The Economic Cost of Noxious Weeds on Montana Grazing Lands
Bibliographic record
Abstract
Abstract We distributed a 16-question survey concerning noxious weed abundance, impacts, and management to livestock producers grazing on privately owned or leased grazing lands in Montana. The noxious weeds most commonly reported as being present on respondents’ grazing units were Canada thistle [Cirsium arvense (L.) Scop.] (64% of grazing units) and leafy spurge (Euphorbia esula L.) (45% of grazing units), and these species also reportedly caused the greatest reductions in livestock forage. Houndstongue (Cynoglossum officinale L.) was more prevalent than either spotted knapweed (Centaurea stoebe L.) or diffuse knapweed (Centaurea diffusa Lam.) (39% vs. 32% and 10%, respectively, of grazing units), but collectively C. stoebe and C. diffusa were reported to cause greater forage reductions than C. officinale. The top three strategies used to manage noxious weeds were chemical control, grazing, and biological control. Combining survey responses with forage-loss models derived from field data for C. stoebe and E. esula, we estimated the combined cost of noxious weed management and forage losses on privately owned rangeland to be $3.54 ha−1 yr−1, or $7,243 annually for an average size grazing unit (i.e., 2,046 ha [5,055 ac]). Our estimates of economic losses are lower than many estimates from previous studies, possibly because we focused only on direct costs related to private grazing land, while other studies often consider indirect impacts. Nonetheless, our estimates are substantial; for example, our estimated loss equates to 24% of the average per-hectare lease rate for Montana grazing land.
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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 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".