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The effect of El Niño Southern Oscillation on U.S. corn production and downside risk

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  • Jesse Tack
  • David Ubilava

Abstract

ENSO teleconnections imply anomalous weather conditions, causing yield shortages, price fluctuations, and civil unrest. We estimate ENSO’s effect on U.S. county-level corn yield distributions and find that temperature and precipitation alone are not sufficient to summarize the effect of global climate on agriculture. We find that acreage-weighted aggregate impacts mask considerable spatial heterogeneity at the county-level for the mean, variance, and downside risk of corn yields. Impacts for mean yields range from − 24 to 33 % for El Niño and − 25 to 36 % for La Niña, with the geographical center of losses shifting from the Eastern to Western corn belt. ENSO’s effect on the variance of crop yields is highly localized and is not representative of a variance-preserving shift. We also find that downside risk impacts are large and spatially correlated across counties. Copyright Springer Science+Business Media Dordrecht 2013

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  • 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.
    • Handle: RePEc:spr:climat:v:121:y:2013:i:4:p:689-700
    DOI: 10.1007/s10584-013-0918-x
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    Cited by:

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    2. 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.
    3. 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.
    4. 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.
    5. Hildegart Ahumada & Magdalena Cornejo, 2019. "How econometrics can help us understand the effects of climate change on crop yields: the case of soybeans," School of Government Working Papers wp_gob_2019_2, Universidad Torcuato Di Tella.
    6. 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.
    7. Sinha Roy,Sutirtha & Van Der Weide,Roy, 2022. "Poverty in India Has Declined over the Last Decade But Not As Much As Previously Thought," Policy Research Working Paper Series 9994, The World Bank.
    8. Massimo Peri, 2017. "Climate variability and the volatility of global maize and soybean prices," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 9(4), pages 673-683, August.
    9. Hildegart Ahumada & Magdalena Cornejo, 2019. "How econometrics can help us understand the effects of climate change on crop yields: the case of soybeans," School of Government Working Papers 201902, Universidad Torcuato Di Tella.
    10. A. Ford Ramsey & Barry K. Goodwin, 2019. "Value-at-Risk and Models of Dependence in the U.S. Federal Crop Insurance Program," JRFM, MDPI, vol. 12(2), pages 1-21, April.
    11. Zhu, Yichen & Ghoshray, Atanu, 2021. "Climate Anomalies and Its Impact on U.S. Corn and Soybean Prices," 2021 Conference, August 17-31, 2021, Virtual 315271, International Association of Agricultural Economists.
    12. Jesse Tack & Andrew Barkley & Lawton Nalley, 2014. "Heterogeneous effects of warming and drought on selected wheat variety yields," Climatic Change, Springer, vol. 125(3), pages 489-500, August.
    13. Patalee, M.A. Buddhika & Tonsor, Glynn T., 2021. "Impact of weather on cow-calf industry locations and production in the United States," Agricultural Systems, Elsevier, vol. 193(C).
    14. Matteo Bonato & Oğuzhan Çepni & Rangan Gupta & Christian Pierdzioch, 2023. "El Niño, La Niña, and forecastability of the realized variance of agricultural commodity prices: Evidence from a machine learning approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 785-801, July.
    15. Jesse Tack & Keith Coble & Barry Barnett, 2018. "Warming temperatures will likely induce higher premium rates and government outlays for the U.S. crop insurance program," Agricultural Economics, International Association of Agricultural Economists, vol. 49(5), pages 635-647, September.

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