Background and Aim: Antibiotic-resistant infections are one of the leading threats to public health globally. Indiscriminate use of antibiotics in food animal production is an important driver of resistance, particularly among foodborne pathogens such as non-typhoidal Salmonella enterica (NTS). While there has been extensive research on antimicrobial-resistant (AMR) S. enterica in India and China, there have been few studies in countries in South Asia, including Nepal. This is particularly important with the rise of commercial poultry farming in Nepal as a means of economic development and nutritional subsistence. This descriptive study seeks to identify the prevalence and resistance patterns of NTS serotypes focusing on Chitwan, Nepal's leading poultry producing district. Materials and Methods: A mixture of purposive and judgment sampling of 18 poultry farms and 20 slaughterhouses representing a broad geographic distribution across multiple municipalities in Chitwan was conducted in May 2019. Environmental samples taken from poultry farms included: Water, litter, feces, feed, farm swabs, and eggshell swabs. Biological samples taken from nearby slaughterhouses included: Muscle, heart, liver, skin, cecum, crop, and spleen. Samples were cultured and tested for the presence of NTS. Positive isolates were serotyped and tested for antimicrobial susceptibility to seven antibiotics known to be important to both human and animal health regionally. Farm practices were also characterized through a survey, the results of which are detailed in the accompanying paper. Results: Out of 708 samples (288 environmental and 420 biological), 103 (15%) tested positive for NTS (9% of environmental; n=26, 18% of biological; n=77). The percentage of positive environmental and biological samples varied by source. Environmental sample positive rates were water (27.5%), feces (10.6%), litter (8.6%), farm swabs (5%), feed (1.8%), and eggshells (0%). Biological sample positive rates were skin (28%), heart (23%), crop (20%), muscle (15%), liver (15%), spleen (15%), and cecum (12%). Out of 103 positive S. enterica isolates, 48.5% were identified as Salmonella Typhimurium, 35% Salmonella Enteritidis, 7.8% Salmonella Gallinarum, 4.9% Salmonella Virchow, and 3.9% were Salmonella Agona. Of the 103 positive isolates, 80 (78%) were resistant to at least one antibiotic, and 21 (20%) were multidrug-resistant (MDR). Conclusion: NTS is highly prevalent among Chitwan's growing poultry industry with higher rates of positivity found in slaughterhouse samples compared with environmental samples from farms. In addition, a high rate of AMR (78%) was revealed, and an extremely concerning number of those were shown to be MDR (20%). This baseline data has important implications for poultry production and consumption in the region. Further research will elucidate the extent to which this contamination and drug resistance is impacting the health of the local population and help inform treatment and management strategies. Note: The characterization of the poultry industry and practices that might be linked to NTS contamination in the Chitwan district are detailed in the previous paper in this series (www.veterinaryworld.org/Vol.14/February-2021/14.pdf).

Background and Aim: Poultry is becoming an increasingly important source of protein in the Nepalese diet. The Chitwan region of Nepal is the hub of the emerging poultry industry. Little is known about the prevalence of non-typhoidal Salmonella (NTS) on poultry farms or the role of farm management practices that may contribute to the presence of NTS on farms. The role of poultry in the transmission of Salmonella enterica to humans is also poorly defined. This descriptive study seeks establish baseline data through estimation of the prevalence of NTS on broiler and layer operations in various farms of the Chitwan district of Nepal. Materials and Methods: Based on district documents on poultry production and meat marketing, a purposive sampling of 18 commercial poultry farms comprising ten broilers farms and eight layers farms was conducted. Environmental samples including water, litter, feces, feed, farm, and eggshell swabs were randomly collected from each farm. Samples were cultured and tested for the presence of NTS; positives were serotyped, and antimicrobial susceptibility determined. A comprehensive farm and practice questionnaire was administered to each farm manager. Results: The farm level point prevalence rate was 55% (10 of 18 farms) for S. enterica. Of the total 288 farm environmental samples collected, 26 samples (9%) were positive. The rate of isolation varied according to the origin of samples: Water (27.5%), feces (10.6%), litter (8.6%), farm swabs (5%), feed (1.8%), and eggshells (0%). Farm management variables/risk factors are summarized and categorized as non-modifiable and modifiable for analysis. Broiler operations were more likely to be positive than layer operations as were poultry houses with two or less open sides. All-in/all-out management style was found to be protective. Due to the small sample size (18 farms), no associations reached statistical significance. Conclusion: Based on environmental sampling results, NTS is highly prevalent on the poultry farms in the Chitwan district of Nepal. Certain risk factors are associated with finding NTS on farms. Our findings are generally in agreement with other studies in similar countries with rapidly emerging poultry industries. The identification of risk factors provides owners, technicians, and veterinarians with some guidance to help reduce the prevalence of NTS on farms. This baseline data are critical to understanding the epidemiology of zoonotic strain of NTS in the region and are necessary for the design of future studies and mitigation plans and underlines the need for a one-health approach to protect public health-related to Salmonella spp. from poultry farms.

Large language models (LLM) hold significant promise to transform dairy science by enhancing research interpretation, supporting decision making, and improving knowledge dissemination. However, without proper systematic design, LLM may generate irrelevant or factually inaccurate responses for domain-specific questions. Moreover, most existing LLM and related tools are not tailored to the needs of the dairy domain, limiting their practical application within the field. To demonstrate the feasibility and practical value of embracing LLM in dairy science, we developed a 2-component agentic system: (1) a decision-support chatbot grounded in the Journal of Dairy Science (JDS) for science-backed insights and (2) a natural language interface for interacting with academic models and visualizing prediction results. All publicly available JDS abstracts and associated metadata dating back to 1917 were compiled using the PubMed application programming interface, forming a scientific knowledge base that enables the chatbot to answer user questions. A retrieval-augmented generation framework was implemented to ensure that responses generated by LLaMA (a LLM developed by Meta) were well-grounded in peer-reviewed literature, with the 5 most relevant sources cited alongside each answer. To address questions beyond the coverage of JDS literature, a web search agent was incorporated into the system to retrieve supplementary information from external online sources. Grading agents, powered by DBRX (a LLM developed by Databricks), were incorporated to evaluate the credibility and relevance of LLM-generated content to mitigate the risk of misinformation or hallucinated responses. The second component of the system facilitates natural language interaction with MilkBot, a published Bayesian milk yield prediction model. After users submit questions in plain language, the system converts the question into model parameters for MilkBot, executes the model prediction, and uses the predicted output to generate visualizations. This work demonstrates the capability of LLM to serve as intuitive, user-friendly interfaces for dairy-specific models. To our knowledge, this is the first chatbot prototype that integrates large-scale information from scientific literature, web-based resources, and academic models, along with self-evaluation capability, to provide dairy-specific insights to scholars, consultants, and farmers. However, challenges remain to realize the full value of LLM-assisted decision making, such as the lack of region-specific data to tailor the answers to the local circumstances, the need for more robust measures to protect data security and privacy, and the need to integrate additional functions to enable more comprehensive decision support.

The purpose of this study was twofold: first, to conduct a situational analysis, and second, to assess the knowledge, attitudes, and practices (KAP) of broiler poultry farmers (BPF) regarding antimicrobial use (AMU) and antimicrobial resistance (AMR). Data were collected from 500 BPF across 40 districts, representing 88.1% of Nepal’s broiler poultry population. Among these farmers, 81.0% were male, 59.6% had up to 4 years of experience in poultry farming, and 50.8% had completed at least a high school education. The most used antimicrobials on the farms were doxycycline (23.5%), neomycin (17.1%), and colistin sulfate (9.6%) out of 27 reported antimicrobials. While the BPF exhibited limited knowledge (62.6%) and practice (55.5%) related to AMU and AMR, their overall attitude toward these issues was positive (91.6%). This study also identified significant factors influencing farmers’ attitudes toward AMU and AMR. Farmers aged 31–40 showed a stronger inclination compared to other age groups (OR = 4.2, p = 0.02), and those using antimicrobials for preventive purposes had a more favorable attitude compared to those using them for other purposes (OR = 5.9, p = 0.02). In light of these findings, this study recommends the implementation of effective regulatory measures for drug usage, along with awareness programs addressing AMU and AMR to address the issue of AMR in poultry production.

Summary: The dairy sector is operating in an environment of rapid technological change, driven by advances in big data and artificial intelligence (AI). To remain competitive and sustainable, farms should adopt intelligent, scalable, and privacy-preserving solutions. This paper presents a comprehensive ecosystem that combines multimodal AI, edge computing, and federated learning to enable real-time, data-driven decision making on farms. With emphasis on privacy, local relevance, and research integration, the framework would enable autonomous AI agents to support everyday farm decisions and long-term planning. The graphical abstract depicts a schematic representation of the proposed framework for a future dairy data ecosystem, illustrating interactions among commercial and research farms, edge devices, and the cloud intelligence layer (enterprise and research and development [R&D] clouds). It shows data and model flows, with solid arrows pointing away from farms and dashed arrows pointing toward farms. E = edge layer; FL = federated learning; RAW = raw data.

Induced molting of laying hens is a common husbandry practiced in many commercial farms. This study was aimed to evaluate the effectiveness of induced molting on production parameters along with production economics of Layers. For this study, Lohmann Brown-Classic hens were reared in a modern cage system from 10 weeks of age. There was no significant difference(P>0.05) in egg production in Hen Day Basis (HDP) before 65 weeks in comparison to standard performance table provided by Lohmann Brown- Classic Layers Management Guide (Cage Housing). A total of 8087 laying hens were divided into two treatment groups after 65 weeks of age; one group (N=4055) without any feed restriction (control=T0) and another (N=4032) had induced molting with 15 days of feed restriction, provided with only water and mineral supplements (T1) followed by five days of exclusive Ground Maize feeding. The average HDP before division of treatment was recorded 83.37%. The T0 and T1 group were reared up to 85 and 109 weeks respectively, in accordance to the economical sustainability. The feed intake per bird, egg production including marketable and non-marketable eggs and mortality along with production economics of both groups were recorded. The T1 group reached its peak egg production (88.48%) on Hen Day Basis (HDP) after 62 days following treatment which was higher (P<0.05) than the T0 group. The ratio of marketable to non-marketable eggs was found higher in T1 (69.32:1) than that of T0 (38.53:1). The production cost per egg was recorded NRs.6.86 and 6.99 respectively for T1 and T0 group with 4.33% more profit on T1 than T0 group. This study suggests that induced molting with feed restriction approach is beneficial to bypass the scarcity period with falling market value of eggs and also increase the ratio of marketable eggs to non-marketable eggs. Int. J. Appl. Sci. Biotechnol. Vol 7(4): 429-433