Modelling livestock infectious disease control policy under differing social perspectives on vaccination behaviour.

The spread of infection amongst livestock depends not only on the traits of the pathogen and the livestock themselves, but also on the veterinary health behaviours of farmers and how this impacts their implementation of disease control measures. Controls that are costly may make it beneficial for individuals to rely on the protection offered by others, though that may be sub-optimal for the population. Failing to account for socio-behavioural properties may produce a substantial layer of bias in infectious disease models. We investigated the role of heterogeneity in vaccine response across a population of farmers on epidemic outbreaks amongst livestock, caused by pathogens with differential speed of spread over spatial landscapes of farms for two counties in England (Cumbria and Devon). Under different compositions of three vaccine behaviour groups (precautionary, reactionary, non-vaccination), we evaluated from population- and individual-level perspectives the optimum threshold distance to premises with notified infection that would trigger responsive vaccination by the reactionary vaccination group. We demonstrate a divergence between population and individual perspectives in the optimal scale of reactive voluntary vaccination response. In general, minimising the population-level perspective cost requires a broader reactive uptake of the intervention, whilst optimising the outcome for the average individual increased the likelihood of larger scale disease outbreaks. When the relative cost of vaccination was low and the majority of premises had undergone precautionary vaccination, then adopting a perspective that optimised the outcome for an individual gave a broader spatial extent of reactive response compared to a perspective wanting to optimise outcomes for everyone in the population. Under our assumed epidemiological context, the findings identify livestock disease intervention receptiveness and cost combinations where one would expect strong disagreement between the intervention stringency that is best from the perspective of a stakeholder responsible for supporting the livestock industry compared to a sole livestock owner. Were such discord anticipated and achieving a consensus view across perspectives desired, the findings may also inform those managing veterinary health policy the requisite reduction in intervention cost and/or the required extent of nurturing beneficial community attitudes towards interventions.

An observational study of ear-tagged calf mortality (1 to 100 days) on Irish dairy farms and associations between biosecurity practices and calf mortality on farms participating in a Johne's disease control program.

Postnatal mortality among replacement stock has a detrimental effect on the social, economic, and environmental sustainability of dairy production. Calf mortality rates vary between countries and show differences in temporal trends; most, however, are characterized by high levels of between-farm variability. Explaining this variation can be difficult because herd-level information on management practices relevant to calf health is often not available. The Irish Johne's Control Programme (IJCP) contains a substantial on-farm monitoring program called the Veterinary Risk Assessment and Management Plan (VRAMP). Although this risk assessment is largely focused on factors relevant to the transmission of paratuberculosis, many of its principles are good practice biocontainment policies that are also advocated for the protection of calf health. The objectives of this study were (1) to quantify mortality in ear-tagged Irish dairy calves between 2016 and 2020 using both survival and risk approaches, (2) to determine risk factors for 100-d cumulative mortality hazard in ear-tagged Irish dairy calves between 2016 and 2020, (3) to determine whether 100-d cumulative mortality hazard was higher in ear-tagged calves within herds registered in the IJCP versus those that were not registered in the IJCP and whether there were differences between these cohorts over time, and (4) within IJCP herds, to determine whether VRAMP score or changes in VRAMP score were associated with 100-d cumulative mortality hazard. Excluding perinatal mortality, the overall 100-d cumulative mortality hazard was 4.1%. Calf mortality was consistently underestimated using risk approaches that did not account for calf censoring. Cox proportional hazards models showed that cumulative mortality hazard was greater in male calves; particularly, calves born to Jersey breed dams and those with a beef breed sire. Mortality hazard increased with increasing herd size, was highest in calves born in herds that contract-reared heifers, and lowest in those born in mixed dairy-beef enterprises. Mortality hazard decreased over time with the mortality hazard in 2020 being 0.83 times that of 2016. Mortality hazard was higher in IJCP-registered herds than nonregistered herds (hazard ratio 1.06, 95% CI 1.01-1.12), likely reflecting differences in herds that enrolled in the national program. However, we detected a significant interaction between IJCP status (enrolled vs. not enrolled) and year (hazard ratio 0.96, 95% CI 0.92-1.00), indicating that the decrease in mortality hazard between 2016 and 2020 was greater in IJCP herds versus non-IJCP herds. Finally, increasing VRAMP scores (indicating higher risk for paratuberculosis transmission) were positively associated with increased calf mortality hazard. Postnatal calf mortality rates in Irish dairy herds declined between 2016 and 2020. Our study suggests that implementation of recommended biocontainment practices to control paratuberculosis in IJCP herds was associated with a reduction in calf mortality hazard.

Predicting lameness in dairy cattle using untargeted liquid chromatography-mass spectrometry-based metabolomics and machine learning.

Lameness in dairy cattle is a highly prevalent condition that impacts on the health and welfare of dairy cows. Prompt detection and implementation of effective treatment is important for managing lameness. However, major limitations are associated with visual assessment of lameness, which is the most commonly used method to detect lameness. The aims of this study were to investigate the use of metabolomics and machine learning to develop novel methods to detect lameness. Untargeted metabolomics using liquid chromatography-mass spectrometry (LC-MS) alongside machine learning models and a stability selection method were utilized to evaluate the predictive accuracy of differences in the metabolomics profile of first-lactation dairy cows before (during the transition period) and at the time of lameness (based on visual assessment using the 0-3 scale of the Agriculture and Horticulture Development Board). Urine samples were collected from 2 cohorts of dairy heifers and stored at -86°C before analysis using LC-MS. Cohort 1 (n = 90) cows were recruited as current first-lactation cows with weekly mobility scores recorded over a 4-mo timeframe, from which newly lame and nonlame cows were identified. Cohort 2 (n = 30) cows were recruited within 3 wk before calving, and lameness events (based on mobility score) were recorded through lactation until a minimum of 70 d in milk (DIM). All cows were matched paired by DIM ± 14 d. The median DIM at lameness identification was 187.5 and 28.5 for cohort 1 and 2, respectively. The best performing machine learning models predicted lameness at the time of lameness with an accuracy of between 81 and 82%. Using stability selection, the prediction accuracy at the time of lameness was 80 to 81%. For samples collected before and after calving, the best performing machine learning model predicted lameness with an accuracy of 71 and 75%, respectively. The findings from this study demonstrate that untargeted LC-MS profiling combined with machine learning methods can be used to predict lameness as early as before calving and before observable changes in gait in first-lactation dairy cows. The methods also provide accuracies for detecting lameness at the time of observable changes in gait of up to 82%. The findings demonstrate that these methods could provide substantial advancements in the early prediction and prevention of lameness risk. Further external validation work is required to confirm these findings are generalizable; however, this study provides the basis from which future work can be conducted.

Mass spectrometry and machine learning for the accurate diagnosis of benzylpenicillin and multidrug resistance of Staphylococcus aureus in bovine mastitis.

Staphylococcus aureus is a serious human and animal pathogen threat exhibiting extraordinary capacity for acquiring new antibiotic resistance traits in the pathogen population worldwide. The development of fast, affordable and effective diagnostic solutions capable of discriminating between antibiotic-resistant and susceptible S. aureus strains would be of huge benefit for effective disease detection and treatment. Here we develop a diagnostics solution that uses Matrix-Assisted Laser Desorption/Ionisation-Time of Flight Mass Spectrometry (MALDI-TOF) and machine learning, to identify signature profiles of antibiotic resistance to either multidrug or benzylpenicillin in S. aureus isolates. Using ten different supervised learning techniques, we have analysed a set of 82 S. aureus isolates collected from 67 cows diagnosed with bovine mastitis across 24 farms. For the multidrug phenotyping analysis, LDA, linear SVM, RBF SVM, logistic regression, naïve Bayes, MLP neural network and QDA had Cohen's kappa values over 85.00%. For the benzylpenicillin phenotyping analysis, RBF SVM, MLP neural network, naïve Bayes, logistic regression, linear SVM, QDA, LDA, and random forests had Cohen's kappa values over 85.00%. For the benzylpenicillin the diagnostic systems achieved up to (mean result ± standard deviation over 30 runs on the test set): accuracy = 97.54% ± 1.91%, sensitivity = 99.93% ± 0.25%, specificity = 95.04% ± 3.83%, and Cohen's kappa = 95.04% ± 3.83%. Moreover, the diagnostic platform complemented by a protein-protein network and 3D structural protein information framework allowed the identification of five molecular determinants underlying the susceptible and resistant profiles. Four proteins were able to classify multidrug-resistant and susceptible strains with 96.81% ± 0.43% accuracy. Five proteins, including the previous four, were able to classify benzylpenicillin resistant and susceptible strains with 97.54% ± 1.91% accuracy. Our approach may open up new avenues for the development of a fast, affordable and effective day-to-day diagnostic solution, which would offer new opportunities for targeting resistant bacteria.

Metabolic alterations in dairy cattle with lameness revealed by untargeted metabolomics of dried milk spots using direct infusion-tandem mass spectrometry and the triangulation of multiple machine learning models.

Lameness is a major challenge in the dairy cattle industry in terms of animal welfare and economic implications. Better understanding of metabolic alteration associated with lameness could lead to early diagnosis and effective treatment, there-fore reducing its prevalence. To determine whether metabolic signatures associated with lameness could be discovered with untargeted metabolomics, we developed a novel workflow using direct infusion-tandem mass spectrometry to rapidly analyse (2 min per sample) dried milk spots (DMS) that were stored on commercially available Whatman® FTA® DMPK cards for a prolonged period (8 and 16 days). An orthogonal partial least squares-discriminant analysis (OPLS-DA) method validated by triangulation of multiple machine learning (ML) models and stability selection was employed to reliably identify important discriminative metabolites. With this approach, we were able to differentiate between lame and healthy cows based on a set of lipid molecules and several small metabolites. Among the discriminative molecules, we identified phosphatidylglycerol (PG 35:4) as the strongest and most sensitive lameness indicator based on stability selection. Overall, this untargeted metabolomics workflow is found to be a fast, robust, and discriminating method for determining lameness in DMS samples. The DMS cards can be potentially used as a convenient and cost-effective sample matrix for larger scale research and future routine screening for lameness.

The effect of environmental temperature on average daily gain in preweaned calves: A randomized controlled trial and Bayesian analysis.

Neonatal calves are relatively susceptible to heat loss, and previous research suggests that reduced environmental temperatures are associated with reduced average daily gain (ADG) during the preweaning phase. Current methods of mitigating negative effects of colder environmental conditions include the use of calf jackets and the provision of supplementary heat sources; however, previous research is limited. The aim of this study was to evaluate the effect of calf jackets and 1-kW heat lamps on the growth rates of preweaning calves and evaluate associations between environmental temperature and ADG using a Bayesian approach to incorporate both current and previous data. Seventy-nine calves from a single British dairy farm were randomly allocated at birth to 1 of the following 4 groups: no jacket and no heat lamp, heat lamp but no jacket, jacket but no heat lamp, or both heat lamp and jacket between January and April of 2021. Calves were weighed at both birth and at approximately 21 d of age. Temperature was recorded both inside and outside of the calf building, and in pens both with and without heat lamps using data loggers. To explore the effect of treatment group and environmental temperature on ADG, a fixed effects model was fitted over 1,000 bootstrap samples. The effect of environmental temperature on ADG was further explored within a Bayesian framework that used temperature and ADG data for 484 calves from 16 farms available from a previous trial as prior information. Calves housed under a 1-kW heat lamp had an increased ADG of 0.09 kg/d (95% bootstrap confidence interval: -0.02 to 0.20 kg/d), and no effect of jacket or interactions between jacket and heat lamp were found. A significant positive association was identified between the mean environmental temperature of the calf building and ADG, with a 1°C increase in temperature being associated with a 0.03 kg/d increase in ADG (95% bootstrap confidence interval: 0.01 to 0.04 kg/d). Associations between environmental temperature and ADG were further evaluated within a Bayesian framework, and posterior estimates were 0.014 kg/d of ADG per 1°C increase (95% credible interval: 0.009 to 0.021 kg/d). This study demonstrated that a 1-kW heat lamp was effective in increasing ADG in calves, and no significant effect of calf jacket on ADG was found. A significant, positive effect of increased pen temperature on calf ADG was identified in this study and was reinforced when including prior information from previous research within a Bayesian framework.

Improving growth rates in preweaning calves on dairy farms: A randomized controlled trial.

Previous research has identified key factors associated with improved average daily gain (ADG) in preweaning dairy calves and these factors have been combined to create a web app-based calf health plan (www.nottingham.ac.uk/herdhealthtoolkit). A randomized controlled trial was conducted to determine the effect of implementing this evidence-based calf health plan on both productivity and health outcomes for calves reared on British dairy farms. Sixty dairy farms were randomized by location (North, South, and Midlands) to either receive the plan at the beginning (INT) or after the end of the trial (CON) and recorded birth and weaning weights by weigh tape, and cases of morbidity and mortality. Calf records were returned for 3,593 calves from 45 farms (21 CON, 24 INT), with 1,760 calves from 43 farms having 2 weights recorded >40 d apart for ADG calculations, with 1,871 calves from 43 farms born >90 d before the end of the trial for morbidity and mortality calculations. Associations between both intervention group and the number of interventions in place with ADG were analyzed using linear regression models. Morbidity and mortality rates were analyzed using beta regression models. Mean ADG was 0.78 kg/d, ranging from 0.33 to 1.13 kg/d, with mean rates of 20.12% (0-96.55%), 16.40% (0-95.24%), and 4.28% (0-18.75%) for diarrhea, pneumonia, and mortality. The INT farms were undertaking a greater number of interventions (9.9) by the end of the trial than CON farms (7.6). Mean farm ADG was higher for calves on INT farms than CON farms for both male beef (MB, +0.22 kg/d) and dairy heifer (DH, +0.03 kg/d) calves. The MB calves on INT farms had significantly increased mean ADG (0.12 kg/d, 95% confidence interval: 0.02-0.22) compared with CON farms. No significant differences were observed between intervention groups for morbidity or mortality. Implementing one additional intervention from the plan, regardless of intervention group, was associated with improvements in mean ADG for DH calves of 0.01 kg/d (0.01, 0-0.03) and MB calves of 0.02 kg/d (0.00-0.04). Model predictions suggest that a farm with the highest number of interventions in place (15) compared with farms with the lowest number of interventions in place (4) would expect an improvement in growth rates from 0.65 to 0.81 kg/d for MB, from 0.73 to 0.88 kg/d for DH, a decrease in mortality rates from 10.9% to 2.8% in MB, and a decrease in diarrhea rates from 42.1% to 15.1% in DH. The calf health plan tested in this study represents a useful tool to aid veterinarians and farmers in the implementation of effective management interventions likely to improve the growth rates, health, and welfare of preweaning calves on dairy farms.

Numbers of close contacts of individuals infected with SARS-CoV-2 and their association with government intervention strategies.

BACKGROUND: Contact tracing is conducted with the primary purpose of interrupting transmission from individuals who are likely to be infectious to others. Secondary analyses of data on the numbers of close contacts of confirmed cases could also: provide an early signal of increases in contact patterns that might precede larger than expected case numbers; evaluate the impact of government interventions on the number of contacts of confirmed cases; or provide data information on contact rates between age cohorts for the purpose of epidemiological modelling. We analysed data from 140,204 close contacts of 39,861 cases in Ireland from 1st May to 1st December 2020. RESULTS: Negative binomial regression models highlighted greater numbers of contacts within specific population demographics, after correcting for temporal associations. Separate segmented regression models of the number of cases over time and the average number of contacts per case indicated that a breakpoint indicating a rapid decrease in the number of contacts per case in October 2020 preceded a breakpoint indicating a reduction in the number of cases by 11 days. CONCLUSIONS: We found that the number of contacts per infected case was overdispersed, the mean varied considerable over time and was temporally associated with government interventions. Analysis of the reported number of contacts per individual in contact tracing data may be a useful early indicator of changes in behaviour in response to, or indeed despite, government restrictions. This study provides useful information for triangulating assumptions regarding the contact mixing rates between different age cohorts for epidemiological modelling.

Estimation of the serial interval and proportion of pre-symptomatic transmission events of COVID- 19 in Ireland using contact tracing data.

BACKGROUND: The serial interval is the period of time between the onset of symptoms in an infector and an infectee and is an important parameter which can impact on the estimation of the reproduction number. Whilst several parameters influencing infection transmission are expected to be consistent across populations, the serial interval can vary across and within populations over time. Therefore, local estimates are preferable for use in epidemiological models developed at a regional level. We used data collected as part of the national contact tracing process in Ireland to estimate the serial interval of SARS-CoV-2 infection in the Irish population, and to estimate the proportion of transmission events that occurred prior to the onset of symptoms. RESULTS: After data cleaning, the final dataset consisted of 471 infected close contacts from 471 primary cases. The median serial interval was 4 days, mean serial interval was 4.0 (95% confidence intervals 3.7, 4.3) days, whilst the 25th and 75th percentiles were 2 and 6 days respectively. We found that intervals were lower when the primary or secondary case were in the older age cohort (greater than 64 years). Simulating from an incubation period distribution from international literature, we estimated that 67% of transmission events had greater than 50% probability of occurring prior to the onset of symptoms in the infector. CONCLUSIONS: Whilst our analysis was based on a large sample size, data were collected for the primary purpose of interrupting transmission chains. Similar to other studies estimating the serial interval, our analysis is restricted to transmission pairs where the infector is known with some degree of certainty. Such pairs may represent more intense contacts with infected individuals than might occur in the overall population. It is therefore possible that our analysis is biased towards shorter serial intervals than the overall population.

Assessment of the prevalence of Streptococcus uberis in dairy cow feces and implications for herd health.

Streptococcus uberis is a major causative agent of bovine mastitis worldwide, negatively affecting both milk production and animal welfare. Mammary infections result from environmental reservoirs, with cattle themselves required to propagate the infection cycle. Two longitudinal studies were performed to investigate the prevalence of Streptococcus uberis within feces and to evaluate factors which may affect gastrointestinal carriage. Bacterial detection was confirmed using a PCR-based method directed against sub0888 that detected S. uberis at an analytical sensitivity of 12 cfu/g of bovine feces. The first study sampled an entire herd at 8-wk intervals, over a 10-mo period and identified that maintenance of S. uberis within the dairy cow environment was due to a high proportion of animals shedding S. uberis and not due to a low number of "super-shedding" cows within the herd. Seasonality influenced detection rates, with detection levels significantly higher for housed cattle compared with those at pasture. Multilevel logistic regression was used to identify significant factors that affected S. uberis detection; these included parity, stage of lactation, and body condition score. An additional study involved screening a smaller cohort of cows housed over a 4-wk period and identified an increased probability of detection if cows were housed in loose straw yards, compared those in straw cubicles. This study highlighted several cow and management related factors that affect both detection of S. uberis and future infection risks.

Quantitative analysis of calf mortality in Great Britain.

National bodies in Great Britain (GB) have expressed concern over young stock health and welfare and identified calf survival as a priority; however, no national data have been available to quantify mortality rates. The aim of this study was to quantify the temporal incidence rate, distributional features, and factors affecting variation in mortality rates in calves in GB since 2011. The purpose was to provide information to national stakeholder groups to inform resource allocation both for knowledge exchange and future research. Cattle birth and death registrations from the national British Cattle Movement Service were analyzed to determine rates of both slaughter and on-farm mortality. The number of births and deaths registered between 2011 and 2018 within GB were 21.2 and 21.6 million, respectively. Of the 3.3 million on-farm deaths, 1.8 million occurred before 24 mo of age (54%) and 818,845 (25%) happened within the first 3 mo of age. The on-farm mortality rate was 3.87% by 3 mo of age, remained relatively stable over time, and was higher for male calves (4.32%) than female calves (3.45%). Dairy calves experience higher on farm mortality rates than nondairy (beef) calves in the first 3 mo of life, with 6.00 and 2.86% mortality rates, respectively. The 0- to 3-mo death rate at slaughterhouse for male dairy calves has increased from 17.40% in 2011 to 26.16% in 2018, and has remained low (<0.5%) for female dairy calves and beef calves of both sexes. Multivariate adaptive regression spline models were able to explain a large degree of the variation in mortality rates (coefficient of determination = 96%). Mean monthly environmental temperature and month of birth appeared to play an important role in neonatal on-farm mortality rates, with increased temperatures significantly reducing mortality rates. Taking the optimal month of birth and environmental temperature as indicators of the best possible environmental conditions, maintaining these conditions throughout the year would be expected to result in a reduction in annual 0- to 3-mo mortality of 37,571 deaths per year, with an estimated economic saving of around £11.6 million (USD $15.3 million) per annum. National cattle registers have great potential for monitoring trends in calf mortality and can provide valuable insights to the cattle industry. Environmental conditions play a significant role in calf mortality rates and further research is needed to explore how to optimize conditions to reduce calf mortality rates in GB.

Relative importance of herd-level risk factors for probability of infection with paratuberculosis in Irish dairy herds.

Control of paratuberculosis is challenging due to the relatively poor performance of diagnostic tests, a prolonged incubation period, and protracted environmental survival. Prioritization of herd-level interventions is not possible because putative risk factors are often not supported by risk factor studies. The objective for this study was to investigate the relative importance of risk factors for an increased probability of herd paratuberculosis infection. Risk assessment data, comprehensive animal purchase history, and diagnostic test data were available for 936 Irish dairy herds. Both logistic regression and a Bayesian ß regression on the outcome of a latent class analysis were conducted. Population attributable fractions and proportional reduction in variance explained were calculated for each variable in the logistic and Bayesian models, respectively. Routine use of the calving area for sick or lame cows was found to be a significant explanatory covariate in both models. Purchasing behavior for the previous 10 yr was not found to be significant. For the logistic model, length of time calves spend in the calving pen (25%) and routine use of the calving pen for sick or lame animals (14%) had the highest attributable fractions. For the Bayesian model, the overall R2 was 16%. Dry cow cleanliness (7%) and routine use of the calving area for sick or lame cows (6%) and had the highest proportional reduction in variance explained. These findings provide support for several management practices commonly recommended as part of paratuberculosis control programs; however, a large proportion of the observed variation in probability of infection remained unexplained, suggesting other important risks factors may exist.

The effect of paratuberculosis on milk yield--A systematic review and meta-analysis.

Bovine paratuberculosis is a disease characterized by chronic granulomatous enteritis causing protein-losing enteropathy. Adverse effects on animal productivity are key drivers in the attempt to control paratuberculosis at the farm level. Economic models require an accurate estimation of the production effects associated with paratuberculosis. The aim of this study was to conduct a systematic review and meta-analysis to investigate the effect of paratuberculosis on milk production. A total of 20 effect estimates from 15 studies were included in the final meta-analysis. Substantial between-study heterogeneity was observed. Subgroup analysis by case definition and study design was carried out to investigate heterogeneity. The majority of between-study variation was attributed to studies that defined cases on serology. Calculation of a pooled effect estimate was only appropriate for studies that defined cases by organism detection. A reduction in milk yield, corrected for lactation number and herd of origin of 1.87 kg/d, equivalent to 5.9% of yield, was associated with fecal culture or PCR positivity in individual cows.

Improvement of mammary gland health in 81 'sentinel herds' in England and Scotland between 2012 and 2021.

BACKGROUND: Achieving a reduction in mastitis in dairy cows is a common industry goal, but there is no recent peer-reviewed record of progress in the UK. METHODS: A convenience sample of 125 herds in England and Scotland was recruited based on the quality of records in 2016, willingness to participate and representative geographical distribution. Individual cow somatic cell counts and clinical mastitis data from 2012 to 2021 were summarised annually, and temporal changes were analysed. Eighty-one herds had sufficient data for comparison between 2012 and 2021, for one or more parameters. RESULTS: Over this period, the median incidence rate of clinical mastitis decreased from 40.0 to 21.0 cases per 100 cows per year (p < 0.001), with improvement in both lactation and dry period indicators. Lactation new infection rate calculated from individual cow somatic cell counts fell from 8.75% to 5.95% (p < 0.001), dry period new infection rate fell from 16.8% to 14.1% (p < 0.05) and proportion of cows over 200,000 cells/mL fell from 20.0% to 14.3% (p < 0.001). LIMITATIONS: Data were necessarily from herds with good records and do not provide absolute values for the industry. CONCLUSION: The findings reflect good progress over a 10-year period in a cohort of well-recorded herds and align with other national datasets.

Predictive modelling of deviation from expected milk yield in transition cows on automatic milking systems.

The transition period is a pivotal time in the production cycle of the dairy cow. It is estimated that between 30% and 50% of all cows experience metabolic or infectious disease during this time. One of the most common and economically consequential effects of disease during the transition period is a reduction in early lactation milk production. This has led to the utilisation of deviation from expected milk yield in early lactation as a proxy measure for transition health. However, to date, this analysis has been used exclusively for the retrospective assessment of transition cow health. Statistical models capable of predicting deviations from expected milk yield may allow producers to proactively manage animals predicted to suffer negative deviations in early lactation milk production. The objective of this retrospective cohort study was first, to explore the accuracy with which cow-level production and behaviour data collected on automatic milking systems (AMS) from 1-3 days in milk (DIM) can predict deviation from expected 30-day cumulative milk yield in multiparous cows. And second, to assess the accuracy with which predicted yield deviations can classify cows into groups which may facilitate improved transition management. Production, rumination, and physical activity data from 31 commercial AMS were accessed. A 3-step analytical procedure was then conducted. In Step 1, expected cumulative yield for 1-30 DIM for each individual cow-lactation was calculated using a mixed effect linear model. In Step 2, 30-Day Yield Deviation (YD) was calculated as the difference between observed and expected cumulative yield. Lactations were then assigned to one of three groups based on their YD, RED Group (0% YD). In Step 3, yield, rumination, and physical activity data from days 1-3 in lactation were used to predict YD using machine learning models. Following external validation, YD was predicted across the test data set with a mean absolute error of 9%. Categorisation of animals suffering large negative deviations (RED group) was achieved with a specificity of 99%, sensitivity of 35%, and balanced accuracy of 67%. Our results suggest that milk yield, rumination and physical activity patterns expressed by dairy cows from 1-3 DIM have utility in the prediction of deviation from expected 30-day cumulative yield. However, these predictions currently lack the sensitivity required to classify cows reliably and completely into groups which may facilitate improved transition cow management.

Use of qualitative behavioural assessment to investigate affective states of housed dairy cows under different environmental conditions.

In addition to the reduction of suboptimal welfare, there is now a need to provide farmed animals with positive opportunities to provide confidence that they have experienced a life worth living. Diversification of the environment through environmental enrichment strategies is one suggested avenue for providing animals with opportunities for positive experiences. The provision of more stimulating environmental conditions has been widely implemented in other animal production industries, based on evidenced welfare benefits. However, the implementation of enrichment on dairy farms is limited. In addition to this, the relationship between enrichment and dairy cows' affective states is an under-researched area. One specific welfare benefit of enrichment strategies which has been observed in a number of species, is increased affective wellbeing. This study investigated whether the provision of different forms of environmental enrichment resources would impact the affective states of housed dairy cows. This was measured by Qualitative Behavioural Assessment, currently a promising positive welfare indicator. Two groups of cows experienced three treatment periods; (i) access to an indoor novel object, (ii) access to an outdoor concrete yard and (iii) simultaneous access to both resources. Principal component analysis was used to analyse qualitative behavioural assessment scores, which yielded two principal components. The first principal component was most positively associated with the terms "content/relaxed/positively occupied" and had the most negative associations with the terms 'fearful/bored'. A second principal component was most positively associated with the terms "lively/inquisitive/playful" and was most negatively associated with the terms "apathetic/bored". Treatment period had a significant effect on both principal components, with cows being assessed as more content, relaxed and positively occupied and less fearful and bored, during periods of access to additional environmental resources. Similarly, cows were scored as livelier, more inquisitive and less bored and apathetic, during treatment periods compared to standard housing conditions. Concurrent with research in other species, these results suggest that the provision of additional environmental resources facilitates positive experiences and therefore enhanced affective states for housed dairy cows.

The use of machine learning to predict somatic cell count status in dairy cows post-calving.

Udder health remains a priority for the global dairy industry to reduce pain, economic losses, and antibiotic usage. The dry period is a critical time for the prevention of new intra-mammary infections and it provides a point for curing existing intra-mammary infections. Given the wealth of udder health data commonly generated through routine milk recording and the importance of udder health to the productivity and longevity of individual cows, an opportunity exists to extract greater value from cow-level data to undertake risk-based decision-making. The aim of this research was to construct a machine learning model, using routinely collected farm data, to make probabilistic predictions at drying off for an individual cow's risk of a raised somatic cell count (hence intra-mammary infection) post-calving. Anonymized data were obtained as a large convenience sample from 108 UK dairy herds that undertook regular milk recording. The outcome measure evaluated was the presence of a raised somatic cell count in the 30 days post-calving in this observational study. Using a 56-farm training dataset, machine learning analysis was performed using the extreme gradient boosting decision tree algorithm, XGBoost. External validation was undertaken on a separate 28-farm test dataset. Statistical assessment to evaluate model performance using the external dataset returned calibration plots, a Scaled Brier Score of 0.095, and a Mean Absolute Calibration Error of 0.009. Test dataset model calibration performance indicated that the probability of a raised somatic cell count post-calving was well differentiated across probabilities to allow an end user to apply group-level risk decisions. Herd-level new intra-mammary infection rate during the dry period was a key driver of the probability that a cow had a raised SCC post-calving, highlighting the importance of optimizing environmental hygiene conditions. In conclusion, this research has determined that probabilistic classification of the risk of a raised SCC in the 30 days post-calving is achievable with a high degree of certainty, using routinely collected data. These predicted probabilities provide the opportunity for farmers to undertake risk decision-making by grouping cows based on their probabilities and optimizing management strategies for individual cows immediately after calving, according to their likelihood of intra-mammary infection.

Incorporating heterogeneity in farmer disease control behaviour into a livestock disease transmission model.

Human behaviour is critical to effective responses to livestock disease outbreaks, especially with respect to vaccination uptake. Traditionally, mathematical models used to inform this behaviour have not taken heterogeneity in farmer behaviour into account. We address this by exploring how heterogeneity in farmers vaccination behaviour can be incorporated to inform mathematical models. We developed and used a graphical user interface to elicit farmers (n = 60) vaccination decisions to an unfolding fast-spreading epidemic and linked this to their psychosocial and behavioural profiles. We identified, via cluster analysis, robust patterns of heterogeneity in vaccination behaviour. By incorporating these vaccination behavioural groupings into a mathematical model for a fast-spreading livestock infection, using computational simulation we explored how the inclusion of heterogeneity in farmer disease control behaviour may impact epidemiological and economic focused outcomes. When assuming homogeneity in farmer behaviour versus configurations informed by the psychosocial profile cluster estimates, the modelled scenarios revealed a disconnect in projected distributions and threshold statistics across outbreak size, outbreak duration and economic metrics.

Fellow cows and conflicting farmers: Public perceptions of dairy farming uncovered through frame analysis.

Divergence in opinion over how farm animals should be cared for is creating a disconnect between livestock farming and the public that risks a loss of "social license" to farm. One proposed solution for the dairy farming community is to engage more constructively with the public to develop a shared vision of the industry's future; however, farmers and veterinarians remain reluctant to validate public opinions on farm animal care, in particular, often viewing them as naïve or impractical. Understanding the interpretive frames through which people make sense of dairy farming could help the dairy farming community engage more constructively with public opinion, thereby reducing conflict and providing opportunities to change communication or practice. Hence, frame analysis was conducted on transcripts of 60 face-to-face interviews with members of the UK public, first defining frames using reflexive thematic analysis, then considering the effect of these frames on those holding them. The results showed that dairy farming was mainly characterized by two entities: the cow and the farmer. Three frames were developed for the cow: she was perceived as i) enduring, which induced a sense of moral responsibility for her well-being among participants; ii) a fellow or companion, which led to feelings of a shared or parallel life with her; and iii) a force of nature, where the cow's connection with the natural world and "otherness" was appreciated, or even longed for. These connections were unexpectedly widespread within the sample, with many participants simultaneously holding two or even three frames. The farmer was seen through two frames: i) traditional; or ii) modernizing, but both frames had positive and negative narratives depending on the perceived care of the cow, causing confusion or even conflict about the care the farmer actually delivered. These findings provide new insights into the interpretive lenses through which the public makes sense of the dairy cow and her care, not least the bond the public themselves feel with the animal. They offer fresh opportunities for the dairy industry to improve engagement through more reflexive communication or modification of farming practices to better fit societal expectations about dairy cow welfare.

Differences in composition of interdigital skin microbiota predict sheep and feet that develop footrot.

Footrot has a major impact on health and productivity of sheep worldwide. The current paradigm for footrot pathogenesis is that physical damage to the interdigital skin (IDS) facilitates invasion of the essential pathogen Dichelobacter nodosus. The composition of the IDS microbiota is different in healthy and diseased feet, so an alternative hypothesis is that changes in the IDS microbiota facilitate footrot. We investigated the composition and diversity of the IDS microbiota of ten sheep, five that did develop footrot and five that did not (healthy) at weekly intervals for 20 weeks. The IDS microbiota was less diverse on sheep 2 + weeks before they developed footrot than on healthy sheep. This change could be explained by only seven of > 2000 bacterial taxa detected. The incubation period of footrot is 8-10 days, and there was a further reduction in microbial diversity on feet that developed footrot in that incubation period. We conclude that there are two stages of dysbiosis in footrot: the first predisposes sheep to footrot and the second occurs in feet during the incubation of footrot. These findings represent a step change in our understanding of the role of the IDS microbiota in footrot pathogenesis.