Search Results (5,346)

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

The utilization of unconventional feed resources offers a sustainable strategy to mitigate feed shortages particularly in tropical and subtropical regions where access to conventional feeds is often limited. Among these, water hyacinth (Eichhornia crassipes) is one of the world’s most aggressive aquatic weeds, which has drawn attention due to its dual role as a problematic invasive species and a potential livestock feed. This plant reduces water quality, contributes to biodiversity loss and causes economic damage in farming systems. At the same time, its high capacity for nutrient absorption makes it a viable source of protein and energy for ruminants when properly harvested and processed into forms such as hay, dried leaves, and silage. However, its utilization requires caution, as the plant can accumulate toxins and heavy metals from polluted water, which may harm animal health if unprocessed. This review focuses on the potential of water hyacinth to improve ruminant growth performance, nutrient digestibility and rumen fermentation. Including water hyacinth in ruminant diet safely can possibly improve animal productivity, contribute to sustainable weed management and also provide a practical strategy to alleviate feed shortage in dry seasons, thereby encouraging resilience and sustainable ruminant production. Full article

Lameness associated with bacterial chondronecrosis with osteomyelitis (BCO) continues to be an important topic of great interest to global poultry production. Caused by bacterial infection of susceptible necrotic bone tissue, the disease severely affects animal health, welfare, and productivity, leading to significant economic losses annually. In recent years, the Kazakhstan poultry industry has enjoyed significant investment and strong growth, with goals of self-sufficiency within the decade. However, there remains a significant knowledge gap in poultry research in the nation, especially regarding the topic of BCO lameness. As such, this study aims to provide a preliminary evaluation of BCO lesion prevalence in different geographical regions of the country―namely Abai, Almaty, and Akmola. In each region, about 200 broilers from local poultry farms were procured, humanely euthanized, and necropsied to evaluate prevalence of femoral and tibial lesions commonly associated with BCO lameness. On average, most broilers had no damage to femoral head (78.17%) followed by femoral head separation (FHS, 11.94%), while the tibial head saw ubiquitous degrees of damage ranging from severe (71.42%) to observable (23.06%). These findings signify potential underlying issues connected to BCO lameness that will necessitate early management and intervention measures to prevent future spread of the disease. Full article

In intensive animal production, the overuse of antibiotics has exacerbated bacterial antimicrobial resistance and environmental pollution. Together with gut microbiota dysbiosis and recurrent disease outbreaks, these challenges severely constrain the sector’s high-quality development. Quorum sensing (QS), a cell-density-dependent bacterial communication mechanism, can be modulated through agents that specifically inhibit or activate QS circuitry to regulate microbial community functions. Such QS modulators possess notable advantages, such as environmental benignity and high target specificity, and thus offer innovative strategies to decrease antibiotic reliance, enhance production efficiency, and reduce environmental emissions. This review examines QS modulators sourced from plants, microorganisms, animals, and synthetic processes, while highlighting key challenges such as environmental interference, resistance development, high costs, and the lack of standardized biosafety evaluations. Future research should focus on enhancing specificity, stability, affordability, and safety, with an emphasis on rational design, synergistic systems, improved manufacturing processes, and multi-target modulators. This review may provide a theoretical basis for translating QS-regulation technologies into farm-level applications, thereby advancing sustainable animal production and antibiotic-free husbandry. Full article

This study explicitly assesses how crop and livestock production, along with real labor productivity, affect greenhouse gas emissions in agriculture across the European Union (EU), considering both per capita and total emissions. Using annual Eurostat data for EU Member States from 2008 to 2024, the research applies multiple regression models and a multivariate General Linear Model (GLM) to evaluate structural relationships, complemented by Holt exponential smoothing and ARIMA models to analyze temporal dynamics and generate forecasts. The empirical results indicate that crop and livestock production have a statistically significant positive effect on emissions, while real labor productivity has a significant negative impact. The models explain over 92% of the variation in total emissions and over 95% of the variation in per capita emissions, confirming strong explanatory power. Forecasts show continued growth in agricultural output but a declining trend in per capita emissions, primarily driven by productivity improvements. These findings demonstrate that improvements in labor efficiency and technological progress can partially offset the environmental pressures associated with increased agricultural production. The study concludes that achieving climate-neutral agriculture in the EU is feasible through sustained productivity gains and innovation-driven transformation. Full article

Background/Objectives: Combination of antiparasitic drugs with different mechanisms of action has been suggested as an effective strategy to delay the development of parasite resistance. Considering the need to understand the pharmacological basis of drug combinations, the current study evaluated the potential pharmacokinetic (PK) interactions and the clinical efficacy (pharmacodynamic response) occurring after the subcutaneous administration of ivermectin (IVM) and levamisole (LEV), administered either as single treatments or concurrently to different groups of parasitized calves on three commercial farms (A, B and C). Methods: Forty-five (45) male calves naturally infected with gastrointestinal nematodes were randomly allocated into three groups (n = 15): IVM, treated with IVM by subcutaneous injection (0.2 mg/kg); LEV, treated subcutaneously with LEV (8 mg/kg); IVM + LEV, simultaneously treated with IVM and LEV (two subcutaneous injections at the same dose rates). Seven animals from each treated group (farm C) were randomly selected to perform the PK study. Drug concentrations were measured by HPLC. The therapeutic response (efficacy) was determined at 14 days after treatment by the fecal egg reduction test. Results: The mean area under the concentration vs time curve (AUC) for IVM obtained after administration of IVM alone (274 ± 65.1 ng.d/mL) was similar to that obtained when IVM was co-administered with LEV (295 ± 111 ng.d/mL). Likewise, mean LEV AUC values were similar after LEV administration alone (8.90 ± 2.69 µg.h/mL) or combined with IVM (9.11 ± 1.82 µg.h/mL). No adverse PK interactions were observed after the combined treatment, with similar PK parameters (p > 0.05) obtained between the single-drug and the combination-based strategies. On farm A, the overall fecal egg reductions were 38% (IVM), 99% (LEV) and 100% (IVM + LEV). While Cooperia spp. and Haemonchus spp. showed reduced susceptibility to IVM treatment, LEV demonstrated high efficacy against both genera, with only a minimal proportion of Haemonchus spp. remaining after treatment. Similarly, total fecal egg reductions were 42% (IVM), 99% (LEV) and 100% (IVM + LEV) on farm B, and 54% (IVM), 99% (LEV) and 100% (IVM + LEV) on farm C. On those farms, IVM was ineffective against Cooperia spp. and/or Haemonchus spp., while LEV failed to control Ostertagia spp. Remarkably, the combination of both molecules was the only treatment that achieved 100% efficacy against all nematode genera (Cooperia, Ostertagia, Haemonchus and Oesophagostomum spp.). Conclusions: Based on the described PK and pharmacodynamic (PD) assessments, the IVM + LEV combination appears to be a promising pharmacological option for controlling resistant gastrointestinal nematodes in cattle, with the additional potential to delay the progression of nematode anthelmintic resistance. Overall, the study provides original and robust pharmacokinetic and efficacy data that contribute to the optimization of parasite control strategies in cattle. This drug combination strategy may enhance treatment efficacy and contribute to improved parasite control in cattle production systems. 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

On-farm welfare assessment of equines is a challenge given the large diversity of management practices, especially in terms of housing and activities. In our study, we tested time budget measures as a complementary tool to more conventional welfare indicators (e.g., stereotypic behaviours, ear position while foraging, neck shape). We observed 174 horses living in eight facilities (in their home environment) for which data on management practices and welfare were available. Time budget was assessed using the scan sampling method (1 min scan sampling over 30 min; 33 scans), while welfare assessment was based on classical measures. The study was performed in two parts: The first part consisted of validating time budget as a correlate of welfare state, which was performed at the individual level and according to the context of observation. In the second part, the data were analysed at the farm level by averaging data from all horses in the same facility. The results showed that the time spent feeding, in exploratory walking and observation behaviour were correlated with indicators of positive welfare and/or good practices in contrast to the time spent standing immobile resting or in fixed attention and/or in negative social interactions. Time budget varied greatly between facilities, reflecting welfare state and management (feeding, working) practices. This study shows that, provided that observations are made at different time periods when animals are calm, three sessions of 10 min. could give a good account of the local «atmosphere». These findings bring new insights into both methodological approaches and the significance of behaviours and, in particular, the importance of precisely defining and measuring the types of attention as a window into an animal’s internal state. Full article

Organic farming can increase the sustainability of farming and support biodiversity and soil health. The aim of this work was to conduct a meta-analysis to evaluate the effects of organic farming on selected soil biotic and abiotic properties in Central Europe and to quantify the possible influences of sampling characteristics, soil type and crop husbandry on these effects. Overall, we integrated data from 59 peer-reviewed studies into our analysis, including 1365 pairwise comparisons (log-response ratios; LRR) that covered carbon and nutrient contents, soil organic matter (SOM), soil microbiology and physicochemical properties. Organic farming increased the levels of microbial activity and this effect decreased with soil depth, whereas generally positive but less robust effects on microbial biomass quantity and soil pH were observed across analyses. In addition, a decrease in cation exchange capacity was observed, but this result should be interpreted with caution due to the limited number of studies. The LRR for the bulk density was affected by the sampling season, while soil type influenced the LRR for the nitrogen contents (with negative values for Chernozems). Tillage modified the LRR for pH and bulk density, as did animal fertilizer for microbial biomass quantity. These differences can be attributed to the differences in pairwise comparisons in these moderators rather than to the use of different farming systems themselves. The lack of effects of organic farming for some categories (SOM, carbon, phosphorus, other macroelements and microbial biomass quality) could be attributed to opposing input–decomposition processes, high variability in site conditions across studies or large scatter in the data. Our results highlight the importance of considering soil characteristics and moderators related to crop husbandry when evaluating the effects of farming systems on soil health. 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

This online survey aimed to elaborate on the salary, working conditions, and job satisfaction of employed veterinarians in Germany. The focus was on factors influencing salaries, violations of German laws, and comparisons between employees of owner- and corporate-managed practices. Answers of up to 1184 veterinarians were analyzed, representing 6% of employed veterinarians. The hourly salary increased by around 19% compared to a study in 2020, but remained significantly lower than in comparable professions and did not rise as much as the national average over the last five years. A multifactorial linear model showed that working experience, additional qualifications, leadership role for other veterinarians, section (pets, equines, farm animals, or non-curative), and gender significantly influenced the salary. The adjusted gender pay gap was about 7%. Employees of corporations earned significantly more than veterinarians being employed in owner-managed practices, but not when salary was adjusted for overtime. Moreover, employees of corporations had significantly lower job satisfaction. Requirements of the German Working Hours Act were regularly not complied with, as e.g., around 40% of respondents reported not being able to take their legally required break at least once per week. Results indicate that, despite improvements, there is still a need to address working conditions to retain veterinarians in the profession. Full article

Ketosis is a prevalent metabolic disorder in early-lactation dairy cows, significantly affecting animal health, milk production, and farm profitability. Developing accurate and non-invasive methods for early risk detection is therefore of critical importance. In this study, a hybrid optimization framework integrating an Improved Whale Migration Algorithm (IWMA) with a Light Gradient Boosting Machine (LightGBM) is proposed to predict ketosis risk based on the milk fat-to-protein ratio (F/P) using Dairy Herd Improvement (DHI) records. The proposed IWMA enhances optimization performance through cubic chaotic initialization, elite opposition-based learning, and a Cauchy–Gaussian hybrid mutation strategy, enabling improved global exploration and convergence stability. A dataset comprising 25,155 DHI records collected from multiple commercial dairy farms over seven months was used for model development and evaluation. Experimental results demonstrate that the IWMA–LightGBM model achieves a classification accuracy of 0.8997 and a mean squared error of 0.289, consistently outperforming six benchmark optimization methods. Feature analysis identifies Herd Within Index (WHI), Energy Corrected Milk (ECM), Days in Milk (DIM), Milk Urea Nitrogen, and Foremilk as key predictors associated with metabolic risk. Overall, the proposed approach provides a robust and effective non-invasive solution for early-stage metabolic risk screening at the herd level, offering practical value for precision dairy management. It should be noted that the model is intended for risk assessment rather than clinical diagnosis of ketosis. Full article

This review outlines the health risks associated with excessive dietary intake of n-6 polyunsaturated fatty acids (PUFAs), particularly linoleic acid (C18:2n-6, LA), which is highly prevalent in the Western diet. It proposes a targeted nutritional strategy to reduce n-6 PUFA overconsumption and increase n-3 PUFA intake, aiming to restore a healthier fatty acid balance and counteract imbalance-induced pathogenetic consequences. The conceptual framework builds on the foundational insights of William E. M. Lands regarding PUFA-driven eicosanoid imbalance. It extends these principles by integrating contemporary models of impaired adipose tissue expandability, functional lipodystrophy, insulin resistance, and ectopic lipid deposition as central mechanisms of lipotoxicity and as unifying drivers of the modern organo-metabolic spectrum of non-communicable diseases. The proposed nutritional strategy combines dietary modifications—such as avoiding seed oils and processed foods as well as products from industrialized animal farming, while prioritizing fatty fish and/or algae-derived supplements—with lifestyle interventions and ongoing laboratory monitoring. This approach is designed to lower chronic disease risk and improve overall metabolic resilience. In addition, Western-diet-related socioeconomic issues and ecological burdens are addressed. The objective of this review is to evaluate the biochemical and clinical relevance of HUFA imbalance and to assess the potential of dietary modulation of n-6 and n-3 PUFAs as a strategy to restore metabolic homeostasis. However, further research is required to corroborate the available findings before broader implementation of the proposed strategy can be recommended. 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

This research paper examines the environmental impact of natural and synthetic fur coats, focusing exclusively on the processing and manufacturing stages. Using one coat weighing approximately 5 kg as the functional unit, a comparative Life Cycle Assessment (LCA) is conducted from raw material processing to final garment production, explicitly excluding animal farming. The analysis includes key processes such as cleaning, tanning, dyeing, and sewing for natural fur, and polymer production, fabric formation, dyeing, and finishing for synthetic fur. Data from international academic literature (Google Scholar and Scopus) are used to evaluate CO₂ emissions, energy and water consumption, chemical inputs, and waste generation. Results indicate that synthetic fur production is energy-intensive but requires relatively low water use, whereas natural fur processing involves high water consumption and chemical treatments, resulting in significantly higher emissions—often reaching hundreds to thousands of kg CO₂e per coat. The study further investigates the role of embedded IoT systems in improving efficiency within tanneries and textile manufacturing. Real-time monitoring and automated dosing systems can reduce emissions and chemical use by approximately 10–20%. Case studies of a smart tannery and an IoT-enabled synthetic fur production line illustrate potential implementation pathways. Although such optimizations can reduce environmental impacts, the findings clearly show that natural fur processing remains considerably more carbon-intensive than synthetic alternatives. This research highlights the importance of integrating digital technologies into industrial processes and suggests directions for future work based on real-world operational data. Full article