Fast Backcross Breeding for Climate-Resilient Cereals: Integrating Speed Breeding, Marker-Assisted Backcrossing and Genomic Selection

Abstract

Abiotic stresses, including drought, heat, salinity, waterlogging, and acidic soils, are increasingly inhibiting the consistency of global food production, valued at USD 3.26 trillion during the last three decades. Although backcrossing efficiently transfers large-effect loci into elite backgrounds, conventional pipelines remain slow and vulnerable to linkage drag and unreliable genotype-to-phenotype translation. Here, we synthesize an operational fast backcross (FB) breeding framework that integrates (i) rapid generation advance (speed breeding), (ii) embryo culture to shorten generation intervals and unlock wide crosses, (iii) marker-assisted backcrossing with coordinated foreground, recombinant, and genome-wide background selection, and (iv) genomic selection to capture residual polygenic adaptation. We propose practical approaches to prioritize stress-adaptive loci and to validate yield and quality neutrality under non-stress conditions before pyramiding. Case studies in rice (SUB1, Saltol, Pup1 and DRO1), wheat (Nax1/Nax2) and barley (aerenchyma formation and HvAACT1 loci) illustrate how FB pipelines can compress variety development timelines from 8-10 years to 3-5 years while maintaining farmer-preferred agronomic and end-use traits; however, they also underscore the constraints of relying on whole-plant phenotyping alone. We show that FB succeeds only when early locus prioritisation, recombinant selection to minimise linkage drag, and pre-pyramiding neutrality testing are enforced, explaining why many accelerated pipelines underperform despite advanced genotyping tools. Further, we propose AI-enabled selection and targeted editing to scale FB breeding for climate-resilient agriculture.

Keywords

Speed breeding / Gene pyramiding / Whole genome selection

Statement of the Use of Generative AI and AI-Assisted Technologies in the Writing Process

During the preparation of this manuscript, the authors used chatbot by OpenAI for English improvement. The authors reviewed and edited the content as needed and take full responsibility for the content of the published article.

Author Contributions

Conceptualization, M.Z.; Writing—Original Draft Preparation, C.Z.; Writing—Review & Editing, M.T.H., K.L., C.L., Z.C., S.S. and M.Z.; Funding Acquisition, M.Z.

Ethics Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Funding

This research was funded by the Grains Research & Development Corporation of Australia.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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