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Effects of Seasonality and Climate on the Propagule Deposition Patterns of the Chestnut Blight Pathogen Cryphonectria parasitica in Orchards of the Alpine District of North Western Italy

Author

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  • Guglielmo Lione

    (Department of Agricultural, Forest and Food Sciences (DISAFA), University of Torino, Largo Paolo Braccini 2, I-10095 Grugliasco, Italy
    Chestnut R&D Center, Regione Gambarello 23, I-12013 Chiusa di Pesio, Italy)

  • Francesca Brescia

    (Department of Agricultural, Forest and Food Sciences (DISAFA), University of Torino, Largo Paolo Braccini 2, I-10095 Grugliasco, Italy
    Current address: National Research Council-Institute for Sustainable Plant Protection (CNR-IPSP), Strada delle Cacce, 73, I-10135 Torino, Italy.)

  • Luana Giordano

    (Department of Agricultural, Forest and Food Sciences (DISAFA), University of Torino, Largo Paolo Braccini 2, I-10095 Grugliasco, Italy
    Chestnut R&D Center, Regione Gambarello 23, I-12013 Chiusa di Pesio, Italy
    Current address: Laboratory of Lombardy Plant Health Service, c/o Fondazione Minoprio, Viale Raimondi 54, I-22070 Vertemate con Minoprio (CO), Italy.)

  • Paolo Gonthier

    (Department of Agricultural, Forest and Food Sciences (DISAFA), University of Torino, Largo Paolo Braccini 2, I-10095 Grugliasco, Italy
    Chestnut R&D Center, Regione Gambarello 23, I-12013 Chiusa di Pesio, Italy)

Abstract

Chestnut blight is the major disease of chestnuts ( Castanea spp.) cultivated worldwide for the production of edible nuts. The disease is caused by the pathogenic fungus Cryphonectria parasitica , which infects trees by means of airborne propagules penetrating through fresh wounds on stems and branches. The aims of this study were to (I) assess the temporal propagule deposition patterns of C. parasitica in the Alpine district of North Western Italy, (II) test and model the effects of seasonality and climate on the above patterns, and (III) investigate the spatial distribution of propagule deposition at the within-site scale. A two-year-long spore trapping experiment was conducted in three chestnut orchards. Approximately 1300 samples were collected and processed with a species-specific qPCR assay to quantitatively assess the propagule deposition of C. parasitica . Results showed that C. parasitica can release propagules all over the year, though with significant seasonal peaks in the spring and fall ( p < 0.05). Large propagule loads were significantly correlated ( p < 0.05) with an increasing number of rainy days of the week (days providing 1–10 mm/day of water). Models predicting periods at high risk of infection based on climate and seasonality were fitted and successfully validated ( p < 0.05).

Suggested Citation

  • Guglielmo Lione & Francesca Brescia & Luana Giordano & Paolo Gonthier, 2022. "Effects of Seasonality and Climate on the Propagule Deposition Patterns of the Chestnut Blight Pathogen Cryphonectria parasitica in Orchards of the Alpine District of North Western Italy," Agriculture, MDPI, vol. 12(5), pages 1-24, April.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:5:p:644-:d:805656
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    References listed on IDEAS

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    1. Piñeiro, Gervasio & Perelman, Susana & Guerschman, Juan P. & Paruelo, José M., 2008. "How to evaluate models: Observed vs. predicted or predicted vs. observed?," Ecological Modelling, Elsevier, vol. 216(3), pages 316-322.
    2. Hui, Wallace & Gel, Yulia R. & Gastwirth, Joseph L., 2008. "lawstat: An R Package for Law, Public Policy and Biostatistics," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i03).
    3. Zeileis, Achim & Leisch, Friedrich & Hornik, Kurt & Kleiber, Christian, 2002. "strucchange: An R Package for Testing for Structural Change in Linear Regression Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 7(i02).
    4. Baddeley, Adrian & Turner, Rolf, 2005. "spatstat: An R Package for Analyzing Spatial Point Patterns," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 12(i06).
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