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

被引:16
|
作者
Galan, Rodrigo Jose [1 ]
Bernal-Vasquez, Angela-Maria [2 ]
Jebsen, Christian [2 ]
Piepho, Hans-Peter [3 ]
Thorwarth, Patrick [1 ,2 ]
Steffan, Philipp [4 ]
Gordillo, Andres [4 ]
Miedaner, Thomas [1 ]
机构
[1] Univ Hohenheim, State Plant Breeding Inst, D-70593 Stuttgart, Germany
[2] KWS SAAT SE, Grimsehlstr 31, D-37574 Einbeck, Germany
[3] Univ Hohenheim, Inst Crop Sci, Biostat Unit, D-70593 Stuttgart, Germany
[4] KWS LOCHOW GMBH, Ferdinand von Lochow Str 5, D-29303 Bergen, Germany
关键词
Biomass; Genetic relatedness; High-throughput phenotyping; Genomic prediction; Prediction ability; Rye; GENETIC-RELATIONSHIP INFORMATION; GRAIN-YIELD; VEGETATION INDEXES; CANOPY TEMPERATURE; SELECTION; REGRESSION; REFLECTANCE; ACCURACY; POPULATIONS; PHENOMICS;
D O I
10.1007/s00122-021-03779-1
中图分类号
S3 [农学(农艺学)];
学科分类号
0901 ;
摘要
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 (H-2) 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 H-2. 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.
引用
收藏
页码:1409 / 1422
页数:14
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