Effect of Different Rates of Poultry Manure and Bio-Slurry on the Yield of Solanum aethiopicum Shum
Bibliographic record
Abstract
Poor soil fertility remains the major cause of low crop productivity on smallholder farms that are engaging in vegetable production in sub-Saharan Africa. Appropriate soil fertility regimes are therefore critical for improving crop productivity. Its yield has remained low mainly due to poor soil fertility. A field experiment in two different seasons was planted in a Completely Randomized Block Design using Solanum aethiopicum Shum (Nakati). The treatments were 3 sole fertilizer options applied at the following rates: poultry manure and bio-slurry manure at 5, 10, 15, 20, 25 and 30 t ha-1, NPK (25:5:5) at the recommend application rate for tomato which is a sister crop and a control without any fertilizer. Crop budgets were used to determine the economic optimum rates of both sole applications of manure and combinations of manure with NPK. The sole applications and showed significantly (p ≤ 0.05) increased the yield of S. aethiopicum compared with the control. The established biological optimum rates were at 24.19 t ha-1 and 21.51 t ha-1 for poultry manure and bio-slurry respectively. Using the crop budgets it was concluded that the established economic optimum rates were 20 t ha-1 and 10 t ha-1 for sole poultry manure and bio-slurry respectively. Recommendations for use of sole poultry manure and bio- slurry at the rate of 20 t ha-1 and 10 t ha-1 respectively were made.
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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) | 0.001 | 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 | 0.001 |
| 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".