Analyzing Internal and External Factors in Livestock Supply Forecasting Using Machine Learning: Sustainable Insights from South Korea

Tserenpurev Chuluunsaikhan, Jeong-Hun Kim, So-Hyun Park () and Aziz Nasridinov ()
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Tserenpurev Chuluunsaikhan: Department of Computer Science, Chungbuk National University, Cheongju 28644, Republic of Korea
Jeong-Hun Kim: Bigdata Research Institute, Chungbuk National University, Cheongju 28644, Republic of Korea
So-Hyun Park: Department of Computer Engineering, Dongguk University WISE, Gyeongju 38066, Republic of Korea
Aziz Nasridinov: Department of Computer Science, Chungbuk National University, Cheongju 28644, Republic of Korea

Sustainability, 2024, vol. 16, issue 16, 1-21

Abstract: The supply of livestock products depends on many internal and external factors. Omitting any one factor can make it difficult to describe the market patterns. So, forecasting livestock indexes such as prices and supplies is challenging due to the effect of unknown factors. This paper proposes a Stacking Forest Ensemble method (SFE-NET) to forecast pork supply by considering both internal and external factors, thereby contributing to sustainable pork production. We first analyze the internal factors to explore features related to pork supply. External factors such as weather conditions, gas prices, and disease information are also collected from different sources. The combined dataset is from 2016 to 2022. Our SFE-NET method utilizes Random Forest, Gradient Boosting, and XGBoost as members and a neural network as the meta-method. We conducted seven experiments for daily, weekly, and monthly pork supply using different sets of factors, such as internal, internal and external, and selected. The results showed the following findings: (a) The proposed method achieved Coefficient of Determination scores between 84% and 91% in short and long periods, (b) the external factors increased the performance of forecasting methods by about 2% to 12%, and (c) the proposed stacking ensemble method outperformed other comparative methods by 1% to 18%. These improvements in forecasting accuracy can help promote more sustainable pork production by enhancing market stability and resilience.

Keywords: sustainability of pork market; livestock forecasting; machine learning (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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