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Novel wheat varieties facilitate deep sowing to beat the heat of changing climates

Abstract

Wheat yields are threatened by global warming and unreliable rainfall, which increase heat and drought stress. A potential adaptation strategy is to sow earlier and deeper, taking advantage of stored soil water. However, the short coleoptiles of modern semi-dwarf wheat varieties reduce emergence when sown deep. Novel genotypes with alternative dwarfing genes have longer coleoptiles to facilitate deep sowing, but the yield benefit has been uncertain. We validated new crop simulation routines with field data to assess the impact of novel genotypes on Australian wheat production. We predict that these genotypes, coupled with deep sowing, can increase national wheat yields by 18–20% under historical climate (1901–2020), without increased yield variability, with benefits also projected under future warming. These benefits are likely to extend to other dryland wheat production regions globally. Our results highlight the impact of synergy between new genetics and management systems to adapt food production to future climates.

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Fig. 1: Comparison of coleoptile length effects on early wheat establishment sown at 120 mm depth.
Fig. 2: Framework for modelling the impact of early establishment in APSIM Next Generation and model validation for the simulation of coleoptile length, emergence capacity and grain yield.
Fig. 3: Mean annual yield benefit (1901–2020) of novel GAS wheat varieties (long coleoptiles and greater early vigour) sown at 120 mm depth compared with baseline wheat sown at 45 mm depth at 37 sites.
Fig. 4: Simulated wheat grain yield and yield benefit under past climate change and projected future warming in Australia.

Data availability

The data used in this study will be available in the CSIRO Data Access Portal: https://data.csiro.au/collection/csiro:53658 (ref. 52). Source data are provided with this paper.

Code availability

The computer code of APSIM NG is available in the GitHub repository: https://github.com/APSIMInitiative/ApsimX (refs. 53,54).

References

  1. World Food and Agriculture—Statistical Yearbook 2020 (FAO, 2020).

  2. Tilman, D., Balzer, C., Hill, J. & Befort, B. L. Global food demand and the sustainable intensification of agriculture. Proc. Natl Acad. Sci. USA 108, 20260–20264 (2011).

    CAS  Google Scholar 

  3. Hedden, P. The genes of the Green Revolution. Trends Genet. 19, 5–9 (2003).

    CAS  Google Scholar 

  4. Hochman, Z., Gobbett, D. L. & Horan, H. Climate trends account for stalled wheat yields in Australia since 1990. Glob. Change Biol. 23, 2071–2081 (2017).

    Google Scholar 

  5. Wang, B. et al. Australian wheat production expected to decrease by the late 21st century. Glob. Change Biol. 24, 2403–2415 (2018).

    Google Scholar 

  6. Rebetzke, G. J. et al. Genotypic increases in coleoptile length improves stand establishment, vigour and grain yield of deep-sown wheat. Field Crops Res. 100, 10–23 (2007).

    Google Scholar 

  7. Gan, Y., Stobbe, E. H. & Moes, J. Relative date of wheat seedling emergence and its impact on grain yield. Crop Sci. 32, 1275–1281 (1992).

    Google Scholar 

  8. Rebetzke, G., Ingvordsen, C., Bovill, W., Trethowan, R. & Fletcher, A. in Australian Agriculture in 2020: From Conservation to Automation (eds Pratley, J. & Kirkegaard, J.) 273–288 (Agronomy Australia and Charles Sturt Univ., 2019).

  9. Schillinger, W. F., Donaldson, E., Allan, R. E. & Jones, S. S. Winter wheat seedling emergence from deep sowing depths. Agron. J. 90, 582–586 (1998).

    Google Scholar 

  10. Hunt, J. R. et al. Early sowing systems can boost Australian wheat yields despite recent climate change. Nat. Clim. Change 9, 244–247 (2019).

    Google Scholar 

  11. Richards, R. The effect of dwarfing genes in spring wheat in dry environments. I. Agronomic characteristics. Aust. J. Agric. Res. 43, 517–527 (1992).

    Google Scholar 

  12. Rebetzke, G. et al. Quantitative trait loci on chromosome 4B for coleoptile length and early vigour in wheat (Triticum aestivum L.). Aust. J. Agric. Res. 52, 1221–1234 (2001).

    CAS  Google Scholar 

  13. Rebetzke, G., Richards, R., Sirault, X. & Morrison, A. Genetic analysis of coleoptile length and diameter in wheat. Aust. J. Agric. Res. 55, 733–743 (2004).

    Google Scholar 

  14. Rebetzke, G. J., Zheng, B. & Chapman, S. C. Do wheat breeders have suitable genetic variation to overcome short coleoptiles and poor establishment in the warmer soils of future climates? Funct. Plant Biol. 43, 961–972 (2016).

    Google Scholar 

  15. Rebetzke, G. J. et al. Height reduction and agronomic performance for selected gibberellin-responsive dwarfing genes in bread wheat (Triticum aestivum L.). Field Crops Res. 126, 87–96 (2012).

    Google Scholar 

  16. Zhao, Z., Rebetzke, G. J., Zheng, B., Chapman, S. C. & Wang, E. Modelling impact of early vigour on wheat yield in dryland regions. J. Exp. Bot. 70, 2535–2548 (2019).

    CAS  Google Scholar 

  17. Brown, H. E. et al. Plant Modelling Framework: software for building and running crop models on the APSIM platform. Environ. Model. Softw. 62, 385–398 (2014).

    Google Scholar 

  18. Holzworth, D. P. et al. APSIM—evolution towards a new generation of agricultural systems simulation. Environ. Model. Softw. 62, 327–350 (2014).

    Google Scholar 

  19. Smith, C. J. et al. Using fertiliser to maintain soil inorganic nitrogen can increase dryland wheat yield with little environmental cost. Agric. Ecosyst. Environ. 286, 106644 (2019).

    CAS  Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  22. Anderson, W. K., Stephens, D. & Siddique, K. H. M. in Innovations in Dryland Agriculture (eds Farooq, M. & Siddique, K. H. M.) 299–319 (Springer International, 2016).

  23. Flohr, B. M., Hunt, J. R., Kirkegaard, J. A., Evans, J. R. & Lilley, J. M. Genotype × management strategies to stabilise the flowering time of wheat in the south-eastern Australian wheatbelt. Crop Pasture Sci. 69, 547–560 (2018).

    Google Scholar 

  24. Rebetzke, G., Botwright, T., Moore, C., Richards, R. & Condon, A. Genotypic variation in specific leaf area for genetic improvement of early vigour in wheat. Field Crops Res. 88, 179–189 (2004).

    Google Scholar 

  25. Richards, R. A. & Lukacs, Z. Seedling vigour in wheat—sources of variation for genetic and agronomic improvement. Aust. J. Agric. Res. 53, 41–50 (2002).

    CAS  Google Scholar 

  26. López-Castañeda, C. & Richards, R. A. Variation in temperate cereals in rainfed environments III. Water use and water-use efficiency. Field Crops Res. 39, 85–98 (1994).

    Google Scholar 

  27. Zerner, M. C., Rebetzke, G. J. & Gill, G. S. Genotypic stability of weed competitive ability for bread wheat (Triticum aestivum) genotypes in multiple environments. Crop Pasture Sci. 67, 695–702 (2016).

    Google Scholar 

  28. Allan, R. E., Vogel, O. A. & Peterson, C. J. Jr Seedling emergence rate of fall-sown wheat and its association with plant height and coleoptile length. Agron. J. 54, 347–350 (1962).

    Google Scholar 

  29. Towards a Global Programme on Sustainable Dryland Agriculture (FAO, 2020); https://www.fao.org/3/nd366en/nd366en.pdf

  30. Antle, J. M., Cho, S., Tabatabaie, S. H. & Valdivia, R. O. Economic and environmental performance of dryland wheat-based farming systems in a 1.5 C world. Mitig. Adapt. Strateg. Glob. Change 24, 165–180 (2019).

    Google Scholar 

  31. Kirkegaard, J. & Hunt, J. Increasing productivity by matching farming system management and genotype in water-limited environments. J. Exp. Bot. 61, 4129–4143 (2010).

    CAS  Google Scholar 

  32. Rebetzke, G. J. et al. Agronomic assessment of the durum Rht18 dwarfing gene in bread wheat. Crop Pasture Sci. https://doi.org/10.1071/CP21645 (2022).

  33. Bathgate, J. The Influence of Wheat (Triticum aestivum L.) Semi-dwarfing Genes and the Lcol-A1 QTL on the Coleoptile, Seedling Vigour, and Establishment from Deep Sowing. Honours thesis, Charles Sturt Univ. (2021).

  34. Brown, H., Huth, N. & Holzworth, D. Crop model improvement in APSIM: using wheat as a case study. Eur. J. Agron. 100, 141–150 (2018).

    Google Scholar 

  35. Botwright, T., Rebetzke, G., Condon, T. & Richards, R. The effect of rht genotype and temperature on coleoptile growth and dry matter partitioning in young wheat seedlings. Funct. Plant Biol. 28, 417–423 (2001).

    Google Scholar 

  36. Ellis, M. H. et al. The effect of different height reducing genes on the early growth of wheat. Funct. Plant Biol. 31, 583–589 (2004).

    CAS  Google Scholar 

  37. Whan, B. The association between coleoptile length and culm length in semidwarf and standard wheats. J. Aust. Inst. Agric. Sci. 42, 194–196 (1976).

    Google Scholar 

  38. Whan, B. The emergence of semidwarf and standard wheats, and its association with coleoptile length. Aust. J. Exp. Agric. 16, 411–416 (1976).

    Google Scholar 

  39. Bush, M. & Evans, L. Growth and development in tall and dwarf isogenic lines of spring wheat. Field Crops Res. 18, 243–270 (1988).

    Google Scholar 

  40. Rebetzke, G. J., Bonnett, D. G. & Ellis, M. H. Combining gibberellic acid-sensitive and insensitive dwarfing genes in breeding of higher-yielding, sesqui-dwarf wheats. Field Crops Res. 127, 17–25 (2012).

    Google Scholar 

  41. Miralles, D., Calderini, D., Pomar, K. & D’Ambrogio, A. Dwarfing genes and cell dimensions in different organs of wheat. J. Exp. Bot. 49, 1119–1127 (1998).

    CAS  Google Scholar 

  42. Radford, B. Effect of constant and fluctuating temperature regimes and seed source on the coleoptile length of tall and semidwarf wheats. Aust. J. Exp. Agric. 27, 113–117 (1987).

    Google Scholar 

  43. Botwright, T., Rebetzke, G., Condon, A. & Richards, R. Influence of variety, seed position and seed source on screening for coleoptile length in bread wheat (Triticum aestivum L.). Euphytica 119, 349–356 (2001).

    Google Scholar 

  44. Cornish, P. & Hindmarsh, S. Seed size influences the coleoptile length of wheat. Aust. J. Exp. Agric. 28, 521–523 (1988).

    Google Scholar 

  45. Zheng, B., Chenu, K. & Doherty, A. The APSIM-Wheat Module (7.5 R3008) (APSIM Initiative, 2015); https://www.apsim.info/wp-content/uploads/2019/09/WheatDocumentation.pdf

  46. Zadoks, J. C., Chang, T. T. & Konzak, C. F. A decimal code for the growth stages of cereals. Weed Res. 14, 415–421 (1974).

    Google Scholar 

  47. Bell, L. W., Lilley, J. M., Hunt, J. R. & Kirkegaard, J. A. Optimising grain yield and grazing potential of crops across Australia’s high-rainfall zone: a simulation analysis. 1. Wheat. Crop Pasture Sci. 66, 332–348 (2015).

    Google Scholar 

  48. Flohr, B. M., Hunt, J. R., Kirkegaard, J. A. & Evans, J. R. Water and temperature stress define the optimal flowering period for wheat in south-eastern Australia. Field Crops Res. 209, 108–119 (2017).

    Google Scholar 

  49. Chen, C. et al. Spatial patterns of estimated optimal flowering period of wheat across the southwest of Western Australia. Field Crops Res. 247, 107710 (2020).

    Google Scholar 

  50. Jeffrey, S. J., Carter, J. O., Moodie, K. B. & Beswick, A. R. Using spatial interpolation to construct a comprehensive archive of Australian climate data. Environ. Model. Softw. 16, 309–330 (2001).

    Google Scholar 

  51. 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).

    Google Scholar 

  52. Zhao, Z., Wang, E., Rebetzke, G. J. & Kirkegaard, J. A. Supporting data for ‘Sowing deep to beat the heat using novel genetics adapts wheat to a changing climate’. CSIRO Data Access Portal https://data.csiro.au/collection/csiro:53658 (2022).

  53. Holzworth, D. et al. APSIM Next Generation: overcoming challenges in modernising a farming systems model. Environ. Model. Softw. 103, 43–51 (2018).

    Google Scholar 

  54. APSIM Initiative. Source code of APSIM Next Generation. GitHub https://github.com/APSIMInitiative/ApsimX (2021).

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Acknowledgements

This study was supported by the CSIRO’s Strategic Investment Project (SIP) ‘SIP268: Modelling informed trait/germplasm phenotyping’. We thank H. E. Brown for constructive discussions held during the period of this research.

Author information

Authors and Affiliations

Authors

Contributions

Z.Z., E.W., J.A.K. and G.J.R. designed the study. G.J.R. provided the experimental data. Z.Z. and E.W. constructed the model, undertook model testing and carried out the crop model simulations. Z.Z., E.W., J.A.K. and G.J.R. undertook the analysis of the simulation results and contributed to writing and revising the paper.

Corresponding author

Correspondence to Enli Wang.

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The authors declare no competing interests.

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Nature Climate Change thanks Qingquan Chu, Ken Giller and Bertrand Muller for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Mean annual yield benefit (1901–2020) of novel GAS varieties (long coleoptiles but without greater early vigour) sown deep at 120 mm compared to baseline wheat sown shallow at 45 mm.

Top: for a slow-developing wheat genotype sown on 10-Apr. Bottom: for a mid-developing wheat genotype sown on 10-May. Baseline: wheat with no new genetics and sown shallow opportunistically after germinating rainfall between 10-Apr and 30-Jun. Map outline © Commonwealth of Australia (Geoscience Australia) 2021. (https://ecat.ga.gov.au/geonetwork/srv/eng/catalog.search#/metadata/61754).

Source data

Extended Data Fig. 2 Performance of novel GAS varieties sown deep at 120 mm compared to the baseline wheat sown shallow at 45 mm under past climate.

a-e, Simulated ranges (across 37 sites using the average at each site) of days after sowing (DAS) to anthesis (a), DAS to maturity (b), above-ground biomass (c), heat and frost stress index (d) and harvest index (HI, calculated from simulated yield and biomass) (e) for three scenarios under two different periods of past climate. In d, ‘Heat and frost index’ (varied between 0 and 1) with 1 representing no stress and no damage to grain yield. ‘1961–1990’: the standard reference period of past climate. ‘1991–2020’: period for warmer temperatures. ‘Scenario1’: a baseline wheat genotype with no new genetics sown shallow opportunistically after germinating rainfall between 10-Apr and 30-Jun. ‘Scenario2’: a slow-developing wheat genotype with the new genetics sown into deep stored water and germinating on 10-Apr. ‘Scenario3’: a mid-developing wheat genotype with the new genetics sown into deep stored water and germinating on 10-May. Boxplots were drawn with the average in the corresponding periods at 37 sites. The bottom, centre and top lines of the box represent the 25th, median and 75th percentiles. The open black circle indicates the average. Whiskers are extended to the most extreme data point that is no more than 1.5 interquartile range from the edge of the box (Tukey style). Black dots beyond the whiskers represent outliers.

Source data

Extended Data Fig. 3 Performance of novel GAS varieties sown deep at 120 mm compared to the baseline wheat sown shallow at 45 mm under projected warming.

a-e, Simulated ranges (across 37 sites using the average at each site) of above-ground biomass (a), potential grain yield (without considering heat and frost reduction on grain yield) (b), days after sowing (DAS) to anthesis (c), DAS to maturity (d), and heat and frost stress index (e) for three scenarios under past climate (Base + 0 °C, 1981–2010) and future temperature rise (+2 and +4 °C temperature increases imposed on the 1981–2010 period). In d, ‘Heat and frost index’ (varied between 0 and 1) with 1 representing no stress and no damage to grain yield. Boxplots were drawn with the average in the corresponding periods at 37 sites. The bottom, centre and top lines of the box represent the 25th, median and 75th percentiles. The open black circle indicates the average. Whiskers are extended to the most extreme data point that is no more than 1.5 interquartile range from the edge of the box (Tukey style). Black dots beyond the whiskers represent outliers.

Source data

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Zhao, Z., Wang, E., Kirkegaard, J.A. et al. Novel wheat varieties facilitate deep sowing to beat the heat of changing climates. Nat. Clim. Chang. 12, 291–296 (2022). https://doi.org/10.1038/s41558-022-01305-9

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