Forecasting drinking milk price based on economic, social, and environmental factors using machine learning algorithms

Atalan A.

Agribusiness, vol.39, no.1, pp.214-241, 2023 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Volume: 39 Issue: 1
  • Publication Date: 2023
  • Doi Number: 10.1002/agr.21773
  • Journal Name: Agribusiness
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Scopus, Periodicals Index Online, ABI/INFORM, Aerospace Database, Agricultural & Environmental Science Database, Business Source Elite, Business Source Premier, CAB Abstracts, Communication Abstracts, EconLit, Food Science & Technology Abstracts, INSPEC, Metadex, Veterinary Science Database, Civil Engineering Abstracts
  • Page Numbers: pp.214-241
  • Keywords: factors, machine learning algorithms, milk price, prediction
  • Çanakkale Onsekiz Mart University Affiliated: Yes


The study aimed to describe and test machine learning (ML)-based algorithms to evaluate the unit price of drinking milk. The algorithms were applied to the data collected over 8 years in 2014 and 2021 related to the price of drinking milk in Turkey. The economic, social, and environmental factors that have an impact on the unit price of drinking milk were evaluated. Five ML algorithms, including random forest, gradient boosting, support vector machine (SVM), neural network, and AdaBoost algorithms, were utilized to predict the drinking milk unit price. ML also applied hyperparameter tuning with nested cross-validation to calculate the prediction accuracy for each algorithm. The results show that the random forest algorithm based on the features of the ML algorithms has the best performance, with the accuracy of 99.30% for training and 98.10% for testing the dataset. The average accuracy of gradient boosting, SVM, neural network, and AdaBoost are obtained as 97.30%, 96.15%, 95.65%, and 96.05%, respectively. Random forest performed best as the target variable with the lowest deviation values of mean squared error (MSE) (0.004), root mean square error (RMSE) (0.060), and mean absolute error (MAE) (0.029) in the training and MSE (0.009), RMSE (0.096), and MA (0.055) in the testing dataset. This study presents an interesting perspective with practical potential to adopt ML methods in the dairy industry. The developed ML algorithms can provide dairy investors and policymakers with important decision-support information. [EconLit Citations: C13, C53, L66, C88].