Cropping Practices and Their Drivers in Various Cropping Systems in Peri-urban Areas of Ouagadougou, Burkina Faso
Bibliographic record
Abstract
The advantages of urban and peri-urban agriculture in West African cities, namely its contribution to food production, income generation and resorbing unemployment are well reported. In the peri-urban areas, cropping systems and practices are various and may affect differently soil properties. Those systems and practices may be driven by farms socio-economic conditions. Here we conducted a study in 133 peri-urban farms located at the vicinity of the city of Ouagadougou. Farmers were questioned on their cropping practices and soil samples were taken and analyzed for their total organic C, available P and K contents. Principal component analysis allowed to study the variability of the farms considering cropping systems, the cropping practices and the farms socio-economic conditions. We found that in the studied cropping systems up to 60% of the farms variability was explained. Monoculture led to low soil organic carbon while polyculture led to low soil available K. The studied socio-economic conditions of the farms explained up to 60% of the variability in cropping practices.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".