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Trends in dietary habits have far-reaching implications, but their impact on animals remains insufficiently explored, as many people continue to dissociate meat from individual animal lives. This research study quantifies the temporal development of the number of animal lives affected by meat consumption within the G20 countries between 1961 and 2020 and forecasts for 2030. Production (including slaughter) and historical and projected food balance data were analyzed to explore these trends. The results indicate an increase in the number of animal lives affected due to increasing consumption, but discrepancies exist between different countries and animal categories. Increases are stronger in emerging countries, such as China, than in more industrialized countries, such as Germany. Overall, the number of animals affected grows 1.7 times as fast as meat consumption due to a shift towards poultry. Poultry birds are affected by far the most, and their dominance in number only slightly reduces when considering the differentiated moral values of the animals, reflecting their sentience. Until 2030, we can expect further increases in the number of animal lives affected. The findings highlight the need for progressive legislation to address the complex trade-offs and challenges in reversing the increasing trends in the number of animals affected.

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Livestock farming substantially contributes to the global economy and food security. However, it poses crucial environmental, animal welfare, and public health challenges. The main objective of this study is to quantify the global antimicrobial use (AMU) in cattle, chicken, and pig farming. This information is important for understanding the potential impact of farm AMU on the emergence and spread of antimicrobial resistance among animals and humans. Using the United States Department of Agriculture Production, Supply, and Distribution and the Food and Agriculture Organization databases, we estimated the total supply of cattle (in heads) and its distribution into four weight categories: calves (26%), cows (41%), heifers (4%), and bulls of more than one year (29%). Similarly, we calculated the total supply of pigs (in heads) and divided it into two weight categories: pigs (96%) and sows (4%). For chickens, we considered one weight category. We attributed to each category a standard weight according to the parameters set by the European Medicines Agency (EMA) to determine the animal biomass at risk of antimicrobial treatment, or population correction unit (PCU). Finally, we estimated the global PCUs and then the global AMU based on the average administered to the three species (in mg of active ingredients per kg PCU). With this method, we estimated a global annual AMU of 76 060 tonnes of antimicrobial active ingredients (2019-2021 average), of which 40 697 tonnes (or 53.5%) for cattle, 4 243 tonnes (or 5.6%) for chickens, and 31 120 tonnes (or 40.9%) for pigs. According to our assessment, global AMU leads to almost 20 000 tonnes less than the previous estimates due to a different evaluation of PCUs. In previous studies, PCUs were calculated on the liveweight at slaughtering of animals, while our method considers the age and sex of animals and their average weight at treatment. Our results are consistent with the World Organization for Animal Health (WOAH) estimate of 76 704 tonnes of veterinary antimicrobials globally consumed in 2018 for the total of food-producing animals (the WOAH estimation is based on sales and import data).

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Attributional life cycle assessment study examines the environmental impact of raw materials, machinery, and unit operations. In the present work, an attributional life cycle assessment (LCA) was employed to assess the environmental and greenhouse gas impacts of a shrimp feed production system. A commercial shrimp feed mill in Tamil Nadu, India, provided inventory data for one-ton shrimp feed (functional unit) for a Cradle-to-Gate evaluation using environmental impact methodologies, specifically Impact 2002+ in SimaPro® (V9.3.0.3) software. The results showed that human health (0.003357 DALY), ecosystem quality (2720.518 PDF × m2 × yr), climate change (2031.696 kg CO2 eq), and resources (71019.42 MJ primary) were the most significantly impacted. The human health category was found to be the most prominent after normalization and weighting (0.47 pt), and strategies were suggested accordingly. The GWP20 and GWP100 measures for long-term climate change were calculated to be 8.7 and 7.33 kg CO2 eq, respectively. Cast iron used in machinery production (GWP 20-15.40%, GWP100-134.5%) and electricity use (GWP 20-6.13%, GWP 100-6.9%) accounted for sizable portions of the burden. Feed production is estimated to contribute 0.2% of global CO2 emissions within the proposed global context. These findings are significant regarding economically and environmentally sustainable shrimp feed production worldwide.

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Meat comprises proteins, fats, vitamins, and trace elements, essential nutrients for the growth and development of the body. The increased demand for meat necessitates the use of antibiotics in intensive farming to sustain and raise productivity. However, the high water activity, the neutral pH, and the high protein content of meat create a favourable milieu for the growth and the persistence of bacteria. Meat serves as a portal for the spread of foodborne diseases. This occurs because of contamination. This review presents information on animal farming in South Africa, the microbial and chemical contamination of meat, and the consequential effects on public health. In South Africa, the sales of meat can be operated both formally and informally. Meat becomes exposed to contamination with different categories of microbes, originating from varying sources during preparation, processing, packaging, storage, and serving to consumers. Apparently, meat harbours diverse pathogenic microorganisms and antibiotic residues alongside the occurrence of drug resistance in zoonotic pathogens, due to the improper use of antibiotics during farming. Different findings obtained across the country showed variations in prevalence of bacteria and multidrug-resistant bacteria studied, which could be explained by the differences in the manufacturer practices, handling processes from producers to consumers, and the success of the hygienic measures employed during production. Furthermore, variation in the socioeconomic and political factors and differences in bacterial strains, geographical area, time, climatic factors, etc. could be responsible for the discrepancy in the level of antibiotic resistance between the provinces. Bacteria identified in meat including Escherichia coli, Listeria monocytogenes, Staphylococcus aureus, Campylobacter spp., Salmonella spp., etc. are incriminated as pathogenic agents causing serious infections in human and their drug-resistant counterparts can cause prolonged infection plus long hospital stays, increased mortality and morbidity as well as huge socioeconomic burden and even death. Therefore, uncooked meat or improperly cooked meat consumed by the population serves as a risk to human health.

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The agricultural sector has historically been the forefront economic sector in Turkey and is crucial for the rural sustainability and the pastures that are critical for biodiversity. However, inadequate policies and factors such as climate change and malpractices result in brittle pastures, rural-urban migration, and a declining agricultural sector. Also, pastures have been left without function and appropriated to other land uses such as quarries, energy power plants, and mines. Although the husbandry sector produces significant greenhouse gas emissions, pastures have a significant capacity of CO2 sequestration. In this study, Life cycle assessment (LCA) is applied to quantify the advantages and disadvantages of the transition between extensive and intensive production. The methodology presents a holistic analysis of the several impact categories and amounts of relevant products, services, and resource emissions along their life cycles. In order to assess the environmental effects of the lamb meat production, three sheep breeding systems in Turkey are evaluated. The study aims to promote a sustainable use of natural resources/assets without compromising the quality, competitiveness, or animal welfare and obtain recommendations for the future husbandry systems and rural development in Turkey. As an overall result, it can be stated that the intensification of sheep farming can lead to a decrease of greenhouse gas emissions per kg of meat. However, extensive sheep farming shows less impacts on soil acidification or eutrophication and can even be beneficial for erosion resistance or biodiversity if properly managed.

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With 9 million hogs, North Carolina (NC) is the second leading hog producer in the United States. Most hogs are housed at concentrated animal feeding operations (CAFOs), where millions of tons of hog waste can pollute air and water with fecal pathogens that can cause diarrhea, vomiting, and/or nausea (known as acute gastrointestinal illness (AGI)). We used NC's ZIP code-level emergency department (ED) data to calculate rates of AGI ED visits (2016-2019) and swine permit data to estimate hog exposure. Case exposure was estimated as the inverse distances from each hog CAFO to census block centroids, weighting with Gaussian decay and by manure amount per CAFO, then aggregated to ZIP code using population weights. We compared ZIP codes in the upper quartile of hog exposure ("high hog exposed") to those without hog exposure. Using inverse probability of treatment weighting, we created a control with similar demographics to the high hog exposed population and calculated rate ratios using quasi-Poisson models. We examined effect measure modification of rurality and race using adjusted models. In high hog exposed areas compared to areas without hog exposure, we observed a 11% increase (95% CI: 1.06, 1.17) in AGI rate and 21% increase specifically in rural areas (95% CI: 0.98, 1.43). When restricted to rural areas, we found an increased AGI rate among American Indian (RR = 4.29, 95% CI: 3.69, 4.88) and Black (RR = 1.45, 95% CI: 0.98, 1.91) residents. The association was stronger during the week after heavy rain (RR = 1.41, 95% CI: 1.19, 1.62) and in areas with both poultry and swine CAFOs (RR = 1.52, 95% CI: 1.48, 1.57). Residing near CAFOs may increase rates of AGI ED visits. Hog CAFOs are disproportionally built near rural Black and American Indian communities in NC and are associated with increased AGI most strongly in these populations.

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With increasing pressure from anthropogenic activity in pristine environments, the comprehensive profiling of antibiotic resistance genes (ARGs) is essential to evaluate the potential risks from human-induced antibiotic resistance in these under-studied places. Here, we characterized the microbial resistome in relatively pristine soil samples collected from four distinct habitats on the Tibetan Plateau, using a Smart chip based high-throughput qPCR approach. We compared these to soils from the same habitats that had been subjected to various anthropogenic activities, including residential sewage discharge, animal farming, atmospheric deposition, and tourism activity. Compared to pristine samples, an average of 23.7% more ARGs were detected in the human-affected soils, and the ARGs enriched in these soils mainly encoded resistances to aminoglycoside and beta-lactam. Of the four habitats studied, soils subjected to animal farming showed the highest risks of ARG enrichment and dissemination. As shown, the number of ARGs enriched (a total of 42), their fold changes (17.6 fold on average), and the co-occurrence complexity between ARGs and mobile genetic elements were all the highest in fecal-polluted soils. As well as antibiotics themselves, heavy metals also influenced ARG distributional patterns in Tibetan environments. However, compared to urban areas, the Tibetan Plateau had a low potential for ARG selection and exhibited low carriage of ARGs by mobile genetic elements, even in environments impacted by humans, suggesting that these ARGs have a limited capacity to disseminate. The present study examined the effects of multiple anthropogenic activities on the soil resistomes in relatively pristine environments.

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The automated quantification of the behaviour of freely moving animals is increasingly needed in applied ethology. State-of-the-art approaches often require tags to identify animals, high computational power for data collection and processing, and are sensitive to environmental conditions, which limits their large-scale utilization, for instance in genetic selection programs of animal breeding. Here we introduce a new automated tracking system based on millimetre-wave radars for real time robust and high precision monitoring of untagged animals. In contrast to conventional video tracking systems, radar tracking requires low processing power, is independent on light variations and has more accurate estimations of animal positions due to a lower misdetection rate. To validate our approach, we monitored the movements of 58 sheep in a standard indoor behavioural test used for assessing social motivation. We derived new estimators from the radar data that can be used to improve the behavioural phenotyping of the sheep. We then showed how radars can be used for movement tracking at larger spatial scales, in the field, by adjusting operating frequency and radiated electromagnetic power. Millimetre-wave radars thus hold considerable promises precision farming through high-throughput recording of the behaviour of untagged animals in different types of environments.

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This paper proposes an algorithm that will allow an autonomous aerial drone to approach and follow a steady or moving herd of cattle using only range measurements. The algorithm is also insensitive to the complexity of the herd's movement and the measurement noise. Once arrived at the herd of cattle, the aerial drone can follow it to a desired destination. The primary motivation for the development of this algorithm is to use simple, inexpensive and robust sensing hence range sensors. The algorithm does not depend on the accuracy of the range measurements, rather the rate of change of range measurements. The proposed method is based on sliding mode control which provides robustness. A mathematical analysis, simulations and experimental results with a real aerial drone are presented to demonstrate the effectiveness of the proposed method.

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Artificial intelligence (AI), machine learning (ML) and big data are consistently called upon to analyze and comprehend many facets of modern daily life. AI and ML in particular are widely used in animal husbandry to monitor both the animals and environment around the clock, which leads to a better understanding of animal behavior and distress, disease control and prevention, and effective business decisions for the farmer. One particularly promising area that advances upon AI is digital twin technology, which is currently used to improve efficiencies and reduce costs across multiple industries and sectors. In contrast to a model, a digital twin is a digital replica of a real-world entity that is kept current with a constant influx of data. The application of digital twins within the livestock farming sector is the next frontier and has the potential to be used to improve large-scale precision livestock farming practices, machinery and equipment usage, and the health and well-being of a wide variety of farm animals. The mental and emotional states of animals can be monitored using recognition technology that examines facial features, such as ear postures and eye white regions. Used with modeling, simulation and augmented reality technologies, digital twins can help farmers to build more energy-efficient housing structures, predict heat cycles for breeding, discourage negative behaviors of livestock, and potentially much more. As with all disruptive technological advances, the implementation of digital twin technology will demand a thorough cost and benefit analysis of individual farms. Our goal in this review is to assess the progress toward the use of digital twin technology in livestock farming, with the goal of revolutionizing animal husbandry in the future.

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Convolutional neural network (CNN)-based computer vision systems have been increasingly applied in animal farming to improve animal management, but current knowledge, practices, limitations, and solutions of the applications remain to be expanded and explored. The objective of this study is to systematically review applications of CNN-based computer vision systems on animal farming in terms of the five deep learning computer vision tasks: image classification, object detection, semantic/instance segmentation, pose estimation, and tracking. Cattle, sheep/goats, pigs, and poultry were the major farm animal species of concern. In this research, preparations for system development, including camera settings, inclusion of variations for data recordings, choices of graphics processing units, image preprocessing, and data labeling were summarized. CNN architectures were reviewed based on the computer vision tasks in animal farming. Strategies of algorithm development included distribution of development data, data augmentation, hyperparameter tuning, and selection of evaluation metrics. Judgment of model performance and performance based on architectures were discussed. Besides practices in optimizing CNN-based computer vision systems, system applications were also organized based on year, country, animal species, and purposes. Finally, recommendations on future research were provided to develop and improve CNN-based computer vision systems for improved welfare, environment, engineering, genetics, and management of farm animals.

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Animal telemetry is a subject of great potential and scientific interest, but it shows design-dependent problems related to price, flexibility and customization, autonomy, integration of elements, and structural design. The objective of this paper is to provide solutions, from the application of design, to cover the niches that we discovered by reviewing the scientific literature and studying the market. The design process followed to achieve the objective involved a development based on methodologies and basic design approaches focused on the human experience and also that of the animal. We present a modular collar that distributes electronic components in several compartments, connected, and powered by batteries that are wirelessly recharged. Its manufacture is based on 3D printing, something that facilitates immediacy in adaptation and economic affordability. The modularity presented by the proposal allows for adapting the size of the modules to the components they house as well as selecting which specific modules are needed in a project. The homogeneous weight distribution is transferred to the comfort of the animal and allows for a better integration of the elements of the collar. This device substantially improves the current offer of telemetry devices for farming animals, thanks to an animal-centered design process.

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The increased manure produced in animal husbandry, especially in beef cattle farming, pose a great threat to the environment. Resource utilization of the manure is an effective way to solve the problem and is conducive to the sustainable development of animal husbandry. Based on the theory of planned behavior, this paper attempts to construct a model of farmers' willingness to utilize livestock manure from behavioral attitude, subjective norm and cognitive behavioral control, and makes an empirical test through a questionnaire on beef cattle farmers. As the findings show, farmers' behavioral attitude, subjective norm and cognitive behavioral control have a significantly positive impact on the resource utilization of the manure. Among them, behavioral attitude exerts the greatest impact, followed by cognitive behavioral control and subjective norm. Farmers surveyed share almost the same attitude towards the effects of resource utilization, who may actively or passively take into consideration the corresponding economic, social and ecological benefits. The pressure from the government plays an important role in farmers' subjective norm, while less pressure comes from village cadres and neighbors. Finally, time or labor endowment and economic strength are the core constraints on the resource utilization of livestock manure by farmers, with relatively little constraint on skill acquisition.

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Antimicrobials are commonly used in conventional livestock production and manure is widely applied to agricultural lands as fertilizer. This practice raises questions regarding the effects of fertilization on (i) soil microbiota composition and (ii) spread of antimicrobials and antimicrobial resistance (AMR) in the environment. This study was conducted in a high-density farming area of Northern Italy and aimed at assessing the impact of (dairy cattle, chickens and swine) manure application on soil microbiome, antimicrobial concentrations and antimicrobial resistance gene (ARG) abundance. We found the microbial community composition in manure to be different and less diverse than in soil, with manure application altering only marginally the soil microbiome. Exceptions were the phyla Firmicutes, Tenericutes and Cloacimonetes, which significantly enriched in fertilized soil. Of the antimicrobials investigated, only flumequine concentrations increased after manure application, albeit non-significantly. ARGs were more abundant in manure, with ermA, ermB, blaOXA-1 and oqxA being significantly enriched in fertilized soil. Positive correlations between oqxA and qnrS abundances and flumequine concentrations were observed, together with the co-occurrence of some ARGs and microbial taxa (e.g. oqxA correlated with Acidobacteria and Gemmatimonadetes). This study showed that manure application has little effect on soil microbiome but may contribute to the dissemination of specific ARGs into the environment. Moreover, flumequine residues seem to enhance the emergence of oqxA and qnrS in soil.

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Rumination time, chewing time and drinking time are indicators that can be assessed in case of cow disease. In this research, two groups of cows were formed: cows with subclinical ketosis (SCK; n = 10) and healthy cows (HG; n = 10). Behaviour such as walking activity, feeding time with head position up, feeding time with head position down, change of activity and average, minimal and maximal ambient temperature of cows were recorded by the RumiWatch noseband system (RWS; RumiWatch System, Itin+Hoch GmbH, Liestal, Switzerland). The RWS comprises a noseband halter with a built-in pressure sensor and a liquid-filled pressure tube. Data from each studied cow were recorded for 420 h. According to the results of our study, it was determined that cows diagnosed with subclinical ketosis showed a tendency to change their activity more frequently. Our data indicates that minimal and maximal ambient temperatures are related with SCK.

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Agricultural digitalization emerged as a radical innovation, punctuating the gradual evolution of the agrifood sector and having the potential to fundamentally restructure the context within which extension and advisory organizations operate. Digital technologies are expected to alter the practice and culture of animal farming in the future. To suit the changing environmental conditions, organizations can make minor adjustments or can call into question their purposes, belief systems, and operating paradigms. Each pattern of change is associated with different types of organizational learning. In this conceptual article, adopting an organizational learning perspective and building upon organizational change models, we present two potential change and learning pathways that extension and advisory organizations can follow to cope with digitalization: morphostasis and morphogenesis. Morphostatic change has a transitional nature and helps organizations survive by adapting to the new environmental conditions. Organizations that follow this pathway learn by recognizing and correcting errors. This way, they increase their competence in specific services and activities. Morphogenetic change, on the other hand, occurs when organizations acknowledge the need to move beyond existing operating paradigms, redefine their purposes, and explore new possibilities. By transforming themselves, organizations learn new ways to understand and interpret contextual cues. We conclude by presenting some factors that explain extension and advisory organizations' tendency to morphostasis.

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Pest regulation by natural enemies has a strong potential to reduce the use of synthetic pesticides in agroecosystems. However, the effective role of predation as an ecosystem service remains largely speculative, especially with minute organisms such as mites.Predatory mites are natural enemies for ectoparasites in livestock farms. We tested for an ecosystem level control of the poultry pest Dermanyssus gallinae by other mites naturally present in manure in poultry farms and investigated differences among farming practices (conventional, free-range, and organic).We used a multiscale approach involving (a) in vitro behavioral predation experiments, (b) arthropod inventories in henhouses with airborne DNA, and (c) a statistical model of covariations in mite abundances comparing farming practices.Behavioral experiments revealed that three mites are prone to feed on D. gallinae. Accordingly, we observed covariations between the pest and these three taxa only, in airborne DNA at the henhouse level, and in mites sampled from manure. In most situations, covariations in abundances were high in magnitude and their sign was positive.Predation on a pest happens naturally in livestock farms due to predatory mites. However, the complex dynamics of mite trophic network prevents the emergence of a consistent assemblage-level signal of predation. Based on these results, we suggest perspectives for mite-based pest control and warn against any possible disruption of ignored services through the application of veterinary drugs or pesticides.

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In various contexts, people talk about animal farming and meat consumption using different arguments to construct and justify their (non-)acceptability. This article presents the results of an in-depth qualitative inquiry into the content of and contextual patterns in the everyday-life framing regarding this issue, performed among consumers in various settings in two extremes in the European sphere: the Netherlands and Turkey. We describe the methodological steps of collecting, coding, and organizing the variety of encountered framing topics, as well as our search for symbolic convergence in groups of consumers from different selected demographic contexts (country, urban-rural areas, gender, age, and education level). The framing of animal farming and meat consumption in everyday-life is not a simple one-issue rational display of facts; people referred to a vast range of topics in the categories knowledge, convictions, pronounced behaviour, values, norms, interests, and feelings. Looking at framing in relation to the researched demographic contexts, most patterns were found on the level of topics; symbolic convergence in lines of reasoning and composite framing was less prominent in groups based on single demographic contexts than anticipated. An explanation for this lies in the complexity of frame construction, happening in relation with multiple interdependent contextual features.

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Background: In recent years, livestock-associated methicillin-resistant Staphylococcus aureus (LA-MRSA) multi locus sequence type CC398 has spread widely in the livestock production in Europe. The rates of LA-MRSA in hospitals have been found to be largely determined by contact to and density of livestock in the area. Methods: This is a cross sectional study of the prevalence of LA-MRSA among hospital staff in a Danish hospital situated in a livestock production region. We analysed nasal swabs, air and dust samples for the presence of MRSA using PCR and mass spectrometry. Results: Of 1745 employees, 545 (31%) contributed nasal swabs. MRSA was not detected in any participant, nor was it detected in air or dust at the hospital or in houses of employees living on farms. Four percent of the participants had contact to pigs either directly or through household members. LA-MRSA was detected in two of 26 samples from animal sheds, both of them from pig farms. The participation rate was relatively low, but participants were representative for the source population with regards to animal contact and job titles. Conclusions: The study suggests a low point prevalence of LA-MRSA carriage in Danish hospital staff even in regions where livestock production is dense. Should more studies confirm our findings we see no need for additional hospital precautions towards LA-MRSA in Denmark at the moment. We think that our data might reduce potential stigmatization of hospital workers with contact to LA-MRSA positive farms at their work places and in their communities.

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