How Drought Affects Agricultural Insurance Policies: The Case of Italy
Bibliographic record
Abstract
Despite their growing intensity and the enormous costs, adverse meteorological events are still perceived as “exceptional”. Among the adverse weather events, the management of drought risk plays a key role due to the more pressing problem of the scarcity of water resources. In this context, agricultural insurance can represent a financial and risk mitigation tool for farmers. In this perspective, the aims of this study are: (1) to analyze, through a systematic review, the main findings of the scientific literature focused on the empirical and theoretical approach to the relation between adverse weather events in agriculture, risk and insurance; (2) to collect agroclimatic and insurance data for each Italian province for the period 2004-2011, (3) to measure the influence of climatic agroclimatic variables on insurance variables, i.e. Total Premiums, Insured Value and Certificates.The results of the analysis show the significance of the precipitation variable and its negative effect with each insurance dependent variable. The same result can be observed focusing on the effect of minimum temperature on two insurance variables, i.e. Total Premiums and Certificates. Models tested explain a range between 44% and 51% of the variation in our insurance dependent variables.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".