Price Estimation of Selected Grains Products Based on Machine Learning for Agricultural Economic Development in Türki̇ye
Year: 2024
Type: article
Abstract: This study aims to estimate the price fluctuations of essential grain products, namely bread wheat (Triticum aestivum), durum wheat (Triticum durum), barley (Hordeum vulgare), and corn (Zea mays), in Türkiye using machine learning (ML) algorithms. Using data from January 2, 2020, to January 10, 2023, the study employs algorithms such as random forest (RF), neural network (NN), support vector machine (SVM), and linear regression (LR). Independent variables include oil prices, currency exchange rates, and grain production volumes. The random forest (RF) algorithm provided the best results with the highest R² values, while NN and LR showed relatively lower performance. The study highlights the significant impact of production and consumption volumes on grain prices and underscores the importance of ML algorithms in predicting these prices amidst changing conditions. Investments in agricultural technologies should be increased to improve data collection and analysis processes, as this is crucial for preventing price fluctuations in the agricultural sector. Keywords: Agricultural products; grains; durum wheat; bread wheat; corn; barley; machine learning algorithms; price estimation
Authors: AbdulKadir Kaskin, Irfan Ersin, AbdulKadir Atalan
Language: English
Subfield: Economics and Econometrics
Domain: Social Sciences
SDG: Zero hunger
Open Access status: hybrid
OpenAlex ID: W4402850809