The Effects of Planting Density on Lodging Resistance, Related Enzyme Activities, and Grain Yield in Different Genotypes of Oilseed Flax
Bibliographic record
Abstract
Knowledge of planting density and lodging resistance is essential to developing effective cropping systems. This study determined (i) the lodging resistance of different genotypes of oilseed flax (Linum usitatissimum L.) under different planting densities, and (ii) the effect of density on lignin content and related enzyme activities in different genotypes in Northwest China. We hypothesized that (i) lignin content and related enzyme activities varied with different planting densities, and (ii) a planting density of 750 × 104 grains ha−1 increased the lignin content and related enzyme activities of stem tissues and improved lodging resistance. The characteristics of oilseed flax from three different cultivars—‘Dingya 23’(V1), ‘Jinya 10’(V2), and ‘Neiya 9’(V3)—were evaluated under different planting densities. Results indicated that increasing plant density increased the phenylalanine ammonia‐lyase (PAL) and tyrosine ammonia‐lyase (TAL) activities but decreased the 4‐coumarate:CoA ligase (4CL) activity and lignin content of a high‐lodging‐resistance cultivar. However, increasing plant density decreased the PAL and TAL activities but increased the 4CL and lignin content of a low‐lodging‐resistance cultivar. We conclude that plant density affected the plant lignin content by regulating the 4CL activity. Flax seed yield increases with density were mainly due to increased lignin content, bending strength, and regulated PAL, TAL, and 4CL activities. The study concluded that optimizing planting density could play an important role in crop lodging resistance and grain yields in the northwest of China.
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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".