Developing a Mastitis Pattern Analysis Tool

Summary

Key outcomes

A mastitis pattern analysis tool was designed and tested. The tool ensures that data are analysed in a structured, consistent and accurate way, based on herd somatic cell count as well as clinical mastitis records. It produces a mastitis pattern report that allows farmers and vets to assess and prioritise the key management areas and potentially detect emerging problems.

Benefits

• If use of the Mastitis Pattern Analysis Tool facilitates a reduction in clinical and subclinical mastitis of only 3% per year for a dairy farm with 150 cows and average levels of clinical and sub-clinical mastitis, this would result in a net financial return of £900 per year or £4,500 in 5 years.

• Reduced culling rates and waste milk as a result of reducing mastitis will result in environmental benefits via reduced greenhouse gas emissions.

• Reducing mastitis levels will also improve animal welfare, and enhance industry image.

Sector:
Dairy
Project code:
41110018
Date:
01 July 2016 - 01 June 2018
Funders:
AHDB Dairy
AHDB sector cost:
£35,227
Total project value:
£35,227
Project leader:
University of Nottingham

About this project

In recent years, the AHDB Dairy Mastitis Control Plan (DMCP) has had a significant positive impact on-farm, with an estimated >15% of GB cows receiving mastitis control through the plan. Despite this excellent performance, the first stage of the plan, making a ‘herd diagnosis’ is particularly difficult. This is an essential first step because control measures later identified are dependent upon a correct diagnosis at this point.

  1. To create a fully automated method of making a herd mastitis diagnosis so that mastitis issues can be identified, quantified and prioritised to enable effective targeting of control measures
  2. To evaluate the new automated method of herd mastitis diagnosis by assessing it against 50 dairy herds in which a known herd diagnosis has been made by an experienced Dairy Mastitis Control Plan Deliverer

Publications:

Hyde et al., 2020. Automated prediction of mastitis infection patterns in dairy herds using machine learning. Nature Scientific Reports 10: 2429

AHDB launches tool to diagnose and tackle mastitis. Farmers weekly January 2020

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