Can the Nitrogen and Silicon Increase the Productivity and Yield in Rice Crops in the Rainfed Environment?
Bibliographic record
Abstract
There is much doubt as to whether, in fact, the combination of nitrogen and silicate fertilization can increase yield and post-harvest yield in rainfed rice. The objective of this study was to investigate the effect of silicate and nitrogen fertilization on the development, quality, yield and post-harvest yield of rice cultivated in the rainfed environment. A field experiment was carried out in the 2014/2015 harvest in a 4 × 4 randomized block design with four replications. The first factor corresponded to the nitrogen doses (0; 50; 100 and 200 kg ha-1 of N), in the form of urea, and the second factor at silicon doses (0; 7.5; 15 and 30 kg ha-1 Si), in the form of calcium silicate. Si affected the chlorophyll content at 45 days after sowing (DAS) suggesting being important in floral differentiation. The increase in N doses caused higher levels of chlorophyll in rice leaves at 55, 65 and 85 DAS, regardless of whether they supplied Si. Plant height, number of tillers, weight of one thousand seeds, total grain yield and of whole grains were not influenced by the Si supply. However, Si doses reduced the yield of broken grains, thus reducing postharvest losses and, consequently, improving grain quality in the beneficiation process.
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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".