A case-control study of herd- and cow-level risk factors associated with an outbreak of Mycoplasma mastitis in Nemuro, Japan

Modeling e-Livestock Indonesia

The demand for beef resources in Indonesia is always increasing every year. However, Indonesia's national beef supply cannot meet those needs. The import of beef in large numbers likely to remain performed. The government has made various efforts to reduce imports and achieve self-sufficiency in beef. However, the government does not yet have a good identification, registration, documentation, and traceability system, so there is no truly valid data regarding the actual stock condition. Inaccuracy of data can lead to inappropriate policymaking in the livestock sector. Therefore, an e-Government initiative in the form of e-Livestock has been proposed. The definitions and success factors regarding e-Livestock have been revealed in our previous researches. Based on those researches, by using soft system methodology, hermeneutics, focus group discussion, and success factors, the business process models for e-Livestock in Indonesia will be created in this research. Apart from that, various kinds of recommendations for action to solve the problem will also be generated from this research. Those recommendations are about the functional requirement, the identification tool, the location numbering rule, the ownership documentation, the socialization of the e-Livestock, the institutional aspect of e-Livestock, the regulations underlie e-Livestock and the conceptual infrastructure diagram of e-Livestock. All of the business process models produced have been validated and their complexities are also calculated. Most of the business process model is very easy to understand. All the business process models and recommendations generated from this research can be a guide for the government when implementing e-Livestock.

Mycoplasma bovis infection in dairy herds-Risk factors and effect of control measures

As Mycoplasma bovis spreads to new countries and becomes increasingly recognized as a disease with major welfare and economic effects, control measures on dairy farms are needed. To minimize the risk of infection spread to naive herds, all possible risk factors for M. bovis infection should be identified and controlled. Mycoplasma bovis was first diagnosed in dairy cattle in Finland in 2012, and by January 2020, 86 Finnish dairy farms (<1.5%) supporting M. bovis infections were identified. We evaluated risk factors for M. bovis infection using a questionnaire provided to 40 infected and 30 control dairy farms. Control measures were advised for 19 of the infected dairy farms during visits by a veterinarian. The course of the infection on those farms was followed by analyzing calf nasal swabs with PCR for presence of M. bovis 4 times at 6-mo intervals. Control measures included culling of M. bovis mastitic cows, isolation of new calves from older animals after initial M. bovis mastitic cows had been culled, prevention of nose-to-nose contact with infected animals, early detection of mastitis cases using M. bovis PCR, and hygiene measures mainly related to milking, calf pens, feeding buckets, and teats. Farms implemented the control measures related to the isolation of calves or avoidance of nose-to-nose contact in various ways, according to farm structures and financial circumstances.
In our study, the control measures recommended to the dairy farms appeared effective, such that 13 of 19 farms reached a low risk level during at least 3 consecutive negative samplings from calves, with no M. bovis mastitis detected subsequently. Among risk factors, insemination with an M. bovis-positive bull indicated a trend of increasing the odds of M. bovis infection on the farm in a multivariable logistic model. In contrast, higher herd average milk yield had an association with lower odds for M. bovis infection. Occurrence of other infectious diseases affecting several animals on the dairy farm in the previous 6 mo before M. bovis infection were more frequent on M. bovis-infected farms.