Forecasting Agricultural input Price Index Using Statistical and Deep Learning Methods




Agriculture input price index prediction, ARIMA, Long Short-Term Memory (LSTM), SARIMA, Convolutional Neural Network (CNN)


Agricultural Input Price Index is calculated and published by Turkish Statistical Institute each month in order to track the changes in prices of products and services that are used for current agricultural production and future investments. The prediction of the index will enable agricultural producers to make timely decisions regarding investment decisions and product preferences and will increase their competitiveness in the domestic and international markets. In order to predict changes in this index, (ARIMA, SARIMA) and deep learning models (CNN, LSTM) were used in a comparative way in the study. It is known that CNN and LSTM models capture both linear and nonlinear data traits. The prediction results are evaluated by Root Mean Squared Error (RMSE) and Mean Squared Error (MSE) metrics. According to the study results, ARIMA (RMSE: 0.16409, MSE: 0.0269247) and CNN (RMSE: 0.16994, MSE: 0.288791) models achieved the best results, and they are followed by LSTM model.


Altug S, Filiztekin A, Pamuk Ş. 2008. Sources of longterm economic growth for Turkey, 1880–2005. European Review of Economic History, 12(3), 393-430.

Arslan B, Ertuğrul İ. 2022. Çoklu regresyon, ARIMA ve yapay sinir ağı yöntemleri ile Türkiye elektrik piyasasında fiyat tahmin ve analizi. Journal of Management and Economics Research, 20 (1), 331-353. doi:10.11611/yead.988146

ArunKumar KE, Kalaga DV, Kumar CMS, Kawaji M, Brenza TM. 2022. Comparative analysis of Gated Recurrent Units (GRU), long Short-Term memory (LSTM) cells, autoregressive Integrated moving average (ARIMA), seasonal autoregressive Integrated moving average (SARIMA) for forecasting COVID-19 trends. Alexandria engineering journal, 61(10), 7585-7603.

Coble KH, Mishra AK, Ferrell S, Griffin T. 2018. Big data in agriculture: A challenge for the future. Applied Economic Perspectives and Policy, 40(1), 79-96.

Deb C, Zhang F, Yang J, Lee SE, Shah KW. 2017. A review on time series forecasting techniques for building energy consumption. Renewable and Sustainable Energy Reviews, 74, 902-924.

Fan M, Shen J, Yuan L, Jiang R, Chen X, Davies WJ, Zhang F. 2012. Improving crop productivity and resource use efficiency to ensure food security and environmental quality in China. Journal of experimental botany, 63(1), 13-24.

Ghimire D, Kil D, Kim S. 2022. A Survey on Efficient Convolutional Neural Networks and Hardware Acceleration. Electronics.11(6):945. doi:10.3390/electronics11060945

Işik S, Özbuğday FC. 2021. The impact of agricultural input costs on food prices in Turkey: A case study. Agricultural Economics, 67(3), 101-110.

Kayişoğlu Ç, Türksoy S. 2023. Tarımda Sürdürülebilirlik ve Gıda Güvenliği. Bursa Uludağ Üniversitesi Ziraat Fakültesi Dergisi, 37(1), 289-303. doi:10.20479/bursauludagziraat.1142135

Konyali S. 2017. Sunflower production and agricultural policies in Turkey. Sosyal Bilimler Araştırma Dergisi, 6(4), 11-19.

Lin J, Ma J, Zhu J, Cui Y. 2022. Short-term load forecasting based on LSTM networkds considering attention mechanism. International Journal of Electrical Power and Energy Systems. 137, 107818. doi:10.1016/j.ijepes.2021.107818

Lunduka R, Ricker‐Gilbert J, Fisher M. 2013. What are the farm‐level impacts of Malawi's farm input subsidy program? A critical review. Agricultural Economics, 44(6), 563-579.

m_Sektor_Raporu_130723.pdf [Erişim: 16 Ağustos 2023]

Montgomery DC, Jennings CL, Kulahci M. 2015. Introduction to time series analysis and forecasting. John Wiley & Sons press. ISBN: 978-1-118-74511-3

Perone G. 2022. Using the SARIMA model to forecast the fourth global wave of cumulative deaths from COVID-19:Evidence from 12 Hard-Hit big countries. Econometrics.10(2):18. doi: 10.3390/econometrics10020018

Puchalsky W, Ribeiro GT, Veiga CP, Freire RZ, Santos-Coelho L. 2018. Agribusiness time series forecasting using Wavelet neural networks and metaheuristic optimization: An analysis of the soybean sack price and perishable products demand. International Journal of Production Economics, 203, 174-189.s

Saridakis G, Georgellis Y, Torres RIM, Mohammed AM, Blackburn R. 2021. From subsistence farming to agribusiness and nonfarm entrepreneurship: Does it improve economic conditions and well-being? Journal of Business Research, 136, 567-579

Sheahan M, Barrett CB. 2017. Ten striking facts about agricultural input use in Sub-Saharan Africa. Food Policy, 67, 12-25.

Soylu AC. 2022. Sürdürülebilir Kalkınma ve Gıda Güvenliği İlişkisi. Paradigma: İktisadi ve İdari Araştırmalar Dergisi, 11(2), 100-111.

TBB. 2023. Türkiye Bankalar Birliği: Tarım Sektörü Raporu. 2023, İstanbul. Dokuman/8960/Tari




How to Cite

Özden, C. (2023). Forecasting Agricultural input Price Index Using Statistical and Deep Learning Methods. Turkish Journal of Agriculture - Food Science and Technology, 11(9), 1751–1755.



Research Paper