Farm Typology and Constraints to the Adoption of Agroecological Practices in the Sudano-Sahelian Zone of Burkina Faso
Bibliographic record
Abstract
Soil degradation and water scarcity pose a major challenge to agricultural production in Sub-Saharan Africa. In Burkina Faso, these conditions are more acute in the north part of the country exhibiting a Sahelian climate. Applying the principles of ecology in agriculture appears to be an option to sustainably improve agricultural productivity in this area. The aim of this study was to identify and characterize agroecological practices and the factors limiting their adoption. To this end, semi-structured interviews were conducted with 84 farmers in the nine municipalities of the Passoré province. The results indicate that the agroecological practices most commonly applied are crop rotation (100%), organic fertilization (98%), crop association (88%), water harvesting techniques like zaï (86%), stone lines (80%), half-moons (69%) as well as improved fallow (62%), mulching (58%), use of biopesticides (26%) and living hedges (25%). Our analysis highlighted that the main factors limiting the adoption of those agroecological practices are the lack of labor (92%), lack of agricultural equipment (88%), lack of organic manure (84%), a lack of financial resources (72%) and a lack of capacity building of farmers (44%). We identified three groups of farms. Group 1 was characterized by small farms with an average workforce and moderate crop diversity. Groups 2 and 3 were characterized by large workforce and larger farm areas. However, group 3 stands out for its very high tree density and high crop diversification. Considering these findings, agroecological practices best suited to the different categories of farms are needed to sustainably improve agricultural productivity in this area.
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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 | 0.002 | 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".