Phenological Model to Predict Budbreak and Flowering Dates of Four Vitis vinifera L. Cultivars Cultivated in DO. Ribeiro (North-West Spain)

The aim of this study was to assess the thermal requirements of the most important grapevine varieties in northwestern Spain to better understand the impact of climate change on their phenology. Different phenological models (GDD, GDD Triangular and UniFORC) were tested and validated to predict budburst and flowering dates of grapevines at the variety level using phenological observations collected from Treixadura, Godello, Loureira and Albariño between 2008 and 2019. The same modeling framework was assessed to obtain the most suitable model for this region. The parametrization of the models was carried out with the Phenological Modeling Platform (PMP) platform by means of an iterative optimization process. Phenological data for all four varieties were used to determine the best-fitted parameters for each variety and model type that best predicted budburst and flowering dates. A model calibration phase was conducted using each variety dataset independently, where the intermediate-fitted parameters for each model formulation were freely-adjusted. Afterwards, the parameter set combination of the model providing the highest performance for each variety was externally validated with the dataset of the other three varieties, which allowed us to establish one overall unique model for budburst and flowering for all varieties. Finally, the performance of this model was compared with the attained one while considering all varieties in one dataset (12 years × 4 varieties giving a total number of observations of 48). For both phenological stages, the results showed no considerable differences between the GDD and Triangular GDD models. The best parameters selected were those provided by the Treixadura GDD model for budburst (day of the year (t(0)) = 49 and base temperature (Tb) = 5) and those corresponding to the Godello model (t(0) = 52 and Tb = 6) for flowering. The modeling approach employed allowed obtaining a global prediction model that can adequately predict budburst and flowering dates for all varieties.