Cattle herbage utilization patterns under high-density rotational grazing in the Aspen Parkland
Bibliographic record
Abstract
Native Aspen Parkland landscapes consist of a complex mosaic of plant communities, including riparian meadows, upland grasslands, and forests. Sustainable livestock production in this environment depends on an understanding of livestock grazing behavior among communities, particularly under contemporary, intensive management rotational grazing systems. This study examined seasonal patterns of absolute (kg ha-1) and relative (%) herbage utilization, as well as plant community visitation, across a Parkland landscape by 150 heifers in two rotations of a high-intensity, low-frequency grazing system. Graminoids constituted more than 92% of the total herbage utilized. Absolute graminoid utilization within each grazing period was greater (P < 0.05) throughout the growing season from riparian meadows (2003 to 2114 kg ha-1) than from upland grasslands (762 to 1041 kg ha-1) or forests (782 to 800 kg ha-1). In contrast, relative graminoid utilization remained similar (P > 0.05) among communities in either rotation, suggesting heifers grazed in proportion to graminoid availability (57–61% in first rotation, 44–54% in second rotation). Although riparian meadows represented a small fraction of the landscape (~ 4%), and provided up to 9.5% of the total forage used, the majority of herbage removal at the paddock level continued to originate from upland grasslands and forests. Patterns of visitation indicated heifers initially visited riparian meadows more often within each 3–4 day grazing period, regardless of entry date. While upland grasslands were least visited in June, forests were least visited in August. Based on these utilization and visitation patterns, we discuss the implications of using high-density, rotational livestock grazing for the sustainable management of Aspen Parkland rangelands. Key words: Aspen forest, cattle grazing, forage quality, herbage utilization, riparian meadow, rotational grazing, upland 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".