Weather risk management by Saskatchewan agriculture producers
Bibliographic record
Abstract
Purpose The purpose of this research is to study the weather risk management practices of agriculture producers. In particular, the authors look at the extent to which farmers use weather derivatives to complement insurance. Unlike insurance, weather derivatives mitigate risk associated with low intensity, high probability events and therefore offer the potential of a more complete hedge than insurance alone. Design/methodology/approach The authors conducted a survey of grain farmers in the province of Saskatchewan, Canada, a typical jurisdiction in which farmers tend to face weather events that are high in frequency but low in severity, to study the usage of weather derivatives compared to insurance and identify the hurdles to their usage. Findings The authors find that fewer than 10 percent of their respondents use weather derivatives. Consistent with previous literature in other contexts, they identify participation costs, especially lack of awareness, to be the most significant hurdle to their usage. Research limitations/implications A limitation of this study is that the data were collected using a survey methodology and are therefore subject to the usual risks of bias associated with that approach. Moreover, because the authors' survey was delivered online, it may have favoured the participation of farmers that were more comfortable with technology and some bias may have also been introduced into the data as a result. Practical implications The authors' findings suggest that there is significant potential to improve farmers' ability to hedge weather risk and thereby improve economic outcomes if the major barriers to the usage of weather derivatives can be overcome. The study paves the way for further research to support the development of public policy strategies that could help farmers take advantage of weather derivatives as part of their inventory of risk management tools. Originality/value To the authors' knowledge this is the first study that quantifies the usage of weather derivatives by agriculture producers and identifies the hurdles.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".