Agronomic soil management and decision-making frequently requires the joint classification of soil variables. Fuzzy set theory is often used to accomplish this task. This paper addresses the issues of objectively defining fuzzy membership functions (FMF) and reducing classification uncertainty with hedge operators. As an example, soil in North-east Thailand was classified according to its inherent potential to support the recovery of a rice crop after a drought spell. The utility of auxiliary information not directly included in the classification was explored. A tree cover density index was employed for an objective definition of the FMF to classify soil organic matter content and plant-available potassium. Mapping units were allocated to classes having low, medium or high availability of these plant nutrients. It was shown that crisp, Boolean style classifications severely misclassify land in all but one class. Adjusted FMF decreased the uncertainty contained in thematic class maps. Single FMF values for soil organic matter and plant-available K were then jointly modelled and the soil classified as having low, medium and high potential for rice plants to recover from drought impacts. The very and more or less hedge operators were applied to increase or decrease the joint FMF values using farmer' knowledge about soil fertility. Overall classification uncertainty using FMF was decreased by 14% if the standard FMF was adjusted and the generated membership values were hedged. It was shown that adjusting FMF influenced the uncertainty components vagueness and ambiguity differently; the former increased slightly but the latter was drastically reduced.
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