Updating test-day milk yield factors for use in genetic evaluations and dairy production systems: A comprehensive review

Authors: WU, XIAO-LIN - Council On Dairy Cattle Breeding; WIGGANS, GEORGE - Council On Dairy Cattle Breeding; NORMAN, H - Council On Dairy Cattle Breeding; CAPUTO, MALIA - Council On Dairy Cattle Breeding; Miles, Asha; Van Tassell, Curtis - Curt; Baldwin, Ransom - Randy; SIEVERT, STEVEN - Collaborator; MATTISON, JAY - Collaborator; BURCHARD, JAVIER - Council On Dairy Cattle Breeding; DURR, JOAO - Council On Dairy Cattle Breeding

Submitted to: Frontiers in Genetics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/27/2023
Publication Date: 12/11/2023
Citation: Wu, X.-L., Wiggans, G.R., Norman, H.D., Caputo, M., Miles, A.M., Van Tassell, C.P., Baldwin, R.L., Sievert, S., Mattison, J., Burchard, J., Durr, J. 2023. Updating test-day milk yield factors for use in genetic evaluations and dairy production systems: A comprehensive review. Frontiers in Genetics. 14:1298114. https://doi.org/10.3389/fgene.2023.1298114.
DOI: https://doi.org/10.3389/fgene.2023.1298114

Interpretive Summary: The genetic evaluation of milk yield is performed using lactation records derived from “test-day” yield records. There are a variety of ways these data are collected and different statistical methodologies for extrapolating test day records to total lactation yields. This paper offers an exhaustive review of daily milk yields, the foundation of lactation records. We aim to provide readers with a multifaceted and in-depth understanding of test-day milk yields, emphasizing daily yield correction factors for estimating daily milk yields and their implications for genetic evaluations. While test-day yields bear immense significance for dairy management, this review does not delve into its expansive scope. Nonetheless, we aspire that this technical overview enlightens a broad spectrum of stakeholders in the dairy sector, encompassing dairy farmers, geneticists, animal scientists, and developers of dairy technology.

Technical Abstract: Accurate lactation records form the foundation for genetic improvement and herd management of dairy cattle, and at its core is the accuracy of test-day yields. While cows are often milked multiple times on a test day, not all milkings are recorded. Various methods have been proposed to estimate daily yield from partial yields, primarily to deal with unequal milking intervals. Seminal advancements in the late 20th century concentrated on two main adjustment metrics: additive correction factors (ACFs) and multiplicative correction factors (MCFs). An ACF model provides additive adjustments to two times AM or PM milk yield, which then becomes the estimated daily yields, whereas an MCF is a ratio of daily yield to the yield from a single milking. Recent studies highlight the potential of alternative approaches, such as exponential regression and other nonlinear models. Biologically, milk secretion rates aren't linear throughout the entire milking interval, influenced by the internal mammary gland pressure. Consequently, nonlinear models are apt for estimating daily milk yields as well. MCFs and ACFs are typically determined for specific milking interval classes, often differentiated by 15- or 30-minute increments. Nonetheless, these discrete intervals can introduce systematic biases. A universal solution for deriving continuous correction factors has been proposed, ensuring reduced bias and enhanced daily milk yield estimation accuracy. When leveraging test-day milk yields for genetic evaluations in dairy cattle, two predominant statistical models are employed: lactation and test-day yield models. A lactation model capitalizes on the high heritability of total lactation yields, aligning closely with dairy producers' needs because the total amount of milk production in a lactation directly determines farm revenue. However, a lactation yield model without harnessing all test-day records may ignore vital data about the shapes of lactation curves needed for informed breeding decisions. In contrast, a test-day model emphasizes individual test-day data, accommodating various intervals and recording plans and allowing the estimation of environmental effects on specific test days. In the United States, the patenting of test-day models in 1993 restricted its use of test-day models to regional and unofficial evaluations by the patent holders. Estimated test-day milk yields have been used as if they were accurate depictions of actual test-day milk yields, neglecting the fact of possible estimation errors. The potential consequences arising from the disturbances linked to these estimates on subsequent genetic evaluations have not been sufficiently addressed. Looking forward, many questions and challenges are still yet to be addressed in this domain.