Total bacterial count and somatic cell count in bulk and individual goat milk around kidding: two longitudinal observational studies.

Total bacterial count (TBC) and somatic cell count (SCC) are important quality parameters in goat milk. Exceeding the bulk milk TBC (BMTBC) thresholds leads to price penalties for Dutch dairy goat farmers. Controlling these milk quality parameters can be challenging, especially around kidding. First, we describe the variation and the peaks around kidding of TBC and SCC in census data on Dutch bulk milk over the last 22 years. Second, to explore causes of these elevations, we studied the variation of TBC and SCC in individual goat milk from 3 weeks before to 5 weeks after kidding and their association with systemic response markers interferon-γ (IFN-γ), calprotectin, ß-hydroxybutyrate (BHB), body condition score (BCS) and fecal consistency. We visited 4 Dutch dairy goat farms weekly for 10 to 16 weeks around kidding. Some of the goats had been dried off, other goats were milked continuously throughout pregnancy. A total of 1,886 milk samples from 141 goats were collected for automated flowcytometric quantification of TBC and SCC measurement. IFN-γ, calprotectin and BHB were determined twice in blood of the same goats, most samples were collected after kidding. The BCS and fecal consistency were scored visually before and after kidding. We found a strong correlation between TBC and SCC (Spearman's rho = 0.87) around kidding. Furthermore, in the third week before kidding, the average TBC (5.67 log10 cfu/mL) and SCC (6.70 log10 cells/mL) were significantly higher compared with the fifth week after kidding, where the average TBC decreased to 4.20 log10 cfu/mL and the average SCC decreased to 5.92 log10 cells/mL. In multivariable linear regression models, farm and stage of lactation were significantly associated with TBC and SCC, but none of the systemic response markers correlated with TBC or SCC. In conclusion, TBC and SCC in dairy goats were high in late lactation and decreased shortly after parturition. For SCC, the dilution effect might have caused the decrease, but this was not plausible for TBC. Moreover, the excretion of bacteria and cells in goat milk was not associated with the selected systemic response markers that were chosen as a read out for general immunity status, intestinal health and metabolic diseases. Therefore, we assume that the TBC increase before kidding and the decrease after parturition is caused by other systemic, possibly hormonal, processes. To reduce BMTBC and BMSCC, it would be advisable to keep milk of goats with highest numbers of bacteria and cells in their milk out of the bulk milk during end lactation. Further studies are needed to investigate the effects of withholding this end lactation milk from the bulk tank.

A New Method to Detect Buffalo Mastitis Using Udder Ultrasonography Based on Deep Learning Network.

Mastitis is one of the most predominant diseases with a negative impact on ranch products worldwide. It reduces milk production, damages milk quality, increases treatment costs, and even leads to the premature elimination of animals. In addition, failure to take effective measures in time will lead to widespread disease. The key to reducing the losses caused by mastitis lies in the early detection of the disease. The application of deep learning with powerful feature extraction capability in the medical field is receiving increasing attention. The main purpose of this study was to establish a deep learning network for buffalo quarter-level mastitis detection based on 3054 ultrasound images of udders from 271 buffaloes. Two data sets were generated with thresholds of somatic cell count (SCC) set as 2 × 105 cells/mL and 4 × 105 cells/mL, respectively. The udders with SCCs less than the threshold value were defined as healthy udders, and otherwise as mastitis-stricken udders. A total of 3054 udder ultrasound images were randomly divided into a training set (70%), a validation set (15%), and a test set (15%). We used the EfficientNet_b3 model with powerful learning capabilities in combination with the convolutional block attention module (CBAM) to train the mastitis detection model. To solve the problem of sample category imbalance, the PolyLoss module was used as the loss function. The training set and validation set were used to develop the mastitis detection model, and the test set was used to evaluate the network's performance. The results showed that, when the SCC threshold was 2 × 105 cells/mL, our established network exhibited an accuracy of 70.02%, a specificity of 77.93%, a sensitivity of 63.11%, and an area under the receiver operating characteristics curve (AUC) of 0.77 on the test set. The classification effect of the model was better when the SCC threshold was 4 × 105 cells/mL than when the SCC threshold was 2 × 105 cells/mL. Therefore, when SCC ≥ 4 × 105 cells/mL was defined as mastitis, our established deep neural network was determined as the most suitable model for farm on-site mastitis detection, and this network model exhibited an accuracy of 75.93%, a specificity of 80.23%, a sensitivity of 70.35%, and AUC 0.83 on the test set. This study established a 1/4 level mastitis detection model which provides a theoretical basis for mastitis detection in buffaloes mostly raised by small farmers lacking mastitis diagnostic conditions in developing countries.

Concentrations of antimicrobial components in milk of Japanese Black cows and their differences from dairy cows.

The components of milk from beef cows remain to be elucidated. This study examined the differences in the antimicrobial components of milk between dairy and beef cows. Quarter milk was collected from both Japanese Black (beef type) and Holstein (dairy type) cows to compare the concentrations of antimicrobial components. The concentration of lingual antimicrobial peptide (LAP) was higher, whereas that of the other antimicrobial components (lactoferrin, S100A7, and S100A8) was lower in beef cows than in dairy cows. Overall, these results indicate that the differences in antimicrobial components between beef and dairy cows may be associated with the difference in the prevalence of mastitis between them.

Comparisons of recycled manure solids and wood shavings/sawdust as bedding material -implications for animal welfare, herd health, milk quality and bedding costs in Swedish dairy herds.

Increasing shortages and costs of common bedding materials have led dairy farmers in Sweden to consider using recycled manure solids (RMS), which are readily available and low-cost, as an alternative bedding material. The main risks are effects on udder health and milk quality, but RMS could also affect animal welfare and claw health. The advantages and disadvantages of using RMS bedding have not been fully investigated, and findings in other countries cannot be directly applied to Swedish conditions and climate. This observational cross-sectional study investigated the use of RMS as bedding regarding associations with certain aspects of animal welfare, herd health, milk quality, and bedding costs in Swedish dairy herds. Thirty-four dairy farms using RMS or wood shavings/sawdust (each n = 17) were compared. Each farm was visited 2 times during the housing period 2020-2021, once in October-December and once in March-May. Dairy barns were observed, animal welfare was assessed, and free-stall dimensions were measured. Farm owners were interviewed about housing system characteristics, herd performance, and herd management. Data on milk production and herd health were obtained from the Swedish official milk recording scheme for the indoor period October-March. The prevalence of claw disorders and abnormal claw conformation were collected from the national claw health database for the period, October-May. On each farm visit, composite samples of unused bedding outside the barn and used bedding material from the free stalls, respectively, were taken for total bacterial count and dry matter analysis. Samples of bulk tank milk for determination of total bacterial count were taken in connection to the visits. In addition, samples of unused and used bedding material and manure from alleys for analysis of 3 Treponema species associated with digital dermatitis (DD) were gathered and analyzed. Total bacterial count was significantly higher in unused (8.50 log10 cfu/g) and used RMS bedding (9.75 log10 cfu/g) than in wood shavings/sawdust (used 4.74; unused 8.63 log10 cfu/g), but there were no significant differences in bulk milk total bacterial count (median 4.07 versus 3.89 log10 cfu/mL) or somatic cell count (median 243,800 versus 229,200 cells /mL). The aspects of animal welfare that were assessed did not differ significantly between the 2 bedding systems, while the prevalence of total claw disorders (25.9 versus 38.0% of trimmed cows), dermatitis (6.9 versus 16.2% of trimmed cows) and sole ulcers (2.0 versus 4.0% of trimmed cows) were significantly lower in the RMS herds. Treponema spp. were not detected in unused RMS material, but all RMS herds had presence of DD recorded at foot trimming. An economic assessment based on the interview results and price level from winter 2021 revealed that the costs of RMS bedding varied with amount of RMS produced. Thus, RMS is a potential alternative bedding material for dairy cows in Sweden and can be a profitable option for large dairy herds. However, the high level of total bacteria in the material requires attention to bedding and milking routines as well as regular monitoring of herd health.

Performance comparison of machine learning models used for predicting subclinical mastitis in dairy cows: bagging, boosting, stacking and super-learner ensembles versus single machine learning models.

Mastitis has a substantial impact on the dairy industry across the world, causing dairy producers to suffer losses due to the reduced quality and quantity of produced milk. A further problem, related to this issue, is the excessive use of antibiotics that leads to the development of resistance in different bacterial strains. The growing consumer awareness oriented toward food safety and rational use of antibiotics has promoted the search for new methods of early identification of cows that may be at risk of developing the disease. Subclinical mastitis does not cause any visible changes to the udder or milk, and therefore it is more difficult to detect than clinical mastitis. The collection of large amounts of data related to milk performance of cows allows using machine learning (ML) methods to build models that could be used for classifying cows into healthy and at risk of subclinical mastitis. The data used for the purpose of this study included information from routine milk recording procedures. The data set consisted of 19,856 records of 2,227 Polish Holstein-Friesian cows from 3 herds. The authors decided to use the approach of building ensemble ML models, in particular bagging, boosting, stacking and super learner models, and comparing them for accuracy of identification of disease-affected cows against single ML models based on the Support Vector Machines, Logistic Regression, Gaussian Naïve Bayes, k-Nearest Neighbors and Decision Tree algorithms. The models were trained and evaluated based on the information recorded for herd 1 and using an 80:20 train-test split ratio according to animal ID (to avoid data leakage). The information recorded for herds 2 and 3 was only used to evaluate on unseen data models developed using the herd 1 data set. Among the single ML models, the Support Vector Machines model was found to be the most accurate in predicting subclinical mastitis at subsequent test-day when used both for the training set (mean F1-score of 0.760) and the testing sets containing data for herds 1, 2 and 3 (F1-score of 0.778, 0.790 and 0.741 respectively). The Gradient Boosting model was found to be the best performing model among the ensemble ML models (F1-score of 0.762, 0.779, 0.791 and 0.723 for the training set and the testing sets respectively). The super learner model, featuring the most advanced design and Logistic Regression in the meta layer, achieved the highest mean F1-score of 0.775 during the cross-validation, however, it was characterized by a slightly worse prediction accuracy of the testing sets (mean F1-score of 0.768, 0.790 and 0.693 for herds 1, 2 and 3 respectively). The study findings confirm the promising role of ensemble ML methods that were found to be slightly superior with respect to most of the single ML models.

Economic losses caused by mastitis and the influence of climate variation on the occurrence of the disease in a dairy cattle farm in southern Brazil.

This study evaluated the economic impacts caused by mastitis in a small dairy farm with similar characteristics and production to most dairy farms in southern Brazil and investigated if climatic variations influenced mastitis occurrence in the region. A farm with, on average, 45 lactating Holstein cattle was monitored from November 2021 to October 2022, and data on mastitis cases, bulk tank milk somatic cell count, animal treatment costs, milk production, animal disposal costs, and production losses were collected. Monthly averages of temperature, relative humidity (RH), and rainfall in the region were obtained. The greatest loss was related to the drop in milk production, resulting in 63.8% of total losses, followed by animal disposal (29.5%), milk disposal (4.6%), and treating animals with mastitis (2.0%), totaling a 10.6% reduction in the annual gross income. There were negative correlations between the clinical mastitis rate and monthly RH and between subclinical mastitis and temperature; the occurrence of subclinical mastitis and average RH were positively correlated. Our findings showed that mastitis negatively impacted the economy and that climate influenced mastitis occurrence.

Effects of Elevating Zinc Supplementation on the Health and Production Parameters of High-Producing Dairy Cows.

This study's objective was to determine the effects of increasing the dietary added zinc (Zn) on the milk production, milk somatic cell count (SCC), and immunoglobulin and antioxidant marker concentrations in the blood of dairy cows. Twelve Holstein cows (67 ± 2.5 days in milk) were assigned randomly to (1) a diet containing Zn-methionine at 76 mg/kg of DM (CTL) or (2) CTL top-dressed with about 21 mg/kg of DM extra Zn-methionine (+Zn) for 70 d. The concentrations of reduced (GSH) and oxidized (GSSG) glutathione, malondialdehyde (MDA), catalase (CAT), superoxide dismutase (SOD), and immunoglobulins in the blood were measured on d 0, 35, and 70. Compared to CTL, +Zn decreased the dry matter intake (DMI) throughout the trial and the milk yield (MY) during the first phase of feeding (0-35 d). It, however, increased the milk yield during the last phase (36-70 d). The +Zn tended to have lower and greater milk protein yields than CTL during the first and last feeding phases, respectively. The +Zn tended to decrease the SCC and was associated with lower plasma GSH: GSSG and lower serum SOD concentrations relative to CTL. The +Zn did not affect the immunoglobulins, MDA, or CAT. Despite the early DMI and MY reduction, the prolonged Zn-methionine supplementation at about 100 mg/kg of DM improved the milk yield, possibly as a result of the improved udder health of dairy cows.

Genetic parameters for novel mastitis traits defined by combining test-day somatic cell score and differential somatic cell count in the first lactation of Japanese Holsteins.

In this study, we aimed to improve current udder health genetic evaluations by addressing the limitations of monthly sampled somatic cell score (SCS) for distinguishing cows with robust innate immunity from those susceptible to chronic infections. The objectives were to (1) establish novel somatic cell traits by integrating SCS and the differential somatic cell count (DSCC), which represents the combined proportion of polymorphonuclear leukocytes and lymphocytes in somatic cells and (2) estimate genetic parameters for the new traits, including their daily heritability and genetic correlations with milk production traits and SCS, using a random regression test-day model (RRTDM). We derived 3 traits, namely ML_SCS_DSCC, SCS_4_DSCC_65_binary, and ML_SCS_DSCC_binary, by using milk loss estimates at corresponding SCS and DSCC levels, thresholds established in previous studies, and a threshold established from milk loss estimates, respectively. Data consisted of test-day records collected during January 2021 through March 2022 from 265 herds in Hokkaido, Japan. From these records, we extracted records between 7 to 305 d in milk (DIM) in the first lactation to fit the RRTDM. The model included the random effect of herd-test-day, the fixed effect of year-month, fixed lactation curves nested with calving age groups, and random regressions with Legendre polynomials of order 3 for additive genetic and permanent environmental effects. The analysis was performed using Gibbs sampling with Gibbsf90+ software. The averages (ranges) of daily heritability estimates over lactation were 0.086 (0.075 to 0.095) for SCS, 0.104 (0.073 to 0.127) for ML_SCS_DSCC, 0.137 (0.014 to 0.297) for SCS_4_DSCC_65_binary, and 0.138 (0.115 to 0.185) for ML_SCS_DSCC_binary; the heritability curve for SCS_4_DSCC_65_binary was erratic. Genetic correlations within the trait decreased as the DIM interval widened, especially for those integrating DSCC, indicating that these traits should be analyzed using RRTDM rather than repeatability models. The averages (ranges) of genetic correlations with milk yield over lactation were 0.01 (-0.22 to 0.28) for SCS, -0.05 (-0.40 to 0.13) for ML_SCS_DSCC, -0.08 (-0.17 to 0.09) for SCS_4_DSCC_65_binary, and -0.08 (-0.22 to 0.27) for ML_SCS_DSCC_binary. Compared with SCS, the newly defined traits exhibited slightly stronger negative genetic correlations with milk yield. Especially in late lactation stages, the genetic correlation between ML_SCS_DSCC and milk yield was significantly below zero, with a posterior median of -0.40. Furthermore, the new traits showed positive correlations with SCS, having estimates varying from 0.68 to 0.85 for ML_SCS_DSCC, 0.14 to 0.47 for SCS_4_DSCC_65_binary, and 0.61 to 0.66 for ML_SCS_DSCC_binary, depending on DIM. Considering that ML_SCS_DSCC and ML_SCS_DSCC_binary have relatively high heritability (compared with SCS) and favorable genetic correlations with milk production traits and SCS, their incorporation into breeding programs appears promising. Nevertheless, their genetic relationships with (sub)clinical mastitis require further investigation.

Pathogen and severity-dependent immune responses in bovine mastitis: highlight the dynamics of differential somatic cell count.

Immune responses in bovine clinical mastitis (CM) probably differ depending on the causative pathogen and disease severity. The observational study aimed to investigate whether both factors are associated with the dynamics of immune indicators, including somatic cell score (SCS), white blood cell count (WBC), serum albumin/globulin (A/G) ratio, and differential somatic cell count (DSCC). We collected blood and milk samples 0, 3, 5, 7, 14, and 21 days after CM occurred in 38 cows, and grouped the cases (n=49) by disease severity and pathogen. We analyzed data using a linear mixed model considering the effects of pathogens and severity, calculated estimated-marginal means for indicators at each time point, and compared the means between groups. The dynamics of WBC varied depending on both pathogen and severity. WBC changed drastically in either severe or coliform-caused CM, slightly elevated in streptococcal mastitis, but unchanged in staphylococcal mastitis. This possibly relates to the deficiency in innate immune response toward staphylococci. The A/G ratio also changed depending on severity, as it dropped sharply only in severe CM. We observed a non-linear relationship between DSCC and SCS, possibly due to mammary epithelial cells shedding in milk when CM occurred. When cows recovering from Streptococcus dysgalatiae mastitis, DSCC decreased while SCS remained high, suggesting a healing process requiring more macrophages. Our results demonstrate that both the severity and pathogen are associated with immune responses in CM, providing insights into mastitis pathogenesis.

Association between somatic cell count or morbidity of chronic subclinical mastitis and longevity in dairy herds in Eastern Hokkaido, Japan: a cross-sectional study.

An epidemiological analysis was conducted on production records in Hokkaido, Japan, to investigate the potential association between improved milk quality and longevity outcomes. The study found significant variations in herd somatic cell count levels and chronic subclinical mastitis morbidity based on geographical area and herd size. The analysis also revealed a positive correlation between herd somatic cell count and chronic subclinical mastitis morbidity. Although the hypothesis of a causal link between milk quality and longevity was examined, no such association was found. However, intensive assistance for identified high-risk areas and farms is expected to enhance overall milk quality.

Effect of intramammary infection and inflammation on milk protein profile assessed at the quarter level in Holstein cows.

In this study we wanted to investigate the associations between naturally occurring subclinical intramammary infection (IMI) caused by different etiological agents (i.e., Staphylococcus aureus, Streptococcus agalactiae, Streptococcus uberis, and Prototheca spp.), in combination with somatic cell count (SCC), on the detailed milk protein profile measured at the individual mammary gland quarter. An initial bacteriological screening (time 0; T0) conducted on individual composite milk from 450 Holstein cows reared in 3 herds, was performed to identify cows with subclinical IMI. We identified 78 infected animals which were followed up at the quarter level at 2 different sampling times: T1 and T2, 2 and 6 wk after T0, respectively. A total of 529 quarter samples belonging to the previously selected animals were collected at the 2 sampling points and analyzed with a reversed phase HPLC (RP-HPLC) validated method. Specifically, we identified and quantified 4 caseins (CN), namely αS1-CN, αS2-CN, κ-CN, and ß-CN, and 3 whey protein fractions, namely ß-lactoglobulin, α-lactalbumin, and lactoferrin (LF), which were later expressed both quantitatively (g/L) and qualitatively (as a percentage of the total milk nitrogen content, % N). Data were analyzed with a hierarchical linear mixed model with the following fixed effects: days in milk (DIM), parity, herd, SCC, bacteriological status (BACT), and the SCC × BACT interaction. The random effect of individual cow, nested within herd, DIM and parity was used as the error term for the latter effects. Both IMI (i.e., BACT) and SCC significantly reduced the proportion of ß-CN and αS1-CN, ascribed to the increased activity of both milk endogenous and microbial proteases. Less evident alterations were found for whey proteins, except for LF, which being a glycoprotein with direct and undirect antimicrobial activity, increased both with IMI and SCC, suggesting its involvement in the modulation of both the innate and adaptive immune response. Finally, increasing SCC in the positive samples was associated with a more marked reduction of total caseins at T1, and αS1-CN at T2, suggesting a synergic effect of infection and inflammation, more evident at high SCC. In conclusion, our work helps clarify the behavior of protein fractions at quarter level in animals having subclinical IMI. The inflammation status driven by the increase in SCC, rather the infection, was associated with the most significant changes, suggesting that the activity of endogenous proteolytic enzymes related to the onset of inflammation might have a pivotal role in directing the alteration of the milk protein profile.

Exploring the sources of variation of electrical conductivity and total and differential somatic cell count in Italian Mediterranean buffaloes.

In the buffalo dairy sector, a huge effort is still needed to improve mastitis prevention, detection, and management. Electrical conductivity (EC) and total somatic cell count (SCC) are well-known indirect indicators of mastitis. Differential somatic cell count (DSCC), which represents the proportion of neutrophils and lymphocytes on the total SCC, is instead a novel phenotype collected in the dairy cattle sector in the last lustrum. As little is known about this novel trait in dairy buffalo, in the present study we explored the nongenetic factors affecting DSCC, as well as EC and total somatic cell score (SCS), in the Italian Mediterranean buffalo. The data set used for the analysis included 14,571 test-day (TD) records of 1,501 animals from 6 herds, and climatic information of the sampling locations. The original data were filtered to exclude animals with less than 3 TD per lactation and, for the investigated traits, outliers beyond 4 standard deviations. In the statistical model we included the fixed effects of herd (6 classes), days in milk (DIM; 10 classes of 30 d, with the last being an open class until 360 d), parity (6 classes, from 1 to 6+), year-season of calving (11 classes, from summer 2019 to winter 2021/2022), year-season of sampling (9 classes, from spring 2020 to spring 2022), production level (4 classes based on quartiles of average milk yield by herd), and temperature-humidity index (THI; 4 classes based on quartiles, calculated using the average temperature and relative humidity of the 5 d before sampling). Average EC, SCS, and DSCC vary across herds. Considering DIM, greater EC values were observed at the beginning and the end of lactation; SCS was slightly lower, but DSCC was greater around the lactation peak. Increased EC, SCS, and DSCC levels with increasing parity were reported. Year-season calving and year-season sampling only slightly affected the variation of the investigated traits. Milk of high-producing buffaloes was characterized by lower EC and SCS mean values, nevertheless it had slightly greater DSCC percentages. Buffaloes grouped in the highest THI classes (classes 3 and 4) showed, on average, greater EC, SCS, and DSCC in comparison to the lower classes, especially to class 2. Results of the present study represent a preliminary as well as necessary step for the possible future inclusion of EC, SCS, or DSCC in breeding programs aimed to improve mastitis resistance in dairy buffaloes.

Novel insights into the associations between immune cell population distribution in mammary glands and milk minerals in Holstein cows.

Udder health has a crucial role in sustainable milk production, and various reports have pointed out that changes in udder condition seem to affect milk mineral content. The somatic cell count (SCC) is the most recognized indicator for the determination of udder health status. Recently, a new parameter, the differential somatic cell count (DSCC), has been proposed for a more detailed evaluation of intramammary infection patterns. Specifically, the DSCC is the combined proportions of polymorphonuclear neutrophils and lymphocytes (PMN-LYM) on the total SCC, with macrophages (MAC) representing the remainder proportion. In this study, we evaluated the association between DSCC in combination with SCC on a detailed milk mineral profile in 1,013 Holstein-Friesian cows reared in 5 herds. An inductively coupled plasma-optical emission spectrometry was used to quantify 32 milk mineral elements. Two different linear mixed models were fitted to explore the associations between the milk mineral elements and first, the DSCC combined with SCC, and second, DSCC expressed as the PMN-LYM and MAC counts, obtained by multiplying the proportion of PMN-LYM and MAC by SCC. We observed a significant positive association between SCC and milk Na, S, and Fe levels. Differential somatic cell count showed an opposite behavior to the one displayed by SCC, with a negative association with Na and positive association with K milk concentrations. When considering DSCC as count, Na and K showed contrasting behavior when associated with PMN-LYM or MAC counts, with decreasing of Na content and increasing K when associated with increasing PMN-LYM counts, and increasing Na and decreasing K when associated with increasing MAC count. These findings confirmed that an increase in SCC is associated with altered milk Na and K amounts. Moreover, MAC count seemed to mirror SCC patterns, with the worsening of inflammation. Differently, PMN-LYM count exhibited patterns of associations with milk Na and K contents attributable more to LYM than PMN, given the nonpathological condition of the majority of the investigated population. An interesting association was observed for milk S content, which increased with increasing of inflammatory conditions (i.e., increased SCC and MAC count) probably attributable to its relationship with milk proteins, especially whey proteins. Moreover, milk Fe content showed positive associations with the PMN-LYM population, highlighting its role in immune regulation during inflammation. Further studies including individuals with clinical condition are needed to achieve a comprehensive view of milk mineral behavior during udder health impairment.

Milk as diagnostic fluid for udder health management.

BACKGROUND: Mastitis is the major disease affecting milk production of dairy cattle, and milk is an obvious substrate for the detection of both the inflammation and its causative infectious agents at quarter, cow, or herd levels. In this review, we examine the use of milk to detect inflammation based on somatic cell count (SCC) and other biomarkers, and for the detection of mastitis pathogens through culture-based and culture-free methods. FINDINGS: The use of SCC at a cow or bulk milk level to guide udder health management in lactation is well-established, and SCC is increasingly used to guide selective dry cow treatment. Other markers of inflammation include electrical conductivity, which is used commercially, and markers of disease severity such as acute phase proteins but are not pathogen-specific. Some pathogen-specific markers based on humoral immune responses are available, but their value in udder health management is largely untested. Commercial pathogen detection is based on culture or polymerase chain reaction, with other tests, for example, loop-mediated isothermal amplification or 16S microbiome analysis still at the research or development stage. Matrix-assisted laser desorption ionisation time of flight (MALDI-ToF) is increasingly used for the identification of cultured organisms whilst application directly to milk needs further development. Details of test sensitivity, specificity, and use of the various technologies may differ between quarter, cow, and bulk milk applications. CONCLUSIONS: There is a growing array of diagnostic assays that can be used to detect markers of inflammation or infection in milk. The value of some of these methods in on-farm udder health improvement programs is yet to be demonstrated whilst methods with proven value may be underutilised.

Effect of Acute and Cumulative Stress on Gene Expression in Mammary Tissue and Their Interactions with Physiological Responses and Milk Yield in Saanen Goats.

This study addresses the hypothesis that different acute stressors can cumulatively decrease milk yield. In fact, in a time of global warming, the impact of environmental stress and farm management practices on milk production remains unclear. In this context, our objective was to investigate the effect of acute and cumulative stress on gene expression in mammary tissue and their interactions with physiological responses and milk yield in Saanen goats. Thirty lactating goats were subjected to two treatments: (1) control (CT), in which goats were maintained following a habitual routine under comfort conditions; (2) stress (ST), in which the goats were subjected to different types of environmental stress: heat stress, adrenocorticotropic hormone administration, hoof care management, and exposure to rain. These stressors were performed sequentially, with one stress per day on four consecutive lactation days, to evaluate their effect on milk quality and milk yield. Our results showed that compared to CT goats, cumulative stress increased the gene expression of glucocorticoid receptor (GR), interferon-gamma (IFN-γ), superoxide dismutase (SOD), and catalase (CAT) in mammary tissue, which are indicators of cortisol action, inflammatory response, and antioxidant enzymes. Furthermore, the acute challenges imposed on ST goats changed their rectal temperature and respiratory frequency and increased cortisol, glucose, cholesterol, triglycerides, and high-density lipoprotein release in plasma when compared to CT goats. Although these physiological and metabolic responses restore homeostasis, ST goats showed lower milk yield and higher somatic cell count in milk than CT goats. In conclusion, the results confirmed our initial hypothesis that different acute stressors cumulatively decrease the milk yield in Saanen goats.

A genome-wide association study reveals novel SNP markers associated with resilience traits in two Mediterranean dairy sheep breeds.

Genetic selection for higher productivity increased dairy sheep susceptibility to diseases and environmental stressors, challenging their health and welfare status and production efficiency. Improving resilience to such stressors can enhance their ability to face these challenges without compromising productivity. Our objective was to estimate genomic heritability and perform genome-wide association studies (GWAS) to detect SNPs and candidate genes associated with three proxy traits for resilience (milk somatic cell count-SCC, lactation persistency-LP, body condition score-BCS) of Chios and Frizarta dairy ewes. We used genome-wide genotypes of 317 Chios and 346 Frizarta ewes. Individual records of milk yield and BCS, and milk samples were collected monthly for two consecutive milking periods; samples were analyzed to determine SCC. The LP was calculated as the regression coefficient of daily milk yield on days from lambing. Within breed, variance components analyses and GWAS were performed using genomic relatedness matrices in single-trait animal linear mixed models. Genomic-based heritability estimates were relatively high (BCS: h2 = 0.54 and 0.55, SCC: h2 = 0.25 and 0.38, LP: h2 = 0.43 and 0.45, for Chios and Frizarta ewes, respectively), compared to previous pedigree-based studies. The GWAS revealed 7 novel SNPs associated with the studied traits; one genome-wide and two suggestive significant SNPs for SCC (Frizarta: rs403061409, rs424064526 and rs428540973, on chromosomes 9, 1 and 12, respectively), one suggestive significant SNP for BCS (Chios: rs424834097 on chromosome 4) and three suggestive significant SNPs for LP (Frizarta: rs193632931 and rs412648955 on chromosomes 1 and 6, Chios: rs428128299 on chromosome 3). Nineteen candidate genes were detected: two for BCS (Chios: POT1, TMEM229A), thirteen for SCC (Frizarta: NTAQ1, ZHX1, ZHX2, LOC101109545, HAS2, DERL1, FAM83A, ATAD2, RBP7, FSTL1, CD80, HCLS1, GSK3B) and four for LP (Frizarta: GRID2, FAIM, CEP70-Chios: GRIP1). Present results show that resilience in the studied dairy sheep breeds is heritable and advance existing knowledge on the genomic background of SCC, LP, and BCS. Future research will quantify effects of different alleles of significant SNPs on the studied traits and search for possible correlations among traits to facilitate their effective incorporation in breeding programs aiming to improve resilience.

Evaluation of factors associated with bulk milk somatic cell count and total plate count in Indonesian smallholder dairy farms.

Increasing milk quality in smallholder dairy farms will result in a greater quantity of milk being delivered to milk collection centers, an increased milk price for farmers and consequently an improved farmers' livelihood. However, little research on milk quality has been performed on smallholder farms in Southeast Asia. The objective of this study was to identify risk factors associated with somatic cell count (SCC) and total plate count (TPC) in Indonesian smallholder dairy farms. One dairy cooperative in West Java, Indonesia was selected based on its willingness to participate. All 119 member farmers in the cooperative, clustered in six groups, were interviewed and a bulk milk sample from all farms was collected in April 2022. Risk factors associated with dairy farms' SCC and TPC were investigated using multivariable population-averaged generalized estimating equations (GEE) models. The mean geometric SCC and TPC from these farms were 529,665 cells/mL of milk and 474,492 cfu/mL of milk, respectively. Five risk factors including manure removal frequency, receiving mastitis treatment training, washing the udder using soap, number of workers, and ownership of the pasture area were associated with SCC. Two risk factors, manure removal frequency and dairy income contribution, were associated with TPC. These findings can therefore be used as a starting point to improve udder health and milk quality in Indonesia and other countries where smallholder farmers play a significant role in milk production.

Microbiota Characterization of the Cow Mammary Gland Microenvironment and Its Association with Somatic Cell Count.

Subclinical mastitis is a common disease that threatens the welfare and health of dairy cows and causes huge economic losses. Somatic cell count (SCC) is the most suitable indirect index used to evaluate the degree of mastitis. To explore the relationship between SCC, diversity in the microbiome, and subclinical mastitis, we performed next-generation sequencing of the 16S rRNA gene of cow's milk with different SCC ranges. The data obtained showed that the microbiota was rich and coordinated with SCC below 2 × 105. SCC above 2 × 105 showed a decrease in the diversity of microbial genera. When SCC was below 2 × 105, the phylum Actinobacteriota accounted for the most. When SCC was between 2 × 105 and 5 × 105, Firmicutes accounted for the most, and when SCC exceeded 5 × 105, Firmicutes and Proteobacteria accounted for the most. Pathogenic genera such as Streptococcus spp. were absent, while SCC above 2 × 105 showed a decrease in the diversity of microbial genera. SCC was positively correlated with the percentage of Romboutsia, Turicibacter, and Paeniclostridium and negatively correlated with the percentage of Staphylococcus, Psychrobacter, Aerococcus, and Streptococcus. Romboutsia decreased 6.19 times after the SCC exceeded 2 × 105; the SCC increased exponentially from 2 × 105 to 5 × 105 and above 1 × 106 in Psychrobacter. Analysis of the microbiota of the different SCC ranges suggests that the development of mastitis may not only be a primary infection but may also be the result of dysbiosis in the mammary gland.

A First Investigation into the Use of Differential Somatic Cell Count as a Predictor of Udder Health in Sheep.

Differential somatic cell count (DSCC), the percentage of somatic cell count (SCC) due to polymorphonuclear leukocytes (PMNs) and lymphocytes (LYMs), is a promising effective diagnostic marker for dairy animals with infected mammary glands. Well-explored in dairy cows, DSCC is also potentially valid in sheep, where clinical and subclinical mastitis outbreaks are among the principal causes of culling. We pioneered the application of DSCC in dairy ewes by applying receiver-operating characteristic (ROC) curve analysis to define the most accurate thresholds to facilitate early discrimination of sheep with potential intramammary infection (IMI) from healthy animals. We tested four predefined SCC cut-offs established in previous research. Specifically, we applied SCC cut-offs of 265 × 103 cells/mL, 500 × 103 cells/mL, 645 × 103 cells/mL, and 1000 × 103 cells/mL. The performance of DSCC as a diagnostic test was assessed by examining sensitivity (Se), specificity (Sp), positive predictive value (PPV), negative predictive value (NPV), and area under curve (AUC) analyses. The designated threshold value for DSCC in the detection of subclinical mastitis is established at 79.8%. This threshold exhibits Se and Sp of 0.84 and 0.81, accompanied by an AUC of 0.88. This study represents the inaugural exploration of the potential use of DSCC in sheep's milk as an early indicator of udder inflammation.

Polymorphism and association study of lactoferrin (LF) gene with milk yield, milk composition, and somatic cell count in Beetal goats.

The sequence analysis of PCR product exhibited four novel SNPs in the promoter region of the LF gene at loci g.98T>C, g.143T>A, g.189AC>A, and g.346A>G. Each SNP yielded three genotypes; the genotypes TT (SNP1), AA (SNP3), and GG (SNP4) decreased SCC and increase milk quality traits such as density, protein, and milk yield (P < 0.01). The genotype CC (SNP2) and CA (SNP4) significantly (P < 0.01) decreased the milk quality parameters, while genotypes TC (SNP2) and GG (SNP4) showed significantly (P < 0.01) less SCC and increase lactose % in milk. Furthermore, screening of the LF promoter sequence explored the gain of four TF binding sites at locus g.98T˃C and three TF binding sites at g.346A˃G. However, the loss of four and two TF binding sites was seen at locus g.143T˃A and g.189C˃A, respectively. We can conclude from the present study that the GG, TT, and AA genotype might be utilized as genetic markers in marker-assisted selection for the breed improvement program of Beetal goats.