¹Akdeniz University, Faculty of Agriculture, Department of Agricultural Biotechnology, TR-07058 Antalya - TÜRKİYE
²Aydın Adnan Menderes University, Faculty of Agriculture, Department of Animal Science, TR-09070 Aydın - TÜRKİYE DOI : 10.9775/kvfd.2026.36392 Accurate monitoring of individual productivity in quail farming is essential for maximizing economic returns and shaping effective, sustainable breeding programs. However, daily variation in egg-laying behavior across individuals presents significant challenges to consistent productivity prediction. While conventional methods often struggle to account for these irregular and long-term behavioral trends, machine learning techniques offer a promising alternative through their capacity for individualized, data-driven modeling. In this study, we propose a machine learning-based approach to forecast daily egg-laying outcomes by identifying production patterns from a longitudinal dataset comprising 193 days of continuous observations on 371 quails. The predictive feature set includes rolling averages, cumulative production metrics, and prior-day laying status engineered from each bird's historical production data. Three supervised classification models, Logistic Regression, Random Forest, and Extreme Gradient Boosting (XGBoost) were employed to predict egg-laying on a daily basis. Performance was assessed using accuracy, precision, recall, and F1-score. Among the models, XGBoost outperformed the others, achieving an F1-score of 91.2% and a recall of 97% on the test set. Feature importance analysis identified the 7-day rolling mean as the most influential predictor. These findings underscore the value of machine learning approaches in modeling individual-level laying patterns and demonstrate their potential application in selection decisions, flock-level management, and automated performance monitoring systems in poultry production. Keywords : Egg-laying prediction, Individual productivity, Machine learning, Precision livestock farming, Quail