Trends, Shocks and Predictions in the Price Development of Food-Grade Wheat
By: Jakub Horák and Jiří Kučera
References
- Abid, A., Souissi, N., Béjaoui, A., & Jeribi, A. (2025). Predicting commodity prices amidst the Ukraine war: Some evidence from artificial intelligence models. Finance Research Open, 1(3), 100023.
- Association of Private Agriculture of the Czech Republic. (2023). Analytik: Ceny pšenice mohou klesnout, zemědělci ji drželi na skladech [Analyst: Wheat prices may fall, farmers kept it in warehouses]. Retrieved from: https://www.asz.cz/clanek/10800/analytik-ceny-psenice-mohou-klesnout-zemedelciji-drzeli-na-skladech/ Accessed 26 April 2025
- Bora, D. J., Kumar, S., & Singh, R. (2024). Multi-step forecasting of agricultural commodity prices using deep learning approaches. Computers and Electronics in Agriculture, 210, 107972.
- Brouns, F., Van Rooy, G., Shewry, P., Rustgi, S., & Jonkers, D. (2019). Adverse reactions to wheat or wheat components. Comprehensive Reviews in Food Science and Food Safety, 18(5), 1437–1452.
- Buczek, J., Jarecki, W., Bobrecka-Jamro, D., & Jańczak-Pieniężek, M. (2020). Hybrid wheat yield and quality related to cultivation intensity and weather conditions. Journal of Elementology, 25(1), 71–83.
- Butler, S., Han, S., & Piotr, P. (2021). Neural network prediction of crude oil futures using B-splines (multilayer feed-forward/MLP). Energy Economics.
- Czech Television. (2025). Ekonomika [Economics]. Retrieved from: https://ct24.ceskatelevize.cz/rubrika/ekonomika-17 Accessed 26 April 2025
- Czech Statistical Office. (2024). Vývoj průměrných cen vybraných potravin [Development of average prices of selected foods]. Retrieved from: https://csu.gov.cz/vyvoj-prumernych-cen-vybranych-potravin-2024 Accessed 23 April 2025
- Demidova, L., Ivkina, M., Zhdankina, E., Krylova, O., Sofyin, E., Reshetova, V., Stepanov, N., Tyart, N. (2016). Software Package STATISTICA and Educational Process. SHS Web of Conferences, 9, 02011.
- Foroutan, R., Chen, L., & Zhang, X. (2024). Predicting energy and precious metal prices using machine learning and deep learning models. Energy Economics, 125, 106743.
- Geetha, V., Gomathy, C. K., Sai, B. P. V. H. N., & Kiran, Ch. S. (2024). Price forecasting of agricultural commodities. AIP Conference Proceedings, 3028(1).
- Goyal, R., & Steinbach, S. (2023). Agricultural commodity markets in the wake of the Black Sea grain initiative. Economics Letters, 231.
- Guindani, M., Rossi, F., & Bianchi, L. (2024). Short-term forecasting of agricultural commodity prices using artificial neural networks. Journal of Commodity Markets, 34.
- Hao, X., Li, X., & Zhang, H. (2020). Forecasting the real prices of crude oil using robust loss functions with regularization (LASSO/Ridge/Elastic Net). Energy Economics, 86,
- Chen, J., Kibriya, S., Bessler, D., & Price, E. (2018). The relationship between conflict events and commodity prices in Sudan. Journal of Policy Modeling, 40(4), 663–684.
- Janković, I., Kovačević, V., & Jeločnik, M. (2020). Production costs and market price of wheat behavior analysis as a support for hedging strategies. Ekonomika poljoprivrede, 67(2), 495–509.
- Jiang, F., Ma, X. Y., Li, Y. Y., Li, J. X., Cao, W. L., Tong, J., Chen, Q. Y., Chen, H. F., & Fu, Z. X. (2023). How deep learning affects price forecasting of agricultural supply chain? Journal of Information Science and Engineering, 39(4), 809–823.
- Kotyza, P., Smutka, L., Czech, K., Wielechowski, M., & Pulkrábek, J. (2021). Sugar prices development in the period of COVID-19 pandemic. Listy cukrovarnické a řepařské, 137(3), 121–127.
- Madaan, L., Sharma, A., Khandelwal, P., Goel, S., & Singla, P. (2019). Price forecasting & anomaly detection for agricultural commodities in India. Proceedings of the 2nd ACM SIGCAS Conference on Computing and Sustainable Societies, 2(2), 52–64.
- Manogna, R., Vasanth, P., & Basha, S. M. (2025). Agricultural commodity price forecasting using machine learning and deep learning models. Agricultural Economics, 66(3), 215–230.
- Manogna, R. L., Dharmaji, V., & Sarang, S. (2025). Enhancing agricultural commodity price forecasting with deep learning. Scientific Reports, 15(1), 20903.
- Martin, W., & Minot, N. (2022). The impacts of price insulation on world wheat markets during the 2022 food price crisis. Australian Journal of Agricultural and Resource Economics, 66(4), 753–774.
- Ministry of Agriculture of the Czech Republic. (2025). Tiskové zprávy [Press releases]. Retrieved from: https://mze.gov.cz/public/portal/mze/tiskovy-servis/tiskove-zpravy Accessed 25 April 2025
- Mottaleb, K. A., Kruseman, G., & Snapp, S. (2022). Potential impacts of Ukraine-Russia armed conflict on global wheat food security: A quantitative exploration. Global Food Security, 35.
- Mutiawani, V., Subianto, M., & Tony, H. R. (2016). A web-based agricultural commodity price information system for the Aceh region, Indonesia. Proceedings of the 12th International Conference on Mathematics, Statistics, and Their Applications (ICMSA), 12(12), 76–79.
- Pandit, R., Sharma, V., & Li, J. (2024). A hybrid CEEMDAN-TDNN model for monthly agricultural commodity price prediction. Expert Systems with Applications, 234, 120789.
- Nayak, G. H., Alam, M. W., Naik, B. S., Varshini, B. S., Avinash, G., Kumar, R. R., Ray, M., & Singh, K. N. (2025). Meta-transformer: leveraging metaheuristic algorithms for agricultural commodity price forecasting. Journal of Big Data, 12(1), 138.
- Pozdílková, A., Zahrádka, J., & Marek, J. (2021). Forecasting of agrarian commodity prices by time series methods. Proceedings of the 39th International Conference on Mathematical Methods in Economics (MME), 39, 339–404.
- Qiu, L., Zhang, H., & Chen, Y. (2024). Forecasting polyester yarn prices using multiple linear regression and Holt–Winters models. Textile Research Journal, 94(5–6), 845–857.
- Ravi Kumar, K. N., Naidu, G. M., & Shafiwu, A. B. (2024). Exploring the drivers of Indian agricultural exports: A dynamic panel data approach. Cogent Economics & Finance, 12(1), 2332–2039.
- Sahinli, M. A. (2021). Determining the influential factors in the modelling of the price of wheat in Turkey. Journal of Environmental Protection and Ecology, 22(1), 424–432.
- Shobande, O. A., & Shodipe, O. T. (2021). Price stickiness in the US-corn market: Evidence from DSGE-VAR simulation. Studia Universitatis „Vasile Goldiș” Arad – Economics Series, 31(2).
- Si, Z., Liu, J., Wu, L., Li, S., Wang, G., Yu, J., Gao, Y., & Duan, A. (2023). A high-yield and high-efficiency cultivation pattern of winter wheat in the North China Plain: High-low seedbed cultivation. Field Crops Research, 300.
- Smutka, L., Pawlak, K., Kotyza, P., & Pulkrábek, J. (2019). Polish sugar market specifics and development. Listy cukrovarnické a řepařské, 135(4), 154–160.
- State Agricultural Intervention Fund. (2023). Zpráva o trhu obilovin, olejnin a krmiv [Cereals, Oilseeds and Feed Market Report]. Retrieved from: https://www.szif.cz/cs Accessed 25 April 2025
- Subhan, A., Khurshid, N., & Shah, Z. (2022). Uncovering price puzzle in the wheat economy of Pakistan: An application of artificial neural networks. 2022 2nd International Conference on Artificial Intelligence (ICAI), IEEE, 2, 84–89.
- Svanidze, M., & Đurić, I. (2021). Global wheat market dynamics: What is the role of the EU and the Black Sea wheat exporters? Agriculture, 11(8), 2077–0472.
- Šuleř, P., & Machová, V. (2020). Better results of artificial neural networks in predicting ČEZ share prices. Journal of International Studies, 13(2), 259–278.
- Tandogan Aktepe, N. S., & Kayral, İ. E. (2024). Unraveling the major determinants behind price changes in four selected representative agricultural products. Agriculture, 14(5), 2077–0472.
- Theofilou, A., Nastis, S. A., Michailidis, A., Bournaris, T., & Mattas, K. (2025). Predicting prices of staple crops using machine learning: A systematic review of studies on wheat, corn, and rice. Sustainability, 17(12), 5456
- Vochozka, M., Šuleř, P., Maroušková, A. (2020). Using regression analysis to predict the development of stock prices in the food industry. Journal of Risk and Financial Management, 13(10).
- Wang, Y. (2023a). Agricultural products price prediction based on an improved RBF neural network model. Applied Artificial Intelligence, 37(1).
- Wang, Y. (2023b). Forecasting agricultural commodity prices using an improved radial basis function neural network. Journal of Applied Economics, 26(4), 345–362.
- Wang, J., Ma, K., Zhang, L., & Wang, J. (2022). Study on price bubbles of China’s agricultural commodity against the background of big data. Electronics, 11(24), 2079–9292.
- Xu, X., & Zhang, Y. (2023). Edible oil wholesale price forecasts via the neural network. Energy Nexus, 12.
- Yi, K., Zhang, Q., Fan, W., Wang, S., Wang, P., He, H., Lian, D., An, N., Cao, L., Niu, Z. (2023). Frequency-domain MLPs are more effective learners in time series forecasting.
- Zhang, Q., Hu, Y., Jiao, J., & Wang, S. (2022). Exploring the trend of commodity prices: A review and bibliometric analysis. Sustainability, 14(15), 2071–105.
Language: English
Page range: 128 - 165
Submitted on: Jul 1, 2025
Accepted on: Oct 1, 2025
Published on: Jul 23, 2026
Published by: Vasile Goldis Western University of Arad
In partnership with: Paradigm Publishing Services
Keywords:
Related subjects:
© 2026 Jakub Horák, Jiří Kučera, published by Vasile Goldis Western University of Arad
This work is licensed under the Creative Commons Attribution 4.0 License.