4.5 Article

Olive flowering phenology variation between different cultivars in Spain and Italy: modeling analysis

期刊

THEORETICAL AND APPLIED CLIMATOLOGY
卷 95, 期 3-4, 页码 385-395

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SPRINGER WIEN
DOI: 10.1007/s00704-008-0016-6

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  1. Centre National de la Recherce Scientifique (CNRS) de Montpellier (France)
  2. Modelizacion y analisis de la influencia de la variacion genetica en la fenologia reproductiva de especies vegetales [PO6-RNM-02195]

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Phenology data are sensitive data to identify how plants are adapted to local climate and how they respond to climatic changes. Modeling flowering phenology allows us to identify the meteorological variables determining the reproductive cycle. Phenology of temperate of woody plants is assumed to be locally adapted to climate. Nevertheless, recent research shows that local adaptation may not be an important constraint in predicting phenological responses. We analyzed variations in flowering dates of Olea europaea L. at different sites of Spain and Italy, testing for a genetic differentiation of flowering phenology among olive varieties to estimate whether local modeling is necessary for olive or not. We build models for the onset and peak dates flowering in different sites of Andalusia and Puglia. Process-based phenological models using temperature as input variable and photoperiod as the threshold date to start temperature accumulation were developed to predict both dates. Our results confirm and update previous results that indicated an advance in olive onset dates. The results indicate that both internal and external validity were higher in the models that used the photoperiod as an indicator to start to cumulate temperature. The use of the unified model for modeling the start and peak dates in the different localities provides standardized results for the comparative study. The use of regional models grouping localities by varieties and climate similarities indicate that local adaptation would not be an important factor in predicting olive phenological responses face to the global temperature increase.

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