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Prediction of seasonal climate-induced variations in global food production


Consumers, including the poor in many countries, are increasingly dependent on food imports1 and are thus exposed to variations in yields, production and export prices in the major food-producing regions of the world. National governments and commercial entities are therefore paying increased attention to the cropping forecasts of important food-exporting countries as well as to their own domestic food production. Given the increased volatility of food markets and the rising incidence of climatic extremes affecting food production, food price spikes may increase in prevalence in future years2,3,4. Here we present a global assessment of the reliability of crop failure hindcasts for major crops at two lead times derived by linking ensemble seasonal climatic forecasts with statistical crop models. We found that moderate-to-marked yield loss over a substantial percentage (26–33%) of the harvested area of these crops is reliably predictable if climatic forecasts are near perfect. However, only rice and wheat production are reliably predictable at three months before the harvest using within-season hindcasts. The reliabilities of estimates varied substantially by crop—rice and wheat yields were the most predictable, followed by soybean and maize. The reasons for variation in the reliability of the estimates included the differences in crop sensitivity to the climate and the technology used by the crop-producing regions. Our findings reveal that the use of seasonal climatic forecasts to predict crop failures will be useful for monitoring global food production and will encourage the adaptation of food systems toclimatic extremes.

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Figure 1: Timing of cropping predictions.
Figure 2: The upper limits of reliability when moderate-to-marked yield losses of maize, soybean, rice and wheat were hindcasted using reanalysis data.
Figure 3: The reliability of the within-season hindcasts of the moderate-to-marked (5% more) yield losses for maize, soybean, rice and wheat.
Figure 4: The dominant climatic factors affecting the year-to-year variations in the yields of maize, soybean, rice and wheat.
Figure 5: The capture reliability of the year-to-year relative wheat yield variations (ΔY) for the reliable areas in four major wheat-exporting countries (the USA, France, Canada and Australia).

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  1. The State of Food Insecurity in The World: How Does International Price Volatility Affect Domestic Economies and Food Security? (FAO, 2011).

  2. Funk, C. C. & Brown, M. E. Declining global per capita agricultural production and warming oceans threaten food security. Food Sec. 1, 271–289 (2009).

    Article  Google Scholar 

  3. Report of the FAO Expert Meeting on How to Feed the World in 2050 (FAO, 2009).

  4. IPCC Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (Cambridge Univ. Press, 2012).

  5. Global Information and Early Warning System on Food and Agriculture (FAO, 2013); available at

  6. Famine Early Warning System Network (FEWS, 2012); available at

  7. Ravallion, M., Chen, S. & Sangraula, P. New Evidence on the Urbanization of Global Poverty (World Bank, 2007).

    Google Scholar 

  8. Ruel, M. T., Garrett, J. L., Hawkes, C. & Cohen, M. J. The food, fuel, and financial crises affect the urban and rural poor disproportionately: A review of the evidence. J. Nutr. 140, 170S–176S (2010).

    Article  CAS  Google Scholar 

  9. Rosegrant, M. W., Msangi, S., Sulser, T. & Valmonte-Santos, R. Biofuels and the Global Food Balance. 2020 Vision for Food, Agriculture and the Environment (International Food Policy Research Institute, 2006).

    Google Scholar 

  10. Headey, D. & Fan, S. Anatomy of a crisis: The causes and consequences of surging food prices. Agric. Econ. 39, 375–391 (2008).

    Article  Google Scholar 

  11. Stringer, L. C. et al. Adaptations to climate change, drought and desertification: Local insights to enhance policy in southern Africa. Environ. Sci. Policy 12, 748–765 (2009).

    Article  Google Scholar 

  12. Ray, D. K., Ramankutty, N., Mueller, N. D., West, P. C. & Foley, J. A. Recent patterns of crop yield growth and stagnation. Nature Commun. 3, 1293 (2012).

    Article  Google Scholar 

  13. Hansen, J. W., Mason, S. J., Sun, L. & Tall, A. Review of seasonal climate forecasting for agriculture in sub-Saharan Africa. Exp. Agric. 47, 205–240 (2011).

    Article  Google Scholar 

  14. Lenton, T. M. What early warning systems are there for environmental shocks? Environ. Sci. Policy 24, S60–S75 (2012).

    Google Scholar 

  15. Cane, M. A., Eshel, G. & Buckland, R. W. Forecasting Zimbabwean maize yield using eastern equatorial Pacific sea surface temperature. Nature 370, 204–205 (1994).

    Article  Google Scholar 

  16. Hansen, J. W., Challinor, A. J., Ines, A. V. M., Wheeler, T. R. & Moron, V. Translating climate forecasts into agricultural terms: Advances and challenges. Clim. Res. 33, 27–41 (2006).

    Article  Google Scholar 

  17. World Water Assessment Programme The United Nations World Water Development Report 4: Managing Water under Uncertainty and Risk (United Nations Educational, Scientific and Cultural Organization, 2012).

  18. Hawkins, E. et al. Increasing influence of heat stress on French maize yields from the 1960s to the 2030s. Glob. Change Biol. 19, 937–947 (2013).

    Article  Google Scholar 

  19. Wilks, D. S. & Godfrey, C. M. Diagnostic verification of the IRI net assessment forecasts, 1997–2000. J. Clim. 15, 1369–1377 (2002).

    Article  Google Scholar 

  20. Luo, J-J., Masson, S., Behera, S., Shingu, S. & Yamagata, T. Seasonal climate predictability in a coupled OAGCM using a different approach for ensemble forecasts. J. Clim. 18, 4474–4497 (2005).

    Article  Google Scholar 

  21. Sacks, W. J., Deryng, D., Foley, J. A. & Ramankutty, N. Crop planting dates: An analysis of global patterns. Glob. Ecol. Biogeogr. 19, 607–620 (2010).

    Google Scholar 

  22. Funk, C. New satellite observations and rainfall forecasts help provide earlier warning of drought in Africa. Earth Observer. 21, 23–27 (2009).

    Google Scholar 

  23. Welton, G. The Impact of Russia’s 2010 Grain Export Ban (Oxfam, 2011); available at

  24. Vogel, C. & O’Brien, K. Who can eat information? Examining the effectiveness of seasonal climate forecasts and regional climate-risk management strategies. Clim. Res. 33, 111–122 (2006).

    Article  Google Scholar 

  25. Challinor, A. J., Ewert, F., Arnold, S., Simelton, E. & Fraser, E. Crops and climate change: Progress, trends, and challenges in simulating impacts and informing adaptation. J. Exp. Bot. 60, 2775–2789 (2009).

    Article  CAS  Google Scholar 

  26. Iizumi, T., Yokozawa, M. & Nishimori, M. Parameter estimation and uncertainty analysis of a large-scale crop model for paddy rice: Application of a Bayesian approach. Agr. For. Meteorol. 149, 333–348 (2009).

    Article  Google Scholar 

  27. Easterling, W. E. et al. in IPCC Climate Change 2007: Impacts, Adaptation and Vulnerability (eds Parry, M. L. et al.) Ch. 5 (Cambridge Univ. Press, 2007).

    Google Scholar 

  28. Vermeulen, S. J. et al. Addressing uncertainty in adaptation planning for agriculture. Proc. Natl Acad. Sci. USA 110, 8357–8362 (2013).

    Article  CAS  Google Scholar 

  29. Onogi, K. et al. The JRA-25 Reanalysis. J. Meteorol. Soc. Jpn 85, 369–432 (2007).

    Article  Google Scholar 

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We thank R. C. Stone, P. McIntosh, H. Kanamaru and M. Otsuka for helpful comments on the earlier version of this manuscript. T.I. was supported by the Japan Society for the Promotion of Science (JSPS) Grant-in-Aid for Research Activity Start-up (23880030). T.I, M.Y. and G.S. were supported by the Environment Research and Technology Development Fund (S-10-2) of the Ministry of the Environment, Japan. The Science and Innovation Section of the British Embassy in Tokyo provided us with the opportunity to conduct this study through a UK–Japan workshop arrangement.

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T.I. was responsible for the study design, data analysis and manuscript preparation. J-J.L. contributed seasonal climatic forecasting and assisted in manuscript preparation. G.S. provided the computational code used to optimize the crop yield modelling. H.S., M.Y., A.J.C., M.E.B and T.Y. assisted with the study design and helped write the manuscript.

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Correspondence to Toshichika Iizumi.

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Iizumi, T., Sakuma, H., Yokozawa, M. et al. Prediction of seasonal climate-induced variations in global food production. Nature Clim Change 3, 904–908 (2013).

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