Breeding goals are broadening with more and more emphasis on health and efficiency related traits. New technologies are revolutionising the dairy industry. In addition to achievements in omics technologies, information and communication technologies (e.g., sensor technologies, Internet of Things, embedded machine learning) are also finding their way into modern dairy herds. Instead of punctual measurements, embedded sensors continuously record animal behavioural patterns that can implyon the animal wellbeing and welfare. Large amounts of data generated by deployed sensor systems along with the integration of related data sources promise ultimately new insights into animal health.

Traditional data pipelines with information about animal performance recordings in combination with indicators for metabolic disturbances, such as veterinary diagnoses, feeding information, test of ketone bodies, body condition score, and mid-infrared spectra have been around for some time already. They provide more precise possibilities to predict diseases, such as ketosis, compared to the methods using fat-protein-ratio. In the context of the claw health, the information about the regular claw trimming visit, veterinary diagnoses and regular lameness scoring has been made available only partly so far. Sensor technology provides alarms and early warnings based on irregularities of normal behaviour for early detection of disorders. Advanced methods and technologies offer the possibility to combine various environmental information and genomic background to get new insights into the occurrence of or susceptibility to disorders.

To explore these opportunities the biggest challenge is the integration of different data sources. In practice, monitoring data is often provided by different hardware and software products. This makes data integration more difficult due to the differences in the data exchange format of the partners involved. Moreover, the same traits may be defined differently by different products. It is therefore necessary to create structures to bring these data sources together in order to provide farmers with maximum support for herd management. Another challenge of data integration from different sources is compliance with legal data protection regulations, since this is often associated with lack of clarity in practice. Cooperation between different partners and integration of different data is a precondition for successfully applying advanced data technologies based on complex trait definitions. We summarize the steps to overcome these challenges based on our research within the project D4Dairy.

F. Grandl, J. Kofler, M. Suntinger, P. Majcen, M. Mayerhofer, F. Papst, O. Saukh, M. Fallast, A. Turkaspa, F. Steininger, K. Linke, J. Duda, T. Wittek, B. Fuerst-Waltl, F. J. Auer, C. Egger-Danner, Opportunities and Challenges of New Technologies for Performance Recording with Focus on Claw Health and Metabolism, in: J. Kucera, P. Bucek, D. Lipovsky, X. Bourrigan and M. Burke (Eds), New traits and adding value to the recording and ID services in the animal production; Proceedings of the 43rd ICAR Conference, Prague, 17–21 June 2019, ICAR Technical Series no. 24 (2019)