Expert Systems with Applications 36 (2009) 5341–5347

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Expert Systems with Applications

journal homepage: www.elsevier .com/locate /eswa

Knowledge-based spatial decision support systems: An assessmentof environmental adaptability of crops

Iftikhar U. Sikder *

Department of Computer and Information Science, Cleveland State University, 1860 E. 18th Street, Monte Ahuja Hall BU 336, Cleveland, OH 44115, USA

a r t i c l e i n f o

Keywords:Knowledge-based system

Geographic information systemDecision support systemLand use planning

0957-4174/$ - see front matter � 2008 Elsevier Ltd. Adoi:10.1016/j.eswa.2008.06.128

* Tel.: +1 216 687 4758; fax: +1 216 687 5448.E-mail address: i.sikder@csuohio.edu

a b s t r a c t

The development of knowledge-based decision support systems for environmental planning requires themanagement of complex geospatial information, the integration of expert judgment with decision mod-els, and the dynamic visualization of geographic terrain. This paper describes the design and implemen-tation of a knowledge-based interactive spatial decision support system for identifying the adaptability ofcrops at a given agro-ecological zone. The system (Eco-SDSS) illustrates the integration of an expert data-base ECOCROP 1, developed by the Food and Agricultural Organization (FAO), with Geographic Informa-tion Systems (GIS) to offer a flexible interface to identify tolerant plant species for a defined use anddescriptions. The use of such tools offers increasing efficiency for potential extension and research in cropmanagement and land use planning.

� 2008 Elsevier Ltd. All rights reserved.

1. Introduction

Intelligent knowledge-based decision support systems offermany promising applications in natural resources management(Bremdal, 1998; Cortés, Sànchez-Marrè, Ceccaroni, R-Roda, & Poch,2000; Fischer, 2006). A key advantage of using knowledge-basedsystems is that the heuristically processed information in thedeclarative knowledge of such systems involves less computationaland cognitive demand and may not necessarily require exhaustivesearches (Samuelson & Allison, 1994). GIS is a computer systemcapable of assembling, storing, manipulating, and displaying geo-graphically referenced information. Managing geo-referencedinformation makes it easy to organize, manipulate and apply toplanning problems (Longley, Goodchild, Maguire, & Rhind, 2001).In GIS, common database operations can be integrated with do-main-specific process models and the results can be visualized withintricate graphics and map rendering. As an integrating tool, GIS hasbeen widely used for comprehensive planning, zoning, land useinventories, agricultural planning and decision making (Angelides& Angelides, 2000; Birkin, Clarke, Clarke, & Wilson, 1996; Sikder &Misra, 2008; Sikder, 2008). While GIS offers tools for planning outenvironmental and natural resources problems, the traditional datadriven approaches fall short of exploiting the full potential of hu-man or expert induced environmental planning. The types of spatialanalysis tools in GIS include visual and spatial query, spatial prox-imity analysis, topological neighborhood analysis, map overlayand geostatistical analysis. Additionally, GIS offers tools for manag-

ll rights reserved.

ing both vector and raster databases in relational and object-based/oriented systems. AGIS-based spatial decision support system is in-tended to support the solution of ill-structured spatial problems(Densham, 1991). Spatial decision support systems (SDSS) providesintegrated set of flexible capabilities: the implementation of such asystem can be achieved using a set of linked software modules(Densham, 1991). The objective of SDSS is to provide a frameworkfor integrating analytical modeling capabilities, database manage-ment systems, graphical display capabilities, tabular reportingcapabilities, and the decision-makers’ expert knowledge. In thecontext of environmental resources planning, in particular, landuse planning requires explicit spatial visualizations of the resultof the expert systems’ output. Moreover, spatial decision problemsare intrinsically complex, often containing intangibles that cannotbe easily modeled because their structures are partially known orburdened by uncertainties (Jankowski & Nyerges, 2001). Hence,the integration of a GIS-based decision support system with tradi-tional knowledge-based system offers significant benefits (Fleming,Merwe, & McFerren, 2007; Rodriguez-Bachiller & Glasson, 2004;Sikder, 2008; Zhu, Healey, & Aspinall, 2004).

This paper illustrates an application of knowledge-based sys-tems to the design of spatial decision support systems (Eco-SDSS).An interactive decision support system is developed that allowsintegration of expert knowledge base ECOCROP 1 (FAO., 2007)which consists of the tolerance limit of crops in a given environ-ment. The Eco-SDSS interface offers options for identifying suitablecrop or tree species for a specified environment, in particular, theidentification of tolerant crops in a given location and the identifi-cation of the locations matching crop requirements for specifieduse. In addition, the system helps identify crop or tree species for

5342 I.U. Sikder / Expert Systems with Applications 36 (2009) 5341–5347

a defined use in a given agro-ecological cell. Section 2 identifies thecharacteristic features of knowledge-based systems and GIS andmethods of integrating them within a decision making framework.Section 3 provides descriptions of the system architecture andmethodology of a tightly coupled system integrating external KBwithin GIS. Finally, the system is evaluated in the context of givenfunctionalities and interoperability.

2. Knowledge-based systems and GIS

Building computer-based expert systems requires eliciting, ana-lyzing, structuring, validating and interpreting the informationwhen researchers deal with a particular problem (Liebowitz,1997). The paucity of methods for extracting useful knowledgefrom large data sets reflects the inadequacy of traditional GIS(Openshaw & Openshaw, 1997). The major impediments of inte-grating knowledge-based systems with currently available com-mercially GIS systems are three: (i) the inherent Boolean logicalfoundation of GIS, (ii) the limited built-in analytical and modelingfunctionality of GIS, and (iii) the low level of intelligence that GISprovides in terms of knowledge representation and processing(Fischer, 2006; Leung, 1992). However, in recent years, the growinginterest in interoperability has stimulated the interest in SpatialExpert System (SES) components, wherein spatial, analytical, andmodeling tasks are increasingly being supported by knowledgeacquisition modules, and domain-specific knowledge base (spatialand non-spatial) and rule-based inference engines. Given that SDSShas to deal with semi-structured spatial problems, the integrationof knowledge-based systems and GIS-based spatial decision sup-port systems (SDSS) offers wide potential for solving complex spa-tial problems, which are difficult to address alone (Malczewski,1999). Thus, spatial knowledge processing and rule-based systemsallow elicitation, representation and application of soft domainknowledge in real world situations. In particular, expert systemsassist in many stages of the decision process (Densham & Rushton,1992) by specifying the problem, selecting data, assisting withusing models (Burrough, 1992), evaluating, comparing solutions,and assessing the reliability of results and the treatment of error.The integration of GIS models and expert systems has led to manyreal world applications in the management of environmental sys-tems (Filis, Sabrakos, Yialouris, Sideridis, & Mahaman, 2003; Maid-ment, 1993; Rafea & Shaalan, 1996).

2.1. Methods of KB systems integration

The currently available methods for integrating intelligent sys-tem tools within GIS can be classified as loose coupling, tight cou-pling and embedded systems (Chang, 2006; Corwin & Vaughan,1997). In loosely coupling systems, data between the GIS and theseparate analytic/knowledge-based systems are exchanged byapplication level file sharing. For example, an expert system coulddirectly read TIGER files and perform some spatial inference basedon the given facts of the map. The GIS packages (e.g., ArcGIS soft-ware) provides interpretive and customization language such asArcObjects, .NET interface and scripting languages like Python,which allows ArcGIS command and function to be automated, to in-voke other programs, and to accept external inputs. When an expertsystem activates rules that require GIS function of graphic visualiza-tion, the system could then generate an ASCII file specifying the typeof thematic map layers to be analyzed. While at the GIS side, nativecustomization language such as the AML (Arc Macro Language) orArcObjects receives the ASCII file and execute necessary commandsto display the results. Although loosely coupled systems can be non-GIS specific, the speed of data transfer between the expert systemmodule and GIS is much lower. In tightly coupled systems knowl-

edge-base utilities exists as coded modules within the frameworkof the GIS that can be developed in a native language provided bythe GIS (e.g., the Arc Macro Language for Arc/Info) or .NET based cus-tomization languages. For example, COM-based expert system hasbeen developed for selecting the suitable map projection in ESRI’sArcGIS (Eldrandaly, 2006). The advantage of this approach is that iteffectively extends the functionality of the GIS and analysis, thus ex-pert system utilities can be seamlessly integrated from a user inter-face point of view. In this kind of tightly coupled system, thecommunication between the inference component and the databasecomponent are made at a low-level access to the mechanism of data-base systems (Klein & Methile, 1995). In embedded systems, GIS andother intelligent systems are bundled with shared memory and acommon interface. For example, commercially available packageslike IDRISI Andes includes soft classifier module that uses Bayesianinference and Dempster-Shafer’s evidence theory for generating be-lief, plausibility and evidence combination modules (Clark-Labs.,2007). ERDAS IMAGINE includes Expert Classifier for building geo-graphic expert systems for image classification, post classificationrefinement, and advanced GIS modeling. The knowledge engineerinterfaces of the systems provide tools for representing theknowledge base as a tree diagram consisting of final and intermedi-ate class definitions (hypotheses), rules (conditional statementsconcerning variables), and variables (raster, vector, or scalar)(Leica-geosystems., 2007).

In a knowledge-based system, a GIS system is required to com-municate with the core of the expert systems, a module for knowl-edge acquisition and a module for interfacing the core with GISusers. The spatial knowledge representation scheme includes man-agement of spatial facts and spatial relations. In a rule-based ex-pert system, for example, the land use planning may requireexpressing heuristic knowledge about the spatial contiguity con-straint or topologic constraint as using spatial predicates (Sikder& Gangapadhayay, 2002):

Rule 1 Zone (Residential) ? PermittedLandUseCategory (multi-family dwellings).

Rule 2 "X, Y $d ResidentialZone (X) ^ PreservationZone(Y)?MinDistApart(X,Y)d).

Here, Rule 1 stipulates the spatial entity class of ‘‘multi-familydwellings” as a valid land use category in the ‘‘residential” zone,while Rule 2 defines that if X and Y are two land use elements thatbelong to two different zones, then the minimum distance be-tween the two elements should always be more than a specifieddistance. In a GIS programming module, the task of ascertainingapplicable spatial meta-rules for a given point P (x,y) can be de-fined with the following:

Zone 1(P) ? ApplicableRules (A)Zone 2(P) ? ApplicableRules (B) ^ ApplicableRules (C)Zone 2(P) ? ApplicableRules (A) ^ ApplicableRules (D)

Within a GIS environment, a spatial inference engine needs toresolve the point-in-polygon topological query in order to decidewhich meta-rule to fire for a specific point. In advanced applica-tions, the Boolean spatial relationships can be replaced by fuzzymembership function (Cross & Firat, 2000; Robinson, 2003; Wang& Brent, 1996) where spatial boundaries and topologic relation-ships are represented in fuzzy logic. Currently, in a tightly coupledsystem reasoning and fuzzy spatial query can be supported by na-tive GIS functionalities.

3. System architecture and methodology: Eco-SDSS

The following sections illustrate an implementation strategyand for integration of Eco-SDSS and knowledge-based systems.

User Interface

GIS Analysis Frame / Processing

External DB & KB

GIS

Spatial

Databases

ExpertSystems

Geo-processing Models

RDBMS

Interoperability Modules /.NET Components

Geo-simulationModules

Spatial Query processingModules

SpatialVisualization

& Report Generation

Fig. 1. System integration architecture of SDSS.

I.U. Sikder / Expert Systems with Applications 36 (2009) 5341–5347 5343

The high-level components are illustrated in Fig. 1. The architec-ture can be regarded as an extension of GIS-mediated expert data-base system that integrates decision models and user interfaces(Yang, 1997). An RDBMS is used to store external knowledge baseto ensure modularity and extensibility. This approach has beenadopted in most architecture that involves the integration of ex-pert systems and databases (Filis et al., 2003; Sonar, 1999). Theadvantage of this approach is that the local autonomy of GIS andRDBMS and KB-ES components are maintained. Hence, by develop-ing the reusable generic geospatial procedures, the system can bemade to communicate with external knowledge components in aheterogeneous environment.

The spatial database is organized in the raster-based GRID mod-ule of Arc/Info (ESRI, 1999b). The smallest resolution for recordingland resources corresponds to a grid cell of 150 m resolution. Thelimiting resolution was defined from the existing Digital TerrainModel (DTM) of the study area. The grid cells have specific geo-graphic coordinates and contain variable elements of land re-sources as follows:

R[mj¼1Dij

where, (i = 1, 2, 3, . . . total no. of cell units). R is the record unit; Dj,

the data element; m, the total no. of data elements and Jmax, is theallowable storage format of data item for each cell.

The grid-cell–based GIS allows the integration of map algebralanguage that provides various spatial operators (e.g., relational,arithmetic, Boolean, Bitwise) and functional operators (e.g., localfocal, zonal, block and global) in raster environment. It is also pos-sible to integrate cell-based process models and external simula-tion models and the output can be visualized in continuous 3-Dsurface. Additionally, handling the GRID cell through data arraysprovides easy manipulation for data access and display. Each gridcell contains land resource data of environmental attributes suchas elevation, slope, aspect, mean annual temperature, soil texture,soil depth, length of growing period, and other required data setscomprising agro-ecological cells for identifying suitability of crops.A relational data model was used to match the environmentalrequirement data to each grid cell. Spatial information (e.g., cli-matic surfaces, soil maps) were developed from primary and sec-ondary data (Trapp, 1996). The information on natural resourcesand land use was retrieved from maps published by the Land Re-source Mapping Project (LRMP, 1986), the ONE INCH topographicalmaps of the Indian Survey and satellite images (LANDSAT MSS andLANDSAT TM). The topographic coverage, raster surface and satel-

lite images were projected and rectified to similar coordinatesystem.

The external knowledge base ECOCROP1 (FAO, 2007) is im-ported into the RDBMS of local GIS. The KB includes the informa-tion on the tolerance and response of both plant and animalspecies to environmental factors for assessing agro-ecologicaladaptability of crops. Such expert information is required for landuse planning, farm management and crop production, soil conser-vation, and plant and animal conservation. The knowledge base en-coded in ECOCROP 1 was used to assess the tolerance of alternatecrops when a farmer needs to increase his production of food, orincrease his income possible for alternative crops. The ECOCROP1 system permits identification of 1710 plant species whose mostimportant climate and soil requirements match the information onsoil and climate of real world databases. The system consists oflibrary of crop environmental requirements. The plant speciesattribute files on crop environmental requirements are comparedwith soil and climate maps in agro-ecological zoning (AEZ) dat-abases or Geographical Information System (GIS) map-based dis-play (FAO, 2007). The knowledge includes detailed constraints ondefining climate, soil and conditions in terms of maximum andminimum values for practical production. Thus the knowledgebase helps a exploratory assessment about the performance of aspecies in a given environment, in terms of growth or output.Eco-SDSS integrates the KB and land resources information in GISallows carrying out suitability classification, or identification of acandidate species for an environment.

3.1. Integration of bio-physical knowledge of crop adaptability

The knowledge base for detecting crop tolerance requires mainlyclimatic and edaphic information. Land resources information wasrecorded to comply with the attribute structure of dominion knowl-edge. This includes development of climatic classifications. The cli-mate classification used is based on regional climates using theKoppen system of classification, as modified by Trewartha & Horn(1968). A broad climate classification is included in the databaseto divide plant species into groups from the point of view of adapt-ability. The system provides the option for the specification of cli-matic classes of the study area concerned. Table 1 represents thetranslation of crop adaptability knowledge into GIS layers.

In ECOCROP, the length of the growing season is defined as theaverage days between start of growth and end of growth. Thesuitability of a given crop is determined for all possible growing

Table 1Representation of crop tolerance knowledge in GIS thematic layers

Croptolerance

Spatial themes/grid layers Interpretation of spatial themes

Soil toleranceSoil texture (ST): L = Light (sand and loamy sand), M = Medium (sandy loam, loam, sandy clay loam, silt loam, clay loam and silty clay

loam), H = Heavy (sandy clay silty clay, and clay), W = Wide (suits a wide range of soil textures)Soil depth (SDP): The minimum soil depth for satisfactory growth. Depth is defined as the thickness of the soil above a layer, which is

impermeable to roots or percolating waterSoil drainage (SDR): Indicates soil moisture condition under which the plant can be grown successfully. The coding definitions were as follows.

I = Poorly drained (rooting zone saturated for more than 50% of the year), W = Well drained (adequate moisture exceptduring very dry spells), E = Excessively drained (dry to moderately dry soil for considerable periods during the growingseason)

Temperature ToleranceKilling temperature (KTMP): This indicates the temperature, in degrees Centigrade, below which the species, under average conditions, will be

damaged or killed. For annual species this refers to the growing period. For perennials it refers to any part of the year, asindicated in the database

Minimum growingtemperature (TMIN):

This is the temperature below which the crop will cease to grow or develop

Optimum temperature(TOPMN):

The most suitable temperature, in degrees Centigrade, for practical production and growth under average conditions

Maximum optimumtemperature(TOPMX):

The maximum average temperature at which the plant will grow optimally

Maximum temperature(TMAX): The ambient temperature at which the plant, under normal conditions, will cease to grow, and above which short or longterm damage may occur

Rainfall toleranceMinimum rainfall (RMIN): The amount of rainfall in millimetres (mm) below which it becomes impractical to grow the species under average

conditions without some supplemental irrigationOptimum minimum rainfall(ROPMIN):

Under average growing conditions the optimum minimum rainfall (mm) during the growing season

Optimum minimum rainfall(ROPMIN):

The optimal maximum rainfall (mm) during the growing season

Maximum rainfall: The amount of rainfall above which it becomes impractical to grow the species or crop under average soil andtopographical conditions

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seasons. With the development of land resources information sys-tems, Agro-ecological zoning at district levels allows researchers toincorporate the complexity of bio-physical processes at the macroscale or the regional scale. The land resources database was devel-oped for a district in the Western Development Region of Nepal.The data set was made available from MENRIS (Mountain Environ-ment & Natural Resources Information Services) of InternationalCenter for Integrated Mountain Development (ICIMOD). TheAgro-climatic zones are characterized by different cropping pat-terns and different crop productivity levels from sources otherthan agricultural land. The search domain for crop adaptabilitywas restricted in Agro-climatic zones and land cover classesderived from remote sensing multispectral satellite s images(LANDSAT 5 Thematic Mapper and LANDSAT 4 Multi Spectral Scanner(MSS)). The land cover classes were derived by initial image seg-mentation and unsupervised ‘‘clustering” followed by supervisedclassification using the maximum likelihood method and valida-tion by knowledge of the terrain condition and ground-truth data.

3.2. System features of Eco-SDSS

Eco-SDSS system offers a user-friendly interface to identify theadaptability level of different crops in agro-ecological cells. It in-volves the evaluation of (i) Agro-climatic assessment and (ii)Agro-edaphic assessment. The data structure was developed con-sidering a relational data model to correlate and evaluate the cli-matic and edaphic variables to aid in structured query in the Arc/Info environment. The overall programming was performed usingAML (Arc Macro Language), which is a high-level algorithmic lan-guage that affords researchers the ability to customize GIS func-tions and use and assign variables, control statement executions,and get and use map or page unit coordinates (ESRI, 1999a). It alsoprovides the option to create Graphical User Interface (GUI) (e.g.,onscreen menus, user forms, etc.). From a user’s perspective, the

search domain can be spatially restricted within specified land cov-er types using the GIS mask option that restricts analysis withinlimited domains. The advantage of using the search domain is thatusers’ queries can be issued based on a single or several combina-tions of land cover units. The selected search domain is highlightedwith distinct color over a shaded relief map generated from DTM(Digital Terrain Model). This option helps to generate graphicdepictions of the suitable areas in 3-D perspective of the selectedland covers types. For instance, in forest resource planning, theintroduction of a new timber species in a forest area requires thecombined analysis of climatic and edaphic factors. In such case, auser can restrict the search domain within the entire forest areaor within a specified type of forest area of interest. The output ofthe analysis and statistical result is displayed graphically withinthe user-defined search domain. Fig. 2 illustrates the system fea-tures and types of output resulting from interactive query. Theinteractive query features include the following:

� Identification of tolerant crops in a given location in agro-eco-logical cells.

� Identification of the location of agro-ecological cells matchingcrop requirements.

� Identify crops for specified use in agro-ecological cells.

Fig. 3 illustrates the main user interface and the users’ navi-gation options. The system provides tools for thematic spatialquery involving multiple GIS layers, map overlay, cartographicrendering by dynamic map composition and automated reportgeneration.

In order to reflect the local knowledge base of agro-ecologicalconstraints, the land suitability for a particular crop is evaluatedbased on real world constraints. For example, a given agro-ecologicalcell may be susceptible to hail or erosion which could limit thegrowth of crop. Moreover, introduction of some species could be

Visualization & Query Processing

in GIS

Identify a crop for

AEZ Cell

Climatic Variables

• Climate Zone• Temperature• Rainfall• Growing period• Light• Day length

•Texture• Depth• Drainage• Salinity

Edaphic Variables

A list of crops with environmentalrequirements

matching AEZ cell

One or more cropswith environmentalrequirements and

use matching specification

A description of the environmental

requirements ofthe crops

Identify theAEZ cells matching a crop requirements

SELECT USE

• Food• energy• Industrial• Spices• Beverage• Fodder• Control• Ornamental

SELECT CROP

Select one or more crops from a list

Fig. 2. System features for user interactions.

Fig. 3. Graphical user interface of Eco-SDSS.

I.U. Sikder / Expert Systems with Applications 36 (2009) 5341–5347 5345

harmful to environment. The system offers option to screen out thecrops in response to particular constraints set up by user.

3.3. Visualization and validation of query results

Given a masked area of a spatial search domain, the relationaldata model of GIS searches agro-ecological cells matching the cli-matic and edaphic requirements for the selected species basedon predefined decision rules expressed in the knowledge base.The decision rules for selecting crop for a given location are firedbased the strength of the rule. The execution of these rules resultsin a graphic display of group of pixels or agro-ecological cells sat-isfying the agro-climatic and agro-edaphic requirements of thecrop. Moreover, the spatial distribution of the selected crop is high-lighted in each search domain with a different color giving detailstatistics of distributions. Users are prompted to graphically pickany location in the search domain to identify potential niche crop.The search option results in a pop up report file giving the list ofcrops (or crops) and its scientific name, common name, its poten-tial usage and its environmental requirements. This option gives

the users the ability to make queries on some selected themes toidentify their attribute values. The suitable grid cells matchingthe crop climatic requirements are rendered over a thematic layer(e.g., shaded relief map of Digital Terrain Model) (see Fig. 4). Thevisual inspection provides users the means to validate the results.

4. Evaluation and conclusion

The integration of a knowledge base with spatial decision sup-port systems involves many issues including the incorporation of adecision support framework, modularization of exploratory spatialdata analysis, spatial inference and abductive reasoning withinintegrated system components. Integrating a spatial relational datamodel with knowledge based systems offers the enhancement ofspatial modeling and spatial data analysis, knowledge processingand enhanced decision support. However, the pre-requisites tohigh-level interoperability of knowledge-based systems and GISare the methodological artifacts for correct description, specifica-tion and interpretation of spatial data resources. Therefore, havinga semantic layer (Vetere & Lenzerini, 2005) of GIS data has a special

Fig. 4. Visualization and validation of search results against a thematic background (e.g., shaded relief map of Digital Elevation model); the colored areas in the valley regionand slopping terraces represent the suitable areas for a given crop.

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significance in information exchange and integration with KB sys-tems. Since GIS data representation is inherently scale-dependent,the application models require that semantic annotations be ableto inform entities or services offered by external KB systems andthereby draw automated inferences. In the absence of an explicitsemantic layer, the system developers are forced to manuallymap the spatial database schema with the knowledge base of theexternal system. For example, in ECOCROP the definition of lengthof growing period involves estimation of monthly average temper-ature and moisture during which plant growth is optimal. In Eco-SDSS, the semantic translation resulted in a number of GRID layerswhere growing period concepts is mapped with spatial layers. In atightly coupled system, such mappings are performed program-matically. However, in distributed heterogeneous systems, wherethe spatial resources and geo-processing services are required tobe discovered dynamically, the implicit semantics of apparentlydisparate spatial services require a semantic mediator. In recentyears, ontology (the specification of knowledge) is regarded as akind of glue for high-level system integration (Agarwal, 2005; Arp-inar et al., 2006; Yandong, Jianya, & Xiaohuang, 2007). In GIS, theontology-driven integration is relevant not only for spatial datamodels to unravel the representational scheme of topologicalpart-and-whole dichotomy but also for the application modelswhich involve an external knowledge base. The research in ontol-ogy-driven semantic interoperability of geo-spatial resources inte-gration is being carried out in the SDSS framework (Sikder & Misra,2008).

The integration effort of KB of crop climatic tolerance limitswithin GIS shows significant potentials for reasoning with complexdata involving spatial and temporal extents. In particular, thestrength of Eco-SDSS arises from the integration of symbolic rea-soning and adaptive knowledge processing abilities with spatialanalysis and dynamic cartographic visualization within a decisionsupport framework. Unlike standalone expert systems, the richvisualization schema of GIS provides additional benefits in termsof validation and evaluation of the areas deemed tolerant or suit-able to certain crops. Such explicit spatial localization may stimu-late further spatial analysis and filtering to search realisticsolutions. In the present context, the integration approach providesflexible and robust tight-coupling strategy involving RDBMS of GIS.

From database point of view, instead of adding a new DBMS, theexisting features of GIS utilities can be harnessed to interface withintelligent systems that offer modularity, extensibility and reus-ability. An additional advantage is that the demand for highly dataintensive operations of GIS can be minimized by incorporating do-main knowledge. However, there still remains a potential source ofuncertainty when KB is significantly lacking. With regard to theECOCROP, it contains a minimum data set, covering only the mostimportant climate and soil factors. However, land use planningrequires other environmental factors, which may be importantfor a particular species, together with non-climate factors, suchas pests and diseases, which could make successful integrationimpractical. Eco-SDSS shows strong potential for integration intolarger modules by involving multi-users in a group dynamic set-ting where preference-based discrete choice models (e.g., AnalyticHierarchic Process (AHP), or Fuzzy Additive Weighting methods)could be linked with multi-criteria decision support systems.

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