Milk fatty acid composition reflects metabolic adaptation of early lactation cows fed hay rich in water-soluble carbohydrates with or without concentrates

Assessing short-term feed efficiency and its association with biological markers in herbage-fed dairy cows

Feed efficiency is an important trait of dairy production. However, assessing feed efficiency is constrained by the associated cost and difficulty in measuring individual feed intake, especially on pastures. The objective of this study was to investigate short-term feed efficiency traits of herbage-fed dairy cows and screening of potential biomarkers (n = 238). Derived feed efficiency traits were ratio-based (i.e., feed conversion ratio (FCR) and N use efficiency (NUE)) or residual-based (i.e., residual feed intake (RFI), residual energy intake (REI), and residual N intake (RNI)). Thirty-eight Holstein and 16 Swiss Fleckvieh dairy cows underwent a 7-d measurement period during mid- and/or late-lactation. The experimental data (n = 100 measurement points) covered different lactational and herbage-fed system situations: mid-lactation grazing (n = 56), late-lactation grazing (n = 28), and late-lactation barn feeding (n = 16). During each measuring period, the individual herbage intake of each cow was estimated using the n-alkane marker technique. For each cow, biomarkers representing milk constituents (n = 109), animal characteristics (n = 13), behaviour, and activity (n = 46), breath emissions (n = 3), blood constituents (n = 35), surface, and rectal temperature (n = 29), hair cortisol (n = 1), and near-infrared (NIR) spectra of faeces and milk (n = 2) were obtained. The relationships between biomarkers and efficiency traits were statistically analysed with univariate linear regression and for NIR spectra using partial least squares regression with feed efficiency traits. The feed efficiency traits were interrelated with each other (r: -0.57 to -0.86 and 0.49-0.81). The biomarkers showed varying R2 values in explaining the variability of feed efficiency traits (FCR: 0.00-0.66, NUE: 0.00-0.74, RFI: 0.00-0.56, REI: 0.00-0.69, RNI: 0.00-0.89). Overall, the feed efficiency traits were best explained by NIR spectral characteristics of milk and faeces (R2: 0.25-0.89). Biomarkers show potential for predicting feed efficiency in herbage-fed dairy cows. NIR spectra data analysis of milk and faeces presents a promising method for estimating individual feed efficiency upon further validation of prediction models. Future applications will depend on the ability to improve the robustness of biomarkers to predict feed efficiency in a greater variety of environments (locations), managing conditions, feeding systems, production intensities, and other aspects.

Variations in colostrum metabolite profiles in association with sow parity

Information about the full spectrum of metabolites present in porcine colostrum and factors that influence metabolite abundances is still incomplete. Parity number appears to modulate the concentration of single metabolites in colostrum. This study aimed to 1) characterize the metabolome composition and 2) assess the effect of parity on metabolite profiles in porcine colostrum. Sows (n = 20) were divided into three parity groups: A) sows in parity 1 and 2 (n = 8), B) sows in parity 3 and 4 (n = 6), and C) sows in parity 5 and 6 (n = 6). Colostrum was collected within 12 h after parturition. A total of 125 metabolites were identified using targeted reversed-phase high-performance liquid chromatography-tandem mass spectrometry and anion-exchange chromatography-high resolution mass spectrometry. Gas chromatography additionally identified 19 fatty acids (FAs). Across parities, colostrum was rich in creatine and creatinine, 1,3-dioleyl-2-palmitatoylglycerol, 1,3-dipalmitoyl-2-oleoylglycerol, and sialyllactose. Alterations in colostrum concentrations were found for eight metabolites among parity groups (P < 0.05) but the effects were not linear. For instance, colostrum from parity group C comprised 75.4% more valine but 15.7%, 34.1%, and 47.9% less citric, pyruvic, and pyroglutamic acid, respectively, compared to group A (P < 0.05). By contrast, colostrum from parity group B contained 39.5% more spermidine than from group A (P < 0.05). Of the FAs, C18:1, C16:0, and C18:2 n6 were the main FAs across parities. Parity affected four FAs (C18:3n3, C14:1, C17:0ai, and C17:1), including 43.1% less α-linolenic acid (C18:3n3) in colostrum from parity group C compared to groups A and B (P < 0.05). Signature feature ranking identified 1-stearoyl-2-hydroxy-sn-glycero-3-phosphatidylcholine and the secondary bile acid hyodeoxycholic acid as the most discriminative metabolites, showing a higher variable importance in the projection score in colostrum from parity group A than from groups B and C. Overall, results provided a comprehensive overview about the metabolome composition of sow colostrum. The consequences of the changes in colostrum metabolites with increasing parity for the nutrient supply of the piglets should be investigated in the future. The knowledge gained in this study could be used to optimize feeding strategies for sows.

Possibility to Estimate Same Day Energy Status of Dairy Cows during First Half of Lactation by Non-Invasive Markers with Emphasis to Milk Fatty Acids

Postpartum negative energy balance (NEB) is detrimental to cows and decreases profitability in dairy farming. The two origins of milk fatty acids (FA), de novo synthesized in the mammary gland and plasma lipids initially originating from feed, rumen microbes and the animal's adipose tissue, make milk FA candidates as possible NEB biomarkers. The aim of this study was to assess the possibility to predict EB in cows in the first 150 days of lactation with BCS, milk traits and selected individual milk FA and the ratios of blood-derived and de novo synthesized FA. The daily EB of Estonian Holstein cows (N = 30) was calculated based on body weights and BCS values. Milk FA were analyzed with gas chromatography. The variance partitioning analysis revealed that milk production traits, BCS at calving, FA ratios and days in milk accounted for 67.1% of the EB variance. Random forest analysis indicated the highest impact of the ratios C18:1cis9/C12:0+C14:0, C18:1cis9+C18:0/C12:0+C14:0, C18:1cis9/C14:0, C18:1cis9+C18:0/C14:0, C18:1cis9/sum C5:0 to C14:0, C18:1cis9+C18:0/sum C5:0 to C14:0 or C18:1cis9/C15:0. FA and their ratios alone explained 63.6% of the EB variance, indicating the possibility to use milk FA and their ratios as sole predictors for the energy status in dairy cows.

Plasma and milk metabolomics in lactating sheep divergent for feed efficiency

Enhancing the ability of animals to convert feed into meat or milk by optimizing feed efficiency (FE) has become a priority in livestock research. Although untargeted metabolomics is increasingly used in this field and may improve our understanding of FE, no information in this regard is available in dairy ewes. This study was conducted to (1) discriminate sheep divergent for FE and (2) provide insights into the physiological mechanisms contributing to FE through high-throughput metabolomics. The ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UHPLC-Q/TOF-MS) technique was applied to easily accessible animal fluids (plasma and milk) to assess whether their metabolome differs between high- and low-feed efficient lactating ewes (H-FE and L-FE groups, respectively; 8 animals/group). Blood and milk samples were collected on the last day of the 3-wk period used for FE estimation. A total of 793 features were detected in plasma and 334 in milk, with 100 and 38 of them, respectively, showing differences between H-FE and L-FE. The partial least-squares discriminant analysis separated both groups of animals regardless of the type of sample. Plasma allowed the detection of a greater number of differential features; however, results also supported the usefulness of milk, more easily accessible, to discriminate dairy sheep divergent for FE. Regarding pathway analysis, nitrogen metabolism (either anabolism or catabolism) seemed to play a central role in FE, with plasma and milk consistently indicating a great impact of AA metabolism. A potential influence of pathways related to energy/lipid metabolism on FE was also observed. The variable importance in the projection plot revealed 15 differential features in each matrix that contributed the most for the separation in H-FE and L-FE, such as l-proline and phosphatidylcholine 20:4e in plasma or l-pipecolic acid and phosphatidylethanolamine (18:2) in milk. Overall, untargeted metabolomics provided valuable information into metabolic pathways that may underlie FE in dairy ewes, with a special relevance of AA metabolism in determining this complex phenotype in the ovine. Further research is warranted to validate these findings.

A review on the potentials of using feeds rich in water-soluble carbohydrates to enhance rumen health and sustainability of dairy cattle production

Cows are adapted to degrade structural plant carbohydrates (SC), such as cellulose and hemicelluloses, prevailing in grasses. Yet, the need for energy-dense diets in many intensive dairy production systems has shifted the dairy cattle's diet from SC-rich to high levels of starch. Feeding of starch-rich diets increases the risk of ruminal acidosis in cows, and feeding starch in the form of grains intensifies the competition over cereal grains and arable land among different livestock species, as well as between livestock and humans. Besides cellulose and hemicelluloses, grasses are also often rich in water-soluble carbohydrates (WSC), which comprise mono-, di-, oligo- and polysaccharides (fructans). Although the ruminal fermentation profile of mono- and disaccharides resembles that of starch, the degradation of oligo- and polysaccharides is slower, and their fermentation elicits a rather protecting effect on ruminal pH. When harvested in an early phase (i.e. ear emergence), grass hay and silages can reach WSC levels up to 150-200 g kg^-1 dry matter and energy levels close to starch-rich diets, allowing a significantly reduced inclusion of concentrate supplements. By doing so, this will enhance both rumen health and the sustainability of milk production. However, because the WSC are chemically very heterogeneous, the patterns and extent of their ruminal fermentation are difficult to predict without a clear analytical characterization. This review article aims to summarize both the benefits and potentials, as well as the challenges, with respect to using WSC-rich feedstuffs in the nutrition of dairy cattle and their effects on ruminal fermentation characteristics and milk production. © 2021 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Effect of Dietary Supplementation with Lipids of Different Unsaturation Degree on Feed Efficiency and Milk Fatty Acid Profile in Dairy Sheep

Lipids of different unsaturation degree were added to dairy ewe diet to test the hypothesis that unsaturated oils would modulate milk fatty acid (FA) profile without impairing or even improving feed efficiency. To this aim, we examined milk FA profile and efficiency metrics (feed conversion ratio (FCR), energy conversion ratio (ECR), residual feed intake (RFI), and residual energy intake (REI)) in 40 lactating ewes fed a diet with no lipid supplementation (Control) or supplemented with 3 fats rich in saturated, monounsaturated and polyunsaturated FA (i.e., purified palmitic acid (PA), olive oil (OO), and soybean oil (SBO)). Compared with PA, addition of OO decreased milk medium-chain saturated FA and improved the concentration of potentially health-promoting FA, such as cis-9 18:1, trans-11 18:1, cis-9 trans-11 CLA, and 4:0, with no impact on feed efficiency metrics. Nevertheless, FA analysis and decreases in FCR and ECR suggested that SBO supplementation would be a better nutritional strategy to further improve milk FA profile and feed efficiency in dairy ewes. The paradox of differences observed depending on the metric used to estimate feed efficiency (i.e., the lack of variation in RFI and REI vs. changes in FCR and ECR) does not allow solid conclusions to be drawn in this regard.

Rumen biohydrogenation and milk fatty acid profile in dairy ewes divergent for feed efficiency

A sustainable increase in livestock production would require selection for improved feed efficiency, but the mechanisms underlying this trait and explaining its large individual variation in dairy ruminants remain unclear. This study was conducted in lactating ewes to test the hypothesis that rumen biohydrogenation (BH) would differ between high- and low-efficiency animals, and these differences would be reflected in rumen fatty acid (FA) profile and affect milk FA composition. A second aim was to identify differences in FA that may serve as biomarkers of feed efficiency. Data of daily feed intake and milk yield and composition, as well as body weight, were collected individually over a 3-wk period in 40 ewes. The difference between the mean actual and predicted feed intake (estimated through metabolizable energy requirements for maintenance, production, and body weight change) over the period was used as the feed efficiency index (FEI) to select 8 of the highest feed efficiency (H-FE) and 8 of the lowest feed efficiency (L-FE) animals. In addition, residual feed intake (RFI) was estimated as the residual term from the regression of feed intake on various energy sinks. Rumen and milk FA composition were characterized by using gas chromatography, and results were analyzed using a statistical model that included the fixed effect of the group (H-FE vs. L-FE). The FEI averaged -0.29 ± 0.046 and 0.81 ± 0.084 in H-FE and L-FE, respectively, whereas RFI averaged -0.16 ± 0.084 and 0.18 ± 0.082, respectively. The correlation coefficient between both metrics was 0.69. Feed intake was similar in both groups, but H-FE showed greater milk yield, with increases in lactose content and yield, and in milk protein and fat production. Results from rumen FA profiles included a lower proportion of 18:2n-6, cis-9 18:1, and of several of their BH metabolites, and a greater concentration of 18:0, which may indicate that the apparent BH would be more complete in more efficient sheep. Milk FA analysis suggested that the greater fat yield in the H-FE group was mostly explained by increased de novo FA synthesis, whereas their milk would have lower proportions of cis-9 18:1 and C20 to 22n-6 polyunsaturated FA than L-FE. Stepwise multiple linear regression suggested that milk C20 to 22n-6 PUFA might be convenient biomarkers to discriminate more efficient dairy sheep. Further research is needed to validate these findings (e.g., under different dietary conditions).