Can Normality of Yields Be Assumed for Crop Insurance?
Bibliographic record
Abstract
Normality of crop yield residuals pooled to the state and regional level is examined using a procedure that accounts for trend and heteroscedasticity. Normality tests are conducted using farm‐level yield data from over 200,000 producers of six crops in seven U.S. states. The results indicate consistent non‐normality of crop yields. The effects of assuming normality on insurance premium rates are examined. Assuming normality is found to generate premiums that can differ substantially from premiums derived using data‐based empirical distributions. Montana Agricultural Experiment Station Journal Series No. 2001‐7. The Risk Management Agency, USDA, also provided support for this research. The views expressed herein are the authors' and do not necessarily represent those of Montana State University or the Risk Management Agency. Les auteurs ont analysé la normalité du rendement résiduel des cultures à l'échelon de l'État et à l'échelon régional au moyen d'une méthode tenant compte des conjectures et de l'hétéroscédasticité. Ils ont utilisé les données sur le rendement fournies par plus de 200 000 cultivateurs produisant six cultures dans sept États américains pour effectuer des tests de normalité. Les résultats révèlent l'anormalité constante des rendements. Les auteurs s'interrogent sur les conséquences d'une normalité hypothétique sur les primes d'assurance. En supposant des rendements normaux, on obtient des primes sensiblement différentes de cedes calculées avec une distribution empirique des données.
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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 | 0.053 | 0.303 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".