Animal Health Monitoring Research Articles

On-farm assessment of grazing behaviour of dairy cows in two pasture management systems by low-cost and reliable cowtrackers

In dairy farming, sensors are commonly applied to monitor animal health and performance. One key task of such sensors is the behaviour classification of cows. The behaviour classification needs to take into account the differences between stable and pasture: the process of feed intake by grazing is different from feed intake in the stable. Pasture management systems differ and this introduces further variability within pasture-based systems, e.g. by different sward heights. Moreover, on pasture, spatial variables like walking distance allow the inference of further information about cow behaviour. Therefore, data accessibility and customizability of sensors is important for the development of pasture specific behaviour classification algorithms. Our objective was to construct an open-source and neck located cowtracker that combines an accelerometer, gyroscope and global navigation satellite system sensors with an Arduino microcontroller. The blueprints of the cowtracker are provided in the manuscript. Cows were tracked on two commercial dairy farms with different breeds and in different grazing systems to ensure high variability of the data. Animal behaviour was classified by training a random forest algorithm that was used to predict animal behaviour from the sensor data. The training data was obtained from visual observations in the field and differentiating between the behaviours grazing, rumination, walking, resting and drinking. The overall accuracy was 0.94 when predicting test data consisting of data retained randomly from all individuals. The predictive performance slightly decreased to 0.89 overall accuracy when whole animal data sets were retained as test data. The decrease in predictive performance was stronger for the rarely observed behaviour drinking. As a showcase, cow behaviour was analysed and differed between the two grazing systems. In continuous grazing at low sward heights (6 cm) the cows allocated more time to grazing and walking whereas in rotational grazing with pre- and post-grazing sward heights of 11.3 cm and 6.3 cm respectively, the cows allocated more time to rumination and less to grazing. The observed grazing time was comparably short in this experiment. The spatial observations revealed longer walking distances and higher herd scatter in continuous grazing which reflects the greater effort in satisfying the feed demand in low swards.

11 Evaluation and Application of Technology in Swine Production Systems

Abstract The rapid development of smart farming technology is improving our real-time and continuous monitoring of animal health status, improvement of animal well-being, and monitoring of growth and performance to maximize revenue capture. Significant innovations are emerging to solve complex tasks in swine barns and to augment the capacity of farmers. Special attention has been given to computer vision technologies because of the low cost of cameras and the advancements in edge computing systems that automatically extract, analyze, and understand complex problems from a single image or a sequence of images. In swine production systems, computer vision technologies have been applied to monitor animal behavior, counting of pigs, estimation of pig body weight, monitoring of feed bin levels, among others. As farmers adopt new technologies, we propose that field evaluation of smart farming tools must include a scientific assessment of accuracy and reliability. Internal validation of the technology PigVision, a smart camera system that estimates the body weight of pigs, used a total of 32 smart cameras installed in a research-commercial finish barn (100 pens, capacity for 2,400 pigs) in Iowa. Estimation of body weights by cameras were compared with pen weights using a built-in pen scale every 2 months for more than 2 years. The initial results of the accuracy of cameras were < 90%. After adjustments on the algorithms related to height of the ceiling and lighting conditions, the accuracy improved to > 96%. The average accuracy during 2022 was 96.7%. Field validation of cameras overtime allowed to assess the reliability of the system by measuring the uptime of cameras. We defined uptime of cameras as the percentage of daily body weight estimations reported to end users relative to the number of cameras placed in the barn. During the first cycle of production (April to July 2020), the uptime averaged 97.2%. The uptime decreased to 85.6% in the next cycle (August to December 2020). Improvements in the hardware, internet reliability and periodicity in lense cleaning resulted in uptimes to > 98.5% during the last 2 cycles tested (February to October 2022). As accuracy and reliability of smart cameras meet expectations, we envision that these can be used to monitor and predict changes in the growth of pigs, early detection of health challenges and support marketing strategies. We anticipate that technology smartness for continuous monitoring of animal growth and health will drive a major shift from our conventional methods of production to more advanced and efficient systems. We expect that the implementation of computer vision technologies in swine barns will accelerate as they continue to prove accuracy, reliability, and cost-effectiveness.

Lumpy Diseases Detection using Machine Learning

Machine learning algorithms for improving animal health monitoring have accelerated the creation of ML applications for behavioral and physiological monitoring systems, including ML-based animal health monitoring systems. Currently, farm animals are raised all over the world, and it is necessary to monitor their physiological processes. It is suggested in this article to use machine learning models to continuously monitor each animal's vital signs and look for biological changes. In this model, crucial data is gathered via IoT devices, and data analysis is carried out using machine learning techniques to identify potential dangers from changes in an animal's physiological state. The results of the experiments demonstrate that the suggested model is accurate and efficient enough to identify animal situations. For our purposes, the CNN and YOLO accuracy of more than 90% is a promising outcome. Keywords- Lumpy disease , Machine learning, Images

Референтные интервалы массовых коэффициентов внутренних органов морских свинок

In toxicological studies for the detection of target organs, organ mass coefficients and absolute organ weights of laboratory animals are one of the monitored parameters. The aim of this study is to determine reference intervals of organ mass coefficients in relation to body weight and brain weight, and absolute organ weights in guinea pigs. The indirect method of obtaining reference values was used to determine reference intervals, and data from 40 male and 61 female outbred guinea pigs obtained during animal health monitoring from 2021 to 2023 were included in the analysis. The initial step in obtaining reference intervals is to determine the presence of statistical outliers and their exclusion, for each parameter evaluated separately. Reference intervals were established based on Clinical and Laboratory Standards Institute recommendations for mass coefficient and absolute values of heart, lung, thymus, liver, spleen, kidney, adrenal glands, brain, testes, and ovaries. Due to the small number of reference values (less than 120), a robust method of calculating reference intervals with confidence intervals was used. To improve the prognostic significance of the obtained reference intervals, it is necessary to increase the size of the analysed sample of reference values (more than 120). From the perspective of bioethical norms, a retrospective approach to obtaining additional reference values should be used to achieve this goal.

Вариабельность биохимических показателей крови и установление референтных интервалов в доклинических исследованиях. Сообщение 10: монгольские песчанки

Gerbils as a type of laboratory animals are widely used as experimental models in a number of areas of biomedical research. This type of laboratory animals is relatively easy to maintain and handle. They are an alternative test system among rodents such as mice and rats. They are most often used in the study of cerebral ischemia or stroke, behavioral research, aging processes, the development of oncological diseases, as well as the study of para­sitic, viral and bacterial infections, studies of the mechanisms and therapy of epileptic seizures and metabolic disorders such as obesity and diabetes mellitus. The aim of this work was to create a database of reference intervals of biochemical parameters of normal healthy mature Mongolian gerbils with further use in daily routine laboratory practice in preclinical centers. The data that were used to form the reference intervals were obtained from intact animals in “Home оf Pharmacy”. All manipulations and experiments were approved by the bioethical commission. The study involved 20 males and 20 females (non-pregnant excluding the estrous cycle phase) of Mongolian gerbils. The age of the animals corresponded to the range of 6–8 weeks, the body weight of males and females was within 60 g. The levels of the following parameters were determined in the blood serum of animals: creatinine, urea, alanine aminotransferase, aspartateaminotransferase, alkaline phosphatase, cholesterol, triglycerides, total protein, albumin, glucose, total bilirubin, the number of globulins was calculated, as well as the ratio of albumin/globulins. The obtained ranges of males and females had no statistical difference between them. These intervals were analyzed for comparison with other reference intervals of gerbils found in the scientific literature, as well as with ranges of similar indicators characteristic of humans. Norms of biochemical parameters of blood serum of gerbils are an important tool necessary for monitoring animal health during biomedical research, monitoring the dynamics of modeling pathological conditions in animals and studying the safety of new pharmacological agents. An important aspect in preclinical research is the creation of any reference intervals of laboratory animals in each scientific center separately. This is due to a large number of pre-analytical, analytical and post-­analytical features, as well as minimizing the risk of misinterpretation of the data obtained during biomedical research.

Analysis of the Drinking Behavior of Beef Cattle Using Computer Vision.

Monitoring the drinking behavior of animals can provide important information for livestock farming, including the health and well-being of the animals. Measuring drinking time is labor-demanding and, thus, it is still a challenge in most livestock production systems. Computer vision technology using a low-cost camera system can be useful in overcoming this issue. The aim of this research was to develop a computer vision system for monitoring beef cattle drinking behavior. A data acquisition system, including an RGB camera and an ultrasonic sensor, was developed to record beef cattle drinking actions. We developed an algorithm for tracking the beef cattle's key body parts, such as head-ear-neck position, using a state-of-the-art deep learning architecture DeepLabCut. The extracted key points were analyzed using a long short-term memory (LSTM) model to classify drinking and non-drinking periods. A total of 70 videos were used to train and test the model and 8 videos were used for validation purposes. During the testing, the model achieved 97.35% accuracy. The results of this study will guide us to meet immediate needs and expand farmers' capability in monitoring animal health and well-being by identifying drinking behavior.

A teacher-to-student information recovery method toward energy-efficient animal activity recognition at low sampling rates

Automated animal activity recognition (AAR) has advanced greatly through recent advances in sensing technologies and deep learning, and improved livestock management efficiency, animal health, and welfare monitoring. In practical automated AAR systems where animals need to be monitored over a long period, the sampling rate dramatically affects the energy consumption and battery life of sensing devices due to continuous data collection and transmission. Considering real-world benefits, existing works have often lowered the sampling rate to reduce energy costs. However, when the sampling rate falls below a threshold, the AAR performance degrades rapidly due to many relevant signals being missed. Therefore, this study proposed a novel method, dubbed teacher-to-student information recovery (T2S-IR), to improve the performance of AAR at low sampling rates. This approach effectively leverages the knowledge obtained from high-sampling-rate data, to assist in recovering the missing information in features extracted by the classification network trained on low-sampling-rate data. The workflow of the T2S-IR contains two main steps. (1) we utilize high-sampling-rate data for training teacher classification and reconstruction networks sequentially. (2) Then, we train a student classification network using low-sampling-rate data, while promoting its performance by exploiting the knowledge learned by trained teacher networks via two novel modules, namely the reconstruction-based information recovery (RIR) module and the correlation-distillation-based information recovery (CDIR) module. Specifically, the RIR module employs the pretrained teacher reconstruction network to enforce the student classification network to learn complete and descriptive features. The CDIR module enforces the feature maps of student network to mimic internal correlations within feature maps of pretrained teacher classification network along temporal and sensor axes directions. To validate our proposed T2S-IR, we conducted experiments on two public datasets acquired for horses and goats using triaxial accelerometers and gyroscopes with an initial sampling rate of 100 Hz. Data having low sampling rates were obtained by downsampling the original data at different frequencies (i.e., 50, 25, 12.5, 10, 5, and 2 Hz). The results demonstrated that our method remarkably boosted the classification network trained on low-sampling-rate data (e.g., percentage-point increments in the precision, recall, F1-score, and accuracy of 3.33%, 3.58%, 3.45%, and 2.19%, respectively, for the 12.5-Hz horse data and 7.6%, 4.44%, 6.9%, and 0.79%, respectively, for the 5-Hz goat data) while outperforming existing knowledge distillation methods. The enhanced classification network can be directly applied in practical AAR tasks with low sampling rates, significantly beneficial for scenarios with constrained energy sources for wearable devices.

Development and evaluation of an animal health and welfare monitoring system for veterinary supervision of pullet farms.

Regular welfare monitoring throughout rearing of pullets may help to identify problems early and take counteractions timely, which helps in guaranteeing good welfare. The aims of our observational study were (i) to establish and test a welfare monitoring system that can be used during (short) routine veterinary and technical staff visits for pullet flocks, (ii) to use the monitoring system to investigate variability between flocks and (iii) to analyse factors that potentially affect pullets' body weight, uniformity in body weight and mortality. The developed monitoring system tries to minimise the time required while not losing important information. Age-specific recording sheets comprise animal-based indicators of welfare and relevant environmental factors (housing, management, care) to allow for identifying causes of problems and targeted action. Finally, the system was implemented in a cross-sectional study and data collected in 100 flocks (67 organic, 33 conventional) on 28 rearing farms in Austria. Linear mixed models were used to identify factors influencing body weight, uniformity and mortality, both including all flocks (A) and only organic flocks (O) and a linear regression model with all flocks to investigate associations within animal-based indicators. High variability was found between flocks in animal-based indicators. Body weight was higher when the pre-rearing period was shorter (p≤0.001, A&O), with higher intensities of light (p=0.012, O), with only one compared to more stockpersons (p≤0.007, A&O), with a higher number of flock visits per day (p≤0.018, A&O), and a lower avoidance distance (p=0.034, A). Body weight uniformity increased, with age and decreased with the duration of the light period (p=0.046, A), and, amongst others, was higher on organic farms (farming type; p=0.041). The latter may reflect a more uniform level of welfare due to a lower stocking density and lowered effects of social competition. Within organic flocks mortality was lower if pullets had access to a covered veranda (p=0.025) resulting in an overall lower stocking density inside the barn, while in the model including all farms mortality was higher in cases where a disease had been diagnosed. We conclude that our monitoring system can easily be implemented in regular veterinary and technical staff visits, but could also be used by the farmers'. Several easy-to-record animal-based indicators of animal welfare could be analysed more frequently to increase early detection of problems. Implementation of such a routine-based monitoring system with easy-to-assess animal-based parameters and input measures can contribute to better animal health and welfare in pullets.

Automated walk-over-weigh system to track daily body mass and growth in grazing steers

Body weight is a critical component for monitoring animal body mass, body condition, nutritional status, and health. However, traditional methods of collecting body weight are stressful, costly, and logistically impractical with cattle on extensive landscapes. Remote systems capable of collecting individual animal body weight provide a potential solution, but automatic systems may collect spurious weight measurements. The objectives of this study were to (1) describe the utility of using a remote weight capture system, (2) develop methods for identifying and removing spurious measurements, and (3) cross-validate automatically collected weights with static chute collected weights. Beef steers (n = 10) were tagged with electronic Radio Frequency Identification tags and grazed for 286 days in an improved pasture (±11.54 ha) containing Bermuda (Cynodon dactylon) and Tall Fescue (Festuca arundinacea) and inter-seeded with Annual Ryegrass (Secale cereale). A walk-over-weigh (WOW) system was designed from a platform scale connected to a TruTest® scale head and paired with a computer to save data files. In total, 5,466 raw weights associated with individuals were collected (0 kg < weight < 1,000 kg). We assessed the use of whole herd and individual means ± 1 standard deviation calculated either daily or over the entire trial to identify potential outliers. Additionally, 3 data smoothing methods were tested: a linear quadratic model, cubic splines, and polynomial regression. Each method was evaluated for total weight events recorded, event frequency, measured body mass and average daily gain. Further, accuracy and precision were calculated using cross-validation with paired chute weights (n = 8). Whole herd means ± 1 SD identified the fewest spurious data points and showed a stronger relationship with paired chute weights (R2 = 0.90) whereas individual daily means ± 1 SD identified the most and exhibited a weaker relationship (R2 = 0.68). Repeated quadratic regression models provided the best results, identifying 3,759 clean weight points and providing an R2 = 0.96 between paired chute and WOW measurements. A real-time simulation analysis found that both individual and the herd means ± 1 SD performed similarly with an average of 30% of data classified as spurious and removed from the analysis, resulting in agreement (R2 = 0.93 for both individual and herd means) between chute and WOW weight measurements. These results indicate the utility of using a simple WOW system to collect data for measuring individual animal body mass and associated metrics, which has implications on making management and marketing decisions in near real-time.

NAHMS 2021 Feedlot study

&#x0D; The National Animal Health Monitoring System (NAHMS) con­ducts national studies on the health and health management of U.S. domestic livestock populations. In the NAHMS Feedlot 2021 study, for feedlots that placed cattle not born and raised on the operation slightly over half of cattle placed (51.1%) came direct­ly from a cow-calf operation or a stocker or backgrounder oper­ation, while 41.2% came from a sale barn. Of the cattle placed in 2020, 85.1% were placed with the intention of meeting no specif­ic marketing claims, 0.5% of cattle were placed to meet certified organic claims, 8.4% were placed to meet no or limited antibi­otic use claims, and 10.2% were placed to meet no hormone use claims. About three-fourths (73.1%) of feedlots used antibiotics in any form in 2020. Of these feedlots: 22.4% gave injectable an­tibiotics to cattle as a group; 67.6% gave injectable antibiotics to sick cattle; 42.8% gave antibiotics in feed; and 4.5% gave antibi­otics in water. Most feedlots (85.1%) used a veterinarian in 2020. About two-thirds of feedlots (64.2%) felt that on January 1, 2017, they had all the resources necessary to manage the Veterinary Feed Directive (VFD) rule change.

Evaluation of multifactorial digestive disorders in a dairy herd at different stages of lactation.

The manager of a large dairy herd (total confinement, total mixed ration feeding, 10130 kg milk sold per cow and year) requested a workup of a digestive disorder problem that had been ongoing for several years. The cows were at all stages of lactation, and the incidence proportion (events/100 cows at risk) of digestive disorders was approximately 5 to 10%. The clinical picture included an abnormal demeanor of varying severity and signs that differed among the cows. The latter included decreased milk production and physical activity, low body condition score, abnormal rumen motility and stratification, small intestinal dilatation, diarrhea, undigested fiber particles in the feces, fever and abdominal pain.The following approach was used to investigate the digestive disorder problem:1. The herd was assessed for production levels, housing requirements, feeding protocols and animal health management. The latter comprised evaluation of different animal scores, metabolic profile analysis, diagnosis of disease, culling records and slaughter data. The results revealed risk factors concerning the feeding and animal health monitoring, (e. g. in dry matter intake and silage quality management, disease detection and diagnosis in fresh cows). The assessment also identified a high occurrence of digestive disorders of unknown origin.2. Fifteen cows that represented the ongoing digestive disorder problem were selected to undergo clinical examination, hematological analysis, urinalysis, and ultrasonography of the ventral abdomen. The clinical examinations revealed different digestive disorders, which were mainly inflammatory in nature, in all the cows. Eight cows had localized reticuloperitonitis and 13 had left displaced abomasum with different degrees of displacement and adhesions between the abomasum and reticulum attributable to reticuloperitonitis.Our results revealed a multifactorial problem caused by several risk factors relating to animal health and feeding management protocols that resulted in different types of digestive disorders. The wires from damaged tires used to hold the pit silo tarps in place were identified as a possible traumatic cause of the reticuloperitonitis. Treatment, prevention and follow-up of the different conditions were discussed.

Agriculture in the face of new digitization technologies

Agriculture plays a vital role in food production, resource utilization, and employment but faces challenges from population growth, climate change, and food shortages. The development of information technology has significantly contributed to the industry's development, and modern technologies such as artificial intelligence, the Internet of Things, computer vision, and machine learning have revolutionized agricultural practices. The purpose of this review is to explore the adoption of digital technologies in agriculture, with a specific focus on their application in livestock breeding. Through the examination of current literature and the utilization of various research methods, this review contributes to the existing knowledge in the field. It is established that the latest information tools allow collecting, analysing data, automating tasks and supporting decision-making, which leads to increased agricultural efficiency, resource management and sustainable development. It has been proven that modern technologies play a crucial role in increasing agricultural production, improving the efficiency of livestock and crop production. These technologies include devices and sensors, data analytics and decision support systems, as well as systems for overall farm productivity assessment. Precision technologies in agriculture, thanks to automation, sensors and machine learning, allow farmers to monitor animal health, optimise feed consumption, detect diseases at early stages and increase overall productivity. IT solutions in agriculture facilitate data processing, visualisation and decision-making, leading to lower costs, greater efficiency and improved food security. The study provides practical insights for farmers and other agricultural stakeholders who can benefit from accurate information, real-time monitoring and automated processes through the integration of modern technologies, ultimately improving agricultural practices and sustainability

Could Fibrinogen Concentration Be a Useful Indicator of Cattle Herd Health Status? Approaches to Setting Reference Values

Fibrinogen is used in the diagnosis of inflammation as an acute phase protein. The research objective set by the authors of this study was to assess the applicability of fibrinogen concentration measurement in the blood plasma of the peak of lactating dairy cows through the evaluation of the reference values by using the functional PT-derived (Prothrombin Time-derived) method. Materials and methods: The study was carried out on 259 HF (Holstein-Friesian) and white-backed cows. The animals were clinically healthy. Fibrinogen concentration was determined by automated PT-derived method. Fibrinogen concentrations were calculated as the mean of duplicate samples. Samples with differences between duplicate results greater than 5% were rejected. Results: In the group of HF cows, the average fibrinogen concentration was 11.75 ± 4.80 g/L. In white-backed cows, it was 9.53 ± 4.79 g/L. At total of 76.01% of the results of the fibrinogen concentration in HF cows and 82.05% of the results obtained in the group of white-backed cows were within the ±1 SD (Standard Deviation) range. Conclusions: Based on our own research, the PT-derived method may be applied in order to determine the concentration of fibrinogen in cattle herds in animal health monitoring studies. An individual laboratory should focus more on verifying reference intervals established elsewhere.

2 Challenges in Breeding and Genetics

Abstract Over the past 50 years tremendous advances were made in reproductive and genetic technologies that are available for beef cow-calf producers. In addition, extensive research has been done to establish the efficacy and economic advantages of adoption of such technologies. Nonetheless, adoption of reproductive and genetic technologies in beef cow-calf operations remain poor, especially when compared with dairy production. The USDA – National Animal Health Monitoring System (NAHMS) performs a survey of cow-calf management practices, with the latest survey performed in 2017. According to the NAHMS survey the majority (58.7%) of cow-calf operations in the U.S. do not have a defined breeding season, with 69.5% of operations in the southeast (SE) failing to adopt a defined breeding season. Reproductive technology adoption is poor, with only 37.5% of all operations utilizing at least one reproductive technology, with 78.1% of larger operations (&amp;gt;200 cows) utilizing at least one reproductive technology, compared with 30.4% of small operations (&amp;lt; 50 cows). Semen evaluation was the leading technology with 19.7% overall adoption, followed by palpation for pregnancy with 19.3%, artificial insemination with 11.6% and estrus synchronization with only 7.3%. The SE is behind the rest of the country on adoption of technology with only 22.1% of the operations adopting at least one reproductive technology, with the North-Central region leading the country with 58.3% of operations adopting at least one reproductive technology. Nationally, most heifers and nearly all cows were exposed only to bulls (76.8 and 92.9 %, respectively), with a greater percentage of heifers (15.1 %) than cows (5.5 %) being artificially inseminated and followed by natural service breeding. A total of 90.7% of all beef females were exposed to natural service only, clearly demonstrating that artificial insemination is not widely adopted by U.S. beef operations. Veterinary consultation also remains below expectations, with only 52.8% of operations consulting a veterinarian every year, and disease diagnosis and treatment being the most common reason (37.3%). The East U.S. remains behind the rest of the country with only 41% of beef operations consulting a veterinarian on a yearly basis. When making decisions about breeding and genetics, 21.5% of producers rated Extension services as slightly useful and 22.6% rated Veterinarians as very useful, with most (23.6) producers consulting other producers. Adoption of reproductive and genetic technologies remains below expectations for the entire U.S. with the East and SE regions lagging behind the rest of the country. Perhaps the fact that the vast majority (84.2% in the U.S., and 89.9% in East) of beef cow-calf producers list their operations as a supplemental source of income remains the most difficult challenge to increase adoption of technologies.

IoT Based Animal Health Monitoring System using Raspberry Pi

IoT has bought many changes in the existing technologies, where its applications are numerous in the field of industry automation, bio-medical, agriculture, transport, smart cities etc. The key benefits of IoT enabled livestock management in real time is enabling farmers to quickly treat animals and prevent the spread of illness or disease, track gazing animals to prevent loss and to identify grazing patterns, gather and analyze historical data to identify trends in cattle health or to track the spread of illness, monitor readiness to give birth , preventing the loss of new calves and optimizing breeding practices. In this paper a smart animal health monitoring system is developed for monitoring the parameters such as body temperature, heart rate and rumination pattern has been monitored. Different kind of sensors are mounted on the body of animals and related information is gathered which can be accessed by the authorized person through internet. Raspberry Pi is used in this work, where different information is gathered from the sensors and placed on to the cloud so that the information can be accessed from the mobile using internet.

Calf management and welfare in the Canadian and US dairy industries: Where do we go from here?

The objective of this narrative review was to compare the results of the 2015 Canadian National Dairy Study and the 2014 US National Animal Health Monitoring System (NAHMS) Dairy Studies, with a specific focus on calf management and welfare, and to interpret these findings within more recent calf health research to describe where we need to go next in the North American dairy industry. Situating results of periodic national studies within the context of past and recent research provides an opportunity to gauge adoption of recommendations and best practices and to help identify persistent and new challenges that the industry is wrestling with to help guide research needs. Through this review of the 2 national studies, we identified several strengths of the Canadian and US dairy industries. In each area of calf health management, improvements relative to previous NAHMS studies and the published literature have been found in the level of mortality, amount of colostrum fed, housing, and the number of producers using pain control for disbudding and dehorning. There were, however, some areas that present clear opportunities for improvement. Specifically, although mortality levels have improved, a large number of calves die at birth, within the first 48 h of life, and during the preweaning period. To improve the health of calves in early life, producers could look at feeding high-quality colostrum at 10% of body weight in the first feeding, as well as feeding a larger amount of milk during the preweaning period. The barriers to making these management changes and improving overall calf health need to be identified in future studies. The majority of preweaning calves in Canada and the United States are housed in individual housing. This represents a clear opportunity for improvement because recent research has identified the positive aspects of group housing. Finally, with respect to pain control, improvements are needed (particularly in the United States) to ensure that pain management is provided when disbudding and dehorning calves. Although the science is clear on pain management, discussions with producers are needed to identify reasons for the lack of uptake.

A one health framework to advance food safety and security: An on-farm case study in the Rwandan dairy sector

In Rwanda, cattle and milk hold a cultural and historical significance, providing an opportunity for pro-dairy governmental policies aimed to alleviate food insecurity, malnutrition, and improve livelihoods. The government of Rwanda has identified strategies to grow the dairy sector through strategic investment to achieve these goals. It is estimated two-thirds of lactating cows in Rwanda have clinical or subclinical mastitis, which reduces milk production and increases the risk of milk as a source for zoonotic disease if the milk is consumed undercooked or unpasteurized. This case study outlines the implementation of a One Health framework that integrates education, research, and outreach in Rwanda to improve food safety and food security, for the social, economic, and health benefit of Rwandans and their livestock. Twenty-five Rwandan Extension Specialists participated in the Dairy Dynamic Management education, research, and outreach program. Once trained, the extension specialists supported 30 small holder dairy farmers in performing proper husbandry and animal health practices for mastitis control and reduction of bacterial counts in the udder. Over the 16-week program, 30 small holder dairy farmers and 100 dairy cows were surveyed weekly for animal husbandry, animal health, and mastitis indicators. Outcomes were evaluated by monitoring animal health, foodborne pathogens in milk, and compliance to animal husbandry protocols. Quarter milk samples were collected weekly and evaluated for the presence of bacteria that are common causes of mastitis. We found a statistically significant reduction of mean total bacterial counts and prevalence of bacterial species in quarters over the 16-week training (P ≤ .01). Smallholders were monitored through observing farmers performing hygienic milking protocols. Farmers conducted the protocol correctly greater than 90% of the time by the end of the 16-week program for 5 of 7 steps for proper hygienic milking procedures, indicating farmers were eager to learn and adopt the procedures. However, follow-up and retraining with Extension Specialists is vital to continued success. We demonstrate that an integrative One Health education, research, and outreach program can be successful in improving animal health, food safety, and food security and this framework can be applied to other agricultural sectors and geographic regions.

A review of Image Processing Technique for Monitoring The Growth and Health of Cows

In general, monitoring of animal growth and health is done directly by farmers (invasive measurement methods) which can cause cows to be injured or experience stress. To avoid this, several studies have been conducted on non-invasive methods using image processing technology. In this study, we systematically reviewed several works of literature to identify and synthesize published articles on image processing technology and image processing applications related to weight estimation and individual cattle identification. Analysis of image processing technologies used for weight estimation and individual cattle identification is the main objective of this article. Articles were searched through several databases and studies that met the inclusion criteria were analyzed and used in the review. The studies were divided into three main themes: image processing technologies, applications using image processing, and image processing research on cattle growth and health. It can be concluded that deep learning approaches are increasingly being studied, tested and considered as a viable and promising approach to monitor cattle weight and health in several aspects

Zoos as Sentinels? A Meta-Analysis of Seroprevalence of Terrestrial Mammalian Viruses in Zoos.

The One Health framework links animal, human, and environmental health, and focuses on emerging zoonotic pathogens. Understanding the interface between wildlife and human activity is critical due to the unpredictable nature of spillover of zoonotic pathogens from animals to humans. Zoos are important partners in One Health because of their contributions to education, conservation, and animal health monitoring. In addition, the housing of wildlife in captive and semi-natural settings makes zoos, especially relevant for detecting animal-related pathogens. A first step to determine the utility of zoos in contributing to pathogen surveillance is to survey the peer-reviewed literature. We, therefore, retrieved data from the previous 20years and performed a meta-analysis to determine global patterns of viral seroprevalence in mammals housed in zoo collections from peer-reviewed literature. We analysed 50 articles, representing a total of 11,300 terrestrial mammals. Increased prevalence was found in viruses strictly targeting specific host taxonomy, especially in viruses transmitted through direct contact. Potentially complex patterns with geography were also identified, despite uneven sampling. This research highlights the role zoos could play in public health and encourages future standardized epidemiological surveillance of zoological collections.

Effect of Different Routes of Blood Collection on the Results of Liver and Kidney Function Test of Adult Wistar Rats

Assessment of blood parameters is often used to monitor animal health and to determine the success of dietary and pharmacological intervention in preclinical animal models. It is important to consider whether hematological and metabolic measurements might differ in relation to different blood sampling sites at the time of blood collection. This research is aimed at evaluating the effects of the different routes of blood collection on the results of kidney function tests and liver function tests of adult Wistar rats. A total of 15 rats weighing 110g-150g were separated into three groups of 5 rats each and then allowed to acclimatize for 2 weeks. Blood was collected from group A using ocular puncture and from groups B and C through cardiac puncture and neck laceration mode of blood collection respectively. Liver function tests to determine serum levels of liver enzymes - aspartate aminotransferase (AST), alanine aminotransferase (ALT) and alkaline phosphatase (ALP) and kidney function tests to determine the serum levels of urea and creatinine were carried out and the results recorded. Our results showed that the different routes of blood collection used have no effect on the serum level of AST, ALT, ALP, urea and creatinine. Hence, it is safe to say that the different routes of blood collection have no effect on the result of the kidney and liver function tests.