Brito Luiz F, Heringstad Bj Rg, Klaas Ilka Christine, Schodl Katharina, Cabrera Victor E, Stygar Anna, Iwersen Michael, Haskell Marie J, Stock Kathrin F, Gengler Nicolas, Bewley Jeffrey, Hostens Miel, Vasseur Elsa, Egger-Danner Christa
Department of Animal Sciences, Purdue University, West Lafayette, IN, 47907.
Norwegian University of Life Sciences, P. O. 5003, 1432, Ås, Norway.
J Dairy Sci. 2025 Aug 20. doi: 10.3168/jds.2025-26554.
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.
奶牛养殖领域对传感器技术和精准养殖工具的采用不断增加,这使得对动物健康、福利和生产力进行实时监测成为可能。这些数字技术进步为育种目的提供了高频、客观和大规模的表型数据。本综述探讨了传感器衍生数据在改进奶牛遗传和基因组评估方面的潜力,并概述了与其实施相关的关键挑战、机遇和方法。虽然这些数据流在遗传评估方面具有巨大潜力,但由于传感器品牌的分散、缺乏标准化以及数据可及性、数据访问和便携权、商业利益和治理等方面的挑战,它们在国家和国际育种计划中的整合仍然有限。在奶牛育种中利用数字技术的一个关键方面是数据协调和整合。我们强调建立标准化数据收集和数据共享协议、实施强大的质量控制和数据清理方法以及定义基于传感器的新性状并估计其遗传背景的重要性。在此背景下,我们汇编了奶牛群体中通过传感器和其他技术记录的数据所衍生新性状的遗传力估计值。育种计划中表型组学的发展,即整合多源数据(包括基于传感器的、基因组的和管理信息),将是加速遗传进展的关键,特别是对于与动物福利、健康、恢复力和效率相关的性状。本综述提出了在遗传评估中有效使用传感器衍生数据的路线图,倡导建立集中式数据基础设施、透明的数据共享协议,以及学术界和产业界不同利益相关者的作用,包括国际动物记录委员会(ICAR)等组织在建立全球标准和指南方面的作用。通过应对这些挑战,奶牛育种计划可以充分利用精准奶牛养殖技术来提高生产和环境效率,改善动物健康和福利,并推动奶牛养殖领域的可持续遗传进展。