An Integrated Hog Supply Forecasting Framework Incorporating the Time-Lagged Piglet Feature: Sustainable Insights from the Hog Industry in China

Mingyu Xu, Xin Lai, Yuying Zhang, Zongjun Li, Bohan Ouyang, Jingmiao Shen and Shiming Deng ()
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Mingyu Xu: School of Management, Huazhong University of Science and Technology, Wuhan 430000, China
Xin Lai: School of Management, Huazhong University of Science and Technology, Wuhan 430000, China
Yuying Zhang: School of Management, Huazhong University of Science and Technology, Wuhan 430000, China
Zongjun Li: School of Management, Huazhong University of Science and Technology, Wuhan 430000, China
Bohan Ouyang: School of Management, Huazhong University of Science and Technology, Wuhan 430000, China
Jingmiao Shen: National Hog Big Data, Rongchang, Chongqing 402460, China
Shiming Deng: School of Management, Huazhong University of Science and Technology, Wuhan 430000, China

Sustainability, 2024, vol. 16, issue 19, 1-24

Abstract: The sustainable development of the hog industry has significant implications for agricultural development, farmers’ income, and the daily lives of residents. Precise hog supply forecasts are essential for both government to ensure food security and industry stakeholders to make informed decisions. This study proposes an integrated framework for hog supply forecast. Granger causality analysis is utilized to simultaneously investigate the causal relationships among piglet, breeding sow, and hog supply, as well as to ascertain the uncertain time lags associated with these variables, facilitating the extraction of valuable time lag features. The Seasonal and Trend decomposition using Loess (STL) is leveraged to decompose hog supply into three components, and Autoregressive Integrated Moving Average (ARIMA) and Xtreme Gradient Boosting (XGBoost) are utilized to forecast the trends, i.e., seasonality and residuals, respectively. Extensive experiments are conducted using monthly data from all the large-scale pig farms in Chongqing, China, covering the period from July 2019 to November 2023. The results demonstrate that the proposed model outperforms the other five baseline models with more than 90% reduction in Mean Squared Logarithm (MSL) loss. The inclusion of the piglet feature can enhance the accuracy of hog supply forecasts by 42.1% MSL loss reduction. Additionally, the findings reveal statistical time lag periods of 4–6 months for piglet and 11–13 months for breeding sow, with significance levels of 99%. Finally, policy recommendations are proposed to promote the sustainability of the pig industry, thereby driving the sustainable development of both upstream and downstream sectors of the swine industry and ensuring food security.

Keywords: machine learning; piglet feature; STL decomposition; sustainability of hog supply (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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