Falcons reduce pre‐harvest food safety risks and crop damage from wild birds
Bibliographic record
Abstract
Abstract Foodborne illness outbreaks have heightened pressures on growers to improve food safety, including mitigating possible threats from wildlife. Among wildlife, birds are particularly challenging to deter, and the risks they pose to pre‐harvest food safety remain unclear. Further, deterrence efforts can jeopardize conservation and biological control, necessitating strategies that effectively lead to co‐management of farmlands for conservation, pest control and pre‐harvest food safety. Promotion of birds of prey with nest boxes may be one promising strategy to promote species of conservation concern that can deter pest birds that damage crops and introduce foodborne pathogens. Here, we evaluate if the American kestrel ( Falco sparverius ), a small falcon, can concurrently reduce crop damage and pre‐harvest food safety risks from birds in sweet cherry orchards in Michigan, USA. In orchards with and without active kestrel nest boxes, we conducted avian transect surveys, estimated the percentage of cherries with bird damage and estimated the percentage of branches and cherries with faeces. We collected faecal samples directly from birds and crop surfaces. We tested faeces for Campylobacter , the most common foodborne pathogen in birds, using both culturing and PCR. Fewer birds were present in fields with nest boxes, which translated into reduced bird damage (0.47% vs 2.50%) and fewer branches with faeces (2.33% vs 6.88%). Faeces on individual cherries were rare (4/15,890 [0.025%] cherries across all sites). We detected one or more species of Campylobacter using culturing and/or PCR in 10.65% (33/310) of bird faeces collected from crops and in 19.67% (24/122) of samples collected directly from birds. Detection rates were similar in fields with and without nest boxes. Despite the somewhat high overall detection, cultivable Campylobacter were only detected in 0.97% of faeces collected from crops. Synthesis and applications . Pre‐harvest food safety and wildlife conservation are often thought to be in conflict, and produce growers have few tools to effectively manage birds. However, our findings suggest that the promotion of birds of prey using nest boxes may be one way for growers to conserve a declining species, reduce crop damage and reduce in‐field faecal contamination that could cause foodborne illness.
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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) | 0.005 | 0.001 |
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".