Dairy herd mastitis and reproduction: using simulation to aid interpretation of results from discrete time survival analysis

Associations of somatic cell count with milk yield and reproductive performance in grazing dairy cows

Poor udder health status can have a detrimental effect on milk yield and reproductive performance, leading to reductions in the dairy farm profit. The objective of this retrospective longitudinal study was to assess the associations of somatic cell count (SCC) with daily milk yield and reproductive performance. A database with 1,930,376 lactations from 867 Argentinean grazing dairy herds records collected for 14 years was used. The association of the evolution of SCC (healthy vs. new case vs. cured vs. chronic; with 150,000 SCC/mL as threshold) and of the severity of SCC [mild (150,000--400,000 SCC/mL) vs. moderate (400,000-1,000,000 SCC/mL) vs. severe (>1,000,000 SCC/mL)] with the odds for conception were estimated. Finally, the associations of the linear score of SCC (LS-SCC) with daily milk yield were estimated depending on parity and milk production quartile. The odds ratios (CI 95%) for conception at first service were 0.921 (0.902-0.941), 0.866 (0.848-0.884), and 0.842 (0.826-0.859) for the new case, cured, and chronic cows compared with healthy cows, respectively. Also, the odds ratios (CI 95%) for conception were 0.902 (0.881-0.925), 0.837 (0.808-0.866) and 0.709 (0.683-0.736) for mild, moderate and severe cases compared with healthy cows, respectively. An increase of one point of LS-SCC was associated with decreases of 0.349, 0.539, and 0.676 kg in daily milk yield for first-, second-, and third-lactation cows, respectively. In conclusion, SCC is negatively associated with the risk for conception and with daily milk yield in grazing dairy cows. This negative relationship with conception is higher when SCC increase occurs after the service date and it is influenced by severity of mastitis, and in the case of milk yield, the negative association is influenced by parity, milk production quartile, and severity of mastitis.

Review: Application and Prospective Discussion of Machine Learning for the Management of Dairy Farms

Dairy farmers use herd management systems, behavioral sensors, feeding lists, breeding schedules, and health records to document herd characteristics. Consequently, large amounts of dairy data are becoming available. However, a lack of data integration makes it difficult for farmers to analyze the data on their dairy farm, which indicates that these data are currently not being used to their full potential. Hence, multiple issues in dairy farming such as low longevity, poor performance, and health issues remain. We aimed to evaluate whether machine learning (ML) methods can solve some of these existing issues in dairy farming. This review summarizes peer-reviewed ML papers published in the dairy sector between 2015 and 2020. Ultimately, 97 papers from the subdomains of management, physiology, reproduction, behavior analysis, and feeding were considered in this review. The results confirm that ML algorithms have become common tools in most areas of dairy research, particularly to predict data. Despite the quantity of research available, most tested algorithms have not performed sufficiently for a reliable implementation in practice. This may be due to poor training data. The availability of data resources from multiple farms covering longer periods would be useful to improve prediction accuracies. In conclusion, ML is a promising tool in dairy research, which could be used to develop and improve decision support for farmers. As the cow is a multifactorial system, ML algorithms could analyze integrated data sources that describe and ultimately allow managing cows according to all relevant influencing factors. However, both the integration of multiple data sources and the obtainability of public data currently remain challenging.

Association of lameness and mastitis with return-to-service oestrus detection in the dairy cow

Oestrus detection is an important part of maintaining efficient reproductive performance in dairy herds. Both lameness and mastitis are common diseases of dairy cows that may impact oestrus detection. A set of data from 28 herds identified as having good recording of clinical mastitis and lameness incidents was used for the study. Logistic regression was used to identify associations between disease episodes within 100 days of insemination and changes in the probability of reinsemination at either 18-24 or 19-26 days after an unsuccessful insemination. Population attributable risk was calculated to understand the impact these diseases may have at a herd level. Lameness 0-28 days after the first insemination of the interval decreased the odds of a reinsemination at an appropriate time by approximately 20 per cent. Clinical mastitis 1-28 days prior to the first insemination of the interval increased the odds of reinsemination at the expected time by approximately 20 per cent. The associations were similar for either interservice interval outcome. Population attributable risk suggested that the effect of these diseases on the probability of reinsemination at the expected time at a population level would likely be extremely small.

Factors affecting the cost-effectiveness of on-farm culture prior to the treatment of clinical mastitis in dairy cows

The objective of this study was to use probabilistic sensitivity analysis to evaluate the cost-effectiveness of using an on-farm culture (OFC) approach to the treatment of clinical mastitis in dairy cows and compare this to a ‘standard’ treatment approach. A specific aim was to identify the herd circumstances under which an OFC approach would be most likely to be cost-effective. A stochastic Monte Carlo model was developed to simulate 5000 cases of clinical mastitis at the cow level and to calculate the associated costs simultaneously when treated according to 2 different treatment protocols; i) a 'conventional' approach (3 tubes of intramammary antibiotic) and ii) an OFC programme, whereby cows are treated according to the results of OFC. Model parameters were taken from recent peer reviewed literature on the use of OFC prior to treatment of clinical mastitis. Spearman rank correlation coefficients were used to evaluate the relationships between model input values and the estimated difference in cost between the standard and OFC treatment protocols. The simulation analyses revealed that both the difference in the bacteriological cure rate due to a delay in treatment when using OFC and the proportion of Gram-positive cases that occur on a dairy unit would have a fundamental impact on whether OFC would be cost-effective. The results of this study illustrated that an OFC approach for the treatment of clinical mastitis would probably not be cost-effective in many circumstances, in particular, not those in which Gram-positive pathogens were responsible for more than 20% of all clinical cases. The results highlight an ethical dilemma surrounding reduced use of antimicrobials for clinical mastitis since it may be associated with financial losses and poorer cow welfare in many instances.