Agronomic Potentials of Rarely Used Agroforestry Species for Smallholder Agriculture in Sub-Saharan Africa: An Exploratory Study
Bibliographic record
Abstract
Despite significant evidence that green manures from agroforestry species can improve soil fertility, green biomasses from many agroforestry species have not been sufficiently explored. In this study, we determined the suitability of green manures of Tithonia diversifolia, Gliricidia sepium, and Senna spectabilis for smallholder agriculture in Africa. Field trials were established to compare them with mineral fertilizer. The results showed that green manures of the three species were of high quality based on their macronutrient compositions. The effect of the green manures (particularly Tithonia) on both the biomass and fruit yield of okro were comparable and in some cases greater than fertilizer treatments. Total yield response in Tithonia treatment was 61% and 20% greater than the control and fertilizer treatments, respectively. In addition, the okro plants recovered a greater percentage of the nitrogen (N), phosphorus (P), and potassium (K) added as green manure compared to fertilizer-treated plots, which received the greatest N, P, and K inputs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".