Pesticides and Associated Impact on Human Health: A Case of Small Farmers in Southern Sindh, Pakistan
Bibliographic record
Abstract
The unbridled use of pesticides in agriculture sector poses a serious environmental degradation problem besides, being intimately associated with public and workers health hazard. Although the use of pesticides help to considerably control diseases, hence reduce crop losses and result in better yield of the crops. Pesticides are particularly under use to improve production of crops like corn, vegetables, potatoes and cotton, although their unfavourable effects on environmental quality and human health have frequently been reported and well documented. Pesticides mismanagement starts at the local area but always exhibits local and global impacts with universal dimensions. The study aimed at determining the extent of pesticide use and farmers’ knowledge of the pesticides and their use on the field. One hundred farmers were interviewed concerning pesticides usage patterns from amongst farmers in vegetable production area in Lower Sindh, Pakistan. Information was obtained through structured Questionnaire coupled with personal interviews. The results revealed that 27 different pesticides were used by farmers and majority of pesticides were insecticides. All pesticides used were registered by Government of Pakistan. Toxicity class of majority pesticides belonged to Class-II, which WHO classifies as moderately hazardous and one highly hazardous pesticide with the class- Ib was also being used. Okra and tomato were more affected by the insect pests. Majority of farmers relied on nicotinoid and organophosphate groups of pesticides. A considerable number of farmers had received education at middle school level only. The source of farmers knowledge of pesticide use was the pesticides dealer who advised them when to spray the crops (s). Half of farmers had no safety protection gears/tools during pesticide application and vegetable harvesting. The survey has shown some overuse, misuse and abuse of pesticides. Farmers were not aware of pesticide hazards and they lacked appropriate knowledge on safe handling and use of pesticides, which can be attributed to inadequate agriculture extension services and state of art trainings to the farmers at their door steps along with lack of education. Since these pesticides were being traditionally used by the farmers in the near by fields and on the same fields with out their deleterious effect knowledge to the farmers, soil and environment combined together as most of the farmers were not educated but also ill equipped about the knowledge of the kind/type of pesticide being used/sprayed what to talk of its chemical nature leading to harmful effects.
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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 | 0.000 |
| 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".