Effects of N and Density Interaction on Dry Matter Distribution in Canopies in Soybean
Bibliographic record
Abstract
A split-plot designed field experiment with three densities( 200 000,250 000 and 300 000 plants·ha-1) and three N treatments( basal fertilizer N 60 kg·ha-1; N 18 kg·ha-1as basal fertilizer plus N 42 kg·ha-1as topdressing at stage R3 /R4) using soybean cultivar Dongnong 52 was conducted to study the effects of starter-N plus top-dressing N on dry matter distribution in leaves,petioles and pods in different canopies under different densities. Results showed that dry matter weight of different organs in middle canopy increased with increment of density at stage R6. At stage R7,dry matter weight of different organs in upper / middle canopy under the density of 250 000 plants·ha-1was significantly higher than the statistics for 300000 plants·ha-1. Different organs dry matter weight in upper / middle canopy after R5 of starter-N plus top-dressing N were significantly higher than those of using N only as basal fertilizer under the same density. Besides,top-dressing N at stage R4 was better than at stage R3. At stage R8,seed dry matter weight in upper canopy of starter-N plus top-dressing N at R4 was15. 2% higher than using N only as basal fertilizer( P 0. 05) under the density of 250 000 plants·ha-1. The number of ≤2-seeded pods in upper / middle canopy reached a maximum under the density of 300 000 plants·ha-1,while the best density for 3 /4-seeded pods was 250 000 plants·ha-1; starter-N plus top-dressing N increased the number of 3 /4-seeded pods. In summary,starter-N plus top-dressing N at R4 under the density of 250 000 plants·ha-1increased the dry matter weight of organs in upper / middle canopy after R6 and the number of 3 /4-seeded pods,and yield under this condition was 10. 8%-36. 5% higher than other treatments( P 0. 05).
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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".