Seed Coated by Boric Acid Enhances Growth, Yield and Kernel quality of both Fine and Coarse Rice (Oryza sativa L.) under Semi-Arid Environmental Condition
Bibliographic record
Abstract
Currently, more than half of the world's population relies on rice as a primary crop, making it a crucial cereal crop in the struggle against food security. Sustainable rice production is under great threat because of poor soil productivity and fertility because of nutrient losses due to temperature stress under semiarid conditions. Boron (B) is a vital micronutrient and plays an eminent role in plant proliferation and development. The Super Basmati and KS-282 seeds were coated with boric acid at dosages of 0.25, 0.50, 0.75 and 1.00 g kg-1 in this experiment. Both rice cultivars exhibited the following traits when boric acid was applied at a rate of 0.75g kg-1 seed: plant height, maximum number of productive tillers, number of kernels per panicle, grain weight in the thousand, biological yield, paddy yield and straw yield. When boric acid was applied at 0.75 g kg-1 seed, the physiological characteristics of Super Basmati and KS-282 rice cultivars, such as crop growth rate, leaf area index, leaf area duration and net absorption rate were significantly greater yielding values of 11.08 gm-2d-1 and 11.96 gm-2 d-1, 4.28 and 4.32, 67.52 and 68.94, 7.33 g m-2 d-1, and 8.93 gm When both the super basmati and KS-282 rice cultivars were treated with boric acid at 0.75 g kg-1 seed, considerably higher paddy yields (3.95 t ha-1) and (5.02 t) were obtained. The kernel quality parameters show maximum results when boric acid applied at 0.75g kg-1 seed. Current research shows that coating seed with micro nano-nutrients are highly effective procedure for increasing rice production and fertilizer use efficiency under semiarid climatic conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 teacher head, 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".