The use of mid-infrared spectrometry to predict body energy status of Holstein cows

Abstract

Energy balance, especially in early lactation, is known to be associated with subsequent health and fertility in dairy cows. However, its inclusion in routine management decisions or breeding programs is hindered by the lack of quick, easy, and inexpensive measures of energy balance. The objective of this study was to evaluate the potential of mid-infrared (MIR) analysis of milk, routinely available from all milk samples taken as part of large-scale milk recording and milk payment operations, to predict body energy status and related traits in lactating dairy cows. The body energy status traits investigated included energy balance and body energy content. The related traits of body condition score and energy intake were also considered. Measurements on these traits along with milk MIR spectral data were available on 17 different test days from 268 cows (418 lactations) and were used to develop the prediction equations using partial least squares regression. Predictions were externally validated on different independent subsets of the data and the results averaged. The average accuracy of predicting body energy status from MIR spectral data was as high as 75% when energy balance was measured across lactation. These predictions of body energy status were considerably more accurate than predictions obtained from the sometimes proposed fat-to-protein ratio in milk. It is not known whether the prediction generated from MIR data are a better reflection of the true (unknown) energy status than the actual energy status measures used in this study. However, results indicate that the approach described may be a viable method of predicting individual cow energy status for a large scale of application.

Summaries

“…The precision of the prediction energy balance calculated to date has been shown to be high and similar to the best estimates of McParland et al (2011) with an R of 0.64 -0.77 for the different traits across lactation. This precision deems these predictions suitable for genetic evaluations and for translation into a sensible reporting trait.…”

“…The duration of time that a cow spends in NEB can be highly detrimental to its health and fertility (McParland et al, 2011). Combining experimental data from our diary research herd in Dumfries and building upon McParland et al (2011), prediction equations have been developed based on routinely available data from the high-throughput mid-infrared (MIR) analysis of milk, due to its proposed relationship with energy balance (McParland et al, 2011;Friggens et al, 2007).…”

“…Combining experimental data from our diary research herd in Dumfries and building upon McParland et al (2011), prediction equations have been developed based on routinely available data from the high-throughput mid-infrared (MIR) analysis of milk, due to its proposed relationship with energy balance (McParland et al, 2011;Friggens et al, 2007). The objective of this research is to use these prediction equations to provide a quick and robust estimation of the energy status in dairy cows which could be directly incorporated into future selection indices to facilitate selection of animals (genotypes) with appropriate energy profiles to optimise profit and animal welfare (Coffey et al, 2004;McParland et al 2011).…”

“…Material and methods Using a non-random selection of animals (dataset 1; c. 3305 records on 500 cows) we have calculated all of the following energy balance traits across lactation (Table 1) (Banos and Coffey, 2010;McParland et al 2011); (i) energy balance (EB; MJ/d), a function of milk yield, fat and protein content, DMI, BW, and BCS; (ii) body energy content (BEC; MJ), a function of body weight and condition score, predicting body lipid and protein weight and (iii) effective energy intake (EEI) per day (MJ/d;Banos and Coffey, 2010). Concurrent MIR spectral records and milk production data were also available for these animals.…”

New phenotypes for new breeding goals in dairy cattle

“…Finally, it should be emphasized that any trait correlated with milk composition could be indirectly predicted by MIR spectra and this opens a completely new way to implement performance recording, not only for genetic purposes but also for traits related to health or reproduction. For instance, body mobilization is correlated with unsaturated fatty acids and body condition score could be predicted by MIR (McParland et al, 2011). The prediction of the reproductive status (pregnant or not) is still a very partially solved question and current methods (progesterone test, echography, rectal palpation) still lack accuracy.…”