LAUSR

In many real world applications of data envelopment analysis (DEA), sample surveys are employed to collect input and output data that may be imprecise or ambiguous. However, accurate input and output data are fundamentally indispensable in conventional DEA. This is due to the assumption underlying DEA, that all data assume specific numerical values. Any imprecise data, missing value or outlier may cause the computed relative efficiency scores to change drastically. A novel estimation technique, fuzzy DEA, has been proposed in the literature as an alternative by incorporating uncertainty in measurement. This paper is an application of fuzzy DEA to agricultural systems. We estimate efficiency scores for a sample of organic farms and compare the results based on fuzzy DEA to conventional DEA. The analysis finds little evidence of large or systematic recall bias in the sample, however depending on the α-cut level, results are more robust.