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Title: Agricultural risk modeling challenges in China : probabilistic modeling of rice losses in Hunan province
Authors: Stojanovski, Pane
Dong, Weimin
Wang, Ming
Ye, Tao
Li, Shuangcai
Mortgat, Christian P.
Keywords: Catastrophe Risk
Agricultural Risk Modeling
Issue Date: 2015
Source: Stojanovski, P., Dong, W., Wang, M., Ye, T., Li, S., & Mortgat, C. P. (2015). Agricultural risk modeling challenges in China : probabilistic modeling of rice losses in Hunan province. International Journal of Disaster Risk Science, 6(4), 335-346. doi:10.1007/s13753-015-0071-4
Series/Report no.: International Journal of Disaster Risk Science
Abstract: This article summarizes a joint research project undertaken under the Risk Management Solutions, Inc. (RMS) banner to investigate some of the possible approaches for agricultural risk modeling in China. Two modeling approaches were investigated—the simulated weather crop index and the burn yield analysis approach. The study was limited to Hunan Province and a single crop—rice. Both modeling approaches were dealt with probabilistically and were able to produce probabilistic risk metrics. Illustrative model outputs are also presented. The article discusses the robustness of the modeling approaches and their dependence on the availability, access to, and quality of weather and yield data. We offer our perspective on the requirements for models and platforms for agricultural risk quantification in China in order to respond to the needs of all stakeholders in agricultural risk transfer.
ISSN: 2095-0055
DOI: 10.1007/s13753-015-0071-4
Rights: © 2015 The Author(s). This article is published with open access at This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Fulltext Permission: open
Fulltext Availability: With Fulltext
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