Estimating breeding values for feed efficiency in dairy cattle by regression on expected feed intake.

The efficiency with which a dairy cow utilises feed for the various physiological and metabolic processes can be evaluated by metrics that contrast realised feed intake with expected feed intake. In this study, we presented a new metric - regression on expected feed intake (ReFI). This metric is based on the idea of regressing DM intake (DMI) on expected DMI using a random regression model, where energy requirement formulations are applied for the calculation of expected DMI covariables. We compared this new metric with the metrics residual feed intake (RFI) and genetic residual feed intake (gRFI), by applying them on 18 581 feed efficiency records from 654 primiparous Nordic Red dairy cows. We estimated variance components for the three metrics and their respective genetic correlations with intake and production traits. In addition, we examined the phenotypes of superior cows. With ReFI, we estimated for feed efficiency a higher genetic variation (4.7%) and heritability (0.23) compared to applying RFI or gRFI. The ReFI metric was genetically uncorrelated with DMI and negatively correlated within energy-corrected milk (ECM), whereas the RFI metric was genetically positively correlated with DMI and metabolic BW. The gRFI metric was genetically positively correlated with DMI and uncorrelated with energy sink traits. Overall, the estimated SE were large. The ReFI metric resulted in a different ranking of cows compared to those based on RFI or gRFI and was superior in selecting the most efficient animals. When the selection was based on ReFI breeding values, then the 10% most efficient cows produced 12.3% more ECM per unit metabolisable energy intake, whereas the corresponding values were only 4.3 or 5.9% when using RFI or gRFI breeding values, respectively. Based on ReFI, superior cows had also higher milk production, whereas based on RFI or gRFI milk production either decreased or was unaffected, respectively. The superiority of the ReFI metric in selecting efficient cows was due to a better modelling of the expected feed intake. The ReFI metric simplified modelling of feed utilisation efficiency in dairy cattle and resulted in breeding values that are equal to percentages of feed saved.

Body and milk traits as indicators of dairy cow energy status in early lactation.

The inclusion of feed intake and efficiency traits in dairy cow breeding goals can lead to increased risk of metabolic stress. An easy and inexpensive way to monitor postpartum energy status (ES) of cows is therefore needed. Cows' ES can be estimated by calculating the energy balance from energy intake and output and predicted by indicator traits such as change in body weight (ΔBW), change in body condition score (ΔBCS), milk fat:protein ratio (FPR), or milk fatty acid (FA) composition. In this study, we used blood plasma nonesterified fatty acids (NEFA) concentration as a biomarker for ES. We determined associations between NEFA concentration and ES indicators and evaluated the usefulness of body and milk traits alone, or together, in predicting ES of the cow. Data were collected from 2 research herds during 2013 to 2016 and included 137 Nordic Red dairy cows, all of which had a first lactation and 59 of which also had a second lactation. The data included daily body weight, milk yield, and feed intake and monthly BCS. Plasma samples for NEFA were collected twice in lactation wk 2 and 3 and once in wk 20. Milk samples for analysis of fat, protein, lactose, and FA concentrations were taken on the blood sampling days. Plasma NEFA concentration was higher in lactation wk 2 and 3 than in wk 20 (0.56 ± 0.30, 0.43 ± 0.22, and 0.13 ± 0.06 mmol/L, respectively; all means ± standard deviation). Among individual indicators, C18:1 cis-9 and the sum of C18:1 in milk had the highest correlations (r = 0.73) with NEFA. Seven multiple linear regression models for NEFA prediction were developed using stepwise selection. Of the models that included milk traits (other than milk FA) as well as body traits, the best fit was achieved by a model with milk yield, FPR, ΔBW, ΔBCS, FPR × ΔBW, and days in milk. The model resulted in a cross-validation coefficient of determination (R2cv) of 0.51 and a root mean squared error (RMSE) of 0.196 mmol/L. When only milk FA concentrations were considered in the model, NEFA prediction was more accurate using measurements from evening milk than from morning milk (R2cv = 0.61 vs. 0.53). The best model with milk traits contained FPR, C10:0, C14:0, C18:1 cis-9, C18:1 cis-9 × C14:0, and days in milk (R2cv = 0.62; RMSE = 0.177 mmol/L). The most advanced model using both milk and body traits gave a slightly better fit than the model with only milk traits (R2cv = 0.63; RMSE = 0.176 mmol/L). Our findings indicate that ES of cows in early lactation can be monitored with moderately high accuracy by routine milk measurements.