Individual Milk Components Research Articles

Dry matter intake in US Holstein cows: Exploring the genomic and phenotypic impact of milk components and body weight composite

Large datasets allow estimation of feed required for individual milk components or body maintenance. Phenotypic regressions are useful for nutrition management, but genetic regressions are more useful in breeding programs. Dry matter intake records from 8,513 lactations of 6,621 Holstein cows were predicted from phenotypes or genomic evaluations for milk components and body size traits. The mixed models also included DIM, age-parity subclass, trial date, management group, and BW change during 28- and 42-d feeding trials in mid lactation. Phenotypic regressions of DMI on milk (0.014 ± 0.006), fat (3.06 ± 0.01), and protein (4.79 ± 0.25) were much less than corresponding genomic regressions (0.08 ± 0.03, 11.30 ± 0.47, and 9.35 ± 0.87, respectively) or sire genomic regressions multiplied by 2 (0.048 ± 0.04, 6.73 ± 0.94, and 4.98 ± 1.75). Thus, marginal feed costs as fractions of marginal milk revenue were higher from genetic than phenotypic regressions. According to the ECM formula, fat production requires 69% more DMI than protein production. In the phenotypic regression, it was estimated that protein production requires 56% more DMI than fat. However, the genomic regression for the animal showed a difference of only 21% more DMI for protein compared with fat, whereas the sire genomic regressions indicated approximately 35% more DMI for fat than protein. Estimates of annual maintenance in kilograms DMI/kilograms BW per lactation were similar from phenotypic regression (5.9 ± 0.14), genomic regression (5.8 ± 0.31), and sire genomic regression multiplied by 2 (5.3 ± 0.55) and are larger than those estimated by the National Academies for Science, Engineering, and Medicine based on NEL equations. Multiple regressions on genomic evaluations for the 5 type traits in body weight composite (BWC) showed that strength was the type trait most associated with BW and DMI, agreeing with the current BWC formula, whereas other traits were less useful predictors, especially for DMI. The Net Merit formula used to weight different genetic traits to achieve an economically optimal overall selection response was revised in 2021 to better account for these estimated regressions. To improve profitability, breeding programs should select smaller cows with negative residual feed intake that produce more milk, fat, and protein.

Evidence for human milk as a biological system and recommendations for study design—a report from “Breastmilk Ecology: Genesis of Infant Nutrition (BEGIN)” Working Group 4

Human milk contains all of the essential nutrients required by the infant within a complex matrix that enhances the bioavailability of many of those nutrients. In addition, human milk is a source of bioactive components, living cells and microbes that facilitate the transition to life outside the womb. Our ability to fully appreciate the importance of this matrix relies on the recognition of short- and long-term health benefits and, as highlighted in previous sections of this supplement, its ecology (i.e., interactions among the lactating parent and breastfed infant as well as within the context of the human milk matrix itself). Designing and interpreting studies to address this complexity depends on the availability of new tools and technologies that account for such complexity. Past efforts have often compared human milk to infant formula, which has provided some insight into the bioactivity of human milk, as a whole, or of individual milk components supplemented with formula. However, this experimental approach cannot capture the contributions of the individual components to the human milk ecology, the interaction between these components within the human milk matrix, or the significance of the matrix itself to enhance human milk bioactivity on outcomes of interest. This paper presents approaches to explore human milk as a biological system and the functional implications of that system and its components. Specifically, we discuss study design and data collection considerations and how emerging analytical technologies, bioinformatics, and systems biology approaches could be applied to advance our understanding of this critical aspect of human biology.

Human Milk Oligosaccharides, Important Milk Bioactives for Child Health: A Perspective.

Human milk contains all nutritive and bioactive compounds to give infants the best possible start in life. Human milk bioactives cover a broad range of components, including immune cells, antimicrobial proteins, microbes, and human milk oligosaccharides (HMOs). Over the last decade, HMOs have gained special attention as their industrial production has allowed the study of their structure-function relation in reductionist experimental setups. This has shed light on how HMOs steer microbiome and immune system development in early life but also how HMOs affect infant health (e.g., antibiotic use, respiratory tract infections). We are on the verge of a new era where we can examine human milk as a complex biological system. This allows not only study of the mode of action and causality of individual human milk components but also investigation of synergistic effects that might exist between different bioactives. This new wave in human milk research is largely fueled by significant advances in analytical tools in the field of systems biology and network analysis. It will be exciting to explore how human milk composition is affected by different factors, how different human milk compounds work together, and how this influences healthy infant development.

PSIV-12 Effects of feeding a novel alfalfa leaf pellet product (ProLEAF MAX™) and alfalfa stems (ProFiber Plus™) on production of lactating dairy cows

Abstract Alfalfa is a commonly grown forage in the Intermountain west and is often included in lactating dairy cow rations. This study investigated the effects of including novel alfalfa products: ProLEAF MAX™ (PLM), an alfalfa leaf pellet; and ProFiber Plus™ (PFP), alfalfa stems, in the diet of lactating dairy cows on dry matter intake, milk yield, and milk components. Holstein cows were housed in a freestall barn and milked in a free-flow automatic milking system. All cows were fed each treatment for 21-days, then switched to the next treatment utilizing a crossover experimental design. The five treatments were: control (CON, typical diet including alfalfa hay; n = 65); low-quality alfalfa hay (LQ+PLM, a diet that replaced alfalfa hay with low-quality alfalfa hay and PLM; n = 62); PLM+PFP (a diet that replaced alfalfa hay with PLM and PFP; n = 65); PLM (a diet that replaced alfalfa hay with PLM; n = 62); and PFP (a diet that replaced alfalfa hay with PFP; n = 60). Cows were group fed a partial mixed ration balanced for 40.8 kg milk, 3.9% milk fat and 3.3% milk protein. Individual milk yield and milk components were recorded daily by the automatic milking system. Dry matter intake was also recorded daily. When fed the PFP diet, cows had decreased (P < 0.01) dry matter intake compared to the other diets. Milk yield was increased (P < 0.01) when cows received the PLM diet when compared to the other diets. When fed the PFP and PLM+PFP diets, milk fat was increased (P < 0.01) when compared to the other diets. Milk protein was decreased (P < 0.01) when cows were fed the PFP diet when compared to the other diets. These data indicate that inclusion of fractionated alfalfa products in the diet of lactating dairy cows has the potential to increase milk yield and milk components.

Whole-farm modelling of grazing dairy systems in Uruguay

CONTEXTModelling grazing dairy systems from the temperate hot-summer climate region of South America is challenging due to the absence of suitable whole-farm models developed or evaluated in this region. The use of whole-farm models developed in other regions represents an opportunity. However, the accuracy and precision of their predictions need to be assessed. OBJECTIVESThe present study evaluated the predictive ability of Farmax Dairy Pro, a whole-farm model developed in New Zealand, to simulate grazing dairy systems in Uruguay. METHODSData used for the model evaluation was obtained from a dairy farmlet study carried out in Uruguay. The study aimed to explore four intensification strategies based on increasing home-grown forage utilisation and milk output per hectare. The field experiment consisted of four farmlets using two feeding strategies: [Grass Maximum (GMAX) and Grass Fixed (GFIX), based on the amount of grazed herbage in the diet], and two cow genotypes [New Zealand (NZHF) or North American Holstein-Friesian (NAHF)]. The four farmlets were modelled with Farmax for two consecutive years. Model evaluation was performed using standard regression, dimensionless and error index statistics. The model was evaluated by comparing predicted versus observed monthly patterns of milk, milk fat, milk protein and milk solids (MS; milk fat + milk protein) yields, body condition score (BCS), body weight (BW), DM intake (DMI) and net pasture growth rate (PGR). An application of the model was demonstrated by modelling three scenarios for the GMAX-NZHF farmlet. RESULTS AND CONCLUSIONSThe predictive ability of Farmax was similar for the four farmlets modelled, including patterns over time, for all the variables evaluated. The model provided a robust prediction for monthly patterns of milk and milk components yields at a herd level for total DMI and PGR. The model had a moderate ability to predict monthly patterns of individual milk and milk components yields and BCS, and a poor ability to predict BW. The scenario modelling results indicate that the model could be used with confidence to simulate different farm system alternatives. Overall, the Farmax Dairy Pro model had the potential to provide adequate predictions for grazing dairy systems from Uruguay. SIGNIFICANCEThis model will allow the exploration of future intensification pathways for grazing dairy systems in Uruguay and the region, including changes in the forage sequence, stocking rate and calving season. Further adjustments of the model will expand the range of systems and latitudes for this model to be utilised.

Potential Protective Protein Components of Cow's Milk against Certain Tumor Entities.

Milk and dairy products, especially from cow’s milk, play a major role in the daily human diet. It is therefore hardly surprising that the subject of milk is being extensively researched and that many effects of individual milk components have been characterized as a result. With the wealth of results available today, the influence of milk on the development of various types of cancer and, in particular, its often protective effects have been shown both in vitro and in vivo and in the evaluation of large-scale cohort and case-control studies. Various caseins, diverse whey proteins such as α-lactalbumin (α-LA), bovine α-lactalbumin made lethal to tumor cells (BAMLET), β-lactoglobulin (β-LG), or bovine serum albumin (BSA), and numerous milk fat components, such as conjugated linoleic acid (CLA), milk fat globule membrane (MFGM), or butyrate, as well as calcium and other protein components such as lactoferrin (Lf), lactoferricin (Lfcin), and casomorphines, show antitumor or cytotoxic effects on cells from different tumor entities. With regard to a balanced and health-promoting diet, milk consumption plays a major role in a global context. This work provides an overview of what is known about the antitumoral properties of proteins derived from cow’s milk and their modes of action.

Association between days open and milk spectral data in dairy cows

Data on 19,489 Brown Swiss cows reared in northeastern Italy were used to associate absorbances of individual wavenumbers within the mid-infrared range with days open (DO). Different postcalving days in milk (DIM) intervals were studied to determine the most informative milk sampling periods for predicting DO. Milk samples were analyzed using a MilkoScan (Foss Electric, Hillerød, Denmark) Fourier-transform infrared (FTIR) spectrometer for 1,060 wavenumbers (wn) ranging from 5,011 to 925 cm-1. To determine DO, we considered an insemination to lead to conception when there was no return of heat (i.e., no successive insemination) and the cow had a subsequent calving date whereby gestation length was required to be within ±30 d of 290 d. Only milk records within the first 90 DIM were considered. Associations were inferred by (1) fitting linear regression models between the DO and each individual wavenumber or milk component, and (2) fitting a Bayesian regression model that included the complete FTIR spectral data. The effects of including systematic effects (parity number, year-season, herd) in the model on these associations were also studied. These analyses were performed for the complete data (5-90 DIM) and for data stratified by DIM period (5 to 30, 31 to 60, and 61 to 90 DIM). Overall, regions of wavenumbers of the milk FTIR spectra that were associated with DO included wn 2,973 to 2,830 cm-1 [related to fat-B (C-H stretch)], wn 2,217 to 1,769 cm-1 [related to fat-A (C = O stretch)], wn 1,546 cm-1 (related to protein), wn 1,465 cm-1 (related to urea and fat), wn 1,399 to 1,245 cm-1 (related to acetone), and wn 1,110 cm-1 (related to lactose). Estimated effects depended on the DIM period, with milk samples drawn during DIM intervals 31 to 60 d and 61 to 90 d being most strongly associated with DO. These DIM intervals are also typically most associated with negative energy balance and peak lactation.

Identifying gram-negative and gram-positive clinical mastitis using daily milk component and behavioral sensor data

Opportunities exist for automated animal health monitoring and early detection of diseases such as mastitis with greater on-farm adoption of precision technologies. Our objective was to evaluate time series changes in individual milk component or behavioral variables for all clinical mastitis (CM) cases (ACM), for CM caused by gram-negative (GN) or gram-positive (GP) pathogens, or CM cases in which no pathogen was isolated (NPI). We developed algorithms using a combination of milk and activity parameters for predicting each of these infection types. Milk and activity data were collated for the 14 d preceding a CM event (n = 170) and for controls (n = 166) matched for breed, parity, and days in milk. Explanatory variables in the univariate and multiple regression models were the slope change in milk (milk yield, conductivity, somatic cell count, lactose percentage, protein percentage, and fat percentage) and activity parameters (steps, lying time, lying bout duration, and number of lying bouts) over 7 d. Slopes were estimated using linear regression between d -7 and -5, d -7 and -4, d -7 and -3, d -7 and -2, and d -7 and -1 relative to CM detection for all parameters. Univariate analyses determined significant slope ranges for explanatory variables against the 4 responses: ACM, GN, GP, and NPI. Next, all slope ranges were offered into the multivariate models for the same 4 responses using 3 baselines: d -10, -7, and -3 relative to CM detection. In the univariate analysis, no explanatory variables were significant indicators of ACM, whereas at least 1 parameter was significant for each of GN, GP, and NPI models. Superior sensitivity (Se) and specificity (Sp) estimates were observed for the best GP (Se = 82%, Sp = 87%) and NPI (Se = 80%, Sp = 94%) multiple regression models compared with the best ACM (Se = 73%, Sp = 75%) and GN (Se = 71%, Sp = 74%) models. Sensitivity for the GN model was greater at the baseline closest to the day of CM detection (d -3), whereas the opposite was observed for the GP and NPI model as Se was maximized at the d -10 baseline. Based on this screening of relationships, milk and activity sensor data could be used in CM detection systems.

Genetic polymorphisms of the OLR1 and DGAT1 genes associated with milk components in Holstein Friesian dairy cattle under an intensive management in Central Java

Oxidized Low Density Lipoprotein Receptor 1 (OLR1) gene serves to encode the binding vascular surface receptors and to degrade the oxidized low-density lipoprotein. In dairy cattle a nucleotide mutation at 3′UTR location of the OLR1 gene results in C allele of which related to higher milk fat content than A allele. Diacylglycerol O-acyltransferase 1 (DGAT1) gene has a K232A mutation of which K allele also associated with higher milk fat content against A allele. This study was aimed to study the association of variant genotypes of the OLR1 and DGAT1 genes on milk fat and other milk components in Holstein Friesian (HF) dairy cattle. The target of this research was to obtain genotypes of the OLR1 and DGAT1 gene affecting highly to milk fat content and other milk components in domestic HF cattle. Animals observed were Holstein Friesian (HF) lactating cows for 53 heads kept intensively at one government dairy station at BBPTU Baturraden, Central Java. Base mutations at 3′UTR location of the OLR1 gene and at K232A mutation of the DGAT1 gene were identified by PCR-RFLP (polymerase chain reaction - restriction fragment length polymorphism) techniques. Allele frequency of the base mutation of each gene was analyzed by PopGen 32 program. Milk components of fat, protein, solid non-fat (SNF) and lactose were generated from daily milk test for morning, afternoon and average of both. Study of the association of variant genotypes of the OLR1 and DGAT1 genes on individual milk component was analyzed by GLM considering the fixed effects of lactation period (1-3, 4-6), calving season (1-4 mo., and 5-8 mo.) and calving year (2011, 2012). Duncan Multiple Range Test was used to test significant differences of the averages among subclasses. The OLR1 gene resulted in A allele and C allele of whose the frequencies were respectively 0.457 and 0.543, while the DGAT1 gene resulted in A allele and K allele with the frequencies respectively 0.426 and 0.575. Variant genotypes of the OLR1 gene gave a significant different on milk fat production (P<0.05). Genetic polymorphism of the OLR1 gene had a significant effect on milk fat content in the noon, the highest was for CC genotype, followed by AC, and the lowest was AA, with the fat contents of CC and AC genotypes higher to AA one by 4.87% and 1.38% respectively. However variant genotypes of the OLR1 gene had no significant effect on other milk components (P>0.05). Similarly, variant genotypes of the DGAT1 gene consisting of genotype AK and genotype KK did not significantly affect on all of milk components. It was concluded that there was a fairly good control of the OLR1 gene to fat content in HF cows. The base mutation at the 3′UTR location of the OLR1 gene can be considered as an initial information in developing a genetic assisted selection (GAS) technique for milk fat content in domestic HF cows.

Modeling homeorhetic trajectories of milk component yields, body composition and dry-matter intake in dairy cows: Influence of parity, milk production potential and breed

The control of nutrient partitioning is complex and affected by many factors, among them physiological state and production potential. Therefore, the current model aims to provide for dairy cows a dynamic framework to predict a consistent set of reference performance patterns (milk component yields, body composition change, dry-matter intake) sensitive to physiological status across a range of milk production potentials (within and between breeds). Flows and partition of net energy toward maintenance, growth, gestation, body reserves and milk components are described in the model. The structure of the model is characterized by two sub-models, a regulating sub-model of homeorhetic control which sets dynamic partitioning rules along the lactation, and an operating sub-model that translates this into animal performance. The regulating sub-model describes lactation as the result of three driving forces: (1) use of previously acquired resources through mobilization, (2) acquisition of new resources with a priority of partition towards milk and (3) subsequent use of resources towards body reserves gain. The dynamics of these three driving forces were adjusted separately for fat (milk and body), protein (milk and body) and lactose (milk). Milk yield is predicted from lactose and protein yields with an empirical equation developed from literature data. The model predicts desired dry-matter intake as an outcome of net energy requirements for a given dietary net energy content. The parameters controlling milk component yields and body composition changes were calibrated using two data sets in which the diet was the same for all animals. Weekly data from Holstein dairy cows was used to calibrate the model within-breed across milk production potentials. A second data set was used to evaluate the model and to calibrate it for breed differences (Holstein, Danish Red and Jersey) on the mobilization/reconstitution of body composition and on the yield of individual milk components. These calibrations showed that the model framework was able to adequately simulate milk yield, milk component yields, body composition changes and dry-matter intake throughout lactation for primiparous and multiparous cows differing in their production level.

Factor analysis as a tool to estimate association among individual proteins and other milk components with casein micelle size and cheese yield

ABSTRACT The present study attempted to identify individual milk proteins and other milk components that are associated with casein micelle size (CMS) and dry matter cheese yield (DMCY) using factor analysis. Here, we used 140 bulk tank milk samples from different farms. Milk composition was determined using a Fourier transform infrared equipament. The individual milk proteins were (αS-casein, β-casein, κ-casein, β-lactoglobulin and α-lactoalbumin) measured by their electrophoretic profile. The CMS was estimated by photon correlation spectroscopy, and the DMCY was determined using reduced laboratory-scale cheese production. Factor analysis partitioned the milk components into three groups that, taken together, explain 68.3% of the total variance. The first factor was defined as “CMS”, while the second as “DMCY” factor, based on their high loadings. The CMS was positively correlated with protein, casein, non-fat solids and αS-casein and negatively associated with κ-casein and β-lactoglubulin. DMCY was positively correlated with fat, protein, casein, total solids and negatively correlated with αs-casein. These results indicate that the variation of individual milk proteins may be an important aspect correlated to milk quality and cheese production.

Significance of litter size, duration of dry period and stage of pregnacy on milk yield and composition in dairy animals

The factors influencing the amount and composition of produced milk can be divided into two groups, namely internal and external factors. This is very important to remember when evaluating the milk quality and in the improvement of milk yield and composition in a dairy enterprise. Of the external factors it is possible to mention a few: litter size, duration of dry period and stage of pregnancy. The present discussion explores the significance of litter size, duration of dry period and pregnancy on milk yield and composition in dairy production. In goats and sheep dams bearing twins or triplets had higher milk yield than single bearing dams, and this significant increase in milk production in these dams that carried twins and triplets was followed by an increase in lactation length. Due to the amount of milk animal produce, the drying-off process is often more complicated for dairy animals (cattle, goats and some sheep). However, this period is essential to enables dams to regain the body condition needed to support the subsequent pregnancy and lactation. The majority of dairy animals dry off spontaneously because of the aforementioned natural decrease in daily milk production. The length of dry period influences milk production in the subsquent lactation, with shortening of the dry period showing a markedly negative effect on milk performance of dams. Lactating animals should have an opportunity to rest and regenerate mammary tissue between lactations. F or optimal dairy animal performance in the next lactation, lactating animals should have an opportunity to rest and regenerate mammary tissue between lactations. The amount of produced milk and its composition are influenced by the stage of pregnancy: in the first half of pregnancy it is not possible to observe any pronounced changes while in the second stage of pregnancy can observe a gradual decrease in milk production as well as an imcrease in levels of individual milk components. During pregnancy and the first few days postpartum, milk supply is hormonally driven – this is called the endocrine control system . This implies that in the course of lactation, changes in milk production are caused by changes in activities of the endocrine system that are caused by hormones sectreted by pitiutary gland (hypophysis cerebi) and placenta. During the latter part of pregnancy, the mammary gland is making colostrum, but high levels of progesterone inhibit milk secretion resulting reduced milk yield. Progesterone influences the growth in size of alveoli and lobes; high levels of progesterone inhibit lactation before birth. Progesterone levels drop after birth; this triggers the onset of copious milk production. Estrogen stimulates the milk duct system to grow and differentiate. Like progesterone, high levels of estrogen also inhibit lactation, while the hormone prolactin must be present for milk synthesis to occur.

Asymmetric time interval between evening and morning milking and its effect on the total daily milk yield

Samples of milk obtained in the course of evening and morning milking performed in variable time intervals of either 11 and 13 hours (n = 1.282) or 10 and 14 hours (n = 370) were collected with the aim to quantify the effect of the length of a variable (asymmetric) time interval between evening and morning milking on the total amount and composition of daily milk production of dairy cows. Milk samples were analysed in an accredited (EN ISO 17025) laboratory in Brno-Tuřany (Czech Republic) and the following contents of individual milk components were estimated: fat (F; g.100g−1), total protein (TP; g.100g−1), lactose (L; lactose monohydrate; g.100g−1), and somatic cell counts (SCC; ­103.ml−1) were estimated in. It was found out that with the increasing total daily milk production the shares of evening and morning milk yield increased as well; however, the percentages of evening and/or morning yields in the total yield remained practically unchanged and represented 43.5 % and 56.5% or 40.4 % and 59.6 % in variants with intervals of 11 and 13 hours and/or 10 and 14 hours, respectively. In the variant with the milking interval of 11 and 13 hours, values of correlation coefficients between the above parameters (i.e. F, TP, L, SCC, and log SCC) of evening and morning milk yields on the one hand and the total milk performance on the other ranged from the minimum r = 0.896 (F) to the maximum r = 0.980 (TP). In the variant with the interval of 10 and 14 hours, the corresponding values of correlation coefficients were r = 0.848 (F) and r = 0.983 (TP). These correlations were statistically highly significant in all cases (P ≤ 0.001). Further, linear regression equations enabling the estimation of milk parameters of the total milk yield on the base of results obtained in evening and morning milking was calculated as well. Values of coefficients of determination (R2) of these equations ranged from 0.803 (F) to 0.960 (TP) and from 0.718 (F) to 0.966 (TP) for intervals of 11:13 hours and of 10:14 hours, respectively.

Effect of Protein Concentrate Supplement on the Qualitative and Quantitative Parameters of Milk from Dairy Cows in Organic Farming

Abstract In our experiment, we studied the effects of protein concentrate addition on the qualitative and quantitative composition of milk in organically farmed dairy cows. A total number of 40 Holstein cows were divided into two groups. Live weight of dairy cows was around 625±25 kg. Average production efficiency of animals was 7600±50 kg milk for the lactation period. Animals of both groups received identical basal feed ration. The first experimental group of cows (n=20) received in the feed ration a protein concentrate from organic production (soybean cake 60%, sunflower cake 20%, linseed cake 20%) at 1 kg per head and day. The second group served as a control (n=20) without the addition of protein concentrate. The experiment lasted 30 days. The goal of the experiment was to investigate whether the addition of protein concentrate can affect individual milk components. The measured values show that the experimental group of dairy cows with the addition of protein concentrate exhibited increases in fat content by 7.4% (P≤0.05), in urea content by 83.1% (P≤0.001) and in citric acid content by 18.6% (P≤0.01), and decreases in free fatty acids by 54.9% (P≤0.001), in ECM by 5.5% (P≤0.05) and in FCM by 6.1% (P≤0.05). The control group of cows exhibited the citric acid content in milk increased by 20.0% (P≤0.05). The results indicate that the protein concentrate composed of soybean, sunflower and linseed cakes can affect the composition of milk from organically farmed dairy cows

Accuracy of milk ketone bodies from flow-injection analysis for the diagnosis of hyperketonemia in dairy cows

The objectives of this study were (1) to determine the correlations between blood β-hydroxybutyrate (BHBA) and milk components [BHBA, acetone, fat, protein, and fat:protein (F:P) ratio], and (2) to establish optimal thresholds for milk components to predict hyperketonemia in dairy cows. Data on 163 cows from 37 herds were used in this cross-sectional study. Herds were visited once during the study period, and cows between 2 and 90 d in milk were blood sampled within 4h of milk sampling for the Dairy Herd Improvement test. Blood BHBA concentrations were measured using a cow-side electronic meter, Precision Xtra, which was considered the gold standard test in this study. Milk BHBA and acetone concentrations were measured in Dairy Herd Improvement milk samples by flow-injection analysis; whereas, milk fat and protein were tested using Fourier transform infrared spectroscopy. Hyperketonemia was defined by a blood BHBA concentration ≥1.4mmol/L. The prevalence of hyperketonemia (based on blood BHBA values) in this study population was 21.0%. Pearson correlation coefficients between blood BHBA and milk BHBA, acetone, fat, protein, and F:P ratio were 0.89, 0.73, 0.21, 0.04, and 0.17, respectively. Receiver operating characteristic curves were generated and thresholds for each individual milk component were determined based on the maximal sum of sensitivity and specificity. Optimal threshold values for hyperketonemia were milk BHBA ≥0.20mmol/L, acetone ≥0.08mmol/L, fat ≥4.2%, and F:P ratio ≥1.3. Based on these thresholds, milk BHBA and acetone had greater sensitivity (84 and 87%, respectively) and greater specificity (96 and 95%, respectively) than the other milk components (fat, protein, and F:P). Series and parallel testing slightly improved the accuracy of milk BHBA and acetone values to predict hyperketonemia. A multivariable model that accounted for milk BHBA and milk acetone values simultaneously had the highest accuracy of all tested models for predicting hyperketonemia. These results support that milk BHBA and milk acetone values from flow-injection analysis are accurate diagnostic tools for hyperketonemia in dairy cows and could potentially be used for herd-level hyperketonemia surveillance programs.

Preliminary investigation of the composition of alpaca (Vicugna pacos) milk in California

The objective of this preliminary investigation was to study the composition of alpaca milk. To define the nutritional requirements of growing alpaca crias, information is needed on the composition of the milk from their dams. Information on milk composition will also contribute to the development of milk formula for alpaca crias. However, there is a paucity of data for the composition of alpaca milk in the literature. The aim of this research was to obtain information on the composition of alpaca milk. Milk samples (n=168) were collected from 11 alpacas on two farms. Average milk concentrations were 3.68% for fat, 4.53% for protein, 6.00% for lactose, and 15.05% for total solids. Milk urea nitrogen averaged 31.4mg/dl of milk. Concentrations of selected minerals (n=22) varied between animals. Concentration of individual milk components, including selected minerals, showed considerable variability. Milk lipids (n=11) had appreciable amounts of fatty acids of 14 carbon chain-length and greater including high amounts of C16:0 (35.04g/100g fatty acids), but there were few fatty acids of 12 carbons and less.

Assessment of dairy cow energy status using milk fat, protein and urea concentrations

One of the ways to make an assessment of the energy status of cows in lactation is to consider the results obtained from the evaluation of the concentrations of organic milk components. The advantages of this method are that the taking of milk samples is not stressful for the cow and that it is also possible to use the results of milk examinations carried out by dairy plants within regular control. A bulk milk sample from all individual cows can be used, or an individual milk sample. In farms that have herds of unequal genetic potential, it is preferable to assess the energy status by analyzing the results for individual samples, because each animals is assessed individually in that way. Furthermore, the use of individual milk samples is recommended at newly-established farms in order to facilitate the establishment of reference values for the herd. The energy status of cows is assessed using the milk samples by analyzing fat, protein and urea concentrations and their mutual ratios. Fat and protein concentrations in cow milk vary depending on the breed, the diet, age, stage of lactation, and the season of the year. A fat content lower than the physiological values can be expected in cases of unfavourable diet of the cows during the period around calving or rumen acidosis, and it can be expected to be higher during ketotic conditions. A higher protein content in milk can be expected during a high-protein, and a lower one during a low-protein diet of the cows. The physiological concentration of urea in milk depends on nutritive factors, the season, age, stage of lactation, and body mass. Specifically, older cows, cows in advanced lactation, and cows in the summer period tend to have higher values for urea concentration in milk. Among nutritive factors, the most important is the ratio between energy and proteins in the cow feed ration. In cases when protein content in milk is optimal or above the recommended values but the energy supply is lower, the urea concentration increases to over the range of physiological values. In the event that the feed ration is deficient in both proteins and energy the urea content in milk drops. This work also presents a model for assessing the energy status in cows by analyzing the mutual ratios between the individual milk components (proteins and fat, or urea and proteins) in the individual milk samples. It is possible to determine the energy status of the animal on the basis of the ratio between proteins and fat, and the supply of the cow with proteins and energy on the basis of the ratio between urea and proteins.

Effect of wheat gluten and extracted, protected soybean meal addition to the diet of cows with different beta-lactoglobulin genotypes on the composition and physical properties of milk

The effect of wheat gluten or extracted soybean meal (300 g of crude protein/head/day) on milk yield, composition and its physical properties was analysed in 53 cows of Polish Holstein-Friesian breed with different milk beta-lactoglobulin (blg) genotypes (AA, AB and BB). The addition of wheat gluten to the diet of cows with the AA and AB blg genotypes caused a significant increase in crude protein (by 0.21; &lt;I&gt;P&lt;/I&gt; ≤ 0.05, and 0.19%; &lt;I&gt;P&lt;/I&gt; ≤ 0.01), casein (by 0.15; &lt;I&gt;P&lt;/I&gt; ≤ 0.05, and 0.15%; &lt;I&gt;P&lt;/I&gt; ≤ 0.01) and dry matter content (0.3 and 0.5%; &lt;I&gt;P&lt;/I&gt; ≤ 0.05) in milk, and a significant increase in beta-casein production (0.6 g/l; &lt;I&gt;P&lt;/I&gt; ≤ 0.05, and 0.6 g/l; &lt;I&gt;P&lt;/I&gt; ≤ 0.01) and kappa-casein yield (0.3 g/l; &lt;I&gt;P&lt;/I&gt; ≤ 0.05) in the case of cows with the AA genotype. Milk acidity and electrical resistance in milk were lowered. The cows with the BB blg genotype did not show any significant changes in milk composition. The supplementation of extracted soybean meal to cows with the AB and BB blg genotype led to a significant increase in milk protein (0.26% and 0.21%; &lt;I&gt;P&lt;/I&gt; ≤ 0.01) and casein content (0.2 and 0.15%; &lt;I&gt;P&lt;/I&gt; ≤ 0.01), beta-casein production (0.60 and 0.50 g/l; &lt;I&gt;P&lt;/I&gt; ≤ 0.01) and to a decrease in kappa-casein content (by 0.7%; &lt;I&gt;P&lt;/I&gt; ≤ 0.05) in cows with the BB genotype. The production of kappa-casein increased in cows with the AB genotype (by 0.2 g/l; &lt;I&gt;P&lt;/I&gt; ≤ 0.01). In the case of all genotypes, an increase in milk active acidity and thermostability and a decrease in electrical resistance in milk were noted. It was observed that cows with different blg genotypes differently utilised the protein from given supplements for the production of individual milk components, which may be used in rationalisation of their feeding and improvement of milk content.

Relationship between somatic cell count, individual leukocyte populations and milk components in bovine udder quarter milk

The somatic cell count (SCC) in bovine milk is an indicator of udder health and milk quality. Individual cell populations, i.e., macrophages, lymphocytes and polymorphonuclear neutrophils, were identified and their relationship to milk components such as acute-phase proteins (serum amyloid A (SAA) and α1-acid glycoprotein), lactic acid and lactoferrin (LF), as well as protein, fat and lactose, were studied in foremilk from separate udder quarters. The whey proteins and peptides were studied using two-dimensional gel electrophoresis, which was shown in the present study to be a useful method of assessing udder health and milk quality. The growth of a starter culture was evaluated using a conductance method as a simulation of fermentation processes relevant for cheese production. The investigation showed that LF, SAA, l-lactate and lactose are reliable indicators of milk quality on udder quarter level. The correlations between SCC, or the individual cell populations, and the potential indicators were highly significant.

Impact of Milk Preacidification with CO2 on Cheddar Cheese Composition and Yield

Preacidification of milk for cheese making may have a beneficial impact on increasing proteolysis during cheese aging. Unlike other acids, CO2 can easily be removed from whey. The objectives of this work were to determine the effect of milk preacidification on Cheddar cheese composition, the recovery of individual milk components, and yield. Carbon dioxide was injected inline after the cooling section of the pasteurizer. Cheeses with and without added CO2 were made simultaneously from the same batch of milk. This procedure was replicated 3 times. Carbon dioxide in the cheese milk was about 1600ppm, which resulted in a milk pH of about 5.9 at 31°C. The starter culture and coagulant addition rates were the same for both the CO2 treatment and the control. The whey pH at draining of the CO2 treatment was lower than the control. Total make time was shorter for the CO2 treatment compared with the control. Cheese manufactured from milk acidified with CO2 retained less of the total calcium and fat than the control cheese. The higher fat loss was primarily in the whey at draining. Preacidification with CO2 did not alter the crude protein recovery in the cheese. The CO2 treatment resulted in a higher added salt recovery in the cheese and produced a cheese that contained too much salt. Considering the higher added salt retention, the salt application rate could be lowered to achieve a typical cheese salt content. Cheese yield efficiency of the CO2 treated milk was 4.4% lower than the control due to fat loss. Future work will focus on modifying the make procedure to achieve a normal fat loss into the whey when CO2 is added to milk.