IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v11y2019i15p4184-d254304.html
   My bibliography  Save this article

The Value of El Niño-Southern Oscillation Forecasts to China’s Agriculture

Author

Listed:
  • Yaling Li

    (College of Economics and Management, Nanjing Agricultural University, Nanjing 210095, China)

  • Fujin Yi

    (College of Economics and Management, Nanjing Agricultural University, Nanjing 210095, China
    China Center for Food Security Studies, Nanjing Agricultural University, Nanjing 210095, China)

  • Yanjun Wang

    (Institute for Disaster Risk Management (iDRM), Nanjing University of Information Science & Technology, Nanjing 210044, China
    School of Geographical Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China)

  • Richard Gudaj

    (College of Economics and Management, Nanjing Agricultural University, Nanjing 210095, China)

Abstract

This study aims to estimate the value of El Niño-Southern Oscillation (ENSO) forecasting to China’s agricultural sector. This study applies the Weibull distribution to model crop yields under different ENSO phases. Under the framework of Bayesian decision theory, this research pioneers the application of China’s Agricultural Sector Model to translate the yield effects resulting from ENSO variations into economic effects. Results show that ENSO exerts noticeable and heterogeneous effects on crop yields over selected crops across different regions. In addition, ENSO forecasting is useful for farmers’ cropping decisions and positively impacts economic surplus. The findings present that the value of this information is generally positive and rises with improved forecast accuracy, with the value of perfect forecasting estimated to be as substantial as CNY 3168 million. However, the value of ENSO forecasting is relatively small in the context of China’s tremendous agricultural output. This study is the first to evaluate the value of ENSO forecasting to China’s agriculture sector and has critical implications for the promotion of a Chinese ENSO forecast system.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:15:p:4184-:d:254304
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/11/15/4184/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/11/15/4184/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chen, Shu-Ling & Miranda, Mario J., 2004. "Modeling Multivariate Crop Yield Densities With Frequent Extreme Events," 2004 Annual meeting, August 1-4, Denver, CO 19970, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    2. Toshichika Iizumi & Jing-Jia Luo & Andrew J. Challinor & Gen Sakurai & Masayuki Yokozawa & Hirofumi Sakuma & Molly E. Brown & Toshio Yamagata, 2014. "Impacts of El Niño Southern Oscillation on the global yields of major crops," Nature Communications, Nature, vol. 5(1), pages 1-7, September.
    3. 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.
    4. R. M. Adams & S. A. Hamilton & B. A. McCarl, 1986. "The Benefits of Pollution Control: The Case of Ozone and U.S. Agriculture," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 68(4), pages 886-893.
    5. Fujin Yi & Bruce McCarl, 2018. "Increasing the effectiveness of the Chinese grain subsidy: a quantitative analysis," China Agricultural Economic Review, Emerald Group Publishing Limited, vol. 10(4), pages 538-557, August.
    6. 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.
    7. 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.
    8. Chen, Chi-Chung & McCarl, Bruce A., 2000. "The Value Of Enso Information To Agriculture: Consideration Of Event Strength And Trade," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 25(2), pages 1-18, December.
    9. 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.
    10. 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.
    11. Denis Nadolnyak & Dmitry Vedenov & James Novak, 2008. "Information Value of Climate-Based Yield Forecasts in Selecting Optimal Crop Insurance Coverage," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 90(5), pages 1248-1255.
    12. Yi, F. & McCarl, B. & Zhou, X., 2018. "Damages of Surface Ozone: Evidence from Agricultural Sector in China," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 276973, International Association of Agricultural Economists.
    13. Richard E. Howitt, 1995. "Positive Mathematical Programming," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 77(2), pages 329-342.
    14. Bruce A. McCarl & Thomas H. Spreen, 1980. "Price Endogenous Mathematical Programming As a Tool for Sector Analysis," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 62(1), pages 87-102.
    15. Christopher B. Barrett, 1998. "The Value of Imperfect ENSO Forecast Information: Discussion," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 80(5), pages 1109-1112.
    16. Ardian Harri & Keith H. Coble & Alan P. Ker & Barry J. Goodwin, 2011. "Relaxing Heteroscedasticity Assumptions in Area-Yield Crop Insurance Rating," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 93(3), pages 703-713.
    17. 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.
    18. 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.
    19. 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.
    Full references (including those not matched with items on IDEAS)

    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. 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.
    2. Fujin Yi & Mengfei Zhou & Yu Yvette Zhang, 2020. "Value of Incorporating ENSO Forecast in Crop Insurance Programs," American Journal of Agricultural Economics, John Wiley & Sons, vol. 102(2), pages 439-457, March.
    3. 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.
    4. Arora, Gaurav & Agarwal, Sandip K., 2020. "Agricultural input use and index insurance adoption: Concept and evidence," 2020 Annual Meeting, July 26-28, Kansas City, Missouri 304508, Agricultural and Applied Economics Association.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. repec:ags:aaea22:335759 is not listed on IDEAS
    10. 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.
    11. 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.
    12. Li, Lisha, 2015. "Three essays on crop yield, crop insurance and climate change," ISU General Staff Papers 201501010800005371, Iowa State University, Department of Economics.
    13. Ozaki, Vitor Augusto & Olinda, Ricardo & Faria, Priscila Neves & Campos, Rogério Costa, 2014. "Estimation of the Agricultural Probability of Loss: evidence for soybean in Paraná State," Revista de Economia e Sociologia Rural (RESR), Sociedade Brasileira de Economia e Sociologia Rural, vol. 52(1), January.
    14. Yu, Tian, 2011. "Three essays on weather and crop yield," ISU General Staff Papers 201101010800002976, Iowa State University, Department of Economics.
    15. Andrea Bastianin & Alessandro Lanza & Matteo Manera, 2018. "Economic impacts of El Niño southern oscillation: evidence from the Colombian coffee market," Agricultural Economics, International Association of Agricultural Economists, vol. 49(5), pages 623-633, September.
    16. Mason, Chuck & Hayes, Dermot J. & Lence, Sergio H., 2003. "Systemic Risk in U.S. Crop Reinsurance Programs," Staff General Research Papers Archive 1944, Iowa State University, Department of Economics.
    17. Ubilava, David & Orlowski, Jan, 2016. "The Predictive Content of Climate Anomalies for Agricultural Production: Does ENSO Really Matter?," 2016 Annual Meeting, July 31-August 2, Boston, Massachusetts 236281, Agricultural and Applied Economics Association.
    18. 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.
    19. 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.
    20. Ramsey, A., 2018. "Conditional Distributions of Crop Yields: A Bayesian Approach for Characterizing Technological Change," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 277253, International Association of Agricultural Economists.
    21. Ker, Alan. P & Tolhurst, Tor & Liu, Yong, 2015. "Rating Area-yield Crop Insurance Contracts Using Bayesian Model Averaging and Mixture Models," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 205211, 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:gam:jsusta:v:11:y:2019:i:15:p:4184-:d:254304. 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.