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Record W4409569389 · doi:10.3390/insects16040427

Farmers’ Perception of Fall Armyworm (Spodoptera frugiperda) as an Invasive Pest and Its Management

2025· article· en· W4409569389 on OpenAlexfundno aff
Waseem Akbar, Sumaira Yousaf, Muhammad Farhan Saeed, Wafa Abdullah Hamad Alkherb, Asim Abbasi, Nazih Y. Rebouh, Nagla N. Suleman

Bibliographic record

VenueInsects · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInsect Resistance and Genetics
Canadian institutionsnot available
FundersDirectorate for Biological SciencesRUDN UniversityPakistan Institute of Engineering and Applied SciencesPakistan Atomic Energy CommissionAlberta Agricultural Research InstituteUniversity of Karachi
KeywordsFall armywormBiologyCropInfestationAgricultureAgronomyCash cropFodderPEST analysisIntegrated pest managementBiotechnologyToxicologySpodopteraEcologyHorticulture

Abstract

fetched live from OpenAlex

This study was carried out with the aim of understanding how farmers perceive and deal with fall armyworm (FAW) infestations in maize crops. Data based on a questionnaire were collected across nine districts of the Punjab province in Pakistan. Findings revealed that about 38% of farmers had medium-sized landholdings and had been cultivating maize crop for the last 11-20 years. Hybrid maize seed was used by 55% of the farmers, and 60% of the maize grown was used as fodder, as well as a cash crop. Surprisingly, only 39% of farmers were able to correctly identify FAW; however, the majority (72%) recognized the larvae as the most damaging stage. Although most of the farmers (71%) grew maize crops during both seasons, only 34% of the farmers recognized autumn as the peak infestation period of FAW. However, despite limited awareness, a high percentage (86%) of farmers managed FAW effectively using various chemical treatments. This study also highlighted the influence of some factors on farmers' perception of FAW such as: age, farming experience, and maize cultivation practices. Overall, the findings emphasize the need for increased awareness of the basic biology and targeted management strategies for FAW to safeguard maize crops in the region.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.213
Threshold uncertainty score0.514

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.007
GPT teacher head0.251
Teacher spread0.244 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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