To implement Area-Wide Pest Management protocols at a regional scale (Liguria, northern Italy), egg deposition and adult flight of olive fruit fly, Bactrocera oleae, were monitored during 2009, 2010 and 2011. The consequence of complete generation in late winter – early spring was also examined. The reliability of a degree-day model was tested to simulate the insect cycle, starting from October oviposition and considering a 379.01°C cumulative degree-day (CDD) needed to complete development. The model was validated and then used to simulate olive fruit fly phenology in the region of Liguria, using a GIS approach and the agrometeorological network in the region. The output of the CDD model was mapped with two different spatialization modelling techniques, geostatistical autocorrelation and regression correlation, and altitude, aspect and distance from the sea were assessed as elements of variability. The regression correlation model provided a more accurate indication of B. oleae diversity at the local scale than the geostatistical autocorrelation model and identified the high spatial climatic variability of Liguria. The potential application of the distribution of days after oviposition and prediction error maps in support of pest management planning is discussed.

Large-scale simulation of temperature-dependent phenology in wintering populations of Bactrocera oleae (Rossi)

PETACCHI, Ruggero;MARCHI, Susanna;RAGAGLINI, Giorgio
2014-01-01

Abstract

To implement Area-Wide Pest Management protocols at a regional scale (Liguria, northern Italy), egg deposition and adult flight of olive fruit fly, Bactrocera oleae, were monitored during 2009, 2010 and 2011. The consequence of complete generation in late winter – early spring was also examined. The reliability of a degree-day model was tested to simulate the insect cycle, starting from October oviposition and considering a 379.01°C cumulative degree-day (CDD) needed to complete development. The model was validated and then used to simulate olive fruit fly phenology in the region of Liguria, using a GIS approach and the agrometeorological network in the region. The output of the CDD model was mapped with two different spatialization modelling techniques, geostatistical autocorrelation and regression correlation, and altitude, aspect and distance from the sea were assessed as elements of variability. The regression correlation model provided a more accurate indication of B. oleae diversity at the local scale than the geostatistical autocorrelation model and identified the high spatial climatic variability of Liguria. The potential application of the distribution of days after oviposition and prediction error maps in support of pest management planning is discussed.
2014
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11382/498140
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