Impact of grazing native prairie on soil and plant nutrients in southwestern Saskatchewan
Bibliographic record
Abstract
A reduction in the sage grouse population could be a result of the export of nutrients from the long term grazing management that has been in place in grassland ecosystems such as southwestern Saskatchewan for the past century. The objective of this study was to measure the supply rates of nitrogen and phosphorus in the soils and their content in the sage plants and determine what impact grazing has had on nutrient availability. Plant and soil samples were taken from five side-by-side normally grazed and ungrazed for ~20 years, native grassland sites in southwestern Saskatchewan and analyzed for nitrogen, phosphorus and selected micronutrients. PRS™-probes were buried in situ for 21 days to measure soil supply rates of nitrogen and phosphorus. Sage plants and grasses were collected and analyzed for nutrient content. At four (Butte Creek Upland and Low Sage; Frenchman Mid and Low slope) of the five sites, grazing had relatively minor, non-statistically significant effects on soil and plant nutrients. At the Consul site, a site that may be considered a drier site of poorer inherent fertility, grazing significantly reduced soil and plant P. Introduction of beef cattle into the pasture in the spring significantly increased supply rates of available N, likely due to fresh addition of N as fecal material and urine. Plant analysis revealed that calcium levels were significantly higher in the ungrazed Butte Creek low-sage and potassium levels were significantly higher in the ungrazed sage at Frenchman low slope and Consul sites. Overall, well managed grazed pastures located on good quality soils do not appear to be at risk of nutrient depletion. Cessation of grazing for ~20 years did not cause major differences in nutrient amounts and supplies compared to normally grazed pastures.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".