A Review of Land‐ and Water‐ Management Technologies for Resilient Agriculture in the Sahel: Insights from Climate Analogues in Sub‐Saharan Africa
Bibliographic record
Abstract
In sub-Saharan Africa, land degradation and climate change threaten food security by reducing soil productivity and water availability. Soil and water conservation (SWC) technologies can restore soil health, enhance moisture retention, and support crop growth under adverse conditions. This review identifies SWC technologies applied in climatically similar African regions with the aim of informing adoption in Senegal, particularly in Sédhiou and Tambacounda regions. Using K-means clustering on 19 WorldClim bioclimatic variables, 35 comparable countries were identified, of which 17 met inclusion criteria based on data availability and ≥60% climatic similarity. Around 85 technologies were reviewed, including water harvesting, soil-moisture conservation, and erosion control, assessed for their compatibility across rainfall patterns, and land gradients and uses. The review highlights 12 successful technologies across Africa with high potential for cross-border transfer and upscaling in Senegal’s agroecological context. While countries such as Burkina Faso, Kenya, and Malawi lead in technology adoption and diversity, Senegal lags behind due to institutional gaps, limited funding, and weak extension systems. The findings highlight the importance of site-specific water management for improving soil conservation, biodiversity protection, climate adaptation, and food security, and emphasize the need for policy integration, stakeholder empowerment, private-sector engagement, and cross-border learning to accelerate adoption.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".