Cattle breeding has been data-driven for decades, with centralized data storage and data processing. Breeding goals have included more and more trait complexes, and over time, increasing amounts of different data sources have been captured.

Pedigree and genomic information in combination with a wide range of phenotypes for milk, meat, functional and conformation traits are commonly available. Limitations exist for traits related to health and welfare. Technological advances are revolutionizing the dairy sector with a huge amount of novel data sources. The increased use of technology on farms with real-time monitoring of animal behavior, improved laboratory technology and advances in statistics are providing new opportunities for prevention and early detection of health problems, genetics and quality assurance. Examples include the increased use of sensors in precision livestock farming systems to support heat detection or early detection of health problems, or the increased use of mid-infrared spectra to improve animal health. The need to improve efficiency has raised interest in feeding and management information. In addition, recording of environmental information is becoming increasingly important in the context of breeding for increased resilience and reduced heat stress. Increasing awareness and need for transparency in regard to animal health and welfare has led to further documentation and novel data sources e.g., animal-related welfare indicators such as lameness and body condition score, or resource-related parameters such as housing information and information on health and drug use. Many of these novel data sources are located in data silos of different stakeholders along the dairy value chain. For the sustainability of the livestock sector, a comprehensive approach with improvements in efficiency, economic and social aspects is important. Data-driven approaches offer new opportunities. To explore the potential of various novel and integrated data sources for its use in genetics, herd management or quality assurance, further data-driven research is valuable.

Therefore, this research topic seeks to highlight approaches and results related to:

• Integration of dairy cattle data

• Technical and legal aspects of dairy cattle data sharing

• Data cleaning procedures with a focus on sensor data from dairy farms

• Re-use of data and advanced statistical models (e.g., machine learning approaches)

• Development of decision support tools for dairy farms

• Definition of auxiliary traits for genetics related to cattle health and welfare.

Of particular interest is the exploration of the joint use of diverse data in combination with advanced statistical models for disease risk prediction and prevention.

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Frontiers in Animal Science

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