Assessing the Response of Sesame to Inorganic and Organic Nutrient Sources
Bibliographic record
Abstract
Sesame (Sesamum indicum. L) is one of the main sources of livelihoods in northern Uganda. However, its production is constrained by low soil fertility and moisture levels. A study was conducted at Serere, Uganda in 2013 and 2014 to investigate the effect of organo-mineral fertilizers on growth, seed yield and nutritional quality of sesame. The design of the experiment was a randomized complete block design with three replications. The treatments comprised: control (no soil amendment), mixtures of 4 crop residues each at (3 and 6 t/ha) and two rates of N, P and K. Finger millet husks (3 t/ha) plus lower fertilizer rate (30 kg N-25 kg P-40 Kg K/ha) had significantly higher seed yield of sesame; while finger millet husks (6 t/ha) plus higher fertilizer rate (60 kg N-50 kg P-80 Kg K/ha) significantly increased vegetative growth of sesame. Finger millet husks (6 t/ha) plus lower fertilizer rate had significantly higher seed crude protein content of sesame; while cowpea husks (3 t/ha) plus higher fertilizer rate and groundnut shells (3 t/ha) plus lower fertilizer rate produced significantly higher seed total ash and seed oil content of sesame, respectively. This study has demonstrated that application of a mixture of crop residues and inorganic fertilizers is the best treatment in enhancing growth, seed yield and nutritional seed quality of sesame.
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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".