Responses of herbage P, Ca, K and Mg content and Ca/P and K/(Ca + Mg) ratios to long‐term continuous and discontinued cattle grazing on a rough fescue grassland
Bibliographic record
Abstract
Abstract Herbage minerals affect performance of grazing cattle. We investigated the response of herbage P, K, Ca and Mg contents and Ca/P and K/(Ca + Mg) ratios to long‐term stocking rate, continuous vs. discontinued grazing practice, and sampling year. Cattle had been stocked at 2·4 and 4·8 animal unit months ha−1since 1949. Exclosures were installed in April 1998. Herbage samples were collected near peak herbage mass in 2001, 2003, 2008 and 2012 and analysed for mineral content. Mineral contents were similar between the two stocking rates, but were lower (P < 0·05) under discontinued than continuous stocking, with the exception of similar P contents. The content of P and K in herbage was higher and the content of Ca and Mg was lower (P < 0·05) in years with greater precipitation and lower temperatures. Herbage mineral content, with the exception of P, exceeded minimum recommended levels for cattle. Given the low P content in herbage (0·74–1·19 g kg−1) and high Ca/P ratios during the dry and hot year (of 2001), a dietary P supplement should be considered for cattle grazing rough fescue grassland in drought years. The low K/(Ca + Mg) ratios (<2·2) suggest there is little risk of grass tetany in cattle grazing on this grassland.
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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 | 0.000 | 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".