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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 #392521

Research Project: Improving Dairy Animals by Increasing Accuracy of Genomic Prediction, Evaluating New Traits, and Redefining Selection Goals

Location: Animal Genomics and Improvement Laboratory

Title: Daily milk yield correction factors: what are they?

item WU, XIAO-LIN - Council On Dairy Cattle Breeding
item WIGGANS, GEORGE - Council On Dairy Cattle Breeding
item NORMAN, HOWARD - Council On Dairy Cattle Breeding
item Miles, Asha
item Van Tassell, Curtis - Curt
item Baldwin, Ransom - Randy
item BURCHARD, JAVIER - Council On Dairy Cattle Breeding
item DURR, JOAO - Council On Dairy Cattle Breeding

Submitted to: Journal of Dairy Science Communications
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/14/2022
Publication Date: 12/1/2022
Citation: Wu, X., Wiggans, G.R., Norman, H.D., Miles, A.M., Van Tassell, C.P., Baldwin, R.L., Burchard, J., Durr, J. 2022. Daily milk yield correction factors: what are they? Journal of Dairy Science Communications.

Interpretive Summary: Few dairy farms have inline milk meters which give a precise measurement of the daily milk yields of their cows. Instead, most herds across the US and the world participate in programs where a technician will come and measure the yield from a single milking, and the total daily yields are estimated from this partial measurement. This technical review explains different methodologies for estimating total yields and compares their accuracy using simulation data.

Technical Abstract: Cost-effective milking plans have been used to supplement the standard supervised four-weekly testing scheme since the 1960s. A cow is typically milked two or more times on a test day, but not all these milkings are sampled and weighed. Statistical methods have been proposed to estimate daily yields in dairy cows, centering on various yield correction factors in two broad categories. This technical note presents a systematic review about additive and multiplicative correction factors concerning their statistical interpretations, model assumptions, and challenges. The initial approach of doubling morning (AM) or evening (PM) milk yield as a test-day yield in an AM-PM plan was inaccurate because it assumed an over-simplified, fixed multiplicative correction factor for all cows. Additive correction factors provide additive adjustments to doubled AM or PM yields, based on the differences between AM and PM milk yield for varied milking interval classes, coupled with other categorical variables whenever applicable. Hence, an equivalent additive correction factor regression model assumes a fixed regression coefficient (2.0) for the yield from single milkings. A linear regression model implemented as an additive correction factor model assumes an unknown regression coefficient for yields from a single milking and estimates it from the data. The latter improved the accuracy. Multiplicative correction factors are ratios of daily yields to yields from single milkings but their statistical interpretations differ. Overall, multiplicative correction factors are more accurate than additive correction factors. Biological and statistical challenges with the current yield factors are discussed. Systematic biases from discretizing milking interval are shown analytically compared to the corresponding linear regression models. An alternative model is proposed, which improved the estimation accuracy based on the simulation data. This new model postulates an exponential function for the daily milk yield curve. It is also analogous to an exponential growth function with a change rate tuned by a meta time as a linear function of milking interval. The methods and principles, though reviewed and evaluated on daily milk yield, similarly apply the estimation of daily fat and protein yields.