Remote sensing integrated modeling of fall armyworm parasitoid interactions in Africa using fractional differential equations
Publication Type
Journal Article
Journal Name
Ecological Modelling
Publication Date
9-1-2026
Abstract
The invasion of fall armyworm (Spodoptera frugiperda; FAW) into Africa poses a major threat to agricultural systems, particularly maize production, necessitating effective biological control strategies. This study presents an ecological modelling framework that integrates temperature data with fractional differential equations (FDEs) to investigate interactions between FAW and its parasitoid natural enemies. By incorporating temperature-driven effects on life-history traits and ecological memory dynamics, the model captures key features of host–parasitoid interactions under variable environmental conditions. Equilibrium states corresponding to FAW extinction, parasitoid extinction, and coexistence were analytically derived, and sensitivity analysis of the FAW reproduction number highlighted its central role in regulating population stability. Numerical simulations demonstrated the influence of fractional-order memory effects on system behaviour, while spatial analysis identified heterogeneous risk zones that support geographically targeted pest management interventions. Model validation revealed a strong positive relationship between simulated reproduction rates and observed oviposition patterns (r = 0.89), indicating high predictive performance. Overall, this framework enhances understanding of FAW–parasitoid dynamics and provides a quantitative basis for developing climate-responsive and biologically based pest management strategies in African agricultural systems.
Keywords
Fall armyworm, Fractional differential equations, Maize biomass, Parasitoid, Remote sensing
Recommended Citation
Adan, M., Affognon, S., Tonnang, H., Greve, K., Borgemeister, C., & Goergen, G. (2026). Remote sensing integrated modeling of fall armyworm parasitoid interactions in Africa using fractional differential equations. Ecological Modelling, 519 https://doi.org/10.1016/j.ecolmodel.2026.111671