Novel wheat varieties facilitate deep sowing to beat the heat of changing climates
World Food and Agriculture—Statistical Yearbook 2020 (FAO, 2020).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
Hedden, P. The genes of the Green Revolution. Trends Genet. 19, 5–9 (2003).CAS
Google Scholar
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
Wang, B. et al. Australian wheat production expected to decrease by the late 21st century. Glob. Change Biol. 24, 2403–2415 (2018).
Google Scholar
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
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
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).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
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
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
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
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
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
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
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
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
Holzworth, D. P. et al. APSIM—evolution towards a new generation of agricultural systems simulation. Environ. Model. Softw. 62, 327–350 (2014).
Google Scholar
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
Asseng, S. et al. Rising temperatures reduce global wheat production. Nat. Clim. Change 5, 143–147 (2015).
Google Scholar
Wang, E. et al. The uncertainty of crop yield projections is reduced by improved temperature response functions. Nat. Plants 3, 17102 (2017).
Google Scholar
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).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
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
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
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
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
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
Towards a Global Programme on Sustainable Dryland Agriculture (FAO, 2020); https://www.fao.org/3/nd366en/nd366en.pdfAntle, 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
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
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).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).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
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
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
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
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
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
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
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
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
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
Cornish, P. & Hindmarsh, S. Seed size influences the coleoptile length of wheat. Aust. J. Exp. Agric. 28, 521–523 (1988).
Google Scholar
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.pdfZadoks, 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
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
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
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
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
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
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).Holzworth, D. et al. APSIM Next Generation: overcoming challenges in modernising a farming systems model. Environ. Model. Softw. 103, 43–51 (2018).
Google Scholar
APSIM Initiative. Source code of APSIM Next Generation. GitHub https://github.com/APSIMInitiative/ApsimX (2021). More