Suitability of somatic cell count, electrical conductivity, and lactate dehydrogenase activity in foremilk before versus after alveolar milk ejection for mastitis detection

Infrared spectroscopy coupled with machine learning algorithms for predicting the detailed milk mineral profile in dairy cattle

Milk minerals are not only essential components for human health, but they can be informative for milk quality and cow's health. Herein, we investigated the feasibility of Fourier Transformed mid Infrared (FTIR) spectroscopy for the prediction of a detailed panel of 17 macro, trace, and environmental elements in bovine milk, using partial least squares regression (PLS) and machine learning approaches. The automatic machine learning significantly outperformed the PLS regression in terms of prediction performances of the mineral elements. For macrominerals, the R2 ranged from 0.59 to 0.78. Promising predictability was achieved for Cu and B (R2 = 0.66 and 0.74, respectively) and more moderate ones for Fe, Mn, Zn, and Al (R2 from 0.48 to 0.58). These results provide a reliable basis for a rapid and cost-effective quantification of these traits, serving as a resource for dairy farmers seeking to enhance the quality of milk production and optimize cheese properties.

Concentrations of antimicrobial components in milk at dry off and postpartum and their relationships to new high somatic cell counts at quarter level in dairy cows

We investigated the antimicrobial components in cow milk at dry off and postpartum and their contribution in preventing new high SCC at quarter level. Milk samples from 72 quarters of 19 lactating cows were collected at last milking before dry off and at 7 d after parturition. Milk yield of each cow was recorded and SCC, IgG, IgA, lactoferrin, lingual antimicrobial peptide (LAP), and S100A7 concentrations in each quarter milk sample were measured. The postpartum milk yield was significantly higher than that at dry off. The IgG, IgA and lactoferrin concentrations in milk at dry off were significantly higher than those at postpartum, whereas the LAP concentration was lower. Quarters with SCC < 300 000 cells/ml at both dry off and postpartum were classified as persistent low SCC (PL) whereas those that rose above that same threshold postpartum were classified as new high SCC (NH). At dry off, IgG and LAP concentrations in milk were significantly higher in PL than in NH. These results suggest that high LAP concentrations during the dry period may contribute toward the prevention of new high SCC.

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.

Electrical conductivity and total dissolved solid of raw milk for the detection of bovine subclinical mastitis

Background and Aim

Bovine subclinical mastitis (SCM) is highly prevalent among dairy cattle. A cross-sectional study was conducted in Bangladesh to evaluate the performance of electric conductivity (EC) and total dissolved solids (TDS) tests for the detection of SCM.

Materials and Methods

We randomly selected 108 milk samples from cows of different breeds in the primary milk-producing region of Pabna and Sirajgonj districts of Bangladesh. Samples were subjected to the California mastitis test (CMT), white side test (WST), electric conductivity (EC), TDS, and culture. A cow was considered positive for SCM if it tested positive in CMT, WST, and culture, whereas a cow was considered negative for SCM if it tested negative in all three methods. These gold standards have been used to evaluate the performance of the EC and TDS tests. The optimal EC and TDS cutoff values for the detection of SCM were determined using the "optimal cutoff" function in R version 4.3.1.

Results

The optimal EC cutoff value for SCM detection was found to be 6159 μS/cm or 6.16 mS/cm. A positive likelihood ratio (LR+) of 31.2 and an area under the curve (AUC) of 0.905 were obtained for this cutoff value. The optimal cutoff value for TDS was 3100 mg/L of milk, which resulted in a positive LR+ of 45.5 and an AUC of 0.924.

Conclusion

To the best of our knowledge, this is the first study to evaluate the performance of EC and TDS tests in detecting SCM in Bangladesh. These results suggest that EC and TDS tests, which are inexpensive, rapid, and easy to conduct, can effectively detect SCM at the farm level.

Skin carotenoid levels in lactating dairy cows as measured using multiple spatially resolved reflection spectroscopy

This research communication describes a pilot study measuring skin carotenoid levels of lactating dairy cows. Carotenoids are natural antioxidants, involved in cell communication and immune function, protecting against oxidative stress. They are precursors of vitamin A, important for reproduction efficiency, growth and male fertility. Therefore, easy-to-use, inexpensive methods to measure carotenoids in cattle would provide interesting data for farmers to monitor the health and nutritional status of their herds. In this study, we used a commercially available sensor based on multiple spatially resolved reflection spectroscopy (MSRRS), intended for human use, to measure the carotenoid content in bovine skin in three research herds in France, Ireland and Scotland. Carotenoid levels were measured by applying the sensor to the teat barrel, avoiding pigmented areas of skin. Mean sensor values differed significantly between herds and between diets, with pasture-based animals showing significantly higher carotenoid levels. Our results suggest that MSRRS can be used to accurately to measure skin carotenoids in cows. However, further calibration in bovines is needed to improve of the accuracy of the MSRRS sensor in cattle.

Precision Livestock Farming Research: A Global Scientometric Review

Precision livestock farming (PLF) utilises information technology to continuously monitor and manage livestock in real-time, which can improve individual animal health, welfare, productivity and the environmental impact of animal husbandry, contributing to the economic, social and environmental sustainability of livestock farming. PLF has emerged as a pivotal area of multidisciplinary interest. In order to clarify the knowledge evolution and hotspot replacement of PLF research, based on the relevant data from the Web of Science database from 1973 to 2023, this study analyzed the main characteristics, research cores and hot topics of PLF research via CiteSpace. The results point to a significant increase in studies on PLF, with countries having advanced livestock farming systems in Europe and America publishing frequently and collaborating closely across borders. Universities in various countries have been leading the research, with Daniel Berckmans serving as the academic leader. Research primarily focuses on animal science, veterinary science, computer science, agricultural engineering, and environmental science. Current research hotspots center around precision dairy and cattle technology, intelligent systems, and animal behavior, with deep learning, accelerometer, automatic milking systems, lameness, estrus detection, and electronic identification being the main research directions, and deep learning and machine learning represent the forefront of current research. Research hot topics mainly include social science in PLF, the environmental impact of PLF, information technology in PLF, and animal welfare in PLF. Future research in PLF should prioritize inter-institutional and inter-scholar communication and cooperation, integration of multidisciplinary and multimethod research approaches, and utilization of deep learning and machine learning. Furthermore, social science issues should be given due attention in PLF, and the integration of intelligent technologies in animal management should be strengthened, with a focus on animal welfare and the environmental impact of animal husbandry, to promote its sustainable development.

Milk Pathogens in Correlation with Inflammatory, Oxidative and Nitrosative Stress Markers in Goat Subclinical Mastitis

Goat mastitis is still frequently diagnosed in dairy farms, with serious consequences on milk quality and composition. The aim of this study was to establish correlations between milk microorganisms and biochemical parameters in goats with no signs of clinical mastitis. Thus, 76 milk samples were collected from a dairy goat farm, Carpathian breed, followed by microbiological, molecular (16S rRNA sequencing) and somatic cells analysis, determination of lactate dehydrogenase (LDH), β-glucuronidase, catalase (CAT), glutathione peroxidase (GPx) activity, total antioxidant capacity (TAC), nitric oxide (NO) and lipid peroxides (LPO) using spectrophotometry and the ELISA method for 8-hydroxy-deoxyguanosine (8-OHdG) as the oxidative DNA damage indicator. Samples positive for bacterial growth showed a significant (p < 0.05) increase in the number of somatic cells, LDH and β-glucuronidase activity, as well as higher levels of CAT, GPx, NO, LPO and 8-OHdG compared with pathogen-free milk whereas TAC was lower in milk from an infected udder. These findings suggest that subclinical mastitis is associated with increased enzymatic activity and induction of oxidative stress. Nevertheless, changes in biochemical parameters tended to vary depending on the pathogen, the most notable mean values being observed overall in milk positive for Staphylococcus aureus.

Mastitis: What It Is, Current Diagnostics, and the Potential of Metabolomics to Identify New Predictive Biomarkers

Periparturient diseases continue to be the greatest challenge to both farmers and dairy cows. They are associated with a decrease in productivity, lower profitability, and a negative impact on cows’ health as well as public health. This review article discusses the pathophysiology and diagnostic opportunities of mastitis, the most common disease of dairy cows. To better understand the disease, we dive deep into the causative agents, traditional paradigms, and the use of new technologies for diagnosis, treatment, and prevention of mastitis. This paper takes a systems biology approach by highlighting the relationship of mastitis with other diseases and introduces the use of omics sciences, specifically metabolomics and its analytical techniques. Concluding, this review is backed up by multiple studies that show how earlier identification of mastitis through predictive biomarkers can benefit the dairy industry and improve the overall animal health.

Sensitivity and Specificity for the Detection of Clinical Mastitis by Automatic Milking Systems in Bavarian Dairy Herds

In automatic milking systems (AMSs), the detection of clinical mastitis (CM) and the subsequent separation of abnormal milk should be reliably performed by commercial AMSs. Therefore, the objectives of this cross-sectional study were (1) to determine the sensitivity (SN) and specificity (SP) of CM detection of AMS by the four most common manufacturers in Bavarian dairy farms, and (2) to identify routinely collected cow data (AMS and monthly test day data of the regional Dairy Herd Improvement Association (DHIA)) that could improve the SN and SP of clinical mastitis detection. Bavarian dairy farms with AMS from the manufacturers DeLaval, GEA Farm Technologies, Lely, and Lemmer-Fullwood were recruited with the aim of sampling at least 40 cows with clinical mastitis per AMS manufacturer in addition to clinically healthy ones. During a single farm visit, cow-level milking information was first electronically extracted from each AMS and then all lactating cows examined for their udder health status in the barn. Clinical mastitis was defined as at least the presence of visibly abnormal milk. In addition, available DHIA test results from the previous six months were collected. None of the manufacturers provided a definition for clinical mastitis (i.e., visually abnormal milk), therefore, the SN and SP of AMS warning lists for udder health were assessed for each manufacturer individually, based on the clinical evaluation results. Generalized linear mixed models (GLMMs) with herd as random effect were used to determine the potential influence of routinely recorded parameters on SN and SP. A total of 7411 cows on 114 farms were assessed; of these, 7096 cows could be matched to AMS data and were included in the analysis. The prevalence of clinical mastitis was 3.4% (239 cows). When considering the 95% confidence interval (95% CI), all but one manufacturer achieved the minimum SN limit of >80%: DeLaval (SN: 61.4% (95% CI: 49.0%−72.8%)), GEA (75.9% (62.4%−86.5%)), Lely (78.2% (67.4%−86.8%)), and Lemmer-Fullwood (67.6% (50.2%−82.0%)). However, none of the evaluated AMSs achieved the minimum SP limit of 99%: DeLaval (SP: 89.3% (95% CI: 87.7%−90.7%)), GEA (79.2% (77.1%−81.2%)), Lely (86.2% (84.6%−87.7%)), and Lemmer-Fullwood (92.2% (90.8%−93.5%)). All AMS manufacturers’ robots showed an association of SP with cow classification based on somatic cell count (SCC) measurement from the last two DHIA test results: cows that were above the threshold of 100,000 cells/mL for subclinical mastitis on both test days had lower chances of being classified as healthy by the AMS compared to cows that were below the threshold. In conclusion, the detection of clinical mastitis cases was satisfactory across AMS manufacturers. However, the low SP will lead to unnecessarily discarded milk and increased workload to assess potentially false-positive mastitis cases. Based on the results of our study, farmers must evaluate all available data (test day data, AMS data, and daily assessment of their cows in the barn) to make decisions about individual cows and to ultimately ensure animal welfare, food quality, and the economic viability of their farm.

Optimising profitability and productivity of pasture-based dairy farms with automatic milking systems

There is a large variability in profitability and productivity between farms operating with automatic milking systems (AMS). The objectives of this study were to identify the physical factors associated with profitability and productivity of pasture-based AMS and quantify how changes in these factors would affect farm productivity. We utilised two different datasets collected between 2015 and 2019 with information from commercial pasture-based AMS farms. One contained annual physical and economic data from 14 AMS farms located in the main Australian dairy regions; the other contained monthly, detailed robot-system performance data from 23 AMS farms located across Australia, Ireland, New Zealand, and Chile. We used linear mixed models to identify the physical factors associated with different profitability (Model 1) and partial productivity measures (Model 2). Additionally, we conducted a Monte Carlo simulation to evaluate how changes in the physical factors would affect productivity. Our results from Model 1 showed that the two main factors associated with profitability in pasture-based AMS were milk harvested/robot (MH; kg milk/robot per day) and total labour on-farm (full-time equivalent). On average, Model 1 explained 69% of the variance in profitability. In turn, Model 2 showed that the main factors associated with MH were cows/robot, milk flow, milking frequency, milking time, and days in milk. Model 2 explained 90% of the variance in MH. The Monte Carlo simulation showed that if pasture-based AMS farms manage to increase the number of cows/robot from 54 (current average) to ∼ 70 (the average of the 25% highest performing farms), the probability of achieving high MH, and therefore profitability, would increase from 23% to 63%. This could make AMS more attractive for pasture-based systems and increase the rate of adoption of the technology.

Suitability of milk lactate dehydrogenase and serum albumin for pathogen-specific mastitis detection in automatic milking systems

In response to intramammary infection (IMI), blood-derived leukocytes are transferred into milk, which can be measured as an increase of somatic cell count (SCC). Additionally, pathogen-dependent IgG increases in milk following infection. The IgG transfer into milk is associated with the opening of the blood-milk barrier, which is much more pronounced during gram-negative than gram-positive IMI. Thus, milk IgG concentration may help to predict the pathogen type causing IMI. Likewise, lactate dehydrogenase (LDH) and serum albumin (SA) cross the blood-milk barrier with IgG if its integrity is reduced. Because exact IgG analysis is complicated and difficult to automate, LDH activity and SA concentration aid as markers to predict the IgG transfer into milk in automatic milking systems (AMS). This study was conducted to test the hypothesis that LDH and SA in milk correlate with the IgG transfer, and in combination with SCC these factors allow the differentiation between gram-positive and gram-negative IMI or even more precisely the infection-causing pathogen. Further, the expression of these parameters in foremilk before (BME) and after (AME) milk ejection was tested. In the AMS, quarter milk samples (n = 686) from 48 Holstein-Friesian cows were collected manually BME and AME, followed by an aseptic sample for bacteriological culture. Mixed models were used to (1) predict the concentration of IgG transmitted from blood into milk based on LDH and SA; (2) use principal component analysis to evaluate joint patterns of SCC (cells/mL), IgG (mg/mL), LDH (U/L), and SA (mg/mL) and use the principal component scores to compare gram-positive, gram-negative, and control IMI types and BME versus AME samples; and (3) predict gram-positive and gram-negative IMI by inclusion of combined SCC-LDH and SCC-SA as predictors in the model. Overall, the SA and LDH had similar ability to predict IgG transmission from blood into milk. Comparing the areas under the curve (AUC) of the receiver operator characteristic curves, the SCC-LDH versus SCC-SA had lower gram-positive (AUC = 0.984 vs. 0.986) but similar gram-negative (AUC = 0.995 vs. 0.998) IMI prediction ability. The SCC, IgG, LDH, and SA were greater in gram-negative than in gram-positive IMI (BME and AME) in early lactation. All measured factors had higher values in milk samples taken BME than AME. In conclusion, LDH and SA could be used as replacement markers to indicate the presence of IgG transfer from blood into milk; in combination with SCC, both SA and LDH are suitable for differentiating IMI type, and BME is better for mastitis detection in AMS.

Adoption of Precision Technologies by Brazilian Dairy Farms: The Farmer's Perception

The use of precision farming technologies, such as milking robots, automated calf feeders, wearable sensors, and others, has significantly increased in dairy operations over the last few years. The growing interest in farming technologies to reduce labor, maximize productivity, and increase profitability is becoming noticeable in several countries, including Brazil. Information regarding technology adoption, perception, and effectiveness in dairy farms could shed light on challenges that need to be addressed by scientific research and extension programs. The objective of this study was to characterize Brazilian dairy farms based on technology usage. Factors such as willingness to invest in precision technologies, adoption of sensor systems, farmer profile, farm characteristics, and production indexes were investigated in 378 dairy farms located in Brazil. A survey with 22 questions was developed and distributed via Google Forms from July 2018 to July 2020. The farms were then classified into seven clusters: (1) top yield farms; (2) medium-high yield, medium-tech; (3) medium yield and top high-tech; (4) medium yield and medium-tech; (5) young medium-low yield and low-tech; (6) elderly medium-low yield and low-tech; and (7) low-tech grazing. The most frequent technologies adopted by producers were milk meters systems (31.7%), milking parlor smart gate (14.5%), sensor systems to detect mastitis (8.4%), cow activity meter (7.1%), and body temperature (7.9%). Based on a scale containing numerical values (1-5), producers indicated "available technical support" (mean; σ²) (4.55; 0.80) as the most important decision criterion involved in adopting technology, followed by "return on investment-ROI" (4.48; 0.80), "user-friendliness" (4.39; 0.88), "upfront investment cost" (4.36; 0.81), and "compatibility with farm management software" (4.2; 1.02). The most important factors precluding investment in precision dairy technologies were the need for investment in other sectors of the farm (36%), the uncertainty of ROI (24%), and lack of integration with other farm systems and software (11%). Farmers indicated that the most useful technologies were automatic milk meters systems (mean; σ²) (4.05; 1.66), sensor systems for mastitis detection (4.00; 1.57), automatic feeding systems (3.50; 2.05), cow activity meter (3.45; 1.95), and in-line milk analyzers (3.45; 1.95). Overall, the concerns related to data integration, ROI, and user-friendliness of technologies are similar to those of dairy farms located in other countries. Increasing available technical support for sensing technology can have a positive impact on technology adoption.

Precision Technologies to Address Dairy Cattle Welfare: Focus on Lameness, Mastitis and Body Condition

Specific animal-based indicators that can be used to predict animal welfare have been the core of protocols for assessing the welfare of farm animals, such as those produced by the Welfare Quality project. At the same time, the contribution of technological tools for the accurate and real-time assessment of farm animal welfare is also evident. The solutions based on technological tools fit into the precision livestock farming (PLF) concept, which has improved productivity, economic sustainability, and animal welfare in dairy farms. PLF has been adopted recently; nevertheless, the need for technological support on farms is getting more and more attention and has translated into significant scientific contributions in various fields of the dairy industry, but with an emphasis on the health and welfare of the cows. This review aims to present the recent advances of PLF in dairy cow welfare, particularly in the assessment of lameness, mastitis, and body condition, which are among the most relevant animal-based indications for the welfare of cows. Finally, a discussion is presented on the possibility of integrating the information obtained by PLF into a welfare assessment framework.

Invited review: The role of the blood-milk barrier and its manipulation for the efficacy of the mammary immune response and milk production

The intact blood-milk barrier (BMB) prevents an uncontrolled exchange of soluble and cellular components between blood and milk in the mammary gland. It enables the sustainability of the optimal milk composition for the nourishment of the offspring. Endothelial cells, connective tissue, the basal membrane, and mainly the epithelial cells provide the semipermeability of this barrier, allowing only a selective transfer of components necessary for milk production. The epithelial cells are closely connected to each other by different formations, in which the tight junctions are the most critical for separating the milk-containing compartments from the surrounding extracellular fluid and vasculature. During mastitis, the integrity of the BMB is reduced. This facilitates the transfer of immune cells and immune factors such as antibodies from blood into milk. Simultaneously, the transfer of soluble blood constituents without an obvious immune function into milk is promoted. Furthermore, a reduced BMB integrity causes a loss of milk constituents into the blood circulation. Different mechanisms are responsible for the barrier impairment including tight junction opening, but also cell degradation. To promote the cure of mastitis, the targeted manipulation of the BMB permeability may be a tool to optimize the immune function of the mammary gland. An intensified opening of the BMB supports the antibody transfer from blood into milk, which is supposed to increase the contribution of the specific immune system in the immune defense. On the contrary, a fast closure of the BMB during the recovery from mastitis can accelerate the normalization of milk composition and milk yield. Various agents have been experimentally shown to either open (e.g., pathogens and pathogen-associated molecular patterns, several nonsteroidal anti-inflammatory drugs, oxytocin, calcium chelators) or close (e.g., glucocorticoids, nonsteroidal anti-inflammatory drugs, natural anti-inflammatory drugs) the BMB.

Genetic, farm, and lactation effects on behavior and performance of US Holsteins in automated milking systems

Selecting for favorable behavior and performance could enhance the efficiency of production in automated milking systems (AMS). The objectives of this study were to describe AMS behavior and performance in Holsteins, estimate genetic parameters among AMS traits, and determine genetic relationships of AMS traits with other routinely recorded traits. The edited data included 1,101,651 individual milking records and 394,636 daily records from 2,531 lactations and 1,714 cows that resided on 3 farms; data were obtained from the Dairy Data Warehouse (Assen, Netherlands) cloud. Traits considered were individual milking and daily totals for milk yield, milking time, milk harvest rate (the ratio of milk yield to milking time), milk flow rate, electrical conductivity, machine kickoffs, incomplete milkings, and blood in milk; the number of milkings per day and 305-d mature-equivalent milk yield (305ME) were also evaluated. Individual milkings were evaluated with mixed models that included fixed effects of week of lactation, lactation group (1, 2, ≥3), hour of day, and farm; random effects included cow within lactation, lactation group by week of lactation, and interactions of farm with date, hour, week of lactation, and year-season of calving. Daily records were evaluated with 3-trait animal models that included 305ME and 2 AMS traits with random additive genetic and permanent environment effects. Estimated breeding values were extracted and correlated with yield, conformation, and udder health genetic evaluations. Farm specific robot access policies had notable effects on week of lactation patterns for milk yield and number of milkings. Mature cows had higher milk harvest rates (2.05 kg/min) than first-lactation cows (1.73 kg/min) with larger differences in early lactation. First-lactation cows were more likely to kick off the machine (15.04%) than mature cows (8.62%), particularly in early lactation. Heritability estimates were generally lower for behavior traits (0.03 for incomplete milkings and 0.08 for kickoffs) than for milk harvest rate (0.30) and flow rate (0.55). Udder conformation traits did not have favorable genetic correlations with AMS traits, with the exception that longer teats were correlated with fewer kickoffs (-0.34) and incomplete milkings (-0.49); increased milk harvest rate and flow rate were unfavorably associated with genetic merit for udder health. There is genetic variation for milking efficiency and behavioral traits, suggesting genetic selection to enhance efficiency in AMS systems is possible. Genetic associations with udder conformation indicate that selection for udder morphology is unlikely to substantially improve milking efficiency. This calls for more direct selection of traits related to AMS efficiency.

Seasonal variations in the concentration of antimicrobial components in milk of dairy cows

The incidence of bovine mastitis and the bulk milk somatic cell count (BMSCC) are influenced by season, which may be associated with innate immune functions, including antimicrobial components in mammary glands. Therefore, the present study was conducted to examine the effect of season on antimicrobial components in milk. Rectal temperature and plasma cortisol, thyroxine, and derivatives of reactive oxygen metabolites (d-ROMs) were measured as stress parameters. Concentrations of lactoferrin (LF), lingual antimicrobial peptide (LAP), psoriasin (S100A7), and Immunoglobulin A (IgA) in milk were measured as indicators of innate immune function. LF and LAP concentrations were significantly lower in summer than in winter and spring, respectively, whereas the concentration of S100A7 was significantly lower in winter than in spring and autumn. The rectal temperature was significantly higher in summer than in other seasons, whereas plasma cortisol, thyroxine, and d-ROMs did not exhibit any seasonal variation. In conclusion, even though stress parameters were not changed, the concentration of antimicrobial components, such as LF and LAP, decreased in summer, which may explain the frequent occurrence of mastitis during this season.

Review: Sensor techniques in ruminants: more than fitness trackers

In this position paper, I shall summarise the current status of sensor technologies in ruminant livestock farming with emphasis on dairy cattle, outline the case for why I believe that sensor technologies could revolutionise global dairy farming in a positive way, describe the significant barriers that exist if that goal is to be achieved and highlight the benefits to animal wellbeing, profitability and sustainability that could result if the technologies are implemented to a significant extent. I shall not provide a comprehensive review of the sensor technology literature since that has been done before, but I intend to provide a sensible amount of background information and data that will allow the reader to obtain a picture not only of today's sensor usage but, more importantly, the possible future direction of dairy animal-oriented sensor technologies, and I shall substantiate my claims and conclusions with relevant literature.

Evaluation of rapid culture, a predictive algorithm, esterase somatic cell count and lactate dehydrogenase to detect intramammary infection in quarters of dairy cows at dry-off

Our objective was to compare four tests to standard milk culture followed by MALDI-ToF in quarters of cows at dry-off. Cows (n = 432) were randomly selected from seven U.S. dairy herds already participating in a multi-site clinical trial in summer 2018. Aseptic foremilk samples were collected from quarters (n = 1728) two days prior to dry-off, and subjected to index and reference tests. The four index tests included rapid culture, a predictive algorithm, an esterase strip test measuring somatic cell count (SCC) and a cow-side lactate dehydrogenase (LDH) test. Rapid culture was performed by inoculating quarter milk samples onto a commercial rapid culture plate. Plates were evaluated by technicians after 30-40 h of incubation at 37 ± 2 °C. Quarters were classified as infected if any bacterial growth was observed. For the algorithm test method, all quarters were classified as infected if the cow met any of the following criteria: 1) any Dairy Herd Improvement Association (DHIA) test with a SCC > 200,000 cells / ml during the current lactation or 2) two or more clinical mastitis cases during the current lactation. Esterase-SCC and cow-side LDH tests involved adding milk to the test strip and reading for color changes. For esterase-SCC and cow-side LDH tests, low (≥250 cells / ml and ≥100 U / L) and high (≥500 cells / ml and ≥200 U / L) thresholds were used to classify quarters as infected or not. Composite samples (4 × 2 mL quarter-milk samples commingled) were also tested for rapid culture, esterase-SCC and cow-side LDH tests, such that if a composite sample was positive, then all quarters contributing to that sample were classified as infected. The reference test was traditional aerobic culture conducted in an accredited laboratory using MALDI-ToF for identification of isolates. Traditional culture was only conducted on quarter-milk samples, and consequently, IMI was always considered at the quarter-level. Unconditional logistic regression was used to estimate sensitivity (SE), specificity (SP), apparent prevalence, positive predictive values (PPV) and negative predictive values (NPV) for each index test. Cohen's Kappa (κ) was used to measure agreement between tests. Algorithm, esterase-SCC and cow-side LDH tests had poor agreement with the reference test (κ ranging from 0.01 to 0.12), while rapid culture had fair agreement (κ = 0.28). No test had concurrently high SE and SP. Negative predictive values were moderate to high for all tests.

Sensitive screen-printed electrodes with the colorimetric zone for simultaneous determination of mastitis and ketosis in bovine milk samples

Bovine metabolic diseases are silent clinical conditions caused by a disturbance in normal cellular metabolic processes majorly due to the demand for nutrients, physiological stress and inflammation in lactating animals. Even though these are non-devastating diseases, conditions like mastitis and ketosis highly influence cow's lactation and fertility performance that leads to economic losses. Hence, in the present study, a combined detection including a sensitive paper-based colorimetric device for the rapid determination of somatic cells (SCC) and electrochemical detection of β-hydroxybutyrate (BHB) in the milk samples has been developed. The working range was 1 × 10⁵ to 2 × 10⁶ for SCC and 0.05 to 2.5 mM BHB in which the normal and extreme cases of mastitis and ketosis values exist. The detection method is based on reaction using substrate 5-Bromo-indoxyl acetate (5BIA) and enzyme (BHB dehydrogenase) for the enzymes (esterase) and substrate (BHB) present in the milk samples during mastitis and ketosis respectively. An increase in blue color development was associated with an increase in the number of SCC; likewise, the increased voltage denoted the increase concentration of BHB in the samples. The repeatability was evaluated, and the % relative standard deviation (RSD) value <8.0 (n = 12). The limit of detection (LOD) was close to that achieved by a desktop spectrophotometer. The applicability of the developed kit demonstrated good sensitivity that can be deployed for other biological samples of interest. The proposed method provides a rapid, sensitive and promising tool for the regular monitoring of cows as a point of care tests (POC) at the field level.