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A DSS for sustainable development and environmental protection of agricultural regions.

A DSS for sustainable development and environmental protection of agricultural regions.
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  Environ Monit AssessDOI 10.1007/s10661-009-0873-1 A DSS for sustainable development and environmentalprotection of agricultural regions Basil D. Manos · Jason Papathanasiou · Thomas Bournaris · Kostas Voudouris Received: 11 September 2008 / Accepted: 10 March 2009© Springer Science + Business Media B.V. 2009 Abstract This paper presents a decision supportsystem (DSS) for sustainable development andenvironmental protection of agricultural regionsdeveloped in the framework of the Interreg-Archimed project entitled WaterMap (develop-ment and utilization of vulnerability maps for themonitoring and management of groundwater re-sources in the ARCHIMED areas). Its aim is tooptimize the production plan of an agriculturalregion taking in account the available resources,the environmental parameters, and the vulnera-bility map of the region. The DSS is based onan optimization multicriteria model. The spatialintegration of vulnerability maps in the DSS en-ables regional authorities to design policies foroptimal agricultural development and groundwa-ter protection from the agricultural land uses. TheDSS can further be used to simulate differentscenarios and policies by the local stakeholdersdue to changes on different social, economic, andenvironmental parameters. In this way, they canachieve alternative production plans and agricul-tural land uses as well as to estimate economic,social, and environmental impacts of differentpolicies. The DSS is computerized and supported B. D. Manos ( B ) · J. Papathanasiou · T. Bournaris · K. VoudourisAristotle University of Thessaloniki,Thessaloniki, Greecee-mail: by a set of relational databases. The correspond-ing software has been developed in a MicrosoftWindows XP platform, using Microsoft VisualBasic, Microsoft Access, and the LINDO library.For demonstration reasons, the paper includes anapplication of the DSS in a region of NorthernGreece. Keywords Decision support systems · Multicriteria mathematical programming · Sustainable development · Environmental management · Vulnerability maps Introduction Decision support systemsDecision support systems (DSS) are defined ascomputerized systems, which include models anddatabases and are used in the decision-makingprocess. They are “tools” that help decision mak-ers in the procedure of decision-making and inchoosing the best (economic, social, or environ-mental) alternative solution.Several scientific sectors support their develop-ment and constitute the necessary background fortheeffectiveplanningofdecisionsupportsystems.The Science of Informatics has contributed inthe planning and applications of decision supportsystems with the supply of tools, material, and  Environ Monit Assess software. The sciences of Operational Researchand Management and Business Administrationprovide the theoretical frame for the analysis of various decisions. The sciences of Behavior, Soci-ology, and the Management of Human Resourcesconstitute sources of information that concern themanner in which humans potential behave at thehandling of information and the decision-makingprocess.A typical decision support system, uponSprague and Carlson (1982), Manos and Voros(1993), Manos et al. (2004a,b), Papathanasiou et al.(2005), Barac et al.(2004), and Kaur et al. (2007), comprises from the following elements(Fig.1):1. The Database and the Database ManagementSystem2. TheModelBaseandtheModelBaseManage-ment System (MBMS)3. The Dialog Generation and ManagementSystemThe Database contains all the data that arerequired for the decision support system opera-tion. The software used for the systematic man-agement of data (storage, briefing, recuperation,and maintenance) constitutes what is known asthe Database Management System. The Data-base constitutes an essential tool for the orga-nized storage of data and information aiming attheir easy renewal, correction, and utilization inevery type analysis. In the DSS of WaterMap,the database was developed using MicrosoftAccess.The Model Base contains the models used fordata processing of the Database contents for theanalysis of input problems and the export of finalresults. The Model Base in a DSS includes mathe-matical,economical,andstatisticalmodels,aswellas models of Operational Research capable of analyzing problems and to support the process of decision-making. All the models are executed andoptimized by the MBMS. The models includedcanbemodelsthatexecutespecializedoperations,modelsforoperational,strategic,ortacticdecisionsupport.In WaterMap DSS, the basic model is anOptimization Multicriteria Mathematical Pro-gramming model. The transformation of data ininformation/results, which support the user/decision maker, is accomplished via the ModelBase Management System. In WaterMap DSS,the Model Base Management System was devel-oped utilizing LINDO. LINDO is a compre-hensive tool designed to build and solve linear,nonlinear, and integer optimization models.The Dialog Generation and Management Sys-tem is responsible for the communication be-tweentheuserordecision-makerandthedecisionsupport system plus the input of data and thepresentation of final results. It determines theinteractions between the user or decision-makerand the DSS, influencing in an important degreethe output, the flexibility, and the manageability Fig. 1 Typical Decisionsupport system DataBaseModelBase ModelBaseManagementSystemDataBaseManagementSystem User InterfaceDecision Maker Decision makerSupport BaseManagement System  Environ Monit Assess of the DSS. WaterMap DSS was developed usingMicrosoft Visual Basic.The need for WaterMap DSSAs underwater pollution levels and water demandtend to increase, and on the same time, availablewater levels are expected in the near future todecrease, the main target of WaterMap DSS isto develop strategies for optimal development of agricultural regions and groundwater protection.WaterMap (Development and utilization of vulnerability maps for the monitoring andmanagement of groundwater resources in theARCHIMED areas) was a project designed tosupportthespatialdevelopmentplanningprocess.It was funded under the ARCHIMED Programof the Community Initiative INTERREG III Bthat specializes in the interventions for improve-ment of the spatial planning integration of theSoutheastern Mediterranean area (Voudouriset al.2007). WaterMap DSS integrates the vulnerabilitymapofaregionandanOptimizationMulticriteriaMathematical Programming model. This integra-tion enables the regional authorities in charge of agricultural and water policy design and imple-mentation to design optimal agricultural devel-opment policies (Sofios et al.2008) and simulatethe impact of different future scenarios (i.e., ex-anteevaluationofhypotheticalimplementationof alternative policy instruments). It optimizes theproduction plan of an agricultural region taking inaccount the available resources (land, labor, andcapital), environmental parameters (agrochemi-cals and water consumption), and the vulnerabil-ity map of the region. The DSS can also be usedto simulate different scenarios and policies by thedecision-makers due to changes on different so-cial, economic, and environmental parameters. Inthis way, they can achieve alternative productionplans and agricultural land uses as well as to esti-mateeconomic,social,andenvironmentalimpactsof different policies.WaterMap DSS is computerized and supportedby a set of relational databases. Input data areprovided by regional authorities and refer to agri-cultural land uses, available resources, etc. Infor-mation for the groundwater resources is providedby vulnerability maps, which are used as a tool todetermine areas where aquifers are in high risk of pollution.For demonstration reasons, the paper includesan application of the DSS in a region of NorthernGreece. Model development The Model Base of WaterMap DSS mainly con-sists of a spatial model that was developed tosupport the sustainable planning process and theenvironmental protection of agricultural regions.The model is an Optimization Multicriteria Math-ematical Programming model (MCDM model)and achieves the optimum production plan in theareacombiningdifferentcriteriatofarmers’utilityfunction under a set of constraints concerningdifferent categories of land, labor, etc. The modelcan then be used to simulate different scenariosand policies due to changes on different social,economic, and environmental parameters (e.g.,differentlevelsofchemicalsorwaterconsumptionper crop). This way, we acquire alternative pro-duction plans and agricultural land uses as well asthe economic, social, and environmental impactsof different policies.ObjectivesThe MCDM model in WaterMap optimizes at thesame time three different objectives or criteria:the profit maximization, the fertilizer minimiza-tion, and the labor minimization.Max GM =  GM i X  i (1)Min TF =  TF i X  i (2)Min TL =  TL i X  i (3)where:GM is the total gross margin of the crop plan of the study region  X  i is the area of crop i in hectaresGM i is the gross margin of crop i per hectare  Environ Monit Assess TF is the sum of fertilizers used for all crops of the crop planTF i isthefertilizerneededbycrop i perhectareTL is the sum of labor for all farm activitiesTL i is the labor needed by crop i per hectareIn WarerMap MCDM model, nitrogen is usedasthefertilizerindicator.Similarindicatorscanbedesigned for other nutrients, such as phosphorusand potassium.ConstraintsThe MCDM model includes a set of constraintsrelated with CAP. There are also market, agro-nomical, and rotational constraints as well asconstraints for labor and fertilizers. Total cultivation area All crops (  X  i ) must add up to 100. This con-straint is only introduced in order to obtain theoutcome of the model (decision variables X  i ) aspercentages. CAP  Alargeproportionofagriculturalincomedependsupon CAP subsidies, and farmers cannot affordto ignore CAP regulations that affect most of thecrops available for cultivation. For this reason, inaccordance with CAP rules, we need to includeset-aside activity (SA) related to the subsidizedcrops (which are the majority):   X  i + SA = 100 Market and other constraints Some of the crops are not subject to CAP rules,but marketing channels put an upper limit onshort-term variations. In our model, we placesome market constraints for hard wheat, oat, andmaize in order to express the market demandof these products in the area, according to thehistorical quotas of the last 5 years (2001–2005) inthe Sarigkhiol basin. Rotational and agronomic considerations Agronomically,itisregardedasasoundpolicynotto cultivate a crop such as a cereal if, during theprevious year, the same plot has grown anothercereal. This is called a rotational constraint. Arotational constraint limits the cultivated area foracroptoamaximumnumberofthetotalavailablearea and applies to all cereals.Policy-relevant attributesAttributesarevaluesofinterestrelevanttopolicy-makers. The analyzed attributes in WaterMapDSS are:1. Water consumption: the projected consump-tion of water measured in cubic meter perhectare is the variable that policy-makershope to control as a consequence of changesin water management policy.2. Environmental impact: the main environmen-tal impact of irrigated agriculture is waterconsumption, with the creation of a mosaiclandscape and a rise in crop diversity andhumid areas. In addition to this positive im-pact, however, comes an increase in the useof fertilizers and chemicals that are the mainsource of nonpoint source pollution in agri-culture. We use the demand for fertilizers asan indicator of the environmental impact of irrigated agriculture, measured in kilogram of nitrogen added per hectare.3. Nitrogen balance in groundwater: Physicaldifference (surplus/deficit) between nitrogeninputs and outputs from an agricultural sys-tem per hectare of agricultural land. Thisis the main form for calculating the sur-pluses of nitrogen potentially dangerous forthe environment. It would also be the mainindicator of the impact of farming on theenvironment as groundwater quality is con-cerned. This way, all nitrogen reaching thecultivated soil is included as input and thenitrogen in groundwater is considered as out-put. The difference is the net amount of ni-trogen that, over 1 year, is released in theenvironment.  Environ Monit Assess Weighted goal programmingTheMCDMmodelisbasedonweightedgoalpro-gramming (WGP). Sumpsi et al. (1993,1997) and Amador et al.(1998) consider this methodology suitable and efficient for the analysis of decisionmaking and the simulation of agricultural systemsbased upon multicriteria techniques applied toagriculture. This methodology has been success-fully implemented on real agricultural systems(Bartolini et al.2007a,b; Berbel and Rodriguez 1998; Gomez-Limon and Berbel2000; Gomez- Limon and Arriaza2000; Gomez-Limon andRiesgo2004; Gomez-Limon et al.2002; Manos et al.2004a,b,c,2006,2007; Pujol et al.2006). We employed this methodology to estimatea surrogate utility function in order to simu-late farmers’ decision-making processes. Briefly,the methodology can be summarized as follows(Romero1991):1. Tentatively establish a set of objectives orcriteria that may be supposed to be most im-portant for farmers.2. Determine the payoff matrix for the aboveobjectives.3. Usingthismatrixestimateasetofweightsthatoptimally reflect farmers’ objectives.In WaterMap MCDM model, the surrogate utilityfunction which is obtained by the application of the WGP methodology combines the three crite-ria 1, 2, and 3 defined in the “Objectives.” Thissurrogate utility function is the new objective of our MCDM model which is used to achieve theoptimum crop plan of the study region as well as aseries of alternative crop plans.Vulnerability map and vulnerability scenariosThe MCDM model is further used to simulatedifferent scenarios and policies due to changeson different social, economic, and environmentalparameters (e.g., different levels of chemicals orwater consumption per crop). In order to definethe different scenarios, we use the vulnerabilityindex from the DRASTIC Method Vulnerabilityindex.Vulnerability refers to the sensitivity of anaquifer system to deterioration due to an externalaction. The concept of the groundwater vulnera-bility is based on the assumption that the physicalenvironment may provide some degree of protec-tion to groundwater against human activities (Al-Zabet2002). Regional assessment of groundwatervulnerability is a useful tool for groundwater re-sources management.The most widely used groundwater vulnerabil-ity mapping method is DRASTIC. The methodwas developed by the US Environmental Protec-tion Agency as a technique for assessing ground-water pollution potential (Aller et al.1987). The acronym DRASTIC corresponds to the initial of the included seven parameters: depth, recharge,aquifer media, soil media, topography, impact of thevadosezonemedia,andhydraulicconductivityof the aquifer.DeterminationoftheDRASTICindexinvolvesmultiplying each parameter weight by its site rat-ing and summing the total. The equation for theDRASTIC (DI) index is:DI = D r D w + R r R w + A r A w + S r S w + T r T w + I r I w + C r C w (4)where: D, R, A, S, T, I, C were defined earlier, r ratingforthestudyareabeingevaluatedand w theimportance weight for the parameter.Each parameter has a rating scale between 1and 10. The higher sum values of DI representgreater potential for groundwater pollution orgreater aquifer vulnerability.In WaterMap, we simulate five scenarios: VeryLow scenario (Drastic index < 75), Low Scenario(Drastic index 75–90), Moderate Scenario (Dras-tic index 90–105), High Scenario (Drastic index105–120), and Very high Scenario (Drastic index > 120). These scenarios are used as an attribute inthe MCDM model in order to take five differentcrop plans for the study region according to theDRASTIC Method and Vulnerability index.DataThe data of WaterMap DSS that feed the MCDMmodel are of the following categories: • Crops of the study region • Yields • Prices • Subsidies
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