IMPROVING WATER USE IN FARMING: IMPLICATIONS DERIVED FROM FRONTIER

FUNCTION STUDIES

Boris E. Bravo-Ureta UCONN, USA & UTalca, Chile

Roberto Jara-Rojas UTalca, Chile

Daniela Martinez UTalca, Chile

Susanne M. Scheierling World Bank, D.C.

David O. Treguer World Bank, D.C.

12th Annual Meeting, International Water Resource Economics Consortium (IWREC)

The World Bank, Washington, DCSeptember 11-13, 2016

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Acknowledgements

This study was partially funded bythe Water Partnership Program (WPP), amulti-donor trust fund at the World Bank.

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Outline

1. Introduction

2. Frontier Methods

3. Approach and Data

4. Results

5. Concluding Remarks

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1. IntroductionPrimary Motivation: WHERE IS THE WATER INFRONTIER STUDIES???

• Scarcity of water resources a growing challengeheightened by climate change.

• Mitigation and adaption strategies needed.

• Irrigation a promising adaptation strategy (IPCC

2014). But irrigation already accounts for approx.70% of all freshwater withdrawals worldwide.

• Future increase in the demand for water fromincome and population growth.

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• Growing scarcity puts pressure to improveproductivity and efficiency of water use in farmingso water can be diverted to other sectors (FAO 2012;World Bank 2013; Scheierling et al. 2014).

• Productivity & efficiency using frontiermethodologies well defined area in economics.

• Frontiers provide measures of efficiency as apotential input reduction or output expansion,relative to a reference “best practice” frontier (Coelli

et al. 2005).

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1. Introduction…

• Despite growth in Frontier literature andimportance of water research bridging both islimited.

• Here we focus on 2 issues:

(i) Role of water in enhancing farm productivity

(ii) Implications from frontier studies on how to

improve water use in farming

• Draw from our recent meta-analyses (Bravo-Ureta et al.2016; Bravo-Ureta et al. 2007).

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1. Introduction…

Outline

1. Introduction

2. Frontier Methods

3. Approach and Data

4. Results

5. Concluding Remarks

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2. Frontier Methods

• In a production model, the frontier represents themaximum output producible given inputs,technology and the environment.

• TE = gap between max and observed output. Is anindex between 0% and 100%, interpreted as ameasure of managerial performance (Farrell 1957;

Martin and Page 1983; Triebs and Kumbhakar 2013).

• Major typologies:• Deterministic vs. Stochastic;• Parametric vs. Deterministic;• Stochastic Frontier Analysis vs. Data Envelopment A.

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Y(Output)

X (Input)

Observed

Output

DeterministicInefficiency

ProductionFrontier

•Random Error (v)

Yi = 0 + ΣX + ΣγZ + vi - ui

Max.Output

2. Frontier Methods….

The ‘Traditional Parametric’ (ALS 1977) Model

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StochasticInefficiency (u)

2. Frontier Methods…

SFA and DEA: key similarities & differences.Both are rigorous tools to measure efficiency relativeto a frontier.

Two key differences (Fried, Lovell & Schmidt, 2008):

1. SFA “naturally” stochastic and is parametric:makes it possible to separate noise from inefficiency,provides basis for statistical inference.

2. DEA nonparametric: avoids functional formmisspecification; “naturally” deterministic.

Narrowing the gap between SFA and DEA camps isan on-going issue!!!!

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2. Frontier Methods…

• Several recent innovations in frontier functionmethods. In SPFs:

• Panel data approaches (Greene 2005a and 2005b).

• Recent: Persistent and Transient TE, andunobserved time invariant heterogeneity (Colombi etal. 2014; Filippini and Greene 2014; Kumbhakar et al. 2014; Tsionas

and Kumbhakar 2014).

• Panel data frontiers and TFP analysis.

• Correction for selectivity bias. Suitable for impactevaluation (Kumbhakar et al. 2009; Lai et al. 2009; Greene 2010;

Bravo-Ureta et al. 2012).• Endogeneity (Tran and Tsionas 2013;Shee and Stefanou 2014).

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Outline

1. Introduction

2. Frontier Methods

3. Approach and Data

4. Results

5. Concluding Remarks

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3. Approach and Data

• Comprehensive search and review of the frontierfunction literature; special focus on water studies.

• 426 studies, 110 with different water features. Wefocus on 92 farm-level water studies that report TE.

• Of the 92, 10 analyze Irrigation Efficiency or IE (Kopp

1981; Reinhard et al. 1999). Karagiannis et al. (2003) first toanalyze IE.

• IE: non-radial measure of the amount of irrigationwater that could be saved holding output, otherinputs and technology constant. IE studies richercompared with non-IE water studies.

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Input Oriented TE & IE (Karagiannis, Tzouvelekas & Xepapadeas, 2003)

Water Studies: 110 classified in 5 groups (A-E)

A. Irrigation: 76 studies subdivided into A1, A2 and A3.

A1. Quantity 56 studies subdivided into 5 classes:

1) Quantity of Water used = 19;

2) Hours of Irrigation = 5;

3) No. Irrigations, Index, or Irrig. Expenses = 22;

4) Percent of Irrigated Land = 7; and

5) Land Area Irrigated= 3.

A2. Dummy: 9 studies dummy Yes/No Irrig.

A3. Mixed: 11 papers combines A1 & A2.

B. Precipitation: 6 papers, quantity or dummy.

C. Both A & B. 13 irrigation and precipitation.

D. Distance Functions: 5 articles.

E. Aggregate: 10 papers.15

Outline

1. Introduction

2. Frontier Methods

3. Approach and Data

4. Results

5. Concluding Remarks

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4. Results

• The 92 farm water studies yielded 189 MTEobservations or cases (MTE = simple average ofindividual reported TEs).

• Cases by Region: Asia 117; Africa 40; WesternEurope, Australia and New Zealand 20; NorthAmerica 8; Latin America 3; Eastern Europe 1.

• Most frequently studied countries: India, followedby China, Pakistan, and Bangladesh.

• Cases by income classification (WB 2015):

LMICs = 64; LICs = 50; UMICS = 38; HICs = 3717

4. Results…

• Cases by Farming Type:Mixed crops and livestock 87; Rice 63;Wheat 23; Dairy 13; Maize 2; Other Animals 1.

• By REGION• Highest MTE: Western Europe, Australia and NewZealand 81.8%.• Lowest MTE: Latin America 55.1%; Africa 65.7%.

• By Farming Type: dairy is highest 84.0%.

• Average MTE for all 92 water studies: 73.2%.

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4. NON-IE STUDIES, OUTPUT RESPONSES

• 82 Non-IE studies searched for estimates ofoutput responses w.r.t. variable incorporatingirrigation water.

• 33 observations listed and grouped by the watervariable used.

• Overall, irrigation water, regardless of how it isincorporated into the analysis, has a positive andsignificant effect on farm output.

• Furthermore, the productivity gap that can beattributed to TE is close to 25-30 % points.

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MTE & Output Response to Water: 33-Farm Non-IE Studies

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MTE & Output Response to Water: 33-Farm Non-IE Studies continued

4. 10 IE STUDIES

• 4 use an SFA model and 6 DEA

• Irrigation water Variable:

* 7 volume of irrigation water applied;

* 3 used mixed measure (volume of water appliedand a dummy variable for irrigation method)

• Average

MTE: 68.9%

IE: 46.6%

ITCE: 84.4%

Y Resp: 0.13422

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MTE, IE, ITCE, Out. Resp., Shadow Value, Market Price: 10 Farm IE Studies

4. Implications for Improved Water Use

• To identify policy implications on waterProductivity from 43 studies: all 10 IE studiesand the 33 non-IE discussed earlier.

• Policy implications clustered into threecategories:

• Farming Practices

• Human Capital of Farmers

• Policy Actions

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4. Implications for Improved Water Use…

FARMING PRACTICES:

• Choice of crops; chemical input use; land tenure(farmers who rent lower IE compared withowners)

• Adoption of modern irrigation technologies

• Improved maintenance of irrigation systemscrucial role in water distribution, which helps toenhance efficiency gains

• Investing in water conservation and usingfertigation can increase IE significantly

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4. Implications for Improved Water Use…

HUMAN CAPITAL OF FARMERS:

• Farmer experience (measured by proxies such asa farmer’s age)

• Participation in training programs focusing onagronomic and irrigation techniques

• Membership in Farmer Associations

• High share of hired labor to all labor higher TElevels

• Farmers with off-farm income tend to showlower TE levels

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4. Implications for Improved Water Use…

POLICY ACTIONS: interventions beyond the farmgate, typically undertaken by public sector

• Financial support through credit and/orsubsidies to foster adoption of modern irrigationtechniques, construction canals and water storage

• Specialized training programs on the correct useof irrigation technologies and irrigation water

• Creation and strengthening water userassociations and co-operatives

• Introducing a pricing system for irrigation water

• Formal water rights27

Outline

1. Introduction

2. Frontier Methods

3. Approach and Data

4. Results

5. Concluding Remarks

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5. Concluding Remarks

• Enhance farmers’ managerial ability related toirrigation.

• Reducing inefficient water use increasinglyimportant as water scarcity rises. Free or low costirrigation water does not help.

• Water pricing analysis needed (based on shadowprices as a point of departure). Evidence from thefrontier literature is very limited.

• Improved farming practices including soil andwater management need to be integrated in theanalysis of irrigation productivity.

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5. Concluding Remarks…

• Dearth of TFP from frontier literature. Recentmethods allow for comprehensive analysis thatcould be useful in informing policy.

• Rigorous impact evaluations of alternativeinterventions designed to improve irrigation wateruse in farming seem to be limited.

• Frontier methods allow for the separation ofproductivity gaps stemming from managerialperformance and from technological levels.

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5. Concluding Remarks…

• Farm studies useful but limited. Most studiesconsider water applied, and assume any reductionwould decrease “waste” and save saving.

• Basin level water use is interdependent.Return flows can be the source of water fordownstream users. This has been ignored in theproductivity literature and deserves attention.

• Irrigation-productivity-farm income researchagenda requires interdisciplinary collaborationto define indicators, data needs, suitablemethodologies provide robust farm and policylevel recommendations.

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IMPROVING WATER USE IN FARMING: IMPLICATIONS DERIVED FROM FRONTIER

FUNCTION STUDIES

Boris E. Bravo-Ureta UCONN, USA & UTalca, ChileRoberto Jara-Rojas UTalca, ChileDaniela Martinez UTalca, Chile

Susanne M. Scheierling World Bank, D.C.David O. Treguer World Bank, D.C.

THANK YOU

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