Identifying opportunities and constraints to effective management of invasive Australian wattle (Acacia) species in grassland landscapes, South Africa
Bibliographic record
Abstract
Land users’ motives for participating in conservation and restoration activities are influenced by the local and broader scale contexts and are often determined by their perceptions of the current situation. Therefore, understanding land users’ views is essential for gaining insights into the opportunities and constraints for ecosystem restoration. In this study, we sought to understand land users’ perceptions of alien wattle (Acacia spp.) clearing activities and explore opportunities and challenges to wattle management as perceived by two groups of land users, i.e., communal land users and commercial livestock farmers, in the upper Umzimvubu catchment, South Africa. The results show marked differences in the key barriers and motives for participation by the two groups. Improvement in water flow was the most cited positive change from wattle clearing mentioned by commercial (75%) and communal (71%) farmers. Most commercial farmers (75%) cited improved grazing as one of the clearing benefits compared to only 39% of communal land users. Employment opportunity was a key motive mentioned by communal land users (25%). In contrast, most commercial farmers mentioned securing good grazing (50%) and water supply (33%) as important motives for removing wattle. Most commercial farmers mentioned high costs (35%) incurred when controlling wattle as the main barrier, whereas communal land users mentioned their old age (20%) and thus physical inability as the main barrier preventing them from maintaining cleared areas. These findings highlight the need to consider a mix of incentives that may effectively engage different land users in invasive alien plant clearing in different contexts.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".