Model training across multiple breeding cycles significantly improves genomic prediction accuracy in rye (Secale cereale L.).
about
Evaluating Methods of Updating Training Data in Long-Term Genomewide SelectionImprovement of Predictive Ability by Uniform Coverage of the Target Genetic Space.Towards a whole-genome sequence for rye (Secale cereale L.).Genomic prediction in early selection stages using multi-year data in a hybrid rye breeding programPredicting genomic selection efficiency to optimize calibration set and to assess prediction accuracy in highly structured populations.Genomic preselection with genotyping-by-sequencing increases performance of commercial oil palm hybrid crosses.QTL mapping and comparative genome analysis of agronomic traits including grain yield in winter rye.Improving the baking quality of bread wheat by genomic selection in early generations.Genomic Selection in Preliminary Yield Trials in a Winter Wheat Breeding Program
P2860
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P2860
Model training across multiple breeding cycles significantly improves genomic prediction accuracy in rye (Secale cereale L.).
description
2016 nî lūn-bûn
@nan
2016年の論文
@ja
2016年論文
@yue
2016年論文
@zh-hant
2016年論文
@zh-hk
2016年論文
@zh-mo
2016年論文
@zh-tw
2016年论文
@wuu
2016年论文
@zh
2016年论文
@zh-cn
name
Model training across multiple ...... cy in rye (Secale cereale L.).
@en
Model training across multiple ...... mic prediction accuracy in rye
@nl
type
label
Model training across multiple ...... cy in rye (Secale cereale L.).
@en
Model training across multiple ...... mic prediction accuracy in rye
@nl
prefLabel
Model training across multiple ...... cy in rye (Secale cereale L.).
@en
Model training across multiple ...... mic prediction accuracy in rye
@nl
P2093
P2860
P50
P1476
Model training across multiple ...... cy in rye (Secale cereale L.).
@en
P2093
Andres Gordillo
Chris-Carolin Schön
Hans-Jürgen Auinger
Hartwig H Geiger
Malthe Schmidt
Manfred Schönleben
Peer Wilde
Viktor Korzun
P2860
P2888
P304
P356
10.1007/S00122-016-2756-5
P407
P577
2016-08-01T00:00:00Z