Search Results (83)

This study examined Croatian consumer attitudes towards fresh pork from the Black Slavonian pig, focusing on the following sustainability dimensions: environmental, social, economic sustainability, and animal welfare. A survey of 410 consumers was conducted in June 2021, using an online questionnaire assessing consumption habits, breed knowledge, and socio-demographic characteristics. Factor analysis identified four key dimensions: attention to animal welfare, support for local production and biodiversity, origin and information, and price and intrinsic quality. Cluster analysis revealed three distinct consumer segments: conscious consumers (32.4%), value-oriented consumers (37.3%), and uninvolved meat consumers (30.2%). Multinomial logistic regression showed that age, region, family economic status, and place of purchase significantly predicted cluster membership (Nagelkerke R² = 0.251, classification accuracy = 52.9%), while gender, education level, and household composition did not. Conscious consumers were characterised by older age, higher income, and a preference for direct purchasing channels, while value-oriented consumers favoured supermarkets and mid-range pricing. These findings highlight the need for improved consumer education, transparent labelling, targeted marketing strategies, and enhanced policy support to promote sustainable indigenous pig breed production and conservation. Full article

This study examines the impact of sociodemographic factors on the attitudes of EU citizens towards animal welfare and their implications for trade policy. Variations in animal welfare legislation across major commercial blocks create ethical and economic challenges, with the EU implementing stringent standards compared with other countries. Data were drawn from the Special Eurobarometer 533 survey, conducted between the 3rd and 26th March 2023, which employed a multistage, clustered sampling method across all 27 EU Member States, yielding a representative sample of 26,368 respondents. The survey collected detailed information on attitudes toward animal welfare alongside sociodemographic characteristics. The results revealed substantial public support for stricter regulations and informative labelling, with attitudes differing with age (p < 0.001), education (p < 0.001), income (p < 0.001), and political orientation (p < 0.001), according to the multinomial regression applied to each of the independent variables. These results emphasise the importance of these factors in shaping consumer expectations. The findings highlight the need for policymakers to integrate ethical and societal values into trade negotiations, ensuring that policies reflect public concerns, promote fair competition, and encourage higher animal welfare standards internationally. Additionally, understanding the perspectives and motivations of livestock industry stakeholders remains critical for implementing effective welfare strategies. By aligning EU trade policy with citizen values and stakeholder practices, it is possible to advance animal welfare globally while balancing economic and ethical considerations and promoting a fair trade. Full article

Meat provides high-quality protein and essential micronutrients such as vitamin B12, heme iron, zinc, and selenium, along with conditionally essential compounds including creatine, carnitine, and taurine. Growing concerns over environmental sustainability, animal welfare, and potential health risks associated with excessive meat consumption have spurred the development of plant-based and cultured alternatives intended to replicate the nutritional and sensory attributes of meat. This review critically examines the extent to which these emerging products achieve nutrient equivalence with conventional meat, focusing on essential and conditionally essential nutrients, their bioavailability, and implications for human health. After outlining the physiological importance of nutrients characteristically supplied by meat, the review compares the composition of plant-based meat analogs (PBMAs) and cultured meat prototypes. Differences in fortification strategies, ingredient formulation, and the presence of anti-nutritional factors are discussed in relation to nutrient absorption and utilization. Current PBMAs can approximate protein content but generally provide lower levels and reduced bioavailability of vitamin B12, heme iron, creatine, taurine, and long-chain omega-3 fatty acids unless fortified. Cultured meat offers theoretical potential for compositional optimization through cellular engineering but remains limited by scarce empirical data. Achieving nutrient equivalence with conventional meat thus represents a major scientific, technological, and regulatory challenge. Future progress will depend on integrating nutritional design into product development, validating bioavailability in human studies, and implementing transparent labeling to ensure that next-generation meat alternatives meet both health and sustainability goals. Full article

This study investigates stress detection in dairy cattle by integrating circadian rhythm analysis and deep learning. Behavioral biomarkers, including feeding, resting, and rumination, were continuously monitored using Nedap CowControl sensors over a 12-month period to capture seasonal variability. Circadian features were extracted using the Fast Fourier Transform (FFT), and deviations from expected 24 h patterns were quantified using Euclidean distance. These features were used to train a Long Short-Term Memory (LSTM) neural network to classify stress into three levels: normal, mild, and high. Expert veterinary observations of anomalous behaviors and environmental records were used to validate stress labeling. We continuously monitored 10 lactating Holstein cows for 365 days, yielding 87,600 raw hours and 3650 cow-days (one day per cow as the analytical unit). The Short-Time Fourier Transform (STFT, 36 h window, 1 h step) was used solely to derive daily circadian characteristics (amplitude, phase, coherence); STFT windows are not statistical samples. A 60 min window prior to stress onset was incorporated to anticipate stress conditions triggered by management practices and environmental stressors, such as vaccination, animal handling, and cold stress. The proposed LSTM model achieved an accuracy of 82.3% and an AUC of 0.847, outperforming a benchmark logistic regression model (65% accuracy). This predictive capability, with a one-hour lead time, provides a critical window for preventive interventions and represents a practical tool for precision livestock farming and animal welfare monitoring. Full article

Interest in more sustainable diets for the global population of 528 million companion dogs is steadily increasing, encompassing nutritionally sound cultivated meat, vegan, and microbial protein-based dog foods. Factors driving these alternative dog foods include lower impacts on the environment, fewer welfare problems related to intensively farmed animals and wild-caught fish, and potentially superior canine health outcomes, relative to conventional meat-based dog food. Through a questionnaire with 2639 responses, this study aimed to gain insights into dog guardians’ current feeding patterns and dog food purchasing determinants, acceptance of more sustainable dog diets, and sources of information used for decisions about dog diets. Key results included that 84% (2188/2596) of respondents currently fed either conventional or raw meat-based dog food. More than 43% (936/2169) of this group of respondents who answered found at least one of the more sustainable alternative dog foods acceptable, with purchases of these alternatives hinging most commonly upon the nutritional soundness of the products. Cultivated meat-based dog food was the most popular alternative (selected by 24%, 529/2169), followed by vegetarian (17%, 359/2169), insect-based (16%, 336/2169), and vegan (13%, 290/2169) dog food. The top three information sources used to make decisions regarding dog diets were labels/packaging (selected by 42% of all respondents, 1080/2596), scientific articles/books (38%, 989/2596), and business webpages (35%, 900/2596). Numerous human and dog demographic variables had impacts on current diets, acceptance of alternative diets, and information sources used. Notably, human diet and dog diet were the factors most commonly associated with current and potential purchasing decisions, as well as with information sources used. For instance, greater reductions by guardians in the consumption of animals were associated with greater acceptance of more sustainable dog diets. It should be noted that, due to the reliance on convenience sampling and the overrepresentation of respondents from the UK, of female guardians, of respondents with higher education, and of vegan guardians, the reported relative frequencies of subgroups were not fully representative of the global dog guardian population. Association estimates were based on regression analyses to minimize any resultant bias effects. Full article

There is increasing awareness about the adverse environmental and ‘food’ animal welfare impacts associated with the production of meat-based pet food. However, little is known about cat guardians’ acceptance of more sustainable food choices for the global population of approximately 476 million pet cats. By surveying 1380 cat guardians, this study explored feeding patterns used by guardians, determinants of their cat food choices, and their acceptance levels of more sustainable cat food alternatives. The sources of information used by cat guardians to obtain information about the cat diets they chose were also investigated. Key results included: (1) 51% (620/1211) of cat guardians currently feeding meat-based cat food (raw or conventional) considered at least one or more sustainable alternatives to be acceptable, with cultivated meat-based cat food being the most popular alternative, followed by nutritionally sound vegan cat food; (2) the top five characteristics alternative diets needed to offer to be considered viable were good health outcomes, nutritional soundness, palatability, quality, and environmental sustainability; (3) diet types consumed by cat guardians and their cats were strongly associated; and (4) labels/packaging and veterinarians were the information sources most used, although veterinary staff may have been less trusted as reliable sources of dietary advice by guardians feeding unconventional diets. It should be noted that, due to the reliance on convenience sampling and the overrepresentation of respondents from the UK, of female guardians, of respondents with higher education and of vegan guardians, the reported relative frequencies of subgroups were not fully representative of the global cat guardian population. Association estimates were based on regression analyses to minimize any resultant bias effects. 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

In vitro meat presents a promising alternative to conventional meat production by addressing environmental and animal welfare concerns. However, broader market adoption depends on increasing consumer acceptance. Labels on product packaging have been shown to be effective in influencing consumer behavior in previous studies. This paper examines the impact of different front-of-package labels on German consumers’ choices regarding in vitro chicken meat, with the goal of identifying effective labeling strategies. To investigate this, an online choice experiment was conducted with 200 participants from Germany. In addition to the label, products varied in terms of price, origin, and calorie content. The data were analyzed using latent class analysis, which identified four distinct consumer segments characterized by their preferences, attitudes, and personal characteristics. The results were used to simulate market scenarios, evaluating the effectiveness of different labeling strategies for in vitro chicken meat. These insights provide a foundation for targeted marketing approaches that promote consumer acceptance and inform the introduction of in vitro meat products in Germany. Full article

This study introduces an innovative dog emotion classification system that integrates four non-invasive physiological indicators—skin potential (SP), muscle potential (MP), respiration frequency (RF), and voice pattern (VP)—with the extreme gradient boosting (XGBoost) algorithm. A four-breed dataset was meticulously constructed by recording and labeling physiological signals from dogs exposed to four fundamental emotional states: happiness, sadness, fear, and anger. Comprehensive feature extraction (time-domain, frequency-domain, nonlinearity) was conducted for each signal modality, and inter-emotional variance was analyzed to establish discriminative patterns. Four machine learning algorithms—Neural Networks (NN), Support Vector Machines (SVM), Gradient Boosting Decision Trees (GBDT), and XGBoost—were trained and evaluated, with XGBoost achieving the highest classification accuracy of 90.54%. Notably, this is the first study to integrate a fusion of two complementary electrophysiological indicators—skin and muscle potentials—into a multi-modal dataset for canine emotion recognition. Further interpretability analysis using Shapley Additive exPlanations (SHAP) revealed skin potential and voice pattern features as the most contributive to model performance. The proposed system demonstrates high accuracy, efficiency, and portability, laying a robust groundwork for future advancements in cross-species affective computing and intelligent animal welfare technologies. Full article

Nowadays, consumers are becoming increasingly concerned about the husbandry conditions under which animals are raised, particularly broilers, since broilers are one of the species whose welfare is most impaired in intensive farming systems. One of the primary means of communicating husbandry practices to consumers is through product labelling. Thus, a rising number of animal welfare labelling schemes for broilers are being developed and used across Europe by initiatives of both public and private stakeholders, including NGOs that advocate for animal welfare. This review aims to identify, analyze, and compare these labelling schemes with a focus on the main animal welfare provisions included in them, which contribute to enhanced animal welfare. The schemes were identified through web searches, so that we could visit their official websites, access their standards and regulations and study them in detail. We included in our research only those schemes whose criteria were publicly available. In total, 16 schemes were selected and analyzed. Although these schemes vary in their criteria, they all enhance the welfare standards of broiler production, primarily through incorporating environmental enrichment and/or access to the outdoors. Most schemes define and specify in detail the required animal welfare provisions, setting a clear application frame for the raising period of the birds. However, the welfare of animals during transport and slaughter is often overlooked. Full article

Ritual slaughter—understood as the killing of animals without prior stunning for religious purposes—constitutes a legally and ethically intricate domain, situated at the intersection of animal welfare, freedom of religion, public health, and consumer protection. This review offers a critical examination of the influence exerted by international and supranational jurisprudence—most notably the case law of the Court of Justice of the European Union—on the regulatory landscape governing ritual slaughter. While the right to religious freedom enjoys robust protection under European constitutional and human rights frameworks, recent judicial decisions have affirmed the legitimacy of national legislative measures mandating pre-slaughter stunning, insofar as such measures pursue objectives of animal welfare and transparency in the public interest. Particular attention is devoted to seminal rulings originating in Belgium and within the broader EU context, with a focus on the application of the principle of proportionality as a legal mechanism for balancing colliding fundamental rights. The analysis further engages with the scientific and ethical discourse surrounding animal suffering and the legal obligations tied to consumer information and labeling. Taken together, these developments reveal an emergent trajectory within EU law toward the progressive tightening of regulatory standards governing ritual slaughter, shaped by an evolving jurisprudential understanding of animal welfare imperatives. Full article

Convolutional neural networks (CNNs) and their application in animal husbandry have in-depth mathematical expressions, which usually revolve around how well they map input data such as images or video frames of animals to meaningful outputs like health status, behavior class, and identification. Likewise, computer vision and deep learning models are driven by CNNs to act intelligently in improving productivity and animal management for sustainable animal husbandry. In animal husbandry, CNNs play a vital role in the management and monitoring of livestock’s health and productivity due to their high-performance accuracy in analyzing images and videos. Monitoring animals’ health is important for their welfare, food abundance, safety, and economic productivity. This paper aims to comprehensively review recent advancements and applications of relevant models that are based on CNNs for livestock health monitoring, covering the detection of their various diseases and classification of their behavior, for overall management gain. We selected relevant articles with various experimental results addressing animal detection, localization, tracking, and behavioral monitoring, validating the high-performance accuracy and efficiency of CNNs. Prominent anchor-based object detection models such as R-CNN (series), YOLO (series) and SSD (series), and anchor-free object detection models such as key-point based and anchor-point based are often used, demonstrating great versatility and robustness across various tasks. From the analysis, it is evident that more significant research contributions to animal husbandry have been made by CNNs. Limited labeled data, variation in data, low-quality or noisy images, complex backgrounds, computational demand, species-specific models, high implementation cost, scalability, modeling complex behaviors, and compatibility with current farm management systems are good examples of several notable challenges when applying CNNs in animal husbandry. By continued research efforts, these challenges can be addressed for the actualization of sustainable animal husbandry. Full article

A representative sample of Maltese citizens (N = 384) was surveyed about their perception and attitude towards animal welfare and animal-friendly foods. Knowledge about animal welfare was self-evaluated as moderate (36%) or good (27%), and mass media (television, web and newspapers) were the primary information source (73%). Dairy cows were perceived as having the highest welfare (average rating 3 on a 1-to-5 scale), while conditions for broilers and pigs were perceived as more critical (average rating 2.7). Respondents consider animal welfare important (64%), the availability of welfare-friendly products in Malta limited (49%), and would support a national animal-friendly label (84%). Although 49% were willing to pay more for animal-friendly products, the accepted price increase was limited (increase below 10% for 37% of respondents, 20% were not willing to pay more and 27% were price-sensitive). Chi-squared analysis showed that the respondents’ profession impacted the support towards an animal welfare label (p < 0.01), with business operators being the least interested. Urban respondents were more critical toward farm animal welfare and more supportive of establishing a national welfare label than rural respondents (p < 0.05). The latter were less convinced that their choices can influence the welfare of farm animals (p < 0.01). Women were more willing to pay for welfare improvements than men (p < 0.01). These findings emphasize a significant concern for animal welfare among respondents, coupled with a moderate willingness to pay. The strong support for a national animal welfare label warrants the question as to whether transparent labelling would allow consumers to translate their sensibility into ethical purchasing behaviour. Full article

Traditional animal behavior recognition models require extensive labeled datasets and frequent retraining, limiting their adaptability across species and environments. Additionally, existing systems rely on predefined behavior categories, making it difficult for researchers to customize recognition models to specific behavioral patterns relevant to their studies. Different research fields, such as animal welfare monitoring, conservation, and behavioral ecology, often require distinct behavior classifications, yet current systems lack the flexibility to accommodate these varying needs. This study aims to develop an expandable and user-driven animal behavior recognition system utilizing DeepLabCut for pose estimation and a BiLSTM-based classification model. By integrating Dynamic Adaptive Elastic Weight Consolidation (DA-EWC), the system enables incremental learning, allowing new behaviors to be added with minimal annotation while preserving previously learned behaviors. The proposed system is trained on dolphin behavior datasets using DeepLabCut for keypoint extraction and a BiLSTM model for sequence classification. Additionally, a user-friendly interface is implemented to facilitate behavior annotation and efficient model updates. The proposed system achieves 96.5% accuracy in behavior classification, surpassing conventional models such as Faster R-CNN. Compared to standard EWC, DA-EWC maintains an average of 8.3% higher accuracy when incorporating new behaviors. Furthermore, the system reduces annotation efforts by 9.3%, enabling users to expand behavior categories efficiently. This expandable behavior recognition system significantly enhances adaptability and efficiency in animal behavior monitoring. By supporting user-driven incremental learning, it provides a scalable solution for behavior analysis across different research domains, addressing the need for customizable and evolving behavior classification. Full article

In 2020, the European Union endorsed its “Farm-to-Fork” strategy, emphasizing the need for transparency in the food production chain and communication of the sustainability level and nutritional value of food products to the consumer through labeling. For animal-based products, this also includes information about the husbandry systems under which the animals are raised. At the same time, people are becoming increasingly concerned both as citizens and as consumers about animal welfare issues in production species, as animal welfare is considered an integral part of sustainability and food security. This has led to the development of various enhanced animal welfare labeling schemes, initiated by public or private entities, or even as a partnership of both. Specifically for cattle, sheep, and goats, various standards have been developed and implemented in Europe, all establishing higher welfare standards compared to conventional farming, and in some cases exceeding the minimum requirements for organic farming as set by Regulation (EU) 2018/848. Most of these standards, especially those developed by NGOs advocating for animal welfare or through public initiative, were developed for semi-intensive to extensive systems. They primarily incorporate animal-based measures, including positive welfare indicators, offering a holistic approach to animal welfare evaluation. Although there is significant heterogeneity in European animal welfare standards, nearly all of them promote access to pasture, comfort, environmental enrichment, and, in some cases, even mother–young bonding. Full article