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Climate factors play a decisive role in crop yield fluctuations. From the perspective of wheat cultivation in Ukraine, three agroclimatic zones can be distinguished. This study investigates the impact of climatic factors on wheat yield fluctuations for each zone. It is demonstrated that the influence of nonlinear climate factors significantly enhances the accuracy of wheat yield prediction. Using machine learning techniques, models are constructed to forecast future yields with a prediction horizon of 3 months. Such forecasts can provide a basis for optimal investment and marketing decisions in the grain market. The proposed methodology can be applied to forecast yields of other agricultural crops as well.

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Modeling of the Nonlinear Impact of Climatic Factors on Wheat Yield Using Machine Learning Techniques

  • 会议论文

  • 发表日期：

    2025年

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  • Petro Hrytsiuk,
  • Maksym Havryliuk

摘要

Climate factors play a decisive role in crop yield fluctuations. From the perspective of wheat cultivation in Ukraine, three agroclimatic zones can be distinguished. This study investigates the impact of climatic factors on wheat yield fluctuations for each zone. It is demonstrated that the influence of nonlinear climate factors significantly enhances the accuracy of wheat yield prediction. Using machine learning techniques, models are constructed to forecast future yields with a prediction horizon of 3 months. Such forecasts can provide a basis for optimal investment and marketing decisions in the grain market. The proposed methodology can be applied to forecast yields of other agricultural crops as well.