The poultry industry is a critical source of affordable protein worldwide; however, it faces continuous threats from various poultry diseases that significantly impact public health, economic stability, and food security. Knowledge of and examination of the transmission routes, risk factors, and environmental survival characteristics of the most important pathogens affecting poultry populations, as well as the importance of strict biosecurity, are pivotal. Transmission routes are split into direct and vector-borne pathways, and indirect ways, which include infections via contaminated surfaces and vector-borne pathways, including insects and rodents. Avian influenza virus and Newcastle disease virus spread through respiratory droplets, and their transmission risk increases with increasing stocking density. While other pathogens (e.g., infectious bursal disease virus and Salmonella spp.), to persist long-term in the environments, for example, feed and litter, increasing the probability to persist long-term in the environments, for example, feed and litter, increasing the probability of infection. The long-term resilience of pathogens in multiple pathogens in various environmental conditions highlights the role of biosecurity, sanitation, and hygiene controls in preventing disease outbreaks. High stocking density in production systems, suboptimal ventilation, and inadequate biosecurity controls further increase transmission risks. This paper summarizes important disease transmissions and reinforces the need for strict biosecurity protocols and routine health monitoring to prevent the spread of pathogens within and beyond poultry facilities. These strategies can support safe poultry production, address growing global demand, and ensure food safety and public health.

Antimicrobial resistance (AMR) is a critical global health concern, with the food production sector, particularly poultry production, playing a significant role in the spread of resistant bacteria. This study aims to identify and analyze the key factors influencing antimicrobial use (AMU) and thereby/or the spread of AMR within the poultry production chain, spanning from farm to consumer. A scoping literature review was conducted to investigate factors influencing AMU and/or the emergence and dissemination of AMR in poultry production. Knowledge synthesis was conducted from 69 relevant publications covering data from 41 countries. A total of 327 factors were identified from the various studies. Most factors (221) were related to knowledge, attitudes, and practices (KAPs) concerning farms. The other significant group, with 57 factors, involved intentional misuse or poor practices related to antimicrobials. The findings highlight the complexity of factors influencing AMR in poultry production, including financial pressures on farmers and varied training effectiveness. Integrated and tailored interventions are essential. Drug/feed sellers’ practices require stronger regulations, while economic motivations and policy gaps must be addressed. Systematic management of these interactions and further research on environmental and genetic influences are crucial for effective strategies to combat this pressing public health challenge.

This research aimed to determine the association between biosecurity and animal welfare measures in large-scale fattening turkey farms in Hungary. Large-scale farms raising male fattening turkeys across Hungary were contacted, with 24 agreeing to participate. The Biocheck.UGent questionnaire was used to evaluate biosecurity measures, and Animal Welfare Indicators (AWIN) protocol was used for welfare assessment. The association between biosecurity and welfare indicators was investigated by correlation testing and prediction accuracy using random forest classification. The areas of biosecurity that were shown to be closely linked to the welfare index were the organization of the farm (farm management, supply of materials), the control and hygienic measures implemented between farm structures, cleaning and disinfection measures on the farm, and control and hygienic measures implemented in the case of farm workers and visitors upon entrance and exit. The study highlighted the link between biosecurity and animal welfare in turkey production, concluding that enhanced biosecurity measures correlate with better welfare outcomes and emphasizing the need for comprehensive and well-implemented biosecurity protocols.

Environmental conditions are increasingly recognized as important contributors to the emergence and spread of antimicrobial resistance (AMR), yet early detection of high-risk situations remains difficult. This study developed a data-driven framework to identify anomalous environmental profiles associated with potential AMR risk. Using an unsupervised anomaly detection method (Isolation Forest) applied to multivariate indicators—including pesticide use, land use change, precipitation, and crop type—we detected unusual environmental patterns without prior AMR data. The anomaly detection analysis highlighted pesticide use, population density, land use change, and fertilizer application as the dominant environmental factors, together explaining the largest share of variation in anomaly scores (each contributing around one-quarter to one-third of the model’s decisions). In the subset of anomalous cases, fertilizer and pesticide intensity exerted the strongest negative impact, confirming their role as key drivers of atypical environmental profiles. Extreme precipitation and crop-specific production patterns also emerged as influential in certain cases. These results show that our interpretable framework can both rank global drivers and reveal context-dependent risks, thereby enabling the development of early-warning strategies for AMR surveillance.

Background: Avian influenza (AI), caused by orthomyxoviruses, is a globally significant disease affecting avian and non-avian species. It manifests in two variants, according to the two biovariants of the virus differentiated as highly pathogenic avian influenza (HPAI) and low pathogenic avian influenza (LPAI) strains, both of which compromise animal welfare, reduce productivity, and cause substantial economic loss. The zoonotic potential of HPAI strains, particularly the currently dominant clade 2.3.4.4b, raises concerns about public health and epidemic risks. This review assesses the results of current vaccine trials targeting HPAI clade 2.3.4.4b, emphasizing these studies because most outbreak strains in domestic poultry currently belong to this dominant clade. Methods: Multiple scientific databases comprised reports of research trials on vaccine efficacy against HPAI clade 2.3.4.4b. The Boolean term “Clade 2.3.4.4b AND vaccine” was entered into the following databases: PubMed, PubAg, Scopus, Cochrane Library, and ScienceDirect. Results: The resulting papers were analyzed. Studies revealed that antigenic similarity between vaccine and field strains enhances protective efficacy (PE), reduces viral shedding, and improves hemagglutination inhibition titers. While multivalent vaccines showed potential, results were inconsistent and varied depending on strain compatibility. Single-dose vaccines may provide sufficient PE for poultry, though ducks and geese often require multiple doses, and long-term PE is yet unknown. It was discovered that vector vaccines can provide appropriate PE against clade 2.3.4.4.b. Conclusions: Further analysis is needed as their effects may be short-lived, and subsequent doses may be required. Limited research exists on the long-term efficacy of these vaccines and their effectiveness in many avian species. Addressing these gaps is crucial for optimizing vaccination strategies. A re-evaluation of vaccination strategies is recommended but essential to implement adequate biosecurity measures on in poultry farms. This review synthesizes current evidence and may assist veterinarians and authorities in deciding whether to apply or license vaccines to reduce economic losses caused by AI.

Forecasting antimicrobial resistance (AMR) is critical for public health, yet most models neglect the interconnected nature of agricultural systems. Focusing on ciprofloxacin resistance in Campylobacter jejuni—a leading foodborne pathogen in poultry—this study aims to develop a probabilistic framework for identifying high-risk environmental conditions. We employed a graph-based machine learning and Bayesian approach, integrating and discretizing data from international databases. An exploratory classification with XGBoost and SVC was followed by core analysis using a Generalized Naive Bayes (GNB) model for feature selection and a Bayesian Network (BN) to uncover conditional dependencies. The GNB model identified pesticides, land use, and precipitation as key features. The BN revealed a complex web of interactions, showing that resistance probability is highly context-dependent. Precipitation was a critical effect modifier; for example, expanded land use correlated with an 18.3% increase in resistance probability during dry conditions but a 73.7% decrease during wet periods. Scenarios with low and high precipitation were associated with high risk, indicating multiple environmental pathways. Our results demonstrate that Bayesian networks can effectively model the complex, non-linear relationships driving AMR. Ciprofloxacin resistance emerges from system-wide interactions rather than isolated factors. This approach provides a valuable framework for generating hypotheses and supports the development of early-warning systems for targeted antimicrobial stewardship in poultry production.