Effect of N fertilizer on the productivity and nutritive values of perennial grass mixtures in the alpine region of Qinghai-Tibetan Plateau, China
Bibliographic record
Abstract
A study was conducted on grass mixtures that included smooth bromegrass (SB) + drooping wild ryegrass (DW), smooth bromegrass + Siberian wild ryegrass (SW) + crested wheatgrass (CW) and smooth bromegrass + Siberian wild ryegrass + drooping wild ryegrass + crested wheatgrass in the alpine region of Qinghai-Tibetan Plateau. The study was conducted from 1998 to 2000 to investigate the effects of N application rates and growing year on herbage dry matter (DM) yield and nutritive values. Herbage DM production increased linearly with N application rates. The effect of N application on DM yields was greater (P < 0.05) in the 2nd and 3rd production years than in the establishment year. Dry matter yields of SB + SW + CW and SB + SW + DW + CW can reach as high as 15 000 kg ha-1 at 345 kg ha-1 N rate in the 3rd growing year. With increased N application rates, crude protein (CP) contents and 48 h in sacco DM degradability of grasses increased (P < 0.05). No effect (P > 0.05) of N application was detected on organic matter (OM) and acid detergent fibre (ADF) concentration. It can be concluded that for increased biomass production in the alpine region of the Qinghai-Tibetan Plateau, a minimum of 345 kg N ha-1 should be applied to grass stands in three split application of 115 kg N ha-1, in early June, early July and late July. Key words: Nitrogen levels, herbage dry matter yield, leaf shoot ratio, crude protein contents, dry matter degradability
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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".