AUTHOR=Cavallini Damiano , Giammarco Melania , Buonaiuto Giovanni , Vignola Giorgio , De Matos Vettori Julio , Lamanna Martina , Prasinou Paraskevi , Colleluori Riccardo , Formigoni Andrea , Fusaro Isa 

TITLE=Two years of precision livestock management: harnessing ear tag device behavioral data for pregnancy detection in free-range dairy cattle on silage/hay-mix ration

JOURNAL=Frontiers in Animal Science

VOLUME=Volume 6 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/animal-science/articles/10.3389/fanim.2025.1547395

DOI=10.3389/fanim.2025.1547395

ISSN=2673-6225

ABSTRACT=IntroductionThe aim of this study was to evaluate the transformative potential of precision livestock farming (PLF) in dairy production, nutrition, and reproductive efficiency by implementing proactive management strategies that utilize real-time data.MethodsData were gathered from a commercial dairy farm, employing a 3D accelerometer system attached to ear-tags to monitor activity and rumination behavior. Additionally, information on animal-related events such as oestrus, artificial insemination, clinical diseases, and treatments was recorded.ResultsIt was observed that there were differences in rumination between positive and negative cows in the days following artificial insemination (AI). Specifically, from day 0 to day 16, negative cows exhibited a higher rumination time compared to positive cows, with this contrast being particularly significant on days 9 and 10. Over the duration of the study, cows that tested negative produced, on average, 0.78 kg more milk per day.DiscussionThis suggests that animals with higher milk production were less likely to be pregnant, potentially due to increased nutrient allocation to the mammary gland, resulting in a more pronounced negative energy balance and consequently lower conception rates. Furthermore, the accelerometer utilized in this study was capable of distinguishing differences in daily lying time between pregnant and non-pregnant animals on the days of oestrus return. This finding highlights the potential for developing a system alert to identify “probable pregnant” and “probable non-pregnant” animals.