ArticleAnimals : an open access journal from MDPI2026
Multi-Horizon Herd-Based Cattle Live-Weight Forecasting Using Irregular Automated Weighing Data.
Article in Animals : an open access journal from MDPI, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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5 authors.
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Abstract
Forecasting individual cattle live weight in herd-managed grazing systems remains challenging because automated weighing observations are irregular and environmental conditions vary seasonally. Evidence remains limited for live-weight forecasting across multiple forecasting periods under commercial grazing conditions. This study developed a machine learning (ML) framework for forecasting the live weight of individual cattle using automated observations from a managed herd. The framework incorporated demographic variables, historical live weights, and climatic lag predictors. The framework compared monthly, weekly, and rolling-window aggregations across forecasting periods of 1, 2, and 3 months. Quality control retained 494 of 1140 cattle (43.3%), yielding 4069 monthly aggregated records. The resulting dataset was more suitable for modelling cattle with regular voluntary weighing records. The respective forecasting datasets contained 3048, 2558, and 2068 records for one-, two-, and three-month periods. Gradient Boosting achieved the strongest testing performance. The corresponding coefficients of determination (R
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