The Dilemma of Fraudulent Pesticides in the Agrifood Sector: Analysis of Factors Affecting Farmers’ Purchasing Behavior in Egypt
Bibliographic record
Abstract
Fraudulent pesticides suggest a solemn risk to sustainable agricultural production, environmental sustainability, and human health due to their unrevealed composition and quality. Nonetheless, their large-scale utilization in the agrifood sector relies on many factors, such as personal, institutional, and legislative ones. This study aimed to evaluate farmers’ perceptions of fraudulent pesticides and examine their marketability elements. The data came from 394 farmers’ structured questionnaires from Dakahlia governorate, Egypt. The factorial analysis revealed beliefs, health and environmental risks, quality recognition, price, and policies as the critical drivers for buying fraudulent pesticides. The cluster analysis disclosed two varied farmer segments—“conventional” and “conscious”—based on perception. “conventional farmers” signify 59.9% of the sample and reveal typical farmer behaviors and give more attention to factors such as beliefs and product price. Contrarily, “conscious farmers” symbolize a more sentient group about policy, product quality, and health and environmental issues. Significant differences (p < 0.01) occurred between the two segments, corresponding to their education, farming activity, farm size, and farming experience. The findings suggest reinforcing the extant pesticide laws and regulations’ administration mechanisms, implementing deliberate measures to increase public awareness of the consequences resulting from fraudulent pesticide use, and improving recognition behavior by detecting fraudulent pesticides with digital technologies among all stakeholders.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".