A Machine Learning Approach to Forecasting Honey Production with Tree-Based Methods

TL;DR

This study applies various machine learning models to forecast honey production in Italy, emphasizing weather variables, and explores ensemble methods to improve prediction accuracy, aiding beekeepers in risk management.

Contribution

It introduces a comprehensive machine learning framework for honey production forecasting using weather data and ensemble techniques, highlighting key production drivers and improving predictive performance.

Findings

Ensemble models enhance forecast accuracy.

Weather variables are primary predictors.

Model explanations reveal main production drivers.

Abstract

The beekeeping sector has experienced significant production fluctuations in recent years, largely due to increasingly frequent adverse weather events linked to climate change. These events can severely affect the environment, reducing its suitability for bee activity. We conduct a forecasting analysis of honey production across Italy using a range of machine learning models, with a particular focus on weather-related variables as key predictors. Our analysis relies on a dataset collected in 2022, which combines hive-level observations with detailed weather data. We train and compare several linear and nonlinear models, evaluating both their predictive accuracy and interpretability. By examining model explanations, we identify the main drivers of honey production. We also ensemble models from different families to assess whether combining predictions improves forecast accuracy. These insights support beekeepers in managing production risks and may inform the development of insurance products against unexpected losses due to poor harvests.