Optimization of plant density and nitrogen regimes to mitigate lodging risk in wheat
Bibliographic record
Abstract
Abstract Influences of planting density and nitrogen rate have been investigated frequently in targeted wheat (Triticum aestivum L.) research. Few studies have investigated interactions between these inputs. The objective was to determine the combine effect of N and seeding rates on culm morph‐physiological traits for lodging tolerance and grain yield. The experiment used a split‐split randomized block design using two wheat varieties ‘AnNong0711’ and ‘YanNong19’, split by four seeding (180, 240, 300, and 375 × 104 ha−1) and four N rates (0, 180, 240, and 300 kg ha−1). Lodging traits of plant height, culm height center of gravity, and internode length, increased (p < .05) however, stem diameter, wall thickness, and stem breaking strength decreased with increasing N and seeding rate. Stem breaking strength was negatively correlated with culm height center of gravity (r = −.869, p = .01), internode length (r = −.872, p < .01), and lignin (r = −.746, p < .01) but positively correlated with internode diameter (r = .715, p < .05) and wall thickness (r = .696, p < .05). Culm lodging index and cellulose showed positive correlation (r = .807 and .913 respectively) with lignin. Compared to YanNong19, AnNong0711 showed higher grain yield and culm lodging index of 9 and 20.49%, respectively. For improved grain yield, 180 plants m−2 was optimal in surface combinations with 210 kg N ha−1 for AnNong0711 and 200 kg N ha−1 for YanNong19. These combinations of seeding and N rates could successfully mitigate lodging and improve grain yield.
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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".