Predicting Seasonal Activity of Blunt-nosed Leafhoppers Using Degree-day Models in Cranberry

Accurate prediction of pest and crop phenology is essential for optimizing integrated pest management (IPM) in cranberry production systems. Degree-day models are widely used to forecast insect development and crop phenology; however, few have been developed for cranberry insect pests. We developed and validated degree-day–based models for the blunt-nosed leafhopper, Limotettix vaccinii (Hemiptera: Cicadellidae), in relation to cranberry bloom phenology in New Jersey, USA. Multiyear field data collected from commercial cranberry beds between 2022 and 2024 were used to develop and optimize the models, whereas independent datasets collected from 289 beds over a 5-year period (2021–25) were used for validation. Phenological progression of both pest and crop was modeled using logistic functions driven by growing degree-day (GDDpest and GDDcrop, respectively), with optimized base temperatures and biofix dates. The optimal L. vaccinii model used a base temperature of 10 °C and a biofix corresponding to floodwater drawdown [day of year (DOY) 112], whereas the cranberry bloom model converged on a base temperature of 4.44 °C and a DOY 94 biofix. Developmental trajectories showed consistent biological patterns, with nymphal activity preceding bloom and adult emergence overlapping the later stages of bloom. Model integration revealed that 10% bloom occurred, on average, at 716 GDDcrop (DOY 160), by which time ∼70% of nymphal development had already been completed. Based on these dynamics, the optimal insecticide application window was identified between 195 GDDpest (DOY 144; 25% nymphal progression) and peak nymph abundance at 266 GDDpest (DOY 153; 50% progression), maximizing nymph suppression while minimizing pollinator exposure. These results demonstrate that empirically optimized degree-day models can effectively synchronize L. vaccinii management with cranberry crop phenology. This framework provides a practical decision support tool for cranberry IPM and highlights the importance of regional calibration of phenology models to local climatic conditions and management practices.