Development of a PDP computational model to enhance the control and management of an aphid pest under greenhouse conditions

Abstract

European Green Deal aims reduce chemical pesticides use by 50 % by 2030, highlighting the need for effective biological pest control. However, predicting interactions between multiple biological control agent remains challenging, due to complex, non-linear dynamics that traditional differential models struggled to capture. Novel modelling approaches such as bioinspired computational Population Dynamics P models (PDP models), have the potential to address these bottlenecks being capable of simulating multiple, simultaneous interactions in biological systems.

We developed an PDP model to evaluated the interactions between two natural enemies in a greenhouse environment - Adalia bipunctata (predator) and Aphidius colemani (parasitoid) - and their shared prey, the peach aphid Myzus persicae, an important economic pest. The model was validated with experimental data, on aphid population growth rates and biocontrol agent introduction time. Using these validated parameters, scenarios were analysed to optimize pest control strategies, balancing both effectiveness and economic costs. Our results revealed that A. bipunctata was more effective at rapidly reducing aphid populations when its quantity was fixed. However, A. colemani was more cost-efficient due to its lower acquisition cost, enabling the deployment of more parasitoids. The model also underscored the importance of timely interventions, showing that delayed introduction of biocontrol agents significantly reduced pest suppression efficacy, also demonstrating the potential in clarifying trade-offs between cost and effectiveness. This study provides a robust framework for developing advanced PDP models, paving the way for improved, cost-effective pest management solutions tailored to complex agroecosystems and designed to meet current and future policy demands.