Quantifying large-scale impacts of cattle grazing on annual burn probability in Napa and Sonoma Counties, California
Bibliographic record
Abstract
Wildfire in California is an increasing threat to life and property. The expansion of urban and suburban development into wildlands limits risk-reduction options like prescribed burning, whereas large-scale mechanical and herbicide treatments can be cost prohibitive and unpalatable to the public. Cattle grazing is a low risk, affordable treatment not frequently considered for use explicitly for fuels reduction in California. To examine the impact of cattle grazing on fire in Napa and Sonoma Counties, California, we quantified its effects as change in average annual burn probability. Probabilities were calculated for 2001–2017 using mixed-effect regression models in combination with a range of grazing intensities and extents. These grazing scenarios were designed to represent current grazing conditions, ungrazed conditions, adding grazing to high priority landscapes, and grazing the full study area. We estimated that under current grazing conditions, cattle grazing reduces average annual burn probability 45% (from 9.9% to 5.4%) compared to ungrazed conditions. Adding grazing to high priority landscapes as identified by the California Department of Forestry and Fire Protection (CAL FIRE) decreased their average annual burn probability by 82% (from 7.6% to 1.4%) compared to under current grazing conditions. Of the scenarios assessed, grazing high priority landscapes heavily while maintaining the current extent and intensity of grazing on other rangelands provided the best return in terms of decreased burn probability per additional area grazed. Finally, we demonstrated how our methodologies can be utilized by fuel managers and planners to identify key areas for treatment with cattle grazing. Our findings suggest cattle grazing provides benefits to the study area by reducing overall burn probability, and that extending its use to treat fuels in priority areas in and around the wildland urban interface could provide further fire-risk reduction on community-adjacent lands. Land managers may find cattle grazing a valuable long term fuel-management tool at the landscape scale.
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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) | 0.000 | 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 | 0.001 | 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".