IDEAS home Printed from https://ideas.repec.org/a/spr/nathaz/v82y2016i1d10.1007_s11069-016-2213-4.html
   My bibliography  Save this article

Assessing agricultural drought vulnerability in the Sanjiang Plain based on an improved projection pursuit model

Author

Listed:
  • Wei Pei

    (Northeast Agricultural University)

  • Qiang Fu

    (Northeast Agricultural University
    Collaborative Innovation Centre of Promote Grain Production in Heilongjiang Province
    Key Laboratory of Water-Saving Agriculture of Regular Institutions of Higher Education in Heilongjiang Province)

  • Dong Liu

    (Northeast Agricultural University)

  • Tian-xiao Li

    (Northeast Agricultural University)

  • Kun Cheng

    (Northeast Agricultural University)

Abstract

Drought is one of the main natural disasters affecting regional agriculture, and regional agricultural drought vulnerability assessment is necessary to establish regional drought forecast, monitoring, and early warning mechanisms. The results can provide a theoretical basis for the identification of drought hazard and disaster prevention. In this study, the concept of the overall dispersion and local aggregation of projection points was proposed by Friedman and Tukey (IEEE Trans Comput 23:881–890, 1974), and improvements to the projection pursuit model are proposed here by measuring discrete projection points according to the information entropy. This improved model was applied to assess the agricultural drought vulnerability of 18 counties located in the Sanjiang Plain for 4 years (2004, 2007, 2010, and 2013). Information entropy was shown to provide improved measurements in the data discreteness relative to standard deviations, and the cutoff radius was defined between 0 and ln 2, thus allowing the use of the exhaustion method to determine the cutoff radius. The overall agricultural drought vulnerability in the Sanjiang Plain area shows a downward trend over time. The main reason for this result is the reduced regional sensitivity and the increased drought resistance ability each year. Economic development speeds up the urbanization process, decreasing the proportion of agricultural population and the proportion of agricultural GDP each year and increasing the irrigation index, per capita GDP, rural per capita net income and other indicators each year. These developments decrease the sensitivity of the agricultural system, improve the adaptive capacity, and reduce the vulnerability. Spatially, the vulnerability of various regions shows some differences. The vulnerabilities of Hulin, Luobei, Youyi, and Fuyuan are the lowest and showed a downward trend over time. The sensitivities of these regions were also low; the population density, the proportion of agricultural population and other sensitive indicators were significantly smaller than those for other regions. Furthermore, the drought threat is small, the region has many state-owned farms, the economic situation is good, and the drought resistance ability is strong. The vulnerabilities of Baoqing, Muling, Raohe, and Tongjiang are moderate, with high sensitivities but strong adaptive capacity. The vulnerabilities of Yilan, Jidong, Mishan, Fujin, and Boli have changed greatly, mainly due to the rapid economic development in recent years, increasing the agricultural drought resistance. The vulnerabilities of Tangyuan, Suibin, Jixian, Huachuan, and Huanan are the highest, and with little change, these regions are highly sensitive and prone to drought. In addition, the regional economic development level is relatively low, and the agricultural drought resistance is not high.

Suggested Citation

  • Wei Pei & Qiang Fu & Dong Liu & Tian-xiao Li & Kun Cheng, 2016. "Assessing agricultural drought vulnerability in the Sanjiang Plain based on an improved projection pursuit model," 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. 82(1), pages 683-701, May.
  • Handle: RePEc:spr:nathaz:v:82:y:2016:i:1:d:10.1007_s11069-016-2213-4
    DOI: 10.1007/s11069-016-2213-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11069-016-2213-4
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s11069-016-2213-4?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. F. Sönmez & Ali Kömüscü & Ayhan Erkan & Ertan Turgu, 2005. "An Analysis of Spatial and Temporal Dimension of Drought Vulnerability in Turkey Using the Standardized Precipitation Index," 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. 35(2), pages 243-264, June.
    2. Jianjun Wu & Bin He & Aifeng Lü & Lei Zhou & Ming Liu & Lin Zhao, 2011. "Quantitative assessment and spatial characteristics analysis of agricultural drought vulnerability 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. 56(3), pages 785-801, March.
    3. Maasoumi, Esfandiar & Racine, Jeff, 2002. "Entropy and predictability of stock market returns," Journal of Econometrics, Elsevier, vol. 107(1-2), pages 291-312, March.
    4. Di Wu & Deng-Hua Yan & Gui-Yu Yang & Xiao-Gang Wang & Wei-Hua Xiao & Hai-Tao Zhang, 2013. "Assessment on agricultural drought vulnerability in the Yellow River basin based on a fuzzy clustering iterative model," 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 919-936, June.
    5. Ebrahimi, Nader & Maasoumi, Esfandiar & Soofi, Ehsan S., 1999. "Ordering univariate distributions by entropy and variance," Journal of Econometrics, Elsevier, vol. 90(2), pages 317-336, June.
    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. Zhuguang Lan & Ming Huang, 2018. "Safety assessment for seawall based on constrained maximum entropy projection pursuit model," 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. 91(3), pages 1165-1178, April.
    2. Kun Cheng & Qiang Fu & Song Cui & Tian-xiao Li & Wei Pei & Dong Liu & Jun Meng, 2017. "Evaluation of the land carrying capacity of major grain-producing areas and the identification of risk factors," 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. 86(1), pages 263-280, March.
    3. Wang, Qiang & Zhan, Lina, 2019. "Assessing the sustainability of the shale gas industry by combining DPSIRM model and RAGA-PP techniques: An empirical analysis of Sichuan and Chongqing, China," Energy, Elsevier, vol. 176(C), pages 353-364.
    4. Dong Liu & Chunlei Liu & Qiang Fu & Tianxiao Li & Muhammad Imran Khan & Song Cui & Muhammad Abrar Faiz, 2018. "Projection Pursuit Evaluation Model of Regional Surface Water Environment Based on Improved Chicken Swarm Optimization Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(4), pages 1325-1342, March.
    5. Yongsheng Jiang & Dong Zhao & Dedong Wang & Yudong Xing, 2019. "Sustainable Performance of Buildings through Modular Prefabrication in the Construction Phase: A Comparative Study," Sustainability, MDPI, vol. 11(20), pages 1-15, October.
    6. Hongpeng Guo & Jia Chen & Chulin Pan, 2021. "Assessment on Agricultural Drought Vulnerability and Spatial Heterogeneity Study in China," IJERPH, MDPI, vol. 18(9), pages 1-17, April.
    7. 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.
    8. Wei Pei & Qiang Fu & Dong Liu & Tianxiao Li & Kun Cheng & Song Cui, 2019. "A Novel Method for Agricultural Drought Risk Assessment," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(6), pages 2033-2047, April.
    9. Abdol Rassoul Zarei & Mohammad Reza Mahmoudi, 2022. "Assessing and Predicting the Vulnerability to Agrometeorological Drought Using the Fuzzy-AHP and Second-order Markov Chain techniques," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(11), pages 4403-4424, 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. Stefania D'Amico, 2004. "Density Estimation and Combination under Model Ambiguity," Computing in Economics and Finance 2004 273, Society for Computational Economics.
    2. Itziar González Tánago & Julia Urquijo & Veit Blauhut & Fermín Villarroya & Lucia De Stefano, 2016. "Learning from experience: a systematic review of assessments of vulnerability 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. 80(2), pages 951-973, January.
    3. 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.
    4. Stefania D'Amico, 2005. "Density selection and combination under model ambiguity: an application to stock returns," Finance and Economics Discussion Series 2005-09, Board of Governors of the Federal Reserve System (U.S.).
    5. Zhaoqi Zeng & Wenxiang Wu & Zhaolei Li & Yang Zhou & Han Huang, 2019. "Quantitative Assessment of Agricultural Drought Risk in Southeast Gansu Province, Northwest China," Sustainability, MDPI, vol. 11(19), pages 1-21, October.
    6. Itziar González Tánago & Julia Urquijo & Veit Blauhut & Fermín Villarroya & Lucia De Stefano, 2016. "Learning from experience: a systematic review of assessments of vulnerability 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. 80(2), pages 951-973, January.
    7. David E. Allen & Michael McAleer & Robert Powell & Abhay K. Singh, 2013. "A Non-Parametric and Entropy Based Analysis of the Relationship between the VIX and S&P 500," JRFM, MDPI, vol. 6(1), pages 1-25, October.
    8. P. Vijaya Kumar & Mohammed Osman & P. K. Mishra, 2019. "Development and application of a new drought severity index for categorizing drought-prone areas: a case study of undivided Andhra Pradesh state, India," 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. 97(2), pages 793-812, June.
    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. Bin He & Jianjun Wu & Aifeng Lü & Xuefeng Cui & Lei Zhou & Ming Liu & Lin Zhao, 2013. "Quantitative assessment and spatial characteristic analysis of agricultural drought risk 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. 66(2), pages 155-166, March.
    11. Gruber, Lutz F. & West, Mike, 2017. "Bayesian online variable selection and scalable multivariate volatility forecasting in simultaneous graphical dynamic linear models," Econometrics and Statistics, Elsevier, vol. 3(C), pages 3-22.
    12. Salois, Matthew & Moss, Charles, 2010. "An Information Approach to the Dynamics in Farm Income: Implications for Farmland Markets," MPRA Paper 26850, University Library of Munich, Germany.
    13. Dionisio, Andreia & Menezes, Rui & Mendes, Diana & Vidigal Da Silva, Jacinto, 2007. "Nonlinear Dynamics Within Macroeconomic Factors And Stock Market In Portugal, 1993-2003," Applied Econometrics and International Development, Euro-American Association of Economic Development, vol. 7(2), pages 57-70.
    14. Zahra Sadat Hosseini & Mahnoosh Moghaddasi & Shahla Paimozd, 2023. "Simultaneous Monitoring of Different Drought Types Using Linear and Nonlinear Combination Approaches," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(3), pages 1125-1151, February.
    15. Javad Bazrafshan & Somayeh Hejabi & Jaber Rahimi, 2014. "Drought Monitoring Using the Multivariate Standardized Precipitation Index (MSPI)," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(4), pages 1045-1060, March.
    16. Shamsuddin Shahid & Houshang Behrawan, 2008. "Drought risk assessment in the western part of Bangladesh," 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. 46(3), pages 391-413, September.
    17. Alexander, Carol & Cordeiro, Gauss M. & Ortega, Edwin M.M. & Sarabia, José María, 2012. "Generalized beta-generated distributions," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1880-1897.
    18. Jin‐Feng Wang & Lian‐Fa Li, 2008. "Improving Tsunami Warning Systems with Remote Sensing and Geographical Information System Input," Risk Analysis, John Wiley & Sons, vol. 28(6), pages 1653-1668, December.
    19. Carol Alexander & Jose Maria Sarabia, 2010. "Endogenizing Model Risk to Quantile Estimates," ICMA Centre Discussion Papers in Finance icma-dp2010-07, Henley Business School, University of Reading.
    20. Kwang-il Choe & Joshua Krausz & Kiseok Nam, 2011. "Technical trading rules for nonlinear dynamics of stock returns: evidence from the G-7 stock markets," Review of Quantitative Finance and Accounting, Springer, vol. 36(3), pages 323-353, April.

    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:spr:nathaz:v:82:y:2016:i:1:d:10.1007_s11069-016-2213-4. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.