Effect of Organic Manure and Mineral Fertilizers on Wheat Growth and Soil Properties
Bibliographic record
Abstract
Sustainable crop management relies on the combined use of organic and inorganic sources of nutrients. The experiment was laid out in a split plot design with manures (control, farmyard manure, sesbania and cluster bean) as main split and mineral fertilizer rates (control, 40-30, 60-45, 80-60, 90-70 and 120-90 kg N-P2O5 ha-1) as sub-split. The manures significantly influenced shoot dry weight, N, P and K uptake and soil properties. Conversely, the rates of mineral fertilizers did not have any effect on soil properties, however, significantly enhanced the shoot dry weight and N, P and K uptake. The combined use of manures and mineral fertilizers had a significant effect on shoot P uptake. Farmyard manure was the best manure amendment with 13% reduction in bulk density and 51% increase in organic matter content over control. Incorporation of farmyard manure increased the shoot dry weight and N, P and K uptake, respectively by 8, 14, 11 and 8% over control. Among rates of mineral fertilizers, recommended rate of mineral fertilizer (120-90 kg N-P2O5 ha-1) was the best treatment with corresponding increase of 26, 81, 56 and 55% in shoot dry weight, N, P and K uptake over control. Integration of farmyard manure with recommended rate of mineral fertilizer enhanced shoot P uptake by 17% as compared to solo application of mineral fertilizers. Through this study, it was concluded that farmyard manure at 6 tons ha-1 coupled with mineral fertilizer rate of 120-90 kg N-P2O5 ha-1 was the best source for sustainable soil health and wheat 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) | 0.001 | 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".