Land Governance in French-Speaking Africa: Comparative Analysis of Legal and Institutional Reforms for Sustainable Management of Community Lands
Bibliographic record
Abstract
In July 2009, African leaders adopted the Declaration on Land Issues in Africa, reaffirming the commitment of African Union member states to effective land management. The declaration emphasizes the protection of land rights for all, with particular attention to women and marginalized groups. Land governance in Africa, which spans various aspects of society, remains a critical issue and is often a source of conflict and instability across the continent. This study examines the legal and institutional reforms of land governance in Francophone Africa (Benin, Burkina Faso, Ivory Coast, Mali, and Senegal), analyzing their objectives, outcomes, and the challenges associated with their implementation. In addition, this study highlights examples of both effective and ineffective reform implementations based on case studies from countries with notable successes (Ethiopia, Rwanda, Mauritius, Ghana, and Madagascar) and failures (South Africa and Zimbabwe). Finally, this study offers recommendations for improving sustainable land management while considering social, economic, political, and environmental dimensions. The methodology employed is based exclusively on a systematic review guided by the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) approach, applied to the ROSES (Reporting Standards for Systematic Evidence Syntheses) protocol. This approach facilitated the selection of 57 relevant documents retrieved from databases such as Scopus, Web of Science, PubMed, and Google Scholar. Land governance in Francophone Africa varies significantly from country to country and cannot be comprehensively addressed in a study of this scope. Nevertheless, this research study identifies common challenges, opportunities, and measures that could inspire reflection in other countries. In several cases, administrative and customary authorities play central roles in land management. However, their overlapping responsibilities, often marked by corruption, extend procedures and exacerbate local conflicts.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".