Implementation of a knowledgebased agricultural geographic decisionsupport system in the Dominican Republic a case study
Identifieur interne : 001305 ( Istex/Corpus ); précédent : 001304; suivant : 001306Implementation of a knowledgebased agricultural geographic decisionsupport system in the Dominican Republic a case study
Auteurs : Severin V. Grabski ; David MendezSource :
- Information Technology & People [ 0959-3845 ] ; 1998-09-01.
Abstract
Effective land use management in lesser developed countries is problematic due to a variety of factors including inexperience and turnover of decision makers, lack of communication among experts in functional areas, and scattered or missing data needed by managers to make informed decisions. This paper describes a first step approach toward the solution of these problems that was implemented in the Dominican Republic. The paper introduces a framework used to organize and facilitate the sharing of data needed for land use decision across multiple disciplines. The framework provided the basis for the development of a prototype agricultural geographic decision support system for use in the Dominican Republic. This system is unique in that it combines concepts from semantic data modeling and database design, geographic information systems, and knowledgebased systems.
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DOI: 10.1108/09593849810227986
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Grabski</name><affiliation><mods:affiliation>Department of Accounting, Michigan State University, East Lansing, Michigan, USA</mods:affiliation></affiliation></author><author><name sortKey="Mendez, David" sort="Mendez, David" uniqKey="Mendez D" first="David" last="Mendez">David Mendez</name><affiliation><mods:affiliation>Department of Health Management & Policy, The University of Michigan, Ann Arbor, Michigan, USA</mods:affiliation></affiliation></author></analytic><monogr></monogr><series><title level="j">Information Technology & People</title><idno type="ISSN">0959-3845</idno><imprint><publisher>MCB UP Ltd</publisher><date type="published" when="1998-09-01">1998-09-01</date><biblScope unit="volume">11</biblScope><biblScope unit="issue">3</biblScope><biblScope unit="page" from="174">174</biblScope><biblScope unit="page" to="193">193</biblScope></imprint><idno type="ISSN">0959-3845</idno></series><idno type="istex">F7F226C775DBDF64FF213B04EA2FDAF7F00FD40B</idno><idno type="DOI">10.1108/09593849810227986</idno><idno type="filenameID">1610110301</idno><idno type="original-pdf">1610110301.pdf</idno><idno type="href">09593849810227986.pdf</idno></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0959-3845</idno></seriesStmt></fileDesc><profileDesc><textClass></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Effective land use management in lesser developed countries is problematic due to a variety of factors including inexperience and turnover of decision makers, lack of communication among experts in functional areas, and scattered or missing data needed by managers to make informed decisions. This paper describes a first step approach toward the solution of these problems that was implemented in the Dominican Republic. The paper introduces a framework used to organize and facilitate the sharing of data needed for land use decision across multiple disciplines. The framework provided the basis for the development of a prototype agricultural geographic decision support system for use in the Dominican Republic. This system is unique in that it combines concepts from semantic data modeling and database design, geographic information systems, and knowledgebased systems.</div></front></TEI><istex><corpusName>emerald</corpusName><author><json:item><name>Severin V. 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Do Not Edit! --><article dtd-version="1.0" xml:lang="en" article-type="research-article"><front><journal-meta><journal-id journal-id-type="publisher-id">itp</journal-id><journal-id journal-id-type="doi">10.1108/itp</journal-id><journal-title-group><journal-title>Information Technology & People</journal-title></journal-title-group><issn pub-type="ppub">0959-3845</issn><publisher><publisher-name>MCB UP Ltd</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.1108/09593849810227986</article-id><article-id pub-id-type="original-pdf">1610110301.pdf</article-id><article-id pub-id-type="filename">1610110301</article-id><article-categories><subj-group subj-group-type="type-of-publication"><compound-subject><compound-subject-part content-type="code">research-article</compound-subject-part><compound-subject-part content-type="label">Research paper</compound-subject-part></compound-subject><compound-subject><compound-subject-part content-type="code">case-report</compound-subject-part><compound-subject-part content-type="label">Case study</compound-subject-part></compound-subject></subj-group><subj-group subj-group-type="subject"><compound-subject><compound-subject-part content-type="code">cat-IKM</compound-subject-part><compound-subject-part content-type="label">Information & knowledge management</compound-subject-part></compound-subject><subj-group><compound-subject><compound-subject-part content-type="code">cat-IMG</compound-subject-part><compound-subject-part content-type="label">Information management & governance</compound-subject-part></compound-subject></subj-group><subj-group><compound-subject><compound-subject-part content-type="code">cat-ISYS</compound-subject-part><compound-subject-part content-type="label">Information systems</compound-subject-part></compound-subject></subj-group></subj-group><subj-group subj-group-type="subject"><compound-subject><compound-subject-part content-type="code">cat-LISC</compound-subject-part><compound-subject-part content-type="label">Library & information science</compound-subject-part></compound-subject><subj-group><compound-subject><compound-subject-part content-type="code">cat-IBRT</compound-subject-part><compound-subject-part content-type="label">Information behaviour & retrieval</compound-subject-part></compound-subject></subj-group></subj-group></article-categories><title-group><article-title>Implementation of a knowledge‐based agricultural geographic decision‐support system in the Dominican Republic: a case study</article-title></title-group><contrib-group><contrib contrib-type="author"><string-name><given-names>Severin V.</given-names> <surname>Grabski</surname></string-name><aff>Department of Accounting, Michigan State University, East Lansing, Michigan, USA</aff></contrib><x></x><contrib contrib-type="author"><string-name><given-names>David</given-names> <surname>Mendez</surname></string-name><aff>Department of Health Management & Policy, The University of Michigan, Ann Arbor, Michigan, USA</aff></contrib></contrib-group><pub-date pub-type="ppub"><day>01</day><month>09</month><year>1998</year></pub-date><volume>11</volume><issue>3</issue><fpage>174</fpage><lpage>193</lpage><permissions><copyright-statement>© MCB UP Limited</copyright-statement><copyright-year>1998</copyright-year><license license-type="publisher"><license-p></license-p></license></permissions><self-uri content-type="pdf" xlink:href="09593849810227986.pdf"></self-uri><abstract><p>Effective land use management in lesser developed countries is problematic due to a variety of factors including inexperience and turnover of decision makers, lack of communication among experts in functional areas, and scattered or missing data needed by managers to make informed decisions. This paper describes a “first step” approach toward the solution of these problems that was implemented in the Dominican Republic. The paper introduces a framework used to organize and facilitate the sharing of data needed for land use decision across multiple disciplines. The framework provided the basis for the development of a prototype agricultural geographic decision support system for use in the Dominican Republic. This system is unique in that it combines concepts from semantic data modeling and database design, geographic information systems, and knowledge‐based systems.</p></abstract><kwd-group><kwd>Decision‐support systems</kwd><x>, </x><kwd>Dominican Republic</kwd><x>, </x><kwd>Geographical information systems</kwd><x>, </x><kwd>Knowledge‐based systems</kwd></kwd-group><custom-meta-group><custom-meta><meta-name>peer-reviewed</meta-name><meta-value>no</meta-value></custom-meta><custom-meta><meta-name>academic-content</meta-name><meta-value>yes</meta-value></custom-meta><custom-meta><meta-name>rightslink</meta-name><meta-value>included</meta-value></custom-meta></custom-meta-group></article-meta></front><body><sec><title>Introduction</title><p>A number of specialized tools and techniques have been developed for land use management. Foremost among these are geographic information systems (GIS), optimization techniques such as linear and quadratic programming, and expert systems. The successful implementation of these tools and techniques requires that knowledge be shared and disseminated across a number of different decision makers. Unfortunately, in a number of lesser‐developed countries (LDCs), this information sharing does not occur due to the socio‐political environment, in which decision makers who are charged with land use management are political appointees. The politicians replace these decision makers whenever public opinion demands that something be done to improve the economic situation of the electorate. The end result is that the managers do not remain in office long enough to learn how to use the information provided by the technical specialists. In addition, the expertise that is needed to answer various land use management questions is often scattered throughout different government institutions. The specialists employed in these various units do not communicate across areas of expertise; a “silo mentality” exists (e.g. an expert on soil conditions does not communicate with a plant expert regarding the conditions needed to grow a particular crop).</p><p>This paper proposes a first step towards solving this problem of scattered data and expertise related to land use decision making in developing countries. The basic constructs proposed by Barlowe (1978) for the development of land use management projects are utilized. This paper introduces a conceptual framework to classify the data required for the analyses, and a logical design of a system to help decision makers perform comprehensive and timely land use evaluations. Additionally, guidelines are presented for the physical implementation along with a prototype knowledge‐based agricultural decision‐support system developed for use in the Dominican Republic. This system is unique in that it combines concepts from semantic data modeling and database design, geographic information systems, and knowledge‐based systems. These basic tools were needed because of the environment and functional requirements that were identified. The system functions as a coordination mechanism that is needed because of the aforementioned “silo mentality” and socio‐political conditions. The different users are now able to perform the task in which they possess expertise and the system disseminates the knowledge. The system developed fosters the identification and gathering of expertise that is scattered among the different user groups, and the system facilitates future coordination efforts.</p><p>This paper is organized in the following manner. First, issues associated with effective land use management and the importance of viable land use plans in LDCs are reviewed. Next, the conceptual framework for data employed in land use management that serves as a basis for the geographical decision‐support system is presented. The characteristics of geographic information systems are then described and some tools currently used in agricultural planning are identified, including other expert systems. Following this, the system requirements as specified by officials from the Dominican Republic are identified. Finally, the conceptual database model employed in this system, the operation of the prototype system and planned extensions for this system are described.</p></sec><sec><title>Land use management</title><p>Decision making in the field of land use management includes the processes of planning, monitoring and evaluating agricultural production in a geographic region (Barlowe, 1978). Planning refers to the process of determining the distribution of the agricultural activity within an area to accomplish a predetermined goal. Monitoring encompasses the inventory of the current land use and the supervision of recommended plans. Evaluation is the appraisal of the results of the implemented plans.</p><p>Land use management is a vital activity in lesser developed countries like the Dominican Republic, Belize, Botswana, Jamaica, and others (Al‐Ankary, 1991; Corbley, 1997; Eyre, 1989; Galeano, 1997; Haack <italic>et al</italic>., 1996; Hyde, 1991; Nkambwe, 1991; Yeh, 1991), in which a great part of the economy depends on agroforestry production controlled by small scale farmers. Effective land use management can correct market inefficiencies that stem from an unorganized use of the land. In the specific case of the Dominican Republic, three major conditions evidence the lack of efficiency in agricultural production. First, intensive exploitation of the land leads to economic externalities such as soil erosion and water contamination by fertilizers. Second, small‐scale producers generally do not have the necessary skills to analyze market information that can be used to predict agricultural products’ demand patterns. Information such as world demand trends and recent developments pertaining to substitute products could be used by the farmers to take advantage of, or shelter against, abrupt changes in the demand for agricultural products. Lacking the ability to assimilate this information, small farmers plan their production guided by the current price level, to often find that the expected price level no longer exists by the time their products have matured. Finally, agricultural production, in general, is not geographically specialized according to the comparative advantage of the different regions of the country (in terms of physical endowment and demographic characteristics) to sustain different crops. Geographic specialization of the agricultural activity leads to increases in crop yields without significantly raising the costs of production, and to the development of economies of scale.</p><p>These conditions, which generally cannot be resolved by private initiative in LDCs, place the responsibility to dictate guidelines for land usage and the development of macroscopic management programmes on the government. The programmes, either of national or regional scope, are aimed to: <list list-type="order"><list-item><label>1. </label><p>correct the externalities derived from agricultural production;</p></list-item><list-item><label>2. </label><p>evaluate the necessary information to forecast future demand trends and transmit the information to the farmers so they can cope with potential demand changes; and</p></list-item><list-item><label>3. </label><p>increase the efficiency of production by reassigning the land use within the regions based on their comparative advantage.</p></list-item></list></p><p>Typical products of these land use management programs include erosion control programs, agricultural zoning programs, and forestry management programs.</p><p>Regrettably, these governmental programs generally turn out to be ineffective in improving the general conditions of agricultural production. A principal cause for the failure of these projects is the ineffective knowledge transfer among the decision makers. Managers, biased by their training, often do not consider all the dimensions that are affected by the problem. For example, soil scientists tend to focus exclusively on improving crop yields ignoring facts such as a product’s demand and production costs. Economists are concerned with maximizing the positive net return of the projects, but because of their lack of training in the biological areas, they usually cannot evaluate the constraints imposed by the physical conditions of the problem.</p><p>As a result, when the land use management plans formulated by the government are implemented, they usually conflict with constraints that were not considered during the planning stage and, therefore, cannot be executed as originally designed. A reformulation of the plans has to be done “on the run”, under the pressure of public criticism, and the final results obtained are far from the original goals. Experience could eventually help the project managers to recognize the complexity of land use decisions and to seek a more systematic and comprehensive approach; unfortunately, these individuals are the ones whose positions are among the most volatile in the government structure, and so they are generally removed from their positions before they have the chance to learn from their mistakes.</p><p>Additionally, the data, as well as the expertise necessary to carry on a comprehensive study, is scattered among multiple institutions within the government. Each group collects and maintains the data that reflect its interest. As a consequence, data formats among institutions in the government are in many instances incompatible, because they refer to nonconforming geographic units and time periods. Data necessary for land use evaluation fall into many categories that have to be coordinated to achieve a common goal. Items so seemingly unrelated as the soil pH values necessary to grow a crop and the population density of a region must be categorized and coordinated to be available to decision makers so they can provide timely responses to problems of current social interest. A framework for organizing the scientific and assembled land use data would help in the coordination of data types and categorization.</p></sec><sec><title>Issues in land use decision making</title><p>Decisions regarding land use have to address three issues that require different expertise (Barlowe, 1978). First, solutions must be physically feasible. Care must be taken that the proposed land use agrees with the physical endowment of the region. For example, type of soil, climate, slope, altitude, etc. must be checked for agreement if a certain crop is being recommended in a certain region. Second, the proposed land use must be economically sound. In other words, out of the possible land use patterns that can physically match the conditions of the region, the chosen land use must provide a positive net benefit. Third, political feasibility must be examined. Do the appropriate legal mechanisms exist to effectively implement the proposed land use change? Does the current land tenure of the zone allow for any change? When a set of alternatives meets the three types of constraints, the one that provides the best fit for the decision maker’s purposes can be selected.</p><sec><title>Proposed framework for land use‐related data</title><p>The above discussion illustrates the diversity of fields and disciplines that are relevant in the development of land management projects. The multidimensionality of the problem indicates the variety of sources of data required for its evaluation. Obtaining and using these data generally presents three obstacles: first, decision makers cannot usually identify all the relevant aspects of the problem and subsequently ignore potential sources of data (i.e. bounded rationality (Simon, 1982)); second, the needed data are often not readily available and the urgency of the problem precludes data collection; and third, existing data across different disciplines are rarely in compatible formats regarding temporal and spatial resolution. These problems indicate the need for a framework that helps to categorize the data used in land use evaluations. Such a framework will help the decision makers to understand the complexity of the land use issues; establish a link between decision makers and technicians with different expertise; stimulate the continuous maintenance and update of land use pertaining data; and promote homogeneity in data formats among the different institutions and groups of experts that participate in land evaluation projects.</p><p>In general, data relevant in many planning and decision making systems are produced from a broad and heterogeneous group of disciplines (e.g. marketing, production, and accounting); however, for data management purposes, the important features of the data are the ones that pertain to acquisition, maintenance, and relationship with other pieces of information in a database. A criterion that is most closely tied to the previously identified conditions is the variability of the data elements. A variable data element, with respect to one dimension, is one that changes continuously along that dimension. These elements require special handling in an information system because they have to be discretized (made into discrete units) along the dimension of continuous change. Two issues arise from the process of discretization: resolution and compatibility. Resolution refers to the size of the dimension of variability that is going to be considered stable or invariant for data storage and analysis purposes. A fine resolution preserves the information contained in the variable dimension, but generates a great volume of data. This large volume creates concerns about storage size, timely accessibility and maintenance of the database. Compatibility refers to whether different data elements that vary in the same dimension are discretized in such a way that a comparison among them is possible.</p><p>Data employed in land use management can exhibit variability along two dimensions: time and space. Accordingly, this data can be classified as dynamic or stable data, depending on whether or not the information measured by the data changes in time; and as geographic or nongeographic, depending on whether or not the information measured by the data changes in space. Following this discussion, the data are classified as: stable‐nongeographic; stable‐geographic; dynamic‐nongeographic; and dynamic‐geographic (see <xref ref-type="fig" rid="F_1610110301001">Table I</xref>).</p><p>The stable‐geographic data include relatively time invariant elements related to the geographic endowment of a given area (e.g. elevation). The dynamic‐geographic data are not time invariant, yet are related to a specific geographic region (e.g. precipitation, population density). The stable‐nongeographic data include relatively time invariant information that is not related to the geographic endowment of a given area (e.g. what are the physical requirements to grow a certain crop). Finally, the dynamic‐nongeographic data consists of data relevant to all geographic zones which change in time (e.g. crop prices). These data are often scattered and difficult to obtain, and as the classification implies, have a short period of time in which they are current (e.g. farm gate prices can change weekly or daily for some commodities).</p><p>This classification scheme, tied to a mechanism to integrate and retrieve the data appropriate for a particular decision, can help the government plan for the data required for land use evaluations. The framework presented accomplishes these objectives and serves as a basis for the geographical decision‐support system developed for the Dominican Republic.</p><p>The physical implementation of this logical framework includes the use of a GIS, a database, and a knowledge‐based system. Given the heterogeneity of the data, different platforms are necessary to store, maintain and handle specific queries. Traditional databases are not well equipped to manage geographic data, which requires intensive coordinate matching and visual display. GISs on the other hand, have been designed to handle these specific issues. In contrast, traditional relational database management systems are the proper platform to handle non‐geographic data. These two different platforms containing data necessary to tackle a common problem must be coordinated either manually, by the analyst, or preferably, by an automatic mechanism that keeps track of the relationships among the various kind of data and make use of them when appropriate (Mennecke and West, 1998). An expert system, able to interact with the user and different platforms, is an ideal tool to function as a coordinating mechanism among data and users. The preceding discussion provides the rationale for our choice of physical implementation. The geographic data (stable and dynamic) are stored in a geographic information system; the nongeographic data (stable and dynamic) are stored in a relational database. Depending on the type of query, the answer can be obtained directly from the relational database, directly from the geographic information system, or from both systems. If the latter is true, the knowledge‐based system is employed to arrive at a response. That is, if a query requires the use of both geographic and non‐geographic data, the knowledge‐based system is required because of the need to integrate data from at least two distinct areas of expertise. However, if a query only requires use of geographic data, only the GIS is needed for a response, or if a query only requires the use of non‐geographic data, only the database management system is needed.</p></sec></sec><sec><title>Geographic information systems</title><p>A GIS is a set of computer programs that are designed to store, retrieve, edit and manipulate data that can be geographically referenced (Burrough, 1986; Cowen, 1988). It consists of five components: <list list-type="order"><list-item><label>1 </label><p>data input and storage;</p></list-item><list-item><label>2 </label><p>data storage and database management;</p></list-item><list-item><label>3 </label><p>data output and presentation;</p></list-item><list-item><label>4 </label><p>data transformation; and</p></list-item><list-item><label>5 </label><p>interaction with the user (Burrough, 1986).</p></list-item></list></p><p>The GIS is used to combine and evaluate different map overlays for the purpose of providing “new” composite information, i.e. the data transformation. This response can be generated in the form of numerical tables or as a new map, and the response can be displayed, printed or plotted. It is this automated creation of a database that helps distinguish a GIS from a database management system or a mapping system. Additionally, the ability of the GIS to synthesize existing levels of geographic data and update a database of spatial entities is a key feature that helps define a GIS (Cowen, 1988). A GIS is also used for the digital storage of maps, the production of maps and graphic displays, and for the reporting of statistical summaries (Yapa, 1991).</p><p>For a given geographical area, a number of different maps can be stored in a GIS, each containing data about a particular attribute of the area under study. Examples of maps used in land evaluation include soil type, climate, rainfall level, current land use, slopes, among others. The maps are recorded by the computer system in several ways, including geocoding, digitizing, scanning, and direct input from video output. In geocoding, a grid is overlaid on each map, dividing it into a certain number of cells. Each cell is considered to be homogeneous in terms of the attribute being analyzed, and it has a unique identifier (which typically consists of the corresponding numbers of its defining row and column). Each cell is then assigned one and only one code, representing the level of the attribute that is the focus or theme of the map (e.g. 1 if the area is at sea level, 2 if the area is between 100 and 200 feet above sea level, and so on). A computer file is created for each map, and each record in the file consists of the row number, column number, and attribute level of a specific cell. A digitizer can be used to input the coordinates and attribute values directly from the map to the computer. Alternatively, the map can be scanned in, or it can be directly input via video output into a special expansion board (Miller <italic>et al</italic>., 1989). Additionally, various for‐profit organizations (and some governments) are now providing data sets that can be directly imported into various GIS applications. In general, regardless of the data entry method used, the GIS creates (uses) a file that stores the coordinates and attributes of geographically identifiable units on the earth surface. These discrete units (i.e. the GIS data elements) are considered to be homogeneous with respect to the theme that is being recorded.</p><p>Geographic information systems have been used successfully in the planning and monitoring of private and public owned projects in lesser‐developed countries. (Galeano, 1997; Haack <italic>et al</italic>., 1996; Klein, 1995; Mogollon, 1997; Ramirez, 1997). However, in general, these projects have not integrated a decision‐making process that involves nongeographic data. Since all the data in the GIS must be geographically identifiable, there are inherent limitations in the use of a GIS for analyses that involve nongeographic data. For these purposes, a database management system must be used.</p><sec><title>GIS knowledge‐based systems</title><p>Knowledge‐based geographic information systems have been developed and used in a variety of settings. Knowledge‐based GISs have been categorized into four areas: map design; geographic feature extraction; geographic database systems; and geographic decision‐support systems (Robinson <italic>et al</italic>., 1987). Map design systems have been developed for the improvement of map quality for GIS derived maps (including remote sensing and satellite imagery), for atlas design, for the placement of feature names on maps, and to emulate a cartographer in map generalization (Corbley, 1997; Freeman and Ahn, 1984; Leddy and Fuller, 1996; Muller <italic>et al</italic>., 1986; Nickerson and Freeman, 1986; Robinson and Jackson, 1985). Geographic feature extraction systems utilize a knowledge base to interpret aerial and satellite images (Brooks, 1983; Goldberg <italic>et al</italic>., 1984). Geographic database systems have focused on obtaining sets of spatial locations which satisfy a query, or obtaining spatial objects that exist in a specific area (Smith <italic>et al</italic>., 1987). Geographic decision‐support systems (GDSS) have incorporated nongeographic factors, such as economic data and/or social criteria. GDSS have been developed in a variety of settings. These include aiding planners evaluate location suitability for particular land‐use activities, aiding in forest management, for planning China’s coal and electricity delivery system, for regional planning which took into consideration the requirements of both the economic sectors (e.g. agriculture, industry, mining, etc.) and the social requirements (e.g. living conditions, employment, education, etc.), and for improving ambulance dispatching including emergency vehicle routing (Barath and Futo, 1984; Beauregard, 1995; Chandra and Goran, 1986; Crossland <italic>et al</italic>., 1995; Evans <italic>et al</italic>., 1993; Gerland, 1996; Kuby <italic>et al</italic>., 1995; Morse, 1987; Vertinsky <italic>et al</italic>., 1994).</p><p>A number of knowledge‐based systems have also been developed for use in agricultural planning. These systems have been developed to aid in irrigation decisions for a particular type of peanut, to aid in cotton farming and harvesting decisions, and to support land use involving alley cropping under defined conditions in the tropics and subtropics (Lemmon, 1986; Ruth and Carlson, 1988; Warkentin <italic>et al</italic>., 1989). These systems are narrow in their problem domain, i.e. the systems were only for a particular crop, and were constructed for use in a limited geographic area. They employed a knowledge‐based GIS; however, they did not use an external database.</p><p>In the following sections the system requirements as identified by planning officials in the Dominican Republic are presented, along with the prototype knowledge‐based GIS and external database that were developed. This knowledge‐based GIS can be classified as a geographic decision‐support system. It is designed to aid in the planning of land use based on crop and environmental characteristics and allows for inclusion of controllable factors of production (e.g. irrigation) in order to expand the list of potential crops that can be grown in an area.</p></sec></sec><sec><title>Planning system requirements in the Dominican Republic</title><p>The planning system requirements were identified by officials in the Agriculture Ministry of the Dominican Republic: the system must be able to identify what crops are being grown in any site, including the total area planted, total crop production, and crop yield; whether the current sites of production have the natural physical endowments required for the crops; or whether the return from the land can be maximized through the planting of other crops. These criteria resulted because this information is critical for planning purposes; it is needed when applications for funding are made to outside organizations such as the World Bank; it is needed to answer questions from potential investors as to what locations of the country are best suited for the farming of crops which are experiencing increased demand and per unit revenues on the international market; and because farmers often contact Ministry employees for information on what crops can be grown on their farm (other than the current plantings). Ministry officials also desire to improve the services provided by the agricultural extension technicians to the local farmers by providing them training based on the crops produced in the region they are assigned and the agrophysical conditions of that region.</p><p>At the present time, manual procedures have not been established to determine the answers for the above requirements and questions. The individuals who possess the expertise are scattered throughout the Ministry and the previously detailed communication problem exists. In addition, the problem of scattered data also exists (e.g. agricultural agents have knowledge of crop production in their area, but this information is often not available on a national level). Based on the framework developed in the previous section, much of the information needed for decision making could be accessible in a database environment. The required geographical information could be accessible through the use of a geographic information system. Finally, information that entails both geographical and nongeographical inputs, such as the ability to cross reference files of areas with those of crops to determine the “best” spatial distribution of crops based on the yield, cost of production, or profit could be accessible through the use a knowledge‐based system. The database design is presented next, followed by a description of the GIS employed, and the knowledge‐based system.</p><sec><title>Database design</title><p>The entity‐relationship model (Chen, 1976) was used for the conceptual design of the database for this system. The entity‐relationship model has become the leading formal structure for conceptual data representation (Batini <italic>et al</italic>., 1992). The entity‐relationship model is semantically expressive. It is relatively easy to present such a model to a decision maker, e.g. a Ministry of Agriculture official, and have them verify that the model reflects the reality of the environment. This is consistent with research that has used the entity‐relationship model in conjunction with other domain specific knowledge as a basis for re‐engineering efforts (see, for example, Denna <italic>et al</italic>., 1993). The markings of the structural constraints (i.e. min, max) for the existence and cardinality respectively) follow Elmasri and Navathe (1989). The entity‐relationship diagram appears as Figure 1.</p><p>Regions, zones, sub‐zones, and areas are all based on political or administrative needs. These entities represent parts of the country without regard to agrophysical conditions. The Dominican Republic is partitioned into regions, which are then separated into zones. These zones are then subdivided into sub‐zones, which are further partitioned into areas.</p><p>The vast majority of farms are family run and are contained within a single area. Agricultural extension agents are assigned to the areas. Unfortunately, the agents may have little knowledge of what crops could grow on a specific farm other than what is growing elsewhere in their area. Since areas are based on administrative or political needs rather than agrophysical characteristics, a crop that grows in one part of a given area and is economically feasible may not be economically feasible (or even grow) in another part of that same area. Fortunately, the government of the Dominican Republic was aware of this problem and entered into a study to identify agrophysically homogeneous areas. The Comprehensive Resource Inventory and Evaluation System (CRIES) project, as part of a contract between the Government of the Dominican Republic and the Agency for the International Development (AID) categorized the country into Resource Planning Units (RPUs) (Schultink <italic>et al</italic>., 1987). A similar project was carried out in a number of LDCs such as Jamaica (Eyre, 1989).</p><p>RPUs are geographic units, not necessarily contiguous, homogenous regarding their potential to sustain agricultural activity. They are defined primarily in terms of their soil type and climate. The family‐run farms are generally contained in a single RPU.</p><p>Data regarding the agricultural activity of the farms are collected at the end of each growing season. These data are also related to the areas since administrative reports detailing farm production are made by area. The Agricultural Activity entity contains event data about specific crop production.</p><p>The prototype database system (implemented in a desktop computer based database) currently has data entered for the RPUs found in the Dominican Republic, and the crops grown in the Caribbean basin. The RPU table contains data about the land units which are categorized according to RPUs. It also contains the digitizing code of each RPU, and this is used to link the regions to the GIS. For each region the physical factors considered relevant for crop development are recorded. These factors include: pH, precipitation, temperature, drainage, soil depth, salinity, and slope (Laureano and Amparo, 1985). The crop table contains the same factors, and it also contains the crop classification. Both tables have almost identical structures. For each of the physical characteristics listed, a range is stored for every crop and region. With respect to crops, this range represents the set of values required for the crop to grow in any given region. With respect to RPUs, the range represents the span of the distribution of the values that the specific physical characteristic attains in that RPU.</p></sec><sec><title>GIS design</title><p>The agricultural information for the Dominican Republic is stored in a GIS that was developed by the CRIES project (Schultink, 1985) as a component of the NARMA project in the Dominican Republic (Kemph and Hernandez, 1987). The CRIES‐GIS provides such features as statistical analyses, data analyses (including erosion modeling and proximity analysis), and terrain analyses (including slope characteristics and spatial filtering) (Schultink <italic>et al</italic>., 1987). It is designed to operate in a microcomputer environment (it has been implemented on systems using the Intel 80 × 86 microprocessors). The data are stored based on RPUs. (The data could be transferred to other commercially available microcomputer‐based products such as ARC/INFO; however, the migration cost is viewed as too high at the present time.)</p></sec><sec><title>Knowledge‐based system design</title><p>The knowledge base component of this geographic information system was developed using the VP‐Expert software[<xref ref-type="bibr" rid="b1">1</xref>]. The objective was to provide as much capability as possible while minimizing cost using standard products. The VP‐Expert software also has the capability to interface directly with the database management software data files. This was also a critical factor in the decision to use this software. <xref ref-type="fig" rid="F_1610110301003">Figure 2</xref> presents an overview of the DREAGIS (Dominican Republic Expert Agricultural Geographic Information System) system components. The expert system shell interfaces with the database files, and queries such as current land usage, or determination of what crops will grow in a particular area, will first use the database, and then use the GIS. A conversion program is used to generate data in a format compatible with the CRIES‐GIS. This data are then read into the GIS and the appropriate maps are generated. The system is designed to be bi‐directional, meaning that a digitizer can be used with the GIS to determine location, and this information can then be converted from GIS format into the database format, and then be used in a consultation with the knowledge‐based system. The nongeographic data are maintained through a standard database management system software with an appropriate “front end” to enhance usability.</p><p>Given a particular crop, physical factors are compared to equivalent factors in a potential region. These factors include pH, precipitation, temperature, drainage, soil depth, salinity, and slope. If the range of the region factor is completely contained within the range of the crop factor, the crop can grow in that region based on that factor. Otherwise the region is rejected and the next region is evaluated. If a region satisfies all the factors it is regarded as suitable for the development of that crop. If the region range factor is not completely contained within the crop range factor, but some overlap exists, the system will classify the region as suitable for the development of that crop regarding the specific factors, but with a warning that the crop may not grow in the entire region. The precipitation factor can also be relaxed if there is the possibility of irrigation and the slope of the region is within acceptable limits.</p><p>The prototype knowledge‐based geographic decision‐support system is menu based. The user is provided with four task choices: <list list-type="order"><list-item><label>1 </label><p>identification of where a specific crop is currently grown;</p></list-item><list-item><label>2 </label><p>identification of where a crop could be grown based upon that crop’s specific agrophysical characteristics;</p></list-item><list-item><label>3 </label><p>identification of where a crop could be grown based upon its similarity to other crops; and</p></list-item><list-item><label>4 </label><p>identification of what crops can be grown in a specific farm or RPU.</p></list-item></list></p><p>After a task has been selected, the system will query the user for any required additional information. This will occur if a user wants to identify where a crop could be grown based on similar crop characteristics. When that task is selected, the user is first asked to identify a similar crop from a list of crops. The crop is classified by the system according to the standard crop family classification (i.e. as a perennial, musoceal, etc.). The user is next presented with a listing of all crops within the selected plant family. The user then selects the crop that most closely matches the characteristics of the desired crop. After this choice is made, a list of crop agrophysical characteristics is displayed. The user may then modify any of the characteristics, and also identify if irrigation will be a possibility (this opportunity is also presented to the user when the other tasks such as “where could a crop be grown” or “what crops can grow in this RPU” are selected). The revised list of characteristics is displayed for user verification. When the user has made all necessary changes and has verified the list, the system then references the database for all regions that match the crop requirements. The system then generates a list of feasible regions and any water deficiency that needs to be made up through irrigation.</p><p>This output can be printed, linked to the CRIES‐GIS, or both. If linked to the GIS, the user can display (print) a scale map of the selected areas and perform other GIS relevant analysis procedures (through the GIS) such as proximity to cities, proximity to rivers, slope, or any other desired output (<xref ref-type="fig" rid="F_1610110301004">Figure 3</xref> presents a map of the area with all RPUs identified within a specific region of the Dominican Republic and <xref ref-type="fig" rid="F_1610110301005">Figure 4</xref> presents the feasible areas for growing a sample crop).</p></sec><sec><title>System validation</title><p>The system prototype was installed and validated in the Ministry of Agriculture of the Dominican Republic. The Ministry’s technicians validated the performance of the system by analyzing its land use recommendations for the Ocoa Watershed (depicted in <xref ref-type="fig" rid="F_1610110301004">Figure 3</xref>), a region located in the south of the country for which extensive data regarding land use and agrophysical characteristics have been gathered.</p><p>The Dominican Republic officials validated the system’s performance by analyzing manually a small subset of the crops used in the study and comparing their solutions to the system’s recommendations. The experts from the Ministry of Agriculture concluded that the system’s recommendations were appropriate for the Ocoa Watershed in that they matched their independent evaluation. They identified two significant contributions of the system: efficiency and consistency. First, the system allowed them to evaluate the suitability of different potential crops for the Ocoa Watershed in a timely manner. They were able to evaluate the performance of 15 new crops proposed for the Ocoa Watershed in several hours. Ministry officials commented that this task, performed manually, would have taken at least a month to complete, not just because of the time consuming comparisons to find a good match between soil and crop, but also because of the dispersion of the relevant data and expertise throughout different governmental institutions and the private sector. Second, the system provides a consistent procedure for potential land use evaluations. The results are not dependent upon which specialist performed the analysis. The Dominican officials also liked the fact that the system is able to recommend a crop for a specific region and to show graphically the area under study; and to also compare the proposed to the current land use and land tenancy of the zone under study to facilitate the implementation of the recommendations. The Dominican experts also offered suggestions to improve the system. Specifically, they recommended that different weights be assigned to the physical characteristics used in the evaluation depending on the crop to stress the relative importance of those characteristics[<xref ref-type="bibr" rid="b2">2</xref>].</p></sec></sec><sec><title>System benefits</title><p>A number of benefits resulted from this system and these are summarized in <xref ref-type="fig" rid="F_1610110301006">Table II</xref> and are detailed in this section. A significant obstacle that exists in effective land use planning in the Dominican Republic is the high level of turnover. In a relatively short time span, seven different individuals needed to be trained in land use management. It was almost as if as soon as the individual understood the process, they were no longer in the position. This system has helped to remedy this situation. Administrators associated with the planning process, after observing the system, have a new appreciation and realization of the need for expertise. Additionally, the officials realized how useful the system was for problem solving (i.e. solving in several hours an evaluation that would normally have taken upwards of a month). Consequently, they realize how the system can provide an impetus for organizational change. The tasks and assignments can be organized around the system. Experts from the different groups can be assigned the task of updating and completing the database, while the specialists in land use management will be expected to use the system to provide consistent answers for queries. All too often organizations forget that a new system results in changes in procedures and in organizational change; these organizations resist the change only to discover that the new system has “failed” and that they must resort to the old methods. Fortunately, officials in the Ministry value the opportunity for changes and they plan to use the system as a catalyst for further improvements.</p><p>Sustained operation of the system will consist of three main activities: land use evaluations, expertise gathering and database management. These are the functions around which the organization will be structured. Land use evaluations constitute the basic decision making activities, the goal for which the system was designed; expertise gathering refers to the systematic incorporation to the rule based subsystem of relevant expertise disseminated throughout the country, i.e. that of knowledge transfer; and database management will entail the collection and updating of the relevant data for the whole country and the incorporation of new data items stemming from the enhanced expertise, i.e. again additional knowledge transfer.</p><p>The expertise gathering and data management activities were unplanned benefits. The prototype was developed with land use management as the goal. This is compatible with the Ministry’s objective to provide informed, consistent advice. To accomplish effective land use management expertise needs to be gathered and then made available. The knowledge‐based geographic decision‐support system is a mechanism that facilitates the goal attainment.</p><sec><title>Extensions</title><p>The immediate extensions to this project are to develop the features indicated in the conceptual framework but not implemented in the prototype. Specifically, the database will be extended to include economic information related to the crops, such as detailed budgets and a history of prices. Referring to the framework presented in <xref ref-type="fig" rid="F_1610110301001">Table I</xref>, three quadrants have been implemented: the stable‐geographic data access through the GIS; the stable‐nongeographic data access through the database; and the dynamic‐geographic data access through the database. The required extension is the inclusion of dynamic‐nongeographic data, which is primarily economic in nature, such as costs of production and farm gate prices.</p><p>The economic information will then be used by the system to perform a cost/benefit analysis, after the agrophysical evaluation had been completed. After determining the crops that are physically suitable to grow in the region, those crops with a positive net benefit will be selected and presented to the user along with the pertinent economic information.</p><p>Another extension to this system is to use mathematical programming to determine the optimal distribution of crops within the agricultural areas that form the region under study. The mathematical program could have different objective functions, such as maximization of the net profit, minimization of cost maintaining the same agricultural production, and so forth. The expert system will formulate the mathematical program. The stable‐ and dynamic‐ nongeographic information such as prices and costs of production could be retrieved from the database, the stable‐geographic information such as available areas from the GIS, and the dynamic‐geographic information from the database. The benefit of this type of an approach is that the decision maker making the inquiry does not need to be skilled in operations research, or in agroeconomics, or in plant and soil sciences. Rather, they need to be able to work with the farmers or others who are desiring the information.</p></sec></sec><sec><title>Conclusions</title><p>This paper has described a problem faced by the Government of the Dominican Republic concerning land use planning. The problem concerns data and expertise sharing across different disciplines that are relevant in the determination of an efficient use of the land for agricultural purposes. The paper also describes the steps that have been taken toward the solution of the problem. A framework for data required in land use decision making was developed. The framework provides the basis for a prototype agricultural geographic decision‐support system for the Ministry of Agriculture in the Dominican Republic. The system combines concepts from semantic data modeling and database design, geographic information systems, and knowledge‐based systems.</p><p>The system was validated by officials from the Ministry of Agriculture. These officials also identified benefits associated with the system beyond those originally expected. The system was developed with land use management as the goal. The system is viewed as enhancing the reputation of the Ministry of Agriculture due to timeliness (able to complete an evaluation in hours rather than a month) and consistency. Additional benefits accruing to the system include expertise gathering and data management activities. The impact of these benefits is causing the reorganization of activities around the functioning of the system.</p></sec><sec><title>Notes</title><p>1. Some issues regarding the construction of the knowledge‐based component of the system have been summarized elsewhere (Mendez‐Emilien and Grabski, 1992). The system is briefly reviewed here for expositional purposes and to present refinements and validation procedures that were not in the earlier summary.</p><p>2. The GIS portion of this system was developed in the early 1980s as part of the NARMA Project (Kemph and Hernandez, 1987). The prototype database and knowledge base were developed in the early 1990s. This system was validated and found to be useful; unfortunately, a lack of funding resulted in a temporary suspension of the project. It is expected that once funding levels are restored the system will be resumed. The temporary suspension of successful projects due to lack of funding is not uncommon in developing countries.</p></sec><sec><fig position="float" id="F_1610110301001"><label><bold>Table I<x> </x></bold></label><caption><p>Conceptual framework of data employed in land use management</p></caption><graphic xlink:href="1610110301001.tif"></graphic></fig></sec><sec><fig position="float" id="F_1610110301002"><label><bold>Figure 1<x> </x></bold></label><caption><p>Entity‐relationship model for the Dominican Republic knowledge‐based geographic information system</p></caption><graphic xlink:href="1610110301002.tif"></graphic></fig></sec><sec><fig position="float" id="F_1610110301003"><label><bold>Figure 2<x> </x></bold></label><caption><p>DREAGIS system components</p></caption><graphic xlink:href="1610110301003.tif"></graphic></fig></sec><sec><fig position="float" id="F_1610110301004"><label><bold>Figure 3<x> </x></bold></label><caption><p>Map of all RPUs within a specific region of the Dominican Republic</p></caption><graphic xlink:href="1610110301004.tif"></graphic></fig></sec><sec><fig position="float" id="F_1610110301005"><label><bold>Figure 4<x> </x></bold></label><caption><p>Map of feasible areas within a specific region for growing a sample crop </p></caption><graphic xlink:href="1610110301005.tif"></graphic></fig></sec><sec><fig position="float" id="F_1610110301006"><label><bold>Table II<x> </x></bold></label><caption><p>System benefits</p></caption><graphic xlink:href="1610110301006.tif"></graphic></fig></sec></body><back><ref-list><title>References</title><ref id="b1"><mixed-citation><person-group person-group-type="author"><string-name><surname>Al‐Ankary</surname>, <given-names>K.M.</given-names></string-name></person-group> (<year>1991</year>), “<article-title><italic>An incremental approach for establishing a geographical information system in a developing country: Saudi Arabia</italic></article-title>”, <source><italic>International Journal of 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type="personal"><namePart type="given">David</namePart><namePart type="family">Mendez</namePart><affiliation>Department of Health Management & Policy, The University of Michigan, Ann Arbor, Michigan, USA</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="research-article" displayLabel="research-article"></genre><originInfo><publisher>MCB UP Ltd</publisher><dateIssued encoding="w3cdtf">1998-09-01</dateIssued><copyrightDate encoding="w3cdtf">1998</copyrightDate></originInfo><language><languageTerm type="code" authority="iso639-2b">eng</languageTerm><languageTerm type="code" authority="rfc3066">en</languageTerm></language><physicalDescription><internetMediaType>text/html</internetMediaType></physicalDescription><abstract lang="en">Effective land use management in lesser developed countries is problematic due to a variety of factors including inexperience and turnover of decision makers, lack of communication among experts in functional areas, and scattered or missing data needed by managers to make informed decisions. This paper describes a first step approach toward the solution of these problems that was implemented in the Dominican Republic. The paper introduces a framework used to organize and facilitate the sharing of data needed for land use decision across multiple disciplines. The framework provided the basis for the development of a prototype agricultural geographic decision support system for use in the Dominican Republic. This system is unique in that it combines concepts from semantic data modeling and database design, geographic information systems, and knowledgebased systems.</abstract><subject><genre>keywords</genre><topic>Decisionsupport systems</topic><topic>Dominican Republic</topic><topic>Geographical information systems</topic><topic>Knowledgebased systems</topic></subject><relatedItem type="host"><titleInfo><title>Information Technology & People</title></titleInfo><genre type="journal">journal</genre><subject><genre>Emerald Subject Group</genre><topic authority="SubjectCodesPrimary" authorityURI="cat-IKM">Information & knowledge management</topic><topic authority="SubjectCodesSecondary" authorityURI="cat-IMG">Information management & governance</topic><topic authority="SubjectCodesSecondary" authorityURI="cat-ISYS">Information systems</topic></subject><subject><genre>Emerald Subject Group</genre><topic authority="SubjectCodesPrimary" authorityURI="cat-LISC">Library & information science</topic><topic authority="SubjectCodesSecondary" authorityURI="cat-IBRT">Information behaviour & retrieval</topic></subject><identifier type="ISSN">0959-3845</identifier><identifier type="PublisherID">itp</identifier><identifier type="DOI">10.1108/itp</identifier><part><date>1998</date><detail type="volume"><caption>vol.</caption><number>11</number></detail><detail type="issue"><caption>no.</caption><number>3</number></detail><extent unit="pages"><start>174</start><end>193</end></extent></part></relatedItem><identifier type="istex">F7F226C775DBDF64FF213B04EA2FDAF7F00FD40B</identifier><identifier type="DOI">10.1108/09593849810227986</identifier><identifier type="filenameID">1610110301</identifier><identifier type="original-pdf">1610110301.pdf</identifier><identifier type="href">09593849810227986.pdf</identifier><accessCondition type="use and reproduction" contentType="copyright">© MCB UP Limited</accessCondition><recordInfo><recordContentSource>EMERALD</recordContentSource></recordInfo></mods></metadata><serie></serie></istex></record>Pour manipuler ce document sous Unix (Dilib)
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