Version 1
: Received: 11 April 2024 / Approved: 11 April 2024 / Online: 11 April 2024 (11:30:33 CEST)
How to cite:
Sahin Demirel, A.N. The Effect of Climate Change on Organic Honey Efficiency in Türkiye via Machine Learning. Preprints2024, 2024040797. https://doi.org/10.20944/preprints202404.0797.v1
Sahin Demirel, A.N. The Effect of Climate Change on Organic Honey Efficiency in Türkiye via Machine Learning. Preprints 2024, 2024040797. https://doi.org/10.20944/preprints202404.0797.v1
Sahin Demirel, A.N. The Effect of Climate Change on Organic Honey Efficiency in Türkiye via Machine Learning. Preprints2024, 2024040797. https://doi.org/10.20944/preprints202404.0797.v1
APA Style
Sahin Demirel, A.N. (2024). The Effect of Climate Change on Organic Honey Efficiency in Türkiye via Machine Learning. Preprints. https://doi.org/10.20944/preprints202404.0797.v1
Chicago/Turabian Style
Sahin Demirel, A.N. 2024 "The Effect of Climate Change on Organic Honey Efficiency in Türkiye via Machine Learning" Preprints. https://doi.org/10.20944/preprints202404.0797.v1
Abstract
This study investigates the impact of climate change on the efficiency of organic honey production in Türkiye using a machine learning approach. The eXtreme Gradient Boosting (XGBoost) algorithm is employed for the Machine Learning (ML) modelling. The analysis includes data on organic honey production performance from 2004 to 2023, spanning a period of 20 years, and 8 different climate variables (16 variables depending on daytime and nighttime values). The dataset created to investigate the impact of climate change on organic honey yield comprises 28 columns and 120,960 rows. The study aimed to determine the significance of different climatic variables on the efficiency of organic honey production. The study found that, in addition to daytime solar radiation and temperature, wind speed and direction were also important factors. The sensitivity analysis has shown that minor alterations in the variables have a significant impact on the model's predictions. Additionally, non-linear relationships between the variables and honey production were identified. These findings suggest that it may be beneficial to consider implementing climate change adaptation and mitigation strategies in agricultural policies and beekeeping practices. The study suggests that machine learning has the potential to provide valuable insights into the complex relationship between climate change and agricultural production. This could aid in the sustainability and economic optimization of the sector.
Environmental and Earth Sciences, Sustainable Science and Technology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
