The Economic Impacts of Pollinator Declines: An Approach to Assessing the Consequences
Bibliographic record
Abstract
Since agricultural activities were first recorded, there have been shortages of pollinators. Today it seems that pollination systems in many areas of agriculture are threatened by the inadequacy or lack of sustainable managed, indigenous, or imported pollinators. Pollinator shortages can adversely affect crop production and commodity markets. This paper presents an economic model than can be used to measure some of the economic impacts of pollinator deficits on traded commodities. This economic analysis indicates that consumers of a commodity affected by a pollinator deficit may suffer because the commodity costs more and becomes less available. At the same time, although the producers of the affected commodity may experience crop declines, they may also experience economic gains in the form of higher prices. The amount the producer gains or loses depends on the shape of the supply and demand functions, and the magnitude of these losses or gains is an empirical question. Although there are few data available to evaluate this model, those we do have indicate that serious problems for world food supply, security, and trade could be in the offing if current declines in pollinator abundance, diversity, and availability are not reversed. Various crops and cropping systems are suggested as practical starting places for economic studies of the effects of pollinator declines, with emphasis on the type of data required.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".