Grazing and ruminating are the most important behaviours for ruminants, as they spend most of their daily time budget performing these. Continuous surveillance of eating behaviour is an important means for monitoring ruminant health, productivity and welfare. However, surveillance performed by human operators is prone to human variance, time-consuming and costly, especially on animals kept at pasture or free-ranging. The use of sensors to automatically acquire data, and software to classify and identify behaviours, offers significant potential in addressing such issues. In this work, data collected from sheep by means of an accelerometer/gyroscope sensor attached to the ear and collar, sampled at 16 Hz, were used to develop classifiers for grazing and ruminating behaviour using various machine learning algorithms: random forest (RF), support vector machine (SVM), k nearest neighbour (kNN) and adaptive boosting (Adaboost). Multiple features extracted from the signals were ranked on their importance for classification. Several performance indicators were considered when comparing classifiers as a function of algorithm used, sensor localisation and number of used features. Random forest yielded the highest overall accuracies: 92% for collar and 91% for ear. Gyroscope-based features were shown to have the greatest relative importance for eating behaviours. The optimum number of feature characteristics to be incorporated into the model was 39, from both ear and collar data. The findings suggest that one can successfully classify eating behaviours in sheep with very high accuracy; this could be used to develop a device for automatic monitoring of feed intake in the sheep sector to monitor health and welfare.

Automated behavioural classification and identification through sensors has the potential to improve health and welfare of the animals. Position of a sensor, sampling frequency and window size of segmented signal data has a major impact on classification accuracy in activity recognition and energy needs for the sensor, yet, there are no studies in precision livestock farming that have evaluated the effect of all these factors simultaneously. The aim of this study was to evaluate the effects of position (ear and collar), sampling frequency (8, 16 and 32 Hz) of a triaxial accelerometer and gyroscope sensor and window size (3, 5 and 7 s) on the classification of important behaviours in sheep such as lying, standing and walking. Behaviours were classified using a random forest approach with 44 feature characteristics. The best performance for walking, standing and lying classification in sheep (accuracy 95%, F -score 91%–97%) was obtained using combination of 32 Hz, 7 s and 32 Hz, 5 s for both ear and collar sensors, although, results obtained with 16 Hz and 7 s window were comparable with accuracy of 91%–93% and F -score 88%–95%. Energy efficiency was best at a 7 s window. This suggests that sampling at 16 Hz with 7 s window will offer benefits in a real-time behavioural monitoring system for sheep due to reduced energy needs.

The aims of this study were to update the prevalence of lameness in sheep in England and identify novel risk factors. A total of 1260 sheep farmers responded to a postal survey. The survey captured detailed information on the period prevalence of lameness from May 2012-April 2013 and the prevalence and farmer naming of lesions attributable to interdigital dermatitis (ID), severe footrot (SFR), contagious ovine digital dermatitis (CODD) and shelly hoof (SH), management and treatment of lameness, and farm and flock details. The global mean period prevalence of lameness fell between 2004 and 2013 from 10.6% to 4.9% and the geometric mean period prevalence of lameness fell from 5.4% (95% CL: 4.7%-6.0%) to 3.5% (95% CI: 3.3%-3.7%). In 2013, more farmers were using vaccination and antibiotic treatment for ID and SFR and fewer farmers were using foot trimming as a routine or therapeutic treatment than in 2004. Two over-dispersed Poisson regression models were developed with the outcome the period prevalence of lameness, one investigated associations with farmer estimates of prevalence of the four foot lesions and one investigated associations with management practices to control and treat lameness and footrot. A prevalence of ID>10%, SFR>2.5% and CODD>2.5% were associated with a higher prevalence of lameness compared with those lesions being absent, however, the prevalence of SH was not associated with a change in risk of lameness. A key novel management risk associated with higher prevalence of lameness was the rate of feet bleeding/100 ewes trimmed/year. In addition, vaccination of ewes once per year and selecting breeding replacements from never-lame ewes were associated with a decreased risk of lameness. Other factors associated with a lower risk of lameness for the first time in a random sample of farmers and a full risk model were: recognising lameness in sheep at locomotion score 1 compared with higher scores, treatment of the first lame sheep in a group compared with waiting until >5 were lame, treatment of lame sheep within 3 days, ease of catching lame sheep and quarantine for >21 days. A previously known factor associated with a lower risk of lameness was footbathing to prevent ID. We conclude that the prevalence of lameness in sheep in England has fallen and that this might be in part because of increased uptake of managements recently reported as beneficial to control lameness. Routine foot trimming should be avoided.

Footrot is an infectious bacterial disease of sheep that causes lameness. The causal agent is Dichelobacter nodosus. There is debate regarding the role of Fusobacterium necrophorum in disease initiation. This research used an observational longitudinal study of footrot, together with quantitative PCR (qPCR) of bacterial load of D. nodosus and F. necrophorum, to elucidate the roles of each species in the development of disease. All feet of 18 a priori selected sheep were monitored for five weeks assessing disease severity (healthy, interdigital dermatitis (ID) and severe footrot (SFR)) and bacterial load. A multinomial model was used to analyse these data. Key unadjusted results were that D. nodosus was detected more frequently on feet with ID, whereas F. necrophorum was detected more frequently on feet with SFR. In the multinomial model, ID was associated with increasing log10 load of D. nodosus the week of observation (OR=1.28 (95% CI=1.08-1.53)) and the week prior to development of ID (OR=1.20 (95% CI=1.01-1.42). There was no association between log10 load(2) of F. necrophorum and presence of ID (OR=0.99 (95% CI=0.96-1.02))). SFR was associated with increasing log10 load of D. nodosus the week before disease onset (OR=1.42 (95% CI=1.02-1.96)) but not once SFR had occurred. SFR was positively associated with log10 load(2) of F. necrophorum once disease was present (OR=1.06 (95% CI=1.01-1.11)). In summary, there was an increased risk of increasing D. nodosus load the week prior to development of ID and SFR and during an episode of ID. In contrast, F. necrophorum load was not associated with ID before or during an episode, and was only associated with SFR once present. These results contribute to our understanding of the epidemiology of footrot and highlight that D. nodosus load plays the primary role in disease initiation and progression, with F. necrophorum load playing a secondary role. Further studies in more flocks and climates would be useful to confirm these findings. This study identifies that D. nodosus load is highest during ID. This supports previous epidemiological findings, which demonstrate that controlling ID is the most effective management strategy to prevent new cases of ID and SFR.

A 2009 UK Government report on veterinary expertise in food animal production highlighted that there was insufficient herd health expertise among veterinarians and lack of appropriate business models to deliver veterinary services to the livestock sector. Approximately two thirds of sheep farmers only contact their veterinarian for emergencies and one fifth have all year round contact. The aim of the current study was to understand sheep farmers' perception, the current and future role of veterinarians in flock health management using qualitative methodology. The eligibility criteria were male farmers with a flock size of at least 200 adult sheep. Seven focus groups of farmers (n=45) stratified by three regions and two age groups (≤50 and >50) were conducted. Thematic analysis of the discussions indicated that most farmers considered and used their veterinarian as a fire-fighter, whilst other advice was gathered free of charge when the veterinarian was on the farm for other reasons (typically seeing cattle) or by telephone. A small group of farmers were using their veterinarian or a sheep consultant proactively with regular contact and found this financially beneficial. Farmers indicated that the key barriers to using a veterinarian proactively were inconsistent service, high turnover of veterinarians, lack of expertise of sheep farming among veterinarians and concern about independence of advice. Although economics was also mentioned as a key barrier to using veterinarians more proactively, most farmers did not know where they gained and lost income from their flock; there was heavy reliance on the single farm payment scheme (SPS) and very few farmers kept records from which they could investigate where there were inefficiencies in production. Overall sheep farmers considered sheep farming complex and that each farm was unique and that they themselves were the experts to manage their flock. We conclude that there is an impasse: veterinarians might need to provide consistency and wide expertise beyond knowledge of disease and a model of how flock planning would be financially beneficial but until sheep farmers keep production records flock health planning cannot be rigorous and the financial benefits cannot be evaluated. Given the reliance on SPS by farmers an alternative model would be to require farmers to keep production records to comply with SPS. This might lead to flock health planning being adopted at a faster rate and so develop the UK sheep industry and make it more environmentally sustainable by reducing waste from disease and low productivity.