Over the past 15 to 20 years, the adoption of automatic milking systems (AMS) has expanded considerably. This growth is driven by multiple factors. For farmers, the primary motivations include labor reduction, improved quality of life, and enhanced management efficiency through automated data recording and decision support. From the cows’ standpoint, key benefits include improvements in milk yield and quality, as well as better welfare and health monitoring. Understanding the interactions between cows and automatic milking systems, as well as how people manage these systems, is essential for developing effective management practices and enhancing system efficiency.

Therefore, this research topic was established to explore these interactions and highlight the state of the art of AMS adoption around the world. Its purpose is to guide management practices and strategies — encompassing nutrition, health, herd management, and others — to improve the efficiency of AMS, reduce human workload and quality of life, and enhance cow performance and welfare.

The specific topic encompasses:

1-Cow–System Interactions

Studies on cow behavior, adaptation, and animal training in automatic milking systems; effects of cow traffic designs (free, guided, semi-guided); differences between primiparous and multiparous cows; and relationships between temperament, milking frequency, and system efficiency.

2-Human–Machine–Animal Interface

Management practices, human factors, and decision-making in AMS; farmer and worker perceptions; training and skill development; labor conditions and quality of life; and barriers to technology adoption.

3-System Performance and Efficiency

Milk yield and composition, milking efficiency, robot utilization (visits, refusals, milking frequency), feeding strategies within AMS, lactation curve characteristics, and economic indicators.

4-Health, Welfare, and Milk Quality

Monitoring of mastitis, lameness, and metabolic or thermal stress using AMS data; welfare indicators and behavioral responses; teat cleaning performance; and milk quality and hygiene under automated milking systems.

5-Nutrition and Feeding Behavior

Concentrate allocation strategies in the robot, relationships between total diet and visit behavior, sensor-based monitoring of feed intake, and nutritional effects on milk production and lactation persistence.

6-Technology, Data, and Artificial Intelligence

Use of sensor data integration, machine learning, and decision-support tools; validation of sensor accuracy; and data interoperability across dairy management platforms.

7-Adoption, Sustainability, and Socioeconomic Impact

Factors influencing AMS adoption across regions, environmental sustainability (energy, emissions, water use), economic assessments, and impacts on farm structure and workforce dynamics.

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Automatic Milking Systems in Modern Dairy Farming