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INTERNATIONAL FOOD AND AGRIBUSINESS MANAGEMENT ASSOCIATION

International Food and Agribusiness Management Review

2025  |  VOLUME 28 ISSUE 2


Published July 2025


Editorial

Machine learning in applied economics and agribusiness: emerging applications and integration with traditional methods
Lucie Maruejols, Xiaohua Yu, and Lauren Chenarides, pp: 237–240


Research Articles

Who buys food products from online influencers? Predictions with machine learning
Xiaoping Zhong and Xiaohua Yu, pp: 241–262

Energy independence, rural sustainability and potential of bioenergy villages in Germany: machine learning perspectives
Lucie Maruejols, Lisa Höschle, and Xiaohua Yu, pp: 263–295

Prediction of household food insecurity in rural China: an application of machine learning methods
Longqiang Zhao, Minda Yang, Shi Min, and Ping Qing, pp: 296–316

Beyond point forecasting: Probability density forecasting of corn yield based on quantile regression forest
Tao Xiong, Meng Xia, Gucheng Li, Jian Li, and Weiyi Xia, pp: 317–336

The cluster characteristics and influencing factors of China’s agricultural product importing countries: an analysis using machine learning
Xiaoheng Zhang, Xiaojie Yu, Yifei Liu, and Yini Xie, pp: 337–356

Prediction and pattern recognition of the large-scale poverty-returning risks: empirical evidence from machine learning
Hanjie Wang, Wenpeng Huang, Zhijian Yu, and Jiali Han, pp: 357–374

Attribute non-attendance in the choice experiment with machine Learning: WTP for organic apples in Germany
Yi Li and Xiaohua Yu, pp: 375–391

Strategic bidding behaviour in agricultural land rental markets: reinforcement learning in an agent-based model
Ruth Dionisia Gicuku Njiru, Changxing Dong, Franziska Appel, and Alfons Balmann, pp: 392–422

Advancing specialty crop management: a review of recent developments in robotics, remote sensing, and machine learning systems
Xiurui Cui, Zhengfei Guan, and Derek Farnsworth, pp: 423–440

Bridging taste and health: the role of machine learning in consumer food selection
Saroj Adhikari and Brandon R. McFadden, pp: 441–456

A practical guide from Ordinary Least Squares to causal machine learning
Thomas Bittmann, pp: 457–486

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