Modelling of post-diapause development and spring emergence of Cydia nigricana (Lepidoptera: Tortricidae)

Abstract

The prediction of the post-diapause emergence is the first step towards a comprehensive decision support system that can contribute to a considerable reduction of pesticide use by forecasting a precise spraying date. The cumulative field emergence can be described as a function of the cumulative development rate. We investigated the impact of seven constant temperatures and five light regimes on post-diapause development in laboratory experiments. Development rate depended significantly on temperature but not on photoperiod. We therefore fit non-linear thermal performance curves, a better and more modern approach over past linear models, to describe the development rate as a function of temperature. The four parameter Brière function was the most suitable and was subsequently applied to temperature data from 36 previous pea fields, where pea moth emergence was measured with pheromone traps in Northern Hesse (Germany). In order to describe the variation in development times between individuals, we fit five nonlinear distribution models to the cumulative development rate as a function of cumulative field emergence. The three parameter Gompertz model was selected as the best fitted model. We validated the model performance with an independent field data set. The model correctly predicted the first moth in the trap and the peak emergence in 81.82% of cases, with an average deviation of only 2.00 and 2.09 days respectively.

The efficacy of spatio‐temporal predictors in forecasting the risk of Cydia nigricana infestation

“…The manual monitoring of pheromone traps would require at least weekly control of all currently growing pea sites subsequent to moth emergence in previous pea sites. A temperature‐based model that predicts pea‐moth emergence already exists and can help to limit the time span needed for placing and maintaining pheromone monitoring traps in currently growing pea sites (Riemer et al., 2021). Automatic pest‐counting insect traps using deep‐learning techniques (Bjerge et al., 2021; Hong et al., 2021; Sütő, 2021; Wang, Li, et al., 2022) would be a substantial improvement on these traps, but they are not market‐ready yet.…”

The efficacy of spatio-temporal predictors in forecasting the risk of Cydia nigricana infestation

“…The manual monitoring of pheromone traps would require at least weekly control of all currently growing pea sites subsequent to moth emergence in previous pea sites. A temperature-based model that predicts pea-moth emergence already exists and can help to restrict the time span needed for placing and maintaining pheromone monitoring traps in currently growing pea sites(Riemer et al 2021). Automatic pest-counting insect traps using deep-learning techniques (Bjerge et al 2021; Hong et al 2021tő 2021; Wang et al 2022a) would be a substantial improvement on these traps, but they are not market ready yet.…”