Predicting bovine daily milk yield by leveraging genomic breeding values

Abstract

The main goal of this work, conducted on a herd of 502 Holstein cows situated in Italy, is to propose a machine learning-based approach to forecast the individual bovine daily milk production by explicitly leveraging genotypic information. As part of our study, we also evaluated the importance in the prediction of genotypic and phenotypic variables usually available within herd. The methodology we propose is based on two consecutive models: a genomic prediction one to calculate the animal’s genomic breeding value from marker data, followed by a feed forward neural network combining such additive genetic effect and the environmental features (parity, days in milk, age at calving in months, month of calving) for milk yield forecasting. In particular, we both assess the inclusion of genomic breeding values calculated within herd or provided by the breeders’ association, discovering that the latter ones allow for better final predictions. The results of our model outperform the ones by a linear mixed model with the same inputs on average, day by day and at the individual level. Moreover, we propose a problem formulation that also leverages additional factors partially controllable by breeders: in this case, features such as the number of milkings and the concentrate consumption inside the automatic milking system prove to highly impact on the final prediction, and hence on milk production. To the best of our knowledge, the proposed problem formulation based on genomic breeding values is a novelty in the individual bovine milk yield machine learning forecasting literature. Given the low genotyping costs and the availability of a larger number of environmental features in farms equipped with a wide range of sensors, as automatic milking systems, our solution can support breeders’ herd management and animal monitoring, thanks to the possibility to forecast the full lactation curve in advance even for primiparous bovines and newborn calves. With this work, we successfully achieve our objectives of including genomic information in bovine milk yield machine learning-based forecasting, thus improving the performance on this task, and of evaluating the impact on prediction of common genotypic and phenotypic information available to breeders.