Agricultural Risk Modeling Challenges in China: Probabilistic Modeling of Rice Losses in Hunan Province

Pane Stojanovski; Weimin Dong; Ming Wang; Tao Ye; Shuangcai Li; Christian P. Mortgat

doi:10.1007/s13753-015-0071-4

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Article Navigation > International Journal of Disaster Risk Science > 2015 > 6(4): 335-346

Pane Stojanovski, Weimin Dong, Ming Wang, Tao Ye, Shuangcai Li, Christian P. Mortgat. Agricultural Risk Modeling Challenges in China: Probabilistic Modeling of Rice Losses in Hunan Province[J]. International Journal of Disaster Risk Science, 2015, 6(4): 335-346. doi: 10.1007/s13753-015-0071-4

Citation:

Pane Stojanovski, Weimin Dong, Ming Wang, Tao Ye, Shuangcai Li, Christian P. Mortgat. Agricultural Risk Modeling Challenges in China: Probabilistic Modeling of Rice Losses in Hunan Province[J]. International Journal of Disaster Risk Science, 2015, 6(4): 335-346. doi: 10.1007/s13753-015-0071-4

Citation:

Pane Stojanovski, Weimin Dong, Ming Wang, Tao Ye, Shuangcai Li, Christian P. Mortgat. Agricultural Risk Modeling Challenges in China: Probabilistic Modeling of Rice Losses in Hunan Province[J]. International Journal of Disaster Risk Science, 2015, 6(4): 335-346. doi: 10.1007/s13753-015-0071-4

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Agricultural Risk Modeling Challenges in China: Probabilistic Modeling of Rice Losses in Hunan Province

doi: 10.1007/s13753-015-0071-4

1 CAT Risk Research LLC, Cupertino, CA 95014, USA;
2 Institute for Catastrophe Risk Management, Nanyang Technological University, Singapore 639978, Singapore;
3 Risk Management Solutions, Inc. (RMS), Newark, CA 94560, USA;
4 Academy of Disaster Reduction and Emergency Management, Ministry of Civil Affairs and Ministry of Education of China, Beijing Normal University, Beijing 100875, China

Available Online: 2021-04-26

Abstract

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.
- Agricultural risk insurance,
- Agricultural risk modeling,
- Burn yield analysis,
- Catastrophe risk,
- China,
- Simulated weather crop index

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References(14)

References

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