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Agricultural Water Management 113 (2012) 10– 18

Contents lists available at SciVerse ScienceDirect

Agricultural Water Management

jo u r n al hom ep age: www.elsev ier .com/ locate /agwat

he economic value of guaranteed water supply for irrigation under scarcityonditions�

. Azahara Mesa-Juradoa,∗, Julia Martin-Ortegab, Eric Rutoc, Julio Berbeld,1

Agroecology Department, El Colegio de la Frontera Sur (ECOSUR), Unidad Villahermosa, Carretera Villahermosa-Reforma s/n Km 15.5, Ranchería Guineo 2a Sección, Villahermosa6280, Tabasco, MexicoSocial, Economic and Geographical Sciences Group, The James Hutton Institute, Craigiebuckler, Aberdeen AB15 8QH, Scotland, UKCentre for Rural Economy, School of Agriculture, Food and Rural Development, Newcastle University, Newcastle upon Tyne NE1 7RU, UKDepartment of Agricultural Economics, University of Cordoba (Spain), Campus Universitario de Rabanales, Edificio Gregor Mendel C-5, Ctra N-IV, Km 396, Cordoba 14014, Spain

r t i c l e i n f o

rticle history:eceived 20 June 2011eceived in revised form 10 June 2012ccepted 13 June 2012vailable online 12 July 2012

a b s t r a c t

We assess the value farmers place on the guarantee of water supply, in the context of water scarcityand we identify the factors that characterise heterogeneity in valuations across respondents. We use acontingent valuation survey approach to elicit farmers’ willingness to pay (WTP) for secured water supplyfor irrigation in the South of Spain (Guadalbullon River within the Guadalquivir River Basin). We find that

eywords:rrigation water

ater scarcityontingent valuationuaranteed supply

farmers are willing to increase by 10% and 20% their current irrigators’ community annual payment and,moreover, they are willing to reduce average supply by 30% of their administrative water concession,to increase their water supply guarantee. These results confirm that when water is scarce, farmers havenon-market values associated with an increased guarantee, in addition to direct use for supplied water.This suggests that farmers perceive benefits in this change as their welfare increases, providing evidenceof the predisposition to measures or strategies that permit such improvement.

. Introduction

The European Water Framework Directive (WFD) requiresember States to achieve good ecological status of all water

odies (European Commission, 2000). For Member States withinhe Mediterranean region, water scarcity represents an additionalbstacle in meeting these requirements. The establishment of

minimal environmental flow enforced by the WFD, togetherith existing constraints from natural climatic variability, cli-ate change and abstraction by competing stakeholders, makes

egulation and allocation of water the core of water resourceanagement. In order to meet these challenges, and in line with

he WFD’s principles, economic analysis of the different elementsegarding water resource allocation under water scarcity condi-ions is needed to inform policy-making (Martin-Ortega, 2012).

Our goal in this paper is to assess the value farmers place onncreased security of water supply under water scarcity condi-ions and to identify the factors that characterise heterogeneity in

� The work presented in this paper was developed while being affiliated to theepartment of Agricultural Economics of the University of Córdoba (Spain).∗ Corresponding author. Tel.: +52 993 3136110x3301; fax: +52 993 3136110.

E-mail addresses: mmesa@ecosur.mx, azamejur@hotmail.comM.A. Mesa-Jurado), julia.martinortega@hutton.ac.uk (J. Martin-Ortega),ric.ruto@ncl.ac.uk (E. Ruto), berbel@uco.es (J. Berbel).1 Tel.: +34 957 21 84 57; fax: +34 957 21 85 39.

378-3774/$ – see front matter © 2012 Elsevier B.V. All rights reserved.ttp://dx.doi.org/10.1016/j.agwat.2012.06.009

© 2012 Elsevier B.V. All rights reserved.

valuations across respondents. We employ the contingent valua-tion method to investigate farmers’ willingness to pay (WTP) forsecured water supply for irrigation using South of Spain (Guadal-bullon River within the Guadalquivir River Basin) as a case study.Our study contributes to the improvement of the design and refine-ment of economic valuation tools which support changes in watermanagement and planning in water scarcity contexts. The effect ofdifferent farmer characteristics on WTP is evaluated to help pol-icy makers to better target their interventions by improving theirknowledge about farmers’ perceptions.

2. The value of water supply guarantee in agriculture

Most studies of the relation between uncertainty over watersupply guarantee and farmers’ benefits pertain to market values.For example, Millan and Berbel (1995) analyse the cost of uncer-tainty in water supply for a Guadalquivir river irrigation area.Calatrava-Leyva and Garrido (2005) show how uncertainty in wateravailability reduces farmer’s benefits because of the fact that deci-sions are taken when they are not sure about the amount of wateravailable for irrigation. Marques et al. (2005) argue that increasingwater supply reliability can increase the probability of higher ben-

efits and promote more effective use of water for permanent crops.In Mesa-Jurado et al. (2010) net margin variability by the produc-tion function is obtained for different water doses applied to olivegroves. Ranjan (2010) studied farmers’ decision of whether or not

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M.A. Mesa-Jurado et al. / Agricultur

o participate in risky water markets. Although these studies takeccount of uncertainty in water supply, none of them estimated theon-market benefit generated by an improvement of water supplyuarantee.

The existing literature on non-market welfare gains of increasedater supply guarantee focuses only on household consumption.owe et al. (1994), Barakat and Chamberlin (1994), Griffin andjelde (2000), Koss and Khawaja (2001), Raje et al. (2002) andatton McDonald et al. (2010) investigated the value householdsssociate with reliability of water supply and customer’s pref-rences. Hensher et al. (2005) and Martin-Ortega et al. (2011)xamined household WTP to avoid drought water restrictions. Thisody of literature looking at household water supply contrastsith the limited literature on irrigation water. Rigby et al. (2010)

stimated the marginal irrigation water value to horticultural pro-ucers in Southern Spain using choice experiments. The effect ofncertainty on the supply of irrigation water is included in Rigbyt al.’s study; however, its value is not estimated. Alcon et al. (2010)arried out a stated preference study aimed at estimating the valuef the use of reclaimed waste water for agriculture, which implicitlyeads to an increase of the guarantee of water supply, but this is notxplicitly assessed in their study. To our knowledge, the analysis ofuaranteed water supply for irrigation has not yet been addressedn the literature.

. Case study description: the Guadalbullon River sub-basin

In this study we use Guadalbullon River sub-basin, within theuadalquivir River Basin as a case study (see Fig. 1). Irrigated olive

roves are widespread in the Guadalquivir River basin and theuadalbullon River sub-basin is a good example for the analysisf supply guarantee because it does not have any regulating infras-ructure. Thus, water supply is subject to high levels of uncertainty.

Fig. 1. Location of the Guadalbullon River Subource: Confederación Hidrográfica del Guadalquivir (2009b).

er Management 113 (2012) 10– 18 11

The Guadalbullon River sub-basin was selected as a pilot sub-basinto study olive irrigation and water guarantee in the context of thenew Hydrological Planning under elaboration, as it was considereda representative example for the whole Guadalquivir Upper Valley(CHG, 2009a).

Summer flows have historically enabled the establishmentof irrigated fields, initially located near the junction with theGuadalquivir River. This has subsequently spread throughout thevalley, due partly to the expansion of irrigated olive groves. Recentstudies show that the apparent water productivity of agriculturein the region has increased by 327% since 1998, however, this hasnot implied any water saving, since the increase in productivity hasbeen accompanied by an increase of the irrigated surface (Carrascoet al., 2010).

The total area under cultivation in the Guadalbullon sub-basinis 70,000 ha, of which 22,000 ha (30%) are irrigated. Agricultureaccounts for 70% of water use, primarily for olive groves assingle-crop farming. It is estimated the basin uses about 65%of available resources and that the minimal environmental flowis not reached in a significant number of days (CHG, 2009a). Adetailed description of the Guadalbullon river basin and the eco-nomics of olive grove irrigation can be found in Mesa-Jurado et al.(2010).

The current policy in the basin is to improve farm irri-gation systems by changing from surface to trickle and dripirrigation as well as improving distribution systems by chang-ing from open channels to pressurized networks. In Spain, eachfarmer receives an amount of water assigned by the waterauthority as a ‘water right’ or concession. However, according

to data obtained from irrigators’ communities and interviewsto farmers, Guadalbullon River basin farmers rarely receive thefull right and often the yearly quota is smaller (Mesa-Jurado,2011).

-basin in the Guadalquivir River Basin.

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Since passage of the 1985 Water Law, Spanish water policy hastarted evolving from the traditional structural approach towards

holistic view focused on the sustainability of the resource (Embidrujo, 2008), promoted by the WFD. The excessive rigidity of thentitlement system made it almost impossible to introduce changesn the use of water, since a new entitlement could be requested only

hen another expired (Garrido, 2005). The 1999 Reform combines more rigorous control by the State upon the natural resourcesith a more flexible use of water (Costejá et al., 2004) through

he regulation of the exchange of water rights and the provisionor water banks. This promotes the contracts for the cession of userights, so that surplus water can be sold to other right-holders (withhe same type of water use) reaching a higher level of efficiency inhe use of this natural resource. Moreover, basin authorities canetup water banks or trading centres in cases of droughts or severecarcity problems. However, there is currently little formal waterrading despite the enactment of the law aimed to promote it. Thiss perhaps due to the widespread farmers’ distrust of formal water

arkets and their perception that these formal markets wouldncrease monitoring, taxes and corruption (Garrido and Llamas,009).

The irrigation of olive groves has been seen as a technologi-al revolution for the sector, especially in this region where oliverove cultivated area has doubled in the last decade (Junta dendalucia, 2008). In 2008 the percentage of olive oil coming from

rrigated areas was estimated at 56% of the total. A recent studyn water productivity in the Guadalquivir River Basin showed thatlive groves reach productivities well above river basin averageBerbel et al., 2011). Mesa-Jurado et al. (2010), using a productionunction approach, also obtained high marginal values for irri-ated olive groves in the Guadalbullon River Basin, in the range of.53–0.60 D /m3 for 1500 and 1000 m3/ha, respectively.2 This highrofitability per area and an increased importance on the use ofamily labour in small farms holdings maybe partly responsibleor the expansion in cropping area during the last decade. Thisrop is also a key to the socio-economic development of someural regions, where there are no other alternative crops (suchs in mountainous areas). However, this expansion of the crop isesponsible of the increased pressure on water resources, soil ero-ion and pollution in the area. Taking the Guadalquivir River basins a whole, the olive grove has more than 50% of irrigated areaespite its low dose (1500 m3/ha versus an average of 6000 m3/haor general irrigation). The transition of agriculture from field cropso higher-value crops, such as olive groves, may have a beneficialffect on water use efficiency, but it increases the financial expo-ure to drought because of the substantial capital investment inhose crops (Bhat and Blomquist, 2004).

In compliance with the “Guadalbullón Sub-basin Irrigable areaodernization Plan” (MARM, 2007), a small artificial pond, known

s Balsa Llano del Cadimo, is currently being built to regulate the flowf the main Guadalbullón river basin’s stream. This infrastructureill increase the reliability of water for irrigation and is expected to

ncrease the environmental river flow allowing for irrigation dur-ng the dry season. The maximum capacity of this reservoir wille 19.75 million m3 and it is expected to improve the irrigationuarantee of approximately 18,000 ha of olive groves in the basin.he “Balsa Llano del Cadimo Feasibility Plan” published on 11/2005aw, estimates that the annual equivalent cost including invest-ent depreciation, operation and maintenance is 0.10 D /(m3 year).

ccording to the WFD’s full cost recovery of water services, the costf the pond should be recovered by charging beneficiary farmers.

2 1500 m3/ha correspond to the administrative concession (i.e. the water rightllocated to the farmer) and 1000 m3/ha corresponds to the average allocation actu-lly received in the last 4 years.

er Management 113 (2012) 10– 18

4. Materials and methods

In economics, the value people attach to unpriced naturalresources and the services these resources provide can be estimatedin monetary terms through the concept of individuals’ willingnessto pay (WTP). The monetary WTP measure indicates how changes inthe provision level of public environmental goods, including qualitychanges of these goods, impact upon individual welfare. Aggregat-ing individual changes in welfare over all those individuals who areaffected by a change in their provision level, provides an indicator ofthe total economic value of that change (Pearce and Turner, 1990).For environmental goods and services that are not traded in themarket, stated preference methods are used to elicit individuals’WTP.

The stated preference method is based on the elicitation of themaximum WTP of the concerned population for a specific environ-mental/resource change (in this case, the increase of the guaranteeof available water for irrigation). It implies the use of surveys, inwhich hypothetical markets are presented to a representative sam-ple of the population (note that in this case, we are not referringto the general public as the population, but the farmers’ popula-tion). These hypothetical markets are characterised by a change inthe environmental good under assessment in exchange for a cer-tain amount of money (Bateman et al., 2002). Among the availablestated preferences techniques, here we use the contingent valua-tion (CV) method.

4.1. Contingent valuation design

The CV design process began with the study of the water rightssystem and water supply guarantee features through a review ofthe literature and legislation (Costejá et al., 2004; Font and Subirats,2010). Also, in-depth interviews of 18 main irrigator communitiesand focus group discussions (including farmers, representativesof irrigator communities and river basin authority experts); anda pilot survey were carried out. Among the goals of these inter-views and focus groups were to cross-check the information givenby farmers with the official data from these communities, gather-ing first-hand information. Following guidelines in Bateman et al.(2002) the focus group discussions and pilot-surveys (40 face-to-face interviews) were used to aid the development and validation ofthe questionnaire, including the wording of questions. During thepre-test, issues raised by farmers included the lack of trust in theadministration, low prices of olive oil, and the high cost of electricityfor pumping water from the river to the farm.

The questionnaire used in the survey was organized in fourmain parts: (i) farm characterisation (e.g. farm size, crop den-sity); (ii) respondents’ perception and knowledge about the currentstate of irrigation water supply guarantee in the study area (e.g.certainty about the quantity of water they will receive at thebeginning of the season, satisfaction with the water quantity theyreceive, etc.); (iii) respondents preferences and values towardsirrigation supply guarantee improvement in the basin elicitedthrough WTP questions; (iv) respondents’ demographic and socio-economic characteristics.

The valuation scenario presented to farmers consisted of adescription of the baseline situation (status-quo) which is the cur-rent official water allocation (1500 m3/ha), though this volume israrely met because of low reliability. Farmers were offered the pos-sibility to pay for an increase of the guarantee of the water supply.The presentation of the valuation scenario was supported by the useof pictograms (Fig. 2). Respondents were also reminded to consider

their budget constraints and were assured that their money wouldonly be used for the purpose of increasing water supply guarantee.Then respondents were asked if they will be willing to pay in prin-ciple to improve the guarantee of the water supplied to them. For

M.A. Mesa-Jurado et al. / Agricultural Water Management 113 (2012) 10– 18 13

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hose who stated not to be willing to pay, a follow-up question wassed to differentiate legitimate zeros from protest answers. Legiti-ate zeros correspond to individuals who attach zero value to the

nvironmental change (Jorgensen et al., 1999). In this case legiti-ate zero answers included respondents who: “think the current

upply guarantee is good”, “cannot afford to pay extra”, considerhat they “already pay enough”. Protest answers are considered as

rejection of the valuation exercise (Hanley, 1996), and in our casenclude: “The administration has to pay it” and different forms ofI don’t trust the improvement of the guarantee would be possi-le”. Those farmers who stated to be willing to pay in principleere then asked their maximum WTP to secure a certain level

f water supply for irrigation for example ‘What is the maximummount of money that you would be willing to pay as an addi-ion to your yearly irrigators community annual payment to ensure0,000 l/tree?’. For a better understanding with the farmers, thenits used in the survey are litre per tree, because it is the measurehat they normally use in their water counts (as came out from there-testing phase). A water allocation of 15,000 l/tree correspondso 1500 m3/ha, assuming that the average density is 100 trees/ha.

During the pre-test it was observed that a 100% guarantee wasot a credible scenario, since farmers are aware of the variable cli-atic conditions and the structural scarcity of the region. This is

ow the guarantee was defined in terms of probability. However,uring the pre-test it was also observed that in general farmers hadroblems regarding probability expressed in terms of percentage.hat is why the guarantee of water supply was defined as the num-er of years that the farmers would receive a specified quantity ofater with certainty in a given time frame. This way of presentingrobabilities of uncertain events have previously been applied inelation to water supply in Martin-Ortega et al. (2011). Two levelsf guarantee were defined in order to check for sensitivity to scopeCarson et al., 2001), i.e. the expectation that the higher the level

f provision of the service, the higher the WTP. The first level waset up on 50% (i.e. 5 years out of the next 10 years, as presentedo farmers) of guarantee of getting the offered water supply and aecond level of 90% (i.e. 9 years out of the next 10 years).

d in the valuation exercise.

The annual increase of the irrigators’ community annual pay-ment was identified in the pre-test as the best payment vehicle asthe respondents were already familiar with this vehicle (expressedin Euros per olive tree). It is comparable with the increment ofthe monthly water bill that has been successfully applied in waterresource valuation studies in the past (e.g. Genius et al., 2008;Martin-Ortega et al., 2009; Alcon et al., 2010). In studies such asRigby et al. (2010) the payment vehicle used is an irrigation contractin which different amounts of water, prices and level of certaintywere offered to the farmers.

The elicitation format was a semi-open ended payment card.Based on information from pre-test interviews and focus groupdiscussions, payment amounts ranging from 0 D to more than 8 Dwere included in the payment card. The NOAA panel advises onthe use of dichotomous choice elicitation formats (Arrow et al.,1993), but the kind of elicitation format used here is said to com-bine the advantages of the open-ended formats at the same time itminimizes the problem of starting point bias (Kallas et al., 2007).Moreover, Ready et al. (2001) argued that this kind of format showsthe quantities that respondents are willing to pay with higher cer-tainty.

4.2. Data collection

Trained interviewers conducted 150 individual interviews inJuly 2009 throughout the Guadalbullon River Sub-basin, targeting arandom sample of irrigated olive farm owners belonging to 18 irri-gator communities. In addition to the survey, in-depth interviewsof the main irrigator communities that manage the water distribu-tion between the farmers were conducted. The goal of the in-depthinterview was to cross check the information given by farmers withthe official data from these communities and to gather backgroundinformation about the case study area.

The sample covers 9% of the irrigated area in the Guadalbul-lon River basin. It was necessary to use crop area as a measure ofgeographical coverage instead of the number of farmers becausemost farmers have more than one farm registered and it was not

14 M.A. Mesa-Jurado et al. / Agricultural Wat

Table 1Summary statistics describing farmer characteristics in our sample of 151 farmersin the Guadalbullon River Basin in 2009.

Characteristics Sample River basinpopulationa

Farm size distribution (%)0.1–5 ha 65.6 68.05–10 ha 19.2 15.910–20 ha 9.3 8.320–50 ha 4.6 4.5>50 ha 1.3 3.2

Average irrigated production (kg/ha) 4560 5056Average olive density (trees/ha) 109 117Age distribution (%)

<35 years 7.3 9.935–54 years 42.4 38.654–64 years 24.5 23.3>64 years 25.8 28.3

Education (%)Without formal education 28.5 18.3Elementary school 49.0 43.9Secondary education 15.9 20.6Higher education level (university) 6.6 17.2

Household size (persons) 2.92 2.83Annual gross household income (%)b

<20,000 D 3320,000–40,000 D 3840,000–60,000 D 1460,000–80,000 D 780,000–100,000 D 5>100,000 D 3

Source: National Institute of Statistics, 2010. Data for the Jaen region in which theentire river basin is located.

a It would be more accurate to check sample representativeness with farmerı̌scharacteristics, rather than with general population. However, and after intensesearch work in different levels (national, regional and municipal) of the official statis-tical databases (INE, 1999, 2001, 2010; Junta de Andalucia, 2008), we have concludedthat this kind of information is simply not available for this region. Therefore, com-paring with the general population is the best approximation possible. However, itis not considered a significant problem since the population of this specific area isprominently rural and agricultural.

b In order to facilitate an easy calculation to the farmers, in the survey they wereasked about “Annual gross household income” that includes the farm total grossincomes and other incomes that support their households from other economica

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of the water that will be available to them for irrigation at the begin-ning of the season and almost 50% felt that there is, in general,insufficient water for their farms. Farmers stated that, on average,

ctivities (subsidies, spouse’s salary, etc.).

ossible to have a reliable number to ensure a representative sam-le. Considering that most of the farmers have small farms, the areaovered by the survey is considered to be a reasonable represen-ation of the population of the river basin. The main demographicnd socio-economic characteristics of the sample are presented inable 1. Descriptive results show that many farms are small in size0.1–5 ha), providing evidence of the high atomization of the sectorn this region. The average density of the olive grove corresponds

ith the traditional system, in which the number of trees variesetween 70 and 110 trees/ha. About 50% of the sample and theopulation are older than 55 years, and young farmers (less than5 years old) represent only 7% of the respondents. On average theespondents in the sample have a farming experience of 33 years.lmost half of the respondents have elementary formal education,nd almost one third have no formal education. Consistent withigby et al. (2010) and Alcon et al. (2011), the socio-demographicrofile of the sample suggest an aging agrarian population operat-

ng on small farm holdings (ranging from 1 to 5 ha). The distributionf income shows that about 30% of the farmers have an annualousehold gross income less than 20,000 D and more than half ofhe farmers in the sample depend on agriculture as the main sourcef income. Overall, the sample is a fairly good representation of

he farmers’ population in the area. Appendix 1 presents a descrip-ion of all the variables in the data together with their descriptivetatistics.

er Management 113 (2012) 10– 18

4.3. Modelling procedure

When analysing data with a high variation, which is the casewith contingent valuation where there is a large accumulation ofzero values, standard linear regressions (OLS) provide inconsistentWTP values (Seung-Hoon et al., 2000). In these cases, Tobit modelshave been proposed (for example, see Adams et al., 2008; Amemiya,1984; Halstead et al., 1991). This type of model also allows theanalysis of preference heterogeneity among respondents. In a Tobitmodel the dependent variable is restricted around a certain value(in our case around the zero value) (Tobin, 1958).

The model specification is given by the following censoring rule:

yi = {y∗i if y∗

i > 0, 0 otherwise} (1)

where yi is the stated WTP of respondent i and y∗i

is the cor-responding latent value of the respondent’s willingness to pay.This expression represents the situation in which zero responsesare generated from the same process as non-zero responses thatrepresent compensating surplus. The expected value of the latentvariable y∗

iand the marginal effects in the model are expressed as:

E(y∗i|xi) = ˇ′xi

∂E(y∗i|xi)

∂xi= ˇ

(2)

where Xi is the vector of the explanatory variables and ̌ is theparameter determining the impact of the explanatory variable onthe WTP. The Tobit model represents the expected value of thevariable yi as:

E(y∗i |xi) = ˇ′xi F(z) + � f (z), (3)

where z = ˇ′xi/�, f(z) is the density function, F(z) is the cumulativedistribution function of a standard normal random variable and �is the standard deviation. Then the marginal effects in the modelare given by:

∂E(yi|xi)∂Xi

= F(z) ̌ + ε (4)

where ̌ is the vector of coefficients to be estimated for the individ-ual respondent characteristics (Xi) and ε is the error term, which isassumed to have a normal distribution centred around zero.

A backward stepwise selection method3 was used in the selec-tion of variables to include in the final (best-fitting) model reportedin Table 3. The backward selection method begins by placing all ofthe predictors under consideration in the model (p variables), fol-lowed by a second model with p − 1 variables; the excluded variableis the one that can be left out with the minimum effect on the fitof predicted ‘y’ values (the least significant, having the largest p-value in the significance test for its effect). Next a model with p − 2predictor variables is fitted, and so on until the removal of anotherpredictor produces an unacceptable increase in the error sum ofsquares (Agresti and Finlay, 2009).

5. Results

5.1. Perceptions on water supply

Water supply guarantee is considered to be a very importantissue for most respondents. Over 65% stated that they are unaware

3 Forward stepwise selection is also a possibility, although it is generally agreedthat backward selection is preferable to forward selection (Harrell et al., 1996).

M.A. Mesa-Jurado et al. / Agricultural Water Management 113 (2012) 10– 18 15

Table 2Willingness to pay to improve the guarantee of water supply for irrigation at 50%and 90% levels, in Euros per tree (n = 141 valid observations).

WTP to ensure10,000 l/tree in 5 of 10years (50%)

WTP to ensure10,000 l/tree 9 of 10years (90%)

Mean 0.39 0.74Median 0.30 0.60Standard deviation 0.38 0.63

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Table 3Coefficient estimates for the Tobit model describing farmer willingness to pay forincreasing the guarantee of water supply.

Variable Coef. Std. error p-Value

Constant −0.51 0.547 0.350Level of guaranteea 0.395 0.087 0.000***

Clusterb 0.0007 0.001 0.484Income 0.163 0.053 0.002***

Age −0.251 0.119 0.037**

Household size 0.072 0.037 0.053**

Agricultural training 0.239 0.105 0.024**

Olive trees per hectare −3.331 × 10−3 0.002 0.058**

Perceived water allocation −4.58 × 10−5 0.000 0.020**

Rainfed production 5.43 × 10−5 0.000 0.148Difficulty of the questionnaire −0.145 0.089 0.110*

Number of observations 302 Pseudo-R2 0.32 Log likelihood −199.98

* Statistical significance level: 10%.** Statistical significance level: 5%.

*** Statistical significance level: 1%.a The level of guarantee is a zero/one variable denoting the level of guarantee as

50% or 90% for the question of willingness to pay (0 = 50%, 1 = 90%).b Data in this study are treated as panel data, i.e. two observations per respondents

are recorded for each of the levels of water supply guarantee (50% and 90%). The vari-able Cluster ID controls for this effect of doubling the observations per respondent.That is, the cluster id variable takes the value 1 for the two observations of the firstrespondent, value 2 for the two observations of the second respondent, and so on.

Minimum 0 0Maximum 1.5 2.8

hey received 9000 l/tree in the last four seasons, which is far belowhe 15,000 l/tree to which they are entitled by the administrativeater concession. Almost 96% declared that irregularity of irrigatedater supply causes negative effects in the productivity of olive

roves.Some farmers (22% of our sample) could not answer exactly how

uch water they receive each year. The quantity of water they per-eived to be receiving was lower than the actual amount declaredy the irrigators community in 55% of the cases, and only 11% per-eive a higher quantity, so their perceived difference is greater thanhe actual difference.

Another important question relates to the irrigators commu-ity’s annual payment, which was compared with the WTP results.he average annual payment paid by farmers per olive tree is.56 D . This value varies between 1 D and 6 D depending on the

rrigators community.

.2. Willingness to pay and preference heterogeneity

Farmers were first asked whether they are willing to pay tomprove the guarantee of water supply for irrigation without men-ioning any guarantee level. About 29% were unwilling to pay. Weonsidered 79% of the negative answers to be legitimate zeros andhe 31% (6% of the total sample) to be protest responses. These fig-res are considered to be within the limits of acceptable levels ofrotest (Carson et al., 2003). A high proportion (89%) of protestesponses involved an affirmative response to “the State should payt”. Following common practice in the literature, protest responses

ere excluded from the analysis, while the legitimate zeros wereetained (Dziegielewska and Mendelsohn, 2007).

Table 2 shows descriptive statistics of the responses regardingTP to improve the guarantee of water supply for irrigation for the

wo levels of guarantee (50% and 90%). The mean WTP to ensure0,000 l/tree in 5 of 10 years (50% guarantee) is 0.39 D /tree perear, and the mean WTP to ensure that supply in 9 of 10 years (90%uarantee) is 0.74 D /tree per year (0.034–0.074 D /m3).

Next we investigate the effects of socio-economic character-stics of respondents and farm characteristics on WTP for themprovement of irrigation water guarantee using a Tobit Model.n addition to respondent socio-economic characteristics, such asnnual household gross income, age, household size or agricul-ural training, variables concerned with farm description, such asensity of trees, perceived water dose and rainfall production,nd aspects of the questionnaire difficulty were considered inhe analysis. Other variables such as annual payment, educationevel, proportion of household income coming from agriculture,ocation of the farm and the importance of the guarantee to thearmers were found to be insignificant determinants of WTP. Ithould be noted, however, that non-significant does not nec-ssarily contradict expectation, but simply that their influence

n WTP is not observed in this dataset. Results of the Tobit

odel are reported in Table 3. As the pseudo R2 is above 0.2,he overall model fit is considered good (Hensher and Johnson,981).

The non-significance of the variable supports the validity of the panel data approachwe use in our analysis.

6. Discussion

The analysis presented here provides evidence that farmers arewilling to pay for improved levels of water supply guarantee, moti-vated by the perceived improvement in their welfare. They arewilling to increase more than 10% and 20% of their current irri-gators’ community annual payment. In addition, they are willing toreduce their administrative water allocation concession by 33%, toincrease this guarantee further. As theoretically expected, sensitiv-ity to scope is confirmed in this case; i.e. higher levels of guaranteeresult in higher WTP values.

Regarding demand heterogeneity, the relevant factors thatsignificantly affect the value of WTP are socioeconomic character-istics: age, annual household gross income, agricultural training,household size and production characteristics: olive trees perhectare and perceived water quota. This additional informationcould help policy makers to better target their interventions forimproving their knowledge about the likely support among differ-ent types of farmers for specific policy actions.

The income variable has the expected significant positive sign,implying that respondents with higher income are willing to paymore for the improvement of the service than farmers with lowerincome. However, farmers with a larger number of olives treeper hectare are less willing to pay. This may be explained in partbecause the annual payment proposed here is expressed in Eurosper tree, and therefore, the total cost per hectare for these specificgroups of farmers is higher. In addition, there might be a greaterWTP for small family farms compared to medium-large farms, asthe larger farms need to hire labour. Agricultural training usu-ally implies a higher productivity of labour and more innovativebehaviour, and this might explain the positive relation betweenthis variable and the WTP value.

Age as a variable was found to be negatively correlated withWTP, with younger farmers willing to pay more than older individ-uals, which is consistent with the finding of Carson et al. (2001).It can be reasoned that farmers close to retirement tend to be less

‘forward-looking’ and do not see investment in water resourcesas a strategic move for their business. Farmers are also willing topay more, when there are greater numbers of people dependant on

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6 M.A. Mesa-Jurado et al. / Agricultur

hat individual farm’s income. From this evidence, it could be inter-reted that farmers are willing to pay more to ensure sustainableates of production to support larger groups of individuals who col-ectively require greater income security. Farmers who perceive tobtain a smaller water dosage are more willing to pay to guaranteehe supply. This significant relationship does not mean, however,hat their behaviour is motivated by a need to receive larger quan-ities of water because a variable which represents the differencesn the perception of water dosage was not significant.

Regarding the non-significant variables, those related to theocation of the farm were expected to be significant. That is, wead anticipated that farmers located further from the main streamuadalquivir River would be more willing to pay than farmers

ocated closer to the junction between the Guadalquivir and theuadalbullon. The latter group might access water from the main

iver instead of the tributary, so that they would have a potentiallyigher level of guarantee. Also, we had previously expected that

armers who have the choice to use water from the Guadalquiviright be less willing to pay for improving the guarantee in theuadalbullon.

A comparative analysis of farmers’ WTP for increasing waterupply guarantee and the estimated cost of the proposed artificialond Balsa Llano del Cadimo (aimed at providing improved levels ofuarantee) shows that the estimated cost of the pond (0.10 D /m3

ncluding the investment in operation and maintenance) is signifi-antly higher than the WTP estimated in our survey (which rangesrom 0.04 to 0.07 D /m3, for 50% and 90% levels of guarantee). Thisuggests that farmers would be reluctant to meet the full cost of thertificial pond. However, an interesting outcome of this researchs that farmers were also willing to trade-off a certain amount ofheir water rights, where 1000 m3/ha was offered out of the originalllocations of 1500 m3/ha. The foregone profits associated with theeduction in water allocations have been estimated through the netenefit function developed in Mesa-Jurado et al. (2010). Accordingo that study, the opportunity cost associated with the reductionf water allocations from 1500 to 1000 m3/ha is about 0.39 D /m3.herefore, the ‘actual’ payment by farmers for increasing the guar-ntee should also take into account the foregone benefits of theecreased water allocation; i.e. 0.78–1.14 D /m3, for the 50% and0% levels of guarantee, which is higher than the annual mainte-ance costs of the Balsa Llano del Cadimo small artificial pond. Thus,here might be no conflict with the cost recovery principle requiredy the Water Framework Directive.

. Conclusions

Our results confirm that, when water is scarce, farmersave non-market values associated with increased guarantee in

Variables Description

Level of guarantee Identifies the level of guarantee as 50% or 90% for tquestion of Willingness to Pay

Water Guadalquivir Identifies farmers who have the choice to use watethe Guadalquivir River

Zone 1 Area located at Upper Basin

Zone 2 Area located at Middle Basin

Zone 3 Area located at Lower Basin

Farm size Farm size (hectares)Guadalbullon area Hectares that the farmer irrigates with water from

GuadalbullonRainfed area Rainfall surface (ha)

Irrigated area Irrigated surface (ha)

Rainfed production Olive production in rainfed system (kg/ha)

Irrigated production Olive production in irrigated system (kg/ha)

Olive trees per hectare Number of olive trees per hectare

er Management 113 (2012) 10– 18

addition to direct use for supplied water. This means that farm-ers perceive benefits in this change as their welfare increases,providing evidence of the predisposition to measures or strate-gies that permit such improvement. It could be interpreted thatdespite these perceived benefits, farmers would not be will-ing to pay for the investment costs of the measure plannedfor increasing supply (artificial pond); thus suggesting a con-flict with the cost recovery principle of the Water FrameworkDirective. However, when the foregone benefits of reducing theallocated water rights are added, the actual WTP is higher thanthe costs of this measure. This gives scope for considering thatfarmers would support the implementation of this infrastruc-ture.

However, it is necessary to combine this type of infrastruc-ture with measures under implementation in the new programsof measures derived from the WFD that aim to introduce demandmanagement tools. This is particularly the case in a context such asthe Guadalquivir River Basin, where the draft hydrological plan isclearly focused on water demand measures (Berbel et al., 2012).This demand-side approach requires careful application and areliable information base, as the implementation of demand man-agement measures is a complex process that farmers might resist(Albiac et al., 2006). Our results enhance the knowledge requiredin this complex process by providing evidence that could serve asa pre-requisite for any type of flexible mechanism, such as partic-ipation in water markets and trade, to ensure that producers havegreater security in their profits during water shortages (throughwater exchanges with other users or water banks, among othermeasures).

Acknowledgements

This research was made possible by funding support of the“Formación al Personal de Investigación” help Program of the Min-istry of Science and Innovation, related to the research projectcalled “Análisis prospectivo de la sostenibilidad de los sistemasagrarios nacionales en el marco de la PAC” (Ref AGL2006-05587-C04-02) and also partially financed by the AGUA-ICAD project(ECO2009-12496-C03-01). The work from Julia Martin-Ortegawas financed by the Scottish Government Research Programme(Theme 2, WP2.3 Effectiveness of measures to manage water qual-ity). We appreciate also the helpful comments of two anonymousreviewers.

Appendix A. Appendix 1

Description and coding of the variables used to construct themultinomial model

Codification Mean

he 0 = guarantee 5 in 10 years1 = guarantee 9 in 10 years

0.5

r from 0 = Using water only for Guadalbullon River1 = Using water also from Gualdalquivir River

0.13

0 = No belonging zone 11 = Belonging zone 1

0.36

0 = No belonging zone 21 = Belonging zone 2

0.40

0 = No belonging zone 31 = Belonging zone 3

0.24

Continuous variable 9.81Continuous variable 6.88

Continuous variable 1.39

Continuous variable 8.24Continuous variable (500–5000) 666.9Continuous variable 4.560Continuous variable (70–203) 109

al Water Management 113 (2012) 10– 18 17

Codification Mean

e farmer Continuous variable (600–15,000) 8089

ficial datas/tree)

Continuous variable 8999

ed by farmers Continuous variable 3.59

t (official data) Continuous variable 3.56

ctare 1 = Less than 5000 D per ha per year2 = From 10,000 to 15,000 D per ha per year3 = From 15,000 to 20,000 D per ha per year4 = More than 20,000 D per ha per year

2.31

r less than 42 0 = Less than 42 years1 = More than 42 years

0.83

ent on farm Continuous variable (1–6) 2.92

ral training 0 = No1 = Yes

0.67

1 = less than 5 years2 = from 5 to 10 years3 = from 10 to 20 years4 = for as long as I can remember

3.54

armers will 1 = Very uncertain2 = Uncertain3 = Slightly certain4 = Certain5 = Very certain

1.45

me received 1 = Strongly disagrees2 = Disagrees3 = Neither Agrees or disagrees4 = Agrees5 = Strongly agrees

2.59

r supply 1 = Not important at all2 = Slightly important3 = Moderately important4 = Important5 = Very important

4.73

gularity in the 1 = Very negative effects2 = Negative effects3 = Moderately effects4 = No negative effects5 = Absolutely no negative effects

1.32

the farmer’s 1 = Very easy to understand2 = Easy to understand3 = More or less easy to understand

1.86

R

A

A

A

A

A

AA

B

B

B

/

M.A. Mesa-Jurado et al. / Agricultur

Variables Description

Perceived water allocation Volume of water received, as perceived by th(litres/tree)

Actual water allocation Actual water volume received by farmers (offrom irrigators community interviews) (litre

Perceived annual payment Irrigators community annual payment declar(D /tree)

Actual annual payment Actual irrigators community annual paymen(D /tree)

Income Household gross income per year and per he

Age Identifies if the age of the farmer is greater oyears old

Household size Number of persons in the household dependincome

Agricultural training Identifies if the farmer has received agricultu(pesticides, ecological agriculture, etc.)

Experience Years devoted to agrarian activity

Certainty Knowledge about the amount of water that freceive at the beginning of the season

Agreement with waterquantity

Farmer’s level of agreement with water voluby him in this area

Importance water supply Importance given by respondent to the wateguarantee

Effects of water uncertainty Respondent’s perception of the effects of irrewater supply in olive production

Difficulty of thequestionnaire

Level of difficulty of the questions, accordingopinion

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