Agroenvironmental transformation in the Sahel: Another kind of “Green Revolution"
Bibliographic record
Abstract
A farmer-managed, agroenvironmental transformation has occurred over the past three decades in the West African Sahel, enabling both land rehabilitation and agricultural intensification to support a dense and growing population. This paper traces the technical and institutional innovations, their impacts, and lessons learned from two successful examples. The first is the story of the improvement and replication of indigenous soil and water conservation practices across the Central Plateau of Burkina Faso. Rehabilitation of at least 200,000 hectares of degraded land enabled farmers to grow cereals on land that had been barren and intensify production through developing agroforestry systems. Additionally, rehabilitation appears to have recharged local wells. The second example is a farmer-managed process of natural regeneration, using improved, local agroforestry practices over an estimated 5 million hectares in southern Niger. This large-scale effort reduced wind erosion and increased the production and marketing of crops, fodder, firewood, fruit, and other products. In both cases, income opportunities were created, reducing incentives for migration. Women benefited from the improved supply of water, fuelwood, and other tree products. Human, social, and political capital was strengthened in a process of farmer-driven change. Fluid coalitions of actors expanded the scale of the transformation. These stories have important lessons for those who seek to create effective agricultural development partnerships and meet the challenges of climate change and food security.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".