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The optimal drought index for designing weather index insurance

Author

Listed:
  • Janic Bucheli
  • Tobias Dalhaus
  • Robert Finger

Abstract

Climate change increases the need for better insurance solutions that enable farmers to cope with drought risks. We design weather index insurance using drought indices based on precipitation, soil moisture and evapotranspiration as underlying drought index and compare their risk-reducing potential for winter wheat producers in Eastern Germany. In general, we find that all drought indices can reduce financial risk exposure. However, the largest risk reduction can be achieved if the underlying drought index is tailored individually for each farm. This implies that insurers should offer insurance with farm-specific underlying drought index.

Suggested Citation

  • Janic Bucheli & Tobias Dalhaus & Robert Finger, 2021. "The optimal drought index for designing weather index insurance," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 48(3), pages 573-597.
    • Handle: RePEc:oup:erevae:v:48:y:2021:i:3:p:573-597.
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    File URL: http://hdl.handle.net/10.1093/erae/jbaa014
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    Citations

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    Cited by:

    1. Mengmeng Qiang & Manhong Shen & Guanjun Xia, 2023. "The effectiveness of weather index insurance in managing mariculture production risk," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 67(2), pages 245-262, April.
    2. Kleiman, Rachel M. & Characklis, Gregory W. & Kern, Jordan D., 2022. "Managing weather- and market price-related financial risks in algal biofuel production," Renewable Energy, Elsevier, vol. 200(C), pages 111-124.
    3. Ruggiero Rippo & Simone Cerroni, 2023. "Farmers' participation in the Income Stabilisation Tool: Evidence from the apple sector in Italy," Journal of Agricultural Economics, Wiley Blackwell, vol. 74(1), pages 273-294, February.
    4. Bucheli, Janic & Dalhaus, Tobias & Finger, Robert, 2022. "Temperature effects on crop yields in heat index insurance," Food Policy, Elsevier, vol. 107(C).
    5. Nordmeyer, Eike Florenz & Danne, Michael & Musshoff, Oliver, 2023. "Can satellite-retrieved data increase farmers' willingness to insure against drought? – Insights from Germany," Agricultural Systems, Elsevier, vol. 211(C).
    6. Nordmeyer, Eike Florenz & Danne, Michael & Mußhoff, Oliver, 2022. "Farmers' Preferences for Satellite-based and Precipitation-based Index Insurance: Insights from Germany," 62nd Annual Conference, Stuttgart, Germany, September 7-9, 2022 329606, German Association of Agricultural Economists (GEWISOLA).
    7. Willemijn Vroege & Robert Finger, 2020. "Insuring Weather Risks in European Agriculture," EuroChoices, The Agricultural Economics Society, vol. 19(2), pages 54-62, August.
    8. Arthur Charpentier & Molly James & Hani Ali, 2021. "Predicting Drought and Subsidence Risks in France," Papers 2107.07668, arXiv.org.
    9. Benson K Kenduiywo & Michael R Carter & Aniruddha Ghosh & Robert J Hijmans, 2021. "Evaluating the quality of remote sensing products for agricultural index insurance," PLOS ONE, Public Library of Science, vol. 16(10), pages 1-24, October.
    10. Tappi, Marco & Carucci, Federica & Gatta, Giuseppe & Giuliani, Marcella Michela & Lamonaca, Emilia & Santeramo, Fabio Gaetano, 2023. "Temporal and design approaches and yield-weather relationships," MPRA Paper 117488, University Library of Munich, Germany.
    11. Matthieu Stigler & David Lobell, 2021. "Optimal index insurance and basis risk decomposition: an application to Kenya," Papers 2111.08601, arXiv.org, revised Mar 2023.
    12. Schmitt, Jonas & Offermann, Frank & Söder, Mareike & Frühauf, Cathleen & Finger, Robert, 2022. "Extreme weather events cause significant crop yield losses at the farm level in German agriculture," Food Policy, Elsevier, vol. 112(C).
    13. Schmidt, Lorenz & Odening, Martin & Schlanstein, Johann & Ritter, Matthias, 2022. "Exploring the weather-yield nexus with artificial neural networks," Agricultural Systems, Elsevier, vol. 196(C).

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