Predictions from algorithmic modeling result in better decisions than from data modeling for soybean iron deficiency chlorosis.
In soybean variety development and genetic improvement projects, iron deficiency chlorosis (IDC) is visually assessed as an ordinal response variable. Linear Mixed Models for Genomic Prediction (GP) have been developed, compared, and used to select continuous plant traits such as yield, height, and...
| Autores principales: | , , , |
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| Formato: | Artículo |
| Lenguaje: | English |
| Publicado: |
Public Library of Science (PLoS)
2021-01-01
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| Colección: | PLoS ONE |
| Acceso en línea: | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0240948&type=printable |