Improving regional wheat drought risk assessment for insurance application by integrating scenario-driven crop model, machine learning, and satellite data

Ziyue Li, Zhao Zhang and Lingyan Zhang

Agricultural Systems, 2021, vol. 191, issue C

Abstract: Accurate estimation of yield losses from natural disasters on a regional scale can guide agronomic management and agricultural insurance, transfer disaster risk, and ensure food security. Conventional yield losses, however, mainly depend on historical events, for which detailed records of locations and losses are unavailable.

Keywords: Crop model; Drought vulnerability model; Wheat yield loss; Machine learning; Drought scenario-driven (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:agisys:v:191:y:2021:i:c:s0308521x21000949

DOI: 10.1016/j.agsy.2021.103141

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