Understanding land reform and the associated consequences for land use and livelihoods
Bibliographic record
Abstract
Land reform is important to correct historical injustices in all districts of South Africa, including the Waterberg District Municipality. There is consensus, globally, that there are trade-offs among diverse land reform objectives, namely: social, economic, political and environmental. The general aim of this thesis, therefore, was to understand land reform: the extent to which it meets objectives and the associated consequences for land use and livelihoods. Restitution- Rest, settlement/land acquisition grant- SLAG, land redistribution for agricultural development phase 1- LRAD1 and phase 2- LRAD2, and proactive land acquisition strategy- PLAS, were the five South Africa’s land reform farm types. The results show that the socio-economic classes of beneficiaries was determined by synergies between policies and their implementation outcomes. Land uses were diverse in Rest, and less diverse and like previous uses in LRAD1, LRAD2 and PLAS. On-farm livestock and overall on-farm livelihood contributions were high for capital-endowed households in LRAD1, LRAD2 and PLAS, and low for capital-poor households in Rest and SLAG. This suggest that land reform was unable to bridge the inequality gap between capital-poor and capital-endowed households, and livestock influenced on-farm contribution in semi-arid conditions. Crop plus ruminants- CR, horticulture- H, ruminants- R, ruminants plus horticulture- RH and monogastrics- M were the five farming systems in land reform farms. These farming systems developed from interactions among the biophysical conditions, land reform policies, socio-economic class of farmers, physical capital endowment of farms, and the type of produce market used. The results suggest that land reform policies could influence land use activities by transferring farms of a particular size, and availability of external physical and financial capital support could determine participation of the poor. Strengths and weaknesses of land reform farms were determined by socio-economic class of farmers and the characteristics of land use activities. Use of farms by the poor led to strengths of low opportunity costs for family labour and farms being prioritised by the state for support, and weakness of being dependent on external support as farmers and farms lacked physical and financial capital, compared to farms used by the better-off. Farms where capital-extensive activities were key had strengths of less capital investment requirements and moderate susceptibility to climate, and a weakness of low possibility for partnerships with large-scale commercial farmers, compared to farm where capital-intensive activities were key. Overall, our results suggest limited use and limited livelihood contributions of land reform farms. These results arise doubts about the relevancy of land reform, and its future depends on stakeholders advocating for it to ensuring that it is implemented through approaches which pays landowners fairly, that correct people benefit, and that appropriate land uses are adopted.
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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 | 0.000 |
| Science and technology studies | 0.001 | 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".