Addressing Research Bottlenecks to Crop Productivity. (June 2021) - Reynolds, Matthew; Atkin, Owen K.; Bennett, Malcolm; Cooper, Mark; Dodd, Ian C.; Foulkes, M. John; Frohberg, Claus; Hammer, Graeme; Henderson, Ian R.; Huang, Bingru; Korzun, Viktor; McCouch, Susan R.; et al.
Abstract : Asymmetry of investment in crop research leads to knowledge gaps and lost opportunities to accelerate genetic gain through identifying new sources and combinations of traits and alleles. On the basis of consultation with scientists from most major seed companies, we identified several research areas with three common features: (i) relatively underrepresented in the literature; (ii) high probability of boosting productivity in a wide range of crops and environments; and (iii) could be researched in 'precompetitive' space, leveraging previous knowledge, and thereby improving models that guide crop breeding and management decisions. Areas identified included research into hormones, recombination, respiration, roots, and source–sink, which, along with new opportunities in phenomics, genomics, and bioinformatics, make it more feasible to explore crop genetic resources and improve breeding strategies. Highlights: More symmetrical investment in crop research will create opportunities to improve crop models, combine new alleles through prebreeding, and suggest novel crop management practices. Consensus among public and private sectors is that more investment is needed to improve understanding of hormone crosstalk, recombination rate, maintenance respiration, root structure and function, and source–sink balance. Greater investment in these areas is expected to benefit a wide range of crops across most environments. New opportunities in phenomics, genomics, and bioinformatics make it feasible to explore the vast untapped collections of crop genetic resources to create novel trait combinations. Filling knowledge gaps is expected to enable a much more integrated understanding of crop yield and adaptation, improving breeding and crop management models.