Experience with a Test-Day Model

Abstract

The Canadian Test-Day Model is a 12-trait random regression animal model in which traits are milk, fat, and protein test-day yields, and somatic cell scores on test days within each of first three lactations. Test-day records from later lactations are not used. Random regressions (genetic and permanent environmental) were based on Wilmink's three parameter function that includes an intercept, regression on days in milk, and regression on an exponential function to the power -0.05 times days in milk. The model was applied to over 22 million test-day records of over 1.4 million cows in seven dairy breeds for cows first calving since 1988. A theoretical comparison of test-day model to 305-d complete lactation animal model is given. Each animal in an analysis receives 36 additive genetic solutions (12 traits by three regression coefficients), and these are combined to give one estimated breeding value (EBV) for each of milk, fat, and protein yields, average daily somatic cell score and milk yield persistency (for bulls only). Correlation of yield EBV with previous 305-d lactation model EBV for bulls was 0.97 and for cows was 0.93 (Holsteins). A question is whether EBV for yield traits for each lactation should be combined into one overall EBV, and if so, what method to combine them. Implementation required development of new methods for approximation of reliabilities of EBV, inclusion of cows without test day records in analysis, but which were still alive and had progeny with test-day records, adjustments for heterogeneous herd-test date variances, and international comparisons. Efforts to inform the dairy industry about changes in EBV due to the model and recovering information needed to explain changes in specific animals' EBV are significant challenges. The Canadian dairy industry will require a year or more to become comfortable with the test-day model and to realize the impact it could have on selection decisions.

Genetic and phenotypic parameters for test day milk yield of Sahiwal cattle in the semi-arid tropics

“…Even for completed lactations, selection on a properly weighted index of TD records could be more accurate than selecting based on phenotypic records of total lactation production (Pander et al, 1992). With improved statistical methods associated with TD models, both genetic and environmental effects associated with each TD are better accounted for (Rekaya et al, 1999;Schaeffer et al, 2000). This gives a more precise definition of the CG, and allows more accurate description of stage of lactation which would be difficult to model with models based on standardised lactation yield Ptak and Schaeffer, 1993;Visscher and Goddard, 1995).…”

“…Selection based on daily yield records would result in reduced cost of recording and maintaining cows and bulls with low breeding values (Schaeffer et al, 2000;Swalve, 2000). Genetic merit of animals can be predicted based on daily milk yield records from early lactations and cost reduced by less frequent recording as opposed to daily recording currently practised (Pander et al, 1992).…”

Predicting the effect of anthelmintic treatment on milk production of dairy cattle in Canada using an Ostertagia ostertagi ELISA from individual milk samples

“…SCCs were transformed to a natural logarithm scale (lnSCC) to linearize their effect (Dohoo et al, 2009). The relationship between DIM and milk yield was assumed to follow Wilmink's function (Schaeffer et al, 2000): Y=DIM+DIM -0.05 , where Y is the 24-hour milk yield in kg/cow/day; the first DIM term was centered to reduce collinearity between the two terms, and the second DIM was computed from the original DIM variable.…”

Genetic variability of milk fatty acids

“…Thanks to a larger data set, a test-day animal mixed model could be used to describe the variability of FAs, as done by Soyeurt et al (2007aSoyeurt et al ( , 2007bSoyeurt et al ( , 2008b. The use of this type of model presents some advantages, such as a more efficient use of the collected data, a genetic model that accounts better for the biology of dairy cows, a better accounting for short-term environmental effects at each test-day milk recording, and finally, more accurate estimations of cow indices (Schaeffer et al 2000;Mayeres et al 2004;Muir et al 2007). Also due to the availability of data, the model could be improved by the addition of some regressions, to take into account the variation of genetic parameters throughout the lactation.…”