The regional impact of irrigation water pricing in Greece under alternative scenarios of European

30 Pages·2017·0.55 MB·English

The regional impact of irrigation water pricing in Greece
under alternative scenarios of European policy: a
multicriteria analysis
Basil D Manos, Thomas Bournaris, Mohd. Kamruzzaman, Moss. Anjuman
Ara Begum, Jason Papathanasiou
To cite this version:
Basil D Manos, Thomas Bournaris, Mohd. Kamruzzaman, Moss. Anjuman Ara Begum, Jason
Papathanasiou. The regional impact of irrigation water pricing in Greece under alternative sce-
narios of European policy: a multicriteria analysis. Regional Studies, 2007, 40 (09), pp.1055-1068.
￿10.1080/00343400600928335￿. ￿hal-00514626￿
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Regional Studies
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The regional impact of irrigation water pricing in Greece
under alternative scenarios of European policy: a
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multicriteria analysis
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Journal: Regional Studies
Manuscript ID: CRES-2005-0072.R3
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Manuscript Type: Main Section
G13 - Contingent Pricing|Futures Pricing < G1 - General Financial
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Markets < G - Financial Economics, Q2 - Renewable Resources and
Conservation|Environmental Management < Q - Agricultural and
JEL codes: Natural Resource Economics, Q01 - Sustnainable Development < Q0
- General < Q - Agricultural and Natural Resolurce Economics, Q00 -
General < Q0 - General < Q - Agricultural and Natural Resource
Economics y
Irrigated agriculture, Water price scenario analysis, Economic,
Keywords:
social and environmental impacts, Multicriteria model

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Page 1 of 28 Regional Studies
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4 The regional impact of irrigation water pricing in Greece under alternative
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scenarios of European policy: a multicriteria analysis
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12 ABSTRACT
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15 This study simulates the impact that various policies based upon the water price have on agricultural
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17 production and analyzes the economic, social and environmental implications of alternative irrigation
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water policies using a multicriteria model. For the purpose of scenario analysis, narratives and
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quantitative indicator values have been compiled. The results show that the increase of water price
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causes almost similar impacts with those that were observed in the status quo scenario. The results
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26 also stress that water pricing as a single instrument for controlling irrigation water use is not a
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28 satisfactory tool for significantly reducring water consumption in agriculture.
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30 Keywords: Irrigated agriculture, Water price, Scenario analysis, Multicriteria model, Utility
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optimization, Economic, social and environmental impacts
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JEL Classifications: Q0, Q1, Q2, G13
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37 INTRODUCTION
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40 European irrigated agriculture is very important in terms ofw area, value of production and employment,
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especially in certain Mediterranean regions devoted to continental agriculture. The management and
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use of water and water resources have been the focus of EU water policy since the 1960s. The Water
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47 Framework Directive 2000/60/EC (WFD) established a structure of commlunity action in the field of
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49 water policy. Several possibilities for water policy have been debated, in payrticular for the pricing of
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51 irrigation water. The discussion is related to the potential savings that might come along with charging
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53 additional water fees. This study, elaborated in the context of the European research project “European
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Irrigated Agriculture under Water Framework Directive-WADI” (BERBEL et al., 2002), contributes
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to that discussion by simulating the impact that various policies based upon the price of water could
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have on agricultural production.
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Regional Studies Page 2 of 28
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3 The analysis of the effects of water pricing on irrigated agriculture and farms behavior ought
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to be an important topic of research for European agricultural and environmental economists
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(ARRIAZA et al., 2002; BERBEL and GOMEZ-LIMON, 2000; GOMEZ-LIMON et al., 2002;
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GOMEZ-LIMON and BERBEL, 1995). Following this observation, this paper aims to analyze the
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12 regional impact of irrigation water pricing under the alternative scenarios of European water policy.
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14 Specifically, the study analyzes the economic, social and environmental implications of alternative
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16 irrigation water policies using a multicriteria model of farmers’ behavior under different scenarios.
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The futureo agricultural and water scenarios are based on a global and national review of future
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21 scenarios developed by the UK foresight program (BERKHOUT et al., 1998; DTI, 1999, 2002) in
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23 which water policy reflecPts a mix of governmental and social preference. The scenarios are further
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25 described in terms of the combeination of policy instruments, policy style and configuration of actors.
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27 The links between the foresight type scenarios and the scenarios for European agriculture, together
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29 with a brief description of the agricultura l policy regime are shown in Table 1.
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The methodology, based on MultRicriteria Decision Making (MCDM) theory (ROMERO and
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REHMAN, 1989; REHMAN and ROMERO, 1993) will be implemented in a real irrigation system,
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36 enabling us to build a model to analyze how the receint CAP reform has influenced the water demand
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38 function and how hypothetical new reforms would affecet the irrigation unit studied.
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40 Specifically, we used an MCDM model in order two achieve better policy-making procedures
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and the simulation of the most realistic decision process. The MCDM model was chosen because of
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the variety of criteria taken into account by farmers when they plan their crop plans broadening in this
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way the traditional assumption of profit maximization. It also assembles the multifuntionality of
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49 irrigated agriculture involving variables related with economic, social and enyvironmental aspects. The
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51 used MCDM model is actually a utility maximization model with multiple criteria.
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The utility MCDM approaches in comparison with other approaches as Linear Programming,
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Cost-Benefit analysis etc. can achieve optimum farm resource allocations (land, labor, capital, water
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58 etc.) that imply the simultaneous optimization of several conflicting criteria, such as the maximization
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60 of gross margin, the minimization of risk, the minimization of labor used etc. However, although
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3 utility optimization is the core of the analysis in this study, the used MCDM model does not
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comply with others schools in MCDA like 'Social MCDM’ which include extensive stakeholder
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8 participation and seekcompromise solutions.
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Both SUMPSI et al. (1997) and AMADOR et al. (1998) have developed methodologies for
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the analysis and simulation of agricultural systems based on multi-criteria techniques applied to
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irrigated agriculture. This methodology has been successfully implemented on real agricultural
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17 systems (BERBEL and RODRIGUGEZ, 1998; GOMEZ-LIMON and BERBEL, 1995). BERBEL and
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19 GOMEZ-LIMON (2000); ARRIAZA, GOMEZ-LIMON and UPTON (2002); GOMEZ-LIMON et al.
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21 (2002); ZEKRI and ROM ERO (1993) have applied this methodology to analyze the local irrigation
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water market in Spain.
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26 CONSTRUCTION OF FUTURE SCENARIOS
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29 For the purpose of scenario analysis, na rratives and quantitative indicator values have been compiled
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31 for each scenario. The quantitative estimates are used as input values in the modeling of irrigation
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33 systems under policy changes. e
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The future agricultural and water scenarios are constructed on a global and national review of
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38 future scenarios developed by the UK foresight programe (DTI, 1999, 2002) as they were specialized in
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WADI project (BERBEL et al., 2002; MORRIS and VASILEIOU, 2003). Scenarios are not intended
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42 to predict the future rather they are tools for thinking about the future, assuming that:
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44 - the future is unlike the past, and is shaped by human choice and action.
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46 - the future cannot be foreseen, but exploring the future can reform presnent decisions.
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- there are many possible futures: scenarios map a ‘possibility space’.
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- scenario development involves a mix of rational analysis and subjective judgment.
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Thus, scenarios are statements of what is possible; of prospective rather than predictive futures;
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55 propositions of what could be. They are often made up of a qualitative storyline and a set of
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57 quantitative indicators, which describe a possible future outcome. Scenarios arise as a consequence of
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59 modeling drivers of economic and social change, new trends and innovation, and of unexpected
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events.
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Regional Studies Page 4 of 28
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3 The baseline is the agricultural policy regime in 2003 (status quo), as determined by CAP at
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that time, which is used to provide a relative reference point for the definition of future scenarios. The
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baseline will also be extrapolated to 2010 based on predictions of agricultural markets and prices from
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EU, OECD and other sources. This extrapolated baseline is perceived to be different from the possible
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12 futures identified in Table 1, although it shows a tendency, due to predicted reform of CAP and greater
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14 influence of WFD, towards global sustainable agriculture.
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16 The foresight program has constructed four possible futures that are distinguished in terms of
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social values and goovernance.
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21 World Markets are char acterized by an emphasis on private consumption and a highly developed and
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23 integrated world trading syPstem.
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25 Global Sustainability is charaecterized by more pronounced social and ecological values, which are
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27 evident in global institutions and trading systems. There is collective action to address social and
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29 environmental issues. Growth is slower but more equitably distributed compared to the world markets
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Provincial Enterprise is characterized by emphasis on private consumption but with decisions made
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36 at national and regional level to reflect local priiorities and interests. Although, market values
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38 dominate, this is at work only within the national/regioenal boundaries. Provincial agricultural markets
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40 are also characterized by protectionist regimes similar to thwat under pre-reform CAP.
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42 Local Stewardship is characterized by strong local or regional governments, which emphasize social
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values, encouraging self-reliance, self-sufficiency, and conservation of natural resources and the
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environment. Local community agriculture, as the label implies, emphasizes sustainability at a local
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49 level. y
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51 These broad generic scenarios define the possible future scenarios in which sectors such as
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realized.
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3 AREA OF STUDY
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6 Greece has arid climate that makes irrigated agriculture more productive than dry-land agriculture.
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Thus irrigation is one of the principal users of water. To analyze the consequences of the application
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of alternative pricing policies for irrigation water, we selected an irrigated area belonging to the
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prefecture of Xanthi in Northeastern Greece. The reason behind selecting this particular area is that it
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15 is a good representative irrigated area of North and Eastern Greece with high water consumption crops
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17 such as corn, cotton and tobacco as well as non-irrigated crops such as wheat, barley and hard wheat
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19 and is fairly homogeneous both in physical terms (soil and climate) and socio-economic conditions.
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Most irrigation in the concerned area is applied by sprinklers and is based on pressure. The climate is
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the usual Mediterranean one with special characteristic of dryness during the summer. The agricultural
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land is constituted by a combination of fertile and poor soils and the dominant system for all types of
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28 crops works to support irrigation fromr late spring to early autumn.
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DATA
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33 The necessary data which refer to the periode of 1995-2001, were gathered from the villages and
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municipalities that are located in the region, the prefectures of Xanthi, the Ministry of Agriculture, the
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38 Statistical Service of Greece, the regional governmeent of East Macedonia and Thrace, etc. The
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40 technical and economic coefficients of crops were collectewd from 25 farms belonging to the irrigation
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42 region of Xanthi using a questionnaire (Table 2). We also used additional data that were provided by
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44 the Department of Agricultural Economics of Aristotle University of Thessaloniki in Greece.
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In this study the possible prices for agricultural inputs and outputs under the alternative
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agricultural policy scenarios by 2010 are expressed as a % of the existing yyear 2001 at fixed values
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(Table 3).
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Crops
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56 Cereals and industrial crops represent the largest proportion of irrigated production in the region
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Regional Studies Page 6 of 28
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3 water policy is being changed. We also included alfalfa because of its significant share of land
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utilization in the study area.
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As it is known, the European “Common Agricultural Policy” (CAP) obliges farmers devoted
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to growing cereals and corn to set aside land if they wish to receive subsidies for agricultural
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12 production.
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Yields
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19 activity (crop) was allocated to a range of different intensities of water usage (deficit watering), giving
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farmers the opportunity to choose between different levels of water supply.
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24 Prices
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Prices applied to crops are averages for the study region obtained from the official statistics of the
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regional authorities. We used historical time series data for the 7-year period 1995-2001, after the
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prices have been adjusted for inflation (2001).
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Subsidies
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36 Subsidies depend upon the European Union’s CAP, iand were obtained from official publications of
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41 Income

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Income is an important attribute of the system as it defines total agricultural output. Income was
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computed by the simple combination of yields and prices, plus subsidies where applicable.
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48 Variable costs
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We took into account six categories of variable costs in order to describe the inputs: (i) seeds, (ii)
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fertilizers, (iii) chemicals, (iv) machinery, (v) labor, and (vi) cost of water. Especially, the cost of
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55 water includes the cost paid to the regional organization of irrigation networks, the electricity/fuel cost
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57 of pumping and the simulated price of water (to be parameterized from zero to 0.15 €/m3).
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Gross margin
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Page 7 of 28 Regional Studies
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3 Gross margin can be computed using the data regarding prices, yields, subsidies and variable costs. It
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was calculated from the total income minus total variable costs. We used this parameter as the best
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estimator of profit and thus the function of the total gross margin of the production plan could be
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considered as an objective for the model.
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12 To run the MCDM model, we need similar units for all inputs. For this reason we transferred
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14 all the inputs into cost (Euro/ha) instead of keeping them in their initial natural units such as kg,
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16 number, area, cubic cm, etc. The inputs were transferred to costs by multiplying each input used by the
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corresponding per ounit input price.
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21 Other attributes
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We estimated the fertilizer use even if it was not a relevant attribute for farmers, since they considered
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it as a cost and not as a decision variable. Nevertheless, this criterion was relevant for policy analysis,
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28 as it might represent the environmentral impact (non-point pollution caused by nitrogen fertilization).
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30 There was also a detailed analysis of water demand and labor use, since both attributes were included
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32 in the MCDM model in the objectives part (labor use) and in the constraints part (water demand).
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Thus, the values of these variables would be known as outcomes of the system and would be used later
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in policy analysis.
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THE UTILITY FUNCTION
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42 In the present study, we used utility functions where the ability to simulate real decision-makers’
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preferences is based on the estimation of relative weightings. These utility functions are a good
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approximation to the farmers’ hypothetical utility functions.
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The relative methodology was developed by SUMPSI et a1. (1993y, 1997) and extended by
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51 AMADOR et α1. (1998). It is based upon Weighted Goal Programming and has previously been used
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53 by BERBEL and RODRIGUEZ (1998); GOMEZ-LIMON and ARRIAZA (2000); GOMEZ-LIMON
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55 and BERBEL (2000). With this methodology a surrogate utility function is estimated, which is used to
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estimate the water demand for crop production.
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The following steps were followed:
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Regional Studies Page 8 of 28
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3 1. Establishment of a set of objectives f (x)...f(x)…f (x) that may be supposed to be the most important
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for farmers and represent the real objectives of the farmers (e.g. profit maximization, risk
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minimization).
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2. Calculation of the pay-off matrix for the above objectives, which has the following formulation:
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Objective/ f (x) f (x)… …f (x)… …f (x)
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f (x) f f * f f (1)
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19 …f(x) f f f* f
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21 …fq(x) fq1 fq2 fqi fq*
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23 The elements of the matrixP need to be calculated by optimizing one objective in each row. Thus, f is
ij
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25 the value of the i-th attribute wehen the j-th objective is optimized.
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3. Estimation of a set of weights that optimally reflect farmers’ preferences. Once the pay-off matrix
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has been obtained, the following system of q (number of objectives) equations is solved:
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32 ∑q w f = f i = 1, 2,…,q; and (2)
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40 where, q is the number of relevant objectives that was fixwed previously, wj are the weights attached
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42 to each objective (the solution), f are the elements of the pay-off matrix and f are the real values
ij i
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that show the observed behavior of farmers in the existing situation.
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4. Since the above system does not result in a set of w (weights of each objective that reproduce the
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actual behavior of the farmer), it is necessary to search for the best possible syolution by minimizing the
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51 sum of deviational variables that finds the closer set of weights. For this purpose (ROMERO, 1991) the
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q n + p
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i=1 i
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subject to:
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