Capitalizing on fine milk composition for breeding and management of dairy cows

Management and breeding of dairy cows face the challenge of permanently adapting to changing production circumstances under socioeconomic constraints. If management and breeding addresses different timeframes of action, both need relevant phenotypes that allow for precise monitoring of the status of the cows, their products (i.e., milk and subsequently dairy products), their behavior and their environmental impact. Milk composition has been identified as an important source of information since it could reflect, at least partially, all these elements. Major milk components such as fat, protein, urea, and lactose contents are routinely predicted by mid-infrared (MIR) spectrometry and have been widely used for these purposes. But, milk composition is much more complex and other components might be informative. Such new milk-based phenotypes should be considered given that they are cheap, rapidly obtained, usable on a large scale, robust and reliable. In a first approach, new phenotypes can be predicted from MIR spectra using classical prediction equation based techniques. This method was used successfully for many novel traits (e.g., fatty acids, lactoferrin, minerals, milk technological properties, citrate), that can then be useful for management and breeding purposes. An innovation was to consider the longitudinal nature of the relationship between the trait of interest and the MIR spectra (e.g., to predict methane from MIR). By avoiding intermediate steps, prediction errors can be minimized when traits of interest (e.g., ketosis) are predicted directly from MIR spectra. In a second approach, in an innovative manner, patterns detected by comparing observed from expected MIR spectra can be used directly. All these traits can then be used to define best practices, adjust feeding and health management, improve animal welfare, improve milk quality and limit environmental impact. Under the condition that MIR data are available on a large scale, phenotypes for these traits will allow genetic and genomic evaluations. Introduction of novel traits into the breeding objectives will need additional research to clarify socio-economic weights and genetic correlation with other traits of interest.