To evaluate the EBLEX KPIs for suckler herds and growing and finishing beef enterprises in England

 

Project number:                    61110007

Lead contractor:                   University of Nottingham

Start and end date:              01 October 2015 – 30 June 2017

 

The Problem:

A Key Performance Indicator (KPI) is a business metric used to evaluate factors that are crucial to success. Livestock businesses can greatly benefit from recording performance and management information to enable more informed decisions to be made, to identify areas of strength and weakness and implement changes that will drive up profitability. Useful performance indicators have been identified and tested in the pig and dairy industries and are widely used to inform management decisions. Within the beef sector, performance indices have been developed in an attempt to guide producers and advisers along the route of data collection and interpretation. However, the practicality of collecting the data required to calculate the performance indicators and the value of them to both farmers and industry professionals requires further investigation.

 

Aims and Objectives:

The main aim of this project is to add significant information towards the development of KPI’s for the beef sector. The project will assess current performance indices whilst at the same time identifying and evaluating new potential KPIs. Since the beef industry comprises of a multitude of farming methods and management systems, it is envisaged that performance indicators will be sought for each major method of production.

Aims:

  • Coordination of a technical advisory group (TAG)
  • Collation of historic data from project farms and calculation of new and existing KPIs
  • Assessment of trends over time in the historic data and comparisons between systems
  • Evaluation of correlations within the data and relationships between KPIs and overall farm performance
  • Appraisal of farmer attitudes to data recording and performance monitoring
  • Maximise knowledge exchange to ensure that results have maximum impact on industry practice
  • Additional period financed by match funding from the University of Nottingham: Advanced stochastic sensitivity analysis to further explore and define KPIs in relation to farm success

 

Approach:

The technical advisory group (TAG) will form a core part of the project, the group will consist of four beef farmers (two suckler producers and two grower/finishers), a minimum of four beef advisors, academics and AHDB Beef & Lamb staff, with the group working together to meet the aims of the project.

Historical cattle performance data relating to production, health, fertility, efficiency and management factors across four cattle systems (lowland sucklers, upland sucklers, semi-extensive finisher and intensive finisher) will be collated. This data will be used to investigate correlations between a number of performance indicators and actual physical performance to improve understanding of the use and value of KPIs to farmers and advisors. A micro-stimulation model will be developed and a probabilistic sensitivity analysis will then be performed to evaluate which of the inputs has most impact on bottom line outcomes and which KPI’s are most predictive. Trends between systems and within systems will be established providing further insight into how data analysis and KPI calculation can provide insight and drive management decisions.

A sample of 50 farmers will be identified and interviewed by telephone using a short, simply structured telephone survey to establish farmer attitudes to data and performance recording. This will identify barriers to the implementation of KPI’s and will inform the design of knowledge materials and approaches best able to overcome these barriers and engage more farmers. Finally, the key outcomes from the project will be disseminated to industry through articles, events and webinars.

 

Results:

Key messages:

  • Optimising efficiency of production through performance monitoring can be used to facilitate both financial and environmental sustainability of beef enterprises.
  • Use of KPIs that reflect the targets of an enterprise can enable goals to be achieved. Goals are often financial, for example to optimise net margin (i.e. profit), however producers will often have more immediate control over physical aspects of production. The ability to link physical performance data with financial, and to analyse both aspects of production together, can allow more complete monitoring of enterprise performance and enable more effective decision-making than monitoring either aspect in isolation.
  • Data is often recorded and not used for performance monitoring, for example statutory movement data and medicine use data. Recording and storage of such data in an appropriate format and in a single place (data is often stored in multiple places making analysis challenging), could facilitate increased performance monitoring.
  • The evidence base behind KPIs used in beef production is limited. This is largely due to the many confounding factors in a farm ‘system’ all having various and interacting effects on production, and challenges around generation of sufficiently large datasets that would enable statistically significant conclusions to be drawn. During this project mathematical modelling has been used to determine individual and independent effects for many variables on an output (for example net margin), and to account for some of these confounding effects. Going forward, simulation modelling will be used to further investigate these relationships.
  • Through discussion with a technical advisory group (TAG), a KPI toolkit has been developed, structured in a hierarchical fashion to provide a decision-making pathway. Definitions of the performance indicators in the toolkit have been provided to facilitate consistency of use.
  • Many of the performance indicators in the toolkit require weight data. The importance of capturing weight data regularly was highlighted as important by the TAG, and the use of EID was seen as a way of facilitating this. EID was also seen as a way of enabling the flow of data along the supply chain both between producers and to the processor, and back again to the primary producer, allowing an individual animals data to be used at all stages of production.
  • During analysis of TAG farm data, methods of displaying data were investigated. Those that displayed data distribution, rather than an individual average figure, were felt to be particularly useful.
  • A questionnaire was distributed to appraise farmer attitudes to data collection and analysis. This highlighted that farmers value their data, and many questioned would like to collect more or make better use of what data they have. Data analysis was perceived as slightly more challenging than data collection, and highlights an area where increased support for farmers could facilitate performance recording. Other than time and cost, lack of technology and knowledge were commonly quoted barriers to data collection and use.
  • Relevant outcomes of TAG discussion and questionnaire results were fed back to herd management software providers, with the aim of promoting ways in which farmers feel their software could enable them to make best use of their data.
  • Throughout the project KE activities have been held to demonstrate how data can be used to inform herd management decision-making, and to engage with beef farmers around data capture and use.