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The impact on the natural characteristics of dairy products during thermal processing warrants the investigation of non-thermal techniques. Ozone has proved to be an effective and sustainable processing technology for the dairy processing sector. This review delves into the effect of ozone processing on the microbiological, physiochemical, nutri-functional, and sensory quality of milk and milk products. Alongside this, the other ozone applications in the dairy processing sector (storage room disinfection, wastewater treatment, benefits in Clean-in-Place (CIP) system, toxin reduction) have been discussed. Current regulatory and industrial status, and safety requirements in the facility have also been highlighted. Overall, ozone treatment has lower microbial inactivation efficiency in milk and milk products than thermal treatment. Further, safety precautions are needed in the processing areas due to its potential health hazard concerns. [ABSTRACT FROM AUTHOR]

ABSTRACT Milk, owing to its nutritional value, is a staple commodity in daily life. Ensuring its integrity and authenticity is paramount for researchers and the food industry. To evaluate spoilage and adulteration of milk, we proposed a robust and reliable optical method to guarantee milk quality by utilizing riboflavin as an internal biomarker. By precisely controlling the content of Eu3+, Tb3+, and Gd3+ ions, a two‐dimensional ratiometric luminescence lanthanide coordination polymer (Ln‐CP) with adjustable emission centers was synthesized for the detection of riboflavin (RF), leveraging a Förster resonance energy transfer (FRET) and photoinduced electron transfer (PET) process. This method demonstrates high sensitivity and selectivity towards RF and has been successfully applied to the detection of RF in both milk and calf serum. [ABSTRACT FROM AUTHOR]

The study aims to explore the effects of composite microecological preparations on lactation performance, milk quality, and immune function of cows. The 160 Chinese Holstein cows with lactation days ranging from 240 to 280 days and 2-3 parity were selected and randomly divided into a control group and experimental groups A, B, and C. The control group cows were fed a basic diet, while experimental groups A, B, and C cows were fed a diet supplemented with 25, 50, and 75 g/(head·d) of composite microecological preparations (yeast culture+fungal enzyme+wall breaking yeast+probiotics) on top of the basic diet. The experimental period is 60 days. After the experiment, samples are taken to measure the milk production, milk quality, number of somatic cells in milk, antioxidant indicators, and immune function indicators of each group of cows, and the significance of the differences is compared and analyzed. The results showed that the average daily milk production, milk fat rate, and milk protein rate of the experimental group were significantly higher than those of the control group (P<0.05), and the number of somatic cells was significantly lower than that of the control group (P<0.05); the content of malondialdehyde (MDA) in serum samples was significantly lower than that in the control group (P<0.05), and the activities of catalase (CAT), superoxide dismutase (SOD), total antioxidant capacity (T-AOC), immunoglobulin G (IgG), immunoglobulin A (IgA), γ-Interferon (γ-IFN) and interleukin-6 (IL-6) were significantly higher than those of the control group (P<0.05), and there was no significant difference in various indicators between experimental group B and experimental group C (P>0.05). Feeding each cow with a compound microecological preparation at a dose of 50 g/(head·d) could effectively improved lactation performance, improved milk quality, and enhanced immune function. [ABSTRACT FROM AUTHOR]

The list of standard abbreviations for JDS is available at adsa.org/jds-abbreviations-24. Nonstandard abbreviations are available in the Notes. Moving from conventional (CMS) to automatic (AMS) milking systems could affect milk quality. Moreover, the type and preservation methods of the forages used in the TMR, such as alfalfa hay (HTMR) or corn silage (STMR) have been demonstrated to modify milk composition. Thus, this study investigated the effect of implementing AMS and different diet forage types on the quality of Italian Holstein-Friesian bulk milk. Milk samples (n = 168) were collected monthly from 21 commercial farms in northern Italy during a period of 8 mo. Farms were categorized into 4 groups according to their milking system (CMS vs. AMS) and diet forage type (HTMR vs. STMR). Milk quality data were analyzed through the mixed procedure for repeated measurement of SAS with the milking system, diet forage type, and sampling day as fixed effects. Milking through the AMS led to lower milk fat, freezing point, and β-LG A; longer coagulation time; and higher K content, pH, and β-LG B than CMS. Cows fed STMR produced milk with greater fat, protein, casein, Mg content, titratable acidity, and β-LG A, but with reduced curd firming time, freezing point, and β-LG B than those fed HTMR. In conclusion, milk quality is not only altered by the diet's forage type and characteristics but also by the milking system. [ABSTRACT FROM AUTHOR]

The list of standard abbreviations for JDS is available at adsa.org/jds-abbreviations-24. Nonstandard abbreviations are available in the Notes. The objective of this study was to determine quarters requiring antimicrobial treatment using either a benchtop somatic cell counter or culture with gram-positive selective media and compare the outcomes in these cows to those receiving blanket dry cow therapy (BDCT) in a randomized, controlled trial. We evaluated 2 novel methods of identifying cows with intramammary infections followed by selective antimicrobial treatment at a commercial dairy farm to determine their usefulness in decreasing antibiotic usage during the dry period without significant detrimental effects on milk quality and production. Cows (n = 840) were randomly allocated to one of 3 groups (BDCT, gram-positive selective media culture-based selective dry cow therapy [C-SDCT], and somatic cell count-based SDCT [S-SDCT]) the day before dry-off, and quarter-level milk samples (QLMS) were collected. The QLMS from cows in the S-SDCT group were evaluated using the cell counter, and quarters were treated if SCC was ≥200,000 cells/mL, whereas the QLMS from cows in the C-SDCT group were cultured, and quarters were treated if the culture showed growth. All cows in the BDCT received antimicrobial therapy, and all cows received an internal teat sealant regardless of treatment group. Outcomes measured were first and second DHIA test SCC, milk production through 60 DIM, cows leaving the farm, clinical mastitis, and bacteriologic new infections in a subset of quarters. Cows in both SDCT groups had fewer antimicrobial treatments than cows in the BDCT group as was expected, and cows in the C-SDCT group had fewer treatments than those in the S-SDCT group. Cows in both SDCT groups had a higher linear score at the first DHIA test (BDCT: 1.8, S-SDCT: 2.2, C-SDCT: 2.2); however, we found no other differences between groups regarding any other outcomes measured. Although antimicrobial use was significantly reduced, farms should use caution in adopting the benchtop analyzer and the selective media described in this study as ways to identify infected cows for dry cow therapy because they may result in increased linear scores early in lactation. [ABSTRACT FROM AUTHOR]

The list of standard abbreviations for JDS is available at adsa.org/jds-abbreviations-24. Nonstandard abbreviations are available in the Notes. The effect of the horn status of cows on their milk composition and quality is a controversial research topic. In this study, 128 milk samples from 64 horned and 64 disbudded Brown Swiss and Original Braunvieh cows were collected from alpine farms where both horned and disbudded cows were grazing on mountain pastures. The samples were analyzed for their detailed composition and protein digestion in a simulated in vitro digestion (INFOGEST). To exclude probable influences on digestion, the β-CN genotype with its variants A1 and A2 was also included in the study. The effects of horn status and β-CN genotype were investigated in linear mixed models, which included additional influencing random factors such as Original Braunvieh blood proportion, stage of lactation, and farm. Horn status did not have any effect on milk composition or digestion. In contrast, milk from A1A1 cows showed a different protein digestion than milk of A1A2 and A2A2 cows in the gastric phase, including smaller amounts of β-casomorphin(BCM)21-associated peptides and larger amounts of BCM11-associated peptides. Abundances of BCM7 did not differ between β-CN genotypes. At the end of the intestinal phase, the digested milk of A1A1 and A2A2 β-CN genotypes did not differ. [Display omitted] [ABSTRACT FROM AUTHOR]

This meta-analysis consolidates various related studies to identify patterns in the impact of feeding algae on performance aspects, including milk fat, milk protein, and carcass yield in several ruminant species, such as cattle, sheep, and goats. The data were collected from 67 articles that examined factors such as the type of algae (macro- and microalgae), algal species, and animal breed. Barki sheep, Moghani sheep, and Zaraibi goats demonstrated an increased average daily gain (P < 0.05) when fed with both macro- and microalgae. Conversely, sheep such as Canadian Arcott and Ile-de-France showed adverse effects on the feed conversion ratio (FCR) (P < 0.05). Elevated FCR values were observed across castrated and young animals (P < 0.05). Algae extract notably increased the hot carcass weight (P < 0.001), particularly among Moghani sheep (P < 0.001). Raw algae significantly reduced the milk fat content (P < 0.001), particularly in cattle and sheep (P < 0.001). A decrease in milk fat was particularly noticeable in lactating females of Assaf sheep, Damascus goats, and Holstein cows (P < 0.001). Overall, algae inclusion tended to decrease the milk protein content (P < 0.05), leading to reduced milk production (P < 0.001) with cumulative algae feeding in Assaf sheep. However, conjugated linoleic acid (CLA; C18:2 c9,t11-CLA and C18:2 c12,t10-CLA) and docosahexaenoic acid (DHA; C22:6n-3) mostly increased in meat and milk from Holstein cow, Assaf sheep, Dorset sheep, and Ile-de-France sheep (P < 0.01). This meta-analysis highlights the necessity for additional research aimed at optimizing the sustainable use of algae in feed for ruminants, despite the demonstrated improvements in performance and the levels of CLA and DHA found in meat and milk. [ABSTRACT FROM AUTHOR]

Artificial insemination (AI) success in bovine reproduction is vital for the cattle industry's economic sustainability and for advancing the understanding of reproductive physiology. Identify high-fertile animals' fertility is a complex task due to multifactorial traits, including hormonal, age-related, and body condition factors. Early high-fertility identification is crucial for timely interventions and enhancing AI success. In this study, we present the potential use of Fourier-transform infrared (FTIR) spectroscopy on blood serum for early identification of high-fertile Nellore female cows for AI protocols. Blood serum FTIR spectra were obtained from Nellore female cows before AI. FTIR spectra underwent data analysis and the results demonstrated successful discrimination between animals that exhibit pregnant and non-pregnant diagnoses 30 days after AI. FTIR spectra revealed consistent vibrational modes, emphasizing Amide I and II bands. Principal Component Analysis (PCA) effectively segregated groups based on molecular information. Linear SVM with C = 10 and 4 PCs achieved 100% accuracy in the group classification. This innovative approach using FTIR spectroscopy and ML algorithms offers a promising means of high-fertile cow identification, potentially improving AI outcomes in Nellore cattle. The study presents valuable insights into advancements in reproductive management practices for this economically significant breed. [ABSTRACT FROM AUTHOR]

Simple Summary: The aims of this study are to analyze prevalence rates, influence, and genetic parameters (heritability, genetic, and phenotypic correlations) of mastitis (MAS), endometritis (MET), and ketosis (KET) in Chinese Holstein Cattle. Data from 37,836 dairy cows in Central China were analyzed using the logistic regression method, a mixed linear model, and an animal model (BLUP). The prevalence rates of MAS, MET, and KET were 20.04%, 10.68%, and 7.33%, respectively. MAS and MET had a negative effect on production traits. High-milk yield cows had high KET prevalence rates. MAS, MET, and KET all had negative influences on reproduction traits. The heritabilities of predispositions to MAS, MET, and KET were 0.09, 0.01, and 0.02, respectively. Mastitis (MAS), endometritis (MET), and ketosis (KET) are prevalent diseases in dairy cows that result in substantial economic losses for the dairy farming industry. This study gathered 26,014 records of the health and sickness of dairy cows and 99,102 data of reproduction from 13 Holstein dairy farms in Central China; the milk protein and milk fat content from 56,640 milk samples, as well as the pedigree data of 37,836 dairy cows were obtained. The logistic regression method was used to analyze the variations in the prevalence rates of MAS, MET, and KET among various parities; the mixed linear model was used to examine the effects of the three diseases on milk production, milk quality, and reproductive traits. DMU software (version 5.2) utilized the DMUAI module in conjunction with the single-trait and two-trait animal model, as well as best linear unbiased prediction (BLUP), to estimate the genetic parameters for the three diseases, milk production, milk quality, and reproductive traits in dairy cows. The primary findings of the investigation comprised the following: (1) The prevalence rates of MAS, MET, and KET in dairy farms were 20.04%, 10.68%, and 7.33%, respectively. (2) MAS and MET had a substantial impact (p < 0.01) on milk production, resulting in significant decreases of 112 kg and 372 kg in 305-d Milk Yield (305-d MY), 4 kg and 12 kg in 305-d Protein Yield (305-d PY), and 6 kg and 16 kg in 305-d Fat Yield (305-d FY). As a result of their excessive 305-d MY, some cows were diagnosed with KET due to glucose metabolism disorder. The 305-d MY of cows with KET was significantly higher than that of healthy cows (205 kg, p < 0.01). (3) All three diseases resulted in an increase in the Interval from Calving to First Service (CTFS, 0.60–1.50 d), Interval from First Service to Conception (FSTC, 0.20–16.20 d), Calving Interval (CI, 4.00–7.00 d), and Number of Services (NUMS, 0.07–0.35). (4) The heritabilities of cows with MAS, MET, and KET were found to be low, with values of 0.09, 0.01, and 0.02, respectively. The genetic correlation between these traits ranged from 0.14 to 0.44. This study offers valuable insights on the prevention and control of the three diseases, as well as feeding management and genetic breeding. [ABSTRACT FROM AUTHOR]

A new video based multi behavior dataset for cows, CBVD-5, is introduced in this paper. The dataset includes five cow behaviors: standing, lying down, foraging,rumination and drinking. The dataset comprises 107 cows from the entire barn, maintaining an 80% stocking density. Monitoring occurred over 96 h for these 20-month-old cows, considering varying light conditions and nighttime data to ensure standardization and inclusivity.The dataset consists of ranch monitoring footage collected by seven cameras, including 687 video segment samples and 206,100 image samples, covering five daily behaviors of cows. The data collection process entailed the deployment of cameras, hard drives, software, and servers for storage. Data annotation was conducted using the VIA web tool, leveraging the video expertise of pertinent professionals. The annotation coordinates and category labels of each individual cow in the image, as well as the generated configuration file, are also saved in the dataset. With this dataset,we propose a slowfast cow multi behavior recognition model based on video sequences as the baseline evaluation model. The experimental results show that the model can effectively learn corresponding category labels from the behavior type data of the dataset, with an error rate of 21.28% on the test set. In addition to cow behavior recognition, the dataset can also be used for cow target detection, and so on.The CBVD-5 dataset significantly influences dairy cow behavior recognition, advancing research, enriching data resources, standardizing datasets, enhancing dairy cow health and welfare monitoring, and fostering agricultural intelligence development. Additionally, it serves educational and training needs, supporting research and practical applications in related fields. The dataset will be made freely available to researchers world-wide. [ABSTRACT FROM AUTHOR]

This study evaluated the effects of supplementing the diet of lactating cows with Acremonium terrestris culture (ATC) on milk production, serum antioxidant capacity, inflammatory indices, and serum lipid metabolomics. Over 90 days, 24 multiparous Chinese Holstein cows in mid-lactation (108 ± 10.4 days in milk, 637 ± 25 kg body weight, 30.23 ± 3.7 kg/d milk yield) were divided into either a control diet (CON) or a diet supplemented with 30 g of ATC daily. All the data were analyzed using Student's t test with SPSS 20.0 software. The results showed that compared with CON feeding, ATC feeding significantly increased milk yield, antioxidant capacity, and immune function. Lipidome screening identified 143 lipid metabolites that differed between the two groups. Further analysis using "random forest" machine learning revealed three glycerophospholipid serum metabolites that could serve as lipid markers with a predictive accuracy of 91.67%. This study suggests that ATC can be a useful dietary supplement for improving lactational performance in dairy cows and provides valuable insights into developing nutritional strategies to maintain metabolic homeostasis in ruminants. [ABSTRACT FROM AUTHOR]

Milk consumption is crucial for a balanced diet, yet recent trends indicate a decline, especially in Italy. A significant factor in this decline is the altered perception of milk quality among consumers, which has created a communication gap between them and other stakeholders. This study aimed to explore the discourse on social media and sentiment towards the concept of milk quality among consumers, farmers, and processors. The research adopted social media analysis to examine online-community messages. A sample of 19,906 Italian comments and posts mentioning keywords "milk", "quality", "cow", and "vaccine" was collected and categorized using term-frequency analysis, correspondence analysis, and sentiment analysis. Results highlighted gaps in perceptions of milk quality: farmers focused on economic issues, consumers on animal welfare and health, and processors on lactose content. For farmers, almost all comments were negative, while for processors, nearly all comments were positive. Consumers presented a more mixed picture. This work contributes to the literature by expanding research on milk quality, using social media as a source of information. The findings suggest that enhancing communication and understanding among these groups could lead to more effective strategies for addressing consumer concerns, potentially reversing the decline in milk consumption. [ABSTRACT FROM AUTHOR]

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The popularity of goat's milk and its products is growing in Slovakia. Among the mandatory health safety features of raw goat's milk is the total bacterial count. It can be determined by the anchor plate-cultivation method, which is, however, tedious, and laborious. Laser flow cytometry is a modern alternative method for measuring the microbiological quality of raw milk. To use the results from laser flow cytometry method for legislative purposes, it is necessary to create a representative conversion of the primarily measured results obtained by this method into the CFU/mL scale. This work describes the creation of such a conversion for raw goat's milk, which will be valid for the Slovak region. After measuring several hundred individual and bulk tank samples of raw goat's milk on a BactoScan FC laser flow cytometer and at the same time using the anchor method according to STN EN ISO 4833-1 and after their subsequent logarithmic transformation, such a conversion was created in the form of log10(CFU/mL) = 1.0174 x log10(IBC/μl) + 2.7483. Subsequently, this was verified by comparing the recalculated and directly measured results in CFU/mL, using additional data set of samples. The influence of the somatic cell count in raw goat's milk, as a possible interfering factor, was also assessed. [ABSTRACT FROM AUTHOR]

The list of standard abbreviations for JDS is available at adsa.org/jds-abbreviations-24. Nonstandard abbreviations are available in the Notes. A total of 18 Italian Mediterranean buffalo cows were subjected to fortnightly milk sampling from May to July 2022. Air temperature and relative humidity were monitored throughout the trial. Temperature-humidity index (THI) was calculated and ranged from 69 to 79, so that 3 classes were set to study the effect of different THI on milk quality: THI less than 72 (THI <72), THI ranging from 72 to 76 (THI 72–76), and THI over 76 (THI >76). Individual milk samples from buffalo cows were analyzed for milk composition and coagulation traits, fatty acid and amino acid composition, and enzymatic and mineral profiles. The THI >76 class registered higher lactose content and poorer milk clot characteristics in comparison with THI <72. Exposure to a THI over 76 resulted in increased SFA and short-chain fatty acid contents in milk, as well as in the highest atherogenic and thrombogenic index values and the lowest essential amino acid content. Moreover, the lowest values of calcium, copper, and selenium contents were found in the milk of buffalo cows exposed to a THI over 72. Results confirm that Italian Mediterranean buffalo express a good level of thermo-tolerance, even though exposure to a daily THI over 76 has a deleterious effect on some nutritional and technological properties of milk. [ABSTRACT FROM AUTHOR]

The list of standard abbreviations for JDS is available at adsa.org/jds-abbreviations-24. Nonstandard abbreviations are available in the Notes. 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 from 2020 to 2021, once from October to December and once from March to May. Dairy barns were observed, animal welfare was assessed, and freestall 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 from October to March. The prevalence of claw disorders and abnormal claw conformation were collected from the national claw health database for the period from October to May. On each farm visit, composite samples of unused bedding outside the barn and used bedding material from the freestalls, respectively, were taken for total bacterial count and DM 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 log 10 cfu/g) and used RMS bedding (9.75 log 10 cfu/g) than in wood shavings/sawdust (used 4.74; unused 8.63 log 10 cfu/g), but there were no significant differences in bulk milk total bacterial count (median 4.07 vs. 3.89 log 10 cfu/mL) or SCC (median 243,800 vs. 229,200 cells/mL). The aspects of animal welfare assessed did not differ significantly between the 2 bedding systems, whereas the prevalence of total claw disorders (25.9% vs. 38.0% of trimmed cows), dermatitis (6.9% vs. 16.2% of trimmed cows) and sole ulcers (2.0% vs. 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. [ABSTRACT FROM AUTHOR]

The list of standard abbreviations for JDS is available at adsa.org/jds-abbreviations-24. Nonstandard abbreviations are available in the Notes. According to our recent necropsy-based study, mastitis is the most common underlying diagnosis of on-farm deaths in Finnish dairy cows. However, it remained unanswered to what extent mastitis has contributed to death of all necropsied cows. In the present study, based on histopathology, we detected one-third of the necropsied dairy cows having active inflammatory udder lesions (n = 110). The role of mastitis varied and was interpreted by causes of death (underlying, intermediate, immediate, other significant). Mastitis was most commonly the underlying (28%) or both immediate and underlying cause of death (48%), and only seldom was it the immediate (4%) or intermediate (4%) cause of death. Mastitis occurred either as the only cause leading to death (mastitis only, MO; 39%), or with many other contributing diseases (multiple diseases, MD; 61%), which were interacting with mastitis and together leading to death. Between these groups (MO vs. MD), time of mastitis occurrence during lactation, producer-reported duration of illness, clinical signs, and medication differed, as well the histopathologic severity of mastitis. The cases where mastitis was the only initial insult occurred evenly throughout the entire lactation, but the cases with many interacting diseases were clustered in early lactation. In MD cows, cases of mastitis occurred concurrently with metritis (31%), aspiration pneumonia (24%), acute trauma or dystocia (15%), or with other diseases, such as ketosis, hepatic lipidosis, rumenitis, and abomasal diseases. For a pathologist, the gross mastitis diagnosis was most challenging at the beginning of the lactation, especially if inflammation was mild to moderate, suggesting the value of histopathologic examination being highest at that time. Also, producers reported mastitis signs less frequently if cow had many simultaneously occurring diseases. Therefore, even if clinical signs of other diseases are present, the udder should be considered a potential cause of illness and should be examined, especially in dry and transition-period cows. [ABSTRACT FROM AUTHOR]

The list of standard abbreviations for JDS is available at adsa.org/jds-abbreviations-24. Nonstandard abbreviations are available in the Notes. This study documents the current state of biosecurity on dairy farms in Québec following the implementation of a mandatory biosecurity risk evaluation that was part of the proAction accreditation program developed by Dairy Farmers of Canada. Using a cross-sectional design, 3,825 risk assessment questionnaires completed between 2018 and 2021 were extracted from Vigil-Vet database, which is a software used by veterinarians for conducting the proAction risk assessment. Descriptive statistics were used to summarize the practices adopted by dairy producers. Additionally, multiple correspondence analysis was used to explore the association between the diseases of most concern and the adoption of biosecurity practices. Moreover, we used a hierarchical cluster analysis on principal components to identify distinct patterns of biosecurity practices among dairy producers. This analysis enabled the identification of typologies or clusters of farms based on the specific biosecurity practices they currently employ. The results of the descriptive statistics indicated that mastitis was the disease of most concern for most dairy farmers (40%). Moreover, given that only 10% of the 2,237 dairy farmers who acquired animals adhered to quarantine practices, there seems to be a need for improved implementation of biosecurity measures aimed at restricting the introduction of diseases when introducing new animals. Conversely, cleaning stalls and health equipment were adequately addressed by 95% and 86% of dairy producers, respectively. The multiple correspondence analysis indicated no significant association between the disease of most concern and the farm's biosecurity profile, except for respondents who identified digital dermatitis as their disease of most concern. Through the hierarchical cluster analysis, 3 clusters were identified among 3,581 farms: (1) Cluster 1 included farms with good management of sick animals; (2) Cluster 2 included farms with good management of young animals; and (3) Cluster 3 included farms with poor management of sick animals and young animals. Our study makes an important contribution by providing valuable insights into the biosecurity practices currently adopted on Québec dairy farms. It establishes a baseline for assessing progress in biosecurity practices adoption and serves as a reference point for future evaluations. In addition, these findings play a key role in monitoring the effectiveness of interventions aimed at improving biosecurity on dairy farms. By making use of this knowledge, stakeholders can make informed decisions that prioritize animal health, increase productivity, and ensure sustainability of the dairy industry. [ABSTRACT FROM AUTHOR]

The list of standard abbreviations for JDS is available at adsa.org/jds-abbreviations-24. Nonstandard abbreviations are available in the Notes. For successful development and adoption of technology on dairy farms, farmers need to be included in the innovation process. However, the design of agricultural technologies usually takes a top-down approach with little involvement of end-users at the early stages. Living Labs offer a methodology that involve end-users throughout the development process and emphasize the importance of understanding users' needs. Currently, exploration of dairy farmers' technology needs has been limited to specific types of technology (e.g., smartphone apps) and adult cattle. The aim of this study was to use a Living Lab approach to identify dairy farmers' data and technology needs to improve herd health and inform innovation development. We conducted 18 focus groups with a total of 80 dairy farmers from Belgium, Ireland, the Netherlands, Norway, Sweden, and the United Kingdom. Data were analyzed using Template Analysis, and 6 themes were generated representing the fundamental needs of autonomy, comfort, competence, community and relatedness, purpose, and security. Farmers favored technologies that provided them with convenience, facilitated their knowledge and understanding of problems on farm, and allowed them to be self-reliant. Issues with data sharing and accessibility and usability of software were barriers to technology use. Furthermore, farmers were facing problems around recruitment and management of labor and needed ways to reduce stress. Controlling aspects of the barn environment, such as air quality, hygiene, and stocking density, were particular concerns in relation to youngstock management. Overall, the findings suggest that developers of farm technologies may want to include farmers in the design process to ensure a positive user experience and improve accessibility. The needs identified in this study can be used as a framework when designing farm technologies to strengthen need satisfaction and reduce any potential harm toward needs. [ABSTRACT FROM AUTHOR]

The distribution of mycotoxins during cheese production and storage is a complex process that depends on several factors, including milk quality, cheese type, storage conditions, and the presence of competing microorganisms. This paper investigates the occurrence and distribution of mycotoxins in curd/cheese/whey during different cheese processes and the effect of ripening/storage time on mycotoxin levels in the final product using a review approach. The paper covers the major mycotoxins that can contaminate cheese, including aflatoxins, ochratoxin‐A, roquefortine‐C, mycophenolic acid, and beauvericin. Overall, it synthesises the existing understanding of the spread of mycotoxins during cheese production and storage. [ABSTRACT FROM AUTHOR]

The effect of using camel milk on the physiochemical, microbiological and sensory properties, and free amino acid and biogenic amine contents of processed Domiati cheese was investigated during pickling (90 days) at 4°C. The results revealed a decrease in biogenic amines and an increase in free amino acids in cheese made from camel milk compared to cheese made from buffalo milk. Histamine and tryptamine were the most predominant amines in all the treatments (13 and 12 mg/kg), respectively. All samples had lower than the allowable level of biogenic amines, according to the Food and Drug Administration (10 mg/100 g). [ABSTRACT FROM AUTHOR]

Summary: Camel milk and its products are gaining higher demand due to their attractive nutritional and therapeutic profile. However, manufacturing of camel milk cheese (CMC) is a challenging task. This study aimed to compare the quality of camel milk cheese made using different starter cultures. Four groups CM (camel milk + mesophilic cultures), CT (camel milk + thermophilic cultures), CBM (camel milk + 10% buffalo milk using mesophilic cultures), and CBT (camel milk + 10% buffalo milk using thermophilic cultures) were created. The cheeses were stored for 60 days at 4 °C to assess their physicochemical, sensory, and proteolytic properties. Results indicated highest moisture content in CM (70.19%) as opposed to the lowest moisture in CBT (55.19%). In addition, CM and CBM revealed lower acidity (0.63% and 0.66%) in contrast with CT and CBT (0.78% and 0.83%, respectively). On the other hand, CBM and CBT exhibited higher contents of protein (21.04, 21.57%) and fat (17.65, 17.70%) during storage. All quality indices of cheese samples significantly declined (P < 0.01) during storage excluding acidity. Sensory analyses revealed that panellists highly preferred CBT treatment followed by CBM and CT. Furthermore, urea‐PAGE and RP‐HPLC were performed for the proteolytic study of cheese samples. The study revealed that the presence of buffalo milk and thermophilic cultures in cheese resulted in a decrease concentration of intact caseins (αs1, β‐, and κ‐caseins) as storage days passed. The degree of proteolysis was found to be higher in CBT compared to CBM, CT, and CM. Based on the results, it was concluded that the use of pasteurisation at 65 °C for 30 min, pH of 5.5, 0.06% CaCl2, and the incorporation of 10% buffalo milk using thermophilic cultures (Lactobaccilus bulgaricus and Streptococcus thermophilus) resulted in desirable quality characteristics. [ABSTRACT FROM AUTHOR]

Simple Summary: This study synthesizes reports on body conformation traits in dairy cows, emphasizing their importance in dairy farming. It elucidates the potential role of these traits in enhancing dairy cattle breeding programs by summarizing their genetic parameters and impact on size, conformation, production, health, and fitness. The review underscores the use of genome-wide association studies (GWAS), a powerful genetic tool that identifies specific genes and genomic regions influencing body conformation. By integrating global research on these genetic markers, the study offers valuable insights for advancing the selection and breeding of dairy cows, ultimately benefiting farmers and the dairy industry through more informed breeding strategies and improved herd quality. Body shape traits are very important and play a crucial role in the economic development of dairy farming. By improving the accuracy of selection for body size traits, we can enhance economic returns across the dairy industry and on farms, contributing to the future profitability of the dairy sector. Registered body conformation traits are reliable and cost-effective tools for use in national cattle breeding selection programs. These traits are significantly related to the production, longevity, mobility, health, fertility, and environmental adaptation of dairy cows. Therefore, they can be considered indirect indicators of economically important traits in dairy cows. Utilizing efficacious genetic methods, such as genome-wide association studies (GWASs), allows for a deeper understanding of the genetic architecture of complex traits through the identification and application of genetic markers. In the current review, we summarize information on candidate genes and genomic regions associated with body conformation traits in dairy cattle worldwide. The manuscript also reviews the importance of body conformation, the relationship between body conformation traits and other traits, heritability, influencing factors, and the genetics of body conformation traits. The information on candidate genes related to body conformation traits provided in this review may be helpful in selecting potential genetic markers for the genetic improvement of body conformation traits in dairy cattle. [ABSTRACT FROM AUTHOR]

Milking methods have significant impacts on the microbiological composition, which could affect the quality of raw buffalo milk. Hence, the current study was conducted on the impact of milking methods on microorganisms in buffalo tank raw milk from 15 farms in Guangxi, China. The farms were divided into two groups based on the milking method: mechanical milking (MM, n = 6) and hand milking (HM, n = 9). Somatic cell counts, bacterial cell counts and nutrients of the raw buffalo milk samples were analyzed. The comparison of raw buffalo milk samples was analyzed using metagenomic sequencing to detect any differences between the two groups. There was no significant difference in the basic nutritional compositions and somatic cell count of raw buffalo milk between the two milking methods. However, the HM samples had significantly higher bacterial counts and diversity compared to the MM samples. The results showed that Staphylococcus spp., Klebsiella spp., Streptococcus spp., and Pseudomonas spp. were the major microbes present in canned raw buffalo milk. However, the differences between the two milking methods were the relative abundance of core microorganisms and their potential mastitis-causing genera, including the content of antibiotic-resistance genes and virulence genes. Our study revealed that Staphylococcus spp. and Streptococcus spp. were significantly more abundant in the MM group, while Klebsiella spp. was more abundant in the HM group. Regardless of the milking method used, Pseudomonas spp. was identified as the primary genus contributing to antibiotic resistance and virulence genes in canned raw buffalo milk. These findings affirm that there are differences in the microbial and genomic levels in canned raw milk. To prove the functional roles of the discovered genes and how these genes affect milk quality, further research and experimental validation are necessary. [ABSTRACT FROM AUTHOR]

In contact with water, chia seeds release mucilage (MC), which is a source of various health-promoting compounds including dietary fibres. MC has been previously used as a thickening agent in cow milk yoghurt, but there are no available data on its application in goat milk. In this study, three goat milk yoghurts (without—MC0, with 1.5%—MC15 and with 3% mucilage—MC30) were produced. The rheology, texture, microbial counts, syneresis, microstructure and sensory acceptance of the yoghurts were investigated. The MC addition resulted in a reduced hysteresis area, but increased yoghurt viscosity at lower shear rates. It also improved all texture parameters at both concentration levels, while syneresis values were reduced only in sample MC30. The MC addition promoted lactobacilli viability in both supplemented yoghurts. The texture perceived by sensory evaluation was rated the highest for the sample MC30, which was also the most accepted by consumers overall. Critical attributes that reduced the acceptability of all yoghurts were flavour and acidity. In conclusion, chia seed mucilage can be used as a functional ingredient in goat milk yoghurt to produce an innovative dairy product and meet consumer expectations. [ABSTRACT FROM AUTHOR]

Milk fat globules (MFGs) are produced by mammary epithelial cells (MECs) and originate from intracellular lipid droplets with a wide size distribution. In the mammary gland and milk, bacteria can thrive on MFGs. Herein, we aimed to investigate whether the response of MECs to the bacterial secretome is dependent on the MFG size used as a substrate for the bacteria, and whether the response differs between pathogenic and commensal bacteria. We used secretomes from both Bacillus subtilis and E. coli. Proinflammatory gene expression in MECs was elevated by the bacteria secretomes from both bacteria sources, while higher expression was found in cells exposed to the secretome of bacteria grown on large MFGs. The secretome of B. subtilis reduced lipid droplet size in MECs. When the secretome originated from E. coli, lipid droplet size in MEC cytoplasm was elevated with a stronger response to the secretome from bacteria grown on large compared with small MFGs. These results indicate that MEC response to bacterial output is modulated by bacteria type and the size of MFGs used by the bacteria, which can modulate the stress response of the milk-producing cells, their lipid output, and consequently milk quality. [ABSTRACT FROM AUTHOR]

The wide adoption of Docker containers for supporting agile and elastic enterprise applications has led to a broad proliferation of container images. The associated storage performance and capacity requirements place a high pressure on the infrastructure of container registries that store and distribute images and container storage systems on the Docker client side that manage image layers and store ephemeral data generated at container runtime. The storage demand is worsened by the large amount of duplicate data in images. Moreover, container storage systems that use Copy-on-Write (CoW) file systems as storage drivers exacerbate the redundancy. Exploiting the high file redundancy in real-world images is a promising approach to drastically reduce the growing storage requirements of container registries and improve the space efficiency of container storage systems. However, existing deduplication techniques significantly degrade the performance of both registries and container storage systems because of data reconstruction overhead as well as the deduplication cost. We propose DupHunter, an end-to-end deduplication scheme that deduplicates layers for both Docker registries and container storage systems while maintaining a high image distribution speed and container I/O performance. DupHunter is divided into three tiers: registry tier, middle tier, and client tier. Specifically, we first build a high-performance deduplication engine at the registry tier that not only natively deduplicates layers for space savings but also reduces layer restore overhead. Then, we use deduplication offloading at the middle tier to eliminate the redundant files from the client tier and avoid bringing deduplication overhead to the clients. To further reduce the data duplicates caused by CoWs and improve the container I/O performance, we utilize a container-aware storage system at the client tier that reserves space for each container and arranges the placement of files and their modifications on the disk to preserve locality. Under real workloads, DupHunter reduces storage space by up to 6.9× and reduces the GET layer latency up to 2.8× compared to the state-of-the-art. Moreover, DupHunter can improve the container I/O performance by up to 93% for reads and 64% for writes. [ABSTRACT FROM AUTHOR]

Climate change is a global problem with an important influence on farm animals, so the entire veterinary medical industry is working to combat the effects of heat stress. In recent years, global warming has been correlated with physiological changes in adaptation that lead to a decrease in milk production and quality. We have chosen to study these mechanisms that are based on hormonal imbalances (LH, TSH, and prolactin) and general imbalances (apathy and lack of appetite). [ABSTRACT FROM AUTHOR]

Simple Summary: Mastitis is an infection of the mammary glands that affects yaks' health and milk quality. The various bacteria found in mastitis-affected yaks were examined using a technique for reading particular genetic information. We discovered that contaminated milk had higher concentrations of bacteria than healthy milk. Our findings highlight the complexity and irregularity in the milk microbiome of yaks. Understanding these bacterial communities may aid in developing more effective mastitis control measures and enhance the health and milk quality of yaks, thereby benefiting dairy producers and yak farmers. Mastitis is an inflammation of the mammary gland that can be caused by various factors, including biological, chemical, mechanical, or physical. Microbiological culture, DNA techniques, and high-throughput next-generation sequencing have been used to identify mastitis-causing pathogens in various animal species. However, little is known about microbiota and microbiome changes linked to yak milk mastitis. This study aimed to characterize the milk microbiota of healthy and mastitis-infected yaks using full-length 16S rRNA sequencing. The results showed that the bacterial microbiota comprises 7 phyla, 9 classes, 20 orders, 39 families, 59 genera, and 72 species. Proteobacteria and Firmicutes were the predominant microbial communities, with lower abundances of Bacteroidota, Actinobacteriota, Acidobacteriota, and other minor groupings also observed. Proteobacteria dominated the clinical and subclinical mastitis groups (95.36% and 89.32%, respectively), in contrast to the healthy group (60.17%). Conversely, Firmicutes were more common in the healthy group (39.7%) than in the subclinical and clinical mastitis groups (10.49% and 2.92%, respectively). The predominant organisms found in the healthy group were Leuconostoc mesenteroides, Lactococcus piscium, Carnobacterium maltaromaticum, and Lactococcus raffinolactis. Low abundances of Staphylococcus aureus species were found in both subclinical and clinical mastitis groups, with Moraxella osloensis and Psychrobacter cibarius dominating the subclinical mastitis group and Pseudomonas fluorescens dominating the clinical mastitis group. An alpha diversity study revealed that the healthy group had a higher microbial diversity than the clinical and subclinical mastitis groups. According to beta-diversity analysis, the principal coordinate analysis identified that mastitis-infected samples significantly differed from healthy ones. The milk microbiota of healthy yaks is more varied, and specific prominent taxa within various groups can act as marker microorganisms for mastitis risk. The genera Leuconostoc and Lactococcus are promising candidates for creating probiotics. [ABSTRACT FROM AUTHOR]

Indoor localization for livestock is important as it facilitates effective monitoring and management of animals within confined spaces, such as barns or stables. By accurately tracking the position of individual animals, farmers and livestock managers can gain valuable insights into their behavior, health, and welfare. This information enables the early detection of potential issues, such as diseases or injuries, allowing for prompt intervention and treatment. While GPS sensors offer global position estimation, they are limited to outdoor environments and inherently exhibit inaccuracies of several meters. In indoor spaces, alternative sensors like lasers and cameras can estimate positions, but they necessitate maps and substantial computational resources to process complex algorithms. Presently, Wireless Networks (WN) are extensively accessible in indoor environments, providing efficient global localization with relatively low cost and computing demands. This paper presents a novel approach to estimate the location of cows in a given area using Deep Neural Networks (DNNs) applied to LQI data. This method aims to improve the efficiency of livestock management, particularly in large-scale farming operations, by enabling precise tracking and monitoring of individual animals. Our proposed model leverages data from wireless sensor networks (WSNs) and demonstrates promising results in terms of accuracy and computational efficiency. This study contributes to the ongoing research in smart agriculture and the application of advanced technologies in the livestock industry. [ABSTRACT FROM AUTHOR]

Introduction: As the intermediate nucleus in the brainstem receiving information from the tongue and transmitting information upstream, the rostral portion of the nucleus tractus solitarius (rNTS) is most often described as a "taste relay". Although recent evidence implicates the caudal NTS in a broad neural circuit involved in regulating ingestion, there is little information about how cells in the rNTS respond when an animal is eating solid food. Methods: Single cells in the rNTS were recorded in awake, unrestrained rats as they explored and ate solid foods (Eating paradigm) chosen to correspond to the basic taste qualities: milk chocolate for sweet, salted peanuts for salty, Granny Smith apples for sour and broccoli for bitter. A subset of cells was also recorded as the animal licked exemplars of the five basic taste qualities: sucrose, NaCl, citric acid, quinine and MSG (Lick paradigm). Results: Most cells were excited by exploration of a food-filled well, sometimes responding prior to contact with the food. In contrast, cells that were excited by food well exploration became significantly less active while the animal was eating the food. Most cells were broadly tuned across foods, and those cells that were recorded in both the Lick and Eating paradigms showed little correspondence in their tuning across paradigms. Discussion: The preponderance of robust responses to the appetitive versus the consummatory phase of ingestion suggests that multimodal convergence onto cells in the rNTS may be used in decision making about ingestion. [ABSTRACT FROM AUTHOR]

Bedding materials are aimed at providing a safe and comfortable resting environment for cows. Control of pathogen proliferation in these substrates is crucial to prevent intramammary infections in dairy cows, as these can significantly impact milk quality, cow health, and farm productivity. This is particularly relevant in the case of organic bedding substrates, includingmanure-derived materials. This study aimed to evaluate the in vitro effect of a lime-based conditioner (LBC), composed of CaCO3MgCO3 and Ca(OH)* 2Mg(OH)2, at increasing concentrations on the physical-chemical characteristics and bacterial counts of untreated anaerobically digested manure solids (ADMS) and separated raw manure solids (SRMS). Unused ADMS and SRMS were evaluated at four LBC weight-based concentrations: 0 (as untreated control), 10, 15, and 20% of LBC inclusion. The bedding materials were assessed immediately after LBC addition (0 h) and after 24, 72, and 168 h of storage at 28?C. The dry matter content (DM), and pH weremeasured for all the time points. Standardmicrobiologicalmethods were used to assess total bacterial counts (TBC), other Gram-negative bacteria, coliforms, Escherichia coli, and streptococci and streptococci-like organism (SSLO). It was observed a linear increase in both DM and pH with increasing concentrations of LBC. Specifically, for each percentage unit increase of LBC, the DM of ADMS and SRMS increased by 0.73 and 0.71%, respectively. Similarly, for each percentage unit of LBC, the pH of ADMS and SRMS increased by 0.15 and 0.19, respectively. Conversely, a linear decrease in TBC, Gram-negative bacteria, coliforms, E. coli, and SSLO was observed with increasing concentrations of the LBC. Manure-derived materials without the inclusion of the LBC had bacterial counts that tended to remain high or increase over time. Otherwise, bedding materials with LBC application had reduced bacterial counts. Based on the results of the present study, it was observed that the higher the concentration of LBC, the more significant the reduction of bacterial counts. Specifically, bacterial recovery was lower when higher concentrations of LBC were applied. Our findings underscore the potential of LBC in effectively controlling environmental bacteria and improving the physical-chemical characteristics of manure-derived bedding materials to improve cow health and welfare. [ABSTRACT FROM AUTHOR]

Simple Summary: Artificial intelligence has become essential for aiding in different knowledge domains by improving knowledge extraction from raw data and process automation. In dairy production, artificial intelligence offers promising applications in detecting and managing bovine mastitis, the most critical disease affecting the mammary gland in dairy cows, impacting milk production and profitability in dairy farms. This research evaluated the evolution of artificial intelligence applications in bovine mastitis between 2011 and 2021 using the Scopus database and the frequency of terms cited in titles, abstracts, and keywords. We selected the 62 papers that were the most relevant according to their citation index. Our results pointed out that the terms "machine learning" and "mastitis" were the most cited, with a significant increase between 2018 and 2021. There was an increase in artificial intelligence applications for bovine mastitis per country, showing applications primarily aimed at improving the current mastitis detection systems. The most cited model was artificial neural networks. We concluded that using artificial intelligence in bovine mastitis was related to mastitis detection as a vital tool to prevent this disease, considering its major impacts on dairy production and economic return. Mastitis, an important disease in dairy cows, causes significant losses in herd profitability. Accurate diagnosis is crucial for adequate control. Studies using artificial intelligence (AI) models to classify, identify, predict, and diagnose mastitis show promise in improving mastitis control. This bibliometric review aimed to evaluate AI and bovine mastitis terms in the most relevant Scopus-indexed papers from 2011 to 2021. Sixty-two documents were analyzed, revealing key terms, prominent researchers, relevant publications, main themes, and keyword clusters. "Mastitis" and "machine learning" were the most cited terms, with an increasing trend from 2018 to 2021. Other terms, such as "sensors" and "mastitis detection", also emerged. The United States was the most cited country and presented the largest collaboration network. Publications on mastitis and AI models notably increased from 2016 to 2021, indicating growing interest. However, few studies utilized AI for bovine mastitis detection, primarily employing artificial neural network models. This suggests a clear potential for further research in this area. [ABSTRACT FROM AUTHOR]

Antimicrobial use (AMU) data are essential to monitor the effect of AMU reduction strategies in animal health. The use of technology and herd recording software to record AMU will be vital to scale the collation of these data in the future. The aim of this study was to determine the barriers and facilitators to Irish dairy farmers recording their AMU using a herd recording software and sharing AMU data. Thirty-three Irish dairy farmers involved in a study on AMU monitoring were asked to record their AMU using a herd recording software over a 12-mo period. At the end of the 12-mo period, 10 of these farmers were selected to take part in semi-structured interviews exploring their opinions on recording AMU, the use of herd recording software, and sharing AMU data. Interviews were transcribed and qualitatively analyzed using inductive thematic analysis. Several barriers and facilitators to farmers recording their AMU using a herd recording software and sharing AMU data were identified. Barriers included the age and generation of the farmer, farm infrastructure, a lack of training and education, a lack of knowledge around the benefits of digital data, a lack of incentive to digitize records, and a fear of repercussions. Facilitators identified by the farmers included the benefits of having instantly available data for making herd management decisions, reduced paperwork, increased organization for inspections, and a potential positive effect on the image of the dairy industry. To increase the uptake of new technology to record AMU at farm-level, farmers will need support in terms of education and training around the software available to them and reassurance around the perceived risks of repercussions with sharing data in a digital format. [ABSTRACT FROM AUTHOR]

Identifying genome-enabled methods that provide more accurate genomic prediction is crucial when evaluating complex traits such as dairy cow behavior. In this study, we aimed to compare the predictive performance of traditional genomic prediction methods and deep learning algorithms for genomic prediction of milking refusals (MREF) and milking failures (MFAIL) in North American Holstein cows measured by automatic milking systems (milking robots). A total of 1,993,509 daily records from 4,511 genotyped Holstein cows were collected by 36 milking robot stations. After quality control, 57,600 SNPs were available for the analyses. Four genomic prediction methods were considered: Bayesian least absolute shrinkage and selection operator (LASSO), multiple layer perceptron (MLP), convolutional neural network (CNN), and GBLUP. We implemented the first 3 methods using the Keras and TensorFlow libraries in Python (v.3.9) but the GBLUP method was implemented using the BLUPF90+ family programs. The accuracy of genomic prediction (mean square error) for MREF and MFAIL was 0.34 (0.08) and 0.27 (0.08) based on LASSO, 0.36 (0.09) and 0.32 (0.09) for MLP, 0.37 (0.08) and 0.30 (0.09) for CNN, and 0.35 (0.09) and 0.31(0.09) based on GBLUP, respectively. Additionally, we observed a lower reranking of top selected individuals based on the MLP versus CNN methods compared with the other approaches for both MREF and MFAIL. Although the deep learning methods showed slightly higher accuracies than GBLUP, the results may not be sufficient to justify their use over traditional methods due to their higher computational demand and the difficulty of performing genomic prediction for nongenotyped individuals using deep learning procedures. Overall, this study provides insights into the potential feasibility of using deep learning methods to enhance genomic prediction accuracy for behavioral traits in livestock. Further research is needed to determine their practical applicability to large dairy cattle breeding programs. [ABSTRACT FROM AUTHOR]

Portable infrared-based instruments have made important contributions in different research fields. Within the dairy supply chain, for example, most of portable devices are based on near-infrared spectroscopy (NIRS) and are nowadays an important support for farmers and operators of the dairy sector, allowing fast and real-time decision-making, particularly for feed and milk quality evaluation and animal health and welfare monitoring. The affordability, portability, and ease of use of these instruments have been pivotal factors for their implementation on farm. In fact, pocket-sized devices enable nonexpert users to perform quick, low-cost, and nondestructive analysis on various matrixes without complex preparation. Because bovine colostrum (BC) quality is mostly given by the IgG level, evaluating the ability of portable NIRS tools to measure antibody concentration is advisable. In this study we used the wireless device SCiO manufactured by Consumer Physics Inc. (Tel Aviv, Israel) to collect BC spectra and then attempt to predict IgG concentration and gross and fine composition in individual samples collected immediately after calving (<6 h) in primiparous and pluriparous Holstein cows on 9 Italian farms. Chemometric analyses revealed that SCiO has promising predictive performance for colostral IgG concentration, total Ig concentration, fat, and AA. The coefficient of determination of cross-validation (R2 CV) was in fact ≥0.75). Excellent accuracy was observed for dry matter, protein, and S prediction in cross-validation and good prediction ability in external validation (R2 CV ≥ 0.93; the coefficient of determination of external validation, R2 V , was ≥0.82). Nonetheless, SCiO's ability to discriminate between good- and low-quality samples (IgG ≥ vs. < 50 g/L) was satisfactory. The affordable cost, the accurate predictions, and the user-friendly design, coupled with the increased interest in BC within the dairy sector, may boost the collection of extensive BC data for management and genetic purposes in the near future. [ABSTRACT FROM AUTHOR]

Currently, the dairy industry is facing many challenges that could affect its sustainability, including climate change and public perception of the industry. As a result, interest is increasing in the concept of identifying resilient animals, those with a long productive lifespan, as well as good reproductive performance and milk yield. There is much evidence that events in utero, that is, the developmental origins of health and disease hypothesis, alter the life-course health of offspring and we hypothesized that these could alter resilience in calves, where resilience is identified using lifetime data. The aim of this study was to quantify lifetime resilience scores (LRS) using an existing scoring system, based on longevity with secondary corrections for age at first calving and calving interval, and to quantify the effects of in utero events on the LRS using 2 datasets. The first was a large dataset of cattle on 83 farms in Great Britain born from 2006 to 2015 and the second was a smaller, more granular dataset of cattle born between 2003 and 2015 in the Langhill research herd at Scotland's Rural College. Events during dam's pregnancy included health events (lameness, mastitis, use of an antibiotic or anti-inflammatory medication), the effect of heat stress as measured by temperature-humidity index, and perturbations in milk yield and quality (somatic cell count, percentage fat, percentage protein and fat:protein ratio). Daughters born to dams that experienced higher temperature-humidity indexes while they were in utero during the first and third trimesters of pregnancy had lower LRS. Daughter LRS were also lower where milk yields or median fat percentages in the first trimester were low, and when milk yields were high in the third trimester. Dam LRS was positively associated with LRS of their offspring; however, as parity of the dam increased, LRS of their calves decreased. Similarly, in the Langhill herd, dams of a higher parity produced calves with lower LRS. Additionally, dams that recorded a high maximum locomotion score in the third trimester of pregnancy were negatively associated with lower calf LRS in the Langhill herd. Our results suggest that events that occur during pregnancy have lifelong consequences for the calf's lifetime performance. However, experience of higher temperature-humidity indexes, higher dam LRS, and mothers in higher parities explained a relatively small proportion of variation in offspring LRS, which suggests that other factors play a substantial role in determining calf LRS. Although "big data" can contain a considerable amount of noise, similar findings between the 2 datasets indicate it is likely these findings are real. [ABSTRACT FROM AUTHOR]

Buffalo milk, recognized for its rich composition, including higher levels of fat, lactose, protein, and ash when compared to cow milk, has been insufficiently studied considering its genetic diversity within the buffalo population in Serbia. This study focuses on the analysis of the CSN1S1 472G>C variant and its potential impact on rennet coagulation properties, critical for cheese production. Rennet coagulation parameters (rennet coagulation time (RCT), setting time (ST), aggregation rate (AR), and gel firmness (GF)), along with fresh curd yield (Yf), were examined in both, raw and high heat-treated buffalo milk. Genotyping results, obtained through Sanger sequencing of blood samples from 30 female buffaloes, revealed the presence of three CSN1S1 472G>C genotypes: GG, GC, and CC. The rheological measurements, conducted using dynamic small amplitude oscillatory rheology (SAOR), indicated significant statistical differences (p<0.05) among the tested parameters for these CSN1S1 472G>C genotypes in both raw and heat-treated buffalo milk. In addition, the influence of genotype on the yield of fresh curd in fresh and thermally treated milk was studied. These results can contribute to optimization in the process of production and processing of buffalo milk, emphasizing the importance of understanding the genetic factors in the composition of buffalo milk and its technological properties. [ABSTRACT FROM AUTHOR]

Around 10.5 million tonnes of sheep milk are produced worldwide, and around 3.1 million tonnes in Europe. In Europe, sheep milk production has stagnated slightly in recent years and currently accounts for around 1/3 of the global production. Due to its high content of dry matter, milk fat, total protein and casein, sheep milk is an excellent raw material for cheese production. Milk processing and cheese making are not only economically important, but also part of the cultural heritage based on a long tradition, often taking place in rural and less developed areas. Milk production and quality are very complex and are influenced by numerous factors, the most important of which are breed, feeding and body condition, parity and stage of lactation, milking and udder health, and environmental factors. In addition, different types of stress such as heat stress, nutritional stress, weaning stress, shearing stress, stress of regrouping, etc. have a significant impact on the welfare and health of the sheep as well as on the quantity of milk produced, its chemical composition, processing characteristics and the economic efficiency of the flock. In recent years, due to climate change, there has been an increasing number of studies on the effects of heat stress on the reproductive and production characteristics of sheep, as well as on their welfare and health. In addition, heat stress is often associated to the feed and the lack of water. The effects of stress are particularly pronounced in extensive sheep farming and milk production, which are prevalent in sheep farming. The aim of this paper is to describe the effects of the main types of stress on the quantity and chemical composition of sheep milk and on animal welfare. [ABSTRACT FROM AUTHOR]

Simple Summary: In Italy, buffalo milk is mainly transformed into 'Mozzarella di Bufala Campana', a Protected Designation of Origin (PDO) cheese. A quick method for predicting the coagulation properties of the milk before cheese production could enhance the efficiency of the industry. Therefore, the aim of this paper was to evaluate the potential use of mid-infrared spectroscopy to predict milk coagulation traits in bulk milk from Mediterranean Italian buffaloes. A total of 1736 bulk milk samples from 55 farms in central Italy were analyzed. Prediction models using mid-infrared spectroscopy were built with a modified partial least-squares regression using an external validation dataset. The best prediction model was obtained for curd firmness, but it was still inaccurate enough to replace traditional methods. This study evaluated the potential use of mid-infrared spectroscopy to predict milk coagulation traits in bulk milk from Mediterranean Italian buffaloes. A total of 1736 bulk milk samples from 55 farms in central Italy were collected during the official milk quality testing system. The prediction models were developed based on modified partial least-squares regression with 75% of the samples and validated with the remaining samples. All bulk milk samples coagulated between 7.37 and 29.45 min. Average values for milk coagulation traits in the calibration set were 17.71 min, 3.29 min, and 38.83 mm for rennet coagulation time, curd firming time, and curd firmness, respectively. The validation set included samples with similar mean and standard deviation for each trait. The prediction models showed the greatest coefficient of determination of external validation (0.57) and the ratio of prediction to deviation (1.52) for curd firmness. Similar fitting statistics of the prediction models were obtained for rennet coagulation time and curd firming time. In conclusion, the prediction models for all three coagulation traits were below the threshold to consider the prediction models adequate even for rough screening of the samples. [ABSTRACT FROM AUTHOR]

Methane emissions from ruminant livestock, particularly cows, have been a topic of interest because of their negative environmental impact, including contributing to greenhouse gas accumulation and accelerating climate change. Understanding the cow's rumen microbiome can provide valuable insights for devising effective emission reduction strategies. Despite the recognized environmental impact of methane emissions from cows, however, a systematic review on the cow rumen microbiome has not yet been done. This study aims to fill this gap by performing an unbiased bibliometric analysis to review the effect of the rumen microbiome on methane emissions through cross-check of multiple data sources. While the bibliometric analysis offers a quantitative approach to assess research trends and identify areas of potential exploration within the field of the cow rumen microbiome, this study also evaluated the concurrently growing interest in the potential of artificial intelligence (AI) tools, specifically ChatGPT by OpenAI in terms of its efficacy and accuracy in enhancing scientific research project. Through the fusion of conventional research methodologies and AI-driven insights, this study aspires to provide a holistic perspective on the cow rumen microbiome's profound influence on methane emissions, while also exploring the untapped potential of AI in advancing scientific inquiry. [ABSTRACT FROM AUTHOR]

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This study aimed to evaluate the effects of the microalgae (MIAs) Schizochytrium sp. as a dietary supplement for dairy cows with respect to the yield, composition, and fatty acid profile of milk using a meta-analytical method. The data used in the statistical analyses were obtained from 11 peer-reviewed scientific publications. The effect size was assessed using the weighted mean differences (WMDs) between MIA-supplemented and control treatments. Dry matter intake, milk fat yield, and milk fat content decreased (p < 0.001) in response to the dietary inclusion of Schizochytrium sp. MIAs. However, Schizochytrium sp. MIAs supplementation increased (p = 0.029) milk yield. The dietary inclusion of Schizochytrium sp. MIAs decreased (p < 0.05) the content of the fatty acids (FAs) butyric, caproic, caprylic, capric, undecanoic, lauric, pentadecanoic, palmitic, heptadecanoic, stearic, arachidic, and total saturated FAs, and it resulted in a ω-6/ω-3 ratio in milk. In contrast, Schizochytrium sp. MIAs supplementation increased (p < 0.05) the content of linoleic, conjugated linoleic, eicosapentaenoic, behenic, docosahexaenoic, total monounsaturated FAs, total polyunsaturated FAs, and total omega-3 FAs in milk. The results showed that Schizochytrium sp. MIAs could be used as a dietary supplement to improve the milk yield and fatty acid profile of milk obtained from dairy cows. [ABSTRACT FROM AUTHOR]

Our study evaluated the impact of incorporating flax seeds or a flax and mustard seeds' mixture into goats' diets to enhance milk polyunsaturated fatty acids (PUFAs). The incorporation of mustard seeds also aimed to slow the lipid oxidation process. A three-week feeding trial was conducted on 18 lactating goats, randomly distributed in three groups: control (C), FS (replacing 12% of the oil source with flax seeds), and FMS (replacing a quarter of flax seeds with mustard seeds). Flax seed inclusion improved the quality of milk fat by reducing saturated fatty acid concentration (p = 0.004) and increasing PUFA levels (p = 0.001). Both experimental groups significantly lowered the omega 6/omega 3 fatty acid ratio (p < 0.001). The FMS group showed a significantly higher total vitamin E concentration (p = 0.007). The fat oxidation parameters revealed that after 24 h of storage at room temperature, the p-anisidine value increased for the FS group compared to the C group, while the FSM group showed no significant difference, suggesting that the combined inclusion of flax and mustard seeds may prolong milk storage time by mitigating secondary oxidation products. This highlights the potential benefits of incorporating the studied seeds into goats' diets for improving milk quality and extending its shelf life. [ABSTRACT FROM AUTHOR]

Objective: To obtain a machine learning (ML) model to predict the milk yield adjusted to 305 d (MY305) from the same lactation period. Design/methodology/approach: A database of test days (TD) was used, made up by 11,892 records of daily milk production from cows with more than 150 days in milk (DIM), from 19 farms in Querétaro, Mexico. The milk production was standardized to specific DIMs (5, 10, 20, 30 and 40) and estimations of MY305 were obtained with these, using ML models. The following were also incorporated as explicative variables of the herd: month of birth of the cow, month of start of lactation, number of lactation, number of days for three daily milking events, and the two first linear scores of somatic cells. Results: The best goodness of fit was achieved with ensemble models, obtaining a deviance of 1503584 in the training with 80% of data chosen randomly, while with 20% of the data reserved to evaluate the deviance model it was 1576776. The relationship between data observed and predictions of MY305 of the ensemble models had a coefficient of determination of r²=0.79 and RMSE of 1256. In the best individual model (deviance of 2281420) of ‘deep learning’ type, the most important variables were daily milk production at 30, 10, 5 and 20 DIM (19.9, 16.6, 16.2 and 12.8%, respectively). Limitations on study/implications: The value of RMSE was high. Although TD databases are generated regularly and following systematic measurement procedures but not many farms are represented. Findings/conclusions: For the database examined, milk production in the early phase of lactation together with a set of automatic learning models resulted in an adequate prediction of MY305. [ABSTRACT FROM AUTHOR]

Kefir milk, known for its high nutritional value and health benefits, is traditionally produced by fermenting milk with kefir grains. These grains are a complex symbiotic community of lactic acid bacteria, acetic acid bacteria, yeasts, and other microorganisms. However, the intricate coexistence mechanisms within these microbial colonies remain a mystery, posing challenges in predicting their biological and functional traits. This uncertainty often leads to variability in kefir milk's quality and safety. This review delves into the unique structural characteristics of kefir grains, particularly their distinctive hollow structure. We propose hypotheses on their formation, which appears to be influenced by the aggregation behaviors of the community members and their alliances. In kefir milk, a systematic colonization process is driven by metabolite release, orchestrating the spatiotemporal rearrangement of ecological niches. We place special emphasis on the dynamic spatiotemporal changes within the kefir microbial community. Spatially, we observe variations in species morphology and distribution across different locations within the grain structure. Temporally, the review highlights the succession patterns of the microbial community, shedding light on their evolving interactions.Furthermore, we explore the ecological mechanisms underpinning the formation of a stable community composition. The interplay of cooperative and competitive species within these microorganisms ensures a dynamic balance, contributing to the community's richness and stability. In kefir community, competitive species foster diversity and stability, whereas cooperative species bolster mutualistic symbiosis. By deepening our understanding of the behaviors of these complex microbial communities, we can pave the way for future advancements in the development and diversification of starter cultures for food fermentation processes. [ABSTRACT FROM AUTHOR]

This study proposed using color components as artificial intelligence (AI) input to predict milk moisture and fat contents. In this sense, an adaptive neuro‐fuzzy inference system (ANFIS) was applied to milk processed by moderate electrical field‐based non‐thermal (NP) and conventional pasteurization (CP). The differences between predicted and experimental data were not significant (p > 0.05) for lightness (L*), redness‐greenness (a*), yellowness‐blueness (b*), total color differences (∆E), hue angle (h), chroma (C), whiteness (WI), yellowness (YI), and browning index (BI). ANFIS well‐predicted milk fat and moisture content using quadratic and two‐factor interaction models with mean errors of.00858–.01260 and correlation coefficient of.8051–.8205. Stability tests showed L* and WI reduced while a*, b*, ΔE, h, C, YI, and BI increased during the storage. NP milk had 77.21% higher half‐life than CP, as predicted by ANFIS modeling. Findings indicated milk quality characteristics could be estimated based on physical parameters (e.g., color components), contributing to sustainable food production. Practical applications: The findings offer practical applications of artificial intelligence (AI) as an innovative monitoring and prediction technique to enhance food quality and sustainability. The proposed methodology makes the real‐time prediction of milk quality feasible by leveraging AI and physical parameters. An adaptive neuro‐fuzzy inference system (ANFIS) accurately predicts moisture and fat contents according to color values, facilitating quality assessment. Stability tests during cold storage provide insights into milk quality changes over time, aiding in determining key parameters in predictive modeling. The proposed approach was found to be applicable to both conventional and non‐thermal pasteurized milk. This study also provides a step‐by‐step protocol, facilitating the implementation of emerging technologies in the food industry. [ABSTRACT FROM AUTHOR]

ABSTRACT: Moving from conventional (CMS) to automatic (AMS) milking systems could affect milk quality. Moreover, the type and preservation methods of the forages used in the TMR, such as alfalfa hay (HTMR) or corn silage (STMR) have been demonstrated to modify milk composition. Thus, this study investigated the effect of implementing AMS and different diet forage types on the quality of Italian Holstein-Friesian bulk milk. Milk samples (n = 168) were collected monthly from 21 commercial farms in northern Italy during a period of 8 mo. Farms were categorized into 4 groups according to their milking system (CMS vs. AMS) and diet forage type (HTMR vs. STMR). Milk quality data were analyzed through the mixed procedure for repeated measurement of SAS with the milking system, diet forage type, and sampling day as fixed effects. Milking through the AMS led to lower milk fat, freezing point, and β-LG A; longer coagulation time; and higher K content, pH, and β-LG B than CMS. Cows fed STMR produced milk with greater fat, protein, casein, Mg content, titratable acidity, and β-LG A, but with reduced curd firming time, freezing point, and β-LG B than those fed HTMR. In conclusion, milk quality is not only altered by the diet's forage type and characteristics but also by the milking system.