Quantification of Yield Losses in Maize Attributed to Fall Armyworm (Spodoptera frugiperda) Infestation

A field experiment was conducted to assess avoidable yield losses caused by the fall armyworm, Spodoptera frugiperda, on maize during the kharif seasons of 2023–24 and 2024–25. Pooled data across both years indicated a significant decline in grain yield with increasing larval density per plant. The highest yield (4794.05 kg/ha) was obtained in infestation-free plots, while the lowest (4005.16 kg/ha) was recorded at a density of five larvae per plant. Grain yield and larval population showed a strong negative correlation (r = –0.99). Avoidable yield losses ranged from 3.71% to 16.46%, increasing proportionally with larval density. The results showed that avoidable yield loss increased consistently with FAW larval density (r = 0.99). Maximum losses occurred at five larvae per plant, reaching 14.26%, 18.75%, and 16.46% in 2023–24, 2024–25, and the pooled dataset, respectively. In contrast, minimum losses were recorded at 0.5 larvae per plant, with 3.74%, 3.67%, and 3.71% across the respective datasets. The relationship between larval density and percent avoidable yield loss was strong and positive, with a high coefficient of determination (R² = 0.97). The findings are useful in defining economic injury levels and economic thresholds for FAW in maize, thereby supporting the development of integrated pest management strategies.

Authors

Publication Details

Journal
Journal of Scientific Research and Reports
Published
2025-09-18
DOI
https://doi.org/10.9734/jsrr/2025/v31i93516
Citations
1
Primary Topic
Crop Yield and Soil Fertility
Type
article
Field-Weighted Citation Impact
1.90
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Quantification of Yield Losses in Maize Attributed to Fall Armyworm (Spodoptera frugiperda) Infestation

Abhishek Shukla, Manish Gadekar, Sunil Kumar Sharma, Subhashree Patnaik et al.
1 citations
Journal of Scientific Research and Reports
Crop Yield and Soil Fertility
1.90
article

Quantification of Yield Losses in Maize Attributed to Fall Armyworm (Spodoptera frugiperda) Infestation

Abhishek Shukla, Manish Gadekar, Sunil Kumar Sharma, Subhashree Patnaik, Sumit Kakade, SB Das
article en
1 citations

Abstract

A field experiment was conducted to assess avoidable yield losses caused by the fall armyworm, Spodoptera frugiperda, on maize during the kharif seasons of 2023–24 and 2024–25. Pooled data across both years indicated a significant decline in grain yield with increasing larval density per plant. The highest yield (4794.05 kg/ha) was obtained in infestation-free plots, while the lowest (4005.16 kg/ha) was recorded at a density of five larvae per plant. Grain yield and larval population showed a strong negative correlation (r = –0.99). Avoidable yield losses ranged from 3.71% to 16.46%, increasing proportionally with larval density. The results showed that avoidable yield loss increased consistently with FAW larval density (r = 0.99). Maximum losses occurred at five larvae per plant, reaching 14.26%, 18.75%, and 16.46% in 2023–24, 2024–25, and the pooled dataset, respectively. In contrast, minimum losses were recorded at 0.5 larvae per plant, with 3.74%, 3.67%, and 3.71% across the respective datasets. The relationship between larval density and percent avoidable yield loss was strong and positive, with a high coefficient of determination (R² = 0.97). The findings are useful in defining economic injury levels and economic thresholds for FAW in maize, thereby supporting the development of integrated pest management strategies.

Journal of Scientific Research and ReportsVol. 31(9)
Openalex Percentile: Top 12%
Crop Yield and Soil Fertility
1.90
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.