Organochlorine Pesticides Residues in Soil of Cocoa Farms in Ondo State Central District, Nigeria
Bibliographic record
Abstract
Ondo State being the highest producer of Cocoa in Nigeria constitutes the most probable area with the highest use of chemical pesticides to enhance cocoa production. As effective as these chemicals may be in achieving this goal, the incidence of their residues on non-targeted substances and the total environment, with the attendant adverse effects have being of serious concerns. Our objective in this paper is to assess contamination of farm soils by organochlorine pesticides applied on cocoa farms within the Central Senatorial District of Ondo State, Nigeria. Soil samples were collected from selected cocoa farms and analysed for organochlorine pesticides residues using GC-MS. Some soil physicochemical properties including pH, particle size and organic matter that may influence the dynamics of the pollutants were also determined. Organochlorine compounds detected at varied concentrations include Endosulfan I and Endosulfan II occurring most frequently with highest concentrations of 350.10 mg/kg and 3.55 mg/kg respectively. Other organochlorine compounds detected were Heptachlor, Heptachlor epoxide, Aldrin, Deldrin,, isomers of Benzene hexachloride: ?-BHC, ?-BHC, ?-BHC, and ?-BHC (lindane). The concentrations of the organochlorine pesticides (mg/kg) measured in the soil samples showed significant (p<0.05) correlation with the total organic matter contents of the soil. Findings from this research thus, provide information on the current and health risk residue levels of organochlorine pesticides in soil from this region with which future environmental performance on the use of pesticides on cocoa farms could be progressively monitored.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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 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".