The Impact of Soil Erosion on Agricultural Potential and Performance of Sheshegu Community Farmers in the Eastern Cape of South Africa
Bibliographic record
Abstract
Soil erosion is one of the unresolved problems of rural agriculture. This study investigates the impact of soil erosion on the agricultural potential and performance of Sheshegu community farmers in the Eastern Cape of South Africa. Structured interview scheduled was used to collect data from 50 respondents using simple random sampling. Findings revealed that most (62%) respondents are male, who are above 46 years old (68%). Most of whom (72%) had education above grade 7. Further, the majority of them (50.8%) depend on social grants as sources of income. Most respondents confirmed that erosion occurred naturally through heavy rainfall and persistent drought while human causes that facilitated erosion include farming activities, deforestation and indiscriminate bush burning that expose soil to impact of rain drop. Respondents affirmed that erosion contributed to poor health of livestocks due to lack of pasture grass to feed on, loss of grazing land and poor bush regrowth. It is recommended that awareness on the negative effect of human causes of erosion should be created while simple technologies on soil erosion control should be pushed to the farmers. Finally, edict on bush burning should be enforced to check indiscriminate bush burning.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".