Multi-objective Breeding Optimization

Multi-Objective Breeding Optimization is a computational framework in plant and animal breeding programs that simultaneously optimizes multiple conflicting genetic and phenotypic objectives—such as yield, resilience, and environmental adaptability—by integrating advanced selection indices, genomic prediction models, and multi-criteria decision-making algorithms to identify optimal breeding strategies under trade-offs.

12

Publications

598

Citations

32

Authors

26

Institutions

Publications per year

2017–2026

7

Authors

32

  1. PS
    Patrick S. Schnable

    Iowa State University, United States

    2 publications · 131 citations

    2

    h-index

  2. AK
    Aaron Kusmec

    Iowa State University, United States

    2 publications · 131 citations

    2

    h-index

  3. LW
    Lizhi Wang

    Iowa State University, United States

    2 publications · 131 citations

    2

    h-index

  4. GH
    Guiping Hu

    Iowa State University, United States

    2 publications · 131 citations

    2

    h-index

  5. PP
    P. P. Cotterill

    Commonwealth Scientific and Industrial Research Organisation, Australia

    1 publications · 20 citations

    1

    h-index

1–5 of 32

Venues

  1. Theoretical and Applied Genetics

    2 publications · 104 citations

    2

    h-index

  2. Bioinformatics

    1 publications · 92 citations

    1

    h-index

  3. Journal of Dairy Science

    JDS · 1 publications · 16 citations

    1

    h-index

  4. Plant Cell & Environment

    1 publications · 40 citations

    1

    h-index

  5. One Earth

    1 publications · 50 citations

    1

    h-index

1–5