Early prediction of biomass in hybrid rye based on hyperspectral data surpasses genomic predictability in less-related breeding material

dc.contributor.authorGalán, Rodrigo José
dc.contributor.authorBernal-Vasquez, Angela-Maria
dc.contributor.authorJebsen, Christian
dc.contributor.authorPiepho, Hans-Peter
dc.contributor.authorThorwarth, Patrick
dc.contributor.authorSteffan, Philipp
dc.contributor.authorGordillo, Andres
dc.contributor.authorMiedaner, Thomas
dc.date.accessioned2024-09-03T13:37:55Z
dc.date.available2024-09-03T13:37:55Z
dc.date.issued2021de
dc.description.abstractKey message: Hyperspectral 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. Abstract: 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 ( ) 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 . 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.en
dc.identifier.urihttps://hohpublica.uni-hohenheim.de/handle/123456789/16464
dc.identifier.urihttps://doi.org/10.1007/s00122-021-03779-1
dc.language.isoengde
dc.rights.licensecc_byde
dc.source1432-2242de
dc.subjectBiomass
dc.subjectGenetic relatedness
dc.subjectHigh-throughput phenotyping
dc.subjectGenomic prediction
dc.subjectPrediction ability
dc.subjectRye
dc.subject.ddc630
dc.titleEarly prediction of biomass in hybrid rye based on hyperspectral data surpasses genomic predictability in less-related breeding materialen
dc.type.diniArticle
dcterms.bibliographicCitationTheoretical and applied genetics, 134 (2021), 5, 1409-1422. https://doi.org/10.1007/s00122-021-03779-1. ISSN: 1432-2242
dcterms.bibliographicCitation.issn1432-2242
dcterms.bibliographicCitation.issue5
dcterms.bibliographicCitation.journaltitleTheoretical and applied genetics
dcterms.bibliographicCitation.volume134
local.export.bibtex@article{Galán2021, url = {https://hohpublica.uni-hohenheim.de/handle/123456789/16464}, doi = {10.1007/s00122-021-03779-1}, author = {Galán, Rodrigo José and Bernal-Vasquez, Angela-Maria and Jebsen, Christian et al.}, title = {Early prediction of biomass in hybrid rye based on hyperspectral data surpasses genomic predictability in less-related breeding material}, journal = {Theoretical and applied genetics}, year = {2021}, volume = {134}, number = {5}, }
local.subject.sdg2
local.subject.sdg12
local.subject.sdg13
local.title.fullEarly prediction of biomass in hybrid rye based on hyperspectral data surpasses genomic predictability in less-related breeding material

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