The Impact of Soil Erosion as a Food Security and Rural Livelihoods Risk in South Africa
Bibliographic record
Abstract
This study evaluates soil erosion/attrition as a major food security and rural livelihoods risk in South Africa, with the Upper and Lower Areas of Didimana, Eastern Cape Province, as a case study. The survey research method was adopted for the study. Farmers and extension officers’ behaviours relating to soil erosion control was negative even though the impact of erosion in the area was high. Approximately 75% of farmers indicated that they lose more than 21% of their crops yearly due to erosion and 55% said their crops and livestock, as well as their household feeding, suffer due to the problem. The results of the multiple linear regression analysis indicate that farm yield and farmers’ access to market are positively related to farmers’ adoption tendencies regarding erosion control, implying that farmers are more willing to adopt recommendations if their yields and access to market can increase. Similarly, age of farmers is positively related with erosion impact, indicating that older people have a higher tendency to cause erosion in the study area. This is true, as the area consists more of older people, who are generally known to resist change, thus low in adoption. Therefore, it is perceived that if farmers manage soil erosion appropriately, they will achieve higher yields. More so, pull factors like improved rural infrastructures and adequate agricultural incentives for youths are suggested to lure more youth in taking into farming in the study area.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".