Predicting enviromically adapted varieties with big data
Our results suggest that big data in combination with machine learning and deep learning methods offers new ways to widen the genetic bottleneck often encountered when advancing candidates from early limited-environment to late stage multi-environment evaluations. This promises faster delivery of breeding progress to farmers' fields.
Authors
- Abhishek Gogna (ORCID: https://orcid.org/0000-0002-5990-1829)
- Yusheng Zhao (ORCID: https://orcid.org/0000-0002-5893-504X)
- Renate Schmidt (ORCID: https://orcid.org/0000-0002-8037-3581)
- Valentin Wimmer
- Jochen C. Reif (ORCID: https://orcid.org/0000-0002-6742-265X)
- Bahareh Kamali (ORCID: https://orcid.org/0000-0002-8070-0175)
- Ehsan Eyshi Rezaei (ORCID: https://orcid.org/0000-0003-2603-8034)
- Wera Maria Eckhoff
Institutions
- Leibniz Centre for Agricultural Landscape Research (DE)
- KWS Saat (Germany) (DE)
- Leibniz-Institut für Pflanzengenetik und Kulturpflanzenforschung (IPK) (DE)
- Institute of Crop Science (JP)
Publication Details
- Journal
- Genome biology
- Published
- 2026-01-07
- DOI
- https://doi.org/10.1186/s13059-025-03914-x
- Citations
- 1
- Primary Topic
- Genetic and phenotypic traits in livestock
- Type
- article
- Field-Weighted Citation Impact
- 30.93