Milk casein and fatty acid fractions in early lactation are affected by nutritional regulation of body condition score at the beginning of the transition period in primiparous and multiparous cows under grazing conditions

Metabolic Changes in Serum and Milk of Holstein Cows in Their First to Fourth Parity Revealed by Biochemical Analysis and Untargeted Metabolomics

The performance of dairy cows is closely tied to the metabolic state, and this performance varies depending on the number of times the cows have given birth. However, there is still a lack of research on the relationship between the metabolic state of Holstein cows and the performance of lactation across multiple parities. In this study, biochemical analyses and metabolomics studies were performed on the serum and milk from Holstein cows of parities 1-4 (H1, N = 10; H2, N = 7; H3, N = 9; H4, N = 9) in mid-lactation (DIM of 141 ± 4 days) to investigate the link between performance and metabolic changes. The results of the milk quality analysis showed that the lactose levels were highest in H1 (p = 0.036). The total protein content in the serum increased with increasing parity (p = 0.013). Additionally, the lipase activity was found to be lowest in H1 (p = 0.022). There was no difference in the composition of the hydrolyzed amino acids in the milk among H1 to H4. However, the free amino acids histidine and glutamate in the serum were lowest in H1 and highest in H3 (p < 0.001), while glycine was higher in H4 (p = 0.031). The metabolomics analysis revealed that 53 and 118 differential metabolites were identified in the milk and serum, respectively. The differential metabolites in the cows' milk were classified into seven categories based on KEGG. Most of the differential metabolites in the cows' milk were found to be more abundant in H1, and these metabolites were enriched in two impact pathways. The differential metabolites in the serum could be classified into nine categories and enriched in six metabolic pathways. A total of six shared metabolites were identified in the serum and milk, among which cholesterol and citric acid were closely related to amino acid metabolism in the serum. These findings indicate a significant influence of blood metabolites on the energy and amino acid metabolism during the milk production process in the Holstein cows across 1-4 lactations, and that an in-depth understanding of the metabolic changes that occur in Holstein cows during different lactations is essential for precision farming, and that it is worthwhile to further investigate these key metabolites that have an impact through controlled experiments.

Milk Fatty Acid Profile of Holstein Cows When Changed from a Mixed System to a Confinement System or Mixed System with Overnight Grazing

This study is aimed at comparing the milk fatty acid profile (FAP) of cows that changed from a mixed system (MS) of double grazing plus total mixed ration (TMR) to a total confinement system (TCS, 100% TMR) with cows that changed to another MS with one overnight grazing plus TMR and compare with cows that were kept unchanged in TCS. The diet change was made in the second month of lactation. The milk samples were collected at one (M1-spring) and three months of lactation (M3-summer). Three treatments are as follows (each n = 10): confined cows fed with TMR throughout the period (GTMR), cows that changed from MS with double grazing plus TMR in M1 to TCS in M3 (GCHD), and cows that changed from a MS with double grazing plus TMR in M1 to a MS with overnight grazing plus TMR in M3 (GTMR+P). Unlike GTMR+P, GCHD improved milk production after change (increased 14% from M1 to M3), but milk FAP was impaired. In M3, conjugated linoleic acid (C18 : 2-CLA) in GTMR and GCHD was lower than GTMR+P (p < 0.05), and linolenic (C18 : 3-n-3) was lower in GCHD than GTMR+P. Maintaining grazing in summer overnight sustained milk fat quality, evidenced by higher C18 : 3 (n-3); C18 : 2 (CLA); and n-6/n-3 ratio than cows that changed to TCS.

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

Cows mobilize body reserves during early lactation, which is reflected in the milk fatty acid (FA) profile. Milk FA can be routinely predicted by Fourier-transform infrared (FTIR) spectroscopy, and be, thus, used to develop an early indicator for bodyweight change (BWC) in early lactating cows in commercial dairy farms. Cow records from 165 herds in Denmark between 2015 and 2017 were used with bodyweight (BW) records at each milking from floor scales in automatic milking systems. Milk FA in monthly test-day samples was predicted by FTIR. Predictions of BWC were based on a random forest model and included parity, stage of lactation, and test day milk production and components (fat, protein, and FA). Bodyweight loss was mainly explained by decreased short-chain FA (C4:0-C10:0) and increased C18:0 FA. The root mean square error (RMSE) of prediction after cross-validation was 1.79 g/kg of BW (R2 of 0.94). Model evaluation with previously unseen BWC records resulted in reduced prediction performance (RMSE of 2.33 g/kg of BW; R2 of 0.31). An early warning system may be implemented for cows with a large BW loss during early lactation based on milk FA profiles, but model performance should be improved, ideally by using the full FTIR milk spectra.

Can We Observe Expected Behaviors at Large and Individual Scales for Feed Efficiency-Related Traits Predicted Partly from Milk Mid-Infrared Spectra?

Phenotypes related to feed efficiency were predicted from records easily acquired by breeding organizations. A total of 461,036 and 354,148 records were collected from the first and second parity Holstein cows. Equations were applied to the milk mid-infrared spectra to predict the main milk components and coupled with animal characteristics to predict the body weight (pBW). Dry matter intake (pDMI) was predicted from pBW using the National Research Council (NRC) equation. The consumption index (pIC) was estimated from pDMI and fat, and protein corrected milk. All traits were modeled using single trait test-day models. Descriptive statistics were within the expected range. Milk yield, pDMI, and pBW were phenotypically positively related (r ranged from 0.08 to 0.64). As expected, pIC was phenotypically negatively correlated with milk yield (-0.77 and -0.80 for the first and second lactation) and slightly positively correlated with pBW (0.16 and 0.07 for the first and second lactation). Later, parity cows seemed to have a better feed efficiency as they had a lower pIC. Although the prediction accuracy was moderate, the observed behaviors of studied traits by year, stage of lactation, and parity were in agreement with the literature. Moreover, as a genetic component was highlighted (heritability around 0.18), it would be interesting to realize a genetic evaluation of these traits and compare the obtained breeding values with the ones estimated for sires having daughters with reference feed efficiency records.

Role of Fatty Acids in Milk Fat and the Influence of Selected Factors on Their Variability-A Review

Fatty acids (FAs) of milk fat are considered to be important nutritional components of the diets of a significant portion of the human population and substantially affect human health. With regard to dairy farming, the FA profile is also seen as an important factor in the technological quality of raw milk. In this sense, making targeted modifications to the FA profile has the potential to significantly contribute to the production of dairy products with higher added value. Thus, FAs also have economic importance. Current developments in analytical methods and their increasing efficiency enable the study of FA profiles not only for scientific purposes but also in terms of practical technological applications. It is important to study the sources of variability of FAs in milk, which include population genetics, type of farming, and targeted animal nutrition. It is equally important to study the health and technological impacts of FAs. This review summarizes current knowledge in the field regarding sources of FA variability, including the impact of factors such as: animal nutrition, seasonal feed changes, type of animal farming (conventional and organic), genetic parameters (influence of breed), animal individuality, lactation, and milk yield. Potential practical applications (to improve food technology and consumer health) of FA profile information are also reviewed.