Luke O'Grady’s research while affiliated with University of Nottingham and other places

Risk assessments are important tools to identify deficits in biosecurity management practices. A major strength of some existing tools is that they facilitate cross-country comparisons. However, a weakness is their failure to account for unique intra-national farming enterprise structures such as, for example, pasture-based dairying. Currently, there are no suitable biosecurity risk assessment tools applicable to pasture-based dairying as practiced in Ireland. In addition to a need for enterprise-specific biosecurity risk assessment tools, the weighting of risk scores generated by these tools needs to be context-specific to ensure validity in assessing biosecurity risks in the farming sector of interest. Furthermore, existing biosecurity audits rely exclusively on respondent recall to answer questions about management practices. To address each of these limitations of existing biosecurity risk assessment tools we developed and optimised a new biosecurity risk assessment tool (BioscoreDairy) designed to assess the biosecurity status of pasture-based dairy farms in Ireland. It consists of two parts, a biosecurity questionnaire and a cattle movement records audit. A questionnaire was developed on biosecurity management practices appropriate for a pasture-based dairy system. Multiple national expert groups were leveraged to provide weightings for the different management practices in the questionnaire using the best-worst scaling methodology of MaxDiff. The results of this process provided a numerical categorisation that could then be used to assign scores to the individual biosecurity management practices. These practices were grouped into three biosecurity areas; risk of disease entry, speed of disease spread and diagnosis of infection. Within each of these three areas, a traffic light system was used to compare a farm’s biosecurity risks to other similar farms—least risk (green; within the top third of farms), concerning practice (amber; middle third) and worst practice or greatest risk (red; lowest third). In addition to these scores, the cattle introduction profile of a herd over the previous 3 years, based on nationally recorded data, was audited, compared amongst dairy farm enterprise subtypes, and included in the BioscoreDairy report. BioscoreDairy is therefore the first biosecurity risk assessment tool tailored to pasture-based dairy farm systems, both for individual farm reporting and for benchmarking against comparable farms.

Mycoplasma bovis is a globally significant pathogen of cattle associated with a wide range of clinical syndromes, including respiratory disease, mastitis, arthritis, otitis, and reproductive failure. Since its detection in Ireland in 1994, M. bovis has become a significant contributor to morbidity and mortality in Irish cattle. This study aimed to investigate herd-level risk factors associated with M. bovis seropositivity in replacement dairy heifers, using data from 105 Irish dairy herds. Ten heifers per herd were sampled on three occasions: spring 2018, spring 2019, and autumn 2019. Seropositivity was evaluated using two thresholds: ≥1 positive heifer (Model ≥ 1POS) and ≥3 positive heifers (Model ≥ 3POS). M. bovis seropositivity varied over time, with at least one positive heifer in 50.4% (95% confidence interval (CI): 40.5–60.3) of herds in spring 2018, 35.2% (95% CI: 26.2–45.1) in spring 2019, and 45.7% (95% CI: 36.0–55.7) in autumn 2019. Herds with three or more positive heifers increased from 31.4% (95%CI: 22.7–41.2) in spring 2018 to 42.9% (95% CI: 33.2–52.9) by autumn 2019. Risk factors for M. bovis seropositivity included the purchase of cattle, which significantly raised the odds of seropositivity across multiple visit periods (Model ≥ 1POS: Odds ratio (OR) 3.84, p = 0.02; Model ≥ 3POS: OR 3.69, p = 0.02). Managing more than three land parcels, housing heifer calves separately from bull calves, and sharing airspace between calves and older animals also increased seropositivity risks. Conversely, more colostrum feeds reduced the risk of seropositivity (Model ≥ 1POS: OR 0.81, p = 0.05), while colostrum quality assessment and feeding waste milk showed a trend toward increased risk. These findings suggest the importance of robust biosecurity measures, including limiting cattle purchases, improving calf management, and enhancing colostrum feeding practices, to control the spread of M. bovis. This study provides valuable insights into the epidemiology of M. bovis in Irish dairy herds, emphasising the need for targeted biosecurity and surveillance to safeguard herd productivity.

Simple Summary The standard method for monitoring lameness in U.K. dairy herds is mobility scoring. Data from mobility scoring can be used to estimate the proportion of cows in the herd that are lame (prevalence), the rate at which cows become lame (incidence), and how long cows remain lame (duration). It is unknown how the frequency and accuracy of mobility scoring impact the accuracy of measurement of these parameters. We developed a model to simulate lameness in a range of herd scenarios with different prevalences and durations of lameness. We used this model to understand how the frequency and accuracy of mobility scoring affected the accuracy of lameness parameters calculated from mobility scoring data. Our results showed that reduced accuracy of mobility scoring results in an over-estimation of lameness incidence and an under-estimation of lameness duration. This effect increased with more frequent scoring. Lameness prevalence and the average number of days to first lameness best identified lameness patterns when simulating monthly mobility scoring. We conclude that the frequency and accuracy of mobility scoring should be considered when using mobility scoring data to inform on lameness patterns on farms. Abstract Mobility scoring data can be used to estimate the prevalence, incidence, and duration of lameness in dairy herds. Mobility scoring is often performed infrequently with variable sensitivity, but how this impacts the estimation of lameness parameters is largely unknown. We developed a simulation model to investigate the impact of the frequency and accuracy of mobility scoring on the estimation of lameness parameters for different herd scenarios. Herds with a varying prevalence (10, 30, or 50%) and duration (distributed around median days 18, 36, 54, 72, or 108) of lameness were simulated at daily time steps for five years. The lameness parameters investigated were prevalence, duration, new case rate, time to first lameness, and probability of remaining sound in the first year. True parameters were calculated from daily data and compared to those calculated when replicating different frequencies (weekly, two-weekly, monthly, quarterly), sensitivities (60–100%), and specificities (95–100%) of mobility scoring. Our results showed that over-estimation of incidence and under-estimation of duration can occur when the sensitivity and specificity of mobility scoring are <100%. This effect increases with more frequent scoring. Lameness prevalence was the only parameter that could be estimated with reasonable accuracy when simulating quarterly mobility scoring. These findings can help inform mobility scoring practices and the interpretation of mobility scoring data.

Currently, the dairy industry is facing many challenges that could affect its sustainability, including climate change and public perception of the industry. As a result, interest is increasing in the concept of identifying resilient animals, those with a long productive lifespan, as well as good reproductive performance and milk yield. There is much evidence that events in utero, that is, the developmental origins of health and disease hypothesis, alter the life-course health of offspring and we hypothesized that these could alter resilience in calves, where resilience is identified using lifetime data. The aim of this study was to quantify lifetime resilience scores (LRS) using an existing scoring system, based on longevity with secondary corrections for age at first calving and calving interval, and to quantify the effects of in utero events on the LRS using 2 datasets. The first was a large dataset of cattle on 83 farms in Great Britain born from 2006 to 2015 and the second was a smaller, more granular dataset of cattle born between 2003 and 2015 in the Langhill research herd at Scotland's Rural College. Events during dam's pregnancy included health events (lameness, mastitis, use of an antibiotic or anti-inflammatory medication), the effect of heat stress as measured by temperature-humidity index, and perturbations in milk yield and quality (somatic cell count, percentage fat, percentage protein and fat:protein ratio). Daughters born to dams that experienced higher temperature-humidity indexes while they were in utero during the first and third trimesters of pregnancy had lower LRS. Daughter LRS were also lower where milk yields or median fat percentages in the first trimester were low, and when milk yields were high in the third trimester. Dam LRS was positively associated with LRS of their offspring; however, as parity of the dam increased, LRS of their calves decreased. Similarly, in the Langhill herd, dams of a higher parity produced calves with lower LRS. Additionally, dams that recorded a high maximum locomotion score in the third trimester of pregnancy were negatively associated with lower calf LRS in the Langhill herd. Our results suggest that events that occur during pregnancy have lifelong consequences for the calf's lifetime performance. However, experience of higher temperature-humidity indexes, higher dam LRS, and mothers in higher parities explained a relatively small proportion of variation in offspring LRS, which suggests that other factors play a substantial role in determining calf LRS. Although “big data” can contain a considerable amount of noise, similar findings between the 2 datasets indicate it is likely these findings are real.