Optimization of selective phenotyping and population design for genomic prediction

DOI10.1007/s13253-020-00415-1OpenAlexW3089241121MaRDI QIDQ2084382

Nicolas Heslot, Vitaliy Feoktistov

Publication date: 18 October 2022

Published in: Journal of Agricultural, Biological, and Environmental Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s13253-020-00415-1

zbMATH Keywords

global optimizationtraining populationexperimental designgenomic selectionpopulation design

Mathematics Subject Classification ID

Applications of statistics to environmental and related topics (62P12)

Related Items (4)

Guest editors' introduction to the special issue on ``Recent advances in design and analysis of experiments and observational studies in agriculture ⋮ Linear variance, P-splines and neighbour differences for spatial adjustment in field trials: how are they related? ⋮ Optimizing the allocation of trials to sub-regions in multi-environment crop variety testing ⋮ Bayesian optimization approaches for identifying the best genotype from a candidate population

Uses Software

• R
• ASReml
• rrBLUP
• Algorithm 971

Cites Work

• Differential evolution. In search of solutions.
• On the design of field experiments with correlated treatment effects
• Construction of more Flexible and Efficient P-rep Designs
• Planning Incomplete Block Experiments When Treatments Are Genetically Related
• Algorithm 971

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