ECONOMIC ANALYSIS OF LEAFY SPURGE INFESTATION IN WEST-CENTRAL SASKATCHEWAN
Bibliographic record
Abstract
Leafy spurge (Euphorbia esula) is a perennial and widely established noxious weed that troubles pasturelands. Cattle avoid grazing pasture areas infested with leafy spurge, in effect, reducing the grazing capacity of the invaded pasture. Saskatchewan is home to 30% of beef cows in Canada and more than half of a cow’s annual diet comes from grazing perennial forages. Thus, leafy spurge infestation signals adverse economic implications for beef cattle production and its related industries. This study uses financial (private cost-benefit) and social cost-benefit analysis to compare and select the best control method for leafy spurge, as well as using an input-output model to assess the economic impact of leafy spurge control methods in Elbow pasture. A frequently asked question is whether leafy spurge should be managed with public or private funds? Elbow pasture has struggled with leafy spurge since at least the 1970s and has been receiving public assistance to aid with control efforts. Any public or private assistance for such a control program must be based on economic efficiency grounds. Hence, financial, and social cost-benefit analysis for three control methods – herbicide, targeted grazing and combined - were conducted to support the decision to select the most optimal method for leafy spurge management. Reduced grazing capacity of the pasture associated with the current leafy spurge infestation was estimated at 6468 Animal Unit Months (AUM) which can support 924 cows per 5 month grazing season. Using the Saskatchewan input-output (I-O) model, the study found that herbicide application generated the highest economic impact, amounting to approximately $2.3 million in labour income. The combined method had the second-highest economic impact at around $151,420, while targeted grazing had the lowest impact at approximately $78,890. The results indicate that using chemical treatment for controlling leafy spurge leads to higher economic growth compared to the other methods. The financial and social cost-benefit analyses yielded a negative net present value (NPV) for herbicide application, indicating its economic undesirability. Conversely, targeted grazing emerged as an economically viable option with positive NPV. Targeted grazing demonstrated a potential cost savings when compared to future costs of uncontrolled infestation, with positive incremental costs. Combined method resulted in a negative NPV in the financial analysis and a positive NPV in the social cost-benefit analysis. However, this study supports existing literature, emphasizing the importance of using integrated or combined control methods to manage leafy spurge infestations effectively.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".