Hedging weather risk for corn production in Northeastern China
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose – The paper analyzes the hedging efficiency of weather-indexed insurance for corn production in Northeast of China. The purpose of this paper is to identify the potential weather variables that impact corn yields and to analyze the efficiency of weather-indexed insurance under varying thresholds for payouts (strike values). Design/methodology/approach – Statistical relationships between climate variables and crop yields are used to construct weather-indexed insurance that enable a farmer to hedge against adverse precipitation outcomes. Mean root square loss is used to compare the efficiency of various weather products. Findings – Based on efficiency comparisons, it turns out that in some, but not all circumstances, cumulative rainfall (CR) insurance can be used to hedge weather risk. When CR explains one-third or more of the variation in corn yields, a hedge can offset the revenue loss caused by the corresponding weather risk; but when it explains much less of the yield variation, it is inefficient for hedgers to buy weather insurance. If CR explains variation in crop yields, it is increasingly efficient to employ CR-indexed insurance as strike values decline for put options or increase for call options. Practical implications – The paper provides a method for calculating the premium for an insurance product that provides a payout if CR in a growing season is too low. Originality/value – This research is important because it illustrates the potential benefits of using weather insurance as an agricultural risk management strategy in China.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it