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Nonlinear kernels, dominance, and envirotyping data increase the accuracy of genome-based prediction in multi-environment trials


Type:
Article
Title:
Nonlinear kernels, dominance, and envirotyping data increase the accuracy of genome-based prediction in multi-environment trials
Creator:
Costa-Neto, G.;
Costa-Neto, G.
ORCID iD iconhttps://orcid.org/0000-0003-1137-6786
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Fritsche-Neto, R.;
Fritsche-Neto, R.
ORCID iD iconhttps://orcid.org/0000-0003-4310-0047
URL Profile
http://www.genetica.esalq.usp.br/alogamas/index2.html
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Crossa, J.
ORCID iD icon
Crossa, J.
ORCID iD iconhttps://orcid.org/0000-0001-9429-5855
ScopusScopus ID
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Year:
2021
Copyright:
CIMMYT manages Intellectual Assets as International Public Goods. The user is free to download, print, store and share this work. In case you want to translate or create any other derivative work and share or distribute such translation/derivative work, please contact CIMMYT-Knowledge-Center@cgiar.org indicating the work you want to use and the kind of use you intend; CIMMYT will contact you with the suitable license for that purpose
Journal:
Heredity
Journal volume:
126
Pages:
92-106
DOI:
10.1038/s41437-020-00353-1
Place of Publication:
Harlow (United Kingdom)
Publisher:
Springer Nature
Citation:
Nonlinear kernels, dominance, and envirotyping data increase the accuracy of genome-based prediction in multi-environment trials. 2021. 126 DOI: 10.1038/s41437-020-00353-1 Springer Nature.

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Genetic ResourcesInstitutionalIntegrated DevelopmentMaizeSocioeconomicsSustainable IntensificationWheat

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