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

Abstract

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 (∆BW), change in body condition score (∆BCS), 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, ∆BW, ∆BCS, FPR × ∆BW, and days in milk. The model resulted in a cross-validation coefficient of determination (R 2 cv) 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 (R 2 cv = 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 (R 2 cv = 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 (R 2 cv = 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.

Fatty Acid Profiles from Routine Milk Recording as a Decision Tool for Body Weight Change of Dairy Cows after Calving

“…Nonetheless, while daily yields of C18:0 and C18:1 were indeed greater in early lactation, daily yields of C16:0 were smaller in early lactation in the present study. These results confirmed previous findings [ 23 , 24 ].…”

Variation of Blood Metabolites of Brown Swiss, Holstein-Friesian, and Simmental Cows

“…On the other hand, the trend of NEFA across parity in BS and SI was similar to that of HF, i.e., NEFA was greater in the first than second lactation (p < 0.05). This trend was previously reported by Mäntysaari et al [25] for Nordic Red cows. The increase of blood BHB and NEFA concentrations with parity is generally expected [26,27].…”

Genetic correlations between energy status indicator traits and female fertility in primiparous Nordic Red Dairy cattle

“…Blood plasma NEFA concentration predicted by multiple regression equation which included DIM, milk FPR and milk FAs C10:0, C14:0, C18:1 cis-9, C14:0 * C18:1 cis-9 (R 2 cv = 0.62 and RMSE = 0.18 mmol/l) was NEFA FA (Mäntysaari et al, 2019). Milk FA concentrations were predicted using equations by Soyeurt et al (2011).…”