Reducing the number of grazing geese on agricultural fields - Effectiveness of different scaring techniques
Bibliographic record
Abstract
Scaring is a widely used damage mitigation tool to make agricultural fields less attractive to wildlife and by that reduce crop damage. However, few experimental studies exist where the numerical response of different scaring devices has been compared. We tested experimentally the effect of three different scaring devices (kite, scarecrow, inflatable man) on the number of geese in fields with cereals, ley, rapeseed, potatoes, and carrots in Sweden. Geese were counted by camera traps and two approaches were used; in a first (model 1) only geese within 50–150m of the scaring devices were counted, and in a second (model 2) all geese in the field were included. A total of 42,281 geese were counted: Greylag goose Anser anser was the most common species (86%), followed by bean goose Anser fabalis (6%), greater white-fronted goose Anser albifrons (3%), barnacle goose Branta leucopsis (2%), and Canada goose Branta canadensis (2%). During scaring the number of geese significantly decreased for all three devices in model 2. The inflatable man decreased goose numbers by 90.0 %, scarecrow 64.6%, and kite 60.5%. A similar pattern was found in model 1, but the decrease was not significant. Our study shows that the scaring devices studied can reduce goose grazing pressure for some time and locally. However, since geese continue to graze during scaring, we conclude that scaring alone is not a final solution to mitigate crop damage. Future work to develop more effective control measures should address the efficiency of other management tools and scaring techniques in combination.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".