Development of a multivariate analytical system to identify lameness in dairy cows

  • Beth Hewitt

Student thesis: Master's ThesisMaster of Science (MSc)


Lameness is a key welfare issue in the dairy industry. With approximately a third of all dairy cows in the UK experiencing lameness at any one time, it poses serious economic losses to the farmer. While advisory tools exist to address the associated risk factors, the problem persists. This project hypothesises two reasons for this: the varied approach to defining the issue, and the tendency for observers to misdiagnose mildly lame cases. As the condition has the potential to cause chronic pain, it is important to identify it early, before lame cows’ experience long-term suffering. This project was formed of two complimentary studies. The first study used historical data on dairy cows’ physiology, to develop a new multivariate analytical system; while the second used interviews to identify gaps in the industry’s understanding of lameness, and any variables that could be used to develop the scoring method. For the first study; mobility score, milk yield, body condition score, fertility (either measured by lactation number or parity) and somatic cell count, were used to determine the severity of lameness. The variables were normalised and combined (using MATLAB) via post-classification fusion, to generate an overall lameness score. An individual’s result was presented as a line on a histogram, so their severity of lameness, along with the distribution among the herd, could be identified. For the second study, eight experts were interviewed (two academics and six qualified veterinarians) to gain their understanding of chronic lameness and any cow variables of interest. The interviews were transcribed and analysed using NVivo. The results from the consultations will support the development of the scoring system in the future, so researchers will be better able to detect lameness before it becomes a chronic problem, greatly improving the chances of full recovery.
Date of Award7 Nov 2018
Original languageEnglish
Awarding Institution
  • The University of Bristol
SupervisorJo Hockenhull (Supervisor) & Becky Whay (Supervisor)

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