Timing and level of nitrogen supply affect nitrogen distribution and recovery in two contrasting oat genotypes
Bibliographic record
Abstract
Abstract Human diets containing oat (Avena sativa L.) grain offer health benefits resulting in an emerging interest in oat improvement. Information on nitrogen (N) uptake, distribution, and use efficiency (NUE) in oat is limited. A greenhouse study using a 15N‐labeling technique was conducted to determine the responses of two contrasting oat genotypes to timing and level of N deficiency. Hulled oat cv. Prescott and hulless cv. AC Gehl were grown in soil‐mix pot culture with five N treatments applied through modified Hoagland solutions. Differences in 15N accumulation, 15N distribution, plant N originating from the labeled source, and NUE between the contrasting cultivars, were examined for each N strategy. Level of N deficiency and timing of N supply of 15NH415NO3 greatly affected 15N distribution, the origins of plant N, and the amount of 15N recovered in the plant. When N was supplied from seedling emergence to maturity (T1), AC Gehl accumulated 61% more 15N in the shoots, but 46% less 15N in the grain than Prescott (0.43 vs. 0.80 mg plant–1), indicating that AC Gehl was less effective in producing grain yield than Prescott as AC Gehl produced greater total dry matter (DM). Withholding N supply until flag‐leaf stage (FL) increased 15N in the grain of both cultivars by 29.6%, resulting in the highest NUE. In most cases, there were larger portions of plant N derived from the labeled source for AC Gehl than for Prescott. Our results suggest that greater NUE in the newly released AC Gehl was associated with N accumulation in the vegetative tissues. It is concluded that genotype improvement of hulless oat should be focused on enhancing N‐translocation efficiency.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 |
| 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".