‘Farms like Mine’: A Novel Method in Peer Matching for Agricultural Benchmarking
Mark A Reader,
Ian Hodge and
Ben GA Lang
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Mark A Reader: University of Cambridge
No 5gjdp, AgriXiv from Center for Open Science
To find opportunities to improve performance, comparisons between farms are often made using aggregates of standard typologies. Being aggregates, farm types in these typologies contain significant numbers of atypical enterprises and thus average figures do not reflect the farming situations of individual farmers wishing to compare their performance with farms of a ‘similar’ type. We present a novel method that matches a specific farm against all farms in a survey (drawing upon the Farm Business Survey sample) and then selects the nearest ‘bespoke farm group’ of matches based on distance (Z-score). We do this across 34 dimensions that capture a wide range of English farm characteristics, including tenure and geographic proximity. Means and other statistics are calculated specifically for that bespoke farm comparator group, or ‘peer set’. This generates a uniquely defined comparator for each individual farm that could substantially improve key-performance-indicators, such as unit costs of production, which can be used for benchmarking purposes. This methodology has potential to be applied across the full range of FBS farm types and in a wider range of benchmarking contexts.
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