Persistence of Selected Pesticides used in Sugarcane Production in Soil and Water in the Northern Lake Victoria Catchment
Bibliographic record
Abstract
Pesticide use in the Lake Victoria catchment area ofUganda has continuously been increasing in the last ten years due to increase in the production of horticultural export crops and sugarcane. The farms have to use pesticides for increased crop productivity in order to meet market demands. This study was conducted to monitor pesticide levels in soils and runoff water following treatment of a sugarcane field in the Northern Lake Victoria watershed. Soil and water samples were collected over a period of 304 days after planting of the sugarcane and analysed for pesticide residues. In soils, glyphosate levels ranged from 0.8-135.5 μglkg. Ametryn ranged from 44.9-1705.4 μg/kg, and Dichlorophenoxy acetic acid (2.4-D) 15.6- 835.2 μg/kg. In water, glyphosate levels ranged from l.3 to 42.2 upstream and 0.4 to 9.3 downstream, ametryn ranged from < 15 to 31.5 μgll upstream and from <1 5- 18.6 g/1 downstream and, 2.4-D<LO to 15.7 μg/1 upstream and <10 to 13.4 μg/1 downstream. The quality of water obtained was compared with the Canadian Environmental Quality thresholds for fresh water, irrigation and livestock water for selected pesticides. At the applied rates the use of herbicides Touch down ( 48% glyphosate trimesium) and Gesapax-H (21% ametryn and 29%, 2,4-D) for the control of weeds in sugarcane farming system at Kakira was found to be within acceptable levels. Keywords : Pesticides, soil, water, pollution, concentration.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".