Search Results (25,581)

Neonatal calf diarrhea (NCD) represents a major cause of economic loss in dairy cattle herds worldwide. The condition is primarily associated with several key pathogens, including enterotoxigenic Escherichia coli (ETEC), viral agents such as bovine rotavirus (BRV) and bovine coronavirus (BCoV), and the protozoan Cryptosporidium parvum. This study aimed to assess the prevalence of NCD-associated pathogens in Italian dairy farms over the period 2020–2022. Among the 598 farms affected by NCD and included in the investigation, ETEC strains were detected in 17.2% of cases. The prevalence of BRV, BCoV, and Cryptosporidium spp. was 22.2%, 20.2%, and 32.3%, respectively. Co-infections were also frequently observed and are considered to significantly exacerbate the clinical severity of the disease. Ongoing surveillance of NCD pathogens is essential to generate reliable and updated epidemiological data, which are critical for guiding effective control and prevention strategies. Full article

Livestock plays a crucial role in the global food system, not only as an important source of nutrients but also as a means of economic and social well-being. It constitutes a critical parameter of agricultural production in Mediterranean countries, with the majority of farms still having a relatively small herd size and depending largely on family labor. The purpose of this study is to record and evaluate the perceptions of livestock farmers in the Region of Western Macedonia, Greece (which represents a typical paradigm of an agricultural region), regarding the future prospects and the actions taken to ensure the sustainability of their farms. The research is based on a survey carried out from May to October, 2024, on ruminant farmers. Selective breeding and crossbreeding with higher-productivity breeds are some of the genetic improvements that are generally applied to increase productivity and were, therefore, investigated in this study. Through gradual crossbreeding, farmers attempt to improve the composition of their initial herds by incorporating high-productivity traits—although without officially participating in any recognized improvement program. This increases the risk of extinction for indigenous breeds, which are abandoned for use by the farmers. Our results also showed that most livestock farms derive from inheritances, with many livestock farmers practicing grazing mainly in mountainous areas and still rearing indigenous breeds. From the farmers’ point of view, more information and education regarding market conditions are needed. Furthermore, the sustainability of farms largely depends on subsidies, which are crucial due to difficulties in economic viability, particularly in mountainous areas. Encouraging the support of market differentiation and public awareness for the nutritional value of products derived from local breeds may serve as a promising agrobiodiversity conservation strategy. Full article

Satellite monitoring of agricultural crops plays a crucial role in ensuring food security and in the sustainable management of agricultural resources, particularly in regions dominated by rainfed farming, such as the Turkestan region of Kazakhstan. Many satellite monitoring tasks rely on remote identification of different types of cultivated crops. In developing the proposed method, we accounted for the temporal characteristics of crop growth and development in various climatic zones of rainfed agriculture, analyzed the dynamics of the Normalized Difference Vegetation Index (NDVI) together with ground-based data, and identified effective time periods and patterns for successful crop recognition. This study aims to develop and comparatively assess two methods for the automatic identification of cultivated crops in rainfed zones using Sentinel-2 satellite data for the years 2018 and 2022. The first method is based on detailed classification of pre-digitized field boundaries, providing high accuracy in satellite-based mapping. The second method represents a fully automated approach applied to large rainfed areas, emphasizing operational efficiency and scalability. The results obtained from both methods were validated against official national statistics, ground-based field surveys, and farm-level data. The findings indicate that the field-boundary-based method delivers significantly higher accuracy (average accuracy of 91.1%). While the automated rainfed-zone approach demonstrates lower accuracy (78%), it still produces acceptable results for large-scale monitoring, confirming its suitability for rapid assessment of sown areas. This research highlights the trade-off between the accuracy achieved through detailed field boundary digitization and the efficiency provided by an automated, scalable approach, offering valuable tools for agricultural production management. Full article

This study evaluates biogas production through the anaerobic digestion of food waste (FW), cow dung (CD), and green waste (GW), with the primary objective of determining the efficacy of co-digesting these organic wastes commonly generated by households and small farms in Central Queensland, Australia. The investigation focuses on both experimental and technoeconomic aspects to support the development of accessible and sustainable energy solutions. A batch anaerobic digestion process was employed using a 1 L jacketed glass digester, simulating small-scale conditions, while technoeconomic feasibility was projected onto a 500 L digester operated without temperature control, reflecting realistic constraints for decentralized rural or residential systems. Three feedstock mixtures (100% FW, 50:50 FW:CD, and 50:25:25 FW:CD:GW) were tested to determine their impact on biogas yield and methane concentration. Experiments were conducted over 14 days, during which biogas production and methane content were monitored. The results showed that FW alone produced the highest biogas volume, but with a low methane concentration of 25%. Co-digestion with CD and GW enhanced methane quality, achieving a methane yield of 48% while stabilizing the digestion process. A technoeconomic analysis was conducted based on the experimental results to estimate the viability of a 500 L biodigester for small-scale use. The evaluation considered costs, benefits, and financial metrics, including Net Present Value (NPV), Internal Rate of Return (IRR), and Dynamic Payback Period (DPP). The biodigester demonstrated strong economic potential, with an NPV of AUD 2834, an IRR of 13.5%, and a payback period of 3.2 years. This study highlights the significance of optimizing feedstock composition and integrating economic assessments with experimental findings to support the adoption of biogas systems as a sustainable energy solution for small-scale, off-grid, or rural applications. Full article

With the accelerating global energy transition, wind power has become a core pillar of renewable energy systems. However, its inherent intermittency and volatility pose significant challenges to the safe, stable, and economical operation of power grids—making ultra-short-term wind power prediction a critical technical link in optimizing grid scheduling and promoting large-scale wind power integration. Current forecasting techniques are plagued by problems like the inadequate representation of features, the poor separation of features, and the challenging clarity of deep learning models. This study introduces a method for the prediction of wind energy using spatiotemporal contrastive learning, employing seasonal trend decomposition to encapsulate the diverse characteristics of time series. A contrastive learning framework and a feature disentanglement loss function are employed to effectively decouple spatiotemporal features. Data on geographical positions are integrated to simulate spatial correlations, and a convolutional network of spatiotemporal graphs, integrated with a multi-head attention system, is crafted to improve the clarity. The proposed method is validated using operational data from two actual wind farms in Northwestern China. The research indicates that, compared with typical baselines (e.g., STGCN), this method reduces the RMSE by up to 38.47% and the MAE by up to 44.71% for ultra-short-term wind power prediction, markedly enhancing the prediction precision and offering a more efficient way to forecast wind power. Full article

Individual recognition of Hu sheep is a core requirement for precision livestock management, significantly improving breeding efficiency and fine management. However, traditional machine vision methods face challenges such as high annotation time costs, the inability to quickly annotate new sheep, and the need for manual intervention and retraining. To address these issues, this study proposes a solution that integrates automatic annotation and transfer learning, developing a sheep face recognition algorithm that adapts to complex farming environments and can quickly learn the characteristics of new Hu sheep individuals. First, through multi-view video collection and data augmentation, a dataset consisting of 82 Hu sheep and a total of 6055 images was created. Additionally, a sheep face detection and automatic annotation algorithm was designed, reducing the annotation time per image to 0.014 min compared to traditional manual annotation. Next, the YOLOv10n-CF-Lite model is proposed, which improved the recognition precision of Hu sheep faces to 92.3%, and the mAP@0.5 to 96.2%. To enhance the model’s adaptability and generalization ability for new sheep, transfer learning was applied to transfer the YOLOv10n-CF-Lite model trained on the source domain (82 Hu sheep) to the target domain (10 new Hu sheep). The recognition precision in the target domain increased from 91.2% to 94.9%, and the mAP@0.5 improved from 96.3% to 97%. Additionally, the model’s convergence speed was improved, reducing the number of training epochs required for fitting from 43 to 14. In summary, the Hu sheep face recognition algorithm proposed in this study improves annotation efficiency, recognition precision, and convergence speed through automatic annotation and transfer learning. It can quickly adapt to the characteristics of new sheep individuals, providing an efficient and reliable technical solution for the intelligent management of livestock. Full article

A perforated aquaculture vessel represents an environmentally sustainable approach to fish farming, leveraging seawater circulation to optimize water quality and enhance fish health and growth. The perforations on the side of the fish tank significantly influence its hydrodynamic characteristics. This study investigated the influence of pore parameters on the perforated fishing tank with various pore designs, such as the asymmetric distribution of the opening in depth, windward, and leeward directions. A numerical study was conducted using STAR-CCM+ to analyze the perforated tank under beam wave conditions. This study aimed to analyze the effects of pore location, opening ratio, and asymmetric distribution on the hydrodynamic performance and flow characteristics within aquaculture tanks. The results demonstrated that an asymmetric pore distribution on the windward and leeward sides of the vessel had a notable impact on the roll motion and the flow velocity in the vicinity of the pores. The findings also indicated that the effects of pore distribution were more significant than those of opening ratio, especially regarding asymmetry. The results revealed that higher flow velocities occurred under a smaller opening ratio. Modifying pore structure parameters on the windward and leeward sides can alter the local flow field. Full article

Magnaporthe oryzae-induced rice blast remains a critical threat to sustainable rice farming, causing extensive losses in many rice-producing regions worldwide. Due to increasing concerns about pesticide overuse and its impact on the environment and human health, alternative control methods are being actively explored. Nanotechnology has recently gained attention as a potential tool for sustainable disease management. This review summarises current progress in the use of nanomaterials—including metal and biopolymer nanoparticles, nanoemulsions, targeted delivery systems, and biosensors—for the detection and control of rice blast. Studies have reported that nanomaterials can reduce disease severity by up to 70% and improve rice yield by 10–20% under field or greenhouse conditions. The mode of action, effectiveness under field conditions, and possible integration into integrated pest management (IPM) programs are discussed. The selection of literature followed the PRISMA-P framework to ensure a systematic and transparent review process. Challenges such as biosafety, environmental risks, and regulatory issues are also addressed, with emphasis on green synthesis methods and the need for field validation before practical application. Full article

Edwardsiella tarda is a zoonotic facultative intracellular bacterium whose impact on farm-raised amphibians is still poorly defined. We recovered seven strains from American bullfrogs (Aquarana catesbeiana) on four farms in Guangdong, China, and combined field surveillance with molecular and pathological investigations. Phylogenetic analysis of 16S rRNA and rpoB sequences confirmed species identity. Quantitative PCR of 192 apparently healthy frogs revealed intestinal carriage at every farm, with prevalence ranging from 39 to 77 percent and bacterial loads of 10⁵–10⁶ CFU/mL, indicating widespread subclinical colonisation. Virulence profiling demonstrated a conserved core gene set (gadB, mukF, citC, fimA, ompA) and accessory variation confined to the flagellar gene fliC. The strains resisted trimethoprim, ampicillin, and tetracyclines, yet remained susceptible to third generation cephalosporins, carbapenems, and most aminoglycosides. Infection trials showed that although very high inocula caused acute fatalities, an inoculum of 10⁸ CFU/mL was sufficient to induce persistent enteritis characterised by suppressed tight junction proteins, elevated cytokine expression, and marked intestinal damage. These findings demonstrate that E. tarda circulates silently in bullfrog culture, carries an amphibian adapted virulence profile and still responds to key antimicrobials, providing a baseline for risk assessment, surveillance, and targeted control in amphibian aquaculture. Full article

The use of exogenous enzymes in the nutrition of dairy cows is an innovative and efficient strategy to maximize productivity and milk quality, with positive applications in the economic and environmental aspects of dairy farming. Therefore, the objective of this study was to evaluate whether the addition of a blend of exogenous enzymes to the diet of lactating Jersey cows has a positive effect on productive performance, milk quality, animal health, ruminal environment, and digestibility. Twenty-one primiparous Jersey cows, with 210 days in lactation (DL), were used. The exogenous enzymes used were blends containing mainly protease, in addition to cellulase, xylanase, and beta-glucanase. The animals were divided into three groups with seven replicates per group (each animal being the experimental unit), as follows: Control (T-0), basal diet without enzyme addition; Treatment (T-80), animals fed enzymes in the diet at a daily dose of 80 mg per kg of dry matter (DM); Treatment (T-160), animals fed enzymes in the diet at a daily dose of 160 mg per kg of DM. The study lasted 84 days, during which higher milk production was observed in the treated groups (T-80 and T-160) compared to the control group (p = 0.04). When calculating feed efficiency from days 1 to 84, greater efficiency was observed in both groups that received the blend compared to the control (p = 0.05). In the centesimal composition of the milk, it was observed that the percentage of protein in the milk of the T-160 group was higher compared to the control group (p = 0.03). The effect of the enzymes was verified for butyric (p = 0.05) and palmitic (p = 0.05) fatty acids. We also observed the effect of the enzyme blend on the amount of volatile fatty acids (VFAs), which were higher in the ruminal fluid of cows that received the enzymes (p = 0.01). Cows that consumed enzymes showed a higher apparent digestibility coefficient of crude protein (p = 0.01). In vitro, the main result is related to lower gas production in 24 and 48 h at T-160. We concluded that the use of a blend of exogenous enzymes in the diet of lactating Jersey cows was able to increase milk production in these animals, resulting in greater feed efficiency and also an increase in milk protein content, positively modulating the fatty acid profile in the rumen and improving the apparent digestibility of nutrients. Full article

The mechanisms through which different types of exogenous carbon enhance the soil organic carbon sequestration rate (C_seq), nitrogen use efficiency (NUE), and corn yield (CY) in rainfed farmland on the Loess Plateau remain inadequately elucidated. This study established a four-year fixed-site experiment in the context of organic materials to increase soil organic carbon storage and enhance corn yield in the dry-farmed areas of the mountainous southern Ningxia region. The research investigates the effects of adding different types of exogenous carbon materials on C_seq, NUE, and CY. The soil type at the experimental base is loessial soil (Huangmian soil), with a soil pH of 8.28 and a baseline organic carbon content of 8.20 g kg⁻¹. The main crop cultivated in this area is corn. The experimental treatments were as follows: (i) N, no fertilization; (ii) CK, 100% nitrogen, phosphorus, and potassium fertilizers; (iii) C, 50%CK + corn straw (pulverized); (iv) M, 50%CK + fermented cow manure; (v) C/M, 50%CK + fermented cow manure + corn straw (1:1). The results show that compared with the CK treatment, the C_seq of C, M, and C/M treatments increased by 488.89%, 355.56%, and 527.78%, respectively. Compared with the CK treatment, the NUE of C, M, and C/M treatments increased by 15.04%, 7.70%, and 12.20%, respectively. Compared with the CK treatment, the CY under the C, M, and C/M treatments were increased by 7.91%, 19.10%, and 11.59%, respectively. The linear regression results show that the C_seq had a significant positive effect on CY (R² = 0.37) and NUE, R² = 0.39) (p < 0.0001). The TOPSIS (technique for order preference by similarity to ideal solution) evaluation results indicate that the C/M treatment was the optimal measure for achieving increased corn yield while enhancing Cseq and NUE. Therefore, incorporating a 1:1 mixture of corn straw and cattle manure in rainfed farmland in the mountainous area of southern Ningxia may be the best strategy to improve C_seq and NUE. Full article

Mechanised agricultural operations are often performed individually, under minimal supervision and across a wide range of unfavourable working conditions, resulting in a complex mixture of hazards and external stressors that severely affect safety conditions. Socio-technical and environmental constraints significantly affect safety culture and require continuous performance adjustments to overcome timing pressures, resource limitations, and unstable weather conditions. This study introduces a FRAM-based safety culture model that embeds the thoroughness-efficiency trade-off (ETTO) in four distinct operational modes that adhere to specific safety cultures, namely, thoroughness, risk awareness, compliance, and efficiency. This model has been instantiated for mechanised ploughing: foreground task functions were coupled with background functions that represent socio-technical constraints and environmental variability, while severity classes for potential incidents were derived from the US OSHA accident database. The framework was also supported by a semi-quantitative Resonance Index based on severity and coupling strength, the Total Resonance Index (TRI), to assess how variability propagates in foreground functions and to identify hot-spot functions where small adjustments can escalate into high resonance and hazardous conditions. Results showed that the negative effects on functional resonance generated by safety detriment on TRI observed between compliance and effective working modes were three times larger than the drift between risk awareness and compliance, demonstrating that efficiency comes with a much higher cost than keeping safety at compliance levels. Extending the proposed approach with quantitative assessments could further support the management of socio-technical and environmental drivers in mechanised farming, strengthening the role of safety as a competitive asset for enhancing resilience and service quality. Full article

Understanding how environmental conditions affect broiler comfort across different seasons is crucial for enhancing welfare in commercial poultry production. This study aimed to identify the relationship between housing environment, litter conditions, and broiler discomfort at different growth stages using data collected from two flocks reared during winter and summer. Environmental variables (temperature, humidity, ammonia, pH, and CO₂) and broiler responses were recorded and analyzed weekly. Discomfort was defined as a binary variable based on threshold deviations in temperature and air quality. Non-parametric statistical tests and a Random Forest model were employed to explore associations and predict comfort status. Results showed that discomfort was significantly higher during winter, particularly in weeks 1 and 6, likely due to thermal instability and rising ammonia levels. Summer flocks exhibited more stable comfort profiles. The predictive model achieved a high test accuracy (97.1%) and identified broiler weight, ammonia, and temperature as the strongest predictors of discomfort. Weekly discomfort patterns and feature importance analyses revealed critical intervention points and variables. These findings provide actionable insights for automating welfare monitoring in commercial broiler production, offering valuable information for season-specific management strategies and demonstrating the potential for integrating predictive models into automated welfare monitoring systems to support precision livestock farming. Full article

Developments in agriculture is reshaping the agribusiness landscape, altering farms’ bargaining power and strategic positioning within supply chains. These dynamics raise important questions about how financial strength influences farmers’ preferences for different components of business relationships with input suppliers. The primary objective of this study is to examine the relationship between a farm’s financial power and the importance it assigns to the behavioral dimension in such relationships. To address this objective, we employ a two-stage research design. In the first stage, qualitative interviews with farmers were conducted to identify the key attributes contributing to relationship value, encompassing economic, strategic, and behavioral dimensions. In the second stage, a quantitative survey was administered to 249 farmers, supplemented with financial data from the Farm Accountancy Data Network (FADN). The Maximum Difference Scaling (MaxDiff) method was applied to assess the relative importance of these attributes, followed by statistical analysis linking the observed preferences to a composite indicator of financial power. The results indicate that financially stronger farms place greater emphasis on economic factors while attaching less importance to behavioral aspects. Among less financially powerful farms, two distinct patterns emerge: one characterized by opportunistic, price-oriented behavior, and another reflecting a relational orientation that values trust, communication, and long-term cooperation alongside economic conditions. These findings contribute to a better understanding of business relationships in agribusiness by explaining how financial power shapes the trade-off between economic and behavioral components. Full article

Pesticides are essential for modern agriculture but leave harmful residues that threaten human health and ecosystems. This paper reviews key pesticide detection technologies, including chromatography and mass spectrometry, spectroscopic methods, biosensing (aptamer/enzyme sensors), and emerging technologies (nanomaterials, AI). Chromatography-mass spectrometry remains the gold standard for lab-based precision, while spectroscopic techniques enable non-destructive, multi-component analysis. Biosensors offer portable, real-time field detection with high specificity. Emerging innovations, such as nano-enhanced sensors and AI-driven data analysis, are improving sensitivity and efficiency. Despite progress, challenges persist in sensitivity, cost, and operational complexity. Future research should focus on biomimetic materials for specificity, femtogram-level nano-enhanced detection, microfluidic “sample-to-result” systems, and cost-effective smart manufacturing. Addressing these gaps will strengthen food safety from farm to table while protecting ecological balance. This overview aids researchers in method selection, supports regulatory optimization, and evaluates sustainable pest control strategies. Full article