Carbohydrate and crude protein fractions in perennial ryegrass as affected by defoliation frequency and nitrogen application rate
Bibliographic record
Abstract
Abstract The objective of this study was to evaluate the effects of defoliation frequency (either at two‐ or three‐leaf stage) and nitrogen (N) application rate (0, 75, 150, 300, 450 kg N ha−1 year−1) on herbage carbohydrate and crude protein (CP) fractions, and the water‐soluble carbohydrate‐to‐protein ratio (WSC:CP) in perennial ryegrass swards. Crude protein fractions were analysed according to the Cornell carbohydrate and protein system. Carbohydrate fractions were analysed by ultra‐high‐performance liquid chromatography. Sward defoliation at two‐leaf stage increased the total CP, reduced the buffer‐soluble CP fractions and decreased carbohydrate fractions of herbage (P < 0·001). The effect of defoliation frequency was less marked during early spring and autumn (P < 0·001) than for the rest of the seasons. An increase in N application rate was negatively associated with WSC, fructans and neutral detergent fibre (P < 0·001), and positively associated with CP and nitrate (N‐NO3) contents of herbage. Nitrogen application rate did not affect CP fractions of herbage (P > 0·05). The fluctuations in CP and WSC contents of herbage resulted in lower WSC:CP ratios during early spring and autumn (0·45:1 and 0·75:1 respectively) than in late spring (1·11:1). The herbage WSC:CP ratio was greater (P < 0·001) at the three‐leaf than the two‐leaf defoliation stage and declined as the N application increased in all seasons (P < 0·001). The results of this study indicate that CP and carbohydrate fractions of herbage can be manipulated by sward defoliation frequency and N application rate. The magnitude of these effects, however, may vary with the season.
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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".