Predicting Prices of Staple Crops Using Machine Learning: A Systematic Review of Studies on Wheat, Corn, and Rice

Asterios Theofilou (), Stefanos A. Nastis, Anastasios Michailidis, Thomas Bournaris and Konstadinos Mattas
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Asterios Theofilou: Department of Agricultural Economics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Stefanos A. Nastis: Department of Agricultural Economics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Anastasios Michailidis: Department of Agricultural Economics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Thomas Bournaris: Department of Agricultural Economics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Konstadinos Mattas: Department of Agricultural Economics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece

Sustainability, 2025, vol. 17, issue 12, 1-34

Abstract: According to the FAO, wheat, corn, and rice are staple crops that support global food security, providing 50% of the world’s dietary energy. The ability to predict accurately these key food crop agricultural commodity prices is important in stabilizing markets, supporting policymaking, and informing stakeholders’ decisions. To this aim, machine learning (ML), ensemble learning (EL), deep learning (DL), and time series methods (TS) have been increasingly used for forecasting due to the rapid development of computational power and data availability. This study presents a systematic literature review (SLR) of peer-reviewed original research articles focused on forecasting the prices of wheat, corn, and rice using machine learning (ML), deep learning (DL), ensemble learning (EL), and time series techniques. The results of the study help uncover suitable forecasting methods, such as hybrid deep learning models that consistently outperform traditional methods, and they identify important limitations in model interpretability and the use of region-specific datasets, highlighting the need for explainable and generalizable forecasting solutions. This systematic review adheres to the PRISMA 2020 reporting guidelines.

Keywords: agriculture; commodities; wheat; corn; maize; rice; paddy; price; prediction; machine learning; deep learning; ensemble learning; time series (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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