Integration of genotypic, hyperspectral, and phenotypic data to improve biomass yield prediction in hybrid rye.

ABSTRACT:

Key message

Hyperspectral and genomic data are effective predictors of biomass yield in winter rye. Variable selection procedures can improve the informativeness of reflectance data. Integrating cutting-edge technologies is imperative to sustainably breed crops for a growing global population. To predict dry matter yield (DMY) in winter rye (Secale cereale L.), we tested single-kernel models based on genomic (GBLUP) and hyperspectral reflectance-derived (HBLUP) relationship matrices, a multi-kernel model combining both matrices and a bivariate model fitted with plant height as a secondary trait. In total, 274 elite rye lines were genotyped using a 10 k-SNP array and phenotyped as testcrosses for DMY and plant height at four locations in Germany in two years (eight environments). Sp

SUBMITTER: Galan RJ 

PROVIDER: S-EPMC7548001 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature