Grazing intensity impacts on pasture carbon and nitrogen flow
Bibliographic record
Abstract
There is little information on the impact of grazing intensity on productivity and sustainability of intensively managed pastures in the humid, short-season parkland of the Canadian prairies. Our hypothesis was that above-ground productivity of dry matter, carbon, nitrogen, and in vitro digestible organic matter would be reduced proportionately with increasing grazing intensity. The study was conducted on a Typic Haplustoll at Lacombe, Alberta. Paddocks of meadow bromegrass (Bromus riparius Rhem.), replicated 4 times, were subjected to heavy, medium and light grazing intensities. Measurements and analyses were carried out for 3 years. Yields of dry matter, carbon, nitrogen, and in vitro digestible organic matter before and after grazing were determined and seasonal pools of above ground production, disappearance and residual were calculated. Concentrations of acid and neutral detergent fiber and lignin were also determined before and after grazing. Increasing grazing intensity tended to increase nitrogen and decrease fiber concentrations for available and residual forage. Heavy and medium grazing intensities produced 83 and 90% as much above ground dry matter and 87 and 90% above ground carbon as the light intensity. All disappearance pools were similar among grazing intensities except in vitro digestible organic matter, where heavy was 116% of light. Heavy grazing reduced the contribution of vegetative dry matter, in vitro digestible organic matter, carbon and nitrogen to the residual to 41, 50, 36, and 52% of that for light grazing. Adding estimated fecal-carbon to the residual significantly increased total residual carbon. Estimated fecal-carbon represented 68, 51, and 42% of all carbon inputs to litter for heavy, medium and light grazing, respectively. Grazing intensity did not affect estimated pools of excreted nitrogen, but increased estimated precent of nitrogen excreted as urine.
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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 | 0.000 |
| 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".