Modelling and forecasting of potato sales prices in Ukraine
Keywords:
potato market, sales price, forecasting, industrial potato growing, time series analysis, Rstudio.
Abstract
Purpose. Under the background of the climate change and other crises, the world food system is becoming increasingly vulnerable to price fluctuations. This highlights the need to consider and better manage the risks associated with price volatility in accordance with the principles of a market economy and simultaneously protecting the most vulnerable groups of population. Responding to these challenges, in this study we aim to determine the main parameters of time series of potato sales prices in agricultural enterprises in Ukraine, to build an appropriate model, and to form a short-term (one-year) forecast.
Methodology / approach. We used in the research the data from the State Statistics Service of Ukraine on average monthly sales prices of potatoes in agricultural enterprises from December 2012 to July 2021 (104 observations) adjusted for the price index of crop products sold by enterprises for the month (with December 2012 base period). Decomposition was used to determine the characteristics of the time series; exponential smoothing methods (Holt-Winters and State Space Framework – ETS) and autoregressive-moving average were used to find the model that fits the actual data the best and has high prognostic quality. We applied the Rstudio forecast package to model and to forecast the time series.
Results. The time series of potato sales prices in enterprises is characterized by seasonality (mainly related to seasonal production) with the lowest prices in November, and the highest – in June; although, other periods of price growth were identified during the year: in January and April. The ARMA (2, 2) (1,0)12 with a non-zero mean was found to be the best model for forecasting potatoes sales prices. ARMA (2, 2) (1,0)12, compared to the state-space exponential smoothing model with additive errors – ETS (A), better fits the observed data and provides more accurate forecasting model (with lower errors). Forecast made with ARMA (2, 2) (1,0)12 shows that potato sale prices in agricultural enterprises in November 2021 (months with the lowest price) will range from 2154.76 UAH/t to 7414.57 UAH/t, in June 2022 – from 3016.72 UAH/t to 14051.63 UAH/t (prices of July 2021) with a probability of 95%. The forecast’s mean absolute percentage error is 14.87%.
Originality / scientific novelty. This research deepens the methodological basis for food prices modelling and forecasting, thus contributing to the agricultural economics science development. The obtained results confirm the previous research findings on the better quality of food prices forecasts made with autoregressive models (for univariate time series) compared with exponential smoothing. Additionally, the study reveals advantages of the state space framework for exponential smoothing (ETS) compared to Holt-Winters methods in case of time series with seasonality: although the ETS model overlaps with the observed (train) data, it is better in terms of information criteria and forecasting (for the test data).
Practical value / implications. The obtained results can serve as an information basis for decision-making on potato production and sales by producers, on more efficient use of resources by the population, on more effective measures to support industrial potato growing, to implement social programs and food security policy by the government.
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27. Degiannakis, S., Filis, G., Klein, T. and Walther, T. (2020), Forecasting realized volatility of agricultural commodities. International Journal of Forecasting. https://doi.org/10.1016/j.ijforecast.2019.08.011.
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39. Rosen, S. (1999), Potato paradoxes. Journal of Political Economy, vol. 107, no. S6, pp. 294–313. https://doi.org/10.1086/250112.
40. Kovalenko, I. M., Kovalenko, V. M., Butenko, Ye. Yu. et al. (2021), Adaptability of Solanum tuberosum to changes of ecological growing condition. Modern Phytomorphology, vol. 15, is. 2, pp. 38–43.
41. Mishenin, Ye., Yarova, I. and Koblianska, I. (2021), Ecologically harmonized agricultural management for global food security in Ecological Intensification of Natural Resources for Sustainable Agriculture, eds M. K. Jhariya, R. S. Meena and A. Banerjee. Springer Nature Singapore Pte Ltd, Singapore. https://doi.org/10.1007/978-981-33-4203-3.
2. Соціально-демографічні характеристики домогосподарств України у 2020 році: стат. зб. Київ: Держслужба статистики України, 2021. 88 с.
3. Сільське господарство України за 2020 рік: стат. щорічник. Київ: Держслужба статистики України, 2021. 232 с.
4. Statistical database of food and agriculture organization of the United Nations. FAOSTAT, 2020. URL: http://faostat.fao.org/faostat.
5. Koblianska І., Pasko О., Hordiyenko M., Yarova I. Are peasant households feasible in terms of policy? The debate on the future of semi-subsistence households in Ukraine. Eastern European Countryside. 2020. Vol. 26. Pp. 127–179. https://doi.org/10.12775/eec.2020.006.
6. Крупа О. М., Крупа В. Р. Кон’юнктура ринку картоплі в Україні та перспективи її оптимізації. Ефективна економіка. 2019. № 12. https://doi.org/10.32702/2307-2105-2019.12.86
7. Макульський К., Житков А. Картопля-2020: проблеми і перспективи промислового виробництва. АгроПрофі. 2020. 21 лютого. URL: http://www.agroprofi.com.ua/statti/1825-2020-3.
8. Староселець І. Українське картоплярство: бізнес чи сізіфів камінь. УкрІнформ. 2020. 4 вересня. URL: https://www.ukrinform.ua/rubric-economy/3092887-ukrainske-kartoplarstvo-biznes-ci-sizifiv-kamin.html.
9. Українська асоціація виробників картоплі. Нестабільність курсу та імпортозаміщення низькосортною картоплею: що призводить до кризи картоплярства в Україні. URL: http://potatoclub.com.ua/news/437-nestablnst-kursu-ta-mportozamschennya-nizkosortnoyu-kartopleyu-scho-prizvodit-do-krizi-kartoplyarstva-v-ukrayin.html.
10. Українська асоціація виробників картоплі. Потужності зберігання картоплі в Україні вчора, сьогодні, завтра. URL: http://potatoclub.com.ua/news/525-potuzhnost-zbergannya-kartopl-v-ukrayin-vchora-sogodn-zavtra.html
11. Про схвалення Концепції Державної цільової програми розвитку промислового картоплярства на період до 2025 року: Розпорядження Кабінету Міністрів України № 1345-2020-р від 21.10.2020 р. URL: https://zakon.rada.gov.ua/laws/show/1345-2020-р#Text.
12. Thorne F. Potato prices as affected by supply and demand factors: an Irish case study. 123rd EAAE Seminar «Price volatility and farm income stabilisation. Modelling outcomes and assessing market and policy based responses» (23–24 February 2012), Dublin: European Association of Agricultural Economists (EAAE). https://doi.org/10.22004/ag.econ.122473.
13. Bolotova Y. V. Recent price developments in the United States potato industry. American Journal of Potato Research. 2017. Vol. 94. Pp. 567–571. https://doi.org/10.1007/s12230-017-9590-4.
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17. Anwar M., Shabbir G., Shahid M. H., Samreen W. Determinants of potato prices and its forecasting: a case study of Punjab, Pakistan. MPRA Paper No. 66678. Punjab: Punjab economic research institute, 2015. 38 p. URL: https://mpra.ub.uni-muenchen.de/66678.
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19. Shahzad M. A. Price forecasting model for perishable commodities: a case of tomatoes in Punjab, Pakistan. MPRA Paper No. 81531. Punjab: Punjab Economic research Institute, 2017. 27 p. URL: https://mpra.ub.uni-muenchen.de/81531.
20. Hossain Md. M., Abdulla F. Forecasting potato production in Bangladesh by ARIMA model. Journal of Advanced Statistics. 2016. Vol. 1. No. 4. Pp. 191–198. https://doi.org/10.22606/jas.2016.14002.
21. Mishra R., Kuma D. Price behaviour of major vegetables in hill region of Nepal: an econometric analysis. SAARC Journal of Agriculture. 2014. Vol. 10. No. 2. Pp. 107–120. https://doi.org/10.3329/sja.v10i2.18332.
22. Paredes-Garcia W. J., Ocampo-Velázquez R. V., Torres-Pacheco I., Cedillo-Jiménez C. A. Price forecasting and span commercialization opportunities for Mexican agricultural products. Agronomy. 2019. Vol. 9(12). 826. https://doi.org/10.3390/agronomy9120826.
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27. Degiannakis S., Filis G., Klein T., Walther T. Forecasting realized volatility of agricultural commodities. International Journal of Forecasting. 2020. https://doi.org/10.1016/j.ijforecast.2019.08.011
28. Sangsefidi S. J., Moghadasi R., Yazdani S., Nejad A. M. Forecasting the prices of agricultural products in Iran with ARIMA and ARCH models. International Journal of Advanced and Applied Sciences. 2015. Vol. 2. Is. 11. Pp. 54–57.
29. Крупа О. М. Економічна ефективність вирощування і реалізації картоплі у господарствах населення Львівської області. Науковий вісник ЛНУВМБТ ім. С. З. Ґжицького. 2014. Т. 16. № 1(58). Ч. 1. С. 277–284.
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31. Комендантова Н. Эксперты дали прогноз цен на картофель на зиму-2021. Когда начнет дорожать овощ. Українські новини, 8 вересня 2021 р. URL: https://ukranews.com/news/799666-eksperty-dali-prognoz-tsen-na-kartofel-na-zimu-2021-kogda-nachnet-dorozhat-ovoshh.
32. Офіційний сайт Державної служби статистики України. Реалізація продукції сільського господарства підприємствами та господарствами населення за 2012–2021 рр. URL: http://www.ukrstat.gov.ua/operativ/menu/menu_u/cg.htm.
33. Офіційний сайт Державної служби статистики України. Індекси цін продукції сільського господарства, реалізованої підприємствами за місяць (продукція рослинництва, 2012–2021 рр.). URL: http://www.ukrstat.gov.ua/operativ/operativ2020/sg/icsh/icsh2020_u.htm.
34. Hyndman R. J., Athanasopoulos G. Forecasting: principles and practice. 2nd ed. Melbourne: Monash University, 2018. URL: https://otexts.org/fpp2.
35. Hyndman R. J., Koehler A. B., Ord J. K., Snyder R. D. Forecasting with exponential smoothing: the state space approach. Berlin: Springer-Verlag, 2008. URL: http://www.exponentialsmoothing.net.
36. RStudio Team. RStudio: Integrated Development for R. RStudio, PBC, Boston, MA. URL: http://www.rstudio.com.
37. Forecasting functions for time series and linear models. R package version 8.15. URL: https://pkg.robjhyndman.com/forecast.
38. Hyndman R. J., Khandakar Y. Automatic time series forecasting: the forecast package for R. Journal of Statistical Software. 2008. Vol. 27. Is. 3. Pp. 1–22. https://doi.org/10.18637/jss.v027.i03.
39. Rosen S. Potato paradoxes. Journal of Political Economy. 1999. Vol. 107. No. S6. Pp. 294–313. https://doi.org/10.1086/250112.
40. Kovalenko I. M., Kovalenko V. M., Butenko Ye. Yu. et al. Adaptability of Solanum tuberosum to changes of ecological growing condition. Modern Phytomorphology. 2021. Vol. 15. Is. 2. Pp. 38–43.
41. Mishenin Ye., Yarova I., Koblianska I. Ecologically harmonized agricultural management for global food security. Ecological Intensification of Natural Resources for Sustainable Agriculture. Eds. M. K. Jhariya, R. S. Meena, A. Banerjee. Springer Nature Singapore Pte Ltd, 2021. Pp. 29–77. https://doi.org/10.1007/978-981-33-4203-3.
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17. Anwar, M., Shabbir, G., Shahid, M. H. and Samreen, W. (2019), Determinants of potato prices and its forecasting: a case study of Punjab, Pakistan. MPRA Paper No. 66678, Punjab economic research institute, Punjab, Pakistan, available at: https://mpra.ub.uni-muenchen.de/66678.
18. Firleja, K. and Kubala, S. (2020), Determinants of variation of potato prices in the European Union. Economia Agro-Alimentare, vol. 3, pp. 697–707. https://doi.org/10.3280/ECAG2019-003007.
19. Shahzad, M. A. (2017), Price forecasting model for perishable commodities: a case of tomatoes in Punjab, Pakistan. MPRA Paper No. 81531, Punjab economic research institute, Punjab, Pakistan, available at: https://mpra.ub.uni-muenchen.de/81531.
20. Hossain, Md. M. and Abdulla, F. (2016), Forecasting potato production in Bangladesh by ARIMA model. Journal of Advanced Statistics, vol. 1, no. 4, pp. 191–198. https://doi.org/10.22606/jas.2016.14002.
21. Mishra, R. and Kumar, D. (2014), Price behaviour of major vegetables in hill region of Nepal: an econometric analysis. SAARC Journal of Agriculture, vol. 10, no. 2, pp. 107–120. https://doi.org/10.3329/sja.v10i2.18332.
22. Paredes-Garcia, W. J., Ocampo-Velázquez, R. V., Torres-Pacheco, I. and Cedillo-Jiménez, C. A. (2019), Price forecasting and span commercialization opportunities for Mexican agricultural products. Agronomy, vol. 9(12), pp. 826–838. https://doi.org/10.3390/agronomy9120826.
23. Mihajlović, Š., Vukelić, N., Novković, N. and Mutavdžić, B. (2019), Vegetable prices in Serbia: tendencies and forecasting. Ekonomika Poljoprivrede, vol. 66, no. 2, pp. 485–498. https://doi.org/10.5937/ekoPolj1902485S.
24. Celik, S. (2019), Modeling and estimation of potato production in Turkey with time series analysis. International Journal of Trend in Research and Development, vol. 6, is. 5, pp. 111–116.
25. Anjoy, P. and Paul, R. K. (2017), Wavelet based hybrid approach for forecasting volatile potato price. Journal of the Indian Society of Agricultural Statistics, vol. 71(1), pp. 7–14.
26. Drachal, K. (2019), Analysis of agricultural commodities prices with new Bayesian model combination schemes. Sustainability, vol. 11(19), pp. 5305–5328. https://doi.org/10.3390/su11195305.
27. Degiannakis, S., Filis, G., Klein, T. and Walther, T. (2020), Forecasting realized volatility of agricultural commodities. International Journal of Forecasting. https://doi.org/10.1016/j.ijforecast.2019.08.011.
28. Sangsefidi, S. J., Moghadasi, R., Yazdani, S. and Nejad, A. M. (2015), Forecasting the prices of agricultural products in Iran with ARIMA and ARCH models. International Journal of Advanced and Applied Sciences, vol. 2, is. 11, pp. 54–57.
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38. Hyndman, R. J. and Khandakar, Y. (2008), Automatic time series forecasting: the forecast package for R. Journal of Statistical Software, vol. 27, is. 3, pp. 1–22. https://doi.org/10.18637/jss.v027.i03.
39. Rosen, S. (1999), Potato paradoxes. Journal of Political Economy, vol. 107, no. S6, pp. 294–313. https://doi.org/10.1086/250112.
40. Kovalenko, I. M., Kovalenko, V. M., Butenko, Ye. Yu. et al. (2021), Adaptability of Solanum tuberosum to changes of ecological growing condition. Modern Phytomorphology, vol. 15, is. 2, pp. 38–43.
41. Mishenin, Ye., Yarova, I. and Koblianska, I. (2021), Ecologically harmonized agricultural management for global food security in Ecological Intensification of Natural Resources for Sustainable Agriculture, eds M. K. Jhariya, R. S. Meena and A. Banerjee. Springer Nature Singapore Pte Ltd, Singapore. https://doi.org/10.1007/978-981-33-4203-3.
Published
2021-12-20
How to Cite
Koblianska, I., Kalachevska, L., Minta, S., Strochenko, N., & Lukash, S. (2021). Modelling and forecasting of potato sales prices in Ukraine. Agricultural and Resource Economics: International Scientific E-Journal, 7(4), 160-179. https://doi.org/10.51599/are.2021.07.04.09
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