The beekeeping sector has undergone considerable production variations over the past years due to adverse weather conditions, occurring more frequently as climate change progresses. These phenomena can be high-impact and cause the environment to be unfavorable to the bees' activity. We disentangle the honey production drivers with tree-based methods and predict honey production variations for hives in Italy, one of the largest honey producers in Europe. The database covers hundreds of beehive data from 2019-2022 gathered with advanced precision beekeeping techniques. We train and interpret the machine learning models making them prescriptive other than just predictive. Superior predictive performances of tree-based methods compared to standard linear techniques allow for better protection of bees' activity and assess potential losses for beekeepers for risk management.
View on arXiv@article{brini2025_2304.01215,
title={ A Machine Learning Approach to Forecasting Honey Production with Tree-Based Methods },
author={ Alessio Brini and Elisa Giovannini and Elia Smaniotto },
journal={arXiv preprint arXiv:2304.01215},
year={ 2025 }
}