Connecting Dairy Farms: Establishing Long-Term Digital Management for Animal Health

General ChatBotSonar Pro English translation: From the perspective of affected farmers, milk production faces multiple challenges. Economic viability and the workload of farms are problems, while animal health, performance, and sustainability are also expected to improve. Digital assistance systems (e.g., activity, rumination, temperature, location, or rumen sensors; networked milking/feeding technology) promise early detection, better decision-making, and reduced workload. In practice, however, their actual usefulness often falls short of expectations: the systems are purchased, initial use begins—and later is reduced or discontinued. Common reasons cited in practice are not primarily the “function” of the devices/software, but rather the human-technology interaction: unclear data flows, lacking data literacy, alarm overload, insufficient integration into work routines, unsuitable interfaces, unclear cost-benefit relationships, support and training needs, and questions about data ownership/cloud solutions. Research-oriented contributions show, however, that, when properly integrated, sensor technology can improve animal welfare, animal health, and efficiency while reducing workload. For Austrian dairy farms, structured handling of data is central: herd-management inputs, milk recording data, and 24/7 sensor data generate large amounts of information that must be selected, checked for plausibility, and transferred into decision-making routines. This does not always succeed: either available data are used too little—or the focus on “dashboards” displaces direct observation of the animals. Practical reports also point to open issues regarding digital infrastructure (connectivity), data sovereignty, and role definitions between farmers, veterinarians, and advisory services. Austrian institutions (e.g., Vetmeduni, HBLFA Raumberg-Gumpenstein) are working on application-oriented solutions and communication, including smart farming, AI applications, and the use of sensors. This leads to a clear need for practice-oriented concepts that clarify technology, work organization, decision rules, and roles—and that support implementation on farms. The present project addresses these practical needs.