A design of spatial decision support system toenhance decision progress in agricultural actionsMeng-Ying Li, Chih-Hong Sun and Min-Fang Lien

Taiwan GIS CenterTaipei, Taiwan

Tsun-Kuo ChangDepartment of Bioenvironmental Systems Engineering

National Taiwan UniversityTaipei, Taiwan

Abstract—Agricultural actions usually involve a decision pro-cess collaborating different decision makers and officers. Dif-ferent decision process may encounter different difficulties de-pending on the type of actions. In order to make efficientprogress in actions, a support system for decision making shouldbe constructed to include not only supporting information forthe decision, but also tools for decision management. For suchdecision support and management system, enhancement in taskcommunication, data summarizing and subtask managementshould be the focus of the development. In this study, we usedMAKOCI (Multi-Agent Knowledge Oriented CyberInfrastruc-ture) as a geospatial platform to convey customized decisionknowledge, working procedures, and data processing capability,where ontology and multi-agent are implemented to facilitatemodularized application design. A case study on detection andresponses of agricultural heavy-metal contamination is given inthis study to exemplify the functions of the decision supportsystem designed. By the proposed design, the spatial decisionsupport system facilitates hierarchical task definition and cross-government communications, in addition to supporting spatialand non-spatial decisional information. The decision progress canconsequently be enhanced by monitoring, communication andmanagement of actions among partaking users of the system.

I. INTRODUCTION

Agricultural actions, such as farmland contamination reme-diation or livestock epidemic prevention, usually involve adecision process that requires collaboration among differentdecision makers and officers. Such decision process may facedifficulties that hinder the progress of the action. For example,calls for decision usually come under stressful situations, andproblem definition are usually unclear. In addition, technicalchallenges, such as the lack of information to support decisionmaking, difficulty in decision communication, and difficulty inrelating pieces of decisional information, also build up barriersin decision making.

In order to make efficient progress in actions, a decisionmaking and implementation management system should beconstructed to include tools that provide not only requiredinformation according to the respective decision, but also amanagement system for the progress of action implementation.In particular, the tools should be able to enhance hierarchi-cal communication in task assigning and reporting, enhancecross-government communication in task collaboration, andconstruct mechanisms for intelligent data summarizing andsubtask management in order to achieve better decisionalresponses.

II. THE NEED OF A SUPPORTING SYSTEM

Decision making for agricultural actions come in differ-ent form, while the idea of making decisions constantlyinvolves the evaluation of alternatives regarding a wide rangeof considerations. The following are examples of challengesthat decision makers are faced when making decisions foragricultural actions:

• Calls for decision usually come under stressful situations[1]

• Objectives for decisions are often conflicting [2]• Decision making involves high complexity [3], [4]• Problem definition are usually unclear [5]• Objectives are usually unclear [4]• Consequences of decision cannot be assessed [4]• Lack of information [4]• Difficulty in implementing decisions [5]• Difficulty in evaluating information sufficiency [5]• Difficulty in decision communication [5]• Difficulty in relating pieces of information [5]• Unknown decision efficacy [4]

In a conventional form of spatial decision support system(SDSS), data sets are visualized for decision makers assummaries of decisional information. The type of difficul-ties resolved by the conventional decision support system,consequently, would be limited to visual judgment of thedata presented. For decision makers, the most difficult partfor decision making, including those listed above, essentiallyoriginate from the nature of the agricultural problem itself,which rely on human’s experience to resolve the situation, anddata visualization may only contribute weakly and indirectlyto the entire decision process. In fact, the examples listedabove are identified as related to the lack of communication ortechnical development, and could be approached with propertechnology. To meet the end for enhancing the communicationin a decision-support system, consequently, we designed acollaboration mechanism with decision-support informationintegrated, such that the efficiency in decision making couldbe improved.

III. WEB APPLICATIONS AND THE MAKOCI PLATFORM

The implementation of collaborative decision making callsfor a communication tool to transport decision transactionsbetween decision makers. Considering the nature of collab-orative communication where users may not be using the

system at all times, the capability for data storage and internetcommunication are indispensable. A communication tool fordecision transactions, consequently, takes the form of mobileapps or web applications in most cases. In this study, wepropose the use of web applications to communicate amongdecision makers, in light of its compatibility to most internet-accessible facilities.

To facilitate the creation of web applications, we developedMAKOCI (Multi-Agent Knowledge Oriented CyberInfrastruc-ture) [6] as a geospatial platform that conveys decision knowl-edge, working procedures, and data processing capability todesignated users of a decision support system. The purposefor constructing the MAKOCI platform is to facilitate creationof customized decision-support system for decision makers,via the management of shared or leased GIS resources. Tomeet this end, a web page for the platform is designed toserve two purposes: (1) to match between the technologicalcapabilities of the industry and requirement between decisionmakers, and (2) to provide catalog service for registering web-serviced data, analytic/simulation models, or developed webapplications. As a conveyor of decision-support knowledge,MAKOCI implemented Web Ontology Language (OWL) [7],[8] on the categorization of resources and users, as wellas computer-coded knowledge for workflows, models andrequirement-oriented application designs. Multi-agents [9] aredeployed on the basis of the ontology structure to allowautomatic data explanation and coded system reaction for theknowledge-based decision support.

IV. SOLUTIONS TO THE CHALLENGES

In this study, we implemented a decision management andsupporting framework for producing web applications, in orderto provide solutions to decision challenges. The frameworkconsists of the following components:

• Clarification of requirements: to resolve the situation withstressful situations, unclear or conflicting problems andobjectives.

• Management of decisions: to resolve the situation withcomplex-structured decision, and the difficulty in hierar-chical and cross-government communication.

• Enhancement of information efficiency: to resolve thesituation with lack of information, information updates,relating pieces of information, implementation difficulty,information sufficiency, and decision consequence.

Implementation of the framework is described in the followingsubsections.

A. Solution for clarification of requirements

We propose to alleviate the stress in decision making byidentifying the workflow and tasks to be accomplished beforea recurrent emergency is encountered. Examples of such re-current emergency includes flood inundation, contamination orepidemic outbreaks, which are usually responded according toa standard operating procedure (SOP) that assigns governmentunits in charge and directs the actions to take under definedstages of the emergency. Problems and objectives would be

Fig. 1. The web portal for the MAKOCI platform

defined clearly in such SOP, while the people in charge ofthe tasks may or may not always be prepared for all typesof emergencies in charge when multiple tasks of variousemergencies are assigned to a single government unit.

The information science community has developed theoriesand instruments of ontology development to enable the system-atic organization of human intelligence and knowledge [10].By applying the instruments of ontology, the SOP as humanknowledge can be clearly defined as computer-readable infor-mation for further use in software development. For clarifyingthe requirements of decision responses, the first step wouldbe defining the functional requirements of the applicationbetween the decision makers and software developers. Toprovide a platform for defining the functional requirements,the main page of the MAKOCI platform was designed as amatching platform for decision makers and developers (Fig. 1).The functional requirements are posted by the decision makersto advertize the need of a custom-made application, and web-application developers would search over the database of therequirements to look for project development opportunities.

Further details of the functional requirements are definedbetween decision makers and developers after the developerscontact the decision makers, such that the SOP and detaileduser/function requirements are define. To ensure the require-ments are clearly defined, the computer-readable knowledge iscoded in the format of OWL file format [11] for the SOP ofan agricultural action across governments involved, to describethe subtasks for each partaking user.

B. Solution for management of decisions

With the SOP defined for decision makers in charge andtasks to be completed in response to the agricultural actions,we designed a decision-communication framework for thecollaboration and management of decisions. The decisioncommunication mechanism includes the following features for

a clear definition of tasks to be completed and progress ofcompletion:

• A visual presentation of the decision relations, usually inthe form of a decision tree that describes the relation ofdecision makers under a hierarchical structure.

• A list of events according to the monitoring data orwarning broadcasts that indicates the possible need ofagricultural actions.

• A list of tasks to be completed for the agricultural actionsin response to the events indicated by monitored data orwarning broadcasts.

• Percentage of tasks completed with respect to the numberof tasks to be completed.

A diagram representing the hierarchical relations of decisionmakers is presented upon login of each user, with percentageof completion indicated for each user to have an overviewof subtasks completed. Administrative assignment is codedas subtasks for administrators of all hierarchical levels, andsubtask reporting and communication is visible to the upperhierarchy to enhance the progress of each agricultural action.

C. Solution for enhancement of information efficiency

In this study, we propose to improving the informationefficiency by providing sources of concurrent data and ana-lytical/simulation model, as well as multi-agents to listen tothe status of the data that decides on which latest and qualityassured data data or models to access or invoke.

The listening and action mechanism is accomplished usingmulti-agent technology. The multi-agent technology is anarea in information science that has rapidly developed inrecent years. This technology enables information systems todynamically exchange and share information through agents,and accomplish tasks and goals in collaboration [12]. Byusing the latest and most quality-assured data and models,a decision support system resolves the situation with the lackof information and updates of information, with informationsufficiency validated. In addition to the updating of data andmodels, multi-agents also provides mechanism to relate piecesof information to decision subtasks, invoke the simulation ofmodels and activate the transmission of data according to thestage of actions, that resolves difficulty in implementing thesubtasks and provide decision consequences in the form of theoutputs of simulation models or analytical functions.

In the decision support system framework designed, multi-agents are assigned to constantly monitor certain backgroundstatus, such as the contamination level, to decide whethera warning of agriculture incidence should be issued andprompt for users’ login. Spatial and non-spatial informationis supported according to the requirement of the subtasks ac-cording to the coded knowledge, via customized combinationof functions created on the MAKOCI platform.

V. CASE STUDY

We will demonstrate the requirement matching platform inthis section, followed by a decision management and supportsystem for remediation actions in response of heavy metal

Fig. 2. The web page for decision makers to post requirements on theMAKOCI platform

Fig. 3. The web page for developers to search and register data, model andapplications on the MAKOCI platform

contamination on farmlands, entitled “Intelligent farmlandcontamination decision management and support system,” toillustrate the communication and information support systemthat provides capability for hierarchical and cross-governmentcollaboration.

The requirement posting page is shown in Fig. 2. Thepage was designed to require only contact information of thedecision maker, and a brief description of the expected func-tions or outcomes that will help decision making. Optionally,categories or entries of the sources of data or models couldbe described to assist web-application developer in searchingrelated web-serviced data or simulation model. The require-ments are made visible to all qualified software developers,and functional requirements are negotiated between decisionmakers and application developers.

Fig. 4. The home page for the farmland contamination decision managementand support system. Titles of each text box indicated “system features”, “usageand limitations”, “system developer”, and “knowledge provider”

Fig. 5. The decision tree for the farmland contamination decision managementand support system, where yellow blocks indicate government units, andpurple blocks indicate officers in charge. In this example, two decision treesare involved: the decision tree for the Agriculture and Food Agency, and thedecision tree for Taichung City. The decision tree for Taichung City is shownin this figure.

Using the catalog service provided on the MAKOCI plat-form (Fig. 3), data and models could be included in thedevelopment of web application. All web applications areconstructed separately according to the theme of the decisionproblem. In this case study, the home page of the decisionsupport system is shown in Fig. 4, which consists of thetitle of the system, a login entry button, description of thecapability and limitation of the system, and information aboutthe developers. Upon login of a decision maker, a user agentidentifies the user, and construct the visible part of the decisiontree according to the particular decision maker under thecertain decision problem, e.g., “farmland contamination” forthis case study, as shown in Fig. 5. Decision makers inthe upper decision hierarchy are granted the visibility to allofficers in the lower decision hierarchy. A mouse click at anofficer in charge will bring up a list of events of contamination,

Fig. 6. The list of events for the farmland contamination decision managementand support system. The titles of the columns indicate respectively “Region”,“event”, “status”, and “detailed tasks and responses”.

Fig. 7. The list of tasks for the certain contamination event. The titles of thecolumns indicate respectively “tasks to be completed”, “completion status”,“assigning and actions”, “comments”, and “decision support information”.

as shown in Fig. 6, for the logged-on user to manage theprogress of the chosen officer. The rightmost column containslinks to the list of detailed tasks to be completed for the certainevent, according the SOP for the decision maker chosen. Thelist of tasks are illustrated in Fig. 7. Information that assiststhe decision making for the tasks are available in the form oftext message, downloads, or map information, and is designedas hyperlinks on the rightmost button. In the case where anassistance map is needed for the task to be completed, thepresentation of the map is illustrated in Fig. 8 to providedecisional information and enhance the efficiency in makingdecision for the corresponding task.

VI. DISCUSSIONS AND CONCLUSIONS

In this study, we presented the MAKOCI platform formatching requirements from decision makers and web-application capabilities from developers, as well as the catalogservice to facilitate production of decision management andsupport system. With the design of the platform for matchingand production, functional requirements are expected to beclarified, decision communications to be better managed, andinformation efficiency to be enhanced. In other words, theMAKOCI platform improves not only the management of dataand model for decision making, but also the management of

Fig. 8. The supporting information in the format of a smart map. The right-hand-side is the map viewing area with a list of related maps. The upper-left isthe information digested from the contamination map, and the lower-left is alist of task-completion reports for similar events that was completed accordingto the same SOP, to convey decision experiences in written reports of pastcompleted remediation of contamination.

decision from people who makes the decisions. Difficulty into solve agricultural decision problems, consequently, couldbe substantially resolved by providing the efficient decisionmaking tools. A case study is demonstrated to shown thecapability of the produced system in supporting and managingdecision under farmland contamination events.

Given that decision making tools in the future requiresrapid renewing to fit the need of changing objectives andgoals, it would be essential to have products that helps withconcurrent updating of data/models and efficient managementof decisions. In addition, to provide knowledge in a decisionmaking tool, particularly by supporting the simulation andanalytic model outcomes, requires substantial collaborationbetween the system developer and academic or researchinstitutes. As a platform that facilitates production of on-line smart maps and coding/updating of standard operationprocedures for decision making, the MAKOCI platform playsan important rule to integrate the rapid renewing of decisiontools with management of changing structure of decision com-munication. By careful commercialization, consequently, theMAKOCI platform will create the opportunity for knowledgetrading, with the collaboration among governments, softwarecompanies, and academic or research institutes.

ACKNOWLEDGMENT

Part of this study was supported by the Ministry of Scienceand Technology of Taiwan (Project MOST 102-2627-M-002-007).

REFERENCES

[1] K. M. Kowalski-Trakofler, C. Vaught, and T. Scharf, “Judgment anddecision making under stress: an overview for emergency managers,”

International Journal of Emergency Management, vol. 1, no. 3, pp. 278–289, 2003.

[2] A. Nardini, “Key issues and challenges in decision making processesto implement river restoration,” in 4 th ECRR Conference on RiverRestoration, Italy, Venice S. Servolo Island, 2008, pp. 619–622.

[3] H. Qudrat-Ullah, P. Davidsen, and J. M. Spector, Complex DecisionMaking. Springer, 2007.

[4] K. J. Sainsbury, A. E. Punt, and A. D. Smith, “Design of operationalmanagement strategies for achieving fishery ecosystem objectives,” ICESJournal of Marine Science: Journal du Conseil, vol. 57, no. 3, pp. 731–741, 2000.

[5] S. Elonen and K. A. Artto, “Problems in managing internal developmentprojects in multi-project environments,” International Journal of ProjectManagement, vol. 21, no. 6, pp. 395–402, 2003.

[6] “MAKOCI: The Intelligent Decision Cloud,” http://www.makoci.com,accessed: 2013-07-02.

[7] “OWL Working Group,” http://www.w3.org/2007/OWL/wiki/OWL -Working Group, accessed: 2013-07-02.

[8] D. L. McGuinness, F. Van Harmelen et al., “Owl web ontology languageoverview,” W3C recommendation, vol. 10, no. 10, p. 2004, 2004.

[9] F. L. Bellifemine, G. Caire, and D. Greenwood, Developing multi-agentsystems with JADE. John Wiley & Sons, 2007, vol. 7.

[10] P. Wongthongtham, E. Chang, and T. Dillon, Ontology-based multi-agentsystems. Springer, 2009, vol. 219.

[11] H. Knublauch, “Ontology-driven software development in the contextof the semantic web: An example scenario with protege/owl,” in 1st In-ternational workshop on the model-driven semantic web (MDSW2004).Monterey, California, USA., 2004.

[12] M. Wooldridge, An introduction to multiagent systems. John Wiley &Sons, 2009.