Evaluation of the usefulness at national level of the dairy cattle health and production recording systems in Great Britain

Recording of calf diseases for potential use in breeding programs: a case study on calf respiratory illness and diarrhea

Calf diseases remain a challenge for dairy producers from both an economic and welfare perspective. Genetically selecting for disease resistance in calves is a promising approach that could contribute to sustainable dairy farming. Genetic evaluations, however, require well-defined and consistently recorded phenotypes to be successful. Therefore, this study aimed to understand the current state of calf disease recording on Ontario farms. Calf disease records of respiratory illness and diarrhea were available from the national milk recording organization (Lactanet Canada, Guelph, Ontario, Canada) from 2009 to 2020. A case study was conducted to describe calf disease diagnoses and recording practices by surveying a subset of 13 Ontario dairy producers. The percentage of milk recorded farms that recorded calf respiratory illness and calf diarrhea increased from 2.6% in 2009 to 11.1% in 2020. Potential sources of data loss were identified along the information chain from farm to genetic evaluation database. Clear definitions and thresholds to diagnose calf disease, standard operating procedures for data recording, as well as a data transfer pipeline, which includes exchange formats, are needed to facilitate the inclusion of calf health traits in genetic evaluations.

The need for national livestock surveillance in Pakistan

Ranked amongst the top five milk-producing countries globally, the Pakistan dairy industry can help to overcome food shortage and hunger, alleviate poverty and positively impact economic growth. This influencing role could potentially be more significant while the COVID-19 pandemic severely affects humanity, challenges the economy and increases the risk of global food shortage. However, its large national population of dairy livestock contrasts with Pakistan's top-five ranking, indeed, four to five Pakistani cows produce milk equivalent to one dairy cow of countries with a well-developed dairy industry. Low milk yield per cow negatively impacts the national production and compromises the development of an efficient processing sector, such that consumers are very often forced to use adulterated milk sold by local 'milkmen.' As a consequence, while committed to alleviating global hunger, Pakistan imports in excess of half a million tons of milk and milk-based products annually. Many studies have identified unproductive, inefficient and imprecise management issues combined with poor genetics and imbalanced nutrition as the leading barriers to improvement in the Pakistani dairy livestock sector. At an individual level, lack of awareness, affordability issues, illiteracy and low ambition of a large percentile of dairy farmers creates additional significant barriers. To address low productivity and poor genetics, Pakistani corporate farms and wealthier individual farmers import genetically improved breeds to attain high milk yields. However, they are then faced with the challenge of managing such breeds to attain sustainable and persistent milk yields under Pakistani climatic conditions, often risking excessive culling even to the point of business liquidation. In developed dairy industries, automated sensor-based livestock management systems are now available to help monitor, compute, and optimize procedures in real-time and are proven to increase productivity and profitability. The term precision livestock farming (PLF) is used to describe systems that monitor individual animals or groups of animals to overcome management deficiencies and optimize productivity. My stance in this Opinion Paper is that adopting and utilizing such precision technologies may support Pakistan in raising its livestock resources toward greater productivity, thereby helping to overcome the global food shortage.

Improving the Utility of Voluntary Ovine Fallen Stock Collection and Laboratory Diagnostic Submission Data for Animal Health Surveillance Purposes: A Development Cycle

There are calls from policy-makers and industry to use existing data sources to contribute to livestock surveillance systems, especially for syndromic surveillance. However, the practical implications of attempting to use such data sources are challenging; development often requires incremental steps in an iterative cycle. In this study the utility of business operational data from a voluntary fallen stock collection service was investigated, to determine if they could be used as a proxy for the mortality experienced by the British sheep population. Retrospectively, Scottish ovine fallen stock collection data (2011-2014) were transformed into meaningful units for analysis, temporal and spatial patterns were described, time-series methods and a temporal aberration detection algorithm applied. Distinct annual and spatial trends plus seasonal patterns were observed in the three age groups investigated. The algorithm produced an alarm at the point of an historic known departure from normal (April 2013) for two age groups, across Scotland as a whole and in specific postcode areas. The analysis was then extended. Initially, to determine if similar methods could be applied to ovine fallen stock collections from England and Wales for the same time period. Additionally, Scottish contemporaneous laboratory diagnostic submission data were analyzed to see if they could provide further insight for interpretation of statistical alarms. Collaboration was required between the primary data holders, those with industry sector knowledge, plus veterinary, epidemiological and statistical expertise, in order to turn data and analytical outcomes into potentially useful information. A number of limitations were identified and recommendations were made as to how some could be addressed in order to facilitate use of these data as surveillance "intelligence." e.g., improvements to data collection and provision. A recent update of the fallen stock collections data has enabled a longer temporal period to be analyzed, with evidence of changes made in line with the recommendations. Further development will be required before a functional system can be implemented. However, there is potential for use of these data as: a proxy measure for mortality in the sheep population; complementary components in a future surveillance system, and to inform the design of additional surveillance system components.

Review of pig health and welfare surveillance data sources in England and Wales

The capability to set baselines and monitor trends of health and welfare conditions is an important requirement for livestock industries in order to maintain economic competitiveness and sustainability. Monitoring schemes evaluate the relative importance of conditions so that: appropriate actions can be determined, prioritised and implemented; new and (re)emerging conditions can be promptly detected and the effectiveness of any actions can be measured. In 2011, the national pig levy board published a strategy document highlighting health and welfare conditions of importance to the pig industry that were to be targeted for control. In this study, existing schemes that could be used to monitor or set baselines for these conditions in pigs were reviewed, in order to evaluate their suitability for this purpose, using a standardised surveillance evaluation framework (SERVAL). The schemes included: government-funded surveillance of endemic and exotic disease and pig welfare; industry surveillance of endemic diseases; regional schemes for improving pig health; national accreditation schemes and information collected by retailers, private veterinary practices and private laboratories. The evaluation of each scheme highlights its capability to monitor any of the targeted conditions. This study identifies the biases, strengths and gaps in each scheme and provides discussion of opportunities for future development.

Herd-level prevalence of selected endemic infectious diseases of dairy cows in Great Britain

To implement appropriate and effective disease control programs at the national level, up-to-date and unbiased information on disease frequency is needed. The aim of this study was to estimate the prevalence of selected endemic infectious diseases in the population of dairy herds in Great Britain. Bulk milk tank (BMT) samples from 225 randomly selected dairy farms, stratified by region and herd size, were tested for antibodies against bovine viral diarrhea virus (BVDV), bovine herpesvirus type 1, Mycobacterium avium ssp. paratuberculosis, Leptospira Hardjo, Salmonella spp., Coxiella burnetii, Fasciola hepatica, Neospora caninum, and Ostertagia ostertagi. Furthermore, the presence of BVDV, C. burnetii, and Chlamydia-like organisms was determined by PCR. The apparent herd prevalence was estimated as a weighted proportion of positive herds. The true prevalence was calculated when a test was used with known test characteristics for the cut-off value used. Among unvaccinated herds, the true prevalence of BMT antibodies against BVDV was estimated at 66% [95% confidence interval (CI): 56-77%], M. avium ssp. paratuberculosis 68% (95% CI: 59-77%), bovine herpesvirus type 1 62% (95% CI: 52-73%), Leptospira Hardjo 47% (95% CI: 34-60%), and Salmonella spp. 48% (95% CI: 39-56%). The apparent prevalence of BMT antibodies against C. burnetii was 80% (95% CI: 75-85%), F. hepatica 55% (95% CI: 48-62%), N. caninum 46% (95% CI: 38-54%), and O. ostertagi 95% (95% CI: 91-98%). The BVDV, C. burnetii, and Chlamydia-like antigens were detected in 5 (95% CI: 2-9%), 29 (95% CI: 21-36%), and 31% (95% CI: 24-38%) of herds, respectively. Our results show that dairy cows across GB are frequently exposed to the studied pathogens, which are endemic at high levels with some geographical variations. These prevalence estimates provide a much-needed basis to assess whether nationwide control programs for the studied pathogens are justified by their potential economic, environmental, and public health implications. Should surveillance and control programs be initiated, the estimates presented here are a baseline against which progress can be assessed.

Methodological challenges to multivariate syndromic surveillance: a case study using Swiss animal health data

BACKGROUND

In an era of ubiquitous electronic collection of animal health data, multivariate surveillance systems (which concurrently monitor several data streams) should have a greater probability of detecting disease events than univariate systems. However, despite their limitations, univariate aberration detection algorithms are used in most active syndromic surveillance (SyS) systems because of their ease of application and interpretation. On the other hand, a stochastic modelling-based approach to multivariate surveillance offers more flexibility, allowing for the retention of historical outbreaks, for overdispersion and for non-stationarity. While such methods are not new, they are yet to be applied to animal health surveillance data. We applied an example of such stochastic model, Held and colleagues' two-component model, to two multivariate animal health datasets from Switzerland.

RESULTS

In our first application, multivariate time series of the number of laboratories test requests were derived from Swiss animal diagnostic laboratories. We compare the performance of the two-component model to parallel monitoring using an improved Farrington algorithm and found both methods yield a satisfactorily low false alarm rate. However, the calibration test of the two-component model on the one-step ahead predictions proved satisfactory, making such an approach suitable for outbreak prediction. In our second application, the two-component model was applied to the multivariate time series of the number of cattle abortions and the number of test requests for bovine viral diarrhea (a disease that often results in abortions). We found that there is a two days lagged effect from the number of abortions to the number of test requests. We further compared the joint modelling and univariate modelling of the number of laboratory test requests time series. The joint modelling approach showed evidence of superiority in terms of forecasting abilities.

CONCLUSIONS

Stochastic modelling approaches offer the potential to address more realistic surveillance scenarios through, for example, the inclusion of times series specific parameters, or of covariates known to have an impact on syndrome counts. Nevertheless, many methodological challenges to multivariate surveillance of animal SyS data still remain. Deciding on the amount of corroboration among data streams that is required to escalate into an alert is not a trivial task given the sparse data on the events under consideration (e.g. disease outbreaks).

Injection-site lesion prevalence and potential risk factors in UK beef cattle

Injectable veterinary medicinal products (VMPs) are widely used in cattle in the UK, and in particular vaccines are often used on large numbers of animals in the herd. The formation of injection-site lesions (ISLs) is a risk when using injectable products and has potential consequences for meat quality, animal welfare and beef industry income. This study used carcase observation in four abattoirs in England to determine ISL prevalence in beef cattle. Additionally, a questionnaire survey was used to investigate vaccination technique among UK beef farmers. The ISL prevalence was 4.1 per cent (95 per cent CI 3.4 per cent to 4.9 per cent). A potential difference between sites being used for vaccination and the distribution of ISLs on carcases suggested that factors other than vaccination were contributing to ISL incidence. Questionnaire responses highlighted deficits in good vaccination practices such as using the recommended site of injection and needle hygiene. The role of the veterinarian in knowledge transfer is crucial in providing practical injection advice when prescribing vaccines and other VMPs. This study identified factors to address when aiming to reduce ISL formation in UK beef animals.