However, the predictive power of both models was largely affected by environmental variances.

HBLUP showed higher prediction abilities (0.41В вЂ“В 0.61) than GBLUP (0.14В вЂ“В 0.28) under a decreased genetic relationship, especially for mid-heritable traits (FMY and DMY), suggesting that HBLUP is much less affected by relatedness and H 2. From 400 discrete narrow bands (410В nmвЂ“993В nm) collected by an uncrewed aerial vehicle (UAV) on two dates in each environment, 32 hyperspectral bands previously selected by Lasso were incorporated into a prediction model. For this, 270 elite rye lines from nine interconnected bi-parental families were genotyped using a 10В k-SNP array and phenotyped as testcrosses at four locations in two years (eight environments). Models were based on genomic (GBLUP) and hyperspectral reflectance-derived (HBLUP) relationship matrices to predict DMY and other biomass-related traits such as dry matter content (DMC) and fresh matter yield (FMY).

To assess the prospect of prediction using reflectance data as a suitable complement to GS for biomass breeding, the influence of trait heritability ( H 2) and genetic relatedness were compared. However, this estimation involves multiple genetic backgrounds and genetic relatedness is a crucial factor in genomic selection (GS). The early prediction of biomass via indirect selection of dry matter yield (DMY) based on hyperspectral and/or genomic prediction is crucial to affordably untap the potential of winter rye ( Secale cereale L.) as a dual-purpose crop. The demand for sustainable sources of biomass is increasing worldwide.

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