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Article
2020
Integration of genotypic, hyperspectral, and phenotypic data to improve biomass yield prediction in hybrid rye
Integration of genotypic, hyperspectral, and phenotypic data to improve biomass yield prediction in hybrid rye
Abstract (English)
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). Spectral data consisted of 400 discrete narrow bands ranging between 410 and 993 nm collected by an unmanned aerial vehicle (UAV) on two dates on each environment. To reduce data dimensionality, variable selection of bands was performed, resulting in the least absolute shrinkage and selection operator (Lasso) as the best method in terms of predictive abilities. The mean heritability of reflectance data was moderate ( h2 = 0.72) and highly variable across the spectrum. Correlations between DMY and single bands were generally significant (p < 0.05) but low (≤ 0.29). Across environments and training set (TRN) sizes, the bivariate model showed the highest prediction abilities (0.56–0.75), followed by the multi-kernel (0.45–0.71) and single-kernel (0.33–0.61) models. With reduced TRN, HBLUP performed better than GBLUP. The HBLUP model fitted with a set of selected bands was preferred. Within and across environments, prediction abilities increased with larger TRN. Our results suggest that in the era of digital breeding, the integration of high-throughput phenotyping and genomic selection
is a promising strategy to achieve superior selection gains in hybrid rye.
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Theoretical and applied genetics, 133 (2020), 11, 3001–3015.
https://doi.org/10.1007/s00122-020-03651-8.
ISSN: 1432-2242
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Galán, R. J., Bernal-Vasquez, A.-M., Jebsen, C., Piepho, H.-P., Thorwarth, P., Steffan, P., Gordillo, A., & Miedaner, T. (2020). Integration of genotypic, hyperspectral, and phenotypic data to improve biomass yield prediction in hybrid rye. Theoretical and applied genetics, 133(11). https://doi.org/10.1007/s00122-020-03651-8
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English
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630 Agriculture
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Sustainable Development Goals
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@article{Galán2020,
url = {https://hohpublica.uni-hohenheim.de/handle/123456789/16323},
doi = {10.1007/s00122-020-03651-8},
author = {Galán, Rodrigo José and Bernal-Vasquez, Angela-Maria and Jebsen, Christian et al.},
title = {Integration of genotypic, hyperspectral, and phenotypic data to improve biomass yield prediction in hybrid rye},
journal = {Theoretical and applied genetics},
year = {2020},
volume = {133},
}