Multivariate Copula Modeling for Improving Agricultural Risk Assessment under Climate Variability
Bibliographic record
Abstract
Agricultural production is highly vulnerable to both short-term extreme weather events and long-term climate variability and change. These impacts propagate further and result in socioeconomic changes affecting farmers, insurers, and other stakeholders across agricultural supply chains. As a result, the most recent challenges in addressing resiliency and sustainability at the face of climate change require development of innovative multivariate methods for quantifying crop yield risk driven by factors that are strongly spatially and temporally dependent. Copulas offer a systematic solution to tackle this spatio-temporal uncertainty quantification problem. However, utility of copulas in agricultural risk assessment and insurance remains largely under-explored. We introduce multivariate copula modeling (MCM) for capturing yield-climate dependence and evaluate its utility by benchmarking its performance on a multi-scale yield-climate dataset against state-of-the-art competing model-based approaches. MCM is found to outperform traditional statistical approaches and better explain complex dependence structure over time and space between crop yield and climate. Our findings highlight the benefits of MCM for reducing basis risk and improving the robustness of insurance premium rate-making. Address for Correspondence: Marwah.Soliman@utdallas.edu
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".