Tactile, Auditory, and Visual Stimulation as Sensory Enrichment for Dairy Cattle.

Several types of enrichment can be used to improve animal welfare. This review summarizes the literature on the use of mechanical brushes, tactile udder stimulation, music, and visual stimuli as enrichment methods for dairy cows. Mechanical brushes and tactile stimulation of the udder have been shown to have a positive effect on milk yield and overall behavioral repertoire, enhancing natural behavior. Classical music reduces stress levels and has similarly been associated with increased milk yield. A slow or moderate tempo (70 to 100 bpm) at frequencies below 70 dB is recommended to have this positive effect. Evidence on the impacts of other types of enrichment, such as visual stimulation through mirrors, pictures, and color lights, or the use of olfactory stimuli, is equivocal and requires further study.

Dynamic Serpentine Convolution with Attention Mechanism Enhancement for Beef Cattle Behavior Recognition.

Behavior recognition in beef cattle is a crucial component of beef cattle behavior warning and intelligent farming. Traditional beef cattle behavior recognition faces challenges in both difficulty in identification and low accuracy. In this study, the YOLOv8n_BiF_DSC (Fusion of Dynamic Snake Convolution and BiFormer Attention) algorithm was employed for the non-intrusive recognition of beef cattle behavior. The specific steps are as follows: 45 beef cattle were observed using a fixed camera (A LINE OF DEFENSE) and a mobile phone (Huawei Mate20Pro) to collect and filter posture data, yielding usable videos ranging from 1 to 30 min in length. These videos cover nine different behaviors in various scenarios, including standing, lying, mounting, fighting, licking, eating, drinking, walking, and searching. After data augmentation, the dataset comprised 34,560 samples. The convolutional layer (CONV) was improved by introducing variable convolution and dynamic snake-like convolution modules. The dynamic snake-like convolution, which yielded the best results, expanded the model's receptive field, dynamically perceived key features of beef cattle behavior, and enhanced the algorithm's feature extraction capability. Attention mechanism modules, including SE (Squeeze-and-Excitation Networks), CBAM (Convolutional Block Attention Module), CA (Coordinate Attention), and BiFormer (Vision Transformer with Bi-Level Routing Attention), were introduced. The BiFormer attention mechanism, selected for its optimal performance, improved the algorithm's ability to capture long-distance context dependencies. The model's computational efficiency was enhanced through dynamic and query-aware perception. Experimental results indicated that YOLOv8n_BiF_DSC achieved the best results among all improved algorithms in terms of accuracy, average precision at IoU 50, and average precision at IoU 50:95. The accuracy of beef cattle behavior recognition reached 93.6%, with the average precision at IoU 50 and IoU 50:95 being 96.5% and 71.5%, respectively. This represents a 5.3%, 5.2%, and 7.1% improvement over the original YOLOv8n. Notably, the average accuracy of recognizing the lying posture of beef cattle reached 98.9%. In conclusion, the YOLOv8n_BiF_DSC algorithm demonstrates excellent performance in feature extraction and high-level data fusion, displaying high robustness and adaptability. It provides theoretical and practical support for the intelligent recognition and management of beef cattle.

Multiview Monitoring of Individual Cattle Behavior Based on Action Recognition in Closed Barns Using Deep Learning.

Cattle behavior recognition is essential for monitoring their health and welfare. Existing techniques for behavior recognition in closed barns typically rely on direct observation to detect changes using wearable devices or surveillance cameras. While promising progress has been made in this field, monitoring individual cattle, especially those with similar visual characteristics, remains challenging due to numerous factors such as occlusion, scale variations, and pose changes. Accurate and consistent individual identification over time is therefore essential to overcome these challenges. To address this issue, this paper introduces an approach for multiview monitoring of individual cattle behavior based on action recognition using video data. The proposed system takes an image sequence as input and utilizes a detector to identify hierarchical actions categorized as part and individual actions. These regions of interest are then inputted into a tracking and identification mechanism, enabling the system to continuously track each individual in the scene and assign them a unique identification number. By implementing this approach, cattle behavior is continuously monitored, and statistical analysis is conducted to assess changes in behavior in the time domain. The effectiveness of the proposed framework is demonstrated through quantitative and qualitative experimental results obtained from our Hanwoo cattle video database. Overall, this study tackles the challenges encountered in real farm indoor scenarios, capturing spatiotemporal information and enabling automatic recognition of cattle behavior for precision livestock farming.

Genetic Variants and Their Putative Effects on microRNA-Seed Sites: Characterization of the 3' Untranslated Region of Genes Associated with Temperament.

The 3' untranslated region has an important role in gene regulation through microRNAs, and it has been estimated that microRNAs regulate up to 50% of coding genes in mammals. With the aim of allelic variant identification of 3' untranslated region microRNA seed sites, the 3' untranslated region was searched for seed sites of four temperament-associated genes (CACNG4, EXOC4, NRXN3, and SLC9A4). The microRNA seed sites were predicted in the four genes, and the CACNG4 gene had the greatest number with 12 predictions. To search for variants affecting the predicted microRNA seed sites, the four 3' untranslated regions were re-sequenced in a Brahman cattle population. Eleven single nucleotide polymorphisms were identified in the CACNG4, and eleven in the SLC9A4. Rs522648682:T>G of the CACNG4 gene was located at the predicted seed site for bta-miR-191. Rs522648682:T>G evidenced an association with both exit velocity (p = 0.0054) and temperament score (p = 0.0097). The genotype TT had a lower mean exit velocity (2.93 ± 0.4 m/s) compared with the TG and GG genotypes (3.91 ± 0.46 m/s and 3.67 ± 0.46 m/s, respectively). The allele associated with the temperamental phenotype antagonizes the seed site, disrupting the bta-miR-191 recognition. The G allele of CACNG4-rs522648682 has the potential to influence bovine temperament through a mechanism associated with unspecific recognition of bta-miR-191.

Impact of sensor data pre-processing strategies and selection of machine learning algorithm on the prediction of metritis events in dairy cattle.

With all the sensor data currently generated at high frequency in dairy farms, there is potential for earlier diagnosis of postpartum diseases compared with traditional monitoring methodologies. Our objectives were 1) to compare the impact of sensor data pre-processing on classifier performance by using multiple time windows before a given metritis event, while considering other cow-level factors and farm-scheduled activities; 2) to compare the performance of random forest (RF), k-nearest neighbors (k-NN), and support vector machine (SVM) classifiers at different decision thresholds using different number of past observations (time-lags) for the detection of behavioral patterns associated with changes in metritis scores; and 3) to compare classifier performance between each one of the five behaviors registered every hour by an ear-tag 3-axis accelerometer (CowManager, Agis Autimatisering, Harmelen, Netherlands). A total of 239 metritis events were created by comparing metritis scores between two consecutive clinical evaluations from cows that were retrospectively selected from a dataset containing sensor data and health information during the first 21 days postpartum from June 2014 to May 2017. Hourly sensor data classified by the accelerometer as either ruminating, eating, not active (including both standing or lying), and two different levels of activity (active and high activity) behaviors corresponding to the 3 days before each metritis event were aggregated every 24-, 12-, 6-, and 3-hour time windows. Multiple time-lags were also used to determine the optimal number of past observations needed for optimal classification. Similarly, different decision thresholds were compared in terms of model performance. Depending on the classifier, algorithm hyperparameters were optimized using grid search (RF, k-NN, SVM) and random search (RF). All behaviors changed throughout the study period and showed distinct daily patterns. From the three algorithms, RF had the highest F1 score followed by k-NN and SVM. Furthermore, sensor data aggregated every 6- or 12-h time windows had the best model performance at multiple time-lags. We concluded that the data from the first 3 days post-partum should be discarded when studying metritis, and either one of the five behaviors measured with CowManager could be used when predicting metritis when sensor data were aggregated every 6- or 12-hour time windows, and using time-lags corresponding to 2-3 days before a given event, depending on the time window used. This study shows how to maximize sensor data in their potential for disease prediction, enhancing the performance of algorithms used in machine learning.

High Precision Classification of Resting and Eating Behaviors of Cattle by Using a Collar-Fitted Triaxial Accelerometer Sensor.

Cattle are less active than humans. Hence, it was hypothesized in this study that transmitting acceleration signals at a 1 min sampling interval to reduce storage load has the potential to improve the performance of motion sensors without affecting the precision of behavior classification. The behavior classification performance in terms of precision, sensitivity, and the F1-score of the 1 min serial datasets segmented in 3, 4, and 5 min window sizes based on nine algorithms were determined. The collar-fitted triaxial accelerometer sensor was attached on the right side of the neck of the two fattening Korean steers (age: 20 months) and the steers were observed for 6 h on day one, 10 h on day two, and 7 h on day three. The acceleration signals and visual observations were time synchronized and analyzed based on the objectives. The resting behavior was most correctly classified using the combination of a 4 min window size and the long short-term memory (LSTM) algorithm which resulted in 89% high precision, 81% high sensitivity, and 85% high F1-score. High classification performance (79% precision, 88% sensitivity, and 83% F1-score) was also obtained in classifying the eating behavior using the same classification method (4 min window size and an LSTM algorithm). The most poorly classified behavior was the active behavior. This study showed that the collar-fitted triaxial sensor measuring 1 min serial signals could be used as a tool for detecting the resting and eating behaviors of cattle in high precision by segmenting the acceleration signals in a 4 min window size and by using the LSTM classification algorithm.

Development of a New Wearable 3D Sensor Node and Innovative Open Classification System for Dairy Cows' Behavior.

Monitoring dairy cattle behavior can improve the detection of health and welfare issues for early interventions. Often commercial sensors do not provide researchers with sufficient raw and open data; therefore, the aim of this study was to develop an open and customizable system to classify cattle behaviors. A 3D accelerometer device and host-board (i.e., sensor node) were embedded in a case and fixed on a dairy cow collar. It was developed to work in two modes: (1) acquisition mode, where a mobile application supported the raw data collection during observations; and (2) operating mode, where data was processed and sent to a gateway and on the cloud. Accelerations were sampled at 25 Hz and behaviors were classified in 10-min windows. Several algorithms were trained with the 108 h of behavioral data acquired from 32 cows on 3 farms, and after evaluating their computational/memory complexity and accuracy, the Decision Tree algorithm was selected. This model detected standing, lying, eating, and ruminating with an average accuracy of 85.12%. The open nature of this system enables for the addition of other functions (e.g., real-time localization of cows) and the integration with other information sources, e.g., microenvironment and air quality sensors, thereby enhancing data processing potential.

Use of radio-frequency identification technology to assess the frequency of cattle visits to mineral feeders.

Three experiments were conducted to evaluate the frequency of visits to a mineral feeder equipped with radio-frequency identification (RFID). In Exp. 1, twelve heifers (Braford, Brahman, and White Angus; n = 4/breed) were fitted with RFID ear tags and placed into a pasture with access to a RFID-equipped mineral feeder. Number of visits were greater (P ≤ 0.05) during daytime than the night period. Brahman and Braford heifers favored (P ≤ 0.05) daytime than night period. White Angus heifers did not display a specific period preference (P = 0.32). In Exp. 2, Black Angus and Brahman cows (n = 15 and 19, respectively) were placed into a pasture with access to a RFID-equipped mineral feeder. Brahman cows made more (P < 0.01) visits to the mineral feeder than Black Angus cows. There were no breed differences on the number of visits during the morning (P = 0.25) and night (P ≤ 0.25) periods, but Brahman cows made more (P ≤ 0.05) visits to the mineral feeder in the afternoon period than Black Angus cows. In Exp. 3, the location of the mineral feeder was tested using 3 groups of Bos indicus-influenced heifers (n = 12/group). The mineral feeder was moved weekly within pasture. The number of visits to the mineral feeder differed for each location (P < 0.001) with visits being greatest when mineral feeder was placed near supplement and water, followed by center of the pasture, and lastly in the shade.

Corn versus Barley in Finishing Diets: Effect on Steer Performance and Feeding Behavior.

This study evaluated the effects of barley and corn finishing rations on feedlot performance and behavior of steers. Feedlot rations in this study were comprised of a main concentrate of either corn or barley. Steers were fed in a GrowSafe system to measure individual animal intake and behavior. Weight gain, average daily gain (ADG), and gain:feed were measured for each steer. Feeding behavior including time spent eating (min/day), visits per day, time per visit (min), eating rate (g/min), intake (kg/day), and intake per visit (g) were measured for each individual. Corn-fed steers had greater ADG (p < 0.01) and heavier hot carcass weights (HCW; p < 0.01). In addition, corn fed steers had a higher yield grade than barley fed steers (p < 0.01). No treatment effects (p ≥ 0.11) were observed for time spent eating, visits per day, time per visit, eating rate, intake g/kg body weight, or intake per visit. Although corn-fed steers had a greater ADG and HCW than barley-fed steers, they tended to consume more feed (p = 0.06). Depending on the difference of costs associated with feeding corn or barley, barley could be a potential high-quality feed source in beef cattle finishing rations.

Padrão de deslocamento de bovinos Nelore em sistemas integrados de produção agropecuária / Displacement patterns of Nellore cattle in integrated crop livestock system

The objective of this study was to characterize the displacement patterns of Nellore cattle in areas of crop livestock and crop livestock forest integration systems with density of 196 and 448 eucalyptus ha-1. Paddock maps were drawn from satellite images of the experimental area. In each evaluated system there was one trained observer, that on the paddock map recorded the place the animals stayed every 10 minutes. The exploration of the area by the animals was observed for 12 hours, starting at 6 a.m. and ending at 6 p.m. The displacement of the animals in the ILP system during the evaluation was bigger than the ILPFs systems, resulting in greater exploration of the area, this fact was due the presence of trees that, for the animals resembled fences, limiting the exploration of the paddock in ILPFs. In the ILPF with higher density of trees, this behavior was more evident. In the period from 10:10 a.m. to 14:00 p.m. the displacement of the animals was decrease by the high temperatures in all systems, although it was more effective in the ILP system. It is concluded that the displacement of cattle is influenced by the presence and density of arboreal component integrated crop livestock system.(AU)

Behavioral characteristics of Hanwoo (Bos taurus coreanae) steers at different growth stages and seasons.

OBJECTIVE: This research analyzed behavioral characteristics of Hanwoo (Bos taurus coreanae) steers during each season and growth stage to enable measurement of the animals' welfare level for precision livestock farming. METHODS: A hundred-eight beef steers were divided into three equal groups at a Hanwoo farm according to their growth stage: growing stage (GS), 8 months; early-fattening stage (EFS), 19 months; and late-fattening stage (LFS), 30 months. Twelve behavioral categories were continuously recorded for 13 day-time hours in each four seasons with three replications. RESULTS: Time spent standing was found to be significantly longer in summer at all growth stages (p<0.05). Hanwoos at the GS spent significantly longer standing time in spring and summer than those at the EFS and LFS (p<0.05). Lying time in summer was the shortest for all growth stages (p<0.05). Steers at the LFS spent significantly longer lying time than that at the GS (p<0.05) in summer. For GS and EFS, time spent eating in spring and autumn were longer than in summer and winter (p<0.05). Eating time was the longest for the GS in spring, autumn, and winter, excluding for the LFS in winter (p<0.05). Regarding ruminating, steers at the LFS spent significantly shorter time than those at other stages in all seasons (p<0.05). GS and EFS steers showed the longest walking time in summer compared with other seasons (p<0.05). At GS and LFS, drinking time in summer was the longest of all seasons (p<0.05). Sleeping time was significantly shorter in summer compared with the other seasons (p<0.05). Self-grooming time was the longest in winter for all growth stages (p<0.05). CONCLUSION: Steers were found to have more variable behavioral patterns during summer and the GS and less active behaviors during the LFS, thus extra care seems necessary during the GS, LFS, and summer period.

Seasonal variation of the lying and standing behavior indexes of dairy cattle at different daily time periods in free-stall housing.

The lying and standing behavior of dairy cattle reveal whether dairy cattle housing is appropriate to cow comfort. Lying and standing behavior indexes, such as cow comfort index (CCI), cow stress index (CSI) and stall usage index (SUI) are often used as an indication of animal welfare. This study was performed to determine the seasonal and hourly variation of cow behavioral indexes at different daily time periods (DTP) and evaluate appropriate DTP better representing daily behavioral activity of dairy cattle in free-stall housing (FHS) in Konya, Turkey. Animal behaviors were videotaped for a total of 24 days (576 h) over four seasons in a FHS at a commercial dairy farm from November 2007 to March 2009 using continuous video data (24 h per day). The behaviors of cows in the barn were evaluated using 60-min scan sampling. All phenotypes were evaluated on an hourly basis during the experimental period. The results show how these indexes can be accurately evaluated by analyzing video recordings taken on DTP between 08.00-15.00 and 19.00-24.00 hours for autumn and summer and 10.00-12.00 and 20.00-22.00 hours for spring, instead of continuous observation (r > 0.93, P < 0.01). Consequently, the evaluated method provides saving time and labor to accurately analyze cow behavior instead of observations over a long time.

Ethological activities, ruminal parameters and weight gain of calves supplemented with different amounts of sodium chloride / Atividades etológicas, variáveis ruminais e ganho de peso de bezerras suplementadas com quantidades crescentes de cloreto de sódio

The daily weight gain, behavioral activities (grazing, ruminating and water consumption) and the number of rumen protozoa, pH, NH3, and the osmolarity of rumen fluid was evaluated for four groups of six calves HPB/Zebu submitted to daily intake of 15, 30, 60 and 90g of NaCl during 135 days (9th Jan. to 24th May 2008).Throughout the experimental period the calves were in perfect health condition and did not show any signs suggestive of chronic sodium poisoning. There was no significant difference in average daily gain of the calves as the intake of NaCl increased. No significant behavioral changes were observed in the activities of grazing and rumination. However, there was a noticeable change in the frequency of water consumption in the calves that ingested 90g/d of NaCl; they went more often to the trough and drank more water than the group that ingested only 15g sodium chloride/d. The osmolarity of ruminal liquid was higher in the group of calves fed 90g of NaCl/d. The pH, NH3 concentration and number of rumen protozoa was within the normal range.

O desempenho ponderal, as atividades comportamentais (pastar, ruminar e beber água) e a estimativa do número de protozoários, pH, NH3 e da osmolaridade do líquido ruminal foram avaliados em quatro grupos de 6 bezerras HPB/Zebú submetidas a ingestão diária de 15, 30, 60 e 90 gramas de NaCl por 135 dias (9 jan. a 24 mai. 2008). Por todo o período experimental, os animais estiveram em perfeita condição de saúde e não exibiram quaisquer sinais sugestivos da intoxicação crônica por sódio. Não houve diferença significativa no ganho médio diário dos animais à medida que a ingestão de NaCl aumentou. Não foram detectadas alterações comportamentais significativas nas atividades de pastejo e ruminação. Entretanto, houve perceptível alteração na atividade de beber água e os animais que ingeriram 90g/d de NaCl aumentaram suas frequências de ida ao bebedouro e beberam mais água em relação ao grupo que ingeriu 15g/d. A osmolaridade do líquido ruminal foi sempre maior no grupo de animais que consumiram 90g/d de NaCl e o pH, a concentração de NH3 e número de protozoários também se situaram na faixa considerada normal.