Peer-reviewed veterinary case report
Engineering to support wellbeing of dairy animals.
- Journal:
- The Journal of dairy research
- Year:
- 2016
- Authors:
- Caja, Gerardo et al.
- Affiliation:
- Department of Animal and Food Sciences · Spain
Abstract
Current trends in the global milk market and the recent abolition of milk quotas have accelerated the trend of the European dairy industry towards larger farm sizes and higher-yielding animals. Dairy cows remain in focus, but there is a growing interest in other dairy species, whose milk is often directed to traditional and protected designation of origin and gourmet dairy products. The challenge for dairy farms in general is to achieve the best possible standards of animal health and welfare, together with high lactational performance and minimal environmental impact. For larger farms, this may need to be done with a much lower ratio of husbandry staff to animals. Recent engineering advances and the decreasing cost of electronic technologies has allowed the development of 'sensing solutions' that automatically collect data, such as physiological parameters, production measures and behavioural traits. Such data can potentially help the decision making process, enabling early detection of health or wellbeing problems in individual animals and hence the application of appropriate corrective husbandry practices. This review focuses on new knowledge and emerging developments in welfare biomarkers (e.g. stress and metabolic diseases), activity-based welfare assessment (e.g. oestrus and lameness detection) and sensors of temperature and pH (e.g. calving alert and rumen function) and their combination and integration into 'smart' husbandry support systems that will ensure optimum wellbeing for dairy animals and thereby maximise farm profitability. Use of novel sensors combined with new technologies for information handling and communication are expected to produce dramatic changes in traditional dairy farming systems.
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Search related cases →Original publication: https://pubmed.ncbi.nlm.nih.gov/27210489/