Optimising Flock Performance, Flock Consistency and Egg Quality in the Australian Egg Industry

Free-range egg production is expanding globally, particularly in Australia, due to increasing consumer awareness of animal welfare. However, challenges such as weather extremes and predators impact productivity. This study developed a machine learning-based decision support tool to address key production issues in commercial free-range systems. The research identified risk factors for short-term production losses, accurately forecasted laying rates and egg fluctuations with Random Forest models, employed cross-farm data to forecast production outcomes and applied K-means clustering to group flock performance. The findings provide data-driven strategies for early intervention, improved productivity and reduced economic losses in free-range egg farming.