The Impact of Agriculture on Waterfowl Abundance: Evidence from Panel Data
Bibliographic record
Abstract
Agricultural expansion and intensification in Canada’s Prairie Pothole Region (PPR) have contributed to declining waterfowl populations since the 1970s. Although this region represents a mere 10% of North America’s waterfowl breeding habitat, it produces over 50% of the continent’s duck population and roughly 60% of Canada’s agricultural output. Thus, intense competition exists between private economic interests and public benefits in the PPR. To better understand the conflict between agricultural and wildlife uses of land, panel methods are used to examine the spatiotemporal variation of waterfowl populations and agricultural land use intensity in the PPR from 1961-2006. For the main static model, we find that a one percent increase in cropland or pasture decreases duck density by 6%, while a similar increase in summerfallow area decreases duck density by 7%. Estimates based on a dynamic specification are more conservative. For the lagged dependent variable model, a 1% increase in cropland and pasture decreases duck density by 4.6%, while a decline of 4.7% is predicted for increases in summerfallow area. The spatial autoregressive model allows the derivation of measures for assessing direct and indirect impacts. The estimated direct impacts fall between those obtained from the standard and dynamic models, but, when spillover effects are included, the impacts exceed those predicted by the standard model. It would appear that conserving wetlands in one location has the added benefit of increasing productivity of wetlands at other locations.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".