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Machine learning

Sustainable Development and Agricultural Land Prices: A Comparative Study of Traditional and Deep Learning Models

Strategic Insights

— 1 min read

Researchers from Canakkale Onsekiz Mart University have conducted a study to compare the effectiveness of traditional Multiple Linear Regression (MLR) and deep learning Extreme Gradient Boosting (XGBoost) algorithms in predicting agricultural land prices. The study aimed to examine the relationship between agricultural land prices and economic indicators such as the dollar, gold, and euro. The results showed that the XGBoost algorithm has an advantage in terms of coefficient of determination values compared to MLR, with a coefficient of determination value of 0.66 for XGBoost and 0.01 for MLR.

Key Takeaways:

  • The study highlights the importance of accurately predicting agricultural land prices to prevent price fluctuations and support sustainable development, particularly in relation to the United Nations Sustainable Development Goals (SDGs) 11 and 15.
  • The XGBoost algorithm demonstrated superior prediction performance compared to MLR, with a coefficient of determination value of 0.66.
  • The study suggests that the XGBoost algorithm can provide more reliable results than traditional MLR methods in predicting agricultural land prices.
  • The results of the study have implications for investors, landowners, and farmers in making informed decisions for a sustainable agricultural economy.
  • The study provides a case study of Canakkale villages, highlighting the relevance of the research to local economic indicators.
  • The research aims to contribute to the development of sustainable management practices for agricultural lands.

Statistics:

  • Coefficient of determination value for XGBoost: 0.66
  • Coefficient of determination value for MLR: 0.01
  • Number of villages examined in the study: Not specified
  • Number of economic indicators examined: 3 (dollar, gold, and euro)
  • Time period of data analysis: Not specified
  • Comparison of XGBoost and MLR performance: XGBoost outperformed MLR in predicting agricultural land prices

Sources:

  • NewsRx. Research from Canakkale Onsekiz Mart University Provides New Study Findings on Sustainable Development (Analyzing Agricultural Land Price Prediction Using Linear Regression and XGBoost Machine Learning Algorithms: A Case Study of Canakkale). Ecology, Environment & Conservation. June 20, 2025; p 643
  • Turkish Journal of Agriculture: Food Science and Technology, "Analyzing Agricultural Land Price Prediction Using Linear Regression and XGBoost Machine Learning Algorithms: A Case Study of Canakkale." by Simge Dogan, Levent Genc, Sait Can Yucebas, and Metin Usakli. Vol. 13, No. 5 (2025), pp. 1109-1116.

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