Sparse kernel models provide optimization of training set design for genomic prediction in multiyear wheat breeding data
Type:
Title:
Sparse kernel models provide optimization of training set design for genomic prediction in multiyear wheat breeding data
Creator:
Lopez-Cruz, M.;
Dreisigacker, S.;
Crespo Herrera, L.A.;
Bentley, A.R.;
Singh, R.P.;
Poland, J.;
Shrestha, S.;
Huerta-Espino, J.;
https://orcid.org/0000-0001-8334-9862
Scopus ID
Researcher ID
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Velu, G.;
Juliana, P.;
Mondal, S.;
Perez-Rodriguez, P.;
Crossa, J.
Dreisigacker, S.;
Crespo Herrera, L.A.;
Bentley, A.R.;
Singh, R.P.;
Poland, J.;
Shrestha, S.;
Huerta-Espino, J.;
Huerta-Espino, J.
https://orcid.org/0000-0001-8334-9862
Scopus ID
Researcher ID
Items in this Repository
View
Velu, G.;
Juliana, P.;
Mondal, S.;
Perez-Rodriguez, P.;
Crossa, J.
Year:
2022
Copyright:
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Journal:
Plant Genome
DOI:
Place of Publication:
USA
Publisher:
John Wiley & Sons Inc.
CGIAR Initiatives
Initiative:
Accelerated Breeding
Breeding Resources
Breeding Resources
Impact Area:
Nutrition, health & food security
Poverty reduction, livelihoods & jobs
Poverty reduction, livelihoods & jobs
Action Area:
Genetic Innovation
Donor or Funder:
Bill & Melinda Gates Foundation
CGIAR Trust Fund
Foundation for Research Levy on Agricultural Products
Agricultural Agreement Research Fund
National Institute of Food and Agriculture
United States Agency for International Development
CGIAR Trust Fund
Foundation for Research Levy on Agricultural Products
Agricultural Agreement Research Fund
National Institute of Food and Agriculture
United States Agency for International Development
CGSpace URL:
