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An EPIC model-based vulnerability assessment of wheat subject to drought

Author

Listed:
  • Yaojie Yue
  • Jian Li
  • Xinyue Ye
  • Zhiqiang Wang
  • A-Xing Zhu
  • Jing-ai Wang

Abstract

This paper presents a regionalized vulnerability curve-building approach to vulnerability and risk assessment of wheat subjected to drought that uses the Environmental Policy Integrated Climate (EPIC) model and statistical analysis. We defined wheat vulnerability as the degree to which a wheat production system is likely to experience yield loss due to a perturbation or drought hazard. Wheat vulnerability in a given region is thus the yield loss divided by the drought hazard index (DHI). By simulating a variety of wheat yield losses and associated DHIs, wheat drought vulnerability curves can be developed. We propose that agricultural systems be considered uniform within each wheat-planting region and different in different regions, according to territorial differentiation, when regionalized vulnerability curves are built. Based on this principle, a detailed regional crop calendar was improved, and optimized wheat varieties were refined that can differentiate agricultural systems within wheat-planting regions. The crop calendar was improved based on the assumption that local farmers have perfect knowledge in selecting sowing and harvesting dates. The wheat varieties were optimized by adjusting the genetic parameters of wheat in the EPIC model using the Shuffled Complex Evolution algorithm–University of Arizona (SCE-UA) method. Based on these improvements and innovations, the precision of most vulnerability curves was improved, and the curves were compared favorably to those observed in previous studies related to differences in the genetic character of wheat, the crop calendar, environmental conditions, and other relevant factors. Differences within each region were smaller than differences between regions. More detailed wheat vulnerability curves allow for the assessment of expected wheat yield loss and also allow for a high level of precision in an evaluation, at a variety of scales, of risk of wheat subject to drought. The proposed approach to building regionalized vulnerability curves has the potential to be the basis for crop drought vulnerability curves in different geographical areas at multiple scales. Copyright Springer Science+Business Media Dordrecht 2015

Suggested Citation

  • Yaojie Yue & Jian Li & Xinyue Ye & Zhiqiang Wang & A-Xing Zhu & Jing-ai Wang, 2015. "An EPIC model-based vulnerability assessment of wheat subject to drought," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 78(3), pages 1629-1652, September.
    • Handle: RePEc:spr:nathaz:v:78:y:2015:i:3:p:1629-1652
    DOI: 10.1007/s11069-015-1793-8
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    References listed on IDEAS

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    1. Olga Wilhelmi & Donald Wilhite, 2002. "Assessing Vulnerability to Agricultural Drought: A Nebraska Case Study," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 25(1), pages 37-58, January.
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    6. Zhiqiang Wang & Fei He & Weihua Fang & Yongfeng Liao, 2013. "Assessment of physical vulnerability to agricultural drought in China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 67(2), pages 645-657, June.
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    Cited by:

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    2. Zhu, Xiufang & Xu, Kun & Liu, Ying & Guo, Rui & Chen, Lingyi, 2021. "Assessing the vulnerability and risk of maize to drought in China based on the AquaCrop model," Agricultural Systems, Elsevier, vol. 189(C).
    3. Wang, Zhiqiang & Ye, Li & Jiang, Jingyi & Fan, Yida & Zhang, Xiaoran, 2022. "Review of application of EPIC crop growth model," Ecological Modelling, Elsevier, vol. 467(C).
    4. Beatrice Monteleone & Iolanda Borzí & Brunella Bonaccorso & Mario Martina, 2023. "Quantifying crop vulnerability to weather-related extreme events and climate change through vulnerability curves," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 116(3), pages 2761-2796, April.
    5. Yaojie Yue & Yao Zhou & Jing’ai Wang & Xinyue Ye, 2016. "Assessing Wheat Frost Risk with the Support of GIS: An Approach Coupling a Growing Season Meteorological Index and a Hybrid Fuzzy Neural Network Model," Sustainability, MDPI, vol. 8(12), pages 1-21, December.
    6. Yaojie Yue & Lin Wang & Jian Li & A-xing Zhu, 2018. "An EPIC model-based wheat drought risk assessment using new climate scenarios in China," Climatic Change, Springer, vol. 147(3), pages 539-553, April.
    7. Peng Su & Shiqi Li & Jing’ai Wang & Fenggui Liu, 2021. "Vulnerability Assessment of Maize Yield Affected by Precipitation Fluctuations: A Northeastern United States Case Study," Land, MDPI, vol. 10(11), pages 1-15, November.
    8. Kieu N. Le & Manoj K. Jha & Jaehak Jeong & Philip W. Gassman & Manuel R. Reyes & Luca Doro & Dat Q. Tran & Lyda Hok, 2018. "Evaluation of Long-Term SOC and Crop Productivity within Conservation Systems Using GFDL CM2.1 and EPIC," Sustainability, MDPI, vol. 10(8), pages 1-17, July.
    9. Huifang Sun & Yaoguo Dang & Wenxin Mao, 2019. "Identifying key factors of regional agricultural drought vulnerability using a panel data grey combined method," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 98(2), pages 621-642, September.
    10. Monteleone, Beatrice & Borzí, Iolanda & Arosio, Marcello & Cesarini, Luigi & Bonaccorso, Brunella & Martina, Mario, 2023. "Modelling the response of wheat yield to stage-specific water stress in the Po Plain," Agricultural Water Management, Elsevier, vol. 287(C).

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