Response of a Leafy and Non‐Leafy Maize Hybrid to Population Densities and Fertilizer Nitrogen Levels
Bibliographic record
Abstract
Optimum plant population density (PPD) of maize (Zea mays L.) for grain and/or silage production depends on hybrid type, soil fertility and agronomic management. Limited information exists on the yield response of Leafy maize hybrids to different PPD under varying N application rates. A field study was conducted during 2003 and 2004 in Ottawa, Canada to evaluate grain and silage yields of a Leafy hybrid (‘Maizex LF850 RR’) with a conventional hybrid (‘Pioneer 3893’), under three PPD (60 000, 75 000, and 90 000 plants ha−1) and four N fertilizer (0, 75, 150, and 225 kg N ha−1) regimes. Canopy light interception, plant dry matter (DM), silage, and grain yield were measured. The Leafy hybrid had 20 to 25% more leaf area, on an individual plant basis, and produced significantly greater silage DM (21.1 vs. 20.0 Mg ha−1), but had a significantly smaller grain yield (9.4 Mg ha−1) than the conventional hybrid (9.7 Mg ha−1). The Leafy hybrid was more sensitive to high density and low N stresses, resulting in more barren plants (up to 15%), lower harvest index (HI), thus significantly lower grain yield than the conventional hybrid in 2003. Grain yield reached to a maximum with 225 kg N ha−1 followed by 75 and 150 kg N ha−1, but silage DM was not different between 150 and 225 kg N ha−1 Plant population density had no effect on grain yield but silage yield increased linearly as PPD increased from 60 000 to 90 000 plants ha−1 Within the tested range of PPD, no differential response of hybrids was observed in terms of grain yield or silage DM, and N treatments had no effect on response of hybrids to PPD. We conclude that the Leafy hybrid was more sensitive to high PPD and low N stresses than the conventional hybrid especially for grain production. The optimum PPD for silage production may be beyond 90 000 plants ha−1 for both types of hybrids.
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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".