Journal of Poultry Sciences and Avian Diseases

Scientific journals do more than publish manuscripts. At their best, they help shape research priorities, strengthen methodological standards, promote ethical scholarship, encourage scientific dialogue, and support the translation of evidence into practice. As the Journal of Poultry Sciences and Avian Diseases (JPSAD) enters a new stage of international visibility, including its indexing in Scopus, it is important to reflect on both the opportunities and responsibilities that accompany this progress.
Poultry and avian health research is becoming increasingly multidisciplinary. Disease outbreaks, antimicrobial resistance, vaccination strategies, biosecurity, environmental conditions, flock management, nutrition, animal welfare, production efficiency, food safety, and public health are no longer isolated areas of study. They are interconnected components of a larger biological, veterinary, environmental, and societal system. This reality requires research that is not only technically sound, but also contextually meaningful, ethically responsible, and practically relevant.

Artificial intelligence (AI), machine learning, computer vision, sensor-based systems, and Internet of Things (IoT) technologies are increasingly used for automated monitoring of poultry health and welfare, but reported model performance varies across tasks, data sources, validation strategies, and production settings. This systematic review and meta-analysis evaluated the reported performance of AI-based models for poultry health and welfare monitoring. PubMed/MEDLINE, Scopus, and Web of Science were searched for peer-reviewed studies involving poultry, reporting health- or welfare-related monitoring outcomes, and providing extractable model-performance data. Data were extracted independently by two reviewers, and pooled accuracy was estimated using a Restricted Maximum Likelihood random-effects model in STATA version 17. Twenty-one studies published between 2012 and 2025 were included, of which ten were eligible for meta-analysis. Included systems used convolutional neural networks, random forests, support vector machines, and other machine-learning approaches for behavior recognition, disease detection, lesion assessment, mortality detection, environmental monitoring, and welfare assessment. The pooled accuracy estimate was 90.39% (95% confidence interval: 84.89–95.89; P < 0.001), with substantial heterogeneity among studies. Reported performance varied by application domain, data source, model type, assessment dimension, sample size, and integration with IoT-based systems. AI-based technologies show promise for automated poultry health and welfare monitoring; however, heterogeneous methods and limited external validation restrict the generalizability of pooled estimates. Future studies should prioritize standardized reporting, open datasets, external validation, and testing under commercial farm conditions.

This study aimed to evaluate the physiological relationship between body weight, selected biochemical and hormonal serum parameters, and their association with productive performance in ISA Brown laying hens during the mid-laying period (42–50 weeks of age). A total of 135 ISA Brown laying hens were into three experimental groups according to body weight category: light, medium, and heavy body weight, with 45 hens per treatment. Productive performance traits, including hen-day egg production, egg weight, and egg mass, were recorded. In addition, serum concentrations of reproductive hormones (FSH, LH, and estradiol) and selected biochemical parameters, including glucose, total protein, albumin, cholesterol, and triglycerides, were determined.

The results demonstrated that medium-weight hens showed significantly higher (P ≤ 0.05) hen-day egg production and increased concentrations of reproductive hormones, total protein, and albumin compared with the other groups, indicating a more favorable physiological and metabolic balance associated with improved reproductive efficiency and laying persistency. In contrast, heavy-weight hens recorded significantly higher (P ≤ 0.05) egg weight, glucose, cholesterol, and triglyceride concentrations, reflecting increased metabolic activity and fat deposition associated with greater body weight. Pearson correlation analysis within body weight categories revealed significant positive correlations (P ≤ 0.05) between reproductive hormones and productive performance traits, particularly in medium-weight hens, whereas elevated cholesterol and triglyceride levels were negatively associated with some productive parameters.

In conclusion, maintaining moderate body weight in ISA Brown laying hens contributes to improved physiological balance and enhanced productive performance during the mid-laying period.

This paper develops an automated non-invasive system for disease detection in poultry farming by classifying chicken droppings. This study examines multiple deep learning architectures employing transfer learning to classify chicken droppings. Based on comparative analysis, a knowledge distillation network was proposed utilizing DenseNet as the teacher model due to its superior performance metrics. A lightweight student network was proposed and trained using knowledge distillation. This effectively transfers discriminative feature representations from the teacher model while significantly reducing computational complexity. Experimental evaluation on a publicly available poultry faecal image dataset demonstrated that the proposed knowledge distillation network achieved an accuracy of 98.40%, precision of 97.72%, specificity of 99.44%, recall of 98.21%, and F1-score of 97.96%.

This study investigated the effects of graded dietary digestible methionine plus cystine (dMC) on hepatic and ovarian gene expression, reproductive hormones, and antioxidant status in post-molt Hy-Line W-80 laying hens (79 to 95 weeks of age; n = 180). Hens were randomly allocated to five isonitrogenous and isoenergetic diets providing dMC: Lys ratios of 75, 83, 91, 99, and 107 (dMC75 to dMC107), corresponding to dMC levels of 0.490% to 0.698%. Hepatic mRNA expression of yolk precursor genes (VTG1, VTG3, VLDLR), apolipoproteins (APOA1, APOA4), and lipogenic enzymes (DGAT2, LPIN1) showed quadratic responses, with maximal expression at dMC99. Ovarian mRNA expression of growth factors (IGF1, IGF1R, BMP6), folliculogenesis markers (AMH, INHA, INHBA), angiogenic factors (VEGFA, KDR), and transcription factors (GATA4, FOXL2) followed similar quadratic patterns, peaking at dMC99. Circulating estradiol, progesterone, insulin-like growth factor-1, and anti-Müllerian hormone exhibited significant quadratic responses, with peak values generally observed at dMC99. Malondialdehyde decreased from 6.49 to 3.55 nmol/mL, while total antioxidant capacity increased from 1.11 to 1.77 mmol/L, and catalase activity rose from 44.62 to 78.32 U/mL. Principal component analysis separated treatments along PC1 (54.9% variance), with dMC99 clustering with favorable parameters. Strong positive correlations were observed among estradiol-17β, catalase, anti-Müllerian hormone, and lipid metabolites (r = 0.78 to 0.92). These results suggest that dietary sulfur amino acid supply is associated with quadratic regulation of the hepatic-ovarian axis in post-molt laying hens, with optimal effects observed at approximately 0.648% dMC (dMC: Lys = 99).

This study compared the preliminary serum immune-marker responses induced by two vaccine preparations against avian pathogenic Escherichia coli (APEC) in broiler chickens: a bacterial ghost preparation (BGs) and a liposome-based preparation (LIPO). Serum IL-4, IL-10, IL-12, IL-1β, IFN-γ, and total IgY were measured by ELISA to evaluate humoral, cellular, and inflammatory responses relative to a negative control group. Overall differences among the three groups were statistically significant for all measured markers (p<0.0001). Direct comparison of the vaccinated groups showed significantly higher IL-10, IL-1β, and total IgY concentrations in the BGs group, whereas IL-4 was significantly higher in the LIPO group. IL-12 and IFN-γ did not differ significantly between the two vaccinated groups. These findings indicate that the two preparations generated distinct preliminary serum immune-marker profiles. Because protective efficacy was not evaluated in an APEC challenge model, further studies are required before either preparation can be considered an effective vaccine against colibacillosis.

Green banana powder is a functional feed ingredient rich in resistant starch and dietary fiber, with potential applications in poultry nutrition. However, information regarding its use in indigenous Vietnamese chicken breeds under practical tropical rearing conditions remains limited. This study evaluated the effects of dietary green banana powder supplementation on growth performance, feed efficiency, watery droppings incidence, and litter quality in H’Mong chickens from 1 to 56 days of age. A total of 120 one-day-old H’Mong chickens were randomly assigned to four dietary treatments containing 0%, 1%, 2%, or 3% green banana powder, with three replicates of 10 chickens per treatment. Dietary supplementation significantly affected final body weight, average daily gain, feed conversion ratio, incidence of watery droppings, wet-dropping days, litter moisture, litter quality score, and odor score (p<0.05). Chickens receiving 2% green banana powder showed the best productivity and litter-related responses. Feed intake and survival rate were not significantly influenced by dietary treatment (p>0.05). Under the conditions of the present study, supplementation with 2% green banana powder improved feed efficiency, productive performance, indicators of watery droppings, and litter condition in H’Mong chickens raised under practical tropical farm conditions.

.