Factors Influencing the Take-Up of Agricultural Insurance and the Entry into the Mutual Fund: A Case Study of the Czech Republic
Bibliographic record
Abstract
The objective of the study was to identify the main factors influencing farmers’ willingness to take up agricultural insurance and participate in a mutual fund for non-insurable risks in the Czech Republic. Responses from 214 representative farms were processed using descriptive statistics, paired t-tests, binary logistic regression, and contingency analysis. The regression model showed the influences of agricultural area, distrust in insurance companies, the probability of losing more than 20% of production, the price of insurance premiums, and having a developed formal strategy on the likelihood of taking up agricultural insurance. Unlike previous empirical studies, this study did not attempt to look at agricultural insurance as an isolated risk management tool but rather to show the interrelationship between farmers’ decisions to join a mutual fund and their choice of agricultural insurance. Farmers expect most agricultural production risks to become significantly more important. With the ongoing economic crisis in the EU, there is growing pressure to reduce ad hoc public spending on coverage of non-insurable risks and to seek alternative solutions. The study also shows the need for a holistic approach to the design of risk management support systems in EU countries.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".