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Research Article Open access CC BY 4.0

A Comprehensive Review on Seasonal Incidence of Fall Army Worm, Spodoptera frugiperda (J.E. Smith) and Their Management

Pramod Kumar Mishra, Akshay Kumar, Ashutosh Singh Aman, Shravan Kumar Verma, Arun Kumar, Jony Kumar, Durgesh Kumar Maurya, Pankaj Kumar Rajpoot

International Journal of Environment and Climate Change · pp. 2458–2467 · Published 6 Sep 2023

10.9734/ijecc/2023/v13i102912

Abstract

The Fall Armyworm (FAW), Spodoptera frugiperda, represents a formidable challenge to global agriculture due to its rapid spread and significant impact on crop yields. This comprehensive review focuses on providing an in-depth exploration of FAW's biology, its seasonal dynamics, and the multifaceted strategies employed for its management. Leveraging datasets from multiple geographical regions, we examined the patterns of FAW infestations and their correlation with various climatic and environmental factors. The research emphasized the criticality of predictive modeling tools in forecasting pest incidence and highlighted the potential of machine learning and big data analytics in enhancing the accuracy of these predictive tools. Innovative management solutions, spanning from genetic interventions to the application of nanotechnology, were also discussed, underlining their potential in mitigating FAW damage. Central to our findings was the recurrent theme of international collaboration; the need for globally coordinated efforts in research, monitoring, and the sharing of resources emerged as a pivotal component in the fight against this pest. By incorporating diverse perspectives, including field insights from farmers and advancements in modern technology, this review aims to provide a holistic overview of the present scenario and proffers strategies for future action against the FAW threat.

Fall armyworm Spodoptera frugiperda predictive modeling insect

Cited by 2

Fall armyworm habitat analysis in Africa with multi-source earth observation data

Tonghui Qi, Yingying Dong, XueLing Li · Computers and Electronics in Agriculture · 2024

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