Influence of the Leaf Biomass of Piliostigma reticulatum on Sorghum Production in North Sudanian Region of Burkina Faso
Bibliographic record
Abstract
In northern Sudan zone of Burkina Faso, soils are increasingly vulnerable to degradation from erosion. Leaf biomass of P. reticulatum, is commonly used by farmers. This study aims (i) to quantify the impact of the leaf biomass of P. reticulatum on the growth and yield of sorghum, (ii) to determine an input composition for better performance and easy adoption by farmers. For three successive years, two different doses of foliar biomass (1.25 t/ha and 2.50 t/ha) were tested in combination and/or in comparison with NPK, Urea and Burkina phosphate. Soil moisture, vegetative growth and yield of sorghum were measured. At stage 30 and 90 days after sowing, treatments showed no difference in growth. At the stage of 90 days after sowing, treatment.3 (T3) (100 kg NPK 50 kg Urea), T4 (200 kg of Burkina Phosphate), T5 (1.25 t/ha of leaf biomass of P. reticulatum + 100 kg NPK and 50 kg of urea) and T6 (2.50 t/ha of leaf biomass + 100 kg NPK + 50 kg Urea) did not show differences between the treatments. The contribution of the single organic matter gave a higher grain yield than that of the control. T6 gave the highest grain yield out of all treatments (2.40 t/ha). The addition of Burkina phosphate to various doses of dry matter did not influence the grain yield. T3, seems to have a better effect on soil protection and on improvement of grain yield.
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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".