Indicator Of Udder Health Research Articles

The Accurate and Exclusive Quantification of Somatic Cells in Raw Milk with an OPD-Cu2+ System-Based Colorimetric Method.

The somatic cell count (SCC) refers to the number of somatic cells present in each milliliter of raw milk and serves as a crucial indicator of dairy cow udder health and raw milk quality. Traditional SCC detection methods are often time-consuming, expensive, and susceptible to bacterial interference, rendering them unsuitable for the rapid and unbiased assessment of raw milk quality. Consequently, there is an urgent need for a low-cost, accurate, and user-friendly SCC quantification method. Here, a method based on an OPD-Cu2+ system for SCC quantification was developed. It was found that OPD oxidation signals exhibited a linear correlation with SCC. Following optimization, the detection system was established with a Cu2+ concentration of 25 μM, an OPD concentration of 2 mM, and an incubation time of 15 min. Furthermore, the method demonstrated significant resistance to bacterial interference, though it produced weaker signals in response to bacteria. The somatic cell recovery rate in milk after pretreatment was 88.9%, and SCC was quantified accurately within 45 min, with a linear range of 104-106 cells/mL. In summary, the method developed is cost-effective, straightforward, and facilitates precise somatic cell quantification, offering significant practical value and a new approach for SCC detection in raw milk.

Fluctuations in somatic cell count and their impact on individual goat milk quality throughout lactation

The somatic cell count (SCC) is used by the dairy industry as an indicator of milk quality and udder health. However, in goats, its reliability is significantly masked by non-infectious variables such as the milk secretion process and physiological lactation changes. Additionally, notable individual variability between goats might exist. This study aimed to investigate fluctuations in SCC in individual goats during an entire lactation and to examine the relationship between SCC and milk parameters such as bacterial count and chemical composition. Individual milk samples from 40 Norwegian dairy goats from the University herd were collected monthly across an entire lactation including the pasture period. The goats were categorized based on SCC levels to analyze patterns in chemical components within these groups. Notably, goats exhibited increased SCC and decreased bacterial counts when moved to summer grazing pastures. At that stage milk samples from goats with the highest SCC (>2000 ×103 cells/mL) showed a distinct decrease in individual bacterial counts (IBC). Several milk components, especially lactose, protein, and various minerals were affected by the SCC level, as well as pH. These effects were further amplified when considering the interaction with the lactation stage, influencing a broader range of variables. The results presented in this study provide new insights into the SCC's effect on milk composition and its critical role. These insights underscore the necessity for establishing lactation-specific thresholds for interpretation, as well as for making adjustments in quality payments.

CARACTERIZACIÓN DE SISTEMAS DE PRODUCCIÓN LECHERA Y SALUD DE LA UBRE EN EL NORTE DE ANTIOQUIA, COLOMBIA

<p><strong>Background: </strong>The sociodemographic characterization of milk producers allows knowing their particular conditions in terms of social, political and economic structure through indicators that describe them as social actors, interaction networks, conflict situations and events related to milk production. <strong>Objective:</strong> To characterize the diversity of dairy production systems in Northern Antioquia with a focus on the producer, the farms, access to services and their impact on the biological indicators of udder health. <strong>Methodology:</strong> A cross-sectional study was designed <span style="text-decoration: line-through;">that</span> including 216 milk producers within distributed in 9 dairy municipalities in northern Antioquia. Surveys to characterize the producer, the farm and access to services for health and milk production were carried out. As biological indicators of udder health, the annual average<span style="text-decoration: line-through;">s</span> of bulk tank somatic cell counts (BTSCC) and colony-forming units (CFU) of each farm were analyzed. Statistical analyzes were performed with SPSS version 25.0 and a p < 0.05. <strong>Results: </strong>The biological indicators of udder health in the studied farms presented poor sanitary quality according to the BTSCC for 67 % of the producers and poor hygienic quality with CFU in for 22 % of the participating farms. Briceño producers showed the lowest production in contrast to Santa Rosa de Osos milk producers, who have the highest production. In the linear regression models, 18.2 % of the BTSCC was explained by the location in relation to the municipality, the size of the production system and the type of milking. The CFU values were explained in 29.8 % by the municipality, the cooperative association or livestock association, the milk storage system, the offer of laboratory services for milk and cows, the area of the farm dedicated to grazing, the number of animals on the farm and the average daily production per cow. <strong>Implications:</strong> This study shows the importance of understanding udder health issues not only at the cow level from a biomedical approach. The understanding of udder health from the farm in the sociodemographic context shows that its location, access to services, technology, education and associativity establish opportunities to improve production and competitiveness of milk production. <strong>Conclusions:</strong> udder health can be positively or negatively affected by the location of the production system, the size of the production system, the type of milking, the association to a cooperative or livestock association, the milk storage system, the supply of laboratory services for milk and cows, the area of the farm dedicated to grazing, the number of animals on the farm and the average daily production per cow. To consider these variables is fundamental in decision-making in mastitis control and preventive care for udder health.</p>

Association of Dry Period Length with Automatic Milking System, Mastitis, and Reproductive Indicators in Cows.

The objective of this study was to evaluate the association between dry period (DP) length and various indicators of productivity, reproduction, and udder health in cows managed with an automatic milking system. We analyzed records from 3861 cows, categorizing them into three groups based on their DP duration: (1) <40 days, (2) 40-70 days, and (3) DP > 70 days. Cows with a DP of 40-70 days had an average energy-corrected milk production that was 8.2 kg greater than that of cows with a short DP and 5.0 kg greater than that of cows with a long DP (p < 0.001). Milk from the 40-70-day DP group exhibited the highest lactose concentration (4.64 ± 0.01%). Additionally, cows with the longest DP had the smallest proportion of animals with a milk fat-to-protein ratio of 1.2 to 1.4. Cows with a DP of 40-70 days also showed the lowest milk electrical conductivity across all udder quarters, whereas cows with the shortest DP had the highest conductivity. The highest conception rates were observed in the group with the shortest DP. These results suggest that a DP of 40-70 days is optimal for maximizing milk production and improving both udder health and reproductive performance under AMS. Proper management of DP duration can be an effective strategy for sustainable dairy herd management.

A large population study to assess the magnitude of prenatal programming in dairy cattle

The performance of an adult dairy cow may be influenced by events that occur before her birth. The present study investigated potential effects of 2 prenatal groups of factors, Assisted Reproductive Techniques (ART) and maternal characteristics (e.g., dam parity), on offspring performance during their first lactation, in populations of 2 dairy cow breeds: French Holstein and Montbéliarde. The different ART studied included the type of semen (conventional or X-sorted) used for Artificial Insemination (AI) and the technology of conception used (AI, embryo transfer, or in vitro fertilization). Three maternal characteristics were considered: (1) the dam age at first calving, (2) dam parity number, and (3) indicators of dam udder health during gestation (somatic cell score and events of clinical mastitis). First, we investigated whether heifer survival from 3d to 18 mo old was associated with any of the prenatal factors considered. We then estimated the associations of these prenatal factors with 8 traits of commercial interest: (1) stature, (2-4) milk, fat, and protein yields, (5) somatic cell score, (6) clinical mastitis, and (7-8) heifer and cow conception rate, all measured on genotyped cows. Linear models were used for this study with the prenatal factors as covariates in the model, and for the 8 traits, phenotypes were adjusted for their corresponding genomic estimated breeding value. The results indicated that the survival rate of heifers born from embryo transfer was significantly higher than that of heifers born from AI (probably due to preferential management practices), while the other prenatal factors did not explain differences in heifer survival. Among the Montbéliarde cows born from AI, those born from X-sorted semen showed a lightly but significantly lower milk yield than those born without X-sorting of the semen (-52 kg of milk in the first lactation). Among the Holstein cows, those born from embryo transfer presented significantly lower milk performance than cows born from AI. Regarding the maternal characteristics, none or very weak associations were found between the dam age at first calving and the offspring performance in both breeds. Dam parity, on the other hand, was associated with offspring performance for milk, fat, and protein yield in both breeds, however not in the same direction. In the Holstein breed, an increase in dam parity was favorable for offspring performance for milk, fat, and protein yield, whereas in the Montbéliarde breed, an increase in dam parity was associated with lower milk and protein yield and no association was found for fat yield. The udder health of the dam during gestation was not or only weakly associated with the traits studied in the offspring. Although some significant associations were identified due to the large sample size, the effects were modest, typically less than 1% of the phenotypic mean, and were not consistently observed across the 2 breeds.

Seasonal dynamics of bulk milk somatic cell count in grazing Norwegian dairy goats

Somatic cell count (SCC) is a widely used indicator of milk quality in the dairy industry. It is a relatively good indicator of udder health in dairy cows, but strongly confounded by non-infection factors, including season, in dairy goats. This study's objectives were to estimate the variation in SCC explained by season and stage of lactation. We also investigated associations between SCC and other milk quality parameters routinely measured, including total bacterial count, fat, protein, and lactose content, in different seasons and stages of lactation. In this observational study, we included results from the routine analyses of bulk milk samples (n = 5,180) collected every third day in 88 Norwegian goat herds through one year. Herd information was collected via a questionnaire and from the dairy herd recording system. The herds had a notable increase in bulk SCC associated with the pasture season. The median bulk SCC was 470,000 cells/mL in deliveries from the indoor spring period and 1,100,000 cells/mL in the pasture season. During the indoor fall season, when most goat herds are in late lactation, the median SCC was 940,000 cells/mL. The combined effect of the season and herd stage of lactation explained 53.5% of the variation in bulk milk SCC. Fat and protein contents varied significantly with season and stage of lactation, and the association with SCC was non-significant for fat content and explained less than 3% of the variation in protein content. Lactose content was associated with SCC, stage of lactation, and season. Total bacterial count was associated with SCC and the indoor seasons. Determination of normal seasonal variation of goat bulk milk SCC is necessary to establish thresholds for defining milk as abnormal and unacceptable for further processing and improving the usefulness of SCC as a tool for milk quality improvement in dairy goats. The results suggest that SCC thresholds for taking action to improve the milk quality should be adjusted for season and stage of lactation.

Trust and reciprocity norms in the analysis of social capital related to udder health. A mixed methods approach with dairy farmers and veterinarians from the north of Antioquia.

Understanding trust between dairy farmers and other actors in the dairy chain, as well as the reciprocity norms among them are fundamental to encouraging collective action and decision-making to improve milk production and udder health. The objective of this study was to understand the relationships of trust between dairy farmers and other actors in the dairy chain related to udder health in the north of Antioquia. Mixed methods (cross-sectional and grounded theory) with a convergent triangulation design were used. A total of 216 dairy farmers participated in the quantitative component, and 17 dairy farmers and 9 veterinarians in the qualitative component, they were located in 9 milk-producing municipalities in the north of Antioquia. A characterization survey of the dairy farmers, a survey on reliability and udder health, an analysis of each farm's annual average BTSCC and CFU, and semi-structured interviews on the same topic were conducted. Problems were found in the biological indicators of udder health on the farms: BTSCC was poor for 67% of the dairy farmers, and CFU was poor for 22% of the dairy farmers. Veterinarians are the actors whom dairy farmers trust the most. Trust in dairy chain actors is complex, variable, and depends on many aspects. Trust representation, Socio-cultural Factors, Economic and Commercial Factors, Labor, Clinical and Laboratory Conditions of Milk, and Norms of Reciprocity constitute the categories of analysis when theorizing about trust and udder health. Each of the theoretical and emerging categories in this study describes actors, attitudes, behaviors, relationships between actors, and norms, allowing us to understand that trust between dairy farmers and other actors in the dairy chain in order to face the problems of udder health and milk production depends on technical processes, individual and collective human attitudes and behaviors, supply of services, political, regulative and economic determinants, the latter being transcendental in decision-making to invest in mastitis control and udder health care.

Identification of hub genes associated with somatic cell score in dairy cow.

Somatic cell count (SCC) is used as an indicator of udder health. The log transformation of SCC is called somatic cell score (SCS). Several QTL and genes have been identified that are associated with SCS. This study aimed to identify the most important genes associated with SCS. This study compiled 168 genes that were reported to be significantly linked to SCS. Pathway analysis and network analysis were used to identify hub genes. Pathway analysis of these genes identified 73 gene ontology (GO) terms associated with SCS. These GO terms are associated with molecular function, biological processes, and cellular components, and the identified pathways are directly or indirectly linked with the immune system. In this study, a gene network was constructed, and from this network, the 17 hub genes (CD4, CXCL8, TLR4, STAT1, TLR2, CXCL9, CCR2, IGF1, LEP, SPP1, GH1, GHR, VWF, TNFSF11, IL10RA, NOD2, and PDGFRB) associated to SCS were identified. The subnetwork analysis yielded 10 clusters, with cluster 1 containing all identified hub genes (except for the VWF gene). Most hub genes and pathways identified in our study were mainly involved in inflammatory and cytokine responses. Result obtained in current study provides knowledge of the genetic basis and biological mechanisms controlling SCS. Therefore, the identified hub genes may be regarded as the main gene for the genomic selection of mastitis resistance.

Adoption and decision factors regarding selective treatment of clinical mastitis on Canadian dairy farms

As clinical mastitis (CM) treatments are responsible for a large portion of antimicrobial use on dairy farms, many selective CM treatment protocols have been developed and evaluated against a blanket treatment approach of CM cases. Selective treatment protocols use outcomes of diagnostic tests to exclude CM cases from antimicrobial treatment when they are unlikely to benefit. To tailor interventions to increase uptake of selective treatment strategies, a comprehension of current on-farm treatment practices and factors affecting treatment decisions is vital. Two questionnaires were conducted among 142 farms across 5 provinces participating in the Canadian Dairy Network for Antimicrobial Stewardship and Resistance in this cross-sectional study. Self-reported adoption of selective CM treatments by dairy farmers was 64%, with median of 82% of cows treated in those herds using selective treatment. Using logistic regression models, the odds to implement a selective CM treatment protocol increased with a decreasing average cow somatic cell count. No other associations were identified between use of a selective CM treatment protocol and farm characteristics (herd size, CM incidence, province, milking system, and housing system). Three subsets of farmers making cow-level CM treatment decisions were identified using a cluster analysis approach: those who based decisions almost exclusively on severity of clinical signs, those who used various udder health indicators, and farmers who also incorporated more general cow information such as production, age, and genetics. When somatic cell count was considered, the median threshold used for treating was >300,000 cells/mL at the last Dairy Herd Improvement test. Various thresholds were present among those considering CM case history. Veterinary laboratories were most frequently used for bacteriological testing. Test results were used to start, change, and stop treatments. Regardless of protocol, reasons for antimicrobial treatment withheld included cow being on a cull list, having a chronic intramammary infection, or being at end of lactation (i.e., close to dry off). If clinical signs persisted after treatment, farmers indicated that they would ask veterinarians for advice, stop treatment, or continue with the same or different antibiotics. Results of this study can be used to design interventions targeting judicious mastitis-related antimicrobial use, and aid discussions between veterinarians and dairy producers regarding CM-related antimicrobial use.

Associations of cow- and herd-level factors during the dry period with indicators of udder health in early-lactation cows milked by automated milking systems

This observational study aimed to determine the association of cow-level factors and herd-level housing and management practices during the dry period with indicators of udder health in early-lactation cows in automated milking system (AMS) herds. Data were collected from 166 commercial AMS dairy farms (mean ± standard deviation = 116 ± 111 milking cows; range = 39 to 1,200) across Canada between October 2018 and September 2020. Information on herd demographics, housing, and management practices was obtained on each farm using 2 surveys. On each farm, we selected all cows that had available Dairy Herd Improvement (DHI) somatic cell count (SCC) data for their last milk test before dry-off (>250 d in milk) and their first milk test after calving (5-45 d in milk). Data from 14,007 cows were included after excluding cows with a dry period of <30 d and >120 d. Using the SCC data, we calculated for each cow the somatic cell score (SCS) for the last milk test before dry-off (PreSCS) and the first milk test after calving (PostSCS), which we then averaged per herd at a test-day level. Intramammary infection (IMI) was estimated using cow SCC data. Each cow was classified as not infected (SCC <200,000 cells/mL) or infected (SCC ≥200,000 cells/mL) at her last milk test before dry-off and her first milk test after calving. Based on this classification, cows were further categorized as never infected, always infected, new IMI, or cured IMI. At the cow level, a higher PostSCS was associated with longer dry periods. The odds of having a new IMI were higher for cows of higher parity and that had lower 305-d milk yield before dry-off. Cows with lower parity were more likely to cure an IMI. At the herd level, a higher 305-d milk yield before dry-off was associated with a lower incidence of new IMI and a higher incidence of cured IMI. Separating cows into a different pen as preparation for dry-off tended to be associated with a lower PostSCS and incidence of new IMI. At dry-off, herds that used teat sealants and blanket antibiotic dry cow therapy also had lower PostSCS. During the dry period, housing cows in different groups was associated with a higher PostSCS and a lower incidence of cured IMI, while housing cows in both pack pens and stalls compared with only pack pens was associated with a lower incidence of new IMI. Finally, placing cows onto the AMS to be milked one or more days after calving tended to be associated with a lower PostSCS compared with placing them in the AMS within the first day postpartum. In summary, indicators of udder health in early-lactation cows in AMS herds were associated with several cow-level factors and herd-level housing and management practices before dry-off, at dry-off, during the dry period, and at the beginning of lactation. Thus, if some of the associations identified are causal, AMS producers may be able to improve udder health through modifications of housing and management practices.

CSN1S1 and LALBA Polymorphisms and Other Factors Influencing Yield, Composition, Somatic Cell Score, and Technological Properties of Cow's Milk.

We evaluated the influence of CSN1S1 and LALBA polymorphisms on cow's milk yield and quality. The analysis was done on Czech Simmental and Holstein cows. Non-genetic factors were included as well. CSN1S1 did not influence the milk performance in the first lactation. In the second lactation, cows with the BB genotype had significantly higher milk, protein, and fat yields than BC. The differences between LALBA genotypes were non-significant in the first lactation, while in the second lactation, the fat percentage was significantly higher in BB than in AB. The farm significantly influenced milk, protein, and fat yields in both the first and second lactations and fat percentage in the first lactation. The effect of CSN1S1 and LALBA genotypes on the milk technological quality was non-significant. Breed did not influence any of the evaluated technological traits and SCS. The ethanol test was not influenced by farm, season, lactation phase, protein percentage, breed, or non-fat solids percentage. Farm, season, and protein percentage significantly influenced milk fermentation ability, renneting, and SCS. The lactose content is a good indicator of udder health.

Status of udder health performance indicators and implementation of on farm monitoring on German dairy cow farms: results from a large scale cross-sectional study.

Regional benchmarking data enables farmers to compare their animal health situation to that of other herds and identify areas with improvement potential. For the udder health status of German dairy cow farms, such data were incomplete. Therefore, the aim of this study was (1) to describe the incidence of clinical mastitis (CM), (2) to describe cell count based udder health indicators [annual mean test day average of the proportion of animals without indication of mastitis (aWIM), new infection risk during lactation (aNIR), and proportion of cows with low chance of cure (aLCC); heifer mastitis rate (HM)] and their seasonal variation, and (3) to evaluate the level of implementation of selected measures of mastitis monitoring. Herds in three German regions (North: n = 253; East: n = 252, South: n = 260) with different production conditions were visited. Data on CM incidence and measures of mastitis monitoring were collected via structured questionnaire-based interviews. Additionally, dairy herd improvement (DHI) test day data from the 365 days preceding the interview were obtained. The median (Q0.1, Q0.9) farmer reported incidence of mild CM was 14.8% (3.5, 30.8%) in North, 16.2% (1.9, 50.4%) in East, and 11.8% (0.0, 30.7%) in South. For severe CM the reported incidence was 4.0% (0.0, 12.2%), 2.0% (0.0, 10.8%), and 2.6% (0.0, 11.0%) for North, East, and South, respectively. The median aWIM was 60.7% (53.4, 68.1%), 59.0% (49.7, 65.4%), and 60.2% (51.5, 67.8%), whereas the median aNIR was 17.1% (13.6, 21.6%), 19.9% (16.2, 24.9%), and 18.3% (14.4, 22.0%) in North, East, and South, respectively with large seasonal variations. Median aLCC was ≤1.1% (≤ 0.7%, ≤ 1.8%) in all regions and HM was 28.4% (19.7, 37.2%), 35.7% (26.7, 44.2%), and 23.5% (13.1, 35.9%), in North, East and South, respectively. Participation in a DHI testing program (N: 95.7%, E: 98.8%, S: 89.2%) and premilking (N: 91.1%, E: 93.7%, S: 90.2%) were widely used. Several aspects of udder health monitoring, including exact documentation of CM cases, regular microbiological analysis of milk samples and the use of a veterinary herd health consultancy service were not applied on many farms. The results of this study can be used by dairy farmers and their advisors as benchmarks for the assessment of the udder health situation in their herds.

Physiological Aspects of Milk Somatic Cell Count in Small Ruminants—A Review

The aim of this review was to focus on the physiological aspects of milk somatic cell count (SCC) in small ruminants (SM). The SCC is an important component naturally present in milk and is generally used as an indicator of milk quality and udder health in milk producing ruminants. SCC contains the following cells: polymorphonuclear neutrophils (PMN), macrophages, lymphocytes, and many milk epithelial (MEC) cells, cell fragments, and cytoplasmic particles/vesicles. PMN (40–80%) represent the major cell type in milk in healthy uninfected goats, whereas the macrophages (45–88%) are the major cell type in sheep’s milk. However, dairy goats and sheep have an apocrine secretory system that produces cytoplasmic cellular particles/vesicles and large numbers of cell fragments, resulting in the physiological SCC limit being exceeded. It is obvious that the SCC level in milk of SM can be affected by various influencing factors, such as milk fraction, breed, stage of lactation, parity, type of birth, milking system, and others. An increase in the SCC above the physiological level not only indicates an udder or general health problem but reduces milk production, changes the milk composition, and hence affects milk processing. Moreover, the milking machine plays an important role in maintaining udder health in SM and stable SCC at physiological levels in the milk obtained. So far, there are no healthy or pathological physiological SCC levels defined in SM milk. Furthermore, a differential cell count (DCC) or even a high resolution DCC (HRDCC), which were recently developed for cattle milk, could also help in SM to gain deeper insight into the immunology of the mammary gland and find biomarkers to assess udder health. In conclusion, SCC is an indication of udder health or exposure of the udder to infectious agents or mechanical stress and should therefore always be considered a warning sign.

Prediction of fresh and ripened cheese yield using detailed milk composition and udder health indicators from individual Brown Swiss cows.

The composition of raw milk is of major importance for dairy products, especially fat, protein, and casein (CN) contents, which are used worldwide in breeding programs for dairy species because of their role in human nutrition and in determining cheese yield (%CY). The aim of the study was to develop formulas based on detailed milk composition to disentangle the role of each milk component on %CY traits. To this end, 1,271 individual milk samples (1.5 L/cow) from Brown Swiss cows were processed according to a laboratory model cheese-making procedure. Fresh %CY (%CYCURD), total solids and water retained in the fresh cheese (%CYSOLIDS and %CYWATER), and 60-days ripened cheese (%CYRIPENED) were the reference traits and were used as response variables. Training-testing linear regression modeling was performed: 80% of observations were randomly assigned to the training set, 20% to the validation set, and the procedure was repeated 10 times. Four groups of predictive equations were identified, in which different combinations of predictors were tested separately to predict %CY traits: (i) basic composition, i.e., fat, protein, and CN, tested individually and in combination; (ii) udder health indicators (UHI), i.e., fat + protein or CN + lactose and/or somatic cell score (SCS); (iii) detailed protein profile, i.e., fat + protein fractions [CN fractions, whey proteins, and nonprotein nitrogen (NPN) compounds]; (iv) detailed protein profile + UHI, i.e., fat + protein fractions + NPN compounds and/or UHI. Aside from the positive effect of fat, protein, and total casein on %CY, our results allowed us to disentangle the role of each casein fraction and whey protein, confirming the central role of β-CN and κ-CN, but also showing α-lactalbumin (α-LA) to have a favorable effect, and β-lactoglobulin (β-LG) a negative effect. Replacing protein or casein with individual milk protein and NPN fractions in the statistical models appreciably increased the validation accuracy of the equations. The cheese industry would benefit from an improvement, through genetic selection, of traits related to cheese yield and this study offers new insights into the quantification of the influence of milk components in composite selection indices with the aim of directly enhancing cheese production.

Predictive formulas for different measures of cheese yield using milk composition from individual goat samples

The objective of this study was to develop formulas based on milk composition of individual goat samples for predicting cheese yield (%CY) traits (fresh curd, milk solids, and water retained in the curd). The specific aims were to assess and quantify (1) the contribution of major milk components (fat, protein, and casein) and udder health indicators (lactose, somatic cell count, pH, and bacterial count) on %CY traits (fresh curd, milk solids, and water retained in the curd); (2) the cheese-making method; and (3) goat breed effects on prediction accuracy of the %CY formulas. The %CY traits were analyzed in duplicate from 600 goats, using an individual laboratory cheese-making procedure (9-MilCA method; 9 mL of milk per observation) for a total of 1,200 observations. Goats were reared in 36 herds and belonged to 6 breeds (Saanen, Murciano-Granadina, Camosciata delle Alpi, Maltese, Sarda, and Sarda Primitiva). Fresh %CY (%CYCURD), total solids (%CYSOLIDS), and water retained (%CYWATER) in the curd were used as response variables. Single and multiple linear regression models were tested via different combinations of standard milk components (fat, protein, casein) and indirect udder health indicators (UHI; lactose, somatic cell count, pH, and bacterial count). The 2 %CY observations within animal were averaged, and a cross-validation (CrV) scheme was adopted, in which 80% of observations were randomly assigned to the calibration (CAL) set and 20% to the validation (VAL) set. The procedure was repeated 10 times to account for sampling variability. Further, the model presenting the best prediction accuracy in CrV (i.e., comprehensive formula) was used in a secondary analysis to assess the accuracy of the %CY predictive formulas as part of the laboratory cheese-making procedure (within-animal validation, WAV), in which the first %CY observation within animal was assigned to CAL, and the second to the VAL set. Finally, a stratified CrV (SCrV) was adopted to assess the %CY traits prediction accuracy across goat breeds, again using the best model, in which 5 breeds were included in CAL and the remaining one in the VAL set. Fitting statistics of the formulas were assessed by coefficient of determination of validation (R2VAL) and the root mean square error of validation (RMSEVAL). In CrV, the formula with the best prediction accuracy for all %CY traits included fat, casein, and UHI (R2VAL = 0.65, 0.96, and 0.23 for %CYCURD, %CYSOLIDS, and %CYWATER, respectively). The WAV procedure showed R2VAL higher than those obtained in CrV, evidencing a low effect of the 9-MilCA method and, indirectly, its high repeatability. In the SCrV, large differences for %CYCURD and %CYWATER among breeds evidenced that the breed is a fundamental factor to consider in %CY predictive formulas. These results may be useful to monitor milk composition and quantify the influence of milk traits in the composite selection indices of specific breeds, and for the direct genetic improvement of cheese production.

Quarter-level analyses of the associations among subclinical intramammary infection and milk quality, udder health, and cheesemaking traits in Holstein cows

In this study, we investigated associations among subclinical intra-mammary infection (IMI) and quarter-level milk composition, udder health indicators, and cheesemaking traits. The dataset included records from 450 Holstein cows belonging to three dairy herds. After an initial screening (T0) to identify animals infected by Streptococcus agalactiae, Streptococcus uberis, Staphylococcus aureus, and Prototheca spp., 613 quarter milk samples for 2 different sampling times (T1 and T2, 1 mo after T1) were used for analysis. Milk traits were analyzed using a hierarchical linear mixed model including the effects of days in milk, parity and herd, and bacteriological and inflammatory category [culture negative with somatic cell count (SCC) <200,000 cells/mL; culture negative with SCC ≥200,000 cells/mL; or culture positive]. All udder health indicators were associated with increased SCC and IMI at both sampling times. The largest effects were detected at T2 for milk lactose (-7% and -5%) and milk conductivity (+9% and +8%). In contrast, the increase in differential SCC (DSCC) in samples with elevated SCC was larger at T1 (+17%). Culture-negative samples with SCC ≥200,000 cells/mL had the highest SCC and greatest numbers of polymorphonuclear-neutrophils-lymphocytes and macrophages at both T1 and T2. Regarding milk cheesemaking ability, samples with elevated SCC showed the worst pattern of curd firmness at T1 and T2. At T2, increased SCC and IMI induced large decreases in recoveries of nutrients into the curd, in particular recovered protein (-14% and -16%) and recovered fat (-12% and -14%). Different behaviors were observed between Strep. agalactiae and Prototheca spp., especially at T2. In particular, samples that were positive for Strep. agalactiae had higher proportions of DSCC (+19%) compared with negative samples with low SCC, whereas samples that were positive for Prototheca spp. had lower DSCC (-11%). Intramammary infection with Prototheca spp. increased milk pH compared with culture-negative samples (+3%) and negative samples that had increased SCC (+2%). The greatest impairment in curd firmness at 30 min from rennet addition was observed for samples that were positive for Prototheca spp. (-99% compared with negative samples, and -98% compared with negative samples with high SCC). These results suggest that IMI caused by Prototheca spp. have detrimental effects on milk technological traits that deserve further investigation of the mechanisms underlying animals' responses to infection.

Environmental factors affecting milk composition in Holstein cattle breed

The incidence of mastitis in dairy herds is one of the main difficulties faced by dairy farmers, with a negative effect on the productivity of the herd and the welfare of the animals. Somatic cell count in milk is an indicator of udder health and frequency of clinical and subclinical mastitis incidence in dairy herds, and it is also often used to determine quality payments to dairy producers. Milk urea can be an indicator of the nutritional status of the dairy cows. The interpretation of these parameters assists in making important management decisions with regards to the health status and nutrition of dairy cattle. The objective of this study was to identify and evaluate environmental factors (farm, season, parity and stage of lactation) which affect the milk production and composition of Holstein breed, using field data. The presented research included 25,460 individual milk samples which were analysed as part of the Dairy Herd Improvement program, from 11 Holstein dairy farms in the region of Vojvodina, Northern Serbia. Analyses of raw milk samples were carried out on the FOSS instruments - CombiFossTMFT+, a combination instrument consisting of the MilcoScanTMFT+ and the FossomaticTMFC. Statistical data processing was carried out by applying General Linear Model procedure, Statistics 13. Farm, season of milk control, parity and stage of lactation were included in the models as fixed effects. Significant differences in milk urea concentrations and somatic cell count were observed between farms (P&lt;0.01), seasons (P&lt;0.01), parity (P&lt;0.01) and stage of lactation (P&lt;0.01). Results showed that there were a highly significant (P&lt;0.01) positive relationships between milk urea (MU) concentration and milk yield, MU and milk fat content, and between MU and solids non-fat (SNF), also between somatic cell count (SCC) and milk fat content, between SCC and protein content, as well as in between SCC and SNF. Highly significant negative relationships were found between milk urea and protein content and SCC, and between SCC and daily milk yield and lactose content. Proper analyses and interpretation of obtained results of milk samples obtained within the Dairy Herd Improvement program could contribute to better health management on the farms and it could have a positive impact on composition and nutritional value of milk, as well as on milk safety. It would be important to carry out further research in order to facilitate the detection of subclinical mastitis with MU as a potential indicator.

Levels of milk glycosaminoglycans in Holstein cows with clinical and subclinical mastitis

Mastitis is an inflammation of the mammary gland whose diagnosis is based on clinical criteria and the determination of inflammatory markers. The disease is mainly caused by bacteria causing intra-mammary infections. The etiological agent/s are detected by milk culture or by molecular biology approaches. However, these methodologies are unpractical for on-farm diagnosis, where rapid decisions must be made when infected animals are detected. For this reason, the levels of glycosaminoglycans (GAGs) in milk were evaluated as a tool for the diagnosis of mastitis. GAGs levels were determined by the dimethylmethylene blue assay (DMMB) method in milk from healthy animals and in milk from animals with mastitis caused by different etiological agents. Low levels of GAGs (1.93 ± 0.40 mg) were found in milk samples obtained from healthy animals (H), while intermediate (2.4 ± 0.61 mg) and high levels (2.87 ± 0.7 mg) were found in milk obtained from cows with subclinical (SM) and clinical mastitis (CM), respectively. Statistically significant differences were found between the SM and H groups (p < 0.001) and between CM and the other groups (p < 0.0001). The highest levels of GAGs were detected in the CM group, in which coagulase negative Staphylococcus (CNS) and Streptococcus spp. were isolated. Animals infected by S. aureus had the highest levels of GAGs in the SM group. The sensitivity (Se) and specificity (Sp) for the assessment of GAGs levels for CM diagnosis were 89.7 % and 81.6 %, respectively. For the diagnosis of SM, Se and Sp were 73.2 % and 95 %, respectively. The area under the curve for the diagnosis of CM was 0.902 (p < 0.0001), while that for SM was 0.833 (p < 0.0001). Results suggest that the measurement of GAGs levels in milk can be used as an indicator of udder health in dairy cattle, since increased levels of GAGs were associated with mastitis.

Detection of subclinical mastitis in camels (Camelus dromedarius) using somatic cell count, N-acetyl-β-D-glucosaminidase and lactate dehydrogenase activity

Clinical and subclinical mastitis (SCM), mostly related to intramammary infection (IMI), is prevalent in pastoralist camel herds. An IMI has implications for public and animal health as well as for household economy. As bacterial culturing is expensive, time consuming and impractical in a pastoralist setting, other early detection methods for SCM in camels need to be investigated. Somatic cell count (SCC) is the standard for detecting SCM in cattle. The udder health indicators of N-acetyl-β-D-glucosaminidase (NAGase) and lactate dehydrogenase (LDH) activity are useful as diagnostic markers in cow, sheep and goat milk; they could be of potential use in camel milk production. The aim of this study was to improve the understanding of SCM in camels, and specifically to assess SCC, and NAGase- and LDH activity in camel milk. In addition, potential associations between SCM (defined by a California Mastitis Test (CMT) score ≥3 and no signs of clinical mastitis) and SCC, NAGase- and LDH activity were investigated. In total, 40 healthy camels without clinical mastitis were sampled in four herds in Kenya. Quarter milk samples were collected aseptically and screened using CMT. SCC was analysed using a direct cell counter (DCC, DeLaval), and NAGase and LDH activity was analysed using kinetic fluorometric measures. In total, 116 milk samples were tested with CMT and analysed for SCC. Of these, 88 were analysed further for NAGase and LDH. The median SCC was 151,000 cells/mL (IQR: 49,500–709,000 cells/mL), and median NAGase and LDH were 18.5 U/l (IQR:14.8–24.0 U/l) and 12.0 U/l (IQR: 8.5–16.2 U/l) respectively. All inflammatory markers (SCC, NAGase, LDH) were significantly associated with SCM (P < 0.001). In conclusion, SCC, NAGase and LDH are potential inflammatory indicators in camel milk that can be used for detection of udder quarters with SCM.

Genetic parameters of differential somatic cell count, milk composition, and cheese-making traits measured and predicted using spectral data in Holstein cows

Mastitis is one of the most prevalent diseases in dairy cattle and is the cause of considerable economic losses. Alongside somatic cell count (SCC), differential somatic cell count (DSCC) has been recently introduced as a new indicator of intramammary infection. The DSCC is expressed as a count or a proportion (%) of polymorphonuclear neutrophils plus lymphocytes (PMN-LYM) in milk somatic cells. These numbers are complemented to total somatic cell count or to 100 by macrophages (MAC). The aim of this study was to investigate the genetic variation and heritability of DSCC, and its correlation with milk composition, udder health indicators, milk composition, and technological traits in Holstein cattle. Data used in the analysis consisted in single test-day records from 2,488 Holstein cows reared in 36 herds located in northern Italy. Fourier-transform infrared (FTIR) spectroscopy was used to predict missing information for some milk coagulation and cheese-making traits, to increase sample size and improve estimation of the genetic parameters. Bayesian animal models were implemented via Gibbs sampling. Marginal posterior means of the heritability estimates were 0.13 for somatic cell score (SCS); 0.11 for DSCC, MAC proportion, and MAC count; and 0.10 for PMN-LYM count. Posterior means of additive genetic correlations between SCS and milk composition and udder health were low to moderate and unfavorable. All the relevant genetic correlations between the SCC traits considered and the milk traits (composition, coagulation, cheese yield and nutrients recovery) were unfavorable. The SCS showed genetic correlations of -0.30 with the milk protein proportion, -0.56 with the lactose proportion and -0.52 with the casein index. In the case of milk technological traits, SCS showed genetic correlations of 0.38 with curd firming rate (k20), 0.45 with rennet coagulation time estimated using the curd firming over time equation (RCTeq), -0.39 with asymptotic potential curd firmness, -0.26 with maximum curd firmness (CFmax), and of -0.31 with protein recovery in the curd. Differential somatic cell count expressed as proportion was correlated with SCS (0.60) but had only 2 moderate genetic correlations with milk traits: with lactose (-0.32) and CFmax (-0.33). The SCS was highly correlated with the log PMN-LYM count (0.79) and with the log MAC count (0.69). The 2 latter traits were correlated with several milk traits: fat (-0.38 and -0.43 with PMN-LYM and MAC counts, respectively), lactose percentage (-0.40 and -0.46), RCTeq (0.53 and 0.41), tmax (0.38 and 0.48). Log MAC count was correlated with k20 (+0.34), and log PMN-LYM count was correlated with CFmax (-0.26) and weight of water curd as percentage of weight of milk processed (-0.26). The results obtained offer new insights into the relationships between the indicators of udder health and the milk technological traits in Holstein cows.