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

Mark A Reader,Ian D Hodge,Ben G.A Lang,Stephen Ramsden,Paul Wilson

doi:10.31220/osf.io/5gjdp

Abstract

Abstract 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.

Highlights

Individual farmers face considerable problems when attempting to compare their individual performance with the performance of other farms: every farm is different, with a number of different enterprises, generating a variety of different income streams
This is a problem when benchmarking as comparisons are normally made using averages – atypical enterprises will appear as relatively small amounts that are not representative of either the overall sample or the small number of farms that have these atypical enterprises
The Farm Business Survey (FBS) provides a wide range of candidate variables that can be used to generate the bespoke peer set of comparator data

Summary

Introduction

Individual farmers face considerable problems when attempting to compare their individual performance with the performance of other farms: every farm is different, with a number of different enterprises, generating a variety of different income streams. It is clearly helpful for individual farmers to be able to have some standard against which to judge their performance, in order to identify areas in which they may be underperforming, and which aspects of the business could be improved. There were 30 cattle and 69 sheep on the 2017 mean ‘General Cropping’ farm in

Methods

Results

Conclusion