This PhD project is part of a larger collaborative multi-disciplinary project comprising of experts in the following areas: beef production, carcass evaluation, animal health and welfare, smart-farming (precision livestock farming), advanced imaging techniques, sensing systems and advanced data analytics (specifically machine learning approaches).
There are two main market-driven challenges facing the UK beef sector:
1. Pre-farm gate: Currently, producers monitor performance and select cattle for slaughter through visual assessment and manual weights over weigh platforms. In 2017, 51% of prime beef carcasses in the UK did not meet target fat and conformation grades (AHDB, 2018a). Animals are frequently over-finished leading to increased variable farm costs (feed, bedding and labour), reduced annual capacity of finishing units and increased primary processing costs. The cost to UK producers of sending over-finished cattle to slaughter has been estimated at £8.8 million per year. Over-finished cattle also have an environmental cost, with an estimated 100 year global warming potential of 230 kgCO2eq per animal (14 days over-finished) (AHDB, 2018b).
2. Post-farm gate: Classification of beef carcasses in the UK is undertaken predominantly by visual assessment by trained assessors, who allocate EUROP fat and conformation grades. This forms the basis of payment from processor to producer but presents a number of challenges including cost (manual assessors and re-training), availability of trained staff and consistency. Lack of confidence in the reliability of classification makes it difficult to agree quality-based payment schedules that reflect the true carcass value.
This PhD will focus on the use of engineering solutions (advanced sensing and high-resolution imaging systems) on-farm and in the abattoir, coupled with advanced data analytics (specifically machine learning approaches), to provide more accurate and detailed measurements of the carcass and it’s individual components than currently available (see Miller et al., 2019). A more comprehensive dataset with variables generated on-farm and in the abattoir, will allow for a better understanding of yield development at the individual animal level, and drive improvements in product quality and consistency through enhanced decision support (optimised nutrition, farm advisory services (business management, health and welfare), optimised slaughter selections).
The student will register at the University of Edinburgh where they will have access to the training environments of both SRUC and the University of Edinburgh. Both institutions are committed to the training of PhD students in both specific and transferable skills. The student will receive mentoring and training in the following areas specific to this PhD project: beef production systems, precision livestock farming developments, product quality (carcass) and advanced data analytics (specifically machine learning techniques).
The student will receive further support from two participating SME’s offering access to expertise, facilities and experience of working in a professional environment including engagement with end-users. Innovent Technologies, an agri-technology company, will provide access to expertise in advanced imaging, data science and decision support for livestock applications. Agri-Epi, one of four UK government AgriTech Innovation Centres, brings expertise in precision farming applications.
Applicants should download the required forms from http://www.eastscotbiodtp.ac.uk/how-apply-0
and send the following documents to [email protected]
a. EASTBIO Application Form
b. EASTBIO DTP Equality Form
d. Academic transcripts (a minimum of an upper second class or first class honours degree or equivalent is required for PhD study
e. Two references should be provided by the deadline using the EASTBIO reference form (http://www.eastscotbiodtp.ac.uk/how-apply-0
). Please advise your referees to return the reference form to [email protected]
f. If you are nominated by the supervisor(s) of the EASTBIO PhD project you wish to apply for, they will provide a Supervisor Support Statement.