Beyond Average Hive Performance: Tail Risk Measurement in Italian Apiculture With Honey-at-Risk

This paper provides a framework for measuring honey-production risk that complements standard mean-based analyses byexplicitly targeting downside tail risk. Using hive-weight data from a large sample of Italian hives over the period 2021–2024,downside tail risk is quantified through the Honey-at-Risk (HaR) metric, defined as the quantile of aggregate daily productionshocks for groups of hives exposed to homogeneous climatic conditions. The aggregate shock distribution is modeled based ona flexible meta-distributional framework that combines hive-specific marginal distributions with a copula for the cluster depen-dence structure, allowing for improved accuracy in representing heterogeneous marginal behavior and cross-hive dependence. Theimplementation of HaR documents substantial spatial heterogeneity, with mountain clusters facing not only higher downside tailrisk but also higher variability in the magnitude of the risk measure, compared to plains and hills. Moreover, logistic regressionssuggest that the probability of realizing extreme losses, that is, losses smaller than HaR, is negatively associated with the averageatmospheric temperature but positively associated with the occurrence of abnormal temperature maxima. Taken together, theseresults position HaR as an operational complement to elasticity-based assessments, enabling the identification of high-risk areasand supporting targeted adaptation measures as well as the design of weather-indexed insurance and compensation schemes.