Skip to main navigation
Skip to search
Skip to main content
Experts@Minnesota Home
Home
Profiles
Research units
University Assets
Projects and Grants
Research output
Datasets
Press/Media
Activities
Fellowships, Honors, and Prizes
Impacts
Search by expertise, name or affiliation
Accurate predictions of barley phenotypes using genomewide markers and environmental covariates
Jeffrey Neyhart
,
Kevin A.T. Silverstein
,
Kevin P. Smith
GEMS Informatics Initiative
Research Computing
Minnesota Supercomputing Institute
Agronomy and Plant Genetics
Research output
:
Contribution to journal
›
Article
›
peer-review
10
Scopus citations
Overview
Fingerprint
Fingerprint
Dive into the research topics of 'Accurate predictions of barley phenotypes using genomewide markers and environmental covariates'. Together they form a unique fingerprint.
Sort by
Weight
Alphabetically
Keyphrases
Barley
100%
Prediction Accuracy
100%
Environmental Covariates
100%
Genotype
66%
Multi-environment
66%
Offspring Generation
66%
Agronomic Traits
33%
Real Environment
33%
Environment Modeling
33%
Prediction Model
33%
Quality Traits
33%
Environmental Conditions
33%
Training Set
33%
Hordeum Vulgare
33%
Model Performance
33%
New Genotype
33%
Plant Genotype
33%
Reduced Model
33%
Target Environment
33%
Historical Climate Data
33%
Holdout
33%
Prediction Scenarios
33%
Environmental Prediction
33%
Climate Resilient Cultivars
33%
Prediction Framework
33%
Agricultural and Biological Sciences
Offspring
100%
Cultivar
50%
Hordeum Vulgare
50%
Climate Data
50%