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Understanding dairy cow behaviour to improve production and welfare in robotic milking systems

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  • Full or part time
    Dr G Arnott
    Dr S Buijs
  • Application Deadline
    No more applications being accepted
  • Funded PhD Project (European/UK Students Only)
    Funded PhD Project (European/UK Students Only)

Project Description

This project will investigate learning and motivational strategies in dairy cows to optimize their use of robotic milking systems, to improve cow welfare and productivity.

Dairy farming is a key industry in Northern Ireland with in excess of 2,500 dairy farms (~14,500 UK), yet increasing herd sizes and reductions in the agricultural workforce have increased the pressure on labour both nationally and internationally. In response there has been increasing uptake of robotic milking systems making use of state of the art technology, whereby a cow voluntarily visits an automatic milking machine. This technology has the potential to improve dairy cow welfare and enhance farm labour efficiency. However, despite the recent uptake of robotic milking in NI, with estimates that 10% of the national herd is milked by robots (with numbers expected to continue increasing), there is little independent information regarding the management of these systems. In particular, there is a lack of knowledge regarding cow behaviour and milking frequency with robotic milking, with some cows not transitioning well to these systems and requiring prolonged training. This can have negative effects on cow welfare and production. The success of robotic milking depends on the cows’ voluntary behaviour, yet this remains to be fully understood. Understanding and influencing this behaviour is key to the successful implementation of robotic milking. This project, with co-funding from an industry partner (AgriSearch), as part of a Collaborative PhD Studentship award, will address the following objectives.

Objectives:
The overall project aim is to improve cow welfare and productivity in robotic milking systems by optimizing the visit frequency to the robot.
More specifically using three experiments, this project will address the following five objectives:
1. Assess the effects of changes in concentrate feed allocation on dairy cow behaviour and welfare in robotic milking systems.
2. Use relevant learning theory to investigate training strategies to increase regular voluntary milking visits to the robot.
3. Investigate training strategies to deter ‘loitering’ (cows remaining in the robot area unnecessarily thereby blocking other cows’ access).
4. Evaluate strategies to encourage voluntary visits in animals that are lame and/or have a low social ranking.
5. Evaluate the effect of the developed strategies on dairy cow welfare.

This project will be supervised by Dr Gareth Arnott of Queen’s University School of Biological Sciences, Dr Stephanie Buijs of the Agri-Food and Biosciences Institute, Mr Jason Rankin of AgriSearch, and Dr Deborah McConnell of the Agri-Food and Biosciences Institute.

Start Date: 1 October 2020
Duration: 3 years

Specific skills required by applicants:

Relevant undergraduate/postgraduate (e.g. animal science, veterinary science, biology, animal behaviour and welfare)
Experience with research on animal behaviour/animal training
Experience with scientific writing and statistical analysis
Experience with working with cattle is desirable

All applications must be submitted via https://dap.qub.ac.uk/portal/user/u_login.php

Funding Notes

This project is funded by a Collaborative PhD studentship award from the Department for the Economy (DfE) with additional funding from an industry partner (AgriSearch).

To be considered eligible for a full DfE studentship award you must have been resident in the United Kingdom for the full three year period before the first day of the first academic year of the course. Please read the full information on eligibility criteria: https://www.nidirect.gov.uk/articles/department-economy-studentships
https://www.economy-ni.gov.uk/sites/default/files/publications/economy/post-graduate-studentships-terms-conditions-19-20.pdf

How good is research at Queen’s University Belfast in Agriculture, Veterinary and Food Science?

FTE Category A staff submitted: 33.40

Research output data provided by the Research Excellence Framework (REF)

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