Luxury Vegetative Nitrogen Uptake in Maize Buffers Grain Yield Under Post-silking Water and Nitrogen Stress: A Mechanistic Understanding
Bibliographic record
Abstract
During vegetative growth maize can accumulate luxury nitrogen (N) in excess of what is required for biomass accumulation. When post-silking N uptake is restricted, this luxury N may mitigate N stress by acting as an N reserve that buffers grain yield and maintains plant function. The objective of this study was to determine if and how luxury accumulation of N prior to silking can buffer yield against post-silking N and/or water stress in maize. In a greenhouse experiment, maize was grown in high (Nveg) and low (nveg) N conditions during vegetative growth. The nveg treatment did not affect biomass accumulation or leaf area by silking but did accumulate less total N compared to the Nveg treatment. The Nveg treatment generated a reserve of 1.1 g N plant-1. Plants in both treatments were then subjected to water and/or N stress after silking. 15N isotope tracers were delivered during either vegetative or reproductive growth to measure N remobilization and the partitioning of post-silking N uptake with and without a luxury N reserve. Under post-silking N and/or water stress, yield was consistently greater in Nveg compared to nveg due to a reduction in kernel abortion. The Nveg treatment resulted in greater kernel numbers and increased N remobilization to meet grain N demand under post-silking N stress. Luxury N uptake at silking also improved leaf area longevity in Nveg plants compared nveg under post-silking N stress, leading to greater biomass production and increased yield. While post-silking N uptake was similar across Nveg and nveg, Nveg plants partitioned a greater proportion of post-silking N to vegetative organs, which may have assisted with the maintenance of leaf function and root N uptake capacity. These results indicate that N uptake at silking in excess of vegetative growth requirements can minimize the effect of N and/or water stress during grain-fill.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".