Search Results (362)

Antimicrobial resistance (AMR) in poultry-associated Escherichia coli (E. coli) is a persistent One Health concern, particularly when ESBL/pAmpC determinants co-occur with resistance to multiple antimicrobial classes. Between March and October 2024, we investigated commensal E. coli from three interconnected compartments of the poultry production chain in Bosnia and Herzegovina (parent-breeder flocks, commercial broiler farms, hatchery-associated material). A total of 333 samples were examined, and 99 E. coli isolates were recovered (29.7%). Phenotypic characterization included ESBL confirmation, disk diffusion susceptibility testing, and EUVSEC broth microdilution. Targeted real-time PCR assays were used to screen key ESBL/pAmpC-associated genes and selected carbapenemase and plasmid-mediated colistin resistance targets within the targeted panel. ESBL phenotypes were detected in 52/99 isolates (52.5%), and multidrug resistance was highly prevalent across compartments (93/99; 93.9%). ESBL/pAmpC-associated genes were detected in 91/99 isolates (91.9%), with bla_TEM predominating. Gene pattern analysis indicated that bla_TEM occurred most frequently as a single determinant and as part of the predominant multi-gene combinations, most notably bla_TEM + bla_CMY and bla_TEM + bla_CTX-M, while bla_SHV was sporadic. Carbapenemase genes (bla_KPC, bla_NDM, bla_GES, bla_OXA-48) and mcr-1 to mcr-9 were not detected. Overall, our findings indicate a substantial ESBL/MDR burden throughout the poultry production chain, supporting the need for strengthening antimicrobial stewardship and biosecurity measures across both farms and hatcheries. Full article

The European Union (EU) Green Deal (EGD) aims to significantly transform and modernize the EU economy, while at the same time envisioning significant changes in agricultural production, especially in livestock farming. Generally, EU Member States implement specific measures that contribute to the achievement of various EGD objectives. Most often, these are part of the national strategies of the EU Common Agricultural Policy. At the same time, it is important to identify the available scientific information on measures that contribute to the achievement of the EGD goals and their various impacts. Usually, each individual measure or practice is aimed at achieving one of the ESD goals, for example, reducing GHG emissions, but in practice, these create multiple side effects that can promote or hinder the achievement of other sustainability goals. This study focuses on the livestock sector and outlines key areas where intervention must occur: feeding, housing, grassland/pasture management, manure management, breeding and genetics—these factors interact and contribute to the achievement of EGD targets. At the same time, this research takes a holistic view of the EGD demands on livestock. In this study, the authors use pictograms and a color-coding system that broadens the scope of impact communication. It translates complex, scientific data into a format that is accessible and easily understood by a wider audience. The results of this study reveal that systematic research is needed to examine livestock farming measures that could change agricultural policies in the long term—from supporting existing measures to creating appropriate sustainable farming systems. Full article

The introduction of the article presents the state of renewable energy development in Poland and statistical information on its dynamics in the context of sustainable development, highlighting both the positive aspects of this situation and the potential risks to the national power system. These risks stem from the inherent instability of renewable energy generation and the seasonal variability of production. The main part of the article provides a comprehensive statistical analysis of time series data (wind energy generation and solar energy generation) aimed at identifying the appropriate input variables for forecasting models. In addition to the two time series of electricity generation, other exogenous variables and feature engineering techniques were incorporated. In the forecasting section, short-term forecasts of energy generation in the national power system from wind farms and solar power plants were developed. The forecasts for both types of renewable energy sources (RESs) were conducted separately and then integrated into a single time series to assess which forecasting approach is more effective. A detailed analysis was carried out to determine the optimal hyperparameters for individual machine learning models. Subsequently, an ensemble model was developed, integrating multiple single models. The article concludes with final insights and practical recommendations regarding the selection of preferred models and input variables that ensure the highest forecast accuracy. Additionally, potential future developments of the models and further research in this field are discussed in the context of sustainable development. Full article

Farmland quality protection is an important measure to implement the strategy of “storing grain in the land” and a vital part of promoting ecological agriculture development. This study focuses on the main agents of farmland quality protection, farmers, with a sample of 1013 households from the rice-growing areas of Jiangxi Province, which is one of the major rice-growing provinces in southern China. We used an Ordered Probit model, a mediation effect model, and a moderation effect model to analyze the influence and mechanism of farmers’ social capital and structure on their farmland quality protection behavior. The result shows that: (1) Social capital significantly promotes the farmers’ farmland quality protection behaviors. The promoting effect of bonding social capital is greater than that of linking social capital. These conclusions remain robust after the endogeneity issue has been addressed and robustness tests have been conducted; (2) Ecological cognition plays a mediating role in this relation, while Internet use exerts a significant positive moderating effect; (3) The effect of social capital is greater for full-time farming households than for part-time farming households, and more significant for risk-neutral and risk-seeking farmers than for risk-averse farmers. Accordingly, this study proposes recommendations, including fostering farmers’ social capital, improving their ecological cognition, promoting the penetration and use of the Internet, vigorously cultivating new agricultural business entities, and expanding agricultural insurance coverage. Full article

This article shows the possibilities of using passive air conditioning methods in real conditions during the fattening of chickens in the summer with high outdoor temperatures. On a farm in three halls with the same interior equipment, the effect of different constructions of walls and roofs of buildings on the internal thermal and humidity microclimate was investigated. Microclimatic conditions and chickens’ performance were compared in two identical lightweight panel halls (49,000 chickens each) with light aluminum roof sheets (L* = 81.4 ± 0.4), and against conditions and results in a massive brick house (31,200 chickens) with a dark eternit roof (L* = 35.7 ± 3.5). The dark color of the roof surfaces and parts of the walls of the brick house accelerated the increase in air temperature in the house. The air temperature was 0.7 to 2 K higher in the house with darker surfaces, which was also reflected in a higher THI. The duration of chickens’ stay in conditions of higher heat stress (THI above 28.3) was 1.84 times longer in this house than in houses with light surfaces, which had the effect of increasing water consumption by 30%. The effect of heat accumulation of the brick structure on the attenuation of high temperature was not significant. Full article

Behavior recognition and multi-object tracking of dairy cows in free-style cow barn environments play a crucial role in monitoring their health status and serve as an essential means for intelligent scientific farming. This study proposes an efficient embedded algorithm, MTW-BYTE, for dairy cow behavior recognition and multi-object tracking. It addresses challenges in free-style cow barn environments, including the impact of lighting variations and common occlusions on behavior recognition, as well as trajectory interruptions and identity ID switching during multi-object tracking. First, the MTW-YOLO cow behavior recognition model is constructed based on the YOLOv11n object detection algorithm. Replacing parts of the backbone network and neck network with MANet and introducing the Task Dynamic Align Detection Head (TDADH). The CIoU loss function of YOLOv11n is replaced with the WIoU loss. The improved model not only effectively handles variations in lighting conditions but also addresses common occlusion issues in cows, enhancing multi-scale behavior recognition capabilities and improving overall detection performance. The improved MTW-YOLO algorithm improves Precision, Recall, mAP50 and F1 score by 4.5%, 0.1%, 1.6% and 2.2%, respectively, compared to the original YOLOv11n model. Second, the ByteTrack multi-object tracking algorithm is enhanced by designing a dynamic buffer and re-detection mechanism to address cow trajectory interruptions and identity ID switching. The MTW-YOLO algorithm is cascaded with the improved ByteTrack to form the multi-target tracking algorithm MTW-BYTE. Compared with the original multi-target tracking algorithm YOLOv11n-ByteTrack (a combination of YOLOv11n and the original ByteTrack), this algorithm improves HOTA by 1.1%, MOTA by 3.6%, MOTP by 0.2%, and IDF1 by 1.9%, reduces the number of ID changes by 11, and achieves a frame rate of 43.11 FPS, which can meet the requirements of multi-target tracking of dairy cows in free-style cow barn environments. Finally, to verify the model’s applicability in real-world scenarios, the MTW-BYTE algorithm is deployed on an NVIDIA Jetson AGX Orin edge device. Based on real-time monitoring of cow behavior on the edge device, the pure inference time for a single frame is 16.62 ms, achieving an FPS of 29.95, demonstrating efficient and stable real-time behavior detection and tracking. The ability of MTW-BYTE to be deployed on edge devices to identify and continuously track cow behavior in various scenarios provides hardware feasibility verification and algorithmic support for the subsequent deployment of intelligent monitoring systems in free-style cow barn environments. Full article

In 2020, a dairy farm in northwest Germany reported several cows with severe respiratory disease, fever, and reduced milk production. Multiple direct and indirect diagnostic methods were used to identify the cause of the disease. However, the pathogens detected could not be correlated with the severity of the clinical symptoms, so further diagnostic steps were taken. Blood and nasal swab samples were examined using next-generation sequencing (NGS) as part of a metagenomic analysis. For the first time in Germany, Hepacivirus bovis genotype 2 was detected. Real-time RT-PCR assays confirmed the presence of BovHepV genotypes 1 and 2 in the herd between 2020 and 2023. Analyses of complete and partial genome sequences demonstrated the presence of different virus variants in the herd over several years. In addition, the sequence data indicated that cattle can be reinfected with viruses belonging either to different BovHepV subtypes or to the same subtype. Although no direct link could be established between the detection of bovine hepaciviruses and the observed clinical symptoms, the PCR and sequence data obtained provide valuable insights into the epidemiology and pathogenesis of BovHepV infections. Full article

To contribute to the United Nations’ 17 Sustainable Development Goals (SDGs), this study focuses on improving two specific goals—SDG 2 (Zero Hunger) and SDG 12 (Responsible Consumption and Production)—by examining how off-farm employment affects agricultural green total factor productivity (GTFP) in China, a key link between rural socio-economic transformation and agricultural sustainability. The results show that: First, the part-time operation of farmers significantly reduces the green total factor productivity, and the negative impact is more pronounced for off-farm employment households with higher non-agricultural income shares. It mainly stems from the redundant input of land and machinery elements. Second, the effect showed obvious heterogeneous effects at different stages of family development and land management scale. In addition, the scale effect of continuous agricultural production services and the technological synergy effect driven by the deepening of agricultural division of labor are the key to improving green total factor productivity and alleviating the negative effects of part-time operations. In summary, promoting sustainable agricultural practices requires the government to further deepen the reform of the land property rights system and optimize the agricultural socialization service system to ensure both food security and environmental sustainability. Full article

Food security and supply networks are becoming an ever-increasing concern requiring innovative practices to deal with the contributing factors. Controlled Environment Agriculture (CEA) offers an alternative to conventional cropping systems for increasing the yields of certain produce types. Crop yields (tons/hectare/year) in CEA are reported to range between 10 and 100 times higher than open-field agriculture, and the water use in CEA is typically about 4.5–16% of that from conventional farms per unit mass of produce. However, these systems can be energy intensive due to temperature regulation requirements, compromising their environmental and economic viability. Energy is the second largest overhead cost in CEA with carbon footprints being reported as 5.6–16.7 times and 2.3–3.3 times greater than that of open-field agriculture for indoor vertical farms and greenhouses, respectively. This can be offset, in part, by reducing the reliance on cooling systems. However, high temperature stress negatively impacts crops at morphological, cellular, metabolic, and molecular levels, reducing produce quality and quantity. Biostimulants are additives which can benefit plant growth through ameliorating stress. This review considers recent research on the effects of heat stress on a variety of crops commonly grown in CEA and the categories of biostimulants that have known thermoprotective qualities. Seaweed extracts, chitin/chitosan, protein hydrolysates and amino acids, inorganic compounds, beneficial microorganisms, and humic substances are explored, alongside the known benefits, limitations, and knowledge gaps. Full article

In recent years, animal welfare has become a high priority in livestock production systems owing to the pressure to balance environmental sustainability, productivity, and ethics as demand continues to grow. This review presents the latest advances in small ruminant welfare, with emphasis on the effects of climate change, the main new innovative managerial and husbandry methods, and the use of precision livestock farming (PLF) technologies. In the first part, this review will examine how climate change is already re-shaping environmental and physiological conditions for farmed sheep and goats, with rising heat stress and negative impacts on both productive and reproductive performance. Secondly, more recent advances in small ruminant management will be presented, including improved housing systems, nutritional strategies, and behavioral monitoring, aimed at enhancing animal resilience and performance. Finally, particular focus will be given to the use of PLF tools for assessing milk quality and monitoring animal welfare. Evidence suggests that real-time monitoring technologies and sensor systems can accurately capture physiological and production parameters and provide an early sign of stress or health issues. Overall, the findings suggest that an integrated approach, combining climate adaptation strategies, welfare management, and the integration of precision technologies can serve as a key driver toward more ethical, sustainable, and resilient livestock production systems. Full article

As an important part of the urban ecological environment, urban green space plays a crucial and irreplaceable role in improving air quality, promoting sustainable development, and enhancing residents’ quality of life. This study takes Beijing’s urban green space as the research object. Based on Landsat series satellite remote sensing images, the land use distribution of Beijing is obtained through supervised classification. Combined with data such as PM_2.5 concentration and wind speed, the dry deposition efficiency of PM_2.5 is quantitatively analyzed. The results show that: (1) Beijing’s urban green space has significant advantages in PM_2.5 dry deposition. In terms of dry deposition flux, the order of annual average deposition of different land types is: forest land > farm land > grassland > impervious surface > water body = unutilized land. Among them, forest land has the best dry deposition effect, with an annual average dry deposition of 1.13 g/m², which is 188.41 times that of impervious surface; cultivated land and grassland are 0.22 g/m² and 0.19 g/m² respectively, which are 37.13 times and 32.34 times that of impervious surface. (2) From 2000 to 2020, the PM_2.5 removal rate of green space continued to rise, but the reduction amount showed a trend of first increasing and then decreasing. There are significant seasonal differences. The reduction amount is the highest in autumn (reaching 449.90 tons in October), followed by summer, spring, and winter (the lowest in August, at 190.27 tons). (3) In terms of spatial distribution, the high-value areas of dry deposition are concentrated in the suburbs, showing a “southwest-northeast” axial distribution, while the low-value areas are mainly located in the outer suburbs, reflecting the imbalance of green space layout and the regional differences in PM_2.5 reduction. Combined with the current situation of green space in Beijing, the study puts forward targeted optimization suggestions, providing theoretical support and scientific basis for the construction of Beijing as a “garden city”. Full article

Water quality is an essential part of maintaining a healthy environment for fish farming. The quality of the water is related to a few of the chemical and biological characteristics of water. The conventional evaluation methods of the water quality are often time-consuming and may overlook complex interdependencies among multiple indicators. This study has proposed a robust machine learning framework for aquaculture water quality classification by integrating the Honey Badger Algorithm (HBA) with the XGBoost classifier. The framework enhances classification accuracy and incorporates explainability through SHAP and DiCE, thereby providing both predictive performance and transparency for practical water quality management. For reliability, the dataset has been randomly shuffled, and a custom 5-fold cross-validation strategy has been applied. Later, through the metaheuristic-based HBA, feature selections and hyperparameter tuning have been performed to improve and increase the prediction accuracy. The highest accuracy of 98.45% has been achieved by a particular fold, whereas the average accuracy is 98.05% across all folds, indicating the model’s stability. SHAP analysis reveals Ammonia, Nitrite, DO, Turbidity, BOD, Temperature, pH, and CO₂ as the topmost water quality indicators. Finally, the DiCE analysis has analyzed that Temperature, Turbidity, DO, BOD, CO₂, pH, Ammonia, and Nitrite are more influential parameters of water quality. Full article

Background: In animals, staphylococci constitute a significant part of the normal skin microbiota and mucous membranes. There is limited information available on staphylococci isolated from healthy horses. These skin-associated bacteria can be easily transferred between animals and horse riders via direct contact. Patients undergoing hippotherapy (i.e., medical or therapeutic sessions with horses) are especially at risk of being colonized by horse skin-associated bacteria. However, it remains unclear whether equine skin is colonized by antimicrobial-resistant (AMR) opportunistic pathogens, which may be of concern to human health. Methods: We cultivate staphylococci from samples collected from healthy, non-vet-visiting horses who live on private farms in a rural area. In total, 61 strains were isolated and identified at the species level using matrix-assisted laser desorption ionization time of flight mass spectrometry (MALDI-TOF MS). Results: The diversity of Staphylococcus species in the equine skin microbiota was relatively high and, with the exception of S. aureus, all the other recovered strains were coagulase-negative staphylococci (CoNS). In total, eleven different staphylococcal species were identified: S. xylosus, S. sciuri, S. vitulinus, S. equorum, S. succinus, S. nepalensis, S. lentus, S. fleurettii, S. aureus, S. chromogenes, and S. simulans. Conclusions: These results indicate that healthy equine skin is colonized by opportunistic pathogens that can be causative agents of infections that are also severe in humans. The resistance among the isolated strains was observed in eight antimicrobials of the total tested and 36% (22/61) of the isolates were resistant to at least one antimicrobial. However, their resistance to critically important antibiotics used in human medicine was low. Seven isolates (11.5%; 7/61) were classified as multidrug-resistant (MDR). S. aureus (1/61) showed MDR and was methicillin-resistant. The S. aureus isolate contained genes conferring resistance to antibiotics, i.e., β-lactams (blaZ, mecA), aminoglycosides (aac(6′)/aph(2″)), and macrolide–lincosamide–streptogramin B (erm(B), erm(C), and lun(A/B)). Also CoNS harbored genes conferring resistance to β-lactams (blaZ), aminoglycosides (aac(6′)/aph(2″), ant(4′)-Ia), MLSB (erm(B), erm(C), lun(A/B)), and tetracycline (tetK, tetL). Full article

Understanding the diversity of avocado production systems is crucial for developing effective agricultural policies and extension strategies. This study examined the Colombian avocado variety “Lorena” in the Colombian lowlands of Casanare through spatial typology analysis to inform sustainable agricultural development strategies. We employed spatial autoregressive modeling and clustering techniques to analyze data from 45 production systems, revealing heterogeneity despite small-scale operations with productivity (2.9 ton ha⁻¹) below regional (8 ton ha⁻¹) and national averages (11.03 ton ha⁻¹). Five distinct typologies emerged: transitional traditional (n = 15), intensive technical management (n = 4), experience-based traditional (n = 5), balanced management (n = 10), and comprehensive technical systems (n = 11). In contrast to conventional assumptions about economies of scale, productivity was not primarily determined by farm size, as smaller intensive technical management systems achieved the highest yields (3375 kg) despite having the smallest size (162.50 trees), followed by experience-based traditional systems (3280 kg). The spatial autoregressive model effectively captured spatial dependence in yield patterns, demonstrating the importance of geographic context in agricultural system analysis. Technology/practice adoption patterns varied markedly, with high adoption of established practices (>90%) but low foliar analysis adoption (17.78%). High organic fertilization adoption (93.33%) reflected a commitment to environmental sustainability but may partially explain productivity gaps, highlighting trade-offs between sustainability and short-term yield optimization. Socioeconomic analysis revealed characteristics of part-time farming systems, with 91.11% of producers having additional income sources and 95.56% using hired labor, suggesting evolved livelihood strategies that may enhance resilience. These findings challenge one-size-fits-all development approaches and demonstrate the need for tailored, spatially targeted interventions that account for specific production system characteristics, multiple pathways to sustainable intensification, and the complex interactions between productivity, sustainability, and socioeconomic factors in smallholder agriculture. Full article

Under the strategic goal of agricultural modernization, agricultural socialization services have become an important means of enhancing agricultural efficiency and guaranteeing food security. Based on microdata from 3811 farm households in seven provinces, this paper integrates labor force structural characteristics with digital literacy to construct a comprehensive analytical framework and empirically examines their effects on the degree of access to agricultural socialized services (DASS) through ordered logit model and moderated effects models. The results show that labor force characteristics significantly affect DASS, and the higher the degree of feminization, aging, and part-time employment, the higher the degree of access to services; digital literacy as a whole significantly improves DASS for farm households and shows heterogeneous moderating effects under different labor force characteristics. Therefore, this paper suggests formulating differentiated socialized service promotion strategies, deepening the digitalization of agricultural services, strengthening the digital technology training of rural laborers in various ways, enhancing DASS, effectively improving the efficiency of agricultural production, and supporting the dual goals of food security and rural revitalization. Full article