牛舍环境，牛的监测数据，和牛舍空间

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Maintaining an optimal indoor thermal environment is crucial for enhancing the welfare and productivity of livestock in intensive breeding farms. This paper investigated the application of a combined geothermal heat pump with a precision air supply (GHP-PAS) system for cooling dairy cows on a dairy farm. The effectiveness of the GHP-PAS system in mitigating heat stress in lactating dairy cattle, along with its energy performance and local cooling efficiency in the free stalls were evaluated. A total of 140 multiparous lactating Holstein cows was tested in two groups. One group was housed in a barn equipped with a GHP-PAS system (GP barn, n = 70), and the other was housed in a barn with a conventional fan-sprinkling system (FS barn, n = 70). Results showed that the ambient temperature of both GP and FS barns were lower than that outside the barn (P < 0.05), with no significant difference between the GP and FS barns (P > 0.05). Compared to cows in the FS barn, those in the GP barn exhibited lower skin temperature, rectal temperature, and respiratory rate (P < 0.05). The mean temperature difference between outflow and inflow water was 2.56 °C of the GHP unit. The average energy efficiency ratios (EER) of the GHP unit and the GHP-PAS system were 5.03 and 2.92, respectively. The daily average electricity consumption was 20.4 ± 1.0 kWh. The field test results indicated that the airflow from a single nozzle of the GHP-PAS system effectively covered a stall space with an average width of 1.84 m at a cow reclining height of 0.5 m, with an average air velocity of 1 m/s. The per-cow hourly electricity consumption for cooling was 2.04 kWh for the GHP-PAS system and 0.36 kWh for the FS system, highlighting that the GHP-PAS system is approximately 5.6 times more energy-intensive than the FS system. In conclusion, the GHP-PAS system showed the potential for alleviating heat stress in dairy cows. Further research is needed to enhance the energy efficiency and cooling effectiveness of the current GHP-PAS system.

Simple Summary This paper introduces a new method of finding the best locations to place video cameras inside large cattle barns to monitor the behavior and health of the animals. Current approaches to livestock video monitoring rely on mounting cameras in the most convenient places for installation, but those locations might either be impractical for actual barns and/or might not capture the best views. This work showed that there is short list of the best placement options for the cameras to choose from which will provide the best camera views. Abstract Computer vision has been extensively used for livestock welfare monitoring in recent years, and data collection with a sensor or camera is the first part of the complete workflow. While current practice in computer vision-based animal welfare monitoring often analyzes data collected from a sensor or camera mounted on the roof or ceiling of a laboratory, such camera placement is not always viable in a commercial confined cattle feeding environment. This study therefore sought to determine the optimal camera placement locations in a confined steer feeding operation. Measurements of cattle pens were used to create a 3D farm model using Blender 3D computer graphic software. In the first part of this study, a method was developed to calculate the camera coverage in a 3D farm environment, and in the next stage, a genetic algorithm-based model was designed for finding optimal placements of a multi-camera and multi-pen setup. The algorithm’s objective was to maximize the multi-camera coverage while minimizing budget. Two different optimization methods involving multiple cameras and pen combinations were used. The results demonstrated the applicability of the genetic algorithm in achieving the maximum coverage and thereby enhancing the quality of the livestock visual-sensing data. The algorithm also provided the top 25 solutions for each camera and pen combination with a maximum coverage difference of less than 3.5% between them, offering numerous options for the farm manager.

In tropical environments, dairy cattle production is constrained by several factors, including climate. The seasonal loss of milk due to heat stress is a recurring challenge for many dairy producers. The objective of this study was to detect heat stress thresholds, milk yield loss and individual animal variations using random regression models for dairy cattle from test-day milk records. Data were obtained from the Kenya Livestock Breeders Organization for the years 2000-2017 and merged with weather data. The weather parameters were grid-interpolated solar and meteorological data obtained from the National Aeronautics and Space Administration/Prediction Of Worldwide Energy Resources (NASA/POWER). After editing, the records comprised 49 993, 45 251 and 36 136 test-day records for first, second, and third lactations, respectively, for the four main dairy breeds: Friesian (68.0%), Ayrshire (21.1%), Jersey (7.6%) and Guernsey (3.3%). Variance components were estimated using Restricted Maximum Likelihood in ASReml software. Random regression models with third-order Legendre polynomials were fitted to the average and individual lactation curves and the reaction norms. An extended factor analytic variance structure for the random cow effects was used to estimate (co)variances between days in milk and thermal load. The daily average temperature (TA) and temperature humidity index (THI) were identified as the most suitable thermal load indicators for assessing milk yield losses. Considering a one day lag, the estimated heat stress thresholds were about 22 °C and 69 index units for TA and THI, respectively. Almost no differences were observed for estimated residual variances between the thermal load indicators, indicating there was no better model fit by TA or THI. The heat stress thresholds and milk loss patterns are important for management of dairy production systems in the tropics with climatic conditions similar to this study. Data recording should be improved as a tool to monitor the expected impacts of climate change and mitigation measures.

We present a method for 3D cattle tracking and inter-camera pose transformation using depth information from monocular depth estimation with deep networks. Camerabased animal monitoring offers a minimally invasive and easily adaptable solution for tracking and welfare monitoring, relying solely on commercial RGB camera systems. However, environmental factors and inter-animal occlusion often hinder tracking efficacy and consistency. To address these challenges, we developed a pipeline to extract 3D point cloud data of individual cows in a straw-bedded calving yard environment, generating quasi-3D bounding boxes ($x, y, z$, height, width, $\theta$), where $\theta$ is the polar angle. We then estimate the camera system extrinsic parameters by minimising the rotation, translation, and scale discrepancies between the apparent motion of animals across different frames of reference. This approach demonstrates a strong agreement between the 3D centroids of tracked animals in motion. Our work advances the development of algorithmic occlusion handling and object handover techniques in multicamera systems, particularly pertinent to the high-occlusion, low-locomotion scenario of animals within barn environments.

Accurate long-term tracking of individual cattle is essential for precision livestock farming but remains challenging due to occlusions, posture variability, and identity drift in free-range environments. We propose a multi-camera tracking framework that combines bird’s-eye-view (BEV) trajectory matching with cattle face recognition to ensure identity preservation across long video sequences. A large-scale dataset was collected from five synchronized 4K cameras in a commercial barn, capturing both full-body movements and frontal facial views. The system employs center point detection and BEV projection for cross-view trajectory association, while periodic face recognition during feeding refreshes identity assignments and corrects errors. Evaluations on a two-day dataset of more than 600,000 images demonstrate robust performance, with an AssPr of 84.481% and a LocA score of 78.836%. The framework outperforms baseline trajectory matching methods, maintaining identity consistency under dense crowding and noisy labels. These results demonstrate a practical and scalable solution for automated cattle monitoring, advancing data-driven livestock management and welfare.

Pose estimation serves as a cornerstone of computer vision for understanding animal posture, behavior, and welfare. Yet, agricultural applications remain constrained by the scarcity of large, annotated datasets for livestock, especially dairy cattle. This study evaluates the potential and limitations of cross-species transfer learning by adapting ZebraPose - a vision transformer-based model trained on synthetic zebra imagery - for 27-keypoint detection in dairy cows under real barn conditions. Using three configurations - a custom on-farm dataset (375 images, Sussex, New Brunswick, Canada), a subset of the APT-36K benchmark dataset, and their combination, we systematically assessed model accuracy and generalization across environments. While the combined model achieved promising performance (AP = 0.86, AR = 0.87, PCK 0.5 = 0.869) on in-distribution data, substantial generalization failures occurred when applied to unseen barns and cow populations. These findings expose the synthetic-to-real domain gap as a major obstacle to agricultural AI deployment and emphasize that morphological similarity between species is insufficient for cross-domain transfer. The study provides practical insights into dataset diversity, environmental variability, and computational constraints that influence real-world deployment of livestock monitoring systems. We conclude with a call for agriculture-first AI design, prioritizing farm-level realism, cross-environment robustness, and open benchmark datasets to advance trustworthy and scalable animal-centric technologies.

In livestock production, animal-related data are often registered in specialised databases and are usually not interconnected, except for a common identifier. Analysis of combined datasets and the possible inclusion of third-party information can provide a more complete picture or reveal complex relationships. The aim of this study was to develop a risk index to predict farms with an increased likelihood for animal welfare violations, defined as non-compliance during on-farm welfare inspections. A data-driven approach was chosen for this purpose, focusing on the combination of existing Swiss government databases and registers. Individual animal-level data were aggregated at the herd level. Since data collection and availability were best for cattle and pigs, the focus was on these two livestock species. We present machine learning models that can be used as a tool to plan and optimise risk-based on-farm welfare inspections by proposing a consolidated list of priority holdings to be visited. The results of previous on-farm welfare inspections were used to calibrate a binary welfare index, which is the prediction goal. The risk index is based on proxy information, such as the participation in animal welfare programmes with structured housing and outdoor access, herd type and size, or animal movement data. Since transparency of the model is critical both for public acceptance of such a data-driven index and farm control planning, the Random Forest model, for which the decision process can be illustrated, was investigated in depth. Using historical inspection data with an overall low prevalence of violations of approximately 4% for both species, the developed index was able to predict violations with a sensitivity of 81.2 and 79.5% for cattle and pig farms, respectively. The study has shown that combining multiple and heterogeneous data sources improves the quality of the models. Furthermore, privacy-preserving methods are applied to a research environment to explore the available data before restricting the feature space to the most relevant. This study demonstrates that data-driven monitoring of livestock populations is already possible with the existing datasets and the models developed can be a useful tool to plan and conduct risk-based animal welfare inspection.

In this paper, an AI driven well-being and health monitoring set-up for the science & engineering applications is being presented. "Dhenu Svasthya" represents an initiative at the intersection of agriculture, animal welfare, and artificial intelligence. This project's primary mission is to significantly enhance the overall health and living conditions of cattle, with a focus on improving their well-being and productivity. At the core of "Dhenu Svasthya" lies a sophisticated health monitoring system. Utilizing advanced AI ML method this system continuously tracks and analyzes key health indicators in cattle. It observes and records jaw movements, heart rate, acetone levels, bellowing patterns, and body temperature in real-time. The objective is to detect anomalies, deviations, or early signs of distress promptly. This proactive approach enables timely intervention by cattle owners, reducing the likelihood of disease and ensuring the optimal health of the animals. Environmental Cleanliness is another pivotal aspect of the project is the assessment of the cattle's living environment. Maintaining a clean and hygienic space is paramount for the cattle’s well-being. "Dhenu Svasthya" deploys AI technology to evaluate the cleanliness of the surroundings. The system provides actionable insights to users, allowing them to address potential cleanliness issues promptly and maintain a healthier living environment for their cattle. To extend the project's reach and accessibility, we are actively working on integrating "Dhenu Svasthya" with cloud services and other data management platforms. It aims to empower cattle owners and farmers with the information and insights needed for effective cattle management, thereby improving overall livestock welfare. In summary, "Dhenu Svasthya" is an ambitious undertaking that leverages AI to usher in a new era of cattle care. By continuously monitoring health, ensuring environmental cleanliness, and embracing data integration, this project aspires to be a game-changer in the field of livestock management. Its overarching goal is to promote the well-being of cattle, enhance agricultural sustainability, and ultimately benefit both the animals and those who depend on them for their livelihoods. The work carried out is the seventh semester main-project by the students of Electronics & Communication Engineering under the guidance of the faculties supervision (guide).

Monitoring the movement patterns of dairy cattle can provide important insight into space utilization or space occupancy in a barn. Although several precision livestock technologies have been developed to record dairy cattle movements, there is a lack of open source tools to track and visualize cattle movement patterns. Therefore, we developed an open-source computer vision software tool, AnimalMotionViz, that allows users to track and visualize dairy cattle movement patterns using a motion heatmap. The software comes with an easy-to-use web-based graphical user interface built with the Python Dash package. It implements a set of background subtraction algorithms in the OpenCV package to track animal motion patterns in real time. The software processes each frame of the input video and identifies the background and foreground using these algorithms. Foreground objects are then subtracted from the background across all frames and cumulatively overlaid on an empty mask image created with the first frame of the input video to visualize the intensity or frequency of motion across different regions. The user can generate motion heatmaps in an image and video, and also track specific regional motion with a custom mask. The software also returns the top three peak intensity locations, the total percentage of regions used, and the within-quadrant percentage of regions used. In four 5 min sample videos, quadrants with peak intensity of space use, as identified using the software, aligned with quadrants where calves spent the greatest duration of time, according to continuous recording of behavior from video. The motion heatmaps generated by AnimalMotionViz can be used to understand space utilization or space occupation by animals, as well as to assess how space allocation affects dairy cow movement. We conclude that the newly developed AnimalMotionViz is a user-friendly and efficient tool to support research developments in precision livestock farming towards enhancing cattle management practices and improving pen designs.

Livestock farming is currently undergoing a digital revolution and becoming increasingly data-driven. Yet, such data often reside in disconnected silos making it impossible to leverage their full potential to improve animal well-being. Here, we introduce a precision medicine approach, bringing together information streams from a variety of life domains of dairy cattle to predict eight common and economically important diseases. Dairy cows are part of a highly industrialised environment. The animals and their surroundings are closely monitored and environmental, behavioural and physiological observations are readily accessible yet seldomly integrated. We use random forest classifiers trained on data from 5,828 animals in 166 herds in Austria to predict occurrences of lameness, acute and chronic mastitis, anoestrus, ovarian cysts, metritis, ketosis (hyperketonemia) and periparturient hypocalcemia (milk fever). To assess the importance of specific cattle life domains and individual features for these predictions, we use multivariate logistic regression and feature permutation approaches. We show that disease in dairy cattle is a product of the complex interplay between a multitude of life domains such as housing, nutrition or climate, and identify a range of features that were previously not associated with increased disease risk. For example, we can predict anoestrus with high sensitivity and specificity (F1=0.72) and find that housing, feed and husbandry variables such as barn design and time on pasture are most predictive of this disease. We also find previously unknown associations of features with disease risk, for example humid conditions, which significantly decrease the odds for ketosis. Our findings pave the way towards data-driven point-of-care interventions and demonstrate the added value of integrating all available data in the dairy industry to improve animal well-being and reduce disease risk.

Wildfire smoke exposure poses a significant health risk to individuals, particularly in regions prone to wildfires. The effects of wildfire smoke exposure on livestock are less well-known. To address this gap in knowledge, our team has developed a controlled, indoor simulation of wildfire smoke. This study investigates the physiological and behavioral responses of beef heifers to controlled smoke exposure, aiming to evaluate the impact of such exposure on various health and behavioral parameters before, during, and after exposure. In a two-year study, sixteen heifers (Angus × Hereford; 8 months, 230 kg) were individually housed in pens within a completely enclosed barn for 36 days. The study was conducted in three phases: baseline/acclimation (days -7 to -1), where heifers were acclimated to the environment; smoke exposure (days 0 to 7), where heifers were exposed to controlled, simulated wildfire smoke daily for seven consecutive days; and the post-smoke exposure phase (days 8 to 28), where heifers were monitored for three weeks following the smoke exposure. During the smoke exposure phase, smoke was created every afternoon to reduce air quality, which was monitored daily every minute with two air quality monitors using particulate matter 2.5 (PM 2.5) as a major indicator of air quality. Throughout the study, each heifer had ad libitum access to alfalfa hay and water, and feed intake was recorded daily. Health scores were collected daily by trained technicians using the University of Wisconsin Calf Health Scoring System. Blood samples were collected weekly to evaluate acute phase proteins, cytokines, hormones, and hematological markers. Behavior was recorded continuously for the length of the study using four cameras. Preliminary data were analyzed over time using the MIXED procedure of SAS, with each heifer serving as its own control and with year as a random effect. Air quality, measured with PM2.5, changed over time (P ≤ 0.0001), reaching a daily average of 142 mg/m3 during the smoke exposure phase, which was above the EPA safe threshold for human 24-hr exposure (35 mg/m3). Although air quality was reduced during the smoke exposure phase, no changes (P = 0.86) in blood cortisol concentration were observed over time. Blood hematology analysis revealed changes (P ≤ 0.04) in concentration of white blood cells, lymphocytes, and neutrophils. These preliminary data suggest that smoke exposure or poor air quality may impact animal health and have the potential to initiate an immune response in beef cattle. This research was supported by the USDA-NIFA, Award #2023-68008-39173.

The economic efficiency of intensive livestock farming on an industrial basis depends on the rational housing of animals, which is largely determined by the presence of an optimal microclimate in the premises. Whatever breed and pedigree qualities the animals may have, without creating the necessary microclimate conditions they are unable to maintain their health and show their potential productive capabilities due to heredity. Between 2018 and 2020, 11 farms in Perm Region were surveyed for respiratory and digestive diseases, skin diseases, and in some cases stress was observed in the animals. The costs of heating livestock buildings are, as a rule, much lower than the losses due to mortality, loss of productivity and overconsumption of feed. The physical properties of the air environment are factors that are not constant and are subject to large fluctuations. To optimize the microclimate in a livestock building, a program algorithm has been developed for a computer. That will create a system, which provides optimal conditions for the maintenance and service of animals and increase the life safety on livestock farms. In this regard, in order to improve conditions for keeping calves and cows, a project for a device to control the parameters of the microclimate in farms in the Perm region has been developed. The microclimate control system is developed on the basis of: Order of the Ministry of Agriculture of the Russian Federation from October 21, 2000 № 622 “On approval of Veterinary rules of keeping cattle for its reproduction, rearing and realization” and Set of rules 106.13330.2012 “Cattle - breeding, poultry - breeding and beast - breeding buildings and premises”. At the heart of the monitoring is an automated analysis and regulation of microclimate parameters. Hardware-software implementation is made on PLC Omron.

The main objective of this work was to investigate the influence of environmental factors, including air temperature (AT), relative humidity (RH) and temperature–humidity index (THI), on the difference between rectal temperature (RT) and eye temperature (ET) of dairy cows. The monitoring of these parameters is important for the further possible introduction of digitalization in animal welfare, especially in dairy cattle. The mean calculated difference between rectal temperature and eye temperature (RT–ET) was 1.5 °C. The average value of AT was 16.4 °C, and the average value of RH was 59.2%. The average value of THI was 60.4. The results of the study showed that, for the temperature difference ET-RT, a low degree of correlation was found both with temperature and with the temperature-humidity index THI (R = 0.22; R = 0.23). However, the observed temperature difference of the animal (ET-RT) showed a moderate degree of dependence on the relative humidity of air (R = −0.32). Although the positive correlation coefficient for AT and THI points to the higher criticality of summer measurements, the negative correlation coefficient for RH supports the use of infrared thermography for determining the temperature of animals even in a moister barn environment.

Simple Summary Behaviour indicators of animal welfare were studied in yearling beef belonging to different genetic types (Limousines, Sardo-Bruna (SRB), and crossbred Limousine × Sardo-Bruna) and sex and reared in the same “optimal” environmental conditions, as prescribed in the Classyfarm® manual. Two trained operators evaluated behavioural activities by the scan and focus samplings and feeding behaviour by evaluating video recording. Behaviour indicators of animal welfare did not evidence substantial differences among animals of different genetic types or sexes reared in the same “optimal” environmental conditions, except for some indicators which were mostly expressed in females and in SRB yearling beef. Female beef and the autochthon’s cattle breed of Sardinia, although typically hardy, showed a wide behavioural repertoire, suggesting a high ability to cope with the environment. Abstract The aim of this study was to monitor the behaviour of purebred and crossbred beef cattle reared in the same optimal environmental conditions according to Classyfarm®. Thirty-yearling beef 11.5 months old, including 10 Limousines (LMS), 10 Sardo-Bruna (SRB), and 10 crossbred Limousine × Sardo-Bruna (LMS × SRB), balanced for sex and body weight, were used. Animals were evaluated for five months by two trained operators by SCAN (“sternal resting”, “lateral resting”, “ central or peripheral position in the pen”, standing”, “walking”, “feeding”, “drinking”, and “ruminating) and FOCUS (“displacement for space”, “displacement for feed or water”, “play-fighting”, “self-grooming”, “allo-grooming”, “stereotyping”, and “mounting”) protocols. Feeding behaviour was monitored by a CCTV system. The application of the SCAN sampling evidenced that SRB animals preferred the “standing” activity over the LMS animals, while the LMS × SRB did not differ from them. The “standing” and “ ruminating “activities were observed mostly in females than males (p < 0.05). For behaviour parameters assessed by the FOCUS methodology, the n-events of “allo-grooming” were higher (p < 0.05) in SRB than in LMS and LMS × SRB genetic types. Males showed higher (p < 0.05) n-events than females for “play-fighting”. For feeding behaviour, the “eating concentrate” activity (expressed as n-events) was higher (p < 0.05) in SRB than LMS × SRB and LMS being intermediate (p < 0.05). The duration of “eating concentrate” (expressed in minutes) was higher (p < 0.05) in females than males. In conclusion, behaviour indicators of animal welfare did not evidence substantial differences among genetic types and between sexes reared in the same “optimal” environmental conditions. Female beef and the autochthon’s cattle breed of Sardinia, although typically hardy, showed a wide behavioural repertoire.

Simple Summary In order to keep dairy cows under satisfactory health and welfare conditions, it is very important to monitor the animals in their living environment. With the support of technology, and, in particular, with the installation of sensors on neck-collars, cow behavior can be adequately monitored, and different behavioral patterns can be classified. In this study, an open and customizable device has been developed to classify the behaviors of dairy cows. The device communicates with a mobile application via Bluetooth to acquire raw data from behavioral observations and via an ad hoc radio channel to send the data from the device to the gateway. After observing 32 cows on 3 farms for a total of 108 h, several machine learning algorithms were trained to classify their behaviors. The decision tree algorithm was found to be the best compromise between complexity and accuracy to classify standing, lying, eating, and ruminating. The open nature of the system enables the addition of other functions (e.g., localization) and the integration with other information sources, e.g., climatic sensors, to provide a more complete picture of cow health and welfare in the barn. Abstract 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.

In the domain of precision livestock farming, the integration of diverse data sources is crucial for advancing sustainability and evaluating the implications of farm management practices on cow health. Addressing the challenge of data heterogeneity and management diversity, we propose a key-feature-based clustering method. This approach, merging knowledge-driven feature selection with unsupervised machine learning, enables the systematic investigation of management effects on cow health by forming distinct clusters for analysis. Utilizing data from 3,284 Austrian farms, including 80 features related to feeding, milking, housing, and technology systems, and health information for 56,000 cows, we show how this methodology can be applied to study the impact of technological systems on cow health resulting from the incidence of veterinary diagnoses. Our analysis successfully identified 14 distinct clusters, further divided into four main groups based on their level of technological integration in farm management: “SMART,” “TRADITIONAL,” “AMS (automatic milking system),” and “SENSOR.” We found that “SMART” farms, which integrate both AMS and sensor systems, exhibited a minimally higher disease risk for milk fever (OR 1.09) but lower risks for fertility disorders and udder diseases, indicating a general trend toward reduced disease risks. In contrast, farms with “TRADITIONAL” management, without AMS and sensor systems, showed the lowest risk for milk fever but the highest risk of udder disease (OR 1.12) and a minimally higher incidence of fertility disorders (OR 1.07). Furthermore, across all four groups, we observed that organic farming practices were associated with a reduced incidence of milk fever, udder issues, and particularly fertility diagnoses. However, the size of the effect varied by cluster, highlighting the complex and multifactorial nature of the relationship between farm management practices and disease risk. The study highlights the effectiveness of the key-feature-based clustering approach for high-dimensional data analyses aimed at comparing different management practices and exploring their complex relationships. The adaptable analytical framework of this approach makes it a promising tool for planning optimizing sustainable and efficient animal husbandry practices.

A detailed survey was conducted from September, 2020 to February, 2021 to document the socio-economic status and livestockrearing practices in the flood-prone areas of Cuddalore district. This study assessed the socio-economic profile, livestock holdings, housing practices, stocking density, and feeding management of livestock farmers in flood-prone areas of Cuddalore district, Tamil Nadu. Floods, among the major natural disasters affecting agriculture and livestock-based livelihoods in coastal regions of India. A total of 600 households (20 villages×30 farmers) were surveyed across three Geographical Areas (GGA I: 0–10 m, GGA II: 11–20 m, GGA III: >20 m above mean sea level). Results revealed a predominance of male farmers (63.83%) with an overall literacy rate of 64.48%, lower than the district average. Most farmers were daily wage labourers (65.17%) and had landholdings below one acre, with 33% landless. Thatched-roof housing was the most common (49.17%), and a significant proportion of farmers (40.16%) lacked any animal shelter. Livestock holdings were generally small, with non-descriptive cattle and goats predominating, and significant variations were observed in crossbred cow, goat, and sheep numbers across GGAs. The mean number of animals shed-1 ranged from 9.67 to 12.27, with higher stocking densities in GGA II. Regular concentrate feeding averaged 1.20 kg day-1, increasing to 3.81 kg day-1 after floods to support recovery. The study highlighted that low literacy, small landholdings, and limited infrastructure constrained adaptive capacity among livestock farmers. These findings underscored the need for targeted extension services, flood-resilient animal housing, and improved feeding strategies to enhance livestock resilience in flood-prone areas.

A serious problem for cattle breeding in the warm season is the threat of heat stress in animals, which causes significant economic losses for the entire industry, while having a negative impact on the productivity of dairy cows, as well as the composition of the milk produced. Daily monitoring of temperature and humidity by special electronic sensors in livestock premises and further calculation of the temperature and humidity index (THI) were chosen as an indicator of thermal stress in dairy cows, after which a link was established between the microclimate of the farm and financial losses. Four farms with different herd management systems, ventilation systems, milking methods and manure disposal were selected as experimental agricultural enterprises. The lowest milk losses were recorded at a megafarm with loose maintenance, robotic milking and an innovative automatic ventilation system. The greatest losses, due to the effect of heat shock on dairy cows, were incurred by a farm with tethered housing, milking in a milk pipeline and not equipped with a ventilation system. At the same time, the indicator of the content of the mass fraction of fat in milk remained relatively stable, the decline was noted only after cases of sharp jumps in the temperature and humidity index in the livestock premises. Most often, such jumps, and the subsequent decline in productivity, occurred in the second half of June and the first half of July 2024. Stabilization of both the temperature and humidity conditions on farms and reduction of the negative effects of heat stress occurred at the end of August and the first half of September. On average, 57 days with critical indicators of the temperature and humidity index were registered for the entire season.

Dairy cattle breeding is one of the leading branches of animal husbandry. Poor zoo-hygienic conditions of dairy cows is the main cause of limb diseases, including pododermatitis. The study aimed to identify the effect of zoo-hygenic conditions of dairy cows on pododermatitis. The objectives of the research were to study the zoo-hygenic conditions of dairy cows at the enterprise; to identify animals with clinical signs of pododermatitis; to determine the causes of this disease and to suggest preventive measures. The study was conducted at an enterprise in the south of Tyumen region engaged in breeding black-and-white Holstein dairy cattle in 2022. Dairy cows of the second lactation were studied. The experiment lasted sixty days (11–12.2022). The test and control groups of cows were kept in different sheds, under certain zoo-hygenic conditions after a thorough analysis. Experimental cows were clinically examined daily. Clinical signs of the impaired functional state of the limbs in the studied herd were identified and recorded. Changes in the general clinical condition and results of laboratory blood tests as well as the data from the monitoring of microclimate parameters and cow housing conditions were analysed to assess the effect of zoo-hygenic factors on pododermatitis. The study has revealed the correlation between zoo-hygenic conditions of dairy cows and clinical signs of pododermatitis as the major underlying condition for limb diseases in cows. Results. Pododermatitis was found in four cows (3 with an aseptic type and 1 with a purulent type) in the control group, where zoo-hygenic conditions did not always meet the requirements. The value accounted for 20 % of the experimental livestock and was 15 % higher compared to the test group, where zoo-hygenic conditions almost completely met the requirements.

Heat stress poses a significant challenge for dairy cows, particularly in warm climates, as it hampers their physiology, behaviour, and milk production. This study evaluated the effectiveness of a modern barn cooling system equipped with temperature-dependent fans in alleviating heat stress and enhancing cow welfare and productivity. The research was conducted on a large-scale Holstein Friesian dairy farm, comparing two high-yielding groups of around 100 cows each. The experimental group was housed in a barn with newly installed louvered, temperature-dependent circulation fans (providing high airspeed), while the control group remained in a barn with box ventilators (low airspeed). Data were collected over four three-day sampling periods during the summer. Environmental conditions were monitored using data loggers that recorded temperature and humidity, enabling calculation of the temperature-humidity index (THI). Cow body temperature was measured intravaginally. Additionally, time spent lying, eating, and ruminating was also tracked. Daily milk yields were obtained from the farm's database. Blood samples were also taken for gene expression measurements. Results indicated that THI values in both barns frequently surpassed the heat stress threshold (THI >68), yet air velocity in the experimental barn was notably higher (1.5-2.5 m/s) than in the control barn (0.2-0.5 m/s). Cows in the cooled barn showed significantly lower body temperatures in three of four periods. While lying time did not vary significantly, eating time increased in the experimental group later in the study. Moreover, milk yield was approximately 3 L/day higher (p < 0.05) in the experimental group. Enhanced ventilation led to complex changes in gene expression patterns, suggesting dynamic cellular responses to improved environmental conditions. These outcomes indicate that improved ventilation effectively reduces heat stress and supports better health, feeding behaviour, and milk production in dairy cows.

Calves raised in barns are usually kept in individual pens separated by either solid or mesh partitions. To quantify the effects that the two types of partition have on airflow speed in an axial-ventilated-barn, the indoor environment of a calf barn was simulated using computational fluid dynamics (CFD) with validation accomplished by means of direct measurement. To ascertain the effects that two types of partition have on the physiological parameters and health of pre-weaned calves, 24 calves (3–11-day-olds) were selected, equally divided into four groups and sequestered as follows: calves placed in pens separated by solid partitions receiving “low-speed” or “high-speed” airflow; calves separated by mesh partitions receiving “low-speed” or “high-speed” airflow. The results of the CFD simulation showed that the percentage of airflow speed that exceeded 0.5 m s−1 at a height of 0.4 m above the floor of the animal occupied zone where calves were separated by mesh partitions was 88%, while the speed was 66–70% for calves separated by solid partitions. The duration of treatment provided to the calves in the MP-LA (mesh partitions and subjected to a low-speed airflow) and MP-HA (mesh partitions and subjected to a high-speed airflow) groups, were both lower than the SP-LA (solid partitions and subjected to a low-speed airflow) and SP-HA (solid partitions and subjected to a high-speed airflow) groups. We conclude that when the fan is operating, contact between calves separated by mesh partitions produces no negative impact on the health of calves; furthermore, this arrangement can provide a higher airflow speed than that delivered to calves raised in pens separated by solid partitions, especially to those calves in pens farther from the fans.

Fogging systems control microclimatic parameters of barns to provide immediate cooling to dairy cows during heat stress conditions. However, the benefits of implementing a fogging system to cow comfort behaviors have not been thoroughly investigated in lactating cows. Therefore, the objective of this case study was to evaluate cow comfort behaviors and milk productivity in cows managed in a tunnel-ventilated barn in a commercial dairy farm that uses an automatic milk system. Sixty Holstein lactating dairy cows (45-90 days in milk; DIM) were monitored and housed in group pens (6 pens; n = 10 cows/pen). Four weeks before the fogging system installation, cows had pedometers (IceQube, IceRobotics, Inc., United Kingdom) and SCR rumination collars (SCR by Allflex; Israel) placed for continuous daily activity and rumination levels monitoring, respectively. Productivity information was acquired using automatic robotic milking technology (Lely Astronaut A5; Iowa, United States), and all variables were measured until four weeks after fogger system installation. Activity behavior (lying duration, daily step count, and number of transitions) and productivity (daily milk yield and milk speed) were evaluated before (PRE; 20-day pre-installation period), during (DURING; 2-day period), and after fogger installation (POST; 35-day post-installation period). Data were analyzed using PROC GLM in SAS 9.4 with cow as the experimental unit and temperature-humidity index (THI) included in the model as a covariate. Overall, cows spent more time lying (P < 0.01) in the POST period versus PRE and DURING. Cows performed fewer daily transitions between lying and standing (P < 0.01) in the POST versus PRE period, but no differences were detected (P = 0.67) among the step counts observed PRE, DURING, and POST fogger installation. Average milk speed and average milk yield were decreased (P < 0.01) in the POST period compared with the PRE and DURING periods. Temperature-humidity index significantly influenced (P < 0.01) lying time but did not have an effect on any other variables measured. In summary, installation of a fogger system in a commercial tunnel-ventilated barn improved cow comfort behaviors in lactating cows managed with an automated robotic milking system but did not offer immediate production performance benefits.

Heat stress has vital importance in livestock farming due to the physiological changes on the animals so that meat and milk production is significantly degraded. Recently, the hereditary and mental effects of environmental conditions have also been discussed for future generations. Therefore, high-level automation solutions are required to keep the environmental conditions in the barns as optimal as possible. In this paper, a hangar-type scaled-down barn was experimentally designed for modelling and control of the environmental conditions. First, the temperature-humidity index (THI) which is a measure of the heat stress, was stabilized to its critical value in two regions of the barn by using proportional-integrative-derivative (PID) controller. The ventilation fans were controlled at variable speeds so that energy efficiency was also provided when compared to the on-off control. Second, we proposed the state-space modelling of the coupled temperature and humidity dynamics for the interior space of the barn so that the obtained model can be utilized for mathematical analysis and accurate control. Specifically, state monitoring and prediction, optimal control, and observer-based sensor-less control are to be applied based on its state-space model. The parameters of the state-space model were here estimated with an Extended-Kalman Filter (EKF). Performances are calculated in terms of mean square error (MSE), and the performance values were found to be less than 5% for stabilization and less than 2% for modelling, respectively. The proposed scaled-down barn model is a low-cost design that can be used as an example for those who work in this field to conduct experimental studies before making large investments. Barn design can also be modified for modelling, analysis, and control of new heat stress measures in the future.

Bovine respiratory disease complex (BRDC) is still one of the most challenging problems in calf rearing, therefore identification and continuous real-time monitoring of contributing environmental factors might play a role in mitigation of the damage caused by the disease. Microclimatic variables (temperature, relative humidity, air velocity, airborn particles expressed in particulate matter [PM], aerial germ load and gaseous pollutants) of a conventional calf barn and outdoor placed small-group hutches with pens were real-time monitored in a dairy herd by mounted sensors from summer to winter. Among the risk factors for BRDC, the small-group outdoor rearing units were characterized by high relative humidity, air velocity, and PM2.5 particulate matter concentration. Although the conventional calf barn was poorly ventilated, most variables were more favourable than expected, in which proper farm management may have played a role. We were able to identify long term and intraday periods with limit-breaking values, daily fluctuations as well as specific patterns of individual indicators in different calf husbandry environments. Based on obtained data, contributing technological processes may be reviewed and the effect of changes can be monitored under farm conditions. In addition, assessment of prevailing microclimatic conditions os also possible before investing in modernization of calf rearing units.

With the development of the Internet of Things (IoT), digital technology has been adopted on livestock farms. In this study, a system for monitoring the health status of dairy cows is proposed, utilizing multi-type sensor fusion and IoT technology. The system uses physiological and behavioral data obtained from a tail sensor attached to the cows to establish prenatal and estrus prediction models. Additionally, environmental sensors installed in the barn monitor parameters such as temperature, humidity, carbon dioxide concentration, and organic gas concentration to automatically regulate the barn’s fan and sprinkler based on preset threshold values. The system also monitors the health and tail behavior of the cows and predicts their calving and estrus times based on the collected data. Experimental results demonstrate that the system exhibits high accuracy and reliability in monitoring the health of dairy cows.

Locomotor activity contributes to the fitness and physiological stability of dairy cows and is a key indicator of animal welfare. Modern barn design should therefore aim to promote cow movement. A central question is whether such activity-promoting environments require entirely new construction or whether existing barns can be effectively converted to meet current welfare standards. This observational study investigates the structural and technical factors influencing cow activity under practical farming conditions. Data were collected on 18 commercial dairy farms in Baden-Württemberg, Germany, of which six featured converted existing facilities and twelve newly constructed barns between 2018 and 2022. Cow activity was measured using ALT pedometers (Holz, Falkenhagen, Germany) over 56 measurement periods from 2020 to 2022. A total of 633 cows were monitored, yielding 24,202 daily activity records. Activity pulses were analyzed using a linear mixed-effects model accounting for repeated measures and hierarchical data structure. The results showed no significant differences in locomotor activity between cows housed in newly built versus converted barns. Similarly, no significant effect was observed for floor type (slatted vs flat). In contrast, pasture access, month of measurement, milking system, parity, and days in milk significantly influenced activity levels. Cows with pasture access displayed the highest activity, and seasonal effects pointed to environmental influences. Two farms exhibited markedly elevated activity: presumably one due to long distances to pasture, the other due to feed presentation via an external hayrack—highlighting the impact of specific management features. Contrary to earlier research, rubber flooring did not significantly affect activity. This may be explained by the widespread use of rubber flooring (on average 80% coverage) across nearly all farms, which reduced variability. Herd size and milk yield also showed no significant effect, likely due to the use of automated feeding systems reducing the need to walk for feed. While causal conclusions are limited by the non-randomized study design, the results suggest that well-executed barn conversions can offer locomotor opportunities equivalent to those of new buildings. This supports the view that modern conversions can be a resource-efficient and welfare-compatible solution for updating dairy housing. The findings provide a valuable evidence base for structural planning and policy development in sustainable dairy farming.

Simple Summary Dairy cows are highly sensitive to heat stress, particularly in naturally ventilated barns where indoor conditions depend on the external climate. While the temperature–humidity index (THI) is widely used to estimate thermal load, traditional approaches often rely on average or maximum values alone, overlooking daily patterns and delayed physiological responses. This study presents a five-step analytical approach that combines THI data with herd-level records on milk production, feed intake, mastitis, and lameness to evaluate the overall impact of heat stress. By integrating environmental and clinical indicators, the method improves the detection of heat-related risks and provides a more accurate assessment of cumulative effects on productivity and welfare. This approach supports practical decision-making in dairy systems and may serve as a foundation for predictive models and real-time monitoring strategies.

The objective of this study was to investigate the interactive effect of dietary cation-anion difference (DCAD) and dietary buffer supply on DMI, ruminal fermentation, milk and milk component yields, and gastrointestinal tract (GIT) permeability in lactating dairy cattle exposed to mild heat stress. Sixteen lactating Holstein cows, including 8 ruminally cannulated primiparous (80 ± 19.2 DIM) and 8 non-cannulated multiparous (136 ± 38.8 DIM) cows, were housed in a tie-stall barn programmed to maintain a temperature-humidity index (THI) between 68 and 72 from 0600 h to 1600 h followed by natural night cooling. The experimental design was a replicated 4 × 4 Latin rectangle (21-d periods) with a 2 × 2 factorial treatment arrangement. Diets contained a low DCAD (LD; 17.5 mEq/100g of DM) or high DCAD (HD; 39.6 mEq/100g of DM) adjusted using NH4Cl and Na-acetate, with low (LB; 0% CaMg(CO3)2) or high buffer (HB; 1% CaMg(CO3)2). In addition to measurement of feed intake, ruminal fermentation, and milk and milk component yields, a ruminal dose of Cr-EDTA and an equimolar abomasal dose of Co-EDTA were used to evaluate total and post-ruminal gastrointestinal tract permeability, respectively. Treatments had no effect on DMI, ruminal short-chain fatty acid concentrations, or ruminal pH. Feeding HD improved blood acid-base balance, increased urine volume by 4 ± 1.5 kg/d, and increased milk fat by 0.14 ± 0.044 percentage units and milk fat yield by 36.5 ± 16.71 g/d. HB reduced milk fat percentage by 0.11 ± 0.044 percentage units and had no effect on milk fat yield. The HB treatments reduced urinary excretion of Co by 27% and tended to reduce urinary Cr excretion by 10%. Across all treatments, 72% of the Cr recovery was represented by Co suggesting that much of the permeability responses were post-ruminal during mild heat stress. In conclusion, increasing DCAD through greater Na supply during mild heat stress improved blood acid-base balance and may increase milk fat yield. Dietary inclusion of CaMg(CO3)2 improved post-ruminal GIT barrier function despite a lack of low ruminal pH. As there appeared to be a limited interactive effect between DCAD and buffer, increased DCAD and provision of buffer seem to independently influence physiological and performance responses in lactating dairy cows exposed to mild heat with night cooling.

Heat stress exerts a substantial negative impact on the dairy industry with expected annual economic losses of approximately $40 billion in the United States. Dairy cows are highly susceptible to heat stress, reducing their milk production and reproductive efficiency. Previous studies reported that fans and sprinkler systems can mitigate heat stress to some extent. However, their effects on blood parameters, indicating systemic responses, are largely unexplored. In this study, 24 lactating Holstein cows were divided into heat stress (HS) group and heat stress abatement (HA) group, which had limited or full access to fan and sprinkler (automatically on at 74 °F, for 3 min on and 5 min off throughout the day) cooling system in the barn, with ad libitum access to feed during a 14-day period in summer. Respiration rate, feed intake, and milk yield were recorded regularly. Blood samples were collected on days 0 and 14 to assess the heat shock protein 70 (HSP70) and cortisol levels in the blood plasma. Regardless of fan and sprinkler system, average temperature-humidity indexes of both pens were above 72 (8am: 73.0±4.18 vs 72.5±3.61, 12pm: 77.9±3.28 vs 78.0±3.06, 8pm: 77.1±3.39 vs 76.6±3.09; p=0.46) throughout the daytime of the trial. Respiration rate showed no significant difference on day 0 between the two groups but was significantly higher in the HS group on day 14 compared to the HA group (84.4±8.36 breaths/min vs. 58.3±9.46 breaths/min, p< 0.01). Average feed intake was also significantly lower in the HS group compared to the HA group (44.7±9.36 kg/day vs 57.6±8.18 kg/day, p< 0.01). However, the average milk yield was not found to be significantly different within the 14-day period (15.8±4.62 kg vs 16.3±4.50 kg, p=0.27) between the two groups. There were no significant differences in plasma HSP70 and cortisol concentrations between the HS and HA group on both day 0 (HSP70, p=0.16; Cortisol, p=0.45) and day 14 (HSP70, p=0.97; Cortisol, p=0.87), respectively. Notably, at day 0, a significant positive correlation between respiration rate and HSP70 was observed in both HS (RPearson = 0.58, p=0.05) and HA groups (RPearson = 0.62, p=0.03), but this positive correlation was only observed in HS group on day 14 (RPearson = 0.57, p=0.05) compared to the HA group. Additionally, a significant positive correlation was also observed between HSP70 and cortisol concentration (RPearson = 0.73, p=0.007) in the HS group on day 14 but not in the HA group. These findings suggest that short-term fan and sprinkler cooling strategies can reduce physiological heat stress and mitigate systemic stress responses in certain animals but not efficiently for the overall HA group.

This study aimed to determine the effect of lipopolysaccharide (LPS)-induced mastitis with or without nonsteroidal anti-inflammatory drug (NSAID) on dairy cows' clinical, physiological, and behavioral responses in the milking parlor and freestalls as well as the specificity (Sp) and sensitivity (Se) of behavioral responses in detecting cows with LPS-induced mastitis. Twenty-seven cows received an intramammary infusion of 25 µg of Escherichia coli LPS in 1 healthy quarter. Following LPS infusion, 14 cows received a placebo (LPS cows), and 13 cows received 3 mg/kg of body weight of ketoprofen i.m. (LPS+NSAID cows). Cow response to the challenge was monitored at regular intervals from 24 h before to 48 h postinfusion (hpi) through direct clinical observations, markers of inflammation in milk, and via point-in-time direct behavioral observations in the barn and at milking. In LPS cows, infusion induced a significant increase of plasma cortisol levels at 3 and 8 hpi, milk cortisol levels at 8 hpi, somatic cell counts from 8 to 48 hpi, IL-6 and IL-8 at 8 hpi, milk amyloid A (mAA) and haptoglobin at 8 and 24 hpi, rectal temperature at 8 hpi, and respiratory rate at 8 hpi. Their rumen motility rate decreased at 8 and 32 hpi. Compared with before the challenge, significantly more LPS cows stopped feeding/ruminating and pressed their tail between their legs at 3 and 5 hpi, increased feeding/ruminating at 24 hpi, and had the tendency to be less responsive, dropping their head, and dropping their ears at 5 hpi. At milking, compared with before challenge, significantly more LPS cows lifted their hooves at forestripping at 8 hpi. The 2 groups showed similar patterns of response for milk cortisol, somatic cell count, respiratory rate, mAA, haptoglobin, and IL-6, IL-1β, and IL-8. Compared with LPS cows, LPS+NSAID cows had significantly lower plasma cortisol levels at 3 hpi, their rectal temperature decreased at 8 hpi, their rumen motility rate increased at 8 and 32 hpi, and their heart rate increased at 32 hpi. Compared with LPS cows, a significantly larger proportion of LPS+NSAID cows were feeding/ruminating, a lower proportion had ears down at 5 hpi, and a larger proportion lied down at 24 hpi. At milking, whatever the phase of milking, for "hoof to belly," 9 out of 14 cows did not show this behavior before infusion (Sp = 64%) and 14/14 did not kick during pre-infusion milking (Sp = 100%). Regarding sensitivity, at maximum, 5 cows out of 14 (Se = 36%) displayed "hoof to belly" after infusion. For "lifting hoof," 14/14 did not show hoof-lifting before infusion (Sp = 100%) and 6/14 displayed it after infusion (Se = 43%) at forestripping only. In the freestall barn, 9 behaviors had a Sp >75% (at minimum, 10/14 did not show the behavior) whatever the time point but Se < 60% (at maximum, 8/14 displayed the behavior). Finally, "absence of feeding and ruminating" had Sp of 86% (12/14 ate/ruminated) and Se of 71% (10/14 did not eat/ruminate) at 5 hpi. This study shows that feeding/ruminating, tail position, and reactivity at forestripping could be used as behavioral indictors for early detection of mastitis-related pain in dairy cows.

Simple Summary As the dairy industry strives to improve the sustainability and efficiency of heifer rearing, it is important to understand the potential long-term impacts of early life events, such as social isolation stress, on the efficiency and performance of growing heifers. Pre-weaning pair housing has many immediate cognitive, growth, and feed intake benefits; however, little is known about the duration of these advantages over individually housed heifers. Pre-weaning isolation stress may have lasting adverse effects on heifer growth and feed efficiency, potentially inflating heifer rearing costs and decreasing farm profitability. To investigate possible long-term effects of pre-weaning housing, we measured the growth, feed efficiency, and methane emissions of 18-month-old heifers which had previously been paired or housed individually pre-weaning. Overall, pair-housed heifers maintained their initial body weight advantage over individually housed heifers with no adverse impacts on feed efficiency or methane emissions. Abstract Our objective in this exploratory study was to evaluate the long-term impacts of pre-weaning social isolation vs. contact on subsequent growth and feed efficiency of Holstein heifers. As pre-weaned calves, 41 heifers were housed individually (n = 15 heifers) or in pairs (n = 13 pairs; 26 heifers). At 18 months of age, heifers were blocked by body weight and randomly assigned to one of three pens within a block (six to eight heifers per pen; six pens total), with original pairs maintained. Body weight (BW), hip height and width, and chest girth were measured at the start and end of the study. Each pen was given 3 days of access to a GreenFeed greenhouse gas emissions monitor to assess potential physiological differences between treatments in enteric methane emissions or behavioral differences in propensity to approach a novel object. During the 9-week study, heifers were fed a common diet containing 62.3% male-sterile corn silage, 36.0% haylage, 0.7% urea, and 1.0% mineral (DM basis). To calculate daily feed intake, as-fed weights and refusals were recorded for individual heifers using Calan gates. Feed samples were collected daily, composited by week, and dried to calculate dry matter intake (DMI). Feed refusal and fecal samples were collected on 3 consecutive days at 3 timepoints, composited by heifer, dried, and analyzed to calculate neutral detergent fiber (NDF), organic matter (OM), and DM digestibility. Feed efficiency was calculated as feed conversion efficiency (FCE; DMI/average daily gain [ADG]) and residual feed intake (RFI; observed DMI-predicted DMI). Paired and individually housed heifers did not differ in DMI, ADG, FCE, or RFI. Although no differences were found in initial or final hip height, hip width, or chest girth, heifers which had been pair-housed maintained a greater BW than individually housed heifers during the trial. Methane production, intensity, and yield were similar between treatments. Pre-weaning paired or individual housing did not impact the number of visits or latency to approach the GreenFeed; approximately 50% of heifers in each treatment visited the GreenFeed within 8 h of exposure. Digestibility of OM, DM, and NDF were also similar between housing treatments. In conclusion, pre-weaning pair housing had no adverse effects on growth, feed efficiency, or methane emissions at 18 to 20 months of age.

In dairy cattle farming, heat stress largely impairs production, health, and animal welfare. The goal of this study is to develop a workflow and a numerical analysis procedure to provide a real-time 3D distribution of the THI in a generic cattle barn based on temperature and humidity monitored in sample points, besides characterizing the relationship between indoor THI and outside weather conditions. This research was carried out with reference to the study case of a cattle barn. A model has been developed to define the indoor three-dimensional spatial distribution of the Temperature-Humidity Index of a cattle barn, based on environmental measurements at different heights of the building. As a core of the model, the Discrete Sibson Interpolation method was used to render a point cloud that represents the THI values in the non-sampled areas. The area between 1-2 meters was emphasized as the region of greatest interest to quantify the heat waves perceived by dairy cows. The model represents an effective tool to distinguish different areas of the animal occupied zone characterized by different values of THI.

Heat stress in dairy cattle buildings is a pressing challenge under global warming. While building climate resilience is as critical as improving animal thermal resilience, limited research has evaluated the effectiveness of building adaptations in specific spaces, such as cattle housing and milking parlours, particularly under extreme climate conditions. This study addresses this gap by assessing the impacts of observed and projected heatwaves on dairy housing and a milking parlour and possible mitigation solutions, through indoor heat stress measurements and dynamic livestock building thermal modelling. We advance the modelling capability by incorporating realistic sensible and latent heat dissipation from dairy cattle, accounting for body mass, daily milk production, and ambient temperatures. Measurements during the 2021 UK Heatwave revealed consistently higher indoor Temperature-Humidity Index (THI) levels compared to outdoors. The milking parlour experienced more severe heat stress (Level 3: Severe) than the housing (Level 2: Moderate) due to higher internal heat gains and poor ventilation, with notable differences between morning and afternoon milking times. Projections for the 2080s heatwave indicated that both spaces would experience heat stress day and night, with severity reaching Level 4 (Emergency) for most of the time. Under current heatwave conditions, solar reflective roof paint proved effective for the housing, while hybrid ventilation was effective for the milking parlour. However, these strategies were insufficient for future extreme heatwaves, emphasizing the need for advanced, tailored building adaptations. This study highlights the critical importance of designing climate-resilient dairy buildings to safeguard animal welfare and productivity in a warming world.

This study aimed to evaluate the physiological response of female beef cattle to peatland microclimate stress in Central Kalimantan. This study used direct observation on small holder beef cattle farm.  Microclimate data on 41 units cattle barns and physiological parameters of female cattle were collected in the morning, at noon and in the afternoon.  The physiological parameter measurements involved 215 female beef cattle, consisting of 119 Bali and 96 crossbred cattle with different physiological stages including pregnant cows, lactating cows, dry cows, heifers and calves. The microclimate condition within cattle barns on peatland of Central Kalimantan is not the comfort zone for beef cattle.  It is characterized by high air temperature and relative humidity, and low wind speed, which result in high temperature humidity index (THI).  The barn with gable roof type and asbestos materials gave the lowest THI.   This Microclimate caused heat stress to beef cattle reared on the peatlands, indicated by the high rectal temperature, heart rate, respiratory rate, and heat tolerance coefficient, although it was still categorized as mild to moderate stress. Bali cattle showed better physiological responses to microclimate stress than crossbred cattle.  Pregnant crossbred cows were the most susceptible to peatland microclimate stress.

Abstract Heat stress in dairy cattle is a major concern in modern dairy farming. Potentially, techniques developed in the field of precision livestock farming that monitor cow behaviour could enable an early detection of heat stress. However, for the definition of a behavioural response profile that is specific to heat stress, the identification of novel behavioural indicators of heat stress is still required. This study therefore studied the effect of hot weather conditions on known and novel behavioural parameters on 390 lactation dairy cows from three commercial dairy farms for 7.5 months, from late winter to early autumn. Cows were fitted with collars with accelerometer-based sensors to measure the Overall Dynamic Body Acceleration (ODBA) and six behaviour categories (lying idle, lying ruminating, standing idle, standing ruminating, eating, and other). Sensors in the barn measured temperature, humidity (to calculate the temperature-humidity index; THI), light, and wind speed. We found considerable variation across the three farms in the heat stress response, indicating the importance of studying heat stress across different farms to improve the applicability of the findings. Across the three farms, we found that an increase in THI led to an increase in the time spent standing, standing ruminating, and a decrease in the time spent eating, mean ODBA, and milk yield. This study identified new, sensitive, potential indicators of heat stress, like standing ruminating, which could be combined with established indicators, like eating, to improve the early detection and effective management of heat stress on diverse dairy cattle farms. HIGHLIGHTS Findings from 3 farms suggest that standing ruminating may be a sensitive novel indicator of heat stress in dairy cattle. Variation in behaviour between the 3 farms shows the need to study heat stress across different farms to improve the applicability of the findings. Combining new indicators, like standing ruminating, with known indicators, like eating, can improve the early detection of heat stress in dairy cattle.

The health, longevity, and performance of dairy cattle can be adversely affected by heat stress. This study evaluated the in-barn condition [i.e., temperature, relative humidity, and resulting temperature-humidity index (THI)] at 9 dairy barns with various climates and farm design-management combinations. Hourly and daily indoor and outdoor conditions were compared at each farm, including both mechanically and naturally ventilated barns. On-site conditions were compared with on-farm outdoor conditions, meteorological stations up to 125 km away, and NASA Power data. Canadian dairy cattle face periods of extreme cold and periods of high THI, dependent on the regional climate and season. The northernmost location (53°N) experienced about 75% fewer hours of THI >68 compared with the southernmost location (42°N). Milking parlors had higher THI than the rest of the barn during milking times. The THI conditions inside dairy barns were well correlated with THI conditions measured outside the barns. Naturally ventilated barns with metal roofs and without sprinklers fit a linear relationship (hourly and daily means) with a slope <1, indicating that in-barn THI exceeded outdoor THI more at lower THI and reached equality at higher THI. Mechanically ventilated barns fit nonlinear relationships, which showed the in-barn THI exceeded outdoor THI more at lower THI (e.g., 55-65) and approached equality at higher THI. In-barn THI exceedance was greater in the evening and overnight due to factors such as decreased wind speed and latent heat retention. Eight regression equations were developed (4 hourly, 4 daily) to predict in-barn conditions based on outdoor conditions, considering different barn designs and management systems. Correlations between in-barn and outdoor THI were best when using the on-site weather data from the study, but publicly available weather data from stations within 50 km provided reasonable estimates. Climate stations 75 to 125 km away and NASA Power ensemble data gave poorer fit statistics. For studies involving many dairy barns, the use of NASA Power data with equations for estimating average in-barn conditions in a population is likely appropriate especially when public stations have incomplete data. Results from this study show the importance of adapting recommendation on heat stress to the barn design and guide the selection of appropriate weather data depending on the aim of the study.

This study evaluates the response of dairy cows to short and extended heat stressing conditions (from 1 to 28 days), as expressed in changes in their behavior. Due to climate change, heat stress and strong heat waves are frequently affecting the productivity and behavior of dairy cows. In the five years under study from 2018 to 2022, two were characterized by extremely strong heat waves occurring in the region analyzed in this study (Northern Italy). The dairy cattle farm involved in this study is located in Northern Italy and includes about 1 600 Holstein Friesian lactating dairy cows. Phenotypic data were provided by the Afimilk system and compromised behavioral and productive traits. Behavioral traits analyzed were activity, rest time, rest bouts, rest ratio, rest per bout and restlessness. Production traits were daily milk yield, average milking time, somatic cell count, fat percentage, protein percentage and lactose percentage. Climate data came from the National Aeronautics and Space Administration/Prediction of Worldwide Energy Resources database. Heat stress was analyzed considering Temperature-Humidity Index (THI) averaged over 28 different time windows of continuous heat stress. Results showed that rest time and milk yield were the two traits most affected by the increased THI. Rest time was immediately affected by high THI, showing a marked decrease already from 1d window and maintaining this all over the other windows. Furthermore, results show that rest time and rest ratio were only slightly negatively correlated with milk yield (-0.14 and -0.15). In addition, heat stress has a different effect depending on parity and lactation stages on the studied traits. In conclusion, the results indicate that heat stress increases activity and compromises milk production, rest time and milk quality traits. Results further suggest that rest time can be a better parameter than activity to describe the effects of heat stress on dairy cattle. The novel approach used in this study is based on the use of different time windows (up to 28 days) before the emergence of undesired THI and allows to identify the traits that are immediately influenced by the undesirable THI values and those that are influenced only after a prolonged heat stress period.

Weather station data and test-day production records can be combined to quantify the effects of heat stress on production traits in dairy cattle. However, meteorological data sets that are retrieved from ground-based weather stations can be limited by spatial and temporal data gaps. The National Aeronautics and Space Administration Prediction of Worldwide Energy Resources (NASA POWER) database provides meteorological data over regions where surface measurements are sparse or nonexistent. The first aim of this study was to determine whether NASA POWER data are a viable alternative resource of weather data for studying heat stress in Canadian Holsteins. The results showed that average, minima, and maxima ambient temperature and dewpoint temperature as well as 4 different types of temperature-humidity index (THI) values from NASA POWER were highly correlated to the corresponding values from weather stations (regression R2 > 0.80). However, the NASA POWER values for the daily average, minima, and maxima wind speed and relative humidity were poorly correlated to the corresponding weather station values (regression R2 = 0.10 to 0.49). The second aim of this study was to quantify the influence of heat stress on Canadian dairy cattle. This was achieved by determining the THI values at which milk, protein, and fat yield started to decline due to heat stress as well as the rates of decline in these traits after the respective thresholds, using segmented polynomial regression models. This was completed for both primiparous and multiparous cows from 5 regions in Canada (Ontario, Quebec, British Columbia, the Prairies, and the Atlantic Maritime). The results showed that all production traits were negatively affected by heat stress and that the patterns of responses for milk, fat, and protein yields to increasing THI differed from each other. We found 3 THI thresholds for milk yield, 1 for fat yield, and 2 for protein yield. All thresholds marked a change in rate of decrease in production yield per unit THI, except for the first milk yield threshold, which marked a greater rate of increase. The first thresholds for milk yield ranged between 47 and 50, the second thresholds ranged between 61 and 69, and the third thresholds ranged between 72 and 76 THI units. The single THI threshold for fat yield ranged between 48 and 55 THI units. Finally, the first and second thresholds ranged between 58 and 62 THI units and 72 and 73 THI units for protein yield, respectively.

Modeling the individual animal response to heat stress (HS) conditions is challenging because of the complex interactions that characterize the system behavior. In explaining the resulting animal behavior, the dynamicity, nonlinearity, and delays in the HS response are often unaccounted for or misinterpreted. The system dynamics (SD) methodology, a mathematical modeling approach based on feedback loop structures, allows for modeling and understanding the behavior of complex systems over time. By applying SD methodology, this study developed a preliminary conceptual model to capture the cow response and observed milk yield (MY) under HS. The data on the temperature-humidity index (THI) and MY used for model development were collected from a dairy cattle farm in August 2021. The parameters related to the HS response of 20 selected cows were used for calibration and parameterization of the model. To minimize the effect of the lactation stage on milk production and model results, 20 cows were selected for days in milk (DIM) to be between 70 and 220 d. After the parameter calibration using MY data, it was found that the historical data pattern of 13 out of 20 cows followed the expected behavioral pattern generated by the model. In contrast, the behavior of the remaining seven cows did not align with that generated by the model. Therefore, based on their patterns, the cows were identified as fitting or non-fitting the model’s structure. The structure of the model captured the effect of HS on fitting cows with high accuracy (mean absolute percentage error, MAPE < 5%; R2 > 0.6; concordance correlation coefficient, CCC > 0.6). At the same time, the behavior of the non-fitting cows could not be explained by the defined parameter space. We believe they either had heat-resistant behavior or experienced different biological delays than average. Based on the obtained results, the evaluation of parameter values should be done only for the fitting cows, as the work aimed to develop a model to understand the HS response. The behaviors generated by the model can help farmers and decision-makers distinguish heat-sensitive from heat-tolerant cows and quantify the animal response in terms of MY so that mitigation strategies can be implemented.

Abstract The aim of this study was to evaluate the heat stress influence on milk production from primiparous and multiparous Holstein-Friesian cows and to estimate economic losses associated with the decrease in the farm’s milk yield. The cows selected for the study were in the middle phase of the 1st, 2nd, and 3rd lactation and were characterized by similar daily milk production. Additionally, the animals were kept in the same conditions and fed with the same feeds throughout the season. The analysis covered two 30-day periods – “cold” (April), in which no days with THI >70 were noted, and “hot” (July), in which THI was above 70 for 90% of the days (74.4 on average). The average daily drop in milk production noted in the hot period was 1.25 kg/cow for multiparous cows and 2.78 kg/cow for primiparous cows. The average daily financial loss resulting from a drop in milk production was €0.55/day/cow in primiparous and €0.46/day/cow in multiparous animals. The calculated daily loss in the profit on production of 1 kg of milk was €0.27/day/kg for primiparous and €0.24/day/kg for multiparous animals. Based on test results, economic losses were simulated depending on the daily milk yield and the size of the primary cattle herd. For the multiparous cows, the estimated losses ranged from €6.07/day (farm sizes 25 cows and average daily milk yield 25 kg) to nearly €219/day (900 cows/25 kg). In larger facilities (900 cows) with an average daily milk yield of 55 kg, the daily loss will be about €481. The obtained results confirmed the assumptions made that with a change in microclimate conditions in the barn, a decrease in the daily production and changes in the milk chemical composition were noted, and the economic efficiency of the studied activity decreased.

The aim of the survey was to study the effect of heat stress (HS) on health lipid indices in milk of Holstein-Friesian cows. The study was conducted in a cattle farm with Holstein-Friesian cows in the region of Karnobat (Southeastern Bulgaria) in 2018. Cows were housed in semi-open free stall dairy barn, fed year-round ad libitum with a total mixed ration. The study included 22 cows on different parities studied in two periods - at thermo-neutral environment conditions and at heat stress, respectively, May and August. Extraction of milk fat was performed by the Rose-Gottlieb method. Conditions of HS lead to changes in the values of health lipid indices associated with a decrease in the values of Atherogenic index (AI), Thrombogenic index (TI), Lipid Preventive Score (LPS) and Desaturase (18) index (DI 18) and an increase in Health promoting Index (HPI), polyunsaturated fatty acids/saturated fatty acids (PUFA/SFA), unsaturated fatty acids/saturated fatty acids (UFA/SFA), mono unsaturated fatty acids (MUFA), Desaturase (16) index (DI 16) and hypocholesterolaemic/hypercholesterolaemic ratio (h/H). Increasing the Temperature-humidity index (THI) above 72, results in a decrease in the AI values and an increase in those of the PUFA/SFA. The values of health lipid indices showed a moderate positive correlation with those of THI (PUFA/SFA - 0.36) with rectal temperature (h/H, MUFA/SFA, UFA/SFA) rp from 0.36 to 0.37, and with respiratory rate (h/H, PUFA/SFA), rp of 0.33 and 0.31, respectively. Under the influence of heat stress, changes in the metabolic processes occur in the body of dairy cows leading to changes in the fatty acid content of milk related to the improvement of health lipid indices in terms of human health due to an increase in UFA and reduction in SFA.

Environmental heat load and housing microclimate are major determinants of dairy animal productivity and health in tropical systems, yet farm-level evidence linking within-barn conditions to animal outcomes remains limited. This study evaluated the influence of environment and its modification through naturally ventilated housing on production and health indicators of lactating dairy cattle at Dr Olaola Vineyard Dairy Farm, Abeokuta, Ogun State, Nigeria. A longitudinal observational approach was used in which microclimate and animal-level records were collected concurrently during the warm and humid season (May-October 2025) in 50 Friesian/Holstein × White Fulani/Bunaji crossbred cows managed semi-intensively in an open-sided shade-barn. Temperature and relative humidity were logged at 10-min intervals at cow head height (1.5 m) across three functional barn locations (feed bunk line, resting/lying area, and holding area), while airflow was measured three times daily using a handheld anemometer. Thermal stress was summarized using the temperature–humidity index (THI) and categorized as <72, 72-78, and >78. Production outcomes included daily milk yield, fat, protein, and somatic cell count (SCC), while health outcomes included clinical and subclinical mastitis (SCC ≥200,000 cells/mL and/or CMT ≥1), lameness (locomotion score ≥3), and reproductive records. In an illustrative analysis using assumed values consistent with the study design, severe heat-stress exposure predominated, with higher THI and longer duration above THI 78 associated with reduced milk yield and increased SCC. Subclinical mastitis risk increased across THI categories, and lameness risk was elevated during periods of higher heat load and wetter conditions, highlighting the combined effects of thermal and moisture-related housing challenges. Overall, the study underscores that zone-specific barn microclimate, particularly in resting areas with lower airflow, is a key driver of performance and health in tropical dairy production. Improving ventilation and cooling effectiveness in high-use zones, alongside moisture and hygiene control during humid months, is likely to mitigate heat-related production losses and health risks in naturally ventilated dairy housing.

This study aimed to evaluate the effect of cattle barns microclimate modification on the physiological response of beef cattles reared on peatland. This study used direct observation and experimental research methods. Microclimate modification is done by using asbestos material, gable roof type and roof height ≥3.5 meters, and vegetation arrangement. Data were collected through measurements of microclimatic parameters and physiological responses in the morning (06.30–07.30), at noon (11.30–12.30), and in the afternoon (16.30–17.30), with measurement intervals every month. The number of cattle barns observed was 46 units. The physiological parameter measurements involved 124 female beef cattle, consisting of 70 Bali and 54 crossbred cattle with physiological stages, gestating cows and lactation period. The results showed that modifying cattle barns and the surrounding environment can reduce the microclimate in the cattle barn as indicated by a decrease in THI from emergency to dangerous levels during the noondayand from dangerous to caution levels in the afternoon. The improvement in microclimate conditions was also followed by a decrease in the level of heat stress as indicated by a decrease in the physiological responses of cows.

Simple Summary Sustainability is a necessary goal for animal-derived products due to the mounting pressure on the livestock sector to meet the growing demand of an increasing population with rising incomes and the need to reduce the exploitation of resources and environmental impact, while safeguarding animal welfare. We found that by considering a precision livestock farming approach to feeding, advanced numerical methods could represent a reliable and viable tool for the evaluation of future productive scenarios of dairy cows in the presence of changing climate conditions. We believe that the model proposed here could help to develop and improve decision support for farmers to increase both milk yield and animal welfare and, on the other hand, to reduce the resources needed, hence increasing sustainability of the dairy sector. Abstract Precision Livestock Farming (PLF) relies on several technological approaches to acquire, in the most efficient way, precise and real-time data concerning production and welfare of individual animals. In this regard, in the dairy sector, PLF devices are being increasingly adopted, automatic milking systems (AMSs) are becoming increasingly widespread, and monitoring systems for animals and environmental conditions are becoming common tools in herd management. As a consequence, a great amount of daily recorded data concerning individual animals are available for the farmers and they could be used effectively for the calibration of numerical models to be used for the prediction of future animal production trends. On the other hand, the machine learning approaches in PLF are nowadays considered an extremely promising solution in the research field of livestock farms and the application of these techniques in the dairy cattle farming would increase sustainability and efficiency of the sector. The study aims to define, train, and test a model developed through machine learning techniques, adopting a Random Forest algorithm, having the main goal to assess the trend in daily milk yield of a single cow in relation to environmental conditions. The model has been calibrated and tested on the data collected on 91 lactating cows of a dairy farm, located in northern Italy, and equipped with an AMS and thermo-hygrometric sensors during the years 2016–2017. In the statistical model, having seven predictor features, the daily milk yield is evaluated as a function of the position of the day in the lactation curve and the indoor barn conditions expressed in terms of daily average of the temperature-humidity index (THI) in the same day and its value in each of the five previous days. In this way, extreme hot conditions inducing heat stress effects can be considered in the yield predictions by the model. The average relative prediction error of the milk yield of each cow is about 18% of daily production, and only 2% of the total milk production.

Bunching behavior in cattle may occur for several reasons including enabling social interactions, a response to stress or danger, or due to shared interest in resources such as feeding or watering areas. There is evidence in pasture grazed cattle that bunching may occur more frequently at higher ambient temperatures, possibly due to sharing of fly-load or to seek shade from the direct sun under heat stress conditions. Here we demonstrate how bunching behavior is associated with higher ambient temperatures in a barn-housed UK dairy herd. A real-time local positioning system was used, as part of a precision livestock farming (PLF) approach, to track the spatial position and activity of a commercial dairy herd (∼100 cows) in a freestall barn continuously at high temporal resolution for 4 mo between August and November 2014. Bunching was determined using 4 different spatial measures determined on an hourly basis: herd full and core range size, mean herd intercow distance (ICD), and mean herd nearest-neighbor distance (NND). For hourly mean ambient temperatures above 20°C, the herd showed higher bunching behavior with increasing ambient temperature (i.e., reduced full and core range size, ICD, and NND). Aggregated space-use intensity was found to positively correlate with localized variations in temperature across the barn (as measured by animal-mounted sensors), but the level of correlation decreased at higher ambient barn temperatures. Bunching behavior may increase localized temperatures experienced by individuals and hence may be a maladaptive behavioral response in housed dairy cattle, which are known to suffer heat stress at higher temperatures. Our study is the first to use high-resolution positional data to provide evidence of associations between bunching behavior and higher ambient temperatures for a barn-housed dairy herd in a temperate region (UK). Further studies are needed to explore the exact mechanisms for this response to inform both welfare and production management.

This study aimed to describe air quality dynamics in a commercial dairy farm focusing on 2 locations: a tunnel-ventilated barn (TVB) and a milking parlor (MKP). Assessed air quality components included carbon monoxide (CO), carbon dioxide (CO2), methane (CH4), ammonia (NH3), particulate matter 2.5 µg/m3 (PM2.5), total volatile organic compounds (VOC), and temperature-humidity index (THI), which were continuously monitored from August 16 to December 22, 2023, using a multiple air quality sensor platform. Descriptive analysis revealed significant hourly variability in the air quality dynamics during the study period. Mixed-effects models revealed no significant differences in the overall CO and THI measurements between the barn and milking parlor. However, the location significantly influenced overall concentrations of other air components including CO2, CH4, PM2.5, VOC, and NH3. Overall comparisons between TVB and MKP showed that the TVB had a higher overall CO2 concentration mean during the observation period compared with the MKP (LSM ± SEM; 640 ± 9.02 vs. 612 ± 9.01 ppm), while the MKP recorded highest CH4 levels (11.03 ± 0.52 vs. 8.87 ± 0.52 ppm). In the TVB, the NH3 levels ranged from 0.401 to 44.9 ppm, whereas no NH3 was detected in the MKP. The MKP recorded higher overall PM2.5 compared with the TVB (5.51 ± 0.31vs. 3.21 ± 0.31µg/m3). The VOC levels exhibited higher overall means in the TVB compared with the MKP (153 ± 2.18 vs. 144 ± 2.16 ppm) but were characterized by substantial variability in both locations. Temporal trends suggested that the monitored air components might be influenced by farm activities such as feeding, cleaning, and milking as identifiable peaks we observed at specific hours of the day. We identified hourly pattern dynamics of CO, CO2, CH4, NH3, PM2.5, VOC, and THI within the TVB and the MKP. Understanding these dynamics provides the opportunity to develop mitigation strategies for enhancing air quality within dairy facilities.

Graphical Abstract Summary: Heat stress (HS) occurs when an animal accrues a heat load that disrupts the regulation of internal temperature. The temperature-humidity index (THI) threshold for HS in dairy heifers is not well defined in the literature. The objectives of the current work were to establish THI thresholds for HS in dairy heifers and to develop predictive equations for respiration rate (RR) and core body temperature (BT). It was observed in a series of 2 experiments that a THI threshold of 74 elevated RR and BT in heifers housed in a heat-abated freestall barn, on pasture, and in a climate-controlled chamber. Simple linear regression best modeled RR and BT response to THI in heifers housed in a freestall barn, and a segmented regression best reflected RR and BT response to THI for pasturehoused heifers. Heifers should be monitored for signs of HS when THI approaches 74.

The main objective of this study was to comprehensively evaluate the effects of key barn microclimate parameters, including air temperature, relative humidity, air velocity, lighting level, and gas composition, on the milk productivity and quality characteristics of dairy cattle. The study was conducted at the ―Qaliqanuly‖ dairy farm, located 20 km from the city of Semey in the Abai region. Black-and-white dairy cows were used in the experiment. Microclimate parameters in the barn were measured using specialized instruments across different seasons, and the obtained data were comparatively analyzed in relation to total milk yield, fat content, protein content, milk density, and solids-not-fat (SNF). The results revealed that deviations of microclimate parameters from recommended standards had a significant impact on the physiological condition and productivity of the animals. According to the findings, unfavorable air temperature and relative humidity levels led to a reduction in milk yield by up to 10–15%. In addition, insufficient lighting and low air movement negatively affected milk quality, particularly fat and protein contents. During the summer period, high temperatures combined with increased humidity caused heat stress in cows, resulting in decreased milk production. Overall, the results demonstrate that optimizing the barn microclimate plays a crucial role in increasing milk production and can serve as a scientific basis for developing effective management strategies in the livestock sector.

In the current era of agricultural management practices, known as agricultural 5.0, optimal indoor environments are associated with comfortable temperatures, regulated humidity, and good air quality—essential variables to improve yields. Given this scenario, there is a need for innovative ecosystems that automate indoor environmental monitoring in an affordable and scalable way. This paper presents the scope of the development and validation of an IoT-based ecosystem designed to monitor and control environmental conditions in agricultural barns. The objective is to present a cost-effective and easily accessible environmental monitoring system for barn buildings and agricultural storage areas, promoting the welfare of animals, humans, and crops, and contributing to the sustainable development of the agricultural industry. The system integrates wireless sensors, predictive algorithms, a web interface and cloud infrastructure to optimize temperature and humidity. A proof-of-concept assessment was performed to determine whether the modular architecture offers scalability, while the responsive web interface ensures cross-device accessibility. The results show data accuracy above 95%, prediction efficiency of 96%, and increases in production yields. This solution demonstrates economic and operational advantages over existing technologies, promoting sustainability and automation in agricultural management practices in hangars and barns, in alignment with the United Nations’ Sustainable Development Goals (SDGs).

Effective oestrus detection is essential for optimizing fertility management on dairy farms. This study explores how weather parameters affect oestrus occurrence and the behavioural responses of teaser bulls to oestrus heifers in two housing systems: barn and loose house. Key environmental factors, such as ambient temperature and relative humidity, were monitored throughout the study. Twelve crossbred Holstein heifers were divided into two groups of six, with one group housed in a barn and the other in a loose housing system. Over 60 days, they were observed for signs of oestrus, and teaser bulls were introduced to evaluate their behaviours, including Flehmen responses and mounting attempts. Although there were significant differences in temperature and humidity between the two housing systems, the temperature-humidity index (THI) did not correlate with oestrus occurrence, challenging some previous studies. The research revealed that the loose housing system resulted in a markedly higher frequency of Flehmen responses and mounting attempts, with mean ranks of 8.14 and 8.57, respectively, compared to 4.20 and 3.60 in the barn system (p-value: 0.025 and 0.014). These results highlight the importance of housing conditions in enhancing reproductive management. Loose housing systems, by promoting more natural social interactions and behavioural displays, improve the accuracy and efficiency of oestrus detection, leading to more timely and successful inseminations and boosting overall herd productivity. Moreover, this system enhances animal welfare by reducing stress, providing greater freedom of movement and allowing for effective herd management.

Abstract Stocking density can potentially impact cattle welfare during livestock export voyages. The aim of this study was to assess selected measures that reflect the welfare of cattle housed at 3 allometric stocking densities (k = 0.027, 0.030, 0.047). Bos indicus cross Bos taurus steers were housed in 12 pens, each with 5 steers, for 10 d. Scan sampling of standing and lying behaviors were conducted on days 2, 5, 7, and 9, at hourly intervals. Continuous observations were conducted on the same days between 1030 and 1130 h, to count aggressive interactions. Liveweights were recorded at the start of the study, and on days 6 and 10. For a subset of focal steers (3 per pen), white blood cell counts, and fecal glucocorticoid metabolite (FGCM) concentrations were assessed on days 0, 6, and 10. More pen space led to a small increase in the number of steers lying, as well as a small increase in lying synchronicity. Results also indicated that the number of cattle lying in isolation from conspecifics is higher when more space is available. More pen space also resulted in more steers lying with outstretched legs on days 2 and 5, but there was no evidence of this after day 5. Stocking density had no important effect on day 6 or 10 liveweights or FGCM concentrations. Only small decreases in total white blood cell and lymphocyte counts between days 6 and 10 were observed, as well as small increases in neutrophil counts and the neutrophil-to-lymphocyte ratio, but all mean counts still fell within reference intervals for healthy cattle. The lack of important effects on stress physiology and liveweight suggests that the cost of attempting to adapt to pen space restriction was relatively low, leading to behavioral responses only. Results for lying behaviors also suggest that additional pen space may facilitate adaptation upon introduction to a new housing environment and is beneficial in facilitating the expression of some lying behaviors. While designed to emulate stocking densities applicable to Australian cattle export voyages, other environmental factors that may induce stress during these voyages were not present, and so the conclusions must be interpreted in the context of the controlled experimental conditions.

Abstract Dairy cow welfare is shaped by multiple factors, notably the interaction between cattle breed, feeding, and housing conditions. This study evaluated welfare indicators—avoidance behaviour, lameness, body condition score (BCS), cleanliness, integument alterations, udder health, and claw health—in 2168 cows across mountain dairy farms using tie-stall and free-stall housing, with grazing and non-grazing systems. Holstein Friesian (HF) consistently showed poorer welfare outcomes, with significantly higher avoidance behaviour (47.2%), integument alterations (62.1%), and dirty spots (47.6%) compared to dual-purpose and local breeds (p < 0.05). Alpine Grey (AG) and Simmental (SI) cows generally performed better, especially under tie-stall and grazing conditions. In free-stalls, HF exhibited elevated avoidance behaviour (β = 0.55 vs. AG, p = 0.008; β = 0.60 vs. Brown Swiss [BS], p = 0.005) and lameness (β = 0.92 vs. AG, p < 0.001; β = 1.02 vs. BS, p < 0.001). Similar trends were observed in tie-stalls and grazing systems. AG consistently achieved the best welfare scores, particularly under tie-stall and grazing conditions. BS performed well overall but showed slightly poorer claw health than AG (β = 0.25–0.30, p < 0.01). No significant breed differences were found for udder health, underscoring the importance of milking hygiene and management across breeds. These findings emphasize the importance of aligning breed selection with suitable housing and feeding systems to optimize welfare, especially in mountain dairy farms, which often operate under challenging climatic and topographic conditions compared to lowland farms. HIGHLIGHTS Holstein Friesian (HF) cows consistently showed poorer welfare outcomes, including higher lameness, avoidance behaviour and dirtiness. Local dual-purpose breeds, Alpine Grey (AG) and Simmental (SI), demonstrated better welfare. Results emphasise the importance of matching cattle breed with suitable housing and feeding systems, especially in challenging mountainous environments. Findings provide practical insights to improve animal welfare and management strategies in small-scale mountain dairy farming.

Simple Summary Lameness is known to impact many feedlot cattle and is both a welfare and a management concern. For cattle housed indoors, the quality of the lying surface may be a contributing factor. Early-weaned beef cattle entering indoor feedlot facilities in the winter were provided supplemental rubber mats for three months. This management change was implemented to determine if creating a more comfortable lying area was feasible and if the animals would cope well with this flooring change. Additionally clinical, behavioral, and other welfare variables were observed to help determine normal behavior of these calves and if the mats were causing any problems. Both heifer and steer calves were used in this project, and differences were observed in their outcomes. Abstract Indoor housed cattle, particularly those housed in slatted floor barns, may develop specific types of lameness associated with their housing environment. Previous studies have demonstrated that cattle raised on slats that are fitted with rubber perform better than cattle that are on concrete slats alone; however, lameness continues to be a problem even with this modification. This project investigated the feasibility of adding additional commercially available solid mats to the rubber-coated slatted floor barn and observing animal behavior and outcomes in a group setting. The objective was to determine if creating an improved lying area through a relatively simple management change could positively impact the outcome of these animals. Commercial mats were simple to install and were used immediately and extensively by the cattle. However, the outcome provided mixed results. The additional mats provided challenges with cleanliness. Steer calves became dirty faster and more severely than heifers. Forty-three percent of the heifer calves and 19% of the steers were culled early. More work is needed to better understand and provide solutions for this welfare issue.

The thermal stress and lack of ventilation of livestock housing, as well as the fluctuating humidity levels, are a common characteristic of livestock housing in tropical areas and in semi-arid regions, which influence animal welfare, health, and productivity adversely. The use of microclimate conditions towards sustainable livestock management is hence crucial in semi-intensive production systems of cattle and sheep that are commonly used in the sector. This paper explores how natural ventilation improvement and low-cost structural changes can help in the improvement of the indoor environment conditions in the current livestock housing units. The main solutions involved installation of constant ridge ventilation to facilitate escaping of air vertically, expansion of sidewall openings to encourage rising of air horizontally, using flexible shading screens to lower the radiant heating load on the building and use of reflective roofing materials to reduce the amount of heat taken in by the sun. The environmental parameters, such as temperatures, relative humidity, air speed and Temperature-Humidity Index (THI) were monitored and compared in modified and conventional housing units. At the same time, physiological measures of the animals such as rectal temperature, respiratory speed and the panting score were checked in order to determine thermal comfort. The altered architectures were with a consistent 35C lowering of the indoor temperatures, and a 1.21.8 m/s growth of the air circulation, and a significant decrease in the indicators of the heat-stress with the animals showing lower panting levels and stable body temperatures. These findings substantiate that structural interventions, such as highly specific low-cost ones, can greatly raise the effectiveness of natural ventilation, decrease heat stress, and play a role in the overall improvement of welfare and productivity of semi-intensive livestock systems. In general, the research represents the relevance and scalability of passive design interventions with regard to the promotion of sustainable and climate-resilient livestock housing.

Simple Summary The aims of this study were (1) to evaluate the prevalence of lameness, dirtiness of the body surface, and abomasal disorders of slaughter cattle; and (2) to determine the association between these welfare indicators and animal-related factors (e.g., housing type, carcass weight, and transportation and waiting duration of the animals). In contrast to dirtiness (level of contamination of the body surface, also referred to as cleanliness) and the prevalence of abomasal disorders, the determined lameness prevalence was very low. The husbandry of cattle was identified as a significant influencing factor for both the dirtiness and occurrence of abomasal disorders of slaughter cattle. Abstract Three cattle welfare indicators (lameness, dirtiness, and abomasal disorders) were evaluated in 412 slaughter cattle in a cross-sectional study in Austria. The aims of this study were (1) to evaluate the prevalence of lameness, dirtiness of slaughter cattle, and abomasal disorders; and (2) to determine the association between these welfare indicators and animal-related factors (e.g., housing type, carcass weight, transportation and waiting duration of the animals). The lameness prevalence was 0.73%, the abomasal disorders prevalence was 52.43%, and 88.59% of all cattle were contaminated. The latter result indicates that the cattle were kept in a dirty environment. The occurrence of abomasal disorders was associated with cattle housing systems (p ≤ 0.00) and slaughter weight (p = 0.03). The odds for abomasal disorders were 28.0 times higher for cattle housed on slatted flooring compared to cattle kept in a tethered system. The chance for occurrence of abomasal disorders was 3.6 times higher for cattle with a low carcass weight compared to cattle with a high carcass weight. Furthermore, significant associations were found between dirtiness (also referred to as cleanliness or contamination) and husbandry system, sex, and breed. Cattle housed in deep litter boxes had 40.8 times higher odds of being contaminated compared to cattle in a tethered housing system. Cows (odds: 32.9) and heifers (odds: 4.4) had higher odds of being contaminated with feces compared to bulls, whereby female calves (odds: 0.09) and male calves (odds: 0.02) had significantly lower odds of being contaminated. Furthermore, the breeds Brown Swiss (odds: 0.26) and Holstein-Friesian (odds: 0.14) had a significantly lower chance of being contaminated compared to Simmental cattle. Other collected factors, such as production system, transportation duration, life days of the cattle, average daily weight gain, carcass classification, and fat coverage, showed no association with the collected welfare indicators. The study presented here indicates that welfare indicators evaluated for slaughter cattle are suitable to assess cattle welfare, and improvements in husbandry may positively impact both the abomasal physiology and cleanliness of cattle.

Microclimatic conditions in facilities for housing and rearing young category of breeding dairy cattle at the first 30 days after birth, have a significant impact on the quality of welfare, especially in intensive production. The parameters most often taken into account when evaluating microclimate conditions are: temperature and air humidity, the mutual relationship of which represents the THI (temperature-humid index) index; speed of air flow; air quality (presence of dust and ammonia) and level of light in the facility. The quality of the microclimate in the facilities is directly influenced by the climatic conditions in the external environment, therefore study period on 2 farms (A and B) with an intensive production system was divided into 4 seasons (autumn, winter, spring and summer). Holstein Friesian calves were observed in the period from birth to 30 days of age. The worst microclimatic conditions were recorded during the summer season on both farms (1129 on farm A and 1114 calves on farm B suffered), while the situation was more favorable during the colder period. Also, the best conditions, on both farms, were provided for calves in the first 7 days of life. The most unfavorable impact was the high air temperature, while the air flow, paradoxically, improved the air quality, especially during that period. The overall welfare quality score was similar on the observed farms, 2.25 on farm A and 2.12 on farm B, which can be considered acceptable. At the same time, it indicates the presence of serious problems, the solution of which must be approached most seriously.

Agro-horticultural vegetation buffers have been discovered to be a promising nature-based intervention in moderating thermal stress, and livestock production systems to improve livestock welfare. This paper has discussed the microclimatic and welfare advantages of planned multi-species tree-shrub buffers with fruits created strategically around open sided dairy cattle sheds in a sub-tropical environment. The critical environmental parameters were monitored during a 12-month monitoring period in sheds with vegetation buffers (VB) and with control sheds without them (CT) measuring the air temperature, relative humidity, black globe temperature, wind, and temperature humidity index (THI). At the same time physiological indicators (rectal temperature, respiration rate, skin temperature) was considered, behavioural time budgets (lying, feeding, ruminating, shade-seeking, agonistic interactions) as well as simple performance characteristics were assessed in 60 crossbred dairy cows. The findings indicated that VB sheds offered superior and steady microclimate atmosphere with a lower afternoon air temperature of 1.52.8 C and THI of 36 without a sharp difference in radiation burden and a controlled wind speed in comparison to CT. In hot climates, cows in VB were much less affected by heat stress as shown by a reduction in respiration rate and skin temperature, increased lying and ruminating time, and lesser discomfort behaviour. There was also the presence of slight gains in milk production and feed consumption in hot months of summer. In general, the results show that agro-horticultural vegetation buffer is indeed efficient to control near-animal microclimate, alleviate heat stress, and enhance superior behavioural welfare as well as provide ecological co-benefits, such as fruit and biomass production. Such results oppose the reason why vegetation buffers are an effective, inexpensive, and climatically sustainable approach to enhancing livestock housing conditions in subtropical areas.

Livestock farming is an important segment of agricultural production that faces challenges in terms of sustainability and effective management. Growing requirements for food production, stricter environmental regulations and economic demands are forcing farmers to look for innovations aimed at long-term stability and profitability of farms. Sustainability in this area includes ecological, economic and social aspects, with an emphasis on minimizing negative environmental impacts, optimizing costs and ensuring animal welfare. Effective breeding management is closely related to the use of modern technologies that enable accurate monitoring of housing conditions and automated operation management. Sensor systems play a key role in monitoring microclimatic conditions, air quality, dust, or light and noise levels. The implementation of smart sensors makes it possible to optimize ventilation, temperature control and improve overall breeding conditions, thus achieving higher productivity and lower operating costs. The article analyzes key aspects of the indoor environment of agricultural buildings and the importance of sensors in the monitoring of housing areas. The benefits of these systems in terms of improving animal health, reducing the ecological burden and managing operations more efficiently are discussed. Research suggests that the use of sensory technologies and IoT solutions in livestock farming represents a necessary step towards sustainable and technologically advanced agriculture.

Dairy cow health and welfare is of critical importance to the industry. Citizens and consumers must accept husbandry practices in order to continue purchasing behaviours. A common management practice is the housing of dairy cows, with this method increasing in duration or turning to year-round housing. Housed infrastructure recommendations that improve a cow’s environment based on animal welfare are likely to simultaneously improve health and production, which have historically been used to assess environment suitability. The conditions within dairy cow housing are therefore fundamental to the sustainability of dairy farming and the well-being of farmed cattle. Although the majority of dairy cows are housed for a duration of time in a year, little scientific research has been undertaken to assess the fundamental impact on behaviours the housed infrastructure may have. Concerningly, even the most fundamental feature of the housed environment, living space, has limited evidence for how it affects dairy cows. This case study focuses on the results of a long-term randomized controlled trial that assessed the impact that increasing living space had on a group of high-yielding dairy cows. The trial was undertaken in a unique, purpose-built, robotic milking facility, which allowed internal layout reconfiguration. Location sensors were used to calculate daily time budgets and assess how cows interacted with the different living space options provided. There will be discussion based on how to assess the housed environment, questions to ask the herdsperson and differential diagnoses to explore. © The Authors 2023

There is an increasing challenge to livestock production systems in terms of increasing thermal stress, exploitation of poor air quality and the lack of natural shelter which have a negative impact on animal welfare, health and productivity. The present study assessed the efficiency of agro-horticultural vegetation buffers that included the strategic placement of shade trees, shrubs, and windbreaks species in terms of controlling the microclimate and enhancing welfare parameters of cattle and small ruminant confinement. A comparative study was done on field-based investigation of representative mixed livestock farms in buffered and non-buffered housing conditions in a 90 day summer period. The microclimatic conditions, such as ambient temperature, relative humidity, wind speed, and particulates, were measured continuously and welfare parameters of animals, such as respiration rate, panting score, resting behaviour, feed intake reliability, and social interactions were measured. Vegetation buffers drastically changed the microenvironment surrounding animal shelters by a reduction in ambient temperature of 1.5-3.2 o C, relative humidity stabilisation, wind velocity reduction, and up to 40 percent lowering the concentration of particulate matter relative to non-buffered systems. Animals kept near vegetation buffers had significantly better thermal comfort, which was lower respiratory stress, longer resting periods, regular food intake patterns, and the animals were less aggressive. The combined buffer systems also enhanced the quality of the environment, through mitigating the amount of dust loaded and enhancing the quality of air circulating in houses. The results reveal clearly that agro-horticultural vegetation buffers are a low-cost and climate-sensitive solution in mitigating heat stress and enhancing livestock welfare in general in semi-intensive production systems. Vegetation buffers should be integrated into the livestock landscapes to provide a sustainable solution of promoting animal welfare, protecting the environment, and managing livestock in Smart climate conditions.

Introduction Knowing the national status of animal welfare, one can identify welfare problems and set a benchmark against which improvements can be compared. Such a status is potentially invaluable for tangible, sustained animal welfare improvement. The objective of this cross-sectional study was to report the status of animal welfare in Norwegian loose-housed dairy herds as assessed using the Welfare Quality® Assessment Protocol. Additionally, we investigated if the welfare status varied on a regional basis. Methods In total, 155 herds in eight of Norway's eleven counties were assessed by six trained Welfare Quality® assessors. This article presents the herd prevalences of common welfare issues in dairy production in Norway, as well as integrated welfare scores. To determine whether welfare status varied regionally in Norway, generalized linear modeling was used to estimate the mean welfare score for five regions in the four Welfare Quality® principles: A. Good feeding, B. Good housing, C. Good health, and D. Appropriate behavior. These estimated mean welfare scores and their 95% confidence intervals were subsequently assessed for significant variation. Results Encouraging findings included the low mean herd prevalence of ‘very lean' cows (3.0%) and the high proportion of cows (59.8%) which could be touched during avoidance distance testing, indicating a positive relationship between stockpeople and their cattle. Challenges affecting the welfare of Norwegian dairy cows were also identified. Of particular concern were issues related to the cows' environment such as prolonged times needed to complete lying down movements and integument alterations. No herd was completely free of changes to the integument and, on average, 77.9% of each herd were affected either mildly or severely. Animal welfare did not appear to vary much between the five regions assessed. Our investigation revealed significant regional variation between two regions (Trøndelag and Vestlandet North) in only the Welfare Quality® principle Good housing (p < 0.01). Discussion The almost complete absence of regional variation demonstrates that animal welfare status generally varies most at herd level. In conclusion, both welfare challenges and encouraging findings were identified in loose-housed Norwegian dairy herds. To improve animal welfare, herd-specific interventions are most likely to be effective in these herds.

The study took place in a dairy cattle farm with 500 Holstein-Friesian cows. Animals were reared under conditions of a freestall housing system and milked in a 2 × 8 "Herringbone" type milking parlor. Noise level reporting was performed three times during each milking (at start, in the middle, and at the end of milking) during the morning, midday, and evening milkings, every month within one year. The noise level in the working environment was measured by means of a Lutron SL-4023SD sound meter. The highest average noise values were recorded during the winter season, especially during the midday and evening milking, 75–76 dB, with deviations reaching over 80 dB. The next season, in terms of noise level, was the summer season, with average values of 72–74 dB. A study on noise levels in a “fishbone” type milking parlor found average values corresponding to moderately high noise levels, exceeding 65–70 dB. Such levels may negatively affect the welfare of dairy cows, as maximum values above the permissible limits were also recorded, particularly during the winter season. Therefore, it is recommended to optimize technological processes in order to reduce noise levels during milking as much as possible, which is essential both for the operators (milkers) and for the comfort of the animals.

The housed environment for dairy cattle is of critical importance to their health, wellbeing, and productivity. Lack of space is an important factor for housing quality assessment due to links with increased likelihood of disease. A recently published randomized controlled trial identified that greater living space provision increased lying time, milk volume production, and also increased time to conception. However, despite probable improvements in cow welfare, the question remains as to whether offering increased living space is a cost-effective option for farmers. The costs associated with financing new housing facilities are escalating, and the industry urgently requires an evidence base for ensuring these investments are financially sustainable. This research used stochastic simulation modeling to explore theoretical net returns on infrastructure investment differences between two living space scenarios (3 m2 vs. 6.5 m2). A cow entered a simulation at the point of first calving, and milk production, reproductive performance, and points of exit were stochastically determined over the cow’s lifetime simultaneously based on living space scenario. This allowed for direct financial comparison over specified sets of parameter inputs. Where cows exited the herd within their second to fourth lactation, the median difference in financial return was observed to be +£87.61 per cow per year (mean + £86.74). The estimated return on investment to provide extra living space access varied dependent on provision method, interest rates, and loan repayment duration. Under the circumstances and contexts investigated, the results suggest that building for increased living space would be cost-effective. When building a new shed with a high living space versus control at a 4.00% interest rate, a median net return on infrastructure investment of +£23.00 per cow per year was identified (range –£25.91 to +£64.16 for 10th to 90th percentile). Since decreased living space is likely to lead to poorer welfare, it can be considered a negative production externality associated with current production systems, the cost of which should also be accounted for when analyzing the economics of housing. Further research is essential to gain a complete understanding of the cost-effectiveness of providing increased living space per cow under different management scenarios.

Heat stress presents a significant challenge in livestock farming, leading to decreased productivity, impaired reproductive performance, and increased morbidity. In the context of global warming, the need for effective systems to monitor and regulate the microclimate in animal environments is becoming increasingly important. ( Research purpose ) The aim of this study is to develop a mathematical framework and control algorithms for a system that regulates heat stress levels based on ethological indicators. ( Materials and methods ) A systematic analysis was conducted on the ethological responses of cattle based on observation of 20 dairy cows. The study included the assessment of behavioral markers, physiological parameters, and microclimatic conditions. Heat stress levels were evaluated using the Temperature-Humidity Index (THI). ( Results and discussion ) The study identified 10 dominant behavioral markers of heat stress out of 16 possible, including elevated heart rate, reduced digestive activity, increased food selectivity, increased water intake, rapid breathing, seeking shaded areas, prolonged lying time, alterations in behavior patterns, and suppression of estrus. A mathematical framework was developed, incorporating equations for radiant energy, moisture exchange, relative humidity, air temperature, and carbon dioxide concentration. Additionally, algorithms were designed for the automated analysis of photo and video data to detect ethological indicators of stress. The proposed control system ensures accurate measurement of the Temperature-Humidity Index (±1) and achieves a 25 percent reduction in energy consumption compared to existing systems. ( Conclusions ) The developed system enables early detection of heat stress symptoms and contributes to mitigating their negative impact on animal productivity and welfare. By integrating microclimate data with behavioral responses, the system offers a comprehensive approach to climate control in livestock housing. The proposed mathematical framework and control algorithms can be incorporated into existing microclimate control systems, thereby improving the economic efficiency of dairy farming under changing climate conditions.

This paper is composed of 5 datasets describing primiparous milk production, reproduction, body weight, activity and whole life longevity and reproductional data in dairy cows that had been reared either with or without mother for the first four days after birth and either in single housing or housing in groups of four between 1 and 8 weeks of age. The datasets contain the following variables- survival to the first lactation, date of first successful insemination, milk parameters per day (such as sum of milk yield, milk electrical conductivity and milking time), activity and body weight, all these collected during the first standardized lactation of 305 days. Cows’ longevity, reproduction and other management events were recorded during the whole life of experimental animals (such as inseminations, pregnancy diagnostics, group changes etc.). Calves’ body weight was measured first 12 weeks of life of the experimental animals. The data include the information about the type of housing (with or without mother, individual vs group housing) in the early ontogeny period and two different breeds (Holstein and Czech Fleckvieh). Data on the milk parameters, body weight and activity were collected twice a day by commercially used precision dairy monitoring technologies. Data on survival to the first lactation, longevity, first successful insemination and other events were recorded by farm managers on farm basis. Data on body weight of animals during early ontogeny were taken after birth, at 4 d of age, at 7 d of age, and then weekly until 12 weeks of age. The data can be used for further analyses of the influence of parameters from early ontogeny on cow performance, especially during the first lactation. This information can be useful for researchers and other stakeholders investigating the influence of early ontogenetic social environment on the dairy cattle performance and welfare.

Climate change is a worldwide problem that is manifested in livestock farming with a decrease in animal health and welfare and economic losses due to heat stress. Therefore, a precise and continuous recording of the barn climate is essential to be able to implement actions at a certain threshold. The aim of this study was to evaluate a logger for temperature and humidity (Kestrel Drop D2) marketed for on-farm use in comparison to various other temperature/humidity data loggers under field conditions. Four different sensors were used and placed in different settings in cattle barns to correlate temperature and humidity measurements. Data were recorded for over a year in total. The data were very highly correlated. Furthermore, the area under the curve for the evaluated logger in comparison to the other ones was 0.99 to 1.0, using a temperature–humidity index cut-off of 72, often set to define heat stress. In conclusion, the evaluated logger performed equally well as the other used devices. For on-farm use, it is suitable.

Heat stress, caused by a warming climate and the increasingly high milk-producing dairy cattle, is one of the major threats to the well-being of dairy cattle as well as the economic, environmental, and social sustainability of dairy farming around the world. Timely identification of cows under heat stress is crucial to improving animal welfare, preventing milk production losses, and preserving water and energy for cooling. This paper presents a smart ear tag, named eTag, and an associated system that can read a passive microchip temperature sensor subcutaneously injected into the animal with minimal discomfort. It features a lightweight design using a single coil shared for microchip scanning and wireless charging. eTag is autonomously recharged by a wireless charger over the head during daily milking sessions, enabling perpetual operation without battery replacement after deployment. The real-world performance of the proposed system was examined intensively in a three-week deployment on seven lactating Holstein cows. We demonstrate that eTag can reliably collect accurate body temperature in real time while maintaining a positive energy flow. The deployment of eTag will enable the timely detection of heat stress and facilitate precision control of barn cooling systems.

The aim of this experiment was to determine whether reticulorumen temperature (ReT), rumination, activity or pH captured by a rumen sensor bolus system (smaXtec animal care GmbH, Graz, Austria) can be used as an early indicator of heat load (HL) and to assess how its daily patterns are influenced by diurnal effects. Physiological and behavioral data from 70 male feedlot cattle (Uckermärker, Hereford, Simmentaler) housed in a closed barn were investigated using the calculated temperature-humidity index (THI) from remote HOBO Onset climate sensors over a period of 210 days. Using time series analysis and seasonal ARIMA modeling, it was found that ReT followed the same patterns throughout days with a THI < 74 as well as days under heat load conditions. Time series and correlation analyses were also performed for the rumen pH, rumination index and activity index. The collective mean ReT over the winter days assessed (n = 14,971) was 39.48 °C, with a minimum mean of 38.31 °C and a maximum mean of 40.69 °C. In comparison, the collective mean ReT over the summer days assessed (n = 14,030) was 39.53 °C, with a minimum mean of 38.39 °C and a maximum mean of 42.02 °C. Pearson’s correlation did not reveal a relationship between THI and ReT (r = −0.06; p < 0.001) and only minimally for rumination (r = −0.11; p < 0.001). Rumination clearly decreased with increasing ambient temperature in comparison to days with a THI < 74. A long-term effect is also visible when the monthly mean rumination from all bulls tends to decrease slightly from February to May and then increases beginning in June. The mean pH values decreased throughout the summer months. Nevertheless, the comparison between daily fluctuations in pH values under HL failed to yield significant deviations from those captured on days of winter. The Pearson correlation for rumen pH showed a weak negative linear relationship with THI (r = −0.3; p < 0.001). The monthly means of the motion activity index could also not verify that HL led to increasing activity (Pearson correlation for motion activity and THI: r = 0.04; p < 0.001). The heat load had no visible short-term effects on the ReT or rumen pH, but rumination and peak motion activity were reduced on days with high ambient temperatures.