Impact of Recapitalisation and Development Programme on Performance of Land Reform Beneficiary Farmers in KwaZulu-Natal, South Africa
Bibliographic record
Abstract
Providing appropriate post-settlement support to farmers is crucial for sustainable development of smallholder agriculture in South Africa. In unravelling this, the South Africa’s Recapitalization and Development Programme (RADP) was initiated. Hence, this study analysed the impact of RADP on performance of land reform beneficiary farmers in KwaZulu-Natal, South Africa. A multistage sampling procedure was used to select (n = 264) respondents for the study. Accounting for endogeneity issues in RADP assessments and its impact on the performance of land reform farmers, an endogenous switching regression model (ESRM) was employed. In the same vein, a doubly-robust inverse probability weighted regression adjustment was used as credible remedy for potentially biased estimates of ATT and POM of endogenous treatment model. The main findings revealed that tax compliance, secondary organization, legal entity, farm potential income at acquisition, farmers receiving third party assistance and strategic partnership were statistically significant in influencing the participation of farmers in RADP. Mentorship remains an extremely challenging element in post-settlement. However, through the strategic partnership of RADP farmers had likelihood to improve the farm and increase farm income. The results of the suggest that the RADP can contribute to a deep process of change and empowerment of farmers. In the same vein, strategic partnership of RADP is likely to improve the farmers’ performance. Therefore, there is a need to strongly improve mentorship and strategic partnership programme to encourage participation of land reform farmers in the support programmes.
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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 | 0.001 |
| 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".