Bulk Milk Quality Research Articles

Milking system and diet forage type effect on milk quality of Italian Holstein-Friesian

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 effects of dietary iodine content, milking system, and farming practices on milk iodine concentration and quality traits

Various management practices can influence milk quality traits in dairy cattle. As an example, an increasing investment in automatic milking system to substitute milking parlors has been observed in the last 2 decades in dairy farms which could have affected certain bulk milk quality traits. What is more, milking practices can also affect certain milk parameters; as an example, teat disinfectants containing I are used in commercial farms where pre- or postdipping is performed, leading to presence of some I in the bulk milk. However, this trace mineral is also supplied in cows' diet to fulfill their nutritional requirements, partly contributing to the milk I final concentration. Therefore, the aim of this study was to evaluate the sources of variation of milk I along with other traditional milk quality traits. A total of 91 dairy farms in northeastern Italy were enrolled in the study. In each farm, diet and bulk milk samples were collected on the same day for chemical analysis. Concentration of I, in particular, was determined in both milk and feed with gold standard. Pearson correlations were calculated among the traits available for milk and diet, and a general linear model was used to test significance of fixed effects (feeding system, milking system, farming system, herd size, herd stage of lactation, and sampling month) on milk quality traits including the I concentration. In the case of milk I, diet I and presence of I-based predipping and postdipping teat disinfect application were also tested as fixed effects. Results showed a positive linear correlation between milk and diet I content (correlation coefficient [r] = 0.78). Although milk I was also positively correlated with lactose content (r = 0.25), dietary I was not correlated with other milk traits. Milk I content was significantly affected by dietary I, I-based predipping teat disinfectant application, and herd composition. Compared with conventional farms, organic farms showed lower protein content and greater somatic cell score (SCS) but similar milk I. Milking system significantly affected only lactose content and SCS of milk. Sampling month was only significant for milk urea nitrogen and herd composition, feeding system, herd size, and herd average days in milk did not modify milk gross composition and SCS. In conclusion, dietary supply of I is the main factor affecting milk I concentration and findings suggest that I level in milk can be naturally improved in dairy cows by modulating the I content in the diet administered. However, further research is needed to evaluate the effect of I-based sanitizers on milk I.

How heat stress conditions affect milk yield, composition, and price in Italian Holstein herds

An edited data set of 700 bulk and 46,338 test-day records collected between 2019 and 2021 in 42 Holstein-dominated farms in the Veneto Region (North of Italy) was available for the present study. Information on protein, fat and lactose content, somatic cell count, and somatic cell score was available in bulk milk as well as individual test-day records, whereas urea concentration (mg/dL), differential somatic cell count (%), and milk yield (kg/d) were available for test-day records only. Milk features were merged with meteorological data retrieved from 8 weather stations located maximum 10 km from the farms. The daily and weekly temperature-humidity index (THI; wTHI) and maximum daily (MTHI) and weekly temperature-humidity index were associated with each record to evaluate the effect of heat stress conditions on milk-related traits through linear mixed models. Least squares means were estimated to evaluate the effect of THI and, separately, of MTHI on milk characteristics correcting for conventional systematic factors. Overall, heat stress conditions lowered the quality of both bulk milk and test-day records, with fat and protein content being greatly reduced, and somatic cell score and differential somatic cell count augmented. Milk yield was not affected by either THI or MTHI in this data set, but the effect of elevated THI and MTHI was in general stronger on test-day records than on bulk milk. Farm-level economic losses of reduced milk quality rather than reduced yield as consequence of elevated THI or MTHI was estimated to be between $23.57 and $43.98 per farmer per day, which is of comparable magnitude to losses resulting from reduced production. Furthermore, MTHI was found to be a more accurate indicator of heat stress experienced by a cow, explaining more variability of traits compared with THI. The negative effect of heat stress conditions on quality traits commences at lower THI/MTHI values compared with milk yield. Thus, a progressive farmers' income loss due to climatic changes is already a reality and it is mainly due to deterioration of milk quality rather than quantity in the studied area.

Factors affecting the microbiological quality and contamination of farm bulk milk by Staphylococcus aureus in dairy farms in Asella, Ethiopia

BackgroundThe determination of the microbiological quality and safety of raw milk and the associated influencing factors at the farm level is very critical given that the quality or safety of subsequent products that are further produced depends on this. Therefore, this study aimed to determine the microbiological quality and safety of bulk milk and identify associated risk factors, and assess the presence/absence of S. aureus in bulk milk with potential contaminating sources in dairy farms in Asella, Ethiopia.ResultsThe geometric means of bacterial counts in farm bulk milk were 5.25 log cfu/ml, 3.1 log cfu/ml and 2.97 log cfu/ml for total bacterial count (TBC), coliform count (CC) and coagulase-positive staphylococci count (CPS), respectively. Of the 50 dairy farms, 66, 88, and 32% had TBC, CC and CPS counts, respectively, that exceeded the standard international limits for raw cow’s milk intended for direct human consumption. TBC tended to increase as CC increased in bulk milk (r = 0.5). In the final regression model, increased TBC, CC and the contamination of farm bulk milk by S. aureus were significantly associated with dirty barns, dirty cows and soiled udder and teats. TBC was higher during the rainy season than during the dry season. The reported practice of washing teats with warm water significantly decreased CC and CPS. The occurrence of S. aureus was significantly (p < 0.05) higher in bulk farm milk (42%) than in pooled udder milk (37.3%), teat swabs (22.5%), milkers’ hand swabs (18%), bulking bucket swabs (16.7%), milking container swabs (14%), and water for cleaning of udder and milkers’ hands (10%). The questionnaire survey result showed widespred raw milk consumption habits, low level of training and poor hygienic milking practices.ConclusionsThis study revealed low-quality bulk farm milk with high bacterial counts and a high occurrence of S. aureus. This indicates the potential food safety risks due to consumption of raw milk or its products. This study suggests awareness creation to dairy farmers and the public on hygienic milk production and heat treatment of milk before consumption.

Bulk milk quality as affected by cattle breed composition of the herd in mountain area

Abstract The aim of this study was to investigate the variation of gross composition, somatic cell count, urea content, and fatty acids (FA) composition of bulk milk from single-breed and multi-breed farms in alpine area, keeping either Brown Swiss (BS), Holstein Friesian (HF), Simmental (SI), or their combinations. Gross milk composition, urea content, and FA composition were predicted using mid-infrared spectroscopy. Observations were grouped in 7 combinations consisting of 3 singlebreed and 4 multi-breed types of herd. A mixed linear model was used for data analysis, accounting for the fixed effects of herd composition (7 combinations), month of sampling, year of sampling, and the interactions between herd composition and month of sampling, and between herd composition and year of sampling. Farm was included as random effect. Results highlighted that about two thirds of South Tyrolean farms were single-breed and herds with more than 20 lactating cows changed herd structure over time, switching from multi- to single-breed. Single-breed BS farms produced milk with greater fat, protein, casein, lactose, and FA content than single-breed HF and SI farms. Further, multi-breed herds including BS cows produced milk with greater fat, protein, casein, and polyunsaturated FA content than multi-breed HF+SI herds. Overall, single-breed SI farms produced milk with lower somatic cell count than other herd combinations. Despite the number of BS cows in South Tyrol has decreased in favor of SI in the last years, this breed is still the most interesting for alpine dairy farming to achieve optimal milk quality in both single- and multi-breed scenarios. The tendency to move to SI is mainly related to good milk performance of SI cows coupled with their robustness, high carcass value, high market value of calves, and adaptability to mountain farming system.

On the Effect of the Temperature-Humidity Index on Buffalo Bulk Milk Composition and Coagulation Traits.

Little is known about the effects of high levels of environmental temperature and humidity on milk yield and quality in buffaloes since this species is known to be more heat tolerant than cattle. However, the distribution of sweat glands and the dark skin color can negatively affect heat tolerance. Moreover, due to increased global temperatures, concerns regarding heat stress and thermoregulation in dairy animals, including buffaloes, have been extended to the northern hemisphere. In this study, the effects of both the temperature-humidity index (THI) and the maximum daily temperature-humidity index (MTHI) were estimated on bulk milk traits, namely fat, protein, lactose, urea content, pH levels, somatic cell score, coagulation properties, and bacteria count. The dataset consisted of repeated data from 99 Mediterranean water buffalo farms, and mixed models were used for the analyses. Supporting the negative correlations observed, bulk milk fat, protein, and lactose content were significantly lower when THI and MTHI were higher. Similarly, milk pH was lower when THI and MTHI were high; however, high levels of THI or MTHI seemed to not be markedly associated with the milk's coagulation ability. According to both analysis of variance and correlations, the somatic cell score was not significantly affected by the THI and MTHI. This is the first study based on a large dataset that evaluates the impact of high temperature and humidity in Italian buffalo milk and that provides correlations with traits of interest for the dairy industry, i.e., milk acidity and coagulation ability. In general, findings show that the effects of elevated THI and heat stress on bulk milk quality in buffalo is less evident than in cattle. These preliminary results intend to open debate on the issue of heat stress in dairy buffaloes that are reared in temperate regions. Further studies should focus on individual milk and performance and should investigate the relationship between high THI and buffalo fertility, behavior, and welfare.

Effect of seasonal conditions and milk management practices on bulk milk quality in Minas Gerais State - Brazil

ABSTRACT The influence of seasonality and the training and implementation of good dairy farming practices on raw milk production and quality was evaluated on dairy farms in Minas Gerais State, Brazil. The physico-chemical composition, somatic cell count (SCC) and total bacterial count (TBC) were determined in 3,096 milk samples collected from bulk tanks originated from 43 dairy farms over a three-year period and correlated with key climatic factors. The recommended milking management practices were applied through a training program and correlated with the seasonal data in three stages: I) prior to training (dry period); II) 48 days after the training (transition period); III) 96 days after the training (rainy period). In the first stage, a diagnosis of the situation was performed with raw milk samplings for laboratory analysis, and training for implementation of good milking practices. In stages II and II, the checklist and laboratory analysis were also performed. The rainfall and high temperatures were found to represent the main factors affecting the milk composition and production, and TBC. The composition and physical properties of raw milk, and the TBC and SCC parameters can be controlled or minimized by applying proper milking management practices and constant monitoring.

Monitoring bulk milk quality by an integral traceability system of milk

ABSTRACTThe Integral Traceability System for tracking and tracing the milk samples used in quality control was checked for one year while monitoring 526 milk samples from sheep's, goats’ and cows’. This system includes a customized automated cooler for carrying samples with a smart sensor inside to store the data collected during the process, and a dongle to transfer the collected data to a computer to be further analysed. The technologies combined to record and trace milk samples on trips from farms to the laboratory (e.g. microcontrollers, sensors, radio frequency identification and global positioning system) were linked. This system allowed us to objectively know the duration of the sampling route and the temperature and time conditions of samples travelled in until they were analysed in an official dairy laboratory. These conditions ensured that the baseline milk quality was preserved, and was therefore adequate according to both European regulations and the price set to be paid for quality. Hardware and software prototypes worked successfully under the real study conditions, and this system may be proposed to become a reference method in the dairy sector.

Association of bedding types with management practices and indicators of milk quality on larger Wisconsin dairy farms

The objective of this study was to identify associations of bedding type and selected management practices with bulk milk quality and productivity of larger Wisconsin dairy farms. Dairy herds (n=325) producing ≥11,340kg of milk daily were surveyed during a single farm visit. Monthly bulk milk SCC and total bacteria counts were obtained from milk buyers for 255 farms for a 2-yr period. Of farms with the same type of bedding in all pens during the study period, most used inorganic bedding (IB), followed by organic nonmanure bedding (OB) and manure products (MB). Almost all bulk milk total bacterial counts were <10,000 cfu/mL and total bacterial count was not associated with bedding type. Bulk milk somatic cell score (BMSCS) was least for farms using IB, varied seasonally, and was greatest in the summer. The BMSCS was reduced when new bedding was added to stalls at intervals greater than 1wk and when teats were dried before attaching the milking unit. The BMSCS for farms using OB was reduced when bedding in the backs of stalls was removed and replaced regularly and when fewer cows with nonfunctioning mammary quarters were present. The BMSCS for farms using MB was reduced when the proportion of cows with milk discarded was less. The rolling herd average (RHA) of herds using IB was 761 and 1,153kg greater than the RHA of herds using OB and MB, respectively. The RHA was 353kg greater on farms where farmers understood subclinical mastitis and 965kg greater on farms milking 3 times daily. Each 1% increase of cows with nonfunctioning mammary quarters was associated with a decrease of 57kg of RHA. The BMSCS, proportions of cows with milk discarded and proportion of cows with nonfunctioning mammary quarters were least for herds using IB and were associated with increased productivity. Large Wisconsin dairy farms that used inorganic bedding had greater productivity and better milk quality compared with herds using other bedding types.

Visible and near-infrared bulk optical properties of raw milk

The implementation of optical sensor technology to monitor the milk quality on dairy farms and milk processing plants would support the early detection of altering production processes. Basic visible and near-infrared spectroscopy is already widely used to measure the composition of agricultural and food products. However, to obtain maximal performance, the design of such optical sensors should be optimized with regard to the optical properties of the samples to be measured. Therefore, the aim of this study was to determine the visible and near-infrared bulk absorption coefficient, bulk scattering coefficient, and scattering anisotropy spectra for a diverse set of raw milk samples originating from individual cow milkings, representing the milk variability present on dairy farms. Accordingly, this database of bulk optical properties can be used in future simulation studies to efficiently optimize and validate the design of an optical milk quality sensor. In a next step of the current study, the relation between the obtained bulk optical properties and milk quality properties was analyzed in detail. The bulk absorption coefficient spectra were found to mainly contain information on the water, fat, and casein content, whereas the bulk scattering coefficient spectra were found to be primarily influenced by the quantity and the size of the fat globules. Moreover, a strong positive correlation (r≥0.975) was found between the fat content in raw milk and the measured bulk scattering coefficients in the 1,300 to 1,400nm wavelength range. Relative to the bulk scattering coefficient, the variability on the scattering anisotropy factor was found to be limited. This is because the milk scattering anisotropy is nearly independent of the fat globule and casein micelle quantity, while it is mainly determined by the size of the fat globules. As this study shows high correlations between the sample’s bulk optical properties and the milk composition and fat globule size, a sensor that allows for robust separation between the absorption and scattering properties would enable accurate prediction of the raw milk quality parameters.

A proteomic perspective on the changes in milk proteins due to high somatic cell count

Although cows with subclinical mastitis have no difference in the appearance of their milk, milk composition and milk quality are altered because of the inflammation. To know the changes in milk quality with different somatic cell count (SCC) levels, 5 pooled bovine milk samples with SCC from 105 to 106 cells/mL were analyzed qualitatively and quantitatively using both one-dimension sodium dodecyl sulfate PAGE and filter-aided sample preparation coupled with dimethyl labeling, both followed by liquid chromatography tandem mass spectrometry. Minor differences were found on the qualitative level in the proteome from milk with different SCC levels, whereas the concentration of milk proteins showed remarkable changes. Not only immune-related proteins (cathelicidins, IGK protein, CD59 molecule, complement regulatory protein, lactadherin), but also proteins with other biological functions (e.g., lipid metabolism: platelet glycoprotein 4, butyrophilin subfamily 1 member A1, perilipin-2) were significantly different in milk from cows with high SCC level compared with low SCC level. The increased concentration of protease inhibitors in the milk with higher SCC levels may suggest a protective role in the mammary gland against protease activity. Prostaglandin-H2 D-isomerase showed a linear relation with SCC, which was confirmed with an ELISA. However, the correlation coefficient was lower in individual cows compared with bulk milk. These results indicate that prostaglandin-H2 D-isomerase may be used as an indicator to evaluate bulk milk quality and thereby reduce the economic loss in the dairy industry. The results from this study reflect the biological phenomena occurring during subclinical mastitis and in addition provide a potential indicator for the detection of bulk milk with high SCC.

Exploring the characteristics and dynamics of Ontario dairy herds experiencing increases in bulk milk somatic cell count during the summer

Regionally aggregated bulk milk somatic cell count (BMSCC) data from around the world shows a repeatable cyclicity, with the highest levels experienced during warm, humid seasons. No studies have evaluated this seasonal phenomenon at the herd level. The objectives of this study were to define summer seasonality in BMSCC on an individual herd basis, and subsequently to describe the characteristics and dynamics of herds with increased BMSCC in the summer. The data used for this analysis were from all dairy farms in Ontario, Canada, between January 2000 and December 2011 (n ≈ 4,000 to 6,000 herds/yr). Bulk milk data were obtained from the milk marketing board and consisted of bulk milk production, components (fat, protein, lactose, other solids), and quality (BMSCC, bacterial count, inhibitor presence, freezing point), total milk quota of the farm, and milk quota and incentive fill percentage. A time-series linear mixed model, with random slopes and intercepts, was constructed using sine and cosine terms as predictors to describe seasonality, with herd as a random effect. For each herd, seasonality was described with reference to 1 cosine function of variable amplitude and phase shift. The predicted months of maximal and minimal BMSCC were then calculated. Herds were assigned as low, medium, and high summer increase (LSI, MSI, and HSI, respectively) based on percentiles of amplitude in BMSCC change for each of the 4 seasons. Using these seasonality classifications, 2 transitional repeated measures logistic regression models were built to assess the characteristics of MSI and HSI herds, using LSI herds as controls. Based on the analyses performed, a history of summer BMSCC increases increased the odds of experiencing a subsequent increase. As herd size decreased, the odds of experiencing HSI to MSI in BMSCC increased. Herds with more variability in daily BMSCC were at higher odds of experiencing MSI and HSI in BMSCC, as were herds with lower annual mean BMSCC. Finally, a negative association was noted between filling herd production targets and experiencing MSI to HSI in BMSCC. These findings provide farm advisors direction for predicting herds likely to experience increases in SCC over the summer, allowing them to proactively focus udder health prevention strategies before the high-risk summer period.

Factors associated with coliform count in unpasteurized bulk milk

The objective of this study was to identify factors associated with bulk milk coliform count (CC). Dairy farms (n=10) were visited once weekly on sequential weekdays over a period of 10 wk. During each visit, in-line drip samplers were used to collect 1 milk sample from 2 points of the milk line (between the receiver jar and milk filters, and after the plate cooler). During the same period that in-line milk samples were collected, university personnel observed milking performance and hygiene and collected liner (n=40) and teat skin swabs (n=40). Coliform counts were determined for milk samples and swabs using Petrifilm CC plates (3M, St. Paul, MN). A mixed model was used to assess the association between in-line milk CC (ILCC) and several potential predictor variables. The mean duration of each visit was 73min and the time between start of milking and beginning of milk sampling was 154min. The mean number of cows milked during each visit was 236. For all milk samples (n=181), geometric mean ILCC was 37cfu/mL. In-line milk CC varied by farm, ranging from 5 to 1,198cfu/mL. Rate of fall-offs, rate of cluster washes, outdoor and indoor temperature, indoor humidity, sampling duration, and parity group were unconditionally associated with ILCC but did not enter the final multivariate model. In-line milk CC was 4 times greater (115cfu/mL) when milking machine wash failures occurred compared with ILCC after normal washes (26cfu/mL). Pre-filter and post-cooler ILCC were not different when milk samples were collected at the beginning (<33% of herd milked) or at mid-milking (33 to 66% of the herd milked), whereas pre-filter ILCC was less than post-cooler for samples collected at the end of milking (>67% of the herd milked). Geometric mean ILCC (cfu/mL) increased 6.3% for every 10% increase in in-line milk SCC (cells/mL). Geometric mean ILCC increased 2.3% for every 10% increase in liner CC (cfu/mL). Results of this study provide novel information about farm factors associated with CC, as estimated in milk before storage in tankers or bulk tanks, and highlight the importance of proper and consistent milking machine washes in minimizing bulk milk coliform contamination. The nature of the associations between liner CC, rate of cluster washes, rate of milking units fall-offs, and ILCC indicates that managing and monitoring such events has the potential for improving bacteriological quality of farm bulk milk.

Process audits versus product quality monitoring of bulk milk

Assessment of milk quality is based on bulk milk testing and farm certification on process quality audits. It is unknown to what extent dairy farm audits improve milk quality. A statistical analysis was conducted to quantify possible associations between bulk milk testing and dairy farm audits. The analysis comprised 64.373 audit outcomes on 26,953 dairy farms, which were merged with all conducted laboratory tests of bulk milk samples 12 mo before the audit. Each farm audit record included 271 binary checklist items and 52 attention point variables (given to farmers if serious deviations were observed), both indicating possible deviations from the desired farm situation. Test results included somatic cell count (SCC), total bacterial count (TBC), antimicrobial drug residues (ADR), level of butyric acid spores (BAB), freezing point depression (FPD), level of free fatty acid (FFA), and milk sediment (SED). Results show that numerous audit variables were related to bulk milk test results, although the goodness of fit of the models was generally low. Cow hygiene, clean cubicles, hygiene of milking parlor, and utility room were positively correlated with superior product quality, mainly with respect to SCC, TBC, BAB, FPD, FFA, and SED. Animal health or veterinary drugs management (i.e., drug treatment recording, marking of treated animals, and storage of veterinary drugs) related to SCC, FPD, FFA, and SED. The availability of drinking water was related to TBC, BAB, FFA, and SED, whereas maintenance of the milking equipment was related mainly to SCC, FPD, and FFA. In summary, bulk milk quality and farm audit outcomes are, to some degree, associated: if dairy farms are assessed negatively on specific audit aspects, the bulk milk quality is more likely to be inferior. However, the proportion of the total variance in milk test results explained by audits ranged between 4 and 13% (depending on the specific bulk milk test), showing that auditing dairy farms provides additional information but has a limited association with the outcome of a product quality control program. This study suggests that farm audits could be streamlined to include only relevant checklist items and that bulk milk quality monitoring could be used as a basis of selecting farms for more or less frequent audits.

Short communication: Relationships between α-lactalbumin and quality traits in bulk milk

The main objective of this study was to investigate whether the α-lactalbumin (α-LA) content of bulk milk is related with some known inflammatory markers and milk quality traits. An additional objective was to study whether combining α-LA, haptoglobin (Hp), and serum amyloid A (SAA) in an acute phase index (API) could be useful as an alternative marker for bulk milk quality. For the dairy industry, it is of great importance to receive high quality bulk milk for manufacture of liquid milk and dairy products. The somatic cell count (SCC) is currently used as an indirect marker for bulk milk quality, but because it is somewhat insensitive and unspecific, interest exists in alternative markers. Bulk milk samples were analyzed for α-LA, SCC, polymorphonuclear leukocyte count, Hp, SAA, fat, lactose, total protein and casein contents, casein number, protein composition, proteolysis, and coagulating properties. No significant differences were found in SCC, polymorphonuclear leukocyte count, Hp, or SAA between milk samples containing low, medium, or high concentrations of α-LA. Differences between α-LA groups were, however, found in some milk quality traits because high α-LA concentration was related to low concentrations of αS1-, αS2-, and β-caseins and high concentrations of lactose and β-lactoglobulin. A high API was related to low lactose content and casein number. Samples with high SCC contained less casein and had a lower casein number than milk with a low SCC, and proteolysis was significantly higher in high SCC milk than in low SCC milk. Neither α-LA nor API proved to be a better marker than SCC for the quality traits investigated, and α-LA was not considered to be a useful inflammatory marker in bulk milk.

Associations among milk quality indicators in raw bulk milk

The objective of this study was to determine characteristics and associations among bulk milk quality indicators from a cohort of dairies that used modern milk harvest, storage, and shipment systems and participated in an intensive program of milk quality monitoring. Bulk milk somatic cell count (SCC), total bacteria count (TBC), coliform count (CC), and laboratory pasteurization count (LPC) were monitored between July 2006 and July 2007. Bulk milk samples were collected 3 times daily (n = 3 farms), twice daily (n = 6 farms), once daily (n = 4 farms), or once every other day (n = 3 farms). Most farms (n = 11) had direct loading of milk into tankers on trucks, but 5 farms had stationary bulk tanks. The average herd size was 924 cows (range = 200 to 2,700), and daily milk produced per herd was 35,220kg (range = 7,500 to 105,000kg). Thresholds for increased bacterial counts were defined according to the 75th percentile and were >8,000cfu/mL for TBC, >160cfu/mL for CC, and ≥310cfu/mL for LPC. Means values were 12,500 (n = 7,241 measurements), 242 (n = 7,275 measurements), and 226cfu/mL (n = 7,220 measurements) for TBC, CC, and LPC, respectively. Increased TBC was 6.3 times more likely for bulk milk loads with increased CC compared with loads containing fewer coliforms. Increased TBC was 1.3 times more likely for bulk milk with increased LPC. The odds of increased TBC increased by 2.4% for every 10,000-cells/mL increase in SCC in the same milk load. The odds of increased CC increased by 4.3% for every 10,000-cells/mL increase in SCC. The odds of increased CC increased by 1% for every 0.1°C increase in the milk temperature upon arrival at the dairy plant (or at pickup for farms with bulk tank). Laboratory pasteurization count was poorly associated with other milk quality indicators. Seasonal effects on bacterial counts and milk temperature varied substantially among farms. Results of this study can be used to aid the interpretation and analysis of indicators of milk quality intensively produced by dairy processors’ laboratories.

Environmental Sampling To Predict Fecal Prevalence of Salmonella in an Intensively Monitored Dairy Herd

Although dairy cattle are known reservoirs for salmonellae, cattle that are shedding this organism are often asymptomatic and difficult to identify. A dairy herd that was experiencing a sustained, subclinical outbreak of Salmonella enterica subsp. enterica Cerro was monitored for 2 years. Fecal samples from the lactating cows were collected every 6 to 8 weeks and tested for the presence of Salmonella. Fecal prevalence of Salmonella fluctuated throughout the observation period and ranged from 8 to 88%. Manure composites and water trough samples were collected along with the fecal samples, and bulk milk and milk filters were cultured for the presence of Salmonella on a weekly basis. Over 90% of the manure composites—representing high-animal-traffic areas—were positive at each sampling. Salmonella was detected in 11% of milk samples and in 66% of the milk filters. Results of weekly bulk milk quality testing (i.e., bulk tank somatic cell score, standard plate count, preliminary incubation count) were typically well within acceptable ranges. Milk quality variables had low correlations with herd Salmonella fecal prevalence. When observed over time, sampling period average prevalence of Salmonella in milk filters closely paralleled fecal prevalence of Salmonella in the herd. Based on results of this study, milk filters appear to be an effective method for monitoring shedding prevalence at the herd level. In-line filter testing is also a more sensitive measure of Salmonella, and perhaps other pathogens, in raw milk than testing the milk alone.

Milk quality assurance for paratuberculosis: simulation of within-herd infection dynamics and economics

A bulk milk quality assurance programme for Mycobacterium avium subsp. paratuberculosis (Map) in dairy herds was simulated with a stochastic simulation model (JohneSSim). The aim of this study was to evaluate the epidemiological and economic effects of preventive management measures and various test schemes in a simulated population of closed Dutch dairy herds over a 20-year period. Herds were certified as ;low-Map bulk milk' if, with a certain probability, the concentration of Map in bulk milk did not exceed a maximum acceptable concentration of 10(3) Map organisms per litre (based on pasteurisation studies). The programme started with an initial assessment; test-negative herds entered a surveillance procedure and test-positive herds a control procedure. The simulations showed that herd examinations by ELISA for the initial assessment, surveillance and control procedures effectively ensure the quality of ;low-Map bulk milk': > 75% of simulated herds were certified and > 96% of certified herds produced bulk milk with < 10(3) Map/L if the initial herd-level prevalence was 30%. Preventive management measures only had a minor effect on bulk milk quality of certified herds. Culling based on biennial faecal culture was more effective than culling based on annual ELISA. Average total discounted costs for 20-year participation in a programme consisting of initial assessment by ELISA, surveillance by biennial ELISA and control by biennial faecal culture were 16 Euro x 10(3) per herd. In conclusion, this study shows that a bulk milk quality assurance programme for closed Dutch dairy herds is feasible and provides information on the cost-effectiveness of different programmes. The concepts of this study equally apply to other countries because mechanisms of paratuberculosis infection, disease, and testing are comparable in other dairy cattle populations.

Milk Quality on Danish Farms with Automatic Milking Systems

The bulk-milk quality of 98 Danish farms with automatic milking systems was analyzed from 1 yr before introduction of automatic milking until 1 yr after. Bulk-milk total bacterial count, spores of anaerobes, somatic cell count (SCC), and freezing point increased when automatic milking was introduced and the frequency of milk-quality failures almost doubled. Milk-quality failures were most frequent in the first 3 mo after the start of automatic milking. The increase in spores of anaerobes indicated that the increase in total bacterial count originated partly from contamination of milk from the teat surface and partly from lack of cleaning of the milking equipment or cooling of the milk. The increase in bulk-milk SCC indicated that milk from clinically infected cows and cows with high cell counts was not diverted to the same degree, milking automatically rather than milking conventionally. A self-monitoring program including survey of the bulk-milk quality was established to help farmers in the transition period going from conventional to automatic milking. The program was introduced on 84 farms. Farms on the self-monitoring program reduced bulk-milk cell count. Application of the program did not reduce the frequency of high total bacterial counts and freezing points of the bulk milk to the level of conventional milking. However, the program reduced the overall frequency of milk-quality failures.

Effects of storage conditions on the composition of raw milk

Abstract The aim of this work was to study the changes of milk composition, especially of the casein fraction, during storage under different conditions. This study was conducted on raw milk with two different levels of psychrotrophic bacteria. Two treatments for raw bulk milk were compared: storage at 4 °C for 48 h and storage at 8 °C after inoculation with L. lactis subsp. lactis for 48 h. Analysis of the composition of milk before and after storage was conducted on the basis of microbiological and physico-chemical composition: colloidal minerals and protein fractions. The results show the importance of the initial microbiological quality of raw bulk milk for its preservation and the efficiency of ripening as a storage treatment for milk with a low-bacterial level.