ArticleTAG. Theoretical and applied genetics. Theoretische und angewandte Genetik2021
Early prediction of biomass in hybrid rye based on hyperspectral data surpasses genomic predictability in less-related breeding material.
Article in TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Leveraging UAV hyperspectral imaging for crop physiology and biochemistry: A comprehensive review of feature extraction and selection methods.Plant phenomics (Washington, D.C.) · 2026Review
- Sequencing of historical plastid genomes reveal exceptional genetic diversity in early domesticated rye plants.iScience · 2025Article
- Near-infrared reflectance spectroscopy phenomic prediction can perform similarly to genomic prediction of maize agronomic traits across environments.The plant genome · 2024Article
- DHFS-ECM: Design of a Dual Heuristic Feature Selection-based Ensemble Classification Model for the Identification of Bamboo Species from Genomic Sequences.Current genomics · 2024Article
- Performance of phenomic selection in rice: Effects of population size and genotype-environment interactions on predictive ability.PloS one · 2024Article
- Phenomic Selection for Hybrid Rapeseed Breeding.Plant phenomics (Washington, D.C.) · 2024Article
- Temporal phenomic predictions from unoccupied aerial systems can outperform genomic predictions.G3 (Bethesda, Md.) · 2023Article
- Heuristic Analysis of Genomic Sequence Processing Models for High Efficiency Prediction: A Statistical Perspective.Current genomics · 2022Review
- Phenomic selection in wheat breeding: prediction of the genotype-by-environment interaction in multi-environment breeding trials.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2022Article
- Interest of phenomic prediction as an alternative to genomic prediction in grapevine.Plant methods · 2022Article
- Phenomic data-facilitated rust and senescence prediction in maize using machine learning algorithms.Scientific reports · 2022Article
- The performance of phenomic selection depends on the genetic architecture of the target trait.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2022Article
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8 authors.
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Abstract
key messageHyperspectral data is a promising complement to genomic data to predict biomass under scenarios of low genetic relatedness. Sufficient environmental connectivity between data used for model training and validation is required. The demand for sustainable sources of biomass is increasing worldwide. 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. However, this estimation involves multiple genetic backgrounds and genetic relatedness is a crucial factor in genomic selection (GS). To assess the prospect of prediction using reflectance data as a suitable complement to GS for biomass breeding, the influence of trait heritability ([Formula: see text]) and genetic relatedness were compared. 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). For this, 270 elite rye lines from nine interconnected bi-parental families were genotyped using a 10 k-SNP array and phenotyped as testcrosses at four locations in two years (eight environments). From 400 discrete narrow bands (410 nm-993 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. HBLUP showed higher prediction abilities (0.41 - 0.61) than GBLUP (0.14 - 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 [Formula: see text]. However, the predictive power of both models was largely affected by environmental variances. Prediction abilities for DMY were further enhanced (up to 20%) by integrating both matrices and plant height into a bivariate model. Thus, data derived from high-throughput phenotyping emerges as a suitable strategy to efficiently leverage selection gains in biomass rye breeding; however, sufficient environmental connectivity is needed.
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