Effects of Three Different Planting Techniques on Soil Water Content, Survival, and Growth of <i>Senegalia</i> Seedlings on Semi-Arid Degraded Lands in Burkina Faso
Bibliographic record
Abstract
Land degradation exacerbates poverty and food shortages in Sub-Saharan Africa. Tree planting is traditionally used to restore degraded lands, but the tree species used are often poorly adapted to the local climate conditions. We evaluated the suitability and efficiency of three planting techniques (half-moon, standard plantation and zaï) in a semi-arid climate using seedlings from two native Senegalia species: Senegalia gourmaensis and Senegalia dudgeonii. A total of 116 nursery-grown seedlings were planted on degraded lands using these three planting techniques. Data on soil water content, seedling survival and growth rates were measured over 1.5 years. The effects of the planting techniques on these variables were significantly different ( p < 0.001). The lowest water content was measured in the topsoil horizon (0–10 cm) and the highest in the deeper horizons (∼50 cm). At the end of the experiment, the survival rate of S. gourmaensis was 72.2% - 62.5% and 57.5% in half-moon, standard plantation and zaï, respectively. For S. dudgeonii, it was 50%, 62.5% and 47.5% in half-moon, standard plantation and zaï, respectively. There was a significant difference in height and collar diameter between S. gourmaensis and S. dudgeonii using the three planting techniques ( p < 0.001). Based on our results, we recommend using the half-moon or standard plantation for Senegalia species. Senegalia species are suitable for planting in degraded land in semi-arid areas when using the appropriate planting technique.
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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 | 0.001 | 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) | 0.000 | 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 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".