The Impact Of Agroclimatic Variables On Crop Insurance Claims In Saskatchewan
Bibliographic record
Abstract
The study investigated the impact of agroclimatic variables on the loss cost of hard red spring wheat (HRSW), durum, barely and canola in Saskatchewan. Using daily data on temperature and precipitation, we estimated the water balance or soil moisture using American Society of Civil Engineers standard reference evapotranspiration formula. Accounting for model uncertainty by using Bayesian modeling averaging (BMA), we find that loss cost is influence by monthly temperature and water balance. We find that water balance in June and August impact loss cost of the HRSW, durum, barley and canola. Depending on the crop, one percent increase June water balance, above its long-term average, decreases loss cost between 0.35 percent and 0.64 percent while a one percent increase in August water balance, above its long-term average, increases the loss cost between 0.24 percent and 0.36 percent. A one percent increase in water balance variability increases the loss cost between 0.35 percent and 0.66 percent. Temperature also affects loss cost, depending on the crop and month. For the early stage of the growing season, a percent increase in GDD increases loss cost between 0.75 and 1.99 percent. However, at the later stages of the growing season, a one per increase in GDD decreases loss cost between 0.7 percent and 2.25 percent. We find that BMA, in general, outperforms OLS model for out-sample-forecast. Lastly, we find that the forecasted premium rate based on weather probabilities from BMA predictors performed better than simple or 10 year moving average.
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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 | 0.003 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".