Predicting budbreak dates for ‘Delaware’ grape considering chilling and heat requirements using PhenoFlex

Masahiro Kamimori and Akihiro Hosomi

Ecological Modelling, 2025, vol. 501, issue C

Abstract: Owing to recent climate change, budbreak in the ‘Delaware’ grape (Vitis vinifera × (V. labrusca × V. aestivalis)) in Osaka, Japan, is occurring earlier. Therefore, the development of phenology models for budbreak in ‘Delaware’ based on the relationship between budbreak and temperature (a key environmental factor) is important for both short-term (vineyard management or frost risk assessment) and long-term (assessment of suitable land or effect of future climate change) needs. The recently developed PhenoFlex modeling framework combines the Dynamic model and Growing Degree Hour models as sub-models for chilling and heat requirements with a flexible transition, showing good prediction accuracy in several temperate woody perennials. However, case studies of PhenoFlex in grapes are absent. In this present study, we aimed to evaluate the applicability of the PhenoFlex modeling framework to grapes using ‘Delaware’ dataset from 1963 to 2023 in Osaka, Japan. PhenoFlex showed a high prediction accuracy, with root mean square error values ranging from 2.28 to 2.79 days. Furthermore, the PhenoFlex model with the Dynamic model parameters adjusted using our data showed higher prediction accuracy and robustness than the model with the original parameters of the Dynamic model, indicating that the Dynamic model parameters should be set for each target species or varieties. The PhenoFlex modeling framework was effective in predicting budbreak in ‘Delaware’ grape although some points of chill response curves derived from the parameters fitted using PhenoFlex were unrealistic.

Keywords: Dormancy; Dynamic model; Growing degree hour model; Parameter estimation; Phenology (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecomod:v:501:y:2025:i:c:s0304380024003739

DOI: 10.1016/j.ecolmodel.2024.110985

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