Impact of conservation tillage on runoff, soil loss, and soil properties on acrisols and ferralsols in central Benin
Bibliographic record
Abstract
The present study is aimed at assessing the impact of different tillage practices and mulch input rates on soil erosion and soil properties in Central Benin. The experiment was carried out at two sites (Dan and Za-zounmè) using a randomized complete block design. The effect of three tillage practices: contour ridging (CR), slope ridging (SR), and no-tillage (NT) and four mulch input rates (0, 3, 5, and 7 t·ha−1) was investigated. The runoff, the soil, and nutrients losses were measured during the major rainy seasons of 2018 and 2019. Bulk density, gravimetric moisture, and water infiltration were collected in 2019. The effect of the interaction between tillage practices and mulch input rates was significant on runoff amount, runoff coefficient, soil loss, N, P, and K losses, and soil moisture. Over the investigated seasons, CR + 7M decreased runoff amount, runoff coefficient, soil loss, and N, P, and K losses by 100% compared to the treatments. NT was found to be effective in runoff and soil erosion controlling when combined with a mulch quantity greater than 3 t·ha−1, and NT + 5M and NT + 7M reduced the soil loss, respectively, by more than 30% compared to the farmer’s practice (SR + 0M) at both sites. Contour ridge treatments yielded more soil moisture than NT and slope ridge treatments. Whatever the tillage practice, the greatest gravimetric moisture was recorded on 5 and 7 t·ha−1 plots (i.e., CR + 7M, NT + 7M, and SR + 7M). This study provides decision makers with requisite information for effective soil erosion management in Benin where mechanization aids are limited.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".