Profitability of sheep grazing in young conifer plantations of British Columbia, Canada
Bibliographic record
Abstract
In British Columbia (BC), sheep grazing, as a silvopastoral system, is occasionally used as a biological weed control method in young conifer plantations. As it is a relatively new method in BC, there is presently a lack of information about the profitability of the practice. For instance, there is no scientific data about the gain in profitability as a function of the number of grazing treatments applied. What is known is that at least two grazing treatments in a given year or one treatment per year for two or more consecutive years are required to effectively deplete fireweed (Epilobium angustifolium) root reserves, the predominant herbaceous competing vegetation. Our main objective was to analyze the profitability of sheep grazing for herbaceous vegetation control in young conifer plantations. The profitability was tested under two grazing treatments: a single sheep grazing treatment and two sheep grazing treatments applied over two years. Since comparing the profitability of treatments requires financial data on all the rotational period and is often not available, we used a simple approach that can determine profitability with little detailed information. Using this approach, grazing treatments could be compared in terms of time gain. Assuming that a grazing treatment shortens the rotational period by “X” years allowing a certain time gain, the additional relative cost can be compared with the break-even additional relative cost. Preliminary results indicated that two grazing treatments have the potential to decrease the time to declare a stand free-growing compared to one grazing treatment. Thus, this time gain, at a young age (e.g., 10 yr) could potentially result in a time gain at the end of the rotational period (e.g., 80 yr). This presentation will examinate the profitability of sheep grazing with more detail and show how this method has the potential to shortened the rotational period.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".