Improving Maize Yield on Ferric Lixisol by NPK Fertilizer Use
Bibliographic record
Abstract
The experiment was conducted to refined profitable NPK fertilizer rate for maize production on Tanchera series (Ferric Lixisol, FAO, 2006) in the Sudan savanna agro-ecological zone of Ghana. RCBD design with four replications was used. Treatments evaluated were N = 0, 40, 80, 120, 160; P = 0, 45, 90 and K = 0, 45, 90 kg/ha. Results showed significant N rate effect on grain yield, benefit cost ratio and gross return (P ≤ 0.01). P and K did not show significant effect among these parameters. Application of nitrogen from 80-120 kg/ha may be recommended for improve grain yield and gross return of maize production on Ferric lixisol. Due to poor nature of soils, application of P and K up to 45 kg/ha may also be recommended for maize production.
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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".