Forecasting the sale price of pork in agricultural enterprises


Keywords: price, pork, correlation and regression analysis, concentration, purchasing power, influence of factors.

Abstract

Purpose. The purpose of the study is to build and analyze an econometrical model to establish correlational relationships between the main factors influencing the price of pork sales.

Methodology / approach. In the research process, general scientific and special research methods were used, namely: monographic – to detail the functioning of the pork market; comparative – for comparing indicators and identifying trends in their change over time; statistical – for studying processes, actual data, identifying trends and regularities in the development of the pork market; tabular – for a visual representation of the obtained results; graphic – to illustrate the trends of the studied economic phenomena; correlation and regression analysis – to determine the influence of the main factors on the selling price of pork and forecasting the selling price of products; abstract and logical – for formulating conclusions and research results. The information base of the study is the data of the State Statistics Service of Ukraine and 100 agricultural enterprises of the Cherkasy region that are engaged in the production of pork.

Results. With the help of regression analysis, the dependence between the factors affecting the selling price of pork was determined, the quantitative assessment of the parameters and their statistical reliability was carried out. The obtained results made it possible to draw reasonable conclusions about the current state of the process and its development in the future. A quantitative assessment of the dependence of the retail price of pork on the income of the population in the regions of Ukraine was carried out. The main results of the study can be used to predict performance characteristics based on actual, random and calculated factors. The developed econometrical model of forecasting and planning the selling price of pork will contribute to the improvement of the system of forming production plans for the short-term period and will make it possible to increase the company’s income.

Originality / scientific novelty. In order to determine the forecast prices of pork sales and reduce their variability, it is substantiated the importance of using multiple correlation analysis to assess the interdependencies between statistical features and identify factors of direct or indirect influence on the price level. The results of the econometrical model are the basis for forecasting the sale price of pork, taking into account such factors as the number of animals and the average weight of one sold head, the increase of which will increase the profitability of the enterprise.

Practical value / implications. The practical value of the obtained research results lies in the use of correlation and regression analysis as a flexible tool for determining the quantitative expression of relationships between the factor characteristic and independent variables. The results of the study can be useful for pork producers to better understand their current state, to be able to manage the ongoing events, and to more accurately predict the future state.

References

Mohr D., Wilson W., Freund R. Statistical Methods; 4th edition. Academic Press, 2021. 784 р. Available at: https://www.elsevier.com/books/statistical-methods/mohr/978-0-12-823043-5.

Panukhnyk O., Popadynets N., Fedotova Y. Analysis and modeling of factor determinants of food provision at consumer market of Ukraine. Global Journal of Environmental Science and Management. 2019. Vol. 5. Spec. is. Pp. 215–226. https://doi.org/10.22034/gjesm.2019.05.SI.24.

Haibo Z., Yi Y., Yao C., Joe Z. Data envelopment analysis application in sustainability: the origins, development and future directions. European Journal of Operational Research. 2018. Vol. 264. Is. 1. Pp. 1–16. https://doi.org/10.1016/j.ejor.2017.06.023.

Zhu H., Xu R., Deng H. A novel STL-based hybrid model for forecasting hog price in China. Computers and Electronics in Agriculture. 2022. Vol. 198. 107068. https://doi.org/10.1016/j.compag.2022.107068.

Janse R., Hoekstra T., Jager K., Zoccali C. et al. Conducting correlation analysis: important limitations and pitfalls. Clinical Kidney Journal. 2021. Vol. 14(11). Pp. 2332–2337. https://doi.org/10.1093/ckj/sfab085.

Yang F., Lin S., Zhang J. Pork price forecast based on the comparison of KPCA-ARIMA-LSTM and DBN Multi-Model. 2021 2nd International Conference on Computer Science and Management Technology (ICCSMT) (Shanghai, 12–14 November, 2021). Shanghai: IEEE, 2021. Pp. 124–130. https://doi.org/10.1109/ICCSMT54525.2021.00033.

Ibatullin M., Varchenko O., Svynous I., Khakhula B. Organizational and economic bases of pig breeding in Ukraine. International Journal of Management and Business Research. 2019. Vol. 9(1). Pp. 59–72.

Калінчик С., Алєксєєнко І., Калінчик М. Проблеми стратегії ефективності свинарства. Агросвіт. 2017. № 13. С. 14–18. URL: http://www.agrosvit.info/index.php?op=1&z=2436&i=2.

Mazur A., Bondarenko V., Mazur S. Organizational reformation of agribusiness entities in Ukraine. Baltic journal of economic studies. 2018. Vol. 4. No. 2. Pp. 126–133. https://doi.org/10.30525/2256-0742/2018-4-2-126-133.

Prylipko S., Shevchenko N., Hryshchenko O. Efficiency of small farms functioning in Ukraine. Economic Annals-XXI. 2016. Vol. 158. Is. 3–4(2). Pp. 17–21. https://doi.org/10.21003/ea.V158-04.

Рибалко В., Сагло О. М’ясні генотипи свиней та їх подальше використання. Свинарство. 2019. № 72. С. 145–146. URL: http://nbuv.gov.ua/UJRN/svun_2019_72_20.

Varchenko O., Svynous I., Grynchuk Y., Khakhula B., Ibatullin M. Improvement of eco-taxation of goods producer of pig husbandry in Ukraine. Academy of Accounting and Financial Studies Journal. 2018. Vol. 22. Is. 5. URL: https://www.abacademies.org/articles/improvement-of-ecotaxation-of-goods-producer-of-pig-husbandry-in-ukraine-7492.html.

Шпичак О. Проблеми ціноутворення в контексті купівельної спроможності населення та інфляційних процесів. Економіка АПК. 2016. № 6. С. 59–70. URL: http://eapk.org.ua/contents/2016/06/59.

Підгорний А. Підвищення ефективності виробництва продукції свинарства у сільськогосподарських підприємствах: дис. ... канд. екон. наук: 08.00.04. Вінниця, 2020. 185 с.

Ібатуллін M. І. Ринок продукції свинарства: оптимальне поєднання ринкового і державного регулювання: монографія. Київ: Аграр. наука, 2017. 383 c.

Daninga P., Qiao Z. Managing price risk of pork through gross margin: a depiction from China and US. European Journal of Business and Management Research. 2020. Vol. 5. No. 5. https://doi.org/10.24018/ejbmr.2020.5.5.504.

Zhao J. Analysis of the rise and fall of pork prices and prediction of the future pork market. 7th International Conference on Financial Innovation and Economic Development (ICFIED) (14-16 January). Atlantis Press, 2022. Pp. 350–354. https://doi.org/10.2991/aebmr.k.220307.055.

Патика Н. Пріоритети забезпечення конкурентоспроможності сільського господарства України на світових ринках. Agricultural and Resource Economics. 2018. Vol. 4. No. 4. Pp. 130–145. https://doi.org/10.51599/are.2018.04.04.10.

Підгорний А. Пріоритетні напрями підвищення ефективності виробництва продукції свинарства у сільськогосподарських підприємствах. Економіка та управління АПК. 2019. № 1. С. 50–64. URL: http://nbuv.gov.ua/UJRN/ecupapk_2019_1_7.

Зомчак Л., Умриш Г. Моделювання й прогнозування виробництва м’яса та яєць в Україні за допомогою сезонної ARIMA-моделі. Agricultural and Resource Economics. 2017. Vol. 3. No. 3. Pp. 16–27. https://doi.org/10.51599/are.2017.03.03.02.

Гайдаєнко О., Коваленко Л. Застосування кореляційно-регресійного аналізу для прогнозування результатів діяльності підприємства. Облік, економіка, менеджмент: наукові нотатки. 2017. Вип. 1(13). Ч. 1. С. 16–23. URL: http://surl.li/ebteb.

Гросул В., Іщейкін Т. Використання багатофакторного кореляційно-регресійного аналізу для оцінки ефективності діяльності підприємств та організацій споживчої кооперації. Науковий вісник Полтавського університету економіки і торгівлі. 2016. № 4. С. 47–61. URL: http://nbuv.gov.ua/UJRN/Nvpusk_2016_4_8.

Пехота М. А., Грищенко О. Ю. Основи економетрії. Київ: ННЦ «ІАЕ», 2007. 180 с.

Palát M., Palátová Š. Microeconomic appraisal of pork market indicators including correlation matrices and developmental trend models in the EU. Bulgarian Journal of Agricultural Science. 2022. Vol. 28. No. 1. Pp. 10–18. URL: https://www.agrojournal.org/28/01-02.pdf.

Офіційний сайт Державної служби статистики України. URL: http://www.ukrstat.gov.ua.

Чередніченко О. Економічні аспекти виробництва та споживання м’яса та м’ясопродуктів. Agricultural and Resource Economics. 2017. Vol. 3. No. 3. Pp. 130–144. https://doi.org/10.51599/are.2017.03.03.10.

Stepasyuk L., Dramaretska K., Titenko Z., Babiak N. The competitive environment diagnostics in the animal husbandry products market. International Journal of Advanced Science and Technology. 2020. Vol. 29. No. 8s. Pp. 2551–2558. URL: http://sersc.org/journals/index.php/IJAST/article/view/14759.

Бабенко М. Якими будуть ціни на свинину у 2021 році? Agroexpert. 2021. URL: https://agroexpert.ua/18360-2.

Брик М. Сучасний стан та перспективи розвитку галузі тваринництва в Україні. Економічний аналіз. 2018. Vol. 28. No. 4. C. 331–337. https://doi.org/10.35774/econa2018.04.331.

Nikolaienko M., Bal-Prylypko L. Development of an integrated food quality management system. Potravinarstvo Slovak Journal of Food Sciences. 2020. Vol. 14. Pp. 862–873. https://doi.org/10.5219/1434.

Кравченко О. Особливості економічних відносин між учасниками ринку продукції тваринництва. Agricultural and Resource Economics. 2019. Vol. 5. No. 1. Pp. 71–91. https://doi.org/10.51599/are.2019.05.01.05.

Chen T., Chen Z., Zhou Z. Computational research and implementation of prediction of pork price based on deeplearning. Journal of Physics: Conference Series. 2nd International Conference on Computer, Communications and Mechatronics Engineering (CCME 2020). Vol. 1815. (Xiamen, 20–21 December 2020). Xiamen: IOP Publishing, 2021. 012032. https://doi.org/10.1088/1742-6596/1815/1/012032.

Zielińska-Sitkiewicz M., Chrzanowska M. Prediction of pork meat prices by selected methods as an element supporting the decision-making process. Operations Research and Decisions. 2021. Vol. 31. Is. 3. Pp. 137–152. https://doi.org/10.37190/ord210307.

References

Mohr, D., Wilson, W., & Freund, R. (2022). Statistical Methods (4 th ed.). Academic Press. Available at: https://www.elsevier.com/books/statistical-methods/mohr/978-0-12-823043-5.

Panukhnyk, O., Popadynets, N., & Fedotova, Y. (2019). Analysis and modeling of factor determinants of food provision at consumer market of Ukraine. Global Journal of Environmental Science and Management, 5(spec. is.), 215–226. https://doi.org/10.22034/gjesm.2019.SI.24.

Haibo, Z., Yi, Y., Yao, C., & Joe, Z. (2018). Data envelopment analysis application in sustainability: the origins, development and future directions. European Journal of Operational Research, 264(1), 1–16. https://doi.org/10.1016/j.ejor.2017.06.023.

Zhu, H., Xu, R., & Deng, H. (2022). A novel STL-based hybrid model for forecasting hog price in China. Computers and Electronics in Agriculture, 198, 107068. https://doi.org/10.1016/j.compag.2022.107068.

Janse, R., Hoekstra, T., Jager, K., Zoccali, C., Tripepi, G., Dekker, F., & Diepen, M. (2021). Conducting correlation analysis: important limitations and pitfalls. Clinical Kidney Journal, 14(11), 2332–2337. https://doi.org/10.1093/ckj/sfab085.

Yang, F., Lin, S., & Zhang, J. (2021). Pork price forecast based on the comparison of KPCA-ARIMA-LSTM and DBN Multi-Model. 2021 2nd International Conference on Computer Science and Management Technology (ICCSMT). Shanghai, IEEE. https://doi.org/10.1109/ICCSMT54525.2021.00033.

Ibatullin, M., Varchenko, O., Svynous, I., Khakhula, B., & Dragan, O. (2019). Organizational and economic bases of pig breeding in Ukraine. International Journal of Management and Business Research, 9(1), 59–72.

Kalinchyk, S., Alekseenko, I., & Kalinchyk, M. (2017). Problems of the efficiency strategy of pig farming. Agrosvit, 13, 14–18. Available at: http://www.agrosvit.info/index.php?op=1&z=2436&i=2.

Mazur, A., Bondarenko, V., & Mazur, S. (2018). Organizational reformation of agribusiness entities in Ukraine. Baltic journal of economic studies, 4(2), 126–133. https://doi.org/10.30525/2256-0742/2018-4-2-126-133.

Prylipko, S., Shevchenko, N., & Hryshchenko, O. (2016). Efficiency of small farms functioning in Ukraine. Economic Annals-XXI, 158(3–4)2), 17–21. https://doi.org/10.21003/ea.V158-04.

Rybalko, V. P., & Saglo, O. F. (2019). Meaty genotypes of pigs and their further using. Pig Breeding, 72, 145–146. Available at: http://nbuv.gov.ua/UJRN/svun_2019_72_20.

Varchenko, O., Svynous, I., Grynchuk, Y., Khakhula, B., & Ibatullin, M. (2018). Improvement of eco-taxation of goods producer of pig husbandry in Ukraine. Academy of Accounting and Financial Studies Journal, 22(5). Available at: https://www.abacademies.org/articles/improvement-of-ecotaxation-of-goods-producer-of-pig-husbandry-in-ukraine-7492.html.

Shpychak, O. (2016). Problems of pricing in the context of purchasing power of the population and inflationary processes. Ekonomika APK, 6, 59–70. Available at: http://eapk.org.ua/contents/2016/06/59.

Podgorny A. V. Improving the efficiency of pig production in agricultural enterprises (PhD thesis). Vinnytsia National Agrarian University, Vinnytsia.

Ibatullin, M. (2017). Rynok produktsii svynarstva: optymalne poiednannia rynkovoho i derzhavnoho rehuliuvannia [Pig production market: optimal combination of market and state regulation]. Kyiv, Agrarian science.

Daninga, P., & Qiao, Z. (2020). Managing price risk of pork through gross margin: a depiction from China and US. European Journal of Business and Management Research, 5(5). https://doi.org/10.24018/ejbmr.2020.5.5.504.

Zhao, J. (2022). Analysis of the rise and fall of pork prices and prediction of the future pork market. 7th International Conference on Financial Innovation and Economic Development (ICFIED). Atlantis Press. https://doi.org/10.2991/aebmr.k.220307.055.

Patyka, N. (2018). Priorities of ensuring the competitiveness of Ukrainian agriculture on world markets. Agricultural and Resource Economics, 4(4), 130–145. https://doi.org/10.51599/are.2018.04.04.10.

Podgorny, A. (2019). Priority directions for increasing the efficiency of pig production in agricultural enterprises. Ekonomika ta upravlinnya APK, 1, 50–64. Available at: http://nbuv.gov.ua/UJRN/ecupapk_2019_1_7.

Zomchak, L., & Umrysh, H. (2017). Modeling and forecasting of meat and egg production in Ukraine using the seasonal ARIMA model. Agricultural and Resource Economics, 3(3), 16–27. Available at: https://doi.org/10.51599/are.2017.03.03.02.

Haydayenko, O., & Kovalenko, L. (2017). Application of correlation-regression analysis for forecasting the results of enterprise activity. Accounting, Economics, Management: scientific notes, 1(13), part 1, 16–23. Available at: http://surl.li/ebteb.

Hrosul V. A., & Ischejkin, T Ye (2016). Use of multifactor correlation-regression analysis to assess the effectiveness of enterprises and consumer cooperation organizations. Naukovyy visnyk Poltavsʹkoho universytetu ekonomiky i torhivli, 4, 47–61. Available at: http://nbuv.gov.ua/UJRN/Nvpusk_2016_4_8.

Pehota, M., & Hryshchenko, O. (2007). Osnovy ekonometrii [Basics of econometrics]. Kyiv, NSC “IAE”.

Palát, M., & Palátová, Š. (2022). Microeconomic appraisal of pork market indicators including correlation matrices and developmental trend models in the EU. Bulgarian Journal of Agricultural Science, 28(1), 10–18. Available at: https://www.agrojournal.org/28/01-02.pdf.

Official website of State Statistics Service of Ukraine (n.d.). Available at: http://www.ukrstat.gov.ua.

Cherednichenko, O. (2017). Economic aspects of production and consumption of meat and meat products. Agricultural and Resource Economics, 3(3), 130–144. https://doi.org/10.51599/are.2017.03.03.10.

Stepasyuk, L., Dramaretska, K., Titenko, Z., & Babiak, N. (2020). The competitive environment diagnostics in the animal husbandry products market. International Journal of Advanced Science and Technology, 29(8s), 2551–2558. Available at: http://sersc.org/journals/index.php/IJAST/article/view/14759.

Babenko, M. (2021). What will pork prices be like in 2021? Agroexpert. Available at: https://agroexpert.ua/18360-2.

Bryk, M. (2018). Current state and prospects of livestock development in Ukraine. Ekonomichnyi analiz, 28(4), 331–337. https://doi.org/10.35774/econa2018.04.331.

Nikolaienko, M., & Bal-Prylypko, L. (2020). Development of an integrated food quality management system. Potravinarstvo Slovak Journal of Food Sciences, 14, 862–873. https://doi.org/10.5219/1434.

Kravchenko, O. (2019). Peculiarities of economic relations between market participants of animal husbandry products. Agricultural and Resource Economics, 5(1), 71–91. https://doi.org/10.22004/ag.econ.287145.

Chen, T., Chen, Z., & Zhou, Z. (2021). Computational research and implementation of prediction of pork price based on deeplearning. Journal of Physics: Conference Series. 2nd International Conference on Computer, Communications and Mechatronics Engineering (CCME 2020), 1815(124), 012032. https://doi.org/10.1088/1742-6596/1815/1/012032.

Zielińska-Sitkiewicz, M., & Chrzanowska, M. (2021). Prediction of pork meat prices by selected methods as an element supporting the decision-making process. Operations Research and Decisions, 31(3), 137–152. https://doi.org/10.37190/ord210307.

Published
2022-12-20
How to Cite
Bal-Prylypko, L., Nikolaenko, M., Stepasyuk, L., Cherednichenko, O., & Lialyk, A. (2022). Forecasting the sale price of pork in agricultural enterprises. Agricultural and Resource Economics: International Scientific E-Journal, 8(4), 170-187. https://doi.org/10.51599/are.2022.08.04.08
Section
Articles