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Biometeorological and autoregressive indices for predicting olive pollen intensity

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Abstract

This paper reports on modelling to predict airborne olive pollen season severity, expressed as a pollen index (PI), in Córdoba province (southern Spain) several weeks prior to the pollen season start. Using a 29-year database (1982–2010), a multivariate regression model based on five indices—the index-based model—was built to enhance the efficacy of prediction models. Four of the indices used were biometeorological indices: thermal index, pre-flowering hydric index, dormancy hydric index and summer index; the fifth was an autoregressive cyclicity index based on pollen data from previous years. The extreme weather events characteristic of the Mediterranean climate were also taken into account by applying different adjustment criteria. The results obtained with this model were compared with those yielded by a traditional meteorological-based model built using multivariate regression analysis of simple meteorological-related variables. The performance of the models (confidence intervals, significance levels and standard errors) was compared, and they were also validated using the bootstrap method. The index-based model built on biometeorological and cyclicity indices was found to perform better for olive pollen forecasting purposes than the traditional meteorological-based model.

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Acknowledgements

The authors are grateful to the European Social Fund for co-financing with the Spanish Science Ministry the “Ramón y Cajal” contract of Dr. García Mozo and also the Project FENOCLIM (CGL2011-24146). The authors are grateful to the Andalusia Regional Government for funding the project entitled “Análisis de la dinámica del polen atmosférico en Andalucía” (P10-RNM-5958). This work was subsidized partially by the TIN2011-22794 project of the Spanish Inter-Ministerial Commission of Science and Technology(MICYT), FEDER funds, and the P08-TIC-3745 project of the Junta de Andalucia" (Spain). The authors also thank the Spanish Meteorological Agency (AEMET) and the Andalusian Government Agroclimatic Information Network (RIA) for providing meteorological data.

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Authors and Affiliations

  1. Department of Botany, Ecology and Plant Physiology, University of Córdoba, Agrifood Campus of International Excellence (CeiA3), 14071, Cordoba, Spain

    J. Oteros, H. García-Mozo & C. Galán

  2. Department of Computing and Numerical Analysis, University of Córdoba, Agrifood Campus of International Excellence (CeiA3), 14071, Cordoba, Spain

    C. Hervás

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  1. J. Oteros
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  2. H. García-Mozo
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  3. C. Hervás
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  4. C. Galán
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Correspondence to J. Oteros.

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Oteros, J., García-Mozo, H., Hervás, C. et al. Biometeorological and autoregressive indices for predicting olive pollen intensity. Int J Biometeorol 57, 307–316 (2013). https://doi.org/10.1007/s00484-012-0555-5

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  • DOI: https://doi.org/10.1007/s00484-012-0555-5

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