Improving the accuracy of predictions for cow survival by multivariate evaluation model

Context Cow survival measures the ability of cows to survive from current subsequent lactation. In addition economic gain, genetic selection for could improve animal welfare by increasing adaptability and resilience both environmental health challenges. However, is a complex trait because it results diverse range reasons culling herd. Consequently, accuracy predictions direct are often low. Aims Our aim was increase in Holstein Jersey sires including important predictor traits multi-trait evaluation models. Methods Phenotypic correlations between deviations (TDs) 35 routinely measured (including milk yield, fertility type traits) were estimated using bivariate sire Survival TDs 538 394 63 839 used our study; these or their close relatives also had milk, records 2002 2019. These parameters required assess potential usefulness prediction survival. Key genetically correlated with fat protein yields, overall type, composite mammary system Jersey. Further, most related feet legs, rump, For sires, increased 0.05 Holsteins (from 0.54 0.59) Jerseys 0.48 0.53) through use multivariate models compared univariate Conclusions associated affecting voluntary involuntary when included models, they moderately improved Implications Predictor can be The inclusion breed-specific should considered, especially evaluations