How do soil hydro-physical properties vary among land uses and soil depth in Parakou, Northern Benin?
Bibliographic record
Abstract
This study investigates the effect of land use on soil hydro-physical properties under four soil depths. Five land uses including Bare soils, Grassland, Agricultural land, Natural Forest and Teck plantation were considered to assess their effects on soil hydro-physical properties including hydraulic conductivity, water stock, bulk density, field capacity, capillary retention capacity, saturated water retention capacity, gravimetric water content, total porosity, and capillary porosity. Twelve soil samples were collected 24 h after infiltration test at four different depths (5, 15, 30, and 50 cm) of vertical soil profile from each land use site. Strong correlation occurred between total porosity, field capacity, capillary retention capacity, saturated water retention capacity, and capillary porosity. However, the bulk density was negatively correlated with the other abovementioned soil properties. The soil hydro-physical properties, especially the hydraulic conductivity, was significantly affected by the land uses. The total porosity showed opposite trend with bulk density that followed the order Teak plantation > Agricultural land > Grassland > Bare soil > Natural Forest. The field capacity, capillary water retention capacity, and saturated water retention capacity were lower in Bare soils, and higher in Teck plantation. The water stock was higher in Grassland and lower in Bare soil, then was positively correlated with soil hydraulic conductivity, which showed highest value in Agricultural land. This study shows that Bare soils performed less well in terms of water retention and hydraulic conductivity, highlighting the need to maintain adequate vegetation cover (grasses and trees) to preserve soil health and soil hydrological functions.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 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".