{
  "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.",
  "abstract_shown": true,
  "affiliations": [
    "Queensland 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"
  ],
  "article_type": "review",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "name": "Owen M. Powell",
      "orcid": "0000-0001-5131-4286"
    },
    {
      "affs": [
        0
      ],
      "name": "Lee Thomas Hickey",
      "orcid": "0000-0001-6909-7101"
    },
    {
      "affs": [
        0
      ],
      "name": "Shunichiro Tomura",
      "orcid": "0009-0005-9050-9963"
    },
    {
      "affs": [
        0
      ],
      "name": "Mark Cooper",
      "orcid": "0000-0002-9418-3359"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/90y9d0rw5qcav.bib",
  "cited_by": 6,
  "doi": "10.1016/j.tplants.2026.05.004",
  "fulltext_url": null,
  "id": "90y9d0rw5qcav",
  "issns": [
    "1360-1385",
    "1878-4372"
  ],
  "landing_url": "https://doi.org/10.1016/j.tplants.2026.05.004",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1110|Genetics and Plant Breeding"
    ]
  ],
  "openalex": "W7166200257",
  "pdf_url": null,
  "pmcid": null,
  "pmid": "42362447",
  "published": "2026-06-01",
  "publisher": "Elsevier BV",
  "ris": "https://research.draconitic.com/paper/90y9d0rw5qcav.ris",
  "sources": [
    {
      "first_seen": "2026-11-04T13:21:08Z",
      "native_id": "W7166200257",
      "source": "openalex",
      "url": "https://doi.org/10.1016/j.tplants.2026.05.004"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "openalex:1110|Genetics and Plant Breeding",
      "subfield": "plant-biology"
    }
  ],
  "title": "Better breeding leveraging more biology",
  "title_text": "Better breeding leveraging more biology",
  "type": "peer-reviewed",
  "updated_at": "2026-11-09T13:21:09Z",
  "url": "https://research.draconitic.com/paper/90y9d0rw5qcav",
  "venue": "Trends in Plant Science",
  "versions": []
}