Effects of Plant Density and the Application of Silica on Seed Yield and Yield Components of Rice (Oryza sativa L.)
Bibliographic record
Abstract
In order to investigate the effects of plant density and the role silicon plays in determining the agronomic features of rice transplanted as single plants and planted in hills, an experiment in the split factorial design in the format of randomized complete block design with three replications was conducted in Sari in 2007. The main factor consisted of two levels of silica (applying and not applying it), and the subordinate factor included two modes of planting rice (in hills and as individual plants) and three plant densities (40, 80, and 120 plants/m2). Results obtained showed that applying silica improved some agronomic features. For example, the total number of tillers per plant increased by 11.6%, the number of effective tillers per plant by 14.2%, and the seed yield by 18.2%; and, therefore, the harvest index, compared to the control (in which silica was not applied), increased by 4.4%; but the percentage of filled spikelets decreased by 13.9% (which was a significant reduction). Increasing plant density from 40 to 120 plants per m2 caused deterioration in some agronomic features. For example, plant height, the total number of tillers, the number of effective tillers per plant, and the total number of spikelets per panicle decreased by 5.04, 51.94, 55.1, and 20.44%, respectively. The mode of planting had a significant effect only on the total number of effective tillers per plant, and on the total number of spikelets per panicle, at the one percent probability level; and it also significantly influenced the percentage of filled spikelets at the five percent probability level. Under the interactive effects of the three variables, the maximum seed yield was obtained in the treatment of applying silica and planting density of 80 plants per m2 in transplanting rice as single plants and in hill planting (424.1 and 414.6 g/m-2, respectively).
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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 | 0.000 | 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 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".