| Summary: | The increased occurrence of extreme weather events due to climate change has heightened the need to develop support decision systems that can help farmers to mitigate losses in agriculture. Environmental hazards, such as frost, have a relevant economic impact on crops since they may cause damage and injuries to sensitive crops and, therefore, lead to production losses. The probability of frost occurrences is heavily influenced by local climate conditions. In addition, the extent of damage due to frost also depends on the phenology stages of the crops present in the area of interest. Hence, an early frost warning system at a local scale has the potential to minimize damage to the crops as one can deploy protective mechanisms. In this article, we present models for early forecasting (24 and 48 h) of frost occurrences using stacked machine learning models. We trained the machine-learning models with hourly historical data from a local weather station. The trained model is validated within the timeframe when the crops (organic fruits) are most susceptible to frost for the area of study. We also show the applicability of the model by extrapolating it to a new region. This development is carried out within the framework of the H2020 CYBELE project.
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