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...

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Detalles Bibliográficos
Autores principales: Xu, Zhanyou, Kurek, Andreomar, Cannon, Steven B., Beavis, William D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8270216/
https://www.ncbi.nlm.nih.gov/pubmed/34242220
http://dx.doi.org/10.1371/journal.pone.0240948

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8270216/
https://www.ncbi.nlm.nih.gov/pubmed/34242220
http://dx.doi.org/10.1371/journal.pone.0240948

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