The effect of grazing duration on forage quality and production of meadow foxtail
Bibliographic record
Abstract
For the past 50 yr, meadow foxtail (Alopecurus pratensis L.) has been invading native flood meadows throughout the Harney Basin in southeastern Oregon. The expansion of this grass species has been the result of its broad climatic adaptation and ability to withstand drought while thriving in saturated soil conditions for a large part of the growing season. The growth of meadow foxtail starts as soon as adequate soil moisture exists. Managing this early-maturing hay species can prove to be a challenge because soil saturation and elevated water tables make it difficult to harvest hay when forage quality and yield are maximized. The purpose of this study was to evaluate whether planned grazing would retard maturation and thus prolong forage quality. Treatments included a non-grazed control and grazing durations of 2, 4, 6, and 8 wk. Grazing was initiated in May of 1998 and 1999 on six replications of each treatment arranged in a randomized block design. Within each treatment/replication combination, ten 0.2-m2 plots were clipped to ground level at about 2-wk intervals from May to August. The samples were weighed and dried for standing crop estimation and 4 of the 10 samples were selected at random and analyzed for acid detergent fiber (ADF), neutral detergent fiber (NDF), and crude protein (CP). We found that early spring grazing decreased forage yield significantly (P ≤ 0.05). Grazing tended to slow the seasonal decline in CP. The effects of grazing on the forage fiber components, however, were inconsistent. The relatively small increase in forage quality does not appear to compensate for the large decline in hay yield (a 40% decline in the shortest grazing duration treatment). We recommend that unfertilized meadow foxtail pastures be used for either haying or grazing, but not both in a given growing season. Key words: Grazing, beef cattle, regrowth, forage yield
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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".