Enhance Animal Welfare Research Articles

Plugging biologging into animal welfare: An opportunity for advancing wild animal welfare science

Abstract Animal welfare science is currently expanding beyond its traditional boundaries, from captive animals to those living in the wild. This current development is conceptually and methodologically challenging, but it could benefit from adjacent and more established research fields. Among these fields, biologging appears to be a strong candidate, as most intrinsic, location and environmental variables collected through biologging approaches could be used to assess animal welfare in the wild. To provide an objective view of the suitability of biologging to assess wild animal welfare, biologging was evaluated against the criteria that are currently recommended to assess animal welfare. This evaluation shows that biologging approaches could enhance animal welfare assessments in terms of completeness, informativeness and feasibility in the wild. However, their full implementation may be complicated by limitations in terms of validity, representativeness and disturbance, and by the different welfare perspectives taken by wildlife biologists using biologging approaches and animal welfare biologists. To exploit the full potential that biologging approaches could offer to assess wild animal welfare, their current limitations need to be overcome. Towards this end, recommendations are explicitly provided to enhance the validity and the representativeness of biologging measurements as welfare indicators, while reducing disturbance. To increase the visibility and the impact of biologging studies examining wild animal welfare, we also encourage wildlife biologists using biologging approaches to adopt the same language and perspectives as those used by animal welfare biologists. If current limitations are overcome, biologging is likely to be instrumental for the future study of animal welfare in the wild. Reciprocally, integrating animal welfare in biologging studies is expected to have a great impact on the whole biologging field by extending its current scope to a new and promising research area.

AUTOMATED TECHNOLOGY OF MEASUREMENT OF PHYSIOLOGICAL PARAMETERS OF FARM ANIMALS

Abstract. The development and application of automated technologies in the agricultural sector have significantly transformed the management and monitoring of farm animals. This paper presents a comprehensive analysis of existing technical systems for measuring the physiological parameters of farm animals, particularly focusing on cows. The primary goal of these technologies is to enhance the accuracy and efficiency of health monitoring and to optimize livestock management through continuous data collection and analysis. Key technological solutions include the use of wireless data transmission systems, radio monitoring devices, and unmanned aerial vehicles (UAVs). These technologies enable real-time tracking of animal health parameters such as body temperature, heart rate, movement patterns, and other critical indicators. The use of various sensors, including ear, neck, and intra-body sensors, plays a crucial role in collecting primary physiological data. These sensors are integrated into a broader system that includes radio modules with built-in controllers for converting raw data into digital formats, enabling wireless transmission to central systems for processing and storage. The radio monitoring of animals allows for the continuous tracking of physiological and behavioral parameters across large areas, providing crucial insights into their well-being. It is particularly beneficial in detecting early signs of disease or health issues, ensuring timely intervention, and preventing potential economic losses for farmers. The integration of unmanned aerial vehicles (UAVs) further expands the capabilities of monitoring systems, enabling the visual assessment of animals' behavior and movement across vast pastures. Equipped with video cameras and data relays, UAVs collect video footage that is transmitted to a central processing unit for real-time analysis and storage. The combined use of radio monitoring and UAV technology forms a comprehensive framework for livestock management, allowing farmers to make data-driven decisions, optimize resource allocation, and improve overall herd health and productivity. By leveraging these automated systems, it becomes possible to reduce the dependency on manual labor, minimize human error, and ensure that animals are closely monitored without the need for constant physical presence. This represents a significant advancement in modern animal farming, aligning with global trends toward smart farming and precision agriculture. In conclusion, the integration of automated physiological monitoring technologies in farm animal management presents an innovative solution for enhancing animal welfare, improving farm efficiency, and reducing operational costs. These systems hold considerable potential for further advancements in the field of animal health monitoring, ultimately contributing to more sustainable and productive farming practices.

The effect of laboratory diet and feeding on growth parameters in juvenile zebrafish

Despite being one of the most used laboratory species in biomedical, behavioral and physiological research, the nutritional requirements of zebrafish (Danio rerio) are poorly understood, and no standardized laboratory diet exists. Diet and feeding regimen can substantially impact the welfare of the fish and, in turn, experimental reproducibility. Consequently, the establishment of a standardized diet and feeding protocol for laboratory zebrafish is imperative to enhance animal welfare, guarantee research reproducibility and advance the economic and environmental sustainability of laboratory dietary practices. Here the aim of this systematic review is to provide an overview of the parameters that need to be standardized in future nutritional studies to facilitate future meta-analyses for confirmation of an optimal juvenile diet for growth. A comprehensive search was conducted in PubMed and Scopus to identify relevant studies published up to August 2023, and the studies were selected on the basis of the predefined inclusion/exclusion criteria. The databases yielded a total of 1,065 articles, of which 14 were included in this review. We conducted data extraction and risk-of-bias analysis in the included studies. Statistical comparisons for specific growth rate, weight gain (%) and length gain (%) parameters were performed to determine the optimal feed for enhanced juvenile growth. We identified significant heterogeneity and caveats to our findings owing to a lack of standardization of experimental conditions in nutritional studies. Our findings highlight an urgent need for research on zebrafish nutrition. Therefore, the standardized parameters we have reported here represent a critical starting point for studies.

Unveiling the Microbial Symphony: Exploring Emerging Contaminants in Zoological Environments for Enhanced Animal Welfare

Unveiling the Microbial Symphony: Exploring Emerging Contaminants in Zoological Environments for Enhanced Animal Welfare

An Update on Australian Policy around Lamb Marking with Examination of Potential Drivers. Comment on Johnston et al. How Well Does Australian Animal Welfare Policy Reflect Scientific Evidence: A Case Study Approach Based on Lamb Marking. Animals 2023, 13, 1358.

This commentary provides an update and clarification on the legislative landscape surrounding mulesing in Australia since the publication of the 2023 study, "How Well Does Australian Animal Welfare Policy Reflect Scientific Evidence: A Case Study Approach Based on Lamb Marking". The article explores legislative changes mandating the use of pain relief for mulesing in various states, emphasising Victoria's original role, and highlighting the fragmented state-based legislative approach to animal welfare. It discusses the impact of these legislative changes on industry practices and animal welfare outcomes. The commentary highlights the complexities of policy development in this area, due in part to the diverse and often conflicting interests of stakeholders and the public. It underscores the importance of transparency, stakeholder collaboration, and scientifically informed policymaking to effectively enhance animal welfare standards.

Poultry Preslaughter Operations in Hot Environments: The Present Knowledge and the Next Steps Forward.

Poultry production faces significant challenges, including high feed prices, diseases, and thermal stress, which impact broiler welfare and productivity. Despite advances in cooling technologies and ventilation, preslaughter operations still lead to considerable losses. This review highlights the need for the improved management of thermal environments and animal logistics. Preslaughter operations typically involve fasting broilers for 8-12 h to reduce gastrointestinal contents and contamination. Following fasting, broilers are caught, crated, and transported. Stress levels vary based on distance and conditions, with manual catching often causing stress and injuries. Catching should occur during cooler periods to minimise these issues, and transport conditions must be carefully managed. Lairage, the waiting period after transport, should be kept short (1-2 h) in climate-controlled environments to avoid stress and deterioration. Proper handling and efficient unloading are essential to prevent injuries and reduce economic losses. Stunning methods, such as electronarcosis and a controlled atmosphere, aim to minimise suffering before slaughter, though practices vary culturally and religiously. Logistics and real-time monitoring technology are crucial for enhancing animal welfare during transportation. Effective planning and the optimisation of transport processes is vital for reducing stress and losses, especially with regard to rising global temperatures and production demands.

Making Noah's Ark Work for Fishing Cat Conservation: A Blueprint for Connecting Populations across an Interactive Wild Ex Situ Spectrum.

The One Plan Approach advocates for a hybrid species management framework, wherein captive-bred populations are considered metapopulations nested within a broader network of zoos and wild populations Additionally, the Opportunities to Thrive framework aims to enhance animal welfare by addressing the physiological, psychological, and emotional needs of captive individuals, thereby improving conservation outcomes. Here, we present an integrated framework for the conservation of a globally threatened wetland wild cat species, the fishing cat, by synthesizing optimal ex situ management practices and in situ conservation strategies. Further, we examined the genetic constitution of the founder population in a fishing cat captive breeding program that was recently initiated by the West Bengal Zoo Authority, India and conducted a population viability analysis to suggest how best to maintain the genetic diversity of the population. We found that the present genetic diversity of 56% and maximum carrying capacity of the captive population (30 individuals) can be maintained for more than 100 years with a combination of supplementation and harvesting. Keeping stochastic events in mind, the introduction of two adult males and females to the existing population each year will seamlessly allow the harvesting of two adult males and two adult females every alternate year to supplement wild populations. Further, we adopted the proposed integrated framework to delineate recommendations for the supplementation of wild populations in West Bengal. We used environmental criteria known to influence fishing cat occurrence to identify 21 potential reintroduction zones in the Sundarbans landscape and Terai region in northern West Bengal with habitable areas for the fishing cat that are larger than the maximum known species' home range. Our study is timely and insightful because it provides a holistic blueprint for implementing the One Plan Approach in safeguarding a threatened species.

Optimized Machine Learning Models for Predicting Core Body Temperature in Dairy Cows: Enhancing Accuracy and Interpretability for Practical Livestock Management.

Heat stress poses a significant challenge to livestock farming, particularly affecting the health and productivity of high-yield dairy cows. This study develops a machine learning framework aimed at predicting the core body temperature (CBT) of dairy cows to enable more effective heat stress management and enhance animal welfare. The dataset includes 3005 records of physiological data from real-world production environments, encompassing environmental parameters, individual animal characteristics, and infrared temperature measurements. Employed machine learning algorithms include elastic net (EN), artificial neural networks (ANN), random forests (RF), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and CatBoost, alongside several optimization algorithms such as Bayesian optimization (BO) and grey wolf optimizer (GWO) to refine model performance through hyperparameter tuning. Comparative analysis of various feature sets reveals that the feature set incorporating the average infrared temperature of the trunk (IRTave_TK) excels in CBT prediction, achieving a coefficient of determination (R2) value of 0.516, mean absolute error (MAE) of 0.239 °C, and root mean square error (RMSE) of 0.302 °C. Further analysis shows that the GWO-XGBoost model surpasses others in predictive accuracy with an R2 value of 0.540, RMSE as low as 0.294 °C, and MAE of just 0.232 °C, and leads in computational efficiency with an optimization time of merely 2.41 s-approximately 4500 times faster than the highest accuracy model. Through SHAP (SHapley Additive exPlanations) analysis, IRTave_TK, time zone (TZ), days in lactation (DOL), and body posture (BP) are identified as the four most critical factors in predicting CBT, and the interaction effects of IRTave_TK with other features such as body posture and time periods are unveiled. This study provides technological support for livestock management, facilitating the development and optimization of predictive models to implement timely and effective interventions, thereby maintaining the health and productivity of dairy cows.

Towards enhancing animal welfare standards in UK media: Part 1; insights from public opinion and attitudes

Abstract Animals are frequently used in the UK for film, television, and advertising, but numerous stakeholders, including charities and industry representatives, have significant concerns regarding their on- and off-set welfare. However, it is unclear to what extent the public can identify animal welfare issues on screen or whether they know where to report any incidents. This survey sought to answer three research questions: (1) What are the levels of awareness and concern around animals used in TV, film, and advertising among the general public? (2) Do people know where or how to report any concerns? (3) What would reassure people that animal welfare is safeguarded? We have analysed and presented the results from the 911 responses to paint a more detailed picture of where the public is on this issue and what mechanisms for improvement they would support. Overall, the results show a clear concern for the welfare of animals used in the media, especially once they had read the eight realistic scenarios of animal use in the survey. Yet the public feel disempowered to report these concerns and lack trust that positive change will result. There is clearly public appetite for better protection of animals used in the media, particularly stronger regulations, independent on-set monitoring of animals, qualified animal trainers, and an accreditation scheme.

Design and Implementation of an IoT- Based Weather Monitoring System forEnhanced Chicken Farm

The goal of the project is to address the difficulties faced by chicken farmers in properly controlling the environmental conditions of their farms through the design and implementation of an Internet of Things- based weather monitoring system. It investigates how Internet of Things (IoT) technology can be used to remotely monitor and regulate weather conditionsthat are critical to the health and productivity of chicken. The overall goals of the initiative are to maximize production efficiency, enhance animal welfare, and improve management methods on poultry farms. Chicken farms are extremely vulnerable to changes in temperature, humidity, and air quality, among other environmental factors. Unpredictable or unfavorable weather patterns can cause stress, lower egg production, and higher bird mortality rates. The goal of the project is to create an affordable, user-friendly weather monitoring system that will enable farmers to keep an eye on, control, and maintain the perfect environment for their chickens. The project's primary design focus is on placing wireless sensors all throughout the chicken farm to continuously monitor important meteorological factors. Through a user- friendly online or mobile application, farmers can obtain real-time weather data and receive alerts thanks to the sensors' connection to a central IoT network. In order to adapt the heating, cooling, and ventilation systems to variations in the weather, the system also includes automated actuators. The Internet of Things (IoT)-based weather monitoring system showed considerable advantages for chicken farmers after undergoing extensive testing and deployment.

PSIII-29 Evaluating the efficacy of bioelectrical impedance analysis using machine learning models for the classification of parasitized goats

Abstract Rapid identification and evaluation of animal health condition is one of the most important concerns when it comes to livestock productivity, particularly for small ruminants susceptible to infection from blood-feeding gastrointestinal nematodes, like Haemonchus contortus. The present study was designed to optimize the process of differentiating parasite infected goats from healthy goats. This study used male intact Spanish goats (n = 65), aiming to establish a proof of concept for the efficacy of bioelectrical impedance, specifically through the metrics of Resistance, and Reactance, in identifying the health status of goats. The research involved the development and comparative analysis of seven machine learning (ML) models, divided into four classification-based models and three regression models, to assess their capabilities in accurately classifying goats based on bioelectrical properties. The data for this study were gathered from live animals using CQR 3.0 (Sea food analytics), with measurements taken from both ear and tail ends to ensure comprehensive and reliable bioelectrical readings. Among the classification models evaluated, the Random Forest Classification model emerged as the most effective, demonstrating a validation accuracy of 71.8% and a testing accuracy of 77.1%. This was closely followed by the K-NN Classification model, which showed a promising testing accuracy of 86.7%. Conversely, the Support Vector Machines and Boosting Classification models displayed decreased accuracies, indicating variability in the performance of classification models in this context. The regression models, evaluated through metrics such as Mean Squared Error (MSE) and R-square, revealed the K-NN Regression model as the most proficient, with the least Test MSE of 0.13 and an R-square of 0.482. This suggests a moderate correlation and predictive capability in determining the health status of goats based on bioelectrical impedance measurements. The Decision Tree Regression and Support Vector Regressor models showed varying degrees of effectiveness, with the latter indicating a minimal R-square value, highlighting the challenges in applying regression models to this domain. The conducted proof of concept research not only underscores the potential of bioelectrical impedance analysis in veterinary diagnostics, but also opens avenues for further refinement of ML algorithms in classifying and predicting the health status of farm animals. The outcomes of this study could significantly influence management strategies for small ruminants, offering a non-invasive, rapid, and potentially cost-effective tool for early detection of parasitism, thereby enhancing animal welfare and productivity in the agricultural sector.

463 Cyber-Physical Systems with robots and AI for precision dairy farming

Abstract The emergence of precision livestock farming (PLF) offers a transformative lens through which to prioritize animal welfare. At the heart of this transformation lies the rumen, a complex microbial ecosystem within dairy cows responsible for converting feed into nutrients and impacting cow health, productivity, and environmental footprint. Despite its pivotal role, our understanding of the inner workings of the rumen remains limited due to a lack of real-time monitoring tools. My research leverages AI and robotics in PLF to address this gap, focusing on developing a closed-loop system for continuous, in-vivo rumen monitoring. This system employs cyber-physical systems (CPS) embedded with modular sensors and adaptive algorithms. These sensor modules, powered by AI-based data tracking, autonomously monitor rumen temperature, humidity, pressure, and methane concentration, enabling early detection of imbalances and prompt interventions to maintain optimal rumen health. A key innovation is the self-powered in-vivo robot capable of traversing the rumen’s stratified layers. Guided by AI-powered localization, this robot unlocks unparalleled access to new data, aiding in identifying specific regions associated with conditions like subacute ruminal acidosis (SARA) and methane production. This non-invasive approach eliminates the need for traditional cannulation, minimizing stress on the animal and opening new avenues for animal scientists. The impact of this research extends beyond individual cow health. By optimizing rumen function, we can improve feed efficiency, reduce methane emissions, and promote overall sustainability in dairy farming. Additionally, real-time data insights pave the way for precision nutrition, tailored interventions, and improved disease prevention, directly enhancing animal welfare. This novel approach to rumen monitoring represents a significant step towards realizing the full potential of PLF. By integrating AI and robotics, we can usher in a future where animal welfare and environmental sustainability go hand-in-hand, ultimately contributing to a more responsible and ethical dairy industry.

GFI-YOLOv8: Sika Deer Posture Recognition Target Detection Method Based on YOLOv8.

As the sika deer breeding industry flourishes on a large scale, accurately assessing the health of these animals is of paramount importance. Implementing posture recognition through target detection serves as a vital method for monitoring the well-being of sika deer. This approach allows for a more nuanced understanding of their physical condition, ensuring the industry can maintain high standards of animal welfare and productivity. In order to achieve remote monitoring of sika deer without interfering with the natural behavior of the animals, and to enhance animal welfare, this paper proposes a sika deer individual posture recognition detection algorithm GFI-YOLOv8 based on YOLOv8. Firstly, this paper proposes to add the iAFF iterative attention feature fusion module to the C2f of the backbone network module, replace the original SPPF module with AIFI module, and use the attention mechanism to adjust the feature channel adaptively. This aims to enhance granularity, improve the model's recognition, and enhance understanding of sika deer behavior in complex scenes. Secondly, a novel convolutional neural network module is introduced to improve the efficiency and accuracy of feature extraction, while preserving the model's depth and diversity. In addition, a new attention mechanism module is proposed to expand the receptive field and simplify the model. Furthermore, a new pyramid network and an optimized detection head module are presented to improve the recognition and interpretation of sika deer postures in intricate environments. The experimental results demonstrate that the model achieves 91.6% accuracy in recognizing the posture of sika deer, with a 6% improvement in accuracy and a 4.6% increase in mAP50 compared to YOLOv8n. Compared to other models in the YOLO series, such as YOLOv5n, YOLOv7-tiny, YOLOv8n, YOLOv8s, YOLOv9, and YOLOv10, this model exhibits higher accuracy, and improved mAP50 and mAP50-95 values. The overall performance is commendable, meeting the requirements for accurate and rapid identification of the posture of sika deer. This model proves beneficial for the precise and real-time monitoring of sika deer posture in complex breeding environments and under all-weather conditions.

Determining calf traceability and cow–calf relationships in extensive farming using geolocation collars and BLE ear tags

Extensive farming is often considered very beneficial to animals for its potential to enhance animal welfare, providing animals with free-range access to their natural environment where they can engage in innate behaviors like grazing and exploration. However, despite these benefits, extensive production still faces welfare and health challenges due to unpredictable weather conditions and limited supervision by stockholders. Moreover, increasing consumer demand for information regarding food quality, safety, and production conditions poses a challenge for extensive farming, where animals are less controlled. Precision livestock farming (PLF) emerges as a possible solution by enabling the continuous real-time monitoring of the health, welfare, and behavior of animals. A novel approach combining geolocation collars for cows and Bluetooth low energy (BLE) ear tags for calves appears promising to enhance traceability and monitoring in extensive farming. Nevertheless, challenges persist, including limitations in the data transmission capacity and associated costs. This study evaluated the effectiveness of combining geolocation collars and BLE ear tags for monitoring calf traceability and cow–calf relationships across three scenarios: 1) Equilibrated: same collar/ear tag ratio; good coverage; 2) intermediate: more collars than ear tags; fair coverage; 3) worst: more ear tags than collars; lousy coverage. Our results indicate that the equilibrated scenario (ES) with an equal number of geolocation collars and BLE ear tags, was the best scenario, demonstrating the highest fix rate (22%) and the longest mean consecutive days of detecting the same ear tag (22.30 days), followed by the intermediate scenario (IS) and the worst scenario. In all scenarios, there was a mean period of 14–15 d without detecting a calf. However, this shortcoming can be overcome as calves usually graze alongside their mothers, ensuring comprehensive traceability in farm breeding. Additionally, by comparing differences in the number of ear tags received from offspring compared to other calves, the BLE ear tags successfully associated every mother with their calf in the ES and IS. Finally, this valuable information, would enable the development of a traceability system that ensures transparency and reliability throughout the supply chain and could allow consumers to access to product information related to animal welfare.

Association of Good Dairy Farming Practices, Welfare and Performance: A Review

This review underscores the critical role of good dairy farming practices in enhancing both animal welfare and dairy performance. International organizations like the World Organization for Animal Health (WOAH) and country-specific protocols emphasize the need for tailored Standard Operating Procedures (SOPs) to ensure effective and contextually appropriate practices. Advanced housing systems for calves, such as pair and group housing, have shown significant benefits in improving social interaction, cognitive development, and overall health. Effective pain management, particularly during procedures like disbudding, and optimized feeding strategies, including appropriate feed delivery timing and frequency, are crucial for enhancing health and productivity. Proper flooring, such as rubber mats, significantly improves cow comfort by providing a softer surface that reduces the risk of lameness and injuries associated with concrete floors. Effective pen management helps to mitigate dominance issues and minimize stress, leading to better welfare and productivity. Gentle handling practices, involving positive interactions like patting and stroking, significantly enhance animal welfare and milk production compared to aversive methods. Addressing issues such as mold and mycotoxins in feed is also essential for maintaining animal health and avoiding adverse effects on growth and milk production. Overall, adopting comprehensive dairy farming practices that prioritize animal welfare not only improves health and reduces stress but also leads to increased dairy productivity and sustainability, highlighting the interdependence of welfare and performance in the dairy industry.

Metaverse for Enhancing Animal Welfare - Leveraging Sensor Technology and Ethical Considerations

The metaverse, a virtual world where real-world aspects merge with artificial intelligence, immersive experiences, and high-level digital connectivity, is increasingly being applied in animal farming. This integration offers significant opportunities for addressing climate change and promoting sustainable food production. However, it also raises several ethical issues, particularly concerning animal rights. This paper evaluates these ethical considerations, emphasizing the need for a thorough examination of how sensor technology affects animals' perception and autonomy. Key findings indicate that while metaverse technologies can enhance animal welfare through improved monitoring and optimized living conditions, they also pose risks of detachment and commodification. The design of animal-friendly environments must balance technological advancement with ethical approaches to animal welfare. Critical factors such as ethical reflection, socio-economic impact, and the ability to retrieve meaningful information must be considered to maintain sensitivity and trust in these technologies. Moreover, the paper highlights the importance of addressing inequalities in access and adoption of metaverse technologies, which can significantly benefit animal farming. The potential of the metaverse to revolutionize the agri-food sector, particularly in animal agriculture, remains vast but requires further research to fully understand its implications. This paper concludes that a conscientious and ethical approach is essential for integrating metaverse technologies into animal farming, ensuring that animal welfare and equitable practices are prioritized for a sustainable future.

Improve animal health to reduce livestock emissions: quantifying an open goal.

Greenhouse gas (GHG) emissions from livestock production must be urgently tackled to substantially reduce their contribution to global warming. Simply reducing livestock numbers to this end risks impacting negatively on food security, rural livelihoods and climate change adaptation. We argue that significant mitigation of livestock emissions can be delivered immediately by improving animal health and hence production efficiency, but this route is not prioritized because its benefits, although intuitive, are poorly quantified. Rigorous methodology must be developed to estimate emissions from animal disease and hence achievable benefits from improved health through interventions. If, as expected, climate change is to affect the distribution and severity of health conditions, such quantification becomes of even greater importance. We have therefore developed a framework and identified data sources for robust quantification of the relationship between animal health and greenhouse gas emissions, which could be applied to drive and account for positive action. This will not only help mitigate climate change but at the same time promote cost-effective food production and enhanced animal welfare, a rare win-win in the search for a sustainable planetary future.

Through an animal's eye: the implications of diverse sensory systems in scientific experimentation.

'Accounting for the sensory abilities of animals is critical in experimental design.' No researcher would disagree with this statement, yet it is often the case that we inadvertently fall for anthropocentric biases and use ourselves as the reference point. This paper discusses the risks of adopting an anthropocentric view when working with non-human animals, and the unintended consequences this has on our experimental designs and results. To this aim, we provide general examples of anthropocentric bias from different fields of animal research, with a particular focus on animal cognition and behaviour, and lay out the potential consequences of adopting a human-based perspective. Knowledge of the sensory abilities, both in terms of similarities to humans and peculiarities of the investigated species, is crucial to ensure solid conclusions. A more careful consideration of the diverse sensory systems of animals would improve many scientific fields and enhance animal welfare in the laboratory.

Assessment of Metaverse wearable technologies for smart livestock farming through a neuro quantum spherical fuzzy decision-making model

Livestock wearable technologies are innovations designed to ensure livestock health management. However, the user aspect of these devices from farmers’ perspective is still questionable. Additionally, livestock wearables are still in progress compared to the other wearables. Thus, this research aims to identify key design features regarding wearable smart collars (WSCs) and rank the alternative WSC prototypes within Metaverse, allowing farmers to select the best wearable device. To this end, an integrated neuro quantum spherical fuzzy multi-criteria decision-making (MCDM) framework is introduced via facial expressions to obtain the priority weights of WSC criteria with the improved decision-making trial and evaluation laboratory (DEMATEL) approach and to rank the WSC alternatives in Metaverse through the improved multi-objective optimization based on ratio analysis (MOORA) model. The novelties of this research are: (1) to build and introduce a novel decision support tool based on facial expressions, expert recommendations, and the quantum spherical fuzzy sets, (2) to guide industrial designers about the essential features of WSCs, whereas they are designing these devices, and (3) to help smallholder farmers to decide on the best WSC to enhance animal welfare and efficiency of animal production. Concerning the findings, “sound and stress analyzer” is the most significant feature, followed by “disease detection” and “price.” Moreover, Prototype 3 is the best WSC for farmers to adopt for livestock health management. Some essential implications are further presented.

Effects of parity, season of birth, and sex on within-litter variation and pre-weaning performance of F1 Large White × Landrace pigs.

A piglet's pre-weaning performance significantly influences both animal welfare and profitability in pig production. Understanding piglet pre-weaning performance influencing factors is key to enhancing animal welfare, reducing losses, and boosting profitability. The study aimed to evaluate the impact of parity, season of birth, and sex on within-litter variation and pre-weaning performance of F1 Large White × Landrace pigs. Information regarding total litter size, number of born alive, number of stillbirths, piglet weight at birth, mortality, and count of weaned F1 Large White × Landrace piglets was acquired from the farm database (April 2022-February 2023). 2602 females and 2882 males, a total of 5484 piglets were utilized, with records from 360 sows. The coefficient of variation (CV) of birth weights among piglets within a litter was calculated. The general linear model analysis in MiniTab 17 was used to evaluate the data, with Fisher's least significant difference test (p < 0.05) used for mean separation and Pearson's moment correlation coefficient calculated to assess relationships between survival rates, mortality rates, litter size, birth weight, and birth weight CV. Parity had a statistically significant impact on litter size, birth weight, and survival rate (p < 0.05). The sow's parity did not significantly (p > 0.05) impact the number of piglets born alive or weaned. Multiparous sows had a significantly larger litter size (p < 0.05) than primiparous sows at birth. The litter weights for parities 2, 4, and 5 did not significantly differ (p > 0.05), with averages of 20.95, 20.74, and 20.03 kg, respectively. About 91.29% was the highest survival rate recorded in parity 2 (p < 0.05). The 1st week of life recorded an 8.02% mortality rate. The mortality rate in parity 3-5 group was significantly (p < 0.05) higher (11.90%) in week 1 than in the other groups (parity 1: 6.79%, parity 2: 5.74%, parity 3-5: 8.54 and 9.21%). The litter sizes in autumn (17.34) and spring (17.72) were significantly larger (p < 0.05) than those in summer (16.47) and winter (16.83). In autumn and spring, the survival rate (83.15 and 85.84%, respectively) was significantly lower (p < 0.05) compared to summer (88.40%) and winter (89.07%). In all seasons, the litter weights did not significantly differ (p > 0.05). The birth weight CV was significantly (p < 0.05) lower during summer (20.11%) than during spring (22.43%), autumn (23.71%), and winter (21.69%). The season of birth had no significant effect (p > 0.05) on the number of live piglets. Males (1.34 kg) were heavier (p < 0.05) than females (1.30 kg) at birth. Notably, the birth weight CV was similar between males (22.43%) and females (22.52%). Litter size was positively correlated with average litter weight (rp = 0.576, p < 0.001), birth weight CV (rp = 0.244, p < 0.001), and mortality rate (rp = 0.378, p < 0.001). An insignificant relationship was observed between average litter weight and birth weight CV (rp = -0.028, p > 0.05) and survival rate (rp = -0.032, p > 0.05). In F1 Large White × Landrace pigs, birth uniformity among piglets declines as litter size grows larger. In parity 3-5, multiparous sows yield litters with reduced uniformity. With an increase in litter size, uniformity among piglets at birth worsens. A larger litter size and greater piglet birth weight variation are linked to a higher pre-weaning mortality rate. Producers need a balanced selection approach to boost litter size and must cull aging sows carefully to introduce younger, more productive females.