton growers; including 92 tube-

well owners and 81 water buyers, indicate that considerable technical and irrigation

water use inefficiencies exist. Despite the severe water shortage in Punjab, the IWE

estimates reflect poor irrigation water management practices.

We find that, on average, tube-well owners are technically more efficient than water

buyers. Tube-well owners and water buyers can potentially increase cotton production

by 19% and 28%, respectively without increasing their existing input levels. Meinzen-

Dick (1996) found that tube-well owners were better-off in terms of farm productivity

compared to water buyers, presumably as a result of greater control over groundwater

access and supplies. Nevertheless, water buyers are prone to delayed irrigation water

supplies. As groundwater trading is informal, it is highly influenced by the social ties

between tube-well owners and water buyers. Hence, the absence of formal contracts

sometimes leads to inequities in water allocation and distribution among the buyers

(Jacoby et al., 2004, Rinaudo et al., 1997a). Moreover, on-going energy crises have

further added to uncertainty to water trading. Consequently, water buyers face delays in

obtaining water for irrigation. It is highly likely that delayed water application may

decrease the marginal product of other inputs such as fertilizer, labour and chemical

inputs.

Tube-well owners also irrigate their cotton fields more efficiently than the water buyers.

However, their irrigation water use inefficiencies are more pronounced than their

technical inefficiencies. The mean irrigation water use inefficiency estimates suggest

that a 46% and 54% reduction in current water applications is feasible for tube-well

owners and water buyers respectively. Our estimated IWE scores are generally lower

than those reported in some other studies on irrigation water use efficiency in many

other water stressed regions. Higher IWE estimates are reported by Speelman et al.

(2008) for small-scale irrigators in South Africa, Frija et al. (2009) for small-scale

greenhouse farmers in Tunisia and Manjunatha et al. (2011) for irrigated agriculture in

India. However, the average scores are higher than those of Karagiannis et al. (2003) for

out-of season vegetable farming in Greece. In contrast to our work, however, these

other studies have included multiple crops in their analyses.

The mean IWE estimates suggest that considerable gains in groundwater conservation

can be achieved by improving IWE across all farms. We calculated that cotton growers

on average produce 0.67 kg m-3. Although these estimates are fairly higher than the

previous estimate of 0.22 kg m-3 (Shabbir et al., 2012), there is considerable scope for


106

improving water productivity and efficiency. We estimated that the 173 cotton farms

can save a total of 1.06 million m3 of water if they achieve 100% efficiency in irrigation

water use. Besides its impact on the sustainability of groundwater resources, such

savings in water use would make it possible to reallocate water to other sectors.

Most of the estimated coefficients in the inefficiency model conform to a priori

expectations about their impact on efficiency levels. Our estimates indicate that

farmer’s age significantly impacts the level of technical efficiency. A number of other

studies suggest that old farmers are more sceptical about adopting new farming

techniques and technologies and hence their agricultural production can lag (Villano

and Fleming, 2006, Speelman et al., 2008).The coefficient of land tenure status

indicates that non-owners are more efficient than the land owners. These results

contradict the common intuition that, ceteris paribus, land owners usually invest more

in recent production technologies and, consequently, increase their expected returns

(Frija et al., 2009, Speelman et al., 2008, Gebremedhin and Swinton, 2003). However,

some studies have also reported a negative impact of land ownership on farm efficiency

(Byiringiro and Reardon, 1996). Nonetheless, our results support the notion that farmers

who rent land will also devote extra effort in management oversight to generate returns

above what they pay for rent. Hence, they are more efficient. As expected, education

and extension services have positive impacts on efficiency and support the premise that

increases in human capital enables farmers to improve resource utilisation and thus

achieve higher efficiency. In the literature, we find mixed results for the efficiency and

education relationship, e.g., Karagiannis et al. (2003) and Solı´s et al. (2009) found the

impact of education significant while Haji (2007) and Speelman et al. (2008) found

education’s impact to be non-significant. These mixed results indicate that using general

years of schooling is not being a substitute for specialized education e.g., agricultural

education has different requirements compared to the social sciences. The impact of

agricultural extension services on efficiency is consistent with the commonly

established assumption that the farmers who tend to seek more extension advice and get

involved in training programmes are technically more efficient than those who have less

or no contact with agricultural extension staff (Parikh and Shah, 1994, Frija et al.,

2009).

The results for seed quality show a statistically significant positive association between

seed quality and technical efficiency. We find that off-farm income is positively

associated with technical efficiency, suggesting that with alternative income resources,


107

farmers may have a better edge to purchase and use an optimal input mix which in turn

results in higher efficiency gains (Karagiannis et al., 2003).

Amongst the explanatory variables representing farmers’ perceptions about the

groundwater resource, perception about salinity and the potential impact on future

cropping patterns are positively associated with technical efficiency, while perception

about decline in groundwater tables is negatively associated with technical efficiency.

These results suggest that farmers do not worry about the declining water tables until

the groundwater quality starts deteriorating, as increasing salinity levels decrease crop

yields.

Whilst this study has policy implications for improving technical efficiency of cotton

growers, it also identifies the need to improve irrigation water use efficiency in cotton

production. Cotton production could be potentially increased through greater technical

efficiency; there is also considerable scope for improving irrigation water use efficiency

in cotton production. Both types of cotton producers i.e., tube-well owners and water

buyers, can reduce their current rates of irrigation application by 46% and 54%. The

main limitations to improving irrigation water use efficiency, however, arise mostly

from the lack of information about future viability of groundwater resources and lack of

information about water requirements of different crops at different times. We suggest

that educating farmers about crop water requirements and changing their perceptions

about groundwater resource availability may help achieve greater irrigation water use

efficiency.

Water buyers are generally down the water supply chain and they face more water

uncertainties that lead to reduced efficiency in the use of water. We suggest that policy

interventions are required to improve allocation security and equity of access for water

buyers whilst also providing information of the state and quality of groundwater

resources.


109

4.3 Measuring Production and Irrigation Efficiencies of Rice Farms:

Evidence from Punjab, Pakistan

(Asian Economic Journal, Vol. 28 (3): 301–322)


111

Abstract

We employ a non-parametric approach, data envelopment analysis, to estimate

production and irrigation water use efficiency of rice farms in Punjab, Pakistan. We use

a cross-sectional dataset of 80 rice growers comprising of 45 tube-well owners and 35

water buyers. The mean technical efficiency scores show that tube-well owners and

water buyers are operating at fairly high efficiency levels, indicating that access to

technology is not a major constraint for rice farms. However, irrigation water use

efficiency estimates suggest considerable inefficiencies with water buyers being more

inefficient than tube-well owners. A bootstrap truncated regression is used to investigate

the determinants of technical and irrigation water use efficiency. We suggest that

groundwater management policies should be designed to address efficiency enhancing

factors such as knowledge of crop water consumption requirement, better credit

opportunities, outreach extension services and training programs.

4.3.1 Introduction

Rice is a staple food crop in many parts of the world, and especially in Asian countries.

About 90% of the world’s rice is grown in Asia under different agro-climatic conditions

and geographic locations. Irrigated lowland rice, covering an estimated 80 million

hectares of cultivated land, contributes to approximately 75% of the global rice

production and consumes approximately 40% of the world’s irrigation water.

Approximately 60 million hectares of rainfed lowland rice meet 20% of the world’s rice

demand. The 14 million rainfed upland rice contributes only 4% to the world’s total rice

production (IRRI, 2013).

As in many Asian countries, rice is a major agricultural export commodity for Pakistan.

It holds an important position in the national economy, accounting for 1.4% of the

country’s gross domestic production (GDP). Pakistan is the world’s 5th largest rice

exporter, with the country exporting 3 million tonnes of rice in 2012 (Pakistan, 2009-

10). The two main varieties of rice produced and exported are Basmati 370 and IRRI-

631. However, the country is facing highly fluctuating trends in rice exports due to

increasing water scarcity and widespread stagnation in yields. In 2012, Pakistan’s rice

31 Basmati 370 is a high quality long grained aromatic variety and IRRI-6 is a medium-long grained variety. Prior to 1990s, Pakistan almost monopolised in international trade in Basmati. (Efferson, 1985)


112

exports declined by 13% compared to 2011 exports. The agricultural economy of

Pakistan is operating at its water limits and rice water productivity (0.45 kg/m3) remains

55% lower than the average estimates of 1.0 kg/m3 in Asia (Watch, 2003). Evidences

suggest that inefficient irrigation water application is one of the major reasons for low

rice water productivity. Rice growers generally apply water to uneven bunded fields,

resulting in long irrigation events, poor water uniformity and over-irrigation (Kahlown

et al., 2007, Kahlown and Kemper, 2004). Due to the inefficient irrigation practices,

severe water stress, and low rice productivity, improving rice productivity is a policy

imperative rather than a choice. Besides severe water stress, rising water demands for

the domestic and industrial sectors have added further impetus to improve irrigation

water use efficiencies (Archer et al., 2010, Laghari et al., 2012, Sharma et al., 2010,

Hussain and Hanjra, 2004). In particular, rice production is being overly pointed out for

inefficient irrigation water applications in Pakistan (Kahlown et al., 2007).

Rising water scarcity is one of the key factors limiting agricultural production in

Pakistan. Surface water availability is deficient and unevenly distributed. Whilst the

demand for irrigation water continues to increase, the supply of surface water is

unlikely to increase due to limited potential in surface water developments (Archer et

al., 2010, Laghari et al., 2012, Sharma et al., 2010). As a result, farmers have started

augmenting their irrigation water supplies through groundwater abstractions.

Groundwater use now constitutes more than 50% of the total irrigation water supplies in

Pakistan (Qureshi et al., 2009). Groundwater utilisation has played a key role in

propelling agricultural development in the country but massive groundwater

abstractions have led to the rapid depletion of groundwater aquifers with serious

repercussions to the sustainability of irrigated agriculture over the last few decades

(Kijne, 1999b, Shah et al., 2000, Khan et al., 2008a, Qureshi et al., 2009). Within this

context, pressure is increasing to improve rice productivity and irrigation water use

efficiency.

The objective of this chapter is to investigate the production and irrigation water use

efficiency of rice farms and the factors which affect technical and irrigation water use

efficiency. We employ a non-parametric approach, data envelopment analysis (DEA), to

estimate production and irrigation water use efficiencies using a cross-sectional dataset

of 80 rice growers in the Punjab Province of Pakistan. A second-stage truncated

regression is used to identify the factors influencing technical and irrigation water use

efficiencies. Besides investigating production efficiencies of rice farms, this study


113

contributes to the literature on rice production economics in several ways. First, it is the

only attempt to measure irrigation water use efficiency among rice growers. Second, to

check the robustness of our results, two methods, the data envelopment sub-vector

model and slack-based model, are used to measure irrigation water use efficiency.

The following section provides some literature review. Section 3 provides the

methodological framework for efficiency measurements. Section 4 describes the data

and principle features of the study area. The results are presented in the Section 5, and

Section 6 provides conclusions and policy implication.

4.3.2 Review of Literature

Due to its importance in food security and economic development, the efficiency of rice

production has been extensively investigated in developing countries32 , especially in

Asian countries (Villano and Fleming, 2006, Smith et al., 2011, Battese and Coelli,

1992, Abedullah et al., 2007) . Much of the empirical research suggests improving

production efficiency among rice growers both in the developed and developing

countries. One of the key finding in Asian countries is that low rice productivity is

attributed to disparities in technical efficiency (Thibbotuwawa et al., 2013). Much of the

published empirical work has estimated and explained technical inefficiency based on

either: i) socio-economic characteristics such as farm size, age, education, farming

experience, land tenureship status, family size, off-farm income; ii) institutional factors

such as extension services, farm organization membership; or iii) input use

characteristics such as soil and seed type. Methodologically, both parametric (Villano

and Fleming, 2006, Gedara et al., 2012a) and non-parametric (Dhungana et al., 2004,

Balcombe et al., 2008) approaches have been applied to estimate production efficiency

of rice farming, with some studies comparing the results from both approaches (Linh,

2012, Wadud and White, 2000).

Amongst the different socio-economic characteristics, various studies show a positive

and significant relationship between farm size and technical efficiency (Balcombe et al.,

2008, Coelli et al., 2002, Wadud and White, 2000, Rahman and Rahman, 2009) while

others find a negative relationship (Rahman et al., 2009). Age of the head of household

is one of the most influential characteristics in many studies. Older farmers are found to

32Bravo-Ureta and Pinheiro (1993) and Thiam et al. (2001) provide an excellent literature review on efficiency measurements in developing countries.


114

be more technically efficient than younger ones (Rahman, 2010, Tan et al., 2010, Khan

et al., 2010, Villano and Fleming, 2006). Similar to age, the impact of farming

experience on rice grower’s efficiency level is mixed and inconclusive. Mariano et al.

(2011) and Khan et al. (2010) found experience is positively related with efficiency

while Coelli et al. (2002) found a negative relationship. Many studies confirm to a

priori expectations about the impact of education on efficiency levels (Villano and

Fleming, 2006, Tan et al., 2010). Nonetheless, numerous studies do not find a

significant relationship between education and efficiency improvements (Gedara et al.,

2012a, Rahman and Rahman, 2009). Some studies even suggested a negative

relationship between education and efficiency, presumably due to the reason that formal

education does not focus on rice farming practices (Coelli et al., 2002, Tian and Wan,

2000, Rahman, 2003).There is also mixed results in regard to the impact of different

institutional factors such as extension services and participation in farmer organizations

on rice production efficiency (Rahman, 2003, Gedara et al., 2012a, Rahman and Hasan,

2008).

Abedullah et al. (2007) used a stochastic production frontier (SFA) model to determine

technical efficiency of 200 rice growers in the Punjab province of Pakistan. They

reported a fairly high average technical efficiency (91%) compared to the study by

Ahmad et al. (1999) in the same province who reported a mean technical efficiency

score of 85%. Amongst the other Asian countries Gedara et al. (2012a) used a stochastic

production frontier to estimate technical efficiency of 460 irrigated rice farms in Sri

Lanka. They estimated a mean technical efficiency of 72%, suggesting significant scope

for improving existing efficiency levels. In Vietnam, Huynh Viet Khai (2011) reported

a mean technical efficiency of 82% for a dataset of 3,733 rice growing households. In

Thailand, Rahman and Rahman (2009) reported substantial technical inefficiencies in

Jasmine rice production. Results from the stochastic frontier model suggest that on

average 59% of the rice productivity is lost due to technical inefficiency.Trewin et al.

(1995) employed SFA to estimate technical efficiency for two different data sets of rice

producers in Cimanuk River basin of West Jawa in Indonesia. Results from the panel

data report an average technical efficiency of 90% while those from the cross-sectional

data report an average score of 85% and 84% for the wet and dry season. Battese and

Coelli (1992) used SFA with time varying firm effects to estimate technical efficiency

for a panel data of rice growers from India. They found that technical efficiency is time

variant when year of observation was included in the model. They reported technical

efficiency ranging between 55% -86% for 1975-76 while 84%-96% in 1984-85. Tian


115

and Wan (2000) used the SFA approach to estimate technical efficiency in grain

production including rice in China. They reported mean technical efficiency estimates

of 95%, 94% and 95% for the early, late and mid-season cultivations for Indica rice and

91% for Japonica rice. Most of the above reported studies have considered land, labour,

fertilizer and machinery as the major variables. Despite a wide array of empirical

studies on rice production efficiency analysis, there are few studies which have included

water as an input in the production function. Moreover, empirical studies with an

exclusive focus on irrigation water use efficiency in rice production are rare.


4.3.3.1 Data Envelopment Analysis and Efficiency Measures

The data envelopment analysis (DEA) method is a mathematical programming

approach for measuring efficiency of different decision making units (DMU) e.g., firms

or farms etc. The method was introduced by Charnes et al. (1978), who extended

Farrell’s (Farrell, 1957) idea of measuring technical efficiency relative to a production

frontier. The DEA model proposed by Charnes et al. (1978) assumed a constant returns

to scale (CRS) technology. Later, Banker et al. (1984) introduced a DEA model under

the variable returns to scale (VRS). We refer the readers to the text book by Cooper et

al. (2000) for the detailed description on DEA.

4.3.3.1.1 Estimation of Technical and Scale Efficiency

Let us consider n DMUs that produce an output Y using input X. X is i × n matrix of

inputs and Y represents a k × n output row vector. Following Banker et al. (1984),

technical efficiency under VRS for a test DMUp, can be computed by solving the

following standard linear programming problem:

(λ, )4.3.1 Min

Subject to:

j ij

j kj kp

ip

n

n

j 1

j 1

n

j 1

j

j

x 0,

y y 0,

1,

0

x


116

where is a scalar and represents technical efficiency; j is a vector of j elements which

represents the influence of each farm in determining the technical efficiency of the

observed farm; p , ipx and kpy are the input and output vectors of farm p . The equation

njj 1

1 is a convexity constraint which specifies variable returns to scale in the

model. Technical efficiency can be further decomposed into scale efficiency which

helps adjusting the scale of operation for a DMU under observation. We impose a

restriction njj 1

1 in Equation 4.3.1 to calculate technical efficiency under CRS and

then we compute scale efficiency as follows:

4.3.2 SE TE(CRS) / TE(VRS)

Scale inefficiency can be either due to decreasing (supra optimal) or increasing (sub

optimal) returns to scales. However, the above measure of scale efficiency does not

indicate whether a DMU is operating in an area of increasing or decreasing returns to

scale. This problem can be resolved by solving an additional DEA model with a non-

increasing return to scale restriction njj 1

1 . The relationship (NIRTS) (VRTS)TE TE ,

(NIRTS) (VRTS)TE TE and (VRTS) (CRTS)TE TE indicates the existence of DRTS, IRTS and

CRTS (Coelli et al., 2005).

4.3.3.1.2 Estimation of Cost and Allocative Efficiency

The cost efficiency for a DMUp, is computed by solving the following DEA linear

programming with cost minimisation objective, where *px represents the cost

minimisation vector of input quantities and 'pw is the vector of input prices.

*p(λ,x

' *p) p4.3.3 wn xMi

Subject to:

ij

kj

n

jj 1

n

j pj 1

jj 1

j

*p

n

x 0,

y y 0,

x

1,

0.


117

The total cost efficiency for DMUp is calculated as ' * 'p p p pCE w x w x . That is, CE is

the ratio of minimum cost to actual cost for the DMUp. The allocative efficiency is then

calculated as a ratio of CE and TE:

4.3.4 AE CE / TE

4.3.3.2 Irrigation Water Use Efficiency

In non-parametric research, two approaches are generally used to measure the efficiency

of a particular input: the DEA sub-vector efficiency method (SVM) and the slack-based

DEA method (SBM). The major difference between the two methods is that the SVM is

a non-radial efficiency measure that ignores possible non-zero slacks, while SBM

calculates efficiency scores together with the slack values.

We use Figure 4.3.1 to illustrate the concept of the sub-vector and the slack based DEA

models. Let us consider six farms using two inputs 1x and 2x (e.g., water and fertiliser)

to produce a single output.

Figure 4.3.1: Graphical representation of the sub-vector and the slack-based input oriented efficiency models

Based on the efficiency concept, farms B, C, D, E and F are the best performers because

they are located on the frontier. A linear combination of their input use defines a

production frontier that envelops all of the other observed farms. Farm A is inefficient

because it is not located on the frontier. The radial contraction of inputs 1x and 2x (water

and fertiliser) produces a projected point on the frontier A , which is a linear

combination of all the observed data points. The technical efficiency of farm A with


118

respect to farms B, C, D, E and F can be measured as A OA ATE / O . The sub-vector

efficiency of farm A for input 1x can be measured by reducing 1x to a point 'A while

keeping 2x and the output constant. Hence, the sub-vector efficiency of input 1x for farm

A can be given by the ratio ' ' 'IWE OA / OA . In contrast to the technical efficiency

which involves radial contraction of all inputs, the sub-vector efficiency measure

contracts a particular sub-set of inputs or any individual input non-radially while

keeping other inputs and the output constant. Although the sub-vector measure allows

the estimation of the possible contraction of a particular sub-set of inputs, it does not

allow estimation of the possible slack value F E1 1ox - ox as illustrated in the Figure 4.3.1.

The slack values are useful in estimating excessive use of an input in a production

process.

4.3.3.2.1 The Sub-vector Efficiency Model

Following Speelman et al. (2008), the irrigation water use efficiency w for a given

pDMU can be estimated by solving the following DEA sub-vector model:

w(λ , )w4.3.5 Min

Subject to:

i w , j

w , j w ,

kj

p

p

kp

i

n

jj 1

n

jj 1

nw

jj 1

n

jj 1

j

x x

x x

1,

0

y y 0,

0,

0,

.

where w is the sub-vector efficiency of input"w" for a pDMU . The constraints (i), (iv)

and (v) are the same as in Equation 4.3.1. However, (ii) is the inputs constraint

excluding water while (iii) is the water input constraint.

4.3.3.2.2 The Slack-based Model

We estimate the slack-based DEA model following Cooper et al. (2011) to get the

difference between the optimal values and the observed values of inputs and output. The


119

linear programming that represents the DEA model to calculate slacks under VRS is

formulated as follows:

m

i(λ, ,S , S

s

k)i 1 k 1

4.3.6 Min S S ,

Subject to:

j kj

j ij ip

kp

n

ij 1

n

j 1

n

jj 1

k

j

x S x ,

S y ,

1,

0.

y

where kiS , S 0 i and k. X is an i × n matrix of inputs, Y represents an k × n output row

vector, and kiS ,S represents the inputs and output slacks respectively. The symbol 𝜀 is a

non-Archimedean infinitesimal defined to be smaller than any positive real number. By

solving this programme we are able to interpret the results as follows:

If * 1 and all slacks ki** ,SS 0 , DMU is considered to be strongly efficient.

If * 1 and *iS 0 and/or *

kS 0 , DMU is considered to be weakly efficient.

We used the following equation following Chemak et al. (2010) to measure the

excessive use of water in irrigation:

w

w

Ve4.3.7 IWE TEVo

where TE is the technical efficiency estimated using Equation 4.3.1, wVe is the slack

value of the input w, and wVo is the observed quantity of the input w.

4.3.3.3 Efficiency Determinants

In the literature, we find tobit regression as the most commonly used approach to

investigate the determinants of DEA efficiency measures. Many studies have justified

the use of tobit regression based on the argument that DEA efficiency scores are

censored values (Dhungana et al., 2004, Frija et al., 2009, Speelman et al., 2008, Wadud

and White, 2000). However, McDonald (2009) argues that efficiency scores are not

censored but are actually fractional values and proposed that Ordinary Least Squares

(OLS) in a second stage yields even more consistent results than the tobit model.


120

However, OLS is only consistent under certain assumptions of the data-generating

process (Simar and Wilson, 2011). In an earlier paper, Simar and Wilson (2007) proved

that in the second stage single bootstrap truncated regression performs better in terms of

estimating confidence intervals. Hence, in contrast to the general use of tobit, we use a

single bootstrap truncated regression to identify efficiency determinants as follows:

n

j j j j jj 1

4.3.8 Y z 0 ;

for j 1,...., N and 2j N(0, )

where jY is either technical or irrigation water use efficiency, jZ is the set of explanatory

variables for j 1,....,9 and j is the error term.

4.3.4 Study Area and Data

4.3.4.1 Study Area

This study was conducted in the northern agricultural territory of the Jhang district of

the Punjab province in Pakistan. In the study area, rural households heavily rely on

groundwater as a major source of irrigation. As a result of excessive pumping,

groundwater tables are in gradual decline. Declining water tables have increased

groundwater extraction costs many times over the last two decades. The variation in the

bore depth was observed to be between 33 and 57 metres during this field survey. We

find that due to low water tables and the high installation cost, tube-well populations are

relatively less dense in the northern parts of the Jhang district. As a result, farmers

generally engage in informal groundwater trading. Such informal groundwater

transactions have increased access to irrigation water for tenants and small farmers who

do not own tube-wells. However, for water buyers there are some equity concerns under

such informal market settings (Shah, 1993, Jacoby et al., 2004).

4.3.4.2 Data and Variable Definitions

The dataset for this study is based on a detailed survey conducted during the Kharif 33

season 2010-11 in the Jhang district of the Punjab province, Pakistan. A multi-stage

sampling technique was used in data collection. In the first stage, one tehsil was

selected purposively from the Jhang district. In the next stage, 10 villages34 were

33Rice is a Kharif crop. 34In the study area, a village usually contains of 70-80 household farms.


121

selected at random from the selected tehsil. Finally, from each village 10 groundwater

users (5 tube-well owners and 5 water buyers) were selected randomly to obtain the

differential impact of tube-well ownership and to reveal the difference of amount of

irrigation water applied and production gains of tube-well owners and water buyers,

thus making a total sample size of 100 respondents. However, during the cropping

season of 2010/11, only 45 tube-well owners and 35 water buyers cultivated rice crop

out of total 100 farming households.

Table 4.3.1: Descriptive statistics of the variables used in the DEA analysis

Tube-well owners Water buyers Variable Mean Std.

Dev. Mean Std. Dev.

Economic Data Cropped area (acres) 4.11 1.84 1.57 0.83 Seed (kg/acer) 4.71 0.53 4.58 0.80 Seed cost(Rs./acre) 356 61 321 82 Total labour (hours/acre) 167 51 239 112 Total labour cost (Rs./acre) 6992 2146 9958 4677 Fertiliser (kg/acre) 207 35 173 40 Fertiliser cost (Rs./acre) 5667 1846 4329 1879 Number of chemical applications/acre 2.46 0.69 2.48 0.65 Chemical cost (Rs./acre) 1323 476 1809 603 Number of farm operations/acre 8.00 1.18 6.97 1.54 Machinery cost (Rs./acre) 4694 833 4419 1318 Irrigation cost (Rs./acre) 8320 1378 14671 2336 Groundwater (m3/acre) 5495 1006 6124 1189 Rice yield (100kg/acre) 1547 161 1419 133

Farm level data was collected using an interview schedule on various inputs and output

quantities. The inputs were measured as: (1) seed and fertilizer in kg/acre; (2) pesticide

and farm operations as number of applications/acre; (3) total labour, comprising hired

(casual and permanent) and family labour in hours/acre; and (5) groundwater use in

cubic metres/acre. Output (rice yield) was measured in kg/acre. For different inputs and

output quantities, information on their respective prices was also collected in Pakistani

Rupees. The descriptive statistics used in the DEA model are presented in Table 4.3.1.

We collected information on groundwater use by obtaining data regarding the number

of irrigations applied to rice crop and the duration of each irrigation. We measured

groundwater extraction in litres using the following formula followed by Eyhorn et al.

(2005) and Srivastavaa et al. (2009).


122

2 2 4t 129574.1 BHP

[d (255.4.3.9

5998 BHP

) / d D )]Q

where Q represents the volume of water in litres, t is the total irrigation duration, d is the

depth of the bore, D is the diameter of the suction pipe, and BHP is the power of the

engine. Later, groundwater extraction was converted into m3. Table 4.3.1 compares

selected variables used in the DEA analysis. Descriptive statistics show considerable

variations in the use of the inputs and output produced by tube-well owners and water

buyers. The average farm size of the sample farms is 4 acres for the tube-well owners

and 1.5 for the water buyers. We see considerable variation in the number of hours

worked on farms by tube-well owners and water buyers. Overall, the average rice yield

is 1491 kg/acre with 1547 kg/acre for the tube-well owners and 1422 kg/acre for the

water buyers. There is also a significant difference in the seed rate with an average of

4.6 kg/acre ranging from a minimum of 2 kg/acre to a maximum of 7 kg/acre. Similarly,

there is great variability across the farms in fertiliser and chemical application. In the

case of irrigation water use, water buyers use an average of 10% more groundwater than

tube-well owners. The respective prices of the different inputs also show significant

variability. On average, labour, fertiliser, machinery, and irrigation cost constitute 94%

of the total production cost. The share of the irrigation cost to the total production cost

is observed to be 35% for the tube-well owners and 43% for the water buyers. The

explanatory variables used to explain the efficiency differentials are presented in

Table 4.3.2.

The average farmer’s age is 42 years, ranging from 27 to 60 years. Among the sampled

farmers, approximately 68% of the farming families are living as joint families. The

statistics on education clearly reflect lack of education with 39% of the surveyed

farmers having no formal education. Only 24% of the farmers have an education level

above matriculation. A significant proportion of the surveyed farmers cultivate their

own land. Only 12% of the farmers are tenants. Because farming is a major livelihood

activity among rural communities, only a small proportion (13%) of the farmers has off-

farm income business. Only 33% of the farmers managed to get credit from private

banks or public agencies. Similarly, only one third (33%) of the farmers participated in

agricultural training programmes or obtained advice from the agricultural extension

field staff.


123

Table 4.3.2: Summary statistics of variables included in the truncated regression

Variable definition Continuous variables Proportion of farmers with dummy variables

Mean SD Min. Max. 0 1 2 Farmers’ age (years) 42 1 27 60 Area under rice cultivation (acres) 3 1.95 0.50 9 Family status (0= single family, 1=joint family)

32 68

Education (0=illiterate, 1=up to metric, 2=above metric)

38 37 24

Off-farm income (0=no, 1=yes) 86 14 Land tenure status (0=tenants, 1=owners)

13 87

Credit access (0=no, 1=yes) 66 34 Extension services (0=no, 1=yes) 66 34

4.3.5 Results and Discussion

4.3.5.1 Technical, Scale, Cost and Allocative Efficiencies

The DEA efficiency estimates under variable returns to scale are presented in

Table 4.3.3. The mean technical efficiency is found to be 96% and 94% for the tube-

well owners and water buyers, respectively. Based on the mean estimates, gains from

improving technical efficiency do not seem to be considerable. However, across all the

villages, only 57.8% of the overall tube-well owners and 51.4% of the total water

buyers were fully technically efficient (TE=1). This implies that a significant majority

of the tube-well owners and water buyers are operating with technical inefficiencies.

The mean scale efficiency values for the tube-well owners and water buyers are found

to be 90% and 89% with 35.5% tube-well owners and 34.2% water buyers being fully

scale efficient (SE=1).

The mean scale efficiency implies that the scale of operation did not differ considerably

for the tube-well owners and water buyers. However, the analysis was further

disaggregated into those farms that exhibit increasing returns to scale (IRTS) and

decreasing returns to scale (DRTS). From Table 4.3.3 we see that: (1) more tube-well

owners are operating at an optimal scale than the water buyers; (2) more water buyers

tend to operate at sub-optimal scale than the tube-well owners. Only a small proportion

of farmers, with 2% of tube-well owners and 3% of water buyers, are operating at

supra-optimal scale. The results on returns to scale imply that most of the rice farms


124

should be larger than they presently are to produce efficiently under the given state of

technology.

Table 4.3.3: Frequency distribution of technical, scale, cost and allocative efficiencies

Tube well owners Water buyers Frequency (%) TE SE CE AE TE SE CE AE <30 0 0 0 0 0 0 0 0 30-40 0 0 0 0 0 0 0 0 40-50 0 0 4 4 0 0 4 3 50-60 0 3 5 2 0 0 11 8 60-70 0 1 15 11 0 4 9 8 70-80 0 5 10 16 1 5 4 6 80-90 5 9 3 4 9 4 3 5 90-99 14 11 3 3 7 10 1 2 100 26 16 5 5 18 12 3 3 Mean 0.97 0.90 0.72 0.74 0.94 0.89 0.67 0.71 Std. Deviation 0.05 0.13 0.16 0.16 0.07 0.12 0.16 0.16 Minimum 0.85 0.57 0.41 0.41 0.78 0.63 0.45 0.46 Maximum 1 1 1 1 1 1 1 1

Table 4.3.4: Distribution of returns to scale for tube wells owners and water buyers

Returns to scale Tube well owners Water buyers IRTS (%) 67 86 DRTS (%) 2 3 CRTS (%) 31 11

Note: IRTS, DRTS, CRTS, indicate increasing returns to scale, decreasing returns to scale and constant returns to scale

Rice farms appear to be having low cost and allocative efficiency. The mean cost

efficiency estimates are found to be 71% and 66% for the tube-well owners and water

buyers. Similarly, the mean allocative efficiency is found to be 73% and 70% for the

tube-well owners and water buyers. Only 11.1% of the tube-well owners and 8.5% of

water buyers are found to be fully cost and allocative efficient (CE=AE=1). On average,

allocative inefficiency accounts for 27% and 30% loss in income respectively for the

tube-well owners and water buyers, suggesting the lack of revenue maximizing

behaviour and scope for improving income by increasing allocative efficiency.

4.3.5.2 Efficiency of Irrigation Water Use

The sub-vector and slack-based irrigation water use efficiency estimates are presented

in Table 4.3.5. The results show large scale irrigation water use inefficiency among


125

tube-well owners and water buyers. The mean sub-vector estimates indicate 20% and

22% irrigation water use inefficiency among tube-well owners and water buyers.

The slack-based model, however, indicates average irrigation water use inefficiency of

10% and 13% for tube-well owners and water buyers. These estimates indicate that

there is a considerable scope for reducing irrigation water use by using the observed

quantities of other inputs and maintaining the same output level. The results presented

in Table 4.3.5 indicate that water buyers are less efficient in irrigation water use than the

tube-well owners.

Table 4.3.5: Frequency distribution of sub-vector and slack-based water use efficiencies

Sub-vector IWE Slack based IWE Frequency (%) Tube-well


owners Water buyers

<30 0 0 0 0 30-40 0 0 0 0 40-50 2 1 0 0 50-60 4 5 0 0 60-70 8 9 2 3 70-80 7 5 7 8 80-90 5 2 10 9 90-99 3 2 11 4 100 15 11 15 11 Mean 0.796 0.779 0.900 0.874 Std. Dev. 0.194 0.187 0.109 0.112 Minimum 0.404 0.442 0.649 0.665 Maximum 1 1 1 1

Several reasons can explain this: (1) the functioning of informal groundwater markets

where water buyers sometimes cannot buy water when their crop requires and; (2)

sometimes tube-well owners prefer certain water buyers due to social ties with them,

thus discriminating on whom to sell water to (Jacoby et al., 2004); and (3) the nearby

tube-well owner may not have enough surplus water to sell because of the large farms

and high water demands; therefore, water buyers have to buy water from a far-off tube-

well owner. Due to all these factors, it is highly likely that water buyers will face delays

in obtaining water for irrigation. Such delays in irrigation water applications can have

serious impacts on crop growth and may decrease the marginal product of other inputs

such as fertilizer, labour and chemical inputs etc.


126

Table 4.3.6: Spearman’s rank correlation among technical efficiency and the sub-vector and slack-based irrigation water use efficiencies

TE Sub-vector IWE Slack-based-IWE TE 1.000 SV- IWE 0.899* 1.000 SB-IWE 0.874* 0.965* 1.000

Table 4.3.7: Paired samples t-test demonstrating the difference between technical and irrigation water use efficiencies

Mean difference Std. deviation t-statistics

TE-Sub-vector IWE .1389 .1322 9.399*** TE-Slack-based IWE .0387 .0697 4.966***

Ho: mean (diff) = 0 Ha: mean (diff) ≠0 Pr (|T| > |t|) =0.000

The results presented in Table 4.3.6 suggest that the sub-vector efficiency model

measures relatively lower degrees of irrigation water use efficiency compared to the

slack-based model; however, both estimates are highly correlated.

Figure 4.3.2: Cumulative distribution for technical, sub-vector and slack-based irrigation water use efficiencies

From, the cumulative frequency distribution of technical efficiency and the sub-vector

and slack-based irrigation water use efficiency (Figure 4.3.2), we can infer that

irrigation water use inefficiencies are more pronounced than the respective technical

inefficiency. A paired sample t-test35 was applied to analyse the equality of means for

technical and irrigation water use efficiency. We find significant difference between

35The t-statistics 9.628 with a p-value 0.000 show that there is a significant difference between the technical and the sub-vector irrigation efficiency. Similarly, t-statistics 16.423 with a p-value of 0.000 indicate that the means difference between the technical and the slack-based irrigation efficiency is also significant.

00.050.1

0.150.2

0.250.3

0.350.4

0.450.5

0 0.2 0.4 0.6 0.8 1

Shar

e of

farm

s

Technical efficiency (VRS)SV-WUESB-WUE


127

technical and the irrigation water use efficiency. The test statistics show that irrigation

water use efficiency is significantly lower than the technical efficiency in rice farming.

4.3.5.3 Explaining Efficiency Differentials

The empirical findings concerning the sources of efficiency differentials among rice

farms are presented in Table 4.3.8. The truncated regression estimates indicate that the

exogenous variables have a significant impact on both technical and irrigation water use

efficiency. The impact of farmer’s age, family status and off-farm income do not seem

to significantly affect either technical or irrigation water use efficiency. However, the

level of education, land tenure status, credit and extension services have statistical

significant impact on technical efficiency while farm size is associated significantly

with the irrigation water use efficiency.

The level of education is shown to have a positive significant impact on technical

efficiency while non-significant on irrigation water use efficiency. Similar to our

findings, Karagiannis et al. (2003) found increased schooling years positively associated

with high levels of technical efficiency. However, Speelman et al. (2008) found that

education does not improve irrigation water use efficiency. This implies that more

educated farmers have the ability to utilize the optimal input bundle and the best

available technology when we consider their overall technical efficiency; however,

when we focus on irrigation water use, education does not seem to contribute towards

the optimal use of irrigation water. Similarly, land ownership has significant impact on

technical efficiency but is non-significant on irrigation water use efficiency. This may

be because land owners have greater access to inputs such as water, credit and extension

services compared to water buyers who are usually tenants or subsistent farmers.

We find that farm size is not associated significantly with technical efficiency; however,

irrigation water use efficiency decreases significantly with increasing size of the farm

suggesting that larger farms use irrigation water less efficiently. This may be due to

flood irrigation which increases the chances of over irrigation as farm size increases.

Many other studies have found a positive relationship between farm size and technical

efficiency (Balcombe et al., 2008, Wadud and White, 2000) and negative relationship

between farm size and irrigation water use efficiency (Speelman et al., 2008).


128

Table 4.3.8: Bootstrap truncated estimates of the determinants of technical and irrigation water use efficiency

Explanatory Variables Technical efficiency Irrigation water use efficiency

Coefficient Sd. Dev.

Coefficient Sd. Dev.

Farmer’s ager (years) Age 0.006 0.007 0.0048 0.012 Age2 -0.000 0.000 -0.000 0.000 Family status (0= single, 1= joint family)

-0.021 0.017 0.023 0.026

Education dummy (0= illiterate, 1= up to matriculation, 2=above matriculation) Up to matriculation 0.034** 0.017 0.006 0.029 Above matriculation 0.033 0.027 0.002 0.038 Land tenure status dummy (0= tenants, 1=owners)

0.035* 0.021 0.043 0.039

Off-farm income dummy (0=no, 1=yes)

0.005 0.022 -0.004 0.049

Farm size (acres) 0.006 0.006 -0.019** 0.009 Credit dummy (0=no, 1=yes) 0.051** 0.023 -0.036 0.028 Extension services dummy (0=no, 1=yes)

0.034* 0.019 0.031 0.031

Constant 0.703*** 0.152 0.266 0.266 Log likelihood 141.1 73.72

Note: *, **, *** indicate significant at 10%, 5% and 1% respectively. Number of bootstraps=4000 We found that farmers who opted to get credit were more technically efficient than

those who did not. Many studies argue that access to credit enables farmers to purchase

and use better mix of inputs at the most appropriate time, hence it helps in improving

technical efficiency (Haji, 2007, Karagiannis et al., 2003). The impact of agricultural

extension services on technical efficiency level is consistent with the commonly

established assumption that the farmers who tend to seek more extension advice and get

involved in training programmes are technically more efficient than those who have less

or no contact with the agricultural extension staff (Parikh and Shah, 1994, Karagiannis

et al., 2003).We find technical and irrigation water use efficiencies are highly correlated

(Table 4.3.6). We infer that the statistically significant impact of agricultural extension

advice on technical efficiency could also have a significant impact on increasing

irrigation water use efficiency (Frija et al., 2009, Karagiannis et al., 2003).


129

4.3.6 Conclusion

The objective of this study was to measure production efficiencies and irrigation water

use efficiency among rice growers in Punjab, Pakistan. We used data envelopment

analysis to compute technical, scale, cost and allocative efficiencies using a survey data

of 80 randomly selected rice growers. Irrigation water use efficiency is estimated using

the sub-vector and slack-based DEA models to estimate excessive use of irrigation

water.

The empirical results on technical efficiency indicate that on average rice growers

operate at fairly high efficiency levels. Likewise, the mean estimates of scale efficiency

show that scale inefficiencies are nearly absent among tube-well owners and water

buyers. However, the results on returns to scale suggest that farm efficiency can be

improved by expanding the scale of operation. The cost and allocative efficiency

estimates indicate that rice growers are not utilising optimal quantities of inputs given

their respective prices. This study finds that rice production could be potentially

increased without increasing current input levels; likewise there is a considerable scope

for improving irrigation water use efficiency by using less water than the current levels.

The average sub-vector irrigation water use efficiency estimates for the tube-well

owners and water buyers suggest 20% and 22% savings in their current use of irrigation

water. In terms of total groundwater volumes, based on the sub-vector estimates, we

calculated that the tube-well owners and water buyers can save a total of 0.28 million

m3 of groundwater, with 0.19 million m3 reductions for tube-well owners and 0.08

million m3 for water buyers. Put in monetary terms, tube-well owners and water buyers

can save up to Rs. 0.28 million and Rs. 0.22 million of their total irrigation costs during

one cropping season of rice.

A key finding of the present study is that access to technology is not a major constraint

in rice production. However, high cost of inputs does affect cost and allocative

efficiency. We suggest that to improve technical and irrigation efficiency in rice

production, efforts and development strategies should be directed towards educating

farmers, providing better credit opportunities and agricultural extension services. In

particular, rice growers would benefit from better education and extension advice about

crop water requirements. Moreover, because water buyers face uncertainty regarding

access to water, policy intervention in groundwater markets to improve water allocation

could improve equity of access and, presumably, irrigation water use efficiency among

water buyers.

131

CHAPTER 5

5. Derived Demand for Irrigation Water

Abstract

We employ the Positive Mathematical Programming (PMP) approach to estimate the

derived demand for groundwater for irrigation using a cross-sectional data of 200

households who predominately use groundwater to produce wheat, cotton, rice and

sugarcane crops in the Punjab province of Pakistan. First, we find that the optimal

solution uses less water than what is being extracted for irrigation among the sampled

households. Second, farmers do not allocate land to different crops based on their water

requirement but based on their profitability. We suggest that water pricing can facilitate

appropriate and efficient use of groundwater for irrigation sector. Our analysis indicate

that that introducing water pricing at Rs. 41/1000 m3 for water sellers and Rs.

36/1000m3 for water buyers can help to achieve a 2% reductions in irrigation water

demand. The high shadow price (marginal value) is due to the higher expected net

returns per hectare. We suggest that water pricing should be used alongside other

policies geared towards improving irrigation water use efficiency.

5.1 Introduction

The agriculture sector is the largest consumer of global water withdrawals with

irrigation accounting for approximately 70% of the withdrawals (Siebert et al., 2010b,

Döll, 2009). The irrigated area comprise of less than 20% of the global cropland but

contributes to more than 40% of global food production (Döll and Siebert, 2002).

Irrigated agriculture heavily relies on groundwater resources in many regions.

Currently, groundwater contributes to about 42% of the global irrigation water supplies

(Döll et al., 2012, Siebert et al., 2010b, Rodell et al., 2009). The huge increase in

groundwater use over the past half-century is as a result of development of large-

capacity wells and wide water distribution technologies (Schwartz and Ibaraki, 2011,

Scanlon et al., 2012). A recent study reports an approximate 1500 km3y-1 use of tapped

water at global scale (Döll et al., 2012) and the area equipped for groundwater irrigation

is about 113 million hectares, which is about 38% of the global irrigated cropland

(Siebert et al., 2010b). With the growing dependence on groundwater and high

abstraction rates, groundwater resources are rapidly declining in many parts of the

world (Giordano, 2009, Werner and Tom, 2012, Schwartz and Ibaraki, 2011). Although

Derived Demand for Irrigation Water

132

global groundwater abstractions (1500 Km3 yr-1) are far lower than the global recharge

(12,600 Km3 yr-1), groundwater resources measured locally in different regions are in

gradual decline (Döll et al., 2012, Döll and Fiedler, 2008, Konikow, 2011, Scanlon et

al., 2012, Wada et al., 2010). A recent study in Nature Geoscience has re-mapped global

groundwater depletion. It shows the highest depletion rates in USA, Mexico, Saudi

Arabia, China, India and Pakistan (Aeschbach-Hertig and Gleeson, 2012).

It is widely recognised that Pakistan is amongst those countries where groundwater is a

depleting resource (Rodel et al., 2009, Wada et al., 2010, Khan et al., 2008b). Pakistan

is the 3rd largest groundwater consumer accounting for about 9% of the global

groundwater withdrawals (Giordano, 2009). Having 5.2 million hectares of land area

equipped with groundwater irrigation, Pakistan irrigates 4.6% of the global

groundwater-fed cropland (Siebert et al., 2010b). A sharp increase in groundwater use

in Pakistan was started after the 1960s’ Green Revolution. The continued expansion of

irrigated area and the introduction of high yielding but water intensive crop varieties

during the Green Revolution increased irrigation water demands by about three times

(Shiva, 1991, Ahmad et al., 2004b, Rodel et al., 2009). In 1960 groundwater

contribution to the total irrigation water supplies at the farm gate was about 8%, but 25

years later in 1985 this share had gone up to 40% (Byrelle and Siddiq, 1994). In recent

years, overall groundwater dependence has increased to more than 50% in different

parts of the country (Qureshi et al., 2010, Strosser and Rieu, 1997). Nevertheless,

renewable groundwater resources are not sufficient enough to meet the escalating

irrigation water demands. As a result, groundwater resources are under immense

pressure from overdrafting and this has led to many negative economic and

environmental externalities with serious repercussions to the sustainability of irrigated

agriculture (Kijne, 1999b, Shah et al., 2000, Khan et al., 2008a, Qureshi et al., 2009).

Given the well-documented spatial and temporal externalities associated with

groundwater overdrafting, some sort of regulatory mechanism is necessary to ensure its

sustainability. Many policy mechanisms propose water pricing to allocate irrigation

water more efficiently by regulating demand and supply (Tsur et al., 2004a, Tsur and

Dinar, 1997). It is considered that ‘getting the price right’ is important to improve water

allocation and conservation (Johansson et al., 2002). An effective water pricing policy

must consider the elasticity of demand for irrigation water and any collateral effects

which may come in the form of reduced agricultural production as a result of

constrained water supplies (Huang et al., 2010). Many studies have demonstrated that


133

the demand for irrigation water is inelastic and hence water pricing would not stimulate

the intended changes in water re-allocation and conservation (Varela-Ortega et al.,

1998, Berbel and Gómez-Limón, 2000, Moore et al., 1994, Schoengold et al., 2006).

Generally, farmers are considered to be unresponsive to low water pricing36 and make

no on-farm investments in water conservation technologies to make efficient use of

water resources (Chaudhry and Young, 1989).

In the past, the Pakistan government tried various indirect groundwater management

strategies such increasing electricity tariffs to control groundwater withdrawal but

achieved limited success. The successful implementation of an effective water

management requires the assessment of the levels at which farmers' demand for

irrigation water becomes elastic and remains socio-economically acceptable. If

groundwater users in Pakistan are not responsive to existing water pricing (cost of

extraction and cost of buying water), imposing price37 on groundwater will not

significantly reduce demand. Therefore, it is necessary to understand how farmers

would respond to water pricing policy and determine the optimal water allocation

among the different uses (Storm et al., 2011).

The goal of this study is to estimate the derived demand for groundwater for irrigation.

We use the Positive Mathematical Programming (PMP) approach to assess how

different irrigators, i.e., tube-well owners (water sellers) and water buyers, value

groundwater resources. Several studies have discussed water pricing policies in Pakistan

36In Pakistan, some water charges are levied as a user charge (Abiana) for canal water distribution by the respective provincial irrigation departments while groundwater is a free resource. Over the last half a decade the Abiana charges are enforced on flat rate basis. These flat rates are different for different crops and very among different provinces. The above mentioned study was done more than two decades ago and at that time Pakistan was not facing the sever water crises. 37Markets for groundwater have been in operation for informal trading of surplus groundwater pumping capacities between the tube-well owners and water buyers without involving the exchange of permanent water rights (Khair et al., 2012, Meinzen-Dick, 1996). Basically, these markets offer a win-win situation to tube-well owners by offering economic benefits and to non-owners offering opportunities to increase agricultural productivity (Manjunatha et al., 2011, Meinzen-Dick, 1996, Shiferaw et al., 2008). Due to open access to groundwater resources farmers who have means to invest in tube-well technology, they can extract and even sell groundwater without any interference (Meinzen-Dick, 1996, van Steenbergen and Oliemans, 2002). The private tube-well owners bear only the energy and machinery costs to extract groundwater. Nonetheless, water buyers have to pay extra charges in terms of wear and tear charges besides paying pumping costs. Traditionally, price for groundwater is determined through a social consensus in the beginning of new cropping season or with increasing energy prices as an hourly flat rate basis or fixed share in crop production per unit of land. However, in many instances tube-well owners set the price first and then inform the water buyers. The price usually varies with the type of tube-well i.e., electric or diesel operated tube-well and based on the horse power of the engine etc.


134

(Sahibzada, 2002, Chaudhry et al., 1993, Sufi, 2011, Farooqi et al., 2012). Nevertheless,

those studies explicitly focus on surface water by addressing gaps between water supply

costs and operation and maintenance costs. None of the studies have empirically

estimated the derived demand for groundwater for irrigation, especially in the context of

depleting groundwater resources. Therefore, this study is amongst the very few

quantitative studies to estimate the derived demand for groundwater for irrigation in

Pakistan.

The rest of the chapter is organised as follows. The next section provides literature

review within the context of irrigation water demand and pricing. Section 3, describes

the methodological frameworks to estimate irrigation water demand. The results are

presented in Section 4. The final section draws conclusions and provides some policy

implications.

5.2 Irrigation Water Pricing and Demand

The neoclassical economic literature argues that irrigation water pricing is necessary to

induce farmers to rationalize their irrigation water demands and adopt water saving

technologies (Berbel and Gómez-Limón, 2000, Frija et al., 2011). Moreover, due to

heavy investments in irrigation infrastructure and the resulting temporal and spatial

unintended consequences, some sort of regulation is required to create an equilibrium

between the supply and demand (Tsur et al., 2004a) and make farmers aware of the

value of water (Perry, 2001, Easter and Yang, 2007). Consequently, a lot of policy

mechanisms to allocate water and rationalize irrigation water demand have emerged

both at the managerial and the institutional levels. Amongst these many policy options,

some seek to regulate water allocations by water pricing (Tsur, 2004, Dinar, 1998, Tsur

and Dinar, 1997). However, there is no consensus on what is the right pricing

mechanism and how pricing should be implemented (Tsur, 2004). Many authors

propose that for the successful implementation of water pricing policy, it should be

accompanied by a set of complimentary policies that help to improve water productivity

and efficiency simultaneously (Gómez-Limón and Berbel, 2000, Gómez-Limón and

Riesgo, 2004, Liao et al., 2007, Molle et al., 2008).

Empirical studies from several developed countries have shown that demand for

irrigation water is inelastic (Berbel and Gómez-Limón, 2000, Salman and Al-Karablieh,

2004, Moore et al., 1994). These studies indicate that water pricing would not reduce

agricultural water consumption until prices negatively affect farm income. This implies


135

that raising the price of water will not significantly reduce demand and will not be

effective because water users are not responsive to water pricing (Huang et al., 2010).

Berbel and Gómez-Limón (2000), in a study in Spain, demonstrated if water is priced,

farm incomes would decrease by 25% to 40% before water demand starts to decrease

significantly. Likewise, Salman and Al-Karablieh (2004) conducted a study in Jordan

and demonstrated that pricing water at 0.35$/m3 would not significantly reduce

irrigation water demand. Moore et al. (1994) investigated multi-cropping production

decisions in western United States and concluded that irrigation water demand is

inelastic for short-run water use decisions and is elastic for long term crop-choice and

land allocation decisions. In contrast, few other studies indicate more elastic demand

and show that the price of water is a strong determinant of water demand and an

important incentive for farmers to adjust their irrigation water requirements (Scheierling

et al., 2006)38.

Numerous methods to price and allocate irrigation water have been proposed in theory

and practice, some more efficient and some easier to implement than others. These

methods include: volumetric pricing, non-volumetric pricing, quotas and market-based

mechanisms (Johansson et al., 2002, Dinar, 1998). Amongst the pricing methods,

volumetric pricing is more effective in inducing efficient use of water. Yet volumetric

pricing is an exception worldwide due to its high implementation costs (Tsur, 2004,

Tsur and Dinar, 1997). On financial grounds, water pricing is considered a means to

recover the cost of supplying water and on economic grounds, a tool to signal water

scarcity (Dinar and Saleth, 2005). Nevertheless, the most commonly used marginal-cost

pricing (MCP) method which equate water price to marginal-cost of supply does not

cover the economic dimensions of water price such as capital depreciation and other

fixed costs (Tsur et al., 2004a, Huang et al., 2010). In the case of groundwater, the

marginal opportunity cost is associated with the unavailability of a unit of groundwater

that is over-extracted today (Koundouri, 2004). Hence, it is necessary to estimate the

social cost of groundwater over-exploitation (Huang et al., 2010). The social cost of

groundwater extraction arises as a result of over-exploitation of the resource by some

users that increases the cost for other users (Harou and Lund, 2008). Because of its non-

excludability nature, there is little incentive for a groundwater user to forego his current

38Scheierling et al. (2006) provide detailed description of irrigation water demand using meta-analysis.


136

water needs for future needs, resulting in an increased rate of extraction and more rapid

resource depletion (Reddy, 2005, Pfeiffer and Lin, 2012). Generally, groundwater is not

regulated by well-defined property rights or by competitive water markets. Hence, both

the opportunity cost and social cost largely remain unrecognised (Huang et al., 2010,

Koundouri, 2004, Lynne, 1989).

5.3 Theoretical Framework

5.3.1 Approaches to Derive Demand for Irrigation Water

Irrigation water demand can be estimated by both statistical (econometrics) and

mathematical programming techniques. We explain the two techniques theoretically

following Tsur et al. (2004a) and Tsur et al. (2004b) . Consider the case of a farmer who

produces m crops using a single input i.e., irrigation water. Let j j jy f q represent a

yield-water response function for crop j , where jy is yield, jq is water input and j jf q is

an increasing and strictly concave production function and j 1,2,......,m . With jp

representing price of crop jand w for water price, a farmer’s operating profit is

represented by j j

mj jj 1

p f q wq and the necessary conditions for profit

maximization are j j j j'/ q f q w w p 0 which give rise to the individual crops

derived demand for water; j j' 1jq w f w p , j 1,2,......,m .Thus, the water demand can

be represented as:

j j j j

m m ' 1j 1 j 1

5.1 q w q w f w p

The above equation can easily be

extended to the general case of n farmers as:

i ij j

n n m' 1

i 1 i 1 j 1

5.2 q w q w f w p

Alternatively, the derived demand for

irrigation water can be obtained as follows. Let us consider that water is not priced but

is constrained at the level x . Here we are interested on how much farmers are willing to

pay to relax the water constraint represented by x units (i.e. x x ). Suppose that a

farmer uses water up to constraint x , the revenue generated is pf x and the additional

water ( x ) will generate additional revenue p f x x f x . For the additional

quantity of water demanded, the farmer is willing to pay at most p f x x f x

x

.

For small enough change in water constraint x , the marginal revenue is 'pf x . This


137

represents the (maximal) price the farmer is willing to pay to relax the water constraint

by one unit; it is also called the shadow price of water. The shadow price varies at

different levels of water constraint and will only be positive when the constraint is

binding. This approach of computing the shadow price of irrigation water can be

extended to situations involving multiple inputs and additional constraints are easy to

impose.

Suppose production of crop j involves inputs j j jq ,z and b , where jq is water input, jz

represents k inputs like seed, fertilizer, pesticide and machinery that can be purchased at

unlimited quantity at the going market price 1 2 kr (r , r ,...r ) and jb denote primary inputs

e.g., land and family labour 1 2 Ls s ,s ,....,s that are available at limited quantities

1 2 Lb b ,b ,....b . Let the production function for crop j be denoted by j j j jf q ,z ,b , the

input output decisions problem of profit maximizing and price-taking is:

j j j j j k jk

m k

q,z,sj 1 k 1

5.3 x,b,p,r Max p f q ,z ,b r z

j

m

j 1 q

Subject to

x

:

jl l

m

j 1

b s ,

where jq 1,2,....,m, j j1 j2 jkz z ,z ,......,z , j j1 j2 jLb b ,b ,......,b , and L 1,2,....,L the non-

negativity constraints of some variables. This problem can be solved by forming the

Lagrangian: j j j j j k jk j l l jl

m k m L m

j 1 k 1 j 1 l 1 j 1

p f q ,z ,b r z x q s b

(other

constraints times their multipliers)

The multiplier, ,of the water constraint evaluated at the optimum level is the shadow

price of water, which when calculated for all feasible water levels x , constitute the

inverse derived demand for water.

5.3.2 Method of Analysis-Positive Mathematical Programming (PMP)

The Positive Mathematical Programming (PMP) approach emerged in the late 1980s as

means to analyse agricultural, environmental and land use policy decisions in

accordance with the economic behaviour. In this work, we apply the Positive

Mathematical Programming (PMP) approach formalized by Howitt (1995) as a method

to model economic behaviour where a concave profit maximization function is solved


138

using the non-linear marginal cost (MC) parameters of a variable cost function. The

term “positive” implies that the parameters of the non-linear objective function are

derived from an economic behaviour and they are considered to be rational given the

observed and non-observed conditions that generate the observed activity level. Later,

Tsur et al. (2004b) advanced this model to estimate the derived demand for irrigation

water. The PMP calibration approach consists of three stages. Let n represent the

number of crops j 1,2,......, n, in the base year, jp price of crop j , jy yield/ha of crop j , jx

water requirement/ha for crop j , jL land allocation to crop j in the base year and jc

production cost/ha excluding water cost for crop j . In the first step, a linear

programming model is set to solve a constrained profit maximization problem with

calibration constraints on the total amount of water available to the system and the total

land available to cultivate j crops. We choose crop area allocation so as to maximize net

farm return subject to land and water constraint. The optimization model is represented

as:

j j j jj

n

Lj 1

5.4 Max p y c L

Subject to:

j j

n

j 1

x L W

(water constraint)

j

n

j 1

L L

(land constraint)

where is small positive perturbation and the usual non-negativity constraint holds.

By solving the constrained profit maximization problem, we generate shadow values

for different crop allocations and optimal allocation of crop area that is devoted to

various crops. In the second step, we use the estimated shadow values (dual values)

along with the data based average yield function to derive the calibration parameters

that represent the crop yield function parameters. Letting j represent the shadow price

derived in step1, we define the yield slope coefficient j as j j j jp L and the intercept

coefficient j as j j j j y L .

The third step involves specifying the PMP using the yield parameters ( and ) along

with the base-year data on all crops. The specified PMP involves reformatting the

constrained optimization as a quadratic programming model using the crop yield

function parameters and solving for the shadow value of water based on the water

availability constraint and the prices for crops. The specified PMP is solved using the

following optimization model:


139

j j j j j j j

n

Lj 1

5.5 Max p L c L

Subject to:

j j

n

j 1

x L W

(total water constraint)

j

n

j 1

L L

(total land constraint)

The dual multiplier of the water constraint is the shadow price of water and constitutes

the marginal value of water which means that if an additional increment of water

resource x (which is constrained at certain level x ) becomes available, output would

increase by some amount * *y x ; in other words * is the marginal value of water

(Silberberg and Suen, 2001).

By changing the annual water constraint x in step 3 only and recording the shadow price

that corresponds to each x level, we obtain the correspondence between x and the

shadow price of water , which constitutes the (inverse) derived demand for irrigation

water.

5.4 Study Areas and Data Description

The data used in this study is based on a detailed farm survey conducted over two

cropping seasons i.e., Rabi and Kharif in Punjab, Pakistan, in the period 2010 to11. We

collected farm level data from 200 groundwater-fed agricultural farms located in two

districts, Jhang and Lodhran, in the cotton-wheat region and mixed cropping region.

The major crops in the cotton-wheat zone are wheat and cotton while wheat, rice, cotton

and sugarcane are major crops in the mixed cropping region. Due to limited canal water

supplies, irrigated agriculture in the study districts heavily rely on groundwater

resources. Despite having unreliable canal water supplies and deep groundwater tables,

both districts grow the most water intensive crops: sugarcane, rice and cotton. Detailed

input and output data on physical quantities and prices were collected for each crop

enterprise. Data was also collected for irrigation water used.

5.4.1 Nature of Irrigation Water Demand in the Central and South Punjab

As in many other districts of Punjab, agriculture relies heavily on groundwater in Jhang

and Lodhran districts due to the arid and semi-arid climate. Both districts receive very

little rainfall. The average precipitation rate in the Lodhran district is only 71 mm-1


140

while it is 180 mm-1in the Jhang district. The potential irrigation water requirement for

cotton in the south of the Indus basin (Punjab) is 27% higher than in the northern part.

The high irrigation water requirement in the south is mainly due to high temperature

and sandy soils. Similarly, for rice and sugarcane, the potential irrigation requirements

are 20% and 25% higher in the southern part compared to the northern side of the Indus

basin. Our sample data show large variation in the depths of installed tube-wells. In the

Lodhran district, the variation was observed to be between 60 to 99 meters compared to

the Jhang district where it was between 33 to 57 meters. Low water tables not only

contribute to high groundwater extraction costs but also to high tube-well installation

costs. The total installation cost to bore a 24 meter deep tube-well is seven times higher

compared to boring a tube-well at a depth of 6 meters (Qureshi and Akhtar., 2003).

Table 5.1 provides the breakdown of the major crops cultivated in both districts by

water sellers and water buyers.

Table 5.1: Number of sample households that grew wheat and cotton in the Lodhran and Jhang districts during 2010-2011

Name of district and type of GW user Number of households that grew different crops Wheat Cotton Rice Sugarcane

Lodhran district total 100 100 0 0 Tube-well owners 50 50 0 0 Water buyers 50 50 0 0

Jhang district total 100 89 80 85 Tube-well owners 50 45 45 40 Water buyers 50 44 35 45

Table 5.2 shows area allocation, yield, irrigation water requirements and different crop

prices. Wheat covers the largest area and is cultivated by all the faming households in

the study districts. It is more popular than any other crop because it is a staple food.

Amongst the cultivated crops, wheat is the least water consumptive crop while

sugarcane is the most water consumptive crop. Column 4 of Table 5.2 gives the

standard irrigation water requirements of the cultivated crops in the regions. In

monetary terms, one cubic meter (m3) of groundwater utilized by wheat crop generates

an average of Rs. 54 while one cubic meter of irrigation water in cotton, sugarcane and

rice generates Rs. 34, Rs. 24 and Rs. 15, respectively. In terms of water productivity,

wheat is the most water productive while sugarcane is the least productive crop.

However, in terms of total returns per hectare, sugarcane is the most profitable crop

followed by rice and cotton.


141

Table 5.2: Area allocation to different crops, yield, irrigation water requirements and crop prices

Groundwater Sellers Crop enterprise

Area jL

ha

Yield jy

metric tons/ha year-1

Irrigation requirement jx

m3 /ha year-1

Crop price jP

Rs./kg Wheat 489 3.8 2,870 23 Cotton 386 2.1 7,770 88 Rice 75 3.8 6,640 55 Sugarcane 97 75 1,5,650 5

Groundwater Buyers Wheat 244 3.6 2,870 23 Cotton 202 2 7,770 86 Rice 22 3.5 6,640 54 Sugarcane 23 70 1,5,650 5

Table 5.3 shows the input and farm operation cost for different crops. We see that there

is a large variation in the price39 for groundwater across different crops. On per hectare

basis, we observe that water buyers, on average, pay 52% more for groundwater for all

crops compared to water sellers. The total groundwater extraction cost, including

electricity and maintenance, is Rs. 2 m-3 for water sellers and Rs. 3.85 m-3 for water

buyers.

39 Groundwater is not priced in Pakistan. The above mentioned groundwater pricing refers to different costs associated with groundwater extraction such as energy costs. Under informal groundwater marketing, tube-well owners bear only the extraction costs (energy and machinery costs) whereas water buyers have to pay extra charges to cover wear and tear charges besides paying pumping costs. Traditionally, price for groundwater is determined through a social consensus in the beginning of new cropping season or with increasing energy prices as an hourly flat rate basis or fixed share in crop production per unit of land. However, in many instances tube-well owners set the price first and then inform the water buyers. The price usually varies with the type of tube-well i.e., electric or diesel operated tube-well and based on the horse power of the engine etc.


142

Table 5.3: Input cost for different farm operations in Rs.ha-1

Groundwater Sellers Crops Wheat Cotton Rice Sugarcane Seed costs 3,583 5,964 881 n.a Labour costs 5,812 33,210 17,271 45,936 Fertilizer costs 17,419 13,506 13,998 16,689 Chemical costs 3,272 11,068 3,269 4,880 Farm operation costs 9,940 9,787 11,596 26,307 Groundwater irrigation costs 5,608 10,472 20,552 29,543 Total costs 45,634 84,007 67,567 1,23,355 Total cost, excluding groundwater cost

40,026 73,535 47,015 93,812

Groundwater Buyers Seed costs 3,361 5,485 795 n.a Labour costs 6,358 33,823 24,599 38,946 Fertilizer costs 14,874 12,449 10,695 12,821 Chemical costs 3,271 10,423 4,470 4,863 Farm operation costs 10,805 10,005 10,916 24,233 Groundwater irrigation costs 10,469 18,536 36,239 51,047 Total costs 49,139 90,721 87,713 1,31,910 Total cost, excluding groundwater cost

38,670 72,185 51,474 80,863

5.5 Results and Discussion

The first step PMP results for water sellers and water buyers are presented in Table 5.4

and Table 5.5, respectively. In the first step, we chose crop area allocation to maximize

net farm returns given the total amount of water and land available for the sample

farming households. The Step1constrained profit maximization model indicates that

given the annual water (total groundwater that was extracted for the observed cropping

season) constraint, total land constraint is binding. It means that area allocation to each

crop is equal to the total land available to the sample farms of both water sellers and

water buyers. However, the water constraint is not binding, indicating that the total

irrigation water requirement is lower than the amount of groundwater that is being

extracted for irrigation. We computed total groundwater extraction for water sellers and

water buyers which indicate an over-extraction of 1, 23,228 m3 for water sellers and

50,510 m3 for water buyers. By taking the total groundwater availability (total

extraction) as an annual water constraint, we calibrate the dual multipliers (shadow

price) for land allocation to each crop.


143

Table 5.4: PMP step 1, water sellers

Crop Wheat Cotton Rice Sugarcane Constraint Level

Return per ha (excluding water cost)

47374 111265 161985 281188

Crops area 489 386 75 97 The (PMP chosen)

The Objective j j j jj

n

Lj 1

max p y c L

9.87E+07

Constraints Total land 1 1 1 1 1047 <= 1,047 Wheat 1 489 <= 489 Cotton 1 386 <= 386 Rice 1 75 <= 75 Sugarcane 1 97 <= 97 Groundwater 2780 7770 6640 15650 6.37E+06 <= 6.50E+06

Table 5.5: PMP step 1, water buyers


Return per ha (excluding water cost)

43432 103004 137399 263932

Crops area 243 202 22 23 The (PMP chosen)

The Objective j j j jj

n

Lj 1

max p y c L

4.05E+07

Constraints Total land 1 1 1 1 490 <= 490 Wheat 1 243 <= 243 Cotton 1 202 <= 202 Rice 1 22 <= 22 Sugarcane 1 23 <= 23 Groundwater 2,780 7,770 6,640 15,650 2.75E+06 <= 2.80E+06

The dual multipliers (calibrated in Step 1) are then used to compute the optimization

function parameters i.e., yield slope coefficient and the intercept coefficient which are

presented in Table 5.6.


144

Table 5.6: PMP Step 2, dual multipliers, yield slope coefficient and the intercept coefficient

Constraint/crop Duals j j j jp L

j j j jy L

Groundwater Sellers Total land 0.00 0.00 0.00 Wheat 47374 0.00 5.86 Cotton 111265 0.00 3.36 Rice 161985 0.04 6.75 Sugarcane 281188 0.58 131.24 Groundwater Buyers Total land 0.00 0.00 0.00 Wheat 43431.58 0.01 5.53 Cotton 103004.11 0.01 3.22 Rice 137398.92 0.12 6.06 Sugarcane 263932.00 2.23 120.95

Table 5.7 and Table 5.8 report the third Step PMP results under the available total land

and groundwater constraints. At this stage, total land constraint is binding while water

constraint is not binding for both the water sellers and water buyers. At regional level

PMP calibrations, total land constraint is binding because the cultivated land by all

farms cannot exceed the total agricultural land that is available. Moreover, farmers may

allocate land differently to various crops in different seasons. In contrast to the total

land available at regional level, groundwater is not a limited resource for short term

extractions. In particular, when there is no volumetric restriction on groundwater use, it

can be an expensive resource rather a limited resource. In this situation, some farmers

may extract more groundwater than others even to irrigate the same size of land with

same type of crop. Because groundwater extraction is not limited either at regional or

farm level, the water constraint may or may not be binding. We observe that

groundwater is not binding for both water sellers and water buyers, suggesting that that

the optimal solution uses less water than what is available. As we adjust the water

constraint level, farmers start re-allocating land to different crops in response to water

availability. We compute the shadow price of groundwater at each constraint level to

assess farmer’s responsiveness at different water constraint levels.

145

Table 5.7: PMP Step 3, water sellers


Return per ha (excluding water cost) 47374 111265 161985 281188 Cropped area No. of ha (PMP chosen) 489 386 75 97 Price of crop ( jp ) 87400 184800 209000 375000 Production cost/ha excluding water cost( jc ) 40026 73535 47015 93812

j 0.00 0.00 0.04 0.58

j 5.86 3.36 6.75 131.24 Land allocation jL 489 386 75 97

The Objective j j j j j j

n

j 1p L c L

3.04E+09

j jL 2.06 1.26 2.95 56.24

j j jL 3.80 2.10 3.80 75.00 j j j jp L 3.32E+05 3.88E+05 7.94E+05 2.81E+07 j j j j jp L c 2.92E+05 3.15E+05 7.47E+05 2.80E+07

Quasi-rent/ha j j j j j jp L c L 1.43E+08 1.21E+08 5.60E+07 2.72E+09

Constraints Total land 1 1 1 1 1047 <= 1,047 Groundwater 2,780 7,770 6,640 15,650 6.37E+06 <= 6.50E+06


146

Table 5.8: PMP step 3, water buyers


Return per ha (excluding water cost) 43432 103004 137399 263932 Cropped area No. of ha (PMP chosen) 243 202 22 23 Price of crop ( jp ) 82102 175189 188873 357744 Production cost/ha excluding water cost( jc ) 38670 72185 51474 93812

j 0.01 0.01 0.12 2.23

j 5.53 3.22 6.06 120.95 Land allocation jL 243 202 22 23

The Objective j j j j j jnj 1

p L c L 7.02E+08

j jL 1.91E+00 1.19E+00 2.55E+00 5.13E+01 j j jL 3.62 2.03 3.51 69.60 j j j jp L 2.97E+05 3.56E+05 6.63E+05 2.49E+07 j j j j jp L c 2.59E+05 2.83E+05 6.11E+05 2.48E+07

Quasi-rent/ha j j j j j jp L c L 6.28E+07 5.73E+07 1.35E+07 5.71E+08

Constraints Total land 1 1 1 1 490.00 <= 490.00 Groundwater 2,780 7,770 6,640 15,650 2.75E+06 <= 2.80E+06


147

The results for the derived demand for groundwater for irrigation for water sellers and

water buyers are presented in Figure 5.1 and Figure 5.2.

Figure 5.1: Derived demand for groundwater for irrigation for water sellers

Figure 5.2: Derived demand for groundwater for irrigation for water buyers

We find a high marginal value of water for both water sellers and buyers at low water

constraint; this is possibly due to the high profitability from cotton and sugarcane crops.

As in the Step 1model, land allocation to each crop cannot exceed the actual land

devoted to that crop. Therefore, when water constraint increases farmers start re-

allocating land to different crops. We observe that as the water constraint increases both

water sellers and buyers keep allocating their land to sugarcane crop and reduce

allocation to other crops. Sugarcane cultivation requires high irrigation water

applications but, higher land allocations to sugarcane are due to the higher per acre net

0.00200.00400.00600.00800.00

1000.001200.001400.001600.001800.002000.00

Wat

er p

rice

(Rs./

m3 )

Annual water constraint (m3)

0200400600800

10001200140016001800

Wat

er p

rice

(Rs./

m3 )

Annual water constraint (m3)


148

returns for sugarcane compared to the other crops. The derived demand for water sellers

is almost inelastic when water is constrained between 4.98E+06 and 1.20E+05 m3; for

water buyers it is inelastic at constraint level between 2.45E+06 and 5.05E+04 m3. The

derived demand for water sellers is responsive to price changes when water is

constrained between 1.20E+05 and below 8.00E+04 m3. For water buyers, the demand

is responsive to price changes at when water is restricted between 5.05E+04 to

3.03E+04 m3.

Table 5.9: Percent change in water demand given and the percent change in shadow price

Water demand (1000 m3)

Shadow price (Rs./1000m3)

% change in consumption

% change in shadow price

Water Sellers 6.37E+06-3.30E+06 40.48-105.06 48% 160% 3.30E+06-2.00E+06 105.06-112.52 40% 7% 2.00E+06-1.52E+06 112.52-1791.13 24% 1499%

Water Buyers 2.75E+06-1.16E+06 36.47-92.088 58% 115% 1.16E+06-9.51E+05 92.088-93 18% 1% 9.51E+05-3.51E+05 93-1585 67% 1604%

Table 5.9 shows percentage changes in water demand corresponding to percentage

change in the shadow prices. For water sellers the groundwater availability of between

6.37E+06 to 3.30E+06 correspond to shadow price between Rs. 40-105/1000m3. It

indicates that the % change in water demand is lower than the % change in shadow

price. Similarly, for water buyers the groundwater availability is between 2.75E+06 to

3.30E+06, the shadow price of water for water buyers corresponds to a shadow price

range of Rs.36-92/1000m3, again with % change in water demand lower than the %

change in shadow price.

The study results suggest that water pricing can induce irrigators to optimise irrigation

water demand. We conjecture that a 2% reduction to the current groundwater volumes

would require that groundwater should be priced at Rs. 41/1000 m3 for water sellers and

Rs. 36/1000m3 for water buyers. At 2% reduction level, farm income does not change

significantly for both water sellers and water buyers. However, a 20% reduction in

irrigation water demand would decrease farm income by 18% and 16% for water sellers

and water buyers, respectively. We believe that imposing price on groundwater is a

complicated issue. The difficulties of implementing water pricing are well documented

in the literature (Dinar, 2000). The direct pricing (e.g., volumetric pricing) is


149

complicated because groundwater extractions are not measured and farmers’ demand

for groundwater has been found to be unresponsive to small price changes, suggesting

that they do not assign any economic value to water. Indirect pricing, such as energy

taxation, is difficult because most of the tube-wells are operated by diesel and the use of

diesel for agricultural purposes and non-purposes are not clearly distinguished.

5.6 Conclusions

It is widely recognised that Pakistan is amongst the most water scarce countries and

dealing with growing water scarcity has become a policy imperative. Amongst the many

ongoing policy discussions to guarantee the sustainability of groundwater resources is

market based solution i.e., pricing water to optimise irrigation water requirements.

This study employed the Positive Mathematical Programming (PMP) approach to

estimate the derived demand for groundwater for irrigation among water sellers and

water buyers. We used a cross-sectional dataset of 200 households who predominately

use groundwater for irrigation in the Punjab province of Pakistan. We estimate the

shadow price of water which represents farmers’ willingness to pay when groundwater

resources become constrained at different levels.

Given the total annual water availability, the land constraint is binding as the total area

allocation to different crops is equal to the total land available for both water sellers and

water buyers. However, the water constraint is not binding suggesting that the optimal

solution uses less water than what is being extracted. We observe that as water

constraint increases, farmers start re-allocating land to crops taking water constraint into

consideration. Under the revised cropping plan, both water sellers and water buyers give

top priority to sugarcane. This is possibly because sugarcane cultivation generates the

highest net returns compared to other crops. This implies that farmers do not re-allocate

land to different crops based on their irrigation water requirements (because sugarcane

is the most water consumptive crop) but based on the expected returns per hectare. We

propose that introducing water pricing at Rs. 41/1000 m3 for water sellers and Rs.

36/1000m3 for water buyers can help achieving 2% reductions in irrigation water

demand.

We suggest that water pricing can facilitate appropriate and efficient use of groundwater

in irrigation sector. However, we suggest that rather solely relying on pricing, additional

policies are required that improves irrigation water use efficiency. First, policy makers

should set a groundwater saving target in tandem with water pricing (such as 2% initial


150

annual water saving target). An introduction of Rs. 0.04/m3 would not decrease farm

income rather it would make farmers aware of the economic value of water. Second, all

subsidies on agricultural tube-wells estimated at Rs. 16.4 billion in 2012 should be

removed and farmers should be encouraged to adopt water conserving irrigation

technologies.

151

Chapter 6 6. Conclusions

6.1 Summary

Water is becoming an increasingly scarce resource for agricultural production in many

regions of the world. In the past, irrigation water policies largely focused on the

development of adequate infrastructure to guarantee water supply as the demand for

agriculture sector was increasing. However, these expansionary policies have resulted in

a massive use of irrigation water and physical scarcity in different parts of the world.

Now water scarcity has become an important economic and social concern for policy

makers on the international and national agendas. Effective management of water

resources raises the challenge of how to use available water resources more efficiently

and sustainably and find possible ways to address and manage water scarcity to meet

the competing inter-sectoral multiple water demands more equitably.

Irrigation water requirements are very high in the Indus basin of Pakistan due to the arid

and semi-arid climate. Existing surface water resources are not only deficient but are

also highly skewed in time and space throughout the Indus basin. Consequently, the

agriculture sector heavily relies on groundwater extraction to meet irrigation water

demands. However, over the last two decades groundwater exploitation has escalated

which has resulted into lowering of groundwater tables. The rapid decline of

groundwater resources and the escalating number of tube-wells has brought into greater

focus the challenge of how to control over-exploitation among policy makers.

Therefore, an understanding of how farmers make decisions to adopt tube-well

technology, their response to constrained water resources and pricing, and the extent of

groundwater-use efficiencies in irrigation is essential to designing or revising and

refining groundwater management policies.

This PhD study focused on the economics of groundwater use in irrigation in the Indus

basin of Pakistan. The specific objectives of the study were to: (1) identify causes and

consequences of groundwater depletion; (2) analyse farmers decision to adopt tube-well

technology under farm profit variability and production uncertainties related to

depleting groundwater resources; (3) investigate the extent of technical and irrigation

water use efficiency for different groundwater irrigated crops and factors that explain

efficiency variability across farms; and (4) estimate the derived demand for

Conclusions

152

groundwater for irrigation among water sellers (tube-well adopters) and water buyers

(non-adopters of tube-wells).

6.2 Methods

The thesis incorporates a number of innovate aspects that contribute to the overall

knowledge of groundwater economics in Pakistan. First, the moment based approach

was used to understand farmer’s decisions to adopt tube-well technology under

production risk and farm profit variability. Although, the moment based approach has

been used to analyse risk exposure (Antle, 1987, Juma et al., 2009, Kassie et al., 2008,

Kim and Chavas, 2003), and except for Koundouri et al. (2006), its application to

analyse irrigation technology adoption decisions under production risk due to water

scarcity issues is rare. In this thesis, the moments of profit distribution and farmer’s

perceptions regarding groundwater resources were simultaneously incorporated into the

technology adoption function.

Second, this study estimates technical and irrigation water use efficiency of different

agricultural crops using both parametric and non-parametric approaches. The DEA sub-

vector model has been used to measure irrigation water use efficiency (Speelman et al.,

2008, Frija et al., 2009, Manjunatha et al., 2011) but application of the DEA slack-based

model is rare (Chemak et al., 2010). The innovative feature of this study is that it used

both the DEA sub-vector and slack-based models to estimate irrigation water use

efficiency in rice farming. Similarly, this study applied the metafrontier framework to

estimate technically efficiency, irrigation water use efficiency, and technology gap

ratios in wheat farming. Many studies have applied the restricted stochastic frontier

model to estimate technical efficiency (Gedara et al., 2012b, Pascoe et al., 2012,

Tiedemann and Latacz-Lohmann, 2013), but no study was found that estimate input-

specific technical efficiency, i.e., irrigation water use efficiency using the restricted

stochastic frontier model. Therefore, this study advanced the input-specific technical

efficiency concept and estimated irrigation water use efficiency in cotton farming by

imposing monotonicity and quasi-concavity restrictions into an input-specific translog

stochastic frontier model.

The third chapter employed the Positive Mathematical Programming (PMP) approach to

estimate the derived demand for groundwater for irrigation. The farmers’

responsiveness to groundwater is assessed by estimating the shadow price of

groundwater at various constraints levels.

Conclusions

153

6.3 Main Results

Chapter 2 identified the causes and consequences of groundwater overdrafting and

draws attention to groundwater resource management issues. The major causes of

groundwater overdrafting were found to include the rigidity of the surface water

allocation system (Warabandi System), the Green Revolution, the Indus Water Treaty,

the increasing population and the ineffective groundwater management policies.

Consequently, massive pumping of groundwater aquifers to meet the increasing

irrigation water demands has started lowering groundwater tables rapidly in different

parts of the country. Besides lowering groundwater tables, overdrafting has led to many

negative environmental, pecuniary and spatial negative externalities which portend

serious repercussions to the sustainability of irrigated agriculture in the region. Major

environmental externalities identified include: soil salinization, salt water and sea water

intrusions, land subsidence and drying up of lakes and vegetation in different parts of

the country. Various pecuniary externalities such as increasing pumping costs and

decreasing land values are also identified. Migration and social conflicts are identified

as potential spatial externalities in the coming years.

Chapter 3 employed a moment-based approach to analyse farmer’s decisions to adopt

tube-well technology under farm profit variability and depleting groundwater resources.

It was found that the sample moments of the profit distribution affect farmers’ adoption

decisions. Estimates show that the higher the expected profit the greater the probability

that a farmer decides to adopt a tube-well technology. It was also found that the

probability of adopting tube-well increases significantly with increasing variance of

profit. These results imply that farmers adopt tube-well technology in the pursuit of

reliable access to irrigation water and hence greater farm profits. Having access to

irrigation water supplies provides a hedge against production risks associated with

unreliable and scarce water supplies. The non-significant skewness of profit distribution

indicates downside profit risk does not have a significant impact on tube-well adoption.

The highly significant kurtosis indicates that farmers’ adoption decreases in the

presence of extreme events like flooding and crop failure due to crop disease outbreak.

Farmers with higher off-farm income, better access to agricultural extension services

and other sources of information, and those who cultivate their own lands are found to

more likely to own a tube-well. The farmers’ perceptions about falling groundwater

tables and deteriorating groundwater quality suggest that they are either not fully aware

Conclusions

154

of the declining groundwater levels and water quality or they are aware but do not care

because of their growing dependence on groundwater resources.

Chapter 4 presented the estimated results of the technical efficiency (TE) and irrigation

water use efficiency (IWE), and the factors affecting TE and IWE of different crop

enterprises.

Section 4.1 employed the data envelopment analysis (DAE) metafrontier approach to

estimate TE and IWE of groundwater-irrigated wheat farms. It was observed that both

TE and IWE were slightly different under the metafrontier and groupfrontier

specifications. We found that the tube-well owners were more efficient than water

buyers. The mean TE estimates for tube-well owners and water buyers were found to be

91% and 90% when estimated under the metafrontier specification whereas the mean

scores were found to be 93% and 94% under the groupfrontier settings. The mean

irrigation water efficiency estimates for tube-well owners and water buyers under the

metafrontier specification were found to be 66% and 65% respectively whereas the

estimates were found to be 71% and 67% under the groupfrontier settings.

In wheat farming, farmers’ education, seed quality and farmer’s perceptions about

salinity levels and groundwater table depth significantly increase wheat growers’

technical and irrigation water use efficiency. However, land tenureship, off-farm

income, access to credit, and access to extension services were found to be significantly

associated only with the TE of wheat farmers.

Section 4.2 estimated a theoretically consistent translog production function and

computed the TE and IWE in cotton production. The mean TE estimates for tube-well

owners was not different (81%) under both the restricted and unrestricted models while

the mean IWE score was 61% and 56% under the unrestricted and restricted models,

respectively. Similarly, the mean TE score for water buyers was the same (71%) under

the both models while the mean IWE score was 47% and 46%. The equality of means

test (t-test) for the unrestricted and restricted TE estimates cannot be rejected at the 1%

significance level for both water sellers and water buyers. However, we reject the null

hypothesis that the mean IWE estimates derived from the unrestricted and restricted

models were not significantly different from zero.

The most important factors having significant impact on cotton growers’ technical and

irrigation efficiency are the seed quality, access to extension services and their

perceptions about groundwater shortage (groundwater table depth). Both age and land

Conclusions

155

tenure status is found to be negatively associated with the TE and IWE of cotton

growers.

Section 4.3 estimated the TE and IWE in rice farming by employing the sub-vector and

slack-based DEA models. It was shown that rice production could potentially be

increased without increasing current input levels. The high mean TE of 96% and 94%

for tube-well owners and water buyers suggest that access to technology is not a major

limiting factor. However, there is considerable scope for improving IWE by using less

water. The mean IWE estimates of 80% and 78% suggest considerable reductions in the

current water usage.

Farmers’ education, land tenureship and access to credit are the most important factors

that are positively associated with technical efficiency. Farm size is the only major

factor that is found to be significantly associated with IWE; with increasing rice farm

size, irrigation water use efficiency decreases significantly.

Chapter 5 used a Positive Mathematical Programming (PMP) approach to estimate the

derived demand for groundwater use in irrigation. We find that the actual crop water

requirement is lower than the amount of groundwater that is being extracted. Given the

land constraint, additional water supplies would not increase the representative farm’s

profit. Therefore, producers are unlikely to respond to any pricing policy at the current

rate of groundwater extraction. Producers would only respond to changes in

groundwater price if groundwater supplies are constrained relative to their demand. The

results indicate that water sellers would be willing to pay a higher price than water

buyers when irrigation water becomes constrained. We propose that introducing water

pricing at Rs. 0.04/ m3 for water sellers and Rs. 0.036/m3 for water buyers can help

achieving 2% reductions in irrigation water demand.

6.4 Synthesis of Main Findings

The overall synthesis of the study findings indicate that over-extraction of groundwater

resources has raised several concerns to the sustainability of groundwater resources and

irrigated agriculture in the Indus basin of Pakistan. Despite that hydrological

assessments indicate that groundwater extraction rates have exceeded the annual

recharge rates, the number of tube-wells has been on the increase since early 1960s.

Nevertheless, the depleting groundwater resources has not only impacted the adoption

of tube-wells but also has raised concerns to improve irrigation efficiency and increase

overall agricultural productivity. Besides suggesting improvements in irrigation water

Conclusions

156

applications, the on-going policy discussions also suggest market based solutions to

induce water resource conservation practices.

It is found that farmers’ profit distribution influences tube-well adoption. More

specifically, farmers who anticipate higher profits are more likely to adopt tube-wells

and invest in the technology in order to hedge against the risks of profit variability.

Among different socio-economic factors, land ownership, off-farm income, access to

extension services and different other sources of information make risk-averse farmers

better off and hence play decisive role in tube-well adoption. However, whilst there are

concerns about the increasing adoption of tube-wells and massive extraction of

groundwater resources, farmers’ perceptions about groundwater resource availability

and utilization indicate that they are not really concerned about the groundwater

resource depletion.

Analysis of irrigation water use efficiency analysis also indicate frivolous attitude

towards groundwater resource utilization. There are considerable inefficiencies in using

groundwater for irrigation purposes. Based on data from the 200 wheat farms in this

study, an over-use of 0.48 million m3 of groundwater can be saved by achieving 100%

irrigation water use efficiency. Similarly, an over-use of 1.06 million m3 from 173

cotton farms and 0.28 million m3 of 80 rice growing farms can be saved if they achieve

100% efficiency in irrigation water use. Extrapolating this to the entire farms in the

study region suggest that improving water use efficiency at the farm level can reduce

over-extraction of groundwater resources.

The impact of different farm and farmer’s characteristics on grower’s efficiency level

was found to be mixed and inconclusive on different crops. Many factors confirm to a

priori expectation about their impact on efficiency levels whereas numerous other

factors do not. These findings suggest that that there is a lot of heterogeneity across the

different farmers and farm characteristics that that eventually influence capacity to use

irrigation water more efficiently.

The results from the derived demand analysis are consistent with those from irrigation

water use efficiency; the optimal solution for water allocation suggests that groundwater

is not a limiting factor and less water can be used that what is being extracted for

irrigation purposes. The optimization results from the Positive Mathematical

Programming (PMP) model suggest that pricing water at Rs. 0.04/ m3 for tube-well

owners and Rs. 0.036/m3 for water buyers can help achieving 2% reductions in

irrigation water demand.

Conclusions

157

6.5 Policy Recommendations

Managing groundwater resources requires multidimensional actions, management

strategies and coordination activities across a range of institutions and stakeholders.

Despite the need for such management and its associated policy design and

implementation activity, this is not evident in Pakistan. The front line challenge to

control over-extraction is to develop and implement both supply and demand

management strategies that improve irrigation water use efficiency and sustainable use

of groundwater resources.

Results from this study have important policy implications covering. We found that

farmers adopt tube-well technology to overcome production risk and variability in farm

profits. However, tube-well adoption does not necessarily improve irrigation water use

efficiency nor conserve groundwater resources. Moreover, farmers’ do not perceive

groundwater as a finite resource and see not need to adjust their production practice to

conserve it. Therefore, further tube-well adoption must be accompanied by

complimentary policies that promote efficient use of groundwater for irrigation by

limiting groundwater extractions. The study results indicate that groundwater is being

over-used in agriculture sector. Thus, there is need for policies that educate farmers on

actual crop water requirements as a way to promote irrigation water use efficiency. This

may involve extending extension advice from crop management to groundwater

management or creating a separate water extension wing. Groundwater metering and

pricing can be explored as an option to induce farmers to reduce irrigation water

demands. Finally, additional policies are also required to improve water allocation,

security and equity of access for both water buyers and sellers. Water buyers are

generally down the water supply chain and face more irrigation water uncertainties than

water sellers. We note that the use of indirect pricing such as putting a tariff on energy

to control over extraction of groundwater is not a viable strategy because the use of

diesel for agricultural purposes and non-purposes is not clearly distinguished.

Therefore, a direct water pricing policy is required to make farmers aware of the

economic value of water. It is suggested that policy makers should set a groundwater

saving target in tandem with water pricing. An introduction of Rs. 0.04/m3 would not

decrease farm income but would decrease groundwater demand, suggesting that farmers

would start to be aware of the economic value of water. Second, current subsidies on

agricultural tube-wells - subsidized electricity and fuel costs - should be reallocated to

promote water conserving irrigation technologies.

Conclusions

158

6.6 Limitations and Future Research Needs

There are several caveats to this analysis. First, survey data was collected in only two

out of the five districts in the Punjab province due to time and financial resource

constraints. As a result, this study relies on a small dataset of only 200 farmers. Second,

there are potential problems related to sample selection and representation. There is no

systematic record of the actual number of tube-well adopters and non-adopters in the

study regions. , Although a multistage sample selection approach was used to select 100

adopters and 100 non-adopters of tube-well technology, the approach was not entirely

random at all stages and therefore sample selection bias cannot be ruled out. The choice

of 50% adopters and 50% non-adopters may not reflect the true proportion in the

population.

Second, like in many other developing countries, farmers in Pakistan generally do not

keep good records of various farm activities over the years. Due to non-availability of

panel data, the presented estimates do not give any indication of the year to year

variability in farm efficiency and productivity. Third, there could be potential errors in

the way groundwater use was computed. Farmers do not have installed meters to

monitor their exact groundwater use levels. The approximation formula used to measure

groundwater extractions is based on the assumptions that the lifting head is equal to the

depth of the tube-well. The formula did not account for variations in efficiency in

groundwater extraction across different types of water pumps. Hence, there is a

likelihood of over-estimation or under-estimation of groundwater extraction. .

Fourth, the demand analysis does not consider inter-seasonal variations in irrigation

water demand. It might be that during the cropping season that underpinned the survey

data irrigation requirements were higher due to climatic conditions. Thus, future studies

in irrigation water demand analyses should control for variability in inter-seasonal

demand for groundwater.

159

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8. Appendix

The Economics of Groundwater Irrigation in the Indus Basin, Pakistan: Production Efficiency, Water-use Efficiency and Optimal Allocation

This research survey is part of a PhD project which focuses on groundwater use efficiency,

production efficiency and optimal allocation of groundwater for sustainable management in Punjab,

Pakistan. The survey will collect data on different production inputs and outputs for small scale

irrigated agriculture at the farm level. It will also look at how the performance of groundwater

markets affects farmers’ (tube well owners and buyers) groundwater use efficiency.

Your participation is completely voluntary, and you can choose not to answer any questions you do

not want to. Your response will be kept confidential and will be used for research purposes only. The

interview will not take more than 2 hours to complete.

I hereby certify that this interview is being conducted for my academic research only (Muhammad Arif Watto).

Date of the interview ---------------------------------- Name of enumerator --------------------------------

A. Identifying Variables and Demographic Information of the Household Head A.1. What is the main source of irrigation water for the household?

A.1.1. Own tube well

A.1.2. Purchased tube well water A.1.3. Own tube well + canal water A.1.4. Purchased tube well water + canal water

A.2. Household ID -------------------------------------

A.3. Name of the cropping region -------------------------------------

A.4. Name of the district -------------------------------------

A.5. Name of the tehsil -------------------------------------

A.6. Name of the village -------------------------------------

A.7. Name of the respondent

(Please get household’s head name, if respondent is not the HH) ------------------------------------A.8. Age of the household head ------------------------------------

A.9. Farming experience of the household head -------------------------------------

A.10. Gender of the household head -------------------------------------

A.10.1. Male -------------------------------------

A.10.2. Female -------------------------------------

A.11. Household family structure

A.11.1. Single ---------------------------------------

A.11.2. Joint ---------------------------------------

A.12. Number of household members ---------------------------------------

A.12.1. Adults (Above 18 years) ---------------------------------------

A.12.2. Children (Under 18 years) ---------------------------------------

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A.13. Formal education of the household head

A.13.1. Illiterate -------------------- A.13.2. Primary ----------------------

A.13.3. Middle----------------------A.13.4. Metric -----------------------

A.13.5. Intermediate ---------------A.13.6.Graduate ---------------------

A.13.7. University --------------------

A.14. Informal education (Training related to water management)

A.14.1. Training by NGO (No=0, Yes=1) ----------------------------

A.14.2. Training by extension field staff (No=0, Yes=1) ----------------------------

A.15. Approximate off-farm annual income of the household during 2010

----------------------------------------------------------------------------------------------------------

B. Farm Characteristics and Production Details

B.1. What is the land tenure status of the household head?

B.1.1. Owner ----------------------------

B.1.2. Tenant ----------------------------

(If, the household head is tenant please do not ask questions B.4. and B.5.)

B.2. Total farm size (Acres) ---------------------------

B.3. Total area under cultivation (Acres) ---------------------------

B.4. Additional area rented in (Acres) ----------------------------

B.5. Area rented out (Acres) ----------------------------

B.6. Which major crops did you grow in the last 12 months among the following crops? (No=0, Yes=1)

B.8.1. Wheat Yes/No B.8.2. Cotton Yes/No B.8.3. Rice Yes/No B.8.4. Sugarcane Yes/No B.8.5. Maize Yes/No

B.7. Please tell me about the production of different crops that you sow in the last year.

Name of Crop B.7.1

Cropped area in acres B.7.2

Production in 100Kg per acre B.7.3

Total farm production in 100Kg B.7.4

Total produce sold in 100Kg B.7.5

Price fetched in Rs./ 100Kg B.7.6

Wheat Cotton Rice Sugarcane Maize

B.8. What are the major objectives of your farm production? Please rate their importance based on the following scale: 1= low importance; 2= No importance; and 3= High importance.

B.8.1. Production for local market 1 2 3

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B.8.2. Production for self-subsistence 1 2 3 B.8.3. Both 1&2 1 2 3

B.9. Is there any difference of land rent for a piece of land with and without tube well? (No=0, Yes=1)

9.1. Yes

9.2. No

If yes, then;

B.10. What is the amount land rent with a tube well per acre? ----------------------------

B.12. What is the amount of land rent without tube well per acre? ----------------------------

Questionnaire

180

C. Input Details

C.1.Seed

Please tell about the seed rates you used for each of the following crops during the last cropping year.

Name of crops Details of seed use

Crop code 1=Wheat 2=Cotton 3=Rice 4=Sugarcane 5=Maize

Crop name C.1.1.

Seed type C.1.2

Seed quality C.1.3

Mode of purchase C.1.4 0=Cash 1=Borrowed

Source of purchase C.1.5 1=Private agency 2=Government agency 3=Local stockist 4=Farmers group 5=Neighbouring farmer 6=Friend/relative

Quantity of seed Kg/Acre C.1.6

Price of seed Rs./Kg C.1.7

Total seed cost for whole farm C.1.8 1=Retained

2=Purchased 3=Ratoon (If retained, please skip C.1.4-C.1.5)

0=Improved 1=Un-improved

Questionnaire

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C.2. Labour

C.2.1. Family Labour

Please provide the details about your family labour for different cropping activities during the last cropping year.

Name of different labour activities for different crops

Labour used in different cropping activities

Crop code 1=Wheat 2=Cotton 3=Rice 4=Sugarcane 5=Maize Activity type code 1=Sowing 2=Transplanting 3=Irrigation 4=Hoeing 5=Spraying 6=Harvesting 7=Threshing 8=Picking 9=Shelling

Adults Children Crop name C.2.1.1

Activity type C.2.1.2

No. of males C.2.1.3

Total days worked C.2.1.4

Total hours worked C.2.1.5

No. of females C.2.1.6



No. of children C.2.1.9



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C.2.2.Hired Labour

Please provide the details about the labour which you hired for different cropping activities during the last cropping year.

Code for different labour activities for different crops

Hired Labour Name of crop C.2.2.1

Activity type C.2.2.2

No. of persons hired C.2.2.3

No. of days worked each C.2.2.5

Total hours worked (average) C.2.2.4

Daily paid wages to labour If in cash, Rs./ person per day C.2.2.6

If in kind, Kg/ person per day C.2.2.7

Crop code 1=Wheat 2=Cotton 3=Rice 4=Sugarcane 5=Maize Activity type code 1=Sowing 2=Transplanting 3=Irrigation 4=Hoeing 5=Spraying 6=Harvesting 7=Threshing 8=Picking 9=Shelling

Questionnaire

183

C.3. Fertilizer

Please provide the details about the different fertilizers which you applied to different crops during the last cropping year.

Crop code 1=Wheat 2=Cotton 3=Rice 4=Sugarcane 5=Maize Input codes 1=Urea 2=DAP 3=SSP 4=TSP 5=NPK 6=ZnSO4 7=NP 8=MOP 9=Other

Name of crop C.3.1

Fertilizer name C.3.2

Number of bags (50Kg)/Unit applied per acre C.3.3

Mode of purchase C.3.4 0=Cash 1=Borrowed cash


Price per bag (50Kg)/Unit in Rs. C.3.6

Total cost for fertilizer used for whole farm C.3.7

Questionnaire

184

C.4. Pesticide

Please provide the details about the different chemicals which you sprayed/applied to different crops during the last cropping year.

Crop code 1=Wheat 2=Cotton 3=Rice 4=Sugarcane 5=Maize Input codes 1=Pesticide 2=Herbicide 3=Fungicide 4=Other

Name of crop C.4.1

Chemical type C.4.2

Mode of purchase C.4.3 0=Cash 1=Borrowed


Number of application of each chemical C.4.5

Cost per application per acre in Rs. C.4.6

Total cost of the chemical used for whole farm C.4.7

Questionnaire

185

C.5. Farm Operations and Machinery Cost

Please provide me the details of your farm operations for the last cropping year.

Different farm operations for different crops Farm operations for different crops Crop code 1=Wheat 2=Cotton 3=Rice 4=Sugarcane 5=Maize Farm operations code 1=Ploughing 2=Harrowing 3=Seed broadcasting 4=Furrowing/ridging 5=Harvesting 6=Threshing 7=Transporting

Name of crop C.5.1

Type of farm operation C.5.2

No./time of operation C.5.3

Type of machinery used C.5.4 1=Cultivator 2=Disc plough 3=Drill 4=Seed bed planter 5=Boom sprayer 6=Ridger 7=Reaper 8=Combined harvester 9=Wheat thresher 10=Tractor trolley

Farm machinery Status C.5.5 0=Own 1=Hired

Cost of operation per acre C.5.6

Cost of operation for whole farm C.5.7

Questionnaire

186

Section D. Irrigation Details

D.1.Volume of Irrigation Water

Please provide the irrigation details of your major crops during the last cropping year.

Crop code Name of crop D.1

Total no. of irrigations applied D.2

Time of each irrigation/Acre D.3

No. of tube well irrigations applied D.4

Amount of groundwater in m3 at farm level D.5

No. of canal irrigations applied D.6

Amount of canal water in m3at farm level D.7

Total amount of water in m3 at farm level D.8

1=Wheat 2=Cotton 3=Rice 4=Sugarcane 5=Maize

D.2. Energy Consumption and Cost Details of Tube Wells

Please provide the energy consumption and energy cost details of your tube well for the last cropping year.

Tube well type by mode of operation

Tube well type

Energy consumption by mode of operation

Energy cost/hour by mode of operation in Rs.

Extraction cost/hour

Lubrication cost/hour

Maintenance cost/hour

Total cost for one irrigation

D.2.1 Diesel L/hour D.2.2

Elec. units/hour D.2.3

Diesel cost/L D.2.4

Elec. cost/unit D.2.5

D.2.6 D.2.7 D.2.8 D.2.9

Tube well type code 1=Tractor 2=Peter engine 3=Electricity 4= Other

Questionnaire

187

D.3.Irrigation water measurement

Please provide me the specifications of your tube well (both engine and bore).

Tube well type code

Type of tube well

Power of the engine (hp)

Depth of bore (m)

Diameter of suction pipe (in)

1=Tractor 2=Peter engine 3=Electricity 4= Other

D.3.1 D.3.2 D.3.3 D.3.4

D.4. Canal Water Irrigation Details

D.4.1. What is the total allocated time of your canal water turn (Warabandi) per acre?

--------------------------------------------------------------------------------------------------------------------

D.4.2. What is the share of canal water to total irrigation for each of the following crop?

Name of the crop D.4.2.1

Share of tube well irrigation in (%) D.4.2.2

Share of canal water irrigation in (%) D.4.2.3

1=Wheat 2=Cotton 3=Rice 4=Sugarcane 5=Maize

D.4.3. How much cost (Abiana) did you pay during the last year? ----------------------------

Note: Please skip section E (E.1. to E.13) if the respondent does not own a tube well and skip questions (E.14. to E.29) if the respondent owns a tube well.

E. Characteristics of Tube Wells and Groundwater Markets

E.1. Tube Well Owners/Groundwater sellers

E.1. How long have you been using groundwater? ----------------------------

E.2. What is the age of the engine/well operating machine? ----------------------------

E.3. Did you test your groundwater before the installation of tube well? (No=0, Yes=1)

E.3.1. Yes E.3.2. No

E.4. Is there any competition among water sellers in the area? (No=0, Yes=1)

E.4.1. Yes E.4.2. No

E.5.To whom do you usually sell water (TSW)?

E.5.1. Relative E.5.2. Friend E.5.3. Neighbour E.5.3. Anyone

E.6. Did you ever refuse to sell water to anyone? (No=0, Yes=1)

E.6.1. Yes

Questionnaire

188

E.6.2. No E.7. If yes, what were the reasons not to sell water to that person? What are the three most important? (Mark as 1, 2, and 3where, 1= low importance; 2= No importance; and 3= High importance.)

E.7.1. Deferred payments by the purchaser 1 2 3 E.7.2. Tube well declined 1 2 3 E.7.3. Unavailability of diesel oil 1 2 3 E.7.4. Negotiation failed on price issue 1 2 3 E.7.5. Long queue of buyers 1 2 3

E.8. Is there any conflict in the region over groundwater extraction among different users? (No=0, Yes=1)

E.8.1. Yes E.8.2. No

E.9. If yes, please explain the nature of conflict?

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

E.10. Do you think that a nearby tube well may affect your tube well extraction? (No=0, Yes=1)

E.10.1. Yes E.10.2. No

E.11. Did it ever happen that a nearby tube well decreases the extraction rate of your tube well? (No=0, Yes=1)

E.11.1. Yes E.11.2. No

E.12. Are people willing to pay for groundwater at the price that you set? (No=0, Yes=1)

E.12.1. Yes E.12.2. No

E.13. If no, do you lower the price or not? (No=0, Yes=1) E.13.1. Yes E.13.2. No

E.2. Non-tube well owners /Water buyers E.14. What are the reasons not to install your own tube well? What are the three most important? (Mark as 1, 2, and 3where, 1= low importance; 2= No importance; and 3= High importance.)

E.14.1. Do not have own land (tenants) 1 2 3 E.14.2. Poor groundwater quality 1 2 3 E.14.3. Low groundwater table 1 2 3 E.14.4. High installation costs 1 2 3 E.14.5. Easy access to water due to water markets 1 2 3

E.15. How important are the factors are that you consider when buying groundwater for irrigation? (Please rank these factors as 1, 2, and 3where, 1= low importance; 2= No importance; and 3= High importance.)

E.15.1. Groundwater quality 1 2 3 E.15.2. Distance from the farm 1 2 3 E.15.3. Water course type 1 2 3 E.15.4. Reliability 1 2 3

E.16. Is there any competition among water buyers in the area? (No=0, Yes=1) E.16.1. Yes E.16.2. No

E.17. From whom do you usually purchase groundwater?

E.17.1. Relative E.17.2. Friend E.17.3. Neighbour E.17.4. Anyone

(Note: In case of 17.4. please skip E.18)

Questionnaire

189

E.18. What are the reasons to buy water from a particular well owner? What are the three most important? (Mark as 1, 2, and 3where, 1= low importance; 2= No importance; and 3= High importance.)

E.18.1. Flexible in terms of payment 1 2 3 E.18.2. Near to the farm 1 2 3 E.18.3. Lined water course 1 2 3 E.18.4. Type of tube well (tractor, peter, electric) 1 2 3 E.18.5. Low price 1 2 3

E.19. Did you ever decide not to buy water from a particular well owner? (No=0, Yes=1)

E.19.1. Yes E.19.2. No

E.20. If yes, what are the three most important reasons not to buy water from that well owner (RNB)? (Mark as 1, 2, and 3where, 1= low importance; 2= No importance; and 3= High importance.)

E.20.1. Rigid in terms of payments 1 2 3 E.20.2. Far from the farm 1 2 3 E.20.3. Unlined water course 1 2 3 E.20.4. Type of tube well (Tractor, Peter, Electric) 1 2 3 E.20.5. High price 1 2 3

E.21. Do you get sufficient groundwater for irrigation when required? (No=0, Yes=1)

E.21.1. Yes E.21.2. No

E.22. If no, what are the reasons, please explain?

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E.23. What is the reliability of water supply of your supplier?

E.23.1. Somewhat reliable E.23.2. Much reliable E.23.3. Not reliable

E.24. What is the reliability of groundwater quality of your water supplier?

E.24.1. Somewhat reliable E.24.2. Much reliable E.24.3. Not reliable

E.25. Do you face any fluctuations in groundwater price during the same cropping season? (No=0, Yes=1)

E.25.1. Yes E.25.2. No

E.26. What is the time/schedule of payment for groundwater purchase?

E.26.1. In advance E.26.2. Monthly E.26.3. After crop harvest E.26.4. Annually

E.27. What are the irrigation costs and payment terms and conditions for water buyer?

Codes for different payment conditions

Payment terms and conditions E.28.1

If flat charge, what is per/h cost of groundwater in Rs.E.28.2

If share in proportion, what is the share for each crop (monds) E.28.3

If share in irrigated plot, what is the share for each crop (kanals) E.28.4

1= Flat charge 2=Share in production 3=Share in irrigated area

a) Wheat a) Wheat b) Cotton b) Cotton c) Rice c) Rice

e) Maize e) Maize f) Sugarcane f) Sugarcane

Questionnaire

190

E.3. Both Well Owners and Water Buyers

E.28.Which irrigation method do you use for irrigating the following crops?

Irrigation method Name of crop Type of irrigation method 1=Flood irrigation 2=Furrow irrigation 3=Sprinkler irrigation 4=Drip irrigation

Wheat Cotton Rice Sugarcane Maize

E.29. What proportion of your farm income did you spend on tube well irrigation last year?

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E.30. In response to declining groundwater table, what mitigation measures are you adopting?

E.30.1. Changing cropping pattern E.30.2. Irrigation infrastructure E.30.3. Drought resistant varieties E.30.4. Increase irrigation intervals E.30.5. None of the above

E.31. What type of conservation techniques are you using to conserve groundwater?

E.31.1. Mulching E.31.2. Watercourse lining E.31.3. Irrigation technology applications E.31.4. Laser land levelling E.31.5. None of the above

E.32. Is there any investment in groundwater saving technology in the region? (No=0, Yes=1), 2= Don’t know)

E.32.1. Yes E.32.2. No E.32.3. Don’t know

E.33. If yes, then what are the major sources of investment?

E.33.1. Government E.33.2. Community groups E.33.3. International donor institutions E.33.4. National donor institutions E.33.5. Farmer himself

E.34. Who does establish/govern the prices for groundwater?

E.34.1. Tube well owners association E.34.2. Well owner and the buyer mutually E.34.3. Well owner individually E.34.4. Any other

E.35. When does the groundwater price change?

E.35.1. Beginning of cropping season E.35.2. Mid of cropping season E.35.3. As the change in prices for diesel and electricity E.35.4. As the change in price of crop

E.36. Do you think that social ties between well owner and water buyer have some effect on water pricing? (No=0, Yes=1)

E.36.1. Yes E.36.2. No

Questionnaire

191

E.37. If yes, please explain how?

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E.38. Is there any social institution that regulates groundwater extraction? (No=0, Yes=1), 2=Don’t know)


E.39. If yes, which is that institution?

E.39.1. Local community group (Punchayat) E.39.2. Farmer’s organization E.39.3. Cooperative association E.39.4. Community development council E.39.5. No. organization

E.40. Do you think that farmers overuse groundwater? (No=0, Yes=1)

E.40.1. Yes E.40.2. No

E.41. Is there any role of farm position relative to water course in groundwater transactions? (No=0, Yes=1)

E.41.1. Yes E.41.2. No

E.42. Is there any role of water course type (lined or unlined) in groundwater transactions? (No=0, Yes=1)

E.42.1. Yes E.42.2. No

E.43. Do you think that groundwater quality is changing in the region? (No=0, Yes=1, 2=Don’t know)


E.44. If yes, can you guess how groundwater quality has changed over the last 10 years?

E.44.1. Somewhat improved E.44.2. Much improved E.44.3. Somewhat declined E.44.4. Much declined

E.45. Do you think that land is degrading in the region due to tube well irrigation? (No=0, Yes=1, 2=Don’t know)


E.46. Do you think that groundwater irrigation is creating some environmental problems? (No=0, Yes=1, 2=Don’t know)


E.47. Do you think that groundwater table is lowering in the region? (No=0, Yes=1, 2=Don’t know)


E.48. If yes, can you guess how groundwater availability has changed over the last 10 years?

E.48.1. Somewhat improved E.48.2. Much improved E.48.3. Somewhat declined E.48.4. Much declined

Questionnaire

192

E.49. How do you rate the importance of preserving groundwater reserves?

E.49.1. Not very important E.49.2. Somewhat Important E.49.3. Very important E.49.4. Highly important E.49.5. Don’t know

E.50. Do you have some limitations to use groundwater in irrigation? If there are any, please specify:

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E.51. Do you have any comments or suggestions regarding sustainable groundwater use in irrigation? If there are any, please do specify:

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G. Perceptions about Water Rights and Water Scarcity G.1. Is there any government policy that affects how you use groundwater? (No=0, Yes=1, 2=Don’t know)

G.1.1.Yes G.1.2.No

G.1.3.Don’t know G.2. Do you think that written laws for the extraction of groundwater can help in controlling misuse of water? (No=0, Yes=1, 2=Don’t know)

G.2.1.Yes G.2.2.No G.2.3.Don’t know

G.3. If the government issues groundwater law are people going to adhere to it and apply to it? (No=0, Yes=1, 2=Don’t know)

G.3.1.Yes G.3.2.No G.3.3.Don’t know

G.4. To whom do you think groundwater belongs?

G.4.1. God G.4.2. State G.4.3. Public property G.4.4. Individual

G.5. Do you think that an individual should own a groundwater source? (No=0, Yes=1)

G.5.1.Yes G.5.2.No

G.6. If an individual has right to groundwater, do you think that after satisfying his demands he should be allowed to sell groundwater? (No=0, Yes=1)

G.6.1. Yes G.6.2. No

G.7. What is the extent of groundwater right according to people in the area? The owner of the water;

G.7.1. May use it any time G.7.2. May use when it upon need G.7.3. Has freedom to behave with water as he wanted G.7.4. Has secure right over water and cannot be confiscated any time

G.8. Do you know that groundwater resources are finite? (No=0, Yes=1)

G.8.1. Yes G.8.2. No

Questionnaire

193

G.9. Are you ready to get water allocated by the government or any agency for irrigation over the next 10 years? (No=0, Yes=1)

G.9.1. Yes G.9.2. No

G.10. Do you think that your region can face groundwater shortage? (No=0, Yes=1, 2=Don’t know)

G.10.1. Yes G.10.2. No G.10.3. Don’t know

G.11. If yes, what are the chances of facing groundwater shortage over the next 10 years in your region?

G.11.1. 10% G.11.2. 20% G.11.3. 30% G.11.4. 40% G.11.5. 50%

G.12. Do you think that groundwater situation may affect your future farming patterns? (No=0, Yes=1, 2=Don’t know)

G.12.1. Yes G.12.2. No G.12.3. Don’t know

F. Credit Details F.1. Do you think that lack of credit affect your use of inputs and ultimately production? (No=0, Yes=1)

F.1.1.Yes F.1.2.No

F.2. Did your household get any cash credit during the last cropping year? (No=0, Yes=1)

F.2.1.Yes F.2.2.No

F.3. If yes, please provide details about the purpose, source and amount of credit which you got. Purpose of the Credit

F.3 Source of the Credit

F.5 Amount of the Credit

F.6

1=Fertilizer (FER) Amount of the Credit in Rs.

2=Pesticide (PES) 1.Commercial bank (CBNK)

3=Diesel OR electricity (DOE) 2.Government bank (GBNK)

4=Seed (SEED) 3. Neighbour (NBR)

5=Land rent (if tenant) (LRNT) 4. Friend/relative (FRND)

6=Irrigation equipment (IREQ) 5.Shopkeeper/Aarhti (SHK)

7=Water purchase for irrigation (WPR)

8=Above all (ALL) H. Extension Services and Sources of Information H.1. Do you have access to different extension services related to different agricultural activities? (No=0, Yes=1)

H.1.1 Yes H.2.1. No

Questionnaire

194

H.2. Did you receive any benefit from the services provided by the extension field staff particularly related to groundwater management during the last year? (No=0, Yes=1)

H.2.1. Yes H.2.2. No

H.3. Did you get any recommendations regarding sustainable use of groundwater in irrigation by the extension field staff last year? (No=0, Yes=1)

H.2.1. Yes H.2.2. No

H.3. What are the main sources of information regarding sustainable groundwater use in irrigation and what was their frequency for the last 12 months?

Source of Information Never Occasionally Often

H.3.1. Newspaper -------- ----------------- ------- H.3.2. Radio -------- ----------------- ------- H.3.3. Television -------- ----------------- ------- H.3.4. Extension field staff -------- ----------------- ------- H.3.5. Private Agencies -------- ----------------- -------

H.4. Did you get any information about groundwater level and quality in your region through any of the following sources? (No=0, Yes=1)

H.4.1. Newspaper Yes/No H.4.2. Radio Yes/No H.4.3. Television Yes/No H.4.4. Extension field staff Yes/No H.3.5. Private Agencies Yes/No H.3.6. Directorate of soil and water Yes/No

the economics of groundwater irrigation in the indus basin...

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