Securing the future of urban agriculture: Legislative reform and planning for sustainability in sub-Saharan African cities
Bibliographic record
Abstract
Urban and peri-urban agriculture (UPA) holds significant promise for advancing sustainable urban development in rapidly urbanizing cities. UPA contributes to food security, enhances urban resilience, and supports the creation of green spaces, all of which are essential in mitigating the adverse impacts of urbanization. Yet, as cities in sub-Saharan Africa (SSA), including Ghana, continue to expand, agricultural lands are increasingly converted into residential and commercial developments. This phenomenon threatens UPA, despite its recognized importance. While there is growing recognition of the importance of UPA in the sustainable city discourse, there is a dearth of empirical evidence on its integration into formal urban planning documents in Ghana, and the challenges associated with its integration remain largely unexplored. This study employed document analysis and key informant interviews to assess the integration of UPA into land use planning frameworks in Ghana, using Greater Kumasi and its peri-urban areas as a case study. It further unpacked the barriers that hinder the integration of UPA into formal planning frameworks. The results indicate a significant legislative gap in the current land use planning frameworks that hinders the integration of UPA into Ghana's land use planning. Land tenure systems, infrastructure deficit and housing design constraints also hinder the integration of UPA into Ghana's land use plans. It is recommended that legislative mandates be formulated and rigorously enforced to formally embed UPA within both national and local land use planning frameworks.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".