Phenological models applied to grapevines are valuable tools to assist in the decision of cultural practices related to winegrowers and winemakers. The two-parameter sigmoid phenological model was used to estimate the three main phenological stages of the grapevine development, i.e., budburst, flowering, and veraison. This model was calibrated and validated with phenology data for 51 grapevine varieties distributed in four wine regions in Portugal (Lisboa, Douro, Dão, and Vinhos Verdes). Meteorological data for the selected sites were also used. Hence, 153 model calibrations (51 varieties × 3 phenological stages) and corresponding parameter estimations were carried out based on an unprecedented comprehensive and systematized dataset of phenology in Portugal. For each phenological stage, the centroid of the estimated parameters was subsequently used, and three generalized sigmoid models (GSM) were constructed (budburst: d = −0.6, e = 8.6; flowering: d = −0.6, e = 13.7; veraison: d = −0.5, e = 13.2). Centroid parameters show high performance for approximately 90% of the varieties and can thereby be used instead of variety-specific parameters. Overall, the RMSE (root-mean-squared-error) is < 7 days, while the EF (efficiency coefficient) is > 0.5. Additionally, according to other studies, the predictive capacity of the models for budburst remains lower than for flowering or veraison. Furthermore, the F-forcing parameter (thermal accumulation) was evaluated for the Lisboa wine region, where the sample size is larger, and for the varieties with model efficiency equal to or greater than 0.5. A ranking and categorization of the varieties in early, intermediate, and late varieties was subsequently undertaken on the basis of F values. These results can be used to more accurately monitor and predict grapevine phenology during a given season, thus supporting decision making in the Portuguese wine sector.

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四个葡萄牙葡萄酒产区的葡萄物候学：建模和预测

应用于葡萄树的物候模型是宝贵的工具，可帮助决定与葡萄种植者和酿酒师有关的文化习俗。两参数乙状结肠物候模型用于估计葡萄发育的三个主要物候阶段，即芽期，开花和确证。该模型已使用物理学数据进行了校准和验证，该数据用于分布在葡萄牙四个葡萄酒产区（里斯本，杜罗，杜昂和维纽斯·维德斯）的51个葡萄品种。还使用了选定地点的气象数据。因此，基于葡萄牙前所未有的全面，系统化的物候数据集，进行了153个模型校准（51个品种×3个物候阶段）和相应的参数估计。对于每个物候阶段，d = -0.6，e = 8.6；开花：d = -0.6，e = 13.7; 检验：d = −0.5，e= 13.2）。质心参数对大约90％的品种表现出较高的性能，因此可以代替特定品种的参数使用。总体而言，RMSE（均方根误差）小于7天，而EF（效率系数）大于0.5。此外，根据其他研究，该模型对萌芽的预测能力仍然低于对开花或Veraison的预测能力。此外，对样本量较大的里斯本葡萄酒产区和模型效率等于或大于0.5的品种，评估了F强迫参数（蓄热）。随后根据F值对早期，中级和晚期品种进行排序和分类。