Prediction of Daily Milk, Fat, and Protein Production by a Random Regression Test-Day Model

Abstract

Test-day genetic evaluation models have many advantages compared with those based on 305-d lactations; however, the possible use of test-day model (TDM) results for herd management purposes has not been emphasized. The aim of this paper was to study the ability of a TDM to predict production for the next test day and for the entire lactation. Predictions of future production and detection of outliers are important factors for herd management (e.g., detection of health and management problems and compliance with quota). Because it is not possible to predict the herdtest-day (HTD) effect per se, the fixed HTD effect was split into 3 new effects: a fixed herd-test month-period effect, a fixed herd-year effect, and a random HTD effect. These new effects allow the prediction of future production for improvement of herd management. Predicted test-day yields were compared with observed yields, and the mean prediction error computed across herds was found to be close to zero. Predictions of performance records at the herd level were even more precise. Discarding herds enrolled in milk recording for <1 yr and animals with very few tests in the evaluation file improved correlations between predicted and observed yields at the next test day (correlation of 0.864 for milk in first-lactation cows as compared with a correlation of 0.821 with no records eliminated). Correlations with the observed 305-d production ranged from 0.575 to 1 for predictions based on 0 to 10 test-day records, respectively. Similar results were found for second and third lactation records for milk and milk components. These findings demonstrate the predictive ability of a TDM. Abbreviation key: BV = breeding value, HTD = herd test day, HTY = herd test year, HTMp = herd test month period, HTDr = random HTD, PE = prediction error, PEV = PE variance, TDM = test-day model.

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.…”

Environmental effects on lactation curves included in a test-day model genetic evaluation

“…Genetic evaluation based on TD models presents a large number of advantages in comparison with 305-day lactation model as mentioned in several reviews (in particular Swalve (2000) and Jensen (2001)). In addition to the usual benefits of TD models from a genetic viewpoint, they are useful for prediction and management purposes such as feeding or cow culling decision support, but only a few published studies specifically refer to these aspects (Mayeres et al, 2004). Each country that already moved from a lactation model to a TD model, developed its own model.…”

Use of Random Regression Test-Day Model to Estimate Genetic Parameters of Milk Yield in Holstein Cows

“…In a study by [20] in order to estimation of variance components of milk yield using random regression models, the residual variance had an increasing rate from beginning to the end months of lactation period that is not in agreement with the results of this study. Also, [14] [17] reported residual variance changes during the lactation period.…”