Heterogeneity in frontier analysis: does it matter for benchmarking farms?

Abstract

Benchmarking farms, in order to advise farmers to cure inefficiency, may be biased if heterogeneity is not accounted for. Technological variability in agriculture indeed happens, but productive efficiency analysis with frontier methods usually assumes homogeneity. Heterogeneity influences investment motives and production strategies, but is not always clear-cut, for example, when gradation in external inputs use occurs. Unfortunately, these indistinct (no clear-cut) differences in technologies are very common within farming communities, but have often been ignored by the advisors focusing on the discrete ones such as organic versus conventional farming. This paper explores indistinct heterogeneity in efficiency analysis, aiming at identifying peers/reference farms while reflecting on their significance for benchmarking. The gradual differentiation between low and high input dairy farms in Flanders is used as a case, based on a five-year balanced panel data for 58 farms. A data envelopment analysis (DEA) version of the meta-frontier approach is used to account for heterogeneity. The research revealed that, although stemming from a continuous distribution, low and high input farming can be considered as different strategies but none can be said to be superior to the other. Coupling the efficiency scores with peer information allows distinguishing good and bad performing efficient farms within each strategy, and thus improves benchmarking using frontier analysis.