Nutrient Content (% Dry Matter) of Maize as Affected by Different Levels of Fertilizers in Asaba Area of Delta State
Bibliographic record
Abstract
This study was carried out in the Teaching and Research Farm of Delta State. University, Asaba campus (Nigeria) from March 2008 to June 2010 to evaluate the nutrient content (% dry matter) of maize as affected by different levels of organic manure and inorganic fertilizer. The experiment was carried out in a Randomized Complete Block Design (RCBD) replicated three times in a factorial layout. Four different rates of poultry droppings, cattle dung and NPK 20: 10: 10 fertilizer were applied to three maize varieties sown at 75 cm x 15 cm spacing and the maize grains produced were evaluate for their nutrient content in percentage dry matter. The results obtained indicated that hybrid variety 9022-13 had the highest N, P and K contents (1.03, 1.68 and 0.26, respectively). Also, plants that received inorganic fertilizer had the highest values of 1.27% N, 1.64% P and 0.29% K. Based on rates of application, plants that received 450 kgha-1 NPK 20: 10: 10 fertilizer had the highest values of 1.74% N, 1.71% P and 0.49% K. The interaction effects showed that only variety, manure type and rates % application were significant (P < 0.05). Based on this study, it is recommended that (i) Hybrid variety, 9022-13, which was outstanding in its nutrient content be grown in the study area. Alternatively, farmers who prefer open-pollinated varieties could grow BR 9922-DMRSF2 or Agbor local variety for people who prefer local varieties in maize production (ii) Spacing of 75 cm x 15 cm (88, 888 plants/ha) which resulted in better growth performance and yield should be adopted in maize production (iii) Farmers who prefer mineral fertilizer for increased growth and yield of maize should apply 450 kg ha-1 of NPK 20: 10: 10 (iv) Farmers who practice organic agriculture in Asaba agro-ecological zone should apply 30 tha-1 of poultry manure to enhance maize 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) | 0.000 | 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".