415 A machine learning approach for price prediction and decision support in United States (U.S.) cow-calf operations.

Abstract Cow-calf operations form the foundation of the U.S. beef industry, with approximately 27.9 million beef cows and 25.1 million calves born in 2024, comprising 61.1% of total cattle inventory. Though their exact economic contribution is hard to predict due to the integrated nature of the beef supply chain, the cattle industry contributed approximately $88.4 billion (17%) to the total U.S. agricultural receipts in 2023. Despite their significance, these operations face economic challenges, including fluctuating feed costs, market volatility, and drought. Given the narrow profit margins of these operations, predictive modeling of profitability can offer valuable insights for sustainable management and decision-making. This study applies three univariate (Auto-Regressive Integrated Moving Average (ARIMA), Seasonal ARIMA (SARIMA), SARIMA with Exogenous regressors (SARIMAX)), three multivariate machine learning models (Random Forest (RF), AdaBoost, Support Vector Regression (SVR)), and one deep learning model (Long Short-Term Memory, LSTM) to assess the economic performance of cow-calf operations, specifically predicting the average farm price received for calves ($/Cwt). The analysis leverages historical data along with key domain-specific variables, including feed costs (monthly average U.S. farm price of corn, $/bushel), fuel prices (industrial natural gas price, $/thousand cubic feet), foreign exchange rates (U.S.-Japan exchange rate), and inflation indices (Consumer Price Index, CPI), to provide robust and enhanced predictive insights. A cow-calf budget dataset spanning from 1996 to 2024 was compiled from sources including the USDA Agricultural Marketing Service and Texas A&M AgriLife Extension. Data acquisition was facilitated through web scraping techniques, enabling the automated extraction of the historical economic indicators. The collected data underwent preprocessing and feature engineering, to enhance model input quality, and both univariate time-series and multivariate machine learning models were applied to capture temporal and nonlinear patterns in calf prices. Model performance was evaluated using multiple metrics, including Mean Absolute Error (MAE), Mean Percentage Error (MPE), Root Mean Square Error (RMSE), Root Mean Square Percentage Error (RMSPE), and Coefficient of Determination (R²) to comprehensively assess predictive performance. The analysis revealed substantial relationships between selling price of cow-calf operations and key economic indicators. Among the evaluated forecasting models, SARIMA exhibited the strongest performance by effectively capturing seasonal trends, while SARIMAX showed comparable accuracy through the integration of external variables. Among the machine learning techniques, AdaBoost performed best, outperforming RF and SVR. The LSTM model achieved the overall best performance, demonstrating strong capability in modeling both nonlinear dependencies and temporal dynamics in calf price trends. These findings demonstrate the potential of combining classical time-series and modern machine learning models to support price forecasting in cow-calf operations. This hybrid modeling approach provides a powerful decision-support tool for producers navigating market volatility, and future research should explore real-time forecasting and broader economic and environmental integrations to further strengthen predictive capabilities.