Research on World Agricultural Economy

Price Volatility and Prediction of Rapeseed as a Market Commodity

Jakub Horák

School of Expertness and Valuation, Institute of Technology and Business, 37001 České Budějovice, Czech Republic; Department of Economics, Management and Marketing, Institute of Technology and Business, 08001 Prešov, Slovakia

DOI: https://doi.org/10.36956/rwae.v7i1.2439

Received: 9 July 2025 | Revised: 26 August 2025 | Accepted: 2 September 2025 | Published Online: 25 February 2026

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Abstract

The price movement of agricultural commodities is decisive for producers and consumers. Rapeseed is one of the essential sources of proteins, vegetable oil and biofuels in European agriculture. Predicting and analysing its price movement is vital for effective economy, considering climate change, growing demand and geopolitical uncertainty. The article aims to analyse the rapeseed price movement in the Czech Republic from January 2010 to November 2024, forecasting the further price trend through December 2025 based on historical data. We used content analysis, linear regression, MLP and RBF neural networks for the predictions. The data come from publicly available monthly statistics of agricultural commodity prices. Our results show a steadily growing trend in rapeseed prices, peaking at 19,887 CZK/t in 2022, when the effects of the COVID-19 pandemic and war in Ukraine had dramatically swayed the price movement. Regression analysis confirmed an increasing trend, revealing a period with an accelerated price hike between 2020 and 2022. We used MLP and RBF neural networks for forecasts. MLP indicated the most accurate results with the values closest to the real prices between December 2024 and February 2025, while RBF neural structures tended to underestimate reality. The predicted movement suggests that the rapeseed price will be growing, which may affect the behaviour on purchasing, selling or warehousing this strategic commodity.        

Keywords: Price Movement; Price Prediction; Rapeseed; Neural Networks; Agriculture

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References

[1] Zhang, Q., Hu, Y., Jiao, J., et al., 2022. Exploring the Trend of Commodity Prices: A Review and Bibliometric Analysis. Sustainability. 14(15), 9536. DOI: https://doi.org/10.3390/su14159536

[2] Mutiawani, V., Subianto, M., Tony, H., 2016. A web-based agricultural commodity price information system for Aceh region, Indonesia. In Proceedings of the 12th International Conference on Mathematics, Statistics, and Their Applications (ICMSA), Banda Aceh, Indonesia, 4–6 October 2016; pp. 76–79. DOI: https://doi.org/10.1109/ICMSA.2016.7954312

[3] Jiayue, W., Ma, K., Zhang, L., et al., 2022. Study on Price Bubbles of China’s Agricultural Commodity against the Background of Big Data. Electronics. 11(24), 4067. DOI: https://doi.org/10.3390/electronics11244067

[4] Tatarintsev, M., Korchagin, S., Nikitin, P., et al., 2021. Analysis of the Forecast Price as a Factor of Sustainable Development of Agriculture. Agronomy. 11(6), 1235. DOI: https://doi.org/10.3390/agronomy11061235

[5] Madaan, L., Sharma, A., Khandelwal, P., et al., 2019. Price forecasting & anomaly detection for agricultural commodities in India. In Proceedings of the 2nd ACM SIGCAS Conference on Computing and Sustainable Societies, Accra Ghana, Ghana, 3 July 2019; pp. 52–64. DOI: https://doi.org/10.1145/3314344.3332488

[6] Sengupta, J., Nag, R.N., Goswami, B., 2017. Commodity price fluctuations and unemployment in a dependent economy. Contemporary economics. 11(3), 315–325.

[7] Jannat, A., Ishikawa-Ishiwata, Y., Furuya, J., 2022. Does Climate Change Affect Rapeseed Production in Exporting and Importing Countries? Evidence from Market Dynamics Syntheses. Sustainability. 14(10), 6051. DOI: https://doi.org/10.3390/su14106051

[8] 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), 2344733. DOI: https://doi.org/10.1080/23322039.2024.2344733

[9] Chen, J., Kibriya, S., Bessler, D., et al., 2018. The relationship between conflict events and commodity prices in Sudan. Journal of Policy Modeling. 40(4), 663–684. DOI: https://doi.org/10.1016/j.jpolmod.2018.01.014

[10] Jiang, F., Ma, X.Y., Li, Y.Y., et al., 2023. How Deep Learning Affect Price Forecasting of Agricultural Supply Chain? Journal of Information Science and Engineering. 39(4). DOI: https://doi.org/10.6688/JISE.202307_39(4).0007

[11] Pozdílková, A., Zahrádka, J., Marek, J., 2021. Forecasting of agrarian commodity prices by time series methods. In Proceedings of the 39th International Conference on Mathematical Methods in Economics (MME 2021), Prague, Czech Republic, 3 September 2021; pp. 339–404.

[12] Geetha, V., Gomathy, C.K., Sai, B.P.V.H.N., et al., 2024. Price forecasting of agricultural commodities. In Proceedings of the International Conference On Emerging Trends In Electronics And Communication Engineering—2023, Nandyala, India, 28–30 April 2023; p. 020015. DOI: https://doi.org/10.1063/5.0212568

[13] Tandogan Aktepe, N.S., Kayral, İ.E., 2024. Unraveling the Major Determinants behind Price Changes in Four Selected Representative Agricultural Products. Agriculture. 14(5), 782. DOI: https://doi.org/10.3390/agriculture14050782

[14] Tang, H., Liao, G., 2021. Correlation between agricultural parameters and spectral vegetation index of rape based on artificial intelligence. Journal of Intelligent & Fuzzy Systems. 40(4), 6803–6813. DOI: https://doi.org/10.3233/JIFS-189513

[15] Gourrion, A., Simon, C., Vallée, P., et al., 2020. Enlarging the genetic diversity of winter oilseed rape (WOSR) by crossing with spring oilseed rape (SOSR). OCL. 27, 16. DOI: https://doi.org/10.1051/ocl/2020013

[16] Luo, T., Lin, R., Cheng, T., et al., 2022. Low Temperature Rather Than Nitrogen Application Mainly Modulates the Floral Initiation of Different Ecotypes of Rapeseed (Brassica napus L.). Agronomy. 12(7), 1624. DOI: https://doi.org/10.3390/agronomy12071624

[17] Fridrihsone, A., Romagnoli, F., Cabulis, U., 2020. Environmental Life Cycle Assessment of Rapeseed and Rapeseed Oil Produced in Northern Europe: A Latvian Case Study. Sustainability. 12(14), 5699. DOI: https://doi.org/10.3390/su12145699

[18] Raboanatahiry, N., Li, H., Yu, L., et al., 2021. Rapeseed (Brassica napus): Processing, Utilization, and Genetic Improvement. Agronomy. 11(9), 1776. DOI: https://doi.org/10.3390/agronomy11091776

[19] Tofanica, B.M., 2019. Rapeseed—a Valuable Renewable Bioresource. Cellulose Chemistry and Technology. 53(9–10), 837–849. DOI: https://doi.org/10.35812/CelluloseChemTechnol.2019.53.81

[20] Hu, H., Ren, Y., Zhou, H., et al., 2024. Oilseed Rape Yield Prediction from UAVs Using Vegetation Index and Machine Learning: A Case Study in East China. Agriculture. 14(8), 1317. DOI: https://doi.org/10.3390/agriculture14081317

[21] Gimbasanu, G.F., Rebega, D.E., Tudor, V.C., 2021. Comparative analysis of the price of rapeseed and sunflower during the pre-accession and post-accession to the European union. Scientific Papers-Series Management Economic Engineering in Agriculture and Rural Development. 21(2), 275–280.

[22] Simotova, V., Petrach, F., Slaninova, T., 2023. Price development of wheat and rapeseed. AD Alta-journal of interdisciplinary research. 13 (2), 228–232.

[23] Saeed, N., Shafi, I., Pervez, S., et al., 2025. Intelligent Decision Making for Commodities Price Prediction: Opportunities, Challenges and Future Avenues. Computational Economics. DOI: https://doi.org/10.1007/s10614-024-10837-5

[24] Sun, F., Meng, X., Zhang, Y., et al., 2023. Agricultural Product Price Forecasting Methods: A Review. Agriculture. 13(9), 1671. DOI: https://doi.org/10.3390/agriculture13091671

[25] Gu, Y.H., Jin, D., Yin, H., et al., 2022. Forecasting Agricultural Commodity Prices Using Dual Input Attention LSTM. Agriculture. 12(2), 256. DOI: https://doi.org/10.3390/agriculture12020256

[26] Pusporani, E., Mardianto, M.F.F., Sediono, et al., 2022. Prediction of national strategic commodity prices based on multivariate nonparametric time series analysis. Communications in Mathematical Biology and Neuroscience. DOI: https://doi.org/10.28919/cmbn/7712

[27] Dharmayanti, D., Akm, A.O., Soegoto, E.S., et al., 2024. Application of data mining for predicting horticultural commodities price. Journal of Engineering Science and Technology. 19(1), 163–175.

[28] Oktoviany, P., Knobloch, R., Korn, R., 2021. A machine learning-based price state prediction model for agricultural commodities using external factors. Decisions in Economics and Finance. 44(2), 1063–1085. DOI: https://doi.org/10.1007/s10203-021-00354-7

[29] Rana, H., Farooq, M.U., Kazi, A.K., et al., 2024. Prediction of Agricultural Commodity Prices using Big Data Framework. Engineering, Technology & Applied Science Research. 14(1), 12652–12658. DOI: https://doi.org/10.48084/etasr.6468

[30] Tule, M.K., Salisu, A.A., Chiemeke, C.C., 2019. Can agricultural commodity prices predict Nigeria’s inflation? Journal of Commodity Markets. 16, 100087. DOI: https://doi.org/10.1016/j.jcomm.2019.02.002

[31] Sun, T.-T., Su, C.-W., Tao, R., et al., 2021. Are Agricultural Commodity Prices on a Conventional Wisdom with Inflation? Sage Open. 11(3), 21582440211038347. DOI: https://doi.org/10.1177/21582440211038347

[32] Porto, B.M., 2022. Forecasting agricultural commodity prices: a bibliometric review of models. Journal of Management and Secretariat Studies. 13(3), 881–912. DOI: https://doi.org/10.7769/gesec.v13i3.1380 (in Portuguese)

[33] Wang, Z., French, N., James, T., et al., 2023. Climate and environmental data contribute to the prediction of grain commodity prices using deep learning. Journal of Sustainable Agriculture and Environment. 2(3), 251–265. DOI: https://doi.org/10.1002/sae2.12041

[34] Hennessy, D.A., Moschini, G., 2001. Uncertainty, risk aversion, and risk management for agricultural producers. In: Handbook of Agricultural Economics. Elsevier: Amsterdam, Netherlands. pp. 87–153. DOI: https://doi.org/10.1016/S1574-0072(01)10005-8

[35] Just, R.E., Pope, R.D. (Eds.), 2002. A Comprehensive Assessment of the Role of Risk in U.S. Agriculture. Springer: Boston, MA, USA. DOI: https://doi.org/10.1007/978-1-4757-3583-3

[36] Chavas, J.P., 2004. Risk analysis in theory and practice. Elsevier: Amsterdam, Netherlands.

[37] Arrow, K.J., 1964. The Role of Securities in the Optimal Allocation of Risk-bearing. The Review of Economic Studies. 31(2), 91. DOI: https://doi.org/10.2307/2296188

[38] Pratt, J.W., 1964. Risk Aversion in the Small and in the Large. Econometrica. 32(1/2), 122. DOI: https://doi.org/10.2307/1913738

[39] Akerlof, G.A., 1970. The Market for “Lemons”: Quality Uncertainty and the Market Mechanism. The Quarterly Journal of Economics. 84(3), 488. DOI: https://doi.org/10.2307/1879431

[40] Just, R.E., Pope, R.D., 1978. Stochastic specification of production functions and economic implications. Journal of Econometrics. 7(1), 67–86. DOI: https://doi.org/10.1016/0304-4076(78)90006-4

[41] Antle, J.M., 1983. Testing the Stochastic Structure of Production: A Flexible Moment-Based Approach. Journal of Business & Economic Statistics. 1(3), 192–201. DOI: https://doi.org/10.1080/07350015.1983.10509339

[42] Di Falco, S., Chavas, J., 2009. On Crop Biodiversity, Risk Exposure, and Food Security in the Highlands of Ethiopia. American Journal of Agricultural Economics. 91(3), 599–611. DOI: https://doi.org/10.1111/j.1467-8276.2009.01265.x

[43] Chavas, J.-P., 2023. Agricultural Risk Management and Technology, in: Mishra, A.K., Kumbhakar, S., Lien, G. (Eds.), Managing Risk in Agriculture. CABI, GB, pp. 130–143. DOI: https://doi.org/10.1079/9781800622289.0009

[44] Wuepper, D., Bukchin‐Peles, S., Just, D., et al., 2023. Behavioral agricultural economics. Applied Economic Perspectives and Policy. 45(4), 2094–2105. DOI: https://doi.org/10.1002/aepp.13343

[45] Ray, D.K., Mueller, N.D., West, P.C., et al., 2013. Yield Trends Are Insufficient to Double Global Crop Production by 2050. PLoS ONE. 8(6), e66428. DOI: https://doi.org/10.1371/journal.pone.0066428

[46] Lobell, D.B., Schlenker, W., Costa-Roberts, J., 2011. Climate Trends and Global Crop Production Since 1980. Science. 333(6042), 616–620. DOI: https://doi.org/10.1126/science.1204531

[47] Nelson, G.C., Valin, H., Sands, R.D., et al., 2014. Climate change effects on agriculture: Economic responses to biophysical shocks. Proceedings of the National Academy of Sciences. 111(9), 3274–3279. DOI: https://doi.org/10.1073/pnas.1222465110

[48] Gammans, M., Mérel, P., Ortiz-Bobea, A., 2017. Negative impacts of climate change on cereal yields: statistical evidence from France. Environmental Research Letters. 12(5), 054007. DOI: https://doi.org/10.1088/1748-9326/aa6b0c

[49] Bukovsky, M.S., Mearns, L.O., 2020. Regional climate change projections from NA-CORDEX and their relation to climate sensitivity. Climatic Change. 162(2), 645–665. DOI: https://doi.org/10.1007/s10584-020-02835-x

[50] Jones, J.W., Antle, J.M., Basso, B., et al., 2017. Toward a new generation of agricultural system data, models, and knowledge products: State of agricultural systems science. Agricultural Systems. 155, 269–288. DOI: https://doi.org/10.1016/j.agsy.2016.09.021

[51] Amundson, R., Berhe, A.A., Hopmans, J.W., et al., 2015. Soil and human security in the 21st century. Science. 348(6235), 1261071. DOI: https://doi.org/10.1126/science.1261071

[52] Stevens, C.J., 2019. Nitrogen in the environment. Science. 363(6427), 578–580. DOI: https://doi.org/10.1126/science.aav8215

[53] Frew, A., Weston, L.A., Reynolds, O.L., et al., 2018. The role of silicon in plant biology: a paradigm shift in research approach. Annals of Botany. 121(7), 1265–1273. DOI: https://doi.org/10.1093/aob/mcy009

[54] Ciliberto, F., Moschini, G., Perry, E.D., 2019. Valuing product innovation: genetically engineered varieties in US corn and soybeans. The RAND Journal of Economics. 50(3), 615–644. DOI: https://doi.org/10.1111/1756-2171.12290

[55] Fuglie, K.O., Walker, T.S., 2001. Economic Incentives and Resource Allocation in U.S. Public and Private Plant Breeding. Journal of Agricultural and Applied Economics. 33(3), 459–473. DOI: https://doi.org/10.1017/S1074070800020939

[56] Zhang, Y., Wang, X., Glauben, T., et al., 2011. The impact of land reallocation on technical efficiency: evidence from China. Agricultural Economics. 42(4), 495–507. DOI: https://doi.org/10.1111/j.1574-0862.2010.00532.x

[57] State Agricultural Intervention Fund, 2023. Market report on cereals, oilseeds and feed. Available from: https://www.szif.cz/cs (cited 3 April 2025). (in Czech)

[58] Agrokompas, 2025. Stock market news. Available from: https://www.agrokompas.cz/burzovni-zpravodajstvi (cited 3 April 2025). (in Czech)

[59] Ministry of Agriculture, 2025. Press releases. Available from: https://mze.gov.cz/public/portal/mze/tiskovy-servis/tiskove-zpravy (cited 5 April 2025).(in Czech)

[60] Czech Television, 2025. China to offer new visas to attract highly skilled workers. Available from: https://ct24.ceskatelevize.cz/rubrika/ekonomika-17 (cited 5 April 2025).(in Czech)

[61] Šuleř, P., Machová, V., 2020. Better results of artificial neural networks in predicting ČEZ share prices. Journal of International Studies. 13(2), 259–278. DOI: https://doi.org/10.14254/2071-8330.2020/13-2/18

[62] Czech Statistical Office, 2024. Development of average prices of selected foods. Available from: https://csu.gov.cz/vyvoj-prumernych-cen-vybranych-potravin-2024 (cited 5 April 2025).(in Czech)