L’Évaluation économique de l'investissement dans la conservation des sols: Le cas des aménagements antiérosifs dans le bassin versant du lac Lagdo au Cameroun
Bibliographic record
Abstract
This article presents an analysis of the economic impact of erosion control facilities on farm operations in the watershed of Lake Lagdo in Cameroon. Over the past several decades, erosion control facilities, which are erosion control techniques, have been introduced in Cameroon. No assessment of the impacts of these on farm operations had yet been made. Drawing on data from a survey carried out in 2007 and 2008 and a switching regression model, the study concentrates on the effectiveness of the production factors of parcels of land with and without erosion control facilities. The comparison of the average crop yields of the operations in our sample that either adopted or did not adopt such facilities shows a significant difference, representing up to 10% of the value of the farm production. Proof of a positive selection bias is also found, indicating that the farms with above‐average crop yields are more likely to adopt erosion control facilities. Such facilities on the parcels of land also provide an advantage in terms of increasing the productivity of inputs. The analytical approach developed and the positive conclusion of the selection bias can be pertinent to assessing other soil conservation technologies promoted in the 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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".