Dumpsites farmers’ awareness of physicochemical properties of dumpsites soil in two agro-ecological zones, Ghana
Bibliographic record
Abstract
In Ghana, widespread backyard farming on arable land with refuse waste is driven by the enriched physicochemical properties of dumpsite soils, supporting crop growth. However, the enhanced levels of heavy metals have raised concerns among policymakers focusing on the environmental challenges posed by dumpsites soil. The purpose of the study was to assess the awareness of dumpsites farmers on soil physicochemical properties and dangers posed by accumulated toxic heavy metals. A total of 100 vegetable farmers were interviewed in three communities to gauge their awareness of dumpsite soil properties and potential contamination. A semi-structured questionnaire was purposively used to solicit for information from farmers. Chi-square (χ2) test of homogeneity was used to ascertain if there was any uniformity among respondents in the different communities studied. The findings showed that the level of education of the famers had no relationship (p = 0.21) with farmers’ educational level. Dumpsites farmers’ knowledge on soil physicochemical properties had a significant (p = 0.02) relationship with farmers' awareness of toxic elements in dumpsites. Farmers’ soil physicochemical knowledge further had a significant (p = 0.03) relationship with their awareness that, plants on dumpsites absorb toxic elements. Furthermore, ailments commonly associated with heavy metals as reported by farmers included skin rashes, cough, diarrhoea, and cholera. It can be concluded that although dumpsite farmers exhibit some awareness about the physicochemical properties of their soil and the risks associated with toxic heavy metals, there is still room for improvement. Despite education levels not significantly impacting awareness, targeted intervention programs are necessary to enhance understanding and address this pressing issue effectively. We recommend an implementation of a tailored educational programs for dumpsite farmers to enhance understanding of soil properties and heavy metal risks. There should be improved access to soil testing and interpretation to enable informed decision-making among farmers. Foster collaborations among stakeholders for sustainable waste management, soil remediation, and health monitoring to mitigate heavy metal contamination are also recommended.
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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 | 0.000 | 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".