Combining Environmental Variables and Machine Learning Methods to Determine the Most Significant Factors Influencing Honey Production

Honey bees are essential for food production and biodiversity, but extreme weather events and harsh winters can significantly affect bee colonies and reduce honey yields. This study investigated if winter weather could be used to predict honey production in the subsequent harvest season. By analyzing environmental data from both winter and harvest seasons, using variables such as temperature, humidity, precipitation, pressure, wind, and vegetation, three different machine learning (ML) models were used to predict honey yields. Data were collected from five Italian apiaries within a breeding population to train the models from 2015 to 2019. Model performance was evaluated using accuracy, sensitivity, specificity, precision, and area under the ROC curve (AUC). The results showed that winter weather conditions have a substantial impact on honey production and can serve as predictors. Understanding and forecasting these patterns can assist beekeepers in making informed decisions to protect their colonies and optimize honey yields.