Seasonal and spatial distribution of fall armyworm larvae in maize fields: implications for integrated pest management

Zanzana, K., Sinzogan, A., Tepa-Yotto, G. T., Dannon, E., Goergen, G. & Tamò, M. (2025). Seasonal and spatial distribution of fall armyworm larvae in maize fields: implications for integrated pest management. Insects, 16(2): 145, 1-17.

The fall armyworm (FAW), Spodoptera frugiperda, a major pest in maize production, was assessed for its temporal and spatial distribution in maize fields during both the dry and rainy seasons of 2021 and 2022 in two agroecological regions in Benin (zone 6 and 8). Zone 6 (AEZ 6) “called zone of terre de barre” (Southern and Central Benin) consisted of ferralitic soils, a Sudano-Guinean climate (two rainy seasons alternating with two dry seasons) with a rainfall ranging between 800 and 1400 mm of rainfall per year; while zone 8 (AEZ 8) called “fisheries region” (Southern Benin” is characterized by coastal gleysols and arenosols with a Sudano-Guinean climate and a rainfall of 900–1400 mm of rainfall per year. In this study, 30 and 50 maize plants were randomly sampled using a “W” pattern during the dry and rainy seasons, respectively. Larval density, larval infestation rates, and damage severity were monitored over time. Taylor’s power law and the mean crowding aggregation index were applied to evaluate the dispersion patterns of the larvae. The results indicate a higher larval infestation rate and larval density in AEZ 8 compared to AEZ 6 during the dry season. In the rainy season, while the percentage of damaged plants was higher in AZE 8, no significant differences in larval density between the two zones were observed. The dispersion analysis revealed moderate aggregation (aggregation index = 1.25) with a basic colony of 2.08 larvae, i.e., an average initial cluster of 2.08 larvae observed per plant, reflecting the aggregation oviposition behavior of FAW. This study provides valuable monitoring data on the FAW’s distribution, offering insights for further research on population dynamics and developing predictive models for integrated pest management strategies.