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Global wheat production could benefit from closing the genetic yield gap

Nature Food volume 3, pages 532–541 (2022)Cite this article

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Abstract

Global food security requires food production to be increased in the coming decades. The closure of any existing genetic yield gap (Yig) by genetic improvement could increase crop yield potential and global production. Here we estimated present global wheat Yig, covering all wheat-growing environments and major producers, by optimizing local wheat cultivars using the wheat model Sirius. The estimated mean global Yig was 51%, implying that global wheat production could benefit greatly from exploiting the untapped global Yig through the use of optimal cultivar designs, utilization of the vast variation available in wheat genetic resources, application of modern advanced breeding tools, and continuous improvements of crop and soil management.

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Fig. 1: Global wheat yields and the ‘classical’ versus ‘genetic’ yield gaps.
Fig. 2: Study sites and potential yield of current wheat cultivars at country level.
Fig. 3: Potential yield of current wheat cultivars in CIMMYT MEs, and under different irrigation conditions and growth habits.
Fig. 4: Comparison of potential yield of current wheat cultivars estimated by Sirius and GYGA.
Fig. 5: Wheat genetic yield potential and genetic yield gap at country level.
Fig. 6: Wheat genetic yield potential and genetic yield gap in CIMMYT MEs.
Fig. 7: Wheat genetic yield potential and genetic yield gap under different irrigation conditions and growth habits.

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Data availability

The data that support the findings of this study are publicly available from http://www.fao.org/faostat/en/#data/QC and https://www.fao.org/aquastat/en/. All other data supporting the findings of this study are included within the article and supplementary information. Any further information regarding this study is available from the corresponding authors on request. Source data are provided with this paper.

Code availability

The Sirius model and the stochastic weather generator LARS-WG 6.0 used in the present study are available from https://doi.org/10.5281/zenodo.4572624 and https://doi.org/10.5281/zenodo.4572752, respectively.

References

  1. Evenson, R. E. & Gollin, D. Assessing the impact of the Green Revolution, 1960 to 2000. Science 300, 758–762 (2003).

    Article  ADS  CAS  PubMed  Google Scholar 

  2. FAOSTAT (Food and Agriculture Organization of the United Nations, accessed 28 July 2021); http://www.fao.org/faostat/en/#data/QC

  3. Stewart, B. A. & Lal, R. Chapter one—increasing world average yields of cereal crops: it’s all about water. Adv. Agron. 151, 1–44 (2018).

    Article  Google Scholar 

  4. van Dijk, M., Morley, T., Rau, M. L. & Saghai, Y. A meta-analysis of projected global food demand and population at risk of hunger for the period 2010–2050. Nat. Food 2, 494–501 (2021).

    Article  Google Scholar 

  5. Foley, J. A. et al. Solutions for a cultivated planet. Nature 478, 337–342 (2011).

    Article  ADS  CAS  PubMed  Google Scholar 

  6. Godfray, H. C. J. et al. Food security: the challenge of feeding 9 billion people. Science 327, 812–818 (2010).

    Article  ADS  CAS  PubMed  Google Scholar 

  7. Shiferaw, B. et al. Crops that feed the world 10. Past successes and future challenges to the role played by wheat in global food security. Food Secur. 5, 291–317 (2013).

    Article  Google Scholar 

  8. The 17 Goals (United Nations Department of Economic and Social Affairs Sustainable Development, Division for Sustainable Development Goals, 2021).

  9. Fischer, R. A., Byerlee, D. & Edmeades, G. O. Crop Yield and Global Food Security: Will Yield Increase Continue to Feed the World? ACIAR Monograph Vol. 158 (ACIAR, 2014).

  10. Lobell, D. B., Cassman, K. G. & Field, C. B. Crop yield gaps: their importance, magnitudes, and causes. Annu Rev. Environ. Resour. 34, 179–204 (2009).

    Article  Google Scholar 

  11. van Ittersum, M. K. et al. Yield gap analysis with local to global relevance—a review. Field Crops Res. 143, 4–17 (2013).

    Article  Google Scholar 

  12. Senapati, N. & Semenov, M. A. Large genetic yield potential and genetic yield gap estimated for wheat in Europe. Glob. Food Secur. 24, 100340 (2020).

    Article  Google Scholar 

  13. Senapati, N. & Semenov, M. A. Assessing yield gap in high productive countries by designing wheat ideotypes. Sci Rep. 9, 5516 (2019).

    Article  ADS  PubMed  PubMed Central  CAS  Google Scholar 

  14. Donald, C. M. The breeding of crop ideotypes. Euphytica 17, 385–403 (1968).

    Article  Google Scholar 

  15. Cooper, M. et al. Integrating genetic gain and gap analysis to predict improvements in crop productivity. Crop Sci. 60, 582–604 (2020).

    Article  CAS  Google Scholar 

  16. Dowla, M., Edwards, I., O’Hara, G., Islam, S. & Ma, W. Developing wheat for improved yield and adaptation under a changing climate: optimization of a few key genes. Engineering 4, 514–522 (2018).

    Article  CAS  Google Scholar 

  17. Hall, A. J. & Richards, R. A. Prognosis for genetic improvement of yield potential and water-limited yield of major grain crops. Field Crops Res. 143, 18–33 (2013).

    Article  Google Scholar 

  18. Reynolds, M. et al. Raising yield potential in wheat. J. Exp. Bot. 60, 1899–1918 (2009).

    Article  CAS  PubMed  Google Scholar 

  19. Gbegbelegbe, S. et al. Baseline simulation for global wheat production with CIMMYT mega-environment specific cultivars. Field Crops Res. 202, 122–135 (2017).

    Article  Google Scholar 

  20. Rajaram, S., van Ginkel, M. & Fischer, R. A. CIMMYT’s wheat breeding mega-environments (ME). In Proceedings of the 8th International Wheat Genetic Symposium (eds Li, Z. S. & Xin, Z. Y.) 1101–1106 (China Agricultural Scientech Press, 1995).

  21. Semenov, M. Sirius Crop Model (Sirius 2018) (Zenodo, 2021); https://doi.org/10.5281/zenodo.4572624

  22. Senapati, N., Stratonovitch, P., Paul, M. J. & Semenov, M. A. Drought tolerance during reproductive development is important for increasing wheat yield potential under climate change in Europe. J. Exp. Bot. 70, 2549–2560 (2019).

    Article  CAS  PubMed  Google Scholar 

  23. AQUASTAT (Food and Agriculture Organization of the United Nations, accessed 17 May 2021); http://www.fao.org/aquastat/en/databases/

  24. GYGA. Global Yield Gap and Water Productivity Atlas (accessed 22 October 2021); www.yieldgap.org

  25. Schils, R. et al. Cereal yield gaps across Europe. Eur. J. Agron. 101, 109–120 (2018).

    Article  Google Scholar 

  26. Hammer, G. L., Cooper, M. & Reynolds, M. P. Plant production in water-limited environments. J. Exp. Bot. 72, 5097–5101 (2021).

    Article  CAS  PubMed  Google Scholar 

  27. Senapati, N., Griffiths, S., Hawkesford, M., Shewry, P. R. & Semenov, M. A. Substantial increase in yield predicted by wheat ideotypes for Europe under future climate. Clim. Res. 80, 189–201 (2020).

    Article  Google Scholar 

  28. Hatfield, J. L. & Beres, B. L. Yield gaps in wheat: path to enhancing productivity. Front. Plant Sci. 10, 1603 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  29. Fischer, R. A. & Connor, D. J. Issues for cropping and agricultural science in the next 20 years. Field Crops Res. 222, 121–142 (2018).

    Article  Google Scholar 

  30. Semenov, M. A. & Terkel, D. A. Analysis of convergence of an evolutionary algorithm with self-adaptation using a stochastic Lyapunov function. Evol. Comput. 11, 363–379 (2003).

    Article  PubMed  Google Scholar 

  31. Stratonovitch, P. & Semenov, M. A. Calibration of a crop simulation model using an evolutionary algorithm with self-adaptation. Proc. Social Behav. Sci. 2, 7749–7750 (2010).

    Article  Google Scholar 

  32. Reynolds, M. et al. Breeder friendly phenotyping. Plant Sci. 295, 110396 (2020).

    Article  CAS  PubMed  Google Scholar 

  33. Cooper, M., Voss-Fels, K. P., Messina, C. D., Tang, T. & Hammer, G. L. Tackling G × E × M interactions to close on-farm yield-gaps: creating novel pathways for crop improvement by predicting contributions of genetics and management to crop productivity. Theo. Appl. Genet. 134, 1625–1644 (2021).

    Article  Google Scholar 

  34. Asseng, S. et al. Climate change impact and adaptation for wheat protein. Glob. Change Biol. 25, 155–173 (2019).

    Article  ADS  Google Scholar 

  35. Liu, B. et al. Global wheat production with 1.5 and 2.0 °C above pre-industrial warming. Glob. Change Biol. 25, 1428–1444 (2019).

    Article  ADS  Google Scholar 

  36. Martre, P. et al. Multimodel ensembles of wheat growth: many models are better than one. Glob. Change Biol. 21, 911–925 (2015).

    Article  ADS  Google Scholar 

  37. 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 

  38. Semenov, M. A. & Stratonovitch, P. Adapting wheat ideotypes for climate change: accounting for uncertainties in CMIP5 climate projections. Clim. Res. 65, 123–139 (2015).

    Article  Google Scholar 

  39. Brooks, R. J., Semenov, M. A. & Jamieson, P. D. Simplifying Sirius: sensitivity analysis and development of a meta-model for wheat yield prediction. Eur. J. Agron. 14, 43–60 (2001).

    Article  Google Scholar 

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

    Article  ADS  Google Scholar 

  41. Cooper, M. et al. Modelling selection response in plant-breeding programs using crop models as mechanistic gene-to-phenotype (CGM-G2P) multi-trait link functions in silico. Plants 3, diaa016 (2021).

    Google Scholar 

  42. Reynolds, M. et al. Addressing research bottlenecks to crop productivity. Trends Plant Sci. 26, 607–630 (2021).

    Article  CAS  PubMed  Google Scholar 

  43. Peng, S. B., Khush, G. S., Virk, P., Tang, Q. Y. & Zou, Y. B. Progress in ideotype breeding to increase rice yield potential. Field Crops Res. 108, 32–38 (2008).

    Article  Google Scholar 

  44. Christopher, M. et al. QTL for stay-green traits in wheat in well-watered and water-limited environments. Field Crops Res. 217, 32–44 (2018).

    Article  Google Scholar 

  45. Royo, C., Dreisigacker, S., Alfaro, C., Ammar, K. & Villegas, D. Effect of Ppd-1 genes on durum wheat flowering time and grain filling duration in a wide range of latitudes. J. Agric. Sci. 154, 612–631 (2016).

    Article  CAS  Google Scholar 

  46. Pingali, P. L. Green Revolution: impacts, limits, and the path ahead. Proc. Natl Acad. Sci. USA 109, 12302–12308 (2012).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  47. Senapati, N., Brown, H. E. & Semenov, M. A. Raising genetic yield potential in high productive countries: designing wheat ideotypes under climate change. Agric. Forest Meteorol. 271, 33–45 (2019).

    Article  ADS  Google Scholar 

  48. Gouache, D. et al. Bridging the gap between ideotype and genotype: challenges and prospects for modelling as exemplified by the case of adapting wheat (Triticum aestivum L.) phenology to climate change in France. Field Crops Res. 202, 108–121 (2017).

    Article  Google Scholar 

  49. Rötter, R. P., Tao, F., Höhn, J. G. & Palosuo, T. Use of crop simulation modelling to aid ideotype design of future cereal cultivars. J. Exp. Bot. 66, 3463–3476 (2015).

    Article  PubMed  CAS  Google Scholar 

  50. Christopher, J. T., Christopher, M. J., Borrell, A. K., Fletcher, S. & Chenu, K. Stay-green traits to improve wheat adaptation in well-watered and water-limited environments. J. Exp. Bot. 67, 5159–5172 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Manschadi, A. M., Christopher, J., deVoil, P. & Hammer, G. L. The role of root architectural traits in adaptation of wheat to water-limited environments. Func. Plant Biol. 33, 823–837 (2006).

    Article  CAS  Google Scholar 

  52. Ramirez-Villegas, J., Watson, J. & Challinor, A. J. Identifying traits for genotypic adaptation using crop models. J. Exp. Bot. 66, 3451–3462 (2015).

    Article  CAS  PubMed  Google Scholar 

  53. Bustos, D. V., Hasan, A. K., Reynolds, M. P. & Calderini, D. F. Combining high grain number and weight through a DH-population to improve grain yield potential of wheat in high-yielding environments. Field Crops Res. 145, 106–115 (2013).

    Article  Google Scholar 

  54. Zhu, X. G., Long, S. P. & Ort, D. R. Improving photosynthetic efficiency for greater yield. Ann. Rev. Plant Biol. 61, 235–261 (2010).

    Article  CAS  Google Scholar 

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Acknowledgements

Rothamsted Research led this study and received grant-aided support from the Biotechnology and Biological Sciences Research Council (BBSRC) through Designing Future Wheat (BB/P016855/1) and Achieving Sustainable Agricultural Systems (NE/N018125/1). This study was also supported by the Agricultural Model Intercomparison and Improvement Project (AgMIP-Wheat) and the International Wheat Yield Program (IWYP115 Project). F.E. acknowledges support by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy (EXC 2070), 390732324. M.K.v.I. acknowledges the Senior Expert Programme of NWO-WOTRO Strategic Partnership NL-CGIAR. P.M. acknowledges support from the metaprogramme Agriculture and forestry in the face of climate change: adaptation and mitigation (CLIMAE) of the French National Research Institute for Agriculture, Food and Environment (INRAE). J.E.O. acknowledges financial support from SustES—adaptation strategies for sustainable ecosystem services and food security under adverse environmental conditions (CZ.02.1.01/0.0/0.0/16_019/0000797). R.P.R. was financially supported by the BARISTA (Advanced tools for breeding BARley for Intensive and SusTainable Agriculture under climate change scenarios) project, grant number 031B0811A.

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Authors and Affiliations

  1. Plant Sciences Department, Rothamsted Research, Harpenden, UK

    Nimai Senapati, Mikhail A. Semenov, Nigel G. Halford & Malcolm J. Hawkesford

  2. TUM School of Life Sciences, Technical University of Munich, Freising, Germany

    Senthold Asseng

  3. The University of Queensland, Queensland Alliance for Agriculture and Food Innovation, Centre for Crop Science, Brisbane, Queensland, Australia

    Mark Cooper

  4. Institute of Crop Science and Resource Conservation INRES, University of Bonn, Bonn, Germany

    Frank Ewert & Heidi Webber

  5. Leibniz Centre for Agricultural Landscape Research, Müncheberg, Germany

    Frank Ewert & Heidi Webber

  6. Plant Production Systems, Wageningen University & Research, Wageningen, Netherlands

    Martin K. van Ittersum

  7. LEPSE, Université Montpellier, INRAE, Institut Agro Montpellier, Montpellier, France

    Pierre Martre

  8. Department of Agroecology, Aarhus University, Tjele, Denmark

    Jørgen E. Olesen

  9. International Maize and Wheat Improvement Center, Texcoco, Mexico

    Matthew Reynolds

  10. Tropical Plant Production & Agricultural Systems Modelling (TROPAGS), University of Göttingen, Göttingen, Germany

    Reimund P. Rötter

  11. Centre of Biodiversity and Sustainable Land Use (CBL), University of Göttingen, Göttingen, Germany

    Reimund P. Rötter

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  1. Nimai Senapati
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Contributions

N.S. and M.A.S. conceived the concept of the study, designed the simulation experiment, performed model simulations, analysed model outputs and wrote the first draft of the manuscript. All coauthors revised the manuscript. Authors from S.A. to H.W. are listed in alphabetical order.

Corresponding authors

Correspondence to Nimai Senapati or Mikhail A. Semenov.

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The authors declare no competing interests.

Peer review

Peer review information

Nature Food thanks Brian Beres, Jerry Hatfield, Toshihiro Hasegawa and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–3, Tables 1–6, methods and references.

Reporting Summary

Supplementary Data 1

Potential yield, harvest index and coefficient of variation of yield of current wheat cultivars at country level under different irrigation conditions.

Supplementary Data 2

Wheat genetic yield potential, harvest index and coefficient of variation of yield potential at country level under different irrigation conditions.

Supplementary Data 3

Wheat genetic yield gap at country level under different irrigation conditions.

Source data

Source Data Fig. 1

Average global wheat grain yield (dry matter: DM t ha−1) and yields in the leading producer countries during 1961–2019.

Source Data Fig. 2

Potential yield of current wheat cultivars at country level.

Source Data Fig. 3

Potential yield of current wheat cultivars in CIMMYT mega-environments, and under different irrigation conditions and growth habits.

Source Data Fig. 4

Comparison of potential yield of current wheat cultivars estimated by Sirius and GYGA.

Source Data Fig. 5

Wheat genetic yield potential and genetic yield gap at a country level.

Source Data Fig. 6

Wheat genetic yield potential and genetic yield gap in CIMMYT mega-environments.

Source Data Fig. 7

Wheat genetic yield potential and genetic yield gap under different irrigation conditions and growth habits.

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Senapati, N., Semenov, M.A., Halford, N.G. et al. Global wheat production could benefit from closing the genetic yield gap. Nat Food 3, 532–541 (2022). https://doi.org/10.1038/s43016-022-00540-9

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