Under these conditions, rye yielded 8.4 t dry matter ha −1, and under better environmental conditions, yields were up to 14.7 t dry matter ha −1. In a previous study, rye demonstrated its high dry matter yield (DMY) potential even on sandy soils and under drought stress (Galán et al. Europe is the largest rye grower worldwide covering about 81% of the global area with Russia, Poland, and Germany being the main producers (FAO 2019). Thus, suitable alternatives are welcome to diversify maize-based biomass production.Īmong the small-grain cereals, winter rye ( Secale cereale L.) stands out for its vigorous growth and enhanced tolerance to abiotic and biotic stress factors. For example, in Germany, the principal European biogas producer, the permitted share of maize ( Zea mays L.) as the most common fermentation substrate has been limited to 44% by 2021 (Renewable Energy Sources Act “EEG”, EEG 2017). New policy directives have established sustainability guidelines for bioenergy production (European Union 2010). In the European Union (EU), for instance, the share of renewable energy is expected to be between 55 and 75% of the total energy consumption in 2050, increasing in proportion the needs for biomass (European Commission 2011). Worldwide, the consumption of energy obtained from renewable origins, especially bio-based sources, is rising (World Bioenergy Association 2019). 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. Prediction abilities for DMY were further enhanced (up to 20%) by integrating both matrices and plant height into a bivariate model. However, the predictive power of both models was largely affected by environmental variances. 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. 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. 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). 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.
0 kommentar(er)
