Precision livestock farming: potential application for sheep systems in harsh environments
dc.contributor.advisor
Waterhouse, Tony
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dc.contributor.advisor
Morgan-Davies, Claire
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dc.contributor.advisor
Metzger, Marc
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dc.contributor.author
Wishart, Harriet Mary
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dc.contributor.sponsor
other
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dc.date.accessioned
2019-09-24T09:49:33Z
dc.date.available
2019-09-24T09:49:33Z
dc.date.issued
2019-11-26
dc.description.abstract
Scottish hill sheep systems are comparable to other extensive sheep systems in
harsh environments around the world. They face a number of difficulties, including
low productivity, poor economic viability, labour availability and capability, and
ensuring good animal welfare. They do however, play an important role for rural
communities, environmental management, and production of sheepmeat and
breeding animals. Exploring alternative approaches, such as Precision Livestock
Farming (PLF), to overcome these difficulties is essential. PLF has been
successfully applied to intensive systems but few examples exist for extensive
systems, such as hill sheep systems.
Pregnancy supplementation and retention and culling decision making are both key
processes within hill sheep systems. How they occur can have major impacts on
productivity and profitability of the system. Therefore, the aim of this thesis was to
investigate and understand the capacity for application and potential impacts of PLF
for hill sheep systems, when considered for two challenge areas: ewe pregnancy
supplementation, and ewe retention and culling decision making.
This research was carried out on a 2,200 ha hill sheep research farm, in the West
Highlands of Scotland. The majority of the land was unimproved hill pasture and
around 230 ha of improved fields and semi-improved parks. Methods were applied
and data collected from October 2013 to October 2016 from a long-term
performance recorded research flock of 600 Scottish Blackface and 300 Lleyn
breeding ewes. All 900 breeding ewes were assigned to one of two management
approaches. The first approach was a conventional one, where management
decisions were carried out at a flock level, or without the assistance of Electronic
Identification (EID) technology, and was comparable to conventional hill sheep
systems. The other approach was PLF, where management decisions were carried
out at an individual animal level, assisted by EID and weighing technology.
A PLF approach was applied to allocate supplementation to pregnant hill ewes
based on liveweight change. Inputs required (feed) and outputs (number of lambs
born and liveweight of lambs) were similar between ewes allocated supplementation
in the PLF approach and those allocated supplementation based on a stockperson’s
assessment of their condition. However the PLF approach successfully moved more
individual ewes out of higher supplementation levels. This PLF supplementary
approach could be consistently applied to any sheep system and constitutes a
framework which can be easily modified and further developed. The work also
demonstrated that liveweights could be collected quickly and reliably using
automated weighing technology. Such technology and liveweight data are likely to
be key for future developments of PLF approaches for hill sheep systems.
The second challenge targeted was retention and culling decision making of ewes
from the breeding flock. Questionnaires carried out with stockpeople revealed many
different reasons are used to make culling decisions. These reasons were mostly
based on the stockperson’s opinion and subjective assessment, and that little
recorded information was used to inform decision making. Culling at a fixed age
occurs on some hill sheep systems but this limits longevity and the associated
benefits of increased longevity. Within the research flock, used in this thesis, the
majority of ewes left the flock as a result of culling decisions and not because of
death. However, findings showed that ewes retained beyond a standard cull age,
were able to perform as well or better than younger ewes. A flock that did not cull
based on age had the potential to improve profitability.
Comparison between individual ewes’ performance, genetic and appearance
attributes and their following year’s performance, found that many common culling
reasons (including stockpersons opinion, number of lambs previously weaned, ewe
age and ewe appearance) had little association with future ewe performance.
Conversely, promising attributes included liveweight, Body Condition Scores and
liveweight change, all of which require data to be collected prior to any decision
making. A PLF approach, that uses data collected about a ewe over its lifetime to
make retention and culling decisions, has the potential to improve productivity and
profitability of the system.
In conclusion, this thesis established that PLF can be applied to hill sheep systems
using commercially available EID and automated weighing technology, as well as
individual sheep data to inform decision making. Such application has the potential
to improve productivity and profitability. For PLF to be widely adopted onto
commercial sheep farms, further research will be required to demonstrate greater
benefits to the system, including labour and welfare, and to better understand
farmers’ motivation towards uptake. Adoption of PLF approaches into hill sheep
systems has the potential to benefit individual farms and the industry as a whole.
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dc.identifier.uri
http://hdl.handle.net/1842/36196
dc.language.iso
en
dc.publisher
The University of Edinburgh
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dc.relation.hasversion
Wishart, H., Morgan-Davies, C., Stott, A., Wilson, R., Waterhouse, T., 2017. Liveweight loss associated with handling and weighing of grazing sheep. Small Ruminant Research, 153, 163-170.
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dc.relation.hasversion
Wishart, H., Lambe, N., Morgan-Davies, C., Waterhouse, A., 2014. The effect of duration of removal from grazing on body weight in sheep measured with an automated weighing system, in: British Society of Animal Science. Nottingham, UK, p. 15.
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dc.relation.hasversion
Wishart, H., Morgan-Davies, C., Waterhouse, A., 2015. A PLF approach for allocating supplementary feed to pregnant ewes in an extensive hill sheep system, in: Proceedings of Precision Livestock Farming ’15. Milan, Italy, pp. 256-265.
en
dc.relation.hasversion
Wishart, H., Lambe, N., Morgan-Davies, C., Waterhouse, A., 2016. Brief Communication: Which traits best predict ewe performance and survival the following year on a UK hill farm?, in: Proceedings of the New Zealand Society of Animal Production. Adelaide, Australia, pp. 159–162.
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Kenyon, F., Morgan-Davies, C., Lambe, N.R., Wishart, H., Waterhouse, A., McBean, D., McCracken, D., 2017. Application of weight-based targeted selective anthelmintic treatment (TST) strategy on hill and upland sheep flocks, in: Proceedings of the 9th International Sheep Veterinary Congress. Harrogate, UK.
en
dc.relation.hasversion
Morgan-Davies, C., Lambe, N., McLaren, A., Wishart, H., Waterhouse, A., McCracken, D., 2015. Labour profiles and Electronic Identification (EID) technology: assessing different management approaches on extensive sheep farming systems, in: Agro2015, 5th International Symposium for Farming Systems Design. Montpellier, France.
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Morgan-Davies, C., Lambe, N., Wishart, H., Waterhouse, A., Kenyon, F., McBean, D., McCracken, D., 2018. Impacts of using a precision livestock system targeted approach in mountain sheep flocks. Livestock Science 208, 67–76.
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dc.relation.hasversion
Morgan-Davies, C., Lambe, N.R., Kenyon, F., McBean, D., Wishart, H., McCracken, D., 2016. Introducing a Targeted Selective Treatment worming approach on a hill farm using Electronic Identification of lambs, in: BSAS - Advances in Animal Biosciences - Animal Science for a Sustainable Future. Chester, UK.
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Morgan-Davies, C., Lambe, N.R., Waterhouse, A., Kenyon, F., McBean, D., Wishart, H., McLaren, A., McCracken, D., 2017. Evaluating precision management of sheep in a hill farming system, in: Advances in Animal Biosciences, Precision Management of Grassland and Grazing Livestock. Edinburgh, UK, p. 892.
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Morgan-Davies, C., Lambe, N.R., Wishart, H., Kenyon, F., McBean, D., Waterhouse, A., McLaren, A., Borthwick, F., McCracken, D., 2015. Integrating electronic identification in hill farming management, in: EAAP 66th Annual Meeting. Warsaw, Poland, p. 195.
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Morgan-Davies, C., Wishart, H., 2015. Electronic Identification : Making the most out of compulsory tagging. SRUC Rural Policy Centre, Edinburgh.
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Morgan-Davies, C., Wishart, H., Lambe, N., McLaren, A., Kenyon, F., McBean, D., Waterhouse, A., Umstätter, C., 2014. Improving efficiency in hill ewes for a better climate, in: McCracken, K. (Ed.), Delivering Multiple Benefits from Our Land: Sustainable Development in Practice. Edinburgh, UK, pp. 241–244.
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Morgan-Davies, C., Wishart, H., Lambe, N.R., Kenyon, F., McBean, D., 2015. Integrating EID technology into hill sheep farming management, in: 4th Farm Animal Imaging Conference. Edinburgh, UK.
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dc.relation.hasversion
Morgan-Davies, C., Wishart, H., Lambe, N.R., Kenyon, F., McBean, D., Waterhouse, A., McCracken, D., 2015. EID and other Technological Advances in Small Ruminant Research. International Animal Health Journal 2, 64–66.
en
dc.relation.hasversion
Umstätter, C., Morgan-Davies, C., Stevens [Wishart], H., Kenyon, F., McBean, D., Lambe, N., Waterhouse, A., 2013. Integrating Electronic Identification into Hill Sheep Management, in: Berckmans, D., Vandermeulen, J. (Eds.), Precision Livestock Farming ’13. Leuven, Belgium, pp. 412–420.
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dc.relation.hasversion
Wishart, H., Lambe, N., 2019. Variation in individual feed intake profiles recorded in group-housed lambs, in: British Society of Animal Science. Edinburgh, UK, p. 21.
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dc.subject
Scottish hill sheep systems
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dc.subject
Precision Livestock Farming
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dc.subject
ewe pregnancy supplementation
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dc.subject
ewe retention
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dc.subject
culling decision making
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dc.subject
Electronic Identification technology
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dc.subject
EID
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dc.subject
PLF
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dc.subject
Body Condition Scores
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dc.subject
liveweight change
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dc.title
Precision livestock farming: potential application for sheep systems in harsh environments
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dc.type
Thesis or Dissertation
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dc.type.qualificationlevel
Doctoral
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dc.type.qualificationname
PhD Doctor of Philosophy
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