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Harnessing translational research in wheat for climate resilience.

Matthew Paul ReynoldsJanet M LewisKarim AmmarBhoja Raj BasnetLeonardo A Crespo-HerreraJosé CrosaKanwarpal S DhuggaSusanne DreisigackerPhilomin JulianaHannes KarwatMasahiro KishiiMargaret R KrausePeter LangridgeAzam LashkariSuchismita MondalThomas PayneDiego N L PequenoFrancisco PintoCarolina Paola SansaloniUrs SchulthessRavi Prakash SinghKai SonderSivakumar SukumaranWei XiongHans-Joachim Braun
Published in: Journal of experimental botany (2021)
Despite being the world's most widely grown crop, research investments in wheat (Triticum aestivum and Triticum durum) fall behind those in other staple crops. Current yield gains will not meet 2050 needs, and climate stresses compound this challenge. However, there is good evidence that heat and drought resilience can be boosted through translating promising ideas into novel breeding technologies using powerful new tools in genetics and remote sensing, for example. Such technologies can also be applied to identify climate resilience traits from among the vast and largely untapped reserve of wheat genetic resources in collections worldwide. This review describes multi-pronged research opportunities at the focus of the Heat and Drought Wheat Improvement Consortium (coordinated by CIMMYT), which together create a pipeline to boost heat and drought resilience, specifically: improving crop design targets using big data approaches; developing phenomic tools for field-based screening and research; applying genomic technologies to elucidate the bases of climate resilience traits; and applying these outputs in developing next-generation breeding methods. The global impact of these outputs will be validated through the International Wheat Improvement Network, a global germplasm development and testing system that contributes key productivity traits to approximately half of the global wheat-growing area.
Keyphrases
  • climate change
  • big data
  • genome wide
  • heat stress
  • machine learning
  • social support
  • dna methylation
  • copy number
  • depressive symptoms
  • plant growth
  • network analysis