Land management in rural Burkina Faso: the role of socio‐cultural and institutional factors
Bibliographic record
Abstract
Farmers in the Sahel have been acknowledged for reclaiming degraded lands and improving food security by ingeniously modifying traditional agroforestry, water, and soil management practices. Despite the advantages offered by this range of farming techniques, their adoption rate is influenced by several factors. Using multivariate probit models and a correlation coefficient, this article examines the factors influencing the adoption of five land management practices based on 220 household and 40 farm surveys in four adjacent rural communities in southern Burkina Faso. The model results indicate that household labor force, education of household head, land tenure security, livestock holding, and membership in farmers’ groups influence the adoption of zaï practice, composting, improved fallow, stone bunds, and live hedges. However, two of the surveyed factors ‐ number of farms and visit by agricultural extension staff during the 12 months prior to the survey ‐ were not significant. Furthermore, a significant correlation was found between different land management practices, e.g., the decision to practice zaï is significantly linked to that of live hedges and composting. Zaï practice and stone bunds are considered labor intensive, which explains their significant correlations with household labor force at the 1% and 5% levels of significance, respectively.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".