The development of a spatial decision support system to optimise agricultural resource use in the Western Cape

[摘要] ENGLISH ABSTRACT: INTRODUCTIONThis thesis describes the development of a decision support model for regional agriculturalresource utilisation. The analysis was generated in a spatial context and the optimisationtechnique was interactive with a geographical information system (GIS). Economic andoperational research methodologies were linked to the GIS in the process of determining theappropriate resource uses for the region. The optimisation technique was applied for theWestern Cape Province for eight crops.The spatial decision support system (SDSS) developed by this research was constructedthrough an eclectic approach, utilising a number of features of economic models andgeographic information systems. The FAO/IIASA study on resource optimisation in Kenyaprovided the starting point for the development of the optimisation methodology. A partialequilibrium multi-market model was used for the study.APPLICATION OF THE SDSSThe model was applied for the Western Cape Province for eight crops or product groups, viz.apples, citrus, olives, peaches, pears, plums, table grapes, and wine grapes. The LP matrixhad 72 557 activities and 22 032 constraints. The results of the model - pertaining to theutilisation of resource units for specific crops were exported to a mapping module to enablethe spatial representation of results.Three examples of the model results were extracted to illustrate the utility of the model asdecision support system. The first case was in support of public sector information needs.Thereafter the model results were interpreted from an agribusiness perspective. Finally, theindividual investor's information requirements were analysed.The public sector - as provider of infrastructure and other public goods - needs to ensuremaximum effectiveness and efficiency in its activities. In a market economy, the public sectorhas a limited number of economic and other tools at its disposal to support the developmentof the agricultural sector. Most important are to provide incentives and infrastructure to guidefarm-level decision-making - and thus resource-use patterns - towards efficient productionsystems at a national or provincial level. The public sector also needs to ensure that it obtainsmaximum 'returns' or benefit on its expenditure. The spatial decision support system wasapplied successfully in this regard by identifying and evaluating areas that need to beearmarked for future development for selected crops.The spatial decision support system was also applied in support of location decisions for.aqribustness. For example, in the case of deciduous fruit packaging and canning, a locationcloser to the source of the products could be profitable since the handling conditions may beless restrictive for the processed product than the inputs. The land-use pattern foreseen fordeciduous fruit production, for example peaches, was examined in this regard.Linear programming models are widely used for farm-level investment decisions. Theparticular advantage of using this spatial decision support system is its ability to includeregion-wide competitive forces and local, national and international market constraints.CONCLUSION AND FURTHER APPLICATION OF THE SDSSThe most apparent advantages of the optimisation technique can be summarised as follows:.:. The technique integrated resource potential and economic determinants in predictingland-use patterns. This interactive capability determined the relative profitability andcompetitive advantage of each of the selected crops vis-a-vis the resource units. .:. Each component enhanced the modelling capacity of the other - the GIS (in the landcapability model) and linear programming (the multi-market partial equilibrium model) - inthe optimisation technique. Greater levels of detail concerning the particularcharacteristics of the resource units could be included in the optimisation model..:. The visual representation of the solution of a mathematical model of this size greatlyassisted the analysis and interpretation of the model results. The integration of the modelresults into the GIS makes further spatial analysis of the solution possible (for example,overlay analysis) .•:. The visual representation also assisted in the verification of the model results. This was amajor advantage of using a GIS indicate the spatial distribution or address of the modelresults that would otherwise be listed in tables in terms of quantities only.Further applications of the optimisation model are possible through changes in any of itscomponents and/or level of detail of the analysis. For example, the spatial decision supportsystem could be applied to simulate the effect of global climate change on the (agricultural)resource-use patterns of a region. Changes to the resource characteristics in the landcapability model could simulate the anticipated change in temperature and rainfall regimes.The subsequent change in resource potential for the selected crops can then be incorporatedin the linear programming model.Secondly, the effect of wide spread adoption of changes in technology can be determined inthe spatial decision support system. The way in which technology changes are incorporatedin the model depends on where in the production process it is developed.The spatial decision support system was flexible with regard to level of detail of the analysis.The optimisation model can be applied for district, provincial, national and regional levelanalyses. Evidently, the decision-maker needs to be conscious of the trade-offs between levelof detail of the spatial (and economic) data and model size. The large data requirements ofthe model are implicit to all spatial decision support systems and linear programming models.Finally, the opportunities for developing the model to determine competitive advantages andguide agricultural development at national and regional level are numerous. Regionalapplications - for example, for Southern Africa - could also be useful for agribusiness, whichare planning business expansion to the region. However, some generalisation of the resourceand economic data would be necessary to keep the information load to manageable levels.

[发布机构] Stellenbosch University