| Abstract |
From a financial point of view, one of the most important problems for almost any horticultural farmer is to predict the future, profit expectations, above all, if the investment is very high as happens in greenhouses. In this situation, operations and resource management are basically under a controlled risk. On the other hand, production and resource prices could be considered properly as uncertain parameters. Project appraisal using simulation and multicriteria methods is a useful technique to analyse both different feasible alternatives and critical parameters for financial sustainability. Our software PRAPPIS v2.0 has been developed combining Monte Carlo and Goal Programming techniques and has shown satisfactory results in this context. However, some methodological problems appeared because of the technical relationship between agricultural activities and the financial items that describe them. For example: low production always means a decrease in part time hand labour in harvesting operations. In order to design an easy-to-use tool, an inference engine based on fuzzy logic has been developed for the PRAPPIS system. Users can introduce their financial parameters and then relate them using five options: very positive, positive, neutral, negative and very negative relations. For example, a neutral relation between parameter A and B has five inference rules, for example: if A increases, B must increase. This paper shows the statistical differences between traditional simulation, where parameters vary freely, and simulation using controlled relations. The statistical distribution in every financial result is a powerful tool for decision makers in horticulture where uncertainty is so high. In order to test our model, a standard subtropical greenhouse was selected. Green beans and cherry tomatoes were chosen in a 1 ha plastic "parral" structure. Theoretical and real technical and financial items were used for a five year project appraisal in an uncertain environment. |
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