Evaluation of Foliar Sprays to Reduce Crop Damage by Canada Geese
Bibliographic record
Abstract
South Dakota Department of Game, Fish and Parks annually spends >$500,000 managing crop damage caused by grazing Canada geese (Branta canadensis). Foliar applications of a chemical feeding deterrent could provide an effective alternative to the methods currently being used to reduce damage. In 2011 and 2012, we evaluated Rejex-It Migrate Turfguard®, Bird Shield®, Avian Control®, and Avipel® as grazing deterrents. We used a ground sprayer to apply the treatments every 7 days to plots in soybean fields in Day County, South Dakota. We monitored activity in the plots using time-lapse photography. We began treating the plots after geese had begun using them (late June through mid- July). Damage was estimated after geese had abandoned the plots (August). The methyl anthranilate products (Rejex-It, Bird Shield, and Avian Control) were ineffective at reducing crop damage. Damage was 100% on all plots treated with these products. Use of plots significantly increased (P < 0.02) between the pretreatment and postreatment periods for Rejex-It (180 minutes/day and 313 minutes/day) and Bird Shield (200 minutes/day and 299 minutes/day); whereas, use was similar (P = 0.99) between plots treated with Avian Control (111 minutes/day) and reference plots (104 minutes/day). Less time was spent on plots treated with the anthraquinone-based product, Avipel (44 minutes/day) than on reference plots (132 minutes/day; P < 0.01). Additionally, soybean damage was less on Avipel-treated plots than on reference plots (P < 0.01). We recommend more research on Avipel to assess rates and timing of application to make this product efficacious and economical in the field.
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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 | 0.000 |
| 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".