Search Results (647)

Circular agriculture has emerged as a promising framework for addressing the inefficiencies and environmental pressures associated with conventional food production systems. Within this context, livestock systems can play a transformative role by enabling waste valorisation, enhancing nutrient recycling, and improving overall resource-use efficiency. This review critically examines the multifunctional role of livestock in circular agriculture, with a particular focus on their capacity to convert non-human-edible biomass, such as crop residues, agro-industrial by-products, and food waste, into high-value animal-sourced foods. Drawing on the recent literature, the analysis explores how livestock systems can be reconfigured to utilise non-human-edible biomass, including crop residues, agro-industrial by-products, and food waste, thereby reducing competition between feed and food while enhancing sustainability outcomes. The findings highlight that livestock can function as biological upcycles, converting low-value materials into high-quality animal products, while also contributing to closed nutrient loops through manure management and integration with crop production. Additional benefits include the generation of renewable energy through anaerobic digestion and improved economic resilience through diversified outputs. However, the extent of these benefits depends on system design, management practices, and regional context. Despite their potential, circular livestock systems face challenges related to greenhouse gas emissions, regulatory constraints, economic feasibility, and knowledge gaps. These challenges highlight the need for a systems-based evaluation that accounts for environmental, economic, and social dimensions. The study concludes that livestock can contribute meaningfully to sustainable food system transitions when aligned with circular principles, but their role must be critically assessed to avoid burden-shifting and unintended environmental impacts. Full article

The agricultural sector, particularly animal production, faces numerous unprecedented challenges driven by climate change, resource depletion, and an ever-growing global demand for quality food. These challenges are further compounded by the increasing environmental impact of livestock farming, including greenhouse gas emissions, overuse of water and land resources, and the destruction of vital ecosystems. Ensuring the sustainability of animal production systems while mitigating the negative environmental impacts of these factors is essential for future global food security. As the demand for animal-derived products continues to rise, there is a pressing need for innovations that can enhance productivity without compromising environmental integrity or animal welfare. Artificial intelligence (AI) has emerged as a transformative technology with the potential to revolutionize the animal production industry. AI-driven solutions offer promising avenues for optimizing production efficiency, enhancing animal health and welfare, and reducing the environmental footprint of livestock farming. Machine learning, sensor technologies, and advanced data analytics are being increasingly utilized to monitor and predict various aspects of animal farming, such as feed efficiency, disease prevention, and climate resilience. These technologies enable farmers to make data-driven decisions, fostering more sustainable and environmentally responsible practices. This review examines the integration of AI into animal production systems, emphasizing its applications in climate change mitigation, resource management, and advancing sustainability. The discussion addresses how AI technologies can be utilized to improve productivity while minimizing environmental impact and enhancing animal welfare. Additionally, the paper outlines future opportunities, challenges, and potential barriers to integrating AI technologies into livestock farming, thereby ensuring long-term sustainability amid global challenges. Full article

Research on cattle feeding and nutrition has increasingly integrated animal welfare considerations in response to evolving scientific, societal, and production challenges. This study aimed to characterise the global scientific landscape on this topic through a comprehensive bibliometric analysis. A structured methodological framework was applied using the Web of Science database, covering the period from 2009 to 2025, limited to literature published in English, Spanish, and Portuguese. The analysis followed five stages: research design, data collection, analysis, visualisation, and interpretation, using a broad search strategy combining terms related to cattle production, nutrition, feeding, health, stress, and welfare. Bibliometric indicators and science mapping techniques were implemented using the Bibliometrix package in R (Biblioshiny), including collaboration network analysis, keyword co-occurrence, thematic evolution, and Bradford’s Law to identify core journals. In total, 424 documents were analysed. The results showed sustained growth in scientific production, particularly from 2016 onwards, indicating consolidation of the field. Output was concentrated in a limited number of countries, institutions, and journals, supported by increasingly interconnected collaboration networks. Thematic trends revealed a shift towards integrative approaches linking nutrition with stress, health, and productivity, positioning nutrition as a key tool to enhance welfare and efficiency, although behavioural and socio-economic aspects remain underrepresented. Full article

This study proposed a multi-class anomaly detection framework for group-housed pigs by integrating computer vision and machine learning. Nine classification algorithms were trained to identify five pig conditions—normal, heat stress, poor ventilation, infection, and recovery—using 10 combinations of feeding, drinking, and posture variables. The analysis revealed distinct behavioral patterns across stress conditions. Linear Discriminant Analysis (LDA) using all feeding and drinking variables achieved strong performance, with precision, recall, F1-score, and accuracy of 96.2% (95% confidence interval: 89.5–100%), 96.0% (91.5–100%), 96.0% (89.8–100%), and 96.0% (91.6–100%), respectively, and an AUC of 98.7% (88.2–95.5%). However, Random Forest and XGBoost trained on feeding and drinking variables achieved perfect classification on unseen data. With the present dataset, results indicate that feeding and drinking behaviors alone are sufficient for robust anomaly detection when paired with appropriate classifiers. Overall, this pilot study demonstrated that stressor-specific anomaly detection based on behavioral data is feasible and offers a practical, scalable approach for early stress detection, improved health and welfare monitoring, and more efficient precision livestock management. Future studies should utilize larger and more diverse datasets to further validate and strengthen the generalizability of the proposed framework. Full article

This review analyzes the economic, social, and environmental dimensions of water buffalo (Bubalus bubalis) production and its contribution to the Sustainable Development Goals (SDGs). A scoping review following PRISMA-ScR guidelines was conducted using the Web of Science (2020–2026), resulting in 225 included studies. Buffalo production is a multipurpose system that generates value through milk, meat, hides, manure, draft power, and animal-assisted services, with greater longevity than most livestock species. Economically, it supports income diversification, resource efficiency, and functions as a financial asset that can be sold to cover unexpected expenses. Socially, it enhances food security by providing nutrient-dense products, particularly milk with bioactive compounds associated with potential health benefits, and promotes women’s participation in livestock management and household economies. Environmentally, buffalo systems efficiently utilize low-quality forages, are adapted to marginal conditions, contribute to wetland conservation, and provide ecosystem services. These contributions align with several SDGs (1, 2, 5, 8, 12, 13, and 15). However, sector expansion is constrained by limitations in nutrition, management, veterinary services, and reproductive efficiency, as well as environmental challenges related to methane emissions and life cycle impacts. While global methane emissions from buffalo are lower due to their smaller population, emission intensity remains system-dependent and represents a critical challenge. In conclusion, water buffalo production represents a multifunctional and context-dependent system with significant potential to support sustainable development, although targeted innovations are required to improve productivity and address environmental challenges. Future research should integrate One Health and One Welfare approaches, develop long-term studies, and expand research under diverse experimental and field conditions to better characterize the potential health implications of buffalo-derived products. In addition, strengthening circular economy strategies, including region-specific diets to reduce emissions, remains a priority. Full article

Accurate determination of morphometric body measurements is essential for monitoring growth, evaluating production traits, and supporting selection decisions in cattle breeding. However, traditional measurement methods require direct contact with animals, which may increase labor requirements, negatively affect animal welfare, and pose safety risks for operators. This study evaluated the relationship and agreement between traditional tape measurements and smartphone-camera-based morphometric measurements in cattle. A total of 100 cattle raised in the Mediterranean region of Türkiye, including 50 Holstein and 50 Simmental animals, were included in the study. Withers height, body length, rump height, and forechest width were measured using both conventional tools and a smartphone-camera-based method. Regression analyses demonstrated strong linear relationships between methods, particularly for body length and withers height (R² = 0.564–0.961). Bland–Altman analysis revealed small but significant systematic differences between methods, with camera-based measurements generally producing slightly higher values than tape measurements. The strongest agreement was observed for body length measurements, whereas wider limits of agreement were detected for anatomically complex traits, such as rump height and forechest width. Although the findings support the potential applicability of smartphone-based morphometric measurements as a practical and contactless alternative under field conditions, measurements were obtained only from a single lateral view, which should be considered an important methodological limitation. Future studies using multi-view or three-dimensional imaging systems may further improve measurement accuracy and agreement. Full article

Continuous and objective monitoring of livestock behavior plays a key role in precision farming, animal welfare assessment, and reproductive management. This study proposes a non-invasive framework for sheep behavior and reproductive activity monitoring that integrates wearable actigraphy, machine learning, and a cloud-based data processing architecture. Tri-axial accelerometer data were collected at 30 Hz using collar-mounted ActiGraph sensors under real farming conditions. Raw acceleration signals were processed without temporal aggregation, preserving full temporal resolution that includes axis-specific acceleration, vector magnitude, and delta magnitude features. Several supervised learning models were evaluated for behavior classification, including BLSTM, LSTM, CNN–BLSTM, Random Forest, and Support Vector Machine, targeting behaviors such as standing, walking, grazing, lying, flehmen, and mating. The results indicate that both deep learning and classical machine learning approaches achieve high classification performance, with Random Forest obtaining an overall accuracy of 0.82, while deep sequential models effectively capture temporal patterns and behavioral transitions. Furthermore, a scalable cloud architecture is introduced to automate data ingestion, preprocessing, inference, storage in InfluxDB, and visualization through an interactive web application. The proposed framework supports continuous monitoring and offers practical tools for precision livestock management. Full article

Systems that farm non-typical (wild) species for human consumption are on the rise globally, in contrast to more typical livestock production. In some instances, wildlife farming may arguably help alleviate poverty, provide sustainable animal protein, and be a useful strategy for conservation through reducing wildlife poaching or breeding some animals on farms for reintroduction. However, it is unclear whether farming non-typical species within variable and often unregulated systems truly offers these benefits or outweighs the costs including animal welfare implications, public health concerns, and normalising or intensifying the consumption of wild animals. A previous study proposed a sentiocentric ethical decision-making framework for the farming of wild species. In the present study we invited academic ‘key informants’ with specialised knowledge about farming non-typical species to pilot the framework via an online survey using a species of their choice and requested their feedback on its strengths and weaknesses. Thirteen respondents applied ten different mammalian, reptilian, insect, and avian species to the framework, spanning all continents. Ultimately, the framework outcome for 11 appraisals was that the chosen species may be suitable for farming. However, erroneous responses were likely in places, and there was some uncertainty over definitions of framework terminology. We publish resultant amendments to the ethical framework to clarify meaning and suggest that it can be applied proactively or reactively by different stakeholders (e.g., governments, businesses, and NGOs). We reflect our informants’ views, acknowledging the need to solicit expertise from additional stakeholders (e.g., farmers) and the role of cultural significance and rural communities when considering farming non-typical species. Full article

Wearable sensors and remote sensing technologies are rapidly increasing opportunities to measure grazing animal behavior, energetics, and performance in extensive rangeland systems. However, despite significant advances in device capabilities, the livestock sector lacks an ecological framework that connects sensor data to the metabolic processes driving animal growth and efficiency. In this paper, we apply the movement ecology paradigm to grazing beef cattle as a demonstration of how metabolic theory, animal behavior, and landscape heterogeneity interact to influence energy budgets. We first describe the mechanistic relationships among basal metabolism, thermoregulation, activity, and forage intake, highlighting how movement patterns reflect underlying metabolic states. Next, we review key variables measurable through modern sensors, including GPS, accelerometers, rumen temperature boluses, and remote sensing of forage quantity and quality and explain how these data can be integrated into an information system to estimate energy expenditure, resource selection, and physiological stress. Finally, we show how combining movement, behavioral, and landscape data can yield meaningful indicators of performance and health, paving the way for precision livestock management grounded in ecological principles. Integrating metabolic and movement ecology with emerging technologies offers a strong framework for enhancing efficiency, welfare, and sustainability in grazing beef systems. Full article

Precision Livestock Farming (PLF) has emerged as an approach in modern animal production, integrating advanced technologies such as sensors, automation, data analytics, and artificial intelligence to enable continuous, individualised monitoring of livestock and their environment. This review examines the impact of PLF technologies on three critical dimensions of livestock systems: productivity, animal welfare, and environmental sustainability. PLF applications, including wearable and environmental sensors, automated feeding and milking systems, and video-based monitoring, allow for early detection of health and behavioural deviations, optimisation of feed efficiency, and improved reproductive and disease management. These technologies support proactive, data-driven decision-making that enhances productivity while promoting animal welfare and reducing the environmental footprint of livestock production. Despite these benefits, the adoption of PLF faces significant challenges, including high initial investment costs, technical limitations, system integration issues, data ownership and privacy concerns, and ethical considerations related to automation. Future research and policy efforts should focus on developing cost-effective, scalable solutions, standardised data frameworks, and supportive regulatory measures to enable equitable and responsible implementation across diverse production systems. By addressing these challenges, PLF offers a pathway towards more efficient, welfare-oriented, and environmentally sustainable livestock production, contributing to global food security and resilient agricultural systems. Full article

Continuous monitoring of livestock vocalizations offers a non-invasive tool for welfare assessment, but deploying current deep learning models in resource-constrained farm environments remains challenging due to high computational demands. This study proposes a feature-based machine learning pipeline optimized for edge computing to classify caprine vocalizations. Using the VOCAPRA dataset, which comprises 4147 labeled caprine vocalizations categorized into eight distinct welfare states and contexts, a hybrid feature extraction framework was applied to derive 156 spectral, temporal, and bioacoustic descriptors. Dimensionality reduction and a comprehensive comparative screening of 18 algorithms identified the CatBoost Classifier and a Multilayer Perceptron (MLP) as the optimal models. The CatBoost ensemble achieved a robust accuracy of 85.2%, while the optimized MLP reached 87.2% overall accuracy. An edge deployment benchmark revealed that the MLP was the best candidate with for real-time application, featuring a memory footprint of just 0.639 MB and near-instantaneous inference speeds of under 0.005 milliseconds per sample. Furthermore, feature importance and SHAP analyses revealed that mel-frequency cepstral coefficients heavily drove model decisions, particularly for identifying extreme physical distress and maternal reunion. The proposed methodology achieves competitive classification performance while dramatically reducing pre-processing and computational loads compared to image-based deep learning approaches, demonstrating the viability of lightweight-model, energy-efficient, real-time bioacoustic monitoring for precision livestock farming. Full article

Ammonia (NH₃) from intensive agriculture is a primary precursor for secondary fine particulate matter (PM_2.5), necessitating mitigation under the EU National Emission Ceilings (NEC) Directive. This study evaluated a novel feed-based intervention assessed under real-scale commercial conditions in weaning and growing pig units. Indoor NH₃ concentrations were monitored at high frequency (2 h resolution), and treatment effects were analyzed using a Circular Block Bootstrap (CBB) approach to account for diurnal cyclicity and temporal autocorrelation. In the weaning unit, where pits were fully emptied before the trial, the mean indoor NH₃ concentration decreased from 7.51 ppm to 1.37 ppm, representing an 81.7% reduction. In the growing unit, which operated under pre-existing slurry and an overflow system, a significant reduction of 20.9% was observed (from 5.45 ppm to 4.31 ppm). These results demonstrate the intervention’s efficacy in preventing NH₃ release from fresh excreta and suggest that its impact in systems managed under slurry overflow can be further optimized by initially activating pre-existing material. This infrastructure-free solution offers a scalable, economically sustainable pathway to align livestock production with zero-pollution targets while supporting multiple Sustainable Development Goals related to human health, worker welfare, and environmental protection. Full article

This review paper provides a comprehensive overview of the use of water in livestock and poultry nutrition, focusing on both quantitative requirements and quality standards. The review is based on the evaluation and synthesis of the published scientific literature addressing water intake, physiological functions, and quality parameters in farm animals. It summarizes the physiological roles of water in key metabolic processes and examines the primary factors influencing water requirements, including animal species, stage of production, and environmental conditions. Furthermore, the article compiles available data on water intake across major livestock systems and outlines the physicochemical and microbiological characteristics required to ensure animal health and food safety. Water constitutes a large proportion of body weight, ranging from 50% to 95% depending on species, and is essential for nutrient transport, thermoregulation, and waste elimination. Water requirements are highly variable and influenced by multiple interacting factors, such as ambient temperature, humidity, and dietary composition. Ensuring continuous access to adequate quantities of safe, high-quality water is essential for optimizing animal health, productivity, and welfare and should be integrated into routine farm management and regulatory frameworks. Full article

This five-month epidemiological investigation evaluated pre-slaughter welfare, carcass-processing practices, and post-mortem lesion prevalence in 1012 cattle and 413 pigs slaughtered in Enugu State, Nigeria. Direct observations and post-mortem inspections were conducted following OIE standards. Animal welfare was markedly compromised. Cattle were dragged from the lairage to kill floor, restrained in lateral recumbency for over 30 min before bleeding, and slaughtered without stunning. Pigs were transported tied to motorcycles and processed on unsanitary floors. The lairages lacked roofing, clean water, and adequate drainage. Carcass handling was unhygienic, with meat processed near maggot-infested drains and transported in open vans or motorized tricycles used to commute passengers and cement. Of all cattle examined, 45.3% (458/1012) exhibited gross lesions attributable to contagious bovine pleuropneumonia (CBPP, 15.5%), fasciolosis (18%), liver abscessation (6.6%), ascariasis (4.6%), and bovine tuberculosis (0.5%). No lesions were detected in pigs. Lesion occurrence differed significantly (p < 0.05) by sex (males = 44.1%, females = 66.7%), age (<4 years = 54.1%, ≥4 years = 45.4%), breed (White Fulani = 45.5%, others = 36.7%), slaughterhouse location, and season (rainy = 45.2%, dry = 45.5%). Temporal analysis showed the highest lesion rate in April (68.3%), declining to 37.7% in May. Lesions of CBPP and fasciolosis were significantly more frequent in young cattle and during the rainy months (p < 0.05). These findings reveal systemic welfare violations and disease endemicity within the municipal abattoirs surveyed. The combination of poor pre-slaughter welfare, unhygienic meat handling, and high prevalence of zoonotic and economically important livestock disease lesions highlights urgent public health concerns. Strengthening abattoir infrastructure, enforcing pre-slaughter animal welfare and hygiene regulations, mechanizing slaughter processes, and instituting continuous surveillance within the One Health framework are essential for ensuring meat safety and public health security in Nigeria and beyond. Full article

In the Mediterranean principality of Thessaly, Greece, heat stress has become an environmental limitation on animal production and welfare. This study aims to quantify livestock heat stress using the temperature–humidity index (THI) and assess its spatial and temporal distribution across Thessaly during the warm seasons from 2020 to 2025, based on ERA5-Land reanalysis data. For selected livestock units, hourly air temperatures and dew point temperatures were used to generate and calculate maximum temperature fields and the THI under outdoor conditions, with no directly measured physiological responses in animals, but potential heat stress exposure was evaluated using THI derived from ERA5-Land data. The results reveal persistent thermal hotspots in the central and southeastern Thessalian plain, where maximum daily temperatures frequently exceeded 38–40 °C and locally surpassed 45 °C during August. THI values regularly exceeded 72, indicating productivity decline, and reached 82 during peak summer months, corresponding to high and severe stress categories. Mountainous regions were consistently 6–10 °C cooler and exhibited lower THI levels. Thermally stressful conditions extended from May through September, indicating sustained seasonal exposure rather than isolated heatwave events. The spatial coincidence between intensive livestock production and high-THI zones suggests structural vulnerability under current climate conditions. These findings offer a spatially explicit assessment of climate-driven thermal risk and support the development of targeted mitigation strategies and climate-resilient livestock management in Mediterranean agricultural regions. They also offer a data-driven foundation for integration into emerging Digital Twin frameworks for predictive livestock management. Full article