Milk Energy Research Articles

A New Strain of Saccharomyces cerevisiae in Diets of Lactating Holstein Cows Improved Feed Efficiency and Lactation Performance

Abstract This study compared the effects of feeding a new strain of Saccharomyces cerevisiae HSA2020 with a commercial strain on in vitro rumen fermentation and production performance of dairy cows. Permeate was used as a substrate for the laboratory production of the new strain of S. cerevisiae after the hydrolysis by β-galactosidase (5000 µ/mL at 37°C). Two experiments were conducted: in Experiment 1, the effects of three levels (1, 2 and 3 g/kg dry matter) of S. cerevisiae on in vitro ruminal fermentation kinetics were evaluated. In Experiment 2, for 60 days, sixty multiparous Holstein cows (639±24.8 kg BW, 3±1 parity, 7±1 days in milk, with a previous milk production of 23±2.0 kg/d) during the previous lactation, were randomly assigned to 3 treatments in a completely randomized design. Cows were fed without any additives (control treatment) or supplemented with 2 g/kg feed daily of laboratory produced (PY) or commercial (CY) S. cerevisiae. In Experiment 1, inclusion of PY and CY increased (P<0.05) gas production, propionate, and nutrient disappearance, while decreased (P<0.05) methane production and protozoal count. Moreover, in Experiment 2, PY followed by CY increased (P<0.01) nutrient digestibility, and serum concentrations of total protein, albumin, and glucose (P<0.05). Higher daily milk yield, and milk energy output were observed with PY and CY without affecting concentrations of milk components or milk fatty acid profile. Compared to control, increased feed efficiency was observed with PY and CY. Compared to PY, CY increased serum concentrations of urea-N and decreased triglycerides, while PY decreased serum aspartate transaminase and increased concentration of conjugated linoleic acids in milk. In early lactating cow diets, both strains of S. cerevisiae improved production performance at 2 g/kg, and minimal differences between strains were found.

Dietary macronutrient composition and partial soybean meal replacement with slow-release urea: Effects on performance, digestibility, rumen fermentation, and nitrogen metabolism in dairy cows

A 2 × 2 factorial arrangement of treatments was used to investigate the interactive effects of dietary macronutrient composition [high-starch, low-fat, low-fiber (HsLFF) than low-starch, high-fat, high-fiber (LsHFF)] and N source [soybean meal (SBM) or partially replaced by slow-release urea (SRU)] on lactation performance, rumen fermentation, N utilization efficiency, nutrient digestibility, blood metabolites, and feeding behavior in cows. A replicated 4 × 4 Latin square design was used involving 12 multiparous Holstein cows (milk yield of 40.5 ± 5.6 kg/d, BW of 590 ± 20 kg; 81 ± 12 d in milk). The HsLFF diet contained 300 g/kg starch, 31.3 g/kg fat, and 301 g/kg NDF, without straw or additional fat. In contrast, the LsHFF diet contained 195.5 g/kg starch, 60.8 g/kg fat, and 367.5 g/kg NDF, enriched with wheat straw (100 g/kg), and additional fat (34 g/kg). The diets were formulated to be iso-nitrogenous and isocaloric. Cows fed the HsLFF diet had greater DM intake, digestibility of DM and CP, milk yield and milk protein %, but lower intakes of NDF, and physically effective NDF, and milk fat % than cows fed the LsHFF diet. Replacing SBM with SRU significantly increased milk solids yield without affecting other lactation performance or BW. Cows fed the LsHFF diet had higher ruminal pH and branched-chain VFA proportions but lower total VFA concentrations compared with the HsLFF diet, while those on the LsHFF-SRU diet had the highest ruminal ammonia levels. Compared with the HsLFF diet, cows fed LsHFF had lower NE intake, milk energy output and energy requirement for maintenance, although energy balances were similar among groups. The HsLFF diet improved N utilization, resulting in higher N content in milk and lower N excretion in feces. Blood metabolite studies showed significant interactions between the main factors, particularly for blood glucose and creatinine, with the lowest levels in cows fed the LsHFF-SRU and LsHFF-SBM diets. In addition, alanine aminotransferase levels were higher in cows fed the LsHFF diet than in cows fed the HsLFF diet. This could indicate early-stage liver stress due to the metabolic imbalance caused by a high-fat, low-starch diet, which can alter energy metabolism. Cows on the HsLFF-SRU diet had the highest glucose levels, indicating possible changes in carbohydrate metabolism or a higher metabolic rate. The concentration of BUN increased steadily after feeding in the LsHFF diet and peaked after 4 h in the LsHFF-SRU diet, with no difference between N sources in the HsLFF diet. Partial replacement of SRU with SBM had no effect on BUN. The interaction between the main factors had a significant effect on MUN content, which was highest in LsHFF-SRU and lowest in HsLFF-SRU, with no difference between the N sources in the HsLFF diets. Overall, while diets with reduced starch and increased fiber and fat compromised lactation performance, partially substituting SRU with SBM helped maintain milk production and milk nitrogen efficiency. However, the LsHFF-SRU diet was less efficient in N utilization, as shown by higher levels of ruminal ammonia, BUN and MUN.

Does the Composition of Breast Milk in the First Week Postpartum Differ Due to Maternal Factors or Neonatal Birth Weight and Percent Fat Body Mass?

Background: The composition of breast milk is dependent on numerous factors. However, the precise impact of maternal health conditions on breast milk composition remains to be fully elucidated. Similarly, there is a paucity of evidence regarding the correlation between neonatal body composition and human milk. The objective of the study was to evaluate the macronutrient composition of breast milk collected during the first week postpartum from mothers with gestational diabetes and healthy mothers in terms of selected maternal and neonatal factors. Methods: n = 70 breast milk samples were analyzed for fat, protein, carbohydrate, dry matter, true protein, and energy. The results were evaluated in terms of selected maternal factors, as well as neonatal birth weight (BW) and percent fat body mass (%FBM), which was assessed with a bioimpedance method. Results: Energy of breast milk in the study group was as follows: median 55.5 kcal/dL in GDM G1, median 55.5 kcal/dL in GDM G2, and median 65.0 kcal/dL in non-GDM, which differed significantly in Kruskal–Wallis ANOVA. Neonatal %FBM, but not BW, was found to be significantly related to concentrations of protein, true protein and dry matter. Maternal gestational weight gain, history of hypothyroidism, and classification by study group were identified as factors affecting both breast milk and neonatal body composition. Conclusions: The composition of breast milk in the initial week following childbirth is predominantly determined by maternal factors. The relationship between selected macronutrients and neonatal percent fat body mass was found to be weak, yet the significance of this finding is unclear. Further research is required to ascertain the influence of maternal milk composition on early infantile nutritional programming.

The impact of citrus pulp inclusion on milk performance of dairy cows: A meta-analysis

Considering sustainability and circular economy, citrus pulp could become a common dairy feedstuff. Yet, there is no clear indication of the amount of citrus pulp that can be fed without compromising milk performance or that could deliver benefits. In our meta-analysis, we investigated the impact of varying citrus pulp inclusion levels, i.e., no (0 %), low (>0–10 %), medium (>10–20 %), and high (>20 %), on milk performance variables. Replacing cereal grains with citrus pulp increased pectins and sugars while decreasing starch. At the low inclusion level, citrus pulp increased milk yield without affecting feed intake. At higher inclusion levels, citrus pulp reduced feed intake and milk yield but with higher fat contents, the milk energy content was similar to no inclusion. Concluding, citrus pulp is a good energy source with lipogenic properties in dairy cows. We encourage research to fill in the existing gap of knowledge at the rumen and systemic levels.

A simple step to improve fat and energy delivery of human milk delivered via bottle-feeding pump: An experimental study.

Enteral feeding pump systems deliver decreased amounts of macronutrients in human milk to neonates. This study determined the macronutrient loss associated with a bottle-feeding pump system and the effect of manually mixing the human milk during extended feeds. Macronutrient content from samples of donor human milk was analyzed after simulated extended feeds with a bottle-feeding pump system, using a human milk analyzer. Simulations were repeated using manual mixing of the bottle every 30 min during feeding. The percentage of the baseline was calculated, and one-sample t tests and analysis of variance compared the effect of manual mixing and the duration of feeding on macronutrient delivery. The delivery of fat and energy was lower over time, but manual mixing considerably improved retention. The length of feeding impacted fat delivery, with less fat delivered over time (P < 0.001). Manually mixing significantly increased fat delivery (P < 0.001). Similar results were found for energy, with a significant reduction in energy delivery over time (P < 0.001) and significantly more energy delivered with mixing (P < 0.001). Mixing and the duration of feeding had minimal effect on protein or carbohydrate delivery. Bottle-feeding pump systems are associated with a significant reduction in the delivery of fat and energy of donor human milk. The manual mixing of donor human milk during prolonged feeds is a simple way to improve fat and energy delivery to the neonate.

Relationship between Selected SNPs (g.16024A/G, g.16039T/C and g.16060A/C) of the FASN Gene and the Fat Content and Fatty Acid Profile in the Milk of Three Breeds of Cows.

Fat is an important energy and nutritional component of milk and consists of fatty acids. FASN (fatty acid synthase) is an enzyme that regulates the synthesis of fatty acids in the milk and meat of cattle. It was hypothesized that knowing the relationships between the genotypes of the tested single nucleotide polymorphisms (SNPs) and the content of fat and specific fatty acids would make it possible to improve milk quality in the selection process during cattle breeding. This study aimed to analyze the relationships of SNPs (g.16024A/G, g.16039T/C) of the FASN gene and their genotypes with the fat and fatty acid content of the milk of the following breeds: Polish Red-White (ZR), Polish Red (RP), and Polish Holstein-Friesian Red-White (RW). The SNP g.16060A/C was included in the study, although its effect on the fat composition of cow's milk has not yet been widely studied. Milk was obtained during test milkings. SNP genotyping was performed using the real-time PCR (HRM) method. The milk from ZR and RP cows was more often characterized by a more favorable fatty acid profile than the milk from RW cows. This information can be used by cattle breeders and consumers of so-called functional food.

Investigating relationships between the host genome, rumen microbiome, and dairy cow feed efficiency using mediation analysis with structural equation modeling

The rumen microbiome is crucial for converting feed into absorbable nutrients used for milk synthesis, and the efficiency of this process directly impacts the profitability and sustainability of the dairy industry. Recent studies have found that the rumen microbial composition explains part of the variation in feed efficiency traits, including dry matter intake, milk energy, and residual feed intake. The main goal of this study was to reveal relationships between the host genome, rumen microbiome, and dairy cow feed efficiency using structural equation models. Our specific objectives were to (i) infer the mediation effects of the rumen microbiome on feed efficiency traits, (ii) estimate the direct and total heritability of feed efficiency traits, and (iii) calculate the direct and total breeding values of feed efficiency traits. Data consisted of dry matter intake, milk energy, and residual feed intake records, SNP genotype data, and 16S rRNA rumen microbial abundances from 448 mid-lactation Holstein cows from 2 research farms. We implemented structural equation models such that the host genome directly affects the phenotype (GP → P) and the rumen microbiome (GM → P), while the microbiome affects the phenotype (M → P), partially mediating the effect of the host genome on the phenotype (G → M → P). We found that 7 to 30% of microbes within the rumen microbial community had structural coefficients different from zero. We classified these microbes into 3 groups that could have different uses in dairy farming. Microbes with heritability <0.10 but significant causal effects on feed efficiency are attractive for external interventions. On the other hand, 2 groups of microbes with heritability ≥0.10, significant causal effects, and genetic covariances and causal effects with the same or opposite sign to feed efficiency are attractive for selective breeding, improving or decreasing the trait heritability and response to selection, respectively. In general, the inclusion of the different microbes in genomic models tends to decrease the trait heritability rather than increase it, ranging from -15% to +5%, depending on the microbial group and phenotypic trait. Our findings provide more understanding to target rumen microbes that can be manipulated, either through selection or management interventions, to improve feed efficiency traits.

Assessing different cross-validation schemes for predicting novel traits using sensor data: An application to dry matter intake and residual feed intake using milk spectral data

Feed efficiency is important for economic profitability of dairy farms; however, recording daily dry matter intakes (DMI) is expensive. Our objective was to investigate the potential use of milk mid-infrared (MIR) spectral data to predict proxy phenotypes for DMI based on different cross-validation schemes. We were specifically interested in comparisons between a model that included only MIR data (Model M1), a model that incorporated different energy sink predictors, such as body weight, body weight change, and milk energy (Model M2), and an extended model that incorporated both energy sinks and MIR data (Model M3). Models M2 and M3 also included various cow level variables (stage of lactation, age at calving, parity) such that any improvement in model performance from M2 to M3, whether through a smaller root mean squared error (RMSE) or a greater squared predictive correlation (R2), could indicate a potential benefit of MIR to predict residual feed intake. The data used in our study originated from a multi-institutional project on the genetics of feed efficiency in US Holsteins. Analyses were conducted on 2 different trait definitions based on different period lengths: averaged across weeks vs. averaged across 28-d. Specifically, there were 19,942 weekly records on 1,812 cows across 46 experiments or cohorts and 3,724 28-d records on 1,700 cows across 43 different cohorts. The cross-validation analyses involved 3 different k-fold schemes. First, a 10-fold cow-independent cross-validation was conducted whereby all records from any one cow were kept together in either training or test sets. Similarly, a 10-fold experiment-independent cross-validation kept entire experiments together whereas a 4-fold herd-independent cross-validation kept entire herds together in either training or test sets. Based on cow-independent cross-validation for both weekly and 28-d DMI, adding MIR predictors to energy sinks (Models M3 vs M2) significantly (P < 10-10) reduced average RMSE to 1.59 kg and increased average R2 to 0.89. However, adding MIR to energy sinks (M3) to predict DMI either within an experiment-independent or herd-independent cross-validation scheme seemed to demonstrate no merit (P > 0.05) compared with an energy sink model (M2) for either R2 or RMSE (respectively, 0.68 and 2.55 kg for M2 in herd-independent scheme). We further noted that with broader cross-validation schemes, i.e., from cow-independent to experiment-independent to herd-independent schemes, the mean and slope bias increased. Given that proxy DMI phenotypes for cows would need to be almost entirely generated in herds having no DMI or training data of their own, herd-independent cross-validation assessments of predictive performance should be emphasized. Hence, more research on predictive algorithms suitable for broader cross-validation schemes and a more earnest effort on calibration of spectrophotometers against each other should be considered.

Partitioning among-animal variance of energy utilization in lactating Jersey cows

Animals vary in the way in which they utilize energy due to diet, genetics, and management. Energy consumed by the animal supports milk production, but considerable variation among-animals in energy utilization is thought to exist. The study objective was to estimate the among-animal variance in energy utilization in data collected from Jersey cows using indirect calorimetry. Individual animal-period data from 15 studies (n = 560) were used. The data set included 115 animals from 44 to 410 DIM producing 11.5 to 39.1 kg/d of milk. On average, the 63 treatments in the data set ranged 14.8 to 19.5% CP, 21.4 to 43.0% NDF, 16.2 to 33.3% starch, and 2.21 to 6.44% crude fat. Data were analyzed with the Glimmix procedure of SAS (9.4) with random effects of cow, treatment nested within period, square, and experiment. The percentage of among-animal, dietary treatment, and experimental variance was calculated as the variance associated with each fraction divided by the sum of variance from animal, dietary treatment, experiment, and residual which was considered the total variance. The percentage of among-animal variance was characterized as high or low when the value was greater than or less than the mean value of 29.2%. Among-animal variance explained approximately 29.3 - 42.5% of the total variance in DM intake (DMI), gross energy (GE), digestible energy (DE), metabolizable energy (ME), and net energy of lactation (NEL) in Mcal/d. When energetic components of feces, urine, and heat in Mcal/d were expressed per unit of DMI the among-animal variance decreased by 20.4, 4.82, and 9.55% units, respectively. However, among-animal variance explained 4.80, 8.78, and 5.02% units more of the total variation for methane energy, lactation energy, and tissue energy in Mcal/d when expressed per unit of DMI. Variance in energetic efficiencies of DE/GE, ME/GE, and ME/DE were explained to a lesser extent by among-animal variance (averaging 17.8 ± 1.95%). The among-animal contribution to total variance in milk energy was 28.8%. Milk energy was a large proportion of the energy efficiency calculation which included milk energy plus corrected tissue energy over net energy intake which likely contributed to the 22.2% of total among-animal variance in energy efficiency. Results indicate that among-animal variance explains a large proportion of the total variation in DMI. This contributes to the variance observed for energy fractions as well as energy components when expressed in Mcal/d. Variation in energetic loss associated with methane was primarily explained by differences among-animals and was increased when expressed per unit of DMI highlighting the role of inherent animal differences in these losses.

Genetic relationships between behavioral traits and feed efficiency traits in lactating Holstein cows

The evaluation of dairy cow feed efficiency using residual feed intake accounts for known energy sinks. However, behavioral traits may also contribute to the variation in feed efficiency. Our objective was to estimate the heritability and repeatability of behavioral traits and their genetic correlations with feed efficiency and its components in lactating Holstein cows. The first data set consisted of 36,075 daily rumination and lying time records collected using a SMARTBOW ear tag accelerometer (Zoetis, Parsippany, NJ) and 6,371 weekly feed efficiency records of 728 cows from the University of Wisconsin-Madison. The second data set consisted of 59,155 daily activity records, measured as number of steps, recorded by pedometers (AfiAct; S.A.E. Afikim, Kibbutz Afikim, Israel), and 8,626 weekly feed efficiency records of 635 cows from the University of Florida. Feed efficiency and its components included dry matter intake, change in body weight, metabolic body weight, secreted milk energy, and residual feed intake. The statistical models included the fixed effect of cohort, lactation number, and days in milk, and the random effects of animal and permanent environment. Heritability estimates for behavioral traits using daily records were 0.19 ± 0.06 for rumination and activity, and 0.37 ± 0.07 for lying time. Repeatability estimates for behavioral traits using daily data ranged from 0.56 ± 0.02 for activity to 0.62 ± 0.01 for lying time. Both heritability and repeatability estimates were larger when weekly records instead of daily records were used. Rumination and activity had positive genetic correlations with residual feed intake (0.40 ± 0.19 and 0.31 ± 0.22, respectively) while lying time had a negative genetic correlation with this residual feed intake (-0.27 ± 0.11). These results indicate that more efficient cows tend to spend more time lying and less time active. Additionally, less efficient cows tend to eat more and therefore also tend to ruminate longer. Overall, sensor-based behavioral traits are heritable and genetically correlated with feed efficiency and its components and, therefore, they could be used as indicators to identify feed efficient cows within the herd.

295 Development of a model to predict forage intake and growth of nursing beef calves

Abstract Models to predict intake and growth of preweaning calves have received less attention than postweaning calves. The objective was to develop a model to predict forage intake and growth of nursing beef calves. Data on milk intake, forage intake, and body weight (BW) of 51 Hereford calves from birth to 200 d of age were acquired from Boggs (1977). Milk and forage intake equations were those evaluated by Lancaster et. al. (2021; 10.1093/jas/skaa406). Forage digestibility was evaluated with a dataset on in vitro and in vivo measurements. Milk energy concentration was evaluated with a dataset on milk yield and composition. NASEM growth equation was evaluated using the dataset of Hildebrand et al. (2022; 10.1093/jas/skac064.321). Model precision and accuracy were evaluated using the concordance correlation coefficient (CCC) and mean bias (MB). The NASEM and WOOD milk yield equations predicted milk intake reasonably well (CCC = 0.796 and 0.820; MB = 4.29% and 1.31%, respectively); the WOOD equation was used in subsequent analyses. Forage intake prediction was imprecise (CCC = 0.348 to 0.613) and inaccurate (MB = 19.36 to 57.67%). Body weight prediction was moderately precise (CCC = 0.574 to 0.766) and moderately accurate (MB = 6.39 to 15.79%). With observed BW, forage intake prediction was moderately precise (CCC = 0.528 to 0.834) and highly to poorly accurate (MB = 0.81 to 27.56%). With observed forage intake, BW prediction by the NASEM growth equation resulted in CCC of 0.821 and MB of 9.69%. Published equations relating IVDMD to in vivo OMD were imprecise (CCC = 0.437 to 0.520) and moderately to highly accurate (MB = -9.46 to 7.67%). A new equation was developed: OMD, % = 7.7719 ± 4.3825 + 0.8937 ± 0.0772*IVDMD, % with RMSE = 2.803% and R2 = 0.9055. Milk energy concentration changed with days in milk (DIM; P &amp;lt; 0.05) and milk yield (P &amp;lt; 0.05): Energy, Mcal/kg = 0.7525 ± 0.0037 + 0.00069 ± 0.00016*DIM -0.00012 ± 0.00003*DIM*Yield, kg/d with RMSE = 0.031 and R2 = 0.527. Evaluating the NASEM growth equation in beef cattle resulted in CCC of 0.777 and MB of -13.56%. A new equation was developed: EBG, kg/d = 7.9279 ± 0.8254*(RE, Mcal/d/EBW, kg0.75) ^0.7083 ± 0.0385 with RMSE = 0.1127 and R2 = 0.6940. With observed forage intake, BW prediction with new equations resulted in CCC of 0.901 with MB of 1.58%. With the new equations, forage intake prediction by TED06, BAK76, TED09A, TED09B, and HOL82 resulted in CCC of 0.822, 0.700, 0.434, 0.686, and 0.380 with MB of -7.01, -5.08, 36.87, 13.24, and 10.96%, respectively. Body weight prediction resulted in CCC of 0.935, 0.938, 0.896, 0.926, and 0.886 with MB of -7.34, -6.21, -0.09, -5.35, and -9.41%. In conclusion, prediction of forage intake and BW in nursing beef calves were hindered by use of IVDMD, constant milk energy concentration, and the NASEM growth equation.

PSV-9 Effects of increasing amounts of high-oleic or conventional soybeans in diets fed to mid-lactation dairy cows on measures of dry matter intake and lactation

Abstract The objective of the study was to determine effects of increasing amounts of conventional (CONV) or high-oleic (HO) soybean in diets fed to mid-lactation dairy cows on dry matter intake (DMI) and performance. Rumen and duodenally cannulated cows (n = 8) were split into two groups and placed in a split-plot design. The whole plot factor was type of soybean (CONV vs. HO) and was provided to cows in a crossover arrangement so that each cow was provided both types of soybeans throughout the experiment. The subplot factor was amount of soybeans (0, 4, 8, 12%). Each group of cows receiving the same type of soybean were placed in a 4 x 4 Latin square with treatment sequences balanced for carryover effects. Diets were formulated to contain 18.5% crude protein and to slightly exceed requirements for ruminally degradable protein. Additions of soybeans replaced soybean meal and soybean hulls so that diets provided similar amounts of soy-based protein and fiber. During the last 4 d of each 14-d period, spot samples of feed, orts, and milk were collected together with measures of milk production and feed intake. Data were analyzed using the MIXED procedure of SAS. There were no interactions of soybean type and amount on lactation or DMI except for measures of milkfat, milk energy concentration and milkfat yield. Increasing amounts of HO resulted in a quadratic response in milkfat and energy concentration together with measures of milkfat yield. Specifically, these measures were greater (P ≤ 0.02) when cows were fed diets containing 4 and 8% HO compared with diets containing 0 or 12% HO; increasing amounts of CONV had no impact (P ≥ 0.32) on milkfat and energy concentration or milkfat yield. Measures of milk yield were not affected by type or amount of soybean (P ≥ 0.54), but measures of energy corrected milk yield (ECM) tended to respond differently to increasing amounts of soybean from either CONV or HO (P = 0.07). Interestingly, ECM responded quadratically (P = 0.02) to increasing amounts of HO but not to increasing amounts of CONV (P ≥ 0.58). Overall, however, ECM tended (P = 0.06) to be slightly greater with HO compared with CONV (42.5 vs. 41.1 kg/d). Yet, DMI was also greater (P ≤ 0.01) with HO compared with CONV (26.6 vs. 24.4 kg/d). Differences in DMI, therefore, resulted in a tendency (P = 0.07) for greater efficiency of ECM production when cows were fed CONV vs. HO. Overall, treatments had no effect on measures of milk protein, urea-nitrogen, solids-not-fat or lactose concentrations or yields. These data seem to indicate that performance responses among lactating cows to increasing amounts of HO are dynamic but that responses to CONV are not.

Effects of the level and source of dietary physically effective fiber on feed intake, nutrient utilization, heat energy, ruminal fermentation, and milk production by Alpine goats

Thirty-two primiparous and 31 multiparous Alpine goats were used to determine influences of diets varying in level and source of forage on performance in early to mid-lactation for 16 wk. Diets consisted of 40%, 50%, 60%, and 70% forage (designated as 40F, 50F, 60F, and 70F, respectively) with 60F and 70F containing coarsely ground grass hay (primarily orchardgrass) and 40F and 50F containing cottonseed hulls, alfalfa pellets, and coarsely ground wheat hay. Diets contained 15.9% to 16.3% crude protein and 37.8%, 42.1%, 53.5%, and 55.4% neutral detergent fiber (NDF) with 10.0%, 15.8%, 50.1%, and 55.5% particle retention on a 19-mm sieve, and 26.1%, 29.6%, 38.3%, and 40.0% physically effective NDF (peNDF) for 40F, 50F, 60F, and 70F, respectively. Dry matter intake (2.71, 2.75, 1.96, and 1.95 kg/d) and milk yield (2.82, 2.71, 2.23, and 2.10 kg/d for 40F, 50F, 60F, and 70F, respectively) were lower (P < 0.05) for the two diets highest in forage. Digestion of organic matter was similar among diets (P > 0.05), but digestibility of NDF was greater (P < 0.05) for 60F and 70F (57.5%, 58.4%, 68.9%, and 72.2% for 40F, 50F, 60F, and 70F, respectively). Diet affected (P < 0.05) milk fat (3.16%, 3.37%, 2.93%, and 2.97%) and protein concentrations (2.62%, 2.69%, 2.58%, and 2.52% for 40F, 50F, 60F, and 70F, respectively). Milk energy yield was greater (P < 0.05) for the two diets lowest in forage (7.51, 7.45, 5.68, and 5.34 MJ/d), although yield relative to dry matter intake was not affected (P > 0.05) by diet and was lower (P < 0.05) for primiparous vs. multiparous goats (2.71 and 3.09 MJ/kg). Ruminal pH and acetate proportion were greater for 60F and 70F than for the other diets and the proportion of butyrate was lower for the two diets highest in fiber. The mean lengths of time spent ruminating, eating, standing, and lying were not affected (P > 0.05) by diet or parity, but many interactions involving diet, period, hour, and parity were significant (P < 0.05). In conclusion, lactational performance of Alpine goats in early to mid-lactation will be constrained with diets high in forage of moderate quality, peNDF content, and large particle size, which appeared related to limited feed intake.

Human Milk Components and the Infant Gut Microbiome at 6 Months: Understanding the Interconnected Relationship

BackgroundHuman milk oligosaccharides have been shown to relate to the infant gut microbiome. However, the impact of other human milk components on infant gut bacterial colonization remains unexplored. ObjectivesOur cross-sectional analysis aimed to investigate associations between human milk components (energy, macronutrients, free amino acids, inflammatory markers, and hormones) and infant gut microbiome diversity and composition (phylum, family, and genus) at 6 mo of age. MethodsHuman milk and infant stool samples were collected at 6 mo postpartum. The infant gut microbiome was profiled using 16S rRNA sequencing. Linear regression models were performed to examine associations, adjusting for pregravid BMI (kg/m2), delivery mode, duration of human milk feeding, and infant sex, with q < 0.2 considered significant. ResultsThis analysis included a total of 54 mothers (100% exclusively feeding human milk) and infants (n = 28 male; 51.9%). Total energy in human milk showed a negative association with α-diversity measures (Chao1 and Shannon). Interleukin (IL)-8 in human milk was positively associated with Chao1 and observed operational taxonomic units. At the family level, human milk glutamine and serine levels showed a negative association with the abundance of Veillonellaceae, whereas isoleucine showed a positive association with Bacteroidaceae. Human milk IL-8 and IL-6 concentrations were positively associated with Bacteroidaceae abundance. IL-8 also had a positive relationship with Bifidobacteriaceae, whereas it had a negative relationship with Streptococcacea and Clostridiaceae. Human milk IL-8 was positively associated with the phylum Bacteroidetes, and negatively associated with Proteobacteria. At the genus level, human milk IL-8 exhibited a positive relationship with Bacteroides, whereas human milk isoleucine had a negative relationship with Bacteroides and Ruminococcus. Pregravid BMI and sex effects were observed. ConclusionsIL-8 in human milk could potentially prepare the infant’s immune system to respond effectively to various microorganisms, potentially promoting the growth of beneficial gut bacteria and protecting against pathogens.

Breast size in lactating women and the content of macronutrients in human milk.

Although the primary function of a woman's breast is milk synthesis, only a few studies have evaluated the relationship between breast size and human milk composition, showing equivocal results. This study aims to test if breast size during fully established lactation is related to energy density and content of macronutrients in human milk. Mothers of healthy, born-on-term infants at stage III of lactogenesis (N = 137) provided breast milk samples. Milk composition was analyzed using midinfrared transmission spectroscopy. The breast size index was calculated by dividing the breast by the circumference below the breast. Stepwise forward linear regression showed a negative association between breast size index and lactose concentration in breast milk (β = -.242, p = .003). The final model, which includes breast size index, feeding frequency, and maternal energy intake together explained around 13% of the variance in breast milk lactose content (R2 adj = .126, p < .001). No statistically significant relationship was found between breast size index and milk's energy density, protein, and fat content. This is the first study that shows a negative relationship between breast size during fully established lactation and lactose concentration in milk in a large sample size. No relationship between other macronutrients and breast size indicates that large breast size is not necessary for adequate milk production; however, it may contribute to an altered lactose concentration.

The association between maternal body mass index and breast milk composition: a systematic review.

Breast milk composition is influenced by many factors, ranging from maternal nutritional status to infant sex. Previous studies have explored the relationship between maternal body mass index (BMI) and breast milk composition; however, the findings have been inconsistent and controversial. To systematically review the evidence on the association of maternal weight and BMI with breast milk composition. The PubMed and Scopus databases were searched up to May 3, 2023, using the following search strategy: ("maternal weight" OR "maternal BMI" OR "mother's weight" OR "mother's BMI") AND ("maternal milk" OR "human milk" OR "breast milk"). A total of 83 publications, involving data from more than 11 310 lactating women, were identified. All extracted data were compiled, compared, and critically analyzed. Overall, maternal BMI was associated with higher levels of leptin and insulin, and the ratio of omega-6 to omega-3 polyunsaturated fatty acids in breast milk. However, no conclusive associations were found between maternal BMI and the levels of energy, macronutrients, micronutrients, and other components of breast milk. This systematic review provides robust evidence supporting a positive correlation between maternal BMI and breast milk concentrations of leptin, insulin, and the omega-6 to omega-3 polyunsaturated fatty acid ratio. Nevertheless, disparities in findings are noticeable for other constituents of breast milk. To comprehensively grasp the influence of maternal weight and BMI on breast milk composition, further research endeavors are imperative. PROSPERO registration no. CRD42023458667.

Structural equation models to infer relationships between energy-related blood metabolites and milk daily energy output in Holstein cows.

During lactation, high-yielding cows experience metabolic disturbances due to milk production. Metabolic monitoring offers valuable insights into how cows manage these challenges throughout the lactation period, making it a topic of considerable interest to breeders. In this study, we used Bayesian networks to uncover potential dependencies among various energy-related blood metabolites, i.e., glucose, urea, beta-hydroxybutyrate (BHB), non-esterified fatty acids (NEFA), cholesterol (CHOL), and daily milk energy output (dMEO) in 1,254 Holstein cows. The inferred causal structure was then incorporated into structural equation models (SEM) to estimate heritabilities and additive genetic correlations among these phenotypes using both pedigree and genotypes from a 100k chip. Dependencies among traits were determined using the Hill-Climbing algorithm, implemented with the posterior distribution of the residuals obtained from the standard multiple-trait model. These identified relationships were then used to construct the SEM, considering both direct and indirect relationships. The relevant dependencies and path coefficients obtained, expressed in units of measurement variation of 1σ, were as follows: dMEO → CHOL (0.181), dMEO → BHB (-0.149), dMEO → urea (0.038), glucose → BHB (-0.55), glucose → urea (-0.194), CHOL → urea (0.175), BHB → urea (-0.049), and NEFA → urea (-0.097). Heritabilities for traits of concern obtained with SEM ranged from 0.09 to 0.2. Genetic correlations with a minimum 95% probability (P) of the posterior mean being >0 for positive means or <0 for negative means include those between dMEO and glucose (-0.583, P = 100), dMEO and BHB (0.349, P = 99), glucose and CHOL (0.325, P = 100), glucose and NEFA (-0.388, P = 100), and NEFA and BHB (0.759, P = 100). The results of this analysis revealed the existence of recursive relationships among the energy-related blood metabolites and dMEO. Understanding these connections is paramount for establishing effective genetic selection strategies, enhancing production and animal welfare.

Concentration and composition of odd-chain fatty acids in phospholipids and triacylglycerols in Chinese human milk throughout lactation.

Human milk represents the gold standard for infant nutrition, with approximately 50% of the energy in human milk derived from lipids. Odd-chain fatty acids (OCFAs) have been recognized as a category of bioactive milk fatty acids in recent research; however, limited data exist on OCFAs in human milk. This study collected human milk samples spanning the postpartum period from 0 to 400 days. Phospholipids containing OCFAs (PL-OCFAs) were determined in 486 human milk samples using hydrophilic liquid chromatography-electrospray ionization-triquadrupole-mass spectrometry. Triacylglycerols containing OCFAs (TAG-OCFAs) were analyzed in 296 human milk samples using ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry. The average total concentration of PL-OCFA ranged from 30.89 ± 14.27 mg L-1 to 93.48 ± 36.55 mg L-1 during lactation, and the average total TAG-OCFA content ranged from 103.1 ± 147.15 mg L-1 to 965.41 ± 651.67 mg L-1. Despite the lower absolute concentration of PL-OCFA, its relative concentration (8.75%-11.75%) was significantly higher than that of TAG-OCFA (0.37%-1.85%) throughout lactation. PC-OCFA, SM-OCFA and PE-OCFA are major sub-classes of PL-OCFA. Furthermore, C17:0 was the major chain length in both PL-OCFA and TAG-OCFA, followed by C15:0. C17:1 was characteristic of TAG-OCFA, while long-chain fatty acids C19:0, C21:0 and C23:0 were characteristic of PL-OCFA. Our findings highlighted the importance of bioactive lipids in human milk, suggesting that OCFAs could be targeted in future studies in relation to the health and development of infants.

Supplementation of Multistrain Probiotics Improves Milk Production, Blood Metabolites, Digestibility, and Rectal Microbiota during the Prepartum and Early Lactation Stages in Crossbred Cows

The current study aimed to study the effects of administration with a multistrain probiotic formulation (MPF) initiated from the prepartum stage on lactation performance, digestibility, rectal microbiota, and blood metabolites of postpartum dairy cows. A total of 30 multiparous crossbred (Holstein Friesian × Balady) dairy cows were randomized into three groups: control (no MPF supplementation), MPF1 (0.5 g MPF/cow/d), and MPF2 (1 g MPF/cow/d). The administration of MPF started 28 days before calving and continued for 84 days after. The results revealed that cows in the MPFI and MPF2 groups had higher milk production, 3.5% FCM, and ECM than those in the control group. The MPF‐supplemented cows had significantly greater milk energy output, DMI, and yields of solids‐not‐fat, lactose, total solids, ash, fat, and protein. The MPF supplementation significantly enhanced feed conversion and significantly interacted with milk solids‐not‐fat, lactose, total solids, and protein. Hematological indicators did not significantly alter between the MPF and control groups. However, cows receiving MPF supplements had higher levels of Hct. Serum biochemical measurements showed lower serum concentrations of LDL, total cholesterol, and VLDL in MPF‐fed cows than in the control group. The digestibility of organic and dry matter tended to be higher in MPF‐supplemented cows. The populations of lactobacillus, bacillus, and total bacterial count increased due to MPF administration, while the count of Clostridium, Salmonella, E. coli, and coliforms decreased in MPF‐fed cows. In summary, administering MPF during late gestation and early lactation period improved milk production, fecal microbial composition, and overall health of dairy cows.

Variations in Casein Genes Are Associated with Milk Protein and Fat Contents in Sarda Goats (Capra hircus), with an Important Role of CSN1S2 for Milk Yield.

This work aimed to assess the variability of casein genes in a population of 153 bucks and 825 lactating does of the Sarda breed, and to perform association analysis between polymorphic sites and milk yield and composition traits. To genotype the casein genes, we chose an SNP panel including 44 SNPs mapping to the four casein genes CSN1S1, CSN2, CSN1S2, and CSN3. Genotyping (made by KASP™ genotyping assay, based on competitive allele-specific PCR) revealed the high variability of the Sarda goat, and haplotype analysis revealed linkage disequilibrium (LD) between CSN1S1 and CSN2 genes, in addition to two LD blocks within the CSN1S2 and two LD blocks within the CSN3 gene, in bucks and does. Association analysis revealed that variability at all four casein genes was associated with milk protein content, total solids, and milk energy. The three Ca-sensitive casein genes were associated with lipid content, and CSN1S2 showed a unique pattern, with intron variants associated with milk yield, in addition to milk pH, NaCl, and SCS (Somatic Cell Score). This information might prove useful in selection schemes and in future investigations aiming to better understand the biology of lactation, and the direct link between genotype and phenotype.