Mid-Season Leaf Glutamine Predicts End-Season Maize Grain Yield and Nitrogen Content in Response to Nitrogen Fertilization under Field Conditions
Bibliographic record
Abstract
After uptake in cereal crops, nitrogen (N) is rapidly assimilated into glutamine (Gln) and other amino acids for transport to sinks. Therefore Gln has potential as an improved indicator of soil N availability compared to plant N demand. Gln has primarily been assayed to understand basic plant physiology, rather than to measure plant/soil-N under field conditions. It was hypothesized that leaf Gln at early-to-mid season could report the N application rate and predict end-season grain yield in field-grown maize. A three-year maize field experiment was conducted with N application rates ranging from 30 to 218 kg ha−1. Relative leaf Gln was assayed from leaf disk tissue using a whole-cell biosensor for Gln (GlnLux) at the V3-V14 growth stages. SPAD (Soil Plant Analysis Development) and NDVI (Normalized Difference Vegetation Index) measurements were also performed. When sampled at V6 or later, GlnLux glutamine output consistently correlated with the N application rate, end-season yield, and grain N content. Yield correlation outperformed GreenSeekerTM NDVI, and was equivalent to SPAD chlorophyll, indicating the potential for yield prediction. Additionally, depleting soil N via overplanting increased GlnLux resolution to the earlier V5 stage. The results of the study are discussed in the context of luxury N consumption, leaf N remobilization, senescence, and grain fill. The potential and challenges of leaf Gln and GlnLux for the study of crop N physiology, and future N management are also discussed.
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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) | 0.000 | 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 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".