Fast-forward breeding for a food-secure world

Crop production systems need to expand their outputs sustainably to feed a burgeoning human population. Advances in genome sequencing technologies combined with efficient trait mapping procedures accelerate the availability of beneficial alleles for breeding and research. Enhanced interoperability between different omics and phenotyping platforms, leveraged by evolving machine learning tools, will help provide mechanistic explanations for complex plant traits. Targeted and rapid assembly of beneficial alleles using optimized breeding strategies and precise genome editing techniques could deliver ideal crops for the future. Realizing desired productivity gains in the field is imperative for securing an adequate future food supply for 10 billion people. The rapid advances in plant genome sequencing and phenotyping have enhanced trait mapping and gene discovery in crops.Increasing adoption of machine learning algorithms is crucial to derive meaningful inferences from complex multidimensional phenotyping data.Emerging breeding approaches like optimal contribution selection, alone or in combination with genomic selection, will enhance the genetic base of breeding programs while accelerating genetic gain.Integrating speed breeding with new-age genomic breeding technologies holds promise to relieve the long-standing bottleneck of lengthy crop breeding cycles.Haplotype-based breeding, genomic prediction, and genome editing will hasten targeted assembly of superior alleles in future cultivars for sustainable agricultural development and long-term food security.