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 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 | 0.001 | 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 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".