Publikationsdatenbank

Forecasting egg production performance and fluctuations in commercial free-range poultry systems using a random forest model (2025)

Art

Zeitschriftenartikel / wissenschaftlicher Beitrag

Autoren

Adejola, Yusuf Adewale

Sibanda, Terence Zimazile

Ruhnke, Isabelle (WE 15)

Boshoff, Johan

Pokhrel, Saluna

Welch, Mitchell

Quelle

Smart Agricultural Technology

Bandzählung: 12

Seiten: 101380

Sprache

Englisch

Verweise

URL (Volltext): https://www.sciencedirect.com/science/article/pii/S2772375525006112

Kontakt

Nutztierklinik: Abteilung Geflügel

Königsweg 63
14163 Berlin
+49 30 838 62676
gefluegelkrankheiten@vetmed.fu-berlin.de

Abstract / Zusammenfassung

The free-range poultry industry is faced with numerous challenges that contribute significantly to the flock’s variability in egg production. Forecasting fluctuations and egg laying rate for commercial flocks is important as it allows early implementation of proactive farm management decisions thereby minimising unexpected interruptions to the production rate. This study employed a Random Forest model to forecast egg production fluctuations and near-future laying rates of commercial free-range hens. Datasets from a single free-range commercial farm, comprising 7 flocks including production and environmental variables were used in a machine learning workflow. The workflow involved the use of a classification task to detect problematic fluctuations in egg production and a regression task to forecast laying rates. This approach provides an understanding of the requirements to forecast production measures feasibly with a level of sensitivity and precision suitable for a decision support system. The results from this study showed that the 28-day data window had the best performance, with a 5-day forecast interval. For the classification task, the AUC values were above 0.9 and sensitivity scores exceeded 0.85 indicating the model's ability to predict the problematic production days, while PPV values around 0.4 suggests a relatively high rate of false positives. For the regression task, the RMSE value was 2.5% demonstrating accurate forecasting of laying rates, with lower error rates. Feature importance analysis revealed that production variables such as laying and mortality rates strongly predict laying performance rather than environmental variables. The findings from this study will build towards the development a decision support system for free-range egg producers.