The lifetime production of dairy cows is a complex trait influenced not only by genetics, but also by the environment in which a cow lives and the management practices of the farmer. Moreover, these influential factors show complex interactions with each other, making it difficult to reliably predict the lifetime production of individual animals at birth. However, since well managed dairy farms often have a surplus of youngstock, reliable lifetime production predictions would offer the opportunity to make more substantiated decisions when selecting calves or heifers to sell. Therefore, using data from Dutch herds, we constructed a data set capturing information on genetics, environment and management practices to develop multiple machine learning models capable of predicting the lifetime production of dairy cattle soon after birth. We found that a coupling of trends observed at the country level with farm-specific models largely outperforms off-the-shelf approaches. At birth, our best model could explain up to 47% of the variance in lifetime production, a considerable improvement in comparison with linear regression on the breeding values supplemented with the average lifetime production at farm level, which could only explain 21.7% of the variance in lifetime production. Moreover, we demonstrated surplus youngstock selection according to our model could more than double the surplus animal selection effect in comparison with the benchmark methodology, offering opportunities to increase the average (future) potential lifetime production of the retained heifers significantly. Assuming a static 20% surplus liveborn heifer scenario and random surplus animal selection as the default, our best model for surplus animal selection resulted in a 9.4% greater lifetime production in the retained animals compared with the current Dutch average lifetime production.
Keywords: gene-environment interaction; lifetime production; machine learning; youngstock selection.
The Authors. Published by Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).