NDLI: Overcoming basis risk in agricultural index insurance using crop simulation modeling and satellite crop phenology

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Overcoming basis risk in agricultural index insurance using crop simulation modeling and satellite crop phenology

Content Provider	Consultative Group on International Agricultural Research (CGIAR)
Author	Afshar, Mehdi H. Foster, Tim Higginbottom, Thomas P. Parkes, Ben Hufkens, Koen Mansabdar, Sanjay Ceballos, Francisco Kramer, Berber
Organization	IFPRI - Markets, Trade, and Institutions Division
Organization	IFPRI - Picture-Based Insurance
Spatial Coverage	India [IN]
Description	Extreme weather causes substantial damage to livelihoods of smallholder farmers globally and are projected to become more frequent in the coming decades as a result of climate change. Index insurance can theoretically help farmers to adapt and mitigate the risks posed by extreme weather events, providing a financial safety net in the event of crop damage or harvest failure. However, uptake of index insurance in practice has lagged far behind expectations. A key reason is that many existing index insurance products suffer from high levels of basis risk, where insurance payouts correlate poorly with actual crop losses due to deficiencies in the underlying index relationship, contract structure or data used to trigger insurance payouts to farmers.
Sponsorship	Foreign, Commonwealth and Development Office, United Kingdom Government of India Ministry of Agriculture and Farmers Welfare Department of Agriculture Cooperation and Farmers Welfare Mahalanobis National Crop Forecast Centre Natural Environment Research Council
Related Links	https://cgspace.cgiar.org/items/459714d2-7fc6-4fe7-a4b5-7208dbb628ec
File Format	PDF
DOI	10.5194/egusphere-egu21-9534
Language	English
Publisher	EGU General Assembly
Access Restriction	Open
Rights License	CC-BY-4.0
Subject Keyword	Policies, Institutions, and Markets Big Data Climate Change, Agriculture and Food Security Insurance Models Data Remote Sensing Leaf Area Index Farmers Crop Yield Yield Forecasting Smallholders Extreme Weather Events Livelihoods Yields Crop Modelling
Content Type	Text
Resource Type	Article
Subject	Agronomy and Crop Science Food Science Plant Science

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