4.5 Article

Phenological models to predict the main flowering phases of olive (Olea europaea L.) along a latitudinal and longitudinal gradient across the Mediterranean region

Journal

INTERNATIONAL JOURNAL OF BIOMETEOROLOGY
Volume 59, Issue 5, Pages 629-641

Publisher

SPRINGER
DOI: 10.1007/s00484-014-0876-7

Keywords

Climate change; Flowering phenology; Least-squares regression; Modeling; Olive; Pollen

Funding

  1. Fundacion Ramon Areces (Madrid, Spain)

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The aim of the present study was to develop phenometeorological models to explain and forecast the main olive flowering phenological phases within the Mediterranean basin, across a latitudinal and longitudinal gradient that includes Tunisia, Spain, and Italy. To analyze the aerobiological sampling points, study periods from 13 years (1999-2011) to 19 years (1993-2011) were used. The forecasting models were constructed using partial least-squares regression, considering both the flowering start and full-flowering dates as dependent variables. The percentages of variance explained by the full-flowering models (mean 84 %) were greater than those explained by the flowering start models (mean 77 %). Moreover, given the time lag from the North African areas to the central Mediterranean areas in the main olive flowering dates, the regional full-flowering predictive models are proposed as the most useful to improve the knowledge of the influence of climate on the olive tree floral phenology. The meteorological parameters related to the previous autumn and both the winter and the spring seasons, and above all the temperatures, regulate the reproductive phenology of olive trees in the Mediterranean area. The mean anticipation of flowering start and full flowering for the future period from 2081 to 2100 was estimated at 10 and 12 days, respectively. One question can be raised: Will the olive trees located in the warmest areas be northward displaced or will they be able to adapt their physiology in response to the higher temperatures? The present study can be considered as an approach to design more detailed future bioclimate research.

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