Factors Influencing Soil Erosion Control Practices Adoption in Centre of the Republic of Benin: Use of Multinomial Logistic
Bibliographic record
Abstract
Water erosion threatens large agricultural areas in Benin. This study dealing with the effect of personal household’s attributes and field physical characteristics on erosion control practices was carried out in the watershed of Zou. A total of 390 farmers distributed in six were randomly sampled. Questionnaires, interview, focus group discussion and field observation were used as the main data collection technics. It allowed to collect sociodemographic and institutional characteristics and have a view on the effectiveness of the erosion control practices adoption. The data were analyzed using descriptive statistic and logistic regression. Ridging parallel to the slope (40.77% in adoption); mulching (11.03% in adoption); isohypse ridging (16.67% in adoption) and no-tillage (8.46 in adoption) were inventoried as soil erosion control practices on the watershed. It appears that that the household’s sociodemographic and institutional attributes and field physical characteristics significantly affected the adoption of the inventoried water erosion control practices. Sex, education, farmer’s organization membership, landownerships, access to agricultural advice service, position of the field on the toposequence and presence of water stream significantly influenced the soil erosion control practices adopted on the watershed. The results of this study showing that set of factor sway farmers to adopt soil erosion control practices can help policy makers to upscale the adoption of the practices and soil scientists to orient their research programs on erosion control practices.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".