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Statistical sampling of missing environmental variables improves biophysical genomic prediction in wheat.
Theor Appl Genet. 2024 Apr 18;137(5):108. doi: 10.1007/s00122-024-04613-0.
Theor Appl Genet. 2024.
PMID: 38637355
Integrating biophysical crop growth models and whole genome prediction for their mutual benefit: a case study in wheat phenology.
Jighly A, Weeks A, Christy B, O'Leary GJ, Kant S, Aggarwal R, Hessel D, Forrest KL, Technow F, Tibbits JFG, Totir R, Spangenberg GC, Hayden MJ, Munkvold J, Daetwyler HD.
Jighly A, et al. Among authors: totir r.
J Exp Bot. 2023 Aug 17;74(15):4415-4426. doi: 10.1093/jxb/erad162.
J Exp Bot. 2023.
PMID: 37177829
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Using genomic prediction with crop growth models enables the prediction of associated traits in wheat.
Jighly A, Thayalakumaran T, O'Leary GJ, Kant S, Panozzo J, Aggarwal R, Hessel D, Forrest KL, Technow F, Tibbits JFG, Totir R, Hayden MJ, Munkvold J, Daetwyler HD.
Jighly A, et al. Among authors: totir r.
J Exp Bot. 2023 Mar 13;74(5):1389-1402. doi: 10.1093/jxb/erac393.
J Exp Bot. 2023.
PMID: 36205117
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