Contemporary Challenges Facing the Small Farmers in the Green Scheme Projects in Namibia
Bibliographic record
Abstract
The paper uses a combination of theory and both quantitative and qualitative evidence to demonstrate the significance and challenges of agricultural development in Namibian green scheme projects. For quantitative, a structured questionnaire to produce descriptive statistics was administered to 135 small farmers while eight (8) project manager who were interviewed at the studied schemes as key informant served as source of qualitative information that pin pointed out challenges and opportunities, faced by the small farmers in these schemes. The evidence points to the fact that although there are myriad of challenges, such as challenges related to production, access to efficient and effective market and access to credit faced by farmers, production and access to efficient and effect market challenges emerged as the most stumbling blocks to the optimal production and sales of small farmers’ produce. Usually access to agricultural credit is seen as one of the major challenges of smallholder farmers in Africa. In this study access to agricultural credit was less seen as a major stumbling block to the smallholder farmers’ productivity. This is attributed to the current farmers’ agricultural credit support scheme in place between Agricultural Bank of Namibia (Agribank) and the government of Namibia.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".