Impacts of grazing systems on soil compaction and pasture production in Alberta
Bibliographic record
Abstract
Livestock trampling impacts have been assessed in many Alberta grassland ecosystems, but the impacts of animal trampling on Aspen Boreal ecosystems have not been documented. This study compared the effects of high intensity [4.16 animal unit month per ha (AUM) ha-1] short-duration grazing (SDG) versus moderate intensity (2.08 AUM ha-1) continuous grazing (CG) by wapiti (Cervus elaphus canadensis) on soil compaction as measured by bulk density at field moist condition (Dbf) and penetration resistance (PR). Herbage phytomass was also measured on grazed pastures and compared to an ungrazed control (UNG). The study was conducted at Edmonton, Alberta, on a Dark Gray Luvisolic soil of loam texture. Sampling was conducted in the spring and fall of 1997 and 1998. Soil cores were collected at 2.5-cm intervals to a depth of 15-cm for measurement of bulk density (Dbf) and moisture content. Penetration resistance to 15 cm at 2.5-cm intervals was measured with a hand-pushed cone penetrometer. The Dbf and PR of the top 10-cm of soil were significantly (P ≤ 0.05) greater by 15 and 17% under SDG than CG, respectively, by wapiti. Generally, Dbf in both grazing treatments decreased over winter at the 0-7.5 cm and 12.5-15 cm depths, suggesting that freeze-thaw cycles over the winter alleviated compaction. Soil water content under SDG was significantly (P < 0.05) lower than CG. Total standing crop and fallen litter were significantly (P ≤ 0.05) greater in CG treatment than the SDG. The SDG treatment had significantly (P ≤ 0.05) less pasture herbage than CG areas in the spring (16%) and fall (26%) of 1997, and in the spring (22%) and fall (24%) of 1998, respectively. The SDG did not show any advantage over CG in improving soil physical characteristics and herbage production. Key Words: Bulk density, Cervus elaphus, moisture content, penetration resistance, pasture production
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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 | 0.001 |
| 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".