ArticlePoultry science2021
Using farm management practices to predict Campylobacter prevalence in pastured poultry farms.
Article in Poultry science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed.
- Artificial intelligence in food safety.NPJ science of food · 2026Review
- Microbial Contamination Risks From Adjacent and Nearby Land: Evidence and Implications for Produce Safety.Journal of food science · 2026Review
- RETRACTED: N-Beats architecture for explainable forecasting of multi-dimensional poultry data.PloS one · 2025Article
- Exploring Pathogen Presence Prediction in Pastured Poultry Farms through Transformer-Based Models and Attention Mechanism Explainability.Microorganisms · 2024Article
- Broiler farming practices using new or re-used bedding, inclusive of free-range, have no impact onAIMS microbiology · 2024Article
- Aerotolerance and Multi-Locus Sequence Typing ofFoods (Basel, Switzerland) · 2023Article
- Chicken caecal enterotypes in indigenous Kadaknath and commercial Cobb chicken lines are associated withFrontiers in microbiomes · 2023Article
- Roles of Aerotolerance, Biofilm Formation, and Viable but Non-Culturable State in the Survival ofMicroorganisms · 2022Review
- Artificial Intelligence Models for Zoonotic Pathogens: A Survey.Microorganisms · 2022Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Contamination of poultry products by Campylobacter is often associated with farm management practices and processing plant practices. A longitudinal study was conducted on 11 pastured poultry farms in southeastern United States from 2014 to 2017. In this study, farm practices and processing variables were used as predictors for a random forest (RF) model to predict Campylobacter prevalence in pastured poultry farms and processing environments. Individual RF models were constructed for fecal, soil and whole carcass rinse after processing (WCR-P) samples. The performance of models was evaluated by the area under curve (AUC) from the receiver operating characteristics curve. The AUC values were 0.902, 0.894, and 0.864 for fecal, soil, and WCR-P models, respectively. Relative importance plots were generated to predict the most important variable in each RF model. Animal source of feces was identified as the most important variable in fecal model and the soy content of the brood feed was the most important variable for soil model. For WCR-P model, the average flock age showed the strongest impact on RF model. These RF models can help pastured poultry growers with food safety control strategies to reduce Campylobacter prevalence in pastured poultry farms.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.