Effect of Integrated Nutrient Management on Yield and Quality of Basmati Rice Varieties
Bibliographic record
Abstract
A 2-year study conducted to determine the effect of integrated nutrient management on yield and quality of basmati rice varieties in non-traditional area with 54 plots in split plot design. Variety HUBR 10-9 produced 18.8% higher mean grain yield and superior quality parameters than HUBR 2-1. Mean milling, head rice recovery, amylose content and alkali digestion value noted higher with HUBR 10-9 by 4.1%, 4.1%, 8.5% and 15.1% over HUBR 2-1, respectively. Addition of 75% recommended dose of fertilizers with 25% recommended dose of nitrogen as farmyard manure produced higher mean values by 3.1%, 4.2% and 4.0% for hulling, milling and head rice recovery respectively over 100% recommended dose applied as inorganic sources. Combined use of bio-inoculants (blue green algae plus Azospirillum) exhibited higher values for yield and quality parameters. HUBR 10-9 be grown using 75% recommended dose of fertilizers with 25% nitrogen as farmyard manure and blue green algae plus Azospirillum for enhancement in yield and quality in non-traditional areas of eastern Uttar Pradesh. Further investigation required under diverse conditions.
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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".