How Drought Affects Agricultural Insurance Policies: The Case of Italy
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
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.
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 | 0.001 | 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