Biofertilizer as a Supplement of Chemical Fertilizer for Yield Maximization of Rice
Bibliographic record
Abstract
Biofertilizer performs major role in crop production. A study was conducted to determine the effect of bio-organic fertilizer with reduced chemical fertilizer for rice yield maximization. The treatments were (i) control (without fertilizer), (ii) N, P, K at recommended rate i.e. 100% (120, 30, 60 kg ha-1), (iii) N and P (75%), and K (recommended rate) with biofertilizer (5 t ha-1) and (iv) N and P (50%), and K (recommended rate) with biofertilizer (10 t ha-1). Results showed that N and P (50%) with biofertilizer (10 t ha-1) increased the number of tillers (29), panicle length (28 cm), weight of 1000 grain (21.31 g), and produced the highest grain yield (7.26 t ha-1). There was no significant difference found among the N, P (75%) with biofertilizer (5 t ha-1) and N, P (50%) with biofertilizer (10 t ha-1) treatments for plant height, number of panicle plant-1 and harvest index (%). The application of biofertilizer with beneficial microbes improved the leaf chlorophyll, plant nutrient uptake and grain protein content in rice. Hence, the use of chemical N and P fertilizer can be minimized by 50 percent and improve rice yield with the supplement of 5 ton ha-1 of bio-organic fertilizer.
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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".