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Updated: Jan 8, 2026

A Precise and Autonomous System for the Detection of Insect Emergence Patterns
Published on: January 9, 2019
Adaptation of a Physiologically Based Model to Predict the Phenology of Philaenus spumarius, the Meadow Spittlebug
Zeinab Sweidan1, Oussama Bouaicha2,3, Nuray Baser2
1Department of Sustainable Crop Production, Facoltà di Science Agrarie, Alimentari e Ambientali, Unviersità Cattolica del Sacro Cuore, Piacenza, Italy.
Abstract:
The importance of Philaenus spumarius has grown considerably due to its role as a vector of Xylella fastidiosa (Xf), the bacterium responsible for olive quick decline syndrome (OQDS). Effective containment of Xf largely depends on vector management, including soil tillage to disrupt juvenile stages and timely insecticide applications to prevent adult spittlebugs from acquiring the bacterium from infected plants and becoming infectious. In this study, we adapted a physiologically based model to P. spumarius. As the pest is difficult to rear under laboratory conditions, we used field data from literature describing the temperature-dependent development of eggs, five juvenile stages (N1-N5), and adults across different crops and geographical areas. Development rate functions were developed from each literature source, and the most representative for the species were defined by adapting the Best Total Coverage method. The model was then validated using three independent datasets collected from different olive orchards across the Apulian region, Italy. The model showed good agreement between predicted and observed nymphal stages (N2 to N5), with R2 values ranging from 0.829 to 0.960, depending on the orchard. However, the model showed a tendency to slightly anticipate the insect development. It also predicted the first seasonal emergence of adults with 88% accuracy. These findings demonstrate that field data can be effectively used to develop models for species that are difficult to rear under controlled conditions. The current model has strong potential to support monitoring and management programs by improving the timing of surveys, interventions, and control measures in olive orchards.
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