## 5 Conclusions  
Agricultural commodity price forecasting techniques, commodities, and associated challenges were systematically reviewed herein to objectively and comprehensively analyze advancements in the field up to 31 December 2024. This provides important insights into the adoption of machine learning (ML), ensemble learning (EL), deep learning (DL), and time series approaches, with new predictive performance contributions. Staple crops like wheat, corn, and rice are commonly forecasted for their contribution to universal food security. This study highlighted persistent challenges, such as the generalization of models being limited to region-specific datasets, computational complexity, and lack of interpretability in advanced models. This review highlights the need to develop more robust and scalable forecasting techniques that leverage multiple data sources, such as weather, economic, and trade data. Moreover, explainable AI (XAI) approaches should be emphasized for more interpretable and trusted complex models for adoption in real-world applications.  
The absence of the use of standardized datasets to be used as benchmarks does not allow for cross-comparison of models and cross-study replication. Alongside this deficiency, the use of different/inconsistent evaluation metrics hinders forecasting accuracy assessment and model comparison.  
Future work should engage in the development of hybrid models, which will utilize the strength of ML, DL, and time series methods while addressing their weaknesses. Standardizing and placing datasets into open-access repositories alongside the use of common evaluation metrics could improve model generalizability and benchmarking. Region-specific challenges and socioeconomic factors should be incorporated into forecasting models to improve their relevance and impact. Finally, the utilization of abundant and relevant exogenous data (weather, macroeconomic indicators, sentiment markers, etc.) would probably greatly benefit the prediction accuracy of the models. Additionally, future studies should prioritize the development of explainable AI (XAI) frameworks tailored to agricultural forecasting, enabling model transparency and user trust in real-world decision-making contexts. Another important direction is the design of regionally transferable forecasting systems trained on multi-country datasets and evaluated under variable market conditions. These strategies would help overcome current limitations in generalizability and interpretability while supporting broader adoption of advanced models. This study lays a foundation for the development of agricultural price prediction research, innovation, and sustainable agricultural practices.  
Validation, K.M. and S.A.N.; writing--original draft preparation, A.T.; writing--review and editing, A.T., S.A.N., K.M., A.M. and T.B.; supervision, S.A.N., K.M., A.M. and T.B. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the European Union under the Horizon Europe H2020 grant for the BIOVALUE project, grant number 101000499, \"Fork-to-farm agent-based simulation tool augmenting BIOdiversity in the agri-food VALUE chain\". This work does not necessarily reflect the view of the EU and in no way anticipates the Commission's future policy.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data sharing is not applicable.  
**Acknowledgments:** We would like to thank [PERSON] for his invaluable help and guidance.  
**Conflicts of Interest:** The authors declare no conflicts of interest.