Nitrogen Use Efficiency and Morphological Characteristics of Timothy Populations Selected for Low and High Forage Nitrogen Concentrations
Bibliographic record
Abstract
Improving timothy (Phleum pratense L.) N use efficiency (NUE) through genetic selection aims at producing greater or similar forage dry matter (DM) yields with less N fertilizer while maintaining N concentration close to the optimal level required for ruminant nutrition. Two populations of timothy, arising from divergent selection for high (N+) and low (N−) forage N concentration, and a reference population, ‘Champ’, were studied under controlled conditions with N rates of 1, 5, 10, and 20 mg N plant−1 wk−1 The populations N− and Champ produced more forage DM yield than N+. This difference in forage production was the result of changes in biomass partitioning between shoots and roots because, at the whole plant level, no differences in total biomass (shoots + roots) were found. On the basis of total biomass, there were no population differences in NUE and N accumulation efficiency (NAE). For a given level of forage DM yield, N+ had a greater N accumulation than N− and Champ and, therefore, a greater N concentration. The greater forage N concentration of N+ was not due to a greater leaf N concentration but to a greater proportion of leaves. The population N+ also had a greater proportion of roots than N−. The forage insoluble N concentration of N+ was greater than that of N−, while NO3–N concentrations of the populations were similar. The population N+ had a greater tiller density and specific leaf area (SLA) than N−. Differences in forage DM yield and N concentration between two populations selected for low and high N concentrations were mainly due to the modification of C and N partitioning between shoots and roots, and between leaves and stems. Our results highlight the role of biomass partitioning in improving grass NUE or N concentration.
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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) | 0.001 | 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".