Maize Morphophysiological Responses to Intense Crowding and Low Nitrogen Availability: An Analysis and Review
Bibliographic record
Abstract
Mounting concerns over the cost and environmental impact of N fertilizer combined with progressively higher plant densities in maize (Zea mays L.) production systems make progress in maize N use efficiency (NUE) and N stress tolerance essential. The primary objectives of this 3‐yr field study were to (i) evaluate the N responsiveness, NUE, and N stress tolerance of multiple modern maize genotypes using suboptimal, optimal, and supraoptimal plant densities (54,000, 79,000, and 104,000 plants ha−1, respectively) with three levels of side‐dress N (0, 165, and 330 kg N ha−1), (ii) identify key morphophysiological responses to the simultaneous stresses of intense crowding and low N availability, and (iii) consider our results with extensive reference to literature on maize morphophysiological responses to plant crowding and N availability. At optimal and supraoptimal plant densities, maize receiving 165 kg ha−1 of side‐dress N displayed strong N responsiveness, high NUE, pronounced crowding tolerance, and plant density independence. However, crowding tolerance was contingent on N application. Relative to less crowded, N‐fertilized environments, the 104,000 plants ha−1, 0 kg N ha−1 treatment combination exhibited (i) reduced pre‐ and postanthesis plant height (PHT), stem diameter (SD), and total biomass; (ii) greater preflowering leaf senescence and lower R1 leaf areas at individual‐leaf, per‐plant, and canopy levels; (iii) enhanced floral protandry; (iv) lower pre‐ and postanthesis leaf‐chlorophyll content; (v) lower per‐plant kernel number (KNP), individual kernel weight (KW), grain yield per plant (GYP), andharvest index per plant (HIP); and (vi) enhanced per‐plant grain yield variability (GYCV). Genetic efforts to improve high plant density tolerance should, therefore, simultaneously focus on enhancing NUE and N stress tolerance.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".