Investigating the impact of climate variables on the organic honey yield in Turkey using XGBoost machine learning.

Background: The Turkish organic honey industry, a major player in the global market, faces challenges due to climate fluctuations. Understanding the influence of climate factors on honey production is vital for sustainable farming and economic stability.

Method: This study uses a machine learning approach with the XGBoost algorithm to analyze temperature, wind, humidity, precipitation and surface pressure over a 20-year period from 2004 to 2023.

Results: The results show that these factors significantly impact organic honey production, with temperature, wind, humidity, precipitation and surface pressure having effects of 41.20%, 26.50%, 12.47%, 11.42% and 8.41%, respectively. Sensitivity analysis reveals the model's sensitivity to even minor fluctuations in these variables.

Conclusion: The results of this research underscore the necessity of integrating climate change mitigation and adaptation measures into agricultural policies and beekeeping practices. This study showcases the practical application of machine learning in deciphering the intricate relationship between climate change and the production of crops, emphasizing the importance of data-driven decision-making to guarantee long-term sustainability and financial stability in the sector. © 2024 The Author(s). Journal of the Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.