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 NO 3 –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 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 | 0.000 | 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 | 0.001 |
| 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".