From mechanistic models to breeding digital twins: Predictive plant improvement for a changing climate

Nguyen Trung Duc

Ecological Genetics and Genomics. 40, 100504.

A single-author short review. It treats predictive plant improvement as a breeding digital twin problem and argues that a twin must specify five elements: a breeding referent, a mutable state vector, a counterfactual action space, a utility function, and a prospective audit. Sweet corn is used as a test case to show how endosperm genetics, product-system quality, seed feasibility, genomic prediction, breeding simulation, and decision science can be organized to support auditable breeding decisions.

← All publications