Background Sheep lameness is a major concern among farmers and policymakers with significant impacts on animal welfare standards as well as financial and production performance. The present study attempts to identify the relative importance of environmental and farm-level management characteristics on sheep lameness.
Method To address this objective, data were derived from the SPiLaMM project from 18 farms that used smartphone app to collect data, the British Geological Survey and the Meteorological Office over 2016–2018. Data were analysed using a multilevel Poisson regression model.
Results Temperature and higher length of pasture had a positive relationship with lameness while concentration of Selenium in soil and flock size had a negative relationship with lameness. In addition, results showed lower lameness levels for the bedrock class mudstone, siltstone, limestone and sandstone in comparison to sandstone and finally, lambs and ewes younger than 1year old had lower levels of lameness than older ewes.
Conclusion Findings of the present approach show the potential use of data collected via a smartphone app to study the epidemiology of disease. Furthermore, factors identified could be validated in intervention studies and generate data-driven disease predictive models.
- risk factors
- environmental characteristics
- field characteristics
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Funding Funding for this research has been provided by Innovate UK (https://www.gov.uk/government/organisations/innovate-uk) (Grant Number 102097).
Competing interests None declared.
Patient consent for publication Not required.
Provenance and peer review Not commissioned; externally peer reviewed.
Data availability statement All data relevant to the study are included in the article or uploaded as supplementary information.
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