Behavior Of Cows Research Articles

Monitoring Dairy Cow Rumination Behavior Based on Upper and Lower Jaw Tracking

To address behavioral interferences such as head turning and lowering during rumination in group-housed dairy cows, an enhanced network algorithm combining the YOLOv5s and DeepSort algorithms was developed. Initially, improvements were made to the YOLOv5s algorithm by incorporating the C3_CA module into the backbone to enhance the feature interaction and representation at different levels. The Slim_Neck paradigm was employed to strengthen the feature extraction and fusion, and the CIoU loss function was replaced with the WIoU loss function to improve the model’s robustness and generalization, establishing it as a detector of the upper and lower jaws of dairy cows. Subsequently, the DeepSort tracking algorithm was utilized to track the upper and lower jaws and plot their movement trajectories. By calculating the difference between the centroid coordinates of the tracking boxes for the upper and lower jaws during rumination, the rumination curve was obtained. Finally, the number of rumination chews and the false detection rate were calculated. The system successfully monitored the frequency of the cows’ chewing actions during rumination. The experimental results indicate that the enhanced network model achieved a mean average precision (mAP@0.5) of 97.5% and 97.9% for the upper and lower jaws, respectively, with precision (P) of 95.4% and 97.4% and recall (R) of 97.6% and 98.4%, respectively. Two methods for determining chewing were proposed, which showed false detection rates of 8.34% and 3.08% after the experimental validation. The research findings validate the feasibility of the jaw movement tracking method, providing a reference for the real-time monitoring of the rumination behavior of dairy cows in group housing environments.

Animal behaviour and dietary preference of dairy cows grazing binary and diverse pastures under the leaf regrowth stage defoliation criterion

In New Zealand, intensively managed pasture-based dairy systems rely on binary pastures mostly comprised of Lolium perenne L. and Trifolium repens L.. More frequent and extreme climatic events have been negatively affecting the persistency and production of these pastures, which now present increased seasonality, with marked peaks and troughs of production throughout the year. Diversification of plant species offers a solution to deal with increased seasonality. However, little is known about animal behaviour and dietary preferences of dairy cows grazing diverse pastures. The present study aimed to assess the grazing preferences of dairy cows when unrestrictedly offered binary (L. perenne and T. repens; Bi) and diverse pastures (L. perenne, Bromus valdivianus Phil., Dactylis glomerata L. and T. repens; Mix) subjected to three different leaf regrowth stage (LS) defoliation criteria. Secondarily, the study aimed to determine the main plant-related drivers for any potential animal preference. The treatments were MixLp (defoliated at L. perenne LS), BiLp (defoliated every time MixLp was defoliated), MixBv (defoliated at B. valdivianus LS) and BiBv (defoliated every time MixBv was defoliated), MixDg (defoliated at D. glomerata LS) and BiDg (defoliated every time MixDg was defoliated). Dairy cattle were evaluated over five agricultural seasons. The response variables were grazing time and location, bite rate, animal behavioural activity, pre-grazing herbage mass, undisturbed sward height, lamina:stem ratio, crude protein, metabolisable energy, organic matter digestibility, non-structural carbohydrates, neutral detergent fibre and lignin. Where significant differences were found, binary pastures presented lower sward height and higher non-structural carbohydrate content in comparison to the diverse pastures under the same LS defoliation criteria. However, no significant differences were found in the percentage of time that cows spent grazing both pastures. Season was the greatest contributor to the proportion variation found in all response variables, with values ranging from 47.55 % up to 88.77 %. In winter and spring, cows modulated their grazing behaviour (proportional time spent grazing, ruminating, or idling), investing more time actively grazing pastures under L. perenne LS interval of defoliation (2.5–3.0 LS), the criterion which resulted in shorter grazing rotations. This study allowed us to understand the suitability of diverse pastures from an animal perspective, and highlighted that independent of the pasture type, the positive productive and nutritional effects of defoliation management based on the LS may also extend themselves to positive outcomes in animal preference, interpreted as the percentage of time dairy cows spend grazing rather than ruminating or idling across and within seasons.

Monitoring cow behavior based on lying, standing, eating, and ruminating recognition using YOLOv8

Monitoring cow behavior based on lying, standing, eating, and ruminating recognition using YOLOv8

Dietary macronutrient composition and partial soybean meal replacement with slow-release urea: Effects on performance, digestibility, rumen fermentation, and nitrogen metabolism in dairy cows

A 2 × 2 factorial arrangement of treatments was used to investigate the interactive effects of dietary macronutrient composition [high-starch, low-fat, low-fiber (HsLFF) than low-starch, high-fat, high-fiber (LsHFF)] and N source [soybean meal (SBM) or partially replaced by slow-release urea (SRU)] on lactation performance, rumen fermentation, N utilization efficiency, nutrient digestibility, blood metabolites, and feeding behavior in cows. A replicated 4 × 4 Latin square design was used involving 12 multiparous Holstein cows (milk yield of 40.5 ± 5.6 kg/d, BW of 590 ± 20 kg; 81 ± 12 d in milk). The HsLFF diet contained 300 g/kg starch, 31.3 g/kg fat, and 301 g/kg NDF, without straw or additional fat. In contrast, the LsHFF diet contained 195.5 g/kg starch, 60.8 g/kg fat, and 367.5 g/kg NDF, enriched with wheat straw (100 g/kg), and additional fat (34 g/kg). The diets were formulated to be iso-nitrogenous and isocaloric. Cows fed the HsLFF diet had greater DM intake, digestibility of DM and CP, milk yield and milk protein %, but lower intakes of NDF, and physically effective NDF, and milk fat % than cows fed the LsHFF diet. Replacing SBM with SRU significantly increased milk solids yield without affecting other lactation performance or BW. Cows fed the LsHFF diet had higher ruminal pH and branched-chain VFA proportions but lower total VFA concentrations compared with the HsLFF diet, while those on the LsHFF-SRU diet had the highest ruminal ammonia levels. Compared with the HsLFF diet, cows fed LsHFF had lower NE intake, milk energy output and energy requirement for maintenance, although energy balances were similar among groups. The HsLFF diet improved N utilization, resulting in higher N content in milk and lower N excretion in feces. Blood metabolite studies showed significant interactions between the main factors, particularly for blood glucose and creatinine, with the lowest levels in cows fed the LsHFF-SRU and LsHFF-SBM diets. In addition, alanine aminotransferase levels were higher in cows fed the LsHFF diet than in cows fed the HsLFF diet. This could indicate early-stage liver stress due to the metabolic imbalance caused by a high-fat, low-starch diet, which can alter energy metabolism. Cows on the HsLFF-SRU diet had the highest glucose levels, indicating possible changes in carbohydrate metabolism or a higher metabolic rate. The concentration of BUN increased steadily after feeding in the LsHFF diet and peaked after 4 h in the LsHFF-SRU diet, with no difference between N sources in the HsLFF diet. Partial replacement of SRU with SBM had no effect on BUN. The interaction between the main factors had a significant effect on MUN content, which was highest in LsHFF-SRU and lowest in HsLFF-SRU, with no difference between the N sources in the HsLFF diets. Overall, while diets with reduced starch and increased fiber and fat compromised lactation performance, partially substituting SRU with SBM helped maintain milk production and milk nitrogen efficiency. However, the LsHFF-SRU diet was less efficient in N utilization, as shown by higher levels of ruminal ammonia, BUN and MUN.

Effect of temporal water restrictions on drinking behavior and time budget in lactating dairy cows according to their position in the social hierarchy within the herd

On dairy farms, milking practices can temporarily restrict cows from accessing drinking water. This study examined how different types of temporary water restriction, similar to those commonly encountered on farms, may affect the individual behavior and physiological dehydration status of cows and whether these effects could differ according to the cow's position in the social hierarchy of the group. Four treatments were applied (CTRL = free access to the drinker and feed fence, HL = 2 h of headlock at the feed fence after morning and evening milking, D2H = free access to the feed fence and no access to water for 2 h after milking and D4H = free access to the feed fence and no access to water for 4 h after milking) to 4 groups of 10 lactating dairy cows using a 4 × 4 Latin square design. Each treatment period lasted 1 week. The cows were housed within a free-stall barn, and each group was in a pen equipped with 1 electronic drinker, 10 individual feeders, and 2 cameras. Cow social dominance hierarchies were characterized using normalized David's scores, which were calculated from video-detected replacements at each pen's drinker. During the last 4 d of each treatment period, drinking behavior was measured using interconnected drinkers, and eating, idle, and lying times were measured using accelerometers. On d 6 of each treatment period, during the morning and evening milkings, blood and then milk samples were collected to analyze bioindicators of dehydration and milk composition, respectively. The daily water intake of cows was unaffected by either social dominance or treatment type. Drinking rate and drinking bout frequency were higher during the HL, D2H, and D4H treatments than during the CTRL treatment, and drinking time was lower during the D4H treatment than during the CTRL treatment. Hourly lying time decreased at the end of the water restriction periods, i.e., at 1000 h in HL, at 1100 h in D2H, and at 1200 and 1300 h in D4H, which was not observed in CTRL, which could be interpreted as a sign of cow discomfort. For blood collected before the morning milking, hematocrit levels were lower in the D4H treatment than in the HL treatment. For blood collected before the evening milking, creatinine concentrations were lower in the D4H treatment than in the D2H treatment, with a greater difference seen for dominant cows than for subordinate cows. For both milkings, milk freezing point was higher in the HL, D2H, and D4H treatments than in the CTRL treatment, indicating dilution. In contrast to the other social categories, subordinate cows made more daily visits to the drinker during the D4H treatment than during the CTRL treatment but displayed no differences in drinking bout duration or the duration of drinking interruptions within bouts in the D2H treatment compared with the CTRL treatment. Overall, cows were able to maintain their water intake despite the temporary water restrictions when they had access to the number of drinkers recommended by animal welfare guidelines. That said, their idle and lying time appeared to be disturbed.

Repeatability and predictability of lying and feeding behaviours in dairy cattle

Subtle changes in behaviour, which may be useful as early indicators of disease or stress in farm animals, can be detected using precision livestock technologies. However, as animals often display differences in their baseline behaviours from one another, individual variation needs to be measured and accounted for if we want to successfully detect abnormalities. In addition to consistent individual differences in their mean behaviour, which are called behavioural types and measured by repeatability, animals may differ in the amount of day-to-day variation around their own mean. This is known as predictability and it is very rarely measured in animals as it requires data with high granularity for extended periods of time, which is hard to obtain without precision technologies. Here we aim to quantify the repeatability and predictability of lying and feeding, two behaviours which have been investigated as potential indicators of disease with inconsistent results, in dairy cows. We used data on daily lying behaviours from leg-mounted sensors for 2183 cows and data on daily feeding behaviours from neck-mounted sensors for 2584 cows belonging to multiple herds to quantify the variation between individuals, the individual differences in predictability and any correlations between individual behaviour type and predictability of the same behaviour. Using multivariate double hierarchical generalised linear models, we were able to quantify individual variation in feeding and lying behaviours of adult cows for the first time. Consistent variation between individuals was present for all measures of lying and feeding with repeatability values of 0.38 for daily feeding time and 0.31 for daily lying time. Individuals also varied in their levels of predictability with coefficients of 0.46 and 0.29 for the daily lying and feeding time respectively. Lastly, there were significant positive correlations between several behaviours and the amount of residual intra-individual variation for the same behaviour (mean lying bout length: 0.93, mean feeding bout length: 0.88), indicating that cows with the longest bouts of the two behaviours were also more variable and unpredictable, perhaps due to an opportunistic strategy. These results highlight the importance of measuring individual variation in behaviour, supporting the evidence of personality traits in cattle, as well as differences in predictability, which must be taken into account in any use of behavioural indicators for early disease diagnosis.

Evaluating GreenFeed and respiration chambers for daily and intraday measurements of enteric gaseous exchange in dairy cows housed in tie-stalls

The objective of this study was to evaluate the GreenFeed (GF) and respiration chambers (RC) for daily and intraday measurements of the enteric gaseous exchange, as well as the metabolic heat production, lying behavior, and feed intake (FI) rate of dairy cows at these 2 respective housing conditions [tie-stall barn (TS) vs. RC] during the summer periods. Sixteen multiparous lactating dairy cows were recruited and arranged in a randomized complete block design with a baseline period established for each cow. Cows were given a basal diet (CON) for a baseline period of 7 d and were then fed a 3-nitrooxypropanol (3-NOP)-containing feed for the subsequent 26 d as experimental period. During both the baseline and the last 7 d of treatment period, gaseous exchanges of each animal were measured in the TS using GF for 8 6-hourly staggered measurements over 3 d, immediately followed by the measurement in RC for 2 d. Corresponding DMI, milk yield, and behavior parameters (e.g., lying behavior and FI rate) in TS and RC were recorded. The correlation coefficients of CH4 and H2 using raw data were 0.84 and 0.85, respectively. For all gases, correlation coefficients between GF and RC on individual cow level decreased when the marginal fixed effects (e.g., inhibitor and breed) were corrected by a mixed model. There were no differences in daily CH4 production or intensity between GF and RC (442 vs. 443 g CH4/d or 16.6 vs. 16.2 g CH4 /kg MY). However, greater CH4 yield was measured by GF than RC (19.0 vs. 17.8 g CH4/kg DMI), driven by a lower DMI (23.3 vs. 24.6 kg/d) when cows were housed in TS sampled by GF compared with cows being housed and sampled in RC. The correlations for CO2 production and O2 consumption were moderate and expected due to the variation associated with the mild heat stress condition during GF measurements in the TS (Thermal humidity index (THI) 56 vs. 68), as indicated by the reduced lying time (-2.1 h/d). At the intraday level, there was an interaction between techniques and hour-of-day for CH4 production, as indicated by the discrepancies in post-prandial CH4 emissions between techniques. In summary, this set of results showed that there were strong positive correlations for CH4 and H2 emissions between GF and RC based on individual cow data. However, such relationship should be interpreted with caution, given the data clustering resulting from the use of inhibitor 3-NOP. On treatment level, these 2 techniques detected similar inhibitor effect on the estimated daily CH4 emissions. The intraday patterns of CH4 and H2 production captured by GF provided a close approximation for those measured by RC. Nevertheless, potential underestimation may occur, especially following fresh feed delivery. For measuring CO2 production and O2 consumption, the GF captured similar intraday variations to those in the RC. However, the estimated daily production and consumption were not directly comparable, which was expected due to the variable thermal conditions during the summer. Further evaluations under the same weather conditions are warranted.

Shifting feed delivery time: effects on feeding behavior, milk production, and blood biomarkers in late-lactating dairy cows

Improving the synchronization between the pattern of milk synthesis and nutrient availability throughout the day could enhance production efficiency. In this study, we evaluated the effects of changing feed delivery time on milk production, feeding behavior, and the daily rhythms of blood biomarkers. Eight multiparous Holstein cows housed in a tie-stall barn with controlled environmental conditions were enrolled in a crossover experimental design with three periods of 14 days and three treatments each. Cows were milked twice daily (0530 and 1730 h) and were individually fed with two equal meals of forage and eight equal meals of concentrate during the day. Forage meals were provided at 12-hour intervals either: (i) 5 h before each milking (0030 and 1230 h; BM), (ii) at the end of each milking (0530 and 1730 h; ME), (iii) or 2 h after (0730 and 1930 h; AM). Feed intake and feeding behavior were monitored, and milk production and composition were measured. Blood samples were collected every 4 days at 0700 h and, during the last day of each period, 15 times daily to determine metabolic profiles, hormones, and their daily rhythmicity by the cosinor analysis. Changing forage delivery time did not affect milk yield and dry matter intake. No difference was observed in feeding behavior when expressed relative to the first meal. There were no significant differences in milk component contents and yields. In samples collected at 0700 h, ME had reduced plasma calcium (Ca), magnesium (Mg), and potassium (K) and increased sodium (Na). AM had increased inflammation, as suggested by the greater blood globulin and ceruloplasmin. The patterns of metabolic biomarkers had limited variations when expressed relative to the first forage meal. Nevertheless, the daily rhythms of these biomarkers were remarkably different. Under our conditions, feeding forage meals to cows at different times of the day did not influence productive performance but highlighted the importance of considering the sampling time when interpreting metabolic profiles.

Path planning of manure-robot cleaners using grid-based reinforcement learning

The use of a robot cleaner for manure removal improves housing conditions for dairy cows in the face of labor shortages. However, current robot cleaners follow programmed fixed routes without considering the dynamic behaviors of cows. This cleaning approach is less efficient and leads to more cow-robot encounters or collisions, thus affecting animal welfare. To address these issues, this paper (1) developed heatmap models for cow locations and defecation behaviors; (2) proposed a dynamic path planning approach for the manure robot cleaner using Grid-based Reinforcement Learning; (3) incorporated cow location information and defecation behavior into the path planning process; (4) compared the performance of the proposed approach with two different cleaning methods: the current fixed programmed cleaning in practice and the ideal path produced by simulated annealing for traveling salesman problem. The simulations mimic the situation in a barn at Dairy Campus of Wageningen Livestock Research located in Leeuwarden (the Netherlands). Obviously, the best performance was achieved when the route was executed without cows present, resulting in no cow-robot collision. However, with cows present, the proposed dynamic path planning strategy achieved a 67.6% reduction in cow-robot encounters while maintaining 85.4% of the cleaning performance compared to the current programmed fixed routes. Compared to the ideal path produced by simulated annealing for traveling salesman problem, the proposed dynamic path planning approach achieved 5% better cleaning performance, at the cost of 25% more cow-robot encounters due to its longer working path. We conclude the proposed grid-based Reinforcement Learning solution for manure robots in barns cleaned most efficient with the least interference with cow traffic.

Geometry of milk liners affects milking performance in dairy cows.

The geometry of milk liners may affect milking performance and cow comfort as the milk liner is the only part of the milking machine that comes into contact with the teat. To determine the effect of alternative shape of milk liners we compared square (SQR) vs. the conventional round (RND) teat cup liner on milking performance and comfort of dairy cows. Treatment milk liners were randomly allocated to clusters within each side of the 12 a side double up-herringbone dairy shed in a complete randomised block design over two periods. Milking performance data from a total of 10 065 (late stage of lactation and once-a-day milking frequency, LATE) and 18 048 (early stage of lactation and twice-a-day milking frequency, EARLY) milking events were automatically recorded by a DeLaval milk meter, and separately analysed for LATE and EARLY, respectively. In EARLY, cow comfort behaviour was also recorded during afternoon milking sessions. Across the two study periods, average milk flow rate, milk flow rate during 0-15, 15-30 and 30-60 s after cluster attachment, and milk flow rate at cluster take-off were higher in SQR compared to RND treatment. Proportion of time in a milking session with low milk flow rate and duration of milking session were less in SQR compared to RND treatment. However, effect of geometry of milk liner on peak milk flow rate was inconsistent across the two-study periods. Peak milk flow rate was higher (P < 0.001) in SQR than RND in LATE, but higher (P < 0.001) in RND than SQR in EARLY. Stomping and kicking behaviours of cows were similar between treatments. Results of this study suggest that square milk liners potentially improve milking performance, without adverse effect on cow comfort compared to conventional round liners. Long-term, multi-site studies are required to confirm potential teat-end health benefits associated with square milk liners and further verify these results.

Sensor-type agnostic heat detection in dairy cows using multi-autoencoders with shared latent space

Monitoring heat events in dairy cows is crucial for determining the heat on time, and the heat events have usually been estimated using machine learning on cow behavioral data collected from wireless activity sensors recently. However, ensuring robust performance of heat detection is difficult because of the difference in data domains (e.g., sensor types) and insufficient heat-labeled data. Therefore, this study proposes a multi-autoencoder-based heat detection in dairy cows that can represent the common representation of cow behavior across the different sensors. The proposed method can train a sensor-type agnostic heat detector using entire labeled data from the two different sensor types by aligning the latent spaces for two sensors. In addition, our approach can train the model by combining anomaly detection and weakly supervised classification to improve the performance of heat detection that can reduce the dependency on label accuracy. The results showed that the proposed approach improved cow heat detection performance by approximately 46 % than independently trained autoencoders, and the average F1-score increased by up to 0.70. The proposed method also outperformed other supervised and unsupervised learning models in heat detection using our dataset. From the results, our model effectively estimates cow behaviors by integrating sensor modalities, thereby enhancing data capabilities in low-resource settings. This study can be key for addressing the detection discrepancy in time series data based on the location of the mounted sensor, and offers the advantage of practical applications to various activity sensors currently used on farms.

Motion focus global–local network: Combining attention mechanism with micro action features for cow behavior recognition

The use of machine vision technology for recognizing cow behavior plays a crucial role in daily management, health monitoring, and breeding and reproduction in dairy farming, making it an essential component of modern smart agriculture. This paper presents a novel dual-stream network model, the Motion Focus Global-Local Network (MFGN), for analyzing cow video data. The dual-stream network consists of a global spatiotemporal feature stream and a fine motion feature stream. The global spatiotemporal feature stream extracts key frames to remove redundant information and utilizes a Transformer network for global spatio-temporal feature extraction, reflecting the dynamic changes in cow videos and the temporal relationships between video frames. The fine motion feature stream, based on frame differencing of cow videos, uses focal convolution to capture subtle movements of cows, enhancing the focus on minor behavioral changes. To evaluate the performance of the proposed model, video data samples were collected from eight cows marked on their bodies and heads at an Australian farm site (CSIRO Armidale), including a total of 1715 video sequences across three behavior categories. The model achieved recognition accuracies of 98.1% for drinking, 95.5% for grazing, and 49.3% for other behaviors, with an overall average recognition accuracy of 79.4%, representing a 7.4% improvement over the classic TSN model. Overall, the MFGN network effectively extracts and integrates global spatiotemporal features with fine motion features from cow video data, modeling both the overall sequence characteristics and focusing on local motion details, achieving precise behavioral recognition. This research not only enhances the accuracy of cow behavior recognition but also provides new technological means for precise management in modern smart agriculture, with broad industry application potential to improve farm efficiency, profitability, and disease control and prevention.

Stocking density at feeders and drinkers and temporal feed restriction affects dairy cows' drinking behavior

Water is an essential resource for dairy cows and its consumption is closely linked to feed intake. Social competition can lead to changes in drinking behavior, especially for subordinate cows. We studied how changes in stocking density at the feeder (varied between 1 or 2 cows per feeder), and drinker (varied between 6 or 12 cows per drinker) and a temporal feed restriction (14 or 24 h access) impacts drinking in 4 groups of 6 cows each, following a Latin Square design. Within each group of 6 focal cows, we recorded drinking behavior and 2 were identified as most dominant, 2 as most subordinate, and the 2 others as intermediate, based on success in agonistic interactions at the drinker. Cows drank less water when feed availability was restricted (on average 5.3 L/d less ± 3.4; 4.5% average difference). Subordinate cows also spent less time drinking when overstocked (9.3 min/d less ± 6.7; 24.3% difference). During treatments with more competition, cows were more likely to be observed drinking in the hours after the peak in drinking observed for the control treatment. A high level of competition among group-housed animals is considered a welfare problem and can lead to injuries and reduced production. Our findings provide evidence that feed restriction and higher stocking density of cows at the drinker and feeders changes the drinking behavior of cows, with subordinate animals experiencing more pronounced effects.

Impact of dairy cow personality traits and concentrate allowance on their response to training and adaptation to an automated milking system

The objectives of this study were to determine: 1) if dairy cow personality traits and concentrate allowance are associated with the behavior and performance of cows during training to use an automated milking system (AMS); and 2) if these factors were associated with the behavior and performance of cows after AMS training. Twenty-nine mid- to late-lactation Holstein cows (218 ± 49 DIM), who were milking on a rotary parlor and had never previously been milked in an AMS, were enrolled in this study. Cows were assigned to 1 of 2 dietary treatments, consisting of a basal partial mixed ration (PMR) common to both treatment groups, with a concentrate allowance (on dry matter basis) of: 1) 2.0 kg/d in the AMS (L-Tx); or 2) 6.0 kg/d in the AMS (H-Tx). Cows were trained to use the free-traffic AMS, with supervised milkings, over 72 h and were milked in this system for 63 d after training was complete. Variables relating to feeding behavior, milking activity, and production were measured from the start of AMS training until the end of the study. Between 42 and 63 d after AMS introduction, each cow was assessed for personality traits using a combined arena test consisting of exposure to a novel environment, novel object, and novel human. Principal components analysis of behaviors observed during the personality assessment revealed 2 factors (interpreted as boldness and activeness traits) that together explained 85% of the variance; each cow received a score for each trait. Associations between dietary treatment and personality traits with feeding behavior, milking activity, and production were analyzed using mixed-effect linear and logistic regression models. Cows with greater scores for the active trait produced less milk during the 3 d of AMS training compared with cows with lower scores. Within the H-Tx, more active cows had a 3.92 times greater risk of kicking off teat cups during AMS training than less active cows. However, during the 8 wk after training, more active cows had a 1.37 times lesser risk of teat cup kickoffs than those that were less active. Cows on the H-Tx produced 4.4 kg/d more energy-corrected milk compared with cows on the L-Tx in the 8 wk after training. During the 8 wk after AMS training the cows on the H-Tx consumed an average of 21.4 kg/d of PMR and were delivered 4.6 kg/d of AMS concentrate, while the L-Tx cows consumed 23.4 kg/d PMR and were delivered 2.0 kg/d of AMS concentrate. The results indicate that both dairy cow personality traits and AMS concentrate allocation influence their response to AMS training and subsequent feeding and milking behavior and production.

A New Approach to Recording Rumination Behavior in Dairy Cows.

Rumination behavior in cattle can provide valuable information for monitoring health status and animal welfare, but continuous monitoring is essential to detect changes in rumination behavior. In a previous study validating the use of a respiration rate sensor equipped with a triaxial accelerometer, the regurgitation process was also clearly visible in the pressure and accelerometer data. The aim of the present study, therefore, was to measure the individual lengths of rumination cycles and to validate whether the sensor data showed the same number of regurgitations as those counted visually (video or direct observation). For this purpose, 19 Holstein Friesian cows equipped with a respiration rate sensor were observed for two years, with a focus on rumination behavior. The results showed a mean duration of 59.27 ± 9.01 s (mean ± SD) per rumination cycle and good agreement (sensitivity: 99.1-100%, specificity: 87.8-95%) between the two methods (sensor and visual observations). However, the frequency of data streaming (continuously or every 30 s) from the sensor to the data storage system strongly influenced the classification performance. In the future, an algorithm and a data cache will be integrated into the sensor to provide rumination time as an additional output.

Short-Term Effects of Heat Stress on Cow Behavior, Registered by Innovative Technologies and Blood Gas Parameters.

Heat stress (HS) is one of the key factors affecting an animal's immune system and productivity, as a result of a physiological reaction combined with environmental factors. This study examined the short-term effects of heat stress on cow behavior, as recorded by innovative technologies, and its impact on blood gas parameters, using 56 of the 1070 cows clinically evaluated during the second and subsequent lactations within the first 30 days postpartum. Throughout the experiment (from 4 June 2024 until 1 July 2024), cow behavior parameters (rumination time min/d. (RT), body temperature (°C), reticulorumen pH, water consumption (L/day), cow activity (h/day)) were monitored using specialized SmaXtec boluses and employing a blood gas analyzer (Siemens Healthineers, 1200 Courtneypark Dr E Mississauga, L5T 1P2, Canada). During the study period, the temperature-humidity index (THI), based on ambient temperature and humidity, was recorded and used to calculate THI and to categorize the data into four THI classes as follows: 1-THI 60-63 (4 June 2024-12 June 2024); 2-THI 65-69 (13 June 2024-18 June 2024); 3-THI 73-75 (19 June 2024-25 June 2024); and 4-THI 73-78 (26 June 2024-1 July 2024). The results showed that heat stress significantly reduced rumination time by up to 70% in cows within the highest THI class (73 to 78) and increased body temperature by 2%. It also caused a 12.6% decrease in partial carbon dioxide pressure (pCO2) and a 32% increase in partial oxygen pressure (pO2), also decreasing plasma sodium by 1.36% and potassium by 6%, while increasing chloride by 3%. The findings underscore the critical need for continuous monitoring, early detection, and proactive management to mitigate the adverse impacts of heat stress on dairy cow health and productivity. Recommendations include the use of advanced monitoring technologies and specific blood gas parameter tracking to detect the early signs of heat stress and implement more timely interventions.

An IMU-based machine learning approach for daily behavior pattern recognition in dairy cows

Technological advancements have revolutionized livestock farming, notably in health monitoring. Traditional methods, which have been criticized for subjectivity and treatment delays, can be replaced with efficient health monitoring systems, thereby reducing costs and workload. Implementing cow behavior recognition allows for effective dairy cow health monitoring. In this research, we propose an integrated system using inertial measurement unit (IMU) devices and machine learning techniques for dairy cow behavior recognition. Six main dairy cow behaviors were studied: lying, standing, walking, drinking, feeding, and ruminating. All behavior types were manually labeled into the IMU data by reviewing the recorded footage. The labeled IMU data underwent four processing steps: selecting different window sizes, feature extraction, feature selection, and normalization. These processed data were then used to build the behavior recognition model. Various model structures, including SVM, Random Forest, and XGBoost, were tested. The top-performing model, XGBoost, with its proposed 58 features achieved an F1-score of 0.87, with specific scores of 0.93 for lying, 0.85 for walking, 0.94 for ruminating, 0.89 for feeding, 0.86 for standing, 0.93 for drinking, and 0.59 for other activities. During our online testing, we observed similar patterns for each healthy cow. The cumulative time spent on each behavior also matched the statistics from previous surveys. Additionally, our backend optimization approach resulted in a final overall percentage error of 1.55 % per day during online testing. In conclusion, our study presents an IMU-based system capable of accurately recognizing dairy cow behavior. Feature design and appropriate models are proposed herein. A functional optimization method was introduced indicating that our system has the potential with applications for estrus detection and other reproductive management practices in the dairy industry.

Improving Milk Yield, Milk Quality, and Follicular Functionality Behavior in Dairy Cows from the Implementation of Microencapsulated Chili Pepper Supplements in Their Diets.

The present study evaluates the effect of including microencapsulated hot chili pepper (MHCP) in the diet of crossbred dairy cows on the volume and quality of milk and on ovarian morphofunctionality. Twenty-four crossbred females in their lactating period were used. The cows were divided into two experimental groups, a control (CT) and an MHCP -supplemented group (CP) given 1 g a day per animal of microencapsulated hot chili in concentrate for 42 days. Over seven weeks of daily milk production was measured, and sample milk was collected weekly for composition analysis. Animals were subject to an ovulation synchronization protocol on day 0 (D0), and an intravaginal progesterone (P4) implant, estradiol benzoate, and prostaglandin (PGF2α) were administered. On D8, the P4 implant was removed and PGF2α, equine chorionic gonadotropin, and estradiol cypionate were administered to the animals. The ovarian dynamics were evaluated in B mode and color Doppler. There were significant differences (p < 0.05) in the group X time interaction, the volume of milk produced, and the amount in kg/day of milk components. There was a higher percentage of vascularization in the preovulatory follicle in the CP group (p ≥ 0.10). The findings show that the inclusion of MHCP in the diet of dairy cows does influence their milk production and reproduction.

The Effect of an Exercise Paddock on Dairy Cow Behavior, Health, and Nutrient Digestion during the Transition from Pregnancy to Lactation.

Providing an exercise paddock may improve the behavior and health of cows in their dry period. We compared a control group of cows in a shed with no exercise paddock and an experimental group in the same shed but with access to an exercise paddock. Both groups had ad libitum total mixed ration (TMR) indoors combined with access to a paddock (Group EX). The other group was just offered TMR indoors (Group IN). Total lying time was longer for cows without the exercise paddock (859 min/d) than for those with the paddock (733 min/d) (p = 0.012). Lying bouts were shorter, there were more allogrooming bouts, and drinking time was longer if an exercise paddock was provided. Cows with the paddock spent on average 76 min/d in paddock activity. Non-esterified fatty acids in the blood were increased by providing the exercise paddock. No significant differences in postpartum milk yield and calf weight of dry cows with or without access to exercise paddock were observed. However, crude protein and neutral detergent fiber digestibility were increased by providing the exercise paddock. The results suggest that providing an exercise paddock for cows in their dry period increased activity, including allogrooming, reduced lying, and improved digestibility of some major nutrients in the feed.

A new dataset for video-based cow behavior recognition

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.