Price Forecasting of Feed Raw Materials Used in Dairy Farming: A Methodological Comparison

Abstract

Milk is among the products of strategic importance for countries due to its nutritional value and being a priority foodstuff. Feed raw materials are one of the most important input items in the dairy cattle sector. Ensuring the balance of milk/feed parity is of great importance for producers to maintain their activities and profitability. In countries like Turkey, where inflationary effects are observed, the prices of feed raw materials are not stable. In an environment of high price fluctuations, forecasting feed raw material prices for producers is of vital importance for future planning. In this study, price forecasting of 43 feed raw materials, which are used extensively in the ration preparation process in the dairy cattle sector, was carried out. The performances of 11 methods based on Time Series, Statistics and Grey System Theory are compared. After the comparison using model success criteria, it was found that the DGM (1,1) method forecasts more effectively than Exponential Smoothing and Regression models as well as other Grey Forecasting models. Based on MAD, MSE and MAPE values, it is concluded that Grey Forecasting methods can be a good alternative for price forecasting of feed ingredients.

Keywords

• Price Forecasting
• Exponential Smoothing
• Regression Analysis
• Grey Forecasting

References

1. Ahumada, H., & Cornejo, M. (2016). Forecasting food prices: The case of corn, soybeans and wheat. International Journal of Forecasting, 32(3), 838–848. https://doi.org/10.1016/j.ijforecast.2016.01.002
2. Akan, B., & Baylan, E. B. (2022). Box-Jenkins yöntemiyle çilek satış fiyatları için tahmin modelikurulması ve tahmin sonuçlarının değerlendirilmesi. İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi, 21(42), 211–234. https://doi.org/10.55071/ticaretfbd.1092970
3. Akdemir, H. A., & Çebi, Y. (2023). Tarımsal Ürünlerin İhracat Fiyatlarının Tahminlenmesinde Yapay Sinir Ağlarının Kullanım. 15. Ulusal Tarım Ekonomisi Kongresi, 306–309.
4. Aksoy, E., & Gençtürk, M. (2024). COVID-19 Döneminde Banka Kredi Risk Bilgileri Üzerine Bir Analiz. Afyon Kocatepe Üniversitesi Sosyal Bilimler Dergisi, 26(1), 194–206. https://doi.org/10.32709/akusosbil.1109545
5. Anggraeni, W., Andri, K. B., Sumaryanto, & Mahananto, F. (2017). The Performance of ARIMAX Model and Vector Autoregressive (VAR) Model in Forecasting Strategic Commodity Price in Indonesia. Procedia Computer Science, 124, 189–196. https://doi.org/10.1016/j.procs.2017.12.146
6. Arsy, F. A. (2021). Demand Forecasting of Toyota Avanza Cars in Indonesia: Grey Systems Approach. International Journal of Grey Systems, 1(1), 38–47. https://doi.org/10.52812/ijgs.24
7. Atıcı, E., & Elen, A. (2024). Optimization of Feed Ration Cost in Dairy Cattle by Genetic Algorithm. Mühendislik Bilimleri Ve Araştırmaları Dergisi, 6(1), 65–76. https://doi.org/10.46387/bjesr.1435749
8. Aydemir, E., & Turhan, T. (2022). Comparison of Grey Incidence Degrees of Selected Stock Indices According to BIST100 Index in the Covid19 Pandemic Process. 1st International Conference on Engineering and Applied Natural Sciences, 10–13.

9. Aydın, S., Çetinkaya, A., & Bayrakçı, E. (2010, ). Kars İlinde Üretilen İnek Sütlerinin Bazı Kimyasal Özellikleri. Ulusal Meslek Yüksekokulları Öğrenci Sempozyumu.
10. Bas, E., Egrioglu, E., & Yolcu, U. (2021). Bootstrapped Holt Method with Autoregressive Coefficients Based on Harmony Search Algorithm. Forecasting, 3(4), 839–849. https://doi.org/10.3390/forecast3040050
11. Bessler, D. A., Yang, J., & Wongcharupan, M. (2003). Price Dynamics in the International Wheat Market: Modeling with Error Correction and Directed Acyclic Graphs. Journal of Regional Science, 43(1), 1–33. https://doi.org/10.1111/1467-9787.00287
12. Beşel, C., & Kayıkçı, E. T. (2016). Interpretation of meteorological data with time series and descriptive statistics; Black Sea Region example. TÜCAUM Uluslararası Coğrafya Sempozyumu, 13–14.
13. Bocsi, V., Hajnalka, F., & Pusztai, G. (2022). First-generation Students at Universities from the Aspect of Achievement, Motivation and Integration. Revija Za Sociologiju, 52(1), 61–85. https://doi.org/10.5613/rzs.52.1.3
14. Brandt, J. A., & Bessler, D. A. (1984). Forecasting with Vector Autoregressions versus a Univariate ARIMA Process: An Empirical Example with U.S. Hog Prices. North Central Journal of Agricultural Economics, 6(2), 29. https://doi.org/10.2307/1349248
15. Cahyo, P. W., Aesyi, U. S., & Santosa, B. D. (2024). Topic Sentiment Using Logistic Regression and Latent Dirichlet Allocation as a Customer Satisfaction Analysis Model. JURNAL INFOTEL, 16(1). https://doi.org/10.20895/infotel.v16i1.1081
16. Can, Ş., & Gerşil, M. (2018). Manisa Pamuk Fiyatlarının Zaman Serisi Analizi ve Yapay Sinir Ağı Teknikleri İle Tahminlenmesi Ve Tahmin Performanslarının Karşılaştırılması. Yönetim Ve Ekonomi, 25(3), 1017–1031.
17. Chen, J., Chen, C., Lin, Y., Su, Y., Yu, X., Jiang, Y., Chen, Z., Ke, S., Lin, S., Chen, L., Zhang, Z., & Zhang, T. (2021). Downregulation of SUMO2 inhibits hepatocellular carcinoma cell proliferation, migration and invasion. FEBS Open Bio, 11(6), 1771–1784. https://doi.org/10.1002/2211-5463.13173
18. Dang, H.-S., Huang, Y.-F., Wang, C.-N., & Nguyen, T.-M.-T. (2016). An Application of the Short-Term Forecasting with Limited Data in the Healthcare Traveling Industry. Sustainability, 8(10), 1037. https://doi.org/10.3390/su8101037
19. Dong, Z., & Sun, F. (2011). A novel DGM (1, 1) model for consumer price index forecasting. Proceedings of 2011 IEEE International Conference on Grey Systems and Intelligent Services, 303–307. https://doi.org/10.1109/gsis.2011.6044084
20. Erdoğan, M. A. (2021). Türkiye'de şeftali fiyatlarının analizi ve fiyatların Box-Jenkins yöntemiyle tahmini [Bursa Uludağ University]. http://hdl.handle.net/11452/21704
21. Es, H. A. (2020). Gri Tahmin Modelleri ile Toplam Enerji Talep Tahmini: Türkiye Örneği. Gümüşhane Üniversitesi Fen Bilimleri Enstitüsü Dergisi. https://doi.org/10.17714/gumusfenbil.676909
22. Fan, G.-F., Wang, A., & Hong, W.-C. (2018). Combining Grey Model and Self-Adapting Intelligent Grey Model with Genetic Algorithm and Annual Share Changes in Natural Gas Demand Forecasting. Energies, 11(7), 1625. https://doi.org/10.3390/en11071625
23. Ferbar Tratar, L. (2015). Forecasting method for noisy demand. International Journal of Production Economics, 161, 64–73. https://doi.org/10.1016/j.ijpe.2014.11.019
24. Groebner, D. F., Shannon, P. W., & Fry, P. C. (2018). Business statistics: a decision-making approach (Tenth edition). Pearson.
25. Gülerce, M., & Ünal, G. (2017). Forecasting of Oil and Agricultural Commodity Prices: VARMA Versus ARMA. Annals of Financial Economics, 12(3), 1750012. https://doi.org/10.1142/s2010495217500129
26. Hanke, J., & Wichern, D. (2014). Business Forecasting. Pearson Education.
27. Hasan, M. B., & Dhali, M. N. (2017). Determination of Optimal Smoothing Constants for Exponential Smoothing Method & Holt's Method. Dhaka University Journal of Science, 65(1), 55–59. https://doi.org/10.3329/dujs.v65i1.54509
28. Hu, Y.-C., & Jiang, P. (2017). Forecasting energy demand using neural-network-based grey residual modification models. Journal of the Operational Research Society, 68(5), 556–565. https://doi.org/10.1057/s41274-016-0130-2
29. Huang, K. Y., & Jane, C.-J. (2009). A hybrid model for stock market forecasting and portfolio selection based on ARX, grey system and RS theories. Expert Systems with Applications, 36(3), 5387–5392. https://doi.org/10.1016/j.eswa.2008.06.103
30. Iqelan, B. M. (2017). Forecasts of female breast cancer referrals using grey prediction model GM(1,1). Applied Mathematical Sciences, 11, 2647–2662. https://doi.org/10.12988/ams.2017.79273
31. Javed, S. A., Ikram, M., Tao, L., & Liu, S. (2020). Forecasting key indicators of China's inbound and outbound tourism: optimistic–pessimistic method. Grey Systems: Theory and Application, 11(2), 265–287. https://doi.org/10.1108/gs-12-2019-0064
32. Jha, S. N., Jaiswal, P., Narsaiah, K., Kumar, R., Sharma, R., Gupta, M., Bhardwaj, R., & Singh, A. K. (2013). Authentication of Mango Varieties Using Near-Infrared Spectroscopy. Agricultural Research, 2(3), 229–235. https://doi.org/10.1007/s40003-013-0068-4
33. Jia, W. (2024). Research on pricing and replenishment strategy of superstore goods based on linear regression and gray prediction models. Highlights in Business, Economics and Management, 24, 18–24. https://doi.org/10.54097/6eztb071
34. Ju-Long, D. (1982). Control problems of grey systems. Systems & Control Letters, 1(5), 288–294. https://doi.org/10.1016/s0167-6911(82)80025-x
35. Kayacan, E., Ulutas, B., & Kaynak, O. (2010). Grey system theory-based models in time series prediction. Expert Systems with Applications, 37(2), 1784–1789. https://doi.org/10.1016/j.eswa.2009.07.064
36. Khairina, D. M., Muaddam, A., Maharani, S., & Rahmania, H. (2019). Forecasting of Groundwater Tax Revenue Using Single Exponential Smoothing Method. E3s Web of Conferences, 125, 23006. https://doi.org/10.1051/e3sconf/201912523006
37. Kling, J. L., & Bessler, D. A. (1985). A comparison of multivariate forecasting procedures for economic time series. International Journal of Forecasting, 1(1), 5–24. https://doi.org/10.1016/s0169-2070(85)80067-4
38. Kohzadi, N., Boyd, M. S., Kermanshahi, B., & Kaastra, I. (1996). A comparison of artificial neural network and time series models for forecasting commodity prices. Neurocomputing, 10(2), 169–181. https://doi.org/10.1016/0925-2312(95)00020-8
39. Kutlar, A. (1998). Introduction to Computer Applied Econometrics. Beta Press.
40. Kuzu Yıldırım, S. (2021). Analysis of Mobile Banking Data with R (1st ed.). Dora Publishing.
41. Küçükoflaz, M., Akçay, A., Çelik, E., & Sarıozkan, S. (2019). Türkiye'de kırmızı et ve süt fiyatlarının Box-Jenkins modeller ile geleceğe yönelik kestirimleri. Veteriner Hekimler Derneği Dergisi, 90(2), 122–131. https://doi.org/10.33188/vetheder.534469
42. Li, B., Yang, W., & Li, X. (2018). Application of combined model with DGM(1,1) and linear regression in grain yield prediction. Grey Systems: Theory and Application, 8(1), 25–34. https://doi.org/10.1108/gs-07-2017-0020
43. Li, J., Wang, Y., Li, J., & Jiang, R. (2023). Forecasting the Impact of the COVID-19 Outbreak on China's Cotton Exports by Modified Discrete Grey Model with Limited Data. AATCC Journal of Research, 247234442211479. https://doi.org/10.1177/24723444221147966
44. Lin, Y., & Liu, S. A historical introduction to grey systems theory. 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04ch37583), 3, 2403–2408. https://doi.org/10.1109/icsmc.2004.1400689
45. Liu, S., & Forrest, J. Y.-L. (2010). Grey Systems: Theory and Applications. Springer Verlag.
46. Liu, S., & Yang, Y. (2017). Explanation of terms of grey forecasting models. Grey Systems: Theory and Application, 7(1), 123–128. https://doi.org/10.1108/gs-11-2016-0047
47. Liu, Y., & Li, K. (2019). Research on House Price Forecast Based on Grey System GM (1, 1). 5th International Conference on Finance, Investment, And Law (ICFIL 2019), 200–206.
48. Manalu, A., Roito, D., Rizkiadina, E., & Laia, Y. (2022). Analysis Forecasting Sales With Single Exponential Smoothing Method. Paradigma - Jurnal Komputer Dan Informatika, 24(2), 135–138. https://doi.org/10.31294/paradigma.v24i2.1255
49. Manickam, A., Indrakala, S., & Kumar, P. (2023). A Novel Mathematical Study on the Predictions of Volatile Price of Gold Using Grey Models. Contemporary Mathematics, 270–285. https://doi.org/10.37256/cm.4220232389
50. Norouzi, N., & Fani, M. (2020). Black gold falls, black plague arise - An Opec crude oil price forecast using a gray prediction model. Upstream Oil and Gas Technology, 5, 100015. https://doi.org/10.1016/j.upstre.2020.100015
51. Oladipo, S., Sun, Y., & Adeleke, O. (2023). An Improved Particle Swarm Optimization and Adaptive Neuro-Fuzzy Inference System for Predicting the Energy Consumption of University Residence. International Transactions on Electrical Energy Systems, 2023, 1–16. https://doi.org/10.1155/2023/8508800
52. Ostertagová, E. (2012). Modelling using Polynomial Regression. Procedia Engineering, 48, 500–506. https://doi.org/10.1016/j.proeng.2012.09.545
53. P. Vatcheva, K., & Lee, M. (2016). Multicollinearity in Regression Analyses Conducted in Epidemiologic Studies. Epidemiology: Open Access, 6(2). https://doi.org/10.4172/2161-1165.1000227
54. Petmezas, G., Cheimariotis, G.-A., Stefanopoulos, L., Rocha, B., Paiva, R. P., Katsaggelos, A. K., & Maglaveras, N. (2022). Automated Lung Sound Classification Using a Hybrid CNN-LSTM Network and Focal Loss Function. Sensors, 22(3), 1232. https://doi.org/10.3390/s22031232
55. Ramadhan, A. S., Prabowo, A., Kankarofi, R. H., & Sulaiman, I. M. (2023). Forecasting Human Development Index With Double Exponential Smoothing Method And Acorrect Determination. International Journal of Business, Economics, And Social Development, 4(1), 25–31. https://doi.org/10.46336/ijbesd.v4i1.375
56. Rathnayaka, R. K. T., & Seneviratna, D. (2019). Taylor series approximation and unbiased GM(1,1) based hybrid statistical approach for forecasting daily gold price demands. Grey Systems: Theory and Application, 9(1), 5–18. https://doi.org/10.1108/gs-08-2018-0032
57. Shahwan, T., & Odening, M. (2017). Forecasting Agricultural Commodity Prices using Hybrid Neural Networks. In Computational Intelligence in Economics and Finance (pp. 63–74). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-72821-4\_3
58. Shrestha, N. (2020). Detecting Multicollinearity in Regression Analysis. American Journal of Applied Mathematics and Statistics, 8(2), 39–42. https://doi.org/10.12691/ajams-8-2-1
59. Singh, P. K., Pandey, A. K., & Bose, S. C. (2022). A new grey system approach to forecast closing price of Bitcoin, Bionic, Cardano, Dogecoin, Ethereum, XRP Cryptocurrencies. Quality & Quantity, 57(3), 2429–2446. https://doi.org/10.1007/s11135-022-01463-0
60. Soysal, M., & Ömürgönülşen, M. (2010). Türk turizm sektöründe talep tahmini üzerine bir uygulama. Anatolia: Turizm Araştırmaları Dergisi, 21(1), 128–136.
61. Sukardi, S., Anisa, A. Y., & Herha, S. K. N. (2023). Application of the Single Exponential Smoothing Method For Flood Disaster Prediction. Journal of Computer Networks, Architecture and High Performance Computing, 5(2), 515–525. https://doi.org/10.47709/cnahpc.v5i2.2455
62. Taylor, J. W. (2003). Exponential smoothing with a damped multiplicative trend. International Journal of Forecasting, 19(4), 715–725. https://doi.org/10.1016/s0169-2070(03)00003-7
63. Temuçin, T., & Temiz, İ. (2016). Türkiye Dış Ticaret İhracat Hacminin Projeksiyonu: Holt-Winters ve Box-Jenkins Modellerinin Kıyaslanması. Süleyman Demirel Üniversitesi İktisadi Ve İdari Bilimler Fakültesi Dergisi, 21(3), 937–960.
64. Tulkinov, S. (2023). Grey forecast of electricity production from coal and renewable sources in the USA, Japan and China. Grey Systems: Theory and Application, 13(3), 517–543. https://doi.org/10.1108/gs-10-2022-0107
65. Wang, C.-N., & Le, A. P. (2019). Application of Multi-Criteria Decision-Making Model and GM (1,1) Theory for Evaluating Efficiency of FDI on Economic Growth: A Case Study in Developing Countries. Sustainability, 11(8), 2389. https://doi.org/10.3390/su11082389
66. Weng, Y., Wang, X., Hua, J., Wang, H., Kang, M., & Wang, F.-Y. (2019). Forecasting Horticultural Products Price Using ARIMA Model and Neural Network Based on a Large-Scale Data Set Collected by Web Crawler. IEEE Transactions on Computational Social Systems, 6(3), 547–553. https://doi.org/10.1109/tcss.2019.2914499
67. Wu, L., & Wang, Y. (2009). Modelling DGM(1,1) under the Criterion of the Minimization of Mean Absolute Percentage Error. 2009 Second International Symposium on Knowledge Acquisition and Modeling, 123–126. https://doi.org/10.1109/kam.2009.175
68. Wu, W.-Z., Jiang, J., & Li, Q. (2019). A Novel Discrete Grey Model and Its Application. Mathematical Problems in Engineering, 2019(1). https://doi.org/10.1155/2019/9623878
69. Xu, X., & Zhang, Y. (2021). Corn cash price forecasting with neural networks. Computers and Electronics in Agriculture, 184, 106120. https://doi.org/10.1016/j.compag.2021.106120
70. Xu, Z., Lin, C., Zhuang, Z., & Wang, L. (2023). Research on Multistage Dynamic Trading Model Based on Gray Model and Auto-Regressive Integrated Moving Average Model. Discrete Dynamics in Nature and Society, 2023, 1–15. https://doi.org/10.1155/2023/1552074
71. Yamak, R., & Erkan, E. (2021). Kripto Para Getirilerinde Haftanın Gün Etkisi. Atatürk Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 25(3), 1356–1372. https://doi.org/10.53487/ataunisosbil.883979
72. Yang, X., Zou, J., Kong, D., & Jiang, G. (2018). The analysis of GM (1, 1) grey model to predict the incidence trend of typhoid and paratyphoid fevers in Wuhan City, China. Medicine, 97(34), e11787. https://doi.org/10.1097/md.0000000000011787
73. Yapar, G., Taylan Selamlar, H., Capar, S., & Yavuz, İ. (2019). ATA Method. Hacettepe Journal of Mathematics and Statistics, 48(6), 1838–1844. https://doi.org/10.15672/hujms.461032
74. Yu, L. (2019). Adaptive Variable Weight Accumulation AVWA-DGM(1,1) Model Based on Particle Swarm Optimization. Journal of Advances in Mathematics and Computer Science, 1–17. https://doi.org/10.9734/jamcs/2019/v32i430150
75. Yıldırım, B. F., & Kesintürk, T. (2015). Kredi Kartı Kullanım İstatistiklerinin Gri Tahmin ve Genetik Algoritma Tabanlı Gri Tahmin Metodu İle Tahmini: Karşılaştırmalı Analiz. Bankacılar, 26(94), 65–80.
76. Yıldız, M., & Atış, E. (2019). Estimation of Turkey's organic fig export price using the ARMA method. Journal of Agricultural Economics, 25(2), 141–147.
77. Zhang, D., & Luo, D. (2022). Evaluation of regional agricultural drought vulnerability based on unbiased generalized grey relational closeness degree. Grey Systems: Theory and Application, 12(4), 839–856. https://doi.org/10.1108/GS-12-2021-0187
78. Zhao, Y., Xie, Q., & Zhang, Y. (2021). Assessment and Prediction for China's Regional Agricultural Sustainability. E3s Web of Conferences, 228, 2007. https://doi.org/10.1051/e3sconf/202122802007
79. Zhou, W., & Ding, S. (2021). A novel discrete grey seasonal model and its applications. Communications in Nonlinear Science and Numerical Simulation, 93, 105493. https://doi.org/10.1016/j.cnsns.2020.105493
80. Zong, J., & Zhu, Q. (2012). Price forecasting for agricultural products based on BP and RBF Neural Network. 2012 IEEE International Conference on Computer Science and Automation Engineering, 607–610. https://doi.org/10.1109/icsess.2012.6269540
81. Zou, H., Xia, G., Yang, F., & Wang, H. (2007). An investigation and comparison of artificial neural network and time series models for Chinese food grain price forecasting. Neurocomputing, 70(16–18), 2913–2923. https://doi.org/10.1016/j.neucom.2007.01.009
82. Çuhadar, M. (2006). Turizm sektöründe talep tahmini için yapay sinir ağları kullanımı ve diğer yöntemlerle karşılaştırmalı analizi (Antalya ilinin dış turizm talebinde uygulama). Süleyman Demirel University.
83. Ömürbek, V., Aksoy, E., & Akçakanat, Ö. (2018). Bankaların Grup Bazlı Karlılıklarının Gri Tahmin Yontemi Ile Deg\uerlendirilmesi. Mehmet Akif Ersoy Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 10(23), 75–89. https://doi.org/10.20875/makusobed.375038
84. Özdemir, M., & Çılgın, C. (2022). Buğday Fiyatının Öngörümlenmesinde Makine Öğrenmesi ve Zaman Serisi Tahmin Modellerinin Performanslarının Karşılaştırılması. In M. Özcan (Ed.), 21. Yüzyılda İktisadı Anlamak : Güncel Ekonometrik Zaman Serileri Çalışmaları. Gazi Kitabevi.
85. Özden, C. (2023). İstatistiksel ve Derin Öğrenme Yöntemlerini Kullanarak Tarımsal Girdi Fiyat Endeksi'nin Tahmin Edilmesi. Turkish Journal of Agriculture - Food Science and Technology, 11(9), 1751–1755. https://doi.org/10.24925/turjaf.v11i9.1751-1755.6359
86. Özen, N. S., Saraç, S., & Koyuncu, M. (2021). COVID-19 Vakalarının Makine Öğrenmesi Algoritmaları ile Tahmini: Amerika Birleşik Devletleri Örneği. European Journal of Science and Technology. https://doi.org/10.31590/ejosat.855113
87. Şahin, E. E., & Bağcı, B. (2020). Kripto Para Fiyatlarının Tahmininde Gri Sistem Teorisi: Yöntemsel Karşılaştırma. Anadolu Üniversitesi Sosyal Bilimler Dergisi, 20(1), 219–232. https://doi.org/10.18037/ausbd.700349
88. Şahin, U. (2018). Forecasting of Turkey's electricity generation and consumption with grey prediction method. Mugla Journal of Science and Technology, 4(2), 205–209. https://doi.org/10.22531/muglajsci.450307
89. Şahin, Y., & Aydemir, E. (2019). Akıllı Telefon Teknik Özellik Önem Derecelerinin AHP Ağırlıklı Gri İlişkisel Analizi Yöntemi İle Belirlenmesi. Eskişehir Osmangazi Üniversitesi İktisadi Ve İdari Bilimler Dergisi, 14(1), 225–238. https://doi.org/10.17153/oguiibf.486920
90. Şahin, Y., & Kılınç, M. (2022). Analysis of Economic and Epidemic Performances of Countries During the Covid-19 Pandemic Period. Düzce Üniversitesi Bilim Ve Teknoloji Dergisi, 10(2), 729–747. https://doi.org/10.29130/dubited.934715