Work

Optimizing soybean-pig integration zones in China: a multi-algorithm approach using random forest, LSTM, and clustering for feed grain and meat security balance

Year: 2025

Type: article

Abstract: Introduction The rising concerns about food security and the increasing demands for meat-based diet in China have highlighted the imbalance between the supply and demand of its feed grains. Scholars view the imports of soybeans as an unsustainable way of ensuring feed grain security. Therefore, this study investigates the optimization of soybean and pig integration zones in China. Methods The study has adopted a multi algorithm approach based on Random Forest Model, LSTM model, KMeans clustering, and PCA to highlight the factors influencing the integration of feed grain plantation and meat security. Results The results of the PCA report that consumption, production levels, land availability, and pig quantity play an instrumental role in defining the integration of soybeans and pig farming. The results indicate that LSTM offers accurate predictions regarding the integration zones. Conclusion The study concludes that areas with high consumption of meat and large production volumes offer an opportunity to integrate soybean cultivation and pig farming in China.

Source: Frontiers in Sustainable Food Systems

DOI: 10.3389/fsufs.2025.1488994

Authors: Zixiang Zhao, Yuanjing Fu, Haiyan Wang, Bilal Ahmad, Feiyu Jin

Institutions: Jilin International Studies University, Tianjin Foreign Studies University

Language: English

Topic: Livestock Farming and Management

Subfield: Agronomy and Crop Science

Field: Agricultural and Biological Sciences

Domain: Life Sciences

SDGs: Zero hunger, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,353