Implications of Land Ownership Heterogeneity on Household Food Security: A Case Study of Urban Farming in Pietermaritzburg, KwaZulu-Natal Province
Bibliographic record
Abstract
Understanding the impact of land ownership on household food security is crucial for achieving sustainable rural and agricultural development in developing countries through improved farm performance. Using a multistage sampling technique to collect data from 156 urban farmers, this study analysed the impact of land ownership on household food security of urban farmers in Pietermaritzburg, KwaZulu-Natal Province of South Africa. This study employed the probit model to evaluate the drivers of land ownership among urban farmers, while the marginal treatment effects model was employed to address selection bias attributed to observed and unobserved characteristics. The analysis of food security status reveals varying degrees of food insecurity, with the majority of households experiencing mild food insecurity and a smaller proportion facing moderate food insecurity. Our results show that land ownership likelihood is positively and significantly influenced by monthly income, age, and membership to a cooperative, while gender and distance to market have negative and significant impacts. The empirical results also show that land ownership significantly reduces household food insecurity by 50%. In conclusion, the interplay of educational level, household size, access to water, access to credit, and distance to a market with land ownership significantly shapes food security outcomes. A comprehensive understanding of these relationships is essential for developing effective policies aimed at enhancing food security, particularly in regions where land ownership is a critical determinant of agricultural productivity and food availability.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".