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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Animal Genomics and Improvement Laboratory » Research » Publications at this Location » Publication #401452
Research Project: Increasing Accuracy of Genomic Prediction, Developing Algorithms, Selecting Markers, and Evaluating New Traits to Improve Dairy Cattle

Location: Animal Genomics and Improvement Laboratory

Title: Get test-day milk yields right: what have we learned?

Author
item WU, XIAO LIN - Council On Dairy Cattle Breeding
item WIGGANS, GEORGE - Council On Dairy Cattle Breeding
item NORMAN, H - Council On Dairy Cattle Breeding
item ENZENAUR, HEATHER - Council On Dairy Cattle Breeding
item Miles, Asha
item Van Tassell, Curtis - Curt
item Baldwin, Ransom - Randy
item SIEVERT, STEVEN - Collaborator
item MATTISON, JAY - Collaborator
item BURCHARD, JAVIER - Council On Dairy Cattle Breeding
item DURR, JOAO - Council On Dairy Cattle Breeding

Submitted to: Journal of Dairy Science
Publication Type: Abstract Only
Publication Acceptance Date: 4/6/2023
Publication Date: 6/25/2023
Citation: Wu, X., Wiggans, G.R., Norman, H.D., Enzenaur, H.A., Miles, A.M., Van Tassell, C.P., Baldwin, R.L., Sievert, S.J., Mattison, J., Burchard, J., Durr, J. 2023. Get test-day milk yields right: What have we learned? [abstract]. Journal of Dairy Science. 106(Suppl. 1):154(abstr. 2624).

Interpretive Summary:

Technical Abstract: Milk recording is essential for herd management and genetic improvement in dairy cattle. Normally lactation yields are estimated from test-day milk yields (TDMY). The latter are computed from partial daily milk yields using the correction methods developed primarily in the 1970s and 1980s. Recently, a joint effort was initiated by CDCB, USDA-AGIL, and the National DHIA to revamp the current methodology. Here, we present a summary of the preliminary studies. An additive correction factor (ACF) model is equivalent to a linear model that regresses TDMY on milking interval bins and relevant variables, assuming fixed regression coefficients for single milkings. Linear regression estimating these regression coefficients for single milkings from the data were more accurate than ACF models. Multiplicative correction factors (MCF) differ in their forms and statistical interpretation interpretations, yet they gave closely comparable estimates and, Overall, MCF models were more accurate than ACF models when estimating TDMY. The linearity assumption of the MCF current methods was approximately taken for cows milked three or more times daily, but it did not hold precisely for cows milked twice daily. Instead, non-linear models could improve the estimation accuracy. An exponential regression model was proposed, which had the most accurate TDMY estimates. This model is analogous to an exponential growth function with a single milking yield as the initial state, and the rate of change tuned by a linear function of milking interval time. Still, a major issue with the current MCF methods was a considerable loss in the accuracy due to discretizing the milking interval into large bins. A general approach to compute MCF on a continuous scale of milking interval time has been proposed as well.