Alterations in Rumination, Eating, Drinking and Locomotion Behavior in Dairy Cows Affected by Subclinical Ketosis and Subclinical Acidosis

This study delves into the effects of subclinical ketosis (SCK) and subclinical acidosis (SCA) on various parameters related to dairy cow rumination, eating, drinking and locomotion behavior. The research hypothesized that these subclinical metabolic disorders could affect behaviors such as rumination, feeding, and locomotion. A total of 320 dairy cows, with a focus on those in their second or subsequent lactation, producing an average of 12,000 kg/year milk in their previous lactation, were examined. These cows were classified into three groups: those with SCK, those with SCA, and healthy cows. The health status of the cows was determined based on the milk fat-protein ratio, blood beta-hydroxybutyrate, and the results of clinical examinations performed by a veterinarian. The data collected during the study included parameters from the RumiWatch sensors. The results revealed significant differences between the cows affected by SCK and the healthy cows, with reductions observed in the rumination time (17.47%) and various eating and chewing behaviors. These changes indicated that SCK had a substantial impact on the cows' behavior. In the context of SCA, the study found significant reductions in Eating Time 2 (ET2) of 36.84% when compared to the healthy cows. Additionally, Eating Chews 2 (EC2) exhibited a significant reduction in the SCA group, with an average of 312.06 units (±17.93), compared to the healthy group's average of 504.20 units (±18.87). These findings emphasize that SCA influences feeding behaviors and chewing activity, which can have implications for nutrient intake and overall cow health. The study also highlights the considerable impact of SCK on locomotion parameters, as the cows with SCK exhibited a 27.36% reduction in the walking time levels. These cows also displayed reductions in the Walking Time (WT), Other Activity Time (OAT), and Activity Change (AC). In conclusion, this research underscores the critical need for advanced strategies to prevent and manage subclinical metabolic disorders within the dairy farming industry. The study findings have far-reaching implications for enhancing the well-being and performance of dairy cattle. Effective management practices and detection methods are essential to mitigate the impact of SCK and SCA on dairy cow health and productivity, ultimately benefiting the dairy farming sector.

Assessment of Ruminating, Eating, and Locomotion Behavior during Heat Stress in Dairy Cattle by Using Advanced Technological Monitoring

Heat stress (HS) significantly impacts dairy farming, prompting interest in precision dairy farming (PDF) for gauging its effects on cow health. This study assessed the influence of the Temperature-Humidity Index (THI) on rumination, eating, and locomotor activity. Various parameters, like rumination time, drinking gulps, chews per minute, and others were analyzed. The hypothesis was that precision dairy farming technology could help detect HS. Nine healthy Lithuanian Black-and-White cows were randomly selected for the trial. RumiWatch noseband sensors recorded behaviors, while SmaXtec climate sensors monitored THI. The data collection spanned from 14 June to 30 June. Cows in the THI class ≥ 72 exhibited reduced drinking time (51.16% decrease, p < 0.01), fewer chews per minute (12.9% decrease, p < 0.01), and higher activity levels (16.99% increase, p < 0.01). THI showed an inverse correlation with drinking time (r = -0.191, p < 0.05) and chews per bolus (r = -0.172, p < 0.01). Innovative technologies like RumiWatch are effective in detecting HS effects on behaviors. Future studies should explore the impact of HS on RWS biomarkers, considering factors such as lactation stage, number, yield, and pregnancy.

The Relationship between Reticuloruminal Temperature, Reticuloruminal pH, Cow Activity, and Clinical Mastitis in Dairy Cows

We hypothesized that reticuloruminal temperature, pH as well as cow activity can be used as parameters for the early diagnosis of clinical mastitis in dairy cows. Therefore, we aimed to detect the relationship between these factors and the disease. We randomly selected cows with clinical mastitis and clinically healthy cows (HG) out of 600 milking cows. We recorded the following parameters during the experiment: reticulorumen temperature (RR temp.), reticulorumen pH (RR pH), and cow activity. We used smaXtec boluses (smaXtec animal care technology^®, Graz, Austria). In this investigation, reticulorumen data obtained seven days before diagnosis were compared to HG data from the same time period. CM cows were observed on the same days as the healthy cows. The healthy group's RR pH was 7.32% higher than that of cows with CM. Reticulorumen temperature was also 1.25% higher in the CM group than in the control group. The healthy group had a higher average value for walking activity, which was 17.37% higher than the CM group. The data of reticulorumen pH changes during 24 h showed that during the day, the pH changed from 5.53 to 5.83 in the CM group. By contrast, pH changed from 6.05 to 6.31 in the control group. The lowest reticulorumen pH in the CM group was detected on the third day before diagnosis, which was 15.76% lower than the highest reticulorumen pH detected on the sixth day before diagnosis. The lowest reticulorumen pH in CM cows was detected at 0 and 1 days before diagnosis and it was 1.45% lower than the highest reticulorumen pH detected on the second day before diagnosis. The lowest walking activity in the CM group was detected 0 days before diagnosis, which was 50.60% lower than on the fifth day before diagnosis. Overall, the results confirmed our hypothesis that reticuloruminal temperature, reticuloruminal pH, and cow activity could be used as parameters for the early diagnosis of clinical mastitis in dairy cows.

Ruminating, Eating, and Locomotion Behavior Registered by Innovative Technologies around Calving in Dairy Cows

The hypothesis for this study was that there are correlations between ruminating, eating, and locomotion behavior parameters registered by the RumiWatch sensors (RWS) before and after calving. The aim was to identify correlations between registered indicators, namely, rumination, eating, and locomotion behavior around the calving period. Some 54 multiparous cows were chosen from the entire herd without previous calving or other health problems. The RWS system recorded a variety of parameters such as rumination time, eating time, drinking time, drinking gulps, bolus, chews per minute, chews per bolus, activity up and down time, temp average, temp minimum, temp maximum, activity change, other chews, ruminate chews, and eating chews. The RWS sensors were placed on the cattle one month before expected calving based on service data and removed ten days after calving. Data were registered 10 days before and 10 days after calving. We found that using the RumiWatch system, rumination time was not the predictor of calving outlined in the literature; rather, drinking time, downtime, and rumen chews gave the most clearcut correlation with the calving period. We suggest that using RumiWatch to combine rumination time, eating time, drinking, activity, and down time characteristics from ten days before calving, it would be possible to construct a sensitive calving alarm; however, considerably more data are needed, not least from primiparous cows not examined here.

Change in Rumination Behavior Parameters around Calving in Cows with Subclinical Ketosis Diagnosed during 30 Days after Calving

We hypothesized that cows with SCK (blood BHB over >1.2 mmol/L) diagnosed within the first 30 days of calving can be predicted by changes in rumination and activity behavioral parameters in the period before calving and indeed subsequently. A total of 45 cows were randomly selected from 60 dry cows from at least 40 days before calving. All the cows were fitted with RuniWatch sensors monitoring both intake behaviors (faceband) and general movement and activity behavior (pedometer) (RWS-ITIN + HOCH, Switzerland). Following an adaptation period of 10 days, rumination, eating, and activity parameters were monitored for 30 days before calving and 30 days after calving. Considering the design of the study, we divided the data of cows into three stages for statistical evaluation: (1) the last thirty days before calving (from day -30 to -1 of the study); (2) day of calving; and (3) the first thirty days after calving (from day 1 to 30 of the study). We found that before calving, those cows with a higher risk of having SCK diagnosed after calving had lower rumination time, eating time, drinking gulps, bolus, chews per min, chews per bolus, downtime, maximal temperature, and activity change. On the calving day, in cows with higher risk of SCK after calving, we found lower rumination time, eating time, chews per min, chews per bolus, uptime, downtime, minimal temperature, other chews, eating chews, drinking time, drinking gulps, activity, average temperature, maximal temperature, activity change, rumination chews, and eating chews. After calving in cows with SCK, we found lower rumination time, eating time 1, eating time 2, bolus, chews per bolus, uptime, downtime, minimal temperature, maximal temperature, rumination chews, and eating chews. Moreover, after calving we found higher drinking gulps, drinking time, activity, activity change, average temperature, other chews, and eating chews in cows with SCK. From a practical point of view, we recommend that by tracking changes in rumination and activity behavior parameters registered with RuniWatch sensors (such as rumination time, eating time, drinking time, drinking gulps, bolus, chews per minute, chews per bolus, downtime, maximal temperature, and activity change) before, during, and after calving, we can identify cows with a higher risk of SCK in the herd.

Trends in Changes of Automatic Milking System Biomarkers and Their Relations with Blood Biochemical Parameters in Fresh Dairy Cows

The aim or this study was to determine the relationship between non-esterified fatty acids and biomarkers from an automatic milking system (AMS). Fresh dairy cows (n = 102) were selected and milked in Lely Astronaut® A3 milking robots. The rumination time (RT), body weight (BW), milk content and composition parameters, milk fat/protein ratio (F/P), and milk electrical conductivity were registered by the same milking robots. For examining non-esterified fatty acids (NEFAs), blood samples were acquired from cows in the dry period. According to the NEFA concentrations, all cows were divided into two groups: Group I, with <0.300 mEq/L (n = 66), and Group II, with ≥0.300 mEq/L (n = 36). Albumin (ALB), aspartate aminotransferase (AST), gamma-glutamyl transferase (GGT), and cortisol concentrations were also analyzed once a week up to 30 days in milking. The study revealed that the cows in Group I had higher concentrations of ALB, cortisol, and GGT, but the average concentration of AST was lower. In Group 1, the milk F/P was higher, but the milk yield was lower. We hypothesize that biomarkers from AMS could help in the early diagnosis of metabolic diseases after calving or to control negative energy balance before calving.

Inline changes in lactate dehydrogenase, milk concentration according to the stage and number of lactation periods, including the status of reproduction and milk yield in dairy cows

The aim of the present study was to investigate inline lactate dehydrogenase (LDH) dynamic changes based on different cow factors - different number and stages of lactation, milk yield, and the status of reproduction in clinically healthy dairy cows. In the Herd Navigator system, LDH activity levels (μmol/min per litre) were measured using dry-stick technology. A total of 378 cows were selected. According to their reproductive status, the cows were classified as belonging to the following groups: Fresh (1 - 44 days after calving); Open (45 - 65 days after calving); Inseminated (1 - 35 days after insemination); Pregnant (35 - 60 days after insemination and pregnant). According to their productivity, the cows were classified into the following groups: ⟨15 kg/day, 15 - 25 kg/day, 25 - 35 kg/day and >35 kg/day. The cows were milked with a DeLaval milking robot (DeLaval Inc. Tumba Sweden) in combination with a Herd Navigator analyser (Lattec I/S. Hillerød Denmark). In conclusion inline dynamic changes in the milk LDH concentration may increase together with the rise in the lactation period frequency. The highest LDH level determinated in the group of the fresh cows ranged from 5 to 10 DIM, while the highest LDH concentration level was found in the fresh cow milk. Thus, there was a positive relationship between the milk concentration of LDH and the milk yield.

Evaluation of rumination time, subsequent yield, and milk trait changes dependent on the period of lactation and reproductive status of dairy cows

The aim of this research was to determine rumination time (RT) and the subsequent milk yield, along with trait changes during lactation dependent on the reproductive status of dairy cows. 728 cows were selected for evaluation in regards to 1-150 days of milk production (DIM). According to their period of lactation and reproductive status, the cows were selected for the following groups: Inseminated (1-35 days after insemination, n=182), Open (45-90 days after calving, n=126), Fresh (1-44 days after calving, n=45); Not-pregnant (>35-60 days after inse- mination and not-pregnant, n=55); Pregnant (35-60 days after insemination and pregnant (n=320). The animals were milked with Lely Astronaut® A3 milking robots. The daily milk yield, rumination time, bodyweight, milk composition (fat, protein, lactose, somatic cell count and gynecological status date) were collected from the Lely T4C management program for analysis. We estimated the lowest productivity in the pregnant cows, where the average milk yield was 28.72 kg and the highest productivity in the fresh cow (p⟨0.001)(Table 1). The longest rumina- tion time was determined for the inseminated cows, statistically significantly higher at 9.92% (p⟨0.001) than in the non-pregnant cows, whose rumination time was the shortest. The statisti- cally reliably RT positively correlated with productivity (r=0.384, p⟨0.001) of the cows (from r=0.302 in the second lactation and r=0.471 in the first lactation to r=0.561 in multiparous cows; p0.001). Rumination time, according to groups of cows by milk yield, had a tendency to increase (2.14 times) from 202.0± 87.38 (in cows with a productivity of less than 10 kg milk) to 431.6±33.91 (in cows with a milk yield higher than 50 kg) by the linear regression equation: y = 38.02x + 232, R² = 0.721 (p⟨0.001). The relation between the gynecological status and milk fat-protein ratio of the cows was statistically significant (χ2=2.974, df= 8, p ⟨0.0001). The longest rumination time was determined for the inseminated cows (1 - 35 days after insemination), and the shortest for the not-pregnant cows (>35 - 60 days after insemination and not-pregnant). We can conclude that rumination time, subsequent yield, and milk trait change depends on the period of lactation and reproductive status of a dairy cow.