Precision livestock farming has a crucial function as farming grows in significance. It will help farmers make better decisions, alter their roles and perspectives as farmers and managers, and allow for the tracking and monitoring of product quality and animal welfare as mandated by the government and industry. Farmers can improve productivity, sustainability, and animal care by gaining a deeper understanding of their farm systems as a result of the increased use of data generated by smart farming equipment. Automation and robots in agriculture have the potential to play a significant role in helping society fulfill its future demands for food supply. These technologies have already enabled significant cost reductions in production, as well as reductions in the amount of intensive manual labor, improvements in product quality, and enhancements in environmental management. Wearable sensors can monitor eating, rumination, rumen pH, rumen temperature, body temperature, laying behavior, animal activity, and animal position or placement. Detachable or imprinted biosensors that are adaptable and enable remote data transfer might be highly important in this quickly growing industry. There are already multiple gadgets to evaluate illnesses such as ketosis or mastitis in cattle. The objective evaluation of sensor methods and systems employed on the farm is one of the difficulties presented by the implementation of modern technologies on dairy farms. The availability of sensors and high-precision technology for real-time monitoring of cattle raises the question of how to objectively evaluate the contribution of these technologies to the long-term viability of farms (productivity, health monitoring, welfare evaluation, and environmental effects). This review focuses on biosensing technologies that have the potential to change early illness diagnosis, management, and operations for livestock.

Agriculture produces greenhouse gases. Methane is a result of manure degradation and microbial fermentation in the rumen. Reduced CH₄ emissions will slow climate change and reduce greenhouse gas concentrations. This review compiled studies to evaluate the best ways to decrease methane emissions. Longer rumination times reduce methane emissions and milk methane. Other studies have not found this. Increasing propionate and reducing acetate and butyrate in the rumen can reduce hydrogen equivalents that would otherwise be transferred to methanogenesis. Diet can reduce methane emissions. Grain lowers rumen pH, increases propionate production, and decreases CH₄ yield. Methane generation per unit of energy-corrected milk yield reduces with a higher-energy diet. Bioactive bromoform discovered in the red seaweed Asparagopsis taxiformis reduces livestock intestinal methane output by inhibiting its production. Essential oils, tannins, saponins, and flavonoids are anti-methanogenic. While it is true that plant extracts can assist in reducing methane emissions, it is crucial to remember to source and produce plants in a sustainable manner. Minimal lipid supplementation can reduce methane output by 20%, increasing energy density and animal productivity. Selecting low- CH₄ cows may lower GHG emissions. These findings can lead to additional research to completely understand the impacts of methanogenesis suppression on rumen fermentation and post-absorptive metabolism, which could improve animal productivity and efficiency.

Bovine subclinical mastitis can cause great harm to dairy herds because of its negative impact on milk production and quality and cow health. Improved diagnostic tools are needed to maximise the control of subclinical mastitis distribution and ensure the high quality of milk as an industrial product. Between 2015 and 2020, seventy-two dairy herds were screened for bovine subclinical mastitis causative agents to identify the relationship between seasons, lactose levels and subclinical mastitis infection. The predominant species found in the milk samples were mixed microbiota, coagulase-negative Staphylococcus and Staphylococcus aureus. Yeasts were found exclusively in autumn, while Enterococcus spp. and Escherichia coli were only found in summer and autumn. A negative correlation was detected between milk lactose and number of somatic cells in milk (−0.471; p < 0.001). The lactose levels in milk were closely associated with the prevalence (%) of subclinical mastitis pathogens, such as Streptococcus agalactiae (y = −1.8011x + 10.867, R² = 0.9298), Staph. aureus (y = −3.5216x + 25.957, R² = 0.8604) and other Streptococci (y = −0.5956x + 7.6179, R² = 0.6656). These findings suggest that milk lactose may be used as a biomarker of suspected udder inflammation in modern health prevention programmes for cows to reduce the prevalence of subclinical mastitis pathogens in dairy cattle herds.

According to the past reports, the utility value of monitoring rumination time (RT) around the time at which calving takes place and, in particular, during the first week of lactation, is a way of identifying in a timely fashion those cows that are at a greater level of risk when it comes to developing disease in early lactation. Recent reports have focused on the role of minerals in disease resistance in ruminants, but little is known about the concentrations blood parameters in dairy cows with subclinical acidosis and subclinical clinical ketosis. According this we hypothesised that rumination time and some blood biochemical parameters (including cortisol and lactate) can serve as biomarkers for subclinical acidosis (SARA) and subclinical ketosis (SCK). Accordingly, the aim of the current study was to determinate the impact of subclinical acidosis and ketosis on rumination time and some blood biochemical parameters.

In this study, cow reticulorumen content pH and temperature together with the activity were registered using smaXtec boluses, specially designed for animal care. Body weight, rumination time, milk fat/protein ratio, milk yield, milk lactose, milk electrical conductivity, milk somatic cell count and consumption of concentrated feed were registered by Lely Astronaut^® A3 milking robots. The cows in this study were assigned into two groups according to the milk lactose concentration: group 1—milk lactose < 4.70% (n = 20), group 2—milk lactose ≥ 4.70% (n = 15). The following cows were further classified according to milk fat and protein ratio: F/P < 1.2 (class 1), F/P = 1.2 (class 2) and F/P > 1.2 (class 3). According to our results, we can conclude that inline registered milk lactose concentration can be used to indicate the health status and reproductive success of fresh dairy cows. Cows with an increased lactose concentration (≥4.70%) showed more activity (54.47%) and had less risk of mastitis (determined by lower milk electrical conductivity (EC) and somatic cell counts (SCC)) and metabolic disorders, determined by milk F/P. A higher glucose concentration was also apparent in the cows with higher lactose concentration. Registered lower levels of milk lactose can be used for early identification of metabolic disorders and mastitis (set at milk SCC ≥ 100 thousand/mL). Lactose levels in cows’ milk were positively associated with their reproductive success.

This study endeavors to ascertain alterations in the in-line registered milk fat-to-protein ratio as a potential indicator for evaluating the metabolic status of dairy cows. Over the study period, farm visits occurred biweekly on consistent days, during which milk composition (specifically fat and protein) was measured using a BROLIS HerdLine in-line milk analyzer (Brolis Sensor Technology, Vilnius, Lithuania). Clinical examinations were performed at the same time as the farm visits. Blood was drawn into anticoagulant-free evacuated tubes to measure the activities of GGT and AST and albumin concentrations. NEFA levels were assessed using a wet chemistry analyzer. Using the MediSense and FreeStyle Optium H systems, blood samples from the ear were used to measure the levels of BHBA and glucose in plasma. Daily blood samples were collected for BHBA concentration assessment. All samples were procured during the clinical evaluations. The cows were categorized into distinct groups: subclinical ketosis (SCK; n = 62), exhibiting elevated milk F/P ratios without concurrent clinical signs of other post-calving diseases; subclinical acidosis (SCA; n = 14), characterized by low F/P ratios (<1.2), severe diarrhea, and nondigestive food remnants in feces, while being free of other post-calving ailments; and a healthy group (H; n = 20), comprising cows with no clinical indications of illness and an average milk F/P ratio of 1.2. The milk fat-to-protein ratios were notably higher in SCK cows, averaging 1.66 (±0.29; p < 0.01), compared to SCA cows (0.93 ± 0.1; p < 0.01) and healthy cows (1.22). A 36% increase in milk fat-to-protein ratio was observed in SCK cows, while SCA cows displayed a 23.77% decrease. Significant differences emerged in AST activity, with SCA cows presenting a 26.66% elevation (p < 0.05) compared to healthy cows. Moreover, SCK cows exhibited a 40.38% higher NEFA concentration (p < 0.001). A positive correlation was identified between blood BHBA and NEFA levels (r = 0.321, p < 0.01), as well as a negative association between BHBA and glucose concentrations (r = −0.330, p < 0.01). Notably, AST displayed a robust positive correlation with GGT (r = 0.623, p < 0.01). In light of these findings, this study posits that milk fat-to-protein ratio comparisons could serve as a non-invasive indicator of metabolic health in cows. The connections between milk characteristics and blood biochemical markers of lipolysis and ketogenesis suggest that these markers can be used to check the metabolic status of dairy cows on a regular basis.