Effects of nitrogen application rate on productivity, nutritive value and winter tolerance of timothy and meadow fescue cultivars
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 NO 3 ‐ or CP concentrations in feed, or too high N balance that indicates increasing risk of N leaching.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 it