Milk price modeling and forecasting

Keywords: price of dairy products, dairy markets, dairy products, price correlation, forecasting on dairy market.


Purpose. The purpose of the article is to substantiate the methodological approach to forecasting the selling price of milk, which is produced and sold by agricultural producers in the Ukrainian market of raw milk.

Methodology / approach. The basis of the methodological approach is the construction of models describing the change in sales prices for milk and certain types of dairy products (pasteurized milk with a fat content of up to 2.5 %, sour cream with a fat content of up to 15.0 %, soft fat cheeses) during 2017–2019 and forecasting the sale price of milk for a period of 6 months. The ARIMA model was used in the forecast, taking into account the degree of correlation between changes in the price of milk and the analyzed types of dairy products. The time factor (time lag) of one month and the share of milk in the price of the finished dairy products were also considered.

Results. The forecast results allowed us to determine milk sales prices for January–June 2020. In addition, the study showed the lack of a single direction in their change during the bias period, namely – increase in milk sales price during February–January 2020 and its reduction till June 2020. The obtained forecast values of milk selling price, considering the share of milk in the selling price of finished dairy products, were adjusted according to the time lag.

Originality / scientific novelty. Novelty is an algorithm for using a methodical approach to forecasting the purchase price of milk, taking into account the correlation between milk prices and prices for certain types of dairy products; time lag and the share of milk prices in the sale price of finished dairy products.

Practical value / implications. Comparison of the obtained forecast data of the price with the actual prices of milk sales indicated the existence of insignificant differences between them, proving the adequacy of the proposed methodological approach to be used in forecasting of milk prices at the enterprises.


1. Yanch, E. (1974), Prognozirovanie nauchno-tehnicheskogo progressa [Forecasting scientific and technological progress], Progress, Moscow, Russia.
2. Dobrov, G. M. (1989), Nauka o nauke. Vvedenie v obshee naukovedenie [Science about science. Introduction to general science study], Naukоva dumka, Kyiv, Ukraine.
3. Dobrov, G. M., Korennoy, A. A. and Musienko, V. B. (1989), Prognozirovanie i ocenki nauchno-tehnicheskih novovvedenij [Forecasting and evaluating scientific and technical innovations], Naukоva dumka, Kyiv, Ukraine.
4. Ivakhnenko, A. G. (1981), Induktivnyj metod samoorganizacii modelej slozhnyh sistem [Inductive method of self-organization of complex systems models], Naukоva dumka, Kyiv, Ukraine.
5. Ivakhnenko, A. G. and Muller, J. A. (1984), Samoorganizaciya prognoziruyushih modelej [Self-organization of predictive models], Tekhnika, Kyiv, Ukraine.
6. Glushkov, V. M. (1969), About forecasting based on expert assessments. Cybernetics, no. 2, pp. 2–4.
7. Assumptions of the macroeconomic forecast of Ukraine for 2019–2020, available at: 19_10_02_1_IEPR_NANU_Assumptions.Risks.Consensus.MEPT.pdf.
8. Filipova, K. V. (2007), Methods for predicting innovative development of an enterprise. Visnyk Natsionalnoho universytetu «Lvivska politekhnika». Seriia: Problemy ekonomiky ta upravlinnia, no. 579, pp. 609–613, available at:
9. Allen, P. G. (1994), Economic forecasting in agriculture. International Journal of Forecasting, no. 10, pp. 81–135.
10. Demir, B., Alptekin, N., Kilicaslan, Y., Ergen, M. and Uslu, N. C. (2015), Forecasting agricultural production: a chaotic dynamic approach. World Journal of Applied Economics, vol. 1, is. 1, pp. 65–80.
11. Galvez-Soriano, O. (2018), Forecasting the agricultural sector of Mexico in Economy, finance and social development in Mexico. Asociacion Mexicana de Investigacion Interdisciplinaria Asmiila, Mexico, pp. 42–58.
12. Jadhav, V. et al. (2017), Application of ARIMA models for forecasting agricultural prices. Journal of Agriculture Science and Technology, vol. 19, pp. 981–992.
13. Kolkova, A. (2018), Indicators of technical analysis on the basis of moving averages as prognostic methods in the food industry. Journal of Competitiveness, vol. 10(4), pp. 102–119. joc.2018.04.07.
14. Newbold, P. (1981), Some recent developments in time series analysis, correspondent paper. International Statistical Rewiew, vol. 49, no. 1, pp. 53–66.
15. Padhan, P. C. (2012), Application of ARIMA model for forecasting agricultural productivity in India. Journal of Agricultural & Social Sciences, no. 8, pp. 50–56.
16. Vedenieev, V. A. (2019), Evaluation of the efficiency of pre-urban models of forecasting the selling price of products of the agricultural sector in Ukraine. Economy and the state, no. 9, pp. 46–51.
17. Céspedes, L. F. and Velasco, A. (2012), Macroeconomic performance during commodity price booms and busts. Working Paper 18569, National Bureau of Economic Research, Cambridge, UK.
18. Borawski, P., Guth, M., Truszkowski, W., Zuzek, D., Beldycka-Borawska, A., Mickiewicz, B., Szymanska, E., Harper, J. and Dunn, J. (2020), Milk price changes in Poland in the context of the Common Agricultural Policy. Agricultural Economics – Czech, vol. 66, is. 1, pp. 19–26.
19. Atsbeha, D., Kristofersson, D. and Rickertsen, K. (2016), Component supply responses in dairy production. European Review of Agricultural Economics, vol. 43, is. 2, pp. 193–215.
20. Paura, L. and Arhipova, I. (2016), Analysis of the milk production and milk price in Latvia. Procedia Economics and Finance, no. 39, pp. 39–43.
21. Butler, W. R. (2000), Nutritional interactions with reproductive performance in dairy cattle. Animal Reproduction Science, vol. 60–61, pp. 449–457.
22. Arbel, R., Bigun, Y., Ezra, E., Sturman, H. and Hojman, D. (2001), The effect of extended calvsng intervals in high lactating cows on milk production and profitability. Journal of Dairy Science, vol. 84, is. 3, pp. 600–608.
23. Bergez, J. E., Chabrier, P., Gary, C. Jeuffroy, M. H., Makowski D. and et al. (2013), An open platform to build, evaluate and simulate integrated models of farming and agro-ecosystems. Environmental Modelling & Software, vol. 39, pp. 39–49.
24. Gaillard, C. Martin, O., Blavy, P., Friggens, N. C., Sehested, J. and Phuong, H. N. (2016), Prediction of the lifetime productive and reproductive performance of Holstein cows managed for different lactation durations, using a model of lifetime nutrient partitioning. Journal of Dairy Science, vol. 99, is. 11, pp. 9126–9135.
25. Zhang, F., Murphy, M. D., Shalloo, L., Ruelle, E. and Upton, J. (2016), An automatic model configuration and optimization system for milk production forecasting. Computers and Electronics in Agriculture, vol. 128, pp. 100–111.
26. Murphy, M. D., O’Mahony, M. J., Shalloo, L., French, P. and Upton, J. (2014), Comparison of modelling techniques for milk-production forecasting. Journal of Dairy Science, vol. 97, no. 6. pp. 3352–3363.
27. Dumas, F., Dijkstra, J. and France, J. (2008), Mathematical modelling in animal nutrition: a centenary review. Journal of Agriculture Science, vol. 146, is. 2, pp. 123–142.
28. Gurčík, L., Dobošová, L., Richter, M., Kubicová, L. and Dobák, D. (2016), Controlling as a management system of milk production and consumption in Slovakia and the Czech Republic. International Scientific Days 2016. The Agri-Food Value Chain: Challenges for Natural Resources Management and Society. Scientific paper, pp. 329–338.
29. Bosakovska, V. G. (2013), Problems of pricing in the process of reforming agricultural enterprises. Productivity of agro-industrial production. Produktyvnist ahropromyslovoho vyrobnytstva. Ekonomichni nauky, vol. 24, pp. 102–106.
30. Gurska, I.S. (2013), Development of the regional market of milk and dairy products. Naukovyi visnyk Natsionalnoho universytetu bioresursiv i pryrodokorystuvannia Ukrainy. Seria: Ekonomika, ahrarnyi menedzhment, biznes, vol. 181(1), pp. 31–38.
31. Aranchiy, V. I., Drogan-Pysarenko, L. O. and Rudich, A. I. (2014), Analytical assessment and extrapolation of the dairy market functioning. Economic analysis, vol. 20, pp. 14–22.
32. Rossokha, V. V. and Petrychenko, O. A. (2018), Milk production and distribution by volume and quality and price characteristics. Economika APK, no. 7, pp. 27–36.
33. Yatsiv, I. and Yatsiv, S. (2015), Formation of prices for agricultural products as a factor in the development of the agricultural sector of the economy. Agricultural economic, vol. 8, no. 1–2, pp. 24–31.
34. Ivanova, L. S. (2017), Directions for improving state regulation of milk and dairy products market based on foreign experience. Agrosvit, no. 23, available at: http: //
35. Average consumer prices for goods (services) in 2017–2019 (2020), available at:
36. Price indices of sales of agricultural products (2020), available at:
37. Borovikov, V. P. and Ivchenko, G. I. (2000), Prognozirovanie v sisteme STATISTICA v srede Windows. Osnovy teorii i intensivnaya praktika na kompyutere [Forecasting in the STATISTICA system in the Windows environment. Fundamentals of theory and intensive computer practice], Finance and Statistics, Moscow, Russia.
38. Ruekkasaem, L. and Sasananan, M. (2018), Forecasting agricultural products prices using time series methods for crop planning. International Journal of Mechanical Engineering and Technology (IJMET), vol. 9, is. 7, pp. 957–971.
How to Cite
Shyian, N., Moskalenko, V., Shabinskyi, O., & Pechko, V. (2021). Milk price modeling and forecasting. Agricultural and Resource Economics: International Scientific E-Journal, 7(1), 81-95.