Search Results (282)

This study presents the scientific and methodological foundation of Spain’s first national framework for the ethical management of community cat populations: the Action Plan for the Management of Community Cat Colonies (PACF), launched in 2025 under the mandate of Law 7/2023. This pioneering legislation introduces a standardized, nationwide obligation for trap–neuter–return (TNR)-based management of free-roaming cats, defined as animals living freely, territorially attached, and with limited socialization toward humans. The PACF aims to support municipalities in implementing this mandate through evidence-based strategies that integrate animal welfare, biodiversity protection, and public health objectives. Using standardized data submitted by 1128 municipalities (13.9% of Spain’s total), we estimated a baseline population of 1.81 million community cats distributed across 125,000 colonies. These data were stratified by municipal population size and applied to national census figures to generate a model-ready demographic structure. We then implemented a stochastic simulation using Vortex software to project long-term population dynamics over a 25-year horizon. The model integrated eight demographic–environmental scenarios defined by a combination of urban–rural classification and ecological reproductive potential based on photoperiod and winter temperature. Parameters included reproductive output, mortality, sterilization coverage, abandonment and adoption rates, stochastic catastrophic events, and territorial carrying capacity. Under current sterilization rates (~20%), our projections indicate that Spain’s community cat population could surpass 5 million individuals by 2050, saturating ecological and social thresholds within a decade. In contrast, a differentiated sterilization strategy aligned with territorial reproductive intensity (50% in most areas, 60–70% in high-pressure zones) achieves population stabilization by 2030 at approximately 1.5 million cats, followed by a gradual long-term decline. This scenario prioritizes feasibility while substantially reducing reproductive output, particularly in rural and high-intensity contexts. The PACF combines stratified demographic modeling with spatial sensitivity, offering a flexible framework adaptable to local conditions. It incorporates One Health principles and introduces tools for adaptive management, including digital monitoring platforms and standardized welfare protocols. While ecological impacts were not directly assessed, the proposed demographic stabilization is designed to mitigate population-driven risks to biodiversity and public health without relying on lethal control. By integrating legal mandates, stratified modeling, and realistic intervention goals, this study outlines a replicable and scalable framework for coordinated action across administrative levels. It exemplifies how national policy can be operationalized through data-driven, territorially sensitive planning tools. The findings support the strategic deployment of TNR-based programs across diverse municipal contexts, providing a model for other countries seeking to align animal welfare policy with ecological planning under a multi-level governance perspective. Full article

Population aging poses significant challenges to the sustainability of pension systems. This study presents an integrated methodological approach that uniquely combines actuarial life-cycle modeling with agent-based simulation to assess the potential of Reverse Mortgage Loans (RMLs) as a dual lever for enhancing retiree welfare and supporting pension system resilience under demographic and financial uncertainty. We explore Reverse Mortgage Loans (RMLs) as a potential financial instrument to support retirees while alleviating pressure on public pensions. Unlike prior research that treats individual decisions or policy outcomes in isolation, our hybrid model explicitly captures feedback loops between household-level behavior and system-wide financial stability. To test our hypothesis that RMLs can improve individual consumption outcomes and bolster systemic solvency, we develop a hybrid model combining actuarial techniques and agent-based simulations, incorporating stochastic housing prices, longevity risk, regulatory capital requirements, and demographic shifts. This dual-framework enables a structured investigation of how micro-level financial decisions propagate through market dynamics, influencing solvency, pricing, and adoption trends. Our central hypothesis is that reverse mortgages, when actuarially calibrated and macroprudentially regulated, enhance individual financial well-being while preserving long-run solvency at the system level. Simulation results indicate that RMLs can improve consumption smoothing, raise expected utility for retirees, and contribute to long-term fiscal sustainability. Moreover, we introduce a dynamic regulatory mechanism that adjusts capital buffers based on evolving market and demographic conditions, enhancing system resilience. Our simulation design supports multi-scenario testing of financial robustness and policy outcomes, providing a transparent tool for stress-testing RML adoption at scale. These findings suggest that, when well-regulated, RMLs can serve as a viable supplement to traditional retirement financing. Rather than offering prescriptive guidance, this framework provides insights to policymakers, financial institutions, and regulators seeking to integrate RMLs into broader pension strategies. Full article

Aggressiveness in dogs is a complex behavioral trait with implications for animal welfare and public safety. Despite domestication, dogs retain aggressive tendencies shaped by both genetic and environmental factors. This systematic review synthesizes the literature from 2000 to 2024 on the genetic and environmental bases of canine aggression. Using PRISMA 2020 guidelines, 144 articles were retrieved from Scopus and PubMed and screened in two phases, resulting in 33 studies selected for analysis. These were evaluated using a 20-question grid across seven categories, including phenotyping, genetic analysis, population structure, and future directions. The studies support a polygenic model of aggressiveness, with associations reported for genes involved in neurotransmission, hormone signaling, and brain function. However, inconsistencies in phenotyping, small sample sizes, and a limited consideration of environmental factors hinder robust conclusions. Most studies focused on popular companion breeds, while those commonly labeled as aggressive were underrepresented. The findings highlight the relevance of gene–environment interactions but underscore that aggression is often poorly defined and measured across studies. Future research should prioritize standardized phenotyping tools, broader breed inclusion, and the functional validation of genetic findings. These efforts will improve the understanding of dog aggression and inform breeding, behavioral assessment, and public policy. Full article

Background: Environmental enrichment is a promising strategy to improve the welfare of dairy goats and sheep. However, studies in this field remain scattered, and its effects on productivity are unclear. Objectives: To evaluate the effects of environmental enrichment on behavioral, physiological, and productive parameters in dairy goats and sheep. Data sources: Scopus and Web of Science were searched for studies published from 2010 to 2025. Study eligibility criteria: Experimental or observational peer-reviewed studies comparing enriched vs. non-enriched housing in dairy goats or sheep, reporting on welfare or productivity outcomes. Methods: This review followed PRISMA 2020 guidelines and the PICO framework. Two independent reviewers screened and extracted data. Risk of bias was assessed with the SYRCLE tool. Results: Thirteen studies were included, mostly with goats. Physical, sensory, and social enrichments showed benefits for behavior (e.g., activity, fewer stereotypies) and stress physiology. However, results varied by social rank, enrichment type, and physiological stage. Only three studies assessed productive parameters (weight gain in kids/lambs); none evaluated milk yield or quality. Limitations: Most studies had small samples and short durations. No meta-analysis was conducted due to heterogeneity. Conclusions: Environmental enrichment can benefit the welfare of dairy goats and sheep. However, evidence on productivity is scarce. Long-term studies are needed to evaluate its cost-effectiveness and potential impacts on milk yield and reproductive performance. Full article

Accurate body condition score (BCS) monitoring in dairy cows is essential for optimizing health, productivity, and welfare. Traditional manual scoring methods are labor-intensive and subjective, driving interest in automated imaging-based systems. This study evaluated the effectiveness of 2D imaging and deep learning for BCS classification using three camera perspectives—front, back, and top-down—to identify the most reliable viewpoint. The research involved 56 Norwegian Red milking cows at the Center for Livestock Experiments (SHF) of Norges Miljo-og Biovitenskaplige Universitet (NMBU) in Norway. Images were classified into BCS categories of 2.5, 3.0, and 3.5 using a YOLOv8 model. The back view achieved the highest classification precision (mAP@0.5 = 0.439), confirming that key morphological features for BCS assessment are best captured from this angle. Challenges included misclassification due to overlapping features, especially in Class 2.5 and background data. The study recommends improvements in algorithmic feature extraction, dataset expansion, and multi-view integration to enhance accuracy. Integration with precision farming tools enables continuous monitoring and early detection of health issues. This research highlights the potential of 2D imaging as a cost-effective alternative to 3D systems, particularly for small and medium-sized farms, supporting more effective herd management and improved animal welfare. Full article

Internet-of-Things (IoT) approaches are continually introducing new sensors into the fields of agriculture and animal welfare. The application of multi-sensor data fusion to these domains remains a complex and open-ended challenge that defies straightforward optimization, often requiring iterative testing and refinement. To respond to this need, we have created a new open-source framework as well as a corresponding Python tool which we call the “Data Fusion Explorer (DFE)”. We demonstrated and evaluated the effectiveness of our proposed framework using four early-stage datasets from diverse disciplines, including animal/environmental tracking, agrarian monitoring, and food quality assessment. This included data across multiple common formats including single, array, and image data, as well as classification or regression and temporal or spatial distributions. We compared various pipeline schemes, such as low-level against mid-level fusion, or the placement of dimensional reduction. Based on their space and time complexities, we then highlighted how these pipelines may be used for different purposes depending on the given problem. As an example, we observed that early feature extraction reduced time and space complexity in agrarian data. Additionally, independent component analysis outperformed principal component analysis slightly in a sweet potato imaging dataset. Lastly, we benchmarked the DFE tool with respect to the Vanilla Python3 packages using our four datasets’ pipelines and observed a significant reduction, usually more than 50%, in coding requirements for users in almost every dataset, suggesting the usefulness of this package for interdisciplinary researchers in the field. Full article

The reduction of environmental pollutant emissions—including greenhouse gases, particulate matter, and other harmful substances—represents one of the foremost challenges in climate policy, economics, and industrial management today. Excessive emissions of CO₂, NO_X, and suspended particulates exert significant impacts on climate change as well as human health and welfare. Consequently, numerous studies and regulatory and technological initiatives are underway to mitigate these emissions. One critical area is intra-plant transport within manufacturing facilities, which, despite its localized scope, can substantially contribute to a company’s total emissions. This paper aims to assess the potential of computer simulation using FlexSim software as a decision-support tool for planning inter-operational transport, with a particular focus on environmental aspects. The study analyzes real operational data from a selected production plant (case study), concentrating on the optimization of the number of transport units, their routing, and the layout of workstations. It is hypothesized that reducing the number of trips, shortening transport routes, and efficiently utilizing transport resources can lead to lower emissions of carbon dioxide (CO₂) and nitrogen oxides (NO_X). The findings provide a basis for a broader adoption of digital tools in sustainable production planning, emphasizing the integration of environmental criteria into decision-making processes. Furthermore, the results offer a foundation for future analyses that consider the development of green transport technologies—such as electric and hydrogen-powered vehicles—in the context of their implementation in the internal logistics of manufacturing enterprises. Full article

The presence of aggressive behavior in livestock creates major difficulties for animal welfare, farm safety, economic performance and selective breeding. The two innovative tools of deep learning-based video analysis and transcriptomic profiling have recently appeared to aid the understanding and monitoring of such behaviors. This scoping review assesses the current use of these two methods for aggression research across livestock species and identifies trends while revealing unaddressed gaps in existing literature. A scoping literature search was performed through the PubMed, Scopus and Web of Science databases to identify articles from 2014 to April 2025. The research included 268 original studies which were divided into 250 AI-driven behavioral phenotyping papers and 18 transcriptomic investigations without any studies combining both approaches. Most research focused on economically significant species, including pigs and cattle, yet poultry and small ruminants, along with camels and fish and other species, received limited attention. The main developments include convolutional neural network (CNN)-based object detection and pose estimation systems, together with the transcriptomic identification of molecular pathways that link to aggression and stress. The main barriers to progress in the field include inconsistent behavioral annotation and insufficient real-farm validation together with limited cross-modal integration. Standardized behavior definitions, together with multimodal datasets and integrated pipelines that link phenotypic and molecular data, should be developed according to our proposal. These innovations will speed up the advancement of livestock welfare alongside precision breeding and sustainable animal production. Full article

The recent birth of genetically modified canids phenotypically resembling the extinct dire wolf (Aenocyon dirus) was hailed as a landmark in synthetic biology. Using genome editing and cloning, the biotech company Colossal Biosciences created three such animals from gray wolf cells, describing the project as an effort in “functional de-extinction”. This case raises significant questions regarding animal welfare, moral justification, and regulatory governance. We used the five domains model framework to assess the welfare risks for the engineered animals, the surrogate mothers used in reproduction, and other animals potentially affected by future reintroduction or escape scenarios. Ethical implications are examined through utilitarian, deontological, virtue, relational, and environmental ethics. Our analysis suggests that the project suffers from ontological ambiguity: it is unclear whether the animals created are resurrected species, hybrids, or novel organisms. While the current welfare of the engineered animals may be manageable, their long-term well-being, particularly under rewilding scenarios, is likely to be compromised. The moral arguments for reviving long-extinct species are weak, particularly in cases where extinction was not anthropogenic. Legally, the current EU frameworks lack the clarity and scope to classify, regulate, or protect genetically engineered extinct animals. We recommend that functional de-extinction involving sentient beings be approached with caution, supported by revised welfare tools and regulatory mechanisms. Full article

The research into human–canine interactions (HCIs) has grown substantially, yet limited attention has focused on the welfare of canines involved, particularly pet dogs owned by volunteer participants. To address this gap, we conducted a secondary analysis of data from a randomized controlled trial, examining canine welfare during an acute human stress protocol. Our methodology incorporated evidence-based screening tools, environmental modifications, researchers trained in canine behavior assessments and safe interactions, and canine stress monitoring using the Fear Free™ Canine Fear, Anxiety, and Stress (FAS) Spectrum. Dogs’ stress levels showed a non-significant increase from the rest to stressor phase (0.80 to 1.00, p = 0.073) and a significant decrease during recovery (1.00 to 0.48, p < 0.001). Only two dogs (7.6%) required withdrawal due to elevated stress levels, though these levels remained within acceptable safety parameters. The peak stress remained within acceptable limits, with only 24% (6 of 25) reaching an FAS score of two during the TSST. By final recovery, 96% of dogs achieved FAS scores of zero to one (Green Zone), indicating relaxed states. Salivary collection proved challenging, highlighting limitations in low-invasive physiological measurement techniques. Based on our findings and literature review, we propose standardized guidelines for HCI research, including thorough pre-screening, environmental preparation, researcher training, stress-monitoring protocols, and informed consent procedures emphasizing withdrawal rights. These guidelines aim to establish ethical standards for this rapidly expanding field, protecting canine participant welfare while enabling valuable research to continue. Full article

Virtual fencing (VF) technology represents an innovative approach to livestock management, utilizing GPS-enabled collars to establish invisible boundaries through auditory and mild electrical stimuli. While VF offers potential benefits such as enhanced pasture management flexibility and reduced labor costs, its widespread adoption faces challenges including high initial investment costs, connectivity issues, GPS accuracy limitations, potential device durability concerns, and individual animal variability in learning and response. Furthermore, despite studies showing rapid learning and generally minimal long-term welfare impacts, questions remain regarding optimizing training protocols, addressing occasional short-term behavioral disruptions and collar abrasions, assessing long-term welfare effects across diverse systems (especially intensive and dairy), and improving scalability. To comprehensively assess the potential and limitations of this technology and guide its future development and implementation, a review integrating existing knowledge on the efficacy, welfare implications, and practical applications of VF in cattle production systems is essential. This review examines the efficacy, welfare implications, and practical applications of VF in cattle production systems. Studies demonstrate that cattle rapidly learn to associate auditory cues with electrical pulses, achieving high containment rates (≥90%) within days, with minimal long-term welfare impacts as indicated by stable cortisol levels. However, short-term behavioral disruptions and occasional collar-related abrasions have been reported, particularly in dairy cattle. While VF enhances pasture management flexibility and reduces labor costs, challenges such as connectivity issues, individual animal variability, and high initial investment costs limit its widespread adoption. The findings suggest that VF is a promising tool for precision livestock farming, though further research is needed to optimize training protocols, assess long-term welfare effects, and improve scalability across diverse farming systems. Full article

This study explores the use of Life Cycle Assessments (LCAs), Total Sustainability Assessment, and Life Cycle Sustainability Assessment (LCSA) as tools to evaluate the environmental, social, and economic impacts in Agri-industry. It highlights the unique trajectory of LCA and LCSA implementation in Latin America, shaped by the region’s distinct environmental, social, and economic contexts, contrasted with global research trends. Evidence shows the importance of biodiversity, conservation, and deforestation mitigation in Latin American LCA applications, which differ from the urban-focused impacts seen in regions like Europe or North America. Furthermore, it emphasizes the significant role of LCSA in addressing socio-economic challenges unique to Latin America, such as inequality and labor conditions. The research reveals the benefits of LCA and LCSA methodologies in the agro-industrial sector, particularly in addressing social issues like land use rights and rural community welfare. Despite challenges such as limited access to high-quality data and the need for capacity building, the innovative application of these methodologies in Latin America offers valuable insights for the global community. Our work relies on Latent Dirichlet Allocation (LDA) to analyze the LCSA literature from 1990 to 2024, identifying evolving trends and research focal areas in sustainability. The analysis herein presented highlights the need for a multi-dimensional and holistic approach to sustainability research and practice. Our findings also emphasize the importance of developing comprehensive models and integrated methodologies to effectively address complex sustainability challenges. Environmental information remains crucial for policy processes, acknowledging uncertainties in estimations and the connection between land use change, agriculture, and emissions from the global food economy and bioenergy sectors. The research underscores the dynamic nature of LCSA and the importance of continually reassessing sustainability efforts to address pressing challenges. Full article

The dairy sector plays a fundamental role in the economic development of numerous regions by creating jobs and sustaining the livelihoods of millions of people. However, concerns related to animal welfare and environmental sustainability—particularly greenhouse gas (GHG) emissions—persist in intensive dairy systems. This study aimed to measure and assess the presence of GHGs, such as methane (CH₄) and carbon dioxide (CO₂), in a compost barn facility, using spatial variability tools to analyze the distribution of these gasses at different heights (0.25 m and 1.5 m) relative to the animals’ bedding. Data were collected over five consecutive days using a prototype equipped with low-cost sensors. Geostatistical analysis was performed using R, and spatial distribution maps were generated with Surfer 13^®. Results showed elevated CH₄ concentrations at 0.25 m, exceeding values typically reported for similar systems values (60–117 ppm), while CO₂ concentrations remained within the expected range (970–1480 ppm), suggesting low risk to animals, workers, and the environment. The findings highlight the importance of continuous environmental monitoring to promote sustainability and productivity in confined dairy operations. Full article

Upper respiratory tract diseases are a major concern in sheep, causing economic losses and affecting animal welfare. Accurate diagnosis is crucial for effective treatment and prevention. This case-based study assessed the use of computed tomography for diagnosing these diseases in sheep. Thirty-three sheep showing clinical signs of one or more upper respiratory tract diseases underwent computed tomography scans, which were compared to clinical diagnoses and postmortem examinations. Results showed that clinical diagnosis matched the postmortem findings in 26 of 38 cases, while computed tomography diagnosis matched in 36 of 38 diagnosed conditions, demonstrating higher accuracy. Computed tomography provided detailed anatomical reconstructions of the respiratory tract, allowing precise detection of lesions, identification of their origin or content, and clear visualisation of surrounding structures. Despite limitations such as high costs, risks related to general anaesthesia, and radiation exposure, its accuracy makes it a valuable tool for managing these diseases. The creation of extensive databases on common diseases affecting sheep could improve the accessibility and effectiveness of computed tomography in veterinary clinics. Full article

Amidst a period of sustained inflation and rising living costs, food insecurity is a growing concern in Australia and is correlated with poor diet quality and increased rates of non-communicable diseases. Currently there is a gap in knowledge of the impact of increasing cost-of-living pressures on the affordability of a healthy diet. As affordability plays a key role in food security, this cross-sectional study aimed to examine the costs, affordability, and differential of habitual (unhealthy) and recommended (healthy) diets within the Illawarra region of Australia and compare results to 2022 findings. The Healthy Diets Australian Standardised Affordability and Pricing tool was applied in six locations in the Illawarra, with two randomly selected each from a low, moderate, and high socioeconomically disadvantaged area. Costs were determined for three reference households: a family of four, a single parent family, and a single male. Affordability was determined for the reference households at three levels of income: median gross, minimum-wage, and welfare dependent. Data was compared to data collected in 2022 using the same methods and locations. Recommended diets cost 10.3–36% less than habitual diets depending on household type, but remained unaffordable for welfare dependant households and family households from socioeconomically disadvantaged areas, where diets required 25.5–45.9% of household income. Due to income increases, affordability of both diets has marginally improved since 2022, requiring 0.5–4.8% less household income. This study provides updated evidence that supports the urgent need for policies, interventions, and monitoring to widely assess and improve healthy diet affordability and decrease food insecurity rates. Possible solutions include increasing welfare rates above the poverty line and utilising nudge theory in grocery stores. Full article