ArticlePlants (Basel, Switzerland)2021
Phenological Model to Predict Budbreak and Flowering Dates of Four
Article in Plants (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Phenological modeling for ecological double-cropping of grapes with rain and pest resilience strategies based on MobileNet developed by Augmented Dream Optimizer.Frontiers in plant science · 2026Article
- A Hierarchical Model to Predict Time of Flowering of Kiwifruit Using Weather Data and Budbreak Dynamics.Plants (Basel, Switzerland) · 2024Article
- Towards a Stochastic Model to Simulate Grapevine Architecture: A Case Study on Digitized Riesling Vines Considering Effects of Elevated COPlants (Basel, Switzerland) · 2022Article
- A Phenological Model for Olive (Plants (Basel, Switzerland) · 2021Article
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5 authors.
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Abstract
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
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