Invited review: Using data from sensors and other precision farming technologies to enhance the sustainability of dairy cattle breeding programs. - 2025
[en] The increased uptake of sensor technologies and precision farming tools for the dairy cattle sector is enabling real-time monitoring of animal health, welfare, and productivity. These digital advancements provide high-frequency, objective, and large-scale phenotypic data for breeding purposes. This review explores the potential of sensor-derived data to improve genetic and genomic evaluations in dairy cattle and outlines key challenges, opportunities, and approaches associated with their implementation. While these data streams have great potential for genetic evaluations, their integration into national and international breeding programs remains limited due to fragmentation across sensor brands, lack of standardization, and challenges related to data accessibility, data access and portability rights, business interests, and governance. A crucial aspect of leveraging digital technologies in dairy cattle breeding is data harmonization and integration. We highlight the importance of establishing standardized data collection and data sharing protocols, implementing robust quality control and data cleaning methodologies, as well as defining novel sensor-based traits and estimating their genetic background. In this context, we compiled heritability estimates for novel traits derived from data recorded by sensors and other technologies in dairy cattle populations. The development of phenomics in breeding programs, which involves integrating multisource data-including sensor-based, genomic, and management information-will be key to accelerating genetic progress, especially for traits related to animal welfare, health, resilience, and efficiency. This review presents a roadmap for the effective use of sensor-derived data in genetic evaluations, advocating for centralized data infrastructures, transparent data-sharing agreements, and the role of different stakeholders from academia and industry, including organizations such as the International Committee on Animal Recording (ICAR) in establishing global standards and guidelines. By addressing these challenges, dairy breeding programs can fully harness precision dairy farming technologies to enhance production and environmental efficiency, improve animal health and welfare, and drive sustainable genetic advancements in the dairy cattle sector.
Research Center/Unit :
TERRA Research Centre. Animal Sciences - ULiège
Disciplines :
Animal production & animal husbandry Genetics & genetic processes
Author, co-author :
Brito, Luiz F ; Department of Animal Sciences, Purdue University, West Lafayette, IN 47907. Electronic address: britol@purdue.edu
Heringstad, Bjørg ; Norwegian University of Life Sciences, 1432 Ås, Norway
Klaas, Ilka Christine ; DeLaval International AB, 14721 Tumba, Sweden
Schodl, Katharina ; ZuchtData EDV-Dienstleistungen GmbH, 1200 Vienna, Austria
Cabrera, Victor E ; University of Wisconsin-Madison, Madison, WI 53706
Stygar, Anna ; Bioeconomy and Environment, Natural Resources Institute Finland (Luke), 00790 Helsinki, Finland
Iwersen, Michael ; Centre for Veterinary Systems Transformation and Sustainability, Clinical Department for Farm Animals and Food System Science, University of Veterinary Medicine, 1210 Vienna, Austria
Haskell, Marie J ; SRUC (Scotland's Rural College), Edinburgh EH9 3JG, United Kingdom
Stock, Kathrin F ; IT Solutions for Animal Production (vit), 27283 Verden, Germany
Gengler, Nicolas ; Université de Liège - ULiège > Département GxABT > Animal Sciences (AS)
Bewley, Jeffrey ; Holstein Association USA, Brattleboro, VT 05302
Hostens, Miel ; College of Agriculture and Life Sciences, Cornell University, Ithaca, NY 14853
Vasseur, Elsa ; McGill University, Ste Anne de Bellevue, H9X 3V9, QC, Canada
Egger-Danner, Christa ; ZuchtData EDV-Dienstleistungen GmbH, 1200 Vienna, Austria
USDA NIFA - United States. Department of Agriculture. National Institute of Food and Agriculture ICAR - Indian Council of Agricultural Research Purdue University FFG - Österreichische Forschungsförderungsgesellschaft EU - European Union
Funding text :
Luiz Brito received funding from the Agriculture and Food Research Initiative Competitive Grant number 2022-67021-37022 from the USDA National Institute of Food and Agriculture (Washington, DC) and support from the Purdue College of Agriculture and Purdue Ag Data Services (West Lafayette, IN) for developing the Purdue Animal Sciences Data Ecosystem (PASDE;Boerman et al. 2025). Anna Stygar received financial support from the European Union's Horizon Europe Coordination and Support Action under grant agreement number 101134866 (project Digi4Live). Christa Egger-Danner acknowledges the COMET-Project D4Dairy (Digitalisation, Data integration, Detection and Decision support in Dairying, project number: 872039), which was supported by the Federal Ministry of Mobility, Innovation, and Infrastructure (BMINI, Vienna, Austria) and the Federal Ministry of Economy, Energy, and Tourism (BMWET, Vienna, Austria) from the Republic of Austria, and the provinces of Lower Austria and Vienna in the framework of the Competence Centers for Excellent Technologies (COMET). The COMET program is handled by the Austrian Research Promotion Agency (FFG, Vienna, Austria; grant number 872039). The experiences and lessons learned from the D4Dairy project were the basis for Christa Egger-Danner, Chair of the ICAR FTWG, to initiate the ICAR IDF Sensor Initiative (Egger-Danner et al. 2024) together with Ilka Klaas of the IDF Standing Committee on Animal Health and Welfare. The authors thank all colleagues, scientists, manufacturers, representatives from ICAR members and working groups, especially Robert Fourdraine and Steven J. Sievert, and other stakeholder organizations for many fruitful discussions and contributions to the work of the ICAR IDF FTWG on the topic of sensor data use. We also thank Patrick Majcen for their contribution on the legal aspects of data sharing. 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: CH4_RATIO = the ratio between CH4 and CO2 in the breath of the cows; CH4_MILK = grams of CH4 per liter of milk produced; 3D = 3-dimensional; AMF = automated feeding machine; AMS = automated milking systems; CFHA = calving to first high activity; EC = electrical conductivity; FTIR = Fourier-transform infrared spectroscopy; FTWG = Functional Traits Working Group; ICAR = International Committee on Animal Recording; IDF = International Dairy Federation; MIR = mid-infrared spectroscopy; OCC = online cell count; RFI = residual feed intake.Luiz Brito received funding from the Agriculture and Food Research Initiative Competitive Grant number 2022-67021-37022 from the USDA National Institute of Food and Agriculture (Washington, DC) and support from the Purdue College of Agriculture and Purdue Ag Data Services (West Lafayette, IN) for developing the Purdue Animal Sciences Data Ecosystem (PASDE; Boerman et al., 2025 ). Anna Stygar received financial support from the European Union's Horizon Europe Coordination and Support Action under grant agreement number 101134866 (project Digi4Live). Christa Egger-Danner acknowledges the COMET-Project D4Dairy (Digitalisation, Data integration, Detection and Decision support in Dairying, project number: 872039), which was supported by the Federal Ministry of Mobility, Innovation, and Infrastructure (BMINI, Vienna, Austria) and the Federal Ministry of Economy, Energy, and Tourism (BMWET, Vienna, Austria) from the Republic of Austria, and the provinces of Lower Austria and Vienna in the framework of the Competence Centers for Excellent Technologies (COMET). The COMET program is handled by the Austrian Research Promotion Agency (FFG, Vienna, Austria; grant number 872039). The experiences and lessons learned from the D4Dairy project were the basis for Christa Egger-Danner, Chair of the ICAR FTWG, to initiate the ICAR IDF Sensor Initiative ( Egger-Danner et al., 2024 ) together with Ilka Klaas of the IDF Standing Committee on Animal Health and Welfare. The authors thank all colleagues, scientists, manufacturers, representatives from ICAR members and working groups, especially Robert Fourdraine and Steven J. Sievert, and other stakeholder organizations for many fruitful discussions and contributions to the work of the ICAR IDF FTWG on the topic of sensor data use. We also thank Patrick Majcen for their contribution on the legal aspects of data sharing. 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.
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