Within-season grass herbage crude-protein- and nitrate-N concentrations as affected by rates and seasonal distribution of fertilizer nitrogen in a high yearly rainfall climate
Bibliographic record
Abstract
An orchardgrass study in which three rates of N (100, 200 and 400 kg ha−1) each distributed in 1/0/0/0, 0.75/0.25/0/0, 0.50/0.25/0.25/0 and 0.25/0.25/0.25/0.25 proportions prior to four cut intervals examined crude-protein-N and nitrate-N concentrations in grass herbage at each cut in three trials. Crude-protein-N concentration frequently increased to a greater degree and in a different pattern (based on cut) than yield as the rate of N application increased. This showed that crude-protein-N by itself cannot be used as a method for determining the N sufficiency status of grass. Both rate and distribution of fertilizer N strongly influenced plant nitrate-N concentration; the degree of change varied considerably among cuts and trials. Plant nitrate-N concentration in the control did not correspond to yield responsiveness to N application, making it a poor indicator of the plant's need for fertilizer applications. Residual effects of N applications on plant nitrate-N were noted into the last cut of the season from a single spring application. The effect of N rate and distribution, then, was a function of immediate and residual effects of the applications. There was some evidence that N present in the soil in nitrate-N form enhanced the potential for high nitrate-N in the plant. Plant nitrate-N concentrations accounted for up to 29% of the total N in the plant with concentrations greater than 4000 mg N kg−1 at the highest N application rates. Plant nitrate-N did not exceed 1000 mg N kg−1, a concentration considered safe for ruminants, when 75 kg N ha−1 or less ammonium nitrate was applied as a single application prior to a growth interval for all cuts. Since grass protein- and nitrate-N concentrations respond differently than yield to N applications, a specific combination of rate and distribution of fertilizer will not necessarily produce maximum herbage quantity and quality simultaneously. Key words: Crude-protein-N, plant nitrate-N, residual effect, split applications
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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".