IDEAS home Printed from https://ideas.repec.org/a/spr/nathaz/v83y2016i3d10.1007_s11069-016-2379-9.html
   My bibliography  Save this article

Risk evaluation of agricultural disaster impacts on food production in southern China by probability density method

Author

Listed:
  • Jing Wang

    (China Meteorological Administration
    China Meteorological Administration
    Northwest Regional Climate Center)

  • Feng Fang

    (China Meteorological Administration
    China Meteorological Administration
    Northwest Regional Climate Center)

  • Qiang Zhang

    (China Meteorological Administration
    China Meteorological Administration
    Northwest Regional Climate Center)

  • Jinsong Wang

    (China Meteorological Administration
    China Meteorological Administration
    Northwest Regional Climate Center)

  • Yubi Yao

    (China Meteorological Administration
    China Meteorological Administration
    Northwest Regional Climate Center)

  • Wei Wang

    (China Meteorological Administration
    China Meteorological Administration
    Northwest Regional Climate Center)

Abstract

Meteorological disaster occurred frequently in China and inflicted great losses to agriculture. The strengthening of disaster risk assessment is necessary, which also has important practical significance for reducing the influence of and losses from meteorological disasters. Due to the advantages, risk probability method is adopted. Using the serial provincial crop yield data from 1949 to 2012 and the probability density function algorithm, the meteorological disaster risk of southern China is analyzed. First, the trend yields of various crops are extracted, then the probability density function curves and distribution functions of the relative meteorological yields are constructed and calculated. The probabilities of different yield decrease sections are also estimated. Finally, the risk levels associated with agriculture and various crops suffering from an agricultural disaster are also assessed. The results show that the variations in trend yields can be divided into three stages and it increased greatly from 1962 to 2004. The food, autumn food and summer food yield has increased each year at a mean rate of 48.3, 50.55 and 36.6 kg/ha a. The increasing rates for the trend yields in Yunnan and Guizhou Provinces are slower than the rates for the other provinces. Additionally, the increasing rate in south China is higher than that of southwest China. Additionally, the yields of all food grains and economic crops in southern China have increased each year. Additionally, the relative meteorological yield of the primary crops possessed distinct stochastic, fluctuating features. The relative meteorological yield was reduced year by year for every province. During the early period, the relative meteorological yield clearly fluctuated, but in recent years, this change has been small. The fluctuation extents for different crops or regions exhibited obvious differences. For example, in some regions or during some years, the fluctuant extents of the relative meteorological yields are large, which suggests that the risk level is high during this year and in this area. The rice yield fluctuation at a mean value of 3.7 % is the smallest and is relatively stable. In general, after suffering an agricultural disaster, the yield increase or decrease section for most crops are primarily concentrated in an interval from −10 to 10 %, but for some crops or individual areas, the probabilities of large losses are relatively high, which suggests that the risk level in this area is high, and the ability to prevent agricultural disasters there should be improved. Among these provinces, the agricultural risk rate of Guizhou Province reaches 20 %, and this province possesses the highest risk associated with grain production, but it is also the most unstable. The agricultural risks for Sichuan and Yunnan Provinces are lower, and they show a skewed distribution and an increasing production trend. All of these results suggest that the agricultural costs in Guizhou Province are higher than the costs in other provinces. The results also provide a scientific basis for agricultural production and for government decision-making in relation to disaster prevention and mitigation.

Suggested Citation

  • 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.
  • Handle: RePEc:spr:nathaz:v:83:y:2016:i:3:d:10.1007_s11069-016-2379-9
    DOI: 10.1007/s11069-016-2379-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11069-016-2379-9
    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/s11069-016-2379-9?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. Paul Gallagher, 1987. "U.S. Soybean Yields: Estimation and Forecasting with Nonsymmetric Disturbances," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 69(4), pages 796-803.
    2. Richard E. Just & Quinn Weninger, 1999. "Are Crop Yields Normally Distributed?," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 81(2), pages 287-304.
    3. Gallagher, Paul W., 1987. "U.S. Soybean Yields: Estimation and Forecasting with Non-Symmetric Disturbances," Staff General Research Papers Archive 10779, Iowa State University, Department of Economics.
    4. Bruce J. Sherrick & Fabio C. Zanini & Gary D. Schnitkey & Scott H. Irwin, 2004. "Crop Insurance Valuation under Alternative Yield Distributions," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 86(2), pages 406-419.
    5. A. Cancelliere & G. Mauro & B. Bonaccorso & G. Rossi, 2007. "Drought forecasting using the Standardized Precipitation Index," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 21(5), pages 801-819, May.
    6. 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.
    7. Alan P. Ker & Barry K. Goodwin, 2000. "Nonparametric Estimation of Crop Insurance Rates Revisited," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 82(2), pages 463-478.
    8. L. Feng & C. Huang, 2008. "A Risk Assessment Model of Water Shortage Based on Information Diffusion Technology and its Application in Analyzing Carrying Capacity of Water Resources," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 22(5), pages 621-633, May.
    9. Ardian Harri & Cumhur Erdem & Keith H. Coble & Thomas O. Knight, 2009. "Crop Yield Distributions: A Reconciliation of Previous Research and Statistical Tests for Normality," Review of Agricultural Economics, Agricultural and Applied Economics Association, vol. 31(1), pages 163-182.
    10. McCown, R. L. & Hammer, G. L. & Hargreaves, J. N. G. & Holzworth, D. P. & Freebairn, D. M., 1996. "APSIM: a novel software system for model development, model testing and simulation in agricultural systems research," Agricultural Systems, Elsevier, vol. 50(3), pages 255-271.
    11. Hong Wu & Donald Wilhite, 2004. "An Operational Agricultural Drought Risk Assessment Model for Nebraska, USA," 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. 33(1), pages 1-21, September.
    12. Goodwin, Barry K. & Mahul, Olivier, 2004. "Risk modeling concepts relating to the design and rating of agricultural insurance contracts," Policy Research Working Paper Series 3392, The World Bank.
    13. Barry K. Goodwin & Alan P. Ker, 1998. "Nonparametric Estimation of Crop Yield Distributions: Implications for Rating Group-Risk Crop Insurance Contracts," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 80(1), pages 139-153.
    14. Octavio A. Ramirez & Sukant Misra & James Field, 2003. "Crop-Yield Distributions Revisited," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 85(1), pages 108-120.
    15. Turvey, Calum G. & Zhao, Jinhua, 1999. "Parametric And Non-Parametric Crop Yield Distributions And Their Effects On All-Risk Crop Insurance Premiums," Working Papers 34129, University of Guelph, Department of Food, Agricultural and Resource Economics.
    16. Carl H. Nelson & Paul V. Preckel, 1989. "The Conditional Beta Distribution as a Stochastic Production Function," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 71(2), pages 370-378.
    17. Swati Pandey & A. Pandey & M. Nathawat & Manoj Kumar & N. Mahanti, 2012. "Drought hazard assessment using geoinformatics over parts of Chotanagpur plateau region, Jharkhand, 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. 63(2), pages 279-303, September.
    18. Ralph R. Botts & James N. Boles, 1958. "Use of Normal-Curve Theory in Crop Insurance Ratemaking," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 40(3), pages 733-740.
    19. Richter, G.M. & Semenov, M.A., 2005. "Modelling impacts of climate change on wheat yields in England and Wales: assessing drought risks," Agricultural Systems, Elsevier, vol. 84(1), pages 77-97, April.
    20. 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, December.
    21. 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.
    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. Zhen Shi & Huinan Huang & Yingju Wu & Yung-Ho Chiu & Shijiong Qin, 2020. "Climate Change Impacts on Agricultural Production and Crop Disaster Area in China," IJERPH, MDPI, vol. 17(13), pages 1-23, July.
    2. Xiaobing Yu & Xianrui Yu & Yiqun Lu, 2018. "Evaluation of an Agricultural Meteorological Disaster Based on Multiple Criterion Decision Making and Evolutionary Algorithm," IJERPH, MDPI, vol. 15(4), pages 1-17, March.
    3. Dang Luo & Wenxin Mao & Huifang Sun, 2017. "Risk assessment and analysis of ice disaster in Ning–Meng reach of Yellow River based on a two-phased intelligent model under grey information environment," 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. 88(1), pages 591-610, August.
    4. Wenjuan Hou & Shaohong Wu & Linsheng Yang & Yunhe Yin & Jiangbo Gao & Haoyu Deng & Maowei Wu & Xiaojie Li & Lulu Liu, 2022. "Production–Living–Ecological Risk Assessment and Corresponding Strategies in China’s Provinces under Climate Change Scenario," Land, MDPI, vol. 11(9), pages 1-15, August.
    5. Yu Xiaobing & Li Chenliang & Huo Tongzhao & Ji Zhonghui, 2021. "Information diffusion theory-based approach for the risk assessment of meteorological disasters in the Yangtze River Basin," 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. 107(3), pages 2337-2362, July.

    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. Ozaki, Vitor & Campos, Rogério, 2017. "Reduzindo a Incerteza no Mercado de Seguros: Uma Abordagem via Informações de Sensoriamento Remoto e Atuária," Revista Brasileira de Economia - RBE, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil), vol. 71(4), December.
    2. Vitor A. Ozaki & Sujit K. Ghosh & Barry K. Goodwin & Ricardo Shirota, 2008. "Spatio-Temporal Modeling of Agricultural Yield Data with an Application to Pricing Crop Insurance Contracts," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 90(4), pages 951-961.
    3. Qiujie Zheng & H. Holly Wang & Qing Hua Shi, 2014. "Estimating bivariate yield distributions and crop insurance premiums using nonparametric methods," Applied Economics, Taylor & Francis Journals, vol. 46(18), pages 2108-2118, June.
    4. Tor N. Tolhurst & Alan P. Ker, 2015. "On Technological Change in Crop Yields," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 97(1), pages 137-158.
    5. A Ford Ramsey, 2020. "Probability Distributions of Crop Yields: A Bayesian Spatial Quantile Regression Approach," American Journal of Agricultural Economics, John Wiley & Sons, vol. 102(1), pages 220-239, January.
    6. Christopher N. Boyer & B. Wade Brorsen & Emmanuel Tumusiime, 2015. "Modeling skewness with the linear stochastic plateau model to determine optimal nitrogen rates," Agricultural Economics, International Association of Agricultural Economists, vol. 46(1), pages 1-10, January.
    7. Lanoue, Christopher & Sherrick, Bruce J. & Woodard, Joshua D. & Paulson, Nicholas D., 2010. "Evaluating Yield Models for Crop Insurance Rating," 2010 Annual Meeting, July 25-27, 2010, Denver, Colorado 61761, Agricultural and Applied Economics Association.
    8. Chen, Shu-Ling & Miranda, Mario J., 2006. "Modeling Yield Distribution In High Risk Counties: Application To Texas Upland Cotton," 2006 Annual meeting, July 23-26, Long Beach, CA 21392, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    9. Jesse B. Tack & David Ubilava, 2015. "Climate and agricultural risk: measuring the effect of ENSO on U.S. crop insurance," Agricultural Economics, International Association of Agricultural Economists, vol. 46(2), pages 245-257, March.
    10. Li, Lisha, 2015. "Three essays on crop yield, crop insurance and climate change," ISU General Staff Papers 201501010800005371, Iowa State University, Department of Economics.
    11. Jesse Tack & David Ubilava, 2013. "The effect of El Niño Southern Oscillation on U.S. corn production and downside risk," Climatic Change, Springer, vol. 121(4), pages 689-700, December.
    12. Yong Liu & Alan P. Ker, 2021. "Simultaneous borrowing of information across space and time for pricing insurance contracts: An application to rating crop insurance policies," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 88(1), pages 231-257, March.
    13. Yaling Li & Fujin Yi & Yanjun Wang & Richard Gudaj, 2019. "The Value of El Niño-Southern Oscillation Forecasts to China’s Agriculture," Sustainability, MDPI, vol. 11(15), pages 1-23, August.
    14. Ghahremanzadeh, Mohammad & Mohammadrezaei, Rassul & Dashti, Ghader & Ainollahi, Moharram, 2018. "Designing a whole-farm revenue insurance for agricultural crops in Zanjan province of Iran," Economia Agraria y Recursos Naturales, Spanish Association of Agricultural Economists, vol. 17(02), January.
    15. Yu, Tian, 2011. "Three essays on weather and crop yield," ISU General Staff Papers 201101010800002976, Iowa State University, Department of Economics.
    16. Hennessy, David A., 2009. "Crop Yield Skewness and the Normal Distribution," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 34(1), pages 1-19, April.
    17. Gerlt, Scott & Westhoff, Patrick, 2013. "Analysis of the Supplemental Coverage Option," 2013 AAEA: Crop Insurance and the Farm Bill Symposium 156704, Agricultural and Applied Economics Association.
    18. Jesse Tack & Ardian Harri & Keith Coble, 2012. "More than Mean Effects: Modeling the Effect of Climate on the Higher Order Moments of Crop Yields," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 94(5), pages 1037-1054.
    19. Agarwal, Sandip Kumar, 2017. "Subjective beliefs and decision making under uncertainty in the field," ISU General Staff Papers 201701010800006248, Iowa State University, Department of Economics.
    20. Gerlt, Scott & Westhoff, Patrick, "undated". "Comparison of County ARC and SCO," 2014 AAEA: Crop Insurance and the 2014 Farm Bill Symposium: Implementing Change in U.S. Agricultural Policy, October 8-9, 2014, Louisville, KY 184289, Agricultural and Applied Economics Association.

    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:83:y:2016:i:3:d:10.1007_s11069-016-2379-9. 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.