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The cytokine context present in the reproductive tract of cows is closely involved in normal uterine functions, including the estrous cycle and the establishment and maintenance of pregnancy. However, the roles of some cytokines in the uterus, and their relation with reproductive performance remain to be elucidated. Thus, this study aimed to examine the protein expression of several cytokines such as TNFα, IL-6, IL-8, IFNγ, IL-4, and TGF-ß3 in endometrial biopsies previous to conception, to evaluate the possible association with delayed conception in dairy cows. Protein expression levels were evaluated by immunohistochemistry. Results showed that the protein expression levels of TNFα, IL-6, IL-4 and TGF-ß3 were not associated with the parturition-conception interval, whereas the high protein expression levels of IFNγ were associated with the parturition-conception interval. Finally, the low protein expression of IL-8 showed a statistical tendency to be associated with delayed conception. This is the first report about the protein expression of IFN-γ in the endometrium of dairy cows and also, this cytokine could enhance the favorable conditions to achieve an early pregnancy.

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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.

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The objective of the study was to determine whether adding grape seed oil (GSO) to the diet of primiparous Jersey breeds during the transition period would improve animal health by measuring effects on the rumen environment, serum biochemistry, oxidative response, and the composition and quality of milk. We used 14 Jersey heifers, weighing an average of 430 kg and 240 days of gestation. The animals were divided into two groups and offered a basal diet, including GSO in the concentrate for the GSO group (dose of 25 mL per animal day) and the same dose of soybean oil (SO) for the control group. The animals were allocated and maintained in a compost barn system, receiving an anionic diet (pre-partum) and a diet for postpartum lactating animals. Dry matter intake (DMI), milk production, serum biochemistry, serum and milk oxidative stability, ruminal fluid and milk fatty acid profile, milk qualitative aspects, and ruminal parameters such as pH, bacterial activity, and protozoan count were evaluated. The addition of GSO had a positive effect on the health of the cows, especially on the oxidative stability of the cows, by increasing total thiols (P = 0.03), higher plasma ferric reducing capacity (FRAP) (P = 0.01), and total antioxidant capacity (TAC) (P = 0.01). In the oxidative stability of the milk produced by the treated animals, there was also an increase in TAC (P = 0.05) and FRAP (P = 0.03). Discreet changes were observed in the ruminal environment with a decreasing trend in pH (P = 0.04) but an increase in bacterial activity (P = 0.05) and protozoa counts (P = 0.07) in cows that consumed the additive. GSO consumption affected the fatty acid profile in milk, increasing saturated fatty acids (SFA) (P = 0.05) and reducing unsaturated fatty acids (UFA) (P = 0.03). The oil did not affect milk production or efficiency in the postpartum period. Based on this information, it is concluded that the addition of GSO positively affects the cow's antioxidant system.

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Maternal status during the transition period can significantly impact the health and performance of Holstein dairy calves, with lasting effects on various variables. However, the relationship between maternal late gestation metabolic status, seasonality, and their impact on offspring remains unclear. This study aimed to assess the influence of maternal variables at calving on the performance, metabolism, and immunity of 28 dairy calves during their first month of life. Blood samples were collected from 28 Holstein cows at calving. Median results for maternal variables including non-esterified fatty acids (NEFA), ß-hydroxybutyrate (BHB), glucose, total protein (TP), albumin, triglycerides (TG), total cholesterol (TC), haptoglobin (Hp), body weight (BW), and body condition score (BCS) were determined. These median values served as a basis for categorizing the offspring into two groups based on their dams' high or low degree of each maternal variable. Additionally, calves were categorized by the season of birth (Spring vs. Winter), with 14 in each. Blood samples were collected from the calves at birth and on days 1, 7, 14, and 28 to assess IgG, biochemical parameters, and haptoglobin concentration. Reactive oxygen species (ROS) production by polymorphonuclear cells stimulated by various agents was also evaluated. Clinical assessments were conducted for diarrhea and bovine respiratory disease frequencies. Despite the overall health of the cows, differences were observed in the calves between maternal groups. Heavier cows with high maternal BCS tended to have larger offspring, while high maternal BCS was associated with increased diarrhea prevalence. Low maternal BCS resulted in a stronger innate immune response, indicated by higher ROS production. Calves from cows experiencing metabolic changes during calving displayed elevated Hp concentrations. Spring-born calves were larger but had lower serum IgG concentration and reduced innate immune response compared to winter-born calves. Additionally, spring-born calves exhibited higher Hp and increased diarrhea prevalence on day 28. These findings underscore the importance of the prenatal period in determining neonatal health and suggest further research to elucidate the long-term clinical implications of maternal effects on offspring health and growth. Investigating offspring constituents later in life can provide insight into the persistence of maternal effects over time.

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Lameness in dairy cows is a clinical sign resulting from multiple diseases that affects animal welfare. The aim of this study was to evaluate the consistency of lameness prevalence estimations between farm managers and locomotion scoring conducted by trained observers, in confined and grazing dairy systems. The study was conducted on 18 dairy farms in Argentina. The locomotion of the lactating cows was scored by trained observers using a four-point visual scale from 0 to 3. Farm managers were interviewed about the number of lame cows in the herd. The consistency of lameness prevalence detected by the farm manager and the observers was assessed by computing the Lin's concordance and correlation coefficient. The comparison of grazing systems versus confined systems on lameness prevalence was analyzed using a generalized mixed model, assuming a binomial distribution for the errors. On average, farm managers estimated a lower prevalence of lameness (p < 0.01) compared with the trained observers; 2.24% and 7.06%, respectively. Based on the estimations from trained observers, we could not detect differences (p = 0.19) in lameness prevalence between confined and grazing systems.

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The advancement of technology has significantly transformed the livestock landscape, particularly in the management of dairy cattle, through the incorporation of digital and precision approaches. This study presents a bibliometric analysis focused on these technologies involving dairy farming to explore and map the extent of research in the scientific literature. Through this review, it was possible to investigate academic production related to digital and precision livestock farming and identify emerging patterns, main research themes, and author collaborations. To carry out this investigation in the literature, the entire timeline was considered, finding works from 2008 to November 2023 in the scientific databases Scopus and Web of Science. Next, the Bibliometrix (version 4.1.3) package in R (version 4.3.1) and its Biblioshiny software extension (version 4.1.3) were used as a graphical interface, in addition to the VOSviewer (version 1.6.19) software, focusing on filtering and creating graphs and thematic maps to analyze the temporal evolution of 198 works identified and classified for this research. The results indicate that the main journals of interest for publications with identified affiliations are "Computers and Electronics in Agriculture" and "Journal of Dairy Science". It has been observed that the authors focus on emerging technologies such as machine learning, deep learning, and computer vision for behavioral monitoring, dairy cattle identification, and management of thermal stress in these animals. These technologies are crucial for making decisions that enhance health and efficiency in milk production, contributing to more sustainable practices. This work highlights the evolution of precision livestock farming and introduces the concept of digital livestock farming, demonstrating how the adoption of advanced digital tools can transform dairy herd management. Digital livestock farming not only boosts productivity but also redefines cattle management through technological innovations, emphasizing the significant impact of these trends on the sustainability and efficiency of dairy production.

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Heat stress is a challenge for the milk production chain, especially in tropical environments. Thus, the objective was to evaluate the knowledge and the perception of milk producers about heat stress and determine what corrective measures they adopted to minimize losses in the productive performance of dairy cows due to high temperatures. A questionnaire was applied to 112 dairy farmers in the states of Minas Gerais and Goiás, Brazil in 2019 and 2020. The collected data were submitted to descriptive statistics using the G test of independence and multivariate correspondence analysis. Among the surveyed producers, 89% stated that they had already been informed about what heat stress is and how it affects the animals; 94% declared that they had already noticed in their day-to-day activities that heat stress impairs productivity and milk quality, and 92% stated that they have tried to reduce the heat stress to which the animals in their herd are exposed. There was an association among previous knowledge about heat stress and farm size, daily volume of milk produced, number of lactating cows, type of milking and presence of technical assistance. There was also an association among the adoption of measures that minimize the negative effects of heat stress with the size of the property, the number of cows in lactation and with the producer's perception of heat stress in their daily lives. It was concluded that, in important municipalities of the Brazilian dairy chain, most milk producers surveyed have knowledge and perception of the negative effects of heat stress on the productive performance of dairy cows and sought to adopt measures that could alleviate them.

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This study aimed to evaluate intake, energy and nitrogen balance as well as methane emission in Holstein and ½ Holstein ½ Gyr (Girolando-F1) cows during the transition period. Twenty-four cows (12 Holstein and 12 Girolando-F1) were used to evaluate feed intake, apparent digestibility, heat production and methane emission, carried out in two periods: from 28 to 19 days pre-calving and from 15 to 23 days post-calving. A completely randomised design was used and data were analysed by ANOVA within periods (pre- and post-calving) considering the main effect of genetic groups. Girolando-F1cows presented greater body condition score (BCS) compared with Holstein. During pre-calving, there were no differences between genetic groups, except for highest heat production per kilogram of metabolic body weight for Holstein cows. After calving, Holstein cows had greater intake of DM, nitrogen, NDF per kg of BW and produced more heat per kg of metabolic body weight. Holstein cows yielded more milk and fat-corrected milk (FCM4%) compared with Girolando-F1 cows. Holstein cows presented higher methane emission per unit of BW and of metabolic weight. Emissions of enteric methane per kilogram of milk and per kilogram of FCM4% tended to be lower for Holstein compared with Girolando-F1 cows. Nitrogen and energy retention were similar for both Holstein and Girolando-F1 at pre- and post-calving. Despite differences in BCS, DMI, and milk yield, Girolando-F1 and Holstein cows present overall similar energy efficiency, albeit Holstein cows tended to present less methane emission per kg of eligible product (milk).

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Two trials were conducted to evaluate the effect of a garlic and citrus extract supplement (GCE) on the milk production performance and carbon footprint of grazing dairy cows in a Chilean commercial farm. A total of 36 early- to mid-lactation and 54 late-lactation Irish Holstein-Friesian cows were used in Trial 1 and Trial 2, respectively. In both trials, the cows were reared under grazing conditions and offered a supplementary concentrate without or with GCE (33 g/cow/d) for 12 weeks. The concentrate was fed in the afternoon when the cows visited the milking parlour. Consequently, the results of milk production performance in these trials were used to determine the effect of feeding with GCE on the carbon footprint (CFP) of milk using a life cycle assessment (LCA) model. In Trial 1 and Trial 2, feeding with GCE increased estimated dry matter intake (DMI, kg/d) by 8.15% (18.4 vs. 19.9) and 15.3% (15.0 vs. 17.3), energy-corrected milk (ECM, kg/d) by 11.4% (24.5 vs. 27.3) and 33.5% (15.5 vs. 20.7), and feed efficiency (ECM/DMI) by 3.03% (1.32 vs. 1.36) and 17.8% (1.01 vs. 1.19), respectively. The LCA revealed that feeding with GCE reduced the emission intensity of milk by 8.39% (1.55 vs. 1.42 kg CO2-eq/kg ECM). Overall, these results indicate that feeding with GCE improved the production performance and CFP of grazing cows under the conditions of the current trials.

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The compost-bedded pack barn (CBPB) system has been increasingly adopted by dairy farms due to its ability to enhance animal comfort and milk production. This study evaluated the associations among bedding characteristics, milk quality and composition, and subclinical mastitis (SCM) occurrence in dairy herds housed in CBPB systems. Over a period of six months, data related to milk quality and udder health and bedding sampling were collected from eight dairy farms. Monthly measurements of the bedding temperature and wind speed inside the CBPB were taken, while temperature and relative humidity data inside the CBPB were recorded using a datalogger. Bedding samples were subjected to analysis of moisture, pH, microbiological count, and carbon/nitrogen ratio. Data on milk composition (fat, protein, milk urea nitrogen, and total solids) and quality (somatic cell count and standard plate count) of bulk tank milk were obtained from DHIA results. Canonical correlation analyses were used to evaluate the association between the analyzed group variables, and linear regression models were used to identify associations between bedding characteristics and SCM occurrence in the studied herds. The bedding characteristics that most influenced milk composition and quality were moisture, temperature at 30 cm depth (T30), and bedding pH. Environmental variables played an important role in bedding composting, as they were closely related to the surface temperature and pH. Overall, 62.71% of the variation in milk quality and composition could be explained by the bedding variables, and 77.50% of the variation in the bedding variables was associated with environmental variables. Median SCM prevalence and incidence were 28.6 and 13.8%, respectively. An increase of 1 °C for T30 resulted in a 0.6% reduction in the prevalence of SCM. Additionally, the bedding surface temperature at 22.3 °C resulted in the highest incidence of SCM (~18.1%). Our results demonstrate the importance of controlling microclimatic conditions in the CBPB to optimize the bedding composting process and milk quality.