Performance of Upland Rice Crop Depending on Plant Spacing and Nitrogen Levels
Bibliographic record
Abstract
The aim of the present study was to determine the dose of Nitrogen and plant spacing of BRSMG Caravera, a modern rice cultivar, in order to maximize grain yield and increase rice production in the state of Minas Gerais, Brazil. The following characteristics were evaluated: grain yield (GY), percentage of filled grains (% F), 100 grains weight (100 GW) and plant height (H). It was used a 4 × 4 factorial design, with four row spacings (20, 30, 40 and 50 cm) and four nitrogen rates (0, 40, 80 and 120 kg ha-1) totaling 16 treatments. The phenotypes were analyzed through the Scott-Knott mean test together with an analysis for significant interactions to identify the spacing and the N rates associated with the best trait averages. Differences in plant spacing were associated with significant differences in GY, % F and H, whereas different N rates were associated with significant differences of GY and H. When considering the interaction between plant spacing and N rates, significant differences could be identified for all traits, indicating that these factors should be considered together. The highest grain yield was achieved with spacing of 20 cm and N rate of 120 kg ha-1. The use of modern cultivars, the adequate spacing and N dose can significantly increase grain yield and competitiveness of upland rice cultivation, in the state of Minas Gerais, Brazil.
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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".