# Impact of Farm Management Practices on Salmonella Occurrence at the Farm Level—A Blend of Traditional Methods and Artificial Intelligence

## TL;DR

This paper reviews how farm management and AI can reduce Salmonella in livestock foods by improving implementation and surveillance.

## Contribution

The paper highlights the integration of traditional practices with AI for enhanced Salmonella control in food systems.

## Key findings

- Coherent farm-to-fork programs significantly reduce Salmonella prevalence.
- AI can improve real-time monitoring and risk prediction for Salmonella control.
- High-resolution surveillance and prevention-focused management are critical for sustained progress.

## Abstract

Background: Salmonella enterica remains a leading cause of foodborne illness worldwide despite decades of advances in surveillance and control. Traditional interventions have targeted specific points in the food chain, yet recurrent outbreaks show that Salmonella exploits system-wide gaps and inconsistencies. Methods: This review synthesises recent evidence from epidemiology, experimental microbiology, and regulatory practice to evaluate how management decisions, from farm through processing, influence Salmonella risk in livestock-derived foods. Results: Poultry, pig, and cattle farms employ targeted measures, including rodent control, litter management, batch rearing, and secure feed storage, to reduce contamination. The greatest reductions in Salmonella prevalence occur when these measures are embedded in coherent farm-to-fork programmes. Future gains are likely to come less from novel interventions and more from rigorous implementation, integration, and the validation of existing tools, supported by high-resolution surveillance (including whole-genome sequencing) and prevention-focused management systems. Artificial intelligence can enhance control through real-time surveillance, predictive risk modelling, and targeted interventions informed by diverse farm data. Conclusions: Sustained progress in Salmonella control will depend on rigorously applying existing interventions, supported by high-resolution surveillance and prevention-focused management. Carefully governed AI can enhance real-time monitoring and risk prediction, but its value hinges on addressing data, cost, and regulatory challenges.