Ranchers’ Attitudes toward Managing for Vegetation and Landscape Heterogeneity
Bibliographic record
Abstract
Grasslands are imperiled due to land conversion, fragmentation, woody encroachment, population growth, and global warming. What remains of intact grasslands are vital for the ecosystem services they provide. Wildlife species native to the North American Great Plains evolved in response to very specific and differing habitats. Without variation in vegetation structure and composition (heterogeneity) the number of species that can thrive is minimized, as are the interconnected ecosystem services. Landowners’ assistance in the maintenance of grassland ecosystems is essential because Great Plains grasslands are primarily privately managed. Thus, increasing heterogeneity on working rangelands is a partial solution to balancing the needs of wildlife with that of cattle production. This study tested a predictive model of factors influencing attitudes toward heterogeneous and landscape-scale ranch management. An online survey was sent to ranchers within prescribed-burn and grazing groups in the Great Plains. Predictors of landscape-scale management were spirituality, stewardship, social descriptive norms, consideration of future consequences, and participation in grassland activities. The lone predictor of attitudes toward heterogeneous grassland management was consideration of future consequences. Even though the survey targeted groups that were more likely to be higher in heterogeneous attitudes, a vast majority are still following the “manage to the middle” paradigm. It appears these ranchers are unaware of the benefits of a heterogeneous landscape and the compatibility of its associated management techniques with their cattle production goals. To improve the adoption of techniques that promote vegetation heterogeneity, more resources should be devoted to demonstrating how these practices benefit ranchers’ cattle business alongside the larger landscape.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".