IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v8y2016i12p1308-d85030.html
   My bibliography  Save this article

Assessing Wheat Frost Risk with the Support of GIS: An Approach Coupling a Growing Season Meteorological Index and a Hybrid Fuzzy Neural Network Model

Author

Listed:
  • Yaojie Yue

    (School of Geography, Beijing Normal University, Beijing 100875, China
    State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China)

  • Yao Zhou

    (Department of Geography, University of Florida, Gainesville, FL 32611, USA)

  • Jing’ai Wang

    (School of Geography, Beijing Normal University, Beijing 100875, China
    State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China)

  • Xinyue Ye

    (Department of Geography, Kent State University, Kent, OH 44242, USA)

Abstract

Crop frost, one kind of agro-meteorological disaster, often causes significant loss to agriculture. Thus, evaluating the risk of wheat frost aids scientific response to such disasters, which will ultimately promote food security. Therefore, this paper aims to propose an integrated risk assessment model of wheat frost, based on meteorological data and a hybrid fuzzy neural network model, taking China as an example. With the support of a geographic information system (GIS), a comprehensive method was put forward. Firstly, threshold temperatures of wheat frost at three growth stages were proposed, referring to phenology in different wheat growing areas and the meteorological standard of Degree of Crop Frost Damage (QX/T 88-2008). Secondly, a vulnerability curve illustrating the relationship between frost hazard intensity and wheat yield loss was worked out using hybrid fuzzy neural network model. Finally, the wheat frost risk was assessed in China. Results show that our proposed threshold temperatures are more suitable than using 0 °C in revealing the spatial pattern of frost occurrence, and hybrid fuzzy neural network model can further improve the accuracy of the vulnerability curve of wheat subject to frost with limited historical hazard records. Both these advantages ensure the precision of wheat frost risk assessment. In China, frost widely distributes in 85.00% of the total winter wheat planting area, but mainly to the north of 35°N; the southern boundary of wheat frost has moved northward, potentially because of the warming climate. There is a significant trend that suggests high risk areas will enlarge and gradually expand to the south, with the risk levels increasing from a return period of 2 years to 20 years. Among all wheat frost risk levels, the regions with loss rate ranges from 35.00% to 45.00% account for the largest area proportion, ranging from 58.60% to 63.27%. We argue that for wheat and other frost-affected crops, it is necessary to take the risk level, physical exposure, and growth stages of crops into consideration together for frost disaster risk prevention planning.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:8:y:2016:i:12:p:1308-:d:85030
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/8/12/1308/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/8/12/1308/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Vera Potop & Pavel Zahraniček & Luboš Türkott & Petr Štěpánek & Josef Soukup, 2014. "Risk occurrences of damaging frosts during the growing season of vegetables in the Elbe River lowland, the Czech Republic," 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. 71(1), pages 1-19, March.
    2. 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.
    3. Otar Varazanashvili & Nino Tsereteli & Avtandil Amiranashvili & Emil Tsereteli & Elizbar Elizbarashvili & Jemal Dolidze & Lado Qaldani & Manana Saluqvadze & Shota Adamia & Nika Arevadze & Aleksandre G, 2012. "Vulnerability, hazards and multiple risk assessment for Georgia," 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. 64(3), pages 2021-2056, December.
    4. Maxx Dilley & Robert S. Chen & Uwe Deichmann & Arthur L. Lerner-Lam & Margaret Arnold, 2005. "Natural Disaster Hotspots: A Global Risk Analysis," World Bank Publications - Books, The World Bank Group, number 7376.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jie Xu & Suri Guga & Guangzhi Rong & Dao Riao & Xingpeng Liu & Kaiwei Li & Jiquan Zhang, 2021. "Estimation of Frost Hazard for Tea Tree in Zhejiang Province Based on Machine Learning," Agriculture, MDPI, vol. 11(7), pages 1-16, June.
    2. Chin-Ling Lee & Robert Strong & Kim E. Dooley, 2021. "Analyzing Precision Agriculture Adoption across the Globe: A Systematic Review of Scholarship from 1999–2020," Sustainability, MDPI, vol. 13(18), pages 1-15, September.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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).
    2. 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.
    3. Xiaobing Yu & Hong Chen & Chenliang Li, 2019. "Evaluate Typhoon Disasters in 21st Century Maritime Silk Road by Super-Efficiency DEA," IJERPH, MDPI, vol. 16(9), pages 1-10, May.
    4. Nicolás Bronfman & Pamela Cisternas & Esperanza López-Vázquez & Luis Cifuentes, 2016. "Trust and risk perception of natural hazards: implications for risk preparedness in Chile," 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. 81(1), pages 307-327, March.
    5. Dapeng Huang & Renhe Zhang & Zhiguo Huo & Fei Mao & Youhao E & Wei Zheng, 2012. "An assessment of multidimensional flood vulnerability at the provincial scale in China based on the DEA 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. 64(2), pages 1575-1586, November.
    6. Viet-Ha Nhu & Ataollah Shirzadi & Himan Shahabi & Sushant K. Singh & Nadhir Al-Ansari & John J. Clague & Abolfazl Jaafari & Wei Chen & Shaghayegh Miraki & Jie Dou & Chinh Luu & Krzysztof Górski & Binh, 2020. "Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms," IJERPH, MDPI, vol. 17(8), pages 1-30, April.
    7. Aubin VIGNOBOUL, 2022. "The winds of inequalities: How hurricanes impact inequalities at the macro level?," LEO Working Papers / DR LEO 2986, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
    8. Jun Wang & Zhenlou Chen & Shiyuan Xu & Beibei Hu, 2013. "Medium-scale natural disaster risk scenario analysis: a case study of Pingyang County, Wenzhou, 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. 66(2), pages 1205-1220, March.
    9. Tsegaye Tadesse & Menghestab Haile & Gabriel Senay & Brian D. Wardlow & Cody L. Knutson, 2008. "The need for integration of drought monitoring tools for proactive food security management in sub‐Saharan Africa," Natural Resources Forum, Blackwell Publishing, vol. 32(4), pages 265-279, November.
    10. Jolanta Kryspin-Watson & John Pollner & Sonja Nieuwejaar, 2008. "Climate Change Adaptation in Europe and Central Asia," World Bank Publications - Reports 25985, The World Bank Group.
    11. Fatemeh Jalayer & Raffaele Risi & Francesco Paola & Maurizio Giugni & Gaetano Manfredi & Paolo Gasparini & Maria Topa & Nebyou Yonas & Kumelachew Yeshitela & Alemu Nebebe & Gina Cavan & Sarah Lindley , 2014. "Probabilistic GIS-based method for delineation of urban flooding risk hotspots," 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. 73(2), pages 975-1001, September.
    12. Faraz S. Tehrani & Michele Calvello & Zhongqiang Liu & Limin Zhang & Suzanne Lacasse, 2022. "Machine learning and landslide studies: recent advances and applications," 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. 114(2), pages 1197-1245, November.
    13. Nisar Ali Shah & Muhammad Shafique & Muhammad Ishfaq & Kamil Faisal & Mark Van der Meijde, 2023. "Integrated Approach for Landslide Risk Assessment Using Geoinformation Tools and Field Data in Hindukush Mountain Ranges, Northern Pakistan," Sustainability, MDPI, vol. 15(4), pages 1-21, February.
    14. Thilini Mahanama & Abootaleb Shirvani & Svetlozar Rachev, 2022. "A Natural Disasters Index," Environmental Economics and Policy Studies, Springer;Society for Environmental Economics and Policy Studies - SEEPS, vol. 24(2), pages 263-284, April.
    15. Stefan Kienberger & Thomas Blaschke & Rukhe Zaidi, 2013. "A framework for spatio-temporal scales and concepts from different disciplines: the ‘vulnerability cube’," 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. 68(3), pages 1343-1369, September.
    16. Mohammed M. Al-Humaiqani & Sami G. Al-Ghamdi, 2023. "Assessing the Built Environment’s Reflectivity, Flexibility, Resourcefulness, and Rapidity Resilience Qualities against Climate Change Impacts from the Perspective of Different Stakeholders," Sustainability, MDPI, vol. 15(6), pages 1-30, March.
    17. Chia-Lee Yang & Benjamin J. C. Yuan & Chi-Yo Huang, 2015. "Key Determinant Derivations for Information Technology Disaster Recovery Site Selection by the Multi-Criterion Decision Making Method," Sustainability, MDPI, vol. 7(5), pages 1-40, May.
    18. Jing Wang & Feng Fang & Qiang Zhang & Jinsong Wang & Yubi Yao & Wei Wang, 2016. "Risk evaluation of agricultural disaster impacts on food production in southern China by probability density 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. 83(3), pages 1605-1634, September.
    19. Yong SHI, 2018. "Assessment of Agricultural Vulnerability to Floods in Shanghai by the DEA Method," Chinese Journal of Urban and Environmental Studies (CJUES), World Scientific Publishing Co. Pte. Ltd., vol. 6(01), pages 1-11, March.
    20. Octavio Rojas & María Mardones & Carolina Martínez & Luis Flores & Katia Sáez & Alberto Araneda, 2018. "Flooding in Central Chile: Implications of Tides and Sea Level Increase in the 21st Century," Sustainability, MDPI, vol. 10(12), pages 1-17, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:8:y:2016:i:12:p:1308-:d:85030. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.