Hybrid Machine Learning Framework for Herd-Level Cattle Growth Pattern and Weight Gain Forecasting in Grazing-Based Production Systems

2026-08-06

AI Summary

This study addresses the challenge of predicting herd-level weight gain in commercial grazing systems, where sparse and irregular livestock observation data hinder accurate forecasting. To overcome this limitation, the authors propose a cascaded hybrid machine learning architecture (GB→RF→NN) that integrates individual animal weights, population-level traits, and lagged environmental variables, generating herd-level growth trajectories through temporal aggregation. By synergistically combining residual modeling, stacking, cascading, and ensemble strategies, the proposed approach significantly outperforms baseline models—including ARIMA, LSTM, and GRU—under sparse observational conditions. The optimal model achieves an R² of 0.889, RMSE of 21.319 kg, and MAE of 15.462 kg on the test set, identifying animal age, precipitation, and temperature as key predictive factors, thereby substantially enhancing both accuracy and robustness of weight gain predictions.

Abstract

Commercial grazing systems yield irregular livestock observations, which challenge cattle growth forecasting. This study developed a hybrid machine learning framework for herd level cattle weight forecasting using automated sensing observations collected between 2022 and 2024 in southeastern Australia. Weekly live weight observations, demographic variables, and lagged environmental predictors were integrated into structured forecasting datasets. Herd level forecasting trajectories were generated through temporal aggregation of animal level predictions. Four hybrid architecture families were evaluated, including residual, stacked, cascade, and ensemble assisted frameworks. ARIMA, LSTM, and GRU models were used as comparative baselines. Independent testing demonstrated strong predictive agreement across multiple forecasting horizons. The cascade GB to RF to NN architecture achieved the best performance, with a test R^2 of 0.889, RMSE of 21.319 kg, and MAE of 15.462 kg. Hybrid architectures maintained greater robustness than recurrent sequential models under sparse observation conditions. Forecasting error increased progressively across extended prediction horizons. Feature importance analysis identified animal age, rainfall, and temperature as dominant predictors influencing herd level growth forecasting. The proposed framework may support feed allocation, grazing management, and livestock marketing decisions under heterogeneous sensing environments.

Problem

• cattle growth forecasting
• grazing-based production systems
• irregular livestock observations
• herd-level weight prediction
• sparse sensing data

Innovation

• hybrid machine learning
• herd-level forecasting
• cattle weight prediction
• sparse observation robustness
• cascade architecture