Nitrogen Effects on Grain Yield and Yield Components of Leafy and Nonleafy Maize Genotypes
Bibliographic record
Abstract
Effects of N fertilization have been extensively studied for conventional maize ( Zea mays L.) hybrids but not for genotypes bearing the leafy and reduced‐stature traits which differ significantly in canopy and root morphology. We tested the hypothesis that genotypes carrying the leafy trait (taller plants with more leaves, greater leaf area development, and greater rooting systems) would show differing responses to N availability in terms of grain yield and yield components from those of conventional maize hybrids. The experimental design was a split‐plot in a randomized complete block design with four blocks repeated across two growing seasons at each of two field sites. The treatments were N fertilization rates (0, 85, 170, and 255 kg N ha −1 ) as the main plot factors and genotypes as the subplot factors. The genotypes were leafy reduced stature (LRS), nonleafy normal stature (NLNS), leafy normal stature (LNS), nonleafy reduced stature (NLRS), and conventional hybrid checks of early (P3979) and late maturity (P3905). The latter consistently yielded best and the NLRS hybrid worst; however, the genotypic grain yield ranking varied between sites. Overall, the LRS outyielded its conventional counterpart (P3979) by 12% at one site and by 26% at the other. No significant N × hybrid interactions were detected for grain yield. We inferred that using leafy genotypes in maize production would not require additional N fertilization compared with their conventional maize hybrid counterparts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".