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Combining Environmental Variables and Machine Learning Methods to Determine the Most Significant Factors Influencing
Johanna Ramirez-Diaz1, Arianna Manunza1, Tiago Almeida de Oliveira2
1Institute of Agricultural Biology and Biotechnology, Italian National Research Council (CNR), 20133 Milan, Italy.
Insects
|April 23, 2025
Summary
Harsh winters impact bee colonies and honey production. This study used machine learning to predict Total Honey Harvest (THH) using winter climate data, helping beekeepers manage colonies effectively.
Area of Science:
- Apiculture and environmental science.
- Application of machine learning in ecological studies.
Background:
- Bees are vital for agriculture and ecosystems.
- Extreme weather and harsh winters cause significant bee colony losses and reduced honey yields.
- Predicting Total Honey Harvest (THH) is crucial for beekeeping management.
Purpose of the Study:
- To identify key climatic and environmental factors influencing Total Honey Harvest (THH).
- To develop and evaluate machine learning models for predicting THH.
- To provide insights for beekeepers to mitigate climate-related challenges.
Main Methods:
- Collected 598 Total Honey Harvest (THH) records from five apiaries in Lombardy, Italy (2015-2019).
- Utilized 38 features including temperature, humidity, precipitation, pressure, wind, and Enhanced Vegetation Index (EVI).
- Trained and evaluated Decision Tree, Random Forest, and Extreme Gradient Boosting (XGBoost) models.
Main Results:
- All machine learning models achieved prediction accuracy above 0.75.
- Winter climatic conditions were identified as significant predictors of Total Honey Harvest (THH).
- The models demonstrated effectiveness in both training and testing phases.
Conclusions:
- Winter climate data is a key factor in predicting bee colony productivity.
- Machine learning models can accurately forecast Total Honey Harvest (THH).
- Findings can assist beekeepers in developing proactive strategies against climate impacts and colony decline.
