Effect of Planting Density on Root Lodging Resistance and Its Relationship to Nodal Root Growth Characteristics in Maize (Zea mays L.)
Bibliographic record
Abstract
Increase of planting density has been widely used to increase grain yield in maize. However, it may lead to higher risk of root lodging hence causing significant yield loss of the crop. The objective of this study was to investigate the effect of planting density on maize nodal root growth characteristics and to analyse their relationships to root lodging resistance. Field experiment was conducted in 2010 and 2011, using two maize varieties, Zhengdan 958 (ZD) and Xianyu 335 (XY), under three planting densities, viz., 4.50, 8.25 and 12.00 plants m-2. The results showed the root failure moment, an indicator of root lodging resistance, was significantly affected by the planting density, the maize variety, as well as the crop developmental stages, and was decreased with increasing planting density. The number and the average diameter of the roots on the upper internodes (phytomer 5 to 8) were decreased with increasing planting density, whereas the maize variety had little effects on those variables. On the contrary, the root angle was less responsive to planting density but was significantly affected by the variety being that XY had larger root angle than did ZD. The root failure moment was linearly positively correlated with the total root number and the average root diameter on the upper internodes, indicating that a greater root number and a larger root diameter are important traits for enhancing root lodging resistance in maize plants.
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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".