Submitted to: Journal of Dairy Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: November 17, 2008
Publication Date: April 1, 2009
Repository URL: http://hdl.handle.net/10113/29893
Citation: Cole, J.B., Null, D.J., Van Raden, P.M. 2009. Best Prediction of Yields for Long Lactations. Journal of Dairy Science. 92(4):1796-1810. Interpretive Summary: Best prediction is a more complex procedure than test interval method but also more accurate, particularly when testing is less frequent. Existing programs were reorganized to output better graphics, give users simpler access to options, and provide additional output, such as predictions of daily yields. Lactation curves for the means and standard deviations of milk, fat, protein, and SCS were estimated. Correlations among test day yields were estimated using a function that accounts for biological changes and daily measurement error. Programs were validated by comparison with 305-d yields from the national dairy database and daily yields from on-farm meters. Many cows can produce profitably for > 305 days in milk, and the revised program provides a flexible tool to model these records.
Technical Abstract: Lactation records of any length now can be processed with the selection index methods known as best prediction (BP). Previous programs were limited to the 305-day standard used since 1935. Best prediction was implemented in 1998 to calculate lactation records in USDA genetic evaluations, replacing the test interval method used since 1969 to calculate lactation records. Best prediction is more complex but also more accurate, particularly when testing is less frequent. Programs were reorganized to output better graphics, give users simpler access to options, and provide additional output, such as BP of daily yields. Test-day data for six breeds were extracted from the national dairy database, and lactation lengths were required to be >= 500-d (Ayrshire, Milking Shorthorn) or >= 800-d (all others). Average yield and SD at any day in milk (DIM) were estimated by fitting 3-parameter Wood's curves (milk, fat, protein) and 4-parameter exponential functions (somatic cell score) to means and SD of 15- (<= 300 DIM) and 30-d (> 300 DIM) intervals. Correlations among test day yields were estimated using an autoregressive matrix to account for biological changes and an identity matrix to model daily measurement error. Autoregressive parameters (r) were estimated separately for first- (r=0.998) and later-parities (r=0.995). These r were slightly larger than previous estimates due to the inclusion of the identity matrix, which accounts for test day variation. Correlations between traits were modified so that correlations between somatic cell score and other traits may be non-zero. The new lactation curves and correlation functions were validated by extracting test day data from the national database, estimating 305-d yields using the original and new programs, and correlating those results. Daily BP of yield were validated using daily milk weights from on-farm meters in university research herds. Correlations ranged from 0.983 to 0.996 for 305-d milk yield. High correlations ranged from 0.952 to 0.997 for daily yields, although correlations were as low as 0.015 on day 1 of lactation, which may be due to calving-related disorders that are not accounted for by BP. Many cows can produce profitably for > 305 DIM, and the revised program provides a flexible tool to model these records.