Abstract
Abstract To find opportunities to improve, in efficiency or performance, farms are often compared on the basis of standard typologies (i.e. categorisations). For example the EU "specialist-cereals-oilseeds-pulses" farm type, known in Britain as "cereals" farms. These categories, being aggregates, contain significant numbers of atypical enterprises. For example, in 2017 there were 30 cattle and 69 sheep on the average "general-cropping" farm in England. This means that comparators are averages across farms with widely divergent scales of different enterprises (and hence farm characteristics), that are not relevant for the comparison. Furthermore, farmers may not necessarily even know their own farm "type" when undertaking benchmarking or comparative analysis. We therefore present a novel method that matches a specific farm against all farms in a survey (drawing upon the Farm Business Survey (FBS) sample), and then selects the nearest "bespoke farm group" of matches based on distance (Z-score) away. Across 34 dimensions, including almost all the enterprises characteristic of English farms, as well as tenure and geographic proximity. Means and other statistics are calculated specifically for that bespoke farm comparator group, or "peer set" of 25 farms or more if less than 1 Z-score away. This generates a uniquely defined comparator, for each individual farm and gives a substantially improved key-performance-indicators for benchmarking purposes. This methodology has potential to be applied across the full range of FBS farm types and across a wider range of 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
The "farms like mine" methodology outlined above generates a bespoke peer comparator data set and results from the approach are bespoke to the individual farm business data that users enter into the system
We detail the dimensions of the methodology to demonstrate the breadth of variables that the system draws upon in the generation of a bespoke peer set of comparator data
Summary
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. In the European FADN/RICA classification (European Commission 2013), "Specialist COP" (cerealsoilseeds-pulses, called "cereals" in Britain) or "Specialist other field crops" ( called "mixed-cropping", or in Britain "general-cropping") These categories, being aggregates, contain significant quantities of atypical enterprises. There is a tendency to assume that a farm is "mixed", where there are small numbers within a particular enterprise (to take an extreme example, a farm with cereal crops and a single sheep is not a mixed farm) To address these issues, this paper introduces the innovation of matching to "farms like mine", where matching is primarily on land use areas and livestock numbers contained in farm survey datasets. We demonstrate a novel method for the identification of benchmark standards, that can provide more relevant and useful standards for farm management decision-making
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