Ensuring access to water for food production by emerging farmers in South Africa: What are the missing ingredients?
Bibliographic record
Abstract
One of the key components essential to the productivity of small-scale farmers who secured farms through the land redistribution programme in South Africa is access to reliable sources of water for irrigation. In this study, we deployed a stakeholder-oriented qualitative research methodology to understand the extent to which land reform farming schemes in Bela-Bela and Greater Sekhukhune have been able to access water and use it to enhance their agricultural production. We were keen to identify and articulate the water-related challenges and missing ingredients for successful agricultural production on the new farming schemes. The study found that access to water for irrigated agriculture is not guaranteed for most of the emerging farmers and they do not have the finance needed to invest in sustainable water supply systems for irrigation. As a result, the majority of the farmers in our study sample have not been able to realize any meaningful agricultural production, with their farming schemes being either underutilized or not functioning at all. Other key challenges include lack of finance, high costs of electricity, and lack of farming knowledge among the emerging farmers. The paper concludes that there is need for key actors in the development sector to provide more substantive post–land transfer support and ensure better access to water for the emerging farmers. This will enhance the farmers’ chances of realizing more meaningful agricultural production while improving their livelihoods.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".