Nitrogen cycling in silvopastoral systems in the Pacific Northwest: A review
Bibliographic record
Abstract
Clearcuts in interior British Columbia are often aerially seeded with domestic forages such as orchardgrass and alsike clover to prevent erosion and help control understorey vegetation during establishment of lodgepole pine plantations. These seeded clearcuts provide valuable mid-summer range for cattle and with careful management there is little damage to planted trees. There is growing concern about N losses due to clearcutting and site preparation and what effect these might have on long-term forest productivity. The literature indicates that without cattle or forage seeding natural N inputs from the atmosphere and N fixation are barely sufficient to provide adequate nutrition for a lodgepole pine stand. This literature review addresses what effect introducing domestic forages and cattle grazing can have on N cycling on clearcuts. Forage seeding alone may reduce N losses due to runoff and leaching and legumes can fix atmospheric N, but grazing by cattle can counteract some of these benefits. Estimates of N losses due to cattle grazing were calculated using summary data from a long-term grazing trial conducted in interior British Columbia where experimental pastures were either aerially seeded with clover-orchardgrass mixtures or left unseeded. Using forage yields, cattle gains, and a number of assumptions, N losses due to cattle grazing on the seeded pastures were calculated as 2.4 kg N ha−1 yr−1 compared with 0.8 kg N ha−1 yr−1 on native vegetation. Losses were from N in new cattle tissue and N volatilized from urine as ammonia. Increased losses due to grazing seeded forages were short-lived as the forage production of seeded pastures was equal to that of native vegetation by the seventh year. During this period, the losses due to grazing seeded forages may be balanced by increased capture of labile N by the forages. Key words: N cycling, seeded clearcuts, cattle grazing
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".