Farm Setting Research Articles

Phylogenetic Characterization of Novel Reassortant 2.3.4.4b H5N8 Highly Pathogenic Avian Influenza Viruses Isolated from Domestic Ducks in Egypt During the Winter Season 2021–2022

Avian influenza (AI) is an extremely contagious viral disease of domestic and wild birds that can spread rapidly among bird populations, inducing serious economic losses in the poultry industry. During the winter season 2021–2022, we isolated seventeen highly pathogenic avian influenza (HPAI) H5N8 viruses from outbreaks involving ducks in Egypt, occurring in both backyard and farm settings. The aim of this study was to pinpoint genetic key substitutions (KSs) that could heighten the risk of a human pandemic by influencing the virus’s virulence, replication ability, host specificity, susceptibility to drugs, or transmissibility. To understand their evolution, origin, and potential risks for a human pandemic, whole-genome sequencing and phylogenetic analysis were conducted. Our analysis identified numerous distinctive mutations in the Egyptian H5N8 viruses, suggesting potential enhancements in virulence, resistance to antiviral drugs, and facilitation of transmission in mammals. In this study, at least five genotypes within one genome constellation of H5N8 viruses were identified, raising concerns about the potential emergence of novel viruses with altered characteristics through reassortment between different genotypes and distinct groups. These findings underscore the role of ducks in the virus’s evolutionary process and emphasize the urgent need for enhanced biosecurity measures in domestic duck farms to mitigate pandemic risk.

Dietary glycerides of valerate ameliorate diarrhea and impact intestinal physiology and serum biomarkers in weaned piglets infected with enterotoxigenic Escherichia coli F18.

In the commercial swine farm setting, the post-weaning period is a critical window during which piglets are highly susceptible to infection and enterotoxigenic E. coli (ETEC)-associated diarrhea. Short chain fatty acids and their glycerides are compounds which may influence intestinal health; however, valerate is one that has not been well-characterized for its role as a dietary supplement. Therefore, the major objective of this experiment was to investigate two forms of valerate glycerides on diarrhea, intestinal physiology, and systemic immunity of weaned pigs experimentally infected with ETEC F18. Dietary treatments included a control diet and three additional diets supplemented with 0.075% monovalerin, 0.1% monovalerin, or 0.1% trivalerin, respectively. Piglets were weaned (21-24 d of age), individually housed, and experimental diets were fed throught the 28-day trial period. After a seven-day period, all piglets were inoculated on three consecutive days with 1010 CFU ETEC F18/3mL. Growth performance was monitored throughout the trial and daily diarrhea scores were recorded. Rectal swabs were collected for bacterial culture to confirm the presence or absence of β-hemolytic coliforms throughout the trial. Serum samples were collected and analyzed for inflammatory biomarkers on d 0, 3, 6, and 21 post-inoculation (PI) and untargeted metabolomics on d 6 PI. Intestinal mucosa and tissue sections were harvested from pigs sacrificed on d 7 PI for gene expression and histology analysis. All data, except for frequency of diarrhea and metabolomics, were analyzed by ANOVA using the PROC MIXED of SAS. Dietary trivalerin reduced (P < 0.05) the frequency of severe diarrhea over the entire trial period and the frequency of β-hemolytic coliforms on d 7 PI compared with control. The intestinal villus height on d 7 PI in jejunum tissue was increased (P < 0.05) in pigs fed trivalerin. The mRNA expression of TNF-α was decreased (P < 0.05) in the trivalerin group, while that of ZO1 was increased (P < 0.05) compared with control. Throughout the trial, serum TNF-α was reduced in pigs fed trivalerin compared with control. Serum metabolites, adenosine, inosine, and shikimic acid were reduced (P < 0.05) on d 6 PI in all treatment groups compared with control. In conclusion, the present results indicate supplementing dietary valerate glycerides exhibited beneficial impacts on diarrhea, inflammation, and intestinal gene expression of piglets during the post-weaning period.

Economic optimization of sustainable soil management: a Dutch case study

Soil quality is pivotal for crop productivity and the environmental quality of agricultural ecosystems. Achieving sufficient yearly income and long-term farm continuity are key goals for farmers, making sustainable soil management an economic challenge. Existing bio-economic models often inadequately address soil quality. In this study, we apply the novel FARManalytics model, which integrates chemical, physical, and biological indicators of soil quality indicator, quantitative rules on how these indicators respond to farmers’ production management over time, and an economic calculation framework that accurately calculates the contribution of production management decisions towards farm income. This is the first study applying this model on existing arable farms. FARManalytics optimizes crop rotation design, cover crops, manure and fertilizer application and crop residue management. Nine Dutch arable farms were analyzed with a high variation in farm size, soil type, and cultivated crops. First, we assessed farm differences in soil quality and farm economics. Second, we optimized production management to maximize farm income while meeting soil quality targets using farm-specific scenarios. Third, we explored the impact of recent policy measures to preserve water quality and to increase the contribution of local protein production. The results show that the case farms already perform well regarding soil quality, with 75% of the soil quality indicators above critical levels. The main soil quality bottlenecks are subsoil compaction and soil organic matter input. We show that even in front-runner farms, bio-economic modeling with FARManalytics substantially improves economic performance while increasing soil quality. We found that farm income could be increased by up to €704 ha−1 year−1 while meeting soil quality targets. Additionally, we show that to anticipate on stricter water quality regulation and market shift for protein crops, FARManalytics is able to provide alternative production management strategies that ensure the highest farm income while preserving soil quality for a set of heterogenous farms.

Alternative treatment methods for bovine mastitis: prospects and limitations (review)

Mastitis remains the most common problem of dairy industry despite the preventive measures and treatment schemes being developed. Antibacterial drugs remain first line agents for therapy of the mammary gland inflammatory diseases in animals. Taking into account the risks associated with antibiotic therapy, such as decreased drug effectiveness due to occurrence of bacterial resistant strains, food safety issues, environmental impact and restrictions on the use of antibacterial drugs in veterinary medicine, an increasing number of scientific studies are addressing new therapeutic agents that can serve as an alternative to conventional therapy. The aim of this review is to give an idea of currently available literature data on alternative methods for the prevention and treatment of mastitis in cattle that are not associated with antibiotics. In general, a significant number of in vitro studies aimed at finding new effective and safe drugs are yielding promising results. This review describes the following alternative remedies: probiotics, bacteriocins, bacteriophages, phage enzymes (endolysins), nanoparticles, plant extracts, essential oils and immunobiological agents (vaccines). Understanding the mechanisms of their action will allow recommending the best treatment option for mastitis in each specific case. These treatment methods can potentially reduce use of antibiotics and increase animal productivity, however more in vivo studies are needed to prove the effectiveness of antibiotics used directly in the conditions of farm settings.

Automatic Face Detection of Farm Images Based on an Enhanced Lightweight Deep Learning Model

In the realm of precise management, artificial intelligence has garnered significant attention and adoption, particularly within the domain of smart agriculture. In modern animal husbandry, animal face detection is conducive to individual identification, expression detection and behavior analysis of animals, and this technological advancement holds immense importance in fostering the advancement of intelligent farming practices. In order to solve the challenge of face detection caused by similar appearance features (color, texture, etc.) and no obvious feature differences between the solid-color goats and sheep in the natural environment, this research introduces a novel approach for face detection by combining the capabilities of YOLOv5 and a convolutional block attention module (CBAM). First, datasets of goats and sheep with different angles, scales and densities were constructed. Second, the basic framework of YOLOv5 was used for object detection. To address the obstacle posed by the limited presence of distinguishing features on the faces of goats and sheep, this study aims to overcome the challenge of extracting informative facial characteristics. The CBAM block was introduced to construct the YOLOv5-CBAM model to improve the feature extraction ability. Finally, 2412 images were selected and divided into training set and verification set according to 8:1. The experimental results of this dataset show that the proposed YOLOv5-CBAM model yielded remarkable results with a precision rate of 0.970, a recall rate of 0.890, a mAP@0.5 score of 0.935, an frames per second (FPS) of 140.845, and a model size of 14.680[Formula: see text]MB. In comparison to other approaches such as Faster R-CNN, SSD, YOLOv3, and YOLOv5, the proposed model demonstrated superior performance in some aspects. In addition, it excelled in both lightweight design and overall effectiveness, and it is well-suited for real-time detection of animal faces in real-world farming settings, ensuring efficient identification and monitoring of animals within practical agricultural environments.

Overview of the various methods used to assess walking ability in broiler chickens.

Walking ability is a highly important animal-based indicator of welfare in broilers that warrants monitoring across commercial production. A review of the technical and scientific literature on existing methods for assessing walking ability in broilers was conducted, and the reliability, validity and feasibility of the different methods were assessed when relevant information was available. Visual gait scoring has high validity, minimal equipment needs and is relatively quick to perform; however, this method is subjective and requires extensive training. Due to this, more objective tests, such as the latency to lie test, have been developed, although more validation of these tests is required. Other identified methods, such as obstacle courses, the rotarod test and kinetic and kinematic approaches, require considerable amounts of equipment, making them impractical for use during on-farm inspections. Automated methods that rely on movement or activity data have potential for future development but currently lack the resolution of traditional gait scoring and require equipment such as cameras or accelerometers. Objective, seemingly valid, repeatable and feasible options exist for the assessment of walking ability that would facilitate the monitoring of commercial broiler welfare; however, more validation of these methods is required, especially in commercial farm settings.

Identifying the Drivers Related to Animal Reservoirs, Environment, and Socio-Demography of Human Leptospirosis in Different Community Types of Southern Chile: An Application of Machine Learning Algorithm in One Health Perspective.

Leptospirosis is a zoonosis with global public health impact, particularly in poor socio-economic settings in tropical regions. Transmitted through urine-contaminated water or soil from rodents, dogs, and livestock, leptospirosis causes over a million clinical cases annually. Risk factors include outdoor activities, livestock production, and substandard housing that foster high densities of animal reservoirs. This One Health study in southern Chile examined Leptospira serological evidence of exposure in people from urban slums, semi-rural settings, and farm settings, using the Extreme Gradient Boosting algorithm to identify key influencing factors. In urban slums, age, shrub terrain, distance to Leptospira-positive households, and neighborhood housing density were contributing factors. Human exposure in semi-rural communities was linked to environmental factors (trees, shrubs, and lower vegetation terrain) and animal variables (Leptospira-positive dogs and rodents and proximity to Leptospira-positive households). On farms, dog counts, animal Leptospira prevalence, and proximity to Leptospira-contaminated water samples were significant drivers. The study underscores that disease dynamics vary across landscapes, with distinct drivers in each community setting. This case study demonstrates how the integration of machine learning with comprehensive cross-sectional epidemiological and geospatial data provides valuable insights into leptospirosis eco-epidemiology. These insights are crucial for informing targeted public health strategies and generating hypotheses for future research.

“This place does a lot more than produce milk”: a reflexive thematic analysis of staff experiences of supporting prison dairy workers

ABSTRACT Correctional practice has seen a proliferation of prison animal programmes over the last two decades, but scant attention has been paid to the impact of prison farm animal work. Five members of prison dairy farm staff in the UK were interviewed about their experiences of supporting prison workers, including their perceptions of its potential impact for supporting rehabilitation from offending. Reflexive thematic analysis was used to identify three overarching themes: the positive human-bovine relationship; strengthening interpersonal skills and intrapersonal benefits. This paper suggests that prison dairy work has the potential to support desistance from further offending by facilitating the transition towards a non-offending identity, cultivating and enabling compassionate behaviour, and improving wellbeing. Future research could include: an analysis of the recidivism data of prison farm livestock workers; qualitative explorations of prison livestock workers’ experiences (including those working with cows and other food/production animals); and research with a specific focus on the transferability of compassionate and caring attitudes towards self and others away from the farm setting. Findings from this exploratory study inform debate about the value and transferability of prison farm animal work and should be used to contribute to a larger body of work on this topic.

Influence of the Glutaraldehyde-based and Quaternary Ammonium Compounds Based Aerosol Disinfectant on Some of the Physiological and Zootechnical Indices in Broiler Chickens

Introduction. A huge number of disinfectants are used for aerosol disinfection of poultry premises, most of them are recommended for preventive or terminal disinfection carried out in the absence of poultry. At the same time, some products available to veterinary services are recommended for concurrent disinfection and are used in the presence of poultry, although, not all of them meet the requirements on composition, purpose and mode of use. The one of interest is a glutaraldehyde-based and quaternary ammonium compounds (QAC) based disinfectant, which has already been investigated in the scientific sources, however the influence of this agent on the physiological and zootechnical status of poultry treated therewith has not been fully studied. The aim of this research is to study the physiological status and productive qualities of the broiler chickens exposed to direct aerosol treatment with the glutaraldehydeand QAD based disinfectant.Materials and Methods. The research was conducted from October to November 2019 in the industrial poultry farm settings in a large flock of broiler chickens of the Cobb 500 cross, divided into two groups — experimental and control one. In the experimental poultry house, on the 10th day after putting the broiler chickens in the house, a preventive aerosol disinfection was carried out by means of the Nebulo cold fogger in the presence of poultry. A 0.5% solution of glutaraldehydeand QAD based disinfectant was used in a dosage of 5 ml per 1 m3 (the maximum permissible concentration exceeding the recommended one). In the control poultry house the aerosol disinfection was not carried out. The biochemical tests of blood serum (alanine aminotransferase (ALT), aspartate transaminase (AST), total bilirubin, total protein, creatinine, blood urea, blood uric acid) were made using a biochemical analyser. The zootechnical indices were also studied (livability, mortality, average daily gain, feed and water consumption during the full growing life cycle, feed conversion ratio).Results. The time history of the blood serum biochemical indices showed that the use of the glutaraldehydeand QAD based aerosol disinfectant in the specified above dosage in the presence of broiler chickens did not cause a negative, toxic effect on chickens’ metabolic indicators — all changes fluctuated slightly within the physiological norm. The zootechnical monitoring data have revealed the positive effect of the aerosol disinfection on the growth and maturing of chickens, enhancement of the livestock livability and poultry average daily gain due to the decrease of microbial burden on the growing organisms of chickens. The broiler chickens livability during the entire growing period in the experimental group was 0.84% higher than in the control group. Also, the average daily gain by the end of the growing period in the experimental group was 1.52% higher than in the control group, and the average liveweight of one bird was 2 137.7 g (experimental group) and 2 111.1 g (control group). At the same time, the feed costs in the experimental group were 0.11% less than in the control group.Discussion and Conclusion. It was found that the use of a glutaraldehydeand QAD based aerosol disinfectant, even in the maximum permissible concentration in the presence of poultry, not only didn’t have a negative effect on the physiological and zootechnical status of the broiler chickens, but, on the contrary, fostered creation of the more comfortable conditions for their keeping and growing. The obtained research results allow us to confidently recommend this method of aerosol disinfection to be used in the animal and poultry keeping premises.

Rapid discrimination of African swine fever virus nucleic acid and virions using BenzoNuclease

African swine fever (ASF) is an acute and severe infectious disease caused by the African Swine Fever Virus (ASFV). ASFV exhibits significant resistance and stability in the environment, which, coupled with its double-stranded DNA and large genome, predisposes it to contaminate laboratory samples. This characteristic can lead to false-positive results in swine farm settings even days after disinfection, as detectable through polymerase chain reaction (PCR) or real-time fluorescent quantitative PCR (qPCR) assays. To meet the demand for efficient clinical methods capable of discriminating between ASFV nucleic acid and ASFV virions, this study aims to ascertain the efficacy of the nuclease “BenzoNuclease” in distinguishing ASFV nucleic acid (ASFV-DNA) from ASFV virions. BenzoNuclease is a versatile nucleic acid enzyme with the capacity to degrade nearly all forms of DNA and RNA. Initially, this research established a highly sensitive general PCR detection method for ASFV. Subsequently, a positive control was constructed using the M13 bacteriophage to substitute for active ASFV, facilitating the development of an improved qPCR method. It is important to note that common disinfectants have the potential to deactivate BenzoNuclease. However, in an environment simulating actual production applications, residual disinfectants do not interfere with the enzymatic efficacy of BenzoNuclease, thus not affecting the detection capabilities of this method. Positive clinical samples from pig farms, upon testing with the improved method, revealed that three samples were positive, indicating the presence of viral particles, while the remaining samples were negative, indicating the presence of nucleic acids. This provides an additional new option for sample testing in pig farms.

Advanced enhancement technique for infrared images of wind turbine blades utilizing adaptive difference multi-scale top-hat transformation

Enhancing infrared images is essential for detecting wind turbine blades using infrared technology. This paper introduces an Infrared Image Enhancement Method based on Adaptive Iterative Cutoff Threshold Difference Multi-Scale Top-Hat Transformation (AICT-DMTH) to address the challenge of low image clarity in infrared detection. The method involves performing a black-white difference top-hat transformation by utilizing structural elements of varying scales for dilation and erosion. Additionally, an iterative threshold method is applied to extract more detailed image features, followed by setting a cutoff constant to determine the final scale of the structural element. The effectiveness of the proposed method is evaluated both qualitatively and quantitatively, with infrared images from laboratory and wind farm settings enhanced and compared against existing methods. The experimental results indicate that the proposed method significantly improves the clarity of infrared images, demonstrating robustness in enhancing images from various environments.

Robust deep learning based shrimp counting in an industrial farm setting

Shrimp production is one of the fastest growing sectors in the aquaculture industry. Despite extensive research in recent years, stocking densities in shrimp systems still depend on manual sampling which is neither time nor cost efficient and additionally challenges shrimp welfare. This paper compares the performance of automatic shrimp counting solutions for commercial Recirculating Aquaculture System (RAS) based farming systems, using eight Deep Learning based methods. The entire dataset includes 1379 images of shrimps in RAS farming tanks, taken at a distance using an iPhone 11 mini. These were manually annotated, with bounding boxes for every clearly visible shrimp. The dataset was partitioned into training (60 %, 828 samples), validation (20 %, 276 samples) and test (20 %, 275 samples) splits for purposes of training and evaluating the models. The present work demonstrates that state-of-the-art object detection models outperform manual counting and achieve high performance across the entire production range and at various circumstances known to be challenging for object detection (dim light, overlapping and small animals, various acquisition devices and image resolutions and camera distance to object). Highest counting performance was obtained with models based on YOLOv5m6 and Faster R–CNN (as opposed to neural network autoencoder architecture to estimate a density map). The best model generalizes well on an independent test set and even shows promising performance when tested with different taxa. The model performs best at densities below 200 shrimps per image with an overall error of 5.97 %. It is assumed that this performance can be improved by increasing the dataset size, especially with images at high shrimp stocking density, and it is strongly believed that a performance below the 5 % error threshold is close to being achieved, which will allow for deployment of the model in an industrial setting.

Animal-assisted interventions in a farm setting: Integrating global perspectives, social work, and ecological approaches for overall welfare

Abstract The focus of the article is the integration of global perspectives, social work, and ecological approaches in animal-assisted interventions in a farm setting, presenting a model that addresses animal, human, and environmental welfare. The authors use a literature review and case scenario illustrating this model that suggests that welfare can be achieved through the simultaneous consideration of the following realms: Global initiatives of One Health and United Nations Sustainable Development Goals, the Five Domains of Animal Welfare, social work theory and practice, and regenerative agriculture. Future research should include the practice of this model in a fashion that allows collecting and studying the outcomes for animals, humans, and the environment involved.

Wind farm structural response and wake dynamics for an evolving stable boundary layer: computational and experimental comparisons

Abstract. The wind turbine design process requires performing thousands of simulations for a wide range of inflow and control conditions, which necessitates computationally efficient yet time-accurate models, especially when considering wind farm settings. To this end, FAST.Farm is a dynamic-wake-meandering-based mid-fidelity engineering tool developed by the National Renewable Energy Laboratory targeted at accurately and efficiently predicting wind turbine power production and structural loading in wind farm settings, including wake interactions between turbines. This work is an extension of a study that addressed constructing a diurnal cycle evolution based on experimental data (Quon, 2024). Here, this inflow is used to validate the turbine structural and wake-meandering response between experimental data, FAST.Farm simulation results, and high-fidelity large-eddy simulation results from the coupled Simulator fOr Wind Farm Applications (SOWFA)–OpenFAST tool. The validation occurs within the nocturnal stable boundary layer when corresponding meteorological and turbine data are available. To this end, we compared the load results from FAST.Farm and SOWFA–OpenFAST to multi-turbine measurements from a subset of a full-scale wind farm. Computational predictions of blade-root and tower-base bending loads are compared to 10 min statistics of strain gauge measurements during 3.5 h of the evolving stable boundary layer, generally with good agreement. This time period coincided with an active wake-steering campaign of an upstream turbine, resulting in time-varying yaw positions of all turbines. Wake meandering was also compared between the computational solutions, generally with excellent agreement. Simulations were based on a high-fidelity precursor constructed from inflow measurements and using state-of-the-art mesoscale-to-microscale coupling.

Dairy farmer income, working time, and antimicrobial use under different dry cow therapy protocols

Mastitis is one of the most common diseases of dairy cattle. It has a high impact on farm economy, farmers' working time, and antimicrobial usage (AMU). Selective dry cow therapy (SDCT) is an effective means of reducing AMU without negatively affecting udder health. The objective of our study was to evaluate the impact of SDCT implementation on farmer's income, working time, and AMU, using a bioeconomic model. A stochastic dairy simulation model (DairyHealthSim) based on a weekly model was used to simulate herd dynamics, reproduction, milk production, culling decisions, health outcomes, and the management of health events. A specific module was developed for the simulation of quarter-level intramammary infection (IMI) acquisition and elimination during the lactation and dry-off periods, and 25 different farm settings were defined to represent herds with various udder health situations. We then defined 20 scenarios of SDCT by combining both the use of different thresholds of somatic cell count and milk bacteriology for treatment allocation and the use of internal teat sealant (ITS). All SDCT protocols had a low impact on farmer's income, and we identified some protocols with a positive farm gross margin (up to 15.83 CA$/dried- cow). We also found that adding an ITS to all cows led to greater economic gain. The application of SDCT had a low impact on farmers' working time, except when milk bacteriology was used for decision-making. Antimicrobial treatment to all cows above 200,000 cells/mL at last control, with the use of ITS on all cows, seems a good choice in most dairy farms. These findings could be used to convince farmers to adopt this strategy at dry-off.

Barriers and facilitators to adopting safe farm-machine related behaviors: A focus group study exploring older farmers’ perspectives

Background: Tractors and quad bikes pose a significant risk of fatal injuries among farmers, particularly affecting older farmers. This study aimed to explore the barriers and facilitators to the adoption of machine related safety behaviors among older farmers in Irish farm settings. Method: Four focus groups were conducted via Zoom in February 2021. Nineteen Irish farmers from four farm types participated. The discussions were audio-recorded, transcribed verbatim, and analyzed using an inductive, reflexive thematic analysis approach. The themes identified were then mapped to the COM-B (Capability-Opportunity-Motivation) model, providing a systematic theoretical basis for designing a future intervention to reduce machine-related accidents. Results: The analysis identified five inductive themes that encompassed both barriers and facilitators in farm safety practices: (1) Capability to manage competing responsibilities; (2) Characteristics of the farm and its work environment; (3) Availability and affordability of resources; (4) Prevailing sociocultural opportunities; and (5) Perceived likelihood and cost-benefit analysis in safety decision-making. These themes captured the complex interplay of capability, opportunity, and motivation in farmers’ decision-making processes. The study also revealed limitations in existing interventions, such as voluntary guidelines and educational methods, in effectively addressing these barriers. Conclusions: Farmers’ abilities (capability), prevailing sociocultural factors, resource availability (opportunity), and their perceived consequences and benefits (motivation) affect how safely they work with machines. The study emphasizes the need for comprehensive, theory-driven approaches that consider the interplay of capability, opportunity, and motivational factors that may support or impede machine safety. Understanding the challenges faced by Irish farmers highlights potential strategies for safety intervention, and these strategies should be co-designed with farmers and attentive to the local context. Practical applications: The study provides a template for understanding farmers’ perspectives using the COM-B model. The findings can inform the development of theoretically informed intervention strategies based on the Behavior Change Wheel framework.

Synergizing Nanomaterials and Artificial Intelligence in Advanced Optical Biosensors for Precision Antimicrobial Resistance Diagnosis.

Antimicrobial resistance (AMR) poses a critical global One Health concern, ensuing from unintentional and continuous exposure to antibiotics, as well as challenges in accurate contagion diagnostics. Addressing AMR requires a strategic approach that emphasizes early stage prevention through screening in clinical, environmental, farming, and livestock settings to identify nonvulnerable antimicrobial agents and the associated genes. Conventional AMR diagnostics, like antibiotic susceptibility testing, possess drawbacks, including high costs, time-consuming processes, and significant manpower requirements, underscoring the need for intelligent, prompt, and on-site diagnostic techniques. Nanoenabled artificial intelligence (AI)-supported smart optical biosensors present a potential solution by facilitating rapid point-of-care AMR detection with real-time, sensitive, and portable capabilities. This Review comprehensively explores various types of optical nanobiosensors, such as surface plasmon resonance sensors, whispering-gallery mode sensors, optical coherence tomography, interference reflection imaging sensors, surface-enhanced Raman spectroscopy, fluorescence spectroscopy, microring resonance sensors, and optical tweezer biosensors, for AMR diagnostics. By harnessing the unique advantages of these nanoenabled smart biosensors, a revolutionary paradigm shift in AMR diagnostics can be achieved, characterized by rapid results, high sensitivity, portability, and integration with Internet-of-Things (IoT) technologies. Moreover, nanoenabled optical biosensors enable personalized monitoring and on-site detection, significantly reducing turnaround time and eliminating the human resources needed for sample preservation and transportation. Their potential for holistic environmental surveillance further enhances monitoring capabilities in diverse settings, leading to improved modern-age healthcare practices and more effective management of antimicrobial treatments. Embracing these advanced diagnostic tools promises to bolster global healthcare capacity to combat AMR and safeguard One Health.

Putting gender upfront in One Health AMR research and implementation strategies

Abstract Despite a gendered approach being increasingly applied across global health challenges, this has been a notable oversight in antimicrobial resistance (AMR) research. Failing to consider the complexity of human behaviours and roles in healthcare, animal production, and environmental settings compromises programmatic effectiveness and sustainability, while risking entrenching or widening existing social disparities. Research demonstrates how gender norms influence health-related behaviours across various sectors, from accessing healthcare to livestock rearing, with strong implications for antimicrobial stewardship and zoonotic disease transmission. The sparse literature connecting and investigating gender, equity and AMR – especially in a One Health context – hampers our ability to comprehensively address this global issue. The responsibility is however shared. This commentary advocates for funders to propel the inclusion of gender and equity-focused perspectives in AMR research and to set expectations in research landscapes, thus fostering a more equitable global health landscape and strengthening strategies against AMR. We outline our rationale and recommendations for other funders to support an ecosystem in AMR that supports gender and equity as a common aspect of AMR research and not an exception. One Health impact statement While AMR is increasingly recognised as a One Health challenge encompassing humans, animals, plants and the environment, sector-specific and cross-sectoral solutions needed to address AMR too often lack the multidisciplinary approach needed for a holistic response to this challenge. To date, AMR research has been largely biomedical, with limited social investigation including on gender and its interplay with factors that drive AMR in different settings such as healthcare, community or farm settings. Intentionally integrating gender analysis that informs AMR research design and implementation across sectors, including with supportive research funding opportunities, will help build the evidence base on how research projects and public programs should integrate and address gender disparities in AMR across the One Health spectrum. This is needed to ensure contextually relevant gender-informed solutions with sustainable impact.

Experimental cultivation of Neopyropia kitoi (Bangiales, Rhodophyta) for the establishment of a new marine crop in nori cultivation: A field experiment assessment of growth, pigments and free amino acids

To elucidate whether Neoporphyra kitoi strain OHN-1 has superior characteristics as a new marine crop in the nori farm setting, we compared OHN-1 and Neopyropia yezoensis f. narawaensis strain U-51 cultivated under identical conditions during the early nori season. We assessed the growth and the contents of photosynthetic pigments and free amino acids (FAAs), which affect the quality and taste of nori, between OHN-1 and U-51. The blade length of OHN-1 was found to be significantly longer than that of U-51, and OHN-1 exhibited high-yield characteristics. Notably, the blade thickness of OHN-1 was only slightly greater than that of U-51, despite the significant differences between the two strains. The contents of pigments and FAAs related to the taste were also higher in OHN-1 than in U-51. These results obtained with a N. kitoi cultivar demonstrated that N. kitoi has superior growth and quality characteristics in the nori farm setting. Thus, we concluded that N. kitoi could be used as a new marine crop for nori cultivation.

Productive, Environmental and Economic Effects of Organic and Conventional Farms—Case Study from Poland

Due to the changes in the agricultural sector, there is a demand among policymakers, administration, advisors and farmers for comparisons of the economic efficiency of organic versus conventional farms and their environmental impact. The authors of the paper hypothesised that in some conditions, organic farms can reach similar productive and economic results as conventional farms and, at the same time, achieve better environmental effects. The aim of the research was to compare the production, environmental and economic effects of selected organic and conventional farms from eastern Poland (mixed, crop production, animal production). The basis for the comparative analysis was the data from 12 farms obtained using the questionnaire method (direct interview) from the years 2020 to 2021. The yields of cereals in the tested organic farms were about one-third lower than the average obtained on conventional farms. Total organic crop production in cereal units per ha was 43% of conventional production. Balances of NPK indicated surpluses or deficiency, which suggested that in both systems nutrient management should be improved and optimised. The compared groups of organic farms generally had higher economic efficiency—both with and without subsidies—than conventional farms, despite the fact that the latter obtained significantly higher incomes. Gross farm income on conventional farms was higher than on organic farms by 28%, but conventional farms had higher direct costs by 332% than organic ones. As a result, the economic efficiency of agricultural production of organic farms was higher by 59% than conventional farms. The lower level of inputs incurred on organic farms was the main factor determining their high economic efficiency. Organic farms pursuing an intensive model of agricultural production (milk production), but also with specialisation in crop production, proved to be the most economically efficient. Despite diversified production, reducing the income risk, the mixed production, both organic and conventional, in the studied set of farms turned out to be the least profitable, indicating the need to support it. It is suggested that moderate specialisation increases the efficiency of management in organic farming.