'Farms like mine': a novel method in peer matching for agricultural benchmarking.


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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Preprints for Agriculture and Allied Sciences
Advisory Board
  • Leisa Armstrong, Edith Cowan University, Australia
  • Arianna Becerril García, Autonomous University of the State of Mexico, Redalyc/AmeliCA, Mexico
  • Susmita Das, Bangladesh Agricultural Research Council
  • Abeer Elhalwagi, National Gene Bank, Egypt
  • Gopinath KA, Central Research Institute for Dryland Agriculture
  • Niklaus Grünwald, USDA Agricultural Research Service
  • Sridhar Gutam, ICAR IIHR/Open Access India
  • Vinodh Ilangovan, Max Planck Institute for Biophysical Chemistry
  • Jayalakshmi M, ANGRAU, India
  • Khelif Karima, Institut National de la Recherche Agronomique d'Algérie
  • Dinesh Kumar, Indian Agricultural Statistics Research Institute
  • Satendra Kumar Singh, Indian Council of Agricultural Research
  • Devika P. Madalli, DRTC/Indian Statistical Institute, India
  • Prateek Mahalwar, Cellulosic Technologies UG, Germany
  • Bernard Pochet, University of Liège - Gembloux Agro-Bio Tech
  • Vassilis Protonotarios, NEUROPUBLIC
  • Andy Robinson, CABI
  • Paraj Shukla, King Saud University
  • Chandni Singh, Indian Institute for Human Settlements
  • Kuldeep Singh Jadon, ICAR-Central Arid Zone Research Institute, India
  • Rajeev K Varshney, CGIAR/ICRISAT, India
  • Sumant Vyas, ICAR- National Research Centre on Camel, India
  • Oya Yildirim Rieger, Ithaka S+R/ITHAKA, USA
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