Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Rising temperatures and increasing demand challenge wheat supply in Sudan

Abstract

Climate warming poses challenges for food production at low latitudes, particularly in arid regions. Sudan, where wheat demand could triple by 2050, has the world’s hottest wheat-growing environments, and observed yield declines in hot seasons are prompting the national government to prepare for a warming of 1.5–4.2 °C. Using advanced crop modelling under different climate and socioeconomic scenarios, we show that despite the use of adjusted sowing dates and existing heat-tolerant varieties, by 2050, Sudan’s domestic production share may decrease from 16.0% to 4.5–12.2%. In the relatively cool northern region, yields will need to increase by 3.1–4.7% per year, at non-compounding rates, to meet demand. In the hot central and eastern regions, improvements in heat tolerance are essential, and yields must increase by 0.2–2.7% per year to keep pace with climate warming. These results indicate the potential contribution of climate change adaptation measures and provide targets for addressing the wheat supply challenge.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Wheat-producing regions.
Fig. 2: Observed and modelled wheat responses to temperature for two varieties at the Gezira Research Station in Wad Medani.
Fig. 3: Observed and modelled average wheat yields in the three regions from 1970/1971 to 2016/2017.
Fig. 4: Relationship between the wheat harvested area and the relative error in the modelled yield from the historical run.
Fig. 5: Relationships between sowing date, temperature and yield across wheat-producing regions under the +4.2 °C scenario in 2050 (r85gH).
Fig. 6: Historical and projected annual socioeconomic indicators in Sudan.
Fig. 7: Current and projected domestic wheat production shares for Sudan in 2050.

Similar content being viewed by others

Data availability

All data supporting the simulation and analysis in this study are publicly available from open sources. The historical and future climate scenarios are available at https://doi.org/10.20783/DIAS.524. The counterfactual climate scenarios are available at https://doi.org/10.20783/DIAS.544. The spatial harvest area data are available at http://www.earthstat.org/harvested-area-yield-175-crops. All other data, including simulation outputs (maturity date and yield) and the processed data used in plotting the figures, are available from the corresponding author upon request.

Code availability

All figures presented here are produced using a purpose-build script for the Generic Mapping Tools (GMT) version 4.5.18. The script runs on the MacOS platform but is potentially applicable to other platforms (for example, Windows and Linux). The script is available from the corresponding author upon reasonable request.

References

  1. GIEWS - Global Information and Early Warning System Country Briefs, Sudan (FAO, 2020); http://www.fao.org/giews/countrybrief/country.jsp?code=SDN

  2. Sudan Wheat Imports by Year (IndexMundi, 2019); https://www.indexmundi.com/agriculture/?country=sd&commodity=wheat&graph=imports

  3. World Population Prospects: The 2017 Revision (UN, 2017); https://population.un.org/wpp/Graphs/DemographicProfiles

  4. Sudan Wheat Production by Year (IndexMundi, 2019); https://www.indexmundi.com/agriculture/?country=sd&commodity=wheat&graph=production

  5. Elbushra, A., Elsheikh, O. & Salih, A. Department of agricultural impact of exchange rate reforms on Sudan’s economy: applied general equilibrium analysis. Afr. J. Agric. Res. 5, 442–448 (2010).

    Google Scholar 

  6. Elsheikh, O., Elbushra, A. & Salih, A. Impacts of changes in exchange rate and international prices on agriculture and economy of the Sudan: computable general equilibrium analysis. Sustain. Agric. Res. 1, 201–210 (2012).

    Google Scholar 

  7. IPCC Global warming of 1.5°C (eds Masson-Delmotte, V. et al.) 1–32 (WMO, 2018).

  8. Asseng, S. et al. Rising temperatures reduce global wheat production. Nat. Clim. Change 5, 143–147 (2015).

    Article  ADS  Google Scholar 

  9. Asseng, S. et al. Hot spots of wheat yield decline with rising temperatures. Glob. Change Biol. 23, 2464–2472 (2017).

    Article  ADS  Google Scholar 

  10. Iizumi, T. et al. Crop production losses associated with anthropogenic climate change for 1981–2010 compared with preindustrial levels. Int. J. Climatol. 38, 5405–5417 (2018).

    Article  Google Scholar 

  11. Ray, D. K. et al. Climate change has likely already affected global food production. PLoS ONE 14, e0217148 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Semenov, M. A. & Shewry, P. R. Modelling predicts that heat stress, not drought, will increase vulnerability of wheat in Europe. Sci. Rep. 1, 66 (2011).

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  13. Lobell, D. B. et al. The shifting influence of drought and heat stress for crops in northeast Australia. Glob. Change Biol. 21, 4115–4127 (2015).

    Article  ADS  Google Scholar 

  14. Iizumi, T. in Adaptation to Climate Change in Agriculture (eds Iizumi, T., Hirata, R. & Matsuda, R.) 3–16 (Springer, 2019).

  15. Siddig, K., Stepanyan, D., Wiebelt, M., Grethe, H. & Zhu, T. Climate change and agriculture in the Sudan: impact pathways beyond changes in mean rainfall and temperature. Ecol. Econ. 169, 106566 (2020).

    Article  Google Scholar 

  16. Elbashir, A. A. E. et al. Genetic variation in heat tolerance-related traits in a population of wheat multiple synthetic derivatives. Breed. Sci. 67, 483–492 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  17. Martre, P. et al. The international heat stress genotype experiment for modeling wheat response to heat: field experiments and AgMIP-Wheat multi-model simulations. Open Data J. Agric. Res. 3, 23–28 (2017).

    Google Scholar 

  18. Negassa, A. et al. The Potential for Wheat Production in Africa: Analysis of Biophysical Suitability and Economic Profitability (CIMMYT, 2013).

  19. O’Neill, B. C. et al. A new scenario framework for climate change research: the concept of shared socioeconomic pathways. Clim. Change 122, 387–400 (2014).

    Article  ADS  Google Scholar 

  20. Tahir, I. S. A. et al. Grain Yield and Stability of Elite Stem and Leaf Rusts Resistant Bread Wheat Genotypes Under the Hot Environments of Sudan: A Proposal for the Release of Three Bread Wheat Varieties (National Variety Release Committee, 2018).

  21. Wang, E. et al. The uncertainty of crop yield projections is reduced by improved temperature response functions. Nat. Plants 3, 17102 (2017).

    Article  PubMed  Google Scholar 

  22. Iizumi, T. et al. Responses of crop yield growth to global temperature and socioeconomic changes. Sci. Rep. 7, 7800 (2017).

    Article  PubMed  PubMed Central  ADS  CAS  Google Scholar 

  23. van Oort, P. A. J. et al. Intensification of an irrigated rice system in Senegal: crop rotations, climate risks, sowing dates and varietal adaptation options. Eur. J. Agron. 80, 168–181 (2016).

    Article  Google Scholar 

  24. Ray, D. K., Mueller, N. D., West, P. C. & Foley, J. A. Yield trends are insufficient to double global crop production by 2050. PLoS ONE 8, e66428 (2013).

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  25. Challinor, A. et al. Current warming will reduce yields unless maize breeding and seed systems adapt immediately. Nat. Clim. Change 6, 954–958 (2016).

    Article  ADS  Google Scholar 

  26. Webber, H. et al. Canopy temperature for simulation of heat stress in irrigated wheat in a semi-arid environment: a multi-model comparison. Field Crops Res. 202, 21–35 (2017).

    Article  Google Scholar 

  27. Yin, X. et al. Multi-model uncertainty analysis in predicting grain N for crop rotations in Europe. Euro. J. Agron. 84, 152–165 (2017).

    Article  Google Scholar 

  28. ISIMIP3b Simulation Round Simulation Protocol - Agriculture (ISI-MIP, 2020); https://protocol.isimip.org/protocol/ISIMIP3b/agriculture.html

  29. Fujimori, S. et al. Macroeconomic impacts of climate change driven by changes in crop yields. Sustainability 10, 3673 (2018).

    Article  Google Scholar 

  30. Sultan, B., Defrance, D. & Iizumi, T. Evidence of crop production losses in West Africa due to historical global warming in two crop models. Sci. Rep. 9, 12834 (2019).

    Article  PubMed  PubMed Central  ADS  Google Scholar 

  31. Takakura, J. et al. Dependence of economic impacts of climate change on anthropogenically directed pathways. Nat. Clim. Change 9, 737–741 (2019).

    Article  ADS  Google Scholar 

  32. Iizumi, T. et al. Climate change adaptation cost and residual damage to global crop production. Clim. Res. 80, 203–218 (2020).

    Article  Google Scholar 

  33. Dunne, K. A. & Willmott, C. J. Global distribution of plant-extractable water capacity of soil. Int. J. Climatol. 16, 841–859 (1996).

    Article  Google Scholar 

  34. Challinor, A. J., Wheeler, T. R., Craufurd, P. Q., Slingo, J. M. & Grimes, D. I. F. Design and optimisation of a large-area process-based model for annual crops. Agric. For. Meteorol. 124, 99–120 (2004).

    Article  ADS  Google Scholar 

  35. Sánchez, B., Rasmussen, A. & Porter, J. R. Temperatures and the growth and development of maize and rice: a review. Glob. Change Biol. 20, 408–417 (2014).

    Article  ADS  Google Scholar 

  36. Luo, Q. Temperature thresholds and crop production: a review. Clim. Change 109, 583–598 (2011).

    Article  ADS  Google Scholar 

  37. Kobayashi, S. et al. The JRA-55 Reanalysis: general specifications and basic characteristics. J. Meteorol. Soc. Jpn 93, 5–48 (2015).

    Article  Google Scholar 

  38. Harada, Y. et al. The JRA-55 Reanalysis: representation of atmospheric circulation and climate variability. J. Meteorol. Soc. Jpn 94, 269–302 (2016).

    Article  Google Scholar 

  39. Iizumi, T., Nishimori, M., Ishigooka, Y. & Yokozawa, M. Introduction to climate change scenario derived by statistical downscaling. J. Agric. Meteorol. 66, 131–143 (2010).

    Article  Google Scholar 

  40. Iizumi, T., Nishimori, M., Dairaku, K., Adachi, S. A. & Yokozawa, M. Evaluation and intercomparison of downscaled daily precipitation indices over Japan in present-day climate: strengths and weaknesses of dynamical and bias-correction-type statistical downscaling methods. J. Geophys. Res. Atmos. 116, D01111 (2011).

    Article  ADS  Google Scholar 

  41. Iizumi, T. et al. Future change of daily precipitation indices in Japan: a stochastic weather generator-based bootstrap approach to provide probabilistic climate information. J. Geophys. Res. Atmos. 117, D11114 (2012).

    Article  ADS  Google Scholar 

  42. Iizumi, T., Takikawa, H., Hirabayashi, Y., Hanasaki, N. & Nishimori, M. Contributions of different bias-correction methods and reference meteorological forcing data sets to uncertainty in projected temperature and precipitation extremes. J. Geophys. Res. Atmos. 122, 7800–7819 (2017).

    Article  ADS  Google Scholar 

  43. van Vuuren, D. P. et al. The representative concentration pathways: an overview. Clim. Change 109, 5 (2011).

    Article  ADS  Google Scholar 

  44. Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc. 93, 485–498 (2012).

    Article  ADS  Google Scholar 

  45. Shiogama, H. et al. Attributing historical changes in probabilities of record-breaking daily temperature and precipitation extreme events. SOLA 12, 225–231 (2016).

    Article  ADS  Google Scholar 

  46. Mizuta, R. et al. Over 5000 years of ensemble future climate simulations by 60 km global and 20 km regional atmospheric models. Bull. Am. Meteorol. Soc. 98, 1383–1398 (2017).

    Article  ADS  Google Scholar 

  47. Imada, Y. et al. Recent enhanced seasonal temperature contrast in Japan from large ensemble high-resolution climate simulations. Atmosphere 8, 57 (2017).

    Article  ADS  Google Scholar 

  48. Abdelgadir, E. M., Fadul, E. M., Fageer, E. A. & Ali, E. A. Response of wheat to nitrogen fertilizer at reclaimed high terrace salt-affected soils in Sudan. J. Agric. Soc. Sci. 6, 43–47 (2010).

    Google Scholar 

  49. Hassan, R. M. & Faki, H. Economic Policy and Technology Determinants of the Comparative Advantage of Wheat Production in Sudan CIMMYT Economics Paper No. 6. (CIMMYT, 1993).

  50. Chebil, A. et al. Metafrontier analysis of technical efficiency of wheat farms in sudan. J. Agric. Sci. 8, 179–186 (2016).

    Google Scholar 

  51. Hassan, R., Faki, H. & Byerlee, D. in Wheat in Heat-Stressed Environments: Irrigated, Dry Areas and Rice-Wheat Farming Systems (eds. Saunders, D. A. & Hettel G. P.) 78–95 (CIMMYT, 1993).

  52. Hansen, J. W. & Jones, J. W. Scaling-up crop models for climate variability applications. Agric. Syst. 65, 43–72 (2000).

    Article  Google Scholar 

  53. Iizumi, T., Sakurai, G. & Yokozawa, M. An ensemble approach to the representation of subgrid-scale heterogeneity of crop phenology and yield in coarse-resolution large-area crop models. J. Agric. Meteorol. 69, 243–254 (2013).

    Article  Google Scholar 

  54. Porwollik, V. et al. Spatial and temporal uncertainty of crop yield aggregations. Euro. J. Agron. 88, 10–21 (2017).

    Article  Google Scholar 

  55. World Bank Open Data (World Bank, 2019); https://data.worldbank.org

  56. R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2018).

  57. The SSP Database (IIASA, 2019); https://tntcat.iiasa.ac.at/SspDb

  58. Monfreda, C., Ramankutty, N. & Foley, J. A. Farming the planet: 2. Geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000. Glob. Biogeochem. Cycles 22, GB1022 (2008).

    Article  ADS  CAS  Google Scholar 

Download references

Acknowledgements

T.I. thanks K. Abe for his support in earlier analysis. This study was funded by the Joint Research Program of Arid Land Research Center, Tottori University (No. 30F2001 to T.I.) and JST SATREPS (JPMJSA1805 to H.T.).

Author information

Authors and Affiliations

Authors

Contributions

T.I. and W.K. conceived this study. I.-E.A.A.-B., I.S.A.T., Y.S.A.G. and A.A.M.I. conducted the field experiments. M.T., Y.K. and H.T. helped modify crop models. T.I. drafted the paper and all contributed to the writing.

Corresponding author

Correspondence to Toshichika Iizumi.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Food thanks Michael Baum, Kindie Tesfaye and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Discussion, References, Figs. 1–7 and Tables 1–4.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Iizumi, T., Ali-Babiker, IE.A., Tsubo, M. et al. Rising temperatures and increasing demand challenge wheat supply in Sudan. Nat Food 2, 19–27 (2021). https://doi.org/10.1038/s43016-020-00214-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s43016-020-00214-4

This article is cited by

Search

Quick links

Nature Briefing Anthropocene

Sign up for the Nature Briefing: Anthropocene newsletter — what matters in anthropocene research, free to your inbox weekly.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing: Anthropocene