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Peer-reviewedCC BYTrends in Plant Science

Better breeding leveraging more biology

  • Owen M. Powell
  • Lee Thomas Hickey1
  • Shunichiro Tomura1
  • Mark Cooper1
  1. 1Queensland Alliance for Agriculture and Food Innovation, Centre for Crop Science, University of Queensland, St Lucia, Queensland, Australia; ARC Centre of Excellence for Plant Success in Nature and Agriculture, University of Queensland, St Lucia, Queensland, Australia; ARC Training Centre in Predictive Breeding for Agricultural Futures, University of Queensland, St Lucia, Queensland, Australia
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Abstract

Climate-driven variability is reducing our ability to accurately predict crop performance across environments, limiting genetic gain in breeding programs. Sustained progress requires predictive frameworks that capture plant-environment interactions across diverse genetics and management conditions. Integrating mechanistic insights from plant science into predictive models offers a path to improve the accuracy, precision, and interpretability of breeding decisions under changing environments. We present emerging hierarchical genome-phenome frameworks and outline how they can be leveraged within breeding programs to evaluate how biological knowledge informs predictions across target environments and supports long-term genetic gain.

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Cite

BibTeXRIS
BibTeX
@article{powell2026better,
  title = {{Better breeding leveraging more biology}},
  author = {Owen M. Powell and Lee Thomas Hickey and Shunichiro Tomura and Mark Cooper},
  journal = {Trends in Plant Science},
  year = {2026},
  month = jun,
  doi = {10.1016/j.tplants.2026.05.004},
  url = {https://doi.org/10.1016/j.tplants.2026.05.004},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Better breeding leveraging more biology
AU  - Owen M. Powell
AU  - Lee Thomas Hickey
AU  - Shunichiro Tomura
AU  - Mark Cooper
PY  - 2026
DA  - 2026/06/01
JO  - Trends in Plant Science
DO  - 10.1016/j.tplants.2026.05.004
UR  - https://doi.org/10.1016/j.tplants.2026.05.004
AB  - Climate-driven variability is reducing our ability to accurately predict crop performance across environments, limiting genetic gain in breeding programs. Sustained progress requires predictive frameworks that capture plant-environment interactions across diverse genetics and management conditions. Integrating mechanistic insights from plant science into predictive models offers a path to improve the accuracy, precision, and interpretability of breeding decisions under changing environments. We present emerging hierarchical genome-phenome frameworks and outline how they can be leveraged within breeding programs to evaluate how biological knowledge informs predictions across target environments and supports long-term genetic gain.
SN  - 1360-1385
SN  - 1878-4372
ER  -