Germination, Growth and Yield Responses of Leaf Amaranth (Amaranthus hybridus) to Rates and Times of Poultry Manure Application
Bibliographic record
Abstract
Manures are more environment-friendly and effective in improving soil fertility, hence their recent preference over inorganic fertilizers. Poultry manure is believed to be the most valuable manure produced by livestock. However, the application had always been only before sowing, unlike the inorganic fertilizers that are, generally, applied after sowing/planting of crops. Therefore, the effects of broiler droppings applied at different rates and times were evaluated on seed germination, growth and yield of leaf amaranths, during the raining season of year 2019. Broilers droppings were applied at 0, 30 and 50 tha-1, at 1 and 2 week (-s) before sowing, at sowing and 1 and 2 (week)s after sowing of amaranth seeds. The experiment was factorial and treatments were replicated thrice in a randomized complete block design. Growth parameters measured were: Plant height, number of leaves, stem girth, leaf area, number of germinated seeds , and yield were measured at 7, 8 and 9 weeks after sowing (WAS). The data generated were subjected to ANOVA at α0.05. At 7 WAS, application of 50 tha-1 manure at sowing produced the highest plant height (17 cm), stem girth (7.40 cm), number of harvested stands (18.33) and marketable yield (7.95 tha-1) with significant differences. At 8 WAS, the highest yield (5.83 tha-1 was obtained from plots treated with 30 tha-1 of poultry dung applied at 2 weeks before sowing while more seeds (27) germinated on both plots treated with 50 t/ha of broiler droppings applied at sowing and 1 WAS. The best recorded values for number of germinated seeds (27) and yield (2.83 tha-1) at 9 WAS were obtained from 50 tha-1 of broiler droppings applied at sowing. 50 t/ha of broiler droppings, at sowing compared well with poultry droppings applied at any rate before sowing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".