The “hidden workers”: livestock farmers’ perceptions of ecosystem services provided by dung beetles
Bibliographic record
Abstract
Non-native dung beetles were introduced to Australian agricultural systems to provide ecosystem services, such as pasture cleaning and to control dung-breeding flies, which benefit particularly livestock farmers. A variety of studies have explored the interactions between dung beetles and livestock systems worldwide. Although many studies have quantitatively assessed the ecosystem services provided by dung beetles through measures like increased plant growth and parasite control, less is known about the perceptions of farmers. This gap lies in understanding how farmers perceive the ecosystem services of dung beetles and their associated values. The lack of studies exploring farmers perceptions of dung beetle value may hinder public organizations and individual farmers from making well-informed resource allocation and management decisions. To address this, we conducted 39 semi-structured interviews with Australian livestock farmers to elicit their perceptions of the ecosystem services and associated values of dung beetles, including their monetary value. We also investigated perceived barriers to adopting dung beetle friendly farming. Farmers reported soil improvements most frequently (reported by 90% of participants), followed by improved animal health (69%) and increased agricultural productivity (67%). About two out of three farmers could not provide a quantitative measure of dung beetle monetary value to their farming system. The remaining participants used heuristic measures (e.g., decrease in farm inputs or increase in farm outputs) to estimate monetary values. Perceptions of dung beetle values ranged from AU$45 to AU$2000 per hectare per year. Over half the participants (56%) reported a lack of knowledge about the effects of veterinary anthelmintics as a barrier to adopting dung beetle friendly farming. Raising stakeholder awareness about dung beetle friendly farming may increase the benefits they receive. This is particularly important as dung beetles are a public good and, when adopted collectively by farmers, provide non-exclusive benefits to all farmers in a region.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".