J.B. Cole
Animal Genomics and Improvement Laboratory, ARS, USDA, Beltsville, MD
2020 J. Dairy Sci. (?)
© American Dairy Science Association,
2020. All rights reserved.
Individuals may download, store, or print single
copies solely for personal use.
Do not share personal accounts or passwords
for the purposes of disseminating this article.
Genetic selection has been a very successful tool for the long-term improvement of livestock populations, and the rapid adoption of genomic selection over the last decade has doubled the rate of gain in some populations. Breeding programs seek to identify genetically superior parents of the next generation, typically as a function of an index which combines information about many economically important phenotypes. However, the full expression of genetic potential requires that animals are placed in environments that support such performance. Increasingly complex dairy cattle production systems require that all aspects of animal performance are measured across individuals’ lifetimes. Selection emphasis is shifting away from traits related to animal productivity towards those related to efficient resource utilization and increased animal welfare. However, phenotypes for many of these new traits are difficult or expensive to measure, or both. This is driving interest in sensor-based systems that provide continuous measurements of the farm environment, individual animal performance, and detailed milk composition. The goal of modern phenomics is to integrate information from many sources in support of on-farm management decision-making and genetic improvement. However, many challenges accompany these new technologies, including a lack of standardization, need for high-speed Internet connections, and increased computational requirements. The construction of selection objectives for breeding programs requires the estimation of many different parameters, and it can be difficult to achieve consensus on what should be added to, or removed from, the index over time. There also is a lack of translational research on the use of these data for real-time precision management. This work will review the current literature related to deep-phenotyping of dairy cattle, identify opportunities and challenges associated with new technology for measuring animal performance, and discuss how these new tools may be applied in practice.