Prediction and validation of residual feed intake and dry matter intake in Danish lactating dairy cows using mid-infrared spectroscopy of milk

Abstract

The present study explored the effectiveness of Fourier transform mid-infrared (FT-IR) spectral profiles as a predictor for dry matter intake (DMI) and residual feed intake (RFI). The partial least squares regression method was used to develop the prediction models. The models were validated using different external test sets, one randomly leaving out 20% of the records (validation A), the second randomly leaving out 20% of cows (validation B), and a third (for DMI prediction models) randomly leaving out one cow (validation C). The data included 1,044 records from 140 cows; 97 were Danish Holstein and 43 Danish Jersey. Results showed better accuracies for validation A compared with other validation methods. Milk yield (MY) contributed largely to DMI prediction; MY explained 59% of the variation and the validated model error root mean square error of prediction (RMSEP) was 2.24kg. The model was improved by adding live weight (LW) as an additional predictor trait, where the accuracy R increased from 0.59 to 0.72 and error RMSEP decreased from 2.24 to 1.83kg. When only the milk FT-IR spectral profile was used in DMI prediction, a lower prediction ability was obtained, with R=0.30 and RMSEP=2.91kg. However, once the spectral information was added, along with MY and LW as predictors, model accuracy improved and R increased to 0.81 and RMSEP decreased to 1.49kg. Prediction accuracies of RFI changed throughout lactation. The RFI prediction model for the early-lactation stage was better compared with across lactation or mid- and late-lactation stages, with R=0.46 and RMSEP=1.70. The most important spectral wavenumbers that contributed to DMI and RFI prediction models included fat, protein, and lactose peaks. Comparable prediction results were obtained when using infrared-predicted fat, protein, and lactose instead of full spectra, indicating that FT-IR spectral data do not add significant new information to improve DMI and RFI prediction models. Therefore, in practice, if full FT-IR spectral data are not stored, it is possible to achieve similar DMI or RFI prediction results based on standard milk control data. For DMI, the milk fat region was responsible for the major variation in milk spectra; for RFI, the major variation in milk spectra was within the milk protein region.

Genetic analysis of Fourier transform infrared milk spectra in Danish Holstein and Danish Jersey

“…Increased h 2 of FT-IR milk spectra predicted milk components (protein, fat, and FA content) was observed in Holstein Friesians compared with other breeds (Maurice-Van Eijndhoven et al, 2015). Differences in FT-IR milk-spectra between DH, and DJ have been observed, but were, in contrast with our findings, mainly caused by differences in MIR regions associated with fat (Shetty et al, 2017). Breed differences in h 2 for milk fat predicted from FT-IR milk-spectra, and milk protein predicted from FT-IR milk spectra were explained by differences in allele frequencies in major milk genes, such as DGAT1 (Maurice- Van Eijndhoven et al, 2015), and casein genes (study on DH and DJ, Gustavsson et al, 2014).…”

“…The FT-IR milk spectra, additionally, have the potential to predict traits that are difficult or expensive to record, such as prevalence of metabolic diseases through milk β-BHB (Heuer et al, 2001;de Roos et al, 2007;Grelet et al, 2016). Attempts have been made to predict methane emissions (Dehareng et al, 2012;Vanlierde et al, 2015), energy balance (McParland et al, 2011, and residual feed intake (Shetty et al, 2017) from FT-IR milk spectra.…”

Residual feed intake divergence during the preweaning period is associated with unique hindgut microbiome and metabolome profiles in neonatal Holstein heifer calves

“…In dairy farming systems, feed costs account for approximately 60% of production expenses [1]. Therefore, identifying biological regulators of feed-efficiency in young dairy cattle would maximize profit margins [2]. The RFI is a relatively new measurement of feed efficiency in dairy cattle [3,4], and is defined as the difference between actual and predicted feed intake, whereby predicted intake is calculated using a linear regression of actual intake on metabolic body weight (BW 0.75 ) and average daily gain (ADG) [5].…”

Strategies for noise reduction and standardization of milk mid-infrared spectra from dairy cattle

“…Ongoing research includes studies of individual milk proteins and fatty acids (Lopez-Villalobos et al, 2014;McDermott et al, 2016;Bonfatti et al, 2017b) and technological properties (Cecchinato et al, 2015;Toffanin et al, 2015;Visentin et al, 2015). Studies have predicted indirect traits related to pregnancy (Lainé et al, 2017;Toledo-Alvarado et al, 2018a,b), energy status Grelet et al, 2016;Mehtiö et al, 2018), efficiency Shetty et al, 2017), and methane emissions (Vanlierde et al, 2013(Vanlierde et al, , 2015Bittante and Cipolat-Gotet, 2018).…”