Effect of organic fertilizers rate on plant survival and mineral properties of Moringa oleifera under greenhouse conditions
Bibliographic record
Abstract
Purpose The nursery is the production of vigorous seedlings for field transplantation. Fertilization improves the quality of Moringa oleifera in the greenhouse. Thus, the effect of composts amendments on the survival rate and mineral composition of M. oleifera was studied.Method A randomized complete block (RCB) design comprising fourteen treatments and twelve repetitions (pots referring to as repetitions), was led out in greenhouse. Varying compost receipts (0.1 kg, 0.2 kg, and 0.3 kg) were used and an increasing amount of a chemical fertilizer (NPK: 20:10:10) was added in some treatment at 25 days after sowing. Parameters such as germination rate, survival rate, biomass, and mineral composition of M. oleifera plants were assessed.Results The germination rate was maximal (100%) in the combined treatment PM1 (0.8 kg of soil+0.1 kg). A considerable reduction of germination rate and high plantlet mortality were observed in treatments that received chemical fertilizer, although the mineral components’ uptake in young M. oleifera plants was considerably improved. The highest dry root biomass was obtained from the treatments MF1 (0.8 kg of soil+0.032 kg each of cow dung, goat, chicken manures+0.003 kg NPK) and CM1 (0.8 kg of soil+0.1 kg of cow dung compost), with, respectively, 0.62 g and 0.59 g per plant.Conclusion All composts types used in this study have appeared as appropriate amendments to improve the M. oleifera production in nursery, through an increase of the vigour and mineral composition of this valuable plant.
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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".