data standardization and integration; data-driven technology adoption; economic value of data; precision dairy farming; sustainable data stewardship; Animals; Artificial Intelligence; Data Analytics; Cattle; Dairying/methods; Dairying/economics; Dairying; Milk; Food Science; Animal Science and Zoology; Genetics
Abstract :
[en] Precision dairy farming is rapidly transforming the global dairy sector through the application of data-driven technologies. This review explores current knowledge and emerging ideas across 5 key areas: (1) the economic value of data, highlighting its role in optimizing productivity and profitability; (2) advances in integrating artificial intelligence (AI), sustainability, and innovation, showcasing how these elements drive efficiency; (3) the drivers and barriers to technology adoption, data integration, and connectivity, identifying factors that enable or hinder progress; (4) sustainable data stewardship, addressing governance, standardization, and ethical concerns to ensure responsible data use; and (5) cross-sector insights from healthcare that can inform and strengthen dairy practices. It also provides recommendations for the dairy industry stakeholders on how best to promote, apply, and benefit from data-driven technologies in dairy farming. Emerging technologies such as AI, sensor-based monitoring, and automation are discussed for their potential to disrupt traditional practices and open new possibilities in dairy management. To advance the dairy industry and maximize the value of these technologies, it is essential to prioritize sustainable data stewardship, ensure clear data ownership, and uphold robust cybersecurity measures. Equally important is the need for increased investment in data infrastructure and the integration of computer science into agricultural and veterinary education. Effective interdisciplinary collaboration and structured support for technology adoption are critical to achieving these goals. Emphasizing practical opportunities and challenges, the review offers a forward-looking perspective on shaping a resilient, efficient, and technology-driven dairy industry.
Disciplines :
Animal production & animal husbandry
Author, co-author :
Sharma, Sumit ; Department of Animal Science, College of Agriculture and Life Sciences, Cornell University, Ithaca, NY 14853
Liu, Enhong ; Department of Animal Science, College of Agriculture and Life Sciences, Cornell University, Ithaca, NY 14853
van Leerdam, Meike ; Department of Animal Science, College of Agriculture and Life Sciences, Cornell University, Ithaca, NY 14853
Hu, Haowen ; Department of Animal Science, College of Agriculture and Life Sciences, Cornell University, Ithaca, NY 14853
Villalobos-Barquero, Rebeca ; Department of Animal Science, College of Agriculture and Life Sciences, Cornell University, Ithaca, NY 14853
Dorea, Joao R R ; Department of Animal and Dairy Sciences, College of Agriculture and Life Sciences, University of Wisconsin-Madison, Madison, WI 53706
Cabrera, Victor E ; Department of Animal and Dairy Sciences, College of Agriculture and Life Sciences, University of Wisconsin-Madison, Madison, WI 53706
Iwersen, Michael ; Clinic for Ruminants with Ambulatory and Herd Health Services, Centre for Clinical Veterinary Medicine, Ludwig-Maximilians-University Munich, Oberschleißheim 85764, Germany
Bewley, Jeffrey M ; Holstein Association USA, Brattleboro, VT 05301
Soyeurt, Hélène ; Université de Liège - ULiège > Département GxABT > Modélisation et développement
Hostens, Miel ; Department of Animal Science, College of Agriculture and Life Sciences, Cornell University, Ithaca, NY 14853. Electronic address: miel.hostens@cornell.edu
Language :
English
Title :
Milking the data for value-driven dairy farming
Alternative titles :
[fr] Tirer le meilleur parti des données pour une production laitière axée sur la valeur
This study received no external funding. The authors gratefully acknowledge the American Dairy Science Association (ADSA) for organizing the 46th Discover Conference, themed \u201CMilking the Data: Value-Driven Dairy Farming,\u201D held from May 6 to 9, 2024, in Itasca, Illinois. The conference brought together a diverse group of participants, including researchers, industry leaders, experts, graduate students, government officials, policymakers, software developers, and dairy producers. This event provided a valuable platform for the exchange of ideas and insights, which greatly contributed to the development of this paper. We also express our gratitude to the speakers, moderators, and attendees for their commitment and expertise. Their collective efforts ensured that the event was both productive and enlightening, with discussions centered on data-driven dairy farming that are expected to have a lasting impact on the dairy sector. During the preparation of this work, the authors used ChatGPT ( https://chatgpt.com/) for language editing and Claude.ai ( https://claude.ai/) for generating concepts for figure design, and the content was subsequently reviewed and refined by the authors. No human or animal subjects were used, so this analysis did not require approval by an Institutional Animal Care and Use Committee or Institutional Review Board. The authors have not stated any conflicts of interest. Nonstandard abbreviations used: ADAPT = Ag Data Application Programming Toolkit; AHM = automated health monitoring; AI = artificial intelligence; AMS = automated milking system; API = application programming interfaces; CDCB = Council on Dairy Cattle Breeding; EHR = electronic health record; FAIR = findable, accessible, interoperable, and reusable; FT-MIR = Fourier transform mid-infrared; ICAR = International Committee for Animal Recording; IDF = International Dairy Federation; IOFC = income over feed costs; IoT = Internet of Things; MIR = mid-infrared spectroscopy; ML = machine learning; PDF = precision dairy farming; PLF = precision livestock farming; PLS = partial least squares; RFI = residual feed intake; RFID = radio frequency identification.
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