Effects of nitrogen application rate on productivity, nutritive value and winter tolerance of timothy and meadow fescue cultivars
Bibliographic record
Abstract
Abstract Finnish N fertilizer application regulations for forage grasses are based on field experiments mainly conducted in the 1960–1970s with cultivars and management practices typical of the time. In order to update the yield response function of N, to make it better suited to current grassland farming, field experiments were conducted at two sites in 2015–2017 with two cultivars of timothy (Phleum pratense L.) and one of meadow fescue (Festuca pratensis Huds.). Dry matter (DM) yield, nutritive value and N balance were evaluated, with N application levels 0, 150, 200, 250, 300, 350, 400 and 450 kg N ha−1 year−1. The grasses were harvested three times per season. The data indicate that the DM yield response was significantly stronger, and N was used more efficiently for DM production than earlier without compromising the nutritive value, especially during the first two years. The third harvest produced on average 23% of the annual yield, utilizing N efficiently. N application rates below 350 kg N ha−1 year−1 did not cause substantial overwintering losses or lodging. The data indicate that with changing climate and improved cultivars and management practices, there is a need to modify the rates and timing of N application. The results suggest that N application levels could be increased by at least 50 kg N ha−1 year−1 from the current maximum accepted rate (250 kg N ha−1 year−1) without too high NO3‐ or CP concentrations in feed, or too high N balance that indicates increasing risk of N leaching.
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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 | 0.001 |
| 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 | 0.001 | 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 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".