Effects of Sowing Time and Growing Density on Agronomic Traits, Grain Yield, and Grain Quality of Waxy Sorghum Cultivar Hongliangfeng 1
Bibliographic record
Abstract
The aim of this study was to determine the effects of sowing time and growing density on the yield and quality of grain in waxy sorghum (Sorghum bicolor L. Moench). The main plots were two sowing time: early sowing (5 April) and late sowing (20 April), and the subplots were three growing densities: 0.8 × 105, 1.1 × 105, and 1.4 × 105 plants/ha. Results showed that sowing time and growing density had significant effects on grain yield and grain quality of waxy sorghum cultivar Hongliangfeng 1. Grain yield, plant height, spike length, culm diameter, grain number per spike, grain weight per plant, 1000-grain weight, protein content, starch content, and amylopectin content were reduced by a delay of sowing time, while the tannin content and amylose content were increased by a delay of sowing time. Grain yield, plant height, spike length, culm diameter, grain number per spike, grain weight per plant, 1000-grain weight, protein content, starch content, and amylopectin content increased and then decreased with the increase of growing density, while the tannin content and amylose content decreased and then increased with the increase of growing density. These results hinted that appropriate sowing time and growing density are key cultivation measures to ensure high yield and good quality in waxy sorghum production.
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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".