The list of standard abbreviations for JDS is available at adsa.org/jds-abbreviations-24. Nonstandard abbreviations are available in the Notes.
Moving from conventional (CMS) to automatic (AMS) ...milking systems could affect milk quality. Moreover, the type and preservation methods of the forages used in the TMR, such as alfalfa hay (HTMR) or corn silage (STMR) have been demonstrated to modify milk composition. Thus, this study investigated the effect of implementing AMS and different diet forage types on the quality of Italian Holstein-Friesian bulk milk. Milk samples (n = 168) were collected monthly from 21 commercial farms in northern Italy during a period of 8 mo. Farms were categorized into 4 groups according to their milking system (CMS vs. AMS) and diet forage type (HTMR vs. STMR). Milk quality data were analyzed through the mixed procedure for repeated measurement of SAS with the milking system, diet forage type, and sampling day as fixed effects. Milking through the AMS led to lower milk fat, freezing point, and β-LG A; longer coagulation time; and higher K content, pH, and β-LG B than CMS. Cows fed STMR produced milk with greater fat, protein, casein, Mg content, titratable acidity, and β-LG A, but with reduced curd firming time, freezing point, and β-LG B than those fed HTMR. In conclusion, milk quality is not only altered by the diet's forage type and characteristics but also by the milking system.
The list of standard abbreviations for JDS is available at adsa.org/jds-abbreviations-24. Nonstandard abbreviations are available in the Notes.
The effect of the horn status of cows on their milk ...composition and quality is a controversial research topic. In this study, 128 milk samples from 64 horned and 64 disbudded Brown Swiss and Original Braunvieh cows were collected from alpine farms where both horned and disbudded cows were grazing on mountain pastures. The samples were analyzed for their detailed composition and protein digestion in a simulated in vitro digestion (INFOGEST). To exclude probable influences on digestion, the β-CN genotype with its variants A1 and A2 was also included in the study. The effects of horn status and β-CN genotype were investigated in linear mixed models, which included additional influencing random factors such as Original Braunvieh blood proportion, stage of lactation, and farm. Horn status did not have any effect on milk composition or digestion. In contrast, milk from A1A1 cows showed a different protein digestion than milk of A1A2 and A2A2 cows in the gastric phase, including smaller amounts of β-casomorphin(BCM)21-associated peptides and larger amounts of BCM11-associated peptides. Abundances of BCM7 did not differ between β-CN genotypes. At the end of the intestinal phase, the digested milk of A1A1 and A2A2 β-CN genotypes did not differ.
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The primary objective of this cross-sectional observational study was to determine whether bulk tank milk quality, udder health, udder hygiene and milk production outcomes were associated with ...facility type on organic dairies. A secondary objective was to identify other management-related risk factors associated with bulk tank milk quality, udder health, udder hygiene, and milk production on organic dairy herds in Vermont. We aimed to enroll 40 farms, to compare herds using the 2 most common systems (freestalls, tiestalls) for housing organic dairy cattle in the state with those using a bedded pack during the non-grazing season (typically November–May). Two general styles of bedded packs were observed: cultivated bedded packs and untilled deep bedded packs. Due to the limited number of herds using bedded packs to house lactating dairy cattle in Vermont, we combined untilled and cultivated bedded packs to describe udder hygiene, milk quality, and udder health on these loose-housing systems deeply-bedded with organic material. The study was completed on 21 farms (5 bedded packs, 6 freestalls, 10 tiestalls) before interruption due to the COVID-19 pandemic. Data captured from Dairy Herd Improvement Association records from the test closest to the date of the farm visit included average somatic cell score (SCS), standardized 150-d milk (pounds), % cows with current high SCS (SCS ≥ 4.0), % cows with newly elevated SCS (previous SCS < 4.0 to current ≥ 4.0), and % cows with chronically elevated SCS (SCS ≥ 4.0 last 2 tests). Multivariable linear regression models were used to describe outcomes by facility type, but suffered from limited statistical power due to small group sample sizes. Unconditional comparisons failed to find statistically significant differences between farms grouped by facility type in metrics captured from Dairy Herd Improvement Association test data, bulk tank milk somatic cell count (BTSCC) and aerobic culture data, or udder hygiene scores. A secondary analysis was conducted using univariate linear regression to identify associations between herd management factors and outcomes for all 21 farms combined. Although not all differences found were statistically significant in this secondary analysis combining all farms, numeric differences that may be biologically important are reported showing farms with deeper bedding had a lower BTSCC, lower newly elevated SCS, lower chronically elevated SCS, lower elevated current SCS, lower average SCS, and better udder hygiene metrics. Farms with lower mean udder hygiene scores had numerically lower chronically elevated SCS, lower elevated current SCS, and lower average SCS. We could not reject the null hypothesis that milk quality and udder health outcomes did not differ by facility type, and this does not preclude the existence of biological differences in these outcomes between facility types. The current study provides insight on factors affecting bulk tank milk quality, udder health and hygiene measures on organic dairy farms in Vermont. Bedded packs may be a viable option for confinement housing during the winter non-grazing season for pasture-based herds interested in a loose-housing system in the Northeastern US, but more research such as longitudinal studies with a larger sample size is needed to test this hypothesis.
•Key handling and hygiene practices in the informal dairy industry are critical.•Microbiological assessment scheme gauges hygiene practice effectiveness.•Microbial level in milk reflects the quality ...of hygiene and handling practices.•Microbial criteria based on data from the milk processing chain ensure safety.•Subpar hygiene practices impact milk quality in informal farms.
The current study assessed (i) the microbiological safety level profiles (MSLPs) of milkmen’s hands and milking containers and (ii) the influence of hygiene and handling practices on MSLPs of raw and cultured milk, from six informal dairy farms in Zimbabwe. Interviews and direct observations were carried out during the assessment of hygiene and handling practices at six farms designated A to F. Microbiological criteria of the following six microbiological parameters: Total Bacterial Counts (TBCs), Coliform Counts (CCs), Total Escherichia coli Counts (TECs), Salmonella spp., Listeria monocytogenes and Klebsiella pneumonia, were used to determine contamination level (CL) at four different critical sampling locations (CSLs). The CSLs were raw milk (CSL1), cultured milk (CSL2), milkmen’s hands (CSL3), and milking containers (CSL4). The microbiological criteria of the six microbiological parameters were used to score CLs as: intolerable (0), poor to average (1), average (2), and good (3). MSLPs at each CSL for the six farms were computed based on the CL scores to a maximum score of 18. A total of 192 samples were collected and analyzed. Salmonella spp. and L. monocytogenes were not detected at all the CSLs. All the farms failed to achieve a maximum MSLP score of 18 at all the CSLs. The relationship between MSLPs and hygiene and handling practices was tested using point-biserial correlation coefficients. The correlation study revealed that handling and hygiene practices (such as the duration between milking and storage, the type of milking container utilized at farms, the frequency of cleaning the milking parlor, the water source for hand and equipment washing, and the use of hand sanitizers) generally influenced the MSLPs on the farms. Both training and improvement in infrastructure are needed to improve the quality of milk and its products produced and sold in the informal value chain in Zimbabwe.
Research follow to assess levels of productivity and the main indicators of milk of Romanian Simmental cows and its half blood, exploited in terms of milk production into Alba County environmental ...conditions. There were studied 45 cows from “P.F.A. Munteanu Cornel” farm. The results show that the maximum average of milk production is 5774 kg, registered in the third lactation and the average percentage of fat and protein is 3.92 respectively 3.34. The conclusion learned from the study is that Romanian Simmental cows studied have quantitative and qualitative productions over race standard and it is trying to reduce the period of exploitation and to intensify the process of milk production in first lactations.
The research aimed to analyze the productive level of Romanian breed motley with black and of his half-breed, in three representative different dimensioned livestock farms from Alba County, and to ...observe if this breed is suitable for milk production in conditions existing in this county. Farms whose effectives have been taken in the study are: S.c. Biotera S.R.L from Dostat commune, Boz village, which has 456 caws (from which 330 annually milked; S.c Stazoo S.R.L located on the right side of the Teius city exit through Alba-Iulia, which has 107 caws from which 86 annually milked, and S.c Zooagro S.R.L farm from Galda de Jos commune which has 40 caws(31 annually milked).
Dairy cattle breeding in Ukraine is set the task that require a radical restructuring of the industry, bringing it out of a difficult crisis situation in order to increase the production of valuable ...food for the population and raw materials for industry The dairy industry is a very important part of all agricultural production. The socio-economic development of the state as a whole depends on the current situation in milk production. Quality milk must meet the requirements not only for physicochemical parameters (acidity, density, mass fraction of dry matter, protein, fat), but above all be safe, ie meet the current requirements for levels of contamination with microorganisms, the number of somatic cells, maximum permissible levels toxic elements, mycotoxins, residual amounts of antimicrobials, drugs and other substances. The quality and safety of cow's milk as a raw material is a global and unresolved issue for our country. The quality of milk cannot be improved in the process of its processing, so the milk quality management system at the enterprise should be focused on high-tech processes of its production, primary processing with the use of preventive measures. At present, the issue of high-quality milk production in Ukraine is urgent. Despite the trend, the population's demand for quality dairy products is increasing every year, so the issue of increasing the productivity of the dairy herd and milk quality through the use of modern high-tech conditions of keeping and milking. Therefore, the identification of factors influencing the process of milk production in farms is aimed at solving quality and safety problems and provides information on how to better and more effectively control hazards in the overall process.
Numerous statistical machine learning methods suitable for application to highly correlated features, as those that exist for spectral data, could potentially improve prediction performance over the ...commonly used partial least squares approach. Milk samples from 622 individual cows with known detailed protein composition and technological trait data accompanied by mid-infrared spectra were available to assess the predictive ability of different regression and classification algorithms. The regression-based approaches were partial least squares regression (PLSR), ridge regression (RR), least absolute shrinkage and selection operator (LASSO), elastic net, principal component regression, projection pursuit regression, spike and slab regression, random forests, boosting decision trees, neural networks (NN), and a post-hoc approach of model averaging (MA). Several classification methods (i.e., partial least squares discriminant analysis (PLSDA), random forests, boosting decision trees, and support vector machines (SVM)) were also used after stratifying the traits of interest into categories. In the regression analyses, MA was the best prediction method for 6 of the 14 traits investigated curd firmness at 60 min, αS1-casein (CN), αS2-CN, κ-CN, α-lactalbumin, and β-lactoglobulin B, whereas NN and RR were the best algorithms for 3 traits each (rennet coagulation time, curd-firming time, and heat stability, and curd firmness at 30 min, β-CN, and β-lactoglobulin A, respectively), PLSR was best for pH, and LASSO was best for CN micelle size. When traits were divided into 2 classes, SVM had the greatest accuracy for the majority of the traits investigated. Although the well-established PLSR-based method performed competitively, the application of statistical machine learning methods for regression analyses reduced the root mean square error compared with PLSR from between 0.18% (κ-CN) to 3.67% (heat stability). The use of modern statistical machine learning methods for trait prediction from mid-infrared spectroscopy may improve the prediction accuracy for some traits.
The study aimed to evaluate the use of the azidiol® preservative for psychrotrophic microorganism count (PMC) in cooled raw milk. Two studies were carried out, one under controlled conditions ...(experiment 1) and the other under field conditions (experiment 2), in which samples of raw milk were taken with and without the use of the azidiol® preservative and analyzed at predefined times (0, 6, 12 and 24 hours - experiment 1) and at varying times (experiment 2). Analysis of variance and regression analysis using SAS were applied for data statistical analysis. Milk samples without azidiol® showed higher PMC with increasing time between sampling and analysis, while in samples preserved with azidiol®, this count remained constant. Samples of cooled raw milk intended for PMC should be collected in flasks containing the azidiol® preservative.
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