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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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.
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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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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.
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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.
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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DOI: https://doi.org/10.1038/s43016-022-00540-9
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