DIFFERENT APPROACHES FOR MODELLING SUNFLOWER PHENOLOGY UNDER CLIMATE CHANGE SCENARIOS || APPROCCI MODELLISTICI PER LA SIMULAZIONE DELLA FENOLOGIA DEL GIRASOLE (HELIANTHUS ANNUUS L.) IN SCENARI DI CAMBIAMENTO CLIMATICO

Temperature variations related to climate change are profoundly affecting crop phenological development, highlighting the need to reassess commonly used predictive models. The calculation of Growing Degree Days (GDD) is a key tool for estimating phenological dynamics, but models that do not account for the impact of high temperatures are becoming increasingly inadequate in the face of more frequent heatwaves. This study compares six GDD calculation models to evaluate their effectiveness under current climate conditions in Northern Italy, focusing on the spring-summer crop cycle of sunflower. The models analyzed include the average method, the single triangle method (with three different upper threshold cut-off techniques: horizontal, vertical, and intermediate), and the physiologically based beta-distribution function method. The analysis, conducted using a 22-year historical dataset of phenological and meteorological data from the Cadriano station (Bologna, Italy), showed that traditional models maintain good accuracy in estimating full flowering, but tend to underestimate full maturity in warm years. The beta-distribution method proved to be the most accurate, although it is also the most complex to implement. Conversely, the intermediate cut-off technique offered a good balance between accuracy and ease of application. These findings emphasize the importance of adopting predictive models that are more responsive to thermal stress, in order to support effective agronomic decision-making and promote climate-resilient agriculture