### Model Applications on Other Independent Datasets  
The statistical performances of the phenological models, applied on the respective independent datasets, are reported in Figure 5 and Figures S10-S16 while the comparison between CF and F models during fitting and applications is shown in Figure 6 and Figure S17. In general, higher performances were found for CF compared to F models during fitting and applications (Figure 5), except for those varieties characterized by few observed data in the model application (Gewurztraminer, Riesling; Figure 6 and Figure S17). Figure 5: Highest-performance (HP) and average (AV) parameters’ set applied on the independent datasets by considering all phenological models and starting dates. The example of Chardonnay variety. In particular, the model application showed similar performances for both HP (R\({}^{2}\) = 0.55, AIC = 57.97, RMSE = 9.41) and AV (R\({}^{2}\) = 0.54, AIC = 58.37, RMSE = 9.26) parameter sets. However, this difference slightly increased when CF (R\({}^{2}\) = 0.56, AIC = 59.05, RMSE = 8.75) and F models (R\({}^{2}\) = 0.52, AIC = 57.01, RMSE = 10.01) were compared without distinguishing between HP and AV parameterizations. Therefore, CF models showed better results in terms of R\({}^{2}\) (+8%) and RMSE (-15%) compared to F models while lower results of AIC value were obtained for CF compared to F models (+3.44%). A detailed analysis revealed that the CF models with highest performances (R\({}^{2}\) and AIC) were UNICHILL (R\({}^{2}\) = 0.64, AIC = 56.74) and UNIFII (R\({}^{2}\) = 0.58, AIC = 56.61) models, while, in F models, the better results were provided by UNIFORC (R\({}^{2}\) = 0.54, AIC = 55.98). However, in terms of RMSE, highest performances were found for BRIN hourly (8.37, CF) and GDD (9.67, F). Concerning the starting dates, similar results were found when the 1 st September (R\({}^{2}\) = 0.58, AIC = 58.92, RMSE = 8.55) and 1 st of August (R\({}^{2}\) = 0.57, AIC = 59.17, RMSE = 8.95) were analyzed, with the 1 st September showing better performance (R\({}^{2}\) = +1.83%, AIC = -0.44% and RMSE = -4.68%). Instead, higher differences were found when the 1 st January (R\({}^{2}\) = 0.55, AIC = 60.47, RMSE = 7.74) and 1 st of March (R\({}^{2}\) = 0.49, AIC = 52.57, RMSE = 12.47) were compared, evidencing the most relevant performances for 1 st January (R\({}^{2}\) = +10.83% and RMSE = -61.23%, with the exception of AIC = +14.15%). Moreover, the behavior of grapevine varieties in response to specific climate conditions was noticed during model application. The higher performances of CF compared to F, as well the relevant  
Figure 6: Statistical results (R\({}^{2}\), AIC and RMSE) of the comparison between CF (black bars) and F (red bars) approaches in model fitting and application for all grapevine varieties using HP parameter set. The differences between CF and F models (CF-F) in both fitting and applications are displayed using green and yellow bars, respectively. CS = Cabernet Sauvignon, CH = Chardonnay, GE = Gewürztraminer, GR = Grenache, PG = Pinot Gris, RS = Riesling, TF = Touriga Franca, TN = Touriga Nacional. differences between starting dates when F models were used, may be explained by the influence of temperatures on the dormancy period. The end of the first phase of dormancy (endo-dormancy) in CF models was generally simulated between the end of December (e.g., Touriga Nacional: DOY 356 for t\({}_{0}\) = \(-121\) and \(-152\), respectively) and end of February (e.g., Riesling: DOY 56 and 57 for t\({}_{0}\) = \(-121\) and \(-152\), respectively). More specifically, the period of endo-dormancy fulfilment was similarly reached regardless of starting date (1 st September/August), allowing the next forcing unit accumulation during the eco-dormancy period to be correctly satisfied (differences in days from 0 to 3 days). The analysis further highlighted that the endo-dormancy release occurs later at northern than at southern latitudes and this is consistent with results obtained using F approach.