Bibliographic record
Abstract
The efficient utilization of nitrogen (N) in grass production is essential to reduce the risks of water and air pollution, and the costs of production. Recent findings in grass physiology and agronomy should help in developing new tools to improve N utilization efficiency. A model of N dilution describing the decrease in plant N concentration with increasing shoot biomass under non-limiting N supply is used to define a critical N concentration in grasses required to reach maximum shoot growth and yield. The index of N nutrition (INN) is then calculated as the measured N concentration in a given situation divided by the critical N concentration. The INN is a diagnostic tool to quantify the level of N deficiency during growth cycles, and can also be used in crop modelling and in the interpretation of results from studies conducted over many sites and years. The "universality" of the model of N dilution is based on the increased proportion of structural to metabolic components during crop growth combined with the fact that the structural component has a lower N concentration. Inter- and intra-species differences in N concentration at a given shoot biomass can be related to differences in the proportion of leaves which are assumed to be equivalent to the metabolic component. Under N-deficient conditions, the reduction in grass growth is due to a reduction in the interception of solar radiation primarily through reduced leaf extension, and to a reduction in the conversion efficiency of intercepted radiation into shoot biomass primarily through an effect on biomass partitioning between roots and shoots. The concept of the critical N concentration based on the relationship between plant N concentration and shoot biomass is used to derive general and synthetic expressions of the effect of plant N nutrition on crop growth and crop growth processes. These recent findings on the relationship between N nutrition and the growth of forage grasses should result in the improvement of the efficiency of N utilization by a more precise fertilizer management and the development of more N efficient cultivars. Key words: Physiology, growth, photosynthesis, leaf, partitioning, model
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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 | 0.001 |
| 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 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".