Applications of Weather Data, Satellite Data, and Other Geospatial Data for Improving Crop Insurance and Agricultural Risk Management
Bibliographic record
Abstract
This dissertation consists of three essays investigating improvements to crop insurance by applying weather data, satellite data, and other geospatial data. The first essay investigates the use of weather data and satellite data to improve a temperature-based crop insurance policy in Alberta, Canada. The analysis used advanced methods from geostatistics, including Universal Kriging and Generalized Additive Models. Results suggest that the more advanced interpolation methods reduced interpolation error and provided a more useful measure of temperature for insurance policies. The second essay evaluates a number of traditional crop yield updating methods and also proposes a new updating method that may be used to improve crop insurance. For illustration purposes, farm yield data is used from 1133 canola farms in Alberta, Canada from 2002 to 2017 (16 years) and national yield data from Statistics Canada are used to evaluate the yield trend updating methods. Crop yield updating methods are needed because improvements in technology have led to higher crop yields over time. Lower past yields need to be updated at higher levels to bring them up to the level of today's technology, otherwise the producer may be under insured. The updating approaches investigated are 10-year average (model A), national yield trend (model B), county yield trend (model C), and a new mixed method (model D). The results suggest that all four crop yield updating methods performed similarly. This analysis may be of interest to crop insurance analysts and policy makers in Canada, the United States, and other countries. The third essay investigates if higher satellite resolution can improve the accuracy of crop yield estimation. In the analysis, the years 2008-2018 of NASA's MODIS satellite image collection over the contiguous United States were examined for four crops: corn, soybeans, spring wheat, and winter wheat. Crop yields were regressed on a vegetation index (NDVI) at three satellite resolution levels (i.e., 1km, 500m, and 250m). Improvements in satellite resolution increased the relative accuracy of the crop yield estimation by improving the quality of the vegetation index (NDVI) measurements. Further improvements in yield estimation accuracy may be expected in the future as satellite resolution improves.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| 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".