Effect of Organic Manures on Nutrient Uptake and Seed Quality of Sesame
Bibliographic record
Abstract
The influence of organic manures in nutrient uptake and seed quality of sesame is not fully known. In this context, a study was conducted in northeastern Uganda in 2013 and 2014 short rains, and 2014 long rains to investigate the effect of organic manures on nutrient uptake and seed quality of sesame. The experiment was laid out in randomized complete block design with three replications. The treatments comprised: control, 4 crop residues, 2 animal manures and combinations of 2 animal manures and 4 crop residues all applied at two rates of 3 and 6 t/ha. Poultry manure plus finger millet husks (6 t/ha) produced significantly the highest seed protein content (48.23%) and uptake of N (4.84%), P (0.66%) and K (1.86%) by sesame at 4 weeks after emergence. Poultry manure plus cowpea husks (6 t/ha) and poultry manure plus groundnut shells (3 t/ha) produced the highest total ash (8.71%) and sesame seed oil content (67.95%), respectively. The crop residue effect on seed crude protein content, seed total ash and seed oil content occurred in the order of finger millet > cowpea > groundnut > sorghum, finger millet > cowpea > sorghum > groundnut and groundnut > sorghum > cowpea > finger millet, respectively. This study has demonstrated that finger millet husks and groundnut shells effectively enhance protein and oil content of sesame than other crop residues, respectively. Poultry manure plus finger millet husks (3 t/ha) enhances sesame seed protein content than other treatments.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".