The development of a spatial decision support system to optimise agricultural resource use in the Western Cape
Thesis (MSc)--Stellenbosch University, 2000.
ENGLISH ABSTRACT: INTRODUCTION This thesis describes the development of a decision support model for regional agricultural resource utilisation. The analysis was generated in a spatial context and the optimisation technique was interactive with a geographical information system (GIS). Economic and operational research methodologies were linked to the GIS in the process of determining the appropriate resource uses for the region. The optimisation technique was applied for the Western Cape Province for eight crops. The spatial decision support system (SDSS) developed by this research was constructed through an eclectic approach, utilising a number of features of economic models and geographic information systems. The FAO/IIASA study on resource optimisation in Kenya provided the starting point for the development of the optimisation methodology. A partial equilibrium multi-market model was used for the study. APPLICATION OF THE SDSS The 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 matrix had 72 557 activities and 22 032 constraints. The results of the model - pertaining to the utilisation of resource units for specific crops were exported to a mapping module to enable the spatial representation of results. Three examples of the model results were extracted to illustrate the utility of the model as decision support system. The first case was in support of public sector information needs. Thereafter the model results were interpreted from an agribusiness perspective. Finally, the individual investor's information requirements were analysed. The public sector - as provider of infrastructure and other public goods - needs to ensure maximum effectiveness and efficiency in its activities. In a market economy, the public sector has a limited number of economic and other tools at its disposal to support the development of the agricultural sector. Most important are to provide incentives and infrastructure to guide farm-level decision-making - and thus resource-use patterns - towards efficient production systems at a national or provincial level. The public sector also needs to ensure that it obtains maximum 'returns' or benefit on its expenditure. The spatial decision support system was applied successfully in this regard by identifying and evaluating areas that need to be earmarked 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 location closer to the source of the products could be profitable since the handling conditions may be less restrictive for the processed product than the inputs. The land-use pattern foreseen for deciduous fruit production, for example peaches, was examined in this regard. Linear programming models are widely used for farm-level investment decisions. The particular advantage of using this spatial decision support system is its ability to include region-wide competitive forces and local, national and international market constraints. CONCLUSION AND FURTHER APPLICATION OF THE SDSS The most apparent advantages of the optimisation technique can be summarised as follows: .:. The technique integrated resource potential and economic determinants in predicting land-use patterns. This interactive capability determined the relative profitability and competitive 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 land capability model) and linear programming (the multi-market partial equilibrium model) - in the optimisation technique. Greater levels of detail concerning the particular characteristics of the resource units could be included in the optimisation model. .:. The visual representation of the solution of a mathematical model of this size greatly assisted the analysis and interpretation of the model results. The integration of the model results 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 a major advantage of using a GIS indicate the spatial distribution or address of the model results 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 its components and/or level of detail of the analysis. For example, the spatial decision support system 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 land capability model could simulate the anticipated change in temperature and rainfall regimes. The subsequent change in resource potential for the selected crops can then be incorporated in the linear programming model. Secondly, the effect of wide spread adoption of changes in technology can be determined in the spatial decision support system. The way in which technology changes are incorporated in 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 level analyses. Evidently, the decision-maker needs to be conscious of the trade-offs between level of detail of the spatial (and economic) data and model size. The large data requirements of the model are implicit to all spatial decision support systems and linear programming models. Finally, the opportunities for developing the model to determine competitive advantages and guide agricultural development at national and regional level are numerous. Regional applications - for example, for Southern Africa - could also be useful for agribusiness, which are planning business expansion to the region. However, some generalisation of the resource and economic data would be necessary to keep the information load to manageable levels.
AFRIKAANSE OPSOMMING: Die ontwikkeling van 'n ruimtelike besluitnemingsondersteuningstelsel (RBOS) vir die benutting van landbouhulpbronne van 'n streek word beskryf in hierdie navorsing. Die analise word in 'n ruimtelike konteks gegenereer en is met 'n geografiese inligtingstelsel (GIS) geskakel. Ekonomiese en operasionele navorsingsmetodieke word met die GIS geskakel ten einde die optimale hulpbron gebruike vir die streek te bepaal. Die model was toegepas vir die Wes-Kaap vir agt produkte, naamlik apples, olywe, pere, perskes, pruime, sitrus, tafeldruiwe en wyndruiwe. Die volume data benut in die model het 'n groot lineêre programmeringsmatriks tot gevolg gehad, met meer as 72 500 aktiwiteite en 22 000 beperkings. Die resultate van die lineêre programmeringsmodel is teruggevoer na die GIS ten einde die resultate ruimtelik voor te stel. Die resultate van die RBOS is vanuit drie perspektiewe ontleed, naamlik die owerheidsektor, landbou industrieë en die van die individuele investeerder. Die drie voorbeelde van die model interpretasie is uitgesonder om die nut van die model as 'n besluitnemingsondersteuningstelsel te illustreer. Die belangrikste voordele van die RBOS kan soos volg opgesom word: • Hulpbron kwaliteit en ekonomiese aspekte word in die bepaling van toekomstige grondgebruikspatrone geïntegreer. Hierdie integrasie weerspieël die dinamika tussen die relatiewe winsgewendheid en mededingende voordeel tussen die verskillende gewasse ten opsigte van die hulpbron potensiaal. • Elke komponent van die model - ekomomiese modellering en die GIS - het die vermoë van die ander verbeter in die RBOS. • Die visuele voorstelling van die modeloplossing het die analise en interpretasie van die resultate aansienlik vergemaklik. Die integrasie van die model resulate in die GIS maak die verdere (ruimtlike) analise van die resultate ook moontlik. • Die visuele voorstelling van die resultate het ook bygedra tot die verifiëring van die model oplossing. Hierdie funksie kon inligting wat gewoonlik in terme van hoeveelhede gegee word ook ruimtelik voorstel in terme van ligging en verspreiding. Verdere toepassing van die model is moontlik deur geringe aanpassings in detail vlak van analise en struktuur van die model. Die model kan byvoorbeeld aangewend word om effek van die wêreldwyde klimaatsverandering op die benutting van landbou hulpbronne te simuleer, asook veranderinge in landboutegnologie. Die RBOS kan op distriks-, provinsiale-, nasionale- en streeksvlak toegepas word. Die besluitnemer moet egeter bewus wees van die aansienlike data benodighede van die model wat. inherent aan beide ekonomiese modellering en 'n GIS is. Daar is egter heelwat geleenthede waar die RBOS landbouontwikkeling op streeks en nasionale vlak kan ondersteun, asook verdere toepassings in terme van sub-kontinentale ontwikkeling in Suider- Afrika.