Abstract

Abstract Worldwide, dairy cattle breeding companies and farmers face several challenges, including concerns about climatic impact of milk production, increasing scarcity of natural resources and feed, and concerns about animal welfare and health. The recording of accurate and comprehensive phenotypic data for these new issues is important for both management and breeding. Technological developments play a key role in this context. An increasing spectrum of traits with relevance to the breeding goal has become available (e.g. behavioral traits from sensor-derived activity patterns, milk metabolites reflecting the metabolic status, direct or indirect measurements of methane emissions). The biological background and genetic architecture of many of the evolving novel traits as well as their relationship with other traits of interest is not yet well understood, which hinders appropriate implementation in breeding programs. Especially for traits that are difficult or expensive to measure, such as feed intake or methane emissions, phenotypes are scarce. Interdisciplinary research and across-country data pooling can be enormously helpful to ensure a fast progress. Hence, the development of universal guidelines for recording is a crucial step, also with regard to a successful application of genomic selection, which enables the improvement of difficult-to-measure traits by transferring genomic knowledge from estimates within comparatively small reference populations to the population level. Furthermore, some traits (e.g. feed intake) show a lactation-stage specific genetic architecture. This highlights the importance of repeated measurements as well as knowledge on genetic correlations among all relevant traits across days in milk, the latter being an important prerequisite for designing balanced breeding strategies. With more traits, especially more complex traits, increasing data sources and volumes, setting up reasonable breeding goals becomes much more sophisticated and often requires innovative approaches.

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