Abstract
Purpose: Hail risk management is essential for successful farm management in German fruit production, particularly because hail events and associated losses have increased in recent years. The purpose of this paper is to conduct a detailed risk analysis comparing different strategies to manage hail risk, taking into account farmers’ risk aversion and farm-specific conditions. Design/methodology/approach: Within an expected utility framework, two different strategies for managing hail risk are compared: one belonging to the group of financial instruments (hail insurance) and the other to the group of technical instruments (anti-hail net). A unique data set comprising a ten-year time series of orchard-specific hail damage and hail insurance data is used. Findings: For orchards with low local hail risk and low yield potential, not using hail risk mitigation is most efficient. For orchards with high local hail risk and high yield potential, anti-hail nets provide the highest certainty equivalents. For orchards with high local risk, but low yield potential, hail insurance is most efficient. For orchards, with low local risk, but high yield potential, the certainty equivalents are higher for anti-hail net, when the farmer is risk neutral or slightly risk-averse. With increasing risk aversion, hail insurance is most efficient, which can be explained by the greater degree of the instrument’s flexibility. Originality/value: The novelty of the study lies in the direct comparison of the risk effects of anti-hail nets and hail insurance in fruit production.
| Original language | English |
|---|---|
| Pages (from-to) | 532-550 |
| Number of pages | 19 |
| Journal | Agricultural Finance Review |
| Volume | 78 |
| Issue number | 5 |
| DOIs | |
| State | Published - 17 Sep 2018 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 2 Zero Hunger
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SDG 13 Climate Action
Keywords
- Anti-hail net
- Climate change
- Expected utility
- Hail insurance
- Historical simulation
- Risk management
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